Sharing a ComfyUI workflow for image-to-video generation. This is an adapted and slightly modified version of a workflow originally created by an acquaintance.It utilizes a two-pass KSampler system, focusing on LoRAs effective at low step counts, with a particular emphasis on CausVid for refinement.<ol><li>Independent LoRA for Each Pass (User Customization):<ul><li>Feature: You can apply separate sets of LoRAs to the 1st (initial generation) and 2nd (refinement) KSampler passes.</li><li>Advantage: This gives you granular control. For instance, use foundational LoRAs in the first pass and specialized motion/detail LoRAs (like CausVid) in the second, or experiment with completely different combinations as you see fit.</li></ul></li><li>Addresses Common CausVid LoRA Challenges &amp; Enhances Low-Step Performance:<ul><li>Problem Solved: Directly tackles issues sometimes seen with CausVid LoRA (and other low-step focused LoRAs) where motion can be weak or artifacts appear when generating with very few steps in a single pass.</li><li>How it Improves:<ul><li>The 1st pass quickly establishes a coherent base latent, even at minimal steps (e.g., 2-5).</li><li>The 2nd pass then leverages CausVid (and other LoRAs) on this pre-generated latent. This targeted refinement at low steps (e.g., 4-12, with CFG around 1.0) allows CausVid to perform optimally, enhancing motion and cleaning up potential issues more effectively than a single, rushed low-step generation.</li></ul></li><li>Advantage: You get the speed benefits of low steps while mitigating common quality/motion degradation, leading to better, more consistent results with efficient LoRAs like CausVid.</li></ul></li></ol>This structured, two-stage process allows for more robust and refined outputs, especially when pushing for speed with very low step counts and relying on powerful efficiency LoRAs.<hr />Custom Nodes<a target="_blank" rel="ugc" href="https://github.com/pythongosssss/ComfyUI-Custom-Scripts">https://github.com/pythongosssss/ComfyUI-Custom-Scripts</a><a target="_blank" rel="ugc" href="https://github.com/ltdrdata/ComfyUI-Impact-Pack">https://github.com/ltdrdata/ComfyUI-Impact-Pack</a><a target="_blank" rel="ugc" href="https://github.com/yolain/ComfyUI-Easy-Use">https://github.com/yolain/ComfyUI-Easy-Use</a><a target="_blank" rel="ugc" href="https://github.com/WASasquatch/was-node-suite-comfyui">https://github.com/WASasquatch/was-node-suite-comfyui</a><a target="_blank" rel="ugc" href="https://github.com/kijai/ComfyUI-KJNodes">https://github.com/kijai/ComfyUI-KJNodes</a><a target="_blank" rel="ugc" href="https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite">https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite</a><a target="_blank" rel="ugc" href="https://github.com/Fannovel16/ComfyUI-Frame-Interpolation">https://github.com/Fannovel16/ComfyUI-Frame-Interpolation</a><a target="_blank" rel="ugc" href="https://github.com/ltdrdata/ComfyUI-Inspire-Pack">https://github.com/ltdrdata/ComfyUI-Inspire-Pack</a><a target="_blank" rel="ugc" href="https://github.com/theUpsider/ComfyUI-Logic">https://github.com/theUpsider/ComfyUI-Logic</a><a target="_blank" rel="ugc" href="https://github.com/orssorbit/ComfyUI-wanBlockswap">https://github.com/orssorbit/ComfyUI-wanBlockswap</a>

Wan 2.1 I2V Two-Pass Workflow (Flexible LoRA.ver)

Initial release.Key features: Two-pass low-step KSampling, optimized for CausVid LoRA, flexible model (Standard/GGUF) and LoRA configurations. See description for full details.

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