* Add IP-Adapter example to Flux docs
* Apply suggestions from code review
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* update
* update
* make style
* remove dynamo disable
* add coauthor
Co-Authored-By: Dhruv Nair <dhruv.nair@gmail.com>
* update
* update
* update
* update mixin
* add some basic tests
* update
* update
* non_blocking
* improvements
* update
* norm.* -> norm
* apply suggestions from review
* add example
* update hook implementation to the latest changes from pyramid attention broadcast
* deinitialize should raise an error
* update doc page
* Apply suggestions from code review
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* update docs
* update
* refactor
* fix _always_upcast_modules for asym ae and vq_model
* fix lumina embedding forward to not depend on weight dtype
* refactor tests
* add simple lora inference tests
* _always_upcast_modules -> _precision_sensitive_module_patterns
* remove todo comments about review; revert changes to self.dtype in unets because .dtype on ModelMixin should be able to handle fp8 weight case
* check layer dtypes in lora test
* fix UNet1DModelTests::test_layerwise_upcasting_inference
* _precision_sensitive_module_patterns -> _skip_layerwise_casting_patterns based on feedback
* skip test in NCSNppModelTests
* skip tests for AutoencoderTinyTests
* skip tests for AutoencoderOobleckTests
* skip tests for UNet1DModelTests - unsupported pytorch operations
* layerwise_upcasting -> layerwise_casting
* skip tests for UNetRLModelTests; needs next pytorch release for currently unimplemented operation support
* add layerwise fp8 pipeline test
* use xfail
* Apply suggestions from code review
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>
* add assertion with fp32 comparison; add tolerance to fp8-fp32 vs fp32-fp32 comparison (required for a few models' test to pass)
* add note about memory consumption on tesla CI runner for failing test
---------
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update hunyuan_video.md to rectify the checkpoint id
* bfloat16
* more fixes
* don't update the checkpoint ids.
* update
* t -> T
* Apply suggestions from code review
* fix
---------
Co-authored-by: YiYi Xu <yixu310@gmail.com>
Fix argument name in 8bit quantized example
Found a tiny mistake in the documentation where the text encoder model was passed to the wrong argument in the FluxPipeline.from_pretrained function.
* fix the Positinoal Embedding bug in 2K model;
* Change the default model to the BF16 one for more stable training and output
* make style
* substract buffer size
* add compute_module_persistent_sizes
---------
Co-authored-by: yiyixuxu <yixu310@gmail.com>
* copy transformer
* copy vae
* copy pipeline
* make fix-copies
* refactor; make original code work with diffusers; test latents for comparison generated with this commit
* move rope into pipeline; remove flash attention; refactor
* begin conversion script
* make style
* refactor attention
* refactor
* refactor final layer
* their mlp -> our feedforward
* make style
* add docs
* refactor layer names
* refactor modulation
* cleanup
* refactor norms
* refactor activations
* refactor single blocks attention
* refactor attention processor
* make style
* cleanup a bit
* refactor double transformer block attention
* update mochi attn proc
* use diffusers attention implementation in all modules; checkpoint for all values matching original
* remove helper functions in vae
* refactor upsample
* refactor causal conv
* refactor resnet
* refactor
* refactor
* refactor
* grad checkpointing
* autoencoder test
* fix scaling factor
* refactor clip
* refactor llama text encoding
* add coauthor
Co-Authored-By: "Gregory D. Hunkins" <greg@ollano.com>
* refactor rope; diff: 0.14990234375; reason and fix: create rope grid on cpu and move to device
Note: The following line diverges from original behaviour. We create the grid on the device, whereas
original implementation creates it on CPU and then moves it to device. This results in numerical
differences in layerwise debugging outputs, but visually it is the same.
* use diffusers timesteps embedding; diff: 0.10205078125
* rename
* convert
* update
* add tests for transformer
* add pipeline tests; text encoder 2 is not optional
* fix attention implementation for torch
* add example
* update docs
* update docs
* apply suggestions from review
* refactor vae
* update
* Apply suggestions from code review
Co-authored-by: hlky <hlky@hlky.ac>
* Update src/diffusers/pipelines/hunyuan_video/pipeline_hunyuan_video.py
Co-authored-by: hlky <hlky@hlky.ac>
* Update src/diffusers/pipelines/hunyuan_video/pipeline_hunyuan_video.py
Co-authored-by: hlky <hlky@hlky.ac>
* make fix-copies
* update
---------
Co-authored-by: "Gregory D. Hunkins" <greg@ollano.com>
Co-authored-by: hlky <hlky@hlky.ac>
* first add a script for DC-AE;
* DC-AE init
* replace triton with custom implementation
* 1. rename file and remove un-used codes;
* no longer rely on omegaconf and dataclass
* replace custom activation with diffuers activation
* remove dc_ae attention in attention_processor.py
* iinherit from ModelMixin
* inherit from ConfigMixin
* dc-ae reduce to one file
* update downsample and upsample
* clean code
* support DecoderOutput
* remove get_same_padding and val2tuple
* remove autocast and some assert
* update ResBlock
* remove contents within super().__init__
* Update src/diffusers/models/autoencoders/dc_ae.py
Co-authored-by: YiYi Xu <yixu310@gmail.com>
* remove opsequential
* update other blocks to support the removal of build_norm
* remove build encoder/decoder project in/out
* remove inheritance of RMSNorm2d from LayerNorm
* remove reset_parameters for RMSNorm2d
Co-authored-by: YiYi Xu <yixu310@gmail.com>
* remove device and dtype in RMSNorm2d __init__
Co-authored-by: YiYi Xu <yixu310@gmail.com>
* Update src/diffusers/models/autoencoders/dc_ae.py
Co-authored-by: YiYi Xu <yixu310@gmail.com>
* Update src/diffusers/models/autoencoders/dc_ae.py
Co-authored-by: YiYi Xu <yixu310@gmail.com>
* Update src/diffusers/models/autoencoders/dc_ae.py
Co-authored-by: YiYi Xu <yixu310@gmail.com>
* remove op_list & build_block
* remove build_stage_main
* change file name to autoencoder_dc
* move LiteMLA to attention.py
* align with other vae decode output;
* add DC-AE into init files;
* update
* make quality && make style;
* quick push before dgx disappears again
* update
* make style
* update
* update
* fix
* refactor
* refactor
* refactor
* update
* possibly change to nn.Linear
* refactor
* make fix-copies
* replace vae with ae
* replace get_block_from_block_type to get_block
* replace downsample_block_type from Conv to conv for consistency
* add scaling factors
* incorporate changes for all checkpoints
* make style
* move mla to attention processor file; split qkv conv to linears
* refactor
* add tests
* from original file loader
* add docs
* add standard autoencoder methods
* combine attention processor
* fix tests
* update
* minor fix
* minor fix
* minor fix & in/out shortcut rename
* minor fix
* make style
* fix paper link
* update docs
* update single file loading
* make style
* remove single file loading support; todo for DN6
* Apply suggestions from code review
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* add abstract
* 1. add DCAE into diffusers;
2. make style and make quality;
* add DCAE_HF into diffusers;
* bug fixed;
* add SanaPipeline, SanaTransformer2D into diffusers;
* add sanaLinearAttnProcessor2_0;
* first update for SanaTransformer;
* first update for SanaPipeline;
* first success run SanaPipeline;
* model output finally match with original model with the same intput;
* code update;
* code update;
* add a flow dpm-solver scripts
* 🎉[important update]
1. Integrate flow-dpm-sovler into diffusers;
2. finally run successfully on both `FlowMatchEulerDiscreteScheduler` and `FlowDPMSolverMultistepScheduler`;
* 🎉🔧[important update & fix huge bugs!!]
1. add SanaPAGPipeline & several related Sana linear attention operators;
2. `SanaTransformer2DModel` not supports multi-resolution input;
2. fix the multi-scale HW bugs in SanaPipeline and SanaPAGPipeline;
3. fix the flow-dpm-solver set_timestep() init `model_output` and `lower_order_nums` bugs;
* remove prints;
* add convert sana official checkpoint to diffusers format Safetensor.
* Update src/diffusers/models/transformers/sana_transformer_2d.py
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update src/diffusers/models/transformers/sana_transformer_2d.py
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update src/diffusers/models/transformers/sana_transformer_2d.py
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update src/diffusers/pipelines/pag/pipeline_pag_sana.py
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update src/diffusers/models/transformers/sana_transformer_2d.py
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update src/diffusers/models/transformers/sana_transformer_2d.py
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update src/diffusers/pipelines/sana/pipeline_sana.py
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update src/diffusers/pipelines/sana/pipeline_sana.py
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* update Sana for DC-AE's recent commit;
* make style && make quality
* Add StableDiffusion3PAGImg2Img Pipeline + Fix SD3 Unconditional PAG (#9932)
* fix progress bar updates in SD 1.5 PAG Img2Img pipeline
---------
Co-authored-by: Vinh H. Pham <phamvinh257@gmail.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* make the vae can be None in `__init__` of `SanaPipeline`
* Update src/diffusers/models/transformers/sana_transformer_2d.py
Co-authored-by: hlky <hlky@hlky.ac>
* change the ae related code due to the latest update of DCAE branch;
* change the ae related code due to the latest update of DCAE branch;
* 1. change code based on AutoencoderDC;
2. fix the bug of new GLUMBConv;
3. run success;
* update for solving conversation.
* 1. fix bugs and run convert script success;
2. Downloading ckpt from hub automatically;
* make style && make quality;
* 1. remove un-unsed parameters in init;
2. code update;
* remove test file
* refactor; add docs; add tests; update conversion script
* make style
* make fix-copies
* refactor
* udpate pipelines
* pag tests and refactor
* remove sana pag conversion script
* handle weight casting in conversion script
* update conversion script
* add a processor
* 1. add bf16 pth file path;
2. add complex human instruct in pipeline;
* fix fast \tests
* change gemma-2-2b-it ckpt to a non-gated repo;
* fix the pth path bug in conversion script;
* change grad ckpt to original; make style
* fix the complex_human_instruct bug and typo;
* remove dpmsolver flow scheduler
* apply review suggestions
* change the `FlowMatchEulerDiscreteScheduler` to default `DPMSolverMultistepScheduler` with flow matching scheduler.
* fix the tokenizer.padding_side='right' bug;
* update docs
* make fix-copies
* fix imports
* fix docs
* add integration test
* update docs
* update examples
* fix convert_model_output in schedulers
* fix failing tests
---------
Co-authored-by: Junyu Chen <chenjydl2003@gmail.com>
Co-authored-by: YiYi Xu <yixu310@gmail.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: chenjy2003 <70215701+chenjy2003@users.noreply.github.com>
Co-authored-by: Aryan <aryan@huggingface.co>
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
Co-authored-by: hlky <hlky@hlky.ac>
