Unconditional Image Generation
Diffusers
Safetensors
English
sit
image-generation
class-conditional
imagenet
Instructions to use BiliSakura/SiT-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BiliSakura/SiT-diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("BiliSakura/SiT-diffusers", 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
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Download SiT-XL-2-512/README.md from BiliSakura/SiT-diffusers: direct link, hf CLI and curl.
- Browser
- Download file 945 Bytes
-
https://huggingface.co/BiliSakura/SiT-diffusers/resolve/main/SiT-XL-2-512/README.md
- Command line
-
hf download hf://BiliSakura/SiT-diffusers/SiT-XL-2-512/README.md
-
curl -L -o README.md https://huggingface.co/BiliSakura/SiT-diffusers/resolve/main/SiT-XL-2-512/README.md
945 Bytes
metadata
library_name: diffusers
pipeline_tag: unconditional-image-generation
tags:
- diffusers
- sit
- image-generation
- class-conditional
inference: true
SiT-XL-2-512
Self-contained Diffusers checkpoint repo for SiT.
Demo
Usage
import torch
from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained("./").to("cuda" if torch.cuda.is_available() else "cpu")
generator = torch.Generator(device=pipe.device).manual_seed(0)
image = pipe(
class_labels=207,
height=512,
width=512,
num_inference_steps=250,
guidance_scale=4.0,
generator=generator,
).images[0]
image.save("demo.png")
Components
pipeline.pytransformer/transformer_sit.pyscheduler/scheduling_flow_match_sit.pytransformer/diffusion_pytorch_model.safetensorsvae/diffusion_pytorch_model.safetensors
