Search Results for "auto"

ImageReward is the first general-purpose human preference reward model (RM) designed for evaluating text-to-image generation, introduced alongside the NeurIPS 2023 paper ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation. Trained on 137k expert-annotated image pairs, ImageReward significantly outperforms existing scoring methods like CLIP, Aesthetic, and BLIP in capturing human visual preferences. It is provided as a Python package (image-reward) that enables...

GLM-Image is an open-source generative AI model designed to create high-fidelity images from text prompts using a hybrid architecture that combines autoregressive semantic understanding with diffusion-based detail refinement. It excels at generating images that include complex layouts and detailed text content, making it especially useful for posters, diagrams, info-graphics, social media graphics, and visual content that requires precise text placement and semantic alignment. Because it...

Development happens on GitHub: https://github.com/Publikey/imference-desktop Imference Desktop is an open-source, local-first AI image generation app. It installs an isolated inference engine for you — no terminal, no CUDA wrestling, no Python environment. It auto-detects your GPU and tunes offloading/quantization to fit your VRAM: 8GB runs SDXL smoothly. Run seven model families locally (SDXL, SD 1.5, Z-Image, FLUX, Chroma, Qwen-Image, Anima), load your own .safetensors from Civitai, and keep every prompt and image on your machine. Optional cloud generation for bigger models — no account needed. ...

...You can also skip the training of the VAE altogether, using the pretrained model released by OpenAI! The wrapper class should take care of downloading and caching the model for you auto-magically. You can also use the pretrained VAE offered by the authors of Taming Transformers! Currently only the VAE with a codebook size of 1024 is offered, with the hope that it may train a little faster than OpenAI's, which has a size of 8192. In contrast to OpenAI's VAE, it also has an extra layer of downsampling, so the image sequence length is 256 instead of 1024 (this will lead to a 16 reduction in training costs, when you do the math).