This is an implementation of the SDXL as a Cog model. Cog packages machine learning models as standard containers.
Follow the model pushing guide to push your own fork of SDXL to Replicate.
for prediction,
cog predict -i prompt="a photo of TOK"
cog train -i input_images=@example_datasets/__data.zip -i use_face_detection_instead=True
cog run -p 5000 python -m cog.server.http
2023-08-17
- ROI problem is fixed.
- Now BLIP caption_prefix does not interfere with BLIP captioner.
- Now lora_url can be used to accept lora models from the web.
2023-08-12
- Input types are inferred from input name extensions, or from the
input_images_filetype
argument - Preprocssing are now done with fp16, and if no mask is found, the model will use the whole image
2023-08-11
- Default to 768x768 resolution training
- Rank as argument now, default to 32
- Now uses Swin2SR
caidas/swin2SR-realworld-sr-x4-64-bsrgan-psnr
as default, and will upscale + downscale to 768x768