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: 360 Bytes
4968e7f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | {
"_class_name": "PixelFlowTransformer2DModel",
"_diffusers_version": "0.36.0",
"attention_bias": true,
"attention_head_dim": 72,
"cross_attention_dim": 2048,
"depth": 28,
"dropout": 0.0,
"in_channels": 3,
"init_weights": false,
"num_attention_heads": 16,
"num_classes": 0,
"out_channels": 3,
"patch_size": 4,
"sample_size": 1024
}
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