GitLab

* initial doc

* fix wrong LCM sentence

* implement binary colormap without requiring matplotlib
update section about Marigold for ControlNet
update formatting of marigold_usage.md

* fix indentation

---------
Co-authored-by: anton <anton.obukhov@gmail.com>

* implement marigold depth and normals pipelines in diffusers core

* remove bibtex

* remove deprecations

* remove save_memory argument

* remove validate_vae

* remove config output

* remove batch_size autodetection

* remove presets logic
move default denoising_steps and processing_resolution into the model config
make default ensemble_size 1

* remove no_grad

* add fp16 to the example usage

* implement is_matplotlib_available
use is_matplotlib_available, is_scipy_available for conditional imports in the marigold depth pipeline

* move colormap, visualize_depth, and visualize_normals into export_utils.py

* make the denoising loop more lucid
fix the outputs to always be 4d tensors or lists of pil images
support a 4d input_image case
attempt to support model_cpu_offload_seq
move check_inputs into a separate function
change default batch_size to 1, remove any logic to make it bigger implicitly

* style

* rename denoising_steps into num_inference_steps

* rename input_image into image

* rename input_latent into latents

* remove decode_image
change decode_prediction to use the AutoencoderKL.decode method

* move clean_latent outside of progress_bar

* refactor marigold-reusable image processing bits into MarigoldImageProcessor class

* clean up the usage example docstring

* make ensemble functions members of the pipelines

* add early checks in check_inputs
rename E into ensemble_size in depth ensembling

* fix vae_scale_factor computation

* better compatibility with torch.compile
better variable naming

* move export_depth_to_png to export_utils

* remove encode_prediction

* improve visualize_depth and visualize_normals to accept multi-dimensional data and lists
remove visualization functions from the pipelines
move exporting depth as 16-bit PNGs functionality from the depth pipeline
update example docstrings

* do not shortcut vae.config variables

* change all asserts to raise ValueError

* rename output_prediction_type to output_type

* better variable names
clean up variable deletion code

* better variable names

* pass desc and leave kwargs into the diffusers progress_bar
implement nested progress bar for images and steps loops

* implement scale_invariant and shift_invariant flags in the ensemble_depth function
add scale_invariant and shift_invariant flags readout from the model config
further refactor ensemble_depth
support ensembling without alignment
add ensemble_depth docstring

* fix generator device placement checks

* move encode_empty_text body into the pipeline call

* minor empty text encoding simplifications

* adjust pipelines' class docstrings to explain the added construction arguments

* improve the scipy failure condition
add comments
improve docstrings
change the default use_full_z_range to True

* make input image values range check configurable in the preprocessor
refactor load_image_canonical in preprocessor to reject unknown types and return the image in the expected 4D format of tensor and on right device
support a list of everything as inputs to the pipeline, change type to PipelineImageInput
implement a check that all input list elements have the same dimensions
improve docstrings of pipeline outputs
remove check_input pipeline argument

* remove forgotten print

* add prediction_type model config

* add uncertainty visualization into export utils
fix NaN values in normals uncertainties

* change default of output_uncertainty to False
better handle the case of an attempt to export or visualize none

* fix `output_uncertainty=False`

* remove kwargs
fix check_inputs according to the new inputs of the pipeline

* rename prepare_latent into prepare_latents as in other pipelines
annotate prepare_latents in normals pipeline with "Copied from"
annotate encode_image in normals pipeline with "Copied from"

* move nested-capable `progress_bar` method into the pipelines
revert the original `progress_bar` method in pipeline_utils

* minor message improvement

* fix cpu offloading

* move colormap, visualize_depth, export_depth_to_16bit_png, visualize_normals, visualize_uncertainty to marigold_image_processing.py
update example docstrings

* fix missing comma

* change torch.FloatTensor to torch.Tensor

* fix importing of MarigoldImageProcessor

* fix vae offloading
fix batched image encoding
remove separate encode_image function and use vae.encode instead

* implement marigold's intial tests
relax generator checks in line with other pipelines
implement return_dict __call__ argument in line with other pipelines

* fix num_images computation

* remove MarigoldImageProcessor and outputs from import structure
update tests

* update docstrings

* update init

* update

* style

* fix

* fix

* up

* up

* up

* add simple test

* up

* update expected np input/output to be channel last

* move expand_tensor_or_array into the MarigoldImageProcessor

* rewrite tests to follow conventions - hardcoded slices instead of image artifacts
write more smoke tests

* add basic docs.

* add anton's contribution statement

* remove todos.

* fix assertion values for marigold depth slow tests

* fix assertion values for depth normals.

* remove print

* support AutoencoderTiny in the pipelines

* update documentation page
add Available Pipelines section
add Available Checkpoints section
add warning about num_inference_steps

* fix missing import in docstring
fix wrong value in visualize_depth docstring

* [doc] add marigold to pipelines overview

* [doc] add section "usage examples"

* fix an issue with latents check in the pipelines

* add "Frame-by-frame Video Processing with Consistency" section

* grammarly

* replace tables with images with css-styled images (blindly)

* style

* print

* fix the assertions.

* take from the github runner.

* take the slices from action artifacts

* style.

* update with the slices from the runner.

* remove unnecessary code blocks.

* Revert "[doc] add marigold to pipelines overview"

This reverts commit a505165150afd8dab23c474d1a054ea505a56a5f.

* remove invitation for new modalities

* split out marigold usage examples

* doc cleanup

---------
Co-authored-by: yiyixuxu <yixu310@gmail.com>
Co-authored-by: yiyixuxu <yixu310@gmail,com>
Co-authored-by: sayakpaul <spsayakpaul@gmail.com>

update

* add a more secure way to run tests from a PR.

* make pytest more secure.

* address dhruv's comments.

* improve validation check.

* Update .github/workflows/run_tests_from_a_pr.yml
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>

---------
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>

* Add details about 1-stage implementation

* Add details about 1-stage implementation

* Fix typos

* Fix `pipe.enable_model_cpu_offload()` usage

* Fix cpu offloading

* Update numbers

Fix grammatical error

sampling bug fix in basic_training.md

In the diffusers basic training tutorial, setting the manual seed argument (generator=torch.manual_seed(config.seed)) in the pipeline call inside evaluate() function rewinds the dataloader shuffling, leading to overfitting due to the model seeing same sequence of training examples after every evaluation call. Using generator=torch.Generator(device='cpu').manual_seed(config.seed) avoids this.

* update

* update

* Fix resume_downoad FutureWarning

* only resume download

run the documentation workflow in a custom container.

[Community Pipeline] FRESCO: Spatial-Temporal Correspondence for Zero-Shot Video Translation (#8239)

* code and doc

* update paper link

* remove redundant codes

* add example video

---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* update

* update

* update

* update

update
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

update

* fix?

* fix?

* fix

make VAE compatible to torch.compile()
Co-authored-by: YiYi Xu <yixu310@gmail.com>

Fixed few docstrings according to the Google Style Guide.

* Update pipeline_stable_diffusion_instruct_pix2pix.py

Add `cross_attention_kwargs` to `__call__` method of `StableDiffusionInstructPix2PixPipeline`, which are passed to UNet.

* Update documentation for pipeline_stable_diffusion_instruct_pix2pix.py

* Update docstring

* Update docstring

* Fix typing import

* 1. add doc for PixArtSigmaPipeline;

---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
Co-authored-by: Guillaume LEGENDRE <glegendre01@gmail.com>
Co-authored-by: Álvaro Somoza <asomoza@users.noreply.github.com>
Co-authored-by: Bagheera <59658056+bghira@users.noreply.github.com>
Co-authored-by: bghira <bghira@users.github.com>
Co-authored-by: Hyoungwon Cho <jhw9811@korea.ac.kr>
Co-authored-by: yiyixuxu <yixu310@gmail.com>
Co-authored-by: Tolga Cangöz <46008593+standardAI@users.noreply.github.com>
Co-authored-by: Philip Pham <phillypham@google.com>

* checking tests

* checking ii.

* remove prints.

* test_pixart_1024

* fix 1024.

* make _callback_tensor_inputs consistent between sdxl pipelines

* forgot this one

* fix failing test

* fix test_components_function

* fix controlnet inpaint tests

Fix conditional teacher model check in train_lcm_distill_lora_sdxl_wds.py
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

Fix CLIP to T5 in logger warning

* Merged isinstance calls to make the code simpler.

* Corrected formatting errors using ruff.

---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: YiYi Xu <yixu310@gmail.com>

* add a workflow that can be manually triggered on a PR.

* remove sudo

* add command

* small fixes.

* Init commit

* Removed einops

* Added default movq config for training

* Update explanation of prompts

* Fixed inheritance of discriminator and init_tracker

* Fixed incompatible api between muse and here

* Fixed output

* Setup init training

* Basic structure done

* Removed attention for quick tests

* Style fixes

* Fixed vae/vqgan styles

* Removed redefinition of wandb

* Fixed log_validation and tqdm

* Nothing commit

* Added commit loss to lookup_from_codebook

* Update src/diffusers/models/vq_model.py
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* Adding perliminary README

* Fixed one typo

* Local changes

* Fixed main issues

* Merging

* Update src/diffusers/models/vq_model.py
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* Testing+Fixed bugs in training script

* Some style fixes

* Added wandb to docs

* Fixed timm test

* get testing suite ready.

* remove return loss

* remove return_loss

* Remove diffs

* Remove diffs

* fix ruff format

---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>

decorate StableDiffusion21PipelineSingleFileSlowTests with slow.

add boxdiff to community examples

Fix `added_cond_kwargs` when using IP-Adapter

Fix error when using IP-Adapter in pipeline and passing `ip_adapter_image_embeds` instead of `ip_adapter_image`
Co-authored-by: YiYi Xu <yixu310@gmail.com>

separate the loading utilities in modeling similar to pipelines.

update to use hf-workflows for reporting