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README.md
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<div align="center">
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[[π€ <strong><span style="color:hotpink">Diffusers</span></strong> Implementation](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/visualcloze)]
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</div>
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## π° News
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- [2025-5-15] π€π€π€ VisualCloze has been merged into the [<strong><span style="color:hotpink">official pipelines of diffusers</span></strong>](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/visualcloze).
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## π Key Features
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[](https://huggingface.co/spaces/VisualCloze/VisualCloze)
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while this model uses the `resolution` of 384. The `resolution` means that each image will be resized to it before being
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concatenated to avoid the out-of-memory error. To generate high-resolution images, we use the
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#### Example with Depth-to-Image:
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pipe = VisualClozePipeline.from_pretrained("VisualCloze/VisualClozePipeline-384", resolution=384, torch_dtype=torch.bfloat16)
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pipe.to("cuda")
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# Run the pipeline
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image_result = pipe(
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task_prompt=task_prompt,
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pipe = VisualClozePipeline.from_pretrained("VisualCloze/VisualClozePipeline-384", resolution=384, torch_dtype=torch.bfloat16)
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pipe.to("cuda")
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# Run the pipeline
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image_result = pipe(
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task_prompt=task_prompt,
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<div align="center">
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[[π€ <strong><span style="color:hotpink">Diffusers</span></strong> Implementation](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/visualcloze)]   [[π€ LoRA Model Card for Diffusers]](https://huggingface.co/VisualCloze/VisualClozePipeline-LoRA-384)
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</div>
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## π° News
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- [2025-5-15] π€π€π€ VisualCloze has been merged into the [<strong><span style="color:hotpink">official pipelines of diffusers</span></strong>](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/visualcloze).
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- [2025-5-18] π₯³π₯³π₯³ We have released the LoRA weights supporting diffusers at [LoRA Model Card 384](https://huggingface.co/VisualCloze/VisualClozePipeline-LoRA-384) and [LoRA Model Card 512](https://huggingface.co/VisualCloze/VisualClozePipeline-LoRA-512).
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## π Key Features
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[](https://huggingface.co/spaces/VisualCloze/VisualCloze)
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This model provides the full parameters of our VisualCloze.
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If you find the download size too large, you can use the [LoRA version](https://huggingface.co/VisualCloze/VisualClozePipeline-LoRA-384)
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with the FLUX.1-Fill-dev as the base model.
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A model trained with the `resolution` of 512 is released at [Full Model Card 512](https://huggingface.co/VisualCloze/VisualClozePipeline-512) and [LoRA Model Card 512](https://huggingface.co/VisualCloze/VisualClozePipeline-LoRA-512),
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while this model uses the `resolution` of 384. The `resolution` means that each image will be resized to it before being
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concatenated to avoid the out-of-memory error. To generate high-resolution images, we use the SDEdit technology for upsampling the generated results.
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#### Example with Depth-to-Image:
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pipe = VisualClozePipeline.from_pretrained("VisualCloze/VisualClozePipeline-384", resolution=384, torch_dtype=torch.bfloat16)
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pipe.to("cuda")
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# Loading the VisualClozePipeline via LoRA
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# pipe = VisualClozePipeline.from_pretrained("black-forest-labs/FLUX.1-Fill-dev", resolution=384, torch_dtype=torch.bfloat16)
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# pipe.load_lora_weights('VisualCloze/VisualClozePipeline-LoRA-384', weight_name='visualcloze-lora-384.safetensors')
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# pipe.to("cuda")
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# Run the pipeline
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image_result = pipe(
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task_prompt=task_prompt,
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pipe = VisualClozePipeline.from_pretrained("VisualCloze/VisualClozePipeline-384", resolution=384, torch_dtype=torch.bfloat16)
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pipe.to("cuda")
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# Loading the VisualClozePipeline via LoRA
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# pipe = VisualClozePipeline.from_pretrained("black-forest-labs/FLUX.1-Fill-dev", resolution=384, torch_dtype=torch.bfloat16)
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# pipe.load_lora_weights('VisualCloze/VisualClozePipeline-LoRA-384', weight_name='visualcloze-lora-384.safetensors')
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# pipe.to("cuda")
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# Run the pipeline
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image_result = pipe(
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task_prompt=task_prompt,
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