* remove k-diffusion as we don't use it from the core.
* Revert "remove k-diffusion as we don't use it from the core."
This reverts commit 8bc86925a0.
* pin k-diffusion
* 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>
* feat: support saving a model in sharded checkpoints.
* feat: make loading of sharded checkpoints work.
* add tests
* cleanse the loading logic a bit more.
* more resilience while loading from the Hub.
* parallelize shard downloads by using snapshot_download()/
* default to a shard size.
* more fix
* Empty-Commit
* debug
* fix
* uality
* more debugging
* fix more
* initial comments from Benjamin
* move certain methods to loading_utils
* add test to check if the correct number of shards are present.
* add a test to check if loading of sharded checkpoints from the Hub is okay
* clarify the unit when passed as an int.
* use hf_hub for sharding.
* remove unnecessary code
* remove unnecessary function
* lucain's comments.
* fixes
* address high-level comments.
* fix test
* subfolder shenanigans./
* Update src/diffusers/utils/hub_utils.py
Co-authored-by: Lucain <lucainp@gmail.com>
* Apply suggestions from code review
Co-authored-by: Lucain <lucainp@gmail.com>
* remove _huggingface_hub_version as not needed.
* address more feedback.
* add a test for local_files_only=True/
* need hf hub to be at least 0.23.2
* style
* final comment.
* clean up subfolder.
* deal with suffixes in code.
* _add_variant default.
* use weights_name_pattern
* remove add_suffix_keyword
* clean up downloading of sharded ckpts.
* don't return something special when using index.json
* fix more
* don't use bare except
* remove comments and catch the errors better
* fix a couple of things when using is_file()
* empty
---------
Co-authored-by: Lucain <lucainp@gmail.com>
