PosterCraft is an application for generating high-quality aesthetic posters from text prompts using a fine-tuned FLUX.1 model. The system consists of three main components: a prompt enhancement module (QwenRecapAgent) that rewrites user prompts for better detail, an image generation module (PosterGenerator) that uses a custom FLUX transformer to create posters, and a Gradio web interface for interactive use. The application supports CPU offloading for low-memory GPUs and demonstrates modular design with clear separation between prompt processing and image generation. Key entry points include inference.py (command-line generation), inference_offload.py (memory-efficient generation), and demo_gradio.py (web UI).
RepLDM is a research codebase accompanying a NeurIPS 2025 paper that introduces a training-free method for generating high-resolution images (up to 8K) using pretrained latent diffusion models (SDXL). The core innovation is an Attention Guidance module that enhances detail and color richness by applying a lightweight self-attention transformation to latent representations during denoising. The code provides custom diffusers pipelines (RepLDMSDXLPipeline, RepLDMSDXLControlNetPipeline, FreeScaleSDXLPipeline) that implement a progressive upsampling strategy: initial denoising at base resolution, then iterative VAE encode/decode with additional denoising steps to increase resolution. It supports text-to-image generation and ControlNet-conditioned generation. The repository includes Jupyter notebooks for interactive use and a batch inference script. While primarily a research artifact, the Attention Guidance module is a clean, self-contained component that could be reused in other diffusion pipelines. The code is tightly coupled to specific versions of diffusers and PyTorch, and some internal APIs are used, indicating potential drift with newer library versions.