工作流 /
视频 /
因果驱动：图生视频
因果驱动：图生视频
Causal Forcing: Image to Video
输入文本提示词并选择一个模型检查点。使用因果强制或因果强制++，通过1到4个推理步骤生成高质量视频。
视频
5 个节点 1 个输入 70 次使用 ComfyUI ≥ 0.21.0 ComfyUI 44 KB
需要的权重
这个工作流引用了 3 个权重文件，其中 3 个能在本站模型库里找到。
causal_forcing-framewise.safetensors
在模型库查看
umt5_xxl_fp8_e4m3fn_scaled.safetensors
在模型库查看
wan_2.1_vae.safetensors
在模型库查看
节点构成
MarkdownNote
× 2
SaveVideo
× 1
LoadImage
× 1
96ba6b5d-dd48-49b3-84c3-5b86eafc2a07
× 1
官方标注的模型
Wan2.1
工作流 JSON
和上面「下载工作流 JSON」是同一份文件。
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207 "Guide: [Subgraph](https://docs.comfy.org/interface/features/subgraph)\n\n## Model Links\n\n- [Hugging Face:Comfy-Org/Wan_2.1_ComfyUI_repackaged](https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged)\n\n**ModelScope mirrors** (alternative repo location)\n\n- [ModelScope:Comfy-Org/Wan_2.1_ComfyUI_repackaged](https://modelscope.cn/models/Comfy-Org/Wan_2.1_ComfyUI_repackaged)\n\n**diffusion_models**\n\n- [causal_forcing-framewise.safetensors](https://huggingface.co/TalmajM/causal_forcing_framewise_ComfyUI_repackaged/resolve/main/split_files/diffusion_models/causal_forcing-framewise.safetensors) (5.29 GB)\n\n**vae**\n\n- [wan_2.1_vae.safetensors](https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/resolve/main/split_files/vae/wan_2.1_vae.safetensors) (242.1 MB)\n\n**text_encoders**\n\n- [umt5_xxl_fp8_e4m3fn_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/resolve/main/split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors) (6.27 GB)\n\n## Model Storage Location\n\n```\n📂 ComfyUI/\n├── 📂 models/\n│ ├── 📂 diffusion_models/\n│ │ └── causal_forcing-framewise.safetensors\n│ ├── 📂 vae/\n│ │ └── wan_2.1_vae.safetensors\n│ └── 📂 text_encoders/\n│ └── umt5_xxl_fp8_e4m3fn_scaled.safetensors\n```\n\n## Report Issue\n\nNote: Please update ComfyUI first ([guide](https://docs.comfy.org/installation/update_comfyui)) and prepare required models. Desktop/Cloud updates follow stable releases, so some nightly-supported models may not be available yet.\n\n- Cannot run / runtime errors: [ComfyUI/issues](https://github.com/comfyanonymous/ComfyUI/issues)\n- UI / frontend issues: [ComfyUI_frontend/issues](https://github.com/Comfy-Org/ComfyUI_frontend/issues)\n- Workflow issues: [workflow_templates/issues](https://github.com/Comfy-Org/workflow_templates/issues)\n"
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231 "## About Causal Forcing\n\n[Causal Forcing](https://github.com/thu-ml/Causal-Forcing) is an open-source video generation method from Tsinghua University. It uses **Causal ODE** or **Causal Consistency Distillation** to distill autoregressive diffusion models into a few-step generator, enabling high-quality real-time interactive video generation.\n\nCompared to earlier methods like Self Forcing, Causal Forcing delivers better visual quality and motion dynamics at the same training budget and inference speed.\n\n**This workflow template is image-to-video (I2V) only** — it takes a starting image and animates it with a text prompt. It uses the **Causal Forcing Framewise 4-step** checkpoint with Wan 2.1 components.\n\n## Duration Limit\n\n**The maximum output length in this template is about 5 seconds.**\n\n- Output is encoded at **16 fps** (`CreateVideo`)\n- Frame count is computed as `min(duration × 16 + 1, 81)`\n- The cap of **81 frames** corresponds to roughly **5 seconds** of video\n\nAdjust the **duration** input on the subgraph (default: 5). Values above ~5 s will still be clamped to 81 frames.\n\n## How to Use\n\n1. **Load Image** — upload the first frame of your video\n2. **Image to Video (Causal Forcing Framewise)** subgraph — set the main controls:\n - **first_frame** — connected from Load Image\n - **positive_prompt** — describe the motion and scene you want\n - **width / height** — output resolution (default 832 × 480)\n - **duration** — length in seconds (default 5, max ~5)\n - **noise_seed** — change to get different results\n3. **Save Video** — output is saved under `video/CausalForcing_I2V`\n\nThe **negative prompt** is preset inside the subgraph and is not exposed as an input. Edit it inside the subgraph if you need to customize it.\n\n## Sampling Settings (inside subgraph)\n\nThese are pre-tuned for the framewise 4-step checkpoint and generally should not be changed:\n\n- **4 sampling steps** (`BasicScheduler`)\n- **CFG = 1** (`CFGGuider`) — classifier-free guidance is disabled\n- **SamplerARVideo** — autoregressive frame-by-frame generation\n\n## Tips\n\n- Write prompts that describe **motion and camera behavior**, not just the static image content\n- Match your input image aspect ratio to **width / height** for best results\n- For longer videos, see the upstream project's [long video generation](https://github.com/thu-ml/Causal-Forcing) extensions (Rolling Forcing, etc.)"
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