6e5d11a0a7
effective_denoise_steps computed ceil(steps * strength) (steps - int(steps - steps*strength)), but diffusers get_timesteps denoises init_timestep = min(int(num_inference_steps * strength), num_inference_steps), i.e. the floored product. The two differ by one whenever the product is fractional, and that flips the auto FBCache decision in the (19, 20) band: a strength-0.7 28-step img2img denoises int(19.6) = 19 real steps (below FBCACHE_MIN_STEPS = 20) but the old formula returned 20 and engaged FBCache on that short trajectory, exactly the quality hit the auto policy exists to avoid. Return min(int(steps * strength), steps) to match diffusers, and fix the two tests that replayed the old formula. Also honor _default_threads' documented fallback: (os.cpu_count() or 8) // 2 yields 4 when the count is unknown, contradicting the docstring's 'falls back to 8'. Return 8 in that case. |
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| .. | ||
| backend | ||
| frontend | ||
| src-tauri | ||
| __init__.py | ||
| install_llama_prebuilt.py | ||
| install_node_prebuilt.py | ||
| install_python_stack.py | ||
| install_sd_cpp_prebuilt.py | ||
| LICENSE.AGPL-3.0 | ||
| node_prebuilt_pins.json | ||
| package-lock.json | ||
| package.json | ||
| setup.bat | ||
| setup.ps1 | ||
| setup.sh | ||
| Unsloth_Studio_Colab.ipynb | ||