readme: ---
  language: ja
  license: other
  tags:
  - stable-diffusion
  - stable-diffusion-diffusers
  - text-to-image
  - ja
  - japanese
  inference: true
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  ---
  
  
  # SFCOCO Stable Diffusion Model Card
  
  SFCOCO Stable Diffusion is a Japanese-specific latent text-to-image diffusion model capable of generating photo-realistic images given any text input.
  
  This model was fine-tuned by using a powerful Japanese-specific latent text-to-image diffusion model, [Japanese Stable Diffusion](https://huggingface.co/rinna/japanese-stable-diffusion).
  We use the [Stable Diffusion text-to-image fine-tuning script](https://github.com/huggingface/diffusers/tree/main/examples/text_to_image) of [🤗 Diffusers](https://github.com/huggingface/diffusers)
  
  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/nu-dialogue/clip-prefix-caption-jp/blob/master/notebooks/sfc2022_stable_diffusion.ipynb)
  
  ## Model Details
  - **Developed by:** Atsumoto Ohashi
  - **Model type:** Diffusion-based text-to-image generation model
  - **Language(s):** Japanese
  - **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 (LDM)](https://arxiv.org/abs/2112.10752) that used [Japanese Stable Diffusion](https://huggingface.co/rinna/japanese-stable-diffusion) as a pre-trained model. 
  - **Resources for more information:** [Japanese Stable Diffusion GitHub Repository](https://github.com/rinnakk/japanese-stable-diffusion)
  
  ## Examples
  
  Firstly, install our package as follows. This package is modified [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Japanese Stable Diffusion.
  
  
  ```bash
  pip install git+https://github.com/rinnakk/japanese-stable-diffusion
  ```
  
  Run this command to log in with your HF Hub token if you haven't before:
  
  ```bash
  huggingface-cli login
  ```
  
  Running the pipeline with the k_lms scheduler:
  ```python
  import torch
  from torch import autocast
  from diffusers import LMSDiscreteScheduler
  from japanese_stable_diffusion import JapaneseStableDiffusionPipeline
  model_id = "nu-dialogue/sfc2022-stable-diffusion"
  device = "cuda"
  # Use the K-LMS scheduler here instead
  scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000)
  pipe = JapaneseStableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, use_auth_token=True, torch_dtype=torch.float16)
  pipe = pipe.to(device)
  
  prompt = "福澤諭吉像の写真"
  with autocast("cuda"):
      image = pipe(prompt, guidance_scale=7.5)["sample"][0]  
      
  image.save("output.png")
  ```
  _Note: `JapaneseStableDiffusionPipeline` is almost same as diffusers' `StableDiffusionPipeline` but added some lines to initialize our models properly._ 
  
  
  ## Training
  
  **Training Data**
  We used the SFCOCO2021 and SFCOCO2022 dataset for training the model.
  You can see these datasets in [this repository](https://github.com/nu-dialogue/clip-prefix-caption-jp).
  
  **Training Procedure**
  SFCOCO Stable Diffusion has the same architecture as Japanese Stable Diffusion and was trained by using Japanese Stable Diffusion.
  We use the [Stable Diffusion text-to-image fine-tuning script](https://github.com/huggingface/diffusers/tree/main/examples/text_to_image) of [🤗 Diffusers](https://github.com/huggingface/diffusers)
  
  ## Citation
  
  ```bibtex
  @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}
    }
  ```
  
  ```bibte