Remove <cat-toy> validation prompt from textual_inversion_sdxl.py
The `<cat-toy>` validation prompt is a default choice for the example task in the README. But no other part of `textual_inversion_sdxl.py` references the cat toy and `textual_inversion.py` has a default validation prompt of `None` as well.
So bring `textual_inversion_sdxl.py` in line with `textual_inversion.py` and change default validation prompt to `None`
* feat: standarize model card creation for dreambooth training.
* correct 'inference
* remove comments.
* take component out of kwargs
* style
* add: card template to have a leaner description.
* widget support.
* propagate changes to train_dreambooth_lora
* propagate changes to custom diffusion
* make widget properly type-annotated
* add noise_offset param
* micro conditioning - wip
* image processing adjusted and moved to support micro conditioning
* change time ids to be computed inside train loop
* change time ids to be computed inside train loop
* change time ids to be computed inside train loop
* time ids shape fix
* move token replacement of validation prompt to the same section of instance prompt and class prompt
* add offset noise to sd15 advanced script
* fix token loading during validation
* fix token loading during validation in sdxl script
* a little clean
* style
* a little clean
* style
* sdxl script - a little clean + minor path fix
sd 1.5 script - change default resolution value
* ad 1.5 script - minor path fix
* fix missing comma in code example in model card
* clean up commented lines
* style
* remove time ids computed outside training loop - no longer used now that we utilize micro-conditioning, as all time ids are now computed inside the training loop
* style
* [WIP] - added draft readme, building off of examples/dreambooth/README.md
* readme
* readme
* readme
* readme
* readme
* readme
* readme
* readme
* removed --crops_coords_top_left from CLI args
* style
* fix missing shape bug due to missing RGB if statement
* add blog mention at the start of the reamde as well
* Update examples/advanced_diffusion_training/README.md
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* change note to render nicely as well
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Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* 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>
* initial commit for unconditional/class-conditional consistency training script
* make style
* Add entry for consistency training script in community README.
* Move consistency training script from community to research_projects/consistency_training
* Add requirements.txt and README to research_projects/consistency_training directory.
* Manually revert community README changes for consistency training.
* Fix path to script after moving script to research projects.
* Add option to load U-Net weights from pretrained model.
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Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* sd1.5 support in separate script
A quick adaptation to support people interested in using this method on 1.5 models.
* sd15 prompt text encoding and unet conversions
as per @linoytsaban 's recommendations. Testing would be appreciated,
* Readability and quality improvements
Removed some mentions of SDXL, and some arguments that don't apply to sd 1.5, and cleaned up some comments.
* make style/quality commands
* tracker rename and run-it doc
* Update examples/advanced_diffusion_training/train_dreambooth_lora_sd15_advanced.py
* Update examples/advanced_diffusion_training/train_dreambooth_lora_sd15_advanced.py
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Co-authored-by: Linoy Tsaban <57615435+linoytsaban@users.noreply.github.com>
* move unets to module 🦋
* parameterize unet-level import.
* fix flax unet2dcondition model import
* models __init__
* mildly depcrecating models.unet_2d_blocks in favor of models.unets.unet_2d_blocks.
* noqa
* correct depcrecation behaviour
* inherit from the actual classes.
* Empty-Commit
* backwards compatibility for unet_2d.py
* backward compatibility for unet_2d_condition
* bc for unet_1d
* bc for unet_1d_blocks
* Fixed the bug related to saving DeepSpeed models.
* Add information about training SD models using DeepSpeed to the README.
* Apply suggestions from code review
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Co-authored-by: mhh001 <mahonghao1@huawei.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* add animatediff img2vid
* fix
* Update examples/community/README.md
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* fix code snippet between ip adapter face id and animatediff img2vid
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Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Fixes#6418 Advanced Dreambooth LoRa Training
* change order of import to fix nit
* fix nit, use cast_training_params
* remove torch.compile fix, will move to a new PR
* remove unnecessary import
* Enable image resizing to adjust its height and width in StableDiffusionXLInstructPix2PixPipeline
* Ensure that validation is performed at every 'validation_step', not at every step
Piyush Thakur