Instructions to use hf-internal-testing/tiny-stable-diffusion-pipe 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-pipe with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-pipe", torch_dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
File size: 566 Bytes
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"_class_name": "StableDiffusionPipeline",
"_diffusers_version": "0.4.0.dev0",
"feature_extractor": [
"transformers",
"CLIPImageProcessor"
],
"safety_checker": [
"stable_diffusion",
"FlaxStableDiffusionSafetyChecker"
],
"scheduler": [
"diffusers",
"FlaxDDIMScheduler"
],
"text_encoder": [
"transformers",
"FlaxCLIPTextModel"
],
"tokenizer": [
"transformers",
"CLIPTokenizer"
],
"unet": [
"diffusers",
"FlaxUNet2DConditionModel"
],
"vae": [
"diffusers",
"FlaxAutoencoderKL"
]
}
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