Instructions to use frankleeeee/CausalForcing-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-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-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-Wan2.1-T2V-1.3B-Diffusers
Diffusers-layout conversion of the chunk-wise Causal Forcing generator
(zhuhz22/Causal-Forcing, chunkwise/causal_forcing.pt, generator weights)
from thu-ml/Causal-Forcing —
"Causal Forcing: Autoregressive Diffusion Distillation Done Right" (ICML 2026).
Wan2.1-T2V-1.3B backbone, block-causal DMD student: 3-latent-frame chunks,
21-latent-frame sliding attention window, 4 warped denoising steps
(1000/750/500/250, shift 5.0), no CFG. 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 causal-forcing-chunkwise
for use with the SGLang diffusion runtime:
sglang generate --model-path frankleeeee/CausalForcing-Wan2.1-T2V-1.3B-Diffusers \
--prompt "A stylish woman walks down a Tokyo street..." \
--width 832 --height 480 --num-frames 81 --save-output
Note: the upstream project releases only Wan 1.3B generators; the
Wan2.1-T2V-14B named in its configs is the DMD teacher (real_name), not a
released generator.
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Wan-AI/Wan2.1-T2V-1.3B-Diffusers