* Add controlnet from single file
* Updates
* make style
* finish
* Apply suggestions from code review
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* feat: add act_fn param to OutValueFunctionBlock
* feat: update unet1d tests to not use mish
* feat: add `mish` as the default activation function
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* feat: drop mish tests from unet1d
---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* refactor to support patching LoRA into T5
instantiate the lora linear layer on the same device as the regular linear layer
get lora rank from state dict
tests
fmt
can create lora layer in float32 even when rest of model is float16
fix loading model hook
remove load_lora_weights_ and T5 dispatching
remove Unet#attn_processors_state_dict
docstrings
* text encoder monkeypatch class method
* fix test
---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* add default to unet output to prevent it from being a required arg
* add unit test
* make style
* adjust unit test
* mark as fast test
* adjust assert statement in test
---------
Co-authored-by: Prathik Rao <prathikrao@microsoft.com@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
Co-authored-by: root <root@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
* Improve memory text to video
* Apply suggestions from code review
* add test
* Apply suggestions from code review
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* finish test setup
---------
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* 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>
