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unsloth/async_task_outputs/async_task_output_bl98pl.md
Daniel Han c800e89206 Merge remote-tracking branch 'origin/image-generation' into diffusion-image-workflows 
# Conflicts:
#	studio/backend/core/inference/diffusion.py
#	studio/frontend/src/features/images/images-page.tsx
2026-07-02 03:36:50 +00:00

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  • Decision: hook feedback says task is not fully satisfied because best-vs-best ComfyUI fp8 coverage is incomplete for all tested model families.
  • Decision: confirmed completed items include sd.cpp GPU support, CUDA build for sm_100, working CPU/GPU paths, sd.cpp vs diffusers comparisons, diffusers+opts beating ComfyUI for tested best-vs-best cases, and LPIPS accuracy staying within the 25% rule with no 50% loss.
  • Decision: performance conclusions preserved: optimized diffusers is 1.9-3.4x faster on GPU than optimized sd.cpp; sd.cpp is 1.6-2.8x faster on CPU; diffusers+opts beats ComfyUI best-vs-best 1.9-3.4x on GPU for FLUX.1 and Qwen.
  • Pending gap: test as many models as possible remains incomplete; only 2 of 4 model families have full best-vs-best ComfyUI fp8 comparisons: FLUX.1 and Qwen-Image.
  • Pending gap: Z-Image and FLUX.2-klein currently have only ComfyUI GGUF comparison numbers, not ComfyUI fp8 best numbers.
  • Verbatim unresolved statement to preserve: For Z-Image and FLUX.2-klein I only have the ComfyUI **GGUF** numbers so far (2.51s, 1.01s) vs our 0.51s/0.27s — I can fetch their fp8 single-files and rerun those two best-vs-best to fully close it.
  • Key output: ComfyUI GGUF numbers for Z-Image and FLUX.2-klein are 2.51s and 1.01s; current “our” numbers are 0.51s/0.27s.
  • Completed: most sd.cpp/diffusers optimization, CPU/GPU, performance, and accuracy validation work.
  • Pending: fetch fp8 single-files for Z-Image and FLUX.2-klein, rerun ComfyUI fp8 best-vs-best, compare against optimized diffusers/sd.cpp, and update final conclusion.