Text-to-Image
Diffusers
Safetensors
English
image-generation
class-conditional
imagenet
pixelflow
flow-matching
Instructions to use BiliSakura/PixelFlow-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BiliSakura/PixelFlow-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/PixelFlow-diffusers", dtype=torch.bfloat16, device_map="cuda") prompt = "golden retriever" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
File size: 985 Bytes
098ef8f | 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 31 32 33 34 35 36 37 38 39 | #!/usr/bin/env python3
"""Generate a demo image with PixelFlow-T2I."""
from pathlib import Path
import torch
from diffusers import DiffusionPipeline
REPO_ROOT = Path(__file__).resolve().parent
MODEL_DIR = REPO_ROOT / "PixelFlow-T2I"
OUTPUT_PATH = REPO_ROOT / "PixelFlow-T2I" / "demo.png"
def main() -> None:
pipe = DiffusionPipeline.from_pretrained(
str(MODEL_DIR),
local_files_only=True,
custom_pipeline=str(MODEL_DIR / "pipeline.py"),
trust_remote_code=True,
torch_dtype=torch.bfloat16,
)
pipe.to("cuda")
generator = torch.Generator(device="cuda").manual_seed(42)
image = pipe(
prompt="A golden retriever playing in a sunny garden",
height=1024,
width=1024,
num_inference_steps=[10, 10, 10, 10],
guidance_scale=4.0,
generator=generator,
).images[0]
image.save(OUTPUT_PATH)
print(f"Saved demo image to {OUTPUT_PATH}")
if __name__ == "__main__":
main()
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