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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().

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.