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
| { | |
| "_class_name": "PixelFlowT2IPipeline", | |
| "_diffusers_version": "0.36.0", | |
| "scheduler": [ | |
| "scheduling_pixelflow", | |
| "PixelFlowScheduler" | |
| ], | |
| "transformer": [ | |
| "transformer_pixelflow", | |
| "PixelFlowTransformer2DModel" | |
| ], | |
| "text_encoder": [ | |
| "transformers", | |
| "T5EncoderModel" | |
| ], | |
| "tokenizer": [ | |
| "transformers", | |
| "T5Tokenizer" | |
| ] | |
| } | |