readme: ---
  license: openrail++
  language:
  - en
  - ja
  - es
  - zh
  widget:
  - text: a beautiful illustration of a fantasy forest
    example_title: Fantasy Forest
  - text: environment concept art
    example_title: Concept Art 1
  tags:
  - stable-diffusion
  - sygil-diffusion
  - text-to-image
  - sygil-devs
  - finetune
  - stable-diffusion-1.5
  inference: true
  pinned: true
  metrics:
  - accuracy
  - bertscore
  - bleu
  - bleurt
  - brier_score
  - cer
  - character
  - charcut_mt
  - chrf
  - code_eval
  ---
  
  
  # About the model
  -----------------
  This model is a fine-tune of Stable Diffusion, trained on the [Imaginary Network Expanded Dataset](https://github.com/Sygil-Dev/INE-dataset), with the big advantage of allowing the use of multiple namespaces (labeled tags) to control various parts of the final generation.
  While current models usually are prone to “context errors” and need substantial negative prompting to set them on the right track, the use of namespaces in this model (eg. “species:seal” or “studio:dc”) stop the model from misinterpreting a seal as the singer Seal, or DC Comics as Washington DC. 
  This model is also able to understand other languages besides English, currently it can partially understand prompts in Chinese, Japanese and Spanish. More training is already being done in order to have the model completely understand those languages and have it work just like how it works with English prompts.
  
  As the model is fine-tuned on a wide variety of content, it’s able to generate many types of images and compositions, and easily outperforms the original model when it comes to portraits, architecture, reflections, fantasy, concept art, anime, landscapes and a lot more without being hyper-specialized like other community fine-tunes that are currently available. 
  
  **Note: The prompt engineering techniques needed are slightly different from other fine-tunes and the original Stable Diffusion model, so while you can still use your favorite prompts, for best results you might need to tweak them to make use of namespaces. A more detailed guide will be available later on, but you can use the tags and namespaces found here [Dataset Explorer](https://huggingface.co/spaces/Sygil/INE-dataset-explorer) should be able to start you off on the right track.
  
  If you find our work useful, please consider supporting us on [OpenCollective](https://opencollective.com/sygil_dev)! 
  
  This model is still in its infancy and it's meant to be constantly updated and trained with more and more data as time goes by, so feel free to give us feedback on our [Discord Server](https://discord.gg/UjXFsf6mTu) or on the discussions section on huggingface. We plan to improve it with more, better tags in the future, so any help is always welcome 😛
  [![Join the Discord Server](https://badgen.net/discord/members/fTtcufxyHQ?icon=discord)](https://discord.gg/UjXFsf6mTu)
  
  
  # Showcase
  ![Showcase image](pictures/showcase-6.jpg)
  
  
  ## Examples
  
  Using the [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Sygil Diffusion in a simple and efficient manner.
  
  ```bash
  pip install diffusers transformers accelerate scipy safetensors
  ```
  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 DPMSolverMultistepScheduler):
  
  ```python
  import torch
  from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
  
  model_id = "Sygil/Sygil-Diffusion"
  
  # Use the DPMSolverMultistepScheduler (DPM-Solver++) scheduler here instead
  pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
  pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
  pipe = pipe.to("cuda")
  
  prompt = "a beautiful illustration of a fantasy forest"
  image = pipe(prompt).images[0]
      
  image.save("fantasy_forest_illustration.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).
  
  ## Available Checkpoints:
    - #### Stable:
      - [Sygil Diffusion v0.1](https://huggingface.co/Sygil/Sygil-Diffusion/blob/main/sygil-diffusion-v0.1.ckpt): Trained on Stable Diffusion 1.5 for 800,000 steps.
      - [Sygil Diffusion v0.2](https://huggingface.co/Sygil/Sygil-Diffusion/blob/main/sygil-diffusion-v0.2.ckpt): Resumed from Sygil Diffusion v0.1 and trained for a total of 1.77 million steps.
      - [Sygil Diffusion v0.3](https://huggingface.co/Sygil/Sygil-Diffusion/blob/main/sygil-diffusion-v0.3.ckpt): Resumed from Sygil Diffusion v0.2 and trained for a total of 2.01 million steps so far.
    - #### Beta:
      - [sygil-diffusion-v0.4_2216300_lora.ckpt](https://huggingface.co/Sygil/Sygil-Diffusion/blob/main/sygil-diffusion-v0.4_2216300_lora.ckpt): Resumed from Sygil Diffusion v0.3 and trained for a total of 2.21 million ste