![]() ![]() SDXL-refiner-0.9: The refiner has been trained to denoise small noise levels of high quality data and as such is. ![]() TheĪnd CLIP-ViT/L for text encoding whereas the refiner model only uses SDXL-base-0.9: The base model was trained on a variety of aspect ratios on images with resolution 1024^2.We are releasing two new diffusion models for research purposes:.A technical report on SDXL is now available here.Permissive CreativeML Open RAIL++-M license (see Inference for file We are releasing two new open models with a.Alongside the model, we release a technical report.We provide a streamlit demo scripts/demo/video_sampling.py and a standalone python script scripts/sampling/simple_video_sample.py for inference of both models.SVD-XT: Same architecture as SVD but finetuned.We use the standard image encoder from SD 2.1, but replace the decoder with a temporally-aware deflickering decoder. SVD: This model was trained to generate 14įrames at resolution 576x1024 given a context frame of the same size.We are releasing Stable Video Diffusion, an image-to-video model, for research purposes: Run streamlit run scripts/demo/turbo.py.Download the weights and place them in the checkpoints/ directory.Follow the installation instructions or update the existing environment with pip install streamlit-keyup.We are releasing SDXL-Turbo, a lightning fast text-to image model.Īlongside the model, we release a technical report Following the launch of SDXL-Turbo, we are releasing SD-Turbo. ![]()
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