* handle norm_type of transformer2d_model safely.
* log an info when old model class is being returned.
* Apply suggestions from code review
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>
* remove extra stuff
---------
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>
* Update transformer2d.md title
For the other classes (e.g., UNet2DModel) the title of the documentation coincides with the name of the class, but that was not the case for Transformer2DModel.
* Update model docs titles for consistency with class names
* Modularized the train_lora_sdxl file
* Modularized the train_lora_sdxl file
* Modularized the train_lora_sdxl file
---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* Modularized the train_lora file
* Modularized the train_lora file
* Modularized the train_lora file
* Modularized the train_lora file
* Modularized the train_lora file
---------
Co-authored-by: Sayak Paul <spsayakpaul@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>
* 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>
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.
