Keep selected previews and refresh packaged documentation
Browse files- README.md +2 -2
- SHA256SUMS +6 -7
- csa-inpainting-source.tar.gz +2 -2
- csa_inpainting-0.3.0-py3-none-any.whl +0 -0
- examples/README.md +2 -2
- release-manifest.json +9 -13
- source-manifest.json +2 -2
README.md
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<img src="https://huggingface.co/KumaPower/CSA/resolve/main/preview.jpg" alt="Actual CSA completions: masked input, restored image, and original image." width="960">
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<p><sub>Stage 2 · step 97,397. Columns: masked input · restored · original.<br>
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Selected examples
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</div>
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The portable artifact is for inference. It contains no optimizer or discriminator state and **cannot resume training**. The full training checkpoint is preserved separately and is not included in this release; fresh training is documented in the guide.
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The model retains width 64, depth 8, and a `tanh` refinement head. Export preserves all generator tensors; see [export verification](export-verification.json), [software validation](validation.json), and [checksums](SHA256SUMS). The [evaluation record](evaluation.json) and [
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## Citation and license
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<img src="https://huggingface.co/KumaPower/CSA/resolve/main/preview.jpg" alt="Actual CSA completions: masked input, restored image, and original image." width="960">
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<p><sub>Stage 2 · step 97,397. Columns: masked input · restored · original.<br>
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Selected examples.</sub></p>
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</div>
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The portable artifact is for inference. It contains no optimizer or discriminator state and **cannot resume training**. The full training checkpoint is preserved separately and is not included in this release; fresh training is documented in the guide.
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The model retains width 64, depth 8, and a `tanh` refinement head. Export preserves all generator tensors; see [export verification](export-verification.json), [software validation](validation.json), and [checksums](SHA256SUMS). The [evaluation record](evaluation.json) and [selected preview](preview.jpg) preserve the selected checkpoint's evidence.
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## Citation and license
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SHA256SUMS
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examples/README.md
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The reference was generated on an H100 with FP32 inference. Floating-point
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differences across devices or PyTorch versions can affect final quantization.
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Known pixels are composited from the input. Evaluation details and
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The reference was generated on an H100 with FP32 inference. Floating-point
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differences across devices or PyTorch versions can affect final quantization.
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examples are in [the release notes](../README.md).
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