* Added mark_step for sdxl to run with pytorch xla. Also updated README with instructions for xla
* adding soft dependency on torch_xla
* fix some styling
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Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>
* fix(gligen_inpaint_pipeline): 🐛 Wrap the timestep() 0-d tensor in a list to convert to 1-d tensor. This avoids the TypeError caused by trying to directly iterate over a 0-dimensional tensor in the denoising stage
* test(gligen/gligen_text_image): unit test using the EulerAncestralDiscreteScheduler
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Co-authored-by: zhen-hao.chu <zhen-hao.chu@vitrox.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
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>
* 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
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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
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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
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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
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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>
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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.
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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
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Co-authored-by: bghira <bghira@users.github.com>
* Fix potential type conversion errors in SDXL pipelines
* make sure vae stays in fp16
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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
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Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
