readme: ---
  license: openrail++
  tags:
    - stable-diffusion
    - text-to-image
  widget:
    - text: Cartoon, ocean.
      example_title: Ocean (Cartoon)
  ---
  
  # Clean Diffusion 2.0 PoC Model Card
  
  > That's one small step for artists, one giant leap for engineers. 
  
  ![ocean](ocean.png)
  
  Clean Diffusion 2.0 PoC is Latent Diffusion Model made of public domain images.
  Clean Diffusion 2.0 PoC is for the proof of the concept: Stable Diffusion can be made of public domain images.
  Therefore, the model can only express the ocean.
  If you are Japanese, I recommend Clean Diffusion For Japanese (TBA) instead of Clean Diffusion (For Global). 
  The model is more powerful than this global version.
  
  # Note
  > With great power comes great responsibility.
  
  If you **CANNOT UNDERSTAND THESE WORDS**, I recommend that **YOU SHOULD NOT USE ALL OF DIFFUSION MODELS** what have great powers.
  
  # Getting Started
  You would be able to use Clean Diffusion by the following code soon.
  
  ```python
  from diffusers import StableDiffusionPipeline
  import torch
  
  model_id = "alfredplpl/clean-diffusion-2-0-poc"
  pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float32)
  pipe = pipe.to("cuda")
  
  prompt = "Cartoon, ocean."
  image = pipe(prompt).images[0]
      
  image.save("ocean.png")
  ```
  
  # Tuning
  Clean Diffusion is less powerful than Stable Diffusion.
  Therefore, I recommend to tune Clean Diffusion like Stable Diffusion 
  because Clean Diffusion of the network architecture and Stable Diffusion of the network architecture are same.
  And I repeat the words before I explain the detail.
  
  > With great power comes great responsibility.
  
  Please consider the words before you tune Clean Diffusion.
  
  ## Textual Inversion
  TBA on Colab.
  
  ## Dreambooth on Stable Diffusion
  TBA on Colab.
  
  ## Pure fine-tuning
  TBA
  
  # Transparency of Clean Diffusion
  I proof that clean diffusion is clean by following explanation.
  
  ## Legal information
  TBA
  
  ### Training
  Clean Diffusion is legal and ethical.
  
  Clean Diffusion is MADE IN JAPAN.
  Therefore, Clean Diffusion is subject to [Japanese copyright laws](https://en.wikipedia.org/wiki/Copyright_law_of_Japan).
  
  TBA
  
  ### Generating
  
  TBA
  
  ## Training Images
  TBA
  
  ### List of works
  - [ArtBench](https://github.com/liaopeiyuan/artbench) (public domain is True)
  - Popeye the Sailor Meets Sindbad the Sailor
  
  ### Tiny training images
  I would like to the all training raw images because these images are public domain.
  However, these images are huge (70GB+).
  Therefore, I have opened the tiny version like this.
  
  [Tiny Images](https://1drv.ms/u/s!ApxVlgxlqLRliLpSC58y5qyAlt52tQ?e=3Yfwbt)
  
  ### Training Process of VAE
  
  TBA
  
  ## Training text-image pairs
  TBA
  
  ## Trainning code and config
  TBA
  
  # Acknowledgement
  > Standing on the shoulders of giants
  
  # Citations
  
  ```bibtex
  @misc{rombach2021highresolution,
        title={High-Resolution Image Synthesis with Latent Diffusion Models}, 
        author={Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and Björn Ommer},
        year={2021},
        eprint={2112.10752},
        archivePrefix={arXiv},
        primaryClass={cs.CV}
  }
  ```
  ```bibtex
  @article{liao2022artbench,
    title={The ArtBench Dataset: Benchmarking Generative Models with Artworks},
    author={Liao, Peiyuan and Li, Xiuyu and Liu, Xihui and Keutzer, Kurt},
    journal={arXiv preprint arXiv:2206.11404},
    year={2022}
  }
  ```
  
  