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  1. 19 Feb, 2025 2 commits
    • Sayak Paul's avatar
      [LoRA] make `set_adapters()` robust on silent failures. (#9618) · 6fe05b9b
      Sayak Paul authored 
      * make set_adapters() robust on silent failures.

* fixes to tests

* flaky decorator.

* fix

* flaky to sd3.

* remove warning.

* sort

* quality

* skip test_simple_inference_with_text_denoiser_multi_adapter_block_lora

* skip testing unsupported features.

* raise warning instead of error.
      6fe05b9b
    • hlky's avatar
      DiffusionPipeline mixin `to`+FromOriginalModelMixin/FromSingleFileMixin... · 2bc82d63
      hlky authored 
      DiffusionPipeline mixin `to`+FromOriginalModelMixin/FromSingleFileMixin `from_single_file` type hint (#10811)

* DiffusionPipeline mixin `to` type hint

* FromOriginalModelMixin from_single_file

* FromSingleFileMixin from_single_file
      2bc82d63
  3. 18 Feb, 2025 1 commit
    • puhuk's avatar
      Fix max_shift value in flux and related functions to 1.15 (issue #10675) (#10807) · b75b204a
      puhuk authored 
      This PR updates the max_shift value in flux to 1.15 for consistency across the codebase. In addition to modifying max_shift in flux, all related functions that copy and use this logic, such as calculate_shift in `src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_img2img.py`, have also been updated to ensure uniform behavior.
      b75b204a
  5. 17 Feb, 2025 1 commit
  7. 16 Feb, 2025 1 commit
  9. 15 Feb, 2025 2 commits
    • Yuxuan Zhang's avatar
      CogView4 (supports different length c and uc) (#10649) · d90cd362
      Yuxuan Zhang authored 
      

* 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: default avatarYiYi 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: default avatarYiYi Xu <yixu310@gmail.com>

---------
Co-authored-by: default avatar三洋三洋 <1258009915@qq.com>
Co-authored-by: default avatarOleehyO <leehy0357@gmail.com>
Co-authored-by: default avatarAryan <aryan@huggingface.co>
Co-authored-by: default avatarYiYi Xu <yixu310@gmail.com>
      d90cd362
    • YiYi Xu's avatar
      follow-up refactor on lumina2 (#10776) · 69f919d8
      YiYi Xu authored 
      * up
      69f919d8
  11. 14 Feb, 2025 2 commits
    • SahilCarterr's avatar
      [FIX] check_inputs function in lumina2 (#10784) · a6b843a7
      SahilCarterr authored
      a6b843a7
    • Aryan's avatar
      Module Group Offloading (#10503) · 9a147b82
      Aryan authored 
      

* 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: default avatarSteven 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: default avatarSteven Liu <59462357+stevhliu@users.noreply.github.com>
      9a147b82
  13. 13 Feb, 2025 4 commits
  15. 12 Feb, 2025 5 commits
  17. 11 Feb, 2025 3 commits
  19. 10 Feb, 2025 2 commits
  21. 07 Feb, 2025 1 commit
  23. 06 Feb, 2025 1 commit
  25. 05 Feb, 2025 1 commit
  27. 04 Feb, 2025 2 commits
  29. 01 Feb, 2025 1 commit
  31. 31 Jan, 2025 1 commit
  33. 29 Jan, 2025 3 commits
  35. 28 Jan, 2025 3 commits
  37. 27 Jan, 2025 4 commits