Add MAGI-1: Autoregressive Video Generation at Scale #11713
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…te attention mechanism accordingly. Updated initialization parameters and reshaping logic.
…tering and equal split ratio. Add utility functions for resizing and cropping images while preserving aspect ratio. Enhance 3D rotary positional embeddings Adds `center_grid_hw_indices` and `equal_split_ratio` parameters to the 3D rotary positional embedding function for more flexible configuration. The `center_grid_hw_indices` option centers the spatial grid indices around zero. The `equal_split_ratio` parameter provides an alternative way to divide the embedding dimension equally among the temporal and spatial axes. Updates the Magi1 VAE to utilize these new embedding features, introducing helper functions to prepare the embeddings dynamically based on input tensor dimensions.
Replaces the initial causal 3D convolution in the encoder with a standard `Conv3d` patch embedding layer. This simplifies the model and makes its input processing more consistent with Diffusion Transformer (DiT) architectures. Additionally, this change: - Removes the unused `Magi1CausalConv3d` class. - Updates the attention mechanism to use the standard `scaled_dot_product_attention`. - Sets the default for `sample_posterior` to `True` in the forward pass.
Removes the feature caching logic (`feat_cache`, `feat_idx`) from the encoder, decoder, and their sub-modules. This change significantly simplifies the forward pass implementation by removing stateful cache management. Additionally, this commit replaces the custom `Magi1RMS_norm` with a standard `nn.LayerNorm` and updates several custom causal convolution layers to use standard `nn.Linear` or `nn.Conv3d` layers.
Moves the positional embedding and dropout layers from the main autoencoder class into the decoder module. This improves encapsulation as the embedding is only used within the decoder. The decoder's forward pass is updated to apply the positional embedding and to remove the class token before the final output convolution. Additionally, `quant_conv` is renamed to `quant_linear` to accurately reflect the layer type.
Updates the `Magi1Decoder3d` from a convolutional design to a Transformer-like structure that operates on patches. This change replaces the initial convolutional and middle blocks with a linear projection layer, positional embeddings, and a class token. The logic for these components is moved from the parent `AutoencoderKLMagi1` model into the decoder for better encapsulation.
Removes several custom modules, including `Magi1ResidualBlock`, `Magi1Resample`, and `Magi1UpBlock`. Replaces the previous `Magi1MidBlock` with a more standard transformer-style `Magi1Block`. This change simplifies the overall VAE architecture by consolidating complex, specialized blocks into a more conventional design.
Replaces the custom `Magi1AttentionBlock` with the more generic `diffusers.Attention` module, combined with a new (?) `Magi1AttnProcessor2_0`. This change aligns the implementation with standard library patterns and leverages PyTorch 2.0's `scaled_dot_product_attention` for improved efficiency. The `Magi1Block` is also refactored into a more conventional transformer block structure using `Attention` and `FeedForward` modules.
Refactors the Magi1 VAE decoder to use a more standard transformer-based architecture. This change replaces the previous U-Net-like upsampling blocks with a series of standard transformer blocks, each containing self-attention and a feed-forward network. The custom rotary positional embedding logic and its helper functions have been removed, and the attention processor is simplified to work with the standard `Attention` module. This simplifies the overall model implementation.
Replaces the previous convolutional U-Net style encoder with a Vision Transformer (ViT) based implementation. This new architecture processes the input by dividing it into patches, adding positional embeddings, and then passing the sequence through a series of transformer blocks. The attention processor is also updated to support attention masks, and the model's configuration is adjusted to accommodate the new transformer-specific parameters.
Removes complex and unused parameters from the Magi1 VAE, encoder, and decoder modules. This change refactors the model to use a more standard Transformer architecture, eliminating the previous U-Net-like structure with dimension multipliers and residual blocks. The configuration is now more direct, improving clarity and maintainability.
Simplifies the initialization of the Magi1 VAE, encoder, and decoder. Reorders constructor parameters for clarity and removes unused arguments. The spatial and temporal compression ratios are now derived directly from the `patch_size` configuration, making the relationship more explicit. The pipeline is updated to use these new VAE attributes.
Simplifies the model architecture by removing the quantization and post-quantization convolution layers. This streamlines the `encode` and `decode` methods. The decoder is also updated to process the entire latent tensor at once, removing the previous frame-by-frame processing loop. Additionally, this change updates an import path for the `timm` library and renames an internal variable for consistency.
Updates the conversion script for the MAGI-1 VAE to correctly handle its Vision Transformer (ViT) based architecture. The state dictionary mapping is rewritten to align with the ViT structure. This includes adding logic to split the original checkpoint's combined QKV weights into separate query, key, and value tensors for the `diffusers` model. The model class and its configuration are also updated to reflect the appropriate ViT parameters, ensuring a correct conversion.
Renames the Magi autoencoder class to align with the "MAGI-1" model name. This refactoring improves consistency and clarity throughout the codebase, including documentation and tests.
Aligns the model naming with the source paper, "MAGI-1". This change refactors the model class, associated files, tests, and documentation to use the `Magi1` prefix for better clarity and consistency.
…ross multiple files
Improve compatibility by handling various PyTorch checkpoint formats. The loader now correctly extracts the state dictionary when it is nested under common keys like "model" or "state_dict". Ensure consistent loading of sharded safetensors by sorting the checkpoint files before merging them.
…e attention dispatch
This includes: - Adding `magi` to the list of available attention backends in the documentation. - Adding utility functions to check for the availability and version of the `magi_attention` package.
… updated examples and tests
…cation and partial loading support temply - Added `allow_partial` parameter to `convert_magi1_transformer_checkpoint` for flexible state dict loading. - Improved `convert_transformer_state_dict` to generate a detailed mapping report, including missing and unexpected keys, and shape mismatches. - Updated command-line arguments to support new features. - Enhanced error handling and reporting during conversion verification. - Refactored related functions to accommodate changes in the transformer architecture.
…pelines with new scheduler integration - Added `_magi_varlen_attention` function to support variable-length sequences in MAGI-1 using the magi-attention library. - Updated `Magi1AttnProcessor` to utilize the MAGI backend when variable-length parameters are provided. - Integrated `FlowMatchEulerDiscreteScheduler` into example usage for `Magi1ImageToVideoPipeline`, `Magi1VideoToVideoPipeline`, and `Magi1Pipeline`, emphasizing the required shift parameter for proper functionality. - Refactored code to improve clarity and maintainability, including normalization of timestep calculations in the pipelines.
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Hi @tolgacangoz, i'll have some free time this month and wanna help you merge this pr. Any specific things that i can look into? I can start with vae tiling, i think you had some open comments back then. To be honest, just changing their implementation not to break the diffusers functional structure felt to me like not the best way to implement the tiled decode. So maybe, i can start by reverting that and instead use the original "sample-then-blend" approach (reference). Then move onto other things. Wdyt? |
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I think we could return to VAE again in the end. You can go on with one of the pipeline groups: T2V or (I2V and V2V). |
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That works for me. Sure i can start with T2V then. I saw that there is already a pipeline_magi1.py. Isn't this completed or? |
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Thanks for the opportunity to fix #11519!
Original repo: https://github.com/SandAI-org/MAGI-1
AutoencoderKLMagi1: Tiling option by @kuantuna -> Feat: Implement tiling in VAE tolgacangoz/diffusers#6Magi1Transformer3DModelkernels?MAGI1Pipeline,MAGI1ImageToVideoPipeline,MAGI1VideoToVideoPipeline.Try
MAGI1Pipelines!Who can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR.