This workflow is designed for Anima Preview3 image-to-image generation, focusing on controlled anime-style transformation from an existing reference image. Its main purpose is to let creators upload a source image, guide it with a text prompt, and generate a cleaner Anima Preview3 result while still preserving the basic structure, pose, composition, and subject direction of the original picture.
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. This creates a compact Anima Preview3 img2img pipeline where the input image is first resized, encoded into latent space, edited through the sampler, decoded back into an image, and then previewed or saved. Compared with a pure text-to-image workflow, this setup gives creators a stronger visual anchor because the model is not starting from an empty latent. It starts from the uploaded image and modifies it according to the prompt.
The image preparation section is simple and practical. The source image is loaded through LoadImage, then passed into image_scale_pixel_v2, with the total pixel target set around 1 megapixel and alignment set to 64. This helps normalize the image into a model-friendly size before VAE encoding. The workflow is therefore useful for taking an existing AI draft, sketch, screenshot, character image, animal image, or rough composition and pushing it into a more polished Anima Preview3 style.
The workflow then uses VAEEncode to convert the scaled image into latent space. This is the key difference from text-to-image. In text-to-image, the model creates everything from noise. In image-to-image, the original image becomes the base latent, so the final result can keep more of the original layout. This makes it especially useful for redraws, style refinement, concept cleanup, character reinterpretation, and controlled anime transformation.
The sampling stage uses ClownsharKSampler_Beta with a 30-step setup, beta57 scheduler, linear/euler sampler route, CFG around 3, and denoise around 0.73. That denoise value is important: it is strong enough to visibly transform the image, but still keeps the source image as a meaningful reference. Lower denoise would preserve the original more strictly; higher denoise would push the result closer to a full redraw.
The example prompt is simple: “masterpiece, best quality, score_7, highres, safe, 1 dog is running.” This makes the workflow a clean baseline for testing how Anima Preview3 handles image-guided transformation. Users can replace it with character prompts, action prompts, environment prompts, clothing descriptions, lighting instructions, or style directions depending on the image they upload.
The negative prompt suppresses common generation problems such as worst quality, low quality, low-score output, artist names, blur, extra fingers, bad hands, bad anatomy, malformed limbs, duplicated elements, cropped bodies, deformed faces, poorly drawn eyes, text, and watermark artifacts. This keeps the workflow practical for Civitai previews, RunningHub demos, cover images, prompt experiments, and daily anime-style image production.
This workflow is ideal for creators who want a clean Anima Preview3 image-to-image setup without heavy extra modules. It is suitable for anime redraws, reference-based generation, character refinement, animal motion reinterpretation, concept art cleanup, and visual style testing. If you want to see how the source image, Anima Preview3 model, Qwen text encoder, Qwen VAE, denoise control, and final img2img output work together, watch the full tutorial from the YouTube link above.
⚙️ Try the Workflow Online
👉 Workflow: https://www.runninghub.ai/post/2042205599336763393?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/2042205599336763393?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。
🎁 粉丝福利: 注册即送 1000 积分,每日登录 100 积分,畅玩 4090 体验 48 G 超级性能!
📺 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。
