1. 11 Sep, 2023 1 commit
    • Dhruv Nair's avatar
      Lazy Import for Diffusers (#4829) · b6e0b016
      Dhruv Nair authored
      
      
      * initial commit
      
      * move modules to import struct
      
      * add dummy objects and _LazyModule
      
      * add lazy import to schedulers
      
      * clean up unused imports
      
      * lazy import on models module
      
      * lazy import for schedulers module
      
      * add lazy import to pipelines module
      
      * lazy import altdiffusion
      
      * lazy import audio diffusion
      
      * lazy import audioldm
      
      * lazy import consistency model
      
      * lazy import controlnet
      
      * lazy import dance diffusion ddim ddpm
      
      * lazy import deepfloyd
      
      * lazy import kandinksy
      
      * lazy imports
      
      * lazy import semantic diffusion
      
      * lazy imports
      
      * lazy import stable diffusion
      
      * move sd output to its own module
      
      * clean up
      
      * lazy import t2iadapter
      
      * lazy import unclip
      
      * lazy import versatile and vq diffsuion
      
      * lazy import vq diffusion
      
      * helper to fetch objects from modules
      
      * lazy import sdxl
      
      * lazy import txt2vid
      
      * lazy import stochastic karras
      
      * fix model imports
      
      * fix bug
      
      * lazy import
      
      * clean up
      
      * clean up
      
      * fixes for tests
      
      * fixes for tests
      
      * clean up
      
      * remove import of torch_utils from utils module
      
      * clean up
      
      * clean up
      
      * fix mistake import statement
      
      * dedicated modules for exporting and loading
      
      * remove testing utils from utils module
      
      * fixes from  merge conflicts
      
      * Update src/diffusers/pipelines/kandinsky2_2/__init__.py
      
      * fix docs
      
      * fix alt diffusion copied from
      
      * fix check dummies
      
      * fix more docs
      
      * remove accelerate import from utils module
      
      * add type checking
      
      * make style
      
      * fix check dummies
      
      * remove torch import from xformers check
      
      * clean up error message
      
      * fixes after upstream merges
      
      * dummy objects fix
      
      * fix tests
      
      * remove unused module import
      
      ---------
      Co-authored-by: default avatarPatrick von Platen <patrick.v.platen@gmail.com>
      b6e0b016
  2. 06 Sep, 2023 1 commit
    • Kashif Rasul's avatar
      Würstchen model (#3849) · 541bb6ee
      Kashif Rasul authored
      
      
      * initial
      
      * initial
      
      * added initial convert script for paella vqmodel
      
      * initial wuerstchen pipeline
      
      * add LayerNorm2d
      
      * added modules
      
      * fix typo
      
      * use model_v2
      
      * embed clip caption amd negative_caption
      
      * fixed name of var
      
      * initial modules in one place
      
      * WuerstchenPriorPipeline
      
      * inital shape
      
      * initial denoising prior loop
      
      * fix output
      
      * add WuerstchenPriorPipeline to __init__.py
      
      * use the noise ratio in the Prior
      
      * try to save pipeline
      
      * save_pretrained working
      
      * Few additions
      
      * add _execution_device
      
      * shape is int
      
      * fix batch size
      
      * fix shape of ratio
      
      * fix shape of ratio
      
      * fix output dataclass
      
      * tests folder
      
      * fix formatting
      
      * fix float16 + started with generator
      
      * Update pipeline_wuerstchen.py
      
      * removed vqgan code
      
      * add WuerstchenGeneratorPipeline
      
      * fix WuerstchenGeneratorPipeline
      
      * fix docstrings
      
      * fix imports
      
      * convert generator pipeline
      
      * fix convert
      
      * Work on Generator Pipeline. WIP
      
      * Pipeline works with our diffuzz code
      
      * apply scale factor
      
      * removed vqgan.py
      
      * use cosine schedule
      
      * redo the denoising loop
      
      * Update src/diffusers/models/resnet.py
      Co-authored-by: default avatarPatrick von Platen <patrick.v.platen@gmail.com>
      
      * use torch.lerp
      
      * use warp-diffusion org
      
      * clip_sample=False,
      
      * some refactoring
      
      * use model_v3_stage_c
      
      * c_cond size
      
      * use clip-bigG
      
      * allow stage b clip to be None
      
      * add dummy
      
      * würstchen scheduler
      
      * minor changes
      
      * set clip=None in the pipeline
      
      * fix attention mask
      
      * add attention_masks to text_encoder
      
      * make fix-copies
      
      * add back clip
      
      * add text_encoder
      
      * gen_text_encoder and tokenizer
      
      * fix import
      
      * updated pipeline test
      
      * undo changes to pipeline test
      
      * nip
      
      * fix typo
      
      * fix output name
      
      * set guidance_scale=0 and remove diffuze
      
      * fix doc strings
      
      * make style
      
      * nip
      
      * removed unused
      
      * initial docs
      
      * rename
      
      * toc
      
      * cleanup
      
      * remvoe test script
      
      * fix-copies
      
      * fix multi images
      
      * remove dup
      
      * remove unused modules
      
      * undo changes for debugging
      
      * no  new line
      
      * remove dup conversion script
      
      * fix doc string
      
      * cleanup
      
      * pass default args
      
      * dup permute
      
      * fix some tests
      
      * fix prepare_latents
      
      * move Prior class to modules
      
      * offload only the text encoder and vqgan
      
      * fix resolution calculation for prior
      
      * nip
      
      * removed testing script
      
      * fix shape
      
      * fix argument to set_timesteps
      
      * do not change .gitignore
      
      * fix resolution calculations + readme
      
      * resolution calculation fix + readme
      
      * small fixes
      
      * Add combined pipeline
      
      * rename generator -> decoder
      
      * Update .gitignore
      Co-authored-by: default avatarPatrick von Platen <patrick.v.platen@gmail.com>
      
      * removed efficient_net
      
      * create combined WuerstchenPipeline
      
      * make arguments consistent with VQ model
      
      * fix var names
      
      * no need to return text_encoder_hidden_states
      
      * add latent_dim_scale to config
      
      * split model into its own file
      
      * add WuerschenPipeline to docs
      
      * remove unused latent_size
      
      * register latent_dim_scale
      
      * update script
      
      * update docstring
      
      * use Attention preprocessor
      
      * concat with normed input
      
      * fix-copies
      
      * add docs
      
      * fix test
      
      * fix style
      
      * add to cpu_offloaded_model
      
      * updated type
      
      * remove 1-line func
      
      * updated type
      
      * initial decoder test
      
      * formatting
      
      * formatting
      
      * fix autodoc link
      
      * num_inference_steps is int
      
      * remove comments
      
      * fix example in docs
      
      * Update src/diffusers/pipelines/wuerstchen/diffnext.py
      Co-authored-by: default avatarPatrick von Platen <patrick.v.platen@gmail.com>
      
      * rename layernorm to WuerstchenLayerNorm
      
      * rename DiffNext to WuerstchenDiffNeXt
      
      * added comment about MixingResidualBlock
      
      * move paella vq-vae to pipelines' folder
      
      * initial decoder test
      
      * increased test_float16_inference expected diff
      
      * self_attn is always true
      
      * more passing decoder tests
      
      * batch image_embeds
      
      * fix failing tests
      
      * set the correct dtype
      
      * relax inference test
      
      * update prior
      
      * added combined pipeline test
      
      * faster test
      
      * faster test
      
      * Update src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_combined.py
      Co-authored-by: default avatarPatrick von Platen <patrick.v.platen@gmail.com>
      
      * fix issues from review
      
      * update wuerstchen.md + change generator name
      
      * resolve issues
      
      * fix copied from usage and add back batch_size
      
      * fix API
      
      * fix arguments
      
      * fix combined test
      
      * Added timesteps argument + fixes
      
      * Update tests/pipelines/test_pipelines_common.py
      Co-authored-by: default avatarPatrick von Platen <patrick.v.platen@gmail.com>
      
      * Update tests/pipelines/wuerstchen/test_wuerstchen_prior.py
      Co-authored-by: default avatarPatrick von Platen <patrick.v.platen@gmail.com>
      
      * Update src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_combined.py
      Co-authored-by: default avatarPatrick von Platen <patrick.v.platen@gmail.com>
      
      * Update src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_combined.py
      Co-authored-by: default avatarPatrick von Platen <patrick.v.platen@gmail.com>
      
      * Update src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_combined.py
      Co-authored-by: default avatarPatrick von Platen <patrick.v.platen@gmail.com>
      
      * Update src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_combined.py
      
      * up
      
      * Fix more
      
      * failing tests
      
      * up
      
      * up
      
      * correct naming
      
      * correct docs
      
      * correct docs
      
      * fix test params
      
      * correct docs
      
      * fix classifier free guidance
      
      * fix classifier free guidance
      
      * fix more
      
      * fix all
      
      * make tests faster
      
      ---------
      Co-authored-by: default avatarDominic Rampas <d6582533@gmail.com>
      Co-authored-by: default avatarPatrick von Platen <patrick.v.platen@gmail.com>
      Co-authored-by: default avatarDominic Rampas <61938694+dome272@users.noreply.github.com>
      541bb6ee
  3. 23 Aug, 2023 1 commit
    • Ollin Boer Bohan's avatar
      Fix AutoencoderTiny encoder scaling convention (#4682) · 052bf328
      Ollin Boer Bohan authored
      * Fix AutoencoderTiny encoder scaling convention
      
        * Add [-1, 1] -> [0, 1] rescaling to EncoderTiny
      
        * Move [0, 1] -> [-1, 1] rescaling from AutoencoderTiny.decode to DecoderTiny
          (i.e. immediately after the final conv, as early as possible)
      
        * Fix missing [0, 255] -> [0, 1] rescaling in AutoencoderTiny.forward
      
        * Update AutoencoderTinyIntegrationTests to protect against scaling issues.
          The new test constructs a simple image, round-trips it through AutoencoderTiny,
          and confirms the decoded result is approximately equal to the source image.
          This test checks behavior with and without tiling enabled.
          This test will fail if new AutoencoderTiny scaling issues are introduced.
      
        * Context: Raw TAESD weights expect images in [0, 1], but diffusers'
          convention represents images with zero-centered values in [-1, 1],
          so AutoencoderTiny needs to scale / unscale images at the start of
          encoding and at the end of decoding in order to work with diffusers.
      
      * Re-add existing AutoencoderTiny test, update golden values
      
      * Add comments to AutoencoderTiny.forward
      052bf328
  4. 02 Aug, 2023 1 commit
    • Sayak Paul's avatar
      [Feat] add tiny Autoencoder for (almost) instant decoding (#4384) · 18fc40c1
      Sayak Paul authored
      
      
      * add: model implementation of tiny autoencoder.
      
      * add: inits.
      
      * push the latest devs.
      
      * add: conversion script and finish.
      
      * add: scaling factor args.
      
      * debugging
      
      * fix denormalization.
      
      * fix: positional argument.
      
      * handle use_torch_2_0_or_xformers.
      
      * handle post_quant_conv
      
      * handle dtype
      
      * fix: sdxl image processor for tiny ae.
      
      * fix: sdxl image processor for tiny ae.
      
      * unify upcasting logic.
      
      * copied from madness.
      
      * remove trailing whitespace.
      
      * set is_tiny_vae = False
      
      * address PR comments.
      
      * change to AutoencoderTiny
      
      * make act_fn an str throughout
      
      * fix: apply_forward_hook decorator call
      
      * get rid of the special is_tiny_vae flag.
      
      * directly scale the output.
      
      * fix dummies?
      
      * fix: act_fn.
      
      * get rid of the Clamp() layer.
      
      * bring back copied from.
      
      * movement of the blocks to appropriate modules.
      
      * add: docstrings to AutoencoderTiny
      
      * add: documentation.
      
      * changes to the conversion script.
      
      * add doc entry.
      
      * settle tests.
      
      * style
      
      * add one slow test.
      
      * fix
      
      * fix 2
      
      * fix 2
      
      * fix: 4
      
      * fix: 5
      
      * finish integration tests
      
      * Apply suggestions from code review
      Co-authored-by: default avatarSteven Liu <59462357+stevhliu@users.noreply.github.com>
      
      * style
      
      ---------
      Co-authored-by: default avatarSteven Liu <59462357+stevhliu@users.noreply.github.com>
      18fc40c1
  5. 20 Jul, 2023 1 commit
  6. 30 Jun, 2023 1 commit
    • Steven Liu's avatar
      [docs] Model API (#3562) · 174dcd69
      Steven Liu authored
      * add modelmixin and unets
      
      * remove old model page
      
      * minor fixes
      
      * fix unet2dcondition
      
      * add vqmodel and autoencoderkl
      
      * add rest of models
      
      * fix autoencoderkl path
      
      * fix toctree
      
      * fix toctree again
      
      * apply feedback
      
      * apply feedback
      
      * fix copies
      
      * fix controlnet copy
      
      * fix copies
      174dcd69
  7. 22 Jun, 2023 1 commit
    • Patrick von Platen's avatar
      Correct bad attn naming (#3797) · 88d26946
      Patrick von Platen authored
      
      
      * relax tolerance slightly
      
      * correct incorrect naming
      
      * correct namingc
      
      * correct more
      
      * Apply suggestions from code review
      
      * Fix more
      
      * Correct more
      
      * correct incorrect naming
      
      * Update src/diffusers/models/controlnet.py
      
      * Correct flax
      
      * Correct renaming
      
      * Correct blocks
      
      * Fix more
      
      * Correct more
      
      * mkae style
      
      * mkae style
      
      * mkae style
      
      * mkae style
      
      * mkae style
      
      * Fix flax
      
      * mkae style
      
      * rename
      
      * rename
      
      * rename attn head dim to attention_head_dim
      
      * correct flax
      
      * make style
      
      * improve
      
      * Correct more
      
      * make style
      
      * fix more
      
      * mkae style
      
      * Update src/diffusers/models/controlnet_flax.py
      
      * Apply suggestions from code review
      Co-authored-by: default avatarPedro Cuenca <pedro@huggingface.co>
      
      ---------
      Co-authored-by: default avatarPedro Cuenca <pedro@huggingface.co>
      88d26946
  8. 25 May, 2023 1 commit
  9. 17 May, 2023 1 commit
  10. 26 Apr, 2023 1 commit
  11. 17 Mar, 2023 1 commit
  12. 01 Mar, 2023 1 commit
  13. 04 Jan, 2023 1 commit
    • Patrick von Platen's avatar
      Improve reproduceability 2/3 (#1906) · 9b638548
      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
      9b638548
  14. 30 Dec, 2022 1 commit
  15. 05 Dec, 2022 1 commit
  16. 29 Nov, 2022 1 commit
    • Ilmari Heikkinen's avatar
      StableDiffusion: Decode latents separately to run larger batches (#1150) · c28d3c82
      Ilmari Heikkinen authored
      
      
      * StableDiffusion: Decode latents separately to run larger batches
      
      * Move VAE sliced decode under enable_vae_sliced_decode and vae.enable_sliced_decode
      
      * Rename sliced_decode to slicing
      
      * fix whitespace
      
      * fix quality check and repository consistency
      
      * VAE slicing tests and documentation
      
      * API doc hooks for VAE slicing
      
      * reformat vae slicing tests
      
      * Skip VAE slicing for one-image batches
      
      * Documentation tweaks for VAE slicing
      Co-authored-by: default avatarIlmari Heikkinen <ilmari@fhtr.org>
      c28d3c82
  17. 03 Nov, 2022 1 commit
    • Will Berman's avatar
      VQ-diffusion (#658) · ef2ea33c
      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: default avatarAnton Lozhkov <aglozhkov@gmail.com>
      
      * Apply suggestions from code review
      Co-authored-by: default avatarPedro Cuenca <pedro@huggingface.co>
      
      * finish
      
      * finish
      
      * up
      Co-authored-by: default avatarPatrick von Platen <patrick.v.platen@gmail.com>
      Co-authored-by: default avatarAnton Lozhkov <aglozhkov@gmail.com>
      Co-authored-by: default avatarPedro Cuenca <pedro@huggingface.co>
      ef2ea33c
  18. 25 Oct, 2022 1 commit
  19. 12 Oct, 2022 1 commit
  20. 07 Oct, 2022 1 commit
    • Suraj Patil's avatar
      [img2img, inpainting] fix fp16 inference (#769) · 92d70863
      Suraj Patil authored
      * handle dtype in vae and image2image pipeline
      
      * fix inpaint in fp16
      
      * dtype should be handled in add_noise
      
      * style
      
      * address review comments
      
      * add simple fast tests to check fp16
      
      * fix test name
      
      * put mask in fp16
      92d70863
  21. 15 Sep, 2022 1 commit
  22. 12 Sep, 2022 1 commit
    • Kashif Rasul's avatar
      update expected results of slow tests (#268) · f4781a0b
      Kashif Rasul authored
      
      
      * update expected results of slow tests
      
      * relax sum and mean tests
      
      * Print shapes when reporting exception
      
      * formatting
      
      * fix sentence
      
      * relax test_stable_diffusion_fast_ddim for gpu fp16
      
      * relax flakey tests on GPU
      
      * added comment on large tolerences
      
      * black
      
      * format
      
      * set scheduler seed
      
      * added generator
      
      * use np.isclose
      
      * set num_inference_steps to 50
      
      * fix dep. warning
      
      * update expected_slice
      
      * preprocess if image
      
      * updated expected results
      
      * updated expected from CI
      
      * pass generator to VAE
      
      * undo change back to orig
      
      * use orignal
      
      * revert back the expected on cpu
      
      * revert back values for CPU
      
      * more undo
      
      * update result after using gen
      
      * update mean
      
      * set generator for mps
      
      * update expected on CI server
      
      * undo
      
      * use new seed every time
      
      * cpu manual seed
      
      * reduce num_inference_steps
      
      * style
      
      * use generator for randn
      Co-authored-by: default avatarPatrick von Platen <patrick.v.platen@gmail.com>
      f4781a0b
  23. 08 Sep, 2022 2 commits
    • Kashif Rasul's avatar
      [Docs] Models (#416) · 5e6417e9
      Kashif Rasul authored
      
      
      * docs for attention
      
      * types for embeddings
      
      * unet2d docstrings
      
      * UNet2DConditionModel docstrings
      
      * fix typos
      
      * style and vq-vae docstrings
      
      * docstrings  for VAE
      
      * Update src/diffusers/models/unet_2d.py
      Co-authored-by: default avatarPatrick von Platen <patrick.v.platen@gmail.com>
      
      * make style
      
      * added inherits from sentence
      
      * docstring to forward
      
      * make style
      
      * Apply suggestions from code review
      Co-authored-by: default avatarPedro Cuenca <pedro@huggingface.co>
      
      * finish model docs
      
      * up
      Co-authored-by: default avatarPatrick von Platen <patrick.v.platen@gmail.com>
      Co-authored-by: default avatarPedro Cuenca <pedro@huggingface.co>
      5e6417e9
    • Pedro Cuenca's avatar
      Inference support for `mps` device (#355) · 5dda1735
      Pedro Cuenca authored
      * Initial support for mps in Stable Diffusion pipeline.
      
      * Initial "warmup" implementation when using mps.
      
      * Make some deterministic tests pass with mps.
      
      * Disable training tests when using mps.
      
      * SD: generate latents in CPU then move to device.
      
      This is especially important when using the mps device, because
      generators are not supported there. See for example
      https://github.com/pytorch/pytorch/issues/84288.
      
      In addition, the other pipelines seem to use the same approach: generate
      the random samples then move to the appropriate device.
      
      After this change, generating an image in MPS produces the same result
      as when using the CPU, if the same seed is used.
      
      * Remove prints.
      
      * Pass AutoencoderKL test_output_pretrained with mps.
      
      Sampling from `posterior` must be done in CPU.
      
      * Style
      
      * Do not use torch.long for log op in mps device.
      
      * Perform incompatible padding ops in CPU.
      
      UNet tests now pass.
      See https://github.com/pytorch/pytorch/issues/84535
      
      
      
      * Style: fix import order.
      
      * Remove unused symbols.
      
      * Remove MPSWarmupMixin, do not apply automatically.
      
      We do apply warmup in the tests, but not during normal use.
      This adopts some PR suggestions by @patrickvonplaten.
      
      * Add comment for mps fallback to CPU step.
      
      * Add README_mps.md for mps installation and use.
      
      * Apply `black` to modified files.
      
      * Restrict README_mps to SD, show measures in table.
      
      * Make PNDM indexing compatible with mps.
      
      Addresses #239.
      
      * Do not use float64 when using LDMScheduler.
      
      Fixes #358.
      
      * Fix typo identified by @patil-suraj
      Co-authored-by: default avatarSuraj Patil <surajp815@gmail.com>
      
      * Adapt example to new output style.
      
      * Restore 1:1 results reproducibility with CompVis.
      
      However, mps latents need to be generated in CPU because generators
      don't work in the mps device.
      
      * Move PyTorch nightly to requirements.
      
      * Adapt `test_scheduler_outputs_equivalence` ton MPS.
      
      * mps: skip training tests instead of ignoring silently.
      
      * Make VQModel tests pass on mps.
      
      * mps ddim tests: warmup, increase tolerance.
      
      * ScoreSdeVeScheduler indexing made mps compatible.
      
      * Make ldm pipeline tests pass using warmup.
      
      * Style
      
      * Simplify casting as suggested in PR.
      
      * Add Known Issues to readme.
      
      * `isort` import order.
      
      * Remove _mps_warmup helpers from ModelMixin.
      
      And just make changes to the tests.
      
      * Skip tests using unittest decorator for consistency.
      
      * Remove temporary var.
      
      * Remove spurious blank space.
      
      * Remove unused symbol.
      
      * Remove README_mps.
      Co-authored-by: default avatarSuraj Patil <surajp815@gmail.com>
      Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> 
      5dda1735
  24. 05 Sep, 2022 3 commits
  25. 04 Sep, 2022 1 commit
    • Partho's avatar
      [Type Hints] VAE models (#344) · 5791f4ac
      Partho authored
      * [Type Hints] VAE models
      
      * apply suggestions from code review
      
      apply suggestions to also return the return type
      5791f4ac
  26. 30 Aug, 2022 1 commit
  27. 06 Aug, 2022 1 commit
  28. 28 Jul, 2022 1 commit
  29. 20 Jul, 2022 3 commits
  30. 12 Jul, 2022 1 commit
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  32. 29 Jun, 2022 2 commits