- 01 Mar, 2023 1 commit
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Patrick von Platen authored
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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:
Patrick von Platen <patrick.v.platen@gmail.com> * 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:
yiyixuxu <yixu@yis-macbook-pro.lan> Co-authored-by:
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- 27 Jan, 2023 2 commits
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Patrick von Platen authored
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Patrick von Platen authored
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- 24 Jan, 2023 1 commit
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Takuma Mori authored
* allow passing op to xFormers attention original code by @patil-suraj huggingface/diffusers@ae0cc0b71f28c0f2c5c27026b18f1bea98b505f1 * correct style by `make style` * add attention_op arg documents * add usage example to docstring Co-authored-by:
Suraj Patil <surajp815@gmail.com> * Update docstring code to a valid python example Co-authored-by:
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- 17 Jan, 2023 1 commit
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Kashif Rasul authored
* added dit model * import * initial pipeline * initial convert script * initial pipeline * make style * raise valueerror * single function * rename classes * use DDIMScheduler * timesteps embedder * samples to cpu * fix var names * fix numpy type * use timesteps class for proj * fix typo * fix arg name * flip_sin_to_cos and better var names * fix C shape cal * make style * remove unused imports * cleanup * add back patch_size * initial dit doc * typo * Update docs/source/api/pipelines/dit.mdx Co-authored-by:
William Berman <WLBberman@gmail.com> Co-authored-by:
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- 16 Jan, 2023 1 commit
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Patrick von Platen authored
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- 01 Jan, 2023 1 commit
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Patrick von Platen authored
* [Attention] Finish refactor attention file * correct more * fix * more fixes * correct * up
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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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- 28 Dec, 2022 1 commit
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Patrick von Platen authored
fix versatile
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- 27 Dec, 2022 1 commit
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William Held authored
* Width was typod as weight * Run Black
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- 20 Dec, 2022 3 commits
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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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Patrick von Platen authored
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Ilmari Heikkinen authored
* only check for xformers when xformers are enabled * only test for xformers when enabling them
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- 19 Dec, 2022 3 commits
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Patrick von Platen authored
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Anton Lozhkov authored
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Patrick von Platen authored
* Remove bogus file * [Unclip] Add efficient attention * [Unclip] Add efficient attention
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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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- 09 Dec, 2022 1 commit
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Patrick von Platen authored
* do not automatically enable xformers * uP
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- 07 Dec, 2022 3 commits
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Suraj Patil authored
* fix upcast in slice attention * fix dtype * add test * fix test
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Suraj Patil authored
upcast attention
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Patrick von Platen authored
* add paint by example * mkae loading possibel * up * Update src/diffusers/models/attention.py * up * finalize weight structure * make example work * make it work * up * up * fix * del * add * update * Apply suggestions from code review * correct transformer 2d * finish * up * up * up * up * fix * Apply suggestions from code review Co-authored-by:
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- 05 Dec, 2022 1 commit
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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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- 03 Dec, 2022 1 commit
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Ilmari Heikkinen authored
* Add xformers attention to VAE * Simplify VAE xformers code * Update src/diffusers/models/attention.py Co-authored-by:
Ilmari Heikkinen <ilmari@fhtr.org> Co-authored-by:
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- 02 Dec, 2022 1 commit
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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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- 01 Dec, 2022 1 commit
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Suraj Patil authored
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- 25 Nov, 2022 1 commit
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Kashif Rasul authored
* call contiguous after permute Fixes for MPS device * Fix MPS UserWarning * make style * Revert "Fix MPS UserWarning" This reverts commit b46c32810ee5fdc4c16a8e9224a826490b66cf49.
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- 24 Nov, 2022 1 commit
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Suraj Patil authored
* allow disabling self attention * add class_embedding * fix copies * fix condition * fix copies * do_self_attention -> only_cross_attention * fix copies * num_classes -> num_class_embeds * fix default value
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- 23 Nov, 2022 3 commits
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Suraj Patil authored
don't norm twice
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Suraj Patil authored
* boom boom * remove duplicate arg * add use_linear_proj arg * fix copies * style * add fast tests * use_linear_proj -> use_linear_projection
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Patrick von Platen authored
* up * convert dual unet * revert dual attn * adapt for vd-official * test the full pipeline * mixed inference * mixed inference for text2img * add image prompting * fix clip norm * split text2img and img2img * fix format * refactor text2img * mega pipeline * add optimus * refactor image var * wip text_unet * text unet end to end * update tests * reshape * fix image to text * add some first docs * dual guided pipeline * fix token ratio * propose change * dual transformer as a native module * DualTransformer(nn.Module) * DualTransformer(nn.Module) * correct unconditional image * save-load with mega pipeline * remove image to text * up * uP * fix * up * final fix * remove_unused_weights * test updates * save progress * uP * fix dual prompts * some fixes * finish * style * finish renaming * up * fix * fix * fix * finish Co-authored-by:anton-l <anton@huggingface.co>
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- 22 Nov, 2022 1 commit
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Suraj Patil authored
* use memory_efficient_attention by default * Update src/diffusers/models/attention.py Co-authored-by:
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- 21 Nov, 2022 1 commit
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Birch-san authored
perf: prefer batched matmuls for attention. added fast-path to Decoder when num_heads=1
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- 14 Nov, 2022 1 commit
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Lime-Cakes authored
Older versions of xformers require query, key, value to be contiguous, this calls .contiguous() on q/k/v before passing to xformers.
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- 08 Nov, 2022 1 commit
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Suraj Patil authored
handle dtype xformers
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- 03 Nov, 2022 1 commit
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Will Berman authored
* Changes for VQ-diffusion VQVAE Add specify dimension of embeddings to VQModel: `VQModel` will by default set the dimension of embeddings to the number of latent channels. The VQ-diffusion VQVAE has a smaller embedding dimension, 128, than number of latent channels, 256. Add AttnDownEncoderBlock2D and AttnUpDecoderBlock2D to the up and down unet block helpers. VQ-diffusion's VQVAE uses those two block types. * Changes for VQ-diffusion transformer Modify attention.py so SpatialTransformer can be used for VQ-diffusion's transformer. SpatialTransformer: - Can now operate over discrete inputs (classes of vector embeddings) as well as continuous. - `in_channels` was made optional in the constructor so two locations where it was passed as a positional arg were moved to kwargs - modified forward pass to take optional timestep embeddings ImagePositionalEmbeddings: - added to provide positional embeddings to discrete inputs for latent pixels BasicTransformerBlock: - norm layers were made configurable so that the VQ-diffusion could use AdaLayerNorm with timestep embeddings - modified forward pass to take optional timestep embeddings CrossAttention: - now may optionally take a bias parameter for its query, key, and value linear layers FeedForward: - Internal layers are now configurable ApproximateGELU: - Activation function in VQ-diffusion's feedforward layer AdaLayerNorm: - Norm layer modified to incorporate timestep embeddings * Add VQ-diffusion scheduler * Add VQ-diffusion pipeline * Add VQ-diffusion convert script to diffusers * Add VQ-diffusion dummy objects * Add VQ-diffusion markdown docs * Add VQ-diffusion tests * some renaming * some fixes * more renaming * correct * fix typo * correct weights * finalize * fix tests * Apply suggestions from code review Co-authored-by:
Anton Lozhkov <aglozhkov@gmail.com> * Apply suggestions from code review Co-authored-by:
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- 02 Nov, 2022 2 commits
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Omiita authored
Fix a small typo fix a typo in `models/attention.py`. weight -> width
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MatthieuTPHR authored
* 2x speedup using memory efficient attention * remove einops dependency * Swap K, M in op instantiation * Simplify code, remove unnecessary maybe_init call and function, remove unused self.scale parameter * make xformers a soft dependency * remove one-liner functions * change one letter variable to appropriate names * Remove Env variable dependency, remove MemoryEfficientCrossAttention class and use enable_xformers_memory_efficient_attention method * Add memory efficient attention toggle to img2img and inpaint pipelines * Clearer management of xformers' availability * update optimizations markdown to add info about memory efficient attention * add benchmarks for TITAN RTX * More detailed explanation of how the mem eff benchmark were ran * Removing autocast from optimization markdown * import_utils: import torch only if is available Co-authored-by:Nouamane Tazi <nouamane98@gmail.com>
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- 31 Oct, 2022 1 commit
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Patrick von Platen authored
* Remove nn sequential * up
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- 29 Oct, 2022 1 commit
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Pedro Cuenca authored
* Docs: refer to pre-RC version of PyTorch 1.13.0. * Remove temporary workaround for unavailable op. * Update comment to make it less ambiguous. * Remove use of contiguous in mps. It appears to not longer be necessary. * Special case: use einsum for much better performance in mps * Update mps docs. * MPS: make pipeline work in half precision.
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