* add tags for diffusers training
* add tags for diffusers training
* add tags for diffusers training
* add tags for diffusers training
* add tags for diffusers training
* add tags for diffusers training
* add dora tags for drambooth lora scripts
* style
* log_validation unification for controlnet.
* additional fixes.
* remove print.
* better reuse and loading
* make final inference run conditional.
* Update examples/controlnet/README_sdxl.md
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* resize the control image in the snippet.
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Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* fix minsnr implementation for v-prediction case
* format code
* always compute snr when snr_gamma is specified
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Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* remove validation args from textual onverson tests
* reduce number of train steps in textual inversion tests
* fix: directories.
* debig
* fix: directories.
* remove validation tests from textual onversion
* try reducing the time of test_text_to_image_checkpointing_use_ema
* fix: directories
* speed up test_text_to_image_checkpointing
* speed up test_text_to_image_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints
* fix
* speed up test_instruct_pix2pix_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints
* set checkpoints_total_limit to 2.
* test_text_to_image_lora_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints speed up
* speed up test_unconditional_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints
* debug
* fix: directories.
* speed up test_instruct_pix2pix_checkpointing_checkpoints_total_limit
* speed up: test_controlnet_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints
* speed up test_controlnet_sdxl
* speed up dreambooth tests
* speed up test_dreambooth_lora_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints
* speed up test_custom_diffusion_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints
* speed up test_text_to_image_lora_sdxl_text_encoder_checkpointing_checkpoints_total_limit
* speed up # checkpoint-2 should have been deleted
* speed up examples/text_to_image/test_text_to_image.py::TextToImage::test_text_to_image_checkpointing_checkpoints_total_limit
* additional speed ups
* style
Min-SNR Gamma: correct the fix for SNR weighted loss in v-prediction by adding 1 to SNR rather than the resulting loss weights
Co-authored-by: bghira <bghira@users.github.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* fix: #4206
* add: sdxl controlnet training smoketest.
* remove unnecessary token inits.
* add: licensing to model card.
* include SDXL licensing in the model card and make public visibility default
* debugging
* debugging
* disable local file download.
* fix: training test.
* fix: ckpt prefix.
* add: controlnet sdxl.
* modifications to controlnet.
* run styling.
* add: __init__.pys
* incorporate https://github.com/huggingface/diffusers/pull/4019 changes.
* run make fix-copies.
* resize the conditioning images.
* remove autocast.
* run styling.
* disable autocast.
* debugging
* device placement.
* back to autocast.
* remove comment.
* save some memory by reusing the vae and unet in the pipeline.
* apply styling.
* Allow low precision sd xl
* finish
* finish
* changes to accommodate the improved VAE.
* modifications to how we handle vae encoding in the training.
* make style
* make existing controlnet fast tests pass.
* change vae checkpoint cli arg.
* fix: vae pretrained paths.
* fix: steps in get_scheduler().
* debugging.
* debugging./
* fix: weight conversion.
* add: docs.
* add: limited tests./
* add: datasets to the requirements.
* update docstrings and incorporate the usage of watermarking.
* incorporate fix from #4083
* fix watermarking dependency handling.
* run make-fix-copies.
* Empty-Commit
* Update requirements_sdxl.txt
* remove vae upcasting part.
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* run make style
* run make fix-copies.
* disable suppot for multicontrolnet.
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* run make fix-copies.
* dtyle/.
* fix-copies.
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Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
controlnet training center crop input images to multiple of 8
The pipeline code resizes inputs to multiples of 8.
Not doing this resizing in the training script is causing
the encoded image to have different height/width dimensions
than the encoded conditioning image (which uses a separate
encoder that's part of the controlnet model).
We resize and center crop the inputs to make sure they're the
same size (as well as all other images in the batch). We also
check that the initial resolution is a multiple of 8.
