* Timestep bias for fine-tuning SDXL
* Adjust parameter choices to include "range" and reword the help statements
* Condition our use of weighted timesteps on the value of timestep_bias_strategy
* style
---------
Co-authored-by: bghira <bghira@users.github.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* SDXL microconditioning documentation should indicate the correct default order of parameters, so that developers know
* SDXL microconditioning documentation should indicate the correct default order of parameters, so that developers know
* empty
---------
Co-authored-by: bghira <bghira@users.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* min-SNR gamma for Dreambooth training
* Align the mse_loss_weights style with SDXL training example
---------
Co-authored-by: bghira <bghira@users.github.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* Resolve v_prediction issue for min-SNR gamma weighted loss function
* Combine MSE loss calculation of epsilon and velocity, with a note about the application of the epsilon code to sample prediction
* style
---------
Co-authored-by: bghira <bghira@users.github.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Fixed `get_word_inds` mistake/typo in P2P community pipeline
The function `get_word_inds` was taking a string of text and either a word (str) or a word index (int) and returned the indices of token(s) the word would be encoded to.
However, there was a typo, in which in the second `if` branch the word was checked to be a `str` **again**, not `int`, which resulted in an [example code from the docs](https://github.com/huggingface/diffusers/tree/main/examples/community#prompt2prompt-pipeline) to result in an error
* Initial commit P2P
* Replaced CrossAttention, added test skeleton
* bug fixes
* Updated docstring
* Removed unused function
* Created tests
* improved tests
- made fast inference tests faster
- corrected image shape assertions
* Corrected expected output shape in tests
* small fix: test inputs
* Update tests
- used conditional unet2d
- set expected image slices
- edit_kwargs are now not popped, so pipe can be run multiple times
* Fixed bug in int tests
* Fixed tests
* Linting
* Create prompt2prompt.md
* Added to docs toc
* Ran make fix-copies
* Fixed code blocks in docs
* Using same interface as StableDiffusionPipeline
* Fixed small test bug
* Added all options SDPipeline.__call_ has
* Fixed docstring; made __call__ like in SD
* Linting
* Added test for multiple prompts
* Improved docs
* Incorporated feedback
* Reverted formatting on unrelated files
* Moved prompt2prompt to community
- Moved prompt2prompt pipeline from main to community
- Deleted tests
- Moved documentation to community and shorted it
* Update src/diffusers/utils/dummy_torch_and_transformers_objects.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* add t2i_example script
* remove in channels logic
* remove comments
* remove use_euler arg
* add requirements
* only use canny example
* use datasets
* comments
* make log_validation consistent with other scripts
* add readme
* fix title in readme
* update check_min_version
* change a few minor things.
* add doc entry
* add: test for t2i adapter training
* remove use_auth_token
* fix: logged info.
* remove tests for now.
---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* Add --vae_precision option to the SDXL pix2pix script so that we have the option of avoiding float32 overhead
* style
---------
Co-authored-by: bghira <bghira@users.github.com>
* Fix potential type conversion errors in SDXL pipelines
* make sure vae stays in fp16
---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Increase min accelerate ver to avoid OOM when mixed precision
* Rm re-instantiation of VAE
* Rm casting to float32
* Del unused models and free GPU
* Fix style
* Update textual_inversion.py
fixed safe_path bug in textual inversion training
* Update test_examples.py
update test_textual_inversion for updating saved file's name
* Update textual_inversion.py
fixed some formatting issues
---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* empty PR
* init
* changes
* starting with the pipeline
* stable diff
* prev
* more things, getting started
* more functions
* makeing it more readable
* almost done testing
* var changes
* testing
* device
* device support
* maybe
* device malfunctions
* new new
* register
* testing
* exec does not work
* float
* change info
* change of architecture
* might work
* testing with colab
* more attn atuff
* stupid additions
* documenting and testing
* writing tests
* more docs
* tests and docs
* remove test
* empty PR
* init
* changes
* starting with the pipeline
* stable diff
* prev
* more things, getting started
* more functions
* makeing it more readable
* almost done testing
* var changes
* testing
* device
* device support
* maybe
* device malfunctions
* new new
* register
* testing
* exec does not work
* float
* change info
* change of architecture
* might work
* testing with colab
* more attn atuff
* stupid additions
* documenting and testing
* writing tests
* more docs
* tests and docs
* remove test
* change cross attention
* revert back
* tests
* reverting back to orig
* changes
* test passing
* pipeline changes
* before quality
* quality checks pass
* remove print statements
* doc fixes
* __init__ error something
* update docs, working on dim
* working on encoding
* doc fix
* more fixes
* no more dependent on 512*512
* update docs
* fixes
* test passing
* remove comment
* fixes and migration
* simpler tests
* doc changes
* green CI
* changes
* more docs
* changes
* new images
* to community examples
* selete
* more fixes
* changes
* fix
---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* dreambooth training
* train_dreambooth validation scheduler
* set a particular scheduler via a string
* modify readme after setting a particular scheduler via a string
* modify readme after setting a particular scheduler
* use importlib to set a particular scheduler
* import with correct sort
* Add SDXL long weighted prompt pipeline
* Add SDXL long weighted prompt pipeline usage sample in the readme document
* Add SDXL long weighted prompt pipeline usage sample in the readme document, add result image
