feat: add MoSR upscaler support

[GabrieleLozupone]

Summary

Adds support for MoSR super-resolution models next to ESRGAN, for -M upscale, the hires fix and the server's upscale endpoint.

MoSR is a small GatedCNN-based architecture. A 4x model such as 4xNomos2_hq_mosr (17 MB, CC BY 4.0) gives results comparable to large ESRGAN models at a fraction of the cost: on an RTX 5070 Ti, a 1472x1472 image is upscaled x4 in about 3 s, against about 24 s for 4x-UltraSharp / NMKD-Superscale (ESRGAN).

• src/model/upscaler/mosr.hpp: the network, ported from the reference implementation (spandrel's mosr_arch.py). The config (scale, blocks, widths, kernel size) is detected from the weights.
• src/upscaler.cpp/.h: UpscalerGGML picks MoSR when the weights contain gblocks.0.weight, otherwise ESRGAN as before. ESRGAN behaviour is unchanged.
• --diffusion-conv-direct also applies to the MoSR runner, as it already did to ESRGAN.
• Only the PixelShuffle (ps) upsampler is supported. Weights with the gps/dys upsamplers are rejected at load time with an error.
• docs/esrgan.md: mentions MoSR.

Related Issue / Discussion

Related to the requests for more upscaler architectures: #1301, #1859.

Additional Information

Correctness. Compared against PyTorch (spandrel, fp32) on four 320x256 crops (portrait, text, landscape, skin), upscaled x4. Mean / max absolute difference in 0..255 levels:

Backend	tile 512 mean	tile 512 max
CPU	0.01–0.11	2
CUDA (RTX 5070 Ti)	0.04–0.16	3
Vulkan (RTX 5070 Ti)	0.04–0.16	2
Metal (M2 Pro)	0.01–0.11	2

With small tiles (128) a few pixels at tile seams differ by up to ~23 levels, as with ESRGAN tiling.

Speed. 1472x1472 input, x4, sd-cli -M upscale, time reported by sd-cli:

	RTX 5070 Ti CUDA	RTX 5070 Ti Vulkan	M2 Pro Metal
MoSR, tile 128	4.6 s	6.8 s	78 s
MoSR, tile 768	3.8 s	5.1 s	55 s
MoSR, tile 768, --diffusion-conv-direct	3.1 s (+1.55 GB VRAM)	3.4 s (+1.25 GB)	220 s
ESRGAN 4x-UltraSharp, tile 128	25 s

--diffusion-conv-direct helps on CUDA and Vulkan and is much slower on Metal.

ESRGAN unchanged. A Qwen Image 2.1 edit followed by an NMKD-Superscale (ESRGAN) x4 upscale gives a bit-identical image with and without this change.

Example command:

sd-cli -M upscale --upscale-model 4xNomos2_hq_mosr.pth -i input.png -o output.png --upscale-tile-size 768

Checklist

• I have read and confirmed this PR follows the contribution guidelines.

Summary by CodeRabbit

• New Features
  • Added support for upscaling with MoSR models that use PixelShuffle. The model architecture and scale are detected from the model weights.
  • MoSR models can be used for image upscaling, including tiled processing.
  • Updated the ESRGAN guide to document MoSR support and describe RGBA handling for model upscalers generally.

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• src/model/upscaler/mosr.hpp

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📝 Walkthrough

Walkthrough

The upscaler detects MoSR models from their weights and runs models that use PixelShuffle. ESRGAN remains supported. Scale lookup and tensor computation dispatch to the selected runner.

Changes

MoSR upscaler support

Layer / File(s)	Summary
MoSR configuration and blocks src/model/upscaler/mosr.hpp	Adds weight-based configuration detection and the normalization, convolution, and gated CNN blocks used by MoSR.
MoSR network and runner src/model/upscaler/mosr.hpp	Builds the MoSR network with PixelShuffle and adds runner graph construction and computation for valid configurations.
Upscaler model selection and dispatch src/upscaler.h, src/upscaler.cpp, docs/esrgan.md	Adds MoSR model detection, runner and scale dispatch, and tensor computation. The guide documents MoSR support and describes RGBA processing in model-neutral terms.

Priority: ➖ Normal

Estimated code review effort: 3 (Moderate) | ~20 minutes

Change: Feature

Sequence Diagram(s)

sequenceDiagram
  participant TensorStorage as String2TensorStorage
  participant Upscaler as UpscalerGGML
  participant Runner as MoSR
  TensorStorage->>Upscaler: Provide model tensors
  Upscaler->>Upscaler: Check for gblocks.0.weight
  Upscaler->>Runner: Initialize MoSR and detect configuration
  Runner-->>Upscaler: Provide configuration and parameter tensors
  Upscaler->>Runner: Compute input tensor
  Runner-->>Upscaler: Return output tensor

Loading

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Merge Risk | ⚪ Minimal · up to af46a

Merge Risk: ⚪ Minimal · up to af46a

Non-RGB MoSR models are rejected during loading rather than failing during upscaling. No actionable merge-blocking risk remains in the supplied evidence.

Security Architecture Review

Security architecture risk: 🟡 Moderate · up to af46a

File-derived configuration can accept incompatible dimensions that reach fatal assertions during image processing, potentially affecting the whole hosting process. Remote callers still select installed files rather than arbitrary paths, but the permissions protecting those files are not established.

Retained concerns

• Medium · security · inferred: The new metadata-driven architecture does not enforce the spatial-shape invariant required by its gated blocks. An otherwise consistent model with an even square depthwise kernel passes the configuration checks and can match registered weight shapes. Padding by kernel_size / 2 then enlarges the convolution output by one pixel, causing a native assertion at concatenation or gate multiplication for ordinary image sizes. This can turn an installed malformed model into a process-wide availability failure rather than a rejected load or failed request. Exploitation requires influencing the installed model; remote model-write authority is not established.

Security review details

Security Blast Radius

• inferred — The retained failure path affects the process loading and executing the malformed model, including other work hosted in that server process. The inspected path does not demonstrate cross-service authority, data access, or privilege escalation. Remote exploitation remains conditional on installation or modification of the model file.

Security Findings and Attack Paths

• inferred — A model supplier or actor able to modify installed weights can provide consistent even-kernel metadata. The new configuration accepts that kernel, parameter validation checks matching stored shapes rather than spatial execution invariants, and inference reaches an incompatible-shape assertion. The PR newly accepts this architecture; this is not a claim that all pre-existing model loading was hardened.

Trust Boundaries and Controls

• observed — Request control covers image content, bounded repeat count, tile size, and installed model name—not a directly supplied model path. Configuration checks and parameter-shape matching reject several incompatible models, but neither validates kernel parity. Filesystem ownership of the configured model directory remains unknown.

Resilience and Maintainability Implications

• inferred — Fresh contexts, server serialization, and ordinary cleanup provide meaningful failure containment, but native assertion failures do not follow the normal empty-result and request-error recovery path. Concurrency and repeated-load observations therefore do not justify separate active PR concerns on the inspected production paths.

Hardening Proposals

• proposed — Validate weight-derived configuration against execution invariants before network construction, including positive dimensions, odd depthwise kernel size, checked arithmetic, and explicit resource limits. Treat model-directory write ownership as a deployment trust requirement rather than relying on request-name confinement alone.

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Pre-merge checks | 4 | 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage		Docstring coverage is 3.03% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 33 functions across 3 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Title check		The title clearly and concisely describes the main change: adding MoSR upscaler support.
Description check		The description includes all required sections, explains the implementation and scope, references related issues, provides verification and performance data, and confirms the contribution checklist.
Linked Issues check		Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check		Check skipped because no linked issues were found for this pull request.

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Actionable comments posted: 1

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• 🪄 Fix CodeRabbit comments on this PR

🤖 Prompt to fix review comments

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Inline comments:
Review comments at @src/upscaler.cpp:
- Around line 116-119: Update the MoSR initialization check to reject
configurations where `mosr_upscaler->config.in_ch` is not 3, and update
`get_upscaler_model_scale` to return 0 for invalid configurations or non-RGB
MoSR models while preserving the configured scale for valid RGB models.

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📥 Commits

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📒 Files selected for processing (4)

• docs/esrgan.md
• src/model/upscaler/mosr.hpp
• src/upscaler.cpp
• src/upscaler.h

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[leejet]

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