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
File size: 527 Bytes
5ca2324 dd05978 5ca2324 651c95b 5ca2324 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | {
"_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"
]
}
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