- 27 Mar, 2023 2 commits
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Pedro Cuenca authored
* Helper function to disable custom attention processors. * Restore code deleted by mistake. * Format * Fix modeling_text_unet copy.
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Pedro Cuenca authored
* Apply same ruff settings as in transformers See https://github.com/huggingface/transformers/blob/main/pyproject.toml Co-authored-by:
Aaron Gokaslan <aaronGokaslan@gmail.com> * Apply new style rules * Style Co-authored-by:
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- 23 Mar, 2023 4 commits
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Sanchit Gandhi authored
* Add AudioLDM * up * add vocoder * start unet * unconditional unet * clap, vocoder and vae * clean-up: conversion scripts * fix: conversion script token_type_ids * clean-up: pipeline docstring * tests: from SD * clean-up: cpu offload vocoder instead of safety checker * feat: adapt tests to audioldm * feat: add docs * clean-up: amend pipeline docstrings * clean-up: make style * clean-up: make fix-copies * fix: add doc path to toctree * clean-up: args for conversion script * clean-up: paths to checkpoints * fix: use conditional unet * clean-up: make style * fix: type hints for UNet * clean-up: docstring for UNet * clean-up: make style * clean-up: remove duplicate in docstring * clean-up: make style * clean-up: make fix-copies * clean-up: move imports to start in code snippet * fix: pass cross_attention_dim as a list/tuple to unet * clean-up: make fix-copies * fix: update checkpoint path * fix: unet cross_attention_dim in tests * film embeddings -> class embeddings * Apply suggestions from code review Co-authored-by:
Will Berman <wlbberman@gmail.com> * fix: unet film embed to use existing args * fix: unet tests to use existing args * fix: make style * fix: transformers import and version in init * clean-up: make style * Revert "clean-up: make style" This reverts commit 5d6d1f8b324f5583e7805dc01e2c86e493660d66. * clean-up: make style * clean-up: use pipeline tester mixin tests where poss * clean-up: skip attn slicing test * fix: add torch dtype to docs * fix: remove conversion script out of src * fix: remove .detach from 1d waveform * fix: reduce default num inf steps * fix: swap height/width -> audio_length_in_s * clean-up: make style * fix: remove nightly tests * fix: imports in conversion script * clean-up: slim-down to two slow tests * clean-up: slim-down fast tests * fix: batch consistent tests * clean-up: make style * clean-up: remove vae slicing fast test * clean-up: propagate changes to doc * fix: increase test tol to 1e-2 * clean-up: finish docs * clean-up: make style * feat: vocoder / VAE compatibility check * feat: possibly expand / cut audio waveform * fix: pipeline call signature test * fix: slow tests output len * clean-up: make style * make style --------- Co-authored-by:
Patrick von Platen <patrick.v.platen@gmail.com> Co-authored-by:
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YiYi Xu authored
* add contronet flax --------- Co-authored-by:yiyixuxu <yixu310@gmail,com>
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Kashif Rasul authored
* initial TokenEncoder and ContinuousEncoder * initial modules * added ContinuousContextTransformer * fix copy paste error * use numpy for get_sequence_length * initial terminal relative positional encodings * fix weights keys * fix assert * cross attend style: concat encodings * make style * concat once * fix formatting * Initial SpectrogramPipeline * fix input_tokens * make style * added mel output * ignore weights for config * move mel to numpy * import pipeline * fix class names and import * moved models to models folder * import ContinuousContextTransformer and SpectrogramDiffusionPipeline * initial spec diffusion converstion script * renamed config to t5config * added weight loading * use arguments instead of t5config * broadcast noise time to batch dim * fix call * added scale_to_features * fix weights * transpose laynorm weight * scale is a vector * scale the query outputs * added comment * undo scaling * undo depth_scaling * inital get_extended_attention_mask * attention_mask is none in self-attention * cleanup * manually invert attention * nn.linear need bias=False * added T5LayerFFCond * remove to fix conflict * make style and dummy * remove unsed variables * remove predict_epsilon * Move accelerate to a soft-dependency (#1134) * finish * finish * Update src/diffusers/modeling_utils.py * Update src/diffusers/pipeline_utils.py Co-authored-by:
Anton Lozhkov <anton@huggingface.co> * more fixes * fix Co-authored-by:
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Patrick von Platen authored
* [UNet3DModel] Fix attn processor * make style
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- 22 Mar, 2023 1 commit
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Patrick von Platen authored
* [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:
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- 21 Mar, 2023 1 commit
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Alexander Pivovarov authored
Co-authored-by:
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- 17 Mar, 2023 1 commit
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Andy authored
* 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:
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- 16 Mar, 2023 2 commits
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Patrick von Platen authored
Improve error message
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Nicolas Patry authored
* 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:
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- 15 Mar, 2023 3 commits
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Patrick von Platen authored
* rename file * rename attention * fix more * rename more * up * more deprecation imports * fixes
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Kashif Rasul authored
* 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: -
Henrik Forstén authored
* Controlnet training code initial commit Works with circle dataset: https://github.com/lllyasviel/ControlNet/blob/main/docs/train.md * Script for adding a controlnet to existing model * Fix control image transform Control image should be in 0..1 range. * Add license header and remove more unused configs * controlnet training readme * Allow nonlocal model in add_controlnet.py * Formatting * Remove unused code * Code quality * Initialize controlnet in training script * Formatting * Address review comments * doc style * explicit constructor args and submodule names * hub dataset NOTE - not tested * empty prompts * add conditioning image * rename * remove instance data dir * image_transforms -> -1,1 . conditioning_image_transformers -> 0, 1 * nits * remove local rank config I think this isn't necessary in any of our training scripts * validation images * proportion_empty_prompts typo * weight copying to controlnet bug * call log validation fix * fix * gitignore wandb * fix progress bar and resume from checkpoint iteration * initial step fix * log multiple images * fix * fixes * tracker project name configurable * misc * add controlnet requirements.txt * update docs * image labels * small fixes * log validation using existing models for pipeline * fix for deepspeed saving * memory usage docs * Update examples/controlnet/train_controlnet.py Co-authored-by:
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- 14 Mar, 2023 2 commits
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Ilmari Heikkinen authored
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Haiwen Huang authored
* fix the in-place modification in unet condition when using controlnet, which will cause backprop errors when training * add clone to mid block * fix-copies --------- Co-authored-by:
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- 13 Mar, 2023 2 commits
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Takuma Mori authored
* support for List[ControlNetModel] on init() * Add to support for multiple ControlNetCondition * rename conditioning_scale to scale * scaling bugfix * Manually merge `MultiControlNet` #2621 Co-authored-by:
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Patrick von Platen authored
* [attention] Fix attention * fix * correct
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- 10 Mar, 2023 1 commit
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Patrick von Platen authored
* [From pretrained] Speed-up loading from cache * up * Fix more * fix one more bug * make style * bigger refactor * factor out function * Improve more * better * deprecate return cache folder * clean up * improve tests * up * upload * add nice tests * simplify * finish * correct * fix version * rename * Apply suggestions from code review Co-authored-by:
Lucain <lucainp@gmail.com> * rename * correct doc string * correct more * Apply suggestions from code review Co-authored-by:
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- 09 Mar, 2023 1 commit
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Patrick von Platen authored
* improve error message * upload
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- 07 Mar, 2023 1 commit
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clarencechen authored
* Improve dynamic threshold * Update code * Add dynamic threshold to ddim and ddpm * Encapsulate and leverage code copy mechanism Update style * Clean up DDPM/DDIM constructor arguments * add test * also add to unipc --------- Co-authored-by:
Peter Lin <peterlin9863@gmail.com> Co-authored-by:
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- 06 Mar, 2023 1 commit
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Patrick von Platen authored
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- 03 Mar, 2023 1 commit
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alvanli authored
Remove explicit message argument
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- 02 Mar, 2023 2 commits
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Ilmari Heikkinen authored
* Tiled VAE for high-res text2img and img2img * vae tiling, fix formatting * enable_vae_tiling API and tests * tiled vae docs, disable tiling for images that would have only one tile * tiled vae tests, use channels_last memory format * tiled vae tests, use smaller test image * tiled vae tests, remove tiling test from fast tests * up * up * make style * Apply suggestions from code review * Apply suggestions from code review * Apply suggestions from code review * make style * improve naming * finish * apply suggestions * Apply suggestions from code review Co-authored-by:
Ilmari Heikkinen <ilmari@fhtr.org> Co-authored-by:
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Takuma Mori authored
* add scaffold - copied convert_controlnet_to_diffusers.py from convert_original_stable_diffusion_to_diffusers.py * Add support to load ControlNet (WIP) - this makes Missking Key error on ControlNetModel * Update to convert ControlNet without error msg - init impl for StableDiffusionControlNetPipeline - init impl for ControlNetModel * cleanup of commented out * split create_controlnet_diffusers_config() from create_unet_diffusers_config() - add config: hint_channels * Add input_hint_block, input_zero_conv and middle_block_out - this makes missing key error on loading model * add unet_2d_blocks_controlnet.py - copied from unet_2d_blocks.py as impl CrossAttnDownBlock2D,DownBlock2D - this makes missing key error on loading model * Add loading for input_hint_block, zero_convs and middle_block_out - this makes no error message on model loading * Copy from UNet2DConditionalModel except __init__ * Add ultra primitive test for ControlNetModel inference * Support ControlNetModel inference - without exceptions * copy forward() from UNet2DConditionModel * Impl ControlledUNet2DConditionModel inference - test_controlled_unet_inference passed * Frozen weight & biases for training * Minimized version of ControlNet/ControlledUnet - test_modules_controllnet.py passed * make style * Add support model loading for minimized ver * Remove all previous version files * from_pretrained and inference test passed * copied from pipeline_stable_diffusion.py except `__init__()` * Impl pipeline, pixel match test (almost) passed. * make style * make fix-copies * Fix to add import ControlNet blocks for `make fix-copies` * Remove einops dependency * Support np.ndarray, PIL.Image for controlnet_hint * set default config file as lllyasviel's * Add support grayscale (hw) numpy array * Add and update docstrings * add control_net.mdx * add control_net.mdx to toctree * Update copyright year * Fix to add PIL.Image RGB->BGR conversion - thanks @Mystfit * make fix-copies * add basic fast test for controlnet * add slow test for controlnet/unet * Ignore down/up_block len check on ControlNet * add a copy from test_stable_diffusion.py * Accept controlnet_hint is None * merge pipeline_stable_diffusion.py diff * Update class name to SDControlNetPipeline * make style * Baseline fast test almost passed (w long desc) * still needs investigate. Following didn't passed descriped in TODO comment: - test_stable_diffusion_long_prompt - test_stable_diffusion_no_safety_checker Following didn't passed same as stable_diffusion_pipeline: - test_attention_slicing_forward_pass - test_inference_batch_single_identical - test_xformers_attention_forwardGenerator_pass these seems come from calc accuracy. * Add note comment related vae_scale_factor * add test_stable_diffusion_controlnet_ddim * add assertion for vae_scale_factor != 8 * slow test of pipeline almost passed Failed: test_stable_diffusion_pipeline_with_model_offloading - ImportError: `enable_model_offload` requires `accelerate v0.17.0` or higher but currently latest version == 0.16.0 * test_stable_diffusion_long_prompt passed * test_stable_diffusion_no_safety_checker passed - due to its model size, move to slow test * remove PoC test files * fix num_of_image, prompt length issue add add test * add support List[PIL.Image] for controlnet_hint * wip * all slow test passed * make style * update for slow test * RGB(PIL)->BGR(ctrlnet) conversion * fixes * remove manual num_images_per_prompt test * add document * add `image` argument docstring * make style * Add line to correct conversion * add controlnet_conditioning_scale (aka control_scales strength) * rgb channel ordering by default * image batching logic * Add control image descriptions for each checkpoint * Only save controlnet model in conversion script * Update src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_controlnet.py typo Co-authored-by:
dqueue <dbyqin@gmail.com> * remove controlnet constructor arguments re: @patrickvonplaten * [integration tests] test canny * test_canny fixes * [integration tests] test_depth * [integration tests] test_hed * [integration tests] test_mlsd * add channel order config to controlnet * [integration tests] test normal * [integration tests] test_openpose test_scribble * change height and width to default to conditioning image * [integration tests] test seg * style * test_depth fix * [integration tests] size fixes * [integration tests] cpu offloading * style * generalize controlnet embedding * fix conversion script * Update docs/source/en/api/pipelines/stable_diffusion/controlnet.mdx Co-authored-by:
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- 01 Mar, 2023 2 commits
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Pedro Cuenca authored
Bring flax attention naming in sync with PyTorch.
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Patrick von Platen authored
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- 27 Feb, 2023 1 commit
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Patrick von Platen authored
* [Safetensors] Make sure metadata is saved * make style
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- 17 Feb, 2023 2 commits
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Pedro Cuenca authored
Fix typo in AttnProcessor2_0 symbol.
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Suraj Patil authored
* add sdpa processor * don't use it by default * add some checks and style * typo * support torch sdpa in dreambooth example * use torch attn proc by default when available * typo * add attn mask * fix naming * being doc * doc * Apply suggestions from code review * polish * torctree * Apply suggestions from code review Co-authored-by:
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- 16 Feb, 2023 3 commits
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fxmarty authored
replace torch.concat by torch.cat
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Pedro Cuenca authored
* enable_model_offload PoC It's surprisingly more involved than expected, see comments in the PR. * Rename final_offload_hook * Invoke the vae forward hook manually. * Completely remove decoder. * Style * apply_forward_hook decorator * Rename method. * Style * Copy enable_model_cpu_offload * Fix copies. * Remove comment. * Fix copies * Missing import * Fix doc-builder style. * Merge main and fix again. * Add docs * Fix docs. * Add a couple of tests. * style
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Patrick von Platen authored
[Variant] Add "variant" as input kwarg so to have better UX when downloading no_ema or fp16 weights (#2305) * [Variant] Add variant loading mechanism * clean * improve further * up * add tests * add some first tests * up * up * use path splittetx * add deprecate * deprecation warnings * improve docs * up * up * up * fix tests * Apply suggestions from code review Co-authored-by:
Suraj Patil <surajp815@gmail.com> * Apply suggestions from code review Co-authored-by:
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- 14 Feb, 2023 2 commits
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Will Berman authored
* pipeline_variant * Add docs for when clip_stats_path is specified * Update src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip.py Co-authored-by:
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Will Berman authored
* unet check length input * prep test file for changes * correct all tests * clean up --------- Co-authored-by:
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- 13 Feb, 2023 1 commit
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bddppq authored
* Fix running LoRA with xformers * support disabling xformers * reformat * Add test
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- 07 Feb, 2023 3 commits
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Patrick von Platen authored
* before running make style * remove left overs from flake8 * finish * make fix-copies * final fix * more fixes
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Pedro Cuenca authored
* mps cross-attention hack: don't crash on fp16 * Make conversion explicit.
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YiYi Xu authored
* 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:
yiyixuxu <yixu@yis-macbook-pro.lan> Co-authored-by:
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- 03 Feb, 2023 1 commit
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Jorge C. Gomes authored
Related to #2124 The current implementation is throwing a shape mismatch error. Which makes sense, as this line is obviously missing, comparing to XFormersCrossAttnProcessor and LoRACrossAttnProcessor. I don't have formal tests, but I compared `LoRACrossAttnProcessor` and `LoRAXFormersCrossAttnProcessor` ad-hoc, and they produce the same results with this fix.
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