* 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>
* make _callback_tensor_inputs consistent between sdxl pipelines
* forgot this one
* fix failing test
* fix test_components_function
* fix controlnet inpaint tests
* find & replace all FloatTensors to Tensor
* apply formatting
* Update torch.FloatTensor to torch.Tensor in the remaining files
* formatting
* Fix the rest of the places where FloatTensor is used as well as in documentation
* formatting
* Update new file from FloatTensor to Tensor
* Add Ascend NPU support for SDXL fine-tuning and fix the model saving bug when using DeepSpeed.
* fix check code quality
* Decouple the NPU flash attention and make it an independent module.
* add doc and unit tests for npu flash attention.
---------
Co-authored-by: mhh001 <mahonghao1@huawei.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* chore: reducing model sizes
* chore: shrinks further
* chore: shrinks further
* chore: shrinking model for img2img pipeline
* chore: reducing size of model for inpaint pipeline
---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* give it a shot.
* print.
* correct assertion.
* gather results from the rest of the tests.
* change the assertion values where needed.
* remove print statements.
* get device <-> component mapping when using multiple gpus.
* condition the device_map bits.
* relax condition
* device_map progress.
* device_map enhancement
* some cleaning up and debugging
* Apply suggestions from code review
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* incorporate suggestions from PR.
* remove multi-gpu condition for now.
* guard check the component -> device mapping
* fix: device_memory variable
* dispatching transformers model to have force_hooks=True
* better guarding for transformers device_map
* introduce support balanced_low_memory and balanced_ultra_low_memory.
* remove device_map patch.
* fix: intermediate variable scoping.
* fix: condition in cpu offload.
* fix: flax class restrictions.
* remove modifications from cpu_offload and model_offload
* incorporate changes.
* add a simple forward pass test
* add: torch_device in get_inputs()
* add: tests
* remove print
* safe-guard to(), model offloading and cpu offloading when balanced is used as a device_map.
* style
* remove .
* safeguard device_map with more checks and remove invalid device_mapping strategues.
* make a class attribute and adjust tests accordingly.
* fix device_map check
* fix test
* adjust comment
* fix: device_map attribute
* fix: dispatching.
* max_memory test for pipeline
* version guard the tests
* fix guard.
* address review feedback.
* reset_device_map method.
* add: test for reset_hf_device_map
* fix a couple things.
* add reset_device_map() in the error message.
* add tests for checking reset_device_map doesn't have unintended consequences.
* fix reset_device_map and offloading tests.
* create _get_final_device_map utility.
* hf_device_map -> _hf_device_map
* add documentation
* add notes suggested by Marc.
* styling.
* Apply suggestions from code review
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* move updates within gpu condition.
* other docs related things
* note on ignore a device not specified in .
* provide a suggestion if device mapping errors out.
* fix: typo.
* _hf_device_map -> hf_device_map
* Empty-Commit
* add: example hf_device_map.
---------
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* reduce block sizes for unet1d.
* reduce blocks for unet_2d.
* reduce block size for unet_motion
* increase channels.
* correctly increase channels.
* reduce number of layers in unet2dconditionmodel tests.
* reduce block sizes for unet2dconditionmodel tests
* reduce block sizes for unet3dconditionmodel.
* fix: test_feed_forward_chunking
* fix: test_forward_with_norm_groups
* skip spatiotemporal tests on MPS.
* reduce block size in AutoencoderKL.
* reduce block sizes for vqmodel.
* further reduce block size.
* make style.
* Empty-Commit
* reduce sizes for ConsistencyDecoderVAETests
* further reduction.
* further block reductions in AutoencoderKL and AssymetricAutoencoderKL.
* massively reduce the block size in unet2dcontionmodel.
* reduce sizes for unet3d
* fix tests in unet3d.
* reduce blocks further in motion unet.
* fix: output shape
* add attention_head_dim to the test configuration.
* remove unexpected keyword arg
* up a bit.
* groups.
* up again
* fix
* Skip `test_freeu_enabled ` on MPS
* Small fixes
- import skip_mps correctly
- disable all instances of test_freeu_enabled
* Empty commit to trigger tests
* Empty commit to trigger CI
