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:
- 4479aac938c224dbaef8d126dc178a0650d09140dcc46885be6d7c72bb6f176f
- Size of remote file:
- 168 MB
- SHA256:
- f5b59a26851551b67ae1fe58d32e76486e1e812def4696a4bea97f16604d40a3
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