sd2R 0.1.9

Shiny GUI

• New sd_app() launches an interactive Shiny application for image generation.
  • Auto-detection of model architecture (Flux, SD3, SDXL, SD1/2) from filenames in the models folder — no manual configuration needed.
  • Non-blocking async generation via C++ std::thread: the UI remains responsive during image generation, with a live progress bar and ETA display.
  • Automatic role assignment for multi-file models (diffusion, VAE, CLIP-L, T5-XXL).
  • Prevents loading incompatible model combinations (e.g. SD1.5 + Flux).

Async C++ Generation API

• New internal functions for non-blocking generation from R:
  • sd_generate_async() — launches generation in a background C++ thread.
  • sd_generate_poll() — checks completion status (atomic flags).
  • sd_generate_result() — retrieves results after completion.
• Progress callback writes JSON to a temp file (step, steps, pct, elapsed, eta_sec), read by Shiny via later::later() polling.
• R API calls (Rprintf, R_CheckUserInterrupt) are suppressed in the worker thread to prevent stack corruption.

Build System

• tools/patch_sd_sources.sh rewritten: all sed calls replaced with perl -pi -e for cross-platform compatibility (macOS BSD sed + Linux GNU sed).

sd2R 0.1.8

Bug Fixes

• Fixed undefined symbol: ggml_backend_vk_get_device_count load error on CRAN Fedora (clang and gcc). Root cause: ggmlR’s shared library (ggmlR.so) was built with Vulkan, but the static library (libggml.a) shipped without Vulkan objects. The old configure relied on ggml_vulkan_status() which queries ggmlR.so — it reported “AVAILABLE”, causing sd2R to compile with -DSD_USE_VULKAN against a libggml.a that lacked the symbols. Now configure checks nm libggml.a for a defined (T) symbol directly, ignoring the runtime ggmlR check entirely.

sd2R 0.1.7

Multi-GPU Model Parallelism

• New device_layout parameter in sd_ctx(): distribute sub-models across multiple Vulkan GPUs without separate processes.
  • "mono" — all on one GPU (default, backward-compatible).
  • "split_encoders" — CLIP/T5 on GPU 1, diffusion + VAE on GPU 0.
  • "split_vae" — CLIP/T5 + VAE on GPU 1, diffusion on GPU 0.
  • "encoders_cpu" — text encoders on CPU, diffusion + VAE on GPU.
• Low-level diffusion_gpu, clip_gpu, vae_gpu integer arguments for manual device assignment (override presets).

Profiling

• New profiling API for per-stage timing of image generation:
  • sd_profile_start() / sd_profile_stop() — control event capture.
  • sd_profile_get() — raw event data frame.
  • sd_profile_summary() — formatted summary with durations and percentages.
• Stages tracked: text_encode (with text_encode_clip and text_encode_t5 sub-stages), sampling, vae_decode, vae_encode, model loading.
• Pretty-printed output via print.sd_profile().

sd2R 0.1.6

Pipeline Graph API

• New sd_pipeline() / sd_node() — sequential graph-based pipeline. Node types: "txt2img", "img2img", "upscale", "save".
• sd_run_pipeline(pipeline, ctx) — execute pipeline with a single context.
• sd_save_pipeline() / sd_load_pipeline() — JSON serialization.

sd2R 0.1.5

Flux Support

• Flux model family (flux1-dev, etc.) fully supported: text-to-image, image-to-image, highres fix, tiled sampling, multi-GPU.
• Separate model paths: diffusion_model_path, vae_path, clip_l_path, t5xxl_path in sd_ctx().
• cfg_scale auto-defaults to 1.0 for Flux (guidance-distilled models).

img2img Improvements

• sd_generate() now defaults width/height to init image dimensions when not specified explicitly.

sd2R 0.1.4

Build System

• configure.win rewritten to use template approach (Makevars.win.in → Makevars.win), matching ggmlR pattern.

sd2R 0.1.3

Unified sd_generate() Entry Point

• New sd_generate() — single function for all generation modes. Automatically selects the optimal strategy (direct, tiled sampling, or highres fix) based on output resolution and available VRAM.
• vram_gb parameter in sd_ctx(): set once, auto-routing handles the rest.

Multi-GPU

• New sd_generate_multi_gpu() — parallel generation across multiple Vulkan GPUs via callr, one process per GPU, with progress reporting.

Performance

• Batch compute optimization for tiled sampling: pre-allocated compute context buffer eliminates ~110 MB malloc/free per UNet call.

sd2R 0.1.2

Highres Fix

• New sd_highres_fix() — classic two-pass highres pipeline: txt2img at native resolution → upscale → tiled img2img refinement.
• hr_strength parameter (default 0.4) controls refinement intensity.

Tiled img2img

• New sd_img2img_tiled() — img2img with MultiDiffusion tiled sampling for large images.

sd2R 0.1.1

VAE Tiling

• New vae_mode parameter: "normal", "tiled", "auto" (default). Auto-tiles when image area exceeds threshold.
• vae_tile_rel_x / vae_tile_rel_y for adaptive tile sizing.

High-Resolution Pipeline

• New sd_txt2img_highres() — patch-based generation for 2K, 4K+ images.
• model_type parameter in sd_ctx(): "sd1", "sd2", "sdxl", "flux", "sd3".

Tiled Sampling (MultiDiffusion)

• New sd_txt2img_tiled() — tiled diffusion sampling at any resolution. VRAM bounded by tile size, not output resolution.

sd2R 0.1.0

Core

• Text-to-image generation via stable-diffusion.cpp (C++ backend).
• Support for SD 1.x, SD 2.x, SDXL model versions.
• SafeTensors and GGUF model format loading.
• Vulkan GPU backend via ggmlR.
• Samplers: Euler, Euler A, Heun, DPM2, DPM++ (2M), LCM, DDIM, TCD.
• Schedulers: Discrete, Karras, Exponential, Simple, SGM Uniform, AYS, LCM.

R API

• sd_ctx() — create model context.
• sd_generate() — unified entry point.
• sd_txt2img(), sd_img2img() — low-level generation.
• sd_save_image(), sd_system_info().