Video-to-Video
Diffusers
Safetensors
English
JoyVideoEditPipeline
video
video-editing
reference-image-guided
autoregressive-diffusion
Instructions to use jdopensource/JoyAI-Video-Edit-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jdopensource/JoyAI-Video-Edit-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("jdopensource/JoyAI-Video-Edit-Diffusers", 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
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scheduler: shift=5.159 to match the deployed denoise schedule
The serving stack constructs FlowMatchDiscreteScheduler(shift=5.159) (deploy/xvideo/config.py), giving 2-step sigmas [1.0, 0.8376, 0.0]. With shift=1.0 the pipeline denoised along [1.0, 0.5, 0.0] — a schedule the distilled checkpoint was never trained on. Verified: with shift=5.159 the JoyVideoEditPipeline sigma/timestep trajectory (raw linspace sigmas -> config-shift in set_timesteps) matches the deployment scheduler exactly. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>2f43eeb
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Upload Diffusers-format 0811 checkpoint
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initial commit
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