* add paint by example
* mkae loading possibel
* up
* Update src/diffusers/models/attention.py
* up
* finalize weight structure
* make example work
* make it work
* up
* up
* fix
* del
* add
* update
* Apply suggestions from code review
* correct transformer 2d
* finish
* up
* up
* up
* up
* fix
* Apply suggestions from code review
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* Apply suggestions from code review
* up
* finish
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* add AudioDiffusionPipeline and LatentAudioDiffusionPipeline
* add docs to toc
* fix tests
* fix tests
* fix tests
* fix tests
* fix tests
* Update pr_tests.yml
Fix tests
* parent 499ff34b3edc3e0c506313ab48f21514d8f58b09
author teticio <teticio@gmail.com> 1668765652 +0000
committer teticio <teticio@gmail.com> 1669041721 +0000
parent 499ff34b3edc3e0c506313ab48f21514d8f58b09
author teticio <teticio@gmail.com> 1668765652 +0000
committer teticio <teticio@gmail.com> 1669041704 +0000
add colab notebook
[Flax] Fix loading scheduler from subfolder (#1319)
[FLAX] Fix loading scheduler from subfolder
Fix/Enable all schedulers for in-painting (#1331)
* inpaint fix k lms
* onnox as well
* up
Correct path to schedlure (#1322)
* [Examples] Correct path
* uP
Avoid nested fix-copies (#1332)
* Avoid nested `# Copied from` statements during `make fix-copies`
* style
Fix img2img speed with LMS-Discrete Scheduler (#896)
Casting `self.sigmas` into a different dtype (the one of original_samples) is not advisable. In my img2img pipeline this leads to a long running time in the `integrate.quad` call later on- by long I mean more than 10x slower.
Co-authored-by: Anton Lozhkov <anton@huggingface.co>
Fix the order of casts for onnx inpainting (#1338)
Legacy Inpainting Pipeline for Onnx Models (#1237)
* Add legacy inpainting pipeline compatibility for onnx
* remove commented out line
* Add onnx legacy inpainting test
* Fix slow decorators
* pep8 styling
* isort styling
* dummy object
* ordering consistency
* style
* docstring styles
* Refactor common prompt encoding pattern
* Update tests to permanent repository home
* support all available schedulers until ONNX IO binding is available
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>
* updated styling from PR suggested feedback
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>
Jax infer support negative prompt (#1337)
* support negative prompts in sd jax pipeline
* pass batched neg_prompt
* only encode when negative prompt is None
Co-authored-by: Juan Acevedo <jfacevedo@google.com>
Update README.md: Minor change to Imagic code snippet, missing dir error (#1347)
Minor change to Imagic Readme
Missing dir causes an error when running the example code.
make style
change the sample model (#1352)
* Update alt_diffusion.mdx
* Update alt_diffusion.mdx
Add bit diffusion [WIP] (#971)
* Create bit_diffusion.py
Bit diffusion based on the paper, arXiv:2208.04202, Chen2022AnalogBG
* adding bit diffusion to new branch
ran tests
* tests
* tests
* tests
* tests
* removed test folders + added to README
* Update README.md
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* move Mel to module in pipeline construction, make librosa optional
* fix imports
* fix copy & paste error in comment
* fix style
* add missing register_to_config
* fix class docstrings
* fix class docstrings
* tweak docstrings
* tweak docstrings
* update slow test
* put trailing commas back
* respect alphabetical order
* remove LatentAudioDiffusion, make vqvae optional
* move Mel from models back to pipelines :-)
* allow loading of pretrained audiodiffusion models
* fix tests
* fix dummies
* remove reference to latent_audio_diffusion in docs
* unused import
* inherit from SchedulerMixin to make loadable
* Apply suggestions from code review
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* feat: switch core pipelines to use image arg
* test: update tests for core pipelines
* feat: switch examples to use image arg
* docs: update docs to use image arg
* style: format code using black and doc-builder
* fix: deprecate use of init_image in all pipelines
* StableDiffusionUpscalePipeline
* fix a few things
* make it better
* fix image batching
* run vae in fp32
* fix docstr
* resize to mul of 64
* doc
* remove safety_checker
* add max_noise_level
* fix Copied
* begin tests
* slow tests
* default max_noise_level
* remove kwargs
* doc
* fix
* fix fast tests
* fix fast tests
* no sf
* don't offload vae
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* up
* convert dual unet
* revert dual attn
* adapt for vd-official
* test the full pipeline
* mixed inference
* mixed inference for text2img
* add image prompting
* fix clip norm
* split text2img and img2img
* fix format
* refactor text2img
* mega pipeline
* add optimus
* refactor image var
* wip text_unet
* text unet end to end
* update tests
* reshape
* fix image to text
* add some first docs
* dual guided pipeline
* fix token ratio
* propose change
* dual transformer as a native module
* DualTransformer(nn.Module)
* DualTransformer(nn.Module)
* correct unconditional image
* save-load with mega pipeline
* remove image to text
* up
* uP
* fix
* up
* final fix
* remove_unused_weights
* test updates
* save progress
* uP
* fix dual prompts
* some fixes
* finish
* style
* finish renaming
* up
* fix
* fix
* fix
* finish
Co-authored-by: anton-l <anton@huggingface.co>
* add conversion script for vae
* up
* up
* some fixes
* add text model
* use the correct config
* add docs
* move model in it's own file
* move model in its own file
* pass attenion mask to text encoder
* pass attn mask to uncond inputs
* quality
* fix image2image
* add imag2image in init
* fix import
* fix one more import
* fix import, dummy objetcs
* fix copied from
* up
* finish
Co-authored-by: patil-suraj <surajp815@gmail.com>
* add conversion script for vae
* uP
* uP
* more changes
* push
* up
* finish again
* up
* up
* up
* up
* finish
* up
* uP
* up
* Apply suggestions from code review
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Anton Lozhkov <anton@huggingface.co>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* up
* up
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Anton Lozhkov <anton@huggingface.co>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* re-add RL model code
* match model forward api
* add register_to_config, pass training tests
* fix tests, update forward outputs
* remove unused code, some comments
* add to docs
* remove extra embedding code
* unify time embedding
* remove conv1d output sequential
* remove sequential from conv1dblock
* style and deleting duplicated code
* clean files
* remove unused variables
* clean variables
* add 1d resnet block structure for downsample
* rename as unet1d
* fix renaming
* rename files
* add get_block(...) api
* unify args for model1d like model2d
* minor cleaning
* fix docs
* improve 1d resnet blocks
* fix tests, remove permuts
* fix style
* add output activation
* rename flax blocks file
* Add Value Function and corresponding example script to Diffuser implementation (#884)
* valuefunction code
* start example scripts
* missing imports
* bug fixes and placeholder example script
* add value function scheduler
* load value function from hub and get best actions in example
* very close to working example
* larger batch size for planning
* more tests
* merge unet1d changes
* wandb for debugging, use newer models
* success!
* turns out we just need more diffusion steps
* run on modal
* merge and code cleanup
* use same api for rl model
* fix variance type
* wrong normalization function
* add tests
* style
* style and quality
* edits based on comments
* style and quality
* remove unused var
* hack unet1d into a value function
* add pipeline
* fix arg order
* add pipeline to core library
* community pipeline
* fix couple shape bugs
* style
* Apply suggestions from code review
Co-authored-by: Nathan Lambert <nathan@huggingface.co>
* update post merge of scripts
* add mdiblock / outblock architecture
* Pipeline cleanup (#947)
* valuefunction code
* start example scripts
* missing imports
* bug fixes and placeholder example script
* add value function scheduler
* load value function from hub and get best actions in example
* very close to working example
* larger batch size for planning
* more tests
* merge unet1d changes
* wandb for debugging, use newer models
* success!
* turns out we just need more diffusion steps
* run on modal
* merge and code cleanup
* use same api for rl model
* fix variance type
* wrong normalization function
* add tests
* style
* style and quality
* edits based on comments
* style and quality
* remove unused var
* hack unet1d into a value function
* add pipeline
* fix arg order
* add pipeline to core library
* community pipeline
* fix couple shape bugs
* style
* Apply suggestions from code review
* clean up comments
* convert older script to using pipeline and add readme
* rename scripts
* style, update tests
* delete unet rl model file
* remove imports in src
Co-authored-by: Nathan Lambert <nathan@huggingface.co>
* Update src/diffusers/models/unet_1d_blocks.py
* Update tests/test_models_unet.py
* RL Cleanup v2 (#965)
* valuefunction code
* start example scripts
* missing imports
* bug fixes and placeholder example script
* add value function scheduler
* load value function from hub and get best actions in example
* very close to working example
* larger batch size for planning
* more tests
* merge unet1d changes
* wandb for debugging, use newer models
* success!
* turns out we just need more diffusion steps
* run on modal
* merge and code cleanup
* use same api for rl model
* fix variance type
* wrong normalization function
* add tests
* style
* style and quality
* edits based on comments
* style and quality
* remove unused var
* hack unet1d into a value function
* add pipeline
* fix arg order
* add pipeline to core library
* community pipeline
* fix couple shape bugs
* style
* Apply suggestions from code review
* clean up comments
* convert older script to using pipeline and add readme
* rename scripts
* style, update tests
* delete unet rl model file
* remove imports in src
* add specific vf block and update tests
* style
* Update tests/test_models_unet.py
Co-authored-by: Nathan Lambert <nathan@huggingface.co>
* fix quality in tests
* fix quality style, split test file
* fix checks / tests
* make timesteps closer to main
* unify block API
* unify forward api
* delete lines in examples
* style
* examples style
* all tests pass
* make style
* make dance_diff test pass
* Refactoring RL PR (#1200)
* init file changes
* add import utils
* finish cleaning files, imports
* remove import flags
* clean examples
* fix imports, tests for merge
* update readmes
* hotfix for tests
* quality
* fix some tests
* change defaults
* more mps test fixes
* unet1d defaults
* do not default import experimental
* defaults for tests
* fix tests
* fix-copies
* fix
* changes per Patrik's comments (#1285)
* changes per Patrik's comments
* update conversion script
* fix renaming
* skip more mps tests
* last test fix
* Update examples/rl/README.md
Co-authored-by: Ben Glickenhaus <benglickenhaus@gmail.com>
* Add a reference to the name 'Sampler'
- Facilitate people that are familiar with the name samplers to understand that we call that schedulers
- Better SEO if people are googling for samplers to find our library as well
* Update README.md with a reference to 'Sampler'
* make accelerate hard dep
* default fast init
* move params to cpu when device map is None
* handle device_map=None
* handle torch < 1.9
* remove device_map="auto"
* style
* add accelerate in torch extra
* remove accelerate from extras["test"]
* raise an error if torch is available but not accelerate
* update installation docs
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* improve defautl loading speed even further, allow disabling fats loading
* address review comments
* adapt the tests
* fix test_stable_diffusion_fast_load
* fix test_read_init
* temp fix for dummy checks
* Trigger Build
* Apply suggestions from code review
Co-authored-by: Anton Lozhkov <anton@huggingface.co>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Anton Lozhkov <anton@huggingface.co>
* Changes for VQ-diffusion VQVAE
Add specify dimension of embeddings to VQModel:
`VQModel` will by default set the dimension of embeddings to the number
of latent channels. The VQ-diffusion VQVAE has a smaller
embedding dimension, 128, than number of latent channels, 256.
Add AttnDownEncoderBlock2D and AttnUpDecoderBlock2D to the up and down
unet block helpers. VQ-diffusion's VQVAE uses those two block types.
* Changes for VQ-diffusion transformer
Modify attention.py so SpatialTransformer can be used for
VQ-diffusion's transformer.
SpatialTransformer:
- Can now operate over discrete inputs (classes of vector embeddings) as well as continuous.
- `in_channels` was made optional in the constructor so two locations where it was passed as a positional arg were moved to kwargs
- modified forward pass to take optional timestep embeddings
ImagePositionalEmbeddings:
- added to provide positional embeddings to discrete inputs for latent pixels
BasicTransformerBlock:
- norm layers were made configurable so that the VQ-diffusion could use AdaLayerNorm with timestep embeddings
- modified forward pass to take optional timestep embeddings
CrossAttention:
- now may optionally take a bias parameter for its query, key, and value linear layers
FeedForward:
- Internal layers are now configurable
ApproximateGELU:
- Activation function in VQ-diffusion's feedforward layer
AdaLayerNorm:
- Norm layer modified to incorporate timestep embeddings
* Add VQ-diffusion scheduler
* Add VQ-diffusion pipeline
* Add VQ-diffusion convert script to diffusers
* Add VQ-diffusion dummy objects
* Add VQ-diffusion markdown docs
* Add VQ-diffusion tests
* some renaming
* some fixes
* more renaming
* correct
* fix typo
* correct weights
* finalize
* fix tests
* Apply suggestions from code review
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>
* Apply suggestions from code review
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* finish
* finish
* up
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* feat: add repaint
* fix: fix quality check with `make fix-copies`
* fix: remove old unnecessary arg
* chore: change default to DDPM (looks better in experiments)
* ".to(device)" changed to "device="
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>
* make generator device-specific
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>
* make generator device-specific and change shape
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>
* fix: add preprocessing for image and mask
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>
* fix: update test
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>
* Update src/diffusers/pipelines/repaint/pipeline_repaint.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Add docs and examples
* Fix toctree
Co-authored-by: fja <fja@zurich.ibm.com>
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Anton Lozhkov <anton@huggingface.co>
* 2x speedup using memory efficient attention
* remove einops dependency
* Swap K, M in op instantiation
* Simplify code, remove unnecessary maybe_init call and function, remove unused self.scale parameter
* make xformers a soft dependency
* remove one-liner functions
* change one letter variable to appropriate names
* Remove Env variable dependency, remove MemoryEfficientCrossAttention class and use enable_xformers_memory_efficient_attention method
* Add memory efficient attention toggle to img2img and inpaint pipelines
* Clearer management of xformers' availability
* update optimizations markdown to add info about memory efficient attention
* add benchmarks for TITAN RTX
* More detailed explanation of how the mem eff benchmark were ran
* Removing autocast from optimization markdown
* import_utils: import torch only if is available
Co-authored-by: Nouamane Tazi <nouamane98@gmail.com>
* [Better scheduler docs] Improve usage examples of schedulers
* finish
* fix warnings and add test
* finish
* more replacements
* adapt fast tests hf token
* correct more
* Apply suggestions from code review
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* Integrate compatibility with euler
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* document cpu offloading method
* address review comments
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* start
* add more logic
* Update src/diffusers/models/unet_2d_condition_flax.py
* match weights
* up
* make model work
* making class more general, fixing missed file rename
* small fix
* make new conversion work
* up
* finalize conversion
* up
* first batch of variable renamings
* remove c and c_prev var names
* add mid and out block structure
* add pipeline
* up
* finish conversion
* finish
* upload
* more fixes
* Apply suggestions from code review
* add attr
* up
* uP
* up
* finish tests
* finish
* uP
* finish
* fix test
* up
* naming consistency in tests
* Apply suggestions from code review
Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Nathan Lambert <nathan@huggingface.co>
Co-authored-by: Anton Lozhkov <anton@huggingface.co>
* remove hardcoded 16
* Remove bogus
* fix some stuff
* finish
* improve logging
* docs
* upload
Co-authored-by: Nathan Lambert <nol@berkeley.edu>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Nathan Lambert <nathan@huggingface.co>
Co-authored-by: Anton Lozhkov <anton@huggingface.co>
Suraj Patil