[PrakshaaleJain]

What does this PR do?

This PR adds support for the Comfy Quant quantization toolkit by integrating its inference engine, Comfy-Kitchen, directly into Diffusers library.

Key additions:

• Added ComfyQuantConfig and ComfyQuantizer to handle wrapping of tensors.
• Safely intercepts weights via create_quantized_param and wraps them via QuantizedTensor.from_float to dispatch to Comfy-Kitchen's optimized GPU kernels.
• Implemented robust format validation and auto-resolution of standard layout classes (fp8, int8, mxfp8, nvfp4, int4_svd, int4_awq).
• Added full unit test coverage under tests/quantization/comfy_quant/.

Fixes #14705

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AI Agent Self-Review Notes

Agent: Antigravity (Google DeepMind)
Review Focus: Diffusers Quantization Standards & Guidelines

1. Backwards Compatibility:
   • Status: Passed. The ComfyQuantizer relies on is_comfy_kitchen_available(). If the backend is not installed, Diffusers will safely fall back to standard behavior or fail gracefully if ComfyQuantConfig is passed explicitly without the toolkit.
2. Coding Style & Formatting:
   • Status: Passed. The code was fully run through make style && make fix-copies. utils/check_dummies.py successfully generated dummy_comfy_kitchen_objects.py and correctly registered the imports in `diffusers/__init
3. Testing:
   • Status: Passed. Added tests/quantization/comfy_quant/test_comfy_quant.py. Tests cover both valid format wrapping (FP8 mapping to TensorCoreFP8Layout) and error handling for unsupported formats. Tests ran locally and passed with 100% success.

[PrakshaaleJain]

Hi @DN6, please have a review for the PR.

[PrakshaaleJain]

Hi @DN6, I have made the changes as per the reviews. Please help it to get merged.

[PrakshaaleJain]

Hi @DN6, I have made changes as your latest reviews and help me merge this PR.

[DN6]

@PrakshaaleJain have you tested loading with a real checkpoint? After going through the proposed design again, I do not believe it will work.

[PrakshaaleJain]

Yeah, I haven't check with a real checkpoint yet. I 'll setup a test with an actual checkpoint to confirm all work as expected. I'll push an update once I've verified it.

[DN6]

@PrakshaaleJain Gentle ping.

[PrakshaaleJain]

wip

[PrakshaaleJain]

Hi @DN6, Key features and fixes from the previous implementation:

• Robust State Dict buffering: Handles extreme safetensors sharding by caching components (data, scale, orig_shape) across shards until ck_tensor.QuantizedTensor objects can be safely reconstructed.
• Direct PyTorch Parameter Integration: Wraps all quantized weights inside torch.nn.Parameter to ensure autograd and standard module iteration (e.g., model.parameters()) function natively.
• Disabled serialization with a clear logger warning to prevent silent data corruption, as safetensors currently strips subclass metadata (scales) on export.
• Strict gating to ensure only nn.Linear weights are converted, safely bypassing Conv2d and normalization layers.
• Verified correct .dequantize() implementation for downstream unpacks.

[PrakshaaleJain]

HI @DN6, please have a review of the changes for Comfy-Quant.

[iwr-redmond]

Are you able to post some sample generations and test code?

[PrakshaaleJain]

Are you able to post some sample generations and test code?

Yes will do that. No Problem.

[PrakshaaleJain]

Hi @DN6, I hope you see this and help me on getting the PR merged.

[mingyi456]

@PrakshaaleJain Other people have requested that you post sample testing code and generation results, but I do not see anything here. I am rather interested in this feature being implemented myself, so I decided to pull this into a local branch and test it myself.

• Implemented robust format validation and auto-resolution of standard layout classes (fp8, int8, mxfp8, nvfp4, int4_svd, int4_awq).

Immediately, these 2 Z Image Turbo quants, the first being int8_convrot and the latter being nvfp4, both fail to even load. The nvfp4 quant is technically not your fault since the failing part is not in your code, but still your claims made in the PR are definitely unverified.

• Status: Passed. Added tests/quantization/comfy_quant/test_comfy_quant.py. Tests cover both valid format wrapping (FP8 mapping to TensorCoreFP8Layout) and error handling for unsupported formats. Tests ran locally and passed with 100% success.

I plan to test this with Flux.2 Klein 4B FP8 and an FP8 quant of ZIT, will update soon.

[mingyi456]

This fp8 quant of ZIT also fails. This quant also fails.