Instructions to use hf-internal-testing/tiny-stable-diffusion-pix2pix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hf-internal-testing/tiny-stable-diffusion-pix2pix 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("hf-internal-testing/tiny-stable-diffusion-pix2pix", 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
| { | |
| "_class_name": "StableDiffusionInstructPix2PixPipeline", | |
| "_diffusers_version": "0.4.0.dev0", | |
| "feature_extractor": [ | |
| "transformers", | |
| "CLIPFeatureExtractor" | |
| ], | |
| "safety_checker": [null, null], | |
| "scheduler": [ | |
| "diffusers", | |
| "FlaxDDIMScheduler" | |
| ], | |
| "text_encoder": [ | |
| "transformers", | |
| "FlaxCLIPTextModel" | |
| ], | |
| "tokenizer": [ | |
| "transformers", | |
| "CLIPTokenizer" | |
| ], | |
| "unet": [ | |
| "diffusers", | |
| "FlaxUNet2DConditionModel" | |
| ], | |
| "vae": [ | |
| "diffusers", | |
| "FlaxAutoencoderKL" | |
| ] | |
| } | |