Instructions to use frankleeeee/CausalForcing-Long-Wan2.1-T2V-1.3B-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use frankleeeee/CausalForcing-Long-Wan2.1-T2V-1.3B-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("frankleeeee/CausalForcing-Long-Wan2.1-T2V-1.3B-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
CausalForcing-Long-Wan2.1-T2V-1.3B-Diffusers
Diffusers-layout conversion of the minute-level long-video Causal Forcing
checkpoint (zhuhz22/Causal-Forcing, chunkwise/longvideo.pt,
generator_ema weights) from
thu-ml/Causal-Forcing.
This checkpoint is a Rolling Forcing model (rolling-window joint denoising,
3-frame attention-sink block, 24-latent-frame KV buffer with a 21-frame
attention window) retrained from Causal Forcing's ODE initialization — upstream
adopts the TencentARC/RollingForcing
framework and only changes the ODE init. It therefore runs on the Rolling
Forcing pipeline (WanRollingForcingPipeline), not the 5-second Causal Forcing
pipeline. Non-transformer components are copied from
Wan-AI/Wan2.1-T2V-1.3B-Diffusers.
Converted with
sglang.multimodal_gen.tools.convert_forcing_to_diffusers --preset rolling-forcing
for use with the SGLang diffusion runtime:
sglang generate --model-path frankleeeee/CausalForcing-Long-Wan2.1-T2V-1.3B-Diffusers \
--prompt "A stylish woman walks down a Tokyo street..." \
--width 832 --height 480 --num-frames 321 --save-output
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Model tree for frankleeeee/CausalForcing-Long-Wan2.1-T2V-1.3B-Diffusers
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Wan-AI/Wan2.1-T2V-1.3B-Diffusers