Instructions to use diffusers-internal-dev/chronoedit-modular with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use diffusers-internal-dev/chronoedit-modular with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("diffusers-internal-dev/chronoedit-modular", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Commit History
-
Update README.md
Verified signature428d55f -
Upload folder using huggingface_hub
Verified signaturefd6ab1f -
Update modular_blocks.py
Verified signaturec88a78a -
Upload ChronoEditBlocks
Verified signatureb856b9b -
Upload WanModularPipeline
Verified signature92b0f7e -
Upload before_denoise.py with huggingface_hub
Verified signature52abadf -
Upload inputs.py with huggingface_hub
Verified signaturefc56851 -
Upload demo.png with huggingface_hub
Verified signature2e63ca7 -
Upload denoise.py with huggingface_hub
Verified signaturee9b4bd3 -
Upload encoders.py with huggingface_hub
Verified signature1b761f5 -
Upload __init__.py with huggingface_hub
Verified signature0afe5e3 -
Upload modular_blocks.py with huggingface_hub
Verified signature7bda6d1 -
Upload example.py with huggingface_hub
Verified signatureb0dfa4c -
Upload decoders.py with huggingface_hub
Verified signature1a0cfc6 -
initial commit
Verified signature65b9422