Updated: May 12, 2026
character
This workflow is designed for WAI-ANIMA image-to-image generation and controlled anime-style refinement. Its main purpose is to take an existing image as the visual foundation, then use the WAI-ANIMA model pipeline to redraw, enhance, stylize, or push the image toward a cleaner anime key visual while still keeping the original composition as an anchor.
Compared with a pure text-to-image workflow, this setup gives creators more control because the input image is not discarded. The uploaded image is loaded into the workflow, resized through a pixel-scaling node, encoded into latent space through the Qwen image VAE, and then processed through the WAI-ANIMA generation model. This means the final result is guided by both the source image and the written prompt, making it useful for style conversion, anime redraws, character enhancement, composition preservation, and prompt-based visual refinement.
The workflow uses waiANIMA_v10 as the main UNet model, qwen_3_06b_base as the text encoder, and qwen_image_vae as the VAE. This combination is aimed at anime-style image generation with stronger prompt understanding and cleaner visual rendering. The workflow is compact, direct, and easy to modify, making it suitable for creators who want a simple but practical Anima image-to-image setup instead of a large multi-stage graph.
The input image is scaled to around a 1-megapixel working size through image_scale_pixel_v2. This helps normalize the image before it enters the latent process, avoiding extremely small or oversized inputs that may reduce stability. After that, VAEEncode converts the image into latent space, allowing the sampler to modify the image with controlled denoise rather than generating from a blank latent.
The sampler section uses ClownsharKSampler_Beta with a 30-step beta-style sampling route, CFG control, and a moderate denoise setting. This is important for image-to-image work. If denoise is too low, the result may barely change. If denoise is too high, the original structure can be lost. The setup here is meant to balance source-image preservation with visible anime-style transformation.
The positive prompt in the workflow is built around a high-quality anime key visual: adult anime beauty, Anima style, fantasy atmosphere, cinematic composition, detailed clothing, dramatic scale, and polished illustration finish. The negative prompt suppresses common quality problems such as low quality, bad hands, malformed limbs, duplicate elements, cropped bodies, deformed faces, poorly drawn eyes, text, and watermark artifacts. This makes the workflow suitable for polished anime illustrations rather than rough experimental outputs.
This workflow is useful for anime character redraws, image stylization, reference-based illustration enhancement, fantasy key visual creation, Civitai preview images, RunningHub demos, social media covers, and quick visual testing. It is especially practical when you already have a rough image, AI draft, screenshot, or reference composition, and you want WAI-ANIMA to reinterpret it into a cleaner anime-style result.
If you want to see how the source image, WAI-ANIMA model, Qwen text encoder, Qwen VAE, controlled denoise, and final image output work together, watch the full tutorial from the YouTube link above.
⚙️ Try the Workflow Online
👉 Workflow: https://www.runninghub.ai/post/2046194201804677121/?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/BV1q7drB7Ecp/
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/2046194201804677121/?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/BV1q7drB7Ecp/
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。