Overview

This workflow uses Wan2.2 I2V together with SVI 2.0 PRO LoRA to generate long-form videos of 81 frames or more.
Instead of generating 81+ frames in a single pass, the video is created in multiple segments and then merged.
Compared to traditional extend/long workflows, this approach preserves motion information from the previous segment, resulting in much smoother transitions without noticeable seams.

Latest News (v1.7)

3-pass mode has been removed due to improved motion range achieved through updated recommended Lightx2v LoRA settings.
In the Extend group, you can now specify the Length for each chunk.
Please normally output at 24 FPS. If the result looks like fast-forward in some cases, try using 16 FPS instead.

(1.6) End Frame feature is now available!
You can specify an end frame for each chunk. This allows much finer control, especially over camera work.
Special thanks to Well-Made for updating the SVI node into a more powerful version.

Newly Implemented Feature

Prompt Enhancement via Ollama
Prompts are enhanced using an LLM, allowing for more detailed and structured motion descriptions.
You can also input prompts in Japanese (other languages are possible by adjusting the persona settings).

To use this feature, Ollama and an LLM model must be installed on your local PC.
Please note that I do not provide support for installing or configuring Ollama or LLM models.

Of course, a standard version without using an LLM is also available.

Optional Features

Resume Mode
Allows you to continue generation not only from the first frame, but also from a previously generated video.
If something goes wrong partway through the process, you can save the generated segment and resume from that point without starting over.

Notes

This workflow is configured for the GGUF version, but fp8 ~~and~~ ~~Distill~~ versions can also be used by switching the model loader.
Designed with 480p output in mind.
There are many variations of Lightx2v LoRA, and their suitability depends on the type of video you want to generate.

Acknowledgements

Special thanks to Kijai for providing the excellent LoRA models and custom nodes.

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