* Add draft for lora text encoder scale
* Improve naming
* fix: training dreambooth lora script.
* Apply suggestions from code review
* Update examples/dreambooth/train_dreambooth_lora.py
* Apply suggestions from code review
* Apply suggestions from code review
* add lora mixin when fit
* add lora mixin when fit
* add lora mixin when fit
* fix more
* fix more
---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* add _convert_kohya_lora_to_diffusers
* make style
* add scaffold
* match result: unet attention only
* fix monkey-patch for text_encoder
* with CLIPAttention
While the terrible images are no longer produced,
the results do not match those from the hook ver.
This may be due to not setting the network_alpha value.
* add to support network_alpha
* generate diff image
* fix monkey-patch for text_encoder
* add test_text_encoder_lora_monkey_patch()
* verify that it's okay to release the attn_procs
* fix closure version
* add comment
* Revert "fix monkey-patch for text_encoder"
This reverts commit bb9c61e6fa.
* Fix to reuse utility functions
* make LoRAAttnProcessor targets to self_attn
* fix LoRAAttnProcessor target
* make style
* fix split key
* Update src/diffusers/loaders.py
* remove TEXT_ENCODER_TARGET_MODULES loop
* add print memory usage
* remove test_kohya_loras_scaffold.py
* add: doc on LoRA civitai
* remove print statement and refactor in the doc.
* fix state_dict test for kohya-ss style lora
* Apply suggestions from code review
Co-authored-by: Takuma Mori <takuma104@gmail.com>
---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* Run ControlNet compile test in a separate subprocess
`torch.compile()` spawns several subprocesses and the GPU memory used
was not reclaimed after the test ran. This approach was taken from
`transformers`.
* Style
* Prepare a couple more compile tests to run in subprocess.
* Use require_torch_2 decorator.
* Test inpaint_compile in subprocess.
* Run img2img compile test in subprocess.
* Run stable diffusion compile test in subprocess.
* style
* Temporarily trigger on pr to test.
* Revert "Temporarily trigger on pr to test."
This reverts commit 82d76868dd.
* Cross-attention masks
prefer qualified symbol, fix accidental Optional
prefer qualified symbol in AttentionProcessor
prefer qualified symbol in embeddings.py
qualified symbol in transformed_2d
qualify FloatTensor in unet_2d_blocks
move new transformer_2d params attention_mask, encoder_attention_mask to the end of the section which is assumed (e.g. by functions such as checkpoint()) to have a stable positional param interface. regard return_dict as a special-case which is assumed to be injected separately from positional params (e.g. by create_custom_forward()).
move new encoder_attention_mask param to end of CrossAttn block interfaces and Unet2DCondition interface, to maintain positional param interface.
regenerate modeling_text_unet.py
remove unused import
unet_2d_condition encoder_attention_mask docs
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
versatile_diffusion/modeling_text_unet.py encoder_attention_mask docs
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
transformer_2d encoder_attention_mask docs
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
unet_2d_blocks.py: add parameter name comments
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
revert description. bool-to-bias treatment happens in unet_2d_condition only.
comment parameter names
fix copies, style
* encoder_attention_mask for SimpleCrossAttnDownBlock2D, SimpleCrossAttnUpBlock2D
* encoder_attention_mask for UNetMidBlock2DSimpleCrossAttn
* support attention_mask, encoder_attention_mask in KCrossAttnDownBlock2D, KCrossAttnUpBlock2D, KAttentionBlock. fix binding of attention_mask, cross_attention_kwargs params in KCrossAttnDownBlock2D, KCrossAttnUpBlock2D checkpoint invocations.
* fix mistake made during merge conflict resolution
* regenerate versatile_diffusion
* pass time embedding into checkpointed attention invocation
* always assume encoder_attention_mask is a mask (i.e. not a bias).
* style, fix-copies
* add tests for cross-attention masks
* add test for padding of attention mask
* explain mask's query_tokens dim. fix explanation about broadcasting over channels; we actually broadcast over query tokens
* support both masks and biases in Transformer2DModel#forward. document behaviour
* fix-copies
* delete attention_mask docs on the basis I never tested self-attention masking myself. not comfortable explaining it, since I don't actually understand how a self-attn mask can work in its current form: the key length will be different in every ResBlock (we don't downsample the mask when we downsample the image).
* review feedback: the standard Unet blocks shouldn't pass temb to attn (only to resnet). remove from KCrossAttnDownBlock2D,KCrossAttnUpBlock2D#forward.
* remove encoder_attention_mask param from SimpleCrossAttn{Up,Down}Block2D,UNetMidBlock2DSimpleCrossAttn, and mask-choice in those blocks' #forward, on the basis that they only do one type of attention, so the consumer can pass whichever type of attention_mask is appropriate.
* put attention mask padding back to how it was (since the SD use-case it enabled wasn't important, and it breaks the original unclip use-case). disable the test which was added.
* fix-copies
* style
* fix-copies
* put encoder_attention_mask param back into Simple block forward interfaces, to ensure consistency of forward interface.
* restore passing of emb to KAttentionBlock#forward, on the basis that removal caused test failures. restore also the passing of emb to checkpointed calls to KAttentionBlock#forward.
* make simple unet2d blocks use encoder_attention_mask, but only when attention_mask is None. this should fix UnCLIP compatibility.
* fix copies
* 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
* enable deterministic pytorch and cuda operations.
* disable manual seeding.
* make style && make quality for unet_2d tests.
* enable determinism for the unet2dconditional model.
* add CUBLAS_WORKSPACE_CONFIG for better reproducibility.
* relax tolerance (very weird issue, though).
* revert to torch manual_seed() where needed.
* relax more tolerance.
* better placement of the cuda variable and relax more tolerance.
* enable determinism for 3d condition model.
* relax tolerance.
* add: determinism to alt_diffusion.
* relax tolerance for alt diffusion.
* dance diffusion.
* dance diffusion is flaky.
* test_dict_tuple_outputs_equivalent edit.
* fix two more tests.
* fix more ddim tests.
* fix: argument.
* change to diff in place of difference.
* fix: test_save_load call.
* test_save_load_float16 call.
* fix: expected_max_diff
* fix: paint by example.
* relax tolerance.
* add determinism to 1d unet model.
* torch 2.0 regressions seem to be brutal
* determinism to vae.
* add reason to skipping.
* up tolerance.
* determinism to vq.
* determinism to cuda.
* determinism to the generic test pipeline file.
* refactor general pipelines testing a bit.
* determinism to alt diffusion i2i
* up tolerance for alt diff i2i and audio diff
* up tolerance.
* determinism to audioldm
* increase tolerance for audioldm lms.
* increase tolerance for paint by paint.
* increase tolerance for repaint.
* determinism to cycle diffusion and sd 1.
* relax tol for cycle diffusion 🚲
* relax tol for sd 1.0
* relax tol for controlnet.
* determinism to img var.
* relax tol for img variation.
* tolerance to i2i sd
* make style
* determinism to inpaint.
* relax tolerance for inpaiting.
* determinism for inpainting legacy
* relax tolerance.
* determinism to instruct pix2pix
* determinism to model editing.
* model editing tolerance.
* panorama determinism
* determinism to pix2pix zero.
* determinism to sag.
* sd 2. determinism
* sd. tolerance
* disallow tf32 matmul.
* relax tolerance is all you need.
* make style and determinism to sd 2 depth
* relax tolerance for depth.
* tolerance to diffedit.
* tolerance to sd 2 inpaint.
* up tolerance.
* determinism in upscaling.
* tolerance in upscaler.
* more tolerance relaxation.
* determinism to v pred.
* up tol for v_pred
* unclip determinism
* determinism to unclip img2img
* determinism to text to video.
* determinism to last set of tests
* up tol.
* vq cumsum doesn't have a deterministic kernel
* relax tol
* relax tol
* inital commit for lora test cases
* help a bit with lora for 3d
* fixed lora tests
* replaced redundant code
---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* add only cross attention to simple attention blocks
* add test for only_cross_attention re: @patrickvonplaten
* mid_block_only_cross_attention better default
allow mid_block_only_cross_attention to default to
`only_cross_attention` when `only_cross_attention` is given
as a single boolean
* [Config] Fix config prints and save, load
* Only use potential nn.Modules for dtype and device
* Correct vae image processor
* make sure in_channels is not accessed directly
* make sure in channels is only accessed via config
* Make sure schedulers only access config attributes
* Make sure to access config in SAG
* Fix vae processor and make style
* add tests
* uP
* make style
* Fix more naming issues
* Final fix with vae config
* change more
* [MS Text To Video} Add first text to video
* upload
* make first model example
* match unet3d params
* make sure weights are correcctly converted
* improve
* forward pass works, but diff result
* make forward work
* fix more
* finish
* refactor video output class.
* feat: add support for a video export utility.
* fix: opencv availability check.
* run make fix-copies.
* add: docs for the model components.
* add: standalone pipeline doc.
* edit docstring of the pipeline.
* add: right path to TransformerTempModel
* add: first set of tests.
* complete fast tests for text to video.
* fix bug
* up
* three fast tests failing.
* add: note on slow tests
* make work with all schedulers
* apply styling.
* add slow tests
* change file name
* update
* more correction
* more fixes
* finish
* up
* Apply suggestions from code review
* up
* finish
* make copies
* fix pipeline tests
* fix more tests
* Apply suggestions from code review
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* apply suggestions
* up
* revert
---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* updated black format
* update black format
* make style format
* updated line endings
* update code formatting
* Update examples/research_projects/onnxruntime/text_to_image/train_text_to_image.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/diffusers/models/vae.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/diffusers/models/vae.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* added vae gradient checkpointing test
* make style
---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Will Berman <wlbberman@gmail.com>
* Adding `use_safetensors` argument to give more control to users
about which weights they use.
* Doc style.
* Rebased (not functional).
* Rebased and functional with tests.
* Style.
* Apply suggestions from code review
* Style.
* Addressing comments.
* Update tests/test_pipelines.py
Co-authored-by: Will Berman <wlbberman@gmail.com>
* Black ???
---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Will Berman <wlbberman@gmail.com>
* fix AttnProcessor2_0
Fix use of AttnProcessor2_0 for cross attention with mask
* added scale_qk and out_bias flags
* fixed for xformers
* check if it has scale argument
* Update cross_attention.py
* check torch version
* fix sliced attn
* style
* set scale
* fix test
* fixed addedKV processor
* revert back AttnProcessor2_0
* if missing if
* fix inner_dim
---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Skip variant tests (UNet1d, UNetRL) on mps.
mish op not yet supported.
* Exclude a couple of panorama tests on mps
They are too slow for fast CI.
* Exclude mps panorama from more tests.
* mps: exclude all fast panorama tests as they keep failing.
* Modify UNet2DConditionModel
- allow skipping mid_block
- adding a norm_group_size argument so that we can set the `num_groups` for group norm using `num_channels//norm_group_size`
- allow user to set dimension for the timestep embedding (`time_embed_dim`)
- the kernel_size for `conv_in` and `conv_out` is now configurable
- add random fourier feature layer (`GaussianFourierProjection`) for `time_proj`
- allow user to add the time and class embeddings before passing through the projection layer together - `time_embedding(t_emb + class_label))`
- added 2 arguments `attn1_types` and `attn2_types`
* currently we have argument `only_cross_attention`: when it's set to `True`, we will have a to the
`BasicTransformerBlock` block with 2 cross-attention , otherwise we
get a self-attention followed by a cross-attention; in k-upscaler, we need to have blocks that include just one cross-attention, or self-attention -> cross-attention;
so I added `attn1_types` and `attn2_types` to the unet's argument list to allow user specify the attention types for the 2 positions in each block; note that I stil kept
the `only_cross_attention` argument for unet for easy configuration, but it will be converted to `attn1_type` and `attn2_type` when passing down to the down blocks
- the position of downsample layer and upsample layer is now configurable
- in k-upscaler unet, there is only one skip connection per each up/down block (instead of each layer in stable diffusion unet), added `skip_freq = "block"` to support
this use case
- if user passes attention_mask to unet, it will prepare the mask and pass a flag to cross attention processer to skip the `prepare_attention_mask` step
inside cross attention block
add up/down blocks for k-upscaler
modify CrossAttention class
- make the `dropout` layer in `to_out` optional
- `use_conv_proj` - use conv instead of linear for all projection layers (i.e. `to_q`, `to_k`, `to_v`, `to_out`) whenever possible. note that when it's used to do cross
attention, to_k, to_v has to be linear because the `encoder_hidden_states` is not 2d
- `cross_attention_norm` - add an optional layernorm on encoder_hidden_states
- `attention_dropout`: add an optional dropout on attention score
adapt BasicTransformerBlock
- add an ada groupnorm layer to conditioning attention input with timestep embedding
- allow skipping the FeedForward layer in between the attentions
- replaced the only_cross_attention argument with attn1_type and attn2_type for more flexible configuration
update timestep embedding: add new act_fn gelu and an optional act_2
modified ResnetBlock2D
- refactored with AdaGroupNorm class (the timestep scale shift normalization)
- add `mid_channel` argument - allow the first conv to have a different output dimension from the second conv
- add option to use input AdaGroupNorm on the input instead of groupnorm
- add options to add a dropout layer after each conv
- allow user to set the bias in conv_shortcut (needed for k-upscaler)
- add gelu
adding conversion script for k-upscaler unet
add pipeline
* fix attention mask
* fix a typo
* fix a bug
* make sure model can be used with GPU
* make pipeline work with fp16
* fix an error in BasicTransfomerBlock
* make style
* fix typo
* some more fixes
* uP
* up
* correct more
* some clean-up
* clean time proj
* up
* uP
* more changes
* remove the upcast_attention=True from unet config
* remove attn1_types, attn2_types etc
* fix
* revert incorrect changes up/down samplers
* make style
* remove outdated files
* Apply suggestions from code review
* attention refactor
* refactor cross attention
* Apply suggestions from code review
* update
* up
* update
* Apply suggestions from code review
* finish
* Update src/diffusers/models/cross_attention.py
* more fixes
* up
* up
* up
* finish
* more corrections of conversion state
* act_2 -> act_2_fn
* remove dropout_after_conv from ResnetBlock2D
* make style
* simplify KAttentionBlock
* add fast test for latent upscaler pipeline
* add slow test
* slow test fp16
* make style
* add doc string for pipeline_stable_diffusion_latent_upscale
* add api doc page for latent upscaler pipeline
* deprecate attention mask
* clean up embeddings
* simplify resnet
* up
* clean up resnet
* up
* correct more
* up
* up
* improve a bit more
* correct more
* more clean-ups
* Update docs/source/en/api/pipelines/stable_diffusion/latent_upscale.mdx
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update docs/source/en/api/pipelines/stable_diffusion/latent_upscale.mdx
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* add docstrings for new unet config
* Update src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* # Copied from
* encode the image if not latent
* remove force casting vae to fp32
* fix
* add comments about preconditioning parameters from k-diffusion paper
* attn1_type, attn2_type -> add_self_attention
* clean up get_down_block and get_up_block
* fix
* fixed a typo(?) in ada group norm
* update slice attention processer for cross attention
* update slice
* fix fast test
* update the checkpoint
* finish tests
* fix-copies
* fix-copy for modeling_text_unet.py
* make style
* make style
* fix f-string
* Update src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* fix import
* correct changes
* fix resnet
* make fix-copies
* correct euler scheduler
* add missing #copied from for preprocess
* revert
* fix
* fix copies
* Update docs/source/en/api/pipelines/stable_diffusion/latent_upscale.mdx
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* Update docs/source/en/api/pipelines/stable_diffusion/latent_upscale.mdx
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* Update docs/source/en/api/pipelines/stable_diffusion/latent_upscale.mdx
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* Update docs/source/en/api/pipelines/stable_diffusion/latent_upscale.mdx
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* Update src/diffusers/models/cross_attention.py
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* Update src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* Update src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* clean up conversion script
* KDownsample2d,KUpsample2d -> KDownsample2D,KUpsample2D
* more
* Update src/diffusers/models/unet_2d_condition.py
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* remove prepare_extra_step_kwargs
* Update src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* Update src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* fix a typo in timestep embedding
* remove num_image_per_prompt
* fix fasttest
* make style + fix-copies
* fix
* fix xformer test
* fix style
* doc string
* make style
* fix-copies
* docstring for time_embedding_norm
* make style
* final finishes
* make fix-copies
* fix tests
---------
Co-authored-by: yiyixuxu <yixu@yis-macbook-pro.lan>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* make tests deterministic
* run slow tests
* prepare for testing
* finish
* refactor
* add print statements
* finish more
* correct some test failures
* more fixes
* set up to correct tests
* more corrections
* up
* fix more
* more prints
* add
* up
* up
* up
* uP
* uP
* more fixes
* uP
* up
* up
* up
* up
* fix more
* up
* up
* clean tests
* up
* up
* up
* more fixes
* Apply suggestions from code review
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* make
* correct
* finish
* finish
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* [Lora] first upload
* add first lora version
* upload
* more
* first training
* up
* correct
* improve
* finish loaders and inference
* up
* up
* fix more
* up
* finish more
* finish more
* up
* up
* change year
* revert year change
* Change lines
* Add cloneofsimo as co-author.
Co-authored-by: Simo Ryu <cloneofsimo@gmail.com>
* finish
* fix docs
* Apply suggestions from code review
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* upload
* finish
Co-authored-by: Simo Ryu <cloneofsimo@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* move files a bit
* more refactors
* fix more
* more fixes
* fix more onnx
* make style
* upload
* fix
* up
* fix more
* up again
* up
* small fix
* Update src/diffusers/__init__.py
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* correct
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* first proposal
* rename
* up
* Apply suggestions from code review
* better
* up
* finish
* up
* rename
* correct versatile
* up
* up
* up
* up
* fix
* Apply suggestions from code review
* make style
* Apply suggestions from code review
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* add error message
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* make attn slice recursive
* remove set_attention_slice from blocks
* fix copies
* make enable_attention_slicing base class method of DiffusionPipeline
* fix set_attention_slice
* fix set_attention_slice
* fix copies
* add tests
* up
* up
* up
* update
* up
* uP
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
