- 07 Feb, 2023 2 commits
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Patrick von Platen authored
* before running make style * remove left overs from flake8 * finish * make fix-copies * final fix * more fixes
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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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- 26 Jan, 2023 1 commit
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Patrick von Platen authored
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- 25 Jan, 2023 1 commit
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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:
Suraj Patil <surajp815@gmail.com> * make * correct * finish * finish 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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- 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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- 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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- 09 Dec, 2022 1 commit
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Patrick von Platen authored
* finish * fix * Update tests/models/test_models_unet_2d.py * style Co-authored-by:Anton Lozhkov <anton@huggingface.co>
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- 05 Dec, 2022 2 commits
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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: -
Anton Lozhkov authored
* [CI] Add slow MPS tests * fix yml * temporarily resolve caching * Tests: fix mps crashes. * Skip test_load_pipeline_from_git on mps. Not compatible with float16. * Increase tolerance, use CPU generator, alt. slices. * Move to nightly * style Co-authored-by:
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- 30 Nov, 2022 1 commit
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Patrick von Platen authored
* Add test * up * no bfloat16 for mps * fix * rename test
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- 29 Nov, 2022 1 commit
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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:
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- 23 Nov, 2022 1 commit
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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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- 15 Nov, 2022 1 commit
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Patrick von Platen authored
* add conversion script for vae * uP * uP * more changes * push * up * finish again * up * up * up * up * finish * up * uP * up * Apply suggestions from code review Co-authored-by:
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- 14 Nov, 2022 1 commit
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Nathan Lambert authored
* re-add RL model code * match model forward api * add register_to_config, pass training tests * fix tests, update forward outputs * remove unused code, some comments * add to docs * remove extra embedding code * unify time embedding * remove conv1d output sequential * remove sequential from conv1dblock * style and deleting duplicated code * clean files * remove unused variables * clean variables * add 1d resnet block structure for downsample * rename as unet1d * fix renaming * rename files * add get_block(...) api * unify args for model1d like model2d * minor cleaning * fix docs * improve 1d resnet blocks * fix tests, remove permuts * fix style * add output activation * rename flax blocks file * Add Value Function and corresponding example script to Diffuser implementation (#884) * valuefunction code * start example scripts * missing imports * bug fixes and placeholder example script * add value function scheduler * load value function from hub and get best actions in example * very close to working example * larger batch size for planning * more tests * merge unet1d changes * wandb for debugging, use newer models * success! * turns out we just need more diffusion steps * run on modal * merge and code cleanup * use same api for rl model * fix variance type * wrong normalization function * add tests * style * style and quality * edits based on comments * style and quality * remove unused var * hack unet1d into a value function * add pipeline * fix arg order * add pipeline to core library * community pipeline * fix couple shape bugs * style * Apply suggestions from code review Co-authored-by:
Nathan Lambert <nathan@huggingface.co> * update post merge of scripts * add mdiblock / outblock architecture * Pipeline cleanup (#947) * valuefunction code * start example scripts * missing imports * bug fixes and placeholder example script * add value function scheduler * load value function from hub and get best actions in example * very close to working example * larger batch size for planning * more tests * merge unet1d changes * wandb for debugging, use newer models * success! * turns out we just need more diffusion steps * run on modal * merge and code cleanup * use same api for rl model * fix variance type * wrong normalization function * add tests * style * style and quality * edits based on comments * style and quality * remove unused var * hack unet1d into a value function * add pipeline * fix arg order * add pipeline to core library * community pipeline * fix couple shape bugs * style * Apply suggestions from code review * clean up comments * convert older script to using pipeline and add readme * rename scripts * style, update tests * delete unet rl model file * remove imports in src Co-authored-by:
Ben Glickenhaus <benglickenhaus@gmail.com>
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- 08 Nov, 2022 1 commit
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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.
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- 03 Nov, 2022 2 commits
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Patrick von Platen authored
* correct naming * finish * Apply suggestions from code review * Apply suggestions from code review Co-authored-by:
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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:
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- 02 Nov, 2022 1 commit
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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:
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- 28 Oct, 2022 6 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
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Patrick von Platen authored
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Patrick von Platen authored
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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
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