This workflow is the single-image version of the Krea2 refinement setup. It is designed for cases where you want to take one existing image, keep the original subject and composition recognizable, and let Krea2 rebuild the texture, lighting, detail, and final polish. It is a practical choice for creators who want a controlled redraw instead of a fully new generation: character illustrations, AI covers, anime portraits, product-style renders, and old outputs that need a cleaner second pass.

The active model path uses `krea2_turbo_fp8.safetensors`, with `qwen3vl_4b_fp8_scaled.safetensors` loaded as the Krea2 CLIP route and `qwen_image_HDR_vae_fp32_comfy.safetensors` as the VAE. A standard LoadImage entry brings the source picture into the workflow, and VAEEncode sends it into the latent path. The graph also contains Qwen3VLProcessor and RH LLM API support, which can help with image understanding or prompt assistance depending on how you run the online version. The important structural control is the `depth-control-lora.safetensors` path at strength 1.0, combined with DA3 depth inference and multiple Krea2Control encode/apply stages.

The sampling structure is a three-stage refinement chain. The inspected graph shows 8-step KSampler passes with CFG 1, euler/simple sampling, and denoise values around 0.55, 0.35, and 0.25. This is more conservative than a full redraw: the first pass is strong enough to clean and restyle the image, while the later passes focus on keeping the structure stable and recovering sharper detail. The workflow also includes latent resizing toward 1024 x 1024, `4x-UltraSharp.pth` for image upscaling, and a final 2048 x 2048 image scale path. Because the actual graph points to a 2048-square output route, this description avoids overclaiming a guaranteed 4K result.

Main features:

- Single-image Krea2 refinement workflow
- LoadImage and VAEEncode input path
- Krea2 Turbo FP8 UNET route
- Qwen3VL Krea2 CLIP encoder
- Qwen Image HDR VAE decode path
- Optional Qwen3VL-assisted image understanding route
- Depth-control LoRA enabled at strength 1.0
- DA3 depth inference for structure preservation
- Three Krea2Control encode/apply stages
- Three 8-step euler/simple sampler passes
- Conservative denoise sequence around 0.55, 0.35, and 0.25
- 1024 latent refinement path
- 4x-UltraSharp image upscale stage
- Final 2048-square output scale path
- Useful for controlled cleanup, redraw, and detail recovery

Suggested workflow:

Start with a single image that already has the composition you want to keep. Use a conservative prompt such as image restoration, preserved subject identity, clean texture, natural fine detail, coherent lighting, and realistic depth. If the output changes the face, pose, or main silhouette too much, lower the first-stage denoise or make the control guidance stricter. If the output feels too close to the original and lacks improvement, increase the first-stage freedom slightly before touching the later polish stages.

Use the preview as a quality check before saving the final version. This workflow is especially useful when you are refining one important image rather than processing a whole folder, because you can tune the prompt and denoise balance more carefully for that single source.

⚙️ RunningHub Workflow

Try the workflow online right now — no installation required.
👉 Workflow: https://www.runninghub.ai/post/2079074609300197378?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/BV1i7KU62Esg/

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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/2079074609300197378?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/BV1i7KU62Esg/

本期 Krea2 相关模型资源：
👉 https://pan.quark.cn/s/07bdc81784ce
这些资源主要面向本地用户，方便进行创作与学习。