# Cool Japan Diffusion 2.1.1.1 Model Card

![eyecatch](eyecatch.jpg)

# Introduction
Cool Japan Diffusion is the latent diffusion model created from Stable Diffsion.
Cool Japan Diffusion is suitable for genetrating Cool Japan images such as Anime, Manga, and Game.

# Legal and ethical information
We create this model legally.
However, we think that this model have ethical problems.
Therefore, we cannot use the model for commercially except for news reporting.

TBA.

# Usage
You can try the model by our [Space](https://huggingface.co./spaces/aipicasso/cool-japan-diffusion-latest-demo).
I recommend to use the model by Web UI.
You can download the model [here](https://huggingface.co./aipicasso/cool-japan-diffusion-2-1-2-beta/resolve/main/v2-1-2-beta.ckpt). Safetensor version is [here](https://huggingface.co./aipicasso/cool-japan-diffusion-2-1-2-beta/resolve/main/v2-1-2-beta.safetensors). Japanese manual is [here](https://alfredplpl.hatenablog.com/entry/2023/01/11/182146).

## Model Details
- **Developed by:** Robin Rombach, Patrick Esser, Alfred Increment
- **Model type:** Diffusion-based text-to-image generation model
- **Language(s):** English
- **License:** [CreativeML Open RAIL++-M-NC License](https://huggingface.co./aipicasso/cool-japan-diffusion-2-1-1-beta/blob/main/MODEL-LICENSE)
- **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([OpenCLIP-ViT/H](https://github.com/mlfoundations/open_clip)).
- **Resources for more information:** [GitHub Repository](https://github.com/Stability-AI/).
- **Cite as:**

      @InProceedings{Rombach_2022_CVPR,
          author    = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
          title     = {High-Resolution Image Synthesis With Latent Diffusion Models},
          booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
          month     = {June},
          year      = {2022},
          pages     = {10684-10695}
      }

## Examples

- Web UI
- Diffusers

## Web UI
**Run with --no-half option. I recommend to install [xformers](https://github.com/facebookresearch/xformers).**
Download the model [here](https://huggingface.co./aipicasso/cool-japan-diffusion-2-1-2-beta/resolve/main/v2-1-2-beta.ckpt).
Then, install [Web UI](https://github.com/AUTOMATIC1111/stable-diffusion-webui) by AUTIMATIC1111.

## Diffusers

Using the [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Cool Japan Diffusion 2.1.1 Beta in a simple and efficient manner.

```bash
pip install --upgrade git+https://github.com/huggingface/diffusers.git transformers accelerate scipy
```

Running the pipeline (if you don't swap the scheduler it will run with the default DDIM, in this example we are swapping it to EulerDiscreteScheduler):

```python
from diffusers import StableDiffusionPipeline, EulerAncestralDiscreteScheduler
import torch

model_id = "aipicasso/cool-japan-diffusion-2-1-2-beta"

scheduler = EulerAncestralDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16)
pipe = pipe.to("cuda")

prompt = "anime, masterpiece, a portrait of a girl, good pupil, 4k, detailed"
negative_prompt="deformed, blurry, bad anatomy, bad pupil, disfigured, poorly drawn face, mutation, mutated, extra limb, ugly, poorly drawn hands, bad hands, fused fingers, messy drawing, broken legs censor, low quality, mutated hands and fingers, long body, mutation, poorly drawn, bad eyes, ui, error, missing fingers, fused fingers, one hand with more than 5 fingers, one hand with less than 5 fingers, one hand with more than 5 digit, one hand with less than 5 digit, extra digit, fewer digits, fused digit, missing digit, bad digit, liquid digit, long body, uncoordinated body, unnatural body, lowres, jpeg artifacts, 3d, cg, text, japanese kanji"
images = pipe(prompt,negative_prompt=negative_prompt, num_inference_steps=20).images
images[0].save("girl.png")

```

**Notes**:
- Despite not being a dependency, we highly recommend you to install [xformers](https://github.com/facebookresearch/xformers) for memory efficient attention (better performance)
- If you have low GPU RAM available, make sure to add a `pipe.enable_attention_slicing()` after sending it to `cuda` for less VRAM usage (to the cost of speed)


*This model card was written by: Alfred Increment and is based on the [Stable Diffusion v2](https://huggingface.co./stabilityai/stable-diffusion-2/raw/main/README.md)