[diffusion] default 4-step inference for Flux2-Klein distilled models

[RuixiangMa]

Motivation

Flux2Klein is step-distilled and optimized for 4-step inference. This change provides the optimal default while still allowing users to specify custom step counts.

Modifications

Accuracy Tests

curl -X POST http://localhost:30010/v1/images/generations   -H "Content-Type: application/json"   -d '{
    "model": "black-forest-labs/FLUX.2-klein-4B",
    "prompt": "A futuristic cityscape with flying cars and neon lights at night",
    "width": 1024,
    "height": 1024,
    "num_images": 1,
    "response_format": "b64_json"
  }'   --max-time 300

[01-16 22:25:10] [DenoisingStage] started...
100%|████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 6.92it/s]

CUDA_VISIBLE_DEVICES=1 pytest python/sglang/multimodal_gen/test/server/test_server_a.py -k flux_2_klein_image_t2i
================================================================================================= test session starts =================================================================================================
platform linux -- Python 3.12.12, pytest-9.0.2, pluggy-1.6.0
rootdir: /home/ruixiang/sglang/python
configfile: pyproject.toml
plugins: anyio-4.12.1
collected 13 items / 12 deselected / 1 selected

python/sglang/multimodal_gen/test/server/test_server_a.py . [100%]

========================================================================================== 1 passed, 12 deselected in 44.73s ==========================================================================================

[gemini-code-assist]

Summary of Changes

Hello @RuixiangMa, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request optimizes the default configuration for Flux2-Klein distilled models by pre-setting the number of inference steps to 4. This change leverages the models' inherent optimization for 4-step inference, providing an out-of-the-box optimal experience while maintaining user flexibility for custom step counts.

Highlights

• New Sampling Parameters for Flux2-Klein Models: Introduced a new dataclass, Flux2KleinSamplingParams, which inherits from FluxSamplingParams.
• Default 4-Step Inference: The Flux2KleinSamplingParams class sets num_inference_steps to 4 by default, aligning with the optimal inference steps for step-distilled Flux2-Klein models.
• Registry Update: The registry.py file has been updated to use Flux2KleinSamplingParams when registering the Flux2KleinPipelineConfig, ensuring the new default is applied.

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[gemini-code-assist]

Code Review

This pull request correctly updates the default inference steps for Flux2-Klein distilled models to 4, which is their optimal setting. The changes are implemented by introducing a new Flux2KleinSamplingParams class and applying it in the model registry. The approach is clean and effective. I have one minor suggestion to improve code style consistency.

[mickqian]

Please add a testcase to testcase_configs.py and add a baseline

[RuixiangMa]

Please add a testcase to testcase_configs.py and add a baseline

done

[mickqian]

/tag-and-rerun-ci

[RuixiangMa]

CI failure is unrelated to the commit

[adarshxs]

/rerun-failed-ci