- 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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- 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 3 commits
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Patrick von Platen authored
* up * finish
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Muyang Li authored
The dimension does not match when `inner_dim` is not equal to `in_channels`.
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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 4 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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Suraj Patil authored
* make scaling factor cnfig arg of vae * fix * make flake happy * fix ldm * fix upscaler * qualirty * Apply suggestions from code review Co-authored-by:
Anton Lozhkov <anton@huggingface.co> Co-authored-by:
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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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- 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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- 19 Jan, 2023 1 commit
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Patrick von Platen authored
correct safetensors
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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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- 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 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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- 12 Jan, 2023 1 commit
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camenduru authored
* from_flax * oops * oops * make style with pip install -e ".[dev]" * oops * now code quality happy
😋 * allow_patterns += FLAX_WEIGHTS_NAME * Update src/diffusers/pipelines/pipeline_utils.py Co-authored-by:
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- 04 Jan, 2023 1 commit
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Patrick von Platen authored
* [Repro] Correct reproducability * up * up * uP * up * need better image * allow conversion from no state dict checkpoints * up * up * up * up * check tensors * check tensors * check tensors * check tensors * next try * up * up * better name * up * up * Apply suggestions from code review * correct more * up * replace all torch randn * fix * correct * correct * finish * fix more * up
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- 03 Jan, 2023 1 commit
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Patrick von Platen authored
* [Deterministic torch randn] Allow tensors to be generated on CPU * fix more * up * fix more * up * Update src/diffusers/utils/torch_utils.py Co-authored-by:
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- 02 Jan, 2023 1 commit
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YiYi Xu authored
* add a doc page for each pipeline under api/pipelines/stable_diffusion * add pipeline examples to docstrings * updated stable_diffusion_2 page * updated default markdown syntax to list methods based on https://github.com/huggingface/diffusers/pull/1870 * add function decorator Co-authored-by:
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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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- 29 Dec, 2022 1 commit
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Simon Kirsten authored
* Flax: Add components function * Flax: Fix img2img and align with other pipeline * Flax: Fix PRNGKey type * Refactor strength to start_timestep * Fix preprocess images * Fix processed_images dimen * latents.shape -> latents_shape * Fix typo * Remove "static" comment * Remove unnecessary optional types in _generate * Apply doc-builder code style. 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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- 13 Dec, 2022 1 commit
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Patrick von Platen authored
* [SD] Make sure batched input works correctly * uP * uP * up * up * uP * up * fix mask stuff * up * uP * more up * up * uP * up * finish * Apply suggestions from code review 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 4 commits
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Pedro Cuenca authored
* Make cross-attention check more robust. * Fix copies.
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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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