- 25 May, 2023 1 commit
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YiYi Xu authored
add kandinsky2.1 --------- Co-authored-by:
yiyixuxu <yixu310@gmail,com> Co-authored-by:
Ayush Mangal <43698245+ayushtues@users.noreply.github.com> Co-authored-by:
ayushmangal <ayushmangal@microsoft.com> Co-authored-by:
Patrick von Platen <patrick.v.platen@gmail.com> Co-authored-by:
Sayak Paul <spsayakpaul@gmail.com>
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- 22 May, 2023 1 commit
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Birch-san authored
* 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:
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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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- 01 May, 2023 1 commit
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Patrick von Platen authored
* fix more * Fix more * fix more * Apply suggestions from code review * fix * make style * make fix-copies * fix * make sure torch compile * Clean * fix test
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- 25 Apr, 2023 1 commit
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Patrick von Platen authored
* add * clean * up * clean up more * fix more tests * Improve docs further * improve * more fixes docs * Improve docs more * Update src/diffusers/models/unet_2d_condition.py * fix * up * update doc links * make fix-copies * add safety checker and watermarker to stage 3 doc page code snippets * speed optimizations docs * memory optimization docs * make style * add watermarking snippets to doc string examples * make style * use pt_to_pil helper functions in doc strings * skip mps tests * Improve safety * make style * new logic * fix * fix bad onnx design * make new stable diffusion upscale pipeline model arguments optional * define has_nsfw_concept when non-pil output type * lowercase linked to notebook name --------- Co-authored-by:William Berman <WLBberman@gmail.com>
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- 18 Apr, 2023 2 commits
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Will Berman authored
This mimics the dtype cast for the standard time embeddings
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Will Berman authored
Adding act fn config to the unet timestep class embedding and conv activation. The custom activation defaults to silu which is the default activation function for both the conv act and the timestep class embeddings so default behavior is not changed. The only unet which use the custom activation is the stable diffusion latent upscaler https://huggingface.co/stabilityai/sd-x2-latent-upscaler/blob/main/unet/config.json (I ran a script against the hub to confirm). The latent upscaler does not use the conv activation nor the timestep class embeddings so we don't change its behavior.
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- 17 Apr, 2023 1 commit
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Patrick von Platen authored
* Better deprecation message * Better deprecation message * Better doc string * Fixes * fix more * fix more * Improve __getattr__ * correct more * fix more * fix * Improve more * more improvements * fix more * Apply suggestions from code review Co-authored-by:
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- 11 Apr, 2023 4 commits
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Will Berman authored
add group norm type to attention processor cross attention norm This lets the cross attention norm use both a group norm block and a layer norm block. The group norm operates along the channels dimension and requires input shape (batch size, channels, *) where as the layer norm with a single `normalized_shape` dimension only operates over the least significant dimension i.e. (*, channels). The channels we want to normalize are the hidden dimension of the encoder hidden states. By convention, the encoder hidden states are always passed as (batch size, sequence length, hidden states). This means the layer norm can operate on the tensor without modification, but the group norm requires flipping the last two dimensions to operate on (batch size, hidden states, sequence length). All existing attention processors will have the same logic and we can consolidate it in a helper function `prepare_encoder_hidden_states` prepare_encoder_hidden_states -> norm_encoder_hidden_states re: @patrickvonplaten move norm_cross defined check to outside norm_encoder_hidden_states add missing attn.norm_cross check
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Will Berman authored
* unet time embedding activation function * typo act_fn -> time_embedding_act_fn * flatten conditional
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Will Berman authored
* 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
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Patrick von Platen authored
* [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
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- 10 Apr, 2023 3 commits
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William Berman authored
`encoder_hid_dim` provides an additional projection for the input `encoder_hidden_states` from `encoder_hidden_dim` to `cross_attention_dim`
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William Berman authored
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William Berman authored
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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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- 23 Mar, 2023 1 commit
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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:
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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 1 commit
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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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- 14 Mar, 2023 1 commit
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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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- 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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- 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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- 18 Jan, 2023 1 commit
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Patrick von Platen authored
* [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:
Suraj Patil <surajp815@gmail.com> * upload * finish Co-authored-by:
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- 30 Dec, 2022 1 commit
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Patrick von Platen authored
* 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:
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- 20 Dec, 2022 1 commit
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Patrick von Platen authored
* 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:
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- 18 Dec, 2022 1 commit
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Will Berman authored
* [wip] attention block updates * [wip] unCLIP unet decoder and super res * [wip] unCLIP prior transformer * [wip] scheduler changes * [wip] text proj utility class * [wip] UnCLIPPipeline * [wip] kakaobrain unCLIP convert script * [unCLIP pipeline] fixes re: @patrickvonplaten remove callbacks move denoising loops into call function * UNCLIPScheduler re: @patrickvonplaten Revert changes to DDPMScheduler. Make UNCLIPScheduler, a modified DDPM scheduler with changes to support karlo * mask -> attention_mask re: @patrickvonplaten * [DDPMScheduler] remove leftover change * [docs] PriorTransformer * [docs] UNet2DConditionModel and UNet2DModel * [nit] UNCLIPScheduler -> UnCLIPScheduler matches existing unclip naming better * [docs] SchedulingUnCLIP * [docs] UnCLIPTextProjModel * refactor * finish licenses * rename all to attention_mask and prep in models * more renaming * don't expose unused configs * final renaming fixes * remove x attn mask when not necessary * configure kakao script to use new class embedding config * fix copies * [tests] UnCLIPScheduler * finish x attn * finish * remove more * rename condition blocks * clean more * Apply suggestions from code review * up * fix * [tests] UnCLIPPipelineFastTests * remove unused imports * [tests] UnCLIPPipelineIntegrationTests * correct * make style Co-authored-by:
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- 07 Dec, 2022 2 commits
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Pedro Cuenca authored
* Make cross-attention check more robust. * Fix copies.
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Suraj Patil authored
upcast attention
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- 05 Dec, 2022 2 commits
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Patrick von Platen authored
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Suraj Patil authored
* 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:
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- 02 Dec, 2022 3 commits
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Patrick von Platen authored
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Pedro Cuenca authored
* Do not use torch.long in mps Addresses #1056. * Use torch.int instead of float. * Propagate changes. * Do not silently change float -> int. * Propagate changes. * Apply suggestions from code review Co-authored-by:
Anton Lozhkov <anton@huggingface.co> Co-authored-by:
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Benjamin Lefaudeux authored
* Moving the mem efficiient attention activation to the top + recursive * black, too bad there's no pre-commit ? Co-authored-by:Benjamin Lefaudeux <benjamin@photoroom.com>
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- 24 Nov, 2022 1 commit
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Anton Lozhkov authored
* Support SD2 attention slicing * Support SD2 attention slicing * Add more copies * Use attn_num_head_channels in blocks * fix-copies * Update tests * fix imports
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