GitLab

* up

* fix more

* Apply suggestions from code review

* fix more

* fix more

* Check it

* Remove 16:8

* fix more

* fix more

* fix more

* up

* up

* Test only stable diffusion

* Test only two files

* up

* Try out spinning up processes that can be killed

* up

* Apply suggestions from code review

* up

* up

* add: debugging to enabling memory efficient processing

* add: better warning message.

explicit view kernel size as number elements in flattened indices

* Correct from_ckpt

* make style

* add inpaint lora scale support

* add inpaint lora scale test

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Co-authored-by: yueyang.hyy <yueyang.hyy@alibaba-inc.com>

Release large tensors in attention (as soon as they're no longer required). Reduces peak VRAM by nearly 2 GB for 1024x1024 (even after slicing), and the savings scale up with image size.

* gradient checkpointing bug fix

* bug fix; changes for reviews

* reformat

* reformat

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Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

Added bugfix using f strings.

* Added explanation of 'strength' parameter

* Added get_timesteps function which relies on new strength parameter

* Added `strength` parameter which defaults to 1.

* Swapped ordering so `noise_timestep` can be calculated before masking the image

this is required when you aren't applying 100% noise to the masked region, e.g. strength < 1.

* Added strength to check_inputs, throws error if out of range

* Changed `prepare_latents` to initialise latents w.r.t strength

inspired from the stable diffusion img2img pipeline, init latents are initialised by converting the init image into a VAE latent and adding noise (based upon the strength parameter passed in), e.g. random when strength = 1, or the init image at strength = 0.

* WIP: Added a unit test for the new strength parameter in the StableDiffusionInpaintingPipeline

still need to add correct regression values

* Created a is_strength_max to initialise from pure random noise

* Updated unit tests w.r.t new strength parameter + fixed new strength unit test

* renamed parameter to avoid confusion with variable of same name

* Updated regression values for new strength test - now passes

* removed 'copied from' comment as this method is now different and divergent from the cpy

* Update src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Ensure backwards compatibility for prepare_mask_and_masked_image

created a return_image boolean and initialised to false

* Ensure backwards compatibility for prepare_latents

* Fixed copy check typo

* Fixes w.r.t backward compibility changes

* make style

* keep function argument ordering same for backwards compatibility in callees with copied from statements

* make fix-copies

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Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: William Berman <WLBberman@gmail.com>

* Update pipeline_if_superresolution.py

Allow arbitrary aspect ratio in IFSuperResolutionPipeline by using the input image shape

* IFSuperResolutionPipeline: allow the user to override the height and width through the arguments

* update IFSuperResolutionPipeline width/height doc string to match StableDiffusionInpaintPipeline conventions

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Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

fix tiled vae bleand extent range

Fix incomplete docstrings for resnet.py

* Add DPM-Solver Multistep Inverse Scheduler

* Add draft tests for DiffEdit

* Add inverse sde-dpmsolver steps to tune image diversity from inverted latents

* Fix tests

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Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* refactor controlnet and add img2img and inpaint

* First draft to get pipelines to work

* make style

* Fix more

* Fix more

* More tests

* Fix more

* Make inpainting work

* make style and more tests

* Apply suggestions from code review

* up

* make style

* Fix imports

* Fix more

* Fix more

* Improve examples

* add test

* Make sure import is correctly deprecated

* Make sure everything works in compile mode

* make sure authorship is correctly attributed

* Replace `AttentionBlock` with `Attention`

* use _from_deprecated_attn_block check re: @patrickvonplaten

* Add `sigmoid` beta scheduler to `DDPMScheduler` docstring

* Add `sigmoid` beta scheduler to `RePaintScheduler` docstring

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Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Improve checkpointing lora

* fix more

* Improve doc string

* Update src/diffusers/loaders.py

* make stytle

* Apply suggestions from code review

* Update src/diffusers/loaders.py

* Apply suggestions from code review

* Apply suggestions from code review

* better

* Fix all

* Fix multi-GPU dreambooth

* Apply suggestions from code review
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* Fix all

* make style

* make style

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Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

Add omegaconfg

* [deepspeed] partial ZeRO-3 support

* cleanup

* improve deepspeed fixes

* Improve

* make style

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Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* add inferring_controlnet_cond_batch

* Revert "add inferring_controlnet_cond_batch"

This reverts commit abe8d6311d4b7f5b9409ca709c7fabf80d06c1a9.

* set guess_mode to True
whenever global_pool_conditions is True
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* nit

* add integration test

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Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* StableDiffusionInpaintingPipeline now resizes input images and masks w.r.t to passed input height and width. Default is already set to 512. This addresses the common tensor mismatch error. Also moved type check into relevant funciton to keep main pipeline body tidy.

* Fixed StableDiffusionInpaintingPrepareMaskAndMaskedImageTests

Due to previous commit these tests were failing as height and width need to be passed into the prepare_mask_and_masked_image function, I have updated the code and added a height/width variable per unit test as it seemed more appropriate than the current hard coded solution

* Added a resolution test to StableDiffusionInpaintPipelineSlowTests

this unit test simply gets the input and resizes it into some that would fail (e.g. would throw a tensor mismatch error/not a mult of 8). Then passes it through the pipeline and verifies it produces output with correct dims w.r.t the passed height and width

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Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* add: a warning message when using xformers in a PT 2.0 env.

* Apply suggestions from code review
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>

* clarify safetensor docstring

* fix typo

* apply feedback

* add text2img

* fix-copies

* add

* add all other pipelines

* add

* add

* add

* add

* add

* make style

* style + fix copies

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Co-authored-by: yiyixuxu <yixu310@gmail,com>

* update IF stage I pipelines

add fixed variance schedulers and lora loading

* added kv lora attn processor

* allow loading into alternative lora attn processor

* make vae optional

* throw away predicted variance

* allow loading into added kv lora layer

* allow load T5

* allow pre compute text embeddings

* set new variance type in schedulers

* fix copies

* refactor all prompt embedding code

class prompts are now included in pre-encoding code
max tokenizer length is now configurable
embedding attention mask is now configurable

* fix for when variance type is not defined on scheduler

* do not pre compute validation prompt if not present

* add example test for if lora dreambooth

* add check for train text encoder and pre compute text embeddings

fix docstring
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Batched load of textual inversions

- Only call resize_token_embeddings once per batch as it is the most expensive operation
- Allow pretrained_model_name_or_path and token to be an optional list
- Remove Dict from type annotation pretrained_model_name_or_path as it was not supported in this function
- Add comment that single files (e.g. .pt/.safetensors) are supported
- Add comment for token parameter
- Convert token override log message from warning to info

* Update src/diffusers/loaders.py

Check for duplicate tokens
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update condition for None tokens

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Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* add karras sigma to lms discrete scheduler

* add test for lms_scheduler karras

* reformat test lms

* test: Added test case

* fix: fixed type checking issue on _encode_prompt

* fix: fixed copies consistency

* fix: one copy was not sufficient

The argument `upsample_size` needs to be added to these modules to allow compatibility with other blocks that require this argument.

* add SDE variant of DPM-Solver and DPM-Solver++

* add test

* fix typo

* fix typo

* fix multistep dpmsolver for cosine schedule (deepfloy-if)

* fix a typo

* Update src/diffusers/schedulers/scheduling_dpmsolver_multistep.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update src/diffusers/schedulers/scheduling_dpmsolver_multistep.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update src/diffusers/schedulers/scheduling_dpmsolver_multistep.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update src/diffusers/schedulers/scheduling_dpmsolver_multistep.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update src/diffusers/schedulers/scheduling_dpmsolver_multistep.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* update all dpmsolver (singlestep, multistep, dpm, dpm++) for cosine noise schedule

* add test, fix style

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Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

Fix missing variable assign

lol