Image-to-Image
Diffusers
Safetensors
English
image-restoration
all-in-one
diffusion
flow-matching
mllm
flux
qwen2.5-vl
siglip2
low-level-vision
Instructions to use David0219/FAPEIR_Uniworld with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use David0219/FAPEIR_Uniworld 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("David0219/FAPEIR_Uniworld", dtype=torch.bfloat16, device_map="cuda") 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
- Xet hash:
- c9ae6d7591e82072c51f8b606457124035ee198761ce08f2a385327b41e785cf
- Size of remote file:
- 1.43 kB
- SHA256:
- 060162550534e2416265da9ea57d87d3b40ae7c79d2e2519b0d3260e4adbc885
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