Text-to-Video
Diffusers
Diffusion Single File
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t2v
video generation
comfyui
distillation
LoRA
Instructions to use lightx2v/Wan2.1-T2V-14B-StepDistill-CfgDistill with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lightx2v/Wan2.1-T2V-14B-StepDistill-CfgDistill with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("lightx2v/Wan2.1-T2V-14B-StepDistill-CfgDistill", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Diffusion Single File
How to use lightx2v/Wan2.1-T2V-14B-StepDistill-CfgDistill with Diffusion Single File:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Is this making the diffusion faster?
#8
by AgustinCaniglia - opened
I don't mean in relation to the number of steps, but for each step. Or is it just coincidence on my current configuration? Because I am using this lora and it's going so fast even in 1280x720... And the quality is amazing even with 3 steps for most videos. I guess if I use this I should0t use CauseVid right? WIth what can we mix it though? I am using it on the i2v 720 14B wan base model.