- 24 Jun, 2024 1 commit
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Tolga Cangöz authored
* Fix typos & improve contributing page * `make style && make quality` * fix typos * Fix typo --------- Co-authored-by:Sayak Paul <spsayakpaul@gmail.com>
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- 21 Jun, 2024 1 commit
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Sayak Paul authored
* get rid of the legacy lora remnants and make our codebase lighter * fix depcrecated lora argument * fix * empty commit to trigger ci * remove print * empty
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- 10 May, 2024 1 commit
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Mark Van Aken authored
* find & replace all FloatTensors to Tensor * apply formatting * Update torch.FloatTensor to torch.Tensor in the remaining files * formatting * Fix the rest of the places where FloatTensor is used as well as in documentation * formatting * Update new file from FloatTensor to Tensor
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- 09 May, 2024 1 commit
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Dhruv Nair authored
* refactor unet single file loading a bit. * retrieve the unet from create_diffusers_unet_model_from_ldm * update * update * updae * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * tests * update * update * update * Update docs/source/en/api/single_file.md Co-authored-by:
YiYi Xu <yixu310@gmail.com> * Update src/diffusers/loaders/single_file.py Co-authored-by:
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- 19 Mar, 2024 1 commit
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laksjdjf authored
* Fix ControlNetModel.from_unet do not load add_embedding * delete white space in blank line --------- Co-authored-by:
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- 18 Mar, 2024 1 commit
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M. Tolga Cangöz authored
* Fix PyTorch's convention for inplace functions * Fix import structure in __init__.py and update config loading logic in test_config.py * Update configuration access * Fix typos * Trim trailing white spaces * Fix typo in logger name * Revert "Fix PyTorch's convention for inplace functions" This reverts commit f65dc4afcb57ceb43d5d06389229d47bafb10d2d. * Fix typo in step_index property description * Revert "Update configuration access" This reverts commit 8d44e870b8c1ad08802e3e904c34baeca1b598f8. * Revert "Fix import structure in __init__.py and update config loading logic in test_config.py" This reverts commit 2ad5e8bca25aede3b912da22bd57285b598fe171. * Fix typos * Fix typos * Fix typos * Fix a typo: tranform -> transform
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- 08 Feb, 2024 1 commit
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Sayak Paul authored
change to 2024
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- 23 Jan, 2024 2 commits
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Dhruv Nair authored
* update * update * update * update * update * update * update * update * update * update * update' * update * update * update * update * update * update * up * update * update * update * update * update * update * update * update * update * update * update * update * up * update * update * update * update * update' * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * clean * update * update * clean up * clean up * update * clean * clean * update * updaet * clean up * fix docs * update * update * Revert "update" This reverts commit dbfb8f1ea9c61a2b4e02f926245be2b3d387e577. * update * update * update * update * fix controlnet * fix scheduler * fix controlnet tests
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Sayak Paul authored
* move unets to module
🦋 * parameterize unet-level import. * fix flax unet2dcondition model import * models __init__ * mildly depcrecating models.unet_2d_blocks in favor of models.unets.unet_2d_blocks. * noqa * correct depcrecation behaviour * inherit from the actual classes. * Empty-Commit * backwards compatibility for unet_2d.py * backward compatibility for unet_2d_condition * bc for unet_1d * bc for unet_1d_blocks
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- 03 Jan, 2024 1 commit
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Sayak Paul authored
* handle rest of the stuff related to deprecated lora stuff. * fix: copies * don't modify the uNet in-place. * fix: temporal autoencoder. * manually remove lora layers. * don't copy unet. * alright * remove lora attn processors from unet3d * fix: unet3d. * styl * Empty-Commit
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- 29 Nov, 2023 1 commit
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Marko Kostiv authored
* Add SSD-1B support for controlnet model * Add conditioning_channels into ControlNet init from unet * Fix black formatting * Isort fixes * Adds SSD-1B controlnet pipeline test with UNetMidBlock2D as mid block * Overrides failing ssd-1b tests * Fixes tests after main branch update * Fixes code quality checks --------- Co-authored-by:
Marko Kostiv <marko@linearity.io> Co-authored-by:
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- 16 Nov, 2023 1 commit
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Aryan V S authored
* improvement: docs and type hints * improvement: docs and type hints minor refactor * improvement: docs and type hints * update with suggestions from review Co-Authored-By:
Dhruv Nair <dhruv.nair@gmail.com> --------- Co-authored-by:
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- 23 Oct, 2023 1 commit
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Sayak Paul authored
* fix: controlnet graph? * fix: sample * fix: * remove print * styling * fix-copies * prevent more graph breaks? * prevent more graph breaks? * see? * revert. * compilation. * rpopagate changes to controlnet sdxl pipeline too. * add: clean version checking.
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- 09 Oct, 2023 1 commit
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Aryan V S authored
* add missing docstrings * chore: run make quality * improvement: include docs suggestion by @yiyixuxu --------- Co-authored-by:Patrick von Platen <patrick.v.platen@gmail.com>
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- 27 Sep, 2023 1 commit
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Patrick von Platen authored
* fix xformers lora * improve * fix
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- 01 Sep, 2023 1 commit
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Dhruv Nair authored
* proposal for flaky tests * more precision fixes * move more tests to use cosine distance * more test fixes * clean up * use default attn * clean up * update expected value * make style * make style * Apply suggestions from code review * Update src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py * make style * fix failing tests --------- Co-authored-by:
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- 28 Aug, 2023 1 commit
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Patrick von Platen authored
* [LoRA Attn] Refactor LoRA attn * correct for network alphas * fix more * fix more tests * fix more tests * Move below * Finish * better version * correct serialization format * fix * fix more * fix more * fix more * Apply suggestions from code review * Update src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py * deprecation * relax atol for slow test slighly * Finish tests * make style * make style
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- 26 Aug, 2023 1 commit
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Patrick von Platen authored
Fix torch compile for controlnete
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- 25 Jul, 2023 1 commit
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Sayak Paul authored
* fix: #4206 * add: sdxl controlnet training smoketest. * remove unnecessary token inits. * add: licensing to model card. * include SDXL licensing in the model card and make public visibility default * debugging * debugging * disable local file download. * fix: training test. * fix: ckpt prefix.
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- 19 Jul, 2023 1 commit
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Patrick von Platen authored
* Add controlnet from single file * Updates * make style * finish * Apply suggestions from code review Co-authored-by:
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- 18 Jul, 2023 1 commit
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Sayak Paul authored
* add: controlnet sdxl. * modifications to controlnet. * run styling. * add: __init__.pys * incorporate https://github.com/huggingface/diffusers/pull/4019 changes. * run make fix-copies. * resize the conditioning images. * remove autocast. * run styling. * disable autocast. * debugging * device placement. * back to autocast. * remove comment. * save some memory by reusing the vae and unet in the pipeline. * apply styling. * Allow low precision sd xl * finish * finish * changes to accommodate the improved VAE. * modifications to how we handle vae encoding in the training. * make style * make existing controlnet fast tests pass. * change vae checkpoint cli arg. * fix: vae pretrained paths. * fix: steps in get_scheduler(). * debugging. * debugging./ * fix: weight conversion. * add: docs. * add: limited tests./ * add: datasets to the requirements. * update docstrings and incorporate the usage of watermarking. * incorporate fix from #4083 * fix watermarking dependency handling. * run make-fix-copies. * Empty-Commit * Update requirements_sdxl.txt * remove vae upcasting part. * Apply suggestions from code review Co-authored-by:
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- 30 Jun, 2023 1 commit
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Steven Liu authored
* add modelmixin and unets * remove old model page * minor fixes * fix unet2dcondition * add vqmodel and autoencoderkl * add rest of models * fix autoencoderkl path * fix toctree * fix toctree again * apply feedback * apply feedback * fix copies * fix controlnet copy * fix copies
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- 22 Jun, 2023 1 commit
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Patrick von Platen authored
* relax tolerance slightly * correct incorrect naming * correct namingc * correct more * Apply suggestions from code review * Fix more * Correct more * correct incorrect naming * Update src/diffusers/models/controlnet.py * Correct flax * Correct renaming * Correct blocks * Fix more * Correct more * mkae style * mkae style * mkae style * mkae style * mkae style * Fix flax * mkae style * rename * rename * rename attn head dim to attention_head_dim * correct flax * make style * improve * Correct more * make style * fix more * mkae style * Update src/diffusers/models/controlnet_flax.py * Apply suggestions from code review Co-authored-by:
Pedro Cuenca <pedro@huggingface.co> --------- Co-authored-by:
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- 21 Jun, 2023 1 commit
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Hans Brouwer authored
support ControlNet models with a different hint_channels value (e.g. TemporalNet2)
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- 11 May, 2023 1 commit
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Takuma Mori authored
* add inferring_controlnet_cond_batch * Revert "add inferring_controlnet_cond_batch" This reverts commit abe8d6311d4b7f5b9409ca709c7fabf80d06c1a9. * set guess_mode to True whenever global_pool_conditions is True Co-authored-by:
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- 02 May, 2023 1 commit
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Patrick von Platen authored
* Fix more torch compile breaks * add tests * Fix all * fix controlnet * fix more * Add Horace He as co-author. > > Co-authored-by:
Horace He <horacehe2007@yahoo.com> * Add Horace He as co-author. Co-authored-by:
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- 16 Apr, 2023 1 commit
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Patrick von Platen authored
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- 14 Apr, 2023 1 commit
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Takuma Mori authored
* add guess mode (WIP) * fix uncond/cond order * support guidance_scale=1.0 and batch != 1 * remove magic coeff * add docstring * add intergration test * add document to controlnet.mdx * made the comments a bit more explanatory * fix table
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- 27 Mar, 2023 1 commit
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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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- 21 Mar, 2023 1 commit
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Alexander Pivovarov authored
Co-authored-by:
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- 15 Mar, 2023 2 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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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:
William Berman <WLBberman@gmail.com> Co-authored-by:
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- 13 Mar, 2023 1 commit
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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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- 02 Mar, 2023 1 commit
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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 1 commit
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Patrick von Platen authored
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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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- 07 Feb, 2023 1 commit
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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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- 27 Jan, 2023 1 commit
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Patrick von Platen authored
* [LoRA] All to use in inference with pipeline * [LoRA] allow cross attention kwargs passed to pipeline * finish
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- 26 Jan, 2023 1 commit
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Pedro Cuenca authored
* Allow `UNet2DModel` to use arbitrary class embeddings. We can currently use class conditioning in `UNet2DConditionModel`, but not in `UNet2DModel`. However, `UNet2DConditionModel` requires text conditioning too, which is unrelated to other types of conditioning. This commit makes it possible for `UNet2DModel` to be conditioned on entities other than timesteps. This is useful for training / research purposes. We can currently train models to perform unconditional image generation or text-to-image generation, but it's not straightforward to train a model to perform class-conditioned image generation, if text conditioning is not required. We could potentiall use `UNet2DConditionModel` for class-conditioning without text embeddings by using down/up blocks without cross-conditioning. However: - The mid block currently requires cross attention. - We are required to provide `encoder_hidden_states` to `forward`. * Style * Align class conditioning, add docstring for `num_class_embeds`. * Copy docstring to versatile_diffusion UNetFlatConditionModel
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