* base template file - train_instruct_pix2pix.py
* additional import and parser argument requried for lora
* finetune only instructpix2pix model -- no need to include these layers
* inject lora layers
* freeze unet model -- only lora layers are trained
* training modifications to train only lora parameters
* store only lora parameters
* move train script to research project
* run quality and style code checks
* move train script to a new folder
* add README
* update README
* update references in README
---------
Co-authored-by: Rahul Raman <rahulraman@gmail.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* enable stable-xl textual inversion
* check if optimizer_2 exists
* check text_encoder_2 before using
* add textual inversion for sdxl in a single file
* fix style
* fix example style
* reset for error changes
* add readme for sdxl
* fix style
* disable autocast as it will cause cast error when weight_dtype=bf16
* fix spelling error
* fix style and readme and 8bit optimizer
* add README_sdxl.md link
* add tracker key on log_validation
* run style
* rm the second center crop
---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* add tutorials to toctree.yml
* fix title
* fix words
* add overview ja
* fix diffusion to 拡散
* fix line 21
* add space
* delete supported pipline
* fix tutorial_overview.md
* fix space
* fix typo
* Delete docs/source/ja/tutorials/using_peft_for_inference.md
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* Delete docs/source/ja/tutorials/basic_training.md
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* Delete docs/source/ja/tutorials/autopipeline.md
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* fix toctree
* add: experimental script for diffusion dpo training.
* random_crop cli.
* fix: caption tokenization.
* fix: pixel_values index.
* fix: grad?
* debug
* fix: reduction.
* fixes in the loss calculation.
* style
* fix: unwrap call.
* fix: validation inference.
* add: initial sdxl script
* debug
* make sure images in the tuple are of same res
* fix model_max_length
* report print
* boom
* fix: numerical issues.
* fix: resolution
* comment about resize.
* change the order of the training transformation.
* save call.
* debug
* remove print
* manually detaching necessary?
* use the same vae for validation.
* add: readme.
* unwrap text encoder when saving hook only for full text encoder tuning
* unwrap text encoder when saving hook only for full text encoder tuning
* save embeddings in each checkpoint as well
* save embeddings in each checkpoint as well
* save embeddings in each checkpoint as well
* Update examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* add documentation for DeepCache
* fix typo
* add wandb url for DeepCache
* fix some typos
* add item in _toctree.yml
* update formats for arguments
* Update deepcache.md
* Update docs/source/en/optimization/deepcache.md
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* add StableDiffusionXLPipeline in doc
* Separate SDPipeline and SDXLPipeline
* Add the paper link of ablation experiments for hyper-parameters
* Apply suggestions from code review
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Make WDS pipeline interpolation type configurable.
* Make the VAE encoding batch size configurable.
* Make lora_alpha and lora_dropout configurable for LCM LoRA scripts.
* Generalize scalings_for_boundary_conditions function and make the timestep scaling configurable.
* Make LoRA target modules configurable for LCM-LoRA scripts.
* Move resolve_interpolation_mode to src/diffusers/training_utils.py and make interpolation type configurable in non-WDS script.
* apply suggestions from review
* debug
* debug test_with_different_scales_fusion_equivalence
* use the right method.
* place it right.
* let's see.
* let's see again
* alright then.
* add a comment.
* I added a new doc string to the class. This is more flexible to understanding other developers what are doing and where it's using.
* Update src/diffusers/models/unet_2d_blocks.py
This changes suggest by maintener.
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* Update src/diffusers/models/unet_2d_blocks.py
Add suggested text
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* Update unet_2d_blocks.py
I changed the Parameter to Args text.
* Update unet_2d_blocks.py
proper indentation set in this file.
* Update unet_2d_blocks.py
a little bit of change in the act_fun argument line.
* I run the black command to reformat style in the code
* Update unet_2d_blocks.py
similar doc-string add to have in the original diffusion repository.
* Batter way to write binarize function
* Solve check_code_quality error
* My mistake to run pull request but not reformated file
* Update image_processor.py
* remove extra variable and space
* Update image_processor.py
* Run ruff libarary to reformat my file
---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: YiYi Xu <yixu310@gmail.com>
* add: test to check if peft loras are loadable in non-peft envs.
* add torch_device approrpiately.
* fix: get_dummy_inputs().
* test logits.
* rename
* debug
* debug
* fix: generator
* new assertion values after fixing the seed.
* shape
* remove print statements and settle this.
* to update values.
* change values when lora config is initialized under a fixed seed.
* update colab link
* update notebook link
* sanity restored by getting the exact same values without peft.
