Text-to-Image
Diffusers
Safetensors
English
image-generation
class-conditional
imagenet
pixeldit
flow-matching
pixel-space
dit
Instructions to use BiliSakura/PixelDiT-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BiliSakura/PixelDiT-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/PixelDiT-diffusers", dtype=torch.bfloat16, device_map="cuda") prompt = "A golden retriever playing in a sunny garden" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
| { | |
| "_class_name": [ | |
| "pipeline", | |
| "PixelDiTT2IPipeline" | |
| ], | |
| "_diffusers_version": "0.35.1", | |
| "scheduler": [ | |
| "diffusers", | |
| "FlowMatchEulerDiscreteScheduler" | |
| ], | |
| "transformer": [ | |
| "transformer_pixeldit_t2i", | |
| "PixelDiTT2ITransformer2DModel" | |
| ], | |
| "text_encoder": [ | |
| "transformers", | |
| "Gemma2Model" | |
| ], | |
| "tokenizer": [ | |
| "transformers", | |
| "GemmaTokenizerFast" | |
| ] | |
| } | |