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b3d10d6d65
* 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>