🚀 Multimodal LLM-Guided Semantic Correction in Text-to-Image Diffusion

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PyTorch implementation of Multimodal LLM-Guided Semantic Correction in Text-to-Image Diffusion

📚 Quick Start

Run the following script to start inference:

bash run.sh

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🧾 Arguments

Argument	Description
--save_dir	Directory to save the generated results
--device	Device to use for inference (cuda, cpu, etc.)
--num_images_per_prompt	Number of images to generate per prompt
--num_inference_steps	Total denoising steps; higher means better quality but slower generation
--model_path	Path to the diffusion model (e.g., HunyuanDiT)
--vl_model_path	Path to the vision-language model for evaluation (e.g., Qwen2.5-VL)
--seed	Random seed for reproducibility
--process_steps	Number of self-reflection optimization steps
--process_steps_interval	Interval between each self-reflection step during generation
--process_start	Fraction of inference steps after which to start self-reflection (e.g., 0.1)
--process_end	Fraction of inference steps to stop self-reflection (e.g., 0.9)
--use_self_reflection	Whether to enable self-reflection during image generation

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📁 Output Structure

Generated outputs are saved under the specified --save_dir directory. Example structure:

results/
├── image_0.png
├── image_1.png
├── ...
└── prompts.txt

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📦 Requirements

Make sure the following libraries are installed:

• diffusers
• transformers
• torch

Or install from requirements.txt