Image Editing Models

IC-Light

Lvmin Zhang|1B+

IC-Light (Intrinsic Compositing Light) is an AI relighting model developed by Lvmin Zhang, the creator of ControlNet, that manipulates and transforms lighting conditions in photographs with remarkable realism. Built on a Stable Diffusion backbone with specialized lighting conditioning, the model with over one billion parameters can take any photograph of an object or person and completely alter the light source direction, color temperature, intensity, and ambient lighting while maintaining photorealistic shadows, highlights, and surface reflections. IC-Light operates in two distinct modes: foreground relighting where the subject is extracted and relit independently, and background-compatible relighting where the lighting is adjusted to match a new background environment. The model understands physical light behavior including specular reflections, subsurface scattering on skin, metallic surfaces, and transparent materials, producing results that respect real-world optical properties. IC-Light accepts text descriptions or reference images to define the target lighting setup, offering intuitive control over the final appearance. Released under the Apache 2.0 license, the model is fully open source and has been integrated into ComfyUI with dedicated workflow nodes. Professional photographers, product photographers, digital artists, and e-commerce teams use IC-Light for correcting unfavorable lighting in existing photos, creating studio-quality lighting from casual snapshots, matching product lighting across catalog images, generating dramatic cinematic lighting for creative projects, and preparing composited images with consistent illumination across elements.

InstructPix2Pix v2

UC Berkeley|1.5B

InstructPix2Pix v2 is an advanced diffusion model developed at UC Berkeley that edits images based on natural language instructions, building upon the success of the original InstructPix2Pix by Tim Brooks and collaborators. The model takes an input image and a text instruction such as 'make it sunset' or 'turn the cat into a dog' and generates the edited result while preserving unrelated parts of the image. Built on a Stable Diffusion backbone with instruction tuning, the v2 version introduces significant improvements in instruction comprehension, output quality, and editing precision compared to its predecessor. The architecture learns to follow complex multi-step instructions and handles nuanced editing requests including style changes, object modifications, color adjustments, weather transformations, and compositional alterations. Unlike mask-based editing approaches, InstructPix2Pix v2 requires no manual region selection as it automatically identifies which parts of the image to modify based on the text instruction. The model with approximately 1.5 billion parameters runs efficiently on consumer GPUs with 8GB or more VRAM. Released under the MIT license, it is fully open source and has been integrated into popular creative tools and workflows including ComfyUI and the Diffusers library. Professional photographers, digital artists, e-commerce teams, and content creators use InstructPix2Pix v2 for rapid iterative editing, product photo enhancement, creative experimentation, and batch processing of visual content where traditional manual editing would be time-prohibitive.