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@@ -25,7 +25,7 @@ tags:
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  <div align="center">
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- [[πŸ€— <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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@@ -41,6 +41,7 @@ If you find VisualCloze is helpful, please consider to star ⭐ the [<strong><sp
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  ## πŸ“° News
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  - [2025-5-15] πŸ€—πŸ€—πŸ€— 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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  ## 🌠 Key Features
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  [![Huggingface VisualCloze](https://img.shields.io/static/v1?label=Demo&message=Huggingface%20Gradio&color=orange)](https://huggingface.co/spaces/VisualCloze/VisualCloze)
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- A model trained with the `resolution` of 512 is released at [Model Card](https://huggingface.co/VisualCloze/VisualClozePipeline-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](https://arxiv.org/abs/2108.01073) 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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  # 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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+ [[πŸ€— <strong><span style="color:hotpink">Diffusers</span></strong> Implementation](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/visualcloze)] &emsp; [[πŸ€— LoRA Model Card for Diffusers]](https://huggingface.co/VisualCloze/VisualClozePipeline-LoRA-384)
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  </div>
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  ## πŸ“° News
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  - [2025-5-15] πŸ€—πŸ€—πŸ€— 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] πŸ₯³πŸ₯³πŸ₯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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  ## 🌠 Key Features
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  [![Huggingface VisualCloze](https://img.shields.io/static/v1?label=Demo&message=Huggingface%20Gradio&color=orange)](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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+
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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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+
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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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+
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  # Run the pipeline
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  image_result = pipe(
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  task_prompt=task_prompt,