- 06 Mar, 2023 1 commit
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Sean Sube authored
* [Onnx] add Stable Diffusion Upscale pipeline * add a test for the OnnxStableDiffusionUpscalePipeline * check for VAE config before adjusting scaling factor * update test assertions, lint fixes * run fix-copies target * switch test checkpoint to one hosted on huggingface * partially restore attention mask * reshape embeddings after running text encoder * add longer nightly test for ONNX upscale pipeline * use package import to fix tests * fix scheduler compatibility and class labels dtype * use more precise type * remove LMS from fast tests * lookup latent and timestamp types * add docs for ONNX upscaling, rename lookup table * replace deprecated pipeline names in ONNX docs
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- 03 Mar, 2023 2 commits
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Patrick von Platen authored
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Patrick von Platen authored
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- 02 Mar, 2023 1 commit
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Takuma Mori authored
* add scaffold - copied convert_controlnet_to_diffusers.py from convert_original_stable_diffusion_to_diffusers.py * Add support to load ControlNet (WIP) - this makes Missking Key error on ControlNetModel * Update to convert ControlNet without error msg - init impl for StableDiffusionControlNetPipeline - init impl for ControlNetModel * cleanup of commented out * split create_controlnet_diffusers_config() from create_unet_diffusers_config() - add config: hint_channels * Add input_hint_block, input_zero_conv and middle_block_out - this makes missing key error on loading model * add unet_2d_blocks_controlnet.py - copied from unet_2d_blocks.py as impl CrossAttnDownBlock2D,DownBlock2D - this makes missing key error on loading model * Add loading for input_hint_block, zero_convs and middle_block_out - this makes no error message on model loading * Copy from UNet2DConditionalModel except __init__ * Add ultra primitive test for ControlNetModel inference * Support ControlNetModel inference - without exceptions * copy forward() from UNet2DConditionModel * Impl ControlledUNet2DConditionModel inference - test_controlled_unet_inference passed * Frozen weight & biases for training * Minimized version of ControlNet/ControlledUnet - test_modules_controllnet.py passed * make style * Add support model loading for minimized ver * Remove all previous version files * from_pretrained and inference test passed * copied from pipeline_stable_diffusion.py except `__init__()` * Impl pipeline, pixel match test (almost) passed. * make style * make fix-copies * Fix to add import ControlNet blocks for `make fix-copies` * Remove einops dependency * Support np.ndarray, PIL.Image for controlnet_hint * set default config file as lllyasviel's * Add support grayscale (hw) numpy array * Add and update docstrings * add control_net.mdx * add control_net.mdx to toctree * Update copyright year * Fix to add PIL.Image RGB->BGR conversion - thanks @Mystfit * make fix-copies * add basic fast test for controlnet * add slow test for controlnet/unet * Ignore down/up_block len check on ControlNet * add a copy from test_stable_diffusion.py * Accept controlnet_hint is None * merge pipeline_stable_diffusion.py diff * Update class name to SDControlNetPipeline * make style * Baseline fast test almost passed (w long desc) * still needs investigate. Following didn't passed descriped in TODO comment: - test_stable_diffusion_long_prompt - test_stable_diffusion_no_safety_checker Following didn't passed same as stable_diffusion_pipeline: - test_attention_slicing_forward_pass - test_inference_batch_single_identical - test_xformers_attention_forwardGenerator_pass these seems come from calc accuracy. * Add note comment related vae_scale_factor * add test_stable_diffusion_controlnet_ddim * add assertion for vae_scale_factor != 8 * slow test of pipeline almost passed Failed: test_stable_diffusion_pipeline_with_model_offloading - ImportError: `enable_model_offload` requires `accelerate v0.17.0` or higher but currently latest version == 0.16.0 * test_stable_diffusion_long_prompt passed * test_stable_diffusion_no_safety_checker passed - due to its model size, move to slow test * remove PoC test files * fix num_of_image, prompt length issue add add test * add support List[PIL.Image] for controlnet_hint * wip * all slow test passed * make style * update for slow test * RGB(PIL)->BGR(ctrlnet) conversion * fixes * remove manual num_images_per_prompt test * add document * add `image` argument docstring * make style * Add line to correct conversion * add controlnet_conditioning_scale (aka control_scales strength) * rgb channel ordering by default * image batching logic * Add control image descriptions for each checkpoint * Only save controlnet model in conversion script * Update src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_controlnet.py typo Co-authored-by:
Pedro Cuenca <pedro@huggingface.co> * Update docs/source/en/api/pipelines/stable_diffusion/control_net.mdx Co-authored-by:
dqueue <dbyqin@gmail.com> * remove controlnet constructor arguments re: @patrickvonplaten * [integration tests] test canny * test_canny fixes * [integration tests] test_depth * [integration tests] test_hed * [integration tests] test_mlsd * add channel order config to controlnet * [integration tests] test normal * [integration tests] test_openpose test_scribble * change height and width to default to conditioning image * [integration tests] test seg * style * test_depth fix * [integration tests] size fixes * [integration tests] cpu offloading * style * generalize controlnet embedding * fix conversion script * Update docs/source/en/api/pipelines/stable_diffusion/controlnet.mdx Co-authored-by:
Sayak Paul <spsayakpaul@gmail.com> * Update docs/source/en/api/pipelines/stable_diffusion/controlnet.mdx Co-authored-by:
William Berman <WLBberman@gmail.com> Co-authored-by:
Patrick von Platen <patrick.v.platen@gmail.com>
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- 17 Feb, 2023 7 commits
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Patrick von Platen authored
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Patrick von Platen authored
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Will Berman authored
This reverts commit 024c4376.
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Patrick von Platen authored
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Omer Bar Tal authored
* add MultiDiffusionPanorama pipeline * fix docs naming * update pipeline name, remove redundant tests * apply styling. * debugging information. * fix: assertion values. * fix-copies. * update docs * update docs --------- Co-authored-by: -
Patrick von Platen authored
* add * finish * add tests * add tests * up * up * pull from main * uP * Apply suggestions from code review * finish * Update docs/source/en/_toctree.yml Co-authored-by:
Suraj Patil <surajp815@gmail.com> * finish * clean docs * next * next * Apply suggestions from code review Co-authored-by:
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Manuel Brack authored
* Add semantic guidance pipeline * Fix style * Refactor Pipeline * Pipeline documentation * Add documentation * Fix style and quality * Fix doctree * Add tests for SEGA * Update src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py Co-authored-by:
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- 16 Feb, 2023 4 commits
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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:
evin <evinpinarornek@gmail.com> Co-authored-by:
yiyixuxu <yixu310@gmail,com> Co-authored-by:
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Wenliang Zhao authored
* add UniPC scheduler * add the return type to the functions * code quality check * add tests * finish docs --------- Co-authored-by: -
Susung Hong authored
* Add Stable Diffusion Sw/ elf-Attention Guidance * Modify __init__.py * Register attention storing processor * Update pipeline_stable_diffusion_sag.py * Editing default value * Update pipeline_stable_diffusion_sag.py * Update pipeline_stable_diffusion_sag.py * Update pipeline_stable_diffusion_sag.py * Update dummy_torch_and_transformers_objects.py * Update pipeline_stable_diffusion_sag.py * Update pipeline_stable_diffusion_sag.py * Update pipeline_stable_diffusion_sag.py * Update src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_sag.py Co-authored-by:
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Sayak Paul authored
* add: support for BLIP generation. * add: support for editing synthetic images. * remove unnecessary comments. * add inits and run make fix-copies. * version change of diffusers. * fix: condition for loading the captioner. * default conditions_input_image to False. * guidance_amount -> cross_attention_guidance_amount * fix inputs to check_inputs() * fix: attribute. * fix: prepare_attention_mask() call. * debugging. * better placement of references. * remove torch.no_grad() decorations. * put torch.no_grad() context before the first denoising loop. * detach() latents before decoding them. * put deocding in a torch.no_grad() context. * add reconstructed image for debugging. * no_grad(0 * apply formatting. * address one-off suggestions from the draft PR. * back to torch.no_grad() and add more elaborate comments. * refactor prepare_unet() per Patrick's suggestions. * more elaborate description for . * formatting. * add docstrings to the methods specific to pix2pix zero. * suspecting a redundant noise prediction. * needed for gradient computation chain. * less hacks. * fix: attention mask handling within the processor. * remove attention reference map computation. * fix: cross attn args. * fix: prcoessor. * store attention maps. * fix: attention processor. * update docs and better treatment to xa args. * update the final noise computation call. * change xa args call. * remove xa args option from the pipeline. * add: docs. * first test. * fix: url call. * fix: argument call. * remove image conditioning for now. *
🚨 add: fast tests. * explicit placement of the xa attn weights. * add: slow tests🐢 * fix: tests. * edited direction embedding should be on the same device as prompt_embeds. * debugging message. * debugging. * add pix2pix zero pipeline for a non-deterministic test. * debugging/ * remove debugging message. * make caption generation _ * address comments (part I). * address PR comments (part II) * fix: DDPM test assertion. * refactor doc. * address PR comments (part III). * fix: type annotation for the scheduler. * apply styling. * skip_mps and add note on embeddings in the docs.
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- 14 Feb, 2023 1 commit
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Will Berman authored
* pipeline_variant * Add docs for when clip_stats_path is specified * Update src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip.py Co-authored-by:
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- 07 Feb, 2023 1 commit
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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:
yiyixuxu <yixu@yis-macbook-pro.lan> Co-authored-by:
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- 25 Jan, 2023 2 commits
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Patrick von Platen authored
* [Bump version] 0.13 * Bump model up * up
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Patrick von Platen authored
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- 20 Jan, 2023 1 commit
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Suraj Patil authored
* being pix2pix * ifx * cfg image_latents * fix some docstr * fix * fix * hack * fix * Apply suggestions from code review Co-authored-by:
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- 17 Jan, 2023 3 commits
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Kashif Rasul authored
* added dit model * import * initial pipeline * initial convert script * initial pipeline * make style * raise valueerror * single function * rename classes * use DDIMScheduler * timesteps embedder * samples to cpu * fix var names * fix numpy type * use timesteps class for proj * fix typo * fix arg name * flip_sin_to_cos and better var names * fix C shape cal * make style * remove unused imports * cleanup * add back patch_size * initial dit doc * typo * Update docs/source/api/pipelines/dit.mdx Co-authored-by:
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Pedro Cuenca authored
* Check k-diffusion version is at least 0.0.12 * make style
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Jerry Jiarui XU authored
* [Flax] Add Flax inpainting impl * fixed copies, add README.md * fixed README.md * add test * format * update README.md
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- 04 Jan, 2023 1 commit
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qsh-zh authored
* feat : add log-rho deis multistep deis * docs :fix typo * docs : add docs for impl algo * docs : remove duplicate ref * finish deis * add docs * fix 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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- 28 Dec, 2022 1 commit
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Will Berman authored
* unCLIP image variation * remove prior comment re: @pcuenca * stable diffusion -> unCLIP re: @pcuenca * add copy froms re: @patil-suraj
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- 20 Dec, 2022 1 commit
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Dhruv Naik authored
* add flax img2img pipeline * update pipeline * black format file * remove argg from get_timesteps * update get_timesteps * fix bug: make use of timesteps for for_loop * black file * black, isort, flake8 * update docstring * update readme * update flax img2img readme * update sd pipeline init * Update src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_img2img.py Co-authored-by:
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- 19 Dec, 2022 2 commits
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Anton Lozhkov authored
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anton- authored
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- 18 Dec, 2022 1 commit
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Will Berman authored
* [wip] attention block updates * [wip] unCLIP unet decoder and super res * [wip] unCLIP prior transformer * [wip] scheduler changes * [wip] text proj utility class * [wip] UnCLIPPipeline * [wip] kakaobrain unCLIP convert script * [unCLIP pipeline] fixes re: @patrickvonplaten remove callbacks move denoising loops into call function * UNCLIPScheduler re: @patrickvonplaten Revert changes to DDPMScheduler. Make UNCLIPScheduler, a modified DDPM scheduler with changes to support karlo * mask -> attention_mask re: @patrickvonplaten * [DDPMScheduler] remove leftover change * [docs] PriorTransformer * [docs] UNet2DConditionModel and UNet2DModel * [nit] UNCLIPScheduler -> UnCLIPScheduler matches existing unclip naming better * [docs] SchedulingUnCLIP * [docs] UnCLIPTextProjModel * refactor * finish licenses * rename all to attention_mask and prep in models * more renaming * don't expose unused configs * final renaming fixes * remove x attn mask when not necessary * configure kakao script to use new class embedding config * fix copies * [tests] UnCLIPScheduler * finish x attn * finish * remove more * rename condition blocks * clean more * Apply suggestions from code review * up * fix * [tests] UnCLIPPipelineFastTests * remove unused imports * [tests] UnCLIPPipelineIntegrationTests * correct * make style Co-authored-by:
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- 13 Dec, 2022 1 commit
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Patrick von Platen authored
upgrade version
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- 09 Dec, 2022 3 commits
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anton- authored
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Anton Lozhkov authored
* Adapt to forced transformers version in some dependent libraries * style * Update __init__.py * update requires_backends
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Anton Lozhkov authored
* Re-add xformers enable to UNet2DCondition (#1627) * finish * fix * Update tests/models/test_models_unet_2d.py * style Co-authored-by:
Anton Lozhkov <anton@huggingface.co> * Release: v0.10.1 Co-authored-by:
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- 08 Dec, 2022 4 commits
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anton- authored
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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:
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Anton Lozhkov authored
* Fix PyCharm/VSCode static type checking for dummy objects * Re-add dummies * Fix AudioDiffusion imports * fix import * fix import * Update utils/check_dummies.py Co-authored-by:
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Patrick von Platen authored
* uP * uP
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- 07 Dec, 2022 2 commits
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Cheng Lu authored
* add singlestep dpmsolver * fix a style typo * fix a style typo * add docs * finish Co-authored-by: -
Patrick von Platen authored
* add paint by example * mkae loading possibel * up * Update src/diffusers/models/attention.py * up * finalize weight structure * make example work * make it work * up * up * fix * del * add * update * Apply suggestions from code review * correct transformer 2d * finish * up * up * up * up * fix * Apply suggestions from code review Co-authored-by:
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