- 28 Oct, 2025 1 commit
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galbria authored
* Bria FIBO pipeline * style fixs * fix CR * Refactor BriaFibo classes and update pipeline parameters - Updated BriaFiboAttnProcessor and BriaFiboAttention classes to reflect changes from Flux equivalents. - Modified the _unpack_latents method in BriaFiboPipeline to improve clarity. - Increased the default max_sequence_length to 3000 and added a new optional parameter do_patching. - Cleaned up test_pipeline_bria_fibo.py by removing unused imports and skipping unsupported tests. * edit the docs of FIBO * Remove unused BriaFibo imports and update CPU offload method in BriaFiboPipeline * Refactor FIBO classes to BriaFibo naming convention - Updated class names from FIBO to BriaFibo for consistency across the module. - Modified instances of FIBOEmbedND, FIBOTimesteps, TextProjection, and TimestepProjEmbeddings to reflect the new naming. - Ensured all references in the BriaFiboTransformer2DModel are updated accordingly. * Add BriaFiboTransformer2DModel import to transformers module * Remove unused BriaFibo imports from modular pipelines and add BriaFiboTransformer2DModel and BriaFiboPipeline classes to dummy objects for enhanced compatibility with torch and transformers. * Update BriaFibo classes with copied documentation and fix import typo in pipeline module - Added documentation comments indicating the source of copied code in BriaFiboTransformerBlock and _pack_latents methods. - Corrected the import statement for BriaFiboPipeline in the pipelines module. * Remove unused BriaFibo imports from __init__.py to streamline modular pipelines. * Refactor documentation comments in BriaFibo classes to indicate inspiration from existing implementations - Updated comments in BriaFiboAttnProcessor, BriaFiboAttention, and BriaFiboPipeline to reflect that the code is inspired by other modules rather than copied. - Enhanced clarity on the origins of the methods to maintain proper attribution. * change Inspired by to Based on * add reference link and fix trailing whitespace * Add BriaFiboTransformer2DModel documentation and update comments in BriaFibo classes - Introduced a new documentation file for BriaFiboTransformer2DModel. - Updated comments in BriaFiboAttnProcessor, BriaFiboAttention, and BriaFiboPipeline to clarify the origins of the code, indicating copied sources for better attribution. --------- Co-authored-by:sayakpaul <spsayakpaul@gmail.com>
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- 15 Feb, 2025 1 commit
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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:
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:
三洋三洋 <1258009915@qq.com> Co-authored-by:
OleehyO <leehy0357@gmail.com> Co-authored-by:
Aryan <aryan@huggingface.co> Co-authored-by:
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- 14 Oct, 2024 1 commit
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Yuxuan.Zhang authored
* merge 9588 * max_shard_size="5GB" for colab running * conversion script updates; modeling test; refactor transformer * make fix-copies * Update convert_cogview3_to_diffusers.py * initial pipeline draft * make style * fight bugs
🐛 🪳 * add example * add tests; refactor * make style * make fix-copies * add co-author YiYi Xu <yixu310@gmail.com> * remove files * add docs * add co-author Co-Authored-By:
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- 11 Jul, 2024 1 commit
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Álvaro Somoza authored
* initial draft
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- 12 Jun, 2024 1 commit
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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:
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- 25 Sep, 2023 2 commits
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Anh71me authored
* Fix type annotation on Scheduler.from_pretrained * Fix type annotation on PIL.Image
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Patrick von Platen authored
* [Doc builder] Ensure slow import for doc builder * Apply suggestions from code review * env for doc builder * fix more * [Diffusers] Set import to slow as env variable * fix docs * fix docs * Apply suggestions from code review * Apply suggestions from code review * fix docs * fix docs
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- 22 Sep, 2023 1 commit
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Pedro Cuenca authored
* support transformer_layers_per block in flax UNet * add support for text_time additional embeddings to Flax UNet * rename attention layers for VAE * add shape asserts when renaming attention layers * transpose VAE attention layers * add pipeline flax SDXL code [WIP] * continue add pipeline flax SDXL code [WIP] * cleanup * Working on JIT support Fixed prompt embedding shapes so they work in parallel mode. Assuming we always have both text encoders for now, for simplicity. * Fixing embeddings (untested) * Remove spurious line * Shard guidance_scale when jitting. * Decode images * Fix sharding * style * Refiner UNet can be loaded. * Refiner / img2img pipeline * Allow latent outputs from base and latent inputs in refiner This makes it possible to chain base + refiner without having to use the vae decoder in the base model, the vae encoder in the refiner, skipping conversions to/from PIL, and avoiding TPU <-> CPU memory copies. * Adapt to FlaxCLIPTextModelOutput * Update Flax XL pipeline to FlaxCLIPTextModelOutput * make fix-copies * make style * add euler scheduler * Fix import * Fix copies, comment unused code. * Fix SDXL Flax imports * Fix euler discrete begin * improve init import * finish * put discrete euler in init * fix flax euler * Fix more * make style * correct init * correct init * Temporarily remove FlaxStableDiffusionXLImg2ImgPipeline * correct pipelines * finish --------- Co-authored-by:
Martin Müller <martin.muller.me@gmail.com> Co-authored-by:
patil-suraj <surajp815@gmail.com> Co-authored-by:
Patrick von Platen <patrick.v.platen@gmail.com>
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- 11 Sep, 2023 1 commit
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Dhruv Nair authored
* initial commit * move modules to import struct * add dummy objects and _LazyModule * add lazy import to schedulers * clean up unused imports * lazy import on models module * lazy import for schedulers module * add lazy import to pipelines module * lazy import altdiffusion * lazy import audio diffusion * lazy import audioldm * lazy import consistency model * lazy import controlnet * lazy import dance diffusion ddim ddpm * lazy import deepfloyd * lazy import kandinksy * lazy imports * lazy import semantic diffusion * lazy imports * lazy import stable diffusion * move sd output to its own module * clean up * lazy import t2iadapter * lazy import unclip * lazy import versatile and vq diffsuion * lazy import vq diffusion * helper to fetch objects from modules * lazy import sdxl * lazy import txt2vid * lazy import stochastic karras * fix model imports * fix bug * lazy import * clean up * clean up * fixes for tests * fixes for tests * clean up * remove import of torch_utils from utils module * clean up * clean up * fix mistake import statement * dedicated modules for exporting and loading * remove testing utils from utils module * fixes from merge conflicts * Update src/diffusers/pipelines/kandinsky2_2/__init__.py * fix docs * fix alt diffusion copied from * fix check dummies * fix more docs * remove accelerate import from utils module * add type checking * make style * fix check dummies * remove torch import from xformers check * clean up error message * fixes after upstream merges * dummy objects fix * fix tests * remove unused module import --------- Co-authored-by:
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