stable-diffusion-xl-base-0.9
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Stable Diffusion xl base 0.9 Model Card

SDXL consists of a two-step pipeline for latent diffusion:
First, we use a base model to generate latents of the desired output size.
In the second step, we use a specialized high-resolution model and apply a technique called SDEdit (https://arxiv.org/abs/2108.01073, also known as “img2img”)
to the latents generated in the first step, using the same prompt.

modelscope usage

from modelscope.utils.constant import Tasks
from modelscope.pipelines import pipeline
import cv2

pipe = pipeline(task=Tasks.text_to_image_synthesis, 
                model='AI-ModelScope/stable-diffusion-xl-base-0.9',
                model_revision='v1.0.0')

prompt = 'a dog'
output = pipe({'text': prompt})
cv2.imwrite('result.png', output['output_imgs'][0])

Direct Use

The model is intended for research purposes only. Possible research areas and tasks include

  • Generation of artworks and use in design and other artistic processes.
  • Applications in educational or creative tools.
  • Research on generative models.
  • Safe deployment of models which have the potential to generate harmful content.
  • Probing and understanding the limitations and biases of generative models.

Excluded uses are described below.

Model Sources

Limitations

  • The model does not achieve perfect photorealism
  • The model cannot render legible text
  • The model struggles with 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 autoencoding part of the model is lossy.

Bias

While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.

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.