EverAnimate Long-Video Consistency Generation Workflow

Watch the full video first if you want to understand how this EverAnimate long-video consistency workflow works in practice. The video shows how a reference character can be animated through a driving video, how face and pose information are extracted, and how the workflow extends the result into a longer continuous video while trying to keep identity, motion, and visual style stable.

This ComfyUI workflow is designed for EverAnimate long-video consistency generation. Its main purpose is to solve a common problem in AI character animation: the first few seconds may look good, but as the video becomes longer, the face starts drifting, clothing changes, body proportions become unstable, and the motion loses continuity. This workflow uses a segmented generation structure with motion handoff, pose guidance, face reference, and loop-based continuation to make longer character videos more controllable.

The workflow starts from a driving video. VHS video loading and video information nodes read the selected frames, FPS, width, height, and frame count. This gives the workflow a clear source timeline before generation begins. The driving video is then processed through pose and face detection. The graph includes ViTPose / YOLO-style body detection, PoseAndFaceDetection, DrawViTPose, SDPose keypoint extraction, and face image extraction. These preprocessing steps turn the original video into usable pose_video and face_video conditions.

The EverAnimate generation section is the core of the workflow. ComfyEverAnimate receives the positive prompt, negative prompt, VAE, reference image, face video, pose video, width, height, length, pose strength, face strength, and motion handoff settings. The first EverAnimate pass generates the opening segment. After that, the workflow trims anchor latents and duplicate image frames, then uses continue_motion to pass motion information into the next segment. This is the key mechanism for long-video continuity.

Instead of generating the entire long video in one pass, the workflow uses a ForLoop structure. The first segment establishes the character, motion, and visual identity. The loop then repeatedly generates continuation segments while receiving the previous motion context. Each continuation segment is sampled, decoded, trimmed, and batched back into the full sequence. This makes the workflow more practical for longer AI character videos than a simple one-shot image-to-video setup.

The model route also includes Wan-related components, Wan 2.1 VAE, ModelSamplingSD3, SageAttention acceleration, Torch setting patches, CLIPTextEncode for positive and negative prompts, and KSampler stages using low-step LCM-style sampling. The negative prompt is designed to suppress overexposure, static frames, blurry details, subtitles, bad hands, bad faces, deformed limbs, background clutter, extra legs, nudity, and NSFW output.

Compared with ordinary image-to-video workflows, this EverAnimate workflow is more specialized for long-video character consistency. It is not only trying to animate a still image. It extracts motion and face signals from the driving video, uses pose and facial guidance, and then passes motion context across multiple generated segments. This makes it suitable for AI dancing videos, digital human motion generation, character animation, music video fragments, short drama characters, cosplay-style motion transfer, Bilibili showcases, YouTube tutorials, RunningHub releases, and Civitai workflow publishing.

Main features:

• EverAnimate long-video consistency generation workflow

• Reference character + driving video generation structure

• Face video and pose video condition extraction

• ViTPose / YOLO / SDPose preprocessing support

• PoseAndFaceDetection for body and face analysis

• DrawViTPose pose visualization route

• ComfyEverAnimate first-segment generation

• continue_motion handoff for continuation segments

• ForLoop structure for longer video generation

• TrimVideoLatent anchor latent trimming

• Duplicate image frame trimming

• ImageBatch segment stitching logic

• Wan 2.1 VAE decoding

• SageAttention acceleration support

• Negative prompt for stability and anti-artifacts

Suggested workflow:

Prepare a clean reference image and a clear driving video first. The character in the reference image should have a visible face, readable clothing, and a stable body shape. The driving video should have clear pose movement and not too much camera shake. Load the driving video, check the selected frame count and resolution, then run the pose and face preprocessing section. Confirm that the pose_video and face_video are usable before entering EverAnimate generation. Start with a short test first. If the face drifts, increase face guidance and simplify the prompt. If the body motion is weak, check pose extraction and pose strength. Once the first segment is stable, enable the loop continuation path and let continue_motion carry motion context into the next segments.

⚙️ RunningHub Workflow

Try the workflow online right now — no installation required.
👉 Workflow: https://www.runninghub.ai/post/2063934460369915905?inviteCode=rh-v1111

If the results meet your expectations, you can later deploy it locally for customization.

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📺 Bilibili Updates (Mainland China & Asia-Pacific)

If you’re in the Asia-Pacific region, you can watch the video below to see the workflow demonstration and creative breakdown.
📺 Bilibili Video: https://www.bilibili.com/video/BV1oyET6cEqb/

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⚙️打开下方链接即可在线体验，无需安装。
👉 工作流： https://www.runninghub.ai/post/2063934460369915905?inviteCode=rh-v1111
如果觉得效果理想，你也可以在本地进行自定义部署。

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📺 Bilibili 更新（中国大陆及南亚太地区）

如果你在中国大陆或南亚太地区，可以通过下方视频查看该工作流的实测效果与构思讲解。
📺 B站视频： https://www.bilibili.com/video/BV1oyET6cEqb/

我会在 夸克网盘 持续更新模型资源：
👉 https://pan.quark.cn/s/20c6f6f8d87b
这些资源主要面向本地用户，方便进行创作与学习。