metadata

license: openrail++
language:
  - en
tags:
  - stable-diffusion
  - sygil-diffusion
  - text-to-image
  - sygil-devs
  - finetune
  - stable-diffusion-1.5
inference: true
pinned: true

About the model

This model is a Stable Diffusion v1.5 fine-tune trained on the Imaginary Network Expanded Dataset. It is an advanced version of Stable Diffusion and can generate nearly all kinds of images like humans, reflections, cities, architecture, fantasy, concepts arts, anime, manga, digital arts, landscapes, or nature views. This model allows the user to have total control of the generation as they can use multiple tags and namespaces to control almost everything on the final result including image composition.

**Note that the prompt engineering techniques is a bit different from other models and Stable Diffusion, while you can still use normal prompts like in other Stable Diffusion models in order to get the best out of this model you will need to make use of tags and namespaces. More information about namespace will later be added.

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Showcase

Examples

Using the 🤗's Diffusers library to run Sygil Diffusion in a simple and efficient manner.

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):

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("astronaut_rides_horse.png")

Notes:

Despite not being a dependency, we highly recommend you to install 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).

Training

Training Data The model was trained on the following dataset:

Imaginary Network Expanded Dataset dataset.

Hardware and others

Hardware: 1 x Nvidia RTX 3050 8GB GPU
Hours Trained: 384 approximately.
Optimizer: AdamW
Gradient Accumulations: 1
Batch: 1
Learning rate: warmup to 1e-7 for 10,000 steps and then kept constant
Total Training Steps: 800,0000

Developed by: Sygil-Dev

License

This model is open access and available to all, with a CreativeML Open RAIL++-M License further specifying rights and usage. Please read the full license here