- 07 Nov, 2022 1 commit
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Pedro Cuenca authored
* Change title to match the sidebar in _toctree. * Fix custom pipe link, add link to contribute. * Fix community pipeline links.
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- 06 Nov, 2022 1 commit
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Cheng Lu authored
* add dpmsolver discrete pytorch scheduler * fix some typos in dpm-solver pytorch * add dpm-solver pytorch in stable-diffusion pipeline * add jax/flax version dpm-solver * change code style * change code style * add docs * add `add_noise` method for dpmsolver * add pytorch unit test for dpmsolver * add dummy object for pytorch dpmsolver * Update src/diffusers/schedulers/scheduling_dpmsolver_discrete.py Co-authored-by:
Suraj Patil <surajp815@gmail.com> * Update tests/test_config.py Co-authored-by:
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- 04 Nov, 2022 1 commit
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Chen Wu (吴尘) authored
* Add CycleDiffusion pipeline for Stable Diffusion * Add the option of passing noise to DDIMScheduler Add the option of providing the noise itself to DDIMScheduler, instead of the random seed generator. * Update README.md * Update README.md * Update pipeline_stable_diffusion_cycle_diffusion.py * Update pipeline_stable_diffusion_cycle_diffusion.py * Update pipeline_stable_diffusion_cycle_diffusion.py * Update pipeline_stable_diffusion_cycle_diffusion.py * Update scheduling_ddim.py * Update import format * Update pipeline_stable_diffusion_cycle_diffusion.py * Update scheduling_ddim.py * Update src/diffusers/schedulers/scheduling_ddim.py Co-authored-by:
Patrick von Platen <patrick.v.platen@gmail.com> * Update src/diffusers/schedulers/scheduling_ddim.py Co-authored-by:
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- 03 Nov, 2022 4 commits
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Pedro Cuenca authored
Do not require PyTorch nightlies.
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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:
Anton Lozhkov <anton@huggingface.co> Co-authored-by:
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Will Berman authored
* Changes for VQ-diffusion VQVAE Add specify dimension of embeddings to VQModel: `VQModel` will by default set the dimension of embeddings to the number of latent channels. The VQ-diffusion VQVAE has a smaller embedding dimension, 128, than number of latent channels, 256. Add AttnDownEncoderBlock2D and AttnUpDecoderBlock2D to the up and down unet block helpers. VQ-diffusion's VQVAE uses those two block types. * Changes for VQ-diffusion transformer Modify attention.py so SpatialTransformer can be used for VQ-diffusion's transformer. SpatialTransformer: - Can now operate over discrete inputs (classes of vector embeddings) as well as continuous. - `in_channels` was made optional in the constructor so two locations where it was passed as a positional arg were moved to kwargs - modified forward pass to take optional timestep embeddings ImagePositionalEmbeddings: - added to provide positional embeddings to discrete inputs for latent pixels BasicTransformerBlock: - norm layers were made configurable so that the VQ-diffusion could use AdaLayerNorm with timestep embeddings - modified forward pass to take optional timestep embeddings CrossAttention: - now may optionally take a bias parameter for its query, key, and value linear layers FeedForward: - Internal layers are now configurable ApproximateGELU: - Activation function in VQ-diffusion's feedforward layer AdaLayerNorm: - Norm layer modified to incorporate timestep embeddings * Add VQ-diffusion scheduler * Add VQ-diffusion pipeline * Add VQ-diffusion convert script to diffusers * Add VQ-diffusion dummy objects * Add VQ-diffusion markdown docs * Add VQ-diffusion tests * some renaming * some fixes * more renaming * correct * fix typo * correct weights * finalize * fix tests * Apply suggestions from code review Co-authored-by:
Anton Lozhkov <aglozhkov@gmail.com> * Apply suggestions from code review Co-authored-by:
Pedro Cuenca <pedro@huggingface.co> * finish * finish * up Co-authored-by:
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Revist authored
* feat: add repaint * fix: fix quality check with `make fix-copies` * fix: remove old unnecessary arg * chore: change default to DDPM (looks better in experiments) * ".to(device)" changed to "device=" Co-authored-by:
fja <fja@zurich.ibm.com> Co-authored-by:
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- 02 Nov, 2022 3 commits
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Patrick von Platen authored
* Rename latent * uP
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Suraj Patil authored
* add euler scheduler in docs * add a section for how to use different scheds * address patrck's comments
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MatthieuTPHR authored
* 2x speedup using memory efficient attention * remove einops dependency * Swap K, M in op instantiation * Simplify code, remove unnecessary maybe_init call and function, remove unused self.scale parameter * make xformers a soft dependency * remove one-liner functions * change one letter variable to appropriate names * Remove Env variable dependency, remove MemoryEfficientCrossAttention class and use enable_xformers_memory_efficient_attention method * Add memory efficient attention toggle to img2img and inpaint pipelines * Clearer management of xformers' availability * update optimizations markdown to add info about memory efficient attention * add benchmarks for TITAN RTX * More detailed explanation of how the mem eff benchmark were ran * Removing autocast from optimization markdown * import_utils: import torch only if is available Co-authored-by:Nouamane Tazi <nouamane98@gmail.com>
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- 31 Oct, 2022 2 commits
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Patrick von Platen authored
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Patrick von Platen authored
* [Better scheduler docs] Improve usage examples of schedulers * finish * fix warnings and add test * finish * more replacements * adapt fast tests hf token * correct more * Apply suggestions from code review Co-authored-by:
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- 29 Oct, 2022 2 commits
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Nathan Lambert authored
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Minwoo Byeon authored
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- 28 Oct, 2022 1 commit
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Pedro Cuenca authored
* Update training and fine-tuning docs. * Update examples README. * Update README. * Add Flax fine-tuning section. * Accept suggestion Co-authored-by:
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- 27 Oct, 2022 1 commit
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Pi Esposito authored
* document cpu offloading method * address review comments Co-authored-by:
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- 25 Oct, 2022 3 commits
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Ella Charlaix authored
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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:
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Pedro Cuenca authored
* Docs: refer to pre-RC version of PyTorch 1.13.0. * Remove temporary workaround for unavailable op. * Update comment to make it less ambiguous. * Remove use of contiguous in mps. It appears to not longer be necessary. * Special case: use einsum for much better performance in mps * Update mps docs. * Minor doc update. * Accept suggestion Co-authored-by:
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- 24 Oct, 2022 2 commits
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Nathan Lambert authored
* add community pipeline docs * fix style in code snippets (lol) * clean up loading docs * add license to doc files * fix some weird links
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apolinario authored
* Update README.md Additionally add FLAX so the model card can be slimmer and point to this page * Find and replace all * v-1-5 -> v1-5 * revert test changes * Update README.md Co-authored-by:
apolinario <joaopaulo.passos+multimodal@gmail.com> Co-authored-by:
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- 20 Oct, 2022 1 commit
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Patrick von Platen authored
* [Stable Diffusion] Add components function * uP
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- 19 Oct, 2022 1 commit
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Pedro Cuenca authored
Docs update for new in-painting pipeline. Co-authored-by:
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- 11 Oct, 2022 3 commits
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Patrick von Platen authored
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Pedro Cuenca authored
* mps: alt. implementation for repeat_interleave * style * Bump mps version of PyTorch in the documentation. * Apply suggestions from code review Co-authored-by:
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Omar Sanseviero authored
Update custom_pipelines.mdx
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- 06 Oct, 2022 3 commits
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Anton Lozhkov authored
Temporarily remove Flax modules from the public API
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Patrick von Platen authored
* [Custom Pipelines] * uP * make style * finish * finish * remove ipdb * upload * fix * finish docs * Apply suggestions from code review Co-authored-by:
apolinario <joaopaulo.passos@gmail.com> * finish * final uploads * remove unnecessary test Co-authored-by:
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- 05 Oct, 2022 4 commits
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Patrick von Platen authored
up
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Suraj Patil authored
remove use_auth_token
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Patrick von Platen authored
* up * uP * uP * make style * Apply suggestions from code review * up * finish
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Kashif Rasul authored
* pytorch timesteps * style * get rid of if-else * fix test Co-authored-by:
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- 04 Oct, 2022 1 commit
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Yuta Hayashibe authored
* Fix typos * Update examples/dreambooth/train_dreambooth.py Co-authored-by:
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- 30 Sep, 2022 2 commits
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Nouamane Tazi authored
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Nouamane Tazi authored
* initial commit * make UNet stream capturable * try to fix noise_pred value * remove cuda graph and keep NB * non blocking unet with PNDMScheduler * make timesteps np arrays for pndm scheduler because lists don't get formatted to tensors in `self.set_format` * make max async in pndm * use channel last format in unet * avoid moving timesteps device in each unet call * avoid memcpy op in `get_timestep_embedding` * add `channels_last` kwarg to `DiffusionPipeline.from_pretrained` * update TODO * replace `channels_last` kwarg with `memory_format` for more generality * revert the channels_last changes to leave it for another PR * remove non_blocking when moving input ids to device * remove blocking from all .to() operations at beginning of pipeline * fix merging * fix merging * model can run in other precisions without autocast * attn refactoring * Revert "attn refactoring" This reverts commit 0c70c0e189cd2c4d8768274c9fcf5b940ee310fb. * remove restriction to run conv_norm in fp32 * use `baddbmm` instead of `matmul`for better in attention for better perf * removing all reshapes to test perf * Revert "removing all reshapes to test perf" This reverts commit 006ccb8a8c6bc7eb7e512392e692a29d9b1553cd. * add shapes comments * hardcore whats needed for jitting * Revert "hardcore whats needed for jitting" This reverts commit 2fa9c698eae2890ac5f8e367ca80532ecf94df9a. * Revert "remove restriction to run conv_norm in fp32" This reverts commit cec592890c32da3d1b78d38b49e4307aedf459b9. * revert using baddmm in attention's forward * cleanup comment * remove restriction to run conv_norm in fp32. no quality loss was noticed This reverts commit cc9bc1339c998ebe9e7d733f910c6d72d9792213. * add more optimizations techniques to docs * Revert "add shapes comments" This reverts commit 31c58eadb8892f95478cdf05229adf678678c5f4. * apply suggestions * make quality * apply suggestions * styling * `scheduler.timesteps` are now arrays so we dont need .to() * remove useless .type() * use mean instead of max in `test_stable_diffusion_inpaint_pipeline_k_lms` * move scheduler timestamps to correct device if tensors * add device to `set_timesteps` in LMSD scheduler * `self.scheduler.set_timesteps` now uses device arg for schedulers that accept it * quick fix * styling * remove kwargs from schedulers `set_timesteps` * revert to using max in K-LMS inpaint pipeline test * Revert "`self.scheduler.set_timesteps` now uses device arg for schedulers that accept it" This reverts commit 00d5a51e5c20d8d445c8664407ef29608106d899. * move timesteps to correct device before loop in SD pipeline * apply previous fix to other SD pipelines * UNet now accepts tensor timesteps even on wrong device, to avoid errors - it shouldnt affect performance if timesteps are alrdy on correct device - it does slow down performance if they're on the wrong device * fix pipeline when timesteps are arrays with strides
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- 29 Sep, 2022 1 commit
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Tanishq Abraham authored
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- 27 Sep, 2022 1 commit
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Kashif Rasul authored
* pytorch only schedulers * fix style * remove match_shape * pytorch only ddpm * remove SchedulerMixin * remove numpy from karras_ve * fix types * remove numpy from lms_discrete * remove numpy from pndm * fix typo * remove mixin and numpy from sde_vp and ve * remove remaining tensor_format * fix style * sigmas has to be torch tensor * removed set_format in readme * remove set format from docs * remove set_format from pipelines * update tests * fix typo * continue to use mixin * fix imports * removed unsed imports * match shape instead of assuming image shapes * remove import typo * update call to add_noise * use math instead of numpy * fix t_index * removed commented out numpy tests * timesteps needs to be discrete * cast timesteps to int in flax scheduler too * fix device mismatch issue * small fix * Update src/diffusers/schedulers/scheduling_pndm.py Co-authored-by:
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- 23 Sep, 2022 2 commits
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Younes Belkada authored
* documenting `attention_flax.py` file * documenting `embeddings_flax.py` * documenting `unet_blocks_flax.py` * Add new objs to doc page * document `vae_flax.py` * Apply suggestions from code review * modify `unet_2d_condition_flax.py` * make style * Apply suggestions from code review * make style * Apply suggestions from code review * fix indent * fix typo * fix indent unet * Update src/diffusers/models/vae_flax.py * Apply suggestions from code review Co-authored-by:
Mishig Davaadorj <dmishig@gmail.com> Co-authored-by:
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Ryan Russell authored
Signed-off-by:Ryan Russell <git@ryanrussell.org>
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