Instructions to use biali/texture-diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use biali/texture-diffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("biali/texture-diffusion", dtype=torch.bfloat16, device_map="cuda") prompt = "pbr brick wall" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download model_index.json from biali/texture-diffusion: direct link, hf CLI and curl.
- Browser
- Download file 462 Bytes
-
https://huggingface.co/biali/texture-diffusion/resolve/main/model_index.json
- Command line
-
hf download hf://biali/texture-diffusion/model_index.json
-
curl -L -o model_index.json https://huggingface.co/biali/texture-diffusion/resolve/main/model_index.json
462 Bytes
| { | |
| "_class_name": "StableDiffusionPipeline", | |
| "_diffusers_version": "0.8.0", | |
| "safety_checker": [ | |
| null, | |
| null | |
| ], | |
| "scheduler": [ | |
| "diffusers", | |
| "DDIMScheduler" | |
| ], | |
| "text_encoder": [ | |
| "transformers", | |
| "CLIPTextModel" | |
| ], | |
| "tokenizer": [ | |
| "transformers", | |
| "CLIPTokenizer" | |
| ], | |
| "unet": [ | |
| "diffusers", | |
| "UNet2DConditionModel" | |
| ], | |
| "vae": [ | |
| "diffusers", | |
| "AutoencoderKL" | |
| ], | |
| "requires_safety_checker": false | |
| } | |