Generate anime-style images and videos

Which anime-style models are the fastest?

For quick results, the datacte/proteus-v0.3 model is one of the fastest, often completing generations in just a few seconds on modern GPUs.
The cjwbw/anything-v4.0 model is also lightweight and returns results quickly while supporting a large range of anime styles.
Style-focused models such as aaronaftab/mirage-ghibli may take longer depending on your input image and transformation settings.

Which models offer the best balance of cost and quality?

If you want strong quality without premium cost, the datacte/proteus-v0.3 model offers a great balance of fidelity and speed.
The cjwbw/anything-v4.0 model is also cost-effective and ideal for rapid experimentation across many anime aesthetics.
For stylized output, the aaronaftab/mirage-ghibli model costs slightly more per run but produces a distinct Ghibli-inspired look.

What works best for creating full-scene anime illustrations (characters + background)?

For full-scene anime images with characters, backgrounds, and detailed lighting, the charlesmccarthy/animagine-xl model is built for that level of structure and detail.
The datacte/proteus-v0.3 model is a strong general-purpose engine for both characters and environments.
For faster drafting and broad anime style coverage, the cjwbw/anything-v4.0 model works well.

What works best for transforming an existing image into a Ghibli or stylized anime scene?

If you want image-to-image transformation with a Ghibli feel, use the aaronaftab/mirage-ghibli model. It adds soft edges, painterly shading, and cinematic color.
For broader anime stylization, models like cjwbw/anything-v4.0 or charlesmccarthy/animagine-xl work well for both guided image editing and prompting.

What’s the difference between key subtypes or approaches in this collection?

• High-fidelity anime illustration models: Built for detailed characters and scenes. Examples: charlesmccarthy/animagine-xl, datacte/proteus-v0.3.
• General-purpose anime models: Flexible models that work across many anime styles. Example: cjwbw/anything-v4.0.
• Style-transfer models: Focused on transforming existing images into specific aesthetics. Example: aaronaftab/mirage-ghibli.

Differences appear in prompt structure, how strictly they follow tags, image resolution, and whether they support strong identity consistency.

What kinds of outputs can I expect from these models?

You can generate:

• Anime portraits
• Full-body characters
• Scenic illustrations
• Concept art
• Ghibli-inspired stylized images
• Character variations and multi-angle sheets

Models like datacte/proteus-v0.3 support high-resolution outputs with strong visual fidelity.
The cjwbw/anything-v4.0 model provides broad stylistic flexibility.
The aaronaftab/mirage-ghibli model transforms existing images into a cohesive painterly style.

How can I self-host or push a model to Replicate?

Many anime and diffusion models are open source and can be self-hosted using Cog or Docker.
To publish your own model on Replicate, create a replicate.yaml file describing its inputs, outputs, and environment.
Push the model to Replicate, and it will run automatically on managed GPUs with no extra infrastructure required.

Can I use these models for commercial work?

Yes—as long as the license of each model allows it.
For example, the cjwbw/anything-v4.0 model uses the CreativeML OpenRAIL-M license, which permits commercial use with conditions.
Other models, such as datacte/proteus-v0.3 or aaronaftab/mirage-ghibli, may have different restrictions, so check each model page before using outputs commercially.

How do I run these models?

Pick a model such as datacte/proteus-v0.3, charlesmccarthy/animagine-xl, or cjwbw/anything-v4.0.
Then:

1. Provide your text prompt or upload an image if the model supports image-to-image.
2. Choose parameters like width, height, seed, and guidance scale.
3. Run the model to generate final images or variations.

Most anime models work best with square or portrait resolutions.

What should I know before running a job in this collection?

• Prompt structure matters — some models respond well to tag-style prompts.
• Higher resolution increases cost and runtime.
• Clean source images improve image-to-image outcomes.
• Negative prompts can reduce issues like distorted anatomy.
• To maintain character consistency, use a model known for stable identity rendering such as animagine-xl.

Any other collection-specific tips or considerations?

• Try multiple models — anime styles vary widely.
• For polished concept art or vivid scenes, proteus-v0.3 performs well.
• For fast exploration, anything-v4.0 is forgiving and broad.
• For aesthetic-driven transformation, mirage-ghibli is ideal.
• If you want anime-style video stylization, the shridharathi/ghibli-vid model can stylize short clips into animated sequences.

What if I want to automate anime art workflows in an app?

Choose a model with predictable runtime—like datacte/proteus-v0.3 or cjwbw/anything-v4.0—as the backbone of your pipeline.
Use prompt templates, seeds, and batch processing for consistency.
For stylizing user photos, integrate models like aaronaftab/mirage-ghibli in an image-to-image flow.
If your workflow includes short video clips, incorporate shridharathi/ghibli-vid to create animated transformations.