Transfer frome huggingface<a target="_blank" rel="ugc" href="https://huggingface.co/Kijai/WanVideo_comfy_GGUF/tree/main/Wan22Animate">https://huggingface.co/Kijai/WanVideo_comfy_GGUF/tree/main/Wan22Animate</a> This is GGUF version of Wan2.2-Animate model, the sample of workflow is same as the fp8 version, u just need to change "Wan2_2-Animate-14B_fp8_e5m2_scaled_KJ.safetensors" model to this GGUF version (use gguf loader) sample workflow: <a target="_blank" rel="ugc" href="https://www.runninghub.ai/post/1969059421638828033?inviteCode=rh-v1213">https://www.runninghub.ai/post/1969068684759212034</a><span style="background-color:rgb(26, 27, 30);color:rgb(141, 141, 143);font-family:-apple-system, BlinkMacSystemFont, &quot;Segoe UI&quot;, Roboto, Helvetica, Arial, sans-serif, &quot;Apple Color Emoji&quot;, &quot;Segoe UI Emoji&quot;;font-size:16px">(This workflow is from KJ discord, it's still in experiment and not perfect, but you already can try it on yur own.)****************************** Function Overview Wan-Animate can animate any character based on a performer's video, precisely replicating the performer's facial expressions and movements to generate highly realistic character videos. Wan-Animate can replace characters in a video with animated characters, preserving their expressions and movements while also replicating the original lighting and color tone for seamless environmental integration." Abstract We introduce Wan-Animate, a unified framework for character animation and replacement. Given a character image and a reference video, Wan-Animate can animate the character by precisely replicating the expressions and movements of the character in the video to generate high-fidelity character videos. Alternatively, it can integrate the animated character into the reference video to replace the original character, replicating the scene's lighting and color tone to achieve seamless environmental integration. Wan-Animate is built upon the Wan model. To adapt it for character animation tasks, we employ a modified input paradigm to differentiate between reference conditions and regions for generation. This design unifies multiple tasks into a common symbolic representation. We use spatially-aligned skeleton signals to replicate body motion and implicit facial features extracted from source images to reenact expressions, enabling the generation of character videos with high controllability and expressiveness. Furthermore, to enhance environmental integration during character replacement, we develop an auxiliary Relighting LoRA. This module preserves the character's appearance consistency while applying the appropriate environmental lighting and color tone. Experimental results demonstrate that Wan-Animate achieves state-of-the-art performance. We are committed to open-sourcing the model weights and its source code

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