This workflow is designed for Anima Preview3 tiled upscaling and high-resolution anime detail refinement. Its main purpose is to take an existing anime-style image, enlarge it, split it into manageable tiles, refine each local region with Anima Preview3, and then reconstruct the image into a cleaner high-resolution result. Instead of relying only on a basic pixel upscaler, this workflow combines traditional upscale, tile processing, prompt-assisted regeneration, VAE encoding / decoding, and final image assembly to produce a more polished output.
The workflow uses anima-preview3-base.safetensors as the main generation model, qwen_3_06b_base.safetensors as the text encoder, and qwen_image_vae.safetensors as the VAE. It also includes an optional LoRA route, with Gundam_RX78_Flux used in the example setup, showing that the workflow can be adapted for mecha, armor, character, or specific style enhancement tasks. The initial enlargement stage uses 4x_NMKD-Siax_200k.pth, which helps expand the image before the Anima refinement stage begins.
The core structure is a tiled enhancement pipeline. The source image is loaded, upscaled, resized to a larger total pixel target, and then divided into tiles through TTP_Tile_image_size and TTP_Image_Tile_Batch. Each tile becomes an independent local region that can be encoded, refined, decoded, and later reassembled. This is important because large anime images often lose detail or become unstable when processed in one full-frame pass. Tiled processing lets the workflow focus more generation attention on each region, improving local texture, line clarity, small mechanical details, clothing folds, hair strands, and background elements.
The workflow also uses WD14 tagging. The tile or image content can be analyzed automatically, and the extracted tags can be used to help guide the refinement prompt. This makes the workflow useful when the creator wants the upscaler to understand what is in the image, not just enlarge it mechanically. For anime and illustration work, this can help keep character features, objects, and style direction more coherent during the enhancement stage.
The sampling route uses ClownsharKSampler_Beta with a controlled denoise value. This is critical for tiled upscale work. If denoise is too high, each tile may redraw too aggressively and break consistency across the image. If denoise is too low, the image may only become larger without gaining meaningful detail. This workflow is set up for refinement rather than full redesign: preserve the original composition, improve the local details, and reconstruct the final image with better finish.
After tile refinement, the workflow uses tiled VAE decoding and TTP_Image_Assy to rebuild the full image. Padding and overlap handling help reduce obvious seams between tiles. This makes the final result more suitable for Civitai previews, RunningHub demos, social media covers, high-resolution anime character art, mecha illustration enhancement, and final polish before publishing.
This workflow is ideal for creators who already have a good anime image but want a sharper, larger, more detailed final version. If you want to see how Anima Preview3, tile splitting, WD14 tagging, local latent refinement, and final tile reconstruction work together, watch the full tutorial from the YouTube link above.
⚙️ Try the Workflow Online
👉 Workflow: https://www.runninghub.ai/post/2042211302168338434?inviteCode=rh-v1111
Open the link above to run the workflow directly online and view the generation results in real time.
If the results meet your expectations, you can also deploy it locally for further customization.
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📺 Bilibili Updates (Mainland China & Asia-Pacific)
If you are in Mainland China or the Asia-Pacific region, you can watch the video below for workflow demos and a detailed creative breakdown.
📺 Bilibili Video: https://www.bilibili.com/video/BV1MEQbBsE5z/
I will continue updating model resources on Quark Drive:
👉 https://pan.quark.cn/s/20c6f6f8d87b
These resources are mainly prepared for local users, making creation and learning more convenient.
⚙️ 在线体验工作流
👉 工作流: https://www.runninghub.ai/post/2042211302168338434?inviteCode=rh-v1111
打开上方链接即可直接运行该工作流,实时查看生成效果。
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/BV1MEQbBsE5z/
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。
