- 27 Mar, 2023 1 commit
-
-
Pedro Cuenca authored
* Helper function to disable custom attention processors. * Restore code deleted by mistake. * Format * Fix modeling_text_unet copy.
-
- 22 Mar, 2023 1 commit
-
-
Pedro Cuenca authored
* Remove warmup passes in mps tests. * Update mps docs: no warmup pass in PyTorch 2 * Update imports.
-
- 21 Mar, 2023 1 commit
-
-
Will Berman authored
-
- 18 Mar, 2023 2 commits
-
-
Patrick von Platen authored
-
Patrick von Platen authored
* [Tests] Correct PT2 * correct more * move versatile to nightly * up * up * again * Apply suggestions from code review
-
- 03 Mar, 2023 1 commit
-
-
Will Berman authored
* attend and excite batch test causing timeouts * move test num_images_per_prompt to pipeline mixin * style * prompt_key -> self.batch_params
-
- 01 Mar, 2023 3 commits
-
-
Will Berman authored
* attend and excite batch test causing timeouts * PipelineTesterMixin argument configuration refactor * error message text re: @yiyixuxu * remove eta re: @patrickvonplaten
-
Patrick von Platen authored
* [Tests] Fix slow tests * [Tests] Fix slow tsets
-
Patrick von Platen authored
-
- 28 Feb, 2023 1 commit
-
-
Pedro Cuenca authored
* Sequential cpu offload: require accelerate 0.14.0. * Import is_accelerate_version * Missing copy.
-
- 27 Feb, 2023 2 commits
-
-
Will Berman authored
-
Will Berman authored
-
- 16 Feb, 2023 3 commits
-
-
YiYi Xu authored
* attend and excite pipeline * update update docstring example remove visualization remove the base class attention control remove dependency on stable diffusion pipeline always apply gaussian filter with default setting remove run_standard_sd argument hardcode attention_res and scale_range (related to step size) Update docs/source/en/api/pipelines/stable_diffusion/attend_and_excite.mdx Co-authored-by:
Patrick von Platen <patrick.v.platen@gmail.com> Update src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_attend_and_excite.py Co-authored-by:
Will Berman <wlbberman@gmail.com> Update src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_attend_and_excite.py Co-authored-by:
Suraj Patil <surajp815@gmail.com> Co-authored-by:
evin <evinpinarornek@gmail.com> Co-authored-by:
yiyixuxu <yixu310@gmail,com> Co-authored-by:
-
Pedro Cuenca authored
* enable_model_offload PoC It's surprisingly more involved than expected, see comments in the PR. * Rename final_offload_hook * Invoke the vae forward hook manually. * Completely remove decoder. * Style * apply_forward_hook decorator * Rename method. * Style * Copy enable_model_cpu_offload * Fix copies. * Remove comment. * Fix copies * Missing import * Fix doc-builder style. * Merge main and fix again. * Add docs * Fix docs. * Add a couple of tests. * style
-
Will Berman authored
-
- 15 Feb, 2023 1 commit
-
-
Patrick von Platen authored
* finish * Apply suggestions from code review * fix indent and import error in test_stable_diffusion_depth --------- Co-authored-by:
-
- 14 Feb, 2023 1 commit
-
-
Will Berman authored
* unet check length input * prep test file for changes * correct all tests * clean up --------- Co-authored-by:
-
- 13 Feb, 2023 1 commit
-
-
Patrick von Platen authored
-
- 10 Feb, 2023 1 commit
-
-
Patrick von Platen authored
* correct some * Apply suggestions from code review * correct * Update tests/pipelines/altdiffusion/test_alt_diffusion_img2img.py * Final
-
- 08 Feb, 2023 1 commit
-
-
Patrick von Platen authored
-
- 07 Feb, 2023 3 commits
-
-
Patrick von Platen authored
* before running make style * remove left overs from flake8 * finish * make fix-copies * final fix * more fixes
-
Patrick von Platen authored
-
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:
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:
-
- 25 Jan, 2023 1 commit
-
-
Patrick von Platen authored
* make tests deterministic * run slow tests * prepare for testing * finish * refactor * add print statements * finish more * correct some test failures * more fixes * set up to correct tests * more corrections * up * fix more * more prints * add * up * up * up * uP * uP * more fixes * uP * up * up * up * up * fix more * up * up * clean tests * up * up * up * more fixes * Apply suggestions from code review Co-authored-by:
-
- 17 Jan, 2023 1 commit
-
-
Jerry Jiarui XU authored
* [Flax] Add Flax inpainting impl * fixed copies, add README.md * fixed README.md * add test * format * update README.md
-
- 29 Dec, 2022 1 commit
-
-
Patrick von Platen authored
-
- 25 Dec, 2022 1 commit
-
-
Pedro Cuenca authored
* Make safety_checker optional in more pipelines. * Remove inappropriate comment in inpaint pipeline. * InPaint Test: set feature_extractor to None. * Remove import * img2img test: set feature_extractor to None. * inpaint sd2 test: set feature_extractor to None. Co-authored-by:
-
- 19 Dec, 2022 1 commit
-
-
Patrick von Platen authored
-
- 16 Dec, 2022 1 commit
-
-
Anton Lozhkov authored
* [WIP] Nightly integration tests * initial SD tests * update SD slow tests * style * repaint * ImageVariations * style * finish imgvar * img2img tests * debug * inpaint 1.5 * inpaint legacy * torch isn't happy about deterministic ops * allclose -> max diff for shorter logs * add SD2 * debug * Update tests/pipelines/stable_diffusion_2/test_stable_diffusion.py Co-authored-by:
-
- 13 Dec, 2022 1 commit
-
-
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:
-
- 08 Dec, 2022 1 commit
-
-
Suraj Patil authored
* begin depth pipeline * add depth estimation model * fix prepare_depth_mask * add a comment about autocast * copied from, quality, cleanup * begin tests * handle tensors * norm image tensor * fix batch size * fix tests * fix enable_sequential_cpu_offload * fix save load * fix test_save_load_float16 * fix test_save_load_optional_components * fix test_float16_inference * fix test_cpu_offload_forward_pass * fix test_dict_tuple_outputs_equivalent * up * fix fast tests * fix test_stable_diffusion_img2img_multiple_init_images * fix few more fast tests * don't use device map for DPT * fix test_stable_diffusion_pipeline_with_sequential_cpu_offloading * accept external depth maps * prepare_depth_mask -> prepare_depth_map * fix file name * fix file name * quality * check transformers version * fix test names * use skipif * fix import * add docs * skip tests on mps * correct version * uP * Update docs/source/api/pipelines/stable_diffusion_2.mdx * fix fix-copies * fix fix-copies Co-authored-by:
anton- <anton@huggingface.co>
-
- 07 Dec, 2022 1 commit
-
-
Suraj Patil authored
* fix upcast in slice attention * fix dtype * add test * fix test
-
- 06 Dec, 2022 1 commit
-
-
Anton Lozhkov authored
* [WIP] Standardize fast pipeline tests with PipelineTestMixin * refactor the sd tests a bit * add more common tests * add xformers * add progressbar test * cleanup * upd fp16 * CycleDiffusionPipelineFastTests * DanceDiffusionPipelineFastTests * AltDiffusionPipelineFastTests * StableDiffusion2PipelineFastTests * StableDiffusion2InpaintPipelineFastTests * StableDiffusionImageVariationPipelineFastTests * StableDiffusionImg2ImgPipelineFastTests * StableDiffusionInpaintPipelineFastTests * remove unused mixins * quality * add missing inits * try to fix mps tests * fix mps tests * add mps warmups * skip for some pipelines * style * Update tests/test_pipelines_common.py Co-authored-by:
-
- 05 Dec, 2022 1 commit
-
-
Patrick von Platen authored
-
- 02 Dec, 2022 1 commit
-
-
Patrick von Platen authored
-
- 29 Nov, 2022 2 commits
-
-
Pedro Cuenca authored
* Flax: start adapting to Stable Diffusion 2 * More changes. * attention_head_dim can be a tuple. * Fix typos * Add simple SD 2 integration test. Slice values taken from my Ampere GPU. * Add simple UNet integration tests for Flax. Note that the expected values are taken from the PyTorch results. This ensures the Flax and PyTorch versions are not too far off. * Apply suggestions from code review Co-authored-by:
-
Patrick von Platen authored
-
- 28 Nov, 2022 1 commit
-
-
Patrick von Platen authored
* Add heun * Finish first version of heun * remove bogus * finish * finish * improve * up * up * fix more * change progress bar * Update src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py * finish * up * up * up
-
- 25 Nov, 2022 2 commits
-
-
Patrick von Platen authored
-
Suraj Patil authored
* StableDiffusionUpscalePipeline * fix a few things * make it better * fix image batching * run vae in fp32 * fix docstr * resize to mul of 64 * doc * remove safety_checker * add max_noise_level * fix Copied * begin tests * slow tests * default max_noise_level * remove kwargs * doc * fix * fix fast tests * fix fast tests * no sf * don't offload vae Co-authored-by:
-
