This workflow is designed for a slower but more polished Anima Aesthetic 1.1 rendering process. Instead of relying on one sampler pass, it separates the image into a high-denoise construction stage and additional refinement stages. That structure is useful when you want the first pass to establish the main character, pose, lighting, and scene, then use lower-denoise stages to improve fine detail without completely rebuilding the image. It is a good choice for finished illustrations, key visuals, and presentation images where quality matters more than generation speed.

The inspected graph shows a Qwen Image CLIP loader, Qwen Image HDR VAE, an Anima Aesthetic V1.1 model selection route, and multiple connected sampler passes. The first major pass is around 34 steps, followed by 18-step refinement passes with lower denoise values. The graph also includes latent upscale stages at 1.25x and 1.5x, so the workflow can build extra detail progressively. The sampler route includes er_sde/simple and ClownsharKSampler-style nodes, with CFG values reduced in later stages to avoid overcooking the image. This preview avoids calling it a 4K workflow because the verified chain is staged latent refinement rather than a confirmed final pixel super-resolution pipeline.

Main features:

- Three-stage Anima Aesthetic 1.1 render structure
- Base composition pass followed by lower-denoise refinement
- Qwen Image CLIP text encoder
- Qwen Image HDR VAE decoding
- 34-step first stage observed
- 18-step refinement stages observed
- 1.25x and 1.5x latent upscale nodes in the graph
- Better suited for polished final images than quick drafts
- Helps preserve composition while improving surface detail
- Vertical composition support through the resolution selector
- Conservative description based on connected sampler and upscale routes
- Good for character key visuals and high-quality anime illustrations

Suggested workflow:

Use the first stage to explore composition and subject design. Once a seed gives you the pose and framing you want, keep it stable and let the second and third stages do the detail work. If the final image becomes too sharp, noisy, or over-rendered, reduce the later-stage CFG or denoise rather than changing the entire prompt.

⚙️ RunningHub Workflow

Try the workflow online right now — no installation required.
👉 Workflow: https://www.runninghub.ai/post/2078721272133029889?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/BV1xJKB6vEfp/

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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/2078721272133029889?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/BV1xJKB6vEfp/

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。