sd2R: Stable Diffusion Image Generation
Abstract
Provides Stable Diffusion image generation in R using the 'ggmlR' tensor library. Supports text-to-image and image-to-image generation with multiple model versions (SD 1.x, SD 2.x, 'SDXL', Flux). Implements the full inference pipeline including CLIP text encoding, 'UNet' noise removal, and 'VAE' encoding/decoding. Unified sd_generate() entry point with automatic strategy selection (direct, tiled sampling, high-resolution fix) based on output resolution and available 'VRAM'. High-resolution generation (2K, 4K+) via tiled 'VAE' decoding, tiled diffusion sampling ('MultiDiffusion'), and classic two-pass refinement (text-to-image, then upscale with image-to-image). Multi-GPU parallel generation via sd_generate_multi_gpu(). Multi-GPU model parallelism via 'device_layout' in sd_ctx(): distribute diffusion, text encoders, and 'VAE' across separate 'Vulkan' devices. Built-in profiling (sd_profile_start(), sd_profile_summary()) for per-stage timing of text encoding, sampling, and 'VAE' decode. Supports CPU and 'Vulkan' GPU. No 'Python' or external API dependencies required. Cross-platform: Linux, macOS, Windows.
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