Turn a photo, or any picture, into a painterly cel-shaded anime illustration, with the composition kept. About 15 seconds on our card.
Required models
<ul>
<li>Diffusion model: <a rel="ugc" href="https://civarchive.com/models/958009?modelVersionId=3139241">RedCraft 3.0 Krea 2</a>, the INT8 convrot build, <code>redcraft23_30Krea2_int8_convrot.safetensors</code>, in <code>models/diffusion_models</code>. Plain Krea 2 and Krea 2 Turbo load too; RedCraft is what we tested.</li>
<li>Text encoder: the Qwen3-VL 4B text encoder from the Krea 2 downloads (see <a rel="ugc" href="https://comfyui-wiki.com/en/tutorial/advanced/image/krea/krea-2">the Krea 2 guide on comfyui-wiki.com</a>); we run a community int8 ConvRot build of it (<code>qwen3vl_4b_int8_convrot</code>), and the bf16 or fp8 file works the same. It goes in <code>models/text_encoders</code>, loaded with CLIPLoader type <code>krea2</code>.</li>
<li>VAE: the Qwen-Image VAE, <code>qwen_image_vae.safetensors</code>, in <code>models/vae</code>, from <a rel="ugc" href="https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/tree/main/split_files/vae">Comfy-Org on Hugging Face</a>.</li>
</ul>
Settings
<ul>
<li>Steps <code>8</code>. It is a distilled model, more steps do not help.</li>
<li>CFG <code>1.0</code>, sampler <code>er_sde</code>, scheduler <code>simple</code>, denoise <code>0.75</code></li>
<li>Size: 1 megapixel buckets, <code>896x1152</code>, <code>1152x896</code> or <code>1024x1024</code></li>
<li>Negative: a <code>ConditioningZeroOut</code>. At CFG 1.0 a negative prompt does nothing, so the graph does not pretend.</li>
<li>The picture is scaled to 1 megapixel first.</li>
</ul>
How it works. The picture goes in twice: as the img2img start, and as a Qwen3-VL vision reference (Picture 1) in <code>TextEncodeQwenImageEditPlus</code>. So the model sees what it is redrawing, and only the medium changes.
<ul>
<li>Start at denoise 0.75. Below 0.7 it reads as a filtered photo, not a drawing. Go higher for a stronger drawn look.</li>
<li>Landscapes and wide scenes: raise denoise to 0.80. At 0.75 they can come back looking like the photo with a light paint pass.</li>
<li>Do not wire a VAE into the text-encode node. We tested it without.</li>
<li>This is a restyle. It keeps composition, pose and background, and it is not a likeness tool.</li>
<li>Use your own photos, or pictures you have the rights to.</li>
</ul>
Tips we learned the hard way
<ul>
<li>Density beats brevity. Write a full paragraph: who or what, where, the light, the camera. A short prompt gets you a short picture.</li>
<li>Say what you want, never what you do not. No negative prompt, and no SDXL habits: weight brackets and the old separator keyword do nothing here. Write "alone in the frame, no other people" instead of a negative list.</li>
<li>Name the medium. "Drawn in clean cel-shaded anime line art, drawn rather than photographed" holds a drawing; "semi-realistic" alone drifts to photo.</li>
<li>Stay at 1 MP. The buckets above are what the model was trained around.</li>
</ul>
Tested on ROCm (a 7900 XTX). Core ComfyUI nodes only, so CUDA builds should run it as is.
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Krea 2 Photo to Anime: Vision-Reference Restyle

Turn a photo, or any picture, into a painterly cel-shaded anime illustration, with the composition kept. About 15 seconds on our card.
Required models
<ul>
<li>Diffusion model: <a href="https://civitai.red/models/958009?modelVersionId=3139241" rel="ugc">RedCraft 3.0 Krea 2</a>, the INT8 convrot build, <code>redcraft23_30Krea2_int8_convrot.safetensors</code>, in <code>models/diffusion_models</code>. Plain Krea 2 and Krea 2 Turbo load too; RedCraft is what we tested.</li>
<li>Text encoder: the Qwen3-VL 4B text encoder from the Krea 2 downloads (see <a href="https://comfyui-wiki.com/en/tutorial/advanced/image/krea/krea-2" rel="ugc">the Krea 2 guide on comfyui-wiki.com</a>); we run a community int8 ConvRot build of it (<code>qwen3vl_4b_int8_convrot</code>), and the bf16 or fp8 file works the same. It goes in <code>models/text_encoders</code>, loaded with CLIPLoader type <code>krea2</code>.</li>
<li>VAE: the Qwen-Image VAE, <code>qwen_image_vae.safetensors</code>, in <code>models/vae</code>, from <a href="https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/tree/main/split_files/vae" rel="ugc">Comfy-Org on Hugging Face</a>.</li>
</ul>
Settings
<ul>
<li>Steps <code>8</code>. It is a distilled model, more steps do not help.</li>
<li>CFG <code>1.0</code>, sampler <code>er_sde</code>, scheduler <code>simple</code>, denoise <code>0.75</code></li>
<li>Size: 1 megapixel buckets, <code>896x1152</code>, <code>1152x896</code> or <code>1024x1024</code></li>
<li>Negative: a <code>ConditioningZeroOut</code>. At CFG 1.0 a negative prompt does nothing, so the graph does not pretend.</li>
<li>The picture is scaled to 1 megapixel first.</li>
</ul>
How it works. The picture goes in twice: as the img2img start, and as a Qwen3-VL vision reference (Picture 1) in <code>TextEncodeQwenImageEditPlus</code>. So the model sees what it is redrawing, and only the medium changes.
<ul>
<li>Start at denoise 0.75. Below 0.7 it reads as a filtered photo, not a drawing. Go higher for a stronger drawn look.</li>
<li>Landscapes and wide scenes: raise denoise to 0.80. At 0.75 they can come back looking like the photo with a light paint pass.</li>
<li>Do not wire a VAE into the text-encode node. We tested it without.</li>
<li>This is a restyle. It keeps composition, pose and background, and it is not a likeness tool.</li>
<li>Use your own photos, or pictures you have the rights to.</li>
</ul>
Tips we learned the hard way
<ul>
<li>Density beats brevity. Write a full paragraph: who or what, where, the light, the camera. A short prompt gets you a short picture.</li>
<li>Say what you want, never what you do not. No negative prompt, and no SDXL habits: weight brackets and the old separator keyword do nothing here. Write "alone in the frame, no other people" instead of a negative list.</li>
<li>Name the medium. "Drawn in clean cel-shaded anime line art, drawn rather than photographed" holds a drawing; "semi-realistic" alone drifts to photo.</li>
<li>Stay at 1 MP. The buckets above are what the model was trained around.</li>
</ul>
Tested on ROCm (a 7900 XTX). Core ComfyUI nodes only, so CUDA builds should run it as is.

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