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Hunyuan-DiT : A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding

This repo contains PyTorch model definitions, pre-trained weights and inference/sampling code for our paper exploring Hunyuan-DiT. You can find more visualizations on our project page.

Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding <br>

DialogGen: Multi-modal Interactive Dialogue System for Multi-turn Text-to-Image Generation <br>

🔥🔥🔥 News!!

Jul 15, 2024: 🚀 HunYuanDiT and Shakker.Ai have jointly launched a fine-tuning event based on the HunYuanDiT 1.2 model. By publishing a lora or fine-tuned model based on HunYuanDiT, you can earn up to $230 bonus from Shakker.Ai. See Shakker.Ai for more details.
Jul 15, 2024: :tada: Update ComfyUI to support standardized workflows and compatibility with weights from t2i module and Lora training for versions 1.1/1.2, as well as those trained by Kohya or the official script. See ComfyUI for details.
Jul 15, 2024: :zap: We offer Docker environments for CUDA 11/12, allowing you to bypass complex installations and play with a single click! See dockers for details.
Jul 08, 2024: :tada: HYDiT-v1.2 version is released. Please check HunyuanDiT-v1.2 and Distillation-v1.2 for more details.
Jul 03, 2024: :tada: Kohya-hydit version now available for v1.1 and v1.2 models, with GUI for inference. Official Kohya version is under review. See kohya for details.
Jun 27, 2024: :art: Hunyuan-Captioner is released, providing fine-grained caption for training data. See mllm for details.
Jun 27, 2024: :tada: Support LoRa and ControlNet in diffusers. See diffusers for details.
Jun 27, 2024: :tada: 6GB GPU VRAM Inference scripts are released. See lite for details.
Jun 19, 2024: :tada: ControlNet is released, supporting canny, pose and depth control. See training/inference codes for details.
Jun 13, 2024: :zap: HYDiT-v1.1 version is released, which mitigates the issue of image oversaturation and alleviates the watermark issue. Please check HunyuanDiT-v1.1 and Distillation-v1.1 for more details.
Jun 13, 2024: :truck: The training code is released, offering full-parameter training and LoRA training.
Jun 06, 2024: :tada: Hunyuan-DiT is now available in ComfyUI. Please check ComfyUI for more details.
Jun 06, 2024: 🚀 We introduce Distillation version for Hunyuan-DiT acceleration, which achieves 50% acceleration on NVIDIA GPUs. Please check Distillation for more details.
Jun 05, 2024: 🤗 Hunyuan-DiT is now available in 🤗 Diffusers! Please check the example below.
Jun 04, 2024: :globe_with_meridians: Support Tencent Cloud links to download the pretrained models! Please check the links below.
May 22, 2024: 🚀 We introduce TensorRT version for Hunyuan-DiT acceleration, which achieves 47% acceleration on NVIDIA GPUs. Please check TensorRT-libs for instructions.
May 22, 2024: 💬 We support demo running multi-turn text2image generation now. Please check the script below.

🤖 Try it on the web

Welcome to our web-based Tencent Hunyuan Bot, where you can explore our innovative products! Just input the suggested prompts below or any other imaginative prompts containing drawing-related keywords to activate the Hunyuan text-to-image generation feature. Unleash your creativity and create any picture you desire, all for free!

You can use simple prompts similar to natural language text

画一只穿着西装的猪

draw a pig in a suit

生成一幅画，赛博朋克风，跑车

generate a painting, cyberpunk style, sports car

or multi-turn language interactions to create the picture.

画一个木制的鸟

draw a wooden bird

变成玻璃的

turn into glass

📑 Open-source Plan

Hunyuan-DiT : A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding

Abstract

We present Hunyuan-DiT, a text-to-image diffusion transformer with fine-grained understanding of both English and Chinese. To construct Hunyuan-DiT, we carefully designed the transformer structure, text encoder, and positional encoding. We also build from scratch a whole data pipeline to update and evaluate data for iterative model optimization. For fine-grained language understanding, we train a Multimodal Large Language Model to refine the captions of the images. Finally, Hunyuan-DiT can perform multi-round multi-modal dialogue with users, generating and refining images according to the context. Through our carefully designed holistic human evaluation protocol with more than 50 professional human evaluators, Hunyuan-DiT sets a new state-of-the-art in Chinese-to-image generation compared with other open-source models.

🎉 Hunyuan-DiT Key Features

Chinese-English Bilingual DiT Architecture

Hunyuan-DiT is a diffusion model in the latent space, as depicted in figure below. Following the Latent Diffusion Model, we use a pre-trained Variational Autoencoder (VAE) to compress the images into low-dimensional latent spaces and train a diffusion model to learn the data distribution with diffusion models. Our diffusion model is parameterized with a transformer. To encode the text prompts, we leverage a combination of pre-trained bilingual (English and Chinese) CLIP and multilingual T5 encoder.

Multi-turn Text2Image Generation

Understanding natural language instructions and performing multi-turn interaction with users are important for a text-to-image system. It can help build a dynamic and iterative creation process that bring the user’s idea into reality step by step. In this section, we will detail how we empower Hunyuan-DiT with the ability to perform multi-round conversations and image generation. We train MLLM to understand the multi-round user dialogue and output the new text prompt for image generation.

📈 Comparisons

In order to comprehensively compare the generation capabilities of HunyuanDiT and other models, we constructed a 4-dimensional test set, including Text-Image Consistency, Excluding AI Artifacts, Subject Clarity, Aesthetic. More than 50 professional evaluators performs the evaluation.

<p align="center"> <table> <thead> <tr> <th rowspan="2">Model</th> <th rowspan="2">Open Source</th> <th>Text-Image Consistency (%)</th> <th>Excluding AI Artifacts (%)</th> <th>Subject Clarity (%)</th> <th rowspan="2">Aesthetics (%)</th> <th rowspan="2">Overall (%)</th> </tr> </thead> <tbody> <tr> <td>SDXL</td> <td> ✔ </td> <td>64.3</td> <td>60.6</td> <td>91.1</td> <td>76.3</td> <td>42.7</td> </tr> <tr> <td>PixArt-α</td> <td> ✔</td> <td>68.3</td> <td>60.9</td> <td>93.2</td> <td>77.5</td> <td>45.5</td> </tr> <tr> <td>Playground 2.5</td> <td>✔</td> <td>71.9</td> <td>70.8</td> <td>94.9</td> <td>83.3</td> <td>54.3</td> </tr> <tr> <td>SD 3</td> <td>&#10008</td> <td>77.1</td> <td>69.3</td> <td>94.6</td> <td>82.5</td> <td>56.7</td> </tr> <tr> <td>MidJourney v6</td><td>&#10008</td> <td>73.5</td> <td>80.2</td> <td>93.5</td> <td>87.2</td> <td>63.3</td> </tr> <tr> <td>DALL-E 3</td><td>&#10008</td> <td>83.9</td> <td>80.3</td> <td>96.5</td> <td>89.4</td> <td>71.0</td> </tr> <tr style="font-weight: bold; background-color: #f2f2f2;"> <td>Hunyuan-DiT</td><td>✔</td> <td>74.2</td> <td>74.3</td> <td>95.4</td> <td>86.6</td> <td>59.0</td> </tr> </tbody> </table> </p>