- 12 Apr, 2025 1 commit
-
-
Nikita Starodubcev authored
* add flow matching lcm scheduler * stochastic sampling * upscaling for scale-wise generation * Apply style fixes * Apply suggestions from code review Co-authored-by:
hlky <hlky@hlky.ac> --------- Co-authored-by:
github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by:
YiYi Xu <yixu310@gmail.com> Co-authored-by:
-
- 18 Mar, 2025 1 commit
-
-
Aryan authored
* update --------- Co-authored-by:
-
- 15 Feb, 2025 1 commit
-
-
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:
三洋三洋 <1258009915@qq.com> Co-authored-by:
OleehyO <leehy0357@gmail.com> Co-authored-by:
Aryan <aryan@huggingface.co> Co-authored-by:
-
- 13 Feb, 2025 1 commit
-
-
Aryan authored
update
-
- 09 Jan, 2025 1 commit
-
-
Steven Liu authored
* fix docstrings * add
-
- 17 Dec, 2024 1 commit
-
-
hlky authored
-
- 12 Dec, 2024 1 commit
-
-
Aryan authored
* transformer * make style & make fix-copies * transformer * add transformer tests * 80% vae * make style * make fix-copies * fix * undo cogvideox changes * update * update * match vae * add docs * t2v pipeline working; scheduler needs to be checked * docs * add pipeline test * update * update * make fix-copies * Apply suggestions from code review Co-authored-by:
Steven Liu <59462357+stevhliu@users.noreply.github.com> * update * copy t2v to i2v pipeline * update * apply review suggestions * update * make style * remove framewise encoding/decoding * pack/unpack latents * image2video * update * make fix-copies * update * update * rope scale fix * debug layerwise code * remove debug * Apply suggestions from code review Co-authored-by:
-
- 03 Dec, 2024 1 commit
-
-
hlky authored
-
- 28 Nov, 2024 1 commit
-
-
hlky authored
Add beta, exponential and karras sigmas to FlowMatchEuler
-
- 05 Nov, 2024 1 commit
-
-
Aryan authored
* update * udpate * update transformer * make style * fix * add conversion script * update * fix * update * fix * update * fixes * make style * update * update * update * init * update * update * add * up * up * up * update * mochi transformer * remove original implementation * make style * update inits * update conversion script * docs * Update src/diffusers/pipelines/mochi/pipeline_mochi.py Co-authored-by:
Dhruv Nair <dhruv.nair@gmail.com> * Update src/diffusers/pipelines/mochi/pipeline_mochi.py Co-authored-by:
-
- 01 Aug, 2024 1 commit
-
-
Sayak Paul authored
add flux! Signed-off-by:
Adrien <adrien@huggingface.co> Co-authored-by:
Adrien <adrien.69740@gmail.com> Co-authored-by:
Anatoly Belikov <abelikov@singularitynet.io> Co-authored-by:
-
- 11 Jul, 2024 1 commit
-
-
Sayak Paul authored
* add lavender flow transformer --------- Co-authored-by:
-
- 27 Jun, 2024 1 commit
-
-
Álvaro Somoza authored
* initial fix * apply suggestion * delete step_index line
-
- 12 Jun, 2024 2 commits
-
-
kkj15dk authored
Update scheduling_flow_match_euler_discrete.py Spelling: Foward -> Forward Co-authored-by: -
Dhruv Nair authored
* up * add sd3 * update * update * add tests * fix copies * fix docs * update * add dreambooth lora * add LoRA * update * update * update * update * import fix * update * Update src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py Co-authored-by:
Sayak Paul <spsayakpaul@gmail.com> * Update docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_3.md Co-authored-by:
-
