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: 366 Bytes
4968e7f 098ef8f 4968e7f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | {
"_class_name": "PixelFlowT2IPipeline",
"_diffusers_version": "0.36.0",
"scheduler": [
"scheduling_pixelflow",
"PixelFlowScheduler"
],
"transformer": [
"transformer_pixelflow",
"PixelFlowTransformer2DModel"
],
"text_encoder": [
"transformers",
"T5EncoderModel"
],
"tokenizer": [
"transformers",
"T5Tokenizer"
]
}
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