- 21 May, 2023 1 commit
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Sayak Paul authored
* add: debugging to enabling memory efficient processing * add: better warning message.
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- 17 May, 2023 1 commit
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cmdr2 authored
Release large tensors in attention (as soon as they're no longer required). Reduces peak VRAM by nearly 2 GB for 1024x1024 (even after slicing), and the savings scale up with image size.
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- 12 May, 2023 1 commit
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Will Berman authored
* Replace `AttentionBlock` with `Attention` * use _from_deprecated_attn_block check re: @patrickvonplaten
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- 10 May, 2023 1 commit
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Sayak Paul authored
* add: a warning message when using xformers in a PT 2.0 env. * Apply suggestions from code review Co-authored-by:
Patrick von Platen <patrick.v.platen@gmail.com> --------- Co-authored-by:
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- 09 May, 2023 1 commit
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Will Berman authored
* update IF stage I pipelines add fixed variance schedulers and lora loading * added kv lora attn processor * allow loading into alternative lora attn processor * make vae optional * throw away predicted variance * allow loading into added kv lora layer * allow load T5 * allow pre compute text embeddings * set new variance type in schedulers * fix copies * refactor all prompt embedding code class prompts are now included in pre-encoding code max tokenizer length is now configurable embedding attention mask is now configurable * fix for when variance type is not defined on scheduler * do not pre compute validation prompt if not present * add example test for if lora dreambooth * add check for train text encoder and pre compute text embeddings
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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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- 20 Apr, 2023 1 commit
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nupurkmr9 authored
* diffusers==0.14.0 update * custom diffusion update * custom diffusion update * custom diffusion update * custom diffusion update * custom diffusion update * custom diffusion update * custom diffusion * custom diffusion * custom diffusion * custom diffusion * custom diffusion * apply formatting and get rid of bare except. * refactor readme and other minor changes. * misc refactor. * fix: repo_id issue and loaders logging bug. * fix: save_model_card. * fix: save_model_card. * fix: save_model_card. * add: doc entry. * refactor doc,. * custom diffusion * custom diffusion * custom diffusion * apply style. * remove tralining whitespace. * fix: toctree entry. * remove unnecessary print. * custom diffusion * custom diffusion * custom diffusion test * custom diffusion xformer update * custom diffusion xformer update * custom diffusion xformer update --------- Co-authored-by:
Nupur Kumari <nupurkumari@Nupurs-MacBook-Pro.local> Co-authored-by:
Sayak Paul <spsayakpaul@gmail.com> Co-authored-by:
Nupur Kumari <nupurkumari@nupurs-mbp.wifi.local.cmu.edu>
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- 11 Apr, 2023 4 commits
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Will Berman authored
add AttnAddedKVProcessor2_0 block
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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
* 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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Will Berman authored
* `AttentionProcessor.group_norm` num_channels should be `query_dim` The group_norm on the attention processor should really norm the number of channels in the query _not_ the inner dim. This wasn't caught before because the group_norm is only used by the added kv attention processors and the added kv attention processors are only used by the karlo models which are configured such that the inner dim is the same as the query dim. * add_{k,v}_proj should be projecting to inner_dim
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- 10 Apr, 2023 2 commits
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William Berman authored
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William Berman authored
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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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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:
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- 03 Mar, 2023 1 commit
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alvanli authored
Remove explicit message argument
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- 01 Mar, 2023 1 commit
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Patrick von Platen authored
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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:
Pedro Cuenca <pedro@huggingface.co> * add fp32 benchmakr * Apply suggestions from code review Co-authored-by:
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- 16 Feb, 2023 1 commit
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fxmarty authored
replace torch.concat by torch.cat
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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 2 commits
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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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- 01 Feb, 2023 1 commit
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Asad Memon authored
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- 27 Jan, 2023 3 commits
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Patrick von Platen authored
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Patrick von Platen authored
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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 2 commits
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Patrick von Platen authored
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Will Berman authored
* fuse attention mask * lint * use 0 beta when no attention mask re: @Birch-san
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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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- 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:
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- 16 Jan, 2023 2 commits
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Will Berman authored
re: https://github.com/huggingface/diffusers/issues/1857 We relax some of the checks to deal with unclip reproducibility issues. Mainly by checking the average pixel difference (measured w/in 0-255) instead of the max pixel difference (measured w/in 0-1). - [x] add mixin to UnCLIPPipelineFastTests - [x] add mixin to UnCLIPImageVariationPipelineFastTests - [x] Move UnCLIPPipeline flags in mixin to base class - [x] Small MPS fixes for F.pad and F.interpolate - [x] Made test unCLIP model's dimensions smaller to run tests faster
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Patrick von Platen authored
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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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