* 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>
* Docs: refer to pre-RC version of PyTorch 1.13.0.
* Remove temporary workaround for unavailable op.
* Update comment to make it less ambiguous.
* Remove use of contiguous in mps.
It appears to not longer be necessary.
* Special case: use einsum for much better performance in mps
* Update mps docs.
* Minor doc update.
* Accept suggestion
Co-authored-by: Anton Lozhkov <anton@huggingface.co>
Co-authored-by: Anton Lozhkov <anton@huggingface.co>
* Update README.md
Additionally add FLAX so the model card can be slimmer and point to this page
* Find and replace all
* v-1-5 -> v1-5
* revert test changes
* Update README.md
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update docs/source/quicktour.mdx
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* Update README.md
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* Update docs/source/quicktour.mdx
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* Update README.md
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* Revert certain references to v1-5
* Docs changes
* Apply suggestions from code review
Co-authored-by: apolinario <joaopaulo.passos+multimodal@gmail.com>
Co-authored-by: anton-l <anton@huggingface.co>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* mps: alt. implementation for repeat_interleave
* style
* Bump mps version of PyTorch in the documentation.
* Apply suggestions from code review
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* Simplify: do not check for device.
* style
* Fix repeat dimensions:
- The unconditional embeddings are always created from a single prompt.
- I was shadowing the batch_size var.
* Split long lines as suggested by Suraj.
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* initial commit
* make UNet stream capturable
* try to fix noise_pred value
* remove cuda graph and keep NB
* non blocking unet with PNDMScheduler
* make timesteps np arrays for pndm scheduler
because lists don't get formatted to tensors in `self.set_format`
* make max async in pndm
* use channel last format in unet
* avoid moving timesteps device in each unet call
* avoid memcpy op in `get_timestep_embedding`
* add `channels_last` kwarg to `DiffusionPipeline.from_pretrained`
* update TODO
* replace `channels_last` kwarg with `memory_format` for more generality
* revert the channels_last changes to leave it for another PR
* remove non_blocking when moving input ids to device
* remove blocking from all .to() operations at beginning of pipeline
* fix merging
* fix merging
* model can run in other precisions without autocast
* attn refactoring
* Revert "attn refactoring"
This reverts commit 0c70c0e189cd2c4d8768274c9fcf5b940ee310fb.
* remove restriction to run conv_norm in fp32
* use `baddbmm` instead of `matmul`for better in attention for better perf
* removing all reshapes to test perf
* Revert "removing all reshapes to test perf"
This reverts commit 006ccb8a8c6bc7eb7e512392e692a29d9b1553cd.
* add shapes comments
* hardcore whats needed for jitting
* Revert "hardcore whats needed for jitting"
This reverts commit 2fa9c698eae2890ac5f8e367ca80532ecf94df9a.
* Revert "remove restriction to run conv_norm in fp32"
This reverts commit cec592890c32da3d1b78d38b49e4307aedf459b9.
* revert using baddmm in attention's forward
* cleanup comment
* remove restriction to run conv_norm in fp32. no quality loss was noticed
This reverts commit cc9bc1339c998ebe9e7d733f910c6d72d9792213.
* add more optimizations techniques to docs
* Revert "add shapes comments"
This reverts commit 31c58eadb8892f95478cdf05229adf678678c5f4.
* apply suggestions
* make quality
* apply suggestions
* styling
* `scheduler.timesteps` are now arrays so we dont need .to()
* remove useless .type()
* use mean instead of max in `test_stable_diffusion_inpaint_pipeline_k_lms`
* move scheduler timestamps to correct device if tensors
* add device to `set_timesteps` in LMSD scheduler
* `self.scheduler.set_timesteps` now uses device arg for schedulers that accept it
* quick fix
* styling
* remove kwargs from schedulers `set_timesteps`
* revert to using max in K-LMS inpaint pipeline test
* Revert "`self.scheduler.set_timesteps` now uses device arg for schedulers that accept it"
This reverts commit 00d5a51e5c20d8d445c8664407ef29608106d899.
* move timesteps to correct device before loop in SD pipeline
* apply previous fix to other SD pipelines
* UNet now accepts tensor timesteps even on wrong device, to avoid errors
- it shouldnt affect performance if timesteps are alrdy on correct device
- it does slow down performance if they're on the wrong device
* fix pipeline when timesteps are arrays with strides
* pytorch only schedulers
* fix style
* remove match_shape
* pytorch only ddpm
* remove SchedulerMixin
* remove numpy from karras_ve
* fix types
* remove numpy from lms_discrete
* remove numpy from pndm
* fix typo
* remove mixin and numpy from sde_vp and ve
* remove remaining tensor_format
* fix style
* sigmas has to be torch tensor
* removed set_format in readme
* remove set format from docs
* remove set_format from pipelines
* update tests
* fix typo
* continue to use mixin
* fix imports
* removed unsed imports
* match shape instead of assuming image shapes
* remove import typo
* update call to add_noise
* use math instead of numpy
* fix t_index
* removed commented out numpy tests
* timesteps needs to be discrete
* cast timesteps to int in flax scheduler too
* fix device mismatch issue
* small fix
* Update src/diffusers/schedulers/scheduling_pndm.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Fix typos
* Add a typo check action
* Fix a bug
* Changed to manual typo check currently
Ref: https://github.com/huggingface/diffusers/pull/483#pullrequestreview-1104468010
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>
* Removed a confusing message
* Renamed "nin_shortcut" to "in_shortcut"
* Add memo about NIN
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>
* docs for attention
* types for embeddings
* unet2d docstrings
* UNet2DConditionModel docstrings
* fix typos
* style and vq-vae docstrings
* docstrings for VAE
* Update src/diffusers/models/unet_2d.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* make style
* added inherits from sentence
* docstring to forward
* make style
* Apply suggestions from code review
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* finish model docs
* up
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* Initial version of `fp16` page.
* Fix typo in README.
* Change titles of fp16 section in toctree.
* PR suggestion
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* PR suggestion
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Clarify attention slicing is useful even for batches of 1
Explained by @patrickvonplaten after a suggestion by @keturn.
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Do not talk about `batches` in `enable_attention_slicing`.
* Use Tip (just for fun), add link to method.
* Comment about fp16 results looking the same as float32 in practice.
* Style: docstring line wrapping.
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* init schedulers docs
* add some docstrings, fix sidebar formatting
* add docstrings
* [Type hint] PNDM schedulers (#335)
* [Type hint] PNDM Schedulers
* ran make style
* updated timesteps type hint
* apply suggestions from code review
* ran make style
* removed unused import
* [Type hint] scheduling ddim (#343)
* [Type hint] scheduling ddim
* apply suggestions from code review
apply suggestions to also return the return type
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* make style
* update class docstrings
* add docstrings
* missed merge edit
* add general docs page
* modify headings for right sidebar
Co-authored-by: Partho <parthodas6176@gmail.com>
Co-authored-by: Santiago Víquez <santi.viquez@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Initial description of Stable Diffusion pipeline.
* Placeholder docstrings to test preview.
* Add docstrings to Stable Diffusion pipeline.
* Style
* Docs for all the SD pipelines + attention slicing.
* Style: wrap long lines.
* Update text_inversion.mdx
Getting in a bit of background info
* fixed typo mode -> model
* Link SD and re-write a few bits for clarity
* Copied in info from the example script
As suggested by surajpatil :)
* removed an unnecessary heading
fboulnois