Instructions to use Stable-X/yoso-normal-v0-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Stable-X/yoso-normal-v0-3 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("Stable-X/yoso-normal-v0-3", 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
Download model_index.json from Stable-X/yoso-normal-v0-3: direct link, hf CLI and curl.
- Browser
- Download file 668 Bytes
-
https://huggingface.co/Stable-X/yoso-normal-v0-3/resolve/main/model_index.json
- Command line
-
hf download hf://Stable-X/yoso-normal-v0-3/model_index.json
-
curl -L -o model_index.json https://huggingface.co/Stable-X/yoso-normal-v0-3/resolve/main/model_index.json
668 Bytes
| { | |
| "_class_name": "YOSONormalsPipeline", | |
| "_diffusers_version": "0.28.0", | |
| "_name_or_path": "weights/yoso-normal-v0-3", | |
| "controlnet": [ | |
| "controlnetvae", | |
| "ControlNetVAEModel" | |
| ], | |
| "feature_extractor": [ | |
| null, | |
| null | |
| ], | |
| "image_encoder": [ | |
| null, | |
| null | |
| ], | |
| "requires_safety_checker": true, | |
| "safety_checker": [ | |
| null, | |
| null | |
| ], | |
| "scheduler": [ | |
| "diffusers", | |
| "PNDMScheduler" | |
| ], | |
| "text_encoder": [ | |
| "transformers", | |
| "CLIPTextModel" | |
| ], | |
| "tokenizer": [ | |
| "transformers", | |
| "CLIPTokenizer" | |
| ], | |
| "unet": [ | |
| "diffusers", | |
| "UNet2DConditionModel" | |
| ], | |
| "vae": [ | |
| "diffusers", | |
| "AutoencoderKL" | |
| ] | |
| } | |