Instructions to use madtune/pixeldit-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use madtune/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("nvidia/PixelDiT-1300M-1024px", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("madtune/pixeldit-diffusers") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
File size: 389 Bytes
491af34 26319e9 491af34 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | {
"_class_name": "PixelDiTPipeline",
"_diffusers_version": "0.38.0",
"_name_or_path": "madtune/pixeldit-diffusers",
"scheduler": [
"diffusers",
"FlowMatchEulerDiscreteScheduler"
],
"text_encoder": [
"transformers",
"Gemma2Model"
],
"tokenizer": [
"transformers",
"TokenizersBackend"
],
"transformer": [
"pixeldit",
"PixelDiTModel"
]
}
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