"llm/llama.cpp/vscode:/vscode.git/clone" did not exist on "35934b2e05cd598a6de0a1ed1ef62c11fb078f36"
- 13 Apr, 2023 1 commit
-
-
Patrick von Platen authored
* [Tests] parallelize * finish folder structuring * Parallelize tests more * Correct saving of pipelines * make sure logging level is correct * try again * Apply suggestions from code review Co-authored-by:
Pedro Cuenca <pedro@huggingface.co> --------- Co-authored-by:
-
- 12 Apr, 2023 1 commit
-
-
Andy authored
* inital commit for lora test cases * help a bit with lora for 3d * fixed lora tests * replaced redundant code --------- Co-authored-by:
Patrick von Platen <patrick.v.platen@gmail.com> Co-authored-by:
Sayak Paul <spsayakpaul@gmail.com>
-
- 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.
-
- 23 Mar, 2023 1 commit
-
-
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:
Will Berman <wlbberman@gmail.com> * fix: unet film embed to use existing args * fix: unet tests to use existing args * fix: make style * fix: transformers import and version in init * clean-up: make style * Revert "clean-up: make style" This reverts commit 5d6d1f8b324f5583e7805dc01e2c86e493660d66. * clean-up: make style * clean-up: use pipeline tester mixin tests where poss * clean-up: skip attn slicing test * fix: add torch dtype to docs * fix: remove conversion script out of src * fix: remove .detach from 1d waveform * fix: reduce default num inf steps * fix: swap height/width -> audio_length_in_s * clean-up: make style * fix: remove nightly tests * fix: imports in conversion script * clean-up: slim-down to two slow tests * clean-up: slim-down fast tests * fix: batch consistent tests * clean-up: make style * clean-up: remove vae slicing fast test * clean-up: propagate changes to doc * fix: increase test tol to 1e-2 * clean-up: finish docs * clean-up: make style * feat: vocoder / VAE compatibility check * feat: possibly expand / cut audio waveform * fix: pipeline call signature test * fix: slow tests output len * clean-up: make style * make style --------- Co-authored-by:
-
- 21 Mar, 2023 1 commit
-
-
Alexander Pivovarov authored
Co-authored-by:
-
- 16 Mar, 2023 1 commit
-
-
Nicolas Patry authored
* Adding `use_safetensors` argument to give more control to users about which weights they use. * Doc style. * Rebased (not functional). * Rebased and functional with tests. * Style. * Apply suggestions from code review * Style. * Addressing comments. * Update tests/test_pipelines.py Co-authored-by:
-
- 15 Mar, 2023 2 commits
-
-
Patrick von Platen authored
* rename file * rename attention * fix more * rename more * up * more deprecation imports * fixes
-
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:
-
- 04 Mar, 2023 1 commit
-
-
Nicolas Patry authored
* Fix regression introduced in #2448 * Style.
-
- 03 Mar, 2023 1 commit
-
-
Nicolas Patry authored
* Adding support for `safetensors` and LoRa. * Adding metadata.
-
- 01 Mar, 2023 1 commit
-
-
Patrick von Platen authored
-
- 13 Feb, 2023 1 commit
-
-
bddppq authored
* Fix running LoRA with xformers * support disabling xformers * reformat * Add test
-
- 07 Feb, 2023 2 commits
-
-
Patrick von Platen authored
* before running make style * remove left overs from flake8 * finish * make fix-copies * final fix * more fixes
-
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:
-
- 26 Jan, 2023 1 commit
-
-
Patrick von Platen authored
-
- 18 Jan, 2023 1 commit
-
-
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:
-
- 20 Dec, 2022 1 commit
-
-
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:
-
- 09 Dec, 2022 1 commit
-
-
Patrick von Platen authored
* finish * fix * Update tests/models/test_models_unet_2d.py * style Co-authored-by:Anton Lozhkov <anton@huggingface.co>
-
- 05 Dec, 2022 1 commit
-
-
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:
-
- 29 Nov, 2022 1 commit
-
-
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:
-
- 23 Nov, 2022 1 commit
-
-
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
-
- 08 Nov, 2022 1 commit
-
-
Pedro Cuenca authored
* Schedulers: don't use float64 on mps * Test set_timesteps() on device (float schedulers). * SD pipeline: use device in set_timesteps. * SD in-painting pipeline: use device in set_timesteps. * Tests: fix mps crashes. * Skip test_load_pipeline_from_git on mps. Not compatible with float16. * Use device.type instead of str in Euler schedulers.
-
- 03 Nov, 2022 2 commits
-
-
Patrick von Platen authored
* correct naming * finish * Apply suggestions from code review * Apply suggestions from code review Co-authored-by:
-
Suraj Patil authored
* make accelerate hard dep * default fast init * move params to cpu when device map is None * handle device_map=None * handle torch < 1.9 * remove device_map="auto" * style * add accelerate in torch extra * remove accelerate from extras["test"] * raise an error if torch is available but not accelerate * update installation docs * Apply suggestions from code review Co-authored-by:
-
- 02 Nov, 2022 1 commit
-
-
Lewington-pitsos authored
* improve test precision get tests passing with greater precision using lewington images * make old numpy load function a wrapper around a more flexible numpy loading function * adhere to black formatting * add more black formatting * adhere to isort * loosen precision and replace path Co-authored-by:
-
- 28 Oct, 2022 4 commits
-
-
Patrick von Platen authored
-
Patrick von Platen authored
-
Patrick von Platen authored
-
Patrick von Platen authored
* improve tests * up * finish * upload * add init * up * finish vae * finish * reduce loading time with device_map * remove device_map from CPU * uP
-
- 25 Oct, 2022 1 commit
-
-
Patrick von Platen authored
* start * add more logic * Update src/diffusers/models/unet_2d_condition_flax.py * match weights * up * make model work * making class more general, fixing missed file rename * small fix * make new conversion work * up * finalize conversion * up * first batch of variable renamings * remove c and c_prev var names * add mid and out block structure * add pipeline * up * finish conversion * finish * upload * more fixes * Apply suggestions from code review * add attr * up * uP * up * finish tests * finish * uP * finish * fix test * up * naming consistency in tests * Apply suggestions from code review Co-authored-by:
Nathan Lambert <nathan@huggingface.co> Co-authored-by:
Nathan Lambert <nol@berkeley.edu> Co-authored-by:
-
- 17 Oct, 2022 1 commit
-
-
Anton Lozhkov authored
* [CI] Add Apple M1 tests * setup-python * python build * conda install * remove branch * only 3.8 is built for osx-arm * try fetching prebuilt tokenizers * use user cache * update shells * Reports and cleanup * -> MPS * Disable parallel tests * Better naming * investigate worker crash * return xdist * restart * num_workers=2 * still crashing? * faulthandler for segfaults * faulthandler for segfaults * remove restarts, stop on segfault * torch version * change installation order * Use pre-RC version of PyTorch. To be updated when it is released. * Skip crashing test on MPS, add new one that works. * Skip cuda tests in mps device. * Actually use generator in test. I think this was a typo. * make style Co-authored-by:
-
- 10 Oct, 2022 1 commit
-
-
Patrick von Platen authored
* Fix gradient checkpointing test * more tsets
-
- 06 Oct, 2022 2 commits
-
-
Patrick von Platen authored
-
Suraj Patil authored
* use_deterministic_algorithms for grad ckpt test * remove eval * Apply suggestions from code review * Update tests/test_models_unet.py
-
- 04 Oct, 2022 1 commit
-
-
Pi Esposito authored
* add accelerate to load models with smaller memory footprint * remove low_cpu_mem_usage as it is reduntant * move accelerate init weights context to modelling utils * add test to ensure results are the same when loading with accelerate * add tests to ensure ram usage gets lower when using accelerate * move accelerate logic to single snippet under modelling utils and remove it from configuration utils * format code using to pass quality check * fix imports with isor * add accelerate to test extra deps * only import accelerate if device_map is set to auto * move accelerate availability check to diffusers import utils * format code Co-authored-by:
-
- 03 Oct, 2022 1 commit
-
-
Patrick von Platen authored
* [Utils] Add deprecate function * up * up * uP * up * up * up * up * uP * up * fix * up * move to deprecation utils file * fix * fix * fix more
-
- 29 Sep, 2022 1 commit
-
-
Suraj Patil authored
* lowe tolerance * put model in eval mode
-
- 22 Sep, 2022 1 commit
-
-
Suraj Patil authored
* add grad ckpt to downsample blocks * make it work * don't pass gradient_checkpointing to upsample block * add tests for UNet2DConditionModel * add test_gradient_checkpointing * add gradient_checkpointing for up and down blocks * add functions to enable and disable grad ckpt * remove the forward argument * better naming * make supports_gradient_checkpointing private
-
- 19 Sep, 2022 1 commit
-
-
Anton Lozhkov authored
* [Tests] Mark the ncsnpp model tests as slow * style
-
- 15 Sep, 2022 1 commit
-
-
Suraj Patil authored
* pass norm_num_groups to unet blocs and attention * fix UNet2DConditionModel * add norm_num_groups arg in vae * add tests * remove comment * Apply suggestions from code review
-
