Instructions to use jdp8/Stable-Diffusion-3.5-Small-Preview1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jdp8/Stable-Diffusion-3.5-Small-Preview1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("jdp8/Stable-Diffusion-3.5-Small-Preview1", 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
- Draw Things
- DiffusionBee
File size: 598 Bytes
512c982 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | {
"_class_name": "StableDiffusion3Pipeline",
"_diffusers_version": "0.29.0.dev0",
"scheduler": ["diffusers", "FlowMatchEulerDiscreteScheduler"],
"text_encoder": ["transformers", "CLIPTextModelWithProjection"],
"text_encoder_2": ["transformers", "CLIPTextModelWithProjection"],
"text_encoder_3": ["transformers", "T5EncoderModel"],
"tokenizer": ["transformers", "CLIPTokenizer"],
"tokenizer_2": ["transformers", "CLIPTokenizer"],
"tokenizer_3": ["transformers", "T5TokenizerFast"],
"transformer": ["diffusers", "SD3Transformer2DModel"],
"vae": ["diffusers", "AutoencoderKL"]
}
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