Image-to-Image
Diffusers
Safetensors
super-resolution
image-super-resolution
flux
lora
dpo
diffusion
Instructions to use ngoctham/ASASR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ngoctham/ASASR 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("ngoctham/ASASR") 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 Settings
- Draw Things
- Xet hash:
- b756a7c1f7258c9830381158372f926c00ff15ca1b68ca770e6025b522a89c97
- Size of remote file:
- 4.98 MB
- SHA256:
- b8599a30c27381bbcbccea313b08428e421b1555daa391ff5ed85b8257a9c660
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