A SafeTensors conversion of an anime- and manga-finetuned Big-LaMa model. It fills masked image regions and is intended for removing text or other localized content from illustrated images.
FFCResNetGeneratorRGB image + binary mask)The published checkpoint was described as fine-tuned on approximately 300,000 manga- and anime-style images and as an improvement over the older lama_mpe model.
lama-manga.safetensors: 989 tensors converted from the original TorchScript checkpointconfig.json: architecture and preprocessing metadataanime-manga-big-lama.ptadvimman/lamaThe conversion removes only the TorchScript wrapper prefix (model.generator.); tensor shapes, dtypes, and values are preserved.
Use this model for masked inpainting of manga, anime, and comic artwork. Masks should identify pixels to replace. Results depend on mask quality and surrounding context, and the model can invent textures or line art. It should not be used where reconstructed pixels must be treated as factual source content. No evaluation metrics or training dataset artifacts are included here.
MIT, as declared by this model repository. Review the LaMa implementation and source checkpoint terms for downstream redistribution.
A SafeTensors conversion of an anime- and manga-finetuned Big-LaMa model. It fills masked image regions and is intended for removing text or other localized content from illustrated images.
FFCResNetGeneratorRGB image + binary mask)The published checkpoint was described as fine-tuned on approximately 300,000 manga- and anime-style images and as an improvement over the older lama_mpe model.
lama-manga.safetensors: 989 tensors converted from the original TorchScript checkpointconfig.json: architecture and preprocessing metadataanime-manga-big-lama.ptadvimman/lamaThe conversion removes only the TorchScript wrapper prefix (model.generator.); tensor shapes, dtypes, and values are preserved.
Use this model for masked inpainting of manga, anime, and comic artwork. Masks should identify pixels to replace. Results depend on mask quality and surrounding context, and the model can invent textures or line art. It should not be used where reconstructed pixels must be treated as factual source content. No evaluation metrics or training dataset artifacts are included here.
MIT, as declared by this model repository. Review the LaMa implementation and source checkpoint terms for downstream redistribution.