Image-to-Image
Diffusers
Safetensors
VisualClozePipeline
text-to-image
flux
lora
in-context-learning
universal-image-generation
ai-tools
Instructions to use VisualCloze/VisualClozePipeline-384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use VisualCloze/VisualClozePipeline-384 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fill-in-base-model", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("VisualCloze/VisualClozePipeline-384") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things

- Xet hash:
- d651726a09bca8f421ff135f466c61f3c066bb252a4d11defc23bcd1b31d0a5b
- Size of remote file:
- 190 kB
- SHA256:
- d64c6b66aa944e5c46b7da82c5ad70f302a13ce97badf0347547d8506275514d
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