pip install invisible_watermark transformers safetensors
pip install diffusers==0.18.0
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-1.0',
use_safetensors=True,
model_revision='v1.0.0')
prompt = 'a dog'
output = pipe({'text': prompt})
cv2.imwrite('result.png', output['output_imgs'][0])
SDXL consists of a mixture-of-experts pipeline for latent diffusion:
In a first step, the base model is used to generate (noisy) latents,
which are then further processed with a refinement model specialized for the final denoising steps.
Note that the base model can be used as a standalone module.
Alternatively, we can use a two-stage pipeline as follows:
First, the base model is used 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. This technique is slightly slower than the first one, as it requires more function evaluations.
Source code is available at https://github.com/Stability-AI/generative-models .
For research purposes, we recommned our generative-models
Github repository (https://github.com/Stability-AI/generative-models), which implements the most popoular diffusion frameworks (both training and inference) and for which new functionalities like distillation will be added over time.
Clipdrop provides free SDXL inference.
The chart above evaluates user preference for SDXL (with and without refinement) over SDXL 0.9 and Stable Diffusion 1.5 and 2.1.
The SDXL base model performs significantly better than the previous variants, and the model combined with the refinement module achieves the best overall performance.
Make sure to upgrade diffusers to >= 0.18.0:
pip install diffusers --upgrade
In addition make sure to install transformers
, safetensors
, accelerate
as well as the invisible watermark:
pip install invisible_watermark transformers accelerate safetensors
The model is intended for research purposes only. Possible research areas and tasks include
Excluded uses are described below.
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.
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.