HunyuanImage-2.1

[yjy415]

[gemini-code-assist[bot]]

Summary of Changes

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This pull request introduces the full HunyuanImage 2.1 model architecture and its associated inference pipeline. The changes encompass a novel Diffusion Transformer (DiT) for robust image generation, a Variational Autoencoder (VAE) for efficient latent space manipulation, and a dual text encoding system utilizing both Qwen and ByT5 models to enhance prompt understanding, including support for glyphs and OCR-related features. The new pipeline provides a streamlined and modular approach to text-to-image synthesis, complete with custom scheduling and adaptive guidance mechanisms.

Highlights

• HunyuanImage 2.1 Model Integration: Introduced the complete HunyuanImage 2.1 model suite, including its Diffusion Transformer (DiT), Variational Autoencoder (VAE), and specialized text encoders (Qwen and ByT5).
• Core Diffusion Transformer (DiT): Added HYImageDiffusionTransformer with advanced features such as multimodal attention, rotary positional embeddings, FlashAttention for efficiency, and various modulation and normalization layers.
• Variational Autoencoder (VAE): Implemented HunyuanImageVAE2D for efficient encoding and decoding of images to and from a latent space, featuring Resnet blocks, attention mechanisms, and spatial down/upsampling.
• Dual Text Encoders for Richer Prompts: Integrated HunyuanImage_TextEncoder_Qwen (based on Qwen2.5-VL-7B-Instruct) for general text understanding and HunyuanImage_ByT5 (based on ByT5) with specialized glyph/OCR support for handling font and color tokens in prompts.
• Comprehensive Inference Pipeline: Developed HunyuanImagePipeline to orchestrate the entire image generation process, incorporating a custom flow-matching scheduler, adaptive projected guidance, and modular pipeline units for preprocessing and embedding.
• State Dictionary Converters: Provided dedicated state dictionary converters for the DiT, VAE, Qwen, and ByT5 models to ensure compatibility and proper loading of pre-trained weights.
• Example Usage: Included an example script (HunyuanImage.py) demonstrating how to download the necessary models and run an inference pipeline for text-to-image generation.

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