diff --git a/invokeai/app/api/dependencies.py b/invokeai/app/api/dependencies.py
index b3ba3be75cf..3e9ae338c3d 100644
--- a/invokeai/app/api/dependencies.py
+++ b/invokeai/app/api/dependencies.py
@@ -69,6 +69,7 @@
     FLUXConditioningInfo,
     Ideogram4ConditioningInfo,
     Krea2ConditioningInfo,
+    MiniMaxH3ConditioningInfo,
     QwenImageConditioningInfo,
     SD3ConditioningInfo,
     SDXLConditioningInfo,
@@ -173,6 +174,7 @@ def initialize(
                     Krea2ConditioningInfo,
                     AnimaConditioningInfo,
                     WanConditioningInfo,
+                    MiniMaxH3ConditioningInfo,
                 ],
                 ephemeral=True,
             ),
diff --git a/invokeai/app/invocations/fields.py b/invokeai/app/invocations/fields.py
index 256b0f20376..8989b8fe98e 100644
--- a/invokeai/app/invocations/fields.py
+++ b/invokeai/app/invocations/fields.py
@@ -185,6 +185,10 @@ class FieldDescriptions:
     wan_model = "Wan 2.2 model (Transformer) to load"
     wan_t5_encoder = "UMT5-XXL tokenizer and text encoder for Wan 2.2"
     wan_ref_image = "Reference-image (VAE-latent) conditioning for Wan 2.2 I2V."
+    minimax_h3_model = "MiniMax H3 model (Transformer) to load"
+    minimax_h3_text_encoder = "Qwen3-VL-32B tokenizer, processor and text encoder for MiniMax H3"
+    minimax_h3_frame_conditioning = "First/last-keyframe (VAE-latent) conditioning for MiniMax H3"
+    minimax_h3_audio_vae = "Audio VAE (stereo, 32 kHz) for MiniMax H3"
     sdxl_main_model = "SDXL Main model (UNet, VAE, CLIP1, CLIP2) to load"
     sdxl_refiner_model = "SDXL Refiner Main Modde (UNet, VAE, CLIP2) to load"
     onnx_main_model = "ONNX Main model (UNet, VAE, CLIP) to load"
@@ -444,6 +448,33 @@ class WanRefImageConditioningField(BaseModel):
     )
 
 
+class MiniMaxH3ConditioningField(BaseModel):
+    """A MiniMax H3 conditioning primitive value.
+
+    H3 conditioning is the layer-50 Qwen3-VL hidden state plus the per-row modality tags the
+    packed-sequence layout is built from (vision-block rows are tagged as video).
+    """
+
+    conditioning_name: str = Field(description="The name of conditioning tensor")
+
+
+class MiniMaxH3FrameConditioningField(BaseModel):
+    """First/last-keyframe conditioning for MiniMax H3 (FL2VA).
+
+    Carries the CLEAN (not yet noise-augmented) packed keyframe conditioning rows; the denoise
+    node noise-augments them to t=0.999 with the request seed's first draws. Width/height ride
+    along so the denoise node can reject a canvas mismatch instead of failing inside the
+    transformer.
+    """
+
+    condition_rows_name: str = Field(description="Name of the saved (num_condition_rows, 96) rows tensor.")
+    keyframe_anchors: list[str] = Field(
+        description='Which end each keyframe anchors, in packed order ("first" / "last").'
+    )
+    width: int = Field(description="Canvas width used during VAE encoding (matches denoise width).")
+    height: int = Field(description="Canvas height used during VAE encoding (matches denoise height).")
+
+
 class ConditioningField(BaseModel):
     """A conditioning tensor primitive value"""
 
diff --git a/invokeai/app/invocations/metadata.py b/invokeai/app/invocations/metadata.py
index 5c882660b7d..ae55ca8dc77 100644
--- a/invokeai/app/invocations/metadata.py
+++ b/invokeai/app/invocations/metadata.py
@@ -185,6 +185,9 @@ def invoke(self, context: InvocationContext) -> MetadataOutput:
     "wan_inpaint",
     "wan_outpaint",
     "wan_i2v",
+    "minimax_h3_t2v",
+    "minimax_h3_i2v",
+    "minimax_h3_txt2img",
 ]
 
 
diff --git a/invokeai/app/invocations/minimax_h3_denoise.py b/invokeai/app/invocations/minimax_h3_denoise.py
new file mode 100644
index 00000000000..2c63fd27d4f
--- /dev/null
+++ b/invokeai/app/invocations/minimax_h3_denoise.py
@@ -0,0 +1,268 @@
+"""MiniMax H3 denoise invocation (T2VA / FL2VA).
+
+One packed sequence carries the text conditioning, the optional keyframe conditioning rows,
+the audio latents and the video latents through a single transformer forward per step; video
+and audio step down two different flow schedules (shift 12.0 / 3.0). The checkpoint is
+guidance-distilled: no negative prompt, no CFG, one forward per step.
+"""
+
+import torch
+from tqdm import tqdm
+
+from invokeai.app.invocations.baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    Classification,
+    invocation,
+    invocation_output,
+)
+from invokeai.app.invocations.fields import (
+    FieldDescriptions,
+    Input,
+    InputField,
+    LatentsField,
+    MiniMaxH3ConditioningField,
+    MiniMaxH3FrameConditioningField,
+    OutputField,
+)
+from invokeai.app.invocations.model import MiniMaxH3TransformerField
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.minimax_h3.denoise import denoise
+from invokeai.backend.minimax_h3.packing import (
+    MINIMAX_H3_CANVAS_MULTIPLE,
+    MiniMaxH3PackedSequence,
+    audio_latent_num_frames,
+    unpack_audio_tokens,
+    unpatchify_video_tokens,
+    video_latent_num_frames,
+)
+from invokeai.backend.minimax_h3.sampling import (
+    MINIMAX_H3_PATCH_SIZE,
+    MINIMAX_H3_SPATIAL_COMPRESSION,
+    MINIMAX_H3_VAE_LATENT_CHANNELS,
+    build_denoise_state,
+    validate_num_frames,
+)
+from invokeai.backend.minimax_h3.transformer_minimax_h3 import MiniMaxH3Transformer3DModel
+from invokeai.backend.model_manager.taxonomy import BaseModelType
+from invokeai.backend.stable_diffusion.diffusers_pipeline import PipelineIntermediateState
+from invokeai.backend.stable_diffusion.diffusion.conditioning_data import MiniMaxH3ConditioningInfo
+from invokeai.backend.util.devices import TorchDevice
+
+
+@invocation_output("minimax_h3_denoise_output")
+class MiniMaxH3DenoiseOutput(BaseInvocationOutput):
+    """Joint video + audio latents from one MiniMax H3 denoise run."""
+
+    video_latents: LatentsField = OutputField(description="5D video latents [1, 24, T_lat, H/16, W/16].")
+    audio_latents: LatentsField = OutputField(
+        description="Audio latents [2, 32, T_audio] (one item per stereo channel)."
+    )
+    width: int = OutputField(description="Pixel width of the video latents.")
+    height: int = OutputField(description="Pixel height of the video latents.")
+    num_frames: int = OutputField(description="Pixel-frame count of the video latents.")
+
+
+@invocation(
+    "minimax_h3_denoise",
+    title="Denoise - MiniMax H3",
+    tags=["latents", "video", "audio", "minimax"],
+    category="latents",
+    version="1.0.0",
+    classification=Classification.Prototype,
+)
+class MiniMaxH3DenoiseInvocation(BaseInvocation):
+    """Run the MiniMax H3 joint audio-video denoising loop."""
+
+    transformer: MiniMaxH3TransformerField = InputField(
+        description="MiniMax H3 FL2VA transformer.", input=Input.Connection, title="Transformer"
+    )
+    positive_conditioning: MiniMaxH3ConditioningField = InputField(
+        description=FieldDescriptions.positive_cond, input=Input.Connection
+    )
+    frame_conditioning: MiniMaxH3FrameConditioningField | None = InputField(
+        default=None,
+        description=FieldDescriptions.minimax_h3_frame_conditioning,
+        input=Input.Connection,
+        title="Frame Conditioning",
+    )
+    width: int = InputField(
+        default=1344,
+        gt=0,
+        multiple_of=MINIMAX_H3_CANVAS_MULTIPLE,
+        description="Width of the generated video. H3's native canvas has a 768px short edge (max 768x1344).",
+    )
+    height: int = InputField(
+        default=768,
+        gt=0,
+        multiple_of=MINIMAX_H3_CANVAS_MULTIPLE,
+        description="Height of the generated video.",
+    )
+    num_frames: int = InputField(
+        default=124,
+        ge=5,
+        description="Number of output frames at the fixed 24 fps. Must be of the form 17n+5 "
+        "(5, 22, ..., 124, ...); durations must stay within 5-15 s, except exactly 5 frames "
+        "for a still image.",
+        title="Number of Frames",
+    )
+    steps: int = InputField(
+        default=50,
+        ge=2,
+        description="Number of denoising steps (sigma grid points, terminal included: N steps = N-1 model evaluations).",
+    )
+    seed: int = InputField(default=0, description="Randomness seed for reproducibility.")
+
+    @staticmethod
+    def _estimate_working_memory(layout: MiniMaxH3PackedSequence) -> int:
+        """Estimate peak transformer activation memory (bytes) so the model cache reserves enough headroom.
+
+        The 61.7 GiB bf16 transformer is partially loaded on most cards; without this hint the cache
+        reserves only the small default working memory, packs the device with weights, and the first
+        forward OOMs (a 124-frame 768x1344 t2v is a ~38k-row packed sequence).
+
+        Attention runs through SDPA without materializing scores, so activations scale ~linearly with
+        the packed row count. The dominant per-row bf16 terms concurrently alive inside one block are
+        QKV/attention-out at the 7168-wide attention inner dim and the SwiGLU intermediates at
+        ffn_dim 14336 (~0.16 MiB/row measured together with the residual stream and the fp32 output
+        heads); 0.25 MiB/row leaves ~1.5x margin. Padding rows additionally materialize a boolean
+        (seq x seq) attention mask - and SDPA's convert_boolean_attn_mask then allocates an
+        additive copy at query dtype (2 bytes) plus a possible alignment-pad transient of the same
+        size, so budget 5 bytes per mask entry, not 1. (No current code path emits padding rows;
+        this arms the estimate for ref2va-style layouts.) The fixed base covers streamed block
+        weights arriving on device under partial load (per-op transients up to ~0.5 GiB), the
+        prompt embeds, rotary tables, per-step preview unpatchify, and allocator slack.
+        """
+        MB = 1024**2
+        GB = 1024**3
+        estimated = layout.sequence_length * int(0.25 * MB)
+        if bool((layout.token_tags < 0).any()):
+            estimated += 5 * layout.sequence_length**2
+        estimated += 2 * GB
+        return estimated
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> MiniMaxH3DenoiseOutput:
+        validate_num_frames(self.num_frames)
+
+        device = TorchDevice.choose_torch_device()
+
+        cond_data = context.conditioning.load(self.positive_conditioning.conditioning_name)
+        assert len(cond_data.conditionings) == 1
+        cond_info = cond_data.conditionings[0]
+        assert isinstance(cond_info, MiniMaxH3ConditioningInfo)
+
+        latent_height = self.height // MINIMAX_H3_SPATIAL_COMPRESSION
+        latent_width = self.width // MINIMAX_H3_SPATIAL_COMPRESSION
+        num_latent_frames = video_latent_num_frames(self.num_frames)
+        num_audio_latents = audio_latent_num_frames(self.num_frames)
+
+        keyframe_anchors: tuple[str, ...] = ()
+        clean_condition_rows: torch.Tensor | None = None
+        if self.frame_conditioning is not None:
+            if (self.frame_conditioning.width, self.frame_conditioning.height) != (self.width, self.height):
+                raise ValueError(
+                    f"Frame conditioning canvas ({self.frame_conditioning.width}x"
+                    f"{self.frame_conditioning.height}) must match denoise dimensions "
+                    f"({self.width}x{self.height}). Re-run Frame Conditioning - MiniMax H3."
+                )
+            keyframe_anchors = tuple(self.frame_conditioning.keyframe_anchors)
+            clean_condition_rows = context.tensors.load(self.frame_conditioning.condition_rows_name)
+
+        # Keyframes must reach the text conditioning (vision context) and the VAE condition
+        # rows together, on the same canvas — the model was trained with them coupled.
+        cond_anchors = tuple(cond_info.keyframe_anchors)
+        if cond_anchors != keyframe_anchors:
+            raise ValueError(
+                f"Keyframe mismatch: the prompt was encoded with keyframes {list(cond_anchors) or 'none'} "
+                f"but frame conditioning provides {list(keyframe_anchors) or 'none'}. Wire the same "
+                "first/last images to both Prompt - MiniMax H3 and Frame Conditioning - MiniMax H3."
+            )
+        if cond_anchors and (cond_info.width, cond_info.height) != (self.width, self.height):
+            raise ValueError(
+                f"The prompt's keyframes were prepared at {cond_info.width}x{cond_info.height} but this "
+                f"denoise runs at {self.width}x{self.height}. Re-run Prompt - MiniMax H3 with matching "
+                "width/height."
+            )
+
+        state = build_denoise_state(
+            text_token_tags=cond_info.text_token_tags,
+            num_latent_frames=num_latent_frames,
+            latent_height=latent_height,
+            latent_width=latent_width,
+            num_audio_latents=num_audio_latents,
+            num_inference_steps=self.steps,
+            seed=self.seed,
+            device=device,
+            keyframe_anchors=keyframe_anchors,
+            clean_condition_rows=clean_condition_rows,
+        )
+
+        num_condition_video_rows = state.layout.num_condition_video_rows
+
+        def step_callback(step: int, total_steps: int, video_rows: torch.Tensor) -> None:
+            # Unpack the generated rows to a 5D grid and preview the middle temporal slice.
+            latents_5d = unpatchify_video_tokens(
+                video_rows[num_condition_video_rows:],
+                num_latent_frames,
+                latent_height,
+                latent_width,
+                MINIMAX_H3_VAE_LATENT_CHANNELS,
+                MINIMAX_H3_PATCH_SIZE,
+            )
+            context.util.sd_step_callback(
+                PipelineIntermediateState(
+                    step=step,
+                    order=1,
+                    total_steps=total_steps,
+                    timestep=0,
+                    latents=latents_5d[:, :, num_latent_frames // 2],
+                ),
+                BaseModelType.MiniMaxH3,
+            )
+
+        estimated_working_memory = self._estimate_working_memory(state.layout)
+        transformer_info = context.models.load(self.transformer.transformer)
+        with transformer_info.model_on_device(working_mem_bytes=estimated_working_memory) as (_, transformer):
+            assert isinstance(transformer, MiniMaxH3Transformer3DModel)
+            context.util.signal_progress("Denoising MiniMax H3 audio-video")
+            # steps counts sigma grid points (terminal included) -> steps-1 model evaluations.
+            progress = tqdm(total=len(state.timesteps), desc=f"Denoising MiniMax H3 ({self.num_frames} frames)")
+
+            def callback_with_progress(step: int, total_steps: int, video_rows: torch.Tensor) -> None:
+                progress.update(1)
+                step_callback(step, total_steps, video_rows)
+
+            try:
+                video_rows, audio_rows = denoise(
+                    transformer=transformer,
+                    state=state,
+                    prompt_embeds=cond_info.prompt_embeds.to(device),
+                    step_callback=callback_with_progress,
+                    is_canceled=context.util.is_canceled,
+                )
+            finally:
+                progress.close()
+
+        video_latents = unpatchify_video_tokens(
+            video_rows[num_condition_video_rows:],
+            num_latent_frames,
+            latent_height,
+            latent_width,
+            MINIMAX_H3_VAE_LATENT_CHANNELS,
+            MINIMAX_H3_PATCH_SIZE,
+        )
+        audio_latents = unpack_audio_tokens(audio_rows[state.layout.num_condition_audio_rows :], num_audio_latents)
+
+        video_latents = video_latents.detach().to(device="cpu", dtype=torch.float32)
+        audio_latents = audio_latents.detach().to(device="cpu", dtype=torch.float32)
+
+        video_name = context.tensors.save(tensor=video_latents)
+        audio_name = context.tensors.save(tensor=audio_latents)
+        return MiniMaxH3DenoiseOutput(
+            video_latents=LatentsField(latents_name=video_name, seed=self.seed),
+            audio_latents=LatentsField(latents_name=audio_name, seed=self.seed),
+            width=self.width,
+            height=self.height,
+            num_frames=self.num_frames,
+        )
diff --git a/invokeai/app/invocations/minimax_h3_frame_conditioning.py b/invokeai/app/invocations/minimax_h3_frame_conditioning.py
new file mode 100644
index 00000000000..332a04d239d
--- /dev/null
+++ b/invokeai/app/invocations/minimax_h3_frame_conditioning.py
@@ -0,0 +1,92 @@
+import torch
+
+from invokeai.app.invocations.baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    Classification,
+    invocation,
+    invocation_output,
+)
+from invokeai.app.invocations.fields import (
+    FieldDescriptions,
+    ImageField,
+    Input,
+    InputField,
+    MiniMaxH3FrameConditioningField,
+    OutputField,
+)
+from invokeai.app.invocations.model import VAEField
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.minimax_h3.autoencoder_kl_minimax_h3 import AutoencoderKLMiniMaxH3
+from invokeai.backend.minimax_h3.keyframe_conditioning import encode_keyframes, prepare_keyframes
+from invokeai.backend.minimax_h3.packing import MINIMAX_H3_CANVAS_MULTIPLE
+from invokeai.backend.model_manager.load.model_cache.utils import get_effective_device
+
+
+@invocation_output("minimax_h3_frame_conditioning_output")
+class MiniMaxH3FrameConditioningOutput(BaseInvocationOutput):
+    """Output of the MiniMax H3 keyframe VAE-encoder."""
+
+    frame_conditioning: MiniMaxH3FrameConditioningField = OutputField(
+        description=FieldDescriptions.minimax_h3_frame_conditioning
+    )
+
+
+@invocation(
+    "minimax_h3_frame_conditioning",
+    title="Frame Conditioning - MiniMax H3",
+    tags=["conditioning", "minimax", "video", "i2v"],
+    category="conditioning",
+    version="1.0.0",
+    classification=Classification.Prototype,
+)
+class MiniMaxH3FrameConditioningInvocation(BaseInvocation):
+    """VAE-encodes first/last keyframes into MiniMax H3 conditioning rows.
+
+    The rows are clean (the denoise node noise-augments them with the request seed). The same
+    images and width/height must also be wired to the Prompt - MiniMax H3 node: the keyframes
+    are part of both the packed sequence and the text conditioning.
+    """
+
+    first_image: ImageField | None = InputField(
+        default=None, description="Keyframe the video starts from (stretched onto the canvas)."
+    )
+    last_image: ImageField | None = InputField(
+        default=None, description="Keyframe the video ends on (cover-cropped onto the canvas)."
+    )
+    vae: VAEField = InputField(description=FieldDescriptions.vae, input=Input.Connection, title="Video VAE")
+    width: int = InputField(
+        default=1344, gt=0, multiple_of=MINIMAX_H3_CANVAS_MULTIPLE, description="Target canvas width."
+    )
+    height: int = InputField(
+        default=768, gt=0, multiple_of=MINIMAX_H3_CANVAS_MULTIPLE, description="Target canvas height."
+    )
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> MiniMaxH3FrameConditioningOutput:
+        if self.first_image is None and self.last_image is None:
+            raise ValueError("Frame Conditioning needs a first image, a last image, or both.")
+
+        first = context.images.get_pil(self.first_image.image_name) if self.first_image else None
+        last = context.images.get_pil(self.last_image.image_name) if self.last_image else None
+        keyframes, anchors = prepare_keyframes(first, last, self.height, self.width)
+
+        vae_info = context.models.load(self.vae.vae)
+        if not isinstance(vae_info.model, AutoencoderKLMiniMaxH3):
+            raise TypeError(
+                f"Expected AutoencoderKLMiniMaxH3 for the MiniMax H3 video VAE, got {type(vae_info.model).__name__}."
+            )
+        with vae_info.model_on_device() as (_, vae):
+            assert isinstance(vae, AutoencoderKLMiniMaxH3)
+            context.util.signal_progress("Encoding MiniMax H3 keyframes")
+            rows = encode_keyframes(vae, keyframes, device=get_effective_device(vae))
+
+        name = context.tensors.save(tensor=rows.detach().to("cpu"))
+        return MiniMaxH3FrameConditioningOutput(
+            frame_conditioning=MiniMaxH3FrameConditioningField(
+                condition_rows_name=name,
+                keyframe_anchors=list(anchors),
+                width=self.width,
+                height=self.height,
+            )
+        )
diff --git a/invokeai/app/invocations/minimax_h3_latents_to_image.py b/invokeai/app/invocations/minimax_h3_latents_to_image.py
new file mode 100644
index 00000000000..4606920b78f
--- /dev/null
+++ b/invokeai/app/invocations/minimax_h3_latents_to_image.py
@@ -0,0 +1,53 @@
+"""MiniMax H3 latents-to-image invocation: the still-image (frame extraction) path.
+
+Paired with Denoise - MiniMax H3 at ``num_frames=5`` (the single-block minimum), this is the
+text-to-image mode: decode the clip and save one frame as a regular gallery image.
+"""
+
+import torch
+from PIL import Image
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
+from invokeai.app.invocations.fields import (
+    FieldDescriptions,
+    Input,
+    InputField,
+    LatentsField,
+    WithBoard,
+    WithMetadata,
+)
+from invokeai.app.invocations.minimax_h3_latents_to_video import decode_video_latents
+from invokeai.app.invocations.model import VAEField
+from invokeai.app.invocations.primitives import ImageOutput
+from invokeai.app.services.shared.invocation_context import InvocationContext
+
+
+@invocation(
+    "minimax_h3_latents_to_image",
+    title="Latents to Image - MiniMax H3",
+    tags=["latents", "image", "vae", "l2i", "minimax"],
+    category="latents",
+    version="1.0.0",
+    classification=Classification.Prototype,
+)
+class MiniMaxH3LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
+    """Decode MiniMax H3 video latents and save a single frame as an image."""
+
+    video_latents: LatentsField = InputField(description=FieldDescriptions.latents, input=Input.Connection)
+    vae: VAEField = InputField(description=FieldDescriptions.vae, input=Input.Connection, title="Video VAE")
+    frame_index: int = InputField(default=0, ge=0, description="Which decoded frame to save.")
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        latents = context.tensors.load(self.video_latents.latents_name)
+        decoded = decode_video_latents(context, self.vae, latents)  # [C, T, H, W] in [0, 1]
+
+        num_frames = decoded.shape[1]
+        if self.frame_index >= num_frames:
+            raise ValueError(f"frame_index {self.frame_index} is out of range for a {num_frames}-frame clip.")
+
+        frame = decoded[:, self.frame_index].permute(1, 2, 0)  # [H, W, C]
+        img_pil = Image.fromarray((255.0 * frame).round().clamp(0, 255).byte().cpu().numpy())
+
+        image_dto = context.images.save(image=img_pil)
+        return ImageOutput.build(image_dto)
diff --git a/invokeai/app/invocations/minimax_h3_latents_to_video.py b/invokeai/app/invocations/minimax_h3_latents_to_video.py
new file mode 100644
index 00000000000..bbaf8e1bc80
--- /dev/null
+++ b/invokeai/app/invocations/minimax_h3_latents_to_video.py
@@ -0,0 +1,260 @@
+"""MiniMax H3 latents-to-video invocation.
+
+Decodes the 5D video latents with the H3 video VAE (ImageNet-normalized RGB, reverted here)
+and, when audio latents are wired, decodes the stereo waveform with the H3 audio VAE and muxes
+it into the MP4 as AAC. The audio is trimmed or zero-padded to exactly the video duration
+before muxing: ffmpeg gets no ``-shortest``, so an over-long track would stretch the container.
+"""
+
+import tempfile
+from collections.abc import Iterator
+from pathlib import Path
+
+import numpy as np
+import torch
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
+from invokeai.app.invocations.fields import (
+    FieldDescriptions,
+    Input,
+    InputField,
+    LatentsField,
+    WithBoard,
+    WithMetadata,
+)
+from invokeai.app.invocations.model import VAEField
+from invokeai.app.invocations.primitives import VideoOutput
+from invokeai.app.invocations.wan_latents_to_video import _write_video_frames
+from invokeai.app.services.session_processor.session_processor_common import CanceledException
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.app.util.video_encoding import make_mp4_writer, write_stereo_wav
+from invokeai.backend.minimax_h3.autoencoder_kl_minimax_h3 import AutoencoderKLMiniMaxH3
+from invokeai.backend.minimax_h3.autoencoder_kl_minimax_h3_audio import AutoencoderKLMiniMaxH3Audio
+from invokeai.backend.minimax_h3.packing import (
+    MINIMAX_H3_FPS,
+    MINIMAX_H3_PIXEL_MEAN,
+    MINIMAX_H3_PIXEL_STD,
+)
+from invokeai.backend.model_manager.load.model_cache.utils import get_effective_device
+from invokeai.backend.util.devices import TorchDevice
+from invokeai.backend.util.vae_working_memory import estimate_vae_working_memory_minimax_h3
+
+
+def _iter_decoded_frames(decoded: torch.Tensor) -> Iterator[np.ndarray]:
+    """Yield uint8 HWC frames from a [C, T, H, W] clip already in [0, 1]."""
+    for index in range(decoded.shape[1]):
+        frame = decoded[:, index].clamp(0, 1).permute(1, 2, 0).cpu().float()
+        yield (255.0 * frame).round().clamp(0, 255).byte().numpy()
+
+
+def decode_video_latents(
+    context: InvocationContext,
+    vae_field: VAEField,
+    latents: torch.Tensor,
+) -> torch.Tensor:
+    """Decode 5D H3 video latents to a [C, T, H, W] float clip in [0, 1], on the CPU."""
+    if latents.ndim != 5:
+        raise ValueError(f"MiniMax H3 video latents must be 5D [B, C, T, H, W]; got {tuple(latents.shape)}.")
+    if latents.shape[0] != 1:
+        raise ValueError(f"MiniMax H3 latents-to-video requires batch size 1; got {latents.shape[0]}.")
+
+    vae_info = context.models.load(vae_field.vae)
+    if not isinstance(vae_info.model, AutoencoderKLMiniMaxH3):
+        raise TypeError(
+            f"Expected AutoencoderKLMiniMaxH3 for the MiniMax H3 video VAE, got {type(vae_info.model).__name__}."
+        )
+    if latents.shape[1] != vae_info.model.config.latent_channels:
+        raise ValueError(
+            f"Latent channel mismatch: these latents have {latents.shape[1]} channels but the "
+            f"selected VAE expects {vae_info.model.config.latent_channels}."
+        )
+
+    _, _, t_lat, h_lat, w_lat = latents.shape
+    spatial = vae_info.model.spatial_compression_ratio
+    # 5*n+2 latent frames decode to 17*n+5 pixel frames.
+    t_pixel = (t_lat - 2) // 5 * 17 + 5
+    h_pixel, w_pixel = h_lat * spatial, w_lat * spatial
+
+    estimated_working_memory = estimate_vae_working_memory_minimax_h3(
+        operation="decode",
+        vae=vae_info.model,
+        pixel_height=h_pixel,
+        pixel_width=w_pixel,
+        pixel_frames=t_pixel,
+    )
+
+    with vae_info.model_on_device(working_mem_bytes=estimated_working_memory) as (_, vae):
+        assert isinstance(vae, AutoencoderKLMiniMaxH3)
+        context.logger.info(
+            f"Running MiniMax H3 VAE decode: {t_lat} latent frames -> {t_pixel} pixel frames at {w_pixel}x{h_pixel}"
+        )
+        context.util.signal_progress("Running MiniMax H3 video VAE decode")
+
+        device = get_effective_device(vae)
+        vae_dtype = next(iter(vae.parameters())).dtype
+        latents = latents.to(device=device, dtype=vae_dtype)
+
+        TorchDevice.empty_cache()
+
+        # The H3 VAE tiles spatially by default (256px tiles / 64px overlap) and the released
+        # frames are the blended-tile ones — never toggle tiling on the shared cached instance:
+        # it would silently change every later encode/decode using the same cached model.
+        with torch.inference_mode():
+            latents_mean = torch.tensor(vae.config.latents_mean).view(1, -1, 1, 1, 1).to(latents)
+            latents_std = torch.tensor(vae.config.latents_std).view(1, -1, 1, 1, 1).to(latents)
+            latents = latents * latents_std + latents_mean
+
+            if device.type == "cuda" and torch.version.hip is None:
+                # Upstream's verified decode recipe: float16 autocast over the fp32-pinned weights.
+                with torch.autocast(device_type="cuda", dtype=torch.float16):
+                    decoded = vae.decode(latents, return_dict=False)[0]
+            else:
+                # ROCm/MPS/CPU: fp16 autocast is unverified there; decode in the weights' dtype.
+                decoded = vae.decode(latents, return_dict=False)[0]
+            del latents, latents_mean, latents_std
+
+        # Move the clip off-device before the full-clip pixel math to keep VRAM at ~one copy.
+        decoded = decoded[0].float().cpu()  # [C, T, H, W]
+
+    TorchDevice.empty_cache()
+
+    # The H3 video VAE emits ImageNet-normalized RGB over a [0, 1] base range; revert it.
+    pixel_mean = torch.tensor(MINIMAX_H3_PIXEL_MEAN).view(-1, 1, 1, 1)
+    pixel_std = torch.tensor(MINIMAX_H3_PIXEL_STD).view(-1, 1, 1, 1)
+    return (decoded * pixel_std + pixel_mean).clamp_(0, 1)
+
+
+@invocation(
+    "minimax_h3_latents_to_video",
+    title="Latents to Video - MiniMax H3",
+    tags=["latents", "video", "audio", "vae", "l2v", "minimax"],
+    category="latents",
+    version="1.0.0",
+    classification=Classification.Prototype,
+)
+class MiniMaxH3LatentsToVideoInvocation(BaseInvocation, WithMetadata, WithBoard):
+    """Decode MiniMax H3 video+audio latents and encode an MP4 with an AAC stereo track."""
+
+    video_latents: LatentsField = InputField(description=FieldDescriptions.latents, input=Input.Connection)
+    audio_latents: LatentsField | None = InputField(
+        default=None,
+        description="Audio latents [2, 32, T_audio] from the denoise node. Omit for a silent video.",
+        input=Input.Connection,
+    )
+    vae: VAEField = InputField(description=FieldDescriptions.vae, input=Input.Connection, title="Video VAE")
+    audio_vae: VAEField | None = InputField(
+        default=None,
+        description=FieldDescriptions.minimax_h3_audio_vae,
+        input=Input.Connection,
+        title="Audio VAE",
+    )
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> VideoOutput:
+        latents = context.tensors.load(self.video_latents.latents_name)
+        decoded = decode_video_latents(context, self.vae, latents)
+
+        if context.util.is_canceled():
+            raise CanceledException
+
+        num_frames = decoded.shape[1]
+        if num_frames == 0:
+            raise ValueError("MiniMax H3 VAE decode produced zero frames.")
+        height, width = decoded.shape[2:]
+        fps = MINIMAX_H3_FPS
+        duration = num_frames / float(fps)
+
+        # Decode the soundtrack (if wired) before opening the writer: the WAV must exist and
+        # be trimmed to the video duration when the muxing writer is constructed.
+        wav_path: Path | None = None
+        if self.audio_latents is not None:
+            if self.audio_vae is None:
+                raise ValueError("Audio latents are wired but no Audio VAE is connected.")
+            wav_path = self._decode_audio_to_wav(context, duration)
+
+        tmp = tempfile.NamedTemporaryFile(prefix="invokeai_minimax_h3_video_", suffix=".mp4", delete=False)
+        tmp.close()
+        tmp_path = Path(tmp.name)
+        try:
+            context.logger.info(
+                f"Encoding MP4: {num_frames} frames @ {fps} fps ({duration:.2f}s) at {width}x{height}"
+                + (" + AAC stereo" if wav_path is not None else "")
+            )
+            context.util.signal_progress(f"Encoding MP4 ({num_frames} frames @ {fps} fps)")
+            writer = make_mp4_writer(tmp_path, float(fps), audio_path=wav_path)
+            try:
+                _write_video_frames(writer, _iter_decoded_frames(decoded), context.util.is_canceled)
+            finally:
+                writer.close()
+            del decoded
+            TorchDevice.empty_cache()
+            video_dto = context.videos.save(
+                source_path=tmp_path,
+                width=int(width),
+                height=int(height),
+                duration=duration,
+                fps=float(fps),
+            )
+            context.logger.info(f"Saved video: {video_dto.video_name}")
+            return VideoOutput.build(video_dto)
+        finally:
+            try:
+                tmp_path.unlink(missing_ok=True)
+            except Exception:
+                pass
+            if wav_path is not None:
+                try:
+                    wav_path.unlink(missing_ok=True)
+                except Exception:
+                    pass
+
+    def _decode_audio_to_wav(self, context: InvocationContext, video_duration_s: float) -> Path:
+        assert self.audio_latents is not None and self.audio_vae is not None
+        audio_latents = context.tensors.load(self.audio_latents.latents_name)
+        if audio_latents.ndim != 3 or audio_latents.shape[0] != 2:
+            raise ValueError(
+                f"MiniMax H3 audio latents must be [2, C, T_audio] (one item per stereo channel); "
+                f"got {tuple(audio_latents.shape)}."
+            )
+
+        audio_vae_info = context.models.load(self.audio_vae.vae)
+        if not isinstance(audio_vae_info.model, AutoencoderKLMiniMaxH3Audio):
+            raise TypeError(
+                f"Expected AutoencoderKLMiniMaxH3Audio for the MiniMax H3 audio VAE, "
+                f"got {type(audio_vae_info.model).__name__}."
+            )
+
+        with audio_vae_info.model_on_device() as (_, audio_vae):
+            assert isinstance(audio_vae, AutoencoderKLMiniMaxH3Audio)
+            context.util.signal_progress("Running MiniMax H3 audio VAE decode")
+            device = get_effective_device(audio_vae)
+            vae_dtype = next(iter(audio_vae.parameters())).dtype
+            audio_latents = audio_latents.to(device=device, dtype=vae_dtype)
+
+            with torch.inference_mode():
+                latents_mean = torch.tensor(audio_vae.config.latents_mean, device=device).view(1, -1, 1)
+                latents_std = torch.tensor(audio_vae.config.latents_std, device=device).view(1, -1, 1)
+                audio_latents = audio_latents * latents_std.to(audio_latents) + latents_mean.to(audio_latents)
+                # The audio VAE is mono; the two stereo channels are two batch items.
+                waveform = audio_vae.decode(audio_latents, return_dict=False)[0]
+
+            sample_rate = int(audio_vae.config.sampling_rate)
+            # [2, 1, N] -> [2, N] float on CPU.
+            waveform = waveform.float().cpu().reshape(2, -1)
+
+        # Trim (or zero-pad: the 40-latents/s grid can come up ~8 ms short when
+        # num_frames % 3 == 2) to exactly the video duration — no -shortest at mux time.
+        max_samples = int(round(video_duration_s * sample_rate))
+        waveform = waveform[:, :max_samples]
+        if waveform.shape[1] < max_samples:
+            waveform = torch.nn.functional.pad(waveform, (0, max_samples - waveform.shape[1]))
+
+        wav = tempfile.NamedTemporaryFile(prefix="invokeai_minimax_h3_audio_", suffix=".wav", delete=False)
+        wav.close()
+        wav_path = Path(wav.name)
+        try:
+            write_stereo_wav(wav_path, waveform.numpy(), sample_rate)
+        except Exception:
+            wav_path.unlink(missing_ok=True)
+            raise
+        return wav_path
diff --git a/invokeai/app/invocations/minimax_h3_model_loader.py b/invokeai/app/invocations/minimax_h3_model_loader.py
new file mode 100644
index 00000000000..e3345f155b7
--- /dev/null
+++ b/invokeai/app/invocations/minimax_h3_model_loader.py
@@ -0,0 +1,72 @@
+from invokeai.app.invocations.baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    Classification,
+    invocation,
+    invocation_output,
+)
+from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField
+from invokeai.app.invocations.model import (
+    MiniMaxH3TextEncoderField,
+    MiniMaxH3TransformerField,
+    ModelIdentifierField,
+    VAEField,
+)
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.model_manager.taxonomy import BaseModelType, ModelType, SubModelType
+
+
+@invocation_output("minimax_h3_model_loader_output")
+class MiniMaxH3ModelLoaderOutput(BaseInvocationOutput):
+    """MiniMax H3 model loader output."""
+
+    transformer: MiniMaxH3TransformerField = OutputField(
+        description="MiniMax H3 FL2VA transformer", title="Transformer"
+    )
+    text_encoder: MiniMaxH3TextEncoderField = OutputField(
+        description=FieldDescriptions.minimax_h3_text_encoder, title="Qwen3-VL Encoder"
+    )
+    vae: VAEField = OutputField(description=FieldDescriptions.vae, title="Video VAE")
+    audio_vae: VAEField = OutputField(description=FieldDescriptions.minimax_h3_audio_vae, title="Audio VAE")
+
+
+@invocation(
+    "minimax_h3_model_loader",
+    title="Main Model - MiniMax H3",
+    tags=["model", "minimax", "video"],
+    category="model",
+    version="1.0.0",
+    classification=Classification.Prototype,
+)
+class MiniMaxH3ModelLoaderInvocation(BaseInvocation):
+    """Loads a MiniMax H3 (FL2VA) model, outputting its submodels.
+
+    All six submodels (transformer, text encoder, tokenizer, processor, video VAE, audio VAE)
+    come from the one diffusers-layout install; there is no component mix-and-match yet.
+    """
+
+    model: ModelIdentifierField = InputField(
+        description=FieldDescriptions.minimax_h3_model,
+        input=Input.Direct,
+        ui_model_base=BaseModelType.MiniMaxH3,
+        ui_model_type=ModelType.Main,
+        title="Model",
+    )
+
+    def invoke(self, context: InvocationContext) -> MiniMaxH3ModelLoaderOutput:
+        if not context.models.exists(self.model.key):
+            raise ValueError(f"Unknown model: {self.model.key}")
+
+        transformer = self.model.model_copy(update={"submodel_type": SubModelType.Transformer})
+        tokenizer = self.model.model_copy(update={"submodel_type": SubModelType.Tokenizer})
+        processor = self.model.model_copy(update={"submodel_type": SubModelType.Processor})
+        text_encoder = self.model.model_copy(update={"submodel_type": SubModelType.TextEncoder})
+        vae = self.model.model_copy(update={"submodel_type": SubModelType.VAE})
+        audio_vae = self.model.model_copy(update={"submodel_type": SubModelType.AudioVAE})
+
+        return MiniMaxH3ModelLoaderOutput(
+            transformer=MiniMaxH3TransformerField(transformer=transformer),
+            text_encoder=MiniMaxH3TextEncoderField(tokenizer=tokenizer, processor=processor, text_encoder=text_encoder),
+            vae=VAEField(vae=vae),
+            audio_vae=VAEField(vae=audio_vae),
+        )
diff --git a/invokeai/app/invocations/minimax_h3_text_encoder.py b/invokeai/app/invocations/minimax_h3_text_encoder.py
new file mode 100644
index 00000000000..6c1e94dd5a8
--- /dev/null
+++ b/invokeai/app/invocations/minimax_h3_text_encoder.py
@@ -0,0 +1,106 @@
+import torch
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
+from invokeai.app.invocations.fields import (
+    FieldDescriptions,
+    ImageField,
+    Input,
+    InputField,
+    UIComponent,
+)
+from invokeai.app.invocations.model import MiniMaxH3TextEncoderField
+from invokeai.app.invocations.primitives import MiniMaxH3ConditioningOutput
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.minimax_h3.keyframe_conditioning import prepare_keyframes
+from invokeai.backend.minimax_h3.packing import MINIMAX_H3_CANVAS_MULTIPLE
+from invokeai.backend.minimax_h3.text_conditioning import encode_prompt
+from invokeai.backend.model_manager.load.model_cache.utils import get_effective_device
+from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
+    ConditioningFieldData,
+    MiniMaxH3ConditioningInfo,
+)
+
+
+@invocation(
+    "minimax_h3_text_encoder",
+    title="Prompt - MiniMax H3",
+    tags=["prompt", "conditioning", "minimax", "video"],
+    category="conditioning",
+    version="1.0.0",
+    classification=Classification.Prototype,
+    idle_gpu_offloadable=True,
+)
+class MiniMaxH3TextEncoderInvocation(BaseInvocation):
+    """Encodes a prompt (and optional first/last keyframes) for MiniMax H3.
+
+    The conditioning is Qwen3-VL-32B's *unnormalized* layer-50 hidden state. H3 is
+    guidance-distilled: there is no negative prompt. For first/last-frame video, the keyframes
+    are ALSO part of the text conditioning (a "<Picture i>: " label plus a vision block per
+    keyframe), so the same images must be wired here and to the Frame Conditioning node, with
+    the same width/height as the denoise node.
+    """
+
+    prompt: str = InputField(description="Text prompt for MiniMax H3.", ui_component=UIComponent.Textarea)
+    text_encoder: MiniMaxH3TextEncoderField = InputField(
+        title="Qwen3-VL Encoder",
+        description=FieldDescriptions.minimax_h3_text_encoder,
+        input=Input.Connection,
+    )
+    first_image: ImageField | None = InputField(
+        default=None, description="Optional keyframe the video starts from (must match Frame Conditioning)."
+    )
+    last_image: ImageField | None = InputField(
+        default=None, description="Optional keyframe the video ends on (must match Frame Conditioning)."
+    )
+    width: int = InputField(
+        default=1344, gt=0, multiple_of=MINIMAX_H3_CANVAS_MULTIPLE, description="Target canvas width."
+    )
+    height: int = InputField(
+        default=768, gt=0, multiple_of=MINIMAX_H3_CANVAS_MULTIPLE, description="Target canvas height."
+    )
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> MiniMaxH3ConditioningOutput:
+        keyframes = []
+        anchors: tuple[str, ...] = ()
+        if self.first_image is not None or self.last_image is not None:
+            first = context.images.get_pil(self.first_image.image_name) if self.first_image else None
+            last = context.images.get_pil(self.last_image.image_name) if self.last_image else None
+            keyframes, anchors = prepare_keyframes(first, last, self.height, self.width)
+
+        # Load the largest model first: loading the ~22 GB text encoder evicts unlocked small
+        # entries from the RAM cache, so tokenizer/processor loaded before it would be dropped
+        # and re-read ("model loading order is non-optimal", issue #7513).
+        text_encoder_info = context.models.load(self.text_encoder.text_encoder)
+        tokenizer_info = context.models.load(self.text_encoder.tokenizer)
+        processor_info = context.models.load(self.text_encoder.processor)
+        with (
+            tokenizer_info.model_on_device() as (_, tokenizer),
+            processor_info.model_on_device() as (_, processor),
+            text_encoder_info.model_on_device() as (_, text_encoder),
+        ):
+            device = get_effective_device(text_encoder)
+            context.util.signal_progress("Running Qwen3-VL text encoder")
+            prompt_embeds, text_token_tags = encode_prompt(
+                text_encoder=text_encoder,
+                tokenizer=tokenizer,
+                processor=processor,
+                prompt=self.prompt,
+                keyframe_images=keyframes,
+                device=device,
+            )
+
+        # Persist on CPU; required by the idle-GPU-offload contract of this node.
+        conditioning_data = ConditioningFieldData(
+            conditionings=[
+                MiniMaxH3ConditioningInfo(
+                    prompt_embeds=prompt_embeds.detach().to("cpu"),
+                    text_token_tags=text_token_tags.detach().to("cpu"),
+                    keyframe_anchors=anchors,
+                    width=self.width if anchors else None,
+                    height=self.height if anchors else None,
+                )
+            ]
+        )
+        conditioning_name = context.conditioning.save(conditioning_data)
+        return MiniMaxH3ConditioningOutput.build(conditioning_name)
diff --git a/invokeai/app/invocations/model.py b/invokeai/app/invocations/model.py
index e178635835f..df20c7744b6 100644
--- a/invokeai/app/invocations/model.py
+++ b/invokeai/app/invocations/model.py
@@ -117,6 +117,25 @@ class WanT5EncoderField(BaseModel):
     loras: List[LoRAField] = Field(default_factory=list, description="LoRAs to apply on model loading")
 
 
+class MiniMaxH3TextEncoderField(BaseModel):
+    """Field for the Qwen3-VL-32B conditioner used by MiniMax H3 models.
+
+    Unlike :class:`Qwen3VLEncoderField`, H3 also needs the Qwen3VLProcessor — even for
+    text-only prompts (its multimodal token-type ids drive Qwen3-VL's 3D rotary layout), and
+    for feeding first/last keyframes to the conditioner as vision context.
+    """
+
+    tokenizer: ModelIdentifierField = Field(description="Info to load tokenizer submodel")
+    processor: ModelIdentifierField = Field(description="Info to load processor submodel")
+    text_encoder: ModelIdentifierField = Field(description="Info to load text_encoder submodel")
+
+
+class MiniMaxH3TransformerField(BaseModel):
+    """Transformer field for MiniMax H3 models (FL2VA)."""
+
+    transformer: ModelIdentifierField = Field(description="Info to load Transformer submodel")
+
+
 class VAEField(BaseModel):
     vae: ModelIdentifierField = Field(description="Info to load vae submodel")
     seamless_axes: List[str] = Field(default_factory=list, description='Axes("x" and "y") to which apply seamless')
diff --git a/invokeai/app/invocations/primitives.py b/invokeai/app/invocations/primitives.py
index dfe44d0e6e9..272a092377d 100644
--- a/invokeai/app/invocations/primitives.py
+++ b/invokeai/app/invocations/primitives.py
@@ -27,6 +27,7 @@
     InputField,
     Krea2ConditioningField,
     LatentsField,
+    MiniMaxH3ConditioningField,
     OutputField,
     QwenImageConditioningField,
     SD3ConditioningField,
@@ -561,6 +562,17 @@ def build(cls, conditioning_name: str) -> "WanConditioningOutput":
         return cls(conditioning=WanConditioningField(conditioning_name=conditioning_name))
 
 
+@invocation_output("minimax_h3_conditioning_output")
+class MiniMaxH3ConditioningOutput(BaseInvocationOutput):
+    """Base class for nodes that output a MiniMax H3 conditioning tensor."""
+
+    conditioning: MiniMaxH3ConditioningField = OutputField(description=FieldDescriptions.cond)
+
+    @classmethod
+    def build(cls, conditioning_name: str) -> "MiniMaxH3ConditioningOutput":
+        return cls(conditioning=MiniMaxH3ConditioningField(conditioning_name=conditioning_name))
+
+
 @invocation_output("wan_ref_image_output")
 class WanRefImageOutput(BaseInvocationOutput):
     """Output of a Wan 2.2 reference-image VAE-encoder."""
diff --git a/invokeai/app/util/step_callback.py b/invokeai/app/util/step_callback.py
index a6206448e9a..c3a5d578b00 100644
--- a/invokeai/app/util/step_callback.py
+++ b/invokeai/app/util/step_callback.py
@@ -257,6 +257,12 @@
 
 WAN22_LATENT_RGB_BIAS = [0.0317, -0.0878, -0.1388]
 
+# MiniMax H3's video VAE has 24 latent channels and 16x spatial downscale. No community RGB
+# projection exists yet, so previews use a uniform channel-mean (grayscale) fallback.
+# TODO(minimax-h3): generate real factors with scripts/generate_vae_linear_approximation.py
+# against the H3 video VAE once weights are available locally.
+MINIMAX_H3_LATENT_RGB_FACTORS = [[1.0 / 24.0, 1.0 / 24.0, 1.0 / 24.0] for _ in range(24)]
+
 
 def sample_to_lowres_estimated_image(
     samples: torch.Tensor,
@@ -366,6 +372,9 @@ def diffusion_step_callback(
         else:
             latent_rgb_factors = WAN_LATENT_RGB_FACTORS
             latent_rgb_bias = WAN_LATENT_RGB_BIAS
+    elif base_model == BaseModelType.MiniMaxH3:
+        # 24-ch H3 video VAE; grayscale channel-mean fallback until real factors exist.
+        latent_rgb_factors = MINIMAX_H3_LATENT_RGB_FACTORS
     else:
         raise ValueError(f"Unsupported base model: {base_model}")
 
@@ -388,6 +397,8 @@ def diffusion_step_callback(
     spatial_scale = 8
     if base_model == BaseModelType.Wan and sample.shape[-3] == 48:
         spatial_scale = 16
+    elif base_model == BaseModelType.MiniMaxH3:
+        spatial_scale = 16
     width = image.width * spatial_scale
     height = image.height * spatial_scale
     percentage = calc_percentage(intermediate_state)
diff --git a/invokeai/backend/minimax_h3/__init__.py b/invokeai/backend/minimax_h3/__init__.py
index 2500f851e58..aad18767c25 100644
--- a/invokeai/backend/minimax_h3/__init__.py
+++ b/invokeai/backend/minimax_h3/__init__.py
@@ -2,7 +2,9 @@
 
 Vendored from huggingface/diffusers PR #14355 ("Add MiniMax-H3") at commit
 abc5e9bf71fd38f53cd471bc3acaa84bc5ecbfdc (branch `minimax-h3`), which is not yet
-in any tagged diffusers release. The only local changes are rewriting the
+in any tagged diffusers release: the four model/scheduler modules plus
+``packing.py`` (the packed-sequence geometry and checkpoint constants, from
+``modular_pipelines/minimax_h3``). The only local changes are rewriting the
 package-relative imports to absolute `diffusers.*` imports (all referenced
 symbols exist in the pinned diffusers==0.39.0) and ruff import sorting. Keep
 these files otherwise
diff --git a/invokeai/backend/minimax_h3/denoise.py b/invokeai/backend/minimax_h3/denoise.py
new file mode 100644
index 00000000000..717f39bdd53
--- /dev/null
+++ b/invokeai/backend/minimax_h3/denoise.py
@@ -0,0 +1,85 @@
+"""The MiniMax H3 denoising loop (FL2VA / T2VA).
+
+First-party port of ``modular_pipelines/minimax_h3/denoise.py`` (commit recorded in
+``__init__``): one transformer forward per step over the packed sequence — every row at its own
+noise level — followed by one scheduler step per modality on the *generated* rows only. The
+conditioning rows are never written, so the anchors survive the loop by construction. The
+checkpoint is guidance-distilled: no unconditional pass, no CFG.
+"""
+
+from typing import Callable
+
+import torch
+
+from invokeai.backend.minimax_h3.sampling import MiniMaxH3DenoiseState
+from invokeai.backend.minimax_h3.transformer_minimax_h3 import MiniMaxH3Transformer3DModel
+
+
+def denoise(
+    transformer: MiniMaxH3Transformer3DModel,
+    state: MiniMaxH3DenoiseState,
+    prompt_embeds: torch.Tensor,
+    step_callback: Callable[[int, int, torch.Tensor], None] | None = None,
+    is_canceled: Callable[[], bool] | None = None,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """Run the full denoising schedule over the packed sequence.
+
+    Args:
+        transformer: The FL2VA transformer.
+        state: The prepared denoise state (rows, layout, schedules).
+        prompt_embeds: The layer-50 Qwen3-VL hidden states, shape ``(1, num_text_tokens, text_dim)``.
+        step_callback: Called after every step with ``(step_index, total_steps, video_rows)`` —
+            the current video rows including conditioning rows, for previews.
+        is_canceled: Polled once per step; a True return raises ``KeyboardInterrupt``-free
+            cancellation by letting the caller's exception type propagate from the callback.
+
+    Returns:
+        The denoised ``(video_rows, audio_rows)`` (conditioning/reference rows still included).
+    """
+    from invokeai.app.services.session_processor.session_processor_common import CanceledException
+
+    num_condition_video_rows = state.layout.num_condition_video_rows
+    num_condition_audio_rows = state.layout.num_condition_audio_rows
+
+    latents = state.video_rows
+    audio_latents = state.audio_rows
+    prompt_embeds = prompt_embeds.to(latents.device)
+
+    total_steps = len(state.timesteps)
+    for i, t in enumerate(state.timesteps):
+        if is_canceled is not None and is_canceled():
+            raise CanceledException
+
+        unique_timesteps, timestep_indices = state.row_timestep_plan[i]
+        noise_pred, audio_noise_pred = transformer(
+            hidden_states=latents[None],
+            audio_hidden_states=audio_latents[None],
+            encoder_hidden_states=prompt_embeds,
+            timestep=unique_timesteps,
+            timestep_indices=timestep_indices,
+            token_tags=state.token_tags,
+            position_ids=state.position_ids,
+            video_indices=state.video_indices,
+            audio_indices=state.audio_indices,
+            text_indices=state.text_indices,
+            attention_kwargs=None,
+            return_dict=False,
+        )
+
+        latents[num_condition_video_rows:] = state.scheduler.step(
+            noise_pred[0, num_condition_video_rows:].float(),
+            t,
+            latents[num_condition_video_rows:],
+            return_dict=False,
+        )[0]
+        audio_latents[num_condition_audio_rows:] = state.audio_scheduler.step(
+            audio_noise_pred[0, num_condition_audio_rows:].float(),
+            state.audio_timesteps[i],
+            audio_latents[num_condition_audio_rows:],
+            return_dict=False,
+        )[0]
+
+        if step_callback is not None:
+            step_callback(i + 1, total_steps, latents)
+
+    return latents, audio_latents
diff --git a/invokeai/backend/minimax_h3/keyframe_conditioning.py b/invokeai/backend/minimax_h3/keyframe_conditioning.py
new file mode 100644
index 00000000000..9618cea38be
--- /dev/null
+++ b/invokeai/backend/minimax_h3/keyframe_conditioning.py
@@ -0,0 +1,80 @@
+"""MiniMax H3 keyframe (first/last frame) VAE conditioning.
+
+First-party port of ``MiniMaxH3KeyframeVaeEncoderStep`` and the keyframe half of
+``MiniMaxH3SetupStep`` from the diffusers MiniMax-H3 integration (commit recorded in
+``__init__``). The keyframes are encoded by the video VAE's spatial encoder alone (they are
+single frames), the posterior is *sampled* under a generator seeded with 42 independently of
+the request seed, and the sampled latent is rounded to float16 before normalization — all
+three are part of reproducing the released model's conditioning.
+
+The rows returned here are CLEAN (not yet noise-augmented): the denoise state noises them to
+``t = 0.999`` with the request generator's first draws, keeping the one-generator-three-draws
+reproducibility order in one place (see :mod:`invokeai.backend.minimax_h3.sampling`).
+"""
+
+import numpy as np
+import torch
+from diffusers.models.autoencoders.vae import DiagonalGaussianDistribution
+from PIL import Image, ImageOps
+
+from invokeai.backend.minimax_h3.autoencoder_kl_minimax_h3 import AutoencoderKLMiniMaxH3
+from invokeai.backend.minimax_h3.packing import (
+    MINIMAX_H3_KEYFRAME_ENCODE_SEED,
+    MINIMAX_H3_PIXEL_MEAN,
+    MINIMAX_H3_PIXEL_STD,
+    patchify_video_latents,
+    prepare_keyframe_image,
+)
+from invokeai.backend.minimax_h3.sampling import MINIMAX_H3_PATCH_SIZE
+
+
+def prepare_keyframes(
+    first_image: Image.Image | None,
+    last_image: Image.Image | None,
+    height: int,
+    width: int,
+) -> tuple[list[Image.Image], tuple[str, ...]]:
+    """Put the keyframes onto the target canvas, in packed order.
+
+    The first keyframe *in packed order* is the geometry anchor and is stretched onto the
+    canvas (upstream stretches index 0 even when it is a lone last-frame); any second keyframe
+    follows the canvas and is cover-cropped. Returns the prepared images and their anchors
+    (``"first"`` / ``"last"``), both in packed order. Must be applied identically by the text
+    encoder (vision context) and the VAE conditioning encoder.
+    """
+    keyframes: list[Image.Image] = []
+    anchors: list[str] = []
+    for anchor, image in (("first", first_image), ("last", last_image)):
+        if image is None:
+            continue
+        prepared = ImageOps.exif_transpose(image).convert("RGB")
+        keyframes.append(prepare_keyframe_image(prepared, height, width, stretch=len(keyframes) == 0))
+        anchors.append(anchor)
+    return keyframes, tuple(anchors)
+
+
+@torch.no_grad()
+def encode_keyframes(
+    vae: AutoencoderKLMiniMaxH3,
+    images: list[Image.Image],
+    device: torch.device,
+) -> torch.Tensor:
+    """Encode prepared keyframes into clean, normalized, packed conditioning rows (float32, CPU)."""
+    latents_mean = torch.tensor(vae.config.latents_mean).view(1, -1, 1, 1, 1)
+    latents_std = torch.tensor(vae.config.latents_std).view(1, -1, 1, 1, 1)
+    pixel_mean = torch.tensor(MINIMAX_H3_PIXEL_MEAN, device=device).view(1, -1, 1, 1, 1)
+    pixel_std = torch.tensor(MINIMAX_H3_PIXEL_STD, device=device).view(1, -1, 1, 1, 1)
+
+    rows = []
+    for image in images:
+        pixels = torch.from_numpy(np.array(image)).to(device).permute(2, 0, 1)[None, :, None]
+        pixels = (pixels.to(torch.float32).div(255.0) - pixel_mean) / pixel_std
+        # A keyframe is one frame: the (tiled) spatial encoder alone, no 17-frame temporal chunking.
+        moments = vae._encode_clip(pixels)
+        posterior = DiagonalGaussianDistribution(moments)
+        latents = posterior.sample(generator=torch.Generator().manual_seed(MINIMAX_H3_KEYFRAME_ENCODE_SEED))
+        # The fp16 rounding before normalization is a checkpoint contract (~11 bits of every
+        # conditioning latent); without it the released model's conditioning is not reproduced.
+        latents = latents.to(torch.float16).float().cpu()
+        rows.append(patchify_video_latents((latents - latents_mean) / latents_std, MINIMAX_H3_PATCH_SIZE))
+    return torch.cat(rows)
diff --git a/invokeai/backend/minimax_h3/packing.py b/invokeai/backend/minimax_h3/packing.py
new file mode 100644
index 00000000000..e34de27e74c
--- /dev/null
+++ b/invokeai/backend/minimax_h3/packing.py
@@ -0,0 +1,536 @@
+# Copyright 2026 The MiniMax and HuggingFace Teams. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+r"""
+Packed-sequence and conditioning machinery of the MiniMax-H3 blocks.
+
+This module holds no block of its own: it is the checkpoint's geometry and its constants, imported by every block
+of `modular_pipelines.minimax_h3` that has to place a row, so that none of them reimplements it.
+
+MiniMax-H3 runs its transformer over a single packed 1-D sequence that holds every modality at once. For the
+text/keyframe tasks the row order is
+
+```
+[ text (L) | keyframe conditions (C) | target audio (A) | target video (V) ]
+```
+
+and every piece of geometry in this module exists to place a row in that sequence and to give it its `(t, h, w)`
+rotary coordinate. The coordinates are built in float64 because video and audio share one 40-units-per-second
+rotary clock — video advances `5/3` rotary units per pixel frame at 24 fps, audio advances one unit per latent at
+40 latents/s — and that shared clock *is* the audio/video alignment.
+
+The reference implementation pads the packed sequence up to a multiple of 64 and keeps the padding tail as a
+separate attention document. Padding therefore cannot influence a live row, and this module builds the sequence
+without it: `MiniMaxH3Transformer3DModel` then needs no attention mask, which keeps the unmasked attention
+backends available.
+"""
+
+from dataclasses import dataclass
+
+import numpy as np
+import torch
+from diffusers.utils.torch_utils import randn_tensor
+from PIL import Image
+
+# Per-row modality tags. They index the transformer's AdaLN table, so the values are a checkpoint contract.
+MINIMAX_H3_VIDEO_TAG = 0
+MINIMAX_H3_TEXT_TAG = 1
+MINIMAX_H3_AUDIO_TAG = 2
+
+# MiniMax-H3 generates at a fixed 24 fps and was released for a 768 pixel short edge only, with a soft area cap of
+# 768x1344 and both axes rounded to a multiple of 32.
+MINIMAX_H3_FPS = 24
+MINIMAX_H3_SHORT_EDGE = 768
+MINIMAX_H3_MAX_PIXELS = 768 * 1344
+MINIMAX_H3_CANVAS_MULTIPLE = 32
+MINIMAX_H3_MIN_ASPECT_RATIO = 1 / 4
+MINIMAX_H3_MAX_ASPECT_RATIO = 4
+MINIMAX_H3_MIN_DURATION = 5.0
+MINIMAX_H3_MAX_DURATION = 15.0
+
+# The video VAE encodes 17 pixel frames per chunk and drops the 3 trailing latent frames of every chunk, so
+# `17 * n + 5` pixel frames map to `5 * n + 2` latent frames.
+MINIMAX_H3_FRAMES_PER_CHUNK = 17
+MINIMAX_H3_LATENTS_PER_CHUNK = 5
+
+# The pixel convention of the video VAE: ImageNet-normalized RGB over a `[0, 1]` base range.
+MINIMAX_H3_PIXEL_MEAN = (0.485, 0.456, 0.406)
+MINIMAX_H3_PIXEL_STD = (0.229, 0.224, 0.225)
+
+# MiniMax-H3 conditions on the *unnormalized* hidden state its Qwen3-VL conditioner produces after the 50th of its 64
+# decoder layers, i.e. `hidden_states[50]` (`hidden_states[0]` being the embedding output).
+MINIMAX_H3_TEXT_ENCODER_LAYER = 50
+
+# The audio VAE hops 800 samples at 32 kHz, i.e. 40 latents per second. Stereo is carried as two channel-major
+# blocks of audio rows (and as two batch items at the audio VAE boundary, which is mono).
+MINIMAX_H3_AUDIO_LATENTS_PER_SECOND = 40
+MINIMAX_H3_AUDIO_CHANNELS = 2
+
+# Conditioning rows are not fully clean: the released model noises keyframe latents to `t = 0.999` and runs them at
+# that timestep for every denoising step.
+MINIMAX_H3_KEYFRAME_NOISE_AUG = 0.999
+
+# The seeded posterior sample of the keyframe VAE encode. Fixed at 42 independently of the request seed.
+MINIMAX_H3_KEYFRAME_ENCODE_SEED = 42
+
+# Rotary-time constants. One latent frame spans `5/3 * frames_per_latent` rotary units, where the pattern
+# `(1, 4, 4, 4, 4)` mirrors the VAE's 17-pixel-frames-to-5-latent-frames grouping; the spatial axes are normalized
+# by the square root of the latent area and scaled by 32.
+_ROPE_FRAME_RESCALE = 5.0 / 3.0
+_ROPE_FRAMES_PER_LATENT = (1, 4, 4, 4, 4)
+_ROPE_SPATIAL_SCALE = 32
+
+
+@dataclass
+class MiniMaxH3PackedSequence:
+    r"""
+    The structural description of one packed MiniMax-H3 sequence.
+
+    Attributes:
+        sequence_length (`int`):
+            Total number of rows, `L + C + A + V`.
+        position_ids (`torch.Tensor` of shape `(sequence_length, 3)`, float64):
+            The `(t, h, w)` rotary coordinate of every row.
+        token_tags (`torch.Tensor` of shape `(sequence_length,)`):
+            The modality tag of every row.
+        video_indices (`torch.Tensor`):
+            Sequence positions of the video rows: the keyframe conditioning rows first, then the target rows.
+        audio_indices (`torch.Tensor`):
+            Sequence positions of the audio rows: reference rows first (`ref2va` only), then the target rows.
+        text_indices (`torch.Tensor`):
+            Sequence positions of the text rows.
+        num_condition_video_rows (`int`):
+            How many leading entries of `video_indices` are conditioning rows rather than generated rows.
+        num_condition_audio_rows (`int`):
+            How many leading entries of `audio_indices` are reference rows rather than generated rows.
+    """
+
+    sequence_length: int
+    position_ids: torch.Tensor
+    token_tags: torch.Tensor
+    video_indices: torch.Tensor
+    audio_indices: torch.Tensor
+    text_indices: torch.Tensor
+    num_condition_video_rows: int
+    num_condition_audio_rows: int
+
+
+def resolve_canvas_size(aspect_width: float, aspect_height: float) -> tuple[int, int]:
+    r"""
+    Resolve a display aspect ratio into a MiniMax-H3 canvas.
+
+    The short edge starts at 768, the area is capped at `768 * 1344` and both axes are then rounded to the nearest
+    multiple of 32 — so the final area may end up slightly above the pre-rounding budget. Only the ratio of the two
+    arguments matters; pass either the aspect ratio (`16, 9`) or the source dimensions of a keyframe.
+
+    Args:
+        aspect_width (`float`): Width of the target ratio.
+        aspect_height (`float`): Height of the target ratio.
+
+    Returns:
+        `tuple[int, int]`: the `(height, width)` of the canvas.
+    """
+    if aspect_width <= 0 or aspect_height <= 0:
+        raise ValueError(f"The aspect ratio must be positive, got {aspect_width}:{aspect_height}.")
+
+    ratio = aspect_width / aspect_height
+    if not MINIMAX_H3_MIN_ASPECT_RATIO <= ratio <= MINIMAX_H3_MAX_ASPECT_RATIO:
+        raise ValueError(
+            f"MiniMax-H3 supports aspect ratios from 1:4 to 4:1, got {aspect_width}:{aspect_height} ({ratio:g})."
+        )
+
+    if ratio >= 1.0:
+        width, height = MINIMAX_H3_SHORT_EDGE * ratio, float(MINIMAX_H3_SHORT_EDGE)
+    else:
+        width, height = float(MINIMAX_H3_SHORT_EDGE), MINIMAX_H3_SHORT_EDGE / ratio
+
+    area = width * height
+    if area > MINIMAX_H3_MAX_PIXELS:
+        scale = (MINIMAX_H3_MAX_PIXELS / area) ** 0.5
+        width, height = width * scale, height * scale
+
+    multiple = MINIMAX_H3_CANVAS_MULTIPLE
+    return max(multiple, round(height / multiple) * multiple), max(multiple, round(width / multiple) * multiple)
+
+
+def align_num_frames(num_frames: int) -> int:
+    r"""
+    Snap a frame count up to the next `17 * n + 5` the video VAE can encode.
+
+    Args:
+        num_frames (`int`): The requested number of frames.
+
+    Returns:
+        `int`: The aligned number of frames.
+    """
+    if num_frames < 1:
+        raise ValueError(f"`num_frames` must be positive, got {num_frames}.")
+    while num_frames % MINIMAX_H3_FRAMES_PER_CHUNK != MINIMAX_H3_LATENTS_PER_CHUNK:
+        num_frames += 1
+    return num_frames
+
+
+def video_latent_num_frames(num_frames: int) -> int:
+    r"""
+    The number of latent frames the video VAE produces for a `17 * n + 5` frame count.
+
+    Args:
+        num_frames (`int`): An aligned number of frames.
+
+    Returns:
+        `int`: The number of latent frames, `5 * n + 2`.
+    """
+    if num_frames % MINIMAX_H3_FRAMES_PER_CHUNK != MINIMAX_H3_LATENTS_PER_CHUNK:
+        raise ValueError(f"`num_frames` must be of the form 17 * n + 5, got {num_frames}.")
+    return (
+        num_frames - MINIMAX_H3_LATENTS_PER_CHUNK
+    ) // MINIMAX_H3_FRAMES_PER_CHUNK * MINIMAX_H3_LATENTS_PER_CHUNK + 2
+
+
+def audio_latent_num_frames(num_frames: int) -> int:
+    r"""
+    The number of audio latents that covers a video of `num_frames` frames at 24 fps.
+
+    Args:
+        num_frames (`int`): The number of video frames.
+
+    Returns:
+        `int`: The number of audio latents, rounded at the 40 Hz latent grid.
+    """
+    return int(round(num_frames / MINIMAX_H3_FPS * MINIMAX_H3_AUDIO_LATENTS_PER_SECOND))
+
+
+def prepare_keyframe_image(image, height: int, width: int, stretch: bool):
+    r"""
+    Put a keyframe onto the target canvas.
+
+    The first keyframe of a request is the geometry anchor and is *stretched* onto the canvas, while a second
+    keyframe follows that canvas and is cover-cropped (aspect-preserving max-scale LANCZOS resize plus a centre
+    crop). An image that already is the canvas is returned untouched, without a resampling pass.
+
+    Args:
+        image (`PIL.Image.Image`): The keyframe, in RGB and already EXIF-transposed.
+        height (`int`): Canvas height.
+        width (`int`): Canvas width.
+        stretch (`bool`): Whether to stretch (geometry anchor) instead of cover-cropping (follower).
+
+    Returns:
+        `PIL.Image.Image`: The prepared keyframe.
+    """
+    if image.size == (width, height):
+        return image
+    if stretch:
+        return image.resize((width, height), Image.Resampling.LANCZOS)
+
+    scale = max(width / image.size[0], height / image.size[1])
+    resized_size = (max(width, round(image.size[0] * scale)), max(height, round(image.size[1] * scale)))
+    left = max(0, (resized_size[0] - width) // 2)
+    top = max(0, (resized_size[1] - height) // 2)
+    resized = image.resize(resized_size, Image.Resampling.LANCZOS)
+    return resized.crop((left, top, left + width, top + height))
+
+
+def patchify_video_latents(latents: torch.Tensor, patch_size: tuple[int, int, int]) -> torch.Tensor:
+    r"""
+    Pack video latents into transformer rows.
+
+    Args:
+        latents (`torch.Tensor` of shape `(batch_size, channels, num_frames, height, width)`):
+            The latents to pack.
+        patch_size (`tuple[int, int, int]`): The `(t, h, w)` patch.
+
+    Returns:
+        `torch.Tensor` of shape `(batch_size * num_patches, channels * prod(patch_size))`: The packed rows, ordered
+        frame-major then row-major.
+    """
+    patch_t, patch_h, patch_w = patch_size
+    batch_size, channels, num_frames, height, width = latents.shape
+    if num_frames % patch_t or height % patch_h or width % patch_w:
+        raise ValueError(f"Latents of shape {tuple(latents.shape)} are not divisible by the patch {patch_size}.")
+
+    latents = latents.reshape(
+        batch_size,
+        channels,
+        num_frames // patch_t,
+        patch_t,
+        height // patch_h,
+        patch_h,
+        width // patch_w,
+        patch_w,
+    )
+    latents = latents.permute(0, 2, 4, 6, 1, 3, 5, 7)
+    return latents.reshape(-1, channels * patch_t * patch_h * patch_w).contiguous()
+
+
+def unpatchify_video_tokens(
+    rows: torch.Tensor,
+    num_latent_frames: int,
+    latent_height: int,
+    latent_width: int,
+    channels: int,
+    patch_size: tuple[int, int, int],
+) -> torch.Tensor:
+    r"""
+    Unpack transformer rows back into video latents. The inverse of [`patchify_video_latents`].
+
+    Args:
+        rows (`torch.Tensor` of shape `(num_patches, channels * prod(patch_size))`): The packed rows.
+        num_latent_frames (`int`): Number of latent frames.
+        latent_height (`int`): Latent height.
+        latent_width (`int`): Latent width.
+        channels (`int`): Number of latent channels.
+        patch_size (`tuple[int, int, int]`): The `(t, h, w)` patch.
+
+    Returns:
+        `torch.Tensor` of shape `(batch_size, channels, num_latent_frames, latent_height, latent_width)`.
+    """
+    patch_t, patch_h, patch_w = patch_size
+    rows = rows.reshape(
+        -1,
+        num_latent_frames // patch_t,
+        latent_height // patch_h,
+        latent_width // patch_w,
+        channels,
+        patch_t,
+        patch_h,
+        patch_w,
+    )
+    rows = rows.permute(0, 4, 1, 5, 2, 6, 3, 7)
+    return rows.reshape(-1, channels, num_latent_frames, latent_height, latent_width).contiguous()
+
+
+def unpack_audio_tokens(rows: torch.Tensor, num_audio_latents: int) -> torch.Tensor:
+    r"""
+    Unpack the channel-major audio rows into audio VAE latents.
+
+    Args:
+        rows (`torch.Tensor` of shape `(num_audio_latents * 2, latent_channels)`): The packed audio rows.
+        num_audio_latents (`int`): Number of audio latents per channel.
+
+    Returns:
+        `torch.Tensor` of shape `(2, latent_channels, num_audio_latents)`: One batch item per stereo channel, which
+        is what the mono audio VAE consumes.
+    """
+    rows = rows.reshape(MINIMAX_H3_AUDIO_CHANNELS, num_audio_latents, rows.shape[-1])
+    return rows.permute(0, 2, 1).contiguous()
+
+
+def _spatial_position_grid(dim: int, patch: int, sqrt_area: float) -> torch.Tensor:
+    r"""
+    One aspect-normalized spatial rotary axis: `dim // patch` coordinates centred on the unit interval, scaled up by
+    32. The right endpoint is excluded, so a square canvas spans `[0, 32)`.
+    """
+    ratio = dim / sqrt_area
+    left = (1.0 - ratio) / 2.0
+    # Built with numpy: `np.linspace(..., endpoint=False)` is `start + arange(num) * (stop - start) / num`, which is
+    # not what `torch.linspace` computes, and the float64 grid has to be reproduced exactly.
+    grid = np.linspace(left, left + ratio, dim // patch, endpoint=False) * _ROPE_SPATIAL_SCALE
+    return torch.from_numpy(grid).to(torch.float64)
+
+
+def _temporal_position_grid(num_latent_frames: int, origin: float) -> torch.Tensor:
+    r"""The rotary time of every latent frame, starting at `origin`. Spacing is non-uniform: `5/3 * (1, 4, 4, 4, 4)`."""
+    spans = torch.tensor(
+        [
+            _ROPE_FRAME_RESCALE * _ROPE_FRAMES_PER_LATENT[index % len(_ROPE_FRAMES_PER_LATENT)]
+            for index in range(num_latent_frames)
+        ],
+        dtype=torch.float64,
+    )
+    return origin + torch.cat([torch.zeros(1, dtype=torch.float64), spans[:-1].cumsum(0)])
+
+
+def _temporal_position_span(num_latent_frames: int) -> float:
+    r"""
+    The rotary time spanned by `num_latent_frames` latent frames.
+
+    Summed by numpy (pairwise summation) rather than sequentially: the reference computes the keyframe anchor this
+    way and the two summation orders differ in the last ulp from 16 latent frames onwards.
+    """
+    spans = np.ones(num_latent_frames, dtype=np.float64) * _ROPE_FRAME_RESCALE
+    for index in range(len(_ROPE_FRAMES_PER_LATENT)):
+        spans[index :: len(_ROPE_FRAMES_PER_LATENT)] *= _ROPE_FRAMES_PER_LATENT[index]
+    return float(spans.sum())
+
+
+def build_packed_sequence(
+    text_token_tags: torch.Tensor,
+    num_latent_frames: int,
+    latent_height: int,
+    latent_width: int,
+    num_audio_latents: int,
+    patch_size: tuple[int, int, int],
+    keyframe_anchors: tuple[str, ...] = (),
+) -> MiniMaxH3PackedSequence:
+    r"""
+    Build the `[text | keyframe conditions | target audio | target video]` layout used by the `t2va` and `fl2va`
+    tasks.
+
+    Args:
+        text_token_tags (`torch.Tensor` of shape `(num_text_tokens,)`):
+            The modality tag of every text row. Text is tagged `1`, except for the rows of a keyframe's vision block,
+            which MiniMax-H3 tags `0` (video).
+        num_latent_frames (`int`): Number of target latent frames.
+        latent_height (`int`): Target latent height.
+        latent_width (`int`): Target latent width.
+        num_audio_latents (`int`): Number of target audio latents per channel.
+        patch_size (`tuple[int, int, int]`): The transformer's `(t, h, w)` patch.
+        keyframe_anchors (`tuple[str, ...]`):
+            One entry per keyframe conditioning block, in packed order: `"first"` anchors the block at the first
+            latent frame, `"last"` at the last one.
+
+    Returns:
+        [`MiniMaxH3PackedSequence`]
+    """
+    _, patch_h, patch_w = patch_size
+    rows_per_frame = (latent_height // patch_h) * (latent_width // patch_w)
+    num_text_tokens = text_token_tags.shape[0]
+    num_condition_rows = len(keyframe_anchors) * rows_per_frame
+    num_audio_rows = num_audio_latents * MINIMAX_H3_AUDIO_CHANNELS
+    num_video_rows = num_latent_frames * rows_per_frame
+    sequence_length = num_text_tokens + num_condition_rows + num_audio_rows + num_video_rows
+
+    condition_start = num_text_tokens
+    audio_start = condition_start + num_condition_rows
+    video_start = audio_start + num_audio_rows
+
+    # 1. The (t, h, w) grid. Text rows sit on the time axis at their row index, and the media rows continue the time
+    # axis from there, so text length shifts the whole media clock.
+    position_ids = torch.zeros(sequence_length, 3, dtype=torch.float64)
+    position_ids[:num_text_tokens, 0] = torch.arange(num_text_tokens, dtype=torch.float64)
+
+    sqrt_area = np.sqrt(latent_height * latent_width)
+    height_grid = _spatial_position_grid(latent_height, patch_h, sqrt_area)
+    width_grid = _spatial_position_grid(latent_width, patch_w, sqrt_area)
+    frame_grid = torch.stack([grid.reshape(-1) for grid in torch.meshgrid(height_grid, width_grid, indexing="ij")], -1)
+
+    for index, anchor in enumerate(keyframe_anchors):
+        if anchor == "first":
+            anchor_time = float(num_text_tokens)
+        elif anchor == "last":
+            anchor_time = float(num_text_tokens) + _temporal_position_span(num_latent_frames) - _ROPE_FRAME_RESCALE
+        else:
+            raise ValueError(f"A keyframe anchor must be 'first' or 'last', got {anchor!r}.")
+        rows = slice(condition_start + index * rows_per_frame, condition_start + (index + 1) * rows_per_frame)
+        position_ids[rows, 0] = anchor_time
+        position_ids[rows, 1:] = frame_grid
+
+    # Audio rows are channel-major and share the video's rotary clock: one unit per latent at 40 latents/s equals
+    # 24 fps * 5/3. They carry no height coordinate and are pinned to the two extremes of the width grid.
+    audio_time = float(num_text_tokens) + torch.arange(num_audio_latents, dtype=torch.float64)
+    position_ids[audio_start:video_start, 0] = audio_time.repeat(MINIMAX_H3_AUDIO_CHANNELS)
+    position_ids[audio_start:video_start, 2] = torch.cat(
+        [
+            torch.full((num_audio_latents,), float(width_grid[0]), dtype=torch.float64),
+            torch.full((num_audio_rows - num_audio_latents,), float(width_grid[-1]), dtype=torch.float64),
+        ]
+    )
+
+    video_position_ids = torch.empty(num_latent_frames, rows_per_frame, 3, dtype=torch.float64)
+    video_position_ids[:, :, 0] = _temporal_position_grid(num_latent_frames, float(num_text_tokens))[:, None]
+    video_position_ids[:, :, 1:] = frame_grid[None]
+    position_ids[video_start:] = video_position_ids.reshape(-1, 3)
+
+    # 2. Row indices and modality tags.
+    video_indices = torch.cat([torch.arange(condition_start, audio_start), torch.arange(video_start, sequence_length)])
+    audio_indices = torch.arange(audio_start, video_start)
+    text_indices = torch.arange(num_text_tokens)
+
+    token_tags = torch.empty(sequence_length, dtype=torch.long)
+    token_tags[text_indices] = text_token_tags.to(torch.long)
+    token_tags[audio_indices] = MINIMAX_H3_AUDIO_TAG
+    token_tags[video_indices] = MINIMAX_H3_VIDEO_TAG
+
+    return MiniMaxH3PackedSequence(
+        sequence_length=sequence_length,
+        position_ids=position_ids,
+        token_tags=token_tags,
+        video_indices=video_indices,
+        audio_indices=audio_indices,
+        text_indices=text_indices,
+        num_condition_video_rows=num_condition_rows,
+        num_condition_audio_rows=0,
+    )
+
+
+def build_row_timesteps(
+    layout: MiniMaxH3PackedSequence,
+    video_timestep: float,
+    audio_timestep: float,
+    condition_video_timestep: float,
+    condition_audio_timestep: float,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    r"""
+    Assign a timestep to every row of the packed sequence and reduce it to the transformer's `(timestep,
+    timestep_indices)` pair.
+
+    One forward serves rows at different noise levels: the generated video and audio rows step down their own
+    schedules while the conditioning rows stay pinned at their noise-augmentation level. Text rows never reach an
+    output head and inherit the video timestep.
+
+    Args:
+        layout ([`MiniMaxH3PackedSequence`]): The packed layout.
+        video_timestep (`float`): Timestep of the generated video rows.
+        audio_timestep (`float`): Timestep of the generated audio rows.
+        condition_video_timestep (`float`): Timestep of the video conditioning rows.
+        condition_audio_timestep (`float`): Timestep of the audio reference rows.
+
+    Returns:
+        `tuple[torch.Tensor, torch.Tensor]`: the distinct timesteps, sorted, and the index of every row into them.
+    """
+    row_timesteps = torch.full((layout.sequence_length,), video_timestep, dtype=torch.float32)
+    row_timesteps[layout.video_indices[: layout.num_condition_video_rows]] = condition_video_timestep
+    row_timesteps[layout.audio_indices[layout.num_condition_audio_rows :]] = audio_timestep
+    row_timesteps[layout.audio_indices[: layout.num_condition_audio_rows]] = condition_audio_timestep
+    return torch.unique(row_timesteps, sorted=True, return_inverse=True)
+
+
+def keyframe_condition_noise(
+    condition_latent_shapes: tuple[tuple[int, int, int], ...],
+    patch_size: tuple[int, int, int],
+    latent_channels: int,
+    generator: torch.Generator | list[torch.Generator] | None = None,
+    device: torch.device | None = None,
+    dtype: torch.dtype = torch.float32,
+) -> torch.Tensor:
+    r"""
+    Draw the noise that the keyframe (or reference) conditioning rows are mixed with.
+
+    One draw per condition, in packed order, off the request's generator. The conditioning rows are prepared before
+    the target rows, so these are the *first* draws of a request, ahead of the video and audio noise of
+    [`~MiniMaxH3PrepareLatentsStep.prepare_latents`] — the order is part of what a generator reproduces.
+
+    Args:
+        condition_latent_shapes (`tuple[tuple[int, int, int], ...]`):
+            The `(num_latent_frames, latent_height, latent_width)` of every condition, in packed order.
+        patch_size (`tuple[int, int, int]`): The transformer's `(t, h, w)` patch.
+        latent_channels (`int`): Number of video latent channels.
+        generator (`torch.Generator`, *optional*): The generator of the request.
+        device (`torch.device`, *optional*): The device the noise is drawn on.
+        dtype (`torch.dtype`, defaults to `torch.float32`): The dtype of the noise.
+
+    Returns:
+        `torch.Tensor` of shape `(num_condition_rows, latent_channels * prod(patch_size))`: the noise rows,
+        concatenated in packed order.
+    """
+    rows = []
+    for num_latent_frames, latent_height, latent_width in condition_latent_shapes:
+        noise = randn_tensor(
+            (1, latent_channels, num_latent_frames, latent_height, latent_width),
+            generator=generator,
+            device=device,
+            dtype=dtype,
+        )
+        rows.append(patchify_video_latents(noise, patch_size))
+    return torch.cat(rows)
diff --git a/invokeai/backend/minimax_h3/sampling.py b/invokeai/backend/minimax_h3/sampling.py
new file mode 100644
index 00000000000..3994f7db85b
--- /dev/null
+++ b/invokeai/backend/minimax_h3/sampling.py
@@ -0,0 +1,214 @@
+"""Denoise-state construction for MiniMax H3 (FL2VA / T2VA).
+
+First-party port of the state-preparation blocks of the diffusers MiniMax-H3 integration
+(``modular_pipelines/minimax_h3/before_denoise.py`` at the commit recorded in ``__init__``).
+The packed-sequence geometry itself is the vendored :mod:`invokeai.backend.minimax_h3.packing`;
+this module only sequences it: noise draws, conditioning-row noise augmentation, schedules and
+the per-step row-timestep plan.
+
+Reproducibility contract (mirrors upstream): a request draws every stream from ONE generator,
+in a fixed order — the keyframe conditioning noise first (one draw per keyframe), then the
+video noise as a 5D latent tensor that is patchified afterwards, then the audio noise directly
+in row layout. A CPU-seeded generator keeps the draws identical across CUDA/ROCm/MPS.
+"""
+
+from dataclasses import dataclass
+
+import torch
+from diffusers.utils.torch_utils import randn_tensor
+
+from invokeai.backend.minimax_h3.packing import (
+    MINIMAX_H3_AUDIO_CHANNELS,
+    MINIMAX_H3_FPS,
+    MINIMAX_H3_FRAMES_PER_CHUNK,
+    MINIMAX_H3_KEYFRAME_NOISE_AUG,
+    MINIMAX_H3_LATENTS_PER_CHUNK,
+    MINIMAX_H3_MAX_DURATION,
+    MINIMAX_H3_MIN_DURATION,
+    MiniMaxH3PackedSequence,
+    build_packed_sequence,
+    build_row_timesteps,
+    keyframe_condition_noise,
+    patchify_video_latents,
+)
+from invokeai.backend.minimax_h3.scheduling_minimax_h3 import MiniMaxH3Scheduler
+
+# The released FL2VA checkpoint's two sigma shifts (scheduler/ and audio_scheduler/ configs).
+MINIMAX_H3_VIDEO_FLOW_SHIFT = 12.0
+MINIMAX_H3_AUDIO_FLOW_SHIFT = 3.0
+
+# Transformer geometry of the released checkpoints (transformer/config.json). The vendored
+# transformer validates these against its own config at forward time; keeping them as module
+# constants lets the state be built without the 33B model in memory.
+MINIMAX_H3_PATCH_SIZE = (1, 2, 2)
+MINIMAX_H3_VAE_LATENT_CHANNELS = 24
+MINIMAX_H3_AUDIO_LATENT_CHANNELS = 32
+MINIMAX_H3_SPATIAL_COMPRESSION = 16
+
+# A single 17-frame-block clip (5 pixel frames, ~0.2 s) is the still-image path: the video VAE
+# can decode it, but it sits far below the 5 s floor the model was trained for, so it is only
+# offered for frame extraction, not as a video duration.
+MINIMAX_H3_STILL_NUM_FRAMES = 5
+
+
+def validate_num_frames(num_frames: int) -> None:
+    """Reject frame counts the FL2VA checkpoint cannot generate.
+
+    Valid values are ``17 * n + 5`` (the video VAE's chunk grid). The resulting duration must
+    lie in the released model's 5-15 s window — except for the single-block minimum of 5 frames,
+    which is allowed as the still-image (frame-extraction) path.
+    """
+    if num_frames % MINIMAX_H3_FRAMES_PER_CHUNK != MINIMAX_H3_LATENTS_PER_CHUNK:
+        raise ValueError(
+            f"num_frames must be of the form 17 * n + 5 for the MiniMax H3 video VAE "
+            f"(5, 22, 39, ..., 124, ...); got {num_frames}."
+        )
+    if num_frames == MINIMAX_H3_STILL_NUM_FRAMES:
+        return
+    duration = num_frames / MINIMAX_H3_FPS
+    if not MINIMAX_H3_MIN_DURATION <= duration <= MINIMAX_H3_MAX_DURATION:
+        raise ValueError(
+            f"MiniMax H3 generates between {MINIMAX_H3_MIN_DURATION:g} and {MINIMAX_H3_MAX_DURATION:g} "
+            f"seconds at {MINIMAX_H3_FPS} fps ({int(MINIMAX_H3_MIN_DURATION * MINIMAX_H3_FPS)}-"
+            f"{int(MINIMAX_H3_MAX_DURATION * MINIMAX_H3_FPS)} frames on the 17n+5 grid), or exactly "
+            f"{MINIMAX_H3_STILL_NUM_FRAMES} frames for a still image; got {num_frames}."
+        )
+
+
+def build_schedulers(num_inference_steps: int, device: torch.device) -> tuple[MiniMaxH3Scheduler, MiniMaxH3Scheduler]:
+    """The two schedules of a request: video (shift 12.0) and audio (shift 3.0)."""
+    scheduler = MiniMaxH3Scheduler(shift=MINIMAX_H3_VIDEO_FLOW_SHIFT)
+    audio_scheduler = MiniMaxH3Scheduler(shift=MINIMAX_H3_AUDIO_FLOW_SHIFT)
+    scheduler.set_timesteps(num_inference_steps, device=device)
+    audio_scheduler.set_timesteps(num_inference_steps, device=device)
+    return scheduler, audio_scheduler
+
+
+@dataclass
+class MiniMaxH3DenoiseState:
+    """Everything the denoise loop consumes, on the execution device."""
+
+    layout: MiniMaxH3PackedSequence
+    video_rows: torch.Tensor
+    """Video rows of the packed sequence, conditioning rows first. Shape (C + V, 96), float32."""
+    audio_rows: torch.Tensor
+    """Channel-major audio rows. Shape (A, 32), float32."""
+    position_ids: torch.Tensor
+    token_tags: torch.Tensor
+    video_indices: torch.Tensor
+    audio_indices: torch.Tensor
+    text_indices: torch.Tensor
+    timesteps: torch.Tensor
+    audio_timesteps: torch.Tensor
+    row_timestep_plan: list[tuple[torch.Tensor, torch.Tensor]]
+    scheduler: MiniMaxH3Scheduler
+    audio_scheduler: MiniMaxH3Scheduler
+
+
+def build_denoise_state(
+    text_token_tags: torch.Tensor,
+    num_latent_frames: int,
+    latent_height: int,
+    latent_width: int,
+    num_audio_latents: int,
+    num_inference_steps: int,
+    seed: int,
+    device: torch.device,
+    keyframe_anchors: tuple[str, ...] = (),
+    clean_condition_rows: torch.Tensor | None = None,
+) -> MiniMaxH3DenoiseState:
+    """Build the packed layout, the noise, and the schedules of one FL2VA / T2VA request.
+
+    ``clean_condition_rows`` are the un-noised keyframe conditioning rows (as produced by
+    :func:`invokeai.backend.minimax_h3.keyframe_conditioning.encode_keyframes`); they are
+    noise-augmented to ``t = 0.999`` here so that the request's single generator makes its
+    three draws in upstream's order (keyframe noise, video noise, audio noise).
+    """
+    if (clean_condition_rows is None) != (len(keyframe_anchors) == 0):
+        raise ValueError("clean_condition_rows and keyframe_anchors must be provided together.")
+
+    layout = build_packed_sequence(
+        text_token_tags,
+        num_latent_frames,
+        latent_height,
+        latent_width,
+        num_audio_latents,
+        MINIMAX_H3_PATCH_SIZE,
+        keyframe_anchors,
+    )
+
+    generator = torch.Generator(device="cpu").manual_seed(seed)
+
+    # Draw 1 (optional): keyframe conditioning noise, one draw per keyframe, in packed order.
+    condition_rows: torch.Tensor | None = None
+    if clean_condition_rows is not None:
+        expected_rows = layout.num_condition_video_rows
+        if clean_condition_rows.shape[0] != expected_rows:
+            raise ValueError(
+                f"Keyframe conditioning carries {clean_condition_rows.shape[0]} rows but the layout "
+                f"expects {expected_rows}. The frame-conditioning node must be run with the same "
+                "width/height as the denoise node."
+            )
+        noise = keyframe_condition_noise(
+            ((1, latent_height, latent_width),) * len(keyframe_anchors),
+            MINIMAX_H3_PATCH_SIZE,
+            MINIMAX_H3_VAE_LATENT_CHANNELS,
+            generator=generator,
+            device=device,
+        )
+        scale_noise_scheduler = MiniMaxH3Scheduler(shift=MINIMAX_H3_VIDEO_FLOW_SHIFT)
+        condition_rows = scale_noise_scheduler.scale_noise(
+            clean_condition_rows.to(device=device, dtype=torch.float32), MINIMAX_H3_KEYFRAME_NOISE_AUG, noise
+        )
+
+    # Draw 2: video noise, as a 5D latent tensor, patchified afterwards (upstream order).
+    video_noise = randn_tensor(
+        (1, MINIMAX_H3_VAE_LATENT_CHANNELS, num_latent_frames, latent_height, latent_width),
+        generator=generator,
+        device=device,
+        dtype=torch.float32,
+    )
+    video_rows = patchify_video_latents(video_noise, MINIMAX_H3_PATCH_SIZE).to(device)
+
+    # Draw 3: audio noise, directly in row layout.
+    audio_rows = randn_tensor(
+        (num_audio_latents * MINIMAX_H3_AUDIO_CHANNELS, MINIMAX_H3_AUDIO_LATENT_CHANNELS),
+        generator=generator,
+        device=device,
+        dtype=torch.float32,
+    ).to(device)
+
+    if condition_rows is not None:
+        video_rows = torch.cat([condition_rows, video_rows])
+
+    scheduler, audio_scheduler = build_schedulers(num_inference_steps, device)
+
+    row_timestep_plan = [
+        tuple(
+            tensor.to(device)
+            for tensor in build_row_timesteps(
+                layout,
+                float(timestep),
+                float(audio_timestep),
+                max(float(timestep), MINIMAX_H3_KEYFRAME_NOISE_AUG),
+                1.0,
+            )
+        )
+        for timestep, audio_timestep in zip(scheduler.timesteps, audio_scheduler.timesteps, strict=True)
+    ]
+
+    return MiniMaxH3DenoiseState(
+        layout=layout,
+        video_rows=video_rows,
+        audio_rows=audio_rows,
+        position_ids=layout.position_ids.to(device),
+        token_tags=layout.token_tags.to(device),
+        video_indices=layout.video_indices.to(device),
+        audio_indices=layout.audio_indices.to(device),
+        text_indices=layout.text_indices.to(device),
+        timesteps=scheduler.timesteps,
+        audio_timesteps=audio_scheduler.timesteps,
+        row_timestep_plan=row_timestep_plan,
+        scheduler=scheduler,
+        audio_scheduler=audio_scheduler,
+    )
diff --git a/invokeai/backend/minimax_h3/text_conditioning.py b/invokeai/backend/minimax_h3/text_conditioning.py
new file mode 100644
index 00000000000..ee7b3a163b5
--- /dev/null
+++ b/invokeai/backend/minimax_h3/text_conditioning.py
@@ -0,0 +1,89 @@
+"""MiniMax H3 text (and keyframe-vision) conditioning.
+
+First-party port of the FL2VA half of ``modular_pipelines/minimax_h3/encoders.py`` (commit
+recorded in ``__init__``). The presentation is the verbatim prompt, preceded by a
+``"<Picture i>: "`` label and a vision block per keyframe — no chat template, no special
+tokens. The conditioning is the *unnormalized* hidden state after the 50th of the Qwen3-VL
+conditioner's 64 decoder layers; the language-model head never runs.
+"""
+
+import torch
+
+from invokeai.backend.minimax_h3.packing import (
+    MINIMAX_H3_TEXT_ENCODER_LAYER,
+    MINIMAX_H3_TEXT_TAG,
+    MINIMAX_H3_VIDEO_TAG,
+)
+
+
+def encode_prompt(
+    text_encoder,
+    tokenizer,
+    processor,
+    prompt: str,
+    keyframe_images: list | None = None,
+    device: torch.device | None = None,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """Build MiniMax H3's presentation of a request and encode it.
+
+    Args:
+        text_encoder: A ``Qwen3VLForConditionalGeneration`` (the full released checkpoint).
+        tokenizer: The Qwen2 fast tokenizer.
+        processor: The ``Qwen3VLProcessor`` (needed even for text-only requests: it derives
+            the token-type ids Qwen3-VL's 3D rotary layout keys off).
+        prompt: The prompt, a single string (H3 packs one request into one sequence).
+        keyframe_images: The keyframes already prepared onto the target canvas, in packed
+            order (None or empty for text-to-video).
+        device: The device to run the conditioner on.
+
+    Returns:
+        ``(prompt_embeds, text_token_tags)``: the ``(1, num_text_tokens, text_dim)`` hidden
+        states and the per-row modality tags (vision-block rows are tagged as video).
+    """
+    num_layers = text_encoder.config.text_config.num_hidden_layers
+    if num_layers <= MINIMAX_H3_TEXT_ENCODER_LAYER:
+        raise ValueError(
+            f"MiniMax H3 conditions on hidden_states[{MINIMAX_H3_TEXT_ENCODER_LAYER}] of its Qwen3-VL "
+            f"conditioner, which needs more than {MINIMAX_H3_TEXT_ENCODER_LAYER} decoder layers; the "
+            f"selected text encoder has {num_layers}. A truncated stack's last hidden state is post-norm "
+            "and is not the conditioning MiniMax H3 expects."
+        )
+
+    pixel_values, image_grid_thw = None, None
+    token_ids: list[int] = []
+    token_tags: list[int] = []
+    if keyframe_images:
+        vision = processor.image_processor(images=keyframe_images, return_tensors="pt")
+        pixel_values, image_grid_thw = vision["pixel_values"], vision["image_grid_thw"]
+        merge_size = processor.image_processor.merge_size**2
+        for index in range(len(keyframe_images)):
+            num_image_tokens = int(image_grid_thw[index].prod()) // merge_size
+            label_ids = tokenizer(f"<Picture {index + 1}>: ", add_special_tokens=False)["input_ids"]
+            vision_ids = (
+                [tokenizer.convert_tokens_to_ids("<|vision_start|>")]
+                + [tokenizer.convert_tokens_to_ids("<|image_pad|>")] * num_image_tokens
+                + [tokenizer.convert_tokens_to_ids("<|vision_end|>")]
+            )
+            token_ids += label_ids + vision_ids
+            token_tags += [MINIMAX_H3_TEXT_TAG] * len(label_ids) + [MINIMAX_H3_VIDEO_TAG] * len(vision_ids)
+    prompt_ids = tokenizer(prompt, add_special_tokens=False)["input_ids"]
+    token_ids += prompt_ids
+    token_tags += [MINIMAX_H3_TEXT_TAG] * len(prompt_ids)
+
+    input_ids = torch.tensor([token_ids], dtype=torch.long, device=device)
+    mm_token_type_ids = torch.tensor(processor.create_mm_token_type_ids([token_ids]), dtype=torch.long, device=device)
+    # Call the language-model submodule directly: MiniMax H3 reads hidden_states[50] and never
+    # uses the LM head, whose vocabulary-wide projection is all the top-level forward would add.
+    # (InvokeAI's model cache places the whole module before this is called, so upstream's
+    # accelerate-hook workaround is not needed here.)
+    outputs = text_encoder.model(
+        input_ids=input_ids,
+        attention_mask=torch.ones_like(input_ids),
+        mm_token_type_ids=mm_token_type_ids,
+        pixel_values=None if pixel_values is None else pixel_values.to(device, text_encoder.dtype),
+        image_grid_thw=None if image_grid_thw is None else image_grid_thw.to(device),
+        use_cache=False,
+        output_hidden_states=True,
+    )
+    prompt_embeds = outputs.hidden_states[MINIMAX_H3_TEXT_ENCODER_LAYER]
+    return prompt_embeds, torch.tensor(token_tags, dtype=torch.long)
diff --git a/invokeai/backend/stable_diffusion/diffusion/conditioning_data.py b/invokeai/backend/stable_diffusion/diffusion/conditioning_data.py
index 47372ec9d8d..b271addc287 100644
--- a/invokeai/backend/stable_diffusion/diffusion/conditioning_data.py
+++ b/invokeai/backend/stable_diffusion/diffusion/conditioning_data.py
@@ -181,6 +181,40 @@ def to(self, device: torch.device | None = None, dtype: torch.dtype | None = Non
         return self
 
 
+@dataclass
+class MiniMaxH3ConditioningInfo:
+    """MiniMax H3 text conditioning from the Qwen3-VL-32B conditioner.
+
+    ``prompt_embeds`` is the *unnormalized* hidden state after the conditioner's 50th decoder
+    layer. ``text_token_tags`` is the per-row modality tag of every embedding row (text rows
+    tagged 1, keyframe vision-block rows tagged 0 = video); the denoise node builds the packed
+    sequence layout from it, so it must travel with the embeddings.
+    """
+
+    prompt_embeds: torch.Tensor
+    """Qwen3-VL layer-50 hidden states. Shape: (1, num_text_tokens, 5120)."""
+
+    text_token_tags: torch.Tensor
+    """Per-row modality tags. Shape: (num_text_tokens,), dtype long."""
+
+    keyframe_anchors: tuple[str, ...] = ()
+    """Which keyframes ("first"/"last", packed order) were part of the vision context. The
+    denoise node cross-checks this against its frame-conditioning input: keyframes must reach
+    the text conditioning and the VAE condition rows together, or not at all."""
+
+    width: int | None = None
+    """Canvas width the keyframes were prepared at (None when no keyframes)."""
+
+    height: int | None = None
+    """Canvas height the keyframes were prepared at (None when no keyframes)."""
+
+    def to(self, device: torch.device | None = None, dtype: torch.dtype | None = None):
+        self.prompt_embeds = self.prompt_embeds.to(device=device, dtype=dtype)
+        # Tags are structural (long); only the device moves.
+        self.text_token_tags = self.text_token_tags.to(device=device)
+        return self
+
+
 @dataclass
 class WanConditioningInfo:
     """Wan 2.2 text conditioning information from the UMT5-XXL encoder.
@@ -220,6 +254,7 @@ class ConditioningFieldData:
         | List[Krea2ConditioningInfo]
         | List[AnimaConditioningInfo]
         | List[WanConditioningInfo]
+        | List[MiniMaxH3ConditioningInfo]
     )
 
 
diff --git a/invokeai/backend/util/vae_working_memory.py b/invokeai/backend/util/vae_working_memory.py
index fbd1c0e1280..e1be5c4c606 100644
--- a/invokeai/backend/util/vae_working_memory.py
+++ b/invokeai/backend/util/vae_working_memory.py
@@ -125,6 +125,36 @@ def estimate_vae_working_memory_anima(
     return int(working_memory)
 
 
+def estimate_vae_working_memory_minimax_h3(
+    operation: Literal["encode", "decode"],
+    vae: "torch.nn.Module",
+    pixel_height: int,
+    pixel_width: int,
+    pixel_frames: int,
+) -> int:
+    """Estimate the working memory to encode/decode with the MiniMax H3 video VAE.
+
+    The H3 VAE always tiles spatially (``use_tiling`` defaults to True with 256px tiles / 64px
+    overlap, and the released frames are the blended-tile ones), so the conv working set is
+    per-tile, not per-frame: the tile geometry is read from the instance. The full RGB clip is
+    still assembled on the execution device (fp32 — the VAE's weights are pinned fp32), plus
+    one transient copy. The Wan per-pixel calibration constant is kept as a conservative
+    stand-in until an H3-specific calibration exists.
+    """
+    element_size = next(vae.parameters()).element_size()
+
+    scaling_constant = 2900 if operation == "decode" else 1450
+    tile_height = min(int(getattr(vae, "tile_sample_min_height", 256)), pixel_height)
+    tile_width = min(int(getattr(vae, "tile_sample_min_width", 256)), pixel_width)
+    # 1.25 accounts for tile overlap.
+    tile_working = tile_height * tile_width * element_size * scaling_constant * 1.25
+
+    clip_copies = 2 if operation == "decode" else 1
+    clip_bytes = clip_copies * 3 * pixel_frames * pixel_height * pixel_width * element_size
+
+    return int(tile_working + clip_bytes)
+
+
 def estimate_vae_working_memory_wan(
     operation: Literal["encode", "decode"],
     vae: AutoencoderKLWan,
diff --git a/invokeai/frontend/web/openapi.json b/invokeai/frontend/web/openapi.json
index 2d5b5ee4261..b023b4e09da 100644
--- a/invokeai/frontend/web/openapi.json
+++ b/invokeai/frontend/web/openapi.json
@@ -22095,7 +22095,10 @@
                   "wan_img2img",
                   "wan_inpaint",
                   "wan_outpaint",
-                  "wan_i2v"
+                  "wan_i2v",
+                  "minimax_h3_t2v",
+                  "minimax_h3_i2v",
+                  "minimax_h3_txt2img"
                 ],
                 "type": "string"
               },
@@ -34484,6 +34487,24 @@
                 {
                   "$ref": "#/components/schemas/MetadataToVAEInvocation"
                 },
+                {
+                  "$ref": "#/components/schemas/MiniMaxH3DenoiseInvocation"
+                },
+                {
+                  "$ref": "#/components/schemas/MiniMaxH3FrameConditioningInvocation"
+                },
+                {
+                  "$ref": "#/components/schemas/MiniMaxH3LatentsToImageInvocation"
+                },
+                {
+                  "$ref": "#/components/schemas/MiniMaxH3LatentsToVideoInvocation"
+                },
+                {
+                  "$ref": "#/components/schemas/MiniMaxH3ModelLoaderInvocation"
+                },
+                {
+                  "$ref": "#/components/schemas/MiniMaxH3TextEncoderInvocation"
+                },
                 {
                   "$ref": "#/components/schemas/ModelIdentifierInvocation"
                 },
@@ -35051,6 +35072,18 @@
                 {
                   "$ref": "#/components/schemas/MetadataToSDXLModelOutput"
                 },
+                {
+                  "$ref": "#/components/schemas/MiniMaxH3ConditioningOutput"
+                },
+                {
+                  "$ref": "#/components/schemas/MiniMaxH3DenoiseOutput"
+                },
+                {
+                  "$ref": "#/components/schemas/MiniMaxH3FrameConditioningOutput"
+                },
+                {
+                  "$ref": "#/components/schemas/MiniMaxH3ModelLoaderOutput"
+                },
                 {
                   "$ref": "#/components/schemas/ModelIdentifierOutput"
                 },
@@ -43001,6 +43034,24 @@
               {
                 "$ref": "#/components/schemas/MetadataToVAEInvocation"
               },
+              {
+                "$ref": "#/components/schemas/MiniMaxH3DenoiseInvocation"
+              },
+              {
+                "$ref": "#/components/schemas/MiniMaxH3FrameConditioningInvocation"
+              },
+              {
+                "$ref": "#/components/schemas/MiniMaxH3LatentsToImageInvocation"
+              },
+              {
+                "$ref": "#/components/schemas/MiniMaxH3LatentsToVideoInvocation"
+              },
+              {
+                "$ref": "#/components/schemas/MiniMaxH3ModelLoaderInvocation"
+              },
+              {
+                "$ref": "#/components/schemas/MiniMaxH3TextEncoderInvocation"
+              },
               {
                 "$ref": "#/components/schemas/ModelIdentifierInvocation"
               },
@@ -43525,6 +43576,18 @@
               {
                 "$ref": "#/components/schemas/MetadataToSDXLModelOutput"
               },
+              {
+                "$ref": "#/components/schemas/MiniMaxH3ConditioningOutput"
+              },
+              {
+                "$ref": "#/components/schemas/MiniMaxH3DenoiseOutput"
+              },
+              {
+                "$ref": "#/components/schemas/MiniMaxH3FrameConditioningOutput"
+              },
+              {
+                "$ref": "#/components/schemas/MiniMaxH3ModelLoaderOutput"
+              },
               {
                 "$ref": "#/components/schemas/ModelIdentifierOutput"
               },
@@ -44331,6 +44394,24 @@
               {
                 "$ref": "#/components/schemas/MetadataToVAEInvocation"
               },
+              {
+                "$ref": "#/components/schemas/MiniMaxH3DenoiseInvocation"
+              },
+              {
+                "$ref": "#/components/schemas/MiniMaxH3FrameConditioningInvocation"
+              },
+              {
+                "$ref": "#/components/schemas/MiniMaxH3LatentsToImageInvocation"
+              },
+              {
+                "$ref": "#/components/schemas/MiniMaxH3LatentsToVideoInvocation"
+              },
+              {
+                "$ref": "#/components/schemas/MiniMaxH3ModelLoaderInvocation"
+              },
+              {
+                "$ref": "#/components/schemas/MiniMaxH3TextEncoderInvocation"
+              },
               {
                 "$ref": "#/components/schemas/ModelIdentifierInvocation"
               },
@@ -45285,6 +45366,24 @@
           "metadata_to_vae": {
             "$ref": "#/components/schemas/VAEOutput"
           },
+          "minimax_h3_denoise": {
+            "$ref": "#/components/schemas/MiniMaxH3DenoiseOutput"
+          },
+          "minimax_h3_frame_conditioning": {
+            "$ref": "#/components/schemas/MiniMaxH3FrameConditioningOutput"
+          },
+          "minimax_h3_latents_to_image": {
+            "$ref": "#/components/schemas/ImageOutput"
+          },
+          "minimax_h3_latents_to_video": {
+            "$ref": "#/components/schemas/VideoOutput"
+          },
+          "minimax_h3_model_loader": {
+            "$ref": "#/components/schemas/MiniMaxH3ModelLoaderOutput"
+          },
+          "minimax_h3_text_encoder": {
+            "$ref": "#/components/schemas/MiniMaxH3ConditioningOutput"
+          },
           "mlsd_detection": {
             "$ref": "#/components/schemas/ImageOutput"
           },
@@ -45790,6 +45889,12 @@
           "metadata_to_string_collection",
           "metadata_to_t2i_adapters",
           "metadata_to_vae",
+          "minimax_h3_denoise",
+          "minimax_h3_frame_conditioning",
+          "minimax_h3_latents_to_image",
+          "minimax_h3_latents_to_video",
+          "minimax_h3_model_loader",
+          "minimax_h3_text_encoder",
           "mlsd_detection",
           "model_identifier",
           "mul",
@@ -46564,6 +46669,24 @@
               {
                 "$ref": "#/components/schemas/MetadataToVAEInvocation"
               },
+              {
+                "$ref": "#/components/schemas/MiniMaxH3DenoiseInvocation"
+              },
+              {
+                "$ref": "#/components/schemas/MiniMaxH3FrameConditioningInvocation"
+              },
+              {
+                "$ref": "#/components/schemas/MiniMaxH3LatentsToImageInvocation"
+              },
+              {
+                "$ref": "#/components/schemas/MiniMaxH3LatentsToVideoInvocation"
+              },
+              {
+                "$ref": "#/components/schemas/MiniMaxH3ModelLoaderInvocation"
+              },
+              {
+                "$ref": "#/components/schemas/MiniMaxH3TextEncoderInvocation"
+              },
               {
                 "$ref": "#/components/schemas/ModelIdentifierInvocation"
               },
@@ -47609,6 +47732,24 @@
               {
                 "$ref": "#/components/schemas/MetadataToVAEInvocation"
               },
+              {
+                "$ref": "#/components/schemas/MiniMaxH3DenoiseInvocation"
+              },
+              {
+                "$ref": "#/components/schemas/MiniMaxH3FrameConditioningInvocation"
+              },
+              {
+                "$ref": "#/components/schemas/MiniMaxH3LatentsToImageInvocation"
+              },
+              {
+                "$ref": "#/components/schemas/MiniMaxH3LatentsToVideoInvocation"
+              },
+              {
+                "$ref": "#/components/schemas/MiniMaxH3ModelLoaderInvocation"
+              },
+              {
+                "$ref": "#/components/schemas/MiniMaxH3TextEncoderInvocation"
+              },
               {
                 "$ref": "#/components/schemas/ModelIdentifierInvocation"
               },
@@ -64608,6 +64749,952 @@
           "$ref": "#/components/schemas/VAEOutput"
         }
       },
+      "MiniMaxH3ConditioningField": {
+        "description": "A MiniMax H3 conditioning primitive value.\n\nH3 conditioning is the layer-50 Qwen3-VL hidden state plus the per-row modality tags the\npacked-sequence layout is built from (vision-block rows are tagged as video).",
+        "properties": {
+          "conditioning_name": {
+            "description": "The name of conditioning tensor",
+            "title": "Conditioning Name",
+            "type": "string"
+          }
+        },
+        "required": ["conditioning_name"],
+        "title": "MiniMaxH3ConditioningField",
+        "type": "object"
+      },
+      "MiniMaxH3ConditioningOutput": {
+        "class": "output",
+        "description": "Base class for nodes that output a MiniMax H3 conditioning tensor.",
+        "properties": {
+          "conditioning": {
+            "$ref": "#/components/schemas/MiniMaxH3ConditioningField",
+            "description": "Conditioning tensor",
+            "field_kind": "output",
+            "ui_hidden": false
+          },
+          "type": {
+            "const": "minimax_h3_conditioning_output",
+            "default": "minimax_h3_conditioning_output",
+            "field_kind": "node_attribute",
+            "title": "type",
+            "type": "string"
+          }
+        },
+        "required": ["output_meta", "conditioning", "type", "type"],
+        "title": "MiniMaxH3ConditioningOutput",
+        "type": "object"
+      },
+      "MiniMaxH3DenoiseInvocation": {
+        "category": "latents",
+        "class": "invocation",
+        "classification": "prototype",
+        "description": "Run the MiniMax H3 joint audio-video denoising loop.",
+        "node_pack": "invokeai",
+        "properties": {
+          "id": {
+            "description": "The id of this instance of an invocation. Must be unique among all instances of invocations.",
+            "field_kind": "node_attribute",
+            "title": "Id",
+            "type": "string"
+          },
+          "is_intermediate": {
+            "default": false,
+            "description": "Whether or not this is an intermediate invocation.",
+            "field_kind": "node_attribute",
+            "input": "direct",
+            "orig_required": true,
+            "title": "Is Intermediate",
+            "type": "boolean",
+            "ui_hidden": false,
+            "ui_type": "IsIntermediate"
+          },
+          "use_cache": {
+            "default": true,
+            "description": "Whether or not to use the cache",
+            "field_kind": "node_attribute",
+            "title": "Use Cache",
+            "type": "boolean"
+          },
+          "transformer": {
+            "anyOf": [
+              {
+                "$ref": "#/components/schemas/MiniMaxH3TransformerField"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "MiniMax H3 FL2VA transformer.",
+            "field_kind": "input",
+            "input": "connection",
+            "orig_required": true,
+            "title": "Transformer"
+          },
+          "positive_conditioning": {
+            "anyOf": [
+              {
+                "$ref": "#/components/schemas/MiniMaxH3ConditioningField"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "Positive conditioning tensor",
+            "field_kind": "input",
+            "input": "connection",
+            "orig_required": true
+          },
+          "frame_conditioning": {
+            "anyOf": [
+              {
+                "$ref": "#/components/schemas/MiniMaxH3FrameConditioningField"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "First/last-keyframe (VAE-latent) conditioning for MiniMax H3",
+            "field_kind": "input",
+            "input": "connection",
+            "orig_default": null,
+            "orig_required": false,
+            "title": "Frame Conditioning"
+          },
+          "width": {
+            "default": 1344,
+            "description": "Width of the generated video. H3's native canvas has a 768px short edge (max 768x1344).",
+            "exclusiveMinimum": 0,
+            "field_kind": "input",
+            "input": "any",
+            "multipleOf": 32,
+            "orig_default": 1344,
+            "orig_required": false,
+            "title": "Width",
+            "type": "integer"
+          },
+          "height": {
+            "default": 768,
+            "description": "Height of the generated video.",
+            "exclusiveMinimum": 0,
+            "field_kind": "input",
+            "input": "any",
+            "multipleOf": 32,
+            "orig_default": 768,
+            "orig_required": false,
+            "title": "Height",
+            "type": "integer"
+          },
+          "num_frames": {
+            "default": 124,
+            "description": "Number of output frames at the fixed 24 fps. Must be of the form 17n+5 (5, 22, ..., 124, ...); durations must stay within 5-15 s, except exactly 5 frames for a still image.",
+            "field_kind": "input",
+            "input": "any",
+            "minimum": 5,
+            "orig_default": 124,
+            "orig_required": false,
+            "title": "Number of Frames",
+            "type": "integer"
+          },
+          "steps": {
+            "default": 50,
+            "description": "Number of denoising steps (sigma grid points, terminal included: N steps = N-1 model evaluations).",
+            "field_kind": "input",
+            "input": "any",
+            "minimum": 2,
+            "orig_default": 50,
+            "orig_required": false,
+            "title": "Steps",
+            "type": "integer"
+          },
+          "seed": {
+            "default": 0,
+            "description": "Randomness seed for reproducibility.",
+            "field_kind": "input",
+            "input": "any",
+            "orig_default": 0,
+            "orig_required": false,
+            "title": "Seed",
+            "type": "integer"
+          },
+          "type": {
+            "const": "minimax_h3_denoise",
+            "default": "minimax_h3_denoise",
+            "field_kind": "node_attribute",
+            "title": "type",
+            "type": "string"
+          }
+        },
+        "required": ["type", "id"],
+        "tags": ["latents", "video", "audio", "minimax"],
+        "title": "Denoise - MiniMax H3",
+        "type": "object",
+        "version": "1.0.0",
+        "output": {
+          "$ref": "#/components/schemas/MiniMaxH3DenoiseOutput"
+        }
+      },
+      "MiniMaxH3DenoiseOutput": {
+        "class": "output",
+        "description": "Joint video + audio latents from one MiniMax H3 denoise run.",
+        "properties": {
+          "video_latents": {
+            "$ref": "#/components/schemas/LatentsField",
+            "description": "5D video latents [1, 24, T_lat, H/16, W/16].",
+            "field_kind": "output",
+            "ui_hidden": false
+          },
+          "audio_latents": {
+            "$ref": "#/components/schemas/LatentsField",
+            "description": "Audio latents [2, 32, T_audio] (one item per stereo channel).",
+            "field_kind": "output",
+            "ui_hidden": false
+          },
+          "width": {
+            "description": "Pixel width of the video latents.",
+            "field_kind": "output",
+            "title": "Width",
+            "type": "integer",
+            "ui_hidden": false
+          },
+          "height": {
+            "description": "Pixel height of the video latents.",
+            "field_kind": "output",
+            "title": "Height",
+            "type": "integer",
+            "ui_hidden": false
+          },
+          "num_frames": {
+            "description": "Pixel-frame count of the video latents.",
+            "field_kind": "output",
+            "title": "Num Frames",
+            "type": "integer",
+            "ui_hidden": false
+          },
+          "type": {
+            "const": "minimax_h3_denoise_output",
+            "default": "minimax_h3_denoise_output",
+            "field_kind": "node_attribute",
+            "title": "type",
+            "type": "string"
+          }
+        },
+        "required": ["output_meta", "video_latents", "audio_latents", "width", "height", "num_frames", "type", "type"],
+        "title": "MiniMaxH3DenoiseOutput",
+        "type": "object"
+      },
+      "MiniMaxH3FrameConditioningField": {
+        "description": "First/last-keyframe conditioning for MiniMax H3 (FL2VA).\n\nCarries the CLEAN (not yet noise-augmented) packed keyframe conditioning rows; the denoise\nnode noise-augments them to t=0.999 with the request seed's first draws. Width/height ride\nalong so the denoise node can reject a canvas mismatch instead of failing inside the\ntransformer.",
+        "properties": {
+          "condition_rows_name": {
+            "description": "Name of the saved (num_condition_rows, 96) rows tensor.",
+            "title": "Condition Rows Name",
+            "type": "string"
+          },
+          "keyframe_anchors": {
+            "description": "Which end each keyframe anchors, in packed order (\"first\" / \"last\").",
+            "items": {
+              "type": "string"
+            },
+            "title": "Keyframe Anchors",
+            "type": "array"
+          },
+          "width": {
+            "description": "Canvas width used during VAE encoding (matches denoise width).",
+            "title": "Width",
+            "type": "integer"
+          },
+          "height": {
+            "description": "Canvas height used during VAE encoding (matches denoise height).",
+            "title": "Height",
+            "type": "integer"
+          }
+        },
+        "required": ["condition_rows_name", "keyframe_anchors", "width", "height"],
+        "title": "MiniMaxH3FrameConditioningField",
+        "type": "object"
+      },
+      "MiniMaxH3FrameConditioningInvocation": {
+        "category": "conditioning",
+        "class": "invocation",
+        "classification": "prototype",
+        "description": "VAE-encodes first/last keyframes into MiniMax H3 conditioning rows.\n\nThe rows are clean (the denoise node noise-augments them with the request seed). The same\nimages and width/height must also be wired to the Prompt - MiniMax H3 node: the keyframes\nare part of both the packed sequence and the text conditioning.",
+        "node_pack": "invokeai",
+        "properties": {
+          "id": {
+            "description": "The id of this instance of an invocation. Must be unique among all instances of invocations.",
+            "field_kind": "node_attribute",
+            "title": "Id",
+            "type": "string"
+          },
+          "is_intermediate": {
+            "default": false,
+            "description": "Whether or not this is an intermediate invocation.",
+            "field_kind": "node_attribute",
+            "input": "direct",
+            "orig_required": true,
+            "title": "Is Intermediate",
+            "type": "boolean",
+            "ui_hidden": false,
+            "ui_type": "IsIntermediate"
+          },
+          "use_cache": {
+            "default": true,
+            "description": "Whether or not to use the cache",
+            "field_kind": "node_attribute",
+            "title": "Use Cache",
+            "type": "boolean"
+          },
+          "first_image": {
+            "anyOf": [
+              {
+                "$ref": "#/components/schemas/ImageField"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "Keyframe the video starts from (stretched onto the canvas).",
+            "field_kind": "input",
+            "input": "any",
+            "orig_default": null,
+            "orig_required": false
+          },
+          "last_image": {
+            "anyOf": [
+              {
+                "$ref": "#/components/schemas/ImageField"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "Keyframe the video ends on (cover-cropped onto the canvas).",
+            "field_kind": "input",
+            "input": "any",
+            "orig_default": null,
+            "orig_required": false
+          },
+          "vae": {
+            "anyOf": [
+              {
+                "$ref": "#/components/schemas/VAEField"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "VAE",
+            "field_kind": "input",
+            "input": "connection",
+            "orig_required": true,
+            "title": "Video VAE"
+          },
+          "width": {
+            "default": 1344,
+            "description": "Target canvas width.",
+            "exclusiveMinimum": 0,
+            "field_kind": "input",
+            "input": "any",
+            "multipleOf": 32,
+            "orig_default": 1344,
+            "orig_required": false,
+            "title": "Width",
+            "type": "integer"
+          },
+          "height": {
+            "default": 768,
+            "description": "Target canvas height.",
+            "exclusiveMinimum": 0,
+            "field_kind": "input",
+            "input": "any",
+            "multipleOf": 32,
+            "orig_default": 768,
+            "orig_required": false,
+            "title": "Height",
+            "type": "integer"
+          },
+          "type": {
+            "const": "minimax_h3_frame_conditioning",
+            "default": "minimax_h3_frame_conditioning",
+            "field_kind": "node_attribute",
+            "title": "type",
+            "type": "string"
+          }
+        },
+        "required": ["type", "id"],
+        "tags": ["conditioning", "minimax", "video", "i2v"],
+        "title": "Frame Conditioning - MiniMax H3",
+        "type": "object",
+        "version": "1.0.0",
+        "output": {
+          "$ref": "#/components/schemas/MiniMaxH3FrameConditioningOutput"
+        }
+      },
+      "MiniMaxH3FrameConditioningOutput": {
+        "class": "output",
+        "description": "Output of the MiniMax H3 keyframe VAE-encoder.",
+        "properties": {
+          "frame_conditioning": {
+            "$ref": "#/components/schemas/MiniMaxH3FrameConditioningField",
+            "description": "First/last-keyframe (VAE-latent) conditioning for MiniMax H3",
+            "field_kind": "output",
+            "ui_hidden": false
+          },
+          "type": {
+            "const": "minimax_h3_frame_conditioning_output",
+            "default": "minimax_h3_frame_conditioning_output",
+            "field_kind": "node_attribute",
+            "title": "type",
+            "type": "string"
+          }
+        },
+        "required": ["output_meta", "frame_conditioning", "type", "type"],
+        "title": "MiniMaxH3FrameConditioningOutput",
+        "type": "object"
+      },
+      "MiniMaxH3LatentsToImageInvocation": {
+        "category": "latents",
+        "class": "invocation",
+        "classification": "prototype",
+        "description": "Decode MiniMax H3 video latents and save a single frame as an image.",
+        "node_pack": "invokeai",
+        "properties": {
+          "board": {
+            "anyOf": [
+              {
+                "$ref": "#/components/schemas/BoardField"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "The board to save the image to",
+            "field_kind": "internal",
+            "input": "direct",
+            "orig_required": false,
+            "ui_hidden": false
+          },
+          "metadata": {
+            "anyOf": [
+              {
+                "$ref": "#/components/schemas/MetadataField"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "Optional metadata to be saved with the image",
+            "field_kind": "internal",
+            "input": "connection",
+            "orig_required": false,
+            "ui_hidden": false
+          },
+          "id": {
+            "description": "The id of this instance of an invocation. Must be unique among all instances of invocations.",
+            "field_kind": "node_attribute",
+            "title": "Id",
+            "type": "string"
+          },
+          "is_intermediate": {
+            "default": false,
+            "description": "Whether or not this is an intermediate invocation.",
+            "field_kind": "node_attribute",
+            "input": "direct",
+            "orig_required": true,
+            "title": "Is Intermediate",
+            "type": "boolean",
+            "ui_hidden": false,
+            "ui_type": "IsIntermediate"
+          },
+          "use_cache": {
+            "default": true,
+            "description": "Whether or not to use the cache",
+            "field_kind": "node_attribute",
+            "title": "Use Cache",
+            "type": "boolean"
+          },
+          "video_latents": {
+            "anyOf": [
+              {
+                "$ref": "#/components/schemas/LatentsField"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "Latents tensor",
+            "field_kind": "input",
+            "input": "connection",
+            "orig_required": true
+          },
+          "vae": {
+            "anyOf": [
+              {
+                "$ref": "#/components/schemas/VAEField"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "VAE",
+            "field_kind": "input",
+            "input": "connection",
+            "orig_required": true,
+            "title": "Video VAE"
+          },
+          "frame_index": {
+            "default": 0,
+            "description": "Which decoded frame to save.",
+            "field_kind": "input",
+            "input": "any",
+            "minimum": 0,
+            "orig_default": 0,
+            "orig_required": false,
+            "title": "Frame Index",
+            "type": "integer"
+          },
+          "type": {
+            "const": "minimax_h3_latents_to_image",
+            "default": "minimax_h3_latents_to_image",
+            "field_kind": "node_attribute",
+            "title": "type",
+            "type": "string"
+          }
+        },
+        "required": ["type", "id"],
+        "tags": ["latents", "image", "vae", "l2i", "minimax"],
+        "title": "Latents to Image - MiniMax H3",
+        "type": "object",
+        "version": "1.0.0",
+        "output": {
+          "$ref": "#/components/schemas/ImageOutput"
+        }
+      },
+      "MiniMaxH3LatentsToVideoInvocation": {
+        "category": "latents",
+        "class": "invocation",
+        "classification": "prototype",
+        "description": "Decode MiniMax H3 video+audio latents and encode an MP4 with an AAC stereo track.",
+        "node_pack": "invokeai",
+        "properties": {
+          "board": {
+            "anyOf": [
+              {
+                "$ref": "#/components/schemas/BoardField"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "The board to save the image to",
+            "field_kind": "internal",
+            "input": "direct",
+            "orig_required": false,
+            "ui_hidden": false
+          },
+          "metadata": {
+            "anyOf": [
+              {
+                "$ref": "#/components/schemas/MetadataField"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "Optional metadata to be saved with the image",
+            "field_kind": "internal",
+            "input": "connection",
+            "orig_required": false,
+            "ui_hidden": false
+          },
+          "id": {
+            "description": "The id of this instance of an invocation. Must be unique among all instances of invocations.",
+            "field_kind": "node_attribute",
+            "title": "Id",
+            "type": "string"
+          },
+          "is_intermediate": {
+            "default": false,
+            "description": "Whether or not this is an intermediate invocation.",
+            "field_kind": "node_attribute",
+            "input": "direct",
+            "orig_required": true,
+            "title": "Is Intermediate",
+            "type": "boolean",
+            "ui_hidden": false,
+            "ui_type": "IsIntermediate"
+          },
+          "use_cache": {
+            "default": true,
+            "description": "Whether or not to use the cache",
+            "field_kind": "node_attribute",
+            "title": "Use Cache",
+            "type": "boolean"
+          },
+          "video_latents": {
+            "anyOf": [
+              {
+                "$ref": "#/components/schemas/LatentsField"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "Latents tensor",
+            "field_kind": "input",
+            "input": "connection",
+            "orig_required": true
+          },
+          "audio_latents": {
+            "anyOf": [
+              {
+                "$ref": "#/components/schemas/LatentsField"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "Audio latents [2, 32, T_audio] from the denoise node. Omit for a silent video.",
+            "field_kind": "input",
+            "input": "connection",
+            "orig_default": null,
+            "orig_required": false
+          },
+          "vae": {
+            "anyOf": [
+              {
+                "$ref": "#/components/schemas/VAEField"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "VAE",
+            "field_kind": "input",
+            "input": "connection",
+            "orig_required": true,
+            "title": "Video VAE"
+          },
+          "audio_vae": {
+            "anyOf": [
+              {
+                "$ref": "#/components/schemas/VAEField"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "Audio VAE (stereo, 32 kHz) for MiniMax H3",
+            "field_kind": "input",
+            "input": "connection",
+            "orig_default": null,
+            "orig_required": false,
+            "title": "Audio VAE"
+          },
+          "type": {
+            "const": "minimax_h3_latents_to_video",
+            "default": "minimax_h3_latents_to_video",
+            "field_kind": "node_attribute",
+            "title": "type",
+            "type": "string"
+          }
+        },
+        "required": ["type", "id"],
+        "tags": ["latents", "video", "audio", "vae", "l2v", "minimax"],
+        "title": "Latents to Video - MiniMax H3",
+        "type": "object",
+        "version": "1.0.0",
+        "output": {
+          "$ref": "#/components/schemas/VideoOutput"
+        }
+      },
+      "MiniMaxH3ModelLoaderInvocation": {
+        "category": "model",
+        "class": "invocation",
+        "classification": "prototype",
+        "description": "Loads a MiniMax H3 (FL2VA) model, outputting its submodels.\n\nAll six submodels (transformer, text encoder, tokenizer, processor, video VAE, audio VAE)\ncome from the one diffusers-layout install; there is no component mix-and-match yet.",
+        "node_pack": "invokeai",
+        "properties": {
+          "id": {
+            "description": "The id of this instance of an invocation. Must be unique among all instances of invocations.",
+            "field_kind": "node_attribute",
+            "title": "Id",
+            "type": "string"
+          },
+          "is_intermediate": {
+            "default": false,
+            "description": "Whether or not this is an intermediate invocation.",
+            "field_kind": "node_attribute",
+            "input": "direct",
+            "orig_required": true,
+            "title": "Is Intermediate",
+            "type": "boolean",
+            "ui_hidden": false,
+            "ui_type": "IsIntermediate"
+          },
+          "use_cache": {
+            "default": true,
+            "description": "Whether or not to use the cache",
+            "field_kind": "node_attribute",
+            "title": "Use Cache",
+            "type": "boolean"
+          },
+          "model": {
+            "$ref": "#/components/schemas/ModelIdentifierField",
+            "description": "MiniMax H3 model (Transformer) to load",
+            "field_kind": "input",
+            "input": "direct",
+            "orig_required": true,
+            "title": "Model",
+            "ui_model_base": ["minimax-h3"],
+            "ui_model_type": ["main"]
+          },
+          "type": {
+            "const": "minimax_h3_model_loader",
+            "default": "minimax_h3_model_loader",
+            "field_kind": "node_attribute",
+            "title": "type",
+            "type": "string"
+          }
+        },
+        "required": ["model", "type", "id"],
+        "tags": ["model", "minimax", "video"],
+        "title": "Main Model - MiniMax H3",
+        "type": "object",
+        "version": "1.0.0",
+        "output": {
+          "$ref": "#/components/schemas/MiniMaxH3ModelLoaderOutput"
+        }
+      },
+      "MiniMaxH3ModelLoaderOutput": {
+        "class": "output",
+        "description": "MiniMax H3 model loader output.",
+        "properties": {
+          "transformer": {
+            "$ref": "#/components/schemas/MiniMaxH3TransformerField",
+            "description": "MiniMax H3 FL2VA transformer",
+            "field_kind": "output",
+            "title": "Transformer",
+            "ui_hidden": false
+          },
+          "text_encoder": {
+            "$ref": "#/components/schemas/MiniMaxH3TextEncoderField",
+            "description": "Qwen3-VL-32B tokenizer, processor and text encoder for MiniMax H3",
+            "field_kind": "output",
+            "title": "Qwen3-VL Encoder",
+            "ui_hidden": false
+          },
+          "vae": {
+            "$ref": "#/components/schemas/VAEField",
+            "description": "VAE",
+            "field_kind": "output",
+            "title": "Video VAE",
+            "ui_hidden": false
+          },
+          "audio_vae": {
+            "$ref": "#/components/schemas/VAEField",
+            "description": "Audio VAE (stereo, 32 kHz) for MiniMax H3",
+            "field_kind": "output",
+            "title": "Audio VAE",
+            "ui_hidden": false
+          },
+          "type": {
+            "const": "minimax_h3_model_loader_output",
+            "default": "minimax_h3_model_loader_output",
+            "field_kind": "node_attribute",
+            "title": "type",
+            "type": "string"
+          }
+        },
+        "required": ["output_meta", "transformer", "text_encoder", "vae", "audio_vae", "type", "type"],
+        "title": "MiniMaxH3ModelLoaderOutput",
+        "type": "object"
+      },
+      "MiniMaxH3TextEncoderField": {
+        "description": "Field for the Qwen3-VL-32B conditioner used by MiniMax H3 models.\n\nUnlike :class:`Qwen3VLEncoderField`, H3 also needs the Qwen3VLProcessor \u2014 even for\ntext-only prompts (its multimodal token-type ids drive Qwen3-VL's 3D rotary layout), and\nfor feeding first/last keyframes to the conditioner as vision context.",
+        "properties": {
+          "tokenizer": {
+            "$ref": "#/components/schemas/ModelIdentifierField",
+            "description": "Info to load tokenizer submodel"
+          },
+          "processor": {
+            "$ref": "#/components/schemas/ModelIdentifierField",
+            "description": "Info to load processor submodel"
+          },
+          "text_encoder": {
+            "$ref": "#/components/schemas/ModelIdentifierField",
+            "description": "Info to load text_encoder submodel"
+          }
+        },
+        "required": ["tokenizer", "processor", "text_encoder"],
+        "title": "MiniMaxH3TextEncoderField",
+        "type": "object"
+      },
+      "MiniMaxH3TextEncoderInvocation": {
+        "category": "conditioning",
+        "class": "invocation",
+        "classification": "prototype",
+        "description": "Encodes a prompt (and optional first/last keyframes) for MiniMax H3.\n\nThe conditioning is Qwen3-VL-32B's *unnormalized* layer-50 hidden state. H3 is\nguidance-distilled: there is no negative prompt. For first/last-frame video, the keyframes\nare ALSO part of the text conditioning (a \"<Picture i>: \" label plus a vision block per\nkeyframe), so the same images must be wired here and to the Frame Conditioning node, with\nthe same width/height as the denoise node.",
+        "node_pack": "invokeai",
+        "properties": {
+          "id": {
+            "description": "The id of this instance of an invocation. Must be unique among all instances of invocations.",
+            "field_kind": "node_attribute",
+            "title": "Id",
+            "type": "string"
+          },
+          "is_intermediate": {
+            "default": false,
+            "description": "Whether or not this is an intermediate invocation.",
+            "field_kind": "node_attribute",
+            "input": "direct",
+            "orig_required": true,
+            "title": "Is Intermediate",
+            "type": "boolean",
+            "ui_hidden": false,
+            "ui_type": "IsIntermediate"
+          },
+          "use_cache": {
+            "default": true,
+            "description": "Whether or not to use the cache",
+            "field_kind": "node_attribute",
+            "title": "Use Cache",
+            "type": "boolean"
+          },
+          "prompt": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "Text prompt for MiniMax H3.",
+            "field_kind": "input",
+            "input": "any",
+            "orig_required": true,
+            "title": "Prompt",
+            "ui_component": "textarea"
+          },
+          "text_encoder": {
+            "anyOf": [
+              {
+                "$ref": "#/components/schemas/MiniMaxH3TextEncoderField"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "Qwen3-VL-32B tokenizer, processor and text encoder for MiniMax H3",
+            "field_kind": "input",
+            "input": "connection",
+            "orig_required": true,
+            "title": "Qwen3-VL Encoder"
+          },
+          "first_image": {
+            "anyOf": [
+              {
+                "$ref": "#/components/schemas/ImageField"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "Optional keyframe the video starts from (must match Frame Conditioning).",
+            "field_kind": "input",
+            "input": "any",
+            "orig_default": null,
+            "orig_required": false
+          },
+          "last_image": {
+            "anyOf": [
+              {
+                "$ref": "#/components/schemas/ImageField"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "Optional keyframe the video ends on (must match Frame Conditioning).",
+            "field_kind": "input",
+            "input": "any",
+            "orig_default": null,
+            "orig_required": false
+          },
+          "width": {
+            "default": 1344,
+            "description": "Target canvas width.",
+            "exclusiveMinimum": 0,
+            "field_kind": "input",
+            "input": "any",
+            "multipleOf": 32,
+            "orig_default": 1344,
+            "orig_required": false,
+            "title": "Width",
+            "type": "integer"
+          },
+          "height": {
+            "default": 768,
+            "description": "Target canvas height.",
+            "exclusiveMinimum": 0,
+            "field_kind": "input",
+            "input": "any",
+            "multipleOf": 32,
+            "orig_default": 768,
+            "orig_required": false,
+            "title": "Height",
+            "type": "integer"
+          },
+          "type": {
+            "const": "minimax_h3_text_encoder",
+            "default": "minimax_h3_text_encoder",
+            "field_kind": "node_attribute",
+            "title": "type",
+            "type": "string"
+          }
+        },
+        "required": ["type", "id"],
+        "tags": ["prompt", "conditioning", "minimax", "video"],
+        "title": "Prompt - MiniMax H3",
+        "type": "object",
+        "version": "1.0.0",
+        "output": {
+          "$ref": "#/components/schemas/MiniMaxH3ConditioningOutput"
+        }
+      },
+      "MiniMaxH3TransformerField": {
+        "description": "Transformer field for MiniMax H3 models (FL2VA).",
+        "properties": {
+          "transformer": {
+            "$ref": "#/components/schemas/ModelIdentifierField",
+            "description": "Info to load Transformer submodel"
+          }
+        },
+        "required": ["transformer"],
+        "title": "MiniMaxH3TransformerField",
+        "type": "object"
+      },
       "MiniMaxH3VariantType": {
         "type": "string",
         "enum": ["fl2va"],
diff --git a/invokeai/frontend/web/src/services/api/schema.ts b/invokeai/frontend/web/src/services/api/schema.ts
index 66f90f8950b..09ce344be87 100644
--- a/invokeai/frontend/web/src/services/api/schema.ts
+++ b/invokeai/frontend/web/src/services/api/schema.ts
@@ -8370,7 +8370,7 @@ export type components = {
              * @description The generation mode that output this image
              * @default null
              */
-            generation_mode?: ("txt2img" | "img2img" | "inpaint" | "outpaint" | "sdxl_txt2img" | "sdxl_img2img" | "sdxl_inpaint" | "sdxl_outpaint" | "flux_txt2img" | "flux_img2img" | "flux_inpaint" | "flux_outpaint" | "flux2_txt2img" | "flux2_img2img" | "flux2_inpaint" | "flux2_outpaint" | "sd3_txt2img" | "sd3_img2img" | "sd3_inpaint" | "sd3_outpaint" | "cogview4_txt2img" | "cogview4_img2img" | "cogview4_inpaint" | "cogview4_outpaint" | "z_image_txt2img" | "z_image_img2img" | "z_image_inpaint" | "z_image_outpaint" | "ernie_image_txt2img" | "ideogram4_txt2img" | "qwen_image_txt2img" | "qwen_image_img2img" | "qwen_image_inpaint" | "qwen_image_outpaint" | "anima_txt2img" | "anima_img2img" | "anima_inpaint" | "anima_outpaint" | "krea2_txt2img" | "krea2_img2img" | "krea2_inpaint" | "krea2_outpaint" | "wan_txt2img" | "wan_img2img" | "wan_inpaint" | "wan_outpaint" | "wan_i2v") | null;
+            generation_mode?: ("txt2img" | "img2img" | "inpaint" | "outpaint" | "sdxl_txt2img" | "sdxl_img2img" | "sdxl_inpaint" | "sdxl_outpaint" | "flux_txt2img" | "flux_img2img" | "flux_inpaint" | "flux_outpaint" | "flux2_txt2img" | "flux2_img2img" | "flux2_inpaint" | "flux2_outpaint" | "sd3_txt2img" | "sd3_img2img" | "sd3_inpaint" | "sd3_outpaint" | "cogview4_txt2img" | "cogview4_img2img" | "cogview4_inpaint" | "cogview4_outpaint" | "z_image_txt2img" | "z_image_img2img" | "z_image_inpaint" | "z_image_outpaint" | "ernie_image_txt2img" | "ideogram4_txt2img" | "qwen_image_txt2img" | "qwen_image_img2img" | "qwen_image_inpaint" | "qwen_image_outpaint" | "anima_txt2img" | "anima_img2img" | "anima_inpaint" | "anima_outpaint" | "krea2_txt2img" | "krea2_img2img" | "krea2_inpaint" | "krea2_outpaint" | "wan_txt2img" | "wan_img2img" | "wan_inpaint" | "wan_outpaint" | "wan_i2v" | "minimax_h3_t2v" | "minimax_h3_i2v" | "minimax_h3_txt2img") | null;
             /**
              * Positive Prompt
              * @description The positive prompt parameter
@@ -14223,7 +14223,7 @@ export type components = {
              * @description The nodes in this graph
              */
             nodes?: {
-                [key: string]: components["schemas"]["AddInvocation"] | components["schemas"]["AlibabaCloudImageGenerationInvocation"] | components["schemas"]["AlphaMaskToTensorInvocation"] | components["schemas"]["AnimaDenoiseInvocation"] | components["schemas"]["AnimaImageToLatentsInvocation"] | components["schemas"]["AnimaLLLiteInvocation"] | components["schemas"]["AnimaLatentsToImageInvocation"] | components["schemas"]["AnimaLoRACollectionLoader"] | components["schemas"]["AnimaLoRALoaderInvocation"] | components["schemas"]["AnimaModelLoaderInvocation"] | components["schemas"]["AnimaTextEncoderInvocation"] | components["schemas"]["ApplyMaskTensorToImageInvocation"] | components["schemas"]["ApplyMaskToImageInvocation"] | components["schemas"]["BlankImageInvocation"] | components["schemas"]["BlendLatentsInvocation"] | components["schemas"]["BooleanCollectionInvocation"] | components["schemas"]["BooleanInvocation"] | components["schemas"]["BoundingBoxInvocation"] | components["schemas"]["CLIPSkipInvocation"] | components["schemas"]["CV2InfillInvocation"] | components["schemas"]["CalculateImageTilesEvenSplitInvocation"] | components["schemas"]["CalculateImageTilesInvocation"] | components["schemas"]["CalculateImageTilesMinimumOverlapInvocation"] | components["schemas"]["CallSavedWorkflowInvocation"] | components["schemas"]["CannyEdgeDetectionInvocation"] | components["schemas"]["CanvasOutputInvocation"] | components["schemas"]["CanvasPasteBackInvocation"] | components["schemas"]["CanvasV2MaskAndCropInvocation"] | components["schemas"]["CenterPadCropInvocation"] | components["schemas"]["CogView4DenoiseInvocation"] | components["schemas"]["CogView4ImageToLatentsInvocation"] | components["schemas"]["CogView4LatentsToImageInvocation"] | components["schemas"]["CogView4ModelLoaderInvocation"] | components["schemas"]["CogView4TextEncoderInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["ColorCorrectInvocation"] | components["schemas"]["ColorInvocation"] | components["schemas"]["ColorMapInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ConditioningCollectionInvocation"] | components["schemas"]["ConditioningInvocation"] | components["schemas"]["ContentShuffleInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["CoreMetadataInvocation"] | components["schemas"]["CreateDenoiseMaskInvocation"] | components["schemas"]["CreateGradientMaskInvocation"] | components["schemas"]["CropImageToBoundingBoxInvocation"] | components["schemas"]["CropLatentsCoreInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["DWOpenposeDetectionInvocation"] | components["schemas"]["DecodeInvisibleWatermarkInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["DenoiseLatentsMetaInvocation"] | components["schemas"]["DepthAnythingDepthEstimationInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["ErnieImageDenoiseInvocation"] | components["schemas"]["ErnieImageModelLoaderInvocation"] | components["schemas"]["ErnieImagePromptEnhancerInvocation"] | components["schemas"]["ErnieImageTextEncoderInvocation"] | components["schemas"]["ErnieImageVaeDecodeInvocation"] | components["schemas"]["ExpandMaskWithFadeInvocation"] | components["schemas"]["ExtractVideoRangeInvocation"] | components["schemas"]["FLUXLoRACollectionLoader"] | components["schemas"]["FaceIdentifierInvocation"] | components["schemas"]["FaceMaskInvocation"] | components["schemas"]["FaceOffInvocation"] | components["schemas"]["FloatBatchInvocation"] | components["schemas"]["FloatCollectionInvocation"] | components["schemas"]["FloatGenerator"] | components["schemas"]["FloatInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["FloatMathInvocation"] | components["schemas"]["FloatToIntegerInvocation"] | components["schemas"]["Flux2DenoiseInvocation"] | components["schemas"]["Flux2KleinLoRACollectionLoader"] | components["schemas"]["Flux2KleinLoRALoaderInvocation"] | components["schemas"]["Flux2KleinModelLoaderInvocation"] | components["schemas"]["Flux2KleinTextEncoderInvocation"] | components["schemas"]["Flux2PiDDecodeInvocation"] | components["schemas"]["Flux2VaeDecodeInvocation"] | components["schemas"]["Flux2VaeEncodeInvocation"] | components["schemas"]["FluxControlLoRALoaderInvocation"] | components["schemas"]["FluxControlNetInvocation"] | components["schemas"]["FluxDenoiseInvocation"] | components["schemas"]["FluxDenoiseLatentsMetaInvocation"] | components["schemas"]["FluxFillInvocation"] | components["schemas"]["FluxIPAdapterInvocation"] | components["schemas"]["FluxKontextConcatenateImagesInvocation"] | components["schemas"]["FluxKontextInvocation"] | components["schemas"]["FluxLoRALoaderInvocation"] | components["schemas"]["FluxModelLoaderInvocation"] | components["schemas"]["FluxPiDDecodeInvocation"] | components["schemas"]["FluxReduxInvocation"] | components["schemas"]["FluxTextEncoderInvocation"] | components["schemas"]["FluxVaeDecodeInvocation"] | components["schemas"]["FluxVaeEncodeInvocation"] | components["schemas"]["FreeUInvocation"] | components["schemas"]["GeminiImageGenerationInvocation"] | components["schemas"]["Gemma2EncoderLoaderInvocation"] | components["schemas"]["GetMaskBoundingBoxInvocation"] | components["schemas"]["GroundingDinoInvocation"] | components["schemas"]["HEDEdgeDetectionInvocation"] | components["schemas"]["HeuristicResizeInvocation"] | components["schemas"]["IPAdapterInvocation"] | components["schemas"]["IdealSizeInvocation"] | components["schemas"]["Ideogram4CaptionBuilderInvocation"] | components["schemas"]["Ideogram4DenoiseInvocation"] | components["schemas"]["Ideogram4LatentsToImageInvocation"] | components["schemas"]["Ideogram4ModelLoaderInvocation"] | components["schemas"]["Ideogram4TextEncoderInvocation"] | components["schemas"]["IfInvocation"] | components["schemas"]["ImageBatchInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageChannelMultiplyInvocation"] | components["schemas"]["ImageChannelOffsetInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImageGenerator"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageMaskToTensorInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageNoiseInvocation"] | components["schemas"]["ImagePanelLayoutInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["IntegerBatchInvocation"] | components["schemas"]["IntegerCollectionInvocation"] | components["schemas"]["IntegerGenerator"] | components["schemas"]["IntegerInvocation"] | components["schemas"]["IntegerMathInvocation"] | components["schemas"]["InvertTensorMaskInvocation"] | components["schemas"]["InvokeAdjustImageHuePlusInvocation"] | components["schemas"]["InvokeEquivalentAchromaticLightnessInvocation"] | components["schemas"]["InvokeImageBlendInvocation"] | components["schemas"]["InvokeImageCompositorInvocation"] | components["schemas"]["InvokeImageDilateOrErodeInvocation"] | components["schemas"]["InvokeImageEnhanceInvocation"] | components["schemas"]["InvokeImageValueThresholdsInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["Krea2ConditioningRebalanceInvocation"] | components["schemas"]["Krea2DenoiseInvocation"] | components["schemas"]["Krea2LoRACollectionLoader"] | components["schemas"]["Krea2LoRALoaderInvocation"] | components["schemas"]["Krea2ModelLoaderInvocation"] | components["schemas"]["Krea2SeedVarianceInvocation"] | components["schemas"]["Krea2TextEncoderInvocation"] | components["schemas"]["LaMaInfillInvocation"] | components["schemas"]["LatentsCollectionInvocation"] | components["schemas"]["LatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["LineartAnimeEdgeDetectionInvocation"] | components["schemas"]["LineartEdgeDetectionInvocation"] | components["schemas"]["LlavaOnevisionVllmInvocation"] | components["schemas"]["LoRACollectionLoader"] | components["schemas"]["LoRALoaderInvocation"] | components["schemas"]["LoRASelectorInvocation"] | components["schemas"]["MLSDDetectionInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["MaskCombineInvocation"] | components["schemas"]["MaskEdgeInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["MaskFromIDInvocation"] | components["schemas"]["MaskTensorToImageInvocation"] | components["schemas"]["MediaPipeFaceDetectionInvocation"] | components["schemas"]["MergeMetadataInvocation"] | components["schemas"]["MergeTilesToImageInvocation"] | components["schemas"]["MetadataFieldExtractorInvocation"] | components["schemas"]["MetadataFromImageInvocation"] | components["schemas"]["MetadataInvocation"] | components["schemas"]["MetadataItemInvocation"] | components["schemas"]["MetadataItemLinkedInvocation"] | components["schemas"]["MetadataToBoolCollectionInvocation"] | components["schemas"]["MetadataToBoolInvocation"] | components["schemas"]["MetadataToControlnetsInvocation"] | components["schemas"]["MetadataToFloatCollectionInvocation"] | components["schemas"]["MetadataToFloatInvocation"] | components["schemas"]["MetadataToIPAdaptersInvocation"] | components["schemas"]["MetadataToIntegerCollectionInvocation"] | components["schemas"]["MetadataToIntegerInvocation"] | components["schemas"]["MetadataToLorasCollectionInvocation"] | components["schemas"]["MetadataToLorasInvocation"] | components["schemas"]["MetadataToModelInvocation"] | components["schemas"]["MetadataToSDXLLorasInvocation"] | components["schemas"]["MetadataToSDXLModelInvocation"] | components["schemas"]["MetadataToSchedulerInvocation"] | components["schemas"]["MetadataToStringCollectionInvocation"] | components["schemas"]["MetadataToStringInvocation"] | components["schemas"]["MetadataToT2IAdaptersInvocation"] | components["schemas"]["MetadataToVAEInvocation"] | components["schemas"]["ModelIdentifierInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["NormalMapInvocation"] | components["schemas"]["OklabUnsharpMaskInvocation"] | components["schemas"]["OklchImageHueAdjustmentInvocation"] | components["schemas"]["OpenAIImageGenerationInvocation"] | components["schemas"]["PBRMapsInvocation"] | components["schemas"]["PairTileImageInvocation"] | components["schemas"]["PasteImageIntoBoundingBoxInvocation"] | components["schemas"]["PiDDecoderLoaderInvocation"] | components["schemas"]["PiDUpscaleInvocation"] | components["schemas"]["PiDiNetEdgeDetectionInvocation"] | components["schemas"]["PromptTemplateInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["QwenImageDenoiseInvocation"] | components["schemas"]["QwenImageImageToLatentsInvocation"] | components["schemas"]["QwenImageLatentsToImageInvocation"] | components["schemas"]["QwenImageLoRACollectionLoader"] | components["schemas"]["QwenImageLoRALoaderInvocation"] | components["schemas"]["QwenImageModelLoaderInvocation"] | components["schemas"]["QwenImagePiDDecodeInvocation"] | components["schemas"]["QwenImageTextEncoderInvocation"] | components["schemas"]["RandomFloatInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RectangleMaskInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["RoundInvocation"] | components["schemas"]["SD3DenoiseInvocation"] | components["schemas"]["SD3ImageToLatentsInvocation"] | components["schemas"]["SD3LatentsToImageInvocation"] | components["schemas"]["SD3PiDDecodeInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLLoRACollectionLoader"] | components["schemas"]["SDXLLoRALoaderInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLPiDDecodeInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SaveImageInvocation"] | components["schemas"]["SaveImageToFileInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["SchedulerInvocation"] | components["schemas"]["Sd3ModelLoaderInvocation"] | components["schemas"]["Sd3TextEncoderInvocation"] | components["schemas"]["SeamlessModeInvocation"] | components["schemas"]["SeedreamImageGenerationInvocation"] | components["schemas"]["SegmentAnythingInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["SpandrelImageToImageAutoscaleInvocation"] | components["schemas"]["SpandrelImageToImageInvocation"] | components["schemas"]["StringBatchInvocation"] | components["schemas"]["StringCollectionInvocation"] | components["schemas"]["StringGenerator"] | components["schemas"]["StringInvocation"] | components["schemas"]["StringJoinInvocation"] | components["schemas"]["StringJoinThreeInvocation"] | components["schemas"]["StringReplaceInvocation"] | components["schemas"]["StringSplitInvocation"] | components["schemas"]["StringSplitNegInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["T2IAdapterInvocation"] | components["schemas"]["TextLLMInvocation"] | components["schemas"]["TextLLMWithPresetInvocation"] | components["schemas"]["TileToPropertiesInvocation"] | components["schemas"]["TiledMultiDiffusionDenoiseLatents"] | components["schemas"]["UnsharpMaskInvocation"] | components["schemas"]["VAELoaderInvocation"] | components["schemas"]["VideoConcatInvocation"] | components["schemas"]["VideoFrameExtractInvocation"] | components["schemas"]["VideoInvocation"] | components["schemas"]["WanDenoiseInvocation"] | components["schemas"]["WanI2VIdealDimensionsInvocation"] | components["schemas"]["WanImageToLatentsInvocation"] | components["schemas"]["WanLatentsToImageInvocation"] | components["schemas"]["WanLatentsToVideoInvocation"] | components["schemas"]["WanLoRACollectionLoader"] | components["schemas"]["WanLoRALoaderInvocation"] | components["schemas"]["WanModelLoaderInvocation"] | components["schemas"]["WanRefImageEncoderInvocation"] | components["schemas"]["WanTI2VIdealDimensionsInvocation"] | components["schemas"]["WanTextEncoderInvocation"] | components["schemas"]["WanVideoDenoiseInvocation"] | components["schemas"]["WorkflowReturnGetInvocation"] | components["schemas"]["WorkflowReturnInvocation"] | components["schemas"]["WorkflowReturnValueInvocation"] | components["schemas"]["ZImageControlInvocation"] | components["schemas"]["ZImageDenoiseInvocation"] | components["schemas"]["ZImageDenoiseMetaInvocation"] | components["schemas"]["ZImageImageToLatentsInvocation"] | components["schemas"]["ZImageLatentsToImageInvocation"] | components["schemas"]["ZImageLoRACollectionLoader"] | components["schemas"]["ZImageLoRALoaderInvocation"] | components["schemas"]["ZImageModelLoaderInvocation"] | components["schemas"]["ZImagePiDDecodeInvocation"] | components["schemas"]["ZImageSeedVarianceEnhancerInvocation"] | components["schemas"]["ZImageTextEncoderInvocation"];
+                [key: string]: components["schemas"]["AddInvocation"] | components["schemas"]["AlibabaCloudImageGenerationInvocation"] | components["schemas"]["AlphaMaskToTensorInvocation"] | components["schemas"]["AnimaDenoiseInvocation"] | components["schemas"]["AnimaImageToLatentsInvocation"] | components["schemas"]["AnimaLLLiteInvocation"] | components["schemas"]["AnimaLatentsToImageInvocation"] | components["schemas"]["AnimaLoRACollectionLoader"] | components["schemas"]["AnimaLoRALoaderInvocation"] | components["schemas"]["AnimaModelLoaderInvocation"] | components["schemas"]["AnimaTextEncoderInvocation"] | components["schemas"]["ApplyMaskTensorToImageInvocation"] | components["schemas"]["ApplyMaskToImageInvocation"] | components["schemas"]["BlankImageInvocation"] | components["schemas"]["BlendLatentsInvocation"] | components["schemas"]["BooleanCollectionInvocation"] | components["schemas"]["BooleanInvocation"] | components["schemas"]["BoundingBoxInvocation"] | components["schemas"]["CLIPSkipInvocation"] | components["schemas"]["CV2InfillInvocation"] | components["schemas"]["CalculateImageTilesEvenSplitInvocation"] | components["schemas"]["CalculateImageTilesInvocation"] | components["schemas"]["CalculateImageTilesMinimumOverlapInvocation"] | components["schemas"]["CallSavedWorkflowInvocation"] | components["schemas"]["CannyEdgeDetectionInvocation"] | components["schemas"]["CanvasOutputInvocation"] | components["schemas"]["CanvasPasteBackInvocation"] | components["schemas"]["CanvasV2MaskAndCropInvocation"] | components["schemas"]["CenterPadCropInvocation"] | components["schemas"]["CogView4DenoiseInvocation"] | components["schemas"]["CogView4ImageToLatentsInvocation"] | components["schemas"]["CogView4LatentsToImageInvocation"] | components["schemas"]["CogView4ModelLoaderInvocation"] | components["schemas"]["CogView4TextEncoderInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["ColorCorrectInvocation"] | components["schemas"]["ColorInvocation"] | components["schemas"]["ColorMapInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ConditioningCollectionInvocation"] | components["schemas"]["ConditioningInvocation"] | components["schemas"]["ContentShuffleInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["CoreMetadataInvocation"] | components["schemas"]["CreateDenoiseMaskInvocation"] | components["schemas"]["CreateGradientMaskInvocation"] | components["schemas"]["CropImageToBoundingBoxInvocation"] | components["schemas"]["CropLatentsCoreInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["DWOpenposeDetectionInvocation"] | components["schemas"]["DecodeInvisibleWatermarkInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["DenoiseLatentsMetaInvocation"] | components["schemas"]["DepthAnythingDepthEstimationInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["ErnieImageDenoiseInvocation"] | components["schemas"]["ErnieImageModelLoaderInvocation"] | components["schemas"]["ErnieImagePromptEnhancerInvocation"] | components["schemas"]["ErnieImageTextEncoderInvocation"] | components["schemas"]["ErnieImageVaeDecodeInvocation"] | components["schemas"]["ExpandMaskWithFadeInvocation"] | components["schemas"]["ExtractVideoRangeInvocation"] | components["schemas"]["FLUXLoRACollectionLoader"] | components["schemas"]["FaceIdentifierInvocation"] | components["schemas"]["FaceMaskInvocation"] | components["schemas"]["FaceOffInvocation"] | components["schemas"]["FloatBatchInvocation"] | components["schemas"]["FloatCollectionInvocation"] | components["schemas"]["FloatGenerator"] | components["schemas"]["FloatInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["FloatMathInvocation"] | components["schemas"]["FloatToIntegerInvocation"] | components["schemas"]["Flux2DenoiseInvocation"] | components["schemas"]["Flux2KleinLoRACollectionLoader"] | components["schemas"]["Flux2KleinLoRALoaderInvocation"] | components["schemas"]["Flux2KleinModelLoaderInvocation"] | components["schemas"]["Flux2KleinTextEncoderInvocation"] | components["schemas"]["Flux2PiDDecodeInvocation"] | components["schemas"]["Flux2VaeDecodeInvocation"] | components["schemas"]["Flux2VaeEncodeInvocation"] | components["schemas"]["FluxControlLoRALoaderInvocation"] | components["schemas"]["FluxControlNetInvocation"] | components["schemas"]["FluxDenoiseInvocation"] | components["schemas"]["FluxDenoiseLatentsMetaInvocation"] | components["schemas"]["FluxFillInvocation"] | components["schemas"]["FluxIPAdapterInvocation"] | components["schemas"]["FluxKontextConcatenateImagesInvocation"] | components["schemas"]["FluxKontextInvocation"] | components["schemas"]["FluxLoRALoaderInvocation"] | components["schemas"]["FluxModelLoaderInvocation"] | components["schemas"]["FluxPiDDecodeInvocation"] | components["schemas"]["FluxReduxInvocation"] | components["schemas"]["FluxTextEncoderInvocation"] | components["schemas"]["FluxVaeDecodeInvocation"] | components["schemas"]["FluxVaeEncodeInvocation"] | components["schemas"]["FreeUInvocation"] | components["schemas"]["GeminiImageGenerationInvocation"] | components["schemas"]["Gemma2EncoderLoaderInvocation"] | components["schemas"]["GetMaskBoundingBoxInvocation"] | components["schemas"]["GroundingDinoInvocation"] | components["schemas"]["HEDEdgeDetectionInvocation"] | components["schemas"]["HeuristicResizeInvocation"] | components["schemas"]["IPAdapterInvocation"] | components["schemas"]["IdealSizeInvocation"] | components["schemas"]["Ideogram4CaptionBuilderInvocation"] | components["schemas"]["Ideogram4DenoiseInvocation"] | components["schemas"]["Ideogram4LatentsToImageInvocation"] | components["schemas"]["Ideogram4ModelLoaderInvocation"] | components["schemas"]["Ideogram4TextEncoderInvocation"] | components["schemas"]["IfInvocation"] | components["schemas"]["ImageBatchInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageChannelMultiplyInvocation"] | components["schemas"]["ImageChannelOffsetInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImageGenerator"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageMaskToTensorInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageNoiseInvocation"] | components["schemas"]["ImagePanelLayoutInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["IntegerBatchInvocation"] | components["schemas"]["IntegerCollectionInvocation"] | components["schemas"]["IntegerGenerator"] | components["schemas"]["IntegerInvocation"] | components["schemas"]["IntegerMathInvocation"] | components["schemas"]["InvertTensorMaskInvocation"] | components["schemas"]["InvokeAdjustImageHuePlusInvocation"] | components["schemas"]["InvokeEquivalentAchromaticLightnessInvocation"] | components["schemas"]["InvokeImageBlendInvocation"] | components["schemas"]["InvokeImageCompositorInvocation"] | components["schemas"]["InvokeImageDilateOrErodeInvocation"] | components["schemas"]["InvokeImageEnhanceInvocation"] | components["schemas"]["InvokeImageValueThresholdsInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["Krea2ConditioningRebalanceInvocation"] | components["schemas"]["Krea2DenoiseInvocation"] | components["schemas"]["Krea2LoRACollectionLoader"] | components["schemas"]["Krea2LoRALoaderInvocation"] | components["schemas"]["Krea2ModelLoaderInvocation"] | components["schemas"]["Krea2SeedVarianceInvocation"] | components["schemas"]["Krea2TextEncoderInvocation"] | components["schemas"]["LaMaInfillInvocation"] | components["schemas"]["LatentsCollectionInvocation"] | components["schemas"]["LatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["LineartAnimeEdgeDetectionInvocation"] | components["schemas"]["LineartEdgeDetectionInvocation"] | components["schemas"]["LlavaOnevisionVllmInvocation"] | components["schemas"]["LoRACollectionLoader"] | components["schemas"]["LoRALoaderInvocation"] | components["schemas"]["LoRASelectorInvocation"] | components["schemas"]["MLSDDetectionInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["MaskCombineInvocation"] | components["schemas"]["MaskEdgeInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["MaskFromIDInvocation"] | components["schemas"]["MaskTensorToImageInvocation"] | components["schemas"]["MediaPipeFaceDetectionInvocation"] | components["schemas"]["MergeMetadataInvocation"] | components["schemas"]["MergeTilesToImageInvocation"] | components["schemas"]["MetadataFieldExtractorInvocation"] | components["schemas"]["MetadataFromImageInvocation"] | components["schemas"]["MetadataInvocation"] | components["schemas"]["MetadataItemInvocation"] | components["schemas"]["MetadataItemLinkedInvocation"] | components["schemas"]["MetadataToBoolCollectionInvocation"] | components["schemas"]["MetadataToBoolInvocation"] | components["schemas"]["MetadataToControlnetsInvocation"] | components["schemas"]["MetadataToFloatCollectionInvocation"] | components["schemas"]["MetadataToFloatInvocation"] | components["schemas"]["MetadataToIPAdaptersInvocation"] | components["schemas"]["MetadataToIntegerCollectionInvocation"] | components["schemas"]["MetadataToIntegerInvocation"] | components["schemas"]["MetadataToLorasCollectionInvocation"] | components["schemas"]["MetadataToLorasInvocation"] | components["schemas"]["MetadataToModelInvocation"] | components["schemas"]["MetadataToSDXLLorasInvocation"] | components["schemas"]["MetadataToSDXLModelInvocation"] | components["schemas"]["MetadataToSchedulerInvocation"] | components["schemas"]["MetadataToStringCollectionInvocation"] | components["schemas"]["MetadataToStringInvocation"] | components["schemas"]["MetadataToT2IAdaptersInvocation"] | components["schemas"]["MetadataToVAEInvocation"] | components["schemas"]["MiniMaxH3DenoiseInvocation"] | components["schemas"]["MiniMaxH3FrameConditioningInvocation"] | components["schemas"]["MiniMaxH3LatentsToImageInvocation"] | components["schemas"]["MiniMaxH3LatentsToVideoInvocation"] | components["schemas"]["MiniMaxH3ModelLoaderInvocation"] | components["schemas"]["MiniMaxH3TextEncoderInvocation"] | components["schemas"]["ModelIdentifierInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["NormalMapInvocation"] | components["schemas"]["OklabUnsharpMaskInvocation"] | components["schemas"]["OklchImageHueAdjustmentInvocation"] | components["schemas"]["OpenAIImageGenerationInvocation"] | components["schemas"]["PBRMapsInvocation"] | components["schemas"]["PairTileImageInvocation"] | components["schemas"]["PasteImageIntoBoundingBoxInvocation"] | components["schemas"]["PiDDecoderLoaderInvocation"] | components["schemas"]["PiDUpscaleInvocation"] | components["schemas"]["PiDiNetEdgeDetectionInvocation"] | components["schemas"]["PromptTemplateInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["QwenImageDenoiseInvocation"] | components["schemas"]["QwenImageImageToLatentsInvocation"] | components["schemas"]["QwenImageLatentsToImageInvocation"] | components["schemas"]["QwenImageLoRACollectionLoader"] | components["schemas"]["QwenImageLoRALoaderInvocation"] | components["schemas"]["QwenImageModelLoaderInvocation"] | components["schemas"]["QwenImagePiDDecodeInvocation"] | components["schemas"]["QwenImageTextEncoderInvocation"] | components["schemas"]["RandomFloatInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RectangleMaskInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["RoundInvocation"] | components["schemas"]["SD3DenoiseInvocation"] | components["schemas"]["SD3ImageToLatentsInvocation"] | components["schemas"]["SD3LatentsToImageInvocation"] | components["schemas"]["SD3PiDDecodeInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLLoRACollectionLoader"] | components["schemas"]["SDXLLoRALoaderInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLPiDDecodeInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SaveImageInvocation"] | components["schemas"]["SaveImageToFileInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["SchedulerInvocation"] | components["schemas"]["Sd3ModelLoaderInvocation"] | components["schemas"]["Sd3TextEncoderInvocation"] | components["schemas"]["SeamlessModeInvocation"] | components["schemas"]["SeedreamImageGenerationInvocation"] | components["schemas"]["SegmentAnythingInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["SpandrelImageToImageAutoscaleInvocation"] | components["schemas"]["SpandrelImageToImageInvocation"] | components["schemas"]["StringBatchInvocation"] | components["schemas"]["StringCollectionInvocation"] | components["schemas"]["StringGenerator"] | components["schemas"]["StringInvocation"] | components["schemas"]["StringJoinInvocation"] | components["schemas"]["StringJoinThreeInvocation"] | components["schemas"]["StringReplaceInvocation"] | components["schemas"]["StringSplitInvocation"] | components["schemas"]["StringSplitNegInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["T2IAdapterInvocation"] | components["schemas"]["TextLLMInvocation"] | components["schemas"]["TextLLMWithPresetInvocation"] | components["schemas"]["TileToPropertiesInvocation"] | components["schemas"]["TiledMultiDiffusionDenoiseLatents"] | components["schemas"]["UnsharpMaskInvocation"] | components["schemas"]["VAELoaderInvocation"] | components["schemas"]["VideoConcatInvocation"] | components["schemas"]["VideoFrameExtractInvocation"] | components["schemas"]["VideoInvocation"] | components["schemas"]["WanDenoiseInvocation"] | components["schemas"]["WanI2VIdealDimensionsInvocation"] | components["schemas"]["WanImageToLatentsInvocation"] | components["schemas"]["WanLatentsToImageInvocation"] | components["schemas"]["WanLatentsToVideoInvocation"] | components["schemas"]["WanLoRACollectionLoader"] | components["schemas"]["WanLoRALoaderInvocation"] | components["schemas"]["WanModelLoaderInvocation"] | components["schemas"]["WanRefImageEncoderInvocation"] | components["schemas"]["WanTI2VIdealDimensionsInvocation"] | components["schemas"]["WanTextEncoderInvocation"] | components["schemas"]["WanVideoDenoiseInvocation"] | components["schemas"]["WorkflowReturnGetInvocation"] | components["schemas"]["WorkflowReturnInvocation"] | components["schemas"]["WorkflowReturnValueInvocation"] | components["schemas"]["ZImageControlInvocation"] | components["schemas"]["ZImageDenoiseInvocation"] | components["schemas"]["ZImageDenoiseMetaInvocation"] | components["schemas"]["ZImageImageToLatentsInvocation"] | components["schemas"]["ZImageLatentsToImageInvocation"] | components["schemas"]["ZImageLoRACollectionLoader"] | components["schemas"]["ZImageLoRALoaderInvocation"] | components["schemas"]["ZImageModelLoaderInvocation"] | components["schemas"]["ZImagePiDDecodeInvocation"] | components["schemas"]["ZImageSeedVarianceEnhancerInvocation"] | components["schemas"]["ZImageTextEncoderInvocation"];
             };
             /**
              * Edges
@@ -14260,7 +14260,7 @@ export type components = {
              * @description The results of node executions
              */
             results: {
-                [key: string]: components["schemas"]["AnimaConditioningOutput"] | components["schemas"]["AnimaLLLiteOutput"] | components["schemas"]["AnimaLoRALoaderOutput"] | components["schemas"]["AnimaModelLoaderOutput"] | components["schemas"]["BooleanCollectionOutput"] | components["schemas"]["BooleanOutput"] | components["schemas"]["BoundingBoxCollectionOutput"] | components["schemas"]["BoundingBoxOutput"] | components["schemas"]["CLIPOutput"] | components["schemas"]["CLIPSkipInvocationOutput"] | components["schemas"]["CalculateImageTilesOutput"] | components["schemas"]["CogView4ConditioningOutput"] | components["schemas"]["CogView4ModelLoaderOutput"] | components["schemas"]["CollectInvocationOutput"] | components["schemas"]["ColorCollectionOutput"] | components["schemas"]["ColorOutput"] | components["schemas"]["ConditioningCollectionOutput"] | components["schemas"]["ConditioningOutput"] | components["schemas"]["ControlOutput"] | components["schemas"]["DenoiseMaskOutput"] | components["schemas"]["ErnieImageConditioningOutput"] | components["schemas"]["ErnieImageModelLoaderOutput"] | components["schemas"]["ExtractVideoRangeOutput"] | components["schemas"]["FaceMaskOutput"] | components["schemas"]["FaceOffOutput"] | components["schemas"]["FloatCollectionOutput"] | components["schemas"]["FloatGeneratorOutput"] | components["schemas"]["FloatOutput"] | components["schemas"]["Flux2KleinLoRALoaderOutput"] | components["schemas"]["Flux2KleinModelLoaderOutput"] | components["schemas"]["FluxConditioningCollectionOutput"] | components["schemas"]["FluxConditioningOutput"] | components["schemas"]["FluxControlLoRALoaderOutput"] | components["schemas"]["FluxControlNetOutput"] | components["schemas"]["FluxFillOutput"] | components["schemas"]["FluxKontextOutput"] | components["schemas"]["FluxLoRALoaderOutput"] | components["schemas"]["FluxModelLoaderOutput"] | components["schemas"]["FluxReduxOutput"] | components["schemas"]["Gemma2EncoderOutput"] | components["schemas"]["GradientMaskOutput"] | components["schemas"]["IPAdapterOutput"] | components["schemas"]["IdealSizeOutput"] | components["schemas"]["Ideogram4ConditioningOutput"] | components["schemas"]["Ideogram4ModelLoaderOutput"] | components["schemas"]["IfInvocationOutput"] | components["schemas"]["ImageCollectionOutput"] | components["schemas"]["ImageGeneratorOutput"] | components["schemas"]["ImageOutput"] | components["schemas"]["ImagePanelCoordinateOutput"] | components["schemas"]["IntegerCollectionOutput"] | components["schemas"]["IntegerGeneratorOutput"] | components["schemas"]["IntegerOutput"] | components["schemas"]["IterateInvocationOutput"] | components["schemas"]["Krea2ConditioningOutput"] | components["schemas"]["Krea2LoRALoaderOutput"] | components["schemas"]["Krea2ModelLoaderOutput"] | components["schemas"]["LatentsCollectionOutput"] | components["schemas"]["LatentsMetaOutput"] | components["schemas"]["LatentsOutput"] | components["schemas"]["LoRALoaderOutput"] | components["schemas"]["LoRASelectorOutput"] | components["schemas"]["MDControlListOutput"] | components["schemas"]["MDIPAdapterListOutput"] | components["schemas"]["MDT2IAdapterListOutput"] | components["schemas"]["MaskOutput"] | components["schemas"]["MetadataItemOutput"] | components["schemas"]["MetadataOutput"] | components["schemas"]["MetadataToLorasCollectionOutput"] | components["schemas"]["MetadataToModelOutput"] | components["schemas"]["MetadataToSDXLModelOutput"] | components["schemas"]["ModelIdentifierOutput"] | components["schemas"]["ModelLoaderOutput"] | components["schemas"]["NoiseOutput"] | components["schemas"]["PBRMapsOutput"] | components["schemas"]["PairTileImageOutput"] | components["schemas"]["PiDDecoderOutput"] | components["schemas"]["PromptTemplateOutput"] | components["schemas"]["QwenImageConditioningOutput"] | components["schemas"]["QwenImageLoRALoaderOutput"] | components["schemas"]["QwenImageModelLoaderOutput"] | components["schemas"]["SD3ConditioningOutput"] | components["schemas"]["SDXLLoRALoaderOutput"] | components["schemas"]["SDXLModelLoaderOutput"] | components["schemas"]["SDXLRefinerModelLoaderOutput"] | components["schemas"]["SchedulerOutput"] | components["schemas"]["Sd3ModelLoaderOutput"] | components["schemas"]["SeamlessModeOutput"] | components["schemas"]["String2Output"] | components["schemas"]["StringCollectionOutput"] | components["schemas"]["StringGeneratorOutput"] | components["schemas"]["StringOutput"] | components["schemas"]["StringPosNegOutput"] | components["schemas"]["T2IAdapterOutput"] | components["schemas"]["TileToPropertiesOutput"] | components["schemas"]["UNetOutput"] | components["schemas"]["VAEOutput"] | components["schemas"]["VideoOutput"] | components["schemas"]["WanConditioningOutput"] | components["schemas"]["WanLoRALoaderOutput"] | components["schemas"]["WanModelLoaderOutput"] | components["schemas"]["WanRefImageOutput"] | components["schemas"]["WorkflowReturnGetOutput"] | components["schemas"]["WorkflowReturnOutput"] | components["schemas"]["WorkflowReturnValueOutput"] | components["schemas"]["ZImageConditioningOutput"] | components["schemas"]["ZImageControlOutput"] | components["schemas"]["ZImageLoRALoaderOutput"] | components["schemas"]["ZImageModelLoaderOutput"];
+                [key: string]: components["schemas"]["AnimaConditioningOutput"] | components["schemas"]["AnimaLLLiteOutput"] | components["schemas"]["AnimaLoRALoaderOutput"] | components["schemas"]["AnimaModelLoaderOutput"] | components["schemas"]["BooleanCollectionOutput"] | components["schemas"]["BooleanOutput"] | components["schemas"]["BoundingBoxCollectionOutput"] | components["schemas"]["BoundingBoxOutput"] | components["schemas"]["CLIPOutput"] | components["schemas"]["CLIPSkipInvocationOutput"] | components["schemas"]["CalculateImageTilesOutput"] | components["schemas"]["CogView4ConditioningOutput"] | components["schemas"]["CogView4ModelLoaderOutput"] | components["schemas"]["CollectInvocationOutput"] | components["schemas"]["ColorCollectionOutput"] | components["schemas"]["ColorOutput"] | components["schemas"]["ConditioningCollectionOutput"] | components["schemas"]["ConditioningOutput"] | components["schemas"]["ControlOutput"] | components["schemas"]["DenoiseMaskOutput"] | components["schemas"]["ErnieImageConditioningOutput"] | components["schemas"]["ErnieImageModelLoaderOutput"] | components["schemas"]["ExtractVideoRangeOutput"] | components["schemas"]["FaceMaskOutput"] | components["schemas"]["FaceOffOutput"] | components["schemas"]["FloatCollectionOutput"] | components["schemas"]["FloatGeneratorOutput"] | components["schemas"]["FloatOutput"] | components["schemas"]["Flux2KleinLoRALoaderOutput"] | components["schemas"]["Flux2KleinModelLoaderOutput"] | components["schemas"]["FluxConditioningCollectionOutput"] | components["schemas"]["FluxConditioningOutput"] | components["schemas"]["FluxControlLoRALoaderOutput"] | components["schemas"]["FluxControlNetOutput"] | components["schemas"]["FluxFillOutput"] | components["schemas"]["FluxKontextOutput"] | components["schemas"]["FluxLoRALoaderOutput"] | components["schemas"]["FluxModelLoaderOutput"] | components["schemas"]["FluxReduxOutput"] | components["schemas"]["Gemma2EncoderOutput"] | components["schemas"]["GradientMaskOutput"] | components["schemas"]["IPAdapterOutput"] | components["schemas"]["IdealSizeOutput"] | components["schemas"]["Ideogram4ConditioningOutput"] | components["schemas"]["Ideogram4ModelLoaderOutput"] | components["schemas"]["IfInvocationOutput"] | components["schemas"]["ImageCollectionOutput"] | components["schemas"]["ImageGeneratorOutput"] | components["schemas"]["ImageOutput"] | components["schemas"]["ImagePanelCoordinateOutput"] | components["schemas"]["IntegerCollectionOutput"] | components["schemas"]["IntegerGeneratorOutput"] | components["schemas"]["IntegerOutput"] | components["schemas"]["IterateInvocationOutput"] | components["schemas"]["Krea2ConditioningOutput"] | components["schemas"]["Krea2LoRALoaderOutput"] | components["schemas"]["Krea2ModelLoaderOutput"] | components["schemas"]["LatentsCollectionOutput"] | components["schemas"]["LatentsMetaOutput"] | components["schemas"]["LatentsOutput"] | components["schemas"]["LoRALoaderOutput"] | components["schemas"]["LoRASelectorOutput"] | components["schemas"]["MDControlListOutput"] | components["schemas"]["MDIPAdapterListOutput"] | components["schemas"]["MDT2IAdapterListOutput"] | components["schemas"]["MaskOutput"] | components["schemas"]["MetadataItemOutput"] | components["schemas"]["MetadataOutput"] | components["schemas"]["MetadataToLorasCollectionOutput"] | components["schemas"]["MetadataToModelOutput"] | components["schemas"]["MetadataToSDXLModelOutput"] | components["schemas"]["MiniMaxH3ConditioningOutput"] | components["schemas"]["MiniMaxH3DenoiseOutput"] | components["schemas"]["MiniMaxH3FrameConditioningOutput"] | components["schemas"]["MiniMaxH3ModelLoaderOutput"] | components["schemas"]["ModelIdentifierOutput"] | components["schemas"]["ModelLoaderOutput"] | components["schemas"]["NoiseOutput"] | components["schemas"]["PBRMapsOutput"] | components["schemas"]["PairTileImageOutput"] | components["schemas"]["PiDDecoderOutput"] | components["schemas"]["PromptTemplateOutput"] | components["schemas"]["QwenImageConditioningOutput"] | components["schemas"]["QwenImageLoRALoaderOutput"] | components["schemas"]["QwenImageModelLoaderOutput"] | components["schemas"]["SD3ConditioningOutput"] | components["schemas"]["SDXLLoRALoaderOutput"] | components["schemas"]["SDXLModelLoaderOutput"] | components["schemas"]["SDXLRefinerModelLoaderOutput"] | components["schemas"]["SchedulerOutput"] | components["schemas"]["Sd3ModelLoaderOutput"] | components["schemas"]["SeamlessModeOutput"] | components["schemas"]["String2Output"] | components["schemas"]["StringCollectionOutput"] | components["schemas"]["StringGeneratorOutput"] | components["schemas"]["StringOutput"] | components["schemas"]["StringPosNegOutput"] | components["schemas"]["T2IAdapterOutput"] | components["schemas"]["TileToPropertiesOutput"] | components["schemas"]["UNetOutput"] | components["schemas"]["VAEOutput"] | components["schemas"]["VideoOutput"] | components["schemas"]["WanConditioningOutput"] | components["schemas"]["WanLoRALoaderOutput"] | components["schemas"]["WanModelLoaderOutput"] | components["schemas"]["WanRefImageOutput"] | components["schemas"]["WorkflowReturnGetOutput"] | components["schemas"]["WorkflowReturnOutput"] | components["schemas"]["WorkflowReturnValueOutput"] | components["schemas"]["ZImageConditioningOutput"] | components["schemas"]["ZImageControlOutput"] | components["schemas"]["ZImageLoRALoaderOutput"] | components["schemas"]["ZImageModelLoaderOutput"];
             };
             /**
              * Errors
@@ -18048,7 +18048,7 @@ export type components = {
              * Invocation
              * @description The ID of the invocation
              */
-            invocation: components["schemas"]["AddInvocation"] | components["schemas"]["AlibabaCloudImageGenerationInvocation"] | components["schemas"]["AlphaMaskToTensorInvocation"] | components["schemas"]["AnimaDenoiseInvocation"] | components["schemas"]["AnimaImageToLatentsInvocation"] | components["schemas"]["AnimaLLLiteInvocation"] | components["schemas"]["AnimaLatentsToImageInvocation"] | components["schemas"]["AnimaLoRACollectionLoader"] | components["schemas"]["AnimaLoRALoaderInvocation"] | components["schemas"]["AnimaModelLoaderInvocation"] | components["schemas"]["AnimaTextEncoderInvocation"] | components["schemas"]["ApplyMaskTensorToImageInvocation"] | components["schemas"]["ApplyMaskToImageInvocation"] | components["schemas"]["BlankImageInvocation"] | components["schemas"]["BlendLatentsInvocation"] | components["schemas"]["BooleanCollectionInvocation"] | components["schemas"]["BooleanInvocation"] | components["schemas"]["BoundingBoxInvocation"] | components["schemas"]["CLIPSkipInvocation"] | components["schemas"]["CV2InfillInvocation"] | components["schemas"]["CalculateImageTilesEvenSplitInvocation"] | components["schemas"]["CalculateImageTilesInvocation"] | components["schemas"]["CalculateImageTilesMinimumOverlapInvocation"] | components["schemas"]["CallSavedWorkflowInvocation"] | components["schemas"]["CannyEdgeDetectionInvocation"] | components["schemas"]["CanvasOutputInvocation"] | components["schemas"]["CanvasPasteBackInvocation"] | components["schemas"]["CanvasV2MaskAndCropInvocation"] | components["schemas"]["CenterPadCropInvocation"] | components["schemas"]["CogView4DenoiseInvocation"] | components["schemas"]["CogView4ImageToLatentsInvocation"] | components["schemas"]["CogView4LatentsToImageInvocation"] | components["schemas"]["CogView4ModelLoaderInvocation"] | components["schemas"]["CogView4TextEncoderInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["ColorCorrectInvocation"] | components["schemas"]["ColorInvocation"] | components["schemas"]["ColorMapInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ConditioningCollectionInvocation"] | components["schemas"]["ConditioningInvocation"] | components["schemas"]["ContentShuffleInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["CoreMetadataInvocation"] | components["schemas"]["CreateDenoiseMaskInvocation"] | components["schemas"]["CreateGradientMaskInvocation"] | components["schemas"]["CropImageToBoundingBoxInvocation"] | components["schemas"]["CropLatentsCoreInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["DWOpenposeDetectionInvocation"] | components["schemas"]["DecodeInvisibleWatermarkInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["DenoiseLatentsMetaInvocation"] | components["schemas"]["DepthAnythingDepthEstimationInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["ErnieImageDenoiseInvocation"] | components["schemas"]["ErnieImageModelLoaderInvocation"] | components["schemas"]["ErnieImagePromptEnhancerInvocation"] | components["schemas"]["ErnieImageTextEncoderInvocation"] | components["schemas"]["ErnieImageVaeDecodeInvocation"] | components["schemas"]["ExpandMaskWithFadeInvocation"] | components["schemas"]["ExtractVideoRangeInvocation"] | components["schemas"]["FLUXLoRACollectionLoader"] | components["schemas"]["FaceIdentifierInvocation"] | components["schemas"]["FaceMaskInvocation"] | components["schemas"]["FaceOffInvocation"] | components["schemas"]["FloatBatchInvocation"] | components["schemas"]["FloatCollectionInvocation"] | components["schemas"]["FloatGenerator"] | components["schemas"]["FloatInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["FloatMathInvocation"] | components["schemas"]["FloatToIntegerInvocation"] | components["schemas"]["Flux2DenoiseInvocation"] | components["schemas"]["Flux2KleinLoRACollectionLoader"] | components["schemas"]["Flux2KleinLoRALoaderInvocation"] | components["schemas"]["Flux2KleinModelLoaderInvocation"] | components["schemas"]["Flux2KleinTextEncoderInvocation"] | components["schemas"]["Flux2PiDDecodeInvocation"] | components["schemas"]["Flux2VaeDecodeInvocation"] | components["schemas"]["Flux2VaeEncodeInvocation"] | components["schemas"]["FluxControlLoRALoaderInvocation"] | components["schemas"]["FluxControlNetInvocation"] | components["schemas"]["FluxDenoiseInvocation"] | components["schemas"]["FluxDenoiseLatentsMetaInvocation"] | components["schemas"]["FluxFillInvocation"] | components["schemas"]["FluxIPAdapterInvocation"] | components["schemas"]["FluxKontextConcatenateImagesInvocation"] | components["schemas"]["FluxKontextInvocation"] | components["schemas"]["FluxLoRALoaderInvocation"] | components["schemas"]["FluxModelLoaderInvocation"] | components["schemas"]["FluxPiDDecodeInvocation"] | components["schemas"]["FluxReduxInvocation"] | components["schemas"]["FluxTextEncoderInvocation"] | components["schemas"]["FluxVaeDecodeInvocation"] | components["schemas"]["FluxVaeEncodeInvocation"] | components["schemas"]["FreeUInvocation"] | components["schemas"]["GeminiImageGenerationInvocation"] | components["schemas"]["Gemma2EncoderLoaderInvocation"] | components["schemas"]["GetMaskBoundingBoxInvocation"] | components["schemas"]["GroundingDinoInvocation"] | components["schemas"]["HEDEdgeDetectionInvocation"] | components["schemas"]["HeuristicResizeInvocation"] | components["schemas"]["IPAdapterInvocation"] | components["schemas"]["IdealSizeInvocation"] | components["schemas"]["Ideogram4CaptionBuilderInvocation"] | components["schemas"]["Ideogram4DenoiseInvocation"] | components["schemas"]["Ideogram4LatentsToImageInvocation"] | components["schemas"]["Ideogram4ModelLoaderInvocation"] | components["schemas"]["Ideogram4TextEncoderInvocation"] | components["schemas"]["IfInvocation"] | components["schemas"]["ImageBatchInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageChannelMultiplyInvocation"] | components["schemas"]["ImageChannelOffsetInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImageGenerator"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageMaskToTensorInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageNoiseInvocation"] | components["schemas"]["ImagePanelLayoutInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["IntegerBatchInvocation"] | components["schemas"]["IntegerCollectionInvocation"] | components["schemas"]["IntegerGenerator"] | components["schemas"]["IntegerInvocation"] | components["schemas"]["IntegerMathInvocation"] | components["schemas"]["InvertTensorMaskInvocation"] | components["schemas"]["InvokeAdjustImageHuePlusInvocation"] | components["schemas"]["InvokeEquivalentAchromaticLightnessInvocation"] | components["schemas"]["InvokeImageBlendInvocation"] | components["schemas"]["InvokeImageCompositorInvocation"] | components["schemas"]["InvokeImageDilateOrErodeInvocation"] | components["schemas"]["InvokeImageEnhanceInvocation"] | components["schemas"]["InvokeImageValueThresholdsInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["Krea2ConditioningRebalanceInvocation"] | components["schemas"]["Krea2DenoiseInvocation"] | components["schemas"]["Krea2LoRACollectionLoader"] | components["schemas"]["Krea2LoRALoaderInvocation"] | components["schemas"]["Krea2ModelLoaderInvocation"] | components["schemas"]["Krea2SeedVarianceInvocation"] | components["schemas"]["Krea2TextEncoderInvocation"] | components["schemas"]["LaMaInfillInvocation"] | components["schemas"]["LatentsCollectionInvocation"] | components["schemas"]["LatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["LineartAnimeEdgeDetectionInvocation"] | components["schemas"]["LineartEdgeDetectionInvocation"] | components["schemas"]["LlavaOnevisionVllmInvocation"] | components["schemas"]["LoRACollectionLoader"] | components["schemas"]["LoRALoaderInvocation"] | components["schemas"]["LoRASelectorInvocation"] | components["schemas"]["MLSDDetectionInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["MaskCombineInvocation"] | components["schemas"]["MaskEdgeInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["MaskFromIDInvocation"] | components["schemas"]["MaskTensorToImageInvocation"] | components["schemas"]["MediaPipeFaceDetectionInvocation"] | components["schemas"]["MergeMetadataInvocation"] | components["schemas"]["MergeTilesToImageInvocation"] | components["schemas"]["MetadataFieldExtractorInvocation"] | components["schemas"]["MetadataFromImageInvocation"] | components["schemas"]["MetadataInvocation"] | components["schemas"]["MetadataItemInvocation"] | components["schemas"]["MetadataItemLinkedInvocation"] | components["schemas"]["MetadataToBoolCollectionInvocation"] | components["schemas"]["MetadataToBoolInvocation"] | components["schemas"]["MetadataToControlnetsInvocation"] | components["schemas"]["MetadataToFloatCollectionInvocation"] | components["schemas"]["MetadataToFloatInvocation"] | components["schemas"]["MetadataToIPAdaptersInvocation"] | components["schemas"]["MetadataToIntegerCollectionInvocation"] | components["schemas"]["MetadataToIntegerInvocation"] | components["schemas"]["MetadataToLorasCollectionInvocation"] | components["schemas"]["MetadataToLorasInvocation"] | components["schemas"]["MetadataToModelInvocation"] | components["schemas"]["MetadataToSDXLLorasInvocation"] | components["schemas"]["MetadataToSDXLModelInvocation"] | components["schemas"]["MetadataToSchedulerInvocation"] | components["schemas"]["MetadataToStringCollectionInvocation"] | components["schemas"]["MetadataToStringInvocation"] | components["schemas"]["MetadataToT2IAdaptersInvocation"] | components["schemas"]["MetadataToVAEInvocation"] | components["schemas"]["ModelIdentifierInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["NormalMapInvocation"] | components["schemas"]["OklabUnsharpMaskInvocation"] | components["schemas"]["OklchImageHueAdjustmentInvocation"] | components["schemas"]["OpenAIImageGenerationInvocation"] | components["schemas"]["PBRMapsInvocation"] | components["schemas"]["PairTileImageInvocation"] | components["schemas"]["PasteImageIntoBoundingBoxInvocation"] | components["schemas"]["PiDDecoderLoaderInvocation"] | components["schemas"]["PiDUpscaleInvocation"] | components["schemas"]["PiDiNetEdgeDetectionInvocation"] | components["schemas"]["PromptTemplateInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["QwenImageDenoiseInvocation"] | components["schemas"]["QwenImageImageToLatentsInvocation"] | components["schemas"]["QwenImageLatentsToImageInvocation"] | components["schemas"]["QwenImageLoRACollectionLoader"] | components["schemas"]["QwenImageLoRALoaderInvocation"] | components["schemas"]["QwenImageModelLoaderInvocation"] | components["schemas"]["QwenImagePiDDecodeInvocation"] | components["schemas"]["QwenImageTextEncoderInvocation"] | components["schemas"]["RandomFloatInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RectangleMaskInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["RoundInvocation"] | components["schemas"]["SD3DenoiseInvocation"] | components["schemas"]["SD3ImageToLatentsInvocation"] | components["schemas"]["SD3LatentsToImageInvocation"] | components["schemas"]["SD3PiDDecodeInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLLoRACollectionLoader"] | components["schemas"]["SDXLLoRALoaderInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLPiDDecodeInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SaveImageInvocation"] | components["schemas"]["SaveImageToFileInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["SchedulerInvocation"] | components["schemas"]["Sd3ModelLoaderInvocation"] | components["schemas"]["Sd3TextEncoderInvocation"] | components["schemas"]["SeamlessModeInvocation"] | components["schemas"]["SeedreamImageGenerationInvocation"] | components["schemas"]["SegmentAnythingInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["SpandrelImageToImageAutoscaleInvocation"] | components["schemas"]["SpandrelImageToImageInvocation"] | components["schemas"]["StringBatchInvocation"] | components["schemas"]["StringCollectionInvocation"] | components["schemas"]["StringGenerator"] | components["schemas"]["StringInvocation"] | components["schemas"]["StringJoinInvocation"] | components["schemas"]["StringJoinThreeInvocation"] | components["schemas"]["StringReplaceInvocation"] | components["schemas"]["StringSplitInvocation"] | components["schemas"]["StringSplitNegInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["T2IAdapterInvocation"] | components["schemas"]["TextLLMInvocation"] | components["schemas"]["TextLLMWithPresetInvocation"] | components["schemas"]["TileToPropertiesInvocation"] | components["schemas"]["TiledMultiDiffusionDenoiseLatents"] | components["schemas"]["UnsharpMaskInvocation"] | components["schemas"]["VAELoaderInvocation"] | components["schemas"]["VideoConcatInvocation"] | components["schemas"]["VideoFrameExtractInvocation"] | components["schemas"]["VideoInvocation"] | components["schemas"]["WanDenoiseInvocation"] | components["schemas"]["WanI2VIdealDimensionsInvocation"] | components["schemas"]["WanImageToLatentsInvocation"] | components["schemas"]["WanLatentsToImageInvocation"] | components["schemas"]["WanLatentsToVideoInvocation"] | components["schemas"]["WanLoRACollectionLoader"] | components["schemas"]["WanLoRALoaderInvocation"] | components["schemas"]["WanModelLoaderInvocation"] | components["schemas"]["WanRefImageEncoderInvocation"] | components["schemas"]["WanTI2VIdealDimensionsInvocation"] | components["schemas"]["WanTextEncoderInvocation"] | components["schemas"]["WanVideoDenoiseInvocation"] | components["schemas"]["WorkflowReturnGetInvocation"] | components["schemas"]["WorkflowReturnInvocation"] | components["schemas"]["WorkflowReturnValueInvocation"] | components["schemas"]["ZImageControlInvocation"] | components["schemas"]["ZImageDenoiseInvocation"] | components["schemas"]["ZImageDenoiseMetaInvocation"] | components["schemas"]["ZImageImageToLatentsInvocation"] | components["schemas"]["ZImageLatentsToImageInvocation"] | components["schemas"]["ZImageLoRACollectionLoader"] | components["schemas"]["ZImageLoRALoaderInvocation"] | components["schemas"]["ZImageModelLoaderInvocation"] | components["schemas"]["ZImagePiDDecodeInvocation"] | components["schemas"]["ZImageSeedVarianceEnhancerInvocation"] | components["schemas"]["ZImageTextEncoderInvocation"];
+            invocation: components["schemas"]["AddInvocation"] | components["schemas"]["AlibabaCloudImageGenerationInvocation"] | components["schemas"]["AlphaMaskToTensorInvocation"] | components["schemas"]["AnimaDenoiseInvocation"] | components["schemas"]["AnimaImageToLatentsInvocation"] | components["schemas"]["AnimaLLLiteInvocation"] | components["schemas"]["AnimaLatentsToImageInvocation"] | components["schemas"]["AnimaLoRACollectionLoader"] | components["schemas"]["AnimaLoRALoaderInvocation"] | components["schemas"]["AnimaModelLoaderInvocation"] | components["schemas"]["AnimaTextEncoderInvocation"] | components["schemas"]["ApplyMaskTensorToImageInvocation"] | components["schemas"]["ApplyMaskToImageInvocation"] | components["schemas"]["BlankImageInvocation"] | components["schemas"]["BlendLatentsInvocation"] | components["schemas"]["BooleanCollectionInvocation"] | components["schemas"]["BooleanInvocation"] | components["schemas"]["BoundingBoxInvocation"] | components["schemas"]["CLIPSkipInvocation"] | components["schemas"]["CV2InfillInvocation"] | components["schemas"]["CalculateImageTilesEvenSplitInvocation"] | components["schemas"]["CalculateImageTilesInvocation"] | components["schemas"]["CalculateImageTilesMinimumOverlapInvocation"] | components["schemas"]["CallSavedWorkflowInvocation"] | components["schemas"]["CannyEdgeDetectionInvocation"] | components["schemas"]["CanvasOutputInvocation"] | components["schemas"]["CanvasPasteBackInvocation"] | components["schemas"]["CanvasV2MaskAndCropInvocation"] | components["schemas"]["CenterPadCropInvocation"] | components["schemas"]["CogView4DenoiseInvocation"] | components["schemas"]["CogView4ImageToLatentsInvocation"] | components["schemas"]["CogView4LatentsToImageInvocation"] | components["schemas"]["CogView4ModelLoaderInvocation"] | components["schemas"]["CogView4TextEncoderInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["ColorCorrectInvocation"] | components["schemas"]["ColorInvocation"] | components["schemas"]["ColorMapInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ConditioningCollectionInvocation"] | components["schemas"]["ConditioningInvocation"] | components["schemas"]["ContentShuffleInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["CoreMetadataInvocation"] | components["schemas"]["CreateDenoiseMaskInvocation"] | components["schemas"]["CreateGradientMaskInvocation"] | components["schemas"]["CropImageToBoundingBoxInvocation"] | components["schemas"]["CropLatentsCoreInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["DWOpenposeDetectionInvocation"] | components["schemas"]["DecodeInvisibleWatermarkInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["DenoiseLatentsMetaInvocation"] | components["schemas"]["DepthAnythingDepthEstimationInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["ErnieImageDenoiseInvocation"] | components["schemas"]["ErnieImageModelLoaderInvocation"] | components["schemas"]["ErnieImagePromptEnhancerInvocation"] | components["schemas"]["ErnieImageTextEncoderInvocation"] | components["schemas"]["ErnieImageVaeDecodeInvocation"] | components["schemas"]["ExpandMaskWithFadeInvocation"] | components["schemas"]["ExtractVideoRangeInvocation"] | components["schemas"]["FLUXLoRACollectionLoader"] | components["schemas"]["FaceIdentifierInvocation"] | components["schemas"]["FaceMaskInvocation"] | components["schemas"]["FaceOffInvocation"] | components["schemas"]["FloatBatchInvocation"] | components["schemas"]["FloatCollectionInvocation"] | components["schemas"]["FloatGenerator"] | components["schemas"]["FloatInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["FloatMathInvocation"] | components["schemas"]["FloatToIntegerInvocation"] | components["schemas"]["Flux2DenoiseInvocation"] | components["schemas"]["Flux2KleinLoRACollectionLoader"] | components["schemas"]["Flux2KleinLoRALoaderInvocation"] | components["schemas"]["Flux2KleinModelLoaderInvocation"] | components["schemas"]["Flux2KleinTextEncoderInvocation"] | components["schemas"]["Flux2PiDDecodeInvocation"] | components["schemas"]["Flux2VaeDecodeInvocation"] | components["schemas"]["Flux2VaeEncodeInvocation"] | components["schemas"]["FluxControlLoRALoaderInvocation"] | components["schemas"]["FluxControlNetInvocation"] | components["schemas"]["FluxDenoiseInvocation"] | components["schemas"]["FluxDenoiseLatentsMetaInvocation"] | components["schemas"]["FluxFillInvocation"] | components["schemas"]["FluxIPAdapterInvocation"] | components["schemas"]["FluxKontextConcatenateImagesInvocation"] | components["schemas"]["FluxKontextInvocation"] | components["schemas"]["FluxLoRALoaderInvocation"] | components["schemas"]["FluxModelLoaderInvocation"] | components["schemas"]["FluxPiDDecodeInvocation"] | components["schemas"]["FluxReduxInvocation"] | components["schemas"]["FluxTextEncoderInvocation"] | components["schemas"]["FluxVaeDecodeInvocation"] | components["schemas"]["FluxVaeEncodeInvocation"] | components["schemas"]["FreeUInvocation"] | components["schemas"]["GeminiImageGenerationInvocation"] | components["schemas"]["Gemma2EncoderLoaderInvocation"] | components["schemas"]["GetMaskBoundingBoxInvocation"] | components["schemas"]["GroundingDinoInvocation"] | components["schemas"]["HEDEdgeDetectionInvocation"] | components["schemas"]["HeuristicResizeInvocation"] | components["schemas"]["IPAdapterInvocation"] | components["schemas"]["IdealSizeInvocation"] | components["schemas"]["Ideogram4CaptionBuilderInvocation"] | components["schemas"]["Ideogram4DenoiseInvocation"] | components["schemas"]["Ideogram4LatentsToImageInvocation"] | components["schemas"]["Ideogram4ModelLoaderInvocation"] | components["schemas"]["Ideogram4TextEncoderInvocation"] | components["schemas"]["IfInvocation"] | components["schemas"]["ImageBatchInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageChannelMultiplyInvocation"] | components["schemas"]["ImageChannelOffsetInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImageGenerator"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageMaskToTensorInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageNoiseInvocation"] | components["schemas"]["ImagePanelLayoutInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["IntegerBatchInvocation"] | components["schemas"]["IntegerCollectionInvocation"] | components["schemas"]["IntegerGenerator"] | components["schemas"]["IntegerInvocation"] | components["schemas"]["IntegerMathInvocation"] | components["schemas"]["InvertTensorMaskInvocation"] | components["schemas"]["InvokeAdjustImageHuePlusInvocation"] | components["schemas"]["InvokeEquivalentAchromaticLightnessInvocation"] | components["schemas"]["InvokeImageBlendInvocation"] | components["schemas"]["InvokeImageCompositorInvocation"] | components["schemas"]["InvokeImageDilateOrErodeInvocation"] | components["schemas"]["InvokeImageEnhanceInvocation"] | components["schemas"]["InvokeImageValueThresholdsInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["Krea2ConditioningRebalanceInvocation"] | components["schemas"]["Krea2DenoiseInvocation"] | components["schemas"]["Krea2LoRACollectionLoader"] | components["schemas"]["Krea2LoRALoaderInvocation"] | components["schemas"]["Krea2ModelLoaderInvocation"] | components["schemas"]["Krea2SeedVarianceInvocation"] | components["schemas"]["Krea2TextEncoderInvocation"] | components["schemas"]["LaMaInfillInvocation"] | components["schemas"]["LatentsCollectionInvocation"] | components["schemas"]["LatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["LineartAnimeEdgeDetectionInvocation"] | components["schemas"]["LineartEdgeDetectionInvocation"] | components["schemas"]["LlavaOnevisionVllmInvocation"] | components["schemas"]["LoRACollectionLoader"] | components["schemas"]["LoRALoaderInvocation"] | components["schemas"]["LoRASelectorInvocation"] | components["schemas"]["MLSDDetectionInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["MaskCombineInvocation"] | components["schemas"]["MaskEdgeInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["MaskFromIDInvocation"] | components["schemas"]["MaskTensorToImageInvocation"] | components["schemas"]["MediaPipeFaceDetectionInvocation"] | components["schemas"]["MergeMetadataInvocation"] | components["schemas"]["MergeTilesToImageInvocation"] | components["schemas"]["MetadataFieldExtractorInvocation"] | components["schemas"]["MetadataFromImageInvocation"] | components["schemas"]["MetadataInvocation"] | components["schemas"]["MetadataItemInvocation"] | components["schemas"]["MetadataItemLinkedInvocation"] | components["schemas"]["MetadataToBoolCollectionInvocation"] | components["schemas"]["MetadataToBoolInvocation"] | components["schemas"]["MetadataToControlnetsInvocation"] | components["schemas"]["MetadataToFloatCollectionInvocation"] | components["schemas"]["MetadataToFloatInvocation"] | components["schemas"]["MetadataToIPAdaptersInvocation"] | components["schemas"]["MetadataToIntegerCollectionInvocation"] | components["schemas"]["MetadataToIntegerInvocation"] | components["schemas"]["MetadataToLorasCollectionInvocation"] | components["schemas"]["MetadataToLorasInvocation"] | components["schemas"]["MetadataToModelInvocation"] | components["schemas"]["MetadataToSDXLLorasInvocation"] | components["schemas"]["MetadataToSDXLModelInvocation"] | components["schemas"]["MetadataToSchedulerInvocation"] | components["schemas"]["MetadataToStringCollectionInvocation"] | components["schemas"]["MetadataToStringInvocation"] | components["schemas"]["MetadataToT2IAdaptersInvocation"] | components["schemas"]["MetadataToVAEInvocation"] | components["schemas"]["MiniMaxH3DenoiseInvocation"] | components["schemas"]["MiniMaxH3FrameConditioningInvocation"] | components["schemas"]["MiniMaxH3LatentsToImageInvocation"] | components["schemas"]["MiniMaxH3LatentsToVideoInvocation"] | components["schemas"]["MiniMaxH3ModelLoaderInvocation"] | components["schemas"]["MiniMaxH3TextEncoderInvocation"] | components["schemas"]["ModelIdentifierInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["NormalMapInvocation"] | components["schemas"]["OklabUnsharpMaskInvocation"] | components["schemas"]["OklchImageHueAdjustmentInvocation"] | components["schemas"]["OpenAIImageGenerationInvocation"] | components["schemas"]["PBRMapsInvocation"] | components["schemas"]["PairTileImageInvocation"] | components["schemas"]["PasteImageIntoBoundingBoxInvocation"] | components["schemas"]["PiDDecoderLoaderInvocation"] | components["schemas"]["PiDUpscaleInvocation"] | components["schemas"]["PiDiNetEdgeDetectionInvocation"] | components["schemas"]["PromptTemplateInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["QwenImageDenoiseInvocation"] | components["schemas"]["QwenImageImageToLatentsInvocation"] | components["schemas"]["QwenImageLatentsToImageInvocation"] | components["schemas"]["QwenImageLoRACollectionLoader"] | components["schemas"]["QwenImageLoRALoaderInvocation"] | components["schemas"]["QwenImageModelLoaderInvocation"] | components["schemas"]["QwenImagePiDDecodeInvocation"] | components["schemas"]["QwenImageTextEncoderInvocation"] | components["schemas"]["RandomFloatInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RectangleMaskInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["RoundInvocation"] | components["schemas"]["SD3DenoiseInvocation"] | components["schemas"]["SD3ImageToLatentsInvocation"] | components["schemas"]["SD3LatentsToImageInvocation"] | components["schemas"]["SD3PiDDecodeInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLLoRACollectionLoader"] | components["schemas"]["SDXLLoRALoaderInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLPiDDecodeInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SaveImageInvocation"] | components["schemas"]["SaveImageToFileInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["SchedulerInvocation"] | components["schemas"]["Sd3ModelLoaderInvocation"] | components["schemas"]["Sd3TextEncoderInvocation"] | components["schemas"]["SeamlessModeInvocation"] | components["schemas"]["SeedreamImageGenerationInvocation"] | components["schemas"]["SegmentAnythingInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["SpandrelImageToImageAutoscaleInvocation"] | components["schemas"]["SpandrelImageToImageInvocation"] | components["schemas"]["StringBatchInvocation"] | components["schemas"]["StringCollectionInvocation"] | components["schemas"]["StringGenerator"] | components["schemas"]["StringInvocation"] | components["schemas"]["StringJoinInvocation"] | components["schemas"]["StringJoinThreeInvocation"] | components["schemas"]["StringReplaceInvocation"] | components["schemas"]["StringSplitInvocation"] | components["schemas"]["StringSplitNegInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["T2IAdapterInvocation"] | components["schemas"]["TextLLMInvocation"] | components["schemas"]["TextLLMWithPresetInvocation"] | components["schemas"]["TileToPropertiesInvocation"] | components["schemas"]["TiledMultiDiffusionDenoiseLatents"] | components["schemas"]["UnsharpMaskInvocation"] | components["schemas"]["VAELoaderInvocation"] | components["schemas"]["VideoConcatInvocation"] | components["schemas"]["VideoFrameExtractInvocation"] | components["schemas"]["VideoInvocation"] | components["schemas"]["WanDenoiseInvocation"] | components["schemas"]["WanI2VIdealDimensionsInvocation"] | components["schemas"]["WanImageToLatentsInvocation"] | components["schemas"]["WanLatentsToImageInvocation"] | components["schemas"]["WanLatentsToVideoInvocation"] | components["schemas"]["WanLoRACollectionLoader"] | components["schemas"]["WanLoRALoaderInvocation"] | components["schemas"]["WanModelLoaderInvocation"] | components["schemas"]["WanRefImageEncoderInvocation"] | components["schemas"]["WanTI2VIdealDimensionsInvocation"] | components["schemas"]["WanTextEncoderInvocation"] | components["schemas"]["WanVideoDenoiseInvocation"] | components["schemas"]["WorkflowReturnGetInvocation"] | components["schemas"]["WorkflowReturnInvocation"] | components["schemas"]["WorkflowReturnValueInvocation"] | components["schemas"]["ZImageControlInvocation"] | components["schemas"]["ZImageDenoiseInvocation"] | components["schemas"]["ZImageDenoiseMetaInvocation"] | components["schemas"]["ZImageImageToLatentsInvocation"] | components["schemas"]["ZImageLatentsToImageInvocation"] | components["schemas"]["ZImageLoRACollectionLoader"] | components["schemas"]["ZImageLoRALoaderInvocation"] | components["schemas"]["ZImageModelLoaderInvocation"] | components["schemas"]["ZImagePiDDecodeInvocation"] | components["schemas"]["ZImageSeedVarianceEnhancerInvocation"] | components["schemas"]["ZImageTextEncoderInvocation"];
             /**
              * Invocation Source Id
              * @description The ID of the prepared invocation's source node
@@ -18058,7 +18058,7 @@ export type components = {
              * Result
              * @description The result of the invocation
              */
-            result: components["schemas"]["AnimaConditioningOutput"] | components["schemas"]["AnimaLLLiteOutput"] | components["schemas"]["AnimaLoRALoaderOutput"] | components["schemas"]["AnimaModelLoaderOutput"] | components["schemas"]["BooleanCollectionOutput"] | components["schemas"]["BooleanOutput"] | components["schemas"]["BoundingBoxCollectionOutput"] | components["schemas"]["BoundingBoxOutput"] | components["schemas"]["CLIPOutput"] | components["schemas"]["CLIPSkipInvocationOutput"] | components["schemas"]["CalculateImageTilesOutput"] | components["schemas"]["CogView4ConditioningOutput"] | components["schemas"]["CogView4ModelLoaderOutput"] | components["schemas"]["CollectInvocationOutput"] | components["schemas"]["ColorCollectionOutput"] | components["schemas"]["ColorOutput"] | components["schemas"]["ConditioningCollectionOutput"] | components["schemas"]["ConditioningOutput"] | components["schemas"]["ControlOutput"] | components["schemas"]["DenoiseMaskOutput"] | components["schemas"]["ErnieImageConditioningOutput"] | components["schemas"]["ErnieImageModelLoaderOutput"] | components["schemas"]["ExtractVideoRangeOutput"] | components["schemas"]["FaceMaskOutput"] | components["schemas"]["FaceOffOutput"] | components["schemas"]["FloatCollectionOutput"] | components["schemas"]["FloatGeneratorOutput"] | components["schemas"]["FloatOutput"] | components["schemas"]["Flux2KleinLoRALoaderOutput"] | components["schemas"]["Flux2KleinModelLoaderOutput"] | components["schemas"]["FluxConditioningCollectionOutput"] | components["schemas"]["FluxConditioningOutput"] | components["schemas"]["FluxControlLoRALoaderOutput"] | components["schemas"]["FluxControlNetOutput"] | components["schemas"]["FluxFillOutput"] | components["schemas"]["FluxKontextOutput"] | components["schemas"]["FluxLoRALoaderOutput"] | components["schemas"]["FluxModelLoaderOutput"] | components["schemas"]["FluxReduxOutput"] | components["schemas"]["Gemma2EncoderOutput"] | components["schemas"]["GradientMaskOutput"] | components["schemas"]["IPAdapterOutput"] | components["schemas"]["IdealSizeOutput"] | components["schemas"]["Ideogram4ConditioningOutput"] | components["schemas"]["Ideogram4ModelLoaderOutput"] | components["schemas"]["IfInvocationOutput"] | components["schemas"]["ImageCollectionOutput"] | components["schemas"]["ImageGeneratorOutput"] | components["schemas"]["ImageOutput"] | components["schemas"]["ImagePanelCoordinateOutput"] | components["schemas"]["IntegerCollectionOutput"] | components["schemas"]["IntegerGeneratorOutput"] | components["schemas"]["IntegerOutput"] | components["schemas"]["IterateInvocationOutput"] | components["schemas"]["Krea2ConditioningOutput"] | components["schemas"]["Krea2LoRALoaderOutput"] | components["schemas"]["Krea2ModelLoaderOutput"] | components["schemas"]["LatentsCollectionOutput"] | components["schemas"]["LatentsMetaOutput"] | components["schemas"]["LatentsOutput"] | components["schemas"]["LoRALoaderOutput"] | components["schemas"]["LoRASelectorOutput"] | components["schemas"]["MDControlListOutput"] | components["schemas"]["MDIPAdapterListOutput"] | components["schemas"]["MDT2IAdapterListOutput"] | components["schemas"]["MaskOutput"] | components["schemas"]["MetadataItemOutput"] | components["schemas"]["MetadataOutput"] | components["schemas"]["MetadataToLorasCollectionOutput"] | components["schemas"]["MetadataToModelOutput"] | components["schemas"]["MetadataToSDXLModelOutput"] | components["schemas"]["ModelIdentifierOutput"] | components["schemas"]["ModelLoaderOutput"] | components["schemas"]["NoiseOutput"] | components["schemas"]["PBRMapsOutput"] | components["schemas"]["PairTileImageOutput"] | components["schemas"]["PiDDecoderOutput"] | components["schemas"]["PromptTemplateOutput"] | components["schemas"]["QwenImageConditioningOutput"] | components["schemas"]["QwenImageLoRALoaderOutput"] | components["schemas"]["QwenImageModelLoaderOutput"] | components["schemas"]["SD3ConditioningOutput"] | components["schemas"]["SDXLLoRALoaderOutput"] | components["schemas"]["SDXLModelLoaderOutput"] | components["schemas"]["SDXLRefinerModelLoaderOutput"] | components["schemas"]["SchedulerOutput"] | components["schemas"]["Sd3ModelLoaderOutput"] | components["schemas"]["SeamlessModeOutput"] | components["schemas"]["String2Output"] | components["schemas"]["StringCollectionOutput"] | components["schemas"]["StringGeneratorOutput"] | components["schemas"]["StringOutput"] | components["schemas"]["StringPosNegOutput"] | components["schemas"]["T2IAdapterOutput"] | components["schemas"]["TileToPropertiesOutput"] | components["schemas"]["UNetOutput"] | components["schemas"]["VAEOutput"] | components["schemas"]["VideoOutput"] | components["schemas"]["WanConditioningOutput"] | components["schemas"]["WanLoRALoaderOutput"] | components["schemas"]["WanModelLoaderOutput"] | components["schemas"]["WanRefImageOutput"] | components["schemas"]["WorkflowReturnGetOutput"] | components["schemas"]["WorkflowReturnOutput"] | components["schemas"]["WorkflowReturnValueOutput"] | components["schemas"]["ZImageConditioningOutput"] | components["schemas"]["ZImageControlOutput"] | components["schemas"]["ZImageLoRALoaderOutput"] | components["schemas"]["ZImageModelLoaderOutput"];
+            result: components["schemas"]["AnimaConditioningOutput"] | components["schemas"]["AnimaLLLiteOutput"] | components["schemas"]["AnimaLoRALoaderOutput"] | components["schemas"]["AnimaModelLoaderOutput"] | components["schemas"]["BooleanCollectionOutput"] | components["schemas"]["BooleanOutput"] | components["schemas"]["BoundingBoxCollectionOutput"] | components["schemas"]["BoundingBoxOutput"] | components["schemas"]["CLIPOutput"] | components["schemas"]["CLIPSkipInvocationOutput"] | components["schemas"]["CalculateImageTilesOutput"] | components["schemas"]["CogView4ConditioningOutput"] | components["schemas"]["CogView4ModelLoaderOutput"] | components["schemas"]["CollectInvocationOutput"] | components["schemas"]["ColorCollectionOutput"] | components["schemas"]["ColorOutput"] | components["schemas"]["ConditioningCollectionOutput"] | components["schemas"]["ConditioningOutput"] | components["schemas"]["ControlOutput"] | components["schemas"]["DenoiseMaskOutput"] | components["schemas"]["ErnieImageConditioningOutput"] | components["schemas"]["ErnieImageModelLoaderOutput"] | components["schemas"]["ExtractVideoRangeOutput"] | components["schemas"]["FaceMaskOutput"] | components["schemas"]["FaceOffOutput"] | components["schemas"]["FloatCollectionOutput"] | components["schemas"]["FloatGeneratorOutput"] | components["schemas"]["FloatOutput"] | components["schemas"]["Flux2KleinLoRALoaderOutput"] | components["schemas"]["Flux2KleinModelLoaderOutput"] | components["schemas"]["FluxConditioningCollectionOutput"] | components["schemas"]["FluxConditioningOutput"] | components["schemas"]["FluxControlLoRALoaderOutput"] | components["schemas"]["FluxControlNetOutput"] | components["schemas"]["FluxFillOutput"] | components["schemas"]["FluxKontextOutput"] | components["schemas"]["FluxLoRALoaderOutput"] | components["schemas"]["FluxModelLoaderOutput"] | components["schemas"]["FluxReduxOutput"] | components["schemas"]["Gemma2EncoderOutput"] | components["schemas"]["GradientMaskOutput"] | components["schemas"]["IPAdapterOutput"] | components["schemas"]["IdealSizeOutput"] | components["schemas"]["Ideogram4ConditioningOutput"] | components["schemas"]["Ideogram4ModelLoaderOutput"] | components["schemas"]["IfInvocationOutput"] | components["schemas"]["ImageCollectionOutput"] | components["schemas"]["ImageGeneratorOutput"] | components["schemas"]["ImageOutput"] | components["schemas"]["ImagePanelCoordinateOutput"] | components["schemas"]["IntegerCollectionOutput"] | components["schemas"]["IntegerGeneratorOutput"] | components["schemas"]["IntegerOutput"] | components["schemas"]["IterateInvocationOutput"] | components["schemas"]["Krea2ConditioningOutput"] | components["schemas"]["Krea2LoRALoaderOutput"] | components["schemas"]["Krea2ModelLoaderOutput"] | components["schemas"]["LatentsCollectionOutput"] | components["schemas"]["LatentsMetaOutput"] | components["schemas"]["LatentsOutput"] | components["schemas"]["LoRALoaderOutput"] | components["schemas"]["LoRASelectorOutput"] | components["schemas"]["MDControlListOutput"] | components["schemas"]["MDIPAdapterListOutput"] | components["schemas"]["MDT2IAdapterListOutput"] | components["schemas"]["MaskOutput"] | components["schemas"]["MetadataItemOutput"] | components["schemas"]["MetadataOutput"] | components["schemas"]["MetadataToLorasCollectionOutput"] | components["schemas"]["MetadataToModelOutput"] | components["schemas"]["MetadataToSDXLModelOutput"] | components["schemas"]["MiniMaxH3ConditioningOutput"] | components["schemas"]["MiniMaxH3DenoiseOutput"] | components["schemas"]["MiniMaxH3FrameConditioningOutput"] | components["schemas"]["MiniMaxH3ModelLoaderOutput"] | components["schemas"]["ModelIdentifierOutput"] | components["schemas"]["ModelLoaderOutput"] | components["schemas"]["NoiseOutput"] | components["schemas"]["PBRMapsOutput"] | components["schemas"]["PairTileImageOutput"] | components["schemas"]["PiDDecoderOutput"] | components["schemas"]["PromptTemplateOutput"] | components["schemas"]["QwenImageConditioningOutput"] | components["schemas"]["QwenImageLoRALoaderOutput"] | components["schemas"]["QwenImageModelLoaderOutput"] | components["schemas"]["SD3ConditioningOutput"] | components["schemas"]["SDXLLoRALoaderOutput"] | components["schemas"]["SDXLModelLoaderOutput"] | components["schemas"]["SDXLRefinerModelLoaderOutput"] | components["schemas"]["SchedulerOutput"] | components["schemas"]["Sd3ModelLoaderOutput"] | components["schemas"]["SeamlessModeOutput"] | components["schemas"]["String2Output"] | components["schemas"]["StringCollectionOutput"] | components["schemas"]["StringGeneratorOutput"] | components["schemas"]["StringOutput"] | components["schemas"]["StringPosNegOutput"] | components["schemas"]["T2IAdapterOutput"] | components["schemas"]["TileToPropertiesOutput"] | components["schemas"]["UNetOutput"] | components["schemas"]["VAEOutput"] | components["schemas"]["VideoOutput"] | components["schemas"]["WanConditioningOutput"] | components["schemas"]["WanLoRALoaderOutput"] | components["schemas"]["WanModelLoaderOutput"] | components["schemas"]["WanRefImageOutput"] | components["schemas"]["WorkflowReturnGetOutput"] | components["schemas"]["WorkflowReturnOutput"] | components["schemas"]["WorkflowReturnValueOutput"] | components["schemas"]["ZImageConditioningOutput"] | components["schemas"]["ZImageControlOutput"] | components["schemas"]["ZImageLoRALoaderOutput"] | components["schemas"]["ZImageModelLoaderOutput"];
         };
         /**
          * InvocationErrorEvent
@@ -18112,7 +18112,7 @@ export type components = {
              * Invocation
              * @description The ID of the invocation
              */
-            invocation: components["schemas"]["AddInvocation"] | components["schemas"]["AlibabaCloudImageGenerationInvocation"] | components["schemas"]["AlphaMaskToTensorInvocation"] | components["schemas"]["AnimaDenoiseInvocation"] | components["schemas"]["AnimaImageToLatentsInvocation"] | components["schemas"]["AnimaLLLiteInvocation"] | components["schemas"]["AnimaLatentsToImageInvocation"] | components["schemas"]["AnimaLoRACollectionLoader"] | components["schemas"]["AnimaLoRALoaderInvocation"] | components["schemas"]["AnimaModelLoaderInvocation"] | components["schemas"]["AnimaTextEncoderInvocation"] | components["schemas"]["ApplyMaskTensorToImageInvocation"] | components["schemas"]["ApplyMaskToImageInvocation"] | components["schemas"]["BlankImageInvocation"] | components["schemas"]["BlendLatentsInvocation"] | components["schemas"]["BooleanCollectionInvocation"] | components["schemas"]["BooleanInvocation"] | components["schemas"]["BoundingBoxInvocation"] | components["schemas"]["CLIPSkipInvocation"] | components["schemas"]["CV2InfillInvocation"] | components["schemas"]["CalculateImageTilesEvenSplitInvocation"] | components["schemas"]["CalculateImageTilesInvocation"] | components["schemas"]["CalculateImageTilesMinimumOverlapInvocation"] | components["schemas"]["CallSavedWorkflowInvocation"] | components["schemas"]["CannyEdgeDetectionInvocation"] | components["schemas"]["CanvasOutputInvocation"] | components["schemas"]["CanvasPasteBackInvocation"] | components["schemas"]["CanvasV2MaskAndCropInvocation"] | components["schemas"]["CenterPadCropInvocation"] | components["schemas"]["CogView4DenoiseInvocation"] | components["schemas"]["CogView4ImageToLatentsInvocation"] | components["schemas"]["CogView4LatentsToImageInvocation"] | components["schemas"]["CogView4ModelLoaderInvocation"] | components["schemas"]["CogView4TextEncoderInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["ColorCorrectInvocation"] | components["schemas"]["ColorInvocation"] | components["schemas"]["ColorMapInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ConditioningCollectionInvocation"] | components["schemas"]["ConditioningInvocation"] | components["schemas"]["ContentShuffleInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["CoreMetadataInvocation"] | components["schemas"]["CreateDenoiseMaskInvocation"] | components["schemas"]["CreateGradientMaskInvocation"] | components["schemas"]["CropImageToBoundingBoxInvocation"] | components["schemas"]["CropLatentsCoreInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["DWOpenposeDetectionInvocation"] | components["schemas"]["DecodeInvisibleWatermarkInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["DenoiseLatentsMetaInvocation"] | components["schemas"]["DepthAnythingDepthEstimationInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["ErnieImageDenoiseInvocation"] | components["schemas"]["ErnieImageModelLoaderInvocation"] | components["schemas"]["ErnieImagePromptEnhancerInvocation"] | components["schemas"]["ErnieImageTextEncoderInvocation"] | components["schemas"]["ErnieImageVaeDecodeInvocation"] | components["schemas"]["ExpandMaskWithFadeInvocation"] | components["schemas"]["ExtractVideoRangeInvocation"] | components["schemas"]["FLUXLoRACollectionLoader"] | components["schemas"]["FaceIdentifierInvocation"] | components["schemas"]["FaceMaskInvocation"] | components["schemas"]["FaceOffInvocation"] | components["schemas"]["FloatBatchInvocation"] | components["schemas"]["FloatCollectionInvocation"] | components["schemas"]["FloatGenerator"] | components["schemas"]["FloatInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["FloatMathInvocation"] | components["schemas"]["FloatToIntegerInvocation"] | components["schemas"]["Flux2DenoiseInvocation"] | components["schemas"]["Flux2KleinLoRACollectionLoader"] | components["schemas"]["Flux2KleinLoRALoaderInvocation"] | components["schemas"]["Flux2KleinModelLoaderInvocation"] | components["schemas"]["Flux2KleinTextEncoderInvocation"] | components["schemas"]["Flux2PiDDecodeInvocation"] | components["schemas"]["Flux2VaeDecodeInvocation"] | components["schemas"]["Flux2VaeEncodeInvocation"] | components["schemas"]["FluxControlLoRALoaderInvocation"] | components["schemas"]["FluxControlNetInvocation"] | components["schemas"]["FluxDenoiseInvocation"] | components["schemas"]["FluxDenoiseLatentsMetaInvocation"] | components["schemas"]["FluxFillInvocation"] | components["schemas"]["FluxIPAdapterInvocation"] | components["schemas"]["FluxKontextConcatenateImagesInvocation"] | components["schemas"]["FluxKontextInvocation"] | components["schemas"]["FluxLoRALoaderInvocation"] | components["schemas"]["FluxModelLoaderInvocation"] | components["schemas"]["FluxPiDDecodeInvocation"] | components["schemas"]["FluxReduxInvocation"] | components["schemas"]["FluxTextEncoderInvocation"] | components["schemas"]["FluxVaeDecodeInvocation"] | components["schemas"]["FluxVaeEncodeInvocation"] | components["schemas"]["FreeUInvocation"] | components["schemas"]["GeminiImageGenerationInvocation"] | components["schemas"]["Gemma2EncoderLoaderInvocation"] | components["schemas"]["GetMaskBoundingBoxInvocation"] | components["schemas"]["GroundingDinoInvocation"] | components["schemas"]["HEDEdgeDetectionInvocation"] | components["schemas"]["HeuristicResizeInvocation"] | components["schemas"]["IPAdapterInvocation"] | components["schemas"]["IdealSizeInvocation"] | components["schemas"]["Ideogram4CaptionBuilderInvocation"] | components["schemas"]["Ideogram4DenoiseInvocation"] | components["schemas"]["Ideogram4LatentsToImageInvocation"] | components["schemas"]["Ideogram4ModelLoaderInvocation"] | components["schemas"]["Ideogram4TextEncoderInvocation"] | components["schemas"]["IfInvocation"] | components["schemas"]["ImageBatchInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageChannelMultiplyInvocation"] | components["schemas"]["ImageChannelOffsetInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImageGenerator"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageMaskToTensorInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageNoiseInvocation"] | components["schemas"]["ImagePanelLayoutInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["IntegerBatchInvocation"] | components["schemas"]["IntegerCollectionInvocation"] | components["schemas"]["IntegerGenerator"] | components["schemas"]["IntegerInvocation"] | components["schemas"]["IntegerMathInvocation"] | components["schemas"]["InvertTensorMaskInvocation"] | components["schemas"]["InvokeAdjustImageHuePlusInvocation"] | components["schemas"]["InvokeEquivalentAchromaticLightnessInvocation"] | components["schemas"]["InvokeImageBlendInvocation"] | components["schemas"]["InvokeImageCompositorInvocation"] | components["schemas"]["InvokeImageDilateOrErodeInvocation"] | components["schemas"]["InvokeImageEnhanceInvocation"] | components["schemas"]["InvokeImageValueThresholdsInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["Krea2ConditioningRebalanceInvocation"] | components["schemas"]["Krea2DenoiseInvocation"] | components["schemas"]["Krea2LoRACollectionLoader"] | components["schemas"]["Krea2LoRALoaderInvocation"] | components["schemas"]["Krea2ModelLoaderInvocation"] | components["schemas"]["Krea2SeedVarianceInvocation"] | components["schemas"]["Krea2TextEncoderInvocation"] | components["schemas"]["LaMaInfillInvocation"] | components["schemas"]["LatentsCollectionInvocation"] | components["schemas"]["LatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["LineartAnimeEdgeDetectionInvocation"] | components["schemas"]["LineartEdgeDetectionInvocation"] | components["schemas"]["LlavaOnevisionVllmInvocation"] | components["schemas"]["LoRACollectionLoader"] | components["schemas"]["LoRALoaderInvocation"] | components["schemas"]["LoRASelectorInvocation"] | components["schemas"]["MLSDDetectionInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["MaskCombineInvocation"] | components["schemas"]["MaskEdgeInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["MaskFromIDInvocation"] | components["schemas"]["MaskTensorToImageInvocation"] | components["schemas"]["MediaPipeFaceDetectionInvocation"] | components["schemas"]["MergeMetadataInvocation"] | components["schemas"]["MergeTilesToImageInvocation"] | components["schemas"]["MetadataFieldExtractorInvocation"] | components["schemas"]["MetadataFromImageInvocation"] | components["schemas"]["MetadataInvocation"] | components["schemas"]["MetadataItemInvocation"] | components["schemas"]["MetadataItemLinkedInvocation"] | components["schemas"]["MetadataToBoolCollectionInvocation"] | components["schemas"]["MetadataToBoolInvocation"] | components["schemas"]["MetadataToControlnetsInvocation"] | components["schemas"]["MetadataToFloatCollectionInvocation"] | components["schemas"]["MetadataToFloatInvocation"] | components["schemas"]["MetadataToIPAdaptersInvocation"] | components["schemas"]["MetadataToIntegerCollectionInvocation"] | components["schemas"]["MetadataToIntegerInvocation"] | components["schemas"]["MetadataToLorasCollectionInvocation"] | components["schemas"]["MetadataToLorasInvocation"] | components["schemas"]["MetadataToModelInvocation"] | components["schemas"]["MetadataToSDXLLorasInvocation"] | components["schemas"]["MetadataToSDXLModelInvocation"] | components["schemas"]["MetadataToSchedulerInvocation"] | components["schemas"]["MetadataToStringCollectionInvocation"] | components["schemas"]["MetadataToStringInvocation"] | components["schemas"]["MetadataToT2IAdaptersInvocation"] | components["schemas"]["MetadataToVAEInvocation"] | components["schemas"]["ModelIdentifierInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["NormalMapInvocation"] | components["schemas"]["OklabUnsharpMaskInvocation"] | components["schemas"]["OklchImageHueAdjustmentInvocation"] | components["schemas"]["OpenAIImageGenerationInvocation"] | components["schemas"]["PBRMapsInvocation"] | components["schemas"]["PairTileImageInvocation"] | components["schemas"]["PasteImageIntoBoundingBoxInvocation"] | components["schemas"]["PiDDecoderLoaderInvocation"] | components["schemas"]["PiDUpscaleInvocation"] | components["schemas"]["PiDiNetEdgeDetectionInvocation"] | components["schemas"]["PromptTemplateInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["QwenImageDenoiseInvocation"] | components["schemas"]["QwenImageImageToLatentsInvocation"] | components["schemas"]["QwenImageLatentsToImageInvocation"] | components["schemas"]["QwenImageLoRACollectionLoader"] | components["schemas"]["QwenImageLoRALoaderInvocation"] | components["schemas"]["QwenImageModelLoaderInvocation"] | components["schemas"]["QwenImagePiDDecodeInvocation"] | components["schemas"]["QwenImageTextEncoderInvocation"] | components["schemas"]["RandomFloatInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RectangleMaskInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["RoundInvocation"] | components["schemas"]["SD3DenoiseInvocation"] | components["schemas"]["SD3ImageToLatentsInvocation"] | components["schemas"]["SD3LatentsToImageInvocation"] | components["schemas"]["SD3PiDDecodeInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLLoRACollectionLoader"] | components["schemas"]["SDXLLoRALoaderInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLPiDDecodeInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SaveImageInvocation"] | components["schemas"]["SaveImageToFileInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["SchedulerInvocation"] | components["schemas"]["Sd3ModelLoaderInvocation"] | components["schemas"]["Sd3TextEncoderInvocation"] | components["schemas"]["SeamlessModeInvocation"] | components["schemas"]["SeedreamImageGenerationInvocation"] | components["schemas"]["SegmentAnythingInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["SpandrelImageToImageAutoscaleInvocation"] | components["schemas"]["SpandrelImageToImageInvocation"] | components["schemas"]["StringBatchInvocation"] | components["schemas"]["StringCollectionInvocation"] | components["schemas"]["StringGenerator"] | components["schemas"]["StringInvocation"] | components["schemas"]["StringJoinInvocation"] | components["schemas"]["StringJoinThreeInvocation"] | components["schemas"]["StringReplaceInvocation"] | components["schemas"]["StringSplitInvocation"] | components["schemas"]["StringSplitNegInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["T2IAdapterInvocation"] | components["schemas"]["TextLLMInvocation"] | components["schemas"]["TextLLMWithPresetInvocation"] | components["schemas"]["TileToPropertiesInvocation"] | components["schemas"]["TiledMultiDiffusionDenoiseLatents"] | components["schemas"]["UnsharpMaskInvocation"] | components["schemas"]["VAELoaderInvocation"] | components["schemas"]["VideoConcatInvocation"] | components["schemas"]["VideoFrameExtractInvocation"] | components["schemas"]["VideoInvocation"] | components["schemas"]["WanDenoiseInvocation"] | components["schemas"]["WanI2VIdealDimensionsInvocation"] | components["schemas"]["WanImageToLatentsInvocation"] | components["schemas"]["WanLatentsToImageInvocation"] | components["schemas"]["WanLatentsToVideoInvocation"] | components["schemas"]["WanLoRACollectionLoader"] | components["schemas"]["WanLoRALoaderInvocation"] | components["schemas"]["WanModelLoaderInvocation"] | components["schemas"]["WanRefImageEncoderInvocation"] | components["schemas"]["WanTI2VIdealDimensionsInvocation"] | components["schemas"]["WanTextEncoderInvocation"] | components["schemas"]["WanVideoDenoiseInvocation"] | components["schemas"]["WorkflowReturnGetInvocation"] | components["schemas"]["WorkflowReturnInvocation"] | components["schemas"]["WorkflowReturnValueInvocation"] | components["schemas"]["ZImageControlInvocation"] | components["schemas"]["ZImageDenoiseInvocation"] | components["schemas"]["ZImageDenoiseMetaInvocation"] | components["schemas"]["ZImageImageToLatentsInvocation"] | components["schemas"]["ZImageLatentsToImageInvocation"] | components["schemas"]["ZImageLoRACollectionLoader"] | components["schemas"]["ZImageLoRALoaderInvocation"] | components["schemas"]["ZImageModelLoaderInvocation"] | components["schemas"]["ZImagePiDDecodeInvocation"] | components["schemas"]["ZImageSeedVarianceEnhancerInvocation"] | components["schemas"]["ZImageTextEncoderInvocation"];
+            invocation: components["schemas"]["AddInvocation"] | components["schemas"]["AlibabaCloudImageGenerationInvocation"] | components["schemas"]["AlphaMaskToTensorInvocation"] | components["schemas"]["AnimaDenoiseInvocation"] | components["schemas"]["AnimaImageToLatentsInvocation"] | components["schemas"]["AnimaLLLiteInvocation"] | components["schemas"]["AnimaLatentsToImageInvocation"] | components["schemas"]["AnimaLoRACollectionLoader"] | components["schemas"]["AnimaLoRALoaderInvocation"] | components["schemas"]["AnimaModelLoaderInvocation"] | components["schemas"]["AnimaTextEncoderInvocation"] | components["schemas"]["ApplyMaskTensorToImageInvocation"] | components["schemas"]["ApplyMaskToImageInvocation"] | components["schemas"]["BlankImageInvocation"] | components["schemas"]["BlendLatentsInvocation"] | components["schemas"]["BooleanCollectionInvocation"] | components["schemas"]["BooleanInvocation"] | components["schemas"]["BoundingBoxInvocation"] | components["schemas"]["CLIPSkipInvocation"] | components["schemas"]["CV2InfillInvocation"] | components["schemas"]["CalculateImageTilesEvenSplitInvocation"] | components["schemas"]["CalculateImageTilesInvocation"] | components["schemas"]["CalculateImageTilesMinimumOverlapInvocation"] | components["schemas"]["CallSavedWorkflowInvocation"] | components["schemas"]["CannyEdgeDetectionInvocation"] | components["schemas"]["CanvasOutputInvocation"] | components["schemas"]["CanvasPasteBackInvocation"] | components["schemas"]["CanvasV2MaskAndCropInvocation"] | components["schemas"]["CenterPadCropInvocation"] | components["schemas"]["CogView4DenoiseInvocation"] | components["schemas"]["CogView4ImageToLatentsInvocation"] | components["schemas"]["CogView4LatentsToImageInvocation"] | components["schemas"]["CogView4ModelLoaderInvocation"] | components["schemas"]["CogView4TextEncoderInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["ColorCorrectInvocation"] | components["schemas"]["ColorInvocation"] | components["schemas"]["ColorMapInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ConditioningCollectionInvocation"] | components["schemas"]["ConditioningInvocation"] | components["schemas"]["ContentShuffleInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["CoreMetadataInvocation"] | components["schemas"]["CreateDenoiseMaskInvocation"] | components["schemas"]["CreateGradientMaskInvocation"] | components["schemas"]["CropImageToBoundingBoxInvocation"] | components["schemas"]["CropLatentsCoreInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["DWOpenposeDetectionInvocation"] | components["schemas"]["DecodeInvisibleWatermarkInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["DenoiseLatentsMetaInvocation"] | components["schemas"]["DepthAnythingDepthEstimationInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["ErnieImageDenoiseInvocation"] | components["schemas"]["ErnieImageModelLoaderInvocation"] | components["schemas"]["ErnieImagePromptEnhancerInvocation"] | components["schemas"]["ErnieImageTextEncoderInvocation"] | components["schemas"]["ErnieImageVaeDecodeInvocation"] | components["schemas"]["ExpandMaskWithFadeInvocation"] | components["schemas"]["ExtractVideoRangeInvocation"] | components["schemas"]["FLUXLoRACollectionLoader"] | components["schemas"]["FaceIdentifierInvocation"] | components["schemas"]["FaceMaskInvocation"] | components["schemas"]["FaceOffInvocation"] | components["schemas"]["FloatBatchInvocation"] | components["schemas"]["FloatCollectionInvocation"] | components["schemas"]["FloatGenerator"] | components["schemas"]["FloatInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["FloatMathInvocation"] | components["schemas"]["FloatToIntegerInvocation"] | components["schemas"]["Flux2DenoiseInvocation"] | components["schemas"]["Flux2KleinLoRACollectionLoader"] | components["schemas"]["Flux2KleinLoRALoaderInvocation"] | components["schemas"]["Flux2KleinModelLoaderInvocation"] | components["schemas"]["Flux2KleinTextEncoderInvocation"] | components["schemas"]["Flux2PiDDecodeInvocation"] | components["schemas"]["Flux2VaeDecodeInvocation"] | components["schemas"]["Flux2VaeEncodeInvocation"] | components["schemas"]["FluxControlLoRALoaderInvocation"] | components["schemas"]["FluxControlNetInvocation"] | components["schemas"]["FluxDenoiseInvocation"] | components["schemas"]["FluxDenoiseLatentsMetaInvocation"] | components["schemas"]["FluxFillInvocation"] | components["schemas"]["FluxIPAdapterInvocation"] | components["schemas"]["FluxKontextConcatenateImagesInvocation"] | components["schemas"]["FluxKontextInvocation"] | components["schemas"]["FluxLoRALoaderInvocation"] | components["schemas"]["FluxModelLoaderInvocation"] | components["schemas"]["FluxPiDDecodeInvocation"] | components["schemas"]["FluxReduxInvocation"] | components["schemas"]["FluxTextEncoderInvocation"] | components["schemas"]["FluxVaeDecodeInvocation"] | components["schemas"]["FluxVaeEncodeInvocation"] | components["schemas"]["FreeUInvocation"] | components["schemas"]["GeminiImageGenerationInvocation"] | components["schemas"]["Gemma2EncoderLoaderInvocation"] | components["schemas"]["GetMaskBoundingBoxInvocation"] | components["schemas"]["GroundingDinoInvocation"] | components["schemas"]["HEDEdgeDetectionInvocation"] | components["schemas"]["HeuristicResizeInvocation"] | components["schemas"]["IPAdapterInvocation"] | components["schemas"]["IdealSizeInvocation"] | components["schemas"]["Ideogram4CaptionBuilderInvocation"] | components["schemas"]["Ideogram4DenoiseInvocation"] | components["schemas"]["Ideogram4LatentsToImageInvocation"] | components["schemas"]["Ideogram4ModelLoaderInvocation"] | components["schemas"]["Ideogram4TextEncoderInvocation"] | components["schemas"]["IfInvocation"] | components["schemas"]["ImageBatchInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageChannelMultiplyInvocation"] | components["schemas"]["ImageChannelOffsetInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImageGenerator"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageMaskToTensorInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageNoiseInvocation"] | components["schemas"]["ImagePanelLayoutInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["IntegerBatchInvocation"] | components["schemas"]["IntegerCollectionInvocation"] | components["schemas"]["IntegerGenerator"] | components["schemas"]["IntegerInvocation"] | components["schemas"]["IntegerMathInvocation"] | components["schemas"]["InvertTensorMaskInvocation"] | components["schemas"]["InvokeAdjustImageHuePlusInvocation"] | components["schemas"]["InvokeEquivalentAchromaticLightnessInvocation"] | components["schemas"]["InvokeImageBlendInvocation"] | components["schemas"]["InvokeImageCompositorInvocation"] | components["schemas"]["InvokeImageDilateOrErodeInvocation"] | components["schemas"]["InvokeImageEnhanceInvocation"] | components["schemas"]["InvokeImageValueThresholdsInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["Krea2ConditioningRebalanceInvocation"] | components["schemas"]["Krea2DenoiseInvocation"] | components["schemas"]["Krea2LoRACollectionLoader"] | components["schemas"]["Krea2LoRALoaderInvocation"] | components["schemas"]["Krea2ModelLoaderInvocation"] | components["schemas"]["Krea2SeedVarianceInvocation"] | components["schemas"]["Krea2TextEncoderInvocation"] | components["schemas"]["LaMaInfillInvocation"] | components["schemas"]["LatentsCollectionInvocation"] | components["schemas"]["LatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["LineartAnimeEdgeDetectionInvocation"] | components["schemas"]["LineartEdgeDetectionInvocation"] | components["schemas"]["LlavaOnevisionVllmInvocation"] | components["schemas"]["LoRACollectionLoader"] | components["schemas"]["LoRALoaderInvocation"] | components["schemas"]["LoRASelectorInvocation"] | components["schemas"]["MLSDDetectionInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["MaskCombineInvocation"] | components["schemas"]["MaskEdgeInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["MaskFromIDInvocation"] | components["schemas"]["MaskTensorToImageInvocation"] | components["schemas"]["MediaPipeFaceDetectionInvocation"] | components["schemas"]["MergeMetadataInvocation"] | components["schemas"]["MergeTilesToImageInvocation"] | components["schemas"]["MetadataFieldExtractorInvocation"] | components["schemas"]["MetadataFromImageInvocation"] | components["schemas"]["MetadataInvocation"] | components["schemas"]["MetadataItemInvocation"] | components["schemas"]["MetadataItemLinkedInvocation"] | components["schemas"]["MetadataToBoolCollectionInvocation"] | components["schemas"]["MetadataToBoolInvocation"] | components["schemas"]["MetadataToControlnetsInvocation"] | components["schemas"]["MetadataToFloatCollectionInvocation"] | components["schemas"]["MetadataToFloatInvocation"] | components["schemas"]["MetadataToIPAdaptersInvocation"] | components["schemas"]["MetadataToIntegerCollectionInvocation"] | components["schemas"]["MetadataToIntegerInvocation"] | components["schemas"]["MetadataToLorasCollectionInvocation"] | components["schemas"]["MetadataToLorasInvocation"] | components["schemas"]["MetadataToModelInvocation"] | components["schemas"]["MetadataToSDXLLorasInvocation"] | components["schemas"]["MetadataToSDXLModelInvocation"] | components["schemas"]["MetadataToSchedulerInvocation"] | components["schemas"]["MetadataToStringCollectionInvocation"] | components["schemas"]["MetadataToStringInvocation"] | components["schemas"]["MetadataToT2IAdaptersInvocation"] | components["schemas"]["MetadataToVAEInvocation"] | components["schemas"]["MiniMaxH3DenoiseInvocation"] | components["schemas"]["MiniMaxH3FrameConditioningInvocation"] | components["schemas"]["MiniMaxH3LatentsToImageInvocation"] | components["schemas"]["MiniMaxH3LatentsToVideoInvocation"] | components["schemas"]["MiniMaxH3ModelLoaderInvocation"] | components["schemas"]["MiniMaxH3TextEncoderInvocation"] | components["schemas"]["ModelIdentifierInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["NormalMapInvocation"] | components["schemas"]["OklabUnsharpMaskInvocation"] | components["schemas"]["OklchImageHueAdjustmentInvocation"] | components["schemas"]["OpenAIImageGenerationInvocation"] | components["schemas"]["PBRMapsInvocation"] | components["schemas"]["PairTileImageInvocation"] | components["schemas"]["PasteImageIntoBoundingBoxInvocation"] | components["schemas"]["PiDDecoderLoaderInvocation"] | components["schemas"]["PiDUpscaleInvocation"] | components["schemas"]["PiDiNetEdgeDetectionInvocation"] | components["schemas"]["PromptTemplateInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["QwenImageDenoiseInvocation"] | components["schemas"]["QwenImageImageToLatentsInvocation"] | components["schemas"]["QwenImageLatentsToImageInvocation"] | components["schemas"]["QwenImageLoRACollectionLoader"] | components["schemas"]["QwenImageLoRALoaderInvocation"] | components["schemas"]["QwenImageModelLoaderInvocation"] | components["schemas"]["QwenImagePiDDecodeInvocation"] | components["schemas"]["QwenImageTextEncoderInvocation"] | components["schemas"]["RandomFloatInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RectangleMaskInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["RoundInvocation"] | components["schemas"]["SD3DenoiseInvocation"] | components["schemas"]["SD3ImageToLatentsInvocation"] | components["schemas"]["SD3LatentsToImageInvocation"] | components["schemas"]["SD3PiDDecodeInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLLoRACollectionLoader"] | components["schemas"]["SDXLLoRALoaderInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLPiDDecodeInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SaveImageInvocation"] | components["schemas"]["SaveImageToFileInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["SchedulerInvocation"] | components["schemas"]["Sd3ModelLoaderInvocation"] | components["schemas"]["Sd3TextEncoderInvocation"] | components["schemas"]["SeamlessModeInvocation"] | components["schemas"]["SeedreamImageGenerationInvocation"] | components["schemas"]["SegmentAnythingInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["SpandrelImageToImageAutoscaleInvocation"] | components["schemas"]["SpandrelImageToImageInvocation"] | components["schemas"]["StringBatchInvocation"] | components["schemas"]["StringCollectionInvocation"] | components["schemas"]["StringGenerator"] | components["schemas"]["StringInvocation"] | components["schemas"]["StringJoinInvocation"] | components["schemas"]["StringJoinThreeInvocation"] | components["schemas"]["StringReplaceInvocation"] | components["schemas"]["StringSplitInvocation"] | components["schemas"]["StringSplitNegInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["T2IAdapterInvocation"] | components["schemas"]["TextLLMInvocation"] | components["schemas"]["TextLLMWithPresetInvocation"] | components["schemas"]["TileToPropertiesInvocation"] | components["schemas"]["TiledMultiDiffusionDenoiseLatents"] | components["schemas"]["UnsharpMaskInvocation"] | components["schemas"]["VAELoaderInvocation"] | components["schemas"]["VideoConcatInvocation"] | components["schemas"]["VideoFrameExtractInvocation"] | components["schemas"]["VideoInvocation"] | components["schemas"]["WanDenoiseInvocation"] | components["schemas"]["WanI2VIdealDimensionsInvocation"] | components["schemas"]["WanImageToLatentsInvocation"] | components["schemas"]["WanLatentsToImageInvocation"] | components["schemas"]["WanLatentsToVideoInvocation"] | components["schemas"]["WanLoRACollectionLoader"] | components["schemas"]["WanLoRALoaderInvocation"] | components["schemas"]["WanModelLoaderInvocation"] | components["schemas"]["WanRefImageEncoderInvocation"] | components["schemas"]["WanTI2VIdealDimensionsInvocation"] | components["schemas"]["WanTextEncoderInvocation"] | components["schemas"]["WanVideoDenoiseInvocation"] | components["schemas"]["WorkflowReturnGetInvocation"] | components["schemas"]["WorkflowReturnInvocation"] | components["schemas"]["WorkflowReturnValueInvocation"] | components["schemas"]["ZImageControlInvocation"] | components["schemas"]["ZImageDenoiseInvocation"] | components["schemas"]["ZImageDenoiseMetaInvocation"] | components["schemas"]["ZImageImageToLatentsInvocation"] | components["schemas"]["ZImageLatentsToImageInvocation"] | components["schemas"]["ZImageLoRACollectionLoader"] | components["schemas"]["ZImageLoRALoaderInvocation"] | components["schemas"]["ZImageModelLoaderInvocation"] | components["schemas"]["ZImagePiDDecodeInvocation"] | components["schemas"]["ZImageSeedVarianceEnhancerInvocation"] | components["schemas"]["ZImageTextEncoderInvocation"];
             /**
              * Invocation Source Id
              * @description The ID of the prepared invocation's source node
@@ -18338,6 +18338,12 @@ export type components = {
             metadata_to_string_collection: components["schemas"]["StringCollectionOutput"];
             metadata_to_t2i_adapters: components["schemas"]["MDT2IAdapterListOutput"];
             metadata_to_vae: components["schemas"]["VAEOutput"];
+            minimax_h3_denoise: components["schemas"]["MiniMaxH3DenoiseOutput"];
+            minimax_h3_frame_conditioning: components["schemas"]["MiniMaxH3FrameConditioningOutput"];
+            minimax_h3_latents_to_image: components["schemas"]["ImageOutput"];
+            minimax_h3_latents_to_video: components["schemas"]["VideoOutput"];
+            minimax_h3_model_loader: components["schemas"]["MiniMaxH3ModelLoaderOutput"];
+            minimax_h3_text_encoder: components["schemas"]["MiniMaxH3ConditioningOutput"];
             mlsd_detection: components["schemas"]["ImageOutput"];
             model_identifier: components["schemas"]["ModelIdentifierOutput"];
             mul: components["schemas"]["IntegerOutput"];
@@ -18491,7 +18497,7 @@ export type components = {
              * Invocation
              * @description The ID of the invocation
              */
-            invocation: components["schemas"]["AddInvocation"] | components["schemas"]["AlibabaCloudImageGenerationInvocation"] | components["schemas"]["AlphaMaskToTensorInvocation"] | components["schemas"]["AnimaDenoiseInvocation"] | components["schemas"]["AnimaImageToLatentsInvocation"] | components["schemas"]["AnimaLLLiteInvocation"] | components["schemas"]["AnimaLatentsToImageInvocation"] | components["schemas"]["AnimaLoRACollectionLoader"] | components["schemas"]["AnimaLoRALoaderInvocation"] | components["schemas"]["AnimaModelLoaderInvocation"] | components["schemas"]["AnimaTextEncoderInvocation"] | components["schemas"]["ApplyMaskTensorToImageInvocation"] | components["schemas"]["ApplyMaskToImageInvocation"] | components["schemas"]["BlankImageInvocation"] | components["schemas"]["BlendLatentsInvocation"] | components["schemas"]["BooleanCollectionInvocation"] | components["schemas"]["BooleanInvocation"] | components["schemas"]["BoundingBoxInvocation"] | components["schemas"]["CLIPSkipInvocation"] | components["schemas"]["CV2InfillInvocation"] | components["schemas"]["CalculateImageTilesEvenSplitInvocation"] | components["schemas"]["CalculateImageTilesInvocation"] | components["schemas"]["CalculateImageTilesMinimumOverlapInvocation"] | components["schemas"]["CallSavedWorkflowInvocation"] | components["schemas"]["CannyEdgeDetectionInvocation"] | components["schemas"]["CanvasOutputInvocation"] | components["schemas"]["CanvasPasteBackInvocation"] | components["schemas"]["CanvasV2MaskAndCropInvocation"] | components["schemas"]["CenterPadCropInvocation"] | components["schemas"]["CogView4DenoiseInvocation"] | components["schemas"]["CogView4ImageToLatentsInvocation"] | components["schemas"]["CogView4LatentsToImageInvocation"] | components["schemas"]["CogView4ModelLoaderInvocation"] | components["schemas"]["CogView4TextEncoderInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["ColorCorrectInvocation"] | components["schemas"]["ColorInvocation"] | components["schemas"]["ColorMapInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ConditioningCollectionInvocation"] | components["schemas"]["ConditioningInvocation"] | components["schemas"]["ContentShuffleInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["CoreMetadataInvocation"] | components["schemas"]["CreateDenoiseMaskInvocation"] | components["schemas"]["CreateGradientMaskInvocation"] | components["schemas"]["CropImageToBoundingBoxInvocation"] | components["schemas"]["CropLatentsCoreInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["DWOpenposeDetectionInvocation"] | components["schemas"]["DecodeInvisibleWatermarkInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["DenoiseLatentsMetaInvocation"] | components["schemas"]["DepthAnythingDepthEstimationInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["ErnieImageDenoiseInvocation"] | components["schemas"]["ErnieImageModelLoaderInvocation"] | components["schemas"]["ErnieImagePromptEnhancerInvocation"] | components["schemas"]["ErnieImageTextEncoderInvocation"] | components["schemas"]["ErnieImageVaeDecodeInvocation"] | components["schemas"]["ExpandMaskWithFadeInvocation"] | components["schemas"]["ExtractVideoRangeInvocation"] | components["schemas"]["FLUXLoRACollectionLoader"] | components["schemas"]["FaceIdentifierInvocation"] | components["schemas"]["FaceMaskInvocation"] | components["schemas"]["FaceOffInvocation"] | components["schemas"]["FloatBatchInvocation"] | components["schemas"]["FloatCollectionInvocation"] | components["schemas"]["FloatGenerator"] | components["schemas"]["FloatInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["FloatMathInvocation"] | components["schemas"]["FloatToIntegerInvocation"] | components["schemas"]["Flux2DenoiseInvocation"] | components["schemas"]["Flux2KleinLoRACollectionLoader"] | components["schemas"]["Flux2KleinLoRALoaderInvocation"] | components["schemas"]["Flux2KleinModelLoaderInvocation"] | components["schemas"]["Flux2KleinTextEncoderInvocation"] | components["schemas"]["Flux2PiDDecodeInvocation"] | components["schemas"]["Flux2VaeDecodeInvocation"] | components["schemas"]["Flux2VaeEncodeInvocation"] | components["schemas"]["FluxControlLoRALoaderInvocation"] | components["schemas"]["FluxControlNetInvocation"] | components["schemas"]["FluxDenoiseInvocation"] | components["schemas"]["FluxDenoiseLatentsMetaInvocation"] | components["schemas"]["FluxFillInvocation"] | components["schemas"]["FluxIPAdapterInvocation"] | components["schemas"]["FluxKontextConcatenateImagesInvocation"] | components["schemas"]["FluxKontextInvocation"] | components["schemas"]["FluxLoRALoaderInvocation"] | components["schemas"]["FluxModelLoaderInvocation"] | components["schemas"]["FluxPiDDecodeInvocation"] | components["schemas"]["FluxReduxInvocation"] | components["schemas"]["FluxTextEncoderInvocation"] | components["schemas"]["FluxVaeDecodeInvocation"] | components["schemas"]["FluxVaeEncodeInvocation"] | components["schemas"]["FreeUInvocation"] | components["schemas"]["GeminiImageGenerationInvocation"] | components["schemas"]["Gemma2EncoderLoaderInvocation"] | components["schemas"]["GetMaskBoundingBoxInvocation"] | components["schemas"]["GroundingDinoInvocation"] | components["schemas"]["HEDEdgeDetectionInvocation"] | components["schemas"]["HeuristicResizeInvocation"] | components["schemas"]["IPAdapterInvocation"] | components["schemas"]["IdealSizeInvocation"] | components["schemas"]["Ideogram4CaptionBuilderInvocation"] | components["schemas"]["Ideogram4DenoiseInvocation"] | components["schemas"]["Ideogram4LatentsToImageInvocation"] | components["schemas"]["Ideogram4ModelLoaderInvocation"] | components["schemas"]["Ideogram4TextEncoderInvocation"] | components["schemas"]["IfInvocation"] | components["schemas"]["ImageBatchInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageChannelMultiplyInvocation"] | components["schemas"]["ImageChannelOffsetInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImageGenerator"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageMaskToTensorInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageNoiseInvocation"] | components["schemas"]["ImagePanelLayoutInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["IntegerBatchInvocation"] | components["schemas"]["IntegerCollectionInvocation"] | components["schemas"]["IntegerGenerator"] | components["schemas"]["IntegerInvocation"] | components["schemas"]["IntegerMathInvocation"] | components["schemas"]["InvertTensorMaskInvocation"] | components["schemas"]["InvokeAdjustImageHuePlusInvocation"] | components["schemas"]["InvokeEquivalentAchromaticLightnessInvocation"] | components["schemas"]["InvokeImageBlendInvocation"] | components["schemas"]["InvokeImageCompositorInvocation"] | components["schemas"]["InvokeImageDilateOrErodeInvocation"] | components["schemas"]["InvokeImageEnhanceInvocation"] | components["schemas"]["InvokeImageValueThresholdsInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["Krea2ConditioningRebalanceInvocation"] | components["schemas"]["Krea2DenoiseInvocation"] | components["schemas"]["Krea2LoRACollectionLoader"] | components["schemas"]["Krea2LoRALoaderInvocation"] | components["schemas"]["Krea2ModelLoaderInvocation"] | components["schemas"]["Krea2SeedVarianceInvocation"] | components["schemas"]["Krea2TextEncoderInvocation"] | components["schemas"]["LaMaInfillInvocation"] | components["schemas"]["LatentsCollectionInvocation"] | components["schemas"]["LatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["LineartAnimeEdgeDetectionInvocation"] | components["schemas"]["LineartEdgeDetectionInvocation"] | components["schemas"]["LlavaOnevisionVllmInvocation"] | components["schemas"]["LoRACollectionLoader"] | components["schemas"]["LoRALoaderInvocation"] | components["schemas"]["LoRASelectorInvocation"] | components["schemas"]["MLSDDetectionInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["MaskCombineInvocation"] | components["schemas"]["MaskEdgeInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["MaskFromIDInvocation"] | components["schemas"]["MaskTensorToImageInvocation"] | components["schemas"]["MediaPipeFaceDetectionInvocation"] | components["schemas"]["MergeMetadataInvocation"] | components["schemas"]["MergeTilesToImageInvocation"] | components["schemas"]["MetadataFieldExtractorInvocation"] | components["schemas"]["MetadataFromImageInvocation"] | components["schemas"]["MetadataInvocation"] | components["schemas"]["MetadataItemInvocation"] | components["schemas"]["MetadataItemLinkedInvocation"] | components["schemas"]["MetadataToBoolCollectionInvocation"] | components["schemas"]["MetadataToBoolInvocation"] | components["schemas"]["MetadataToControlnetsInvocation"] | components["schemas"]["MetadataToFloatCollectionInvocation"] | components["schemas"]["MetadataToFloatInvocation"] | components["schemas"]["MetadataToIPAdaptersInvocation"] | components["schemas"]["MetadataToIntegerCollectionInvocation"] | components["schemas"]["MetadataToIntegerInvocation"] | components["schemas"]["MetadataToLorasCollectionInvocation"] | components["schemas"]["MetadataToLorasInvocation"] | components["schemas"]["MetadataToModelInvocation"] | components["schemas"]["MetadataToSDXLLorasInvocation"] | components["schemas"]["MetadataToSDXLModelInvocation"] | components["schemas"]["MetadataToSchedulerInvocation"] | components["schemas"]["MetadataToStringCollectionInvocation"] | components["schemas"]["MetadataToStringInvocation"] | components["schemas"]["MetadataToT2IAdaptersInvocation"] | components["schemas"]["MetadataToVAEInvocation"] | components["schemas"]["ModelIdentifierInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["NormalMapInvocation"] | components["schemas"]["OklabUnsharpMaskInvocation"] | components["schemas"]["OklchImageHueAdjustmentInvocation"] | components["schemas"]["OpenAIImageGenerationInvocation"] | components["schemas"]["PBRMapsInvocation"] | components["schemas"]["PairTileImageInvocation"] | components["schemas"]["PasteImageIntoBoundingBoxInvocation"] | components["schemas"]["PiDDecoderLoaderInvocation"] | components["schemas"]["PiDUpscaleInvocation"] | components["schemas"]["PiDiNetEdgeDetectionInvocation"] | components["schemas"]["PromptTemplateInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["QwenImageDenoiseInvocation"] | components["schemas"]["QwenImageImageToLatentsInvocation"] | components["schemas"]["QwenImageLatentsToImageInvocation"] | components["schemas"]["QwenImageLoRACollectionLoader"] | components["schemas"]["QwenImageLoRALoaderInvocation"] | components["schemas"]["QwenImageModelLoaderInvocation"] | components["schemas"]["QwenImagePiDDecodeInvocation"] | components["schemas"]["QwenImageTextEncoderInvocation"] | components["schemas"]["RandomFloatInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RectangleMaskInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["RoundInvocation"] | components["schemas"]["SD3DenoiseInvocation"] | components["schemas"]["SD3ImageToLatentsInvocation"] | components["schemas"]["SD3LatentsToImageInvocation"] | components["schemas"]["SD3PiDDecodeInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLLoRACollectionLoader"] | components["schemas"]["SDXLLoRALoaderInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLPiDDecodeInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SaveImageInvocation"] | components["schemas"]["SaveImageToFileInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["SchedulerInvocation"] | components["schemas"]["Sd3ModelLoaderInvocation"] | components["schemas"]["Sd3TextEncoderInvocation"] | components["schemas"]["SeamlessModeInvocation"] | components["schemas"]["SeedreamImageGenerationInvocation"] | components["schemas"]["SegmentAnythingInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["SpandrelImageToImageAutoscaleInvocation"] | components["schemas"]["SpandrelImageToImageInvocation"] | components["schemas"]["StringBatchInvocation"] | components["schemas"]["StringCollectionInvocation"] | components["schemas"]["StringGenerator"] | components["schemas"]["StringInvocation"] | components["schemas"]["StringJoinInvocation"] | components["schemas"]["StringJoinThreeInvocation"] | components["schemas"]["StringReplaceInvocation"] | components["schemas"]["StringSplitInvocation"] | components["schemas"]["StringSplitNegInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["T2IAdapterInvocation"] | components["schemas"]["TextLLMInvocation"] | components["schemas"]["TextLLMWithPresetInvocation"] | components["schemas"]["TileToPropertiesInvocation"] | components["schemas"]["TiledMultiDiffusionDenoiseLatents"] | components["schemas"]["UnsharpMaskInvocation"] | components["schemas"]["VAELoaderInvocation"] | components["schemas"]["VideoConcatInvocation"] | components["schemas"]["VideoFrameExtractInvocation"] | components["schemas"]["VideoInvocation"] | components["schemas"]["WanDenoiseInvocation"] | components["schemas"]["WanI2VIdealDimensionsInvocation"] | components["schemas"]["WanImageToLatentsInvocation"] | components["schemas"]["WanLatentsToImageInvocation"] | components["schemas"]["WanLatentsToVideoInvocation"] | components["schemas"]["WanLoRACollectionLoader"] | components["schemas"]["WanLoRALoaderInvocation"] | components["schemas"]["WanModelLoaderInvocation"] | components["schemas"]["WanRefImageEncoderInvocation"] | components["schemas"]["WanTI2VIdealDimensionsInvocation"] | components["schemas"]["WanTextEncoderInvocation"] | components["schemas"]["WanVideoDenoiseInvocation"] | components["schemas"]["WorkflowReturnGetInvocation"] | components["schemas"]["WorkflowReturnInvocation"] | components["schemas"]["WorkflowReturnValueInvocation"] | components["schemas"]["ZImageControlInvocation"] | components["schemas"]["ZImageDenoiseInvocation"] | components["schemas"]["ZImageDenoiseMetaInvocation"] | components["schemas"]["ZImageImageToLatentsInvocation"] | components["schemas"]["ZImageLatentsToImageInvocation"] | components["schemas"]["ZImageLoRACollectionLoader"] | components["schemas"]["ZImageLoRALoaderInvocation"] | components["schemas"]["ZImageModelLoaderInvocation"] | components["schemas"]["ZImagePiDDecodeInvocation"] | components["schemas"]["ZImageSeedVarianceEnhancerInvocation"] | components["schemas"]["ZImageTextEncoderInvocation"];
+            invocation: components["schemas"]["AddInvocation"] | components["schemas"]["AlibabaCloudImageGenerationInvocation"] | components["schemas"]["AlphaMaskToTensorInvocation"] | components["schemas"]["AnimaDenoiseInvocation"] | components["schemas"]["AnimaImageToLatentsInvocation"] | components["schemas"]["AnimaLLLiteInvocation"] | components["schemas"]["AnimaLatentsToImageInvocation"] | components["schemas"]["AnimaLoRACollectionLoader"] | components["schemas"]["AnimaLoRALoaderInvocation"] | components["schemas"]["AnimaModelLoaderInvocation"] | components["schemas"]["AnimaTextEncoderInvocation"] | components["schemas"]["ApplyMaskTensorToImageInvocation"] | components["schemas"]["ApplyMaskToImageInvocation"] | components["schemas"]["BlankImageInvocation"] | components["schemas"]["BlendLatentsInvocation"] | components["schemas"]["BooleanCollectionInvocation"] | components["schemas"]["BooleanInvocation"] | components["schemas"]["BoundingBoxInvocation"] | components["schemas"]["CLIPSkipInvocation"] | components["schemas"]["CV2InfillInvocation"] | components["schemas"]["CalculateImageTilesEvenSplitInvocation"] | components["schemas"]["CalculateImageTilesInvocation"] | components["schemas"]["CalculateImageTilesMinimumOverlapInvocation"] | components["schemas"]["CallSavedWorkflowInvocation"] | components["schemas"]["CannyEdgeDetectionInvocation"] | components["schemas"]["CanvasOutputInvocation"] | components["schemas"]["CanvasPasteBackInvocation"] | components["schemas"]["CanvasV2MaskAndCropInvocation"] | components["schemas"]["CenterPadCropInvocation"] | components["schemas"]["CogView4DenoiseInvocation"] | components["schemas"]["CogView4ImageToLatentsInvocation"] | components["schemas"]["CogView4LatentsToImageInvocation"] | components["schemas"]["CogView4ModelLoaderInvocation"] | components["schemas"]["CogView4TextEncoderInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["ColorCorrectInvocation"] | components["schemas"]["ColorInvocation"] | components["schemas"]["ColorMapInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ConditioningCollectionInvocation"] | components["schemas"]["ConditioningInvocation"] | components["schemas"]["ContentShuffleInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["CoreMetadataInvocation"] | components["schemas"]["CreateDenoiseMaskInvocation"] | components["schemas"]["CreateGradientMaskInvocation"] | components["schemas"]["CropImageToBoundingBoxInvocation"] | components["schemas"]["CropLatentsCoreInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["DWOpenposeDetectionInvocation"] | components["schemas"]["DecodeInvisibleWatermarkInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["DenoiseLatentsMetaInvocation"] | components["schemas"]["DepthAnythingDepthEstimationInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["ErnieImageDenoiseInvocation"] | components["schemas"]["ErnieImageModelLoaderInvocation"] | components["schemas"]["ErnieImagePromptEnhancerInvocation"] | components["schemas"]["ErnieImageTextEncoderInvocation"] | components["schemas"]["ErnieImageVaeDecodeInvocation"] | components["schemas"]["ExpandMaskWithFadeInvocation"] | components["schemas"]["ExtractVideoRangeInvocation"] | components["schemas"]["FLUXLoRACollectionLoader"] | components["schemas"]["FaceIdentifierInvocation"] | components["schemas"]["FaceMaskInvocation"] | components["schemas"]["FaceOffInvocation"] | components["schemas"]["FloatBatchInvocation"] | components["schemas"]["FloatCollectionInvocation"] | components["schemas"]["FloatGenerator"] | components["schemas"]["FloatInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["FloatMathInvocation"] | components["schemas"]["FloatToIntegerInvocation"] | components["schemas"]["Flux2DenoiseInvocation"] | components["schemas"]["Flux2KleinLoRACollectionLoader"] | components["schemas"]["Flux2KleinLoRALoaderInvocation"] | components["schemas"]["Flux2KleinModelLoaderInvocation"] | components["schemas"]["Flux2KleinTextEncoderInvocation"] | components["schemas"]["Flux2PiDDecodeInvocation"] | components["schemas"]["Flux2VaeDecodeInvocation"] | components["schemas"]["Flux2VaeEncodeInvocation"] | components["schemas"]["FluxControlLoRALoaderInvocation"] | components["schemas"]["FluxControlNetInvocation"] | components["schemas"]["FluxDenoiseInvocation"] | components["schemas"]["FluxDenoiseLatentsMetaInvocation"] | components["schemas"]["FluxFillInvocation"] | components["schemas"]["FluxIPAdapterInvocation"] | components["schemas"]["FluxKontextConcatenateImagesInvocation"] | components["schemas"]["FluxKontextInvocation"] | components["schemas"]["FluxLoRALoaderInvocation"] | components["schemas"]["FluxModelLoaderInvocation"] | components["schemas"]["FluxPiDDecodeInvocation"] | components["schemas"]["FluxReduxInvocation"] | components["schemas"]["FluxTextEncoderInvocation"] | components["schemas"]["FluxVaeDecodeInvocation"] | components["schemas"]["FluxVaeEncodeInvocation"] | components["schemas"]["FreeUInvocation"] | components["schemas"]["GeminiImageGenerationInvocation"] | components["schemas"]["Gemma2EncoderLoaderInvocation"] | components["schemas"]["GetMaskBoundingBoxInvocation"] | components["schemas"]["GroundingDinoInvocation"] | components["schemas"]["HEDEdgeDetectionInvocation"] | components["schemas"]["HeuristicResizeInvocation"] | components["schemas"]["IPAdapterInvocation"] | components["schemas"]["IdealSizeInvocation"] | components["schemas"]["Ideogram4CaptionBuilderInvocation"] | components["schemas"]["Ideogram4DenoiseInvocation"] | components["schemas"]["Ideogram4LatentsToImageInvocation"] | components["schemas"]["Ideogram4ModelLoaderInvocation"] | components["schemas"]["Ideogram4TextEncoderInvocation"] | components["schemas"]["IfInvocation"] | components["schemas"]["ImageBatchInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageChannelMultiplyInvocation"] | components["schemas"]["ImageChannelOffsetInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImageGenerator"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageMaskToTensorInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageNoiseInvocation"] | components["schemas"]["ImagePanelLayoutInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["IntegerBatchInvocation"] | components["schemas"]["IntegerCollectionInvocation"] | components["schemas"]["IntegerGenerator"] | components["schemas"]["IntegerInvocation"] | components["schemas"]["IntegerMathInvocation"] | components["schemas"]["InvertTensorMaskInvocation"] | components["schemas"]["InvokeAdjustImageHuePlusInvocation"] | components["schemas"]["InvokeEquivalentAchromaticLightnessInvocation"] | components["schemas"]["InvokeImageBlendInvocation"] | components["schemas"]["InvokeImageCompositorInvocation"] | components["schemas"]["InvokeImageDilateOrErodeInvocation"] | components["schemas"]["InvokeImageEnhanceInvocation"] | components["schemas"]["InvokeImageValueThresholdsInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["Krea2ConditioningRebalanceInvocation"] | components["schemas"]["Krea2DenoiseInvocation"] | components["schemas"]["Krea2LoRACollectionLoader"] | components["schemas"]["Krea2LoRALoaderInvocation"] | components["schemas"]["Krea2ModelLoaderInvocation"] | components["schemas"]["Krea2SeedVarianceInvocation"] | components["schemas"]["Krea2TextEncoderInvocation"] | components["schemas"]["LaMaInfillInvocation"] | components["schemas"]["LatentsCollectionInvocation"] | components["schemas"]["LatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["LineartAnimeEdgeDetectionInvocation"] | components["schemas"]["LineartEdgeDetectionInvocation"] | components["schemas"]["LlavaOnevisionVllmInvocation"] | components["schemas"]["LoRACollectionLoader"] | components["schemas"]["LoRALoaderInvocation"] | components["schemas"]["LoRASelectorInvocation"] | components["schemas"]["MLSDDetectionInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["MaskCombineInvocation"] | components["schemas"]["MaskEdgeInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["MaskFromIDInvocation"] | components["schemas"]["MaskTensorToImageInvocation"] | components["schemas"]["MediaPipeFaceDetectionInvocation"] | components["schemas"]["MergeMetadataInvocation"] | components["schemas"]["MergeTilesToImageInvocation"] | components["schemas"]["MetadataFieldExtractorInvocation"] | components["schemas"]["MetadataFromImageInvocation"] | components["schemas"]["MetadataInvocation"] | components["schemas"]["MetadataItemInvocation"] | components["schemas"]["MetadataItemLinkedInvocation"] | components["schemas"]["MetadataToBoolCollectionInvocation"] | components["schemas"]["MetadataToBoolInvocation"] | components["schemas"]["MetadataToControlnetsInvocation"] | components["schemas"]["MetadataToFloatCollectionInvocation"] | components["schemas"]["MetadataToFloatInvocation"] | components["schemas"]["MetadataToIPAdaptersInvocation"] | components["schemas"]["MetadataToIntegerCollectionInvocation"] | components["schemas"]["MetadataToIntegerInvocation"] | components["schemas"]["MetadataToLorasCollectionInvocation"] | components["schemas"]["MetadataToLorasInvocation"] | components["schemas"]["MetadataToModelInvocation"] | components["schemas"]["MetadataToSDXLLorasInvocation"] | components["schemas"]["MetadataToSDXLModelInvocation"] | components["schemas"]["MetadataToSchedulerInvocation"] | components["schemas"]["MetadataToStringCollectionInvocation"] | components["schemas"]["MetadataToStringInvocation"] | components["schemas"]["MetadataToT2IAdaptersInvocation"] | components["schemas"]["MetadataToVAEInvocation"] | components["schemas"]["MiniMaxH3DenoiseInvocation"] | components["schemas"]["MiniMaxH3FrameConditioningInvocation"] | components["schemas"]["MiniMaxH3LatentsToImageInvocation"] | components["schemas"]["MiniMaxH3LatentsToVideoInvocation"] | components["schemas"]["MiniMaxH3ModelLoaderInvocation"] | components["schemas"]["MiniMaxH3TextEncoderInvocation"] | components["schemas"]["ModelIdentifierInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["NormalMapInvocation"] | components["schemas"]["OklabUnsharpMaskInvocation"] | components["schemas"]["OklchImageHueAdjustmentInvocation"] | components["schemas"]["OpenAIImageGenerationInvocation"] | components["schemas"]["PBRMapsInvocation"] | components["schemas"]["PairTileImageInvocation"] | components["schemas"]["PasteImageIntoBoundingBoxInvocation"] | components["schemas"]["PiDDecoderLoaderInvocation"] | components["schemas"]["PiDUpscaleInvocation"] | components["schemas"]["PiDiNetEdgeDetectionInvocation"] | components["schemas"]["PromptTemplateInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["QwenImageDenoiseInvocation"] | components["schemas"]["QwenImageImageToLatentsInvocation"] | components["schemas"]["QwenImageLatentsToImageInvocation"] | components["schemas"]["QwenImageLoRACollectionLoader"] | components["schemas"]["QwenImageLoRALoaderInvocation"] | components["schemas"]["QwenImageModelLoaderInvocation"] | components["schemas"]["QwenImagePiDDecodeInvocation"] | components["schemas"]["QwenImageTextEncoderInvocation"] | components["schemas"]["RandomFloatInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RectangleMaskInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["RoundInvocation"] | components["schemas"]["SD3DenoiseInvocation"] | components["schemas"]["SD3ImageToLatentsInvocation"] | components["schemas"]["SD3LatentsToImageInvocation"] | components["schemas"]["SD3PiDDecodeInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLLoRACollectionLoader"] | components["schemas"]["SDXLLoRALoaderInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLPiDDecodeInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SaveImageInvocation"] | components["schemas"]["SaveImageToFileInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["SchedulerInvocation"] | components["schemas"]["Sd3ModelLoaderInvocation"] | components["schemas"]["Sd3TextEncoderInvocation"] | components["schemas"]["SeamlessModeInvocation"] | components["schemas"]["SeedreamImageGenerationInvocation"] | components["schemas"]["SegmentAnythingInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["SpandrelImageToImageAutoscaleInvocation"] | components["schemas"]["SpandrelImageToImageInvocation"] | components["schemas"]["StringBatchInvocation"] | components["schemas"]["StringCollectionInvocation"] | components["schemas"]["StringGenerator"] | components["schemas"]["StringInvocation"] | components["schemas"]["StringJoinInvocation"] | components["schemas"]["StringJoinThreeInvocation"] | components["schemas"]["StringReplaceInvocation"] | components["schemas"]["StringSplitInvocation"] | components["schemas"]["StringSplitNegInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["T2IAdapterInvocation"] | components["schemas"]["TextLLMInvocation"] | components["schemas"]["TextLLMWithPresetInvocation"] | components["schemas"]["TileToPropertiesInvocation"] | components["schemas"]["TiledMultiDiffusionDenoiseLatents"] | components["schemas"]["UnsharpMaskInvocation"] | components["schemas"]["VAELoaderInvocation"] | components["schemas"]["VideoConcatInvocation"] | components["schemas"]["VideoFrameExtractInvocation"] | components["schemas"]["VideoInvocation"] | components["schemas"]["WanDenoiseInvocation"] | components["schemas"]["WanI2VIdealDimensionsInvocation"] | components["schemas"]["WanImageToLatentsInvocation"] | components["schemas"]["WanLatentsToImageInvocation"] | components["schemas"]["WanLatentsToVideoInvocation"] | components["schemas"]["WanLoRACollectionLoader"] | components["schemas"]["WanLoRALoaderInvocation"] | components["schemas"]["WanModelLoaderInvocation"] | components["schemas"]["WanRefImageEncoderInvocation"] | components["schemas"]["WanTI2VIdealDimensionsInvocation"] | components["schemas"]["WanTextEncoderInvocation"] | components["schemas"]["WanVideoDenoiseInvocation"] | components["schemas"]["WorkflowReturnGetInvocation"] | components["schemas"]["WorkflowReturnInvocation"] | components["schemas"]["WorkflowReturnValueInvocation"] | components["schemas"]["ZImageControlInvocation"] | components["schemas"]["ZImageDenoiseInvocation"] | components["schemas"]["ZImageDenoiseMetaInvocation"] | components["schemas"]["ZImageImageToLatentsInvocation"] | components["schemas"]["ZImageLatentsToImageInvocation"] | components["schemas"]["ZImageLoRACollectionLoader"] | components["schemas"]["ZImageLoRALoaderInvocation"] | components["schemas"]["ZImageModelLoaderInvocation"] | components["schemas"]["ZImagePiDDecodeInvocation"] | components["schemas"]["ZImageSeedVarianceEnhancerInvocation"] | components["schemas"]["ZImageTextEncoderInvocation"];
             /**
              * Invocation Source Id
              * @description The ID of the prepared invocation's source node
@@ -18572,7 +18578,7 @@ export type components = {
              * Invocation
              * @description The ID of the invocation
              */
-            invocation: components["schemas"]["AddInvocation"] | components["schemas"]["AlibabaCloudImageGenerationInvocation"] | components["schemas"]["AlphaMaskToTensorInvocation"] | components["schemas"]["AnimaDenoiseInvocation"] | components["schemas"]["AnimaImageToLatentsInvocation"] | components["schemas"]["AnimaLLLiteInvocation"] | components["schemas"]["AnimaLatentsToImageInvocation"] | components["schemas"]["AnimaLoRACollectionLoader"] | components["schemas"]["AnimaLoRALoaderInvocation"] | components["schemas"]["AnimaModelLoaderInvocation"] | components["schemas"]["AnimaTextEncoderInvocation"] | components["schemas"]["ApplyMaskTensorToImageInvocation"] | components["schemas"]["ApplyMaskToImageInvocation"] | components["schemas"]["BlankImageInvocation"] | components["schemas"]["BlendLatentsInvocation"] | components["schemas"]["BooleanCollectionInvocation"] | components["schemas"]["BooleanInvocation"] | components["schemas"]["BoundingBoxInvocation"] | components["schemas"]["CLIPSkipInvocation"] | components["schemas"]["CV2InfillInvocation"] | components["schemas"]["CalculateImageTilesEvenSplitInvocation"] | components["schemas"]["CalculateImageTilesInvocation"] | components["schemas"]["CalculateImageTilesMinimumOverlapInvocation"] | components["schemas"]["CallSavedWorkflowInvocation"] | components["schemas"]["CannyEdgeDetectionInvocation"] | components["schemas"]["CanvasOutputInvocation"] | components["schemas"]["CanvasPasteBackInvocation"] | components["schemas"]["CanvasV2MaskAndCropInvocation"] | components["schemas"]["CenterPadCropInvocation"] | components["schemas"]["CogView4DenoiseInvocation"] | components["schemas"]["CogView4ImageToLatentsInvocation"] | components["schemas"]["CogView4LatentsToImageInvocation"] | components["schemas"]["CogView4ModelLoaderInvocation"] | components["schemas"]["CogView4TextEncoderInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["ColorCorrectInvocation"] | components["schemas"]["ColorInvocation"] | components["schemas"]["ColorMapInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ConditioningCollectionInvocation"] | components["schemas"]["ConditioningInvocation"] | components["schemas"]["ContentShuffleInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["CoreMetadataInvocation"] | components["schemas"]["CreateDenoiseMaskInvocation"] | components["schemas"]["CreateGradientMaskInvocation"] | components["schemas"]["CropImageToBoundingBoxInvocation"] | components["schemas"]["CropLatentsCoreInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["DWOpenposeDetectionInvocation"] | components["schemas"]["DecodeInvisibleWatermarkInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["DenoiseLatentsMetaInvocation"] | components["schemas"]["DepthAnythingDepthEstimationInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["ErnieImageDenoiseInvocation"] | components["schemas"]["ErnieImageModelLoaderInvocation"] | components["schemas"]["ErnieImagePromptEnhancerInvocation"] | components["schemas"]["ErnieImageTextEncoderInvocation"] | components["schemas"]["ErnieImageVaeDecodeInvocation"] | components["schemas"]["ExpandMaskWithFadeInvocation"] | components["schemas"]["ExtractVideoRangeInvocation"] | components["schemas"]["FLUXLoRACollectionLoader"] | components["schemas"]["FaceIdentifierInvocation"] | components["schemas"]["FaceMaskInvocation"] | components["schemas"]["FaceOffInvocation"] | components["schemas"]["FloatBatchInvocation"] | components["schemas"]["FloatCollectionInvocation"] | components["schemas"]["FloatGenerator"] | components["schemas"]["FloatInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["FloatMathInvocation"] | components["schemas"]["FloatToIntegerInvocation"] | components["schemas"]["Flux2DenoiseInvocation"] | components["schemas"]["Flux2KleinLoRACollectionLoader"] | components["schemas"]["Flux2KleinLoRALoaderInvocation"] | components["schemas"]["Flux2KleinModelLoaderInvocation"] | components["schemas"]["Flux2KleinTextEncoderInvocation"] | components["schemas"]["Flux2PiDDecodeInvocation"] | components["schemas"]["Flux2VaeDecodeInvocation"] | components["schemas"]["Flux2VaeEncodeInvocation"] | components["schemas"]["FluxControlLoRALoaderInvocation"] | components["schemas"]["FluxControlNetInvocation"] | components["schemas"]["FluxDenoiseInvocation"] | components["schemas"]["FluxDenoiseLatentsMetaInvocation"] | components["schemas"]["FluxFillInvocation"] | components["schemas"]["FluxIPAdapterInvocation"] | components["schemas"]["FluxKontextConcatenateImagesInvocation"] | components["schemas"]["FluxKontextInvocation"] | components["schemas"]["FluxLoRALoaderInvocation"] | components["schemas"]["FluxModelLoaderInvocation"] | components["schemas"]["FluxPiDDecodeInvocation"] | components["schemas"]["FluxReduxInvocation"] | components["schemas"]["FluxTextEncoderInvocation"] | components["schemas"]["FluxVaeDecodeInvocation"] | components["schemas"]["FluxVaeEncodeInvocation"] | components["schemas"]["FreeUInvocation"] | components["schemas"]["GeminiImageGenerationInvocation"] | components["schemas"]["Gemma2EncoderLoaderInvocation"] | components["schemas"]["GetMaskBoundingBoxInvocation"] | components["schemas"]["GroundingDinoInvocation"] | components["schemas"]["HEDEdgeDetectionInvocation"] | components["schemas"]["HeuristicResizeInvocation"] | components["schemas"]["IPAdapterInvocation"] | components["schemas"]["IdealSizeInvocation"] | components["schemas"]["Ideogram4CaptionBuilderInvocation"] | components["schemas"]["Ideogram4DenoiseInvocation"] | components["schemas"]["Ideogram4LatentsToImageInvocation"] | components["schemas"]["Ideogram4ModelLoaderInvocation"] | components["schemas"]["Ideogram4TextEncoderInvocation"] | components["schemas"]["IfInvocation"] | components["schemas"]["ImageBatchInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageChannelMultiplyInvocation"] | components["schemas"]["ImageChannelOffsetInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImageGenerator"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageMaskToTensorInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageNoiseInvocation"] | components["schemas"]["ImagePanelLayoutInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["IntegerBatchInvocation"] | components["schemas"]["IntegerCollectionInvocation"] | components["schemas"]["IntegerGenerator"] | components["schemas"]["IntegerInvocation"] | components["schemas"]["IntegerMathInvocation"] | components["schemas"]["InvertTensorMaskInvocation"] | components["schemas"]["InvokeAdjustImageHuePlusInvocation"] | components["schemas"]["InvokeEquivalentAchromaticLightnessInvocation"] | components["schemas"]["InvokeImageBlendInvocation"] | components["schemas"]["InvokeImageCompositorInvocation"] | components["schemas"]["InvokeImageDilateOrErodeInvocation"] | components["schemas"]["InvokeImageEnhanceInvocation"] | components["schemas"]["InvokeImageValueThresholdsInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["Krea2ConditioningRebalanceInvocation"] | components["schemas"]["Krea2DenoiseInvocation"] | components["schemas"]["Krea2LoRACollectionLoader"] | components["schemas"]["Krea2LoRALoaderInvocation"] | components["schemas"]["Krea2ModelLoaderInvocation"] | components["schemas"]["Krea2SeedVarianceInvocation"] | components["schemas"]["Krea2TextEncoderInvocation"] | components["schemas"]["LaMaInfillInvocation"] | components["schemas"]["LatentsCollectionInvocation"] | components["schemas"]["LatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["LineartAnimeEdgeDetectionInvocation"] | components["schemas"]["LineartEdgeDetectionInvocation"] | components["schemas"]["LlavaOnevisionVllmInvocation"] | components["schemas"]["LoRACollectionLoader"] | components["schemas"]["LoRALoaderInvocation"] | components["schemas"]["LoRASelectorInvocation"] | components["schemas"]["MLSDDetectionInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["MaskCombineInvocation"] | components["schemas"]["MaskEdgeInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["MaskFromIDInvocation"] | components["schemas"]["MaskTensorToImageInvocation"] | components["schemas"]["MediaPipeFaceDetectionInvocation"] | components["schemas"]["MergeMetadataInvocation"] | components["schemas"]["MergeTilesToImageInvocation"] | components["schemas"]["MetadataFieldExtractorInvocation"] | components["schemas"]["MetadataFromImageInvocation"] | components["schemas"]["MetadataInvocation"] | components["schemas"]["MetadataItemInvocation"] | components["schemas"]["MetadataItemLinkedInvocation"] | components["schemas"]["MetadataToBoolCollectionInvocation"] | components["schemas"]["MetadataToBoolInvocation"] | components["schemas"]["MetadataToControlnetsInvocation"] | components["schemas"]["MetadataToFloatCollectionInvocation"] | components["schemas"]["MetadataToFloatInvocation"] | components["schemas"]["MetadataToIPAdaptersInvocation"] | components["schemas"]["MetadataToIntegerCollectionInvocation"] | components["schemas"]["MetadataToIntegerInvocation"] | components["schemas"]["MetadataToLorasCollectionInvocation"] | components["schemas"]["MetadataToLorasInvocation"] | components["schemas"]["MetadataToModelInvocation"] | components["schemas"]["MetadataToSDXLLorasInvocation"] | components["schemas"]["MetadataToSDXLModelInvocation"] | components["schemas"]["MetadataToSchedulerInvocation"] | components["schemas"]["MetadataToStringCollectionInvocation"] | components["schemas"]["MetadataToStringInvocation"] | components["schemas"]["MetadataToT2IAdaptersInvocation"] | components["schemas"]["MetadataToVAEInvocation"] | components["schemas"]["ModelIdentifierInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["NormalMapInvocation"] | components["schemas"]["OklabUnsharpMaskInvocation"] | components["schemas"]["OklchImageHueAdjustmentInvocation"] | components["schemas"]["OpenAIImageGenerationInvocation"] | components["schemas"]["PBRMapsInvocation"] | components["schemas"]["PairTileImageInvocation"] | components["schemas"]["PasteImageIntoBoundingBoxInvocation"] | components["schemas"]["PiDDecoderLoaderInvocation"] | components["schemas"]["PiDUpscaleInvocation"] | components["schemas"]["PiDiNetEdgeDetectionInvocation"] | components["schemas"]["PromptTemplateInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["QwenImageDenoiseInvocation"] | components["schemas"]["QwenImageImageToLatentsInvocation"] | components["schemas"]["QwenImageLatentsToImageInvocation"] | components["schemas"]["QwenImageLoRACollectionLoader"] | components["schemas"]["QwenImageLoRALoaderInvocation"] | components["schemas"]["QwenImageModelLoaderInvocation"] | components["schemas"]["QwenImagePiDDecodeInvocation"] | components["schemas"]["QwenImageTextEncoderInvocation"] | components["schemas"]["RandomFloatInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RectangleMaskInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["RoundInvocation"] | components["schemas"]["SD3DenoiseInvocation"] | components["schemas"]["SD3ImageToLatentsInvocation"] | components["schemas"]["SD3LatentsToImageInvocation"] | components["schemas"]["SD3PiDDecodeInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLLoRACollectionLoader"] | components["schemas"]["SDXLLoRALoaderInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLPiDDecodeInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SaveImageInvocation"] | components["schemas"]["SaveImageToFileInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["SchedulerInvocation"] | components["schemas"]["Sd3ModelLoaderInvocation"] | components["schemas"]["Sd3TextEncoderInvocation"] | components["schemas"]["SeamlessModeInvocation"] | components["schemas"]["SeedreamImageGenerationInvocation"] | components["schemas"]["SegmentAnythingInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["SpandrelImageToImageAutoscaleInvocation"] | components["schemas"]["SpandrelImageToImageInvocation"] | components["schemas"]["StringBatchInvocation"] | components["schemas"]["StringCollectionInvocation"] | components["schemas"]["StringGenerator"] | components["schemas"]["StringInvocation"] | components["schemas"]["StringJoinInvocation"] | components["schemas"]["StringJoinThreeInvocation"] | components["schemas"]["StringReplaceInvocation"] | components["schemas"]["StringSplitInvocation"] | components["schemas"]["StringSplitNegInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["T2IAdapterInvocation"] | components["schemas"]["TextLLMInvocation"] | components["schemas"]["TextLLMWithPresetInvocation"] | components["schemas"]["TileToPropertiesInvocation"] | components["schemas"]["TiledMultiDiffusionDenoiseLatents"] | components["schemas"]["UnsharpMaskInvocation"] | components["schemas"]["VAELoaderInvocation"] | components["schemas"]["VideoConcatInvocation"] | components["schemas"]["VideoFrameExtractInvocation"] | components["schemas"]["VideoInvocation"] | components["schemas"]["WanDenoiseInvocation"] | components["schemas"]["WanI2VIdealDimensionsInvocation"] | components["schemas"]["WanImageToLatentsInvocation"] | components["schemas"]["WanLatentsToImageInvocation"] | components["schemas"]["WanLatentsToVideoInvocation"] | components["schemas"]["WanLoRACollectionLoader"] | components["schemas"]["WanLoRALoaderInvocation"] | components["schemas"]["WanModelLoaderInvocation"] | components["schemas"]["WanRefImageEncoderInvocation"] | components["schemas"]["WanTI2VIdealDimensionsInvocation"] | components["schemas"]["WanTextEncoderInvocation"] | components["schemas"]["WanVideoDenoiseInvocation"] | components["schemas"]["WorkflowReturnGetInvocation"] | components["schemas"]["WorkflowReturnInvocation"] | components["schemas"]["WorkflowReturnValueInvocation"] | components["schemas"]["ZImageControlInvocation"] | components["schemas"]["ZImageDenoiseInvocation"] | components["schemas"]["ZImageDenoiseMetaInvocation"] | components["schemas"]["ZImageImageToLatentsInvocation"] | components["schemas"]["ZImageLatentsToImageInvocation"] | components["schemas"]["ZImageLoRACollectionLoader"] | components["schemas"]["ZImageLoRALoaderInvocation"] | components["schemas"]["ZImageModelLoaderInvocation"] | components["schemas"]["ZImagePiDDecodeInvocation"] | components["schemas"]["ZImageSeedVarianceEnhancerInvocation"] | components["schemas"]["ZImageTextEncoderInvocation"];
+            invocation: components["schemas"]["AddInvocation"] | components["schemas"]["AlibabaCloudImageGenerationInvocation"] | components["schemas"]["AlphaMaskToTensorInvocation"] | components["schemas"]["AnimaDenoiseInvocation"] | components["schemas"]["AnimaImageToLatentsInvocation"] | components["schemas"]["AnimaLLLiteInvocation"] | components["schemas"]["AnimaLatentsToImageInvocation"] | components["schemas"]["AnimaLoRACollectionLoader"] | components["schemas"]["AnimaLoRALoaderInvocation"] | components["schemas"]["AnimaModelLoaderInvocation"] | components["schemas"]["AnimaTextEncoderInvocation"] | components["schemas"]["ApplyMaskTensorToImageInvocation"] | components["schemas"]["ApplyMaskToImageInvocation"] | components["schemas"]["BlankImageInvocation"] | components["schemas"]["BlendLatentsInvocation"] | components["schemas"]["BooleanCollectionInvocation"] | components["schemas"]["BooleanInvocation"] | components["schemas"]["BoundingBoxInvocation"] | components["schemas"]["CLIPSkipInvocation"] | components["schemas"]["CV2InfillInvocation"] | components["schemas"]["CalculateImageTilesEvenSplitInvocation"] | components["schemas"]["CalculateImageTilesInvocation"] | components["schemas"]["CalculateImageTilesMinimumOverlapInvocation"] | components["schemas"]["CallSavedWorkflowInvocation"] | components["schemas"]["CannyEdgeDetectionInvocation"] | components["schemas"]["CanvasOutputInvocation"] | components["schemas"]["CanvasPasteBackInvocation"] | components["schemas"]["CanvasV2MaskAndCropInvocation"] | components["schemas"]["CenterPadCropInvocation"] | components["schemas"]["CogView4DenoiseInvocation"] | components["schemas"]["CogView4ImageToLatentsInvocation"] | components["schemas"]["CogView4LatentsToImageInvocation"] | components["schemas"]["CogView4ModelLoaderInvocation"] | components["schemas"]["CogView4TextEncoderInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["ColorCorrectInvocation"] | components["schemas"]["ColorInvocation"] | components["schemas"]["ColorMapInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ConditioningCollectionInvocation"] | components["schemas"]["ConditioningInvocation"] | components["schemas"]["ContentShuffleInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["CoreMetadataInvocation"] | components["schemas"]["CreateDenoiseMaskInvocation"] | components["schemas"]["CreateGradientMaskInvocation"] | components["schemas"]["CropImageToBoundingBoxInvocation"] | components["schemas"]["CropLatentsCoreInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["DWOpenposeDetectionInvocation"] | components["schemas"]["DecodeInvisibleWatermarkInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["DenoiseLatentsMetaInvocation"] | components["schemas"]["DepthAnythingDepthEstimationInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["ErnieImageDenoiseInvocation"] | components["schemas"]["ErnieImageModelLoaderInvocation"] | components["schemas"]["ErnieImagePromptEnhancerInvocation"] | components["schemas"]["ErnieImageTextEncoderInvocation"] | components["schemas"]["ErnieImageVaeDecodeInvocation"] | components["schemas"]["ExpandMaskWithFadeInvocation"] | components["schemas"]["ExtractVideoRangeInvocation"] | components["schemas"]["FLUXLoRACollectionLoader"] | components["schemas"]["FaceIdentifierInvocation"] | components["schemas"]["FaceMaskInvocation"] | components["schemas"]["FaceOffInvocation"] | components["schemas"]["FloatBatchInvocation"] | components["schemas"]["FloatCollectionInvocation"] | components["schemas"]["FloatGenerator"] | components["schemas"]["FloatInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["FloatMathInvocation"] | components["schemas"]["FloatToIntegerInvocation"] | components["schemas"]["Flux2DenoiseInvocation"] | components["schemas"]["Flux2KleinLoRACollectionLoader"] | components["schemas"]["Flux2KleinLoRALoaderInvocation"] | components["schemas"]["Flux2KleinModelLoaderInvocation"] | components["schemas"]["Flux2KleinTextEncoderInvocation"] | components["schemas"]["Flux2PiDDecodeInvocation"] | components["schemas"]["Flux2VaeDecodeInvocation"] | components["schemas"]["Flux2VaeEncodeInvocation"] | components["schemas"]["FluxControlLoRALoaderInvocation"] | components["schemas"]["FluxControlNetInvocation"] | components["schemas"]["FluxDenoiseInvocation"] | components["schemas"]["FluxDenoiseLatentsMetaInvocation"] | components["schemas"]["FluxFillInvocation"] | components["schemas"]["FluxIPAdapterInvocation"] | components["schemas"]["FluxKontextConcatenateImagesInvocation"] | components["schemas"]["FluxKontextInvocation"] | components["schemas"]["FluxLoRALoaderInvocation"] | components["schemas"]["FluxModelLoaderInvocation"] | components["schemas"]["FluxPiDDecodeInvocation"] | components["schemas"]["FluxReduxInvocation"] | components["schemas"]["FluxTextEncoderInvocation"] | components["schemas"]["FluxVaeDecodeInvocation"] | components["schemas"]["FluxVaeEncodeInvocation"] | components["schemas"]["FreeUInvocation"] | components["schemas"]["GeminiImageGenerationInvocation"] | components["schemas"]["Gemma2EncoderLoaderInvocation"] | components["schemas"]["GetMaskBoundingBoxInvocation"] | components["schemas"]["GroundingDinoInvocation"] | components["schemas"]["HEDEdgeDetectionInvocation"] | components["schemas"]["HeuristicResizeInvocation"] | components["schemas"]["IPAdapterInvocation"] | components["schemas"]["IdealSizeInvocation"] | components["schemas"]["Ideogram4CaptionBuilderInvocation"] | components["schemas"]["Ideogram4DenoiseInvocation"] | components["schemas"]["Ideogram4LatentsToImageInvocation"] | components["schemas"]["Ideogram4ModelLoaderInvocation"] | components["schemas"]["Ideogram4TextEncoderInvocation"] | components["schemas"]["IfInvocation"] | components["schemas"]["ImageBatchInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageChannelMultiplyInvocation"] | components["schemas"]["ImageChannelOffsetInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImageGenerator"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageMaskToTensorInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageNoiseInvocation"] | components["schemas"]["ImagePanelLayoutInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["IntegerBatchInvocation"] | components["schemas"]["IntegerCollectionInvocation"] | components["schemas"]["IntegerGenerator"] | components["schemas"]["IntegerInvocation"] | components["schemas"]["IntegerMathInvocation"] | components["schemas"]["InvertTensorMaskInvocation"] | components["schemas"]["InvokeAdjustImageHuePlusInvocation"] | components["schemas"]["InvokeEquivalentAchromaticLightnessInvocation"] | components["schemas"]["InvokeImageBlendInvocation"] | components["schemas"]["InvokeImageCompositorInvocation"] | components["schemas"]["InvokeImageDilateOrErodeInvocation"] | components["schemas"]["InvokeImageEnhanceInvocation"] | components["schemas"]["InvokeImageValueThresholdsInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["Krea2ConditioningRebalanceInvocation"] | components["schemas"]["Krea2DenoiseInvocation"] | components["schemas"]["Krea2LoRACollectionLoader"] | components["schemas"]["Krea2LoRALoaderInvocation"] | components["schemas"]["Krea2ModelLoaderInvocation"] | components["schemas"]["Krea2SeedVarianceInvocation"] | components["schemas"]["Krea2TextEncoderInvocation"] | components["schemas"]["LaMaInfillInvocation"] | components["schemas"]["LatentsCollectionInvocation"] | components["schemas"]["LatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["LineartAnimeEdgeDetectionInvocation"] | components["schemas"]["LineartEdgeDetectionInvocation"] | components["schemas"]["LlavaOnevisionVllmInvocation"] | components["schemas"]["LoRACollectionLoader"] | components["schemas"]["LoRALoaderInvocation"] | components["schemas"]["LoRASelectorInvocation"] | components["schemas"]["MLSDDetectionInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["MaskCombineInvocation"] | components["schemas"]["MaskEdgeInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["MaskFromIDInvocation"] | components["schemas"]["MaskTensorToImageInvocation"] | components["schemas"]["MediaPipeFaceDetectionInvocation"] | components["schemas"]["MergeMetadataInvocation"] | components["schemas"]["MergeTilesToImageInvocation"] | components["schemas"]["MetadataFieldExtractorInvocation"] | components["schemas"]["MetadataFromImageInvocation"] | components["schemas"]["MetadataInvocation"] | components["schemas"]["MetadataItemInvocation"] | components["schemas"]["MetadataItemLinkedInvocation"] | components["schemas"]["MetadataToBoolCollectionInvocation"] | components["schemas"]["MetadataToBoolInvocation"] | components["schemas"]["MetadataToControlnetsInvocation"] | components["schemas"]["MetadataToFloatCollectionInvocation"] | components["schemas"]["MetadataToFloatInvocation"] | components["schemas"]["MetadataToIPAdaptersInvocation"] | components["schemas"]["MetadataToIntegerCollectionInvocation"] | components["schemas"]["MetadataToIntegerInvocation"] | components["schemas"]["MetadataToLorasCollectionInvocation"] | components["schemas"]["MetadataToLorasInvocation"] | components["schemas"]["MetadataToModelInvocation"] | components["schemas"]["MetadataToSDXLLorasInvocation"] | components["schemas"]["MetadataToSDXLModelInvocation"] | components["schemas"]["MetadataToSchedulerInvocation"] | components["schemas"]["MetadataToStringCollectionInvocation"] | components["schemas"]["MetadataToStringInvocation"] | components["schemas"]["MetadataToT2IAdaptersInvocation"] | components["schemas"]["MetadataToVAEInvocation"] | components["schemas"]["MiniMaxH3DenoiseInvocation"] | components["schemas"]["MiniMaxH3FrameConditioningInvocation"] | components["schemas"]["MiniMaxH3LatentsToImageInvocation"] | components["schemas"]["MiniMaxH3LatentsToVideoInvocation"] | components["schemas"]["MiniMaxH3ModelLoaderInvocation"] | components["schemas"]["MiniMaxH3TextEncoderInvocation"] | components["schemas"]["ModelIdentifierInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["NormalMapInvocation"] | components["schemas"]["OklabUnsharpMaskInvocation"] | components["schemas"]["OklchImageHueAdjustmentInvocation"] | components["schemas"]["OpenAIImageGenerationInvocation"] | components["schemas"]["PBRMapsInvocation"] | components["schemas"]["PairTileImageInvocation"] | components["schemas"]["PasteImageIntoBoundingBoxInvocation"] | components["schemas"]["PiDDecoderLoaderInvocation"] | components["schemas"]["PiDUpscaleInvocation"] | components["schemas"]["PiDiNetEdgeDetectionInvocation"] | components["schemas"]["PromptTemplateInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["QwenImageDenoiseInvocation"] | components["schemas"]["QwenImageImageToLatentsInvocation"] | components["schemas"]["QwenImageLatentsToImageInvocation"] | components["schemas"]["QwenImageLoRACollectionLoader"] | components["schemas"]["QwenImageLoRALoaderInvocation"] | components["schemas"]["QwenImageModelLoaderInvocation"] | components["schemas"]["QwenImagePiDDecodeInvocation"] | components["schemas"]["QwenImageTextEncoderInvocation"] | components["schemas"]["RandomFloatInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RectangleMaskInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["RoundInvocation"] | components["schemas"]["SD3DenoiseInvocation"] | components["schemas"]["SD3ImageToLatentsInvocation"] | components["schemas"]["SD3LatentsToImageInvocation"] | components["schemas"]["SD3PiDDecodeInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLLoRACollectionLoader"] | components["schemas"]["SDXLLoRALoaderInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLPiDDecodeInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SaveImageInvocation"] | components["schemas"]["SaveImageToFileInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["SchedulerInvocation"] | components["schemas"]["Sd3ModelLoaderInvocation"] | components["schemas"]["Sd3TextEncoderInvocation"] | components["schemas"]["SeamlessModeInvocation"] | components["schemas"]["SeedreamImageGenerationInvocation"] | components["schemas"]["SegmentAnythingInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["SpandrelImageToImageAutoscaleInvocation"] | components["schemas"]["SpandrelImageToImageInvocation"] | components["schemas"]["StringBatchInvocation"] | components["schemas"]["StringCollectionInvocation"] | components["schemas"]["StringGenerator"] | components["schemas"]["StringInvocation"] | components["schemas"]["StringJoinInvocation"] | components["schemas"]["StringJoinThreeInvocation"] | components["schemas"]["StringReplaceInvocation"] | components["schemas"]["StringSplitInvocation"] | components["schemas"]["StringSplitNegInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["T2IAdapterInvocation"] | components["schemas"]["TextLLMInvocation"] | components["schemas"]["TextLLMWithPresetInvocation"] | components["schemas"]["TileToPropertiesInvocation"] | components["schemas"]["TiledMultiDiffusionDenoiseLatents"] | components["schemas"]["UnsharpMaskInvocation"] | components["schemas"]["VAELoaderInvocation"] | components["schemas"]["VideoConcatInvocation"] | components["schemas"]["VideoFrameExtractInvocation"] | components["schemas"]["VideoInvocation"] | components["schemas"]["WanDenoiseInvocation"] | components["schemas"]["WanI2VIdealDimensionsInvocation"] | components["schemas"]["WanImageToLatentsInvocation"] | components["schemas"]["WanLatentsToImageInvocation"] | components["schemas"]["WanLatentsToVideoInvocation"] | components["schemas"]["WanLoRACollectionLoader"] | components["schemas"]["WanLoRALoaderInvocation"] | components["schemas"]["WanModelLoaderInvocation"] | components["schemas"]["WanRefImageEncoderInvocation"] | components["schemas"]["WanTI2VIdealDimensionsInvocation"] | components["schemas"]["WanTextEncoderInvocation"] | components["schemas"]["WanVideoDenoiseInvocation"] | components["schemas"]["WorkflowReturnGetInvocation"] | components["schemas"]["WorkflowReturnInvocation"] | components["schemas"]["WorkflowReturnValueInvocation"] | components["schemas"]["ZImageControlInvocation"] | components["schemas"]["ZImageDenoiseInvocation"] | components["schemas"]["ZImageDenoiseMetaInvocation"] | components["schemas"]["ZImageImageToLatentsInvocation"] | components["schemas"]["ZImageLatentsToImageInvocation"] | components["schemas"]["ZImageLoRACollectionLoader"] | components["schemas"]["ZImageLoRALoaderInvocation"] | components["schemas"]["ZImageModelLoaderInvocation"] | components["schemas"]["ZImagePiDDecodeInvocation"] | components["schemas"]["ZImageSeedVarianceEnhancerInvocation"] | components["schemas"]["ZImageTextEncoderInvocation"];
             /**
              * Invocation Source Id
              * @description The ID of the prepared invocation's source node
@@ -27347,6 +27353,526 @@ export type components = {
              */
             type: "metadata_to_vae";
         };
+        /**
+         * MiniMaxH3ConditioningField
+         * @description A MiniMax H3 conditioning primitive value.
+         *
+         *     H3 conditioning is the layer-50 Qwen3-VL hidden state plus the per-row modality tags the
+         *     packed-sequence layout is built from (vision-block rows are tagged as video).
+         */
+        MiniMaxH3ConditioningField: {
+            /**
+             * Conditioning Name
+             * @description The name of conditioning tensor
+             */
+            conditioning_name: string;
+        };
+        /**
+         * MiniMaxH3ConditioningOutput
+         * @description Base class for nodes that output a MiniMax H3 conditioning tensor.
+         */
+        MiniMaxH3ConditioningOutput: {
+            /** @description Conditioning tensor */
+            conditioning: components["schemas"]["MiniMaxH3ConditioningField"];
+            /**
+             * type
+             * @default minimax_h3_conditioning_output
+             * @constant
+             */
+            type: "minimax_h3_conditioning_output";
+        };
+        /**
+         * Denoise - MiniMax H3
+         * @description Run the MiniMax H3 joint audio-video denoising loop.
+         */
+        MiniMaxH3DenoiseInvocation: {
+            /**
+             * Id
+             * @description The id of this instance of an invocation. Must be unique among all instances of invocations.
+             */
+            id: string;
+            /**
+             * Is Intermediate
+             * @description Whether or not this is an intermediate invocation.
+             * @default false
+             */
+            is_intermediate?: boolean;
+            /**
+             * Use Cache
+             * @description Whether or not to use the cache
+             * @default true
+             */
+            use_cache?: boolean;
+            /**
+             * Transformer
+             * @description MiniMax H3 FL2VA transformer.
+             * @default null
+             */
+            transformer?: components["schemas"]["MiniMaxH3TransformerField"] | null;
+            /**
+             * @description Positive conditioning tensor
+             * @default null
+             */
+            positive_conditioning?: components["schemas"]["MiniMaxH3ConditioningField"] | null;
+            /**
+             * Frame Conditioning
+             * @description First/last-keyframe (VAE-latent) conditioning for MiniMax H3
+             * @default null
+             */
+            frame_conditioning?: components["schemas"]["MiniMaxH3FrameConditioningField"] | null;
+            /**
+             * Width
+             * @description Width of the generated video. H3's native canvas has a 768px short edge (max 768x1344).
+             * @default 1344
+             */
+            width?: number;
+            /**
+             * Height
+             * @description Height of the generated video.
+             * @default 768
+             */
+            height?: number;
+            /**
+             * Number of Frames
+             * @description Number of output frames at the fixed 24 fps. Must be of the form 17n+5 (5, 22, ..., 124, ...); durations must stay within 5-15 s, except exactly 5 frames for a still image.
+             * @default 124
+             */
+            num_frames?: number;
+            /**
+             * Steps
+             * @description Number of denoising steps (sigma grid points, terminal included: N steps = N-1 model evaluations).
+             * @default 50
+             */
+            steps?: number;
+            /**
+             * Seed
+             * @description Randomness seed for reproducibility.
+             * @default 0
+             */
+            seed?: number;
+            /**
+             * type
+             * @default minimax_h3_denoise
+             * @constant
+             */
+            type: "minimax_h3_denoise";
+        };
+        /**
+         * MiniMaxH3DenoiseOutput
+         * @description Joint video + audio latents from one MiniMax H3 denoise run.
+         */
+        MiniMaxH3DenoiseOutput: {
+            /** @description 5D video latents [1, 24, T_lat, H/16, W/16]. */
+            video_latents: components["schemas"]["LatentsField"];
+            /** @description Audio latents [2, 32, T_audio] (one item per stereo channel). */
+            audio_latents: components["schemas"]["LatentsField"];
+            /**
+             * Width
+             * @description Pixel width of the video latents.
+             */
+            width: number;
+            /**
+             * Height
+             * @description Pixel height of the video latents.
+             */
+            height: number;
+            /**
+             * Num Frames
+             * @description Pixel-frame count of the video latents.
+             */
+            num_frames: number;
+            /**
+             * type
+             * @default minimax_h3_denoise_output
+             * @constant
+             */
+            type: "minimax_h3_denoise_output";
+        };
+        /**
+         * MiniMaxH3FrameConditioningField
+         * @description First/last-keyframe conditioning for MiniMax H3 (FL2VA).
+         *
+         *     Carries the CLEAN (not yet noise-augmented) packed keyframe conditioning rows; the denoise
+         *     node noise-augments them to t=0.999 with the request seed's first draws. Width/height ride
+         *     along so the denoise node can reject a canvas mismatch instead of failing inside the
+         *     transformer.
+         */
+        MiniMaxH3FrameConditioningField: {
+            /**
+             * Condition Rows Name
+             * @description Name of the saved (num_condition_rows, 96) rows tensor.
+             */
+            condition_rows_name: string;
+            /**
+             * Keyframe Anchors
+             * @description Which end each keyframe anchors, in packed order ("first" / "last").
+             */
+            keyframe_anchors: string[];
+            /**
+             * Width
+             * @description Canvas width used during VAE encoding (matches denoise width).
+             */
+            width: number;
+            /**
+             * Height
+             * @description Canvas height used during VAE encoding (matches denoise height).
+             */
+            height: number;
+        };
+        /**
+         * Frame Conditioning - MiniMax H3
+         * @description VAE-encodes first/last keyframes into MiniMax H3 conditioning rows.
+         *
+         *     The rows are clean (the denoise node noise-augments them with the request seed). The same
+         *     images and width/height must also be wired to the Prompt - MiniMax H3 node: the keyframes
+         *     are part of both the packed sequence and the text conditioning.
+         */
+        MiniMaxH3FrameConditioningInvocation: {
+            /**
+             * Id
+             * @description The id of this instance of an invocation. Must be unique among all instances of invocations.
+             */
+            id: string;
+            /**
+             * Is Intermediate
+             * @description Whether or not this is an intermediate invocation.
+             * @default false
+             */
+            is_intermediate?: boolean;
+            /**
+             * Use Cache
+             * @description Whether or not to use the cache
+             * @default true
+             */
+            use_cache?: boolean;
+            /**
+             * @description Keyframe the video starts from (stretched onto the canvas).
+             * @default null
+             */
+            first_image?: components["schemas"]["ImageField"] | null;
+            /**
+             * @description Keyframe the video ends on (cover-cropped onto the canvas).
+             * @default null
+             */
+            last_image?: components["schemas"]["ImageField"] | null;
+            /**
+             * Video VAE
+             * @description VAE
+             * @default null
+             */
+            vae?: components["schemas"]["VAEField"] | null;
+            /**
+             * Width
+             * @description Target canvas width.
+             * @default 1344
+             */
+            width?: number;
+            /**
+             * Height
+             * @description Target canvas height.
+             * @default 768
+             */
+            height?: number;
+            /**
+             * type
+             * @default minimax_h3_frame_conditioning
+             * @constant
+             */
+            type: "minimax_h3_frame_conditioning";
+        };
+        /**
+         * MiniMaxH3FrameConditioningOutput
+         * @description Output of the MiniMax H3 keyframe VAE-encoder.
+         */
+        MiniMaxH3FrameConditioningOutput: {
+            /** @description First/last-keyframe (VAE-latent) conditioning for MiniMax H3 */
+            frame_conditioning: components["schemas"]["MiniMaxH3FrameConditioningField"];
+            /**
+             * type
+             * @default minimax_h3_frame_conditioning_output
+             * @constant
+             */
+            type: "minimax_h3_frame_conditioning_output";
+        };
+        /**
+         * Latents to Image - MiniMax H3
+         * @description Decode MiniMax H3 video latents and save a single frame as an image.
+         */
+        MiniMaxH3LatentsToImageInvocation: {
+            /**
+             * @description The board to save the image to
+             * @default null
+             */
+            board?: components["schemas"]["BoardField"] | null;
+            /**
+             * @description Optional metadata to be saved with the image
+             * @default null
+             */
+            metadata?: components["schemas"]["MetadataField"] | null;
+            /**
+             * Id
+             * @description The id of this instance of an invocation. Must be unique among all instances of invocations.
+             */
+            id: string;
+            /**
+             * Is Intermediate
+             * @description Whether or not this is an intermediate invocation.
+             * @default false
+             */
+            is_intermediate?: boolean;
+            /**
+             * Use Cache
+             * @description Whether or not to use the cache
+             * @default true
+             */
+            use_cache?: boolean;
+            /**
+             * @description Latents tensor
+             * @default null
+             */
+            video_latents?: components["schemas"]["LatentsField"] | null;
+            /**
+             * Video VAE
+             * @description VAE
+             * @default null
+             */
+            vae?: components["schemas"]["VAEField"] | null;
+            /**
+             * Frame Index
+             * @description Which decoded frame to save.
+             * @default 0
+             */
+            frame_index?: number;
+            /**
+             * type
+             * @default minimax_h3_latents_to_image
+             * @constant
+             */
+            type: "minimax_h3_latents_to_image";
+        };
+        /**
+         * Latents to Video - MiniMax H3
+         * @description Decode MiniMax H3 video+audio latents and encode an MP4 with an AAC stereo track.
+         */
+        MiniMaxH3LatentsToVideoInvocation: {
+            /**
+             * @description The board to save the image to
+             * @default null
+             */
+            board?: components["schemas"]["BoardField"] | null;
+            /**
+             * @description Optional metadata to be saved with the image
+             * @default null
+             */
+            metadata?: components["schemas"]["MetadataField"] | null;
+            /**
+             * Id
+             * @description The id of this instance of an invocation. Must be unique among all instances of invocations.
+             */
+            id: string;
+            /**
+             * Is Intermediate
+             * @description Whether or not this is an intermediate invocation.
+             * @default false
+             */
+            is_intermediate?: boolean;
+            /**
+             * Use Cache
+             * @description Whether or not to use the cache
+             * @default true
+             */
+            use_cache?: boolean;
+            /**
+             * @description Latents tensor
+             * @default null
+             */
+            video_latents?: components["schemas"]["LatentsField"] | null;
+            /**
+             * @description Audio latents [2, 32, T_audio] from the denoise node. Omit for a silent video.
+             * @default null
+             */
+            audio_latents?: components["schemas"]["LatentsField"] | null;
+            /**
+             * Video VAE
+             * @description VAE
+             * @default null
+             */
+            vae?: components["schemas"]["VAEField"] | null;
+            /**
+             * Audio VAE
+             * @description Audio VAE (stereo, 32 kHz) for MiniMax H3
+             * @default null
+             */
+            audio_vae?: components["schemas"]["VAEField"] | null;
+            /**
+             * type
+             * @default minimax_h3_latents_to_video
+             * @constant
+             */
+            type: "minimax_h3_latents_to_video";
+        };
+        /**
+         * Main Model - MiniMax H3
+         * @description Loads a MiniMax H3 (FL2VA) model, outputting its submodels.
+         *
+         *     All six submodels (transformer, text encoder, tokenizer, processor, video VAE, audio VAE)
+         *     come from the one diffusers-layout install; there is no component mix-and-match yet.
+         */
+        MiniMaxH3ModelLoaderInvocation: {
+            /**
+             * Id
+             * @description The id of this instance of an invocation. Must be unique among all instances of invocations.
+             */
+            id: string;
+            /**
+             * Is Intermediate
+             * @description Whether or not this is an intermediate invocation.
+             * @default false
+             */
+            is_intermediate?: boolean;
+            /**
+             * Use Cache
+             * @description Whether or not to use the cache
+             * @default true
+             */
+            use_cache?: boolean;
+            /**
+             * Model
+             * @description MiniMax H3 model (Transformer) to load
+             */
+            model: components["schemas"]["ModelIdentifierField"];
+            /**
+             * type
+             * @default minimax_h3_model_loader
+             * @constant
+             */
+            type: "minimax_h3_model_loader";
+        };
+        /**
+         * MiniMaxH3ModelLoaderOutput
+         * @description MiniMax H3 model loader output.
+         */
+        MiniMaxH3ModelLoaderOutput: {
+            /**
+             * Transformer
+             * @description MiniMax H3 FL2VA transformer
+             */
+            transformer: components["schemas"]["MiniMaxH3TransformerField"];
+            /**
+             * Qwen3-VL Encoder
+             * @description Qwen3-VL-32B tokenizer, processor and text encoder for MiniMax H3
+             */
+            text_encoder: components["schemas"]["MiniMaxH3TextEncoderField"];
+            /**
+             * Video VAE
+             * @description VAE
+             */
+            vae: components["schemas"]["VAEField"];
+            /**
+             * Audio VAE
+             * @description Audio VAE (stereo, 32 kHz) for MiniMax H3
+             */
+            audio_vae: components["schemas"]["VAEField"];
+            /**
+             * type
+             * @default minimax_h3_model_loader_output
+             * @constant
+             */
+            type: "minimax_h3_model_loader_output";
+        };
+        /**
+         * MiniMaxH3TextEncoderField
+         * @description Field for the Qwen3-VL-32B conditioner used by MiniMax H3 models.
+         *
+         *     Unlike :class:`Qwen3VLEncoderField`, H3 also needs the Qwen3VLProcessor — even for
+         *     text-only prompts (its multimodal token-type ids drive Qwen3-VL's 3D rotary layout), and
+         *     for feeding first/last keyframes to the conditioner as vision context.
+         */
+        MiniMaxH3TextEncoderField: {
+            /** @description Info to load tokenizer submodel */
+            tokenizer: components["schemas"]["ModelIdentifierField"];
+            /** @description Info to load processor submodel */
+            processor: components["schemas"]["ModelIdentifierField"];
+            /** @description Info to load text_encoder submodel */
+            text_encoder: components["schemas"]["ModelIdentifierField"];
+        };
+        /**
+         * Prompt - MiniMax H3
+         * @description Encodes a prompt (and optional first/last keyframes) for MiniMax H3.
+         *
+         *     The conditioning is Qwen3-VL-32B's *unnormalized* layer-50 hidden state. H3 is
+         *     guidance-distilled: there is no negative prompt. For first/last-frame video, the keyframes
+         *     are ALSO part of the text conditioning (a "<Picture i>: " label plus a vision block per
+         *     keyframe), so the same images must be wired here and to the Frame Conditioning node, with
+         *     the same width/height as the denoise node.
+         */
+        MiniMaxH3TextEncoderInvocation: {
+            /**
+             * Id
+             * @description The id of this instance of an invocation. Must be unique among all instances of invocations.
+             */
+            id: string;
+            /**
+             * Is Intermediate
+             * @description Whether or not this is an intermediate invocation.
+             * @default false
+             */
+            is_intermediate?: boolean;
+            /**
+             * Use Cache
+             * @description Whether or not to use the cache
+             * @default true
+             */
+            use_cache?: boolean;
+            /**
+             * Prompt
+             * @description Text prompt for MiniMax H3.
+             * @default null
+             */
+            prompt?: string | null;
+            /**
+             * Qwen3-VL Encoder
+             * @description Qwen3-VL-32B tokenizer, processor and text encoder for MiniMax H3
+             * @default null
+             */
+            text_encoder?: components["schemas"]["MiniMaxH3TextEncoderField"] | null;
+            /**
+             * @description Optional keyframe the video starts from (must match Frame Conditioning).
+             * @default null
+             */
+            first_image?: components["schemas"]["ImageField"] | null;
+            /**
+             * @description Optional keyframe the video ends on (must match Frame Conditioning).
+             * @default null
+             */
+            last_image?: components["schemas"]["ImageField"] | null;
+            /**
+             * Width
+             * @description Target canvas width.
+             * @default 1344
+             */
+            width?: number;
+            /**
+             * Height
+             * @description Target canvas height.
+             * @default 768
+             */
+            height?: number;
+            /**
+             * type
+             * @default minimax_h3_text_encoder
+             * @constant
+             */
+            type: "minimax_h3_text_encoder";
+        };
+        /**
+         * MiniMaxH3TransformerField
+         * @description Transformer field for MiniMax H3 models (FL2VA).
+         */
+        MiniMaxH3TransformerField: {
+            /** @description Info to load Transformer submodel */
+            transformer: components["schemas"]["ModelIdentifierField"];
+        };
         /**
          * MiniMaxH3VariantType
          * @description MiniMax H3 model variants (task-specific transformer checkpoints sharing every other component).
diff --git a/tests/backend/minimax_h3/test_denoise.py b/tests/backend/minimax_h3/test_denoise.py
new file mode 100644
index 00000000000..67469f9ba33
--- /dev/null
+++ b/tests/backend/minimax_h3/test_denoise.py
@@ -0,0 +1,152 @@
+"""Tests for the MiniMax H3 denoising loop, on a micro-config CPU transformer."""
+
+import pytest
+import torch
+
+from invokeai.app.services.session_processor.session_processor_common import CanceledException
+from invokeai.backend.minimax_h3.denoise import denoise
+from invokeai.backend.minimax_h3.sampling import build_denoise_state
+from invokeai.backend.minimax_h3.transformer_minimax_h3 import MiniMaxH3Transformer3DModel
+
+TINY_CONFIG = {
+    "num_attention_heads": 2,
+    "attention_head_dim": 16,
+    "hidden_size": 32,
+    "num_layers": 1,
+    "num_refiner_layers": 1,
+    "ffn_dim": 64,
+    "in_channels": 24,
+    "audio_in_channels": 32,
+    "patch_size": [1, 2, 2],
+    "text_dim": 8,
+    "freq_dim": 16,
+    "time_embed_hidden_dim": 32,
+    "time_embed_dim": 16,
+    "rope_freq_dim": 2,
+    "rope_theta": 10000.0,
+}
+
+# Grid points passed to the scheduler; the loop runs NUM_STEPS - 1 model evaluations
+# (num_inference_steps counts sigma grid points, terminal included).
+NUM_STEPS = 3
+NUM_EVALS = NUM_STEPS - 1
+TEXT_TAGS = torch.tensor([1, 1, 0], dtype=torch.long)
+
+
+@pytest.fixture(scope="module")
+def tiny_transformer() -> MiniMaxH3Transformer3DModel:
+    torch.manual_seed(0)
+    model = MiniMaxH3Transformer3DModel(**TINY_CONFIG)
+    model.eval()
+    return model
+
+
+def _state(with_keyframe: bool = False):
+    kwargs = {}
+    if with_keyframe:
+        kwargs = {"keyframe_anchors": ("first",), "clean_condition_rows": torch.zeros(4, 96)}
+    return build_denoise_state(
+        text_token_tags=TEXT_TAGS,
+        num_latent_frames=2,
+        latent_height=4,
+        latent_width=4,
+        num_audio_latents=8,
+        num_inference_steps=NUM_STEPS,
+        seed=42,
+        device=torch.device("cpu"),
+        **kwargs,
+    )
+
+
+def test_denoise_output_shapes(tiny_transformer):
+    state = _state()
+    prompt_embeds = torch.randn(1, 3, TINY_CONFIG["text_dim"], generator=torch.Generator().manual_seed(1))
+    video_rows, audio_rows = denoise(tiny_transformer, state, prompt_embeds)
+    assert video_rows.shape == (8, 96)
+    assert audio_rows.shape == (16, 32)
+    assert torch.isfinite(video_rows).all()
+    assert torch.isfinite(audio_rows).all()
+
+
+def test_conditioning_rows_survive_the_loop(tiny_transformer):
+    state = _state(with_keyframe=True)
+    anchors_before = state.video_rows[:4].clone()
+    prompt_embeds = torch.randn(1, 3, TINY_CONFIG["text_dim"], generator=torch.Generator().manual_seed(1))
+    video_rows, _ = denoise(tiny_transformer, state, prompt_embeds)
+    # The loop only writes generated rows; the noise-augmented anchors are untouched.
+    assert torch.equal(video_rows[:4], anchors_before)
+    assert not torch.equal(video_rows[4:], state.video_rows[4:] * 0)
+
+
+def test_step_callback_called_per_step(tiny_transformer):
+    state = _state()
+    calls: list[tuple[int, int]] = []
+    prompt_embeds = torch.randn(1, 3, TINY_CONFIG["text_dim"])
+    denoise(
+        tiny_transformer,
+        state,
+        prompt_embeds,
+        step_callback=lambda step, total, rows: calls.append((step, total)),
+    )
+    assert calls == [(i + 1, NUM_EVALS) for i in range(NUM_EVALS)]
+
+
+def test_cancellation_raises(tiny_transformer):
+    state = _state()
+    prompt_embeds = torch.randn(1, 3, TINY_CONFIG["text_dim"])
+    polls = iter([False, True, True, True])
+    with pytest.raises(CanceledException):
+        denoise(tiny_transformer, state, prompt_embeds, is_canceled=lambda: next(polls))
+
+
+def test_denoise_is_deterministic(tiny_transformer):
+    prompt_embeds = torch.randn(1, 3, TINY_CONFIG["text_dim"], generator=torch.Generator().manual_seed(2))
+    v1, a1 = denoise(tiny_transformer, _state(), prompt_embeds.clone())
+    v2, a2 = denoise(tiny_transformer, _state(), prompt_embeds.clone())
+    assert torch.equal(v1, v2)
+    assert torch.equal(a1, a2)
+
+
+def _layout(sequence_length: int, num_pad_rows: int):
+    from invokeai.backend.minimax_h3.packing import MiniMaxH3PackedSequence
+
+    token_tags = torch.ones(sequence_length, dtype=torch.long)
+    if num_pad_rows:
+        token_tags[-num_pad_rows:] = -1
+    empty = torch.empty(0, dtype=torch.long)
+    return MiniMaxH3PackedSequence(
+        sequence_length=sequence_length,
+        position_ids=torch.zeros(sequence_length, 3, dtype=torch.float64),
+        token_tags=token_tags,
+        video_indices=empty,
+        audio_indices=empty,
+        text_indices=empty,
+        num_condition_video_rows=0,
+        num_condition_audio_rows=0,
+    )
+
+
+def test_denoise_working_memory_estimate():
+    """Pin the reservation formula: per-row activations plus a fixed base, and the padding-mask
+    term (bool mask + SDPA's additive-copy and alignment-pad transients = 5 bytes/entry) added
+    only when padding rows exist. The band assertion sanity-checks the default linear-UI video
+    (124 frames at 768x1344 = ~38k rows): far above the small cache default, below the size of
+    the weights themselves. Change the constants deliberately - this test is meant to fail on
+    accidental drift."""
+    from invokeai.app.invocations.minimax_h3_denoise import MiniMaxH3DenoiseInvocation
+
+    estimate = MiniMaxH3DenoiseInvocation._estimate_working_memory
+
+    MB = 1024**2
+    GB = 1024**3
+    per_row_bytes = int(0.25 * MB)
+    base_bytes = 2 * GB
+
+    assert estimate(_layout(2_000, 0)) == 2_000 * per_row_bytes + base_bytes
+
+    large = estimate(_layout(38_000, 0))
+    assert large == 38_000 * per_row_bytes + base_bytes
+    assert 8 * GB < large < 20 * GB
+
+    padded = estimate(_layout(38_000, 4))
+    assert padded == large + 5 * 38_000**2
diff --git a/tests/backend/minimax_h3/test_sampling.py b/tests/backend/minimax_h3/test_sampling.py
new file mode 100644
index 00000000000..d8bbe56d3e3
--- /dev/null
+++ b/tests/backend/minimax_h3/test_sampling.py
@@ -0,0 +1,141 @@
+"""Tests for the MiniMax H3 packed-sequence math and denoise-state construction."""
+
+import pytest
+import torch
+
+from invokeai.backend.minimax_h3.packing import (
+    align_num_frames,
+    audio_latent_num_frames,
+    video_latent_num_frames,
+)
+from invokeai.backend.minimax_h3.sampling import build_denoise_state, validate_num_frames
+
+
+class TestFrameGrid:
+    def test_legal_frame_counts_pass(self):
+        # 345 = 17*20+5 (14.375 s) is the longest legal clip: 362 rounds past the 15 s cap.
+        for n in (5, 124, 141, 345):
+            validate_num_frames(n)
+
+    def test_misaligned_frame_counts_rejected(self):
+        with pytest.raises(ValueError, match="17"):
+            validate_num_frames(6)
+        with pytest.raises(ValueError, match="17"):
+            validate_num_frames(121)
+
+    def test_durations_outside_window_rejected(self):
+        # 22/39/107 frames are grid-aligned but below the 5 s floor (and not the still path).
+        for n in (22, 39, 107):
+            with pytest.raises(ValueError, match="seconds"):
+                validate_num_frames(n)
+        # 362 = 17*21+5 is aligned but 15.083 s > 15 s.
+        with pytest.raises(ValueError, match="seconds"):
+            validate_num_frames(362)
+
+    def test_still_image_minimum_allowed(self):
+        validate_num_frames(5)
+
+    def test_align_num_frames(self):
+        assert align_num_frames(121) == 124
+        assert align_num_frames(5) == 5
+        assert align_num_frames(1) == 5
+
+    def test_latent_frame_math(self):
+        assert video_latent_num_frames(5) == 2
+        assert video_latent_num_frames(124) == 37
+        with pytest.raises(ValueError):
+            video_latent_num_frames(6)
+
+    def test_audio_latent_math(self):
+        # 40 latents/s at 24 fps.
+        assert audio_latent_num_frames(24) == 40
+        assert audio_latent_num_frames(124) == round(124 / 24 * 40)
+
+
+def _build_state(seed: int, with_keyframe: bool = False):
+    kwargs = {}
+    if with_keyframe:
+        # One keyframe -> rows_per_frame = (4//2) * (4//2) = 4 condition rows of width 96.
+        kwargs = {
+            "keyframe_anchors": ("first",),
+            "clean_condition_rows": torch.zeros(4, 96),
+        }
+    return build_denoise_state(
+        text_token_tags=torch.tensor([1, 1, 0], dtype=torch.long),
+        num_latent_frames=2,
+        latent_height=4,
+        latent_width=4,
+        num_audio_latents=8,
+        num_inference_steps=4,
+        seed=seed,
+        device=torch.device("cpu"),
+        **kwargs,
+    )
+
+
+class TestDenoiseState:
+    def test_shapes_and_layout(self):
+        state = _build_state(seed=123)
+        # 2 latent frames x (4/2 * 4/2) rows/frame = 8 video rows, patch dim 24*1*2*2 = 96.
+        assert state.video_rows.shape == (8, 96)
+        # 8 audio latents x 2 stereo channels, 32 channels each.
+        assert state.audio_rows.shape == (16, 32)
+        assert state.layout.num_condition_video_rows == 0
+        assert state.layout.sequence_length == 3 + 0 + 16 + 8
+        # num_inference_steps counts sigma grid points (terminal included): N -> N-1 evals.
+        assert len(state.row_timestep_plan) == 3
+        assert len(state.timesteps) == 3
+
+    def test_noise_is_deterministic_per_seed(self):
+        a, b = _build_state(seed=7), _build_state(seed=7)
+        assert torch.equal(a.video_rows, b.video_rows)
+        assert torch.equal(a.audio_rows, b.audio_rows)
+        c = _build_state(seed=8)
+        assert not torch.equal(a.video_rows, c.video_rows)
+        assert not torch.equal(a.audio_rows, c.audio_rows)
+
+    def test_keyframe_draw_shifts_generated_noise(self):
+        # The keyframe conditioning noise is the generator's FIRST draw, so the video/audio
+        # draws of a keyframed request differ from an unkeyframed one at the same seed.
+        plain = _build_state(seed=7)
+        keyframed = _build_state(seed=7, with_keyframe=True)
+        assert keyframed.layout.num_condition_video_rows == 4
+        assert keyframed.video_rows.shape == (12, 96)  # 4 condition rows + 8 generated
+        assert not torch.equal(keyframed.video_rows[4:], plain.video_rows)
+
+    def test_condition_rows_are_noise_augmented(self):
+        keyframed = _build_state(seed=7, with_keyframe=True)
+        # Clean rows were zeros; scale_noise(x0=0, t=0.999, noise) = 0.001 * noise != 0.
+        condition = keyframed.video_rows[:4]
+        assert not torch.equal(condition, torch.zeros_like(condition))
+        assert condition.abs().max() < 0.1  # 0.1% of unit-normal noise
+
+    def test_mismatched_condition_rows_rejected(self):
+        with pytest.raises(ValueError, match="rows"):
+            build_denoise_state(
+                text_token_tags=torch.tensor([1], dtype=torch.long),
+                num_latent_frames=2,
+                latent_height=4,
+                latent_width=4,
+                num_audio_latents=8,
+                num_inference_steps=2,
+                seed=0,
+                device=torch.device("cpu"),
+                keyframe_anchors=("first",),
+                clean_condition_rows=torch.zeros(3, 96),  # wrong: layout expects 4
+            )
+
+    def test_anchors_and_rows_must_travel_together(self):
+        with pytest.raises(ValueError, match="together"):
+            build_denoise_state(
+                text_token_tags=torch.tensor([1], dtype=torch.long),
+                num_latent_frames=2,
+                latent_height=4,
+                latent_width=4,
+                num_audio_latents=8,
+                num_inference_steps=2,
+                seed=0,
+                device=torch.device("cpu"),
+                keyframe_anchors=("first",),
+                clean_condition_rows=None,
+            )