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
- Repository: https://github.com/Stability-AI/generative-models
- Demo [optional]: https://clipdrop.co/stable-diffusion
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.
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