readme: ---
  license: creativeml-openrail-m
  tags:
  - stable-diffusion
  - stable-diffusion-diffusers
  - text-to-image
  inference: true
  language: ko
  ---
  
  # my-korean-stable-diffusion-v1-5
  
  It's [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) model, just text encoder and tokenizer are replaced with my [Bingsu/clip-vit-large-patch14-ko](https://huggingface.co/Bingsu/clip-vit-large-patch14-ko).
  
  If you are looking for a Korean diffusion model that works well in practice, see:
  
  - [BAAI/AltDiffusion-m9](https://huggingface.co/BAAI/AltDiffusion-m9)
  - [Multilingual Stable Diffusion Pipeline](https://github.com/huggingface/diffusers/tree/main/examples/community#multilingual-stable-diffusion-pipeline)
  
  # Usage
  
  ```sh
  pip install transformers accelerate>=0.14.0 diffusers>=0.7.2
  ```
  
  ```python
  import torch
  from diffusers import StableDiffusionPipeline, EulerAncestralDiscreteScheduler
  
  repo = "Bingsu/my-korean-stable-diffusion-v1-5"
  euler_ancestral_scheduler = EulerAncestralDiscreteScheduler.from_config(repo, subfolder="scheduler")
  pipe = StableDiffusionPipeline.from_pretrained(
      repo, scheduler=euler_ancestral_scheduler, torch_dtype=torch.float16,
  )
  pipe.to("cuda")
  ```
  
  ```python
  prompt = "화성에서 말을 타고 있는 우주인 사진"
  seed = 23957
  generator = torch.Generator("cuda").manual_seed(seed)
  image = pipe(prompt, num_inference_steps=25, generator=generator).images[0]
  ```
  
  ```python
  image
  ```
  ![Imgur](https://i.imgur.com/JwthHe1.png)
  
  ## more examples
  
  ```python
  prompt = "고퀄리티 하얀 고양이 사진"
  seed = 46399
  generator = torch.Generator("cuda").manual_seed(seed)
  pipe(prompt, num_inference_steps=25, generator=generator).images[0]
  ```
  ![Imgur](https://i.imgur.com/Ex6zbjN.png)
  
  ```python
  prompt = "고퀄리티 하얀 고양이 사진, 피아노를 치는 중"
  seed = 12345
  generator = torch.Generator("cuda").manual_seed(seed)
  pipe(prompt, num_inference_steps=25, generator=generator).images[0]
  ```
  ![Imgur](https://i.imgur.com/1d4GpTH.png)
  
  ```python
  prompt = "달과 별이 보이는 밤하늘을 배경으로 한 해변가 사진"
  seed = 1234246
  generator = torch.Generator("cuda").manual_seed(seed)
  pipe(prompt, num_inference_steps=25, generator=generator).images[0]
  ```
  ![Imgur](https://i.imgur.com/9NhKaAo.png)
  
  # Uses
  
  ## Direct Use 
  The model is intended for research purposes only. Possible research areas and
  tasks include
  
  - Safe deployment of models which have the potential to generate harmful content.
  - Probing and understanding the limitations and biases of generative models.
  - Generation of artworks and use in design and other artistic processes.
  - Applications in educational or creative tools.
  - Research on generative models.
  
  Excluded uses are described below.
  
   ### Misuse, Malicious Use, and Out-of-Scope Use
  _Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_.
  
  
  The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
  
  #### Out-of-Scope Use
  The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
  
  #### Misuse and Malicious Use
  Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
  
  - Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
  - Intentionally promoting or propagating discriminatory content or harmful stereotypes.
  - Impersonating individuals without their consent.
  - Sexual content without consent of the people who might see it.
  - Mis- and disinformation
  - Representations of egregious violence and gore
  - Sharing of copyrighted or licensed material in violation of its terms of use.
  - Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
  
  ## Limitations and Bias
  
  ### Limitations
  
  - The model does not achieve perfect photorealism
  - The model cannot render legible text
  - The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
  - Faces and people in general may not be generated properly.
  - The model was trained mainly with English captions and will not work as well in other languages.
  - The autoencoding part of the model is lossy
  - The model was trained on a large-scale dataset
    [LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material
    and is not fit for product use without additional safety mechanisms and
    considerations.
  - No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the trai