* feat: pipeline-level quant config.
Co-authored-by: SunMarc <marc.sun@hotmail.fr>
condition better.
support mapping.
improvements.
[Quantization] Add Quanto backend (#10756)
* update
* updaet
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* Update docs/source/en/quantization/quanto.md
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* Update src/diffusers/quantizers/quanto/utils.py
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* update
* update
---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
[Single File] Add single file loading for SANA Transformer (#10947)
* added support for from_single_file
* added diffusers mapping script
* added testcase
* bug fix
* updated tests
* corrected code quality
* corrected code quality
---------
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>
[LoRA] Improve warning messages when LoRA loading becomes a no-op (#10187)
* updates
* updates
* updates
* updates
* notebooks revert
* fix-copies.
* seeing
* fix
* revert
* fixes
* fixes
* fixes
* remove print
* fix
* conflicts ii.
* updates
* fixes
* better filtering of prefix.
---------
Co-authored-by: hlky <hlky@hlky.ac>
[LoRA] CogView4 (#10981)
* update
* make fix-copies
* update
[Tests] improve quantization tests by additionally measuring the inference memory savings (#11021)
* memory usage tests
* fixes
* gguf
[`Research Project`] Add AnyText: Multilingual Visual Text Generation And Editing (#8998)
* Add initial template
* Second template
* feat: Add TextEmbeddingModule to AnyTextPipeline
* feat: Add AuxiliaryLatentModule template to AnyTextPipeline
* Add bert tokenizer from the anytext repo for now
* feat: Update AnyTextPipeline's modify_prompt method
This commit adds improvements to the modify_prompt method in the AnyTextPipeline class. The method now handles special characters and replaces selected string prompts with a placeholder. Additionally, it includes a check for Chinese text and translation using the trans_pipe.
* Fill in the `forward` pass of `AuxiliaryLatentModule`
* `make style && make quality`
* `chore: Update bert_tokenizer.py with a TODO comment suggesting the use of the transformers library`
* Update error handling to raise and logging
* Add `create_glyph_lines` function into `TextEmbeddingModule`
* make style
* Up
* Up
* Up
* Up
* Remove several comments
* refactor: Remove ControlNetConditioningEmbedding and update code accordingly
* Up
* Up
* up
* refactor: Update AnyTextPipeline to include new optional parameters
* up
* feat: Add OCR model and its components
* chore: Update `TextEmbeddingModule` to include OCR model components and dependencies
* chore: Update `AuxiliaryLatentModule` to include VAE model and its dependencies for masked image in the editing task
* `make style`
* refactor: Update `AnyTextPipeline`'s docstring
* Update `AuxiliaryLatentModule` to include info dictionary so that text processing is done once
* simplify
* `make style`
* Converting `TextEmbeddingModule` to ordinary `encode_prompt()` function
* Simplify for now
* `make style`
* Up
* feat: Add scripts to convert AnyText controlnet to diffusers
* `make style`
* Fix: Move glyph rendering to `TextEmbeddingModule` from `AuxiliaryLatentModule`
* make style
* Up
* Simplify
* Up
* feat: Add safetensors module for loading model file
* Fix device issues
* Up
* Up
* refactor: Simplify
* refactor: Simplify code for loading models and handling data types
* `make style`
* refactor: Update to() method in FrozenCLIPEmbedderT3 and TextEmbeddingModule
* refactor: Update dtype in embedding_manager.py to match proj.weight
* Up
* Add attribution and adaptation information to pipeline_anytext.py
* Update usage example
* Will refactor `controlnet_cond_embedding` initialization
* Add `AnyTextControlNetConditioningEmbedding` template
* Refactor organization
* style
* style
* Move custom blocks from `AuxiliaryLatentModule` to `AnyTextControlNetConditioningEmbedding`
* Follow one-file policy
* style
* [Docs] Update README and pipeline_anytext.py to use AnyTextControlNetModel
* [Docs] Update import statement for AnyTextControlNetModel in pipeline_anytext.py
* [Fix] Update import path for ControlNetModel, ControlNetOutput in anytext_controlnet.py
* Refactor AnyTextControlNet to use configurable conditioning embedding channels
* Complete control net conditioning embedding in AnyTextControlNetModel
* up
* [FIX] Ensure embeddings use correct device in AnyTextControlNetModel
* up
* up
* style
* [UPDATE] Revise README and example code for AnyTextPipeline integration with DiffusionPipeline
* [UPDATE] Update example code in anytext.py to use correct font file and improve clarity
* down
* [UPDATE] Refactor BasicTokenizer usage to a new Checker class for text processing
* update pillow
* [UPDATE] Remove commented-out code and unnecessary docstring in anytext.py and anytext_controlnet.py for improved clarity
* [REMOVE] Delete frozen_clip_embedder_t3.py as it is in the anytext.py file
* [UPDATE] Replace edict with dict for configuration in anytext.py and RecModel.py for consistency
* 🆙
* style
* [UPDATE] Revise README.md for clarity, remove unused imports in anytext.py, and add author credits in anytext_controlnet.py
* style
* Update examples/research_projects/anytext/README.md
Co-authored-by: Aryan <contact.aryanvs@gmail.com>
* Remove commented-out image preparation code in AnyTextPipeline
* Remove unnecessary blank line in README.md
[Quantization] Allow loading TorchAO serialized Tensor objects with torch>=2.6 (#11018)
* update
* update
* update
* update
* update
* update
* update
* update
* update
fix: mixture tiling sdxl pipeline - adjust gerating time_ids & embeddings (#11012)
small fix on generating time_ids & embeddings
[LoRA] support wan i2v loras from the world. (#11025)
* support wan i2v loras from the world.
* remove copied from.
* upates
* add lora.
Fix SD3 IPAdapter feature extractor (#11027)
chore: fix help messages in advanced diffusion examples (#10923)
Fix missing **kwargs in lora_pipeline.py (#11011)
* Update lora_pipeline.py
* Apply style fixes
* fix-copies
---------
Co-authored-by: hlky <hlky@hlky.ac>
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Fix for multi-GPU WAN inference (#10997)
Ensure that hidden_state and shift/scale are on the same device when running with multiple GPUs
Co-authored-by: Jimmy <39@🇺🇸.com>
[Refactor] Clean up import utils boilerplate (#11026)
* update
* update
* update
Use `output_size` in `repeat_interleave` (#11030)
[hybrid inference 🍯🐝] Add VAE encode (#11017)
* [hybrid inference 🍯🐝] Add VAE encode
* _toctree: add vae encode
* Add endpoints, tests
* vae_encode docs
* vae encode benchmarks
* api reference
* changelog
* Update docs/source/en/hybrid_inference/overview.md
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* update
---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Wan Pipeline scaling fix, type hint warning, multi generator fix (#11007)
* Wan Pipeline scaling fix, type hint warning, multi generator fix
* Apply suggestions from code review
[LoRA] change to warning from info when notifying the users about a LoRA no-op (#11044)
* move to warning.
* test related changes.
Rename Lumina(2)Text2ImgPipeline -> Lumina(2)Pipeline (#10827)
* Rename Lumina(2)Text2ImgPipeline -> Lumina(2)Pipeline
---------
Co-authored-by: YiYi Xu <yixu310@gmail.com>
making ```formatted_images``` initialization compact (#10801)
compact writing
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: YiYi Xu <yixu310@gmail.com>
Fix aclnnRepeatInterleaveIntWithDim error on NPU for get_1d_rotary_pos_embed (#10820)
* get_1d_rotary_pos_embed support npu
* Update src/diffusers/models/embeddings.py
---------
Co-authored-by: Kai zheng <kaizheng@KaideMacBook-Pro.local>
Co-authored-by: hlky <hlky@hlky.ac>
Co-authored-by: YiYi Xu <yixu310@gmail.com>
[Tests] restrict memory tests for quanto for certain schemes. (#11052)
* restrict memory tests for quanto for certain schemes.
* Apply suggestions from code review
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>
* fixes
* style
---------
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>
[LoRA] feat: support non-diffusers wan t2v loras. (#11059)
feat: support non-diffusers wan t2v loras.
[examples/controlnet/train_controlnet_sd3.py] Fixes#11050 - Cast prompt_embeds and pooled_prompt_embeds to weight_dtype to prevent dtype mismatch (#11051)
Fix: dtype mismatch of prompt embeddings in sd3 controlnet training
Co-authored-by: Andreas Jörg <andreasjoerg@MacBook-Pro-von-Andreas-2.fritz.box>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
reverts accidental change that removes attn_mask in attn. Improves fl… (#11065)
reverts accidental change that removes attn_mask in attn. Improves flux ptxla by using flash block sizes. Moves encoding outside the for loop.
Co-authored-by: Juan Acevedo <jfacevedo@google.com>
Fix deterministic issue when getting pipeline dtype and device (#10696)
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>
[Tests] add requires peft decorator. (#11037)
* add requires peft decorator.
* install peft conditionally.
* conditional deps.
Co-authored-by: DN6 <dhruv.nair@gmail.com>
---------
Co-authored-by: DN6 <dhruv.nair@gmail.com>
CogView4 Control Block (#10809)
* cogview4 control training
---------
Co-authored-by: OleehyO <leehy0357@gmail.com>
Co-authored-by: yiyixuxu <yixu310@gmail.com>
[CI] pin transformers version for benchmarking. (#11067)
pin transformers version for benchmarking.
updates
Fix Wan I2V Quality (#11087)
* fix_wan_i2v_quality
* Update src/diffusers/pipelines/wan/pipeline_wan_i2v.py
Co-authored-by: YiYi Xu <yixu310@gmail.com>
* Update src/diffusers/pipelines/wan/pipeline_wan_i2v.py
Co-authored-by: YiYi Xu <yixu310@gmail.com>
* Update src/diffusers/pipelines/wan/pipeline_wan_i2v.py
Co-authored-by: YiYi Xu <yixu310@gmail.com>
* Update pipeline_wan_i2v.py
---------
Co-authored-by: YiYi Xu <yixu310@gmail.com>
Co-authored-by: hlky <hlky@hlky.ac>
LTX 0.9.5 (#10968)
* update
---------
Co-authored-by: YiYi Xu <yixu310@gmail.com>
Co-authored-by: hlky <hlky@hlky.ac>
make PR GPU tests conditioned on styling. (#11099)
Group offloading improvements (#11094)
update
Fix pipeline_flux_controlnet.py (#11095)
* Fix pipeline_flux_controlnet.py
* Fix style
update readme instructions. (#11096)
Co-authored-by: Juan Acevedo <jfacevedo@google.com>
Resolve stride mismatch in UNet's ResNet to support Torch DDP (#11098)
Modify UNet's ResNet implementation to resolve stride mismatch in Torch's DDP
Fix Group offloading behaviour when using streams (#11097)
* update
* update
Quality options in `export_to_video` (#11090)
* Quality options in `export_to_video`
* make style
improve more.
add placeholders for docstrings.
formatting.
smol fix.
solidify validation and annotation
* Revert "feat: pipeline-level quant config."
This reverts commit 316ff46b76.
* feat: implement pipeline-level quantization config
Co-authored-by: SunMarc <marc@huggingface.co>
* update
* fixes
* fix validation.
* add tests and other improvements.
* add tests
* import quality
* remove prints.
* add docs.
* fixes to docs.
* doc fixes.
* doc fixes.
* add validation to the input quantization_config.
* clarify recommendations.
* docs
* add to ci.
* todo.
---------
Co-authored-by: SunMarc <marc@huggingface.co>
* begin transformer conversion
* refactor
* refactor
* refactor
* refactor
* refactor
* refactor
* update
* add conversion script
* add pipeline
* make fix-copies
* remove einops
* update docs
* gradient checkpointing
* add transformer test
* update
* debug
* remove prints
* match sigmas
* add vae pt. 1
* finish CV* vae
* update
* update
* update
* update
* update
* update
* make fix-copies
* update
* make fix-copies
* fix
* update
* update
* make fix-copies
* update
* update tests
* handle device and dtype for safety checker; required in latest diffusers
* remove enable_gqa and use repeat_interleave instead
* enforce safety checker; use dummy checker in fast tests
* add review suggestion for ONNX export
Co-Authored-By: Asfiya Baig <asfiyab@nvidia.com>
* fix safety_checker issues when not passed explicitly
We could either do what's done in this commit, or update the Cosmos examples to explicitly pass the safety checker
* use cosmos guardrail package
* auto format docs
* update conversion script to support 14B models
* update name CosmosPipeline -> CosmosTextToWorldPipeline
* update docs
* fix docs
* fix group offload test failing for vae
---------
Co-authored-by: Asfiya Baig <asfiyab@nvidia.com>
* Refactor `LTXConditionPipeline` to add text-only conditioning
* style
* up
* Refactor `LTXConditionPipeline` to streamline condition handling and improve clarity
* Improve condition checks
* Simplify latents handling based on conditioning type
* Refactor rope_interpolation_scale preparation for clarity and efficiency
* Update LTXConditionPipeline docstring to clarify supported input types
* Add LTX Video 0.9.5 model to documentation
* Clarify documentation to indicate support for text-only conditioning without passing `conditions`
* refactor: comment out unused parameters in LTXConditionPipeline
* fix: restore previously commented parameters in LTXConditionPipeline
* fix: remove unused parameters from LTXConditionPipeline
* refactor: remove unnecessary lines in LTXConditionPipeline
* minor documentation fixes of the depth and normals pipelines
* update license headers
* update model checkpoints in examples
fix missing prediction_type in register_to_config in the normals pipeline
* add initial marigold intrinsics pipeline
update comments about num_inference_steps and ensemble_size
minor fixes in comments of marigold normals and depth pipelines
* update uncertainty visualization to work with intrinsics
* integrate iid
---------
Co-authored-by: YiYi Xu <yixu310@gmail.com>
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* init
* encode with glm
* draft schedule
* feat(scheduler): Add CogView scheduler implementation
* feat(embeddings): add CogView 2D rotary positional embedding
* 1
* Update pipeline_cogview4.py
* fix the timestep init and sigma
* update latent
* draft patch(not work)
* fix
* [WIP][cogview4]: implement initial CogView4 pipeline
Implement the basic CogView4 pipeline structure with the following changes:
- Add CogView4 pipeline implementation
- Implement DDIM scheduler for CogView4
- Add CogView3Plus transformer architecture
- Update embedding models
Current limitations:
- CFG implementation uses padding for sequence length alignment
- Need to verify transformer inference alignment with Megatron
TODO:
- Consider separate forward passes for condition/uncondition
instead of padding approach
* [WIP][cogview4][refactor]: Split condition/uncondition forward pass in CogView4 pipeline
Split the forward pass for conditional and unconditional predictions in the CogView4 pipeline to match the original implementation. The noise prediction is now done separately for each case before combining them for guidance. However, the results still need improvement.
This is a work in progress as the generated images are not yet matching expected quality.
* use with -2 hidden state
* remove text_projector
* 1
* [WIP] Add tensor-reload to align input from transformer block
* [WIP] for older glm
* use with cogview4 transformers forward twice of u and uc
* Update convert_cogview4_to_diffusers.py
* remove this
* use main example
* change back
* reset
* setback
* back
* back 4
* Fix qkv conversion logic for CogView4 to Diffusers format
* back5
* revert to sat to cogview4 version
* update a new convert from megatron
* [WIP][cogview4]: implement CogView4 attention processor
Add CogView4AttnProcessor class for implementing scaled dot-product attention
with rotary embeddings for the CogVideoX model. This processor concatenates
encoder and hidden states, applies QKV projections and RoPE, but does not
include spatial normalization.
TODO:
- Fix incorrect QKV projection weights
- Resolve ~25% error in RoPE implementation compared to Megatron
* [cogview4] implement CogView4 transformer block
Implement CogView4 transformer block following the Megatron architecture:
- Add multi-modulate and multi-gate mechanisms for adaptive layer normalization
- Implement dual-stream attention with encoder-decoder structure
- Add feed-forward network with GELU activation
- Support rotary position embeddings for image tokens
The implementation follows the original CogView4 architecture while adapting
it to work within the diffusers framework.
* with new attn
* [bugfix] fix dimension mismatch in CogView4 attention
* [cogview4][WIP]: update final normalization in CogView4 transformer
Refactored the final normalization layer in CogView4 transformer to use separate layernorm and AdaLN operations instead of combined AdaLayerNormContinuous. This matches the original implementation but needs validation.
Needs verification against reference implementation.
* 1
* put back
* Update transformer_cogview4.py
* change time_shift
* Update pipeline_cogview4.py
* change timesteps
* fix
* change text_encoder_id
* [cogview4][rope] align RoPE implementation with Megatron
- Implement apply_rope method in attention processor to match Megatron's implementation
- Update position embeddings to ensure compatibility with Megatron-style rotary embeddings
- Ensure consistent rotary position encoding across attention layers
This change improves compatibility with Megatron-based models and provides
better alignment with the original implementation's positional encoding approach.
* [cogview4][bugfix] apply silu activation to time embeddings in CogView4
Applied silu activation to time embeddings before splitting into conditional
and unconditional parts in CogView4Transformer2DModel. This matches the
original implementation and helps ensure correct time conditioning behavior.
* [cogview4][chore] clean up pipeline code
- Remove commented out code and debug statements
- Remove unused retrieve_timesteps function
- Clean up code formatting and documentation
This commit focuses on code cleanup in the CogView4 pipeline implementation, removing unnecessary commented code and improving readability without changing functionality.
* [cogview4][scheduler] Implement CogView4 scheduler and pipeline
* now It work
* add timestep
* batch
* change convert scipt
* refactor pt. 1; make style
* refactor pt. 2
* refactor pt. 3
* add tests
* make fix-copies
* update toctree.yml
* use flow match scheduler instead of custom
* remove scheduling_cogview.py
* add tiktoken to test dependencies
* Update src/diffusers/models/embeddings.py
Co-authored-by: YiYi Xu <yixu310@gmail.com>
* apply suggestions from review
* use diffusers apply_rotary_emb
* update flow match scheduler to accept timesteps
* fix comment
* apply review sugestions
* Update src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py
Co-authored-by: YiYi Xu <yixu310@gmail.com>
---------
Co-authored-by: 三洋三洋 <1258009915@qq.com>
Co-authored-by: OleehyO <leehy0357@gmail.com>
Co-authored-by: Aryan <aryan@huggingface.co>
Co-authored-by: YiYi Xu <yixu310@gmail.com>
* update
* fix
* non_blocking; handle parameters and buffers
* update
* Group offloading with cuda stream prefetching (#10516)
* cuda stream prefetch
* remove breakpoints
* update
* copy model hook implementation from pab
* update; ~very workaround based implementation but it seems to work as expected; needs cleanup and rewrite
* more workarounds to make it actually work
* cleanup
* rewrite
* update
* make sure to sync current stream before overwriting with pinned params
not doing so will lead to erroneous computations on the GPU and cause bad results
* better check
* update
* remove hook implementation to not deal with merge conflict
* re-add hook changes
* why use more memory when less memory do trick
* why still use slightly more memory when less memory do trick
* optimise
* add model tests
* add pipeline tests
* update docs
* add layernorm and groupnorm
* address review comments
* improve tests; add docs
* improve docs
* Apply suggestions from code review
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* apply suggestions from code review
* update tests
* apply suggestions from review
* enable_group_offloading -> enable_group_offload for naming consistency
* raise errors if multiple offloading strategies used; add relevant tests
* handle .to() when group offload applied
* refactor some repeated code
* remove unintentional change from merge conflict
* handle .cuda()
---------
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
