Merge branch 'main' into feature/generatemode

cleanup
can't trigger default features; switch to platform switches
2026-10-22 09:39:31 -04:00 · 2025-03-25 01:38:48 -04:00 · 2025-03-24 23:08:07 -04:00 · 2025-03-24 23:00:29 -04:00 · 2025-03-24 22:31:58 -04:00 · 2025-03-24 21:46:25 -04:00
119 changed files with 2808 additions and 1027 deletions
@@ -66,3 +66,7 @@ scraps/
 # These should be built by the platform
 windows/sqlite/sqlite3.exp
 windows/sqlite/sqlite3.lib
+
+# ArtCraft Windows DLLs
+crates/desktop/src-tauri/*.dll
+windows_dlls/
@@ -658,23 +658,23 @@ name = "app"
 version = "0.1.0"
 dependencies = [
 "anyhow",
+ "assertor",
 "base64 0.22.1",
 "bytes 1.10.0",
- "candle-core",
- "candle-nn",
- "candle-transformers",
- "cudarc",
+ "const_format",
 "directories 6.0.0",
 "dirs 6.0.0",
 "filesys",
 "futures 0.3.29",
 "half",
- "hf-hub 0.3.2",
 "image",
 "libloading 0.8.6",
 "log",
 "memory_caching",
+ "ml_models",
+ "ml_weights_registry",
 "once_cell",
+ "primitives",
 "rand 0.8.5",
 "reqwest 0.12.12",
 "serde",
@@ -686,7 +686,6 @@ dependencies = [
 "tauri-plugin-log",
 "tempdir",
 "tempfile",
- "tokenizers 0.15.2",
 "tokio",
 "tokio-util",
 ]
@@ -855,6 +854,15 @@ version = "1.1.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "10d2119f741b79fe9907f5396d19bffcb46568cfcc315e78677d731972ac7085"

+[[package]]
+name = "assertor"
+version = "0.0.3"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "4b02f2af4042fbbed4d5732c517666629512c574c8e2edb5cfe2cf8a86564d52"
+dependencies = [
+ "num-traits",
+]
+
 [[package]]
 name = "async-channel"
 version = "1.9.0"
@@ -1869,6 +1877,18 @@ dependencies = [
 "toml 0.8.14",
 ]

+[[package]]
+name = "carrot_utils"
+version = "0.1.7"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "3f34277d609ff503187aa8e8604913b103e2eb8f893222a4948748f22527252e"
+dependencies = [
+ "chrono",
+ "colored",
+ "log",
+ "simplelog",
+]
+
 [[package]]
 name = "cast"
 version = "0.3.0"
@@ -2046,9 +2066,9 @@ dependencies = [

 [[package]]
 name = "clap"
-version = "4.4.8"
+version = "4.5.13"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "2275f18819641850fa26c89acc84d465c1bf91ce57bc2748b28c420473352f64"
+checksum = "0fbb260a053428790f3de475e304ff84cdbc4face759ea7a3e64c1edd938a7fc"
 dependencies = [
 "clap_builder",
 "clap_derive",
@@ -2056,23 +2076,23 @@ dependencies = [

 [[package]]
 name = "clap_builder"
-version = "4.4.8"
+version = "4.5.13"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "07cdf1b148b25c1e1f7a42225e30a0d99a615cd4637eae7365548dd4529b95bc"
+checksum = "64b17d7ea74e9f833c7dbf2cbe4fb12ff26783eda4782a8975b72f895c9b4d99"
 dependencies = [
 "anstream",
 "anstyle",
 "clap_lex",
- "strsim 0.10.0",
+ "strsim 0.11.1",
 ]

 [[package]]
 name = "clap_derive"
-version = "4.4.7"
+version = "4.5.13"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "cf9804afaaf59a91e75b022a30fb7229a7901f60c755489cc61c9b423b836442"
+checksum = "501d359d5f3dcaf6ecdeee48833ae73ec6e42723a1e52419c79abf9507eec0a0"
 dependencies = [
- "heck 0.4.1",
+ "heck 0.5.0",
 "proc-macro2",
 "quote",
 "syn 2.0.87",
@@ -2080,9 +2100,9 @@ dependencies = [

 [[package]]
 name = "clap_lex"
-version = "0.6.0"
+version = "0.7.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "702fc72eb24e5a1e48ce58027a675bc24edd52096d5397d4aea7c6dd9eca0bd1"
+checksum = "f46ad14479a25103f283c0f10005961cf086d8dc42205bb44c46ac563475dca6"

 [[package]]
 name = "cloud_storage"
@@ -2175,6 +2195,16 @@ version = "1.0.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "0b6a852b24ab71dffc585bcb46eaf7959d175cb865a7152e35b348d1b2960422"

+[[package]]
+name = "colored"
+version = "2.2.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "117725a109d387c937a1533ce01b450cbde6b88abceea8473c4d7a85853cda3c"
+dependencies = [
+ "lazy_static",
+ "windows-sys 0.59.0",
+]
+
 [[package]]
 name = "combine"
 version = "3.8.1"
@@ -2300,6 +2330,26 @@ dependencies = [
 "tiny-keccak",
 ]

+[[package]]
+name = "const_format"
+version = "0.2.34"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "126f97965c8ad46d6d9163268ff28432e8f6a1196a55578867832e3049df63dd"
+dependencies = [
+ "const_format_proc_macros",
+]
+
+[[package]]
+name = "const_format_proc_macros"
+version = "0.2.34"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "1d57c2eccfb16dbac1f4e61e206105db5820c9d26c3c472bc17c774259ef7744"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "unicode-xid",
+]
+
 [[package]]
 name = "constant_time_eq"
 version = "0.1.5"
@@ -6747,6 +6797,58 @@ dependencies = [
 "ws2_32-sys",
 ]

+[[package]]
+name = "ml_models"
+version = "0.0.1"
+dependencies = [
+ "anyhow",
+ "assertor",
+ "base64 0.22.1",
+ "bytes 1.10.0",
+ "candle-core",
+ "candle-nn",
+ "candle-transformers",
+ "const_format",
+ "cudarc",
+ "directories 6.0.0",
+ "dirs 6.0.0",
+ "filesys",
+ "futures 0.3.29",
+ "half",
+ "hf-hub 0.3.2",
+ "image",
+ "libloading 0.8.6",
+ "log",
+ "memory_caching",
+ "ml_weights_registry",
+ "ndarray",
+ "nvidia-checker",
+ "once_cell",
+ "ort",
+ "primitives",
+ "rand 0.8.5",
+ "reqwest 0.12.12",
+ "serde",
+ "serde_json",
+ "serde_yaml",
+ "serial_test",
+ "tauri",
+ "tauri-plugin-log",
+ "tempdir",
+ "tempfile",
+ "tokenizers 0.15.2",
+ "tokio",
+ "tokio-util",
+]
+
+[[package]]
+name = "ml_weights_registry"
+version = "0.0.1"
+dependencies = [
+ "anyhow",
+ "const_format",
+]
+
 [[package]]
 name = "mockall"
 version = "0.12.1"
@@ -7011,6 +7113,19 @@ dependencies = [
 "tempfile",
 ]

+[[package]]
+name = "ndarray"
+version = "0.15.6"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "adb12d4e967ec485a5f71c6311fe28158e9d6f4bc4a447b474184d0f91a8fa32"
+dependencies = [
+ "matrixmultiply",
+ "num-complex",
+ "num-integer",
+ "num-traits",
+ "rawpointer",
+]
+
 [[package]]
 name = "ndk"
 version = "0.8.0"
@@ -7288,6 +7403,23 @@ version = "0.4.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "830b246a0e5f20af87141b25c173cd1b609bd7779a4617d6ec582abaf90870f3"

+[[package]]
+name = "nvidia-checker"
+version = "0.1.3"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "c8b584e4b0be14d4e3f8b779d79477aa0f36658d7a535c42abc725013d0c8951"
+dependencies = [
+ "carrot_utils",
+ "chrono",
+ "clap",
+ "colored",
+ "dirs 5.0.1",
+ "log",
+ "serde",
+ "serde_derive",
+ "toml 0.8.14",
+]
+
 [[package]]
 name = "objc"
 version = "0.2.7"
@@ -7793,6 +7925,26 @@ dependencies = [
 "hashbrown 0.14.5",
 ]

+[[package]]
+name = "ort"
+version = "1.16.3"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "889dca4c98efa21b1ba54ddb2bde44fd4920d910f492b618351f839d8428d79d"
+dependencies = [
+ "flate2",
+ "half",
+ "lazy_static",
+ "libc",
+ "ndarray",
+ "tar",
+ "thiserror 1.0.63",
+ "tracing",
+ "ureq",
+ "vswhom",
+ "winapi 0.3.9",
+ "zip 0.6.6",
+]
+
 [[package]]
 name = "overload"
 version = "0.1.1"
@@ -8360,6 +8512,7 @@ dependencies = [
 name = "primitives"
 version = "0.0.1"
 dependencies = [
+ "assertor",
 "my-workspace-hack",
 "testing",
 ]
@@ -10382,6 +10535,17 @@ version = "0.1.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "e3a9fe34e3e7a50316060351f37187a3f546bce95496156754b601a5fa71b76e"

+[[package]]
+name = "simplelog"
+version = "0.12.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "16257adbfaef1ee58b1363bdc0664c9b8e1e30aed86049635fb5f147d065a9c0"
+dependencies = [
+ "log",
+ "termcolor",
+ "time",
+]
+
 [[package]]
 name = "siphasher"
 version = "0.3.11"
@@ -11912,6 +12076,15 @@ dependencies = [
 "safetensors",
 ]

+[[package]]
+name = "termcolor"
+version = "1.4.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "06794f8f6c5c898b3275aebefa6b8a1cb24cd2c6c79397ab15774837a0bc5755"
+dependencies = [
+ "winapi-util",
+]
+
 [[package]]
 name = "termtree"
 version = "0.4.1"
@@ -12573,9 +12746,9 @@ dependencies = [

 [[package]]
 name = "tracing-subscriber"
-version = "0.3.19"
+version = "0.3.18"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e8189decb5ac0fa7bc8b96b7cb9b2701d60d48805aca84a238004d665fcc4008"
+checksum = "ad0f048c97dbd9faa9b7df56362b8ebcaa52adb06b498c050d2f4e32f90a7a8b"
 dependencies = [
 "nu-ansi-term",
 "sharded-slab",
@@ -12884,6 +13057,12 @@ version = "0.2.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "1fc81956842c57dac11422a97c3b8195a1ff727f06e85c84ed2e8aa277c9a0fd"

+[[package]]
+name = "unicode-xid"
+version = "0.2.6"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "ebc1c04c71510c7f702b52b7c350734c9ff1295c464a03335b00bb84fc54f853"
+
 [[package]]
 name = "unicode_categories"
 version = "0.1.1"
@@ -23,6 +23,8 @@ members = [

  # Desktop apps
  "crates/desktop/src-tauri",
+  "crates/desktop/ml_models",
+  "crates/desktop/ml_weights_registry",

  # Shared libraries
  "crates/lib/actix_cors_configs",
@@ -0,0 +1,79 @@
+[package]
+name = "ml_models"
+edition = "2021"
+version = "0.0.1"
+authors = ["storyteller"]
+publish = false
+description = ""
+license = ""
+repository = ""
+
+[lib]
+name = "ml_models"
+path = "src/lib.rs"
+crate-type = ["staticlib", "cdylib", "rlib"]
+
+[package.metadata.cargo-udeps.ignore]
+normal = ["my-workspace-hack"]
+
+[dependencies]
+
+# Internal
+filesys = { path = "../../lib/files/filesys" }
+memory_caching = { path = "../../lib/caching/memory_caching" }
+ml_weights_registry = { path = "../ml_weights_registry" }
+primitives = { path = "../../lib/data/primitives" }
+
+# Bypass cudarc issues on Windows https://github.com/coreylowman/cudarc/issues/332
+cudarc = "0.13.7"
+
+# External
+anyhow = "1.0"
+base64 = "0.22.1"
+bytes = "1.10"
+candle-core = { path = "../../vendor/candle/candle-core", features = ["cuda"] }
+candle-nn = { path = "../../vendor/candle/candle-nn", features = ["cuda"] }
+candle-transformers = { path = "../../vendor/candle/candle-transformers", features = ["cuda"] }
+const_format = "0.2.34"
+directories = "6.0.0"
+dirs = "6.0.0"
+futures = "0.3"
+half = { version = "2.3.1", features = ["num-traits", "use-intrinsics", "rand_distr"] }
+hf-hub = "0.3.2"
+image = "0.25.5"
+log = "0.4"
+ndarray = "0.15.6"
+nvidia-checker = "0.1.2"
+once_cell = "1.20.3"
+ort = { version = "1.16.2", features = ["cuda"] }
+rand = "0.8.5"
+# NB(bt,2025-03-10): default-features = false on Windows while I debug openssl / rustls
+reqwest = { version = "0.12.12", features = ["stream"], default-features = false }
+serde = { version = "1.0", features = ["derive"] }
+serde_json = "1.0"
+serde_yaml = "0.9"
+tauri = { version = "2.2.4", features = [] }
+tauri-plugin-log = "2"
+tempdir = "0.3.7"
+tempfile = "3.18.0"
+tokenizers = "0.15.2"
+tokio = { version = "1.42", features = ["rt", "rt-multi-thread", "fs"] }
+tokio-util = { version = "0.7", features = ["codec"] }
+
+# tracing-subscriber = { version = "0.3", features = [ "env-filter", "fmt" ] }
+
+[features]
+default = ["cuda"]
+cuda = [
+    "candle-core/cuda",
+    "candle-nn/cuda",
+    "candle-transformers/cuda",
+    # NB(bt,2025-03-10): Building without Flash attention on Windows for now to bypass linker issues
+    "candle-transformers/flash-attn",
+    "candle-core/cudnn"
+]
+
+[dev-dependencies]
+libloading = "0.8.6"
+serial_test = "3.2.0"
+assertor = "0.0.3"
@@ -0,0 +1 @@
+pub mod ml;
@@ -0,0 +1,2 @@
+pub mod onnx_session;
+pub mod remove_image_background;
@@ -0,0 +1,19 @@
+//! Adapted from https://github.com/dnanhkhoa/rust-background-removal/blob/main/src/onnx.rs
+
+use std::path::Path;
+use ort::{Environment, ExecutionProvider, GraphOptimizationLevel, SessionBuilder};
+
+pub fn onnx_session(onnx_model_file: &Path) -> Result<ort::Session, anyhow::Error> {
+  let environment = Environment::default().into_arc();
+  let session = SessionBuilder::new(&environment)?
+    .with_optimization_level(GraphOptimizationLevel::Level1)? // Configure model optimization level
+    .with_intra_threads(1)? // Configure the number of threads used for inference
+    .with_execution_providers([
+      // Configure execution providers (e.g., CUDA, CoreML, CPU)
+      ExecutionProvider::CUDA(Default::default()),
+      ExecutionProvider::CoreML(Default::default()),
+      ExecutionProvider::CPU(Default::default()),
+    ])?
+    .with_model_from_file(onnx_model_file)?;
+  Ok(session)
+}
@@ -0,0 +1,61 @@
+use crate::ml::background_removal::onnx_session::onnx_session;
+use image::{imageops, DynamicImage};
+use ndarray::{Array, CowArray};
+use ort::Value;
+use std::path::Path;
+
+pub async fn remove_image_background<P: AsRef<Path>>(model_path: P, image: DynamicImage) -> anyhow::Result<DynamicImage> {
+  // NB: Real time needed cuDNN 9, but this uses cuDNN 8:
+  // sudo apt-get install libcudnn8-dev libcudnn8
+  // update-alternatives: warning: forcing reinstallation of alternative /usr/include/x86_64-linux-gnu/cudnn_v9.h because link group libcudnn is broken
+  // update-alternatives: using /usr/include/x86_64-linux-gnu/cudnn_v8.h to provide /usr/include/cudnn.h (libcudnn) in manual mode
+  let session = onnx_session(model_path.as_ref())?;
+  let image = process_dynamic_image(&session, image)?;
+  Ok(image)
+}
+
+fn process_dynamic_image(
+  session: &ort::Session,
+  dynamic_img: DynamicImage,
+) -> Result<DynamicImage, anyhow::Error> {
+  let input_shape = session.inputs[0]
+    .dimensions()
+    .map(|dim| dim.unwrap())
+    .collect::<Vec<usize>>();
+  let input_img = dynamic_img.into_rgba8();
+  let scaling_factor = f32::min(
+    1., // Avoid upscaling
+    f32::min(
+      input_shape[3] as f32 / input_img.width() as f32, // Width ratio
+      input_shape[2] as f32 / input_img.height() as f32, // Height ratio
+    ),
+  );
+  let mut resized_img = imageops::resize(
+    &input_img,
+    input_shape[3] as u32,
+    input_shape[2] as u32,
+    imageops::FilterType::Triangle,
+  );
+  let input_tensor = CowArray::from(
+    Array::from_shape_fn(input_shape, |indices| {
+      let mean = 128.;
+      let std = 256.;
+
+      (resized_img[(indices[3] as u32, indices[2] as u32)][indices[1]] as f32 - mean) / std
+    })
+      .into_dyn(),
+  );
+  let inputs = vec![Value::from_array(session.allocator(), &input_tensor)?];
+  let outputs = session.run(inputs)?;
+  let output_tensor = outputs[0].try_extract::<f32>()?;
+  for (indices, alpha) in output_tensor.view().indexed_iter() {
+    resized_img[(indices[3] as u32, indices[2] as u32)][3] = (alpha * 255.) as u8;
+  }
+  let output_img = imageops::resize(
+    &resized_img,
+    (input_img.width() as f32 * scaling_factor) as u32,
+    (input_img.height() as f32 * scaling_factor) as u32,
+    imageops::FilterType::Triangle,
+  );
+  Ok(DynamicImage::ImageRgba8(output_img))
+}
@@ -0,0 +1,7 @@
+pub mod background_removal;
+pub mod image;
+pub mod model_cache;
+pub mod model_config;
+pub mod model_file;
+pub mod prompt_cache;
+pub mod stable_diffusion;
@@ -1,15 +1,14 @@
-use crate::ml::models::unet_model::UNetModel;
 use anyhow::anyhow;
+use candle_transformers::models::stable_diffusion::unet_2d::UNet2DConditionModel;
 use candle_transformers::models::stable_diffusion::vae::AutoEncoderKL;
 use std::sync::{Arc, RwLock};
-
 // TODO: This data structure is gross. Generalize the locking and loading semantics for reuse.

 // Simple registry for now. We can build complex machinery that aids in 
 // VRAM utilization, disk space saving, intelligent scheduling, etc. in the future.
 pub struct ModelCache {
  vae: RwLock<Option<Arc<AutoEncoderKL>>>,
-  unet2: RwLock<Option<Arc<UNetModel>>>,
+  unet2: RwLock<Option<Arc<UNet2DConditionModel>>>,
 }

 impl ModelCache {
@@ -39,7 +38,7 @@ impl ModelCache {
    }
  }

-  pub fn set_unet(&self, vae: Arc<UNetModel>) -> anyhow::Result<()> {
+  pub fn set_unet(&self, vae: Arc<UNet2DConditionModel>) -> anyhow::Result<()> {
    match self.unet2.write() {
      Err(err) => Err(anyhow!("{}", err)),
      Ok(mut lock) => {
@@ -49,7 +48,7 @@ impl ModelCache {
    }
  }

-  pub fn get_unet(&self) -> anyhow::Result<Option<Arc<UNetModel>>> {
+  pub fn get_unet(&self) -> anyhow::Result<Option<Arc<UNet2DConditionModel>>> {
    match self.unet2.read() {
      Err(err) => Err(anyhow!("{}", err)),
      Ok(ref lock) => {
@@ -0,0 +1,61 @@
+use crate::ml::model_file::StableDiffusionVersion;
+use candle_core::{DType, Device};
+use candle_transformers::models::stable_diffusion::StableDiffusionConfig;
+use hf_hub::api::sync::Api;
+use log::info;
+
+const DEFAULT_SD_IMAGE_WIDTH: usize = 1024;
+const DEFAULT_SD_IMAGE_HEIGHT: usize = 1024;
+const DEFAULT_SD_NUMERIC_DATATYPE: DType = DType::F32;
+const SD_VERSION: StableDiffusionVersion = StableDiffusionVersion::V1_5;
+
+pub struct ModelConfig {
+
+  /// We only support one device type currently, and we can't multiplex
+  /// over several devices yet.
+  pub device: Device,
+
+  /// The core numeric type to use for models.
+  pub dtype: DType,
+
+  pub sd_version: StableDiffusionVersion,
+  pub sd_config: StableDiffusionConfig,
+
+  /// Probably shouldn't be here.
+  pub hf_api: Api,
+}
+
+impl ModelConfig {
+  pub fn init() -> anyhow::Result<Self> {
+    let device = Device::new_cuda(0)
+      .unwrap_or_else(|e| {
+          info!("CUDA not available ({}), falling back to CPU", e);
+          Device::Cpu
+        });
+
+    let sd_config = match SD_VERSION {
+      StableDiffusionVersion::Turbo => {
+        StableDiffusionConfig::sdxl_turbo(None, Some(DEFAULT_SD_IMAGE_HEIGHT), Some(DEFAULT_SD_IMAGE_WIDTH))
+      }
+      StableDiffusionVersion::V1_5 | StableDiffusionVersion::V1_5Inpaint => {
+        StableDiffusionConfig::v1_5(None, Some(DEFAULT_SD_IMAGE_HEIGHT), Some(DEFAULT_SD_IMAGE_WIDTH))
+      }
+      StableDiffusionVersion::V2_1 | StableDiffusionVersion::V2Inpaint => {
+        StableDiffusionConfig::v2_1(None, Some(DEFAULT_SD_IMAGE_HEIGHT), Some(DEFAULT_SD_IMAGE_WIDTH))
+      }
+      StableDiffusionVersion::Xl | StableDiffusionVersion::XlInpaint => {
+        StableDiffusionConfig::sdxl(None, Some(DEFAULT_SD_IMAGE_HEIGHT), Some(DEFAULT_SD_IMAGE_WIDTH))
+      }
+    };
+
+    let hf_api = Api::new()?;
+
+    Ok(Self {
+      device,
+      dtype: DEFAULT_SD_NUMERIC_DATATYPE,
+      sd_version: SD_VERSION,
+      sd_config,
+      hf_api,
+    })
+  }
+}
@@ -3,19 +3,51 @@ extern crate accelerate_src;
 #[cfg(feature = "mkl")]
 extern crate intel_mkl_src;

-use std::path::PathBuf;
 use candle_transformers::models::stable_diffusion;
+use std::path::{Path, PathBuf};

 use crate::ml::model_file::{ModelFile, StableDiffusionVersion};
+use ml_weights_registry::weights_registry::weights::{CLIP_JSON, SDXL_TURBO_CLIP_TEXT_ENCODER, SDXL_TURBO_CLIP_TEXT_ENCODER_2};
 use anyhow::Error as E;
 use candle_core::{DType, Device, Module, Tensor, D};
 use log::info;
 use tokenizers::Tokenizer;
-use crate::ml::model_type::ModelType;
-use crate::state::app_dir::AppWeightsDir;
+
+
+pub struct ClipArgs<'a, P: AsRef<Path>,  Q: AsRef<Path>> {
+  pub prompt: &'a str, 
+  pub uncond_prompt: &'a str, 
+  pub tokenizer: Option<String>, 
+  pub clip_weights: Option<String>, 
+  pub clip2_weights: Option<String>, 
+  pub sd_version: StableDiffusionVersion, 
+  pub sd_config: &'a stable_diffusion::StableDiffusionConfig, 
+  pub use_f16: bool, 
+  pub device: &'a Device, 
+  pub dtype: DType, 
+  pub use_guide_scale: bool, 
+  pub clip_json_path: P,
+  pub clip_weights_path: Q,
+}

 #[allow(clippy::too_many_arguments)]
-pub fn infer_clip_text_embeddings(prompt: &str, uncond_prompt: &str, tokenizer: Option<String>, clip_weights: Option<String>, clip2_weights: Option<String>, sd_version: StableDiffusionVersion, sd_config: &stable_diffusion::StableDiffusionConfig, use_f16: bool, device: &Device, dtype: DType, use_guide_scale: bool, weights_dir: &AppWeightsDir) -> anyhow::Result<Tensor> {
+pub fn infer_clip_text_embeddings<P: AsRef<Path>, Q: AsRef<Path>>(args: ClipArgs<'_, P, Q>) -> anyhow::Result<Tensor> {
+  let ClipArgs {
+    prompt, 
+    uncond_prompt, 
+    tokenizer, 
+    clip_weights, 
+    clip2_weights, 
+    sd_version, 
+    sd_config, 
+    use_f16, 
+    device, 
+    dtype, 
+    use_guide_scale, 
+    clip_json_path,
+    clip_weights_path,
+  } = args;
+
  info!("Preparing text embeddings... for {:?}", sd_version);

  let which = match sd_version {
@@ -39,7 +71,8 @@ pub fn infer_clip_text_embeddings(prompt: &str, uncond_prompt: &str, tokenizer:
        dtype,
        use_guide_scale,
        *first,
-        weights_dir,
+        clip_json_path.as_ref(),
+        clip_weights_path.as_ref(),
      )
    })
    .collect::<anyhow::Result<Vec<_>>>()?;
@@ -50,20 +83,25 @@ pub fn infer_clip_text_embeddings(prompt: &str, uncond_prompt: &str, tokenizer:
 }

 #[allow(clippy::too_many_arguments)]
-fn do_infer_clip_text_embeddings(prompt: &str, uncond_prompt: &str, tokenizer: Option<String>, clip_weights: Option<String>, clip2_weights: Option<String>, sd_version: StableDiffusionVersion, sd_config: &stable_diffusion::StableDiffusionConfig, use_f16: bool, device: &Device, dtype: DType, use_guide_scale: bool, first: bool, weights_dir: &AppWeightsDir) -> anyhow::Result<Tensor> {
+fn do_infer_clip_text_embeddings(
+  prompt: &str, 
+  uncond_prompt: &str, 
+  tokenizer: Option<String>, 
+  clip_weights: Option<String>, 
+  clip2_weights: Option<String>, 
+  sd_version: StableDiffusionVersion, 
+  sd_config: &stable_diffusion::StableDiffusionConfig, 
+  use_f16: bool, 
+  device: &Device, 
+  dtype: DType, 
+  use_guide_scale: bool, 
+  first: bool, 
+  clip_json_path: &Path,
+  clip_weights_path: &Path,
+) -> anyhow::Result<Tensor> {
  info!("do_infer_clip_text_embeddings called with args {:?}", (prompt, uncond_prompt, tokenizer.clone().unwrap_or_else(|| "None".to_string()), clip_weights.clone().unwrap_or_else(|| "None".to_string()), clip2_weights.clone().unwrap_or_else(|| "None".to_string()), sd_version, sd_config, use_f16, device, dtype, use_guide_scale, first));

-  //let tokenizer_file = if first {
-  //  info!("ModelFile::Tokenizer");
-  //  ModelFile::Tokenizer
-  //} else {
-  //  info!("ModelFile::Tokenizer2");
-  //  ModelFile::Tokenizer2
-  //};
-
-  //let tokenizer = tokenizer_file.get(tokenizer, sd_version, use_f16)?;
-
-  let tokenizer = weights_dir.model_path(&ModelType::ClipJson);
+  let tokenizer = clip_json_path;

  info!("Loading Clip Tokenizer path: {:?}", tokenizer);

@@ -85,14 +123,16 @@ fn do_infer_clip_text_embeddings(prompt: &str, uncond_prompt: &str, tokenizer: O
  let tokens = Tensor::new(tokens.as_slice(), device)?.unsqueeze(0)?;

  println!("Building the Clip transformer.");
-  let clip_weights_file = if first { ModelFile::Clip } else { ModelFile::Clip2 };
+  
+  // TODO(bt,2025-03-23): These used to be two different clip models. We should only need one.
  let clip_weights = if first {
-    //clip_weights_file.get(clip_weights, sd_version, use_f16)?
-    weights_dir.model_path(&ModelType::SdxlTurboClipEncoder)
+    println!(">>>>> CLIP 1");
+    clip_weights_path
  } else {
-    //clip_weights_file.get(clip2_weights, sd_version, use_f16)?
-    weights_dir.model_path(&ModelType::SdxlTurboClipEncoder2)
+    println!(">>>>> CLIP 2");
+    clip_weights_path
  };
+  
  let clip_config = if first { &sd_config.clip } else { sd_config.clip2.as_ref().unwrap() };
  let text_model = stable_diffusion::build_clip_transformer(clip_config, clip_weights, device, DType::F32)?;

@@ -1,72 +1,99 @@
-use std::path::PathBuf;
+use std::path::{Path, PathBuf};
 use std::sync::{Arc, RwLock, RwLockReadGuard, RwLockWriteGuard};

-use crate::events::notification_event::{NotificationEvent, NotificationModelType};
-use crate::ml::image::dynamic_image_to_tensor::dynamic_image_to_tensor;
-use crate::ml::image::tensor_to_image_buffer::{tensor_to_image_buffer, RgbImage};
-use crate::ml::model_cache::ModelCache;
-use crate::ml::model_file::{ModelFile, StableDiffusionVersion};
-use crate::ml::model_type::ModelType;
-use crate::ml::models::unet_model::UNetModel;
-use crate::ml::prompt_cache::PromptCache;
-use crate::ml::stable_diffusion::get_vae_scale::get_vae_scale;
-use crate::ml::stable_diffusion::infer_clip_text_embeddings::infer_clip_text_embeddings;
-use crate::state::app_config::AppConfig;
-use crate::state::app_dir::AppDataRoot;
 use anyhow::{anyhow, Error as E, Result};
-use candle_core::{DType, IndexOp, Tensor, D};
+use candle_core::{DType, Device, IndexOp, Tensor, D};
 use candle_nn::VarBuilder;
-use candle_transformers::models::stable_diffusion::lcm::LCMScheduler;
+use candle_transformers::models::stable_diffusion::StableDiffusionConfig;
+use candle_transformers::models::stable_diffusion::lcm::{LCMScheduler, LCMSchedulerConfig};
 use candle_transformers::models::stable_diffusion::unet_2d::BlockConfig;
 use candle_transformers::models::stable_diffusion::unet_2d::UNet2DConditionModel;
 use candle_transformers::models::stable_diffusion::unet_2d::UNet2DConditionModelConfig;
 use candle_transformers::models::stable_diffusion::vae::{AutoEncoderKL, DiagonalGaussianDistribution};
+use crate::ml::image::dynamic_image_to_tensor::dynamic_image_to_tensor;
+use crate::ml::image::tensor_to_image_buffer::{tensor_to_image_buffer, RgbImage};
+use crate::ml::model_cache::ModelCache;
+use crate::ml::model_config::ModelConfig;
+use crate::ml::model_file::{ModelFile, StableDiffusionVersion};
+use crate::ml::prompt_cache::PromptCache;
+use crate::ml::stable_diffusion::get_vae_scale::get_vae_scale;
+use crate::ml::stable_diffusion::infer_clip_text_embeddings::{infer_clip_text_embeddings, ClipArgs};
+use crate::ml::stable_diffusion::remap_lcm_strength_range::remap_lcm_strength_range;
 use image::DynamicImage;
 use log::info;
+use ml_weights_registry::weights_registry::weights::{LYKON_DEAMSHAPER_7_TEXT_ENCODER_FP16, LYKON_DEAMSHAPER_7_VAE, SIMIANLUO_LCM_DREAMSHAPER_V7_UNET};
 use rand::Rng;
-use tauri::{AppHandle, Emitter};

-pub struct Args<'a> {
+const DEFAULT_STRENGTH : f64 = 75.0;
+
+pub struct Args<'a, P1: AsRef<Path>, P2: AsRef<Path>, P3: AsRef<Path>, P4: AsRef<Path>> {
  pub image: &'a DynamicImage,
  pub prompt: String,
  pub uncond_prompt: String,
  pub cfg_scale: Option<f64>,
-  pub i2i_strength: Option<u8>,
-  pub configs: &'a AppConfig,
+  pub img2img_strength: Option<f64>,
  pub model_cache: &'a ModelCache,
  pub prompt_cache: &'a PromptCache,
-  pub app_data_root: &'a AppDataRoot,
-  pub app: &'a AppHandle,
  pub use_flash_attn: bool,
+  pub model_config: &'a ModelConfig,
+  pub maybe_seed: Option<u64>,
+  pub scheduler_steps: usize,
+  pub vae_path: P1,
+  pub unet_path: P2,
+  pub clip_json_path: P3,
+  pub clip_weights_path: P4,
 }

-pub fn lcm_pipeline(args: Args<'_>) -> Result<RgbImage> {
-  let Args { prompt, uncond_prompt, cfg_scale, i2i_strength, configs, model_cache, prompt_cache, app, image, app_data_root, use_flash_attn } = args;
+pub fn lcm_pipeline<P1, P2, P3, P4>(args: Args<'_, P1, P2, P3, P4>) -> Result<RgbImage> 
+  where P1: AsRef<Path>, P2: AsRef<Path>, P3: AsRef<Path>, P4: AsRef<Path>
+{
+  let Args { 
+    prompt, 
+    uncond_prompt, 
+    cfg_scale,
+    img2img_strength, 
+    model_cache, 
+    prompt_cache, 
+    image, 
+    use_flash_attn ,
+    model_config: ModelConfig {
+      device,
+      dtype,
+      sd_version,
+      sd_config,
+      hf_api,
+    },
+    maybe_seed,
+    scheduler_steps,
+    vae_path,
+    unet_path,
+    clip_json_path,
+    clip_weights_path,
+  } = args;
+  
+  let dtype = *dtype;
+  let sd_version = *sd_version;

-  let weights_dir = app_data_root.weights_dir();
+  let img2img_strength = img2img_strength
+    .map(|s| remap_lcm_strength_range(s))
+    .unwrap_or(DEFAULT_STRENGTH);
+  
+  let img2img_strength = img2img_strength / 100.0;

-  let img2img_strength = args.i2i_strength.unwrap_or(75) as f64 / 100.0;
  let use_f16 = true;

-  //println!("Starting image generation with the following configuration:");
-  //println!("  Model: {:?}", configs.sd_version);
-  //println!("  Prompt: {}", prompt);
-  //println!("  Steps: {}", configs.scheduler_steps);
-  //println!("  Device: {:?}", configs.device);
-
-  //println!("Model dimensions: {}x{}", configs.sd_config.width, configs.sd_config.height);
-
  // Use LCM Scheduler instead of Euler Ancestral for better speed and quality
-  let mut scheduler = LCMScheduler::new(configs.scheduler_steps, img2img_strength, candle_transformers::models::stable_diffusion::lcm::LCMSchedulerConfig::default())?;
+  let mut scheduler = LCMScheduler::new(scheduler_steps, img2img_strength, LCMSchedulerConfig::default())?;

-  let seed = configs.seed.unwrap_or_else(|| rand::thread_rng().gen());
-  configs.device.set_seed(seed)?;
+  let seed = maybe_seed.unwrap_or_else(|| rand::thread_rng().gen());

-  //info!("Using seed: {}", seed);
+  let seed = 42;
+  
+  device.set_seed(seed)?;

  let guidance_scale = match cfg_scale {
    Some(guidance_scale) => guidance_scale,
-    None => match configs.sd_version {
+    None => match sd_version {
      StableDiffusionVersion::V1_5 | StableDiffusionVersion::V2_1 | StableDiffusionVersion::Xl => 7.5,
      StableDiffusionVersion::Turbo => 0.,
      _ => 0., // NB: Not sure what the other model families should use, so sticking with "0"
@@ -76,72 +103,50 @@ pub fn lcm_pipeline(args: Args<'_>) -> Result<RgbImage> {
  // let use_guide_scale = guidance_scale > 1.0;
  let use_guide_scale = false;

-  //info!("Using guide scale: {}", use_guide_scale);
-
-  //info!("Checking if prompt is cached");
  let maybe_cached = prompt_cache.get_copy(&prompt)?;

  let mut text_embeddings = if let Some(tensor) = maybe_cached {
    tensor
  } else {
-    info!("Loading clip weights...");
-    //println!("Loading clip weights for {:?} with model {:?}", configs.sd_version, configs.sd_version.repo());
-    //let api = configs.hf_api.clone();
-
-    //let clip_path = api.model(configs.sd_version.repo().to_string()).get(if use_f16 { "text_encoder/model.fp16.safetensors" } else { "text_encoder/model.safetensors" })?;
-
-    //println!(">>>> CLIP REPO = {:?}", configs.sd_version.repo());
-    //println!(">>>> CLIP PATH = {:?}", clip_path);
-    //println!(">>>> USE_FP16 = {:?}", use_f16);
-    //let clip_weights = Some(clip_path.to_string_lossy().to_string());
-
-    let clip_weights = weights_dir.model_path(&ModelType::LykonDreamshaper7TextEncoderFp16);
-    let clip_weights = Some(clip_weights.to_string_lossy().to_string());
-
    info!("Prompt is NOT cached! Calculating embedding...");
-    let tensor = infer_clip_text_embeddings(
-      &prompt,
-      &uncond_prompt,
-      None, // tokenizer
-      clip_weights,
-      None, // clip2_weights
-      configs.sd_version,
-      &configs.sd_config,
+    let tensor = infer_clip_text_embeddings(ClipArgs {
+      prompt: & prompt,
+      uncond_prompt: &uncond_prompt,
+      tokenizer: None, // tokenizer
+      clip_weights: None,
+      clip2_weights: None, // clip2_weights
+      sd_version,
+      sd_config: &sd_config,
      use_f16, // use_f16
-      &configs.device,
-      configs.dtype,
+      device,
+      dtype,
      use_guide_scale,
-      weights_dir,
-    )?;
+      clip_json_path,
+      clip_weights_path,
+    })?;
+
    prompt_cache.store_copy(&prompt, &tensor)?;
    tensor
  };

-  println!("Text embeddings shape: {:?}", text_embeddings.shape());
+  info!("Text embeddings shape: {:?}", text_embeddings.shape());

-  println!("Loading input image into tensor...");
+  info!("Loading input image into tensor...");

-  let input_image = dynamic_image_to_tensor(image, &configs.device, configs.dtype)?;
+  let input_image = dynamic_image_to_tensor(image, &device, dtype)?;

-  println!("Reference image shape: {:?}", input_image.shape());
+  info!("Reference image shape: {:?}", input_image.shape());

-  let vae_scale = get_vae_scale(configs.sd_version);
+  let vae_scale = get_vae_scale(sd_version);

  let maybe_vae = model_cache.get_vae()?;

  let vae = match maybe_vae {
    Some(vae) => vae,
    None => {
-      //let repo = configs.sd_version.repo();
-      //println!(">>>>> VAE REPO = {:?}", repo);
-      //println!("Building VAE model from : {:?} ... (3)", repo);
-      //let vae_file = configs.hf_api.model(repo.to_string()).get("vae/diffusion_pytorch_model.safetensors")?;
-
-      let vae_file = weights_dir.model_path(&ModelType::LykonDreamshaper7Vae);
-
-      println!("Building VAE model from file {:?}...", &vae_file);
-
-      let vae = configs.sd_config.build_vae(vae_file, &configs.device, configs.dtype)?;
+      info!("Building VAE model from file {:?}...", vae_path.as_ref());
+      
+      let vae = sd_config.build_vae(vae_path, &device, dtype)?;

      let vae = Arc::new(vae);

@@ -158,9 +163,7 @@ pub fn lcm_pipeline(args: Args<'_>) -> Result<RgbImage> {
    None => {
      info!("No unet found in cache; loading...");

-      let unet_weights = weights_dir.model_path(&ModelType::SimianLuoLcmDreamshaperV7Unet);
-
-      let in_channels = match configs.sd_version {
+      let in_channels = match sd_version {
        StableDiffusionVersion::XlInpaint | StableDiffusionVersion::V2Inpaint | StableDiffusionVersion::V1_5Inpaint => 9,
        _ => 4,
      };
@@ -195,12 +198,10 @@ pub fn lcm_pipeline(args: Args<'_>) -> Result<RgbImage> {
      };

      // Load the model directly from the safetensors file
-      let vs_unet = unsafe { VarBuilder::from_mmaped_safetensors(&[unet_weights], configs.dtype, &configs.device)? };
+      let vs_unet = unsafe { VarBuilder::from_mmaped_safetensors(&[unet_path], dtype, &device)? };

      let unet = UNet2DConditionModel::new(vs_unet, in_channels, 4, use_flash_attn, unet_config)?;

-      let unet = UNetModel::new_with_inner(unet);
-
      let unet = Arc::new(unet);

      model_cache.set_unet(unet.clone())?;
@@ -211,50 +212,39 @@ pub fn lcm_pipeline(args: Args<'_>) -> Result<RgbImage> {

  let init_latent_dist: DiagonalGaussianDistribution = vae.encode(&input_image)?;

-  // Parse and calculate the img2img strength
-  let img2img_strength = match i2i_strength {
-    None => 0.75f64,
-    Some(strength) => (strength as f64) / 100.0f64,
-  };
-
  // Create LCM guidance scale embeddings
  let embedding_dim = 1280;
-  let guidance_scale_embedding = scheduler.get_guidance_scale_embedding(guidance_scale, embedding_dim, &configs.device, configs.dtype)?;
+  let guidance_scale_embedding = scheduler.get_guidance_scale_embedding(guidance_scale, embedding_dim, &device, dtype)?;

  // Generate latents from input image using the LCM approach
-  println!("Generating latents from input image...");
+  info!("Generating latents from input image...");
  let init_latents = (init_latent_dist.sample()? * vae_scale)?;
-  println!("Initial latents shape: {:?}", init_latents.shape());
+  info!("Initial latents shape: {:?}", init_latents.shape());

  // Calculate timesteps for LCM with proper img2img handling
-  let timesteps = LCMScheduler::get_timesteps_for_steps(configs.scheduler_steps, img2img_strength);
+  let timesteps = LCMScheduler::get_timesteps_for_steps(scheduler_steps, img2img_strength);
  let t_start = timesteps[0]; // First denoising step

  // Add noise at the appropriate timestep for img2img
-  println!("Adding noise to latents for t_start={}", t_start);
+  info!("Adding noise to latents for t_start={}", t_start);
  let noise = init_latents.randn_like(0f64, 1f64)?;
  let latents = scheduler.add_noise(&init_latents, noise, t_start)?;

-  println!("Latents initialized successfully");
-  println!("Initial noise shape: {:?}", latents.shape());
-
-  println!("Starting diffusion process...");
-
  let mut latents = latents;
  let timesteps: Vec<_> = scheduler.timesteps().iter().copied().collect();

  for (timestep_index, &timestep) in timesteps.iter().enumerate() {
-    println!("Processing step {}/{} @ timestamp = {}", timestep_index + 1, timesteps.len(), timestep);
+    info!("Processing step {}/{} @ timestamp = {}", timestep_index + 1, timesteps.len(), timestep);

    let latent_model_input = if use_guide_scale { Tensor::cat(&[&latents, &latents], 0)? } else { latents.clone() };

    let latent_model_input = scheduler.scale_model_input(latent_model_input, timestep);

    // Use the guidance scale embedding in the UNet inference with the proper method name
-    let mut noise_pred = match unet.forward_with_guidance(&latent_model_input, timestep as f64, &text_embeddings, &guidance_scale_embedding) {
+    let mut noise_pred = match unet.forward_with_guidance(&latent_model_input, timestep as f64, &text_embeddings, Some(&guidance_scale_embedding)) {
      Ok(pred) => pred,
      Err(e) => {
-        println!("UNet inference failed with error: {}", e);
+        info!("UNet inference failed with error: {}", e);
        return Err(anyhow::anyhow!("UNet inference failed: {}", e));
      },
    };
@@ -266,24 +256,24 @@ pub fn lcm_pipeline(args: Args<'_>) -> Result<RgbImage> {
    }

    // Apply the LCM scheduler step
-    latents = scheduler.step(&noise_pred, timestep, &latents, timestep_index, configs.scheduler_steps)?;
+    latents = scheduler.step(&noise_pred, timestep, &latents, timestep_index, scheduler_steps)?;
  }

-  println!("Diffusion process completed, decoding image...");
+  info!("Diffusion process completed, decoding image...");
  let image = vae.decode(&(latents / vae_scale)?)?;

-  println!("VAE decode completed");
+  info!("VAE decode completed");

-  println!("Scaling image back");
+  info!("Scaling image back");
  let image = ((image / 2.)? + 0.5)?;

-  println!("Normalized image values");
+  info!("Normalized image values");
  let image = (image.clamp(0f32, 1.)? * 255.)?;

-  println!("Convert to int8");
+  info!("Convert to int8");
  let image = image.to_dtype(DType::U8)?;

-  println!("Converted to 8-bit format");
+  info!("Converted to 8-bit format");

  let image = tensor_to_image_buffer(&image.i(0)?)?;

@@ -1,4 +1,5 @@
 pub mod get_vae_scale;
 pub mod infer_clip_text_embeddings;
 pub mod stable_diffusion_pipeline;
-pub mod lcm_pipeline;
+pub mod lcm_pipeline;
+mod remap_lcm_strength_range;
@@ -0,0 +1,54 @@
+use primitives::numerics::remap_range_f64::{remap_range_f64, Args};
+
+/// Our LCM implementation uses strength [13, 100]. Strength below 13 will take 8 steps, 
+/// so we need to limit ourselves to this range from an input range of [0, 100].
+pub fn remap_lcm_strength_range(value: f64) -> f64 {
+  remap_range_f64(Args {
+    old_min: 0.0,
+    old_max: 100.0,
+    new_min: 13.0,
+    new_max: 100.0,
+    value,
+  })
+}
+
+#[cfg(test)]
+mod test {
+  use crate::ml::stable_diffusion::remap_lcm_strength_range::remap_lcm_strength_range;
+  use assertor::{assert_that, FloatAssertion};
+
+  #[test]
+  fn old_max() {
+    assert_eq!(remap_lcm_strength_range(100.0), 100.0);
+  }
+  
+  #[test]
+  fn old_min() {
+    assert_eq!(remap_lcm_strength_range(0.0), 13.0);
+  }
+
+  #[test]
+  fn under_bounds() {
+    assert_eq!(remap_lcm_strength_range(-100.0), 13.0);
+  }
+  
+  #[test]
+  fn over_bounds() {
+    assert_eq!(remap_lcm_strength_range(200.0), 100.0);
+  }
+
+  #[test]
+  fn other_values() {
+    assert_that!(remap_lcm_strength_range(0.0)).with_abs_tol(0.001).is_approx_equal_to(13.000);
+    assert_that!(remap_lcm_strength_range(5.0)).with_abs_tol(0.001).is_approx_equal_to(17.350);
+    assert_that!(remap_lcm_strength_range(11.0)).with_abs_tol(0.001).is_approx_equal_to(22.570);
+    assert_that!(remap_lcm_strength_range(13.0)).with_abs_tol(0.001).is_approx_equal_to(24.310);
+    assert_that!(remap_lcm_strength_range(14.0)).with_abs_tol(0.001).is_approx_equal_to(25.180);
+    assert_that!(remap_lcm_strength_range(20.0)).with_abs_tol(0.001).is_approx_equal_to(30.400);
+    assert_that!(remap_lcm_strength_range(25.0)).with_abs_tol(0.001).is_approx_equal_to(34.750);
+    assert_that!(remap_lcm_strength_range(50.0)).with_abs_tol(0.001).is_approx_equal_to(56.500);
+    assert_that!(remap_lcm_strength_range(75.0)).with_abs_tol(0.001).is_approx_equal_to(78.250);
+    assert_that!(remap_lcm_strength_range(99.0)).with_abs_tol(0.001).is_approx_equal_to(99.130);
+    assert_that!(remap_lcm_strength_range(100.0)).with_abs_tol(0.001).is_approx_equal_to(100.000);
+  }
+}
@@ -1,76 +1,86 @@
+use std::path::Path;
 use std::sync::{Arc, RwLock, RwLockReadGuard, RwLockWriteGuard};

-use crate::events::notification_event::{NotificationModelType, NotificationEvent};
 use crate::ml::image::dynamic_image_to_tensor::dynamic_image_to_tensor;
 use crate::ml::image::tensor_to_image_buffer::{tensor_to_image_buffer, RgbImage};
 use crate::ml::model_cache::ModelCache;
 use crate::ml::model_file::StableDiffusionVersion;
-use crate::ml::model_type::ModelType;
-use crate::ml::models::unet_model::UNetModel;
 use crate::ml::prompt_cache::PromptCache;
 use crate::ml::stable_diffusion::get_vae_scale::get_vae_scale;
-use crate::ml::stable_diffusion::infer_clip_text_embeddings::infer_clip_text_embeddings;
-use crate::state::app_config::AppConfig;
+use crate::ml::stable_diffusion::infer_clip_text_embeddings::{infer_clip_text_embeddings, ClipArgs};
 use anyhow::{anyhow, Error as E, Result};
-use candle_core::{DType, IndexOp, Tensor, D};
+use candle_core::{DType, Device, IndexOp, Tensor, D};
 use candle_transformers::models::stable_diffusion::vae::{AutoEncoderKL, DiagonalGaussianDistribution};
+use candle_transformers::models::stable_diffusion::StableDiffusionConfig;
 use image::DynamicImage;
 use log::info;
 use rand::Rng;
-use tauri::{AppHandle, Emitter};
-use crate::state::app_dir::AppDataRoot;

-pub struct Args<'a> {
+pub struct Args<'a, P: AsRef<Path>, Q: AsRef<Path>, R: AsRef<Path>, S: AsRef<Path>> {
    pub image: &'a DynamicImage,
    pub prompt: String,
    pub uncond_prompt: String,
    pub cfg_scale: Option<f64>,
    pub i2i_strength: Option<u8>,
-    pub configs: &'a AppConfig,
    pub model_cache: &'a ModelCache,
    pub prompt_cache: &'a PromptCache,
-    pub app_data_root: &'a AppDataRoot,
-    pub app: &'a AppHandle,
+    pub sd_version: StableDiffusionVersion,
+    pub sd_config: StableDiffusionConfig,
+    pub device: &'a Device,
+    pub dtype: DType,
+    pub maybe_seed: Option<u64>,
+    pub scheduler_steps: usize,
+    pub sdxl_turbo_vae_path: P,
+    pub sdxl_turbo_unet_path: Q,
+    pub clip_json_path: R,
+    pub clip_weights_path: S,
 }

-pub fn stable_diffusion_pipeline(args: Args<'_>) -> Result<RgbImage> {
+pub fn stable_diffusion_pipeline<P1, P2, P3, P4>(args: Args<'_, P1, P2, P3, P4>) -> Result<RgbImage> 
+  where P1: AsRef<Path>, P2: AsRef<Path>, P3: AsRef<Path>, P4: AsRef<Path>
+{
    let Args { 
        prompt, 
        uncond_prompt, 
        cfg_scale, 
        i2i_strength, 
-        configs, 
        model_cache, 
        prompt_cache, 
-        app, 
        image,
-        app_data_root,
+        sd_version,
+        sd_config,
+        device,
+        dtype,
+        maybe_seed,
+        scheduler_steps,
+        sdxl_turbo_vae_path,
+        sdxl_turbo_unet_path,
+        clip_json_path,
+        clip_weights_path,
    } = args;
    
-    let weights_dir = app_data_root.weights_dir();
-    
    println!("Starting image generation with the following configuration:");
-    println!("  Model: {:?}", configs.sd_version);
+    println!("  Model: {:?}", sd_version);
    println!("  Prompt: {}", prompt);
-    println!("  Steps: {}", configs.scheduler_steps);
-    println!("  Device: {:?}", configs.device);
+    println!("  Steps: {}", scheduler_steps);
+    println!("  Device: {:?}", device);

-    println!("Model dimensions: {}x{}", configs.sd_config.width, configs.sd_config.height);
+    println!("Model dimensions: {}x{}", sd_config.width, sd_config.height);

    // TODO(bt,2025-02-18): The scheduler is `EulerAncestralDiscreteScheduler`, but we may want to port an LCM scheduler.
    //  This is a target for performance improvement
    //  See: https://github.com/huggingface/candle/issues/1331
-    let mut scheduler = configs.sd_config.build_scheduler(
-        configs.scheduler_steps)?;
+    let mut scheduler = sd_config.build_scheduler(scheduler_steps)?;

-    let seed = configs.seed.unwrap_or_else(|| rand::thread_rng().gen());
-    configs.device.set_seed(seed)?;
+    let seed = maybe_seed.unwrap_or_else(|| rand::thread_rng().gen());
+    
+    device.set_seed(seed)?;

    info!("Using seed: {}", seed);

    let guidance_scale = match cfg_scale {
        Some(guidance_scale) => guidance_scale,
-        None => match configs.sd_version {
+        None => match sd_version {
            StableDiffusionVersion::V1_5
            | StableDiffusionVersion::V2_1
            | StableDiffusionVersion::Xl => 7.5,
@@ -91,20 +101,21 @@ pub fn stable_diffusion_pipeline(args: Args<'_>) -> Result<RgbImage> {
    } else {
        
        info!("Prompt is NOT cached! Calculating embedding...");
-        let tensor = infer_clip_text_embeddings(
-            &prompt,
-            &uncond_prompt,
-            None, // tokenizer
-            None, // clip_weights
-            None, // clip2_weights
-            configs.sd_version,
-            &configs.sd_config,
-            false, // use_f16
-            &configs.device,
-            configs.dtype,
+        let tensor = infer_clip_text_embeddings(ClipArgs {
+            prompt: &prompt,
+            uncond_prompt: &uncond_prompt,
+            tokenizer: None, // tokenizer
+            clip_weights: None, // clip_weights
+            clip2_weights: None, // clip2_weights
+            sd_version: sd_version,
+            sd_config: &sd_config,
+            use_f16: false, // use_f16
+            device: device,
+            dtype: dtype,
            use_guide_scale,
-            weights_dir,
-        )?;
+            clip_json_path: clip_json_path.as_ref(),
+            clip_weights_path: clip_weights_path.as_ref(),
+        })?;
        prompt_cache.store_copy(&prompt, &tensor)?;
        tensor
    };
@@ -113,14 +124,11 @@ pub fn stable_diffusion_pipeline(args: Args<'_>) -> Result<RgbImage> {

    println!("Loading input image into tensor...");

-    let input_image = dynamic_image_to_tensor(
-        image,
-        &configs.device,
-        configs.dtype)?;
+    let input_image = dynamic_image_to_tensor(image, device, dtype)?;

    println!("Reference image shape: {:?}", input_image.shape());

-    let vae_scale = get_vae_scale(configs.sd_version);
+    let vae_scale = get_vae_scale(sd_version);

    let maybe_vae = model_cache.get_vae()?;
    
@@ -129,33 +137,14 @@ pub fn stable_diffusion_pipeline(args: Args<'_>) -> Result<RgbImage> {
        None => {
            info!("No vae found in cache; loading...");
            
-            let mut notify_download_complete = false;
-            //if !vae_file.exists() {
-            if true {
-                notify_download_complete = true;
-                app.emit("notification", NotificationEvent::ModelDownloadStarted {
-                    model_name: "sdxl-turbo-vae",
-                    model_type: NotificationModelType::Vae,
-                })?;
-            }
+            let vae_file = sdxl_turbo_vae_path.as_ref();
            
-            let vae_file = weights_dir.model_path(&ModelType::SdxlTurboVae);
-            
-            let vae = configs
-              .sd_config
-              .build_vae(vae_file, &configs.device, configs.dtype)?;
+            let vae = sd_config.build_vae(vae_file, &device, dtype)?;

            let vae = Arc::new(vae);
            
            model_cache.set_vae(vae.clone())?;
-            
-            if notify_download_complete {
-                app.emit("notification", NotificationEvent::ModelDownloadComplete {
-                    model_name: "sdxl-turbo-vae",
-                    model_type: NotificationModelType::Vae,
-                })?;
-            }
-            
+
            vae
        }
    };
@@ -167,32 +156,15 @@ pub fn stable_diffusion_pipeline(args: Args<'_>) -> Result<RgbImage> {
        None => {
            info!("No unet found in cache; loading...");

-            let mut notify_download_complete = false;
-            //if !unet_file.exists() {
-            if true {
-                notify_download_complete = true;
-                app.emit("notification", NotificationEvent::ModelDownloadStarted {
-                    model_name: "sdxl-turbo-unet",
-                    model_type: NotificationModelType::Unet,
-                })?;
-            }
-            
-            let unet_file = weights_dir.model_path(&ModelType::SdxlTurboUnet);
+            let unet_weights_file = sdxl_turbo_unet_path.as_ref();

-            let unet = UNetModel::new(&configs.sd_config, unet_file, &configs.device, configs.dtype)
+            let unet = sd_config.build_unet(unet_weights_file, &device, 4, true, dtype)
              .map_err(|err| anyhow!("error initializing unet model: {:?}", err))?;
-            
+
            let unet = Arc::new(unet);

            model_cache.set_unet(unet.clone())?;

-            if notify_download_complete {
-                app.emit("notification", NotificationEvent::ModelDownloadComplete {
-                    model_name: "sdxl-turbo-unet",
-                    model_type: NotificationModelType::Unet,
-                })?;
-            }
-
            unet
        }
    };
@@ -216,9 +188,9 @@ pub fn stable_diffusion_pipeline(args: Args<'_>) -> Result<RgbImage> {
    };

    let t_start = {
-        let start = configs.scheduler_steps - (configs.scheduler_steps as f64 * img2img_strength) as usize;
+        let start = scheduler_steps - (scheduler_steps as f64 * img2img_strength) as usize;

-        println!("Starting from step {} of {} (strength: {})", start, configs.scheduler_steps, img2img_strength);
+        println!("Starting from step {} of {} (strength: {})", start, scheduler_steps, img2img_strength);
        start
    };

@@ -257,7 +229,7 @@ pub fn stable_diffusion_pipeline(args: Args<'_>) -> Result<RgbImage> {

        let latent_model_input = scheduler.scale_model_input(latent_model_input, timestep)?;

-        let mut noise_pred = match unet.inference(&latent_model_input, timestep as f64, &text_embeddings) {
+        let mut noise_pred = match unet.forward(&latent_model_input, timestep as f64, &text_embeddings) {
            Ok(pred) => pred,
            Err(e) => {
                println!("UNet inference failed with error: {}", e);
@@ -0,0 +1,18 @@
+[package]
+name = "ml_weights_registry"
+edition = "2021"
+version = "0.0.1"
+authors = ["storyteller"]
+publish = false
+description = ""
+license = ""
+repository = ""
+
+[lib]
+name = "ml_weights_registry"
+path = "src/lib.rs"
+
+# External
+[dependencies]
+anyhow = "1.0"
+const_format = "0.2.34"
@@ -0,0 +1 @@
+pub mod weights_registry;
@@ -0,0 +1,3 @@
+mod weight_descriptor_builder;
+pub mod weight_descriptor;
+pub mod weights;
@@ -0,0 +1,45 @@
+
+// TODO(bt,2025-03-13): When it's time for dynamic, non-const allocation, refer to
+//  http::header::name::HeaderName: https://github.com/hyperium/http/blob/master/src/header/name.rs
+//  as a pattern that will support both compile-time constants and dynamically allocated String-based variants.
+
+#[derive(Clone, Copy)]
+pub enum WeightFunction {
+  ImageSegmentation,
+  TextEncoder,
+  TextTokenizer,
+  Unet,
+  Vae,
+}
+
+pub struct WeightDescriptor {
+  /// Name of the weight, eg. "Lykon Dreamshaper 7"
+  pub name: &'static str,
+
+  /// Name of the file on the filesystem, eg. lykon_dreamshaper_7_text_encoder.fp16.safetensors
+  pub filename: &'static str,
+
+  /// URL of the file in the R2 bucket,
+  /// eg. https://pub-bc5e2bc0cdee4bb5ae8fca9d641ca0d6.r2.dev/lykon_dreamshaper_7_text_encoder.fp16.safetensors
+  pub r2_download_url: &'static str,
+
+  pub function: WeightFunction,
+}
+
+impl WeightFunction {
+  pub fn as_descriptive_str(&self) -> &'static str {
+    match self {
+      WeightFunction::ImageSegmentation => "image segmentation",
+      WeightFunction::TextEncoder => "text encoder",
+      WeightFunction::TextTokenizer => "text tokenizer",
+      WeightFunction::Unet => "UNET",
+      WeightFunction::Vae => "VAE",
+    }
+  }
+}
+
+impl WeightDescriptor {
+  pub fn to_descriptive_name(&self) -> String {
+    format!("{} ({})", self.name, self.function.as_descriptive_str())
+  }
+}
@@ -0,0 +1,17 @@
+pub (super) const R2_BUCKET_URL : &str = "https://pub-bc5e2bc0cdee4bb5ae8fca9d641ca0d6.r2.dev/";
+
+// TODO(bt,2025-03-13): Try to use keyword args from the call site.
+// NB: We're using a macro because we can't use a builder pattern and then concat string literals
+//  and variables (even if they're known to be &'static) and keep allocations static.
+macro_rules! weight {
+    ($name: literal, $file_name:literal, $weight_function:expr) => {
+      $crate::weights_registry::weight_descriptor::WeightDescriptor {
+        name: $name,
+        filename: $file_name,
+        r2_download_url: const_format::concatcp!(crate::weights_registry::weight_descriptor_builder::R2_BUCKET_URL, $file_name),
+        function: $weight_function,
+      }
+    };
+}
+
+pub(crate) use weight;
@@ -0,0 +1,65 @@
+use crate::weights_registry::weight_descriptor::{WeightDescriptor, WeightFunction};
+use crate::weights_registry::weight_descriptor_builder::weight;
+
+pub const CLIP_JSON : WeightDescriptor = weight!(
+  "CLIP",
+  "clip_vit_bigg_14_lion2b_39b_b160k.tokenizer.json",
+  WeightFunction::TextTokenizer
+);
+
+/// For LCM-based image generation
+pub const LYKON_DEAMSHAPER_7_TEXT_ENCODER_FP16 : WeightDescriptor = weight!(
+  "Lykon Dreamshaper 7",
+  "lykon_dreamshaper_7_text_encoder.fp16.safetensors",
+  WeightFunction::TextEncoder
+);
+
+/// For LCM-based image generation
+pub const LYKON_DEAMSHAPER_7_VAE : WeightDescriptor = weight!(
+  "Lykon Dreamshaper 7",
+  "lykon_dreamshaper_7_vae.safetensors",
+  WeightFunction::Vae
+);
+
+/// For LCM-based image generation
+pub const SIMIANLUO_LCM_DREAMSHAPER_V7_UNET : WeightDescriptor = weight!(
+  "SimianLuo LCM Dreamshaper v7",
+  "simianluo_lcm_dreamshaper_v7_unet.safetensors",
+  WeightFunction::Unet
+);
+
+/// Image segmentation and background removal
+/// dis_medium.onnx
+pub const DIS_MEDIUM_ONNX : WeightDescriptor = weight!(
+  "Dichotomous Image Segmentation Medium",
+  "dis_medium.onnx",
+  WeightFunction::ImageSegmentation
+);
+
+#[deprecated]
+pub const SDXL_TURBO_CLIP_TEXT_ENCODER : WeightDescriptor = weight!(
+  "SDXL Turbo CLIP",
+  "clip_text_encoder.safetensors",
+  WeightFunction::TextEncoder
+);
+
+#[deprecated]
+pub const SDXL_TURBO_CLIP_TEXT_ENCODER_2 : WeightDescriptor = weight!(
+  "SDXL Turbo CLIP #2",
+  "clip_text_encoder_2.safetensors",
+  WeightFunction::TextEncoder
+);
+
+#[deprecated]
+pub const SDXL_TURBO_VAE : WeightDescriptor = weight!(
+  "SDXL Turbo",
+  "diffusion_pytorch_model.vae.safetensors",
+  WeightFunction::Vae
+);
+
+#[deprecated]
+pub const SDXL_TURBO_UNET : WeightDescriptor = weight!(
+  "SDXL Turbo",
+  "diffusion_pytorch_model.unet.safetensors",
+  WeightFunction::Unet
+);
@@ -7,6 +7,7 @@ license = ""
 repository = ""
 edition = "2021"
 rust-version = "1.77.2"
+publish = false

 # See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html

@@ -22,22 +23,23 @@ tauri-build = { version = "2.0.4", features = [] }
 # Internal
 filesys = { path = "../../lib/files/filesys" }
 memory_caching = { path = "../../lib/caching/memory_caching" }
+ml_weights_registry = { path = "../ml_weights_registry" }
+primitives = { path = "../../lib/data/primitives" }

-# Bypass cudarc issues on Windows https://github.com/coreylowman/cudarc/issues/332
-cudarc = "0.13.7"
+# Internal ML
+# TODO: Only on Windows and Linux
+#ml_models = { path = "../ml_models", optional = true }

 # External
 #openssl = { version = "0.10", features = ["vendored"] }
 anyhow = "1.0"
 base64 = "0.22.1"
 bytes = "1.10"
-candle-core = { path = "../../vendor/candle/candle-core", features = ["cuda"] }
-candle-nn = { path = "../../vendor/candle/candle-nn", features = ["cuda"] }
-candle-transformers = { path = "../../vendor/candle/candle-transformers", features = ["cuda"] }
-dirs = "6.0.0"
+const_format = "0.2.34"
 directories = "6.0.0"
+dirs = "6.0.0"
 futures = "0.3"
-hf-hub = "0.3.2"
+half = { version = "2.3.1", features = ["num-traits", "use-intrinsics", "rand_distr"] }
 image = "0.25.5"
 log = "0.4"
 once_cell = "1.20.3"
@@ -51,25 +53,22 @@ tauri = { version = "2.2.4", features = [] }
 tauri-plugin-log = "2"
 tempdir = "0.3.7"
 tempfile = "3.18.0"
-tokenizers = "0.15.2"
 tokio = { version = "1.42", features = ["rt", "rt-multi-thread", "fs"] }
 tokio-util = { version = "0.7", features = ["codec"] }
-half = { version = "2.3.1", features = ["num-traits", "use-intrinsics", "rand_distr"] }

-[features]
-default = ["cuda"]
-cuda = [
-    "candle-core/cuda",
-    "candle-nn/cuda",
-    "candle-transformers/cuda",
-# NB(bt,2025-03-10): Building without Flash attention on Windows for now to bypass linker issues
-    "candle-transformers/flash-attn",
-    "candle-core/cudnn"
-]
+# tracing-subscriber = { version = "0.3", features = [ "env-filter", "fmt" ] }
+
+[target.'cfg(not(target_os = "macos"))'.dependencies]
+ml_models = { path = "../ml_models" }

 [dev-dependencies]
 libloading = "0.8.6"
 serial_test = "3.2.0"
+assertor = "0.0.3"
+
+#[target.'cfg(target_os = "macos")'.features]
+#default  = []
+#ml_models = ["dep:ml_models"]

 [profile.dev]
 incremental = true # Compile your binary in smaller steps.
@@ -1,16 +1,12 @@
-use crate::ml::downloads::download_all_models::download_all_models;
 use crate::state::app_dir::AppDataRoot;
 use tauri::State;

 #[tauri::command]
 pub async fn download_models(
-  app_data_root: State<'_, AppDataRoot>,
+  _app_data_root: State<'_, AppDataRoot>,
 ) -> Result<String, String> {
-  
-  download_all_models(&app_data_root).await
-    .map_err(|err| {
-      "error downloading models".to_string()
-    })?;
-  
+
+  // NB: This endpoint has been removed for now as we now download models in the app lifecycle.
+
  Ok(("downloaded".to_string()))
 }
@@ -1,146 +0,0 @@
-use crate::ml::downloads::download_all_models::download_all_models;
-use crate::ml::model_cache::ModelCache;
-use crate::ml::prompt_cache::PromptCache;
-use crate::ml::stable_diffusion::lcm_pipeline::{lcm_pipeline, Args};
-use crate::state::app_config::AppConfig;
-use base64::{engine::general_purpose::STANDARD as BASE64_STANDARD, Engine};
-use bytes::BytesMut;
-use image::imageops::FilterType;
-use image::{DynamicImage, ImageFormat, ImageReader};
-use log::{error, info};
-use std::io::Cursor;
-use std::path::PathBuf;
-use tauri::{AppHandle, State};
-use crate::state::app_dir::AppDataRoot;
-
-const PROMPT_FILENAME : &str = "prompt.txt";
-const PROMPT: &str = "green goblin, blood dripping from lips, detailed, photorealistic, 8k";
-
-const NEGATIVE_PROMPT: &str = "bad quality, bad faces, poor quality, blurry faces, watermark";
-
-const RANDOM_SEED: u32 = 42;
-
-// TODO: Mark async https://github.com/tauri-apps/tauri/discussions/7737
-
-
-/// This handler takes an image (as a base64 encoded string) and a prompt and returns
-/// an image (as a base64-encoded string).
-#[tauri::command]
-pub async fn infer_image(
-  image: &str,
-  prompt: Option<String>,
-  strength: Option<u8>,
-  model_config: State<'_, AppConfig>,
-  model_cache: State<'_, ModelCache>,
-  prompt_cache: State<'_, PromptCache>,
-  app_data_root: State<'_, AppDataRoot>,
-  app: AppHandle,
-) -> Result<String, String> {
-
-  let prompt = get_prompt_or_fallback(prompt);
-
-  info!("Prompt: {}", prompt);
-  info!("Strength: {:?}", strength);
-
-  let image = hydrate_base64_image(image)
-    .map_err(|err| format!("Couldn't hydrate image from base64: {}", err))?;
-
-  // TODO(bt): Move this to another endpoint
-  download_all_models(&app_data_root).await
-    .map_err(|err| {
-      error!("couldn't download models: {:?}", err);
-      "Couldn't download models".to_string()
-    })?;
-
-  let result = do_infer_image(
-    &prompt, 
-    image, 
-    strength, 
-    &model_config, 
-    &model_cache, 
-    prompt_cache, 
-    app, 
-    &app_data_root
-  ).await;
-  
-  if let Err(err) = result.as_deref() {
-    error!("There was an error: {:?}", err);
-  }
-  
-  result
-}
-
-async fn do_infer_image(
-  prompt: &str,
-  image: DynamicImage,
-  strength: Option<u8>,
-  config: &AppConfig,
-  model_cache: &ModelCache,
-  prompt_cache: State<'_, PromptCache>,
-  app: AppHandle,
-  app_data_root: &AppDataRoot,
-) -> Result<String, String> {
-
-  let args = Args {
-    image: &image,
-    prompt: prompt.to_string(),
-    uncond_prompt: NEGATIVE_PROMPT.to_string(),
-    model_cache,
-    configs: config,
-    prompt_cache: &prompt_cache,
-    i2i_strength: strength,
-    cfg_scale: config.cfg_scale,
-    app: &app,
-    app_data_root,
-    use_flash_attn: true,
-  };
-
-  match lcm_pipeline(args) {
-    Ok(image) => {
-      let mut bytes = Vec::with_capacity(1024*1024);
-      
-      image.write_to(&mut Cursor::new(&mut bytes), ImageFormat::Png)
-        .map_err(|err| format!("failure to encode image: {:?}", err))?;
-
-      let bytes = BASE64_STANDARD.encode(bytes);
-
-      Ok(bytes)
-    },
-    Err(e) => Err(format!("Failed to generate image: {}", e)),
-  }
-}
-
-fn get_prompt_or_fallback(user_prompt: Option<String>) -> String {
-  let user_prompt = user_prompt.map(|prompt| prompt.trim().to_string())
-    .filter(|prompt| !prompt.is_empty());
-
-  if let Some(prompt) = user_prompt {
-    return prompt;
-  }
-
-  let prompt_file = PathBuf::from(PROMPT_FILENAME)
-    .canonicalize()
-    .unwrap_or_else(|_| PathBuf::from(PROMPT_FILENAME));
-
-  std::fs::read_to_string(&prompt_file)
-    .map_err(|err| format!("Failed to read prompt file: {}", err))
-    .unwrap_or_else(|_| {
-      error!("Failed to read prompt file: {:?}", prompt_file);
-      PROMPT.to_string()
-    })
-    .trim()
-    .to_string()
-}
-
-fn hydrate_base64_image(base64_image: &str) -> anyhow::Result<DynamicImage> {
-  let bytes = BASE64_STANDARD.decode(base64_image)?;
-
-  let image = ImageReader::new(Cursor::new(bytes))
-    .with_guessed_format()?
-    .decode()?;
-
-  // TODO(bt,2025-02-17): Running out of vram with full image buffer size
-  let image = image.resize(512, 512, FilterType::CatmullRom);
-
-  Ok(image)
-}
@@ -0,0 +1,24 @@
+use crate::ml::model_cache::ModelCache;
+use crate::ml::prompt_cache::PromptCache;
+use crate::state::app_config::AppConfig;
+use crate::state::app_dir::AppDataRoot;
+use image::{ImageReader, RgbImage};
+use log::info;
+use tauri::{AppHandle, State};
+
+
+#[tauri::command]
+pub async fn inpaint_image(
+  image: &str,
+  mask: &str,
+  prompt: String,
+  model_config: State<'_, AppConfig>,
+  model_cache: State<'_, ModelCache>,
+  prompt_cache: State<'_, PromptCache>,
+  app_data_root: State<'_, AppDataRoot>,
+  app: AppHandle,
+) -> Result<String, String> {
+  info!("inpaint_endpoint endpoint called.");
+
+  Ok(mask.to_string())
+}
@@ -1,4 +1,6 @@
 pub mod download_models;
 pub mod flip_image;
-pub mod image_endpoint;
-pub mod test_counter;
+pub mod inpaint_image_endpoint;
+pub mod realtime_image_endpoint;
+pub mod remove_background_endpoint;
+pub mod text_to_image_endpoint;
@@ -0,0 +1,143 @@
+#[cfg(not(target_os = "macos"))]
+use ml_models::ml::stable_diffusion::lcm_pipeline::{lcm_pipeline, Args};
+
+use crate::state::app_config::AppConfig;
+use crate::state::app_dir::AppDataRoot;
+use crate::stubs::model_cache::ModelCache;
+use crate::stubs::prompt_cache::PromptCache;
+use crate::utils::image::decode_base64_image::decode_base64_image;
+use crate::utils::image::encode_dynamic_image_base64_png::encode_dynamic_image_base64_png;
+use crate::utils::image::encode_rgb_image_base64_png::encode_rgb_image_base64_png;
+use image::imageops::FilterType;
+use image::{DynamicImage, RgbImage};
+use log::{error, info};
+use ml_weights_registry::weights_registry::weights::{CLIP_JSON, LYKON_DEAMSHAPER_7_TEXT_ENCODER_FP16, LYKON_DEAMSHAPER_7_VAE, SDXL_TURBO_CLIP_TEXT_ENCODER, SIMIANLUO_LCM_DREAMSHAPER_V7_UNET};
+use std::path::PathBuf;
+use tauri::{AppHandle, State};
+
+
+const PROMPT_FILENAME : &str = "prompt.txt";
+const PROMPT: &str = "shiba inu, cute dog, detailed, walking in a wooded forest, photorealistic, 8k";
+
+const NEGATIVE_PROMPT: &str = "bad quality, bad faces, poor quality, blurry faces, watermark";
+
+const RANDOM_SEED: u32 = 42;
+
+
+/// This handler takes an image (as a base64 encoded string) and a prompt and returns
+/// an image (as a base64-encoded string).
+#[tauri::command]
+pub async fn infer_image(
+  image: &str,
+  prompt: Option<String>,
+  strength: Option<f64>,
+  model_config: State<'_, AppConfig>,
+  model_cache: State<'_, ModelCache>,
+  prompt_cache: State<'_, PromptCache>,
+  app_data_root: State<'_, AppDataRoot>,
+  app: AppHandle,
+) -> Result<String, String> {
+  info!("infer_image endpoint called.");
+
+  let prompt = get_prompt_or_fallback(prompt);
+
+  info!("Strength: {:?}; Prompt: {}", strength, prompt);
+
+  let image = decode_base64_image(image)
+    .map_err(|err| format!("Couldn't hydrate image from base64: {}", err))?;
+  
+  // TODO(bt,2025-02-17): Running out of vram with full image buffer size
+  let mut image = image.resize(512, 512, FilterType::CatmullRom);
+
+  #[cfg(not(target_os = "macos"))]
+  {
+    let result = infer_image_impl(
+      &prompt,
+      image,
+      strength,
+      &model_config,
+      &model_cache,
+      prompt_cache,
+      &app_data_root
+    ).await;
+
+    image = match result {
+      Ok(img) => DynamicImage::ImageRgb8(img),
+      Err(err) => {
+        error!("There was an error generating the image: {:?}", err);
+        return Err(format!("There was an error generating the image: {}", err));
+      }
+    };
+  }
+
+  let bytes = encode_dynamic_image_base64_png(image)
+    .map_err(|err| format!("failure to encode image: {:?}", err))?;
+
+  info!("Inference successful; image converted to base64, serving back to browser...");
+
+  Ok(bytes)
+}
+
+#[cfg(not(target_os = "macos"))]
+async fn infer_image_impl(
+  prompt: &str,
+  image: DynamicImage,
+  strength: Option<f64>,
+  config: &AppConfig,
+  model_cache: &ModelCache,
+  prompt_cache: State<'_, PromptCache>,
+  app_data_root: &AppDataRoot,
+) -> Result<RgbImage, String> {
+  
+  let weights_dir = app_data_root.weights_dir();
+  let vae_path = weights_dir.weight_path(&LYKON_DEAMSHAPER_7_VAE);
+  let unet_path = weights_dir.weight_path(&SIMIANLUO_LCM_DREAMSHAPER_V7_UNET);
+  let clip_json_path = weights_dir.weight_path(&CLIP_JSON);
+  //let clip_weights_path= weights_dir.weight_path(&LYKON_DEAMSHAPER_7_TEXT_ENCODER_FP16);
+  let clip_weights_path= weights_dir.weight_path(&SDXL_TURBO_CLIP_TEXT_ENCODER);
+
+  let args = Args {
+    image: &image,
+    prompt: prompt.to_string(),
+    uncond_prompt: NEGATIVE_PROMPT.to_string(),
+    model_cache,
+    prompt_cache: &prompt_cache,
+    img2img_strength: strength,
+    cfg_scale: config.cfg_scale,
+    use_flash_attn: true,
+    model_config: &config.model_config,
+    maybe_seed: None,
+    scheduler_steps: config.scheduler_steps,
+    vae_path: &vae_path,
+    unet_path: &unet_path,
+    clip_json_path: &clip_json_path,
+    clip_weights_path: &clip_weights_path,
+  };
+
+  let image = lcm_pipeline(args)
+    .map_err(|err| format!("failure to encode image: {:?}", err))?;
+
+  Ok(image)
+}
+
+fn get_prompt_or_fallback(user_prompt: Option<String>) -> String {
+  let user_prompt = user_prompt.map(|prompt| prompt.trim().to_string())
+    .filter(|prompt| !prompt.is_empty());
+
+  if let Some(prompt) = user_prompt {
+    return prompt;
+  }
+
+  let prompt_file = PathBuf::from(PROMPT_FILENAME)
+    .canonicalize()
+    .unwrap_or_else(|_| PathBuf::from(PROMPT_FILENAME));
+
+  std::fs::read_to_string(&prompt_file)
+    .map_err(|err| format!("Failed to read prompt file: {}", err))
+    .unwrap_or_else(|_| {
+      error!("Failed to read prompt file: {:?}", prompt_file);
+      PROMPT.to_string()
+    })
+    .trim()
+    .to_string()
+}
@@ -0,0 +1,45 @@
+//! Adapted from https://github.com/dnanhkhoa/rust-background-removal
+
+#[cfg(not(target_os = "macos"))]
+use ml_models::ml::background_removal::remove_image_background::remove_image_background;
+
+use crate::state::app_dir::AppDataRoot;
+use crate::utils::image::decode_base64_image::decode_base64_image;
+use crate::utils::image::encode_dynamic_image_base64_png::encode_dynamic_image_base64_png;
+use image::{imageops, DynamicImage};
+use log::info;
+use ml_weights_registry::weights_registry::weights::DIS_MEDIUM_ONNX;
+use tauri::State;
+
+/// This handler removes the background from an image.
+#[tauri::command]
+pub async fn remove_background(
+  image: &str,
+  app_data_root: State<'_, AppDataRoot>,
+) -> Result<String, String> {
+  info!("remove_background endpoint called.");
+
+  let mut image = decode_base64_image(image)
+      .map_err(|err| format!("Couldn't hydrate image from base64: {}", err))?;
+
+  #[cfg(not(target_os = "macos"))]
+  {
+    image = remove_background_impl(&app_data_root, image).await?;
+  }
+
+  let image = encode_dynamic_image_base64_png(image)
+    .map_err(|err| format!("failure to encode image: {:?}", err))?;
+
+  Ok(image)
+}
+
+#[cfg(not(target_os = "macos"))]
+async fn remove_background_impl(app_data_root: &AppDataRoot, image: DynamicImage) -> Result<DynamicImage, String> {
+  let model_path = app_data_root.weights_dir().weight_path(&DIS_MEDIUM_ONNX);
+
+  let image = remove_image_background(model_path, image)
+      .await
+      .map_err(|err| format!("Couldn't remove image from image: {}", err))?;
+
+  Ok(image)
+}
@@ -1,24 +0,0 @@
-use std::sync::{Arc, RwLock};
-use once_cell::sync::Lazy;
-
-static COUNTER : Lazy<Arc<RwLock<u64>>> = Lazy::new(|| Arc::new(RwLock::new(0)));
-
-#[tauri::command]
-pub fn test_counter() -> u64 {
-  let value : u64;
-  {
-    match COUNTER.write() {
-      Ok(mut counter) => {
-        *counter += 1;
-        value = *counter;
-      },
-      Err(_e) => {
-        value = 0;
-      }
-    }
-  }
-
-  println!("I was invoked from JavaScript! {:?}", value);
-
-  value
-}
@@ -0,0 +1,115 @@
+#[cfg(not(target_os = "macos"))]
+use ml_models::ml::stable_diffusion::lcm_pipeline::{lcm_pipeline, Args};
+
+use crate::state::app_config::AppConfig;
+use crate::state::app_dir::AppDataRoot;
+use crate::stubs::model_cache::ModelCache;
+use crate::stubs::prompt_cache::PromptCache;
+use crate::utils::image::encode_dynamic_image_base64_png::encode_dynamic_image_base64_png;
+use image::imageops::FilterType;
+use image::{DynamicImage, ImageReader, RgbImage};
+use log::{error, info};
+use ml_weights_registry::weights_registry::weights::{CLIP_JSON, LYKON_DEAMSHAPER_7_VAE, SDXL_TURBO_CLIP_TEXT_ENCODER, SIMIANLUO_LCM_DREAMSHAPER_V7_UNET};
+
+use once_cell::sync::Lazy;
+use std::io::Cursor;
+use tauri::{AppHandle, State};
+
+const RANDOM_SEED: u32 = 42;
+
+const STRENGTH: f64 = 100.0;
+const PNG_BYTES : &[u8] = include_bytes!("../../binary_includes/1024.png");
+
+// NB: We're just hacking a temporary "text to image" test endpoint leveraging the existing image to image modality.
+static IMAGE : Lazy<DynamicImage> = Lazy::new(||{
+  ImageReader::new(Cursor::new(PNG_BYTES))
+    .with_guessed_format()
+    .expect("failed to detect png format")
+    .decode()
+    .expect("failed to decode image")
+});
+
+#[tauri::command]
+pub async fn text_to_image(
+  prompt: String,
+  model_config: State<'_, AppConfig>,
+  model_cache: State<'_, ModelCache>,
+  prompt_cache: State<'_, PromptCache>,
+  app_data_root: State<'_, AppDataRoot>,
+  app: AppHandle,
+) -> Result<String, String> {
+  info!("text_to_image endpoint called.");
+
+  let mut image = IMAGE.resize(512, 512, FilterType::CatmullRom);
+
+  #[cfg(not(target_os = "macos"))]
+  {
+    let result = text_to_image_impl(
+      &prompt,
+      image,
+      Some(STRENGTH),
+      &model_config,
+      &model_cache,
+      prompt_cache,
+      app,
+      &app_data_root
+    ).await;
+
+    image = match result {
+      Ok(img) => DynamicImage::ImageRgb8(img),
+      Err(err) => {
+        error!("There was an error generating the image: {:?}", err);
+        return Err(format!("There was an error generating the image: {}", err));
+      }
+    };
+  }
+
+  let bytes = encode_dynamic_image_base64_png(image)
+    .map_err(|err| format!("failure to encode image: {:?}", err))?;
+
+  info!("Inference successful; image converted to base64, serving back to browser...");
+
+  Ok(bytes)
+}
+
+#[cfg(not(target_os = "macos"))]
+async fn text_to_image_impl(
+  prompt: &str,
+  image: DynamicImage,
+  strength: Option<f64>,
+  config: &AppConfig,
+  model_cache: &ModelCache,
+  prompt_cache: State<'_, PromptCache>,
+  app: AppHandle,
+  app_data_root: &AppDataRoot,
+) -> Result<RgbImage, String> {
+  
+  let weights_dir = app_data_root.weights_dir();
+  let vae_path = weights_dir.weight_path(&LYKON_DEAMSHAPER_7_VAE);
+  let unet_path = weights_dir.weight_path(&SIMIANLUO_LCM_DREAMSHAPER_V7_UNET);
+  let clip_json_path = weights_dir.weight_path(&CLIP_JSON);
+  let clip_weights_path= weights_dir.weight_path(&SDXL_TURBO_CLIP_TEXT_ENCODER);
+
+  let args = Args {
+    image: &image,
+    prompt: prompt.to_string(),
+    uncond_prompt: "".to_string(),
+    model_cache,
+    prompt_cache: &prompt_cache,
+    img2img_strength: strength,
+    cfg_scale: config.cfg_scale,
+    use_flash_attn: true,
+    model_config: &config.model_config,
+    maybe_seed: None,
+    scheduler_steps: config.scheduler_steps,
+    vae_path: &vae_path,
+    unet_path: &unet_path,
+    clip_json_path: &clip_json_path,
+    clip_weights_path: &clip_weights_path,
+  };
+
+  let image = lcm_pipeline(args)
+    .map_err(|err| format!("failure to encode image: {:?}", err))?;
+
+  Ok(image)
+}
@@ -1,13 +1,13 @@
 use serde::Serialize;

-
 #[derive(Clone, Copy, Serialize)]
 #[serde(rename_all = "snake_case")]
 pub enum NotificationModelType {
+  ImageSegmentation,
+  TextEncoder,
+  TextTokenizer,
  Unet,
  Vae,
-  Json,
-  ClipEncoder,
 }

 #[derive(Clone, Serialize)]
@@ -1,17 +1,22 @@
 pub mod endpoints;
 pub mod events;
-pub mod ml;
 pub mod state;
+pub mod stubs;
 pub mod threads;
 pub mod transfer;
+pub mod utils;

 use crate::endpoints::download_models::download_models;
-use crate::endpoints::image_endpoint::infer_image;
-use crate::endpoints::test_counter::test_counter;
-use crate::ml::model_cache::ModelCache;
-use crate::ml::prompt_cache::PromptCache;
+use crate::endpoints::inpaint_image_endpoint::inpaint_image;
+use crate::endpoints::flip_image::flip_image;
+use crate::endpoints::realtime_image_endpoint::infer_image;
+use crate::endpoints::remove_background_endpoint::remove_background;
+use crate::endpoints::text_to_image_endpoint::text_to_image;
 use crate::state::app_config::AppConfig;
+use crate::stubs::model_cache::ModelCache;
+use crate::stubs::prompt_cache::PromptCache;
 use crate::threads::downloader_thread::downloader_thread;
+
 use tauri_plugin_log::Target;
 use tauri_plugin_log::TargetKind;

@@ -24,6 +29,7 @@ pub fn run() {
  let config = AppConfig::init()
    .expect("config should load");

+
  let prompt_cache = PromptCache::with_capacity(8)
    .expect("prompt cache should load");

@@ -42,6 +48,9 @@ pub fn run() {
      .targets(vec![Target::new(TargetKind::Stdout)])
      .build())
    .setup(|app| {
+      // TODO(bt): This is broken on windows
+      // log_environment_details();
+
      //if cfg!(debug_assertions) {
      //  app.handle().plugin(
      //    tauri_plugin_log::Builder::default()
@@ -60,9 +69,12 @@ pub fn run() {
    .manage(model_cache)
    .manage(app_data_root)
    .invoke_handler(tauri::generate_handler![
-      test_counter, 
-      infer_image,
      download_models,
+      flip_image,
+      infer_image,
+      inpaint_image,
+      remove_background,
+      text_to_image,
    ])
    .run(tauri::generate_context!())
    .expect("error while running tauri application");
@@ -1,15 +0,0 @@
-use crate::ml::downloads::download_model_registry_file::download_model_registry_file;
-use crate::ml::model_type::ModelType;
-use crate::state::app_dir::AppDataRoot;
-
-pub async fn download_all_models(app_data_root: &AppDataRoot) -> anyhow::Result<()> {
-  let weights_dir = app_data_root.weights_dir();
-  
-  //download_model_registry_file(ModelType::ClipJson, weights_dir).await?;
-  //download_model_registry_file(ModelType::SdxlTurboUnet, weights_dir).await?;
-  //download_model_registry_file(ModelType::SdxlTurboVae, weights_dir).await?;
-  //download_model_registry_file(ModelType::SdxlTurboClipEncoder, weights_dir).await?;
-  //download_model_registry_file(ModelType::SdxlTurboClipEncoder2, weights_dir).await?;
-  
-  Ok(())
-}
@@ -1,30 +0,0 @@
-use crate::ml::model_type::ModelType;
-use crate::state::app_dir::AppWeightsDir;
-use log::info;
-use std::fs::File;
-use std::path::{Path, PathBuf};
-
-pub async fn download_model_registry_file(
-  model_type: ModelType,
-  weights_dir: &AppWeightsDir,
-) -> anyhow::Result<PathBuf> {
-  
-  let path = weights_dir.model_path(&model_type);
-
-  if path.exists() {
-    return Ok(path);
-  }
-
-  info!("downloading model: {:?} to {:?}", model_type, path);
-
-  let body = reqwest::get(model_type.get_download_url())
-    .await?
-    .bytes()
-    .await?;
-  
-  let _file = File::create(&path)?;
-  
-  std::fs::write(&path, body.as_ref())?;
-  
-  Ok(path)
-}
@@ -1,2 +0,0 @@
-pub mod download_model_registry_file;
-pub mod download_all_models;
@@ -1,8 +0,0 @@
-pub mod image;
-pub mod model_cache;
-pub mod model_file;
-pub mod models;
-pub mod prompt_cache;
-pub mod stable_diffusion;
-pub mod model_type;
-pub mod downloads;
@@ -1,86 +0,0 @@
-use crate::events::notification_event::NotificationModelType;
-use crate::state::app_dir::AppWeightsDir;
-use std::path::PathBuf;
-
-#[derive(Copy, Clone, Debug)]
-pub enum ModelType {
-  ClipJson,
-  SdxlTurboUnet,
-  SdxlTurboVae,
-  SdxlTurboClipEncoder,
-  SdxlTurboClipEncoder2,
-  SimianLuoLcmDreamshaperV7Unet,
-  LykonDreamshaper7Vae,
-  LykonDreamshaper7TextEncoderFp16,
-}
-
-impl ModelType {
-  pub fn get_name(&self) -> &'static str {
-    match self {
-      Self::ClipJson => "CLIP (JSON)",
-      Self::SdxlTurboUnet => "SDXL Turbo UNET",
-      Self::SdxlTurboVae => "SDXL Turbo VAE",
-      Self::SdxlTurboClipEncoder => "Clip Encoder #1",
-      Self::SdxlTurboClipEncoder2 => "Clip Encoder #2",
-      Self::SimianLuoLcmDreamshaperV7Unet => "SimianLuo LCM Dreamshaper v7 UNET",
-      Self::LykonDreamshaper7Vae => "Lykon Dreamshaper 7 VAE",
-      Self::LykonDreamshaper7TextEncoderFp16 => "Lykon Dreamshaper Text Encoder (fp16)",
-    }
-  }
-
-  pub fn get_notification_type(&self) -> NotificationModelType {
-    match self {
-      Self::ClipJson => NotificationModelType::Json,
-      Self::SdxlTurboUnet => NotificationModelType::Unet,
-      Self::SdxlTurboVae => NotificationModelType::Vae,
-      Self::SdxlTurboClipEncoder => NotificationModelType::ClipEncoder,
-      Self::SdxlTurboClipEncoder2 => NotificationModelType::ClipEncoder,
-      Self::SimianLuoLcmDreamshaperV7Unet => NotificationModelType::Unet,
-      Self::LykonDreamshaper7Vae => NotificationModelType::Vae,
-      Self::LykonDreamshaper7TextEncoderFp16 => NotificationModelType::ClipEncoder,
-    }
-  }
-
-  pub fn get_hf_id(&self) -> Option<&'static str> {
-    match self {
-      Self::ClipJson => Some("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k"),
-      Self::SdxlTurboUnet 
-      | Self::SdxlTurboVae
-      | Self::SdxlTurboClipEncoder
-      | Self::SdxlTurboClipEncoder2 => Some("stabilityai/sdxl-turbo"),
-      Self::SimianLuoLcmDreamshaperV7Unet => Some("SimianLuo/LCM_Dreamshaper_v7"),
-      Self::LykonDreamshaper7Vae
-      | Self::LykonDreamshaper7TextEncoderFp16 => Some("Lykon/dreamshaper-7"),
-    }
-  }
-
-  pub fn get_download_url(&self) -> &'static str {
-    match self {
-      Self::ClipJson => "https://pub-bc5e2bc0cdee4bb5ae8fca9d641ca0d6.r2.dev/tokenizer.json",
-      Self::SdxlTurboUnet => "https://pub-bc5e2bc0cdee4bb5ae8fca9d641ca0d6.r2.dev/diffusion_pytorch_model.unet.safetensors",
-      Self::SdxlTurboVae => "https://pub-bc5e2bc0cdee4bb5ae8fca9d641ca0d6.r2.dev/diffusion_pytorch_model.vae.safetensors",
-      Self::SdxlTurboClipEncoder => "https://pub-bc5e2bc0cdee4bb5ae8fca9d641ca0d6.r2.dev/clip_text_encoder.safetensors",
-      Self::SdxlTurboClipEncoder2 => "https://pub-bc5e2bc0cdee4bb5ae8fca9d641ca0d6.r2.dev/clip_text_encoder_2.safetensors",
-      Self::SimianLuoLcmDreamshaperV7Unet => "https://pub-bc5e2bc0cdee4bb5ae8fca9d641ca0d6.r2.dev/simianluo_lcm_dreamshaper_v7_unet.safetensors",
-      Self::LykonDreamshaper7Vae => "https://pub-bc5e2bc0cdee4bb5ae8fca9d641ca0d6.r2.dev/lykon_dreamshaper_7_vae.safetensors",
-      Self::LykonDreamshaper7TextEncoderFp16 => "https://pub-bc5e2bc0cdee4bb5ae8fca9d641ca0d6.r2.dev/lykon_dreamshaper_7_text_encoder.fp16.safetensors",
-    }
-  }
-
-  pub fn get_filename(&self) -> &'static str {
-    match self {
-      Self::ClipJson => "clip_vit_bigg_14_lion2b_39b_b160k.tokenizer.json",
-      Self::SdxlTurboUnet => "diffusion_pytorch_model.unet.safetensors",
-      Self::SdxlTurboVae => "diffusion_pytorch_model.vae.safetensors",
-      Self::SdxlTurboClipEncoder => "clip_text_encoder.safetensors",
-      Self::SdxlTurboClipEncoder2 => "clip_text_encoder_2.safetensors",
-      Self::SimianLuoLcmDreamshaperV7Unet => "simianluo_lcm_dreamshaper_v7_unet.safetensors",
-      Self::LykonDreamshaper7Vae => "lykon_dreamshaper_7_vae.safetensors",
-      Self::LykonDreamshaper7TextEncoderFp16 => "lykon_dreamshaper_7_text_encoder.fp16.safetensors",
-    }
-  }
-  
-  pub fn get_path(&self, weights_dir: &AppWeightsDir) -> PathBuf {
-    weights_dir.model_path(self)
-  }
-}
@@ -1,130 +0,0 @@
-use crate::ml::model_file::StableDiffusionVersion;
-use anyhow::anyhow;
-use candle_core::{DType, Device, Tensor};
-use candle_transformers::models::stable_diffusion::vae::{AutoEncoderKL, DiagonalGaussianDistribution};
-use candle_transformers::models::stable_diffusion::StableDiffusionConfig;
-use hf_hub::api::sync::Api;
-use std::sync::{Arc, RwLock};
-
-pub struct LazyLoadVaeModel {
-  lazy_model: Arc<RwLock<Option<AutoEncoderKL>>>,
-  model_configs: VaeConfigs,
-}
-
-// TODO: Maybe we pass these via a central config read at startup, passed as an Arc<ConfigT>
-pub struct VaeConfigs {
-  sd_config: StableDiffusionConfig,
-  sd_version: StableDiffusionVersion,
-  device: Device,
-  dtype: DType,
-  hf_api: Api,
-}
-
-impl LazyLoadVaeModel {
-  pub fn new(
-    sd_config: &StableDiffusionConfig,
-    sd_version: &StableDiffusionVersion,
-    hf_api: &Api,
-    device: &Device,
-    dtype: DType,
-  ) -> anyhow::Result<Self> {
-    println!("building vae model... (1)");
-    
-    Ok(Self {
-      lazy_model: Arc::new(RwLock::new(None)),
-      model_configs: VaeConfigs {
-        sd_config: sd_config.clone(),
-        sd_version: sd_version.clone(),
-        device: device.clone(),
-        hf_api: hf_api.clone(),
-        dtype
-      }
-    })
-  }
-
-  pub fn encode(&self, xs: &Tensor) -> anyhow::Result<DiagonalGaussianDistribution> {
-    println!("vae encode...");
-    match self.try_encode(xs) {
-      Err(err) => Err(err),
-      Ok(Some(result)) => Ok(result),
-      Ok(None) => {
-        // Model wasn't previously loaded
-        self.load_model()?;
-        self.try_encode(xs)?
-          .ok_or(anyhow!("model was not previously loaded"))
-      }
-    }
-  }
-  
-  pub fn decode(&self, xs: &Tensor) -> anyhow::Result<Tensor> {
-    println!("vae decode...");
-    match self.try_decode(xs) {
-      Err(err) => Err(err),
-      Ok(Some(result)) => Ok(result),
-      Ok(None) => {
-        // Model wasn't previously loaded
-        self.load_model()?;
-        self.try_decode(xs)?
-          .ok_or(anyhow!("model was not previously loaded"))
-      }
-    }
-  }
-
-  fn try_encode(&self, xs: &Tensor) -> anyhow::Result<Option<DiagonalGaussianDistribution>> {
-    match self.lazy_model.read() {
-      Err(err) => Err(anyhow!("lock error: {:?}", err)),
-      Ok(model) => {
-        match &*model {
-          None => Ok(None), // Model is not yet loaded
-          Some(model) => {
-            Ok(Some(model.encode(xs)?))
-          }
-        }
-      }
-    }
-  }
-
-  fn try_decode(&self, xs: &Tensor) -> anyhow::Result<Option<Tensor>> {
-    match self.lazy_model.read() {
-      Err(err) => Err(anyhow!("lock error: {:?}", err)),
-      Ok(model) => {
-        match &*model {
-          None => Ok(None), // Model is not yet loaded
-          Some(model) => {
-            Ok(Some(model.decode(xs)?))
-          }
-        }
-      }
-    }
-  }
-
-  fn load_model(&self) -> anyhow::Result<()> {
-    println!("load vae");
-    match self.lazy_model.write() {
-      Err(err) => return Err(anyhow!("lock error: {:?}", err)),
-      Ok(mut maybe_model) => {
-        match &*maybe_model {
-          Some(_model) => {} // Already loaded; fall through
-          None => {
-            let repo = self.model_configs.sd_version.repo();
-
-            println!("Building VAE model from : {:?} ... (3)", repo);
-            
-            let vae_file = self.model_configs.hf_api.model(repo.to_string())
-              .get("vae/diffusion_pytorch_model.safetensors")?;
-
-            println!("Building VAE model from file {:?}...", &vae_file);
-            let vae = self.model_configs
-              .sd_config
-              .build_vae(vae_file, &self.model_configs.device, self.model_configs.dtype)?;
-
-            println!("Built VAE...");
-            *maybe_model = Some(vae);
-          }
-        }
-      }
-    }
-    Ok(())
-  }
- 
-}
@@ -1,2 +0,0 @@
-pub mod unet_model;
-pub mod lazy_load_vae_model;
@@ -1,30 +0,0 @@
-use candle_core::{DType, Device, Tensor};
-use candle_transformers::models::stable_diffusion::unet_2d::UNet2DConditionModel;
-use candle_transformers::models::stable_diffusion::StableDiffusionConfig;
-use std::path::Path;
-
-pub struct UNetModel {
-  model: UNet2DConditionModel,
-}
-
-impl UNetModel {
-  pub fn new<P: AsRef<Path>>(sd_config: &StableDiffusionConfig, unet_weights_file: P, device: &Device, dtype: DType) -> anyhow::Result<Self> {
-    println!("building unet model... (1)");
-
-    let model = sd_config.build_unet(unet_weights_file, &device, 4, true, dtype)?;
-
-    Ok(Self { model })
-  }
-
-  pub fn new_with_inner(model: UNet2DConditionModel) -> Self {
-    Self { model }
-  }
-
-  pub fn inference(&self, latent_model_input: &Tensor, timestep: f64, text_embeddings: &Tensor) -> anyhow::Result<Tensor> {
-    Ok(self.model.forward(&latent_model_input, timestep, &text_embeddings)?)
-  }
-
-  pub fn forward_with_guidance(&self, latent_model_input: &Tensor, timestep: f64, text_embeddings: &Tensor, guidance_scale_embedding: &Tensor) -> anyhow::Result<Tensor> {
-    Ok(self.model.forward_with_guidance(&latent_model_input, timestep, &text_embeddings, Some(guidance_scale_embedding))?)
-  }
-}
@@ -1,32 +1,14 @@
-use crate::ml::model_file::StableDiffusionVersion;
 use crate::state::app_dir::AppDataRoot;
 use crate::state::os_platform::OsPlatform;
 use crate::state::yaml_config::YamlConfig;
-use candle_core::{DType, Device};
-use candle_transformers::models::stable_diffusion::StableDiffusionConfig;
-use filesys::recursively_find_file_by_name::recursively_find_file_by_name;
-use hf_hub::api::sync::Api;
-use log::info;
-use std::path::PathBuf;
+use crate::stubs::model_config::ModelConfig;

 const DEFAULT_SD_IMAGE_WIDTH: usize = 1024;
 const DEFAULT_SD_IMAGE_HEIGHT: usize = 1024;

-const DEFAULT_SD_NUMERIC_DATATYPE: DType = DType::F32;
-const SD_VERSION: StableDiffusionVersion = StableDiffusionVersion::V1_5;

 /// A central place to configure app-wide details.
 pub struct AppConfig {
-  /// We only support one device type currently, and we can't multiplex 
-  /// over several devices yet.
-  pub device: Device,
-  
-  /// The core numeric type to use for models.
-  pub dtype: DType,
-  
-  pub sd_version: StableDiffusionVersion,
-  pub sd_config: StableDiffusionConfig,
-
  pub image_height: usize,
  pub image_width: usize,
  pub scheduler_steps: usize,
@@ -36,11 +18,10 @@ pub struct AppConfig {

  pub cfg_scale: Option<f64>,
  
-  /// Probably shouldn't be here.
-  pub hf_api: Api,
-  
  /// Root location for application data.
  pub app_data_root: AppDataRoot,
+  
+  pub model_config: ModelConfig,
 }

 impl AppConfig {
@@ -50,30 +31,6 @@ impl AppConfig {
    
    println!("Configs: {:?}", yaml_configs);
    
-    let device = Device::new_cuda(0)
-      .unwrap_or_else(|e| {
-          info!("CUDA not available ({}), falling back to CPU", e);
-          Device::Cpu
-        });
-
-    
-    let sd_config = match SD_VERSION {
-      StableDiffusionVersion::Turbo => {
-        StableDiffusionConfig::sdxl_turbo(None, Some(DEFAULT_SD_IMAGE_HEIGHT), Some(DEFAULT_SD_IMAGE_WIDTH))
-      }
-      StableDiffusionVersion::V1_5 | StableDiffusionVersion::V1_5Inpaint => {
-        StableDiffusionConfig::v1_5(None, Some(DEFAULT_SD_IMAGE_HEIGHT), Some(DEFAULT_SD_IMAGE_WIDTH))
-      }
-      StableDiffusionVersion::V2_1 | StableDiffusionVersion::V2Inpaint => {
-        StableDiffusionConfig::v2_1(None, Some(DEFAULT_SD_IMAGE_HEIGHT), Some(DEFAULT_SD_IMAGE_WIDTH))
-      }
-      StableDiffusionVersion::Xl | StableDiffusionVersion::XlInpaint => {
-        StableDiffusionConfig::sdxl(None, Some(DEFAULT_SD_IMAGE_HEIGHT), Some(DEFAULT_SD_IMAGE_WIDTH))
-      }
-    };
-    
-    let hf_api = Api::new()?;
-    
    let os_platform = OsPlatform::get();

    let default_data_root = match os_platform {
@@ -92,18 +49,14 @@ impl AppConfig {
    println!("App data root: {:?}", app_data_root.path());

    Ok(Self {
-      device,
-      dtype: DEFAULT_SD_NUMERIC_DATATYPE,
-      sd_version: SD_VERSION,
-      sd_config,
      image_height: yaml_configs.image_height.unwrap_or(DEFAULT_SD_IMAGE_HEIGHT),
      image_width: yaml_configs.image_width.unwrap_or(DEFAULT_SD_IMAGE_WIDTH),
      scheduler_steps: yaml_configs.scheduler_steps.unwrap_or(4),
      scheduler_samples: yaml_configs.scheduler_samples.unwrap_or(15),
      seed: yaml_configs.seed,
      cfg_scale: yaml_configs.cfg_scale,
-      hf_api,
      app_data_root,
+      model_config: ModelConfig::init()?,
    })
  }
 }
@@ -1,11 +1,12 @@
-use crate::ml::model_type::ModelType;
 use crate::state::expanduser::expanduser;
 use crate::state::os_platform::OsPlatform;
 use anyhow::anyhow;
 use directories::UserDirs;
+use ml_weights_registry::weights_registry::weight_descriptor::WeightDescriptor;
 use std::path::{Path, PathBuf};
 use tempdir::TempDir;
 use tempfile::{Builder, NamedTempFile};
+
 const DEFAULT_DATA_DIR : &str = "artcraft";
 const ASSETS_SUBDIRECTORY : &str = "assets";
 const WEIGHTS_SUBDIRECTORY : &str = "weights";
@@ -139,9 +140,9 @@ impl AppWeightsDir {
  pub fn path(&self) -> &Path {
    &self.path
  }
-  
-  pub fn model_path(&self, model_registry: &ModelType) -> PathBuf {
-    self.path.join(model_registry.get_filename())
+
+  pub fn weight_path(&self, descriptor: &WeightDescriptor) -> PathBuf {
+    self.path.join(descriptor.filename)
  }
 }

@@ -0,0 +1,3 @@
+pub mod model_cache;
+pub mod model_config;
+pub mod prompt_cache;
@@ -0,0 +1,14 @@
+#[cfg(not(target_os = "macos"))]
+pub use ml_models::ml::model_cache::ModelCache;
+
+#[cfg(not(not(target_os = "macos")))]
+pub struct ModelCache {
+  // Intentionally left blank
+}
+
+#[cfg(not(not(target_os = "macos")))]
+impl ModelCache {
+  pub fn new() -> Self {
+    Self {}
+  }
+}
@@ -0,0 +1,14 @@
+#[cfg(not(target_os = "macos"))]
+pub use ml_models::ml::model_config::ModelConfig;
+
+#[cfg(not(not(target_os = "macos")))]
+pub struct ModelConfig {
+  // Intentionally left blank
+}
+
+#[cfg(not(not(target_os = "macos")))]
+impl ModelConfig {
+  pub fn init() -> anyhow::Result<Self> {
+    Ok(Self {})
+  }
+}
@@ -0,0 +1,15 @@
+
+#[cfg(not(target_os = "macos"))]
+pub use ml_models::ml::prompt_cache::PromptCache;
+
+#[cfg(not(not(target_os = "macos")))]
+pub struct PromptCache {
+  // Intentionally left blank
+}
+
+#[cfg(not(not(target_os = "macos")))]
+impl PromptCache {
+  pub fn with_capacity(capacity: usize) -> anyhow::Result<Self> {
+    Ok(Self {})
+  }
+}
@@ -1,10 +1,11 @@
 use crate::events::notification_event::{NotificationEvent, NotificationModelType};
-use crate::ml::model_type::ModelType;
 use crate::state::app_dir::AppDataRoot;
 use crate::transfer::download::{download_async, ProgressUpdate};
-use futures::stream::FuturesUnordered;
 use futures::StreamExt;
+use futures::stream::FuturesUnordered;
 use log::{debug, error, info};
+use ml_weights_registry::weights_registry::weight_descriptor::WeightFunction;
+use ml_weights_registry::weights_registry::weights::{CLIP_JSON, DIS_MEDIUM_ONNX, LYKON_DEAMSHAPER_7_TEXT_ENCODER_FP16, LYKON_DEAMSHAPER_7_VAE, SDXL_TURBO_CLIP_TEXT_ENCODER, SDXL_TURBO_CLIP_TEXT_ENCODER_2, SDXL_TURBO_UNET, SDXL_TURBO_VAE, SIMIANLUO_LCM_DREAMSHAPER_V7_UNET};
 use std::collections::VecDeque;
 use std::path::PathBuf;
 use std::sync::mpsc::Receiver;
@@ -39,22 +40,22 @@ pub async fn downloader_thread(app_data_root: AppDataRoot, app: AppHandle) -> !

  let mut download_queue = VecDeque::new();

-  // TODO: Automatic enqueue of known important models + enqueue new models on-demand
-  download_queue.push_back(ModelType::ClipJson);
-  //download_queue.push_back(ModelType::SdxlTurboUnet);
-  //download_queue.push_back(ModelType::SdxlTurboVae);
-  download_queue.push_back(ModelType::SdxlTurboClipEncoder); // TODO(bt): Why is this still needed?
-  //download_queue.push_back(ModelType::SdxlTurboClipEncoder2);
-  download_queue.push_back(ModelType::SimianLuoLcmDreamshaperV7Unet);
-  download_queue.push_back(ModelType::LykonDreamshaper7Vae);
-  download_queue.push_back(ModelType::LykonDreamshaper7TextEncoderFp16);
+  download_queue.push_back(CLIP_JSON);
+  download_queue.push_back(LYKON_DEAMSHAPER_7_TEXT_ENCODER_FP16);
+  download_queue.push_back(LYKON_DEAMSHAPER_7_VAE);
+  download_queue.push_back(SIMIANLUO_LCM_DREAMSHAPER_V7_UNET);
+  download_queue.push_back(SDXL_TURBO_CLIP_TEXT_ENCODER); // TODO(bt): Why is this still needed?
+  download_queue.push_back(DIS_MEDIUM_ONNX);
+  //download_queue.push_back(SDXL_TURBO_CLIP_TEXT_ENCODER_2); // TODO(bt): REMOVE
+  //download_queue.push_back(SDXL_TURBO_VAE); // TODO(bt): REMOVE
+  //download_queue.push_back(SDXL_TURBO_UNET); // TODO(bt): REMOVE

  let mut handles = FuturesUnordered::new();

  while let Some(model) = download_queue.pop_front() {
-    let url = model.get_download_url().to_string();
+    let url = model.r2_download_url.to_string();

-    let filename = app_data_root.weights_dir().model_path(&model);
+    let filename = app_data_root.weights_dir().weight_path(&model);

    if filename.exists() {
      info!("File already exists: {:?}", &filename);
@@ -75,9 +76,17 @@ pub async fn downloader_thread(app_data_root: AppDataRoot, app: AppHandle) -> !
      }
    };

+    let model_type = match model.function {
+      WeightFunction::TextEncoder => NotificationModelType::TextEncoder,
+      WeightFunction::TextTokenizer => NotificationModelType::TextTokenizer,
+      WeightFunction::Unet => NotificationModelType::Unet,
+      WeightFunction::Vae => NotificationModelType::Vae,
+      WeightFunction::ImageSegmentation => NotificationModelType::ImageSegmentation,
+    };
+
    let result = app.emit(NOTIFICATION_CHANNEL_NAME, NotificationEvent::ModelDownloadStarted {
-      model_name: model.get_name(),
-      model_type: model.get_notification_type(),
+      model_name: model.name,
+      model_type,
    });

    if let Err(err) = result {
@@ -102,8 +111,8 @@ pub async fn downloader_thread(app_data_root: AppDataRoot, app: AppHandle) -> !
        lock.push(ModelDownloadStatusChannel {
          receiver: rx,
          final_file_path: filename.clone(),
-          model_name: model.get_name().to_string(),
-          model_type: model.get_notification_type(),
+          model_name: model.to_descriptive_name(),
+          model_type,
        })
      }
    }
@@ -0,0 +1,14 @@
+use base64::prelude::BASE64_STANDARD;
+use base64::Engine;
+use image::{DynamicImage, ImageReader};
+use std::io::Cursor;
+
+pub fn decode_base64_image(base64_image: &str) -> anyhow::Result<DynamicImage> {
+  let bytes = BASE64_STANDARD.decode(base64_image)?;
+
+  let image = ImageReader::new(Cursor::new(bytes))
+    .with_guessed_format()?
+    .decode()?;
+
+  Ok(image)
+}
@@ -0,0 +1,14 @@
+use base64::prelude::BASE64_STANDARD;
+use base64::Engine;
+use image::{DynamicImage, ImageFormat};
+use std::io::Cursor;
+
+pub fn encode_dynamic_image_base64_png(image: DynamicImage) -> anyhow::Result<String> {
+  let mut bytes = Vec::with_capacity(1024*1024);
+
+  image.write_to(&mut Cursor::new(&mut bytes), ImageFormat::Png)?;
+
+  let bytes = BASE64_STANDARD.encode(bytes);
+
+  Ok(bytes)
+}
@@ -0,0 +1,16 @@
+use base64::prelude::BASE64_STANDARD;
+use base64::Engine;
+use image::{ImageFormat, RgbImage};
+use std::io::Cursor;
+
+pub fn encode_rgb_image_base64_png(
+  image: RgbImage
+) -> anyhow::Result<String> {
+  let mut bytes = Vec::with_capacity(1024*1024);
+
+  image.write_to(&mut Cursor::new(&mut bytes), ImageFormat::Png)?;
+
+  let bytes = BASE64_STANDARD.encode(bytes);
+
+  Ok(bytes)
+}
@@ -0,0 +1,3 @@
+pub mod decode_base64_image;
+pub mod encode_dynamic_image_base64_png;
+pub mod encode_rgb_image_base64_png;
@@ -0,0 +1,11 @@
+use log::info;
+//use nvidia_checker::{get_cuda_version, get_cudnn_version, get_kernel_version, get_nvidia_driver_version, get_os_version, get_tensorrt_version};
+
+pub fn log_environment_details() {
+  //info!("NVIDIA driver version: {}", get_nvidia_driver_version(false));
+  //info!("CUDA version: {}", get_cuda_version(false));
+  //info!("cuDNN version: {}", get_cudnn_version(false));
+  //info!("TensorRT version: {}", get_tensorrt_version(false));
+  //info!("OS version: {}", get_os_version(false));
+  //info!("Kernel version: {}", get_kernel_version(false));
+}
@@ -0,0 +1,2 @@
+pub mod image;
+pub mod log_environment_details;
@@ -32,3 +32,4 @@ my-workspace-hack = { version = "0.1", path = "../../../my-workspace-hack" }

 [dev-dependencies]
 testing = { path = "../../testing" }
+assertor = "0.0.3"
@@ -1,5 +1,6 @@
 pub mod i32_to_u32_zero_clamped;
 pub mod i64_to_u64_zero_clamped;
+pub mod remap_range_f64;
 pub mod u64_to_i32_saturating;
 pub mod u64_to_i64_saturating;
 pub mod u64_to_u32_saturating;
@@ -0,0 +1,131 @@
+
+pub struct Args {
+  pub old_min: f64,
+  pub old_max: f64,
+  pub new_min: f64,
+  pub new_max: f64,
+  
+  /// The value to remap to the new range.
+  pub value: f64,
+}
+
+pub fn remap_range_f64(args: Args) -> f64 {
+  let old_range = args.old_max - args.old_min;
+  let new_range = args.new_max -  args.new_min;
+
+  let new_value = (args.value -  args.old_min) * new_range / old_range + args.new_min;
+  
+  if new_value > args.new_max {
+    args.new_max
+  } else if new_value < args.new_min { 
+    args.new_min
+  } else {
+    new_value
+  }
+}
+
+#[cfg(test)]
+mod tests {
+  use crate::numerics::remap_range_f64::{remap_range_f64, Args};
+  use assertor::{assert_that, FloatAssertion};
+
+  #[test]
+  fn test_base_cases() {
+    assert_that!(remap_range_f64(Args {
+      old_min: 0.0,
+      old_max: 1.0,
+      new_min: 0.0,
+      new_max: 1.0,
+      value: 0.0,
+    })).with_abs_tol(0.01).is_approx_equal_to(0.0);
+    
+    assert_that!(remap_range_f64(Args {
+      old_min: 0.0,
+      old_max: 1.0,
+      new_min: 0.0,
+      new_max: 1.0,
+      value: 1.0,
+    })).with_abs_tol(0.01).is_approx_equal_to(1.0);
+  }
+
+  #[test]
+  fn test_maxima() {
+    assert_that!(remap_range_f64(Args {
+      old_min: 0.0,
+      old_max: 100.0,
+      new_min: 0.0,
+      new_max: 1.0,
+      value: 100.0,
+    })).with_abs_tol(0.01).is_approx_equal_to(1.0);
+
+    assert_that!(remap_range_f64(Args {
+      old_min: 0.0,
+      old_max: 1.0,
+      new_min: 0.0,
+      new_max: 50.0,
+      value: 1.0,
+    })).with_abs_tol(0.01).is_approx_equal_to(50.0);
+
+    assert_that!(remap_range_f64(Args {
+      old_min: 0.0,
+      old_max: 1.0,
+      new_min: -100.0,
+      new_max: 100.0,
+      value: 1.0,
+    })).with_abs_tol(0.01).is_approx_equal_to(100.0);
+  }
+
+  #[test]
+  fn test_minima() {
+    assert_that!(remap_range_f64(Args {
+      old_min: 0.0,
+      old_max: 100.0,
+      new_min: 0.0,
+      new_max: 1.0,
+      value: 0.0,
+    })).with_abs_tol(0.01).is_approx_equal_to(0.0);
+
+    assert_that!(remap_range_f64(Args {
+      old_min: 0.0,
+      old_max: 1.0,
+      new_min: 0.0,
+      new_max: 50.0,
+      value: 0.0,
+    })).with_abs_tol(0.01).is_approx_equal_to(0.0);
+
+    assert_that!(remap_range_f64(Args {
+      old_min: 0.0,
+      old_max: 1.0,
+      new_min: -100.0,
+      new_max: 100.0,
+      value: 0.0,
+    })).with_abs_tol(0.01).is_approx_equal_to(-100.0);
+  }
+
+  #[test]
+  fn test_other_values() {
+    assert_that!(remap_range_f64(Args {
+      old_min: 0.0,
+      old_max: 100.0,
+      new_min: 0.0,
+      new_max: 1.0,
+      value: 50.0,
+    })).with_abs_tol(0.01).is_approx_equal_to(0.50);
+
+    assert_that!(remap_range_f64(Args {
+      old_min: 0.0,
+      old_max: 1.0,
+      new_min: 0.0,
+      new_max: 50.0,
+      value: 0.10,
+    })).with_abs_tol(0.01).is_approx_equal_to(5.0);
+
+    assert_that!(remap_range_f64(Args {
+      old_min: 0.0,
+      old_max: 1.0,
+      new_min: -100.0,
+      new_max: 100.0,
+      value: 0.25,
+    })).with_abs_tol(0.01).is_approx_equal_to(-50.0);
+  }
+}
@@ -8,6 +8,7 @@
      "name": "storyteller-board",
      "version": "0.0.0",
      "dependencies": {
+        "@devnomic/marquee": "^1.0.3",
        "@fortawesome/free-brands-svg-icons": "^6.6.0",
        "@headlessui/react": "^2.1.3",
        "@preact/signals-react": "^2.2.0",
@@ -381,6 +382,21 @@
        "node": ">=6.9.0"
      }
    },
+    "node_modules/@devnomic/marquee": {
+      "version": "1.0.3",
+      "resolved": "https://registry.npmjs.org/@devnomic/marquee/-/marquee-1.0.3.tgz",
+      "integrity": "sha512-q8QUtNO8ff+UvfrKJsHBtxh4fbOfhGk6SSdK0Uu/XZFdEzrmiGJ/l0FuVHRYsPAmzgnhVVnz1dGNB5CWGLMVvw==",
+      "dependencies": {
+        "clsx": "^2.1.0",
+        "tailwind-merge": "^2.2.0"
+      },
+      "peerDependencies": {
+        "@types/react": "^18.2.47",
+        "@types/react-dom": "^18.2.18",
+        "react": "^18.2.0",
+        "react-dom": "^18.2.0"
+      }
+    },
    "node_modules/@emotion/is-prop-valid": {
      "version": "1.3.0",
      "resolved": "https://registry.npmjs.org/@emotion/is-prop-valid/-/is-prop-valid-1.3.0.tgz",
@@ -1510,14 +1526,12 @@
    "node_modules/@types/prop-types": {
      "version": "15.7.12",
      "resolved": "https://registry.npmjs.org/@types/prop-types/-/prop-types-15.7.12.tgz",
-      "integrity": "sha512-5zvhXYtRNRluoE/jAp4GVsSduVUzNWKkOZrCDBWYtE7biZywwdC2AcEzg+cSMLFRfVgeAFqpfNabiPjxFddV1Q==",
-      "dev": true
+      "integrity": "sha512-5zvhXYtRNRluoE/jAp4GVsSduVUzNWKkOZrCDBWYtE7biZywwdC2AcEzg+cSMLFRfVgeAFqpfNabiPjxFddV1Q=="
    },
    "node_modules/@types/react": {
      "version": "18.3.4",
      "resolved": "https://registry.npmjs.org/@types/react/-/react-18.3.4.tgz",
      "integrity": "sha512-J7W30FTdfCxDDjmfRM+/JqLHBIyl7xUIp9kwK637FGmY7+mkSFSe6L4jpZzhj5QMfLssSDP4/i75AKkrdC7/Jw==",
-      "dev": true,
      "dependencies": {
        "@types/prop-types": "*",
        "csstype": "^3.0.2"
@@ -1527,7 +1541,6 @@
      "version": "18.3.0",
      "resolved": "https://registry.npmjs.org/@types/react-dom/-/react-dom-18.3.0.tgz",
      "integrity": "sha512-EhwApuTmMBmXuFOikhQLIBUn6uFg81SwLMOAUgodJF14SOBOCMdU04gDoYi0WOJJHD144TL32z4yDqCW3dnkQg==",
-      "dev": true,
      "dependencies": {
        "@types/react": "*"
      }
@@ -2364,8 +2377,7 @@
    "node_modules/csstype": {
      "version": "3.1.3",
      "resolved": "https://registry.npmjs.org/csstype/-/csstype-3.1.3.tgz",
-      "integrity": "sha512-M1uQkMl8rQK/szD0LNhtqxIPLpimGm8sOBwU7lLnCpSbTyY3yeU1Vc7l4KT5zT4s/yOxHH5O7tIuuLOCnLADRw==",
-      "dev": true
+      "integrity": "sha512-M1uQkMl8rQK/szD0LNhtqxIPLpimGm8sOBwU7lLnCpSbTyY3yeU1Vc7l4KT5zT4s/yOxHH5O7tIuuLOCnLADRw=="
    },
    "node_modules/data-view-buffer": {
      "version": "1.0.1",
@@ -11,6 +11,7 @@
    "preview": "vite preview"
  },
  "dependencies": {
+    "@devnomic/marquee": "^1.0.3",
    "@fortawesome/free-brands-svg-icons": "^6.6.0",
    "@headlessui/react": "^2.1.3",
    "@preact/signals-react": "^2.2.0",
@@ -0,0 +1,25 @@
+import { useCallback, useRef } from "react";
+import { useRenderCounter } from "~/hooks/useRenderCounter";
+import { EditEngine } from "~/KonvaApp/EditEngine";
+import { KonvaCanvasContainer } from "~/KonvaRootComponent/KonvaCanvasContainer";
+
+export const EditModeRootComponent = ({ className }: { className: string }) => {
+  useRenderCounter("EditModeRootComponent");
+
+  const editEngineRef = useRef<EditEngine | null>(null);
+
+  const konvaContainerCallbackRef = useCallback((node: HTMLDivElement) => {
+    if (node !== null && editEngineRef.current === null) {
+      editEngineRef.current = new EditEngine(node);
+    }
+  }, []);
+
+  return (
+    <>
+      <KonvaCanvasContainer
+        className={className}
+        ref={konvaContainerCallbackRef}
+      />
+    </>
+  );
+};
@@ -0,0 +1,145 @@
+import { useEffect, useState } from "react";
+import { Marquee } from "@devnomic/marquee";
+import "@devnomic/marquee/dist/index.css";
+
+function getRandomImages(count: number) {
+  const images = Array.from({ length: count }, (_, i) => ({
+    id: i + 1,
+    src: `/images/generate/image${i + 1}.png`,
+    alt: `Generated image ${i + 1}`,
+  }));
+
+  // Shuffle the array
+  return [...images].sort(() => Math.random() - 0.5);
+}
+
+interface ImageColumnProps {
+  images: Array<{ id: number; src: string; alt: string }>;
+  delay: number;
+  reverse?: boolean;
+  isLoaded: boolean;
+  duration?: number;
+}
+
+function ImageColumn({
+  images,
+  delay,
+  reverse = false,
+  duration = 100,
+  isLoaded,
+}: ImageColumnProps) {
+  return (
+    <div className="flex h-screen flex-col gap-6">
+      <Marquee
+        fade={false}
+        direction="up"
+        reverse={reverse}
+        pauseOnHover={false}
+        className={`h-full gap-[24px]`}
+        style={{ "--duration": `${duration}s` } as React.CSSProperties}
+        innerClassName="gap-[24px] [--gap:24px]"
+        numberOfCopies={2}
+      >
+        {images.map((image, i) => (
+          <div
+            key={`${image.id}-${i}`}
+            className={`relative aspect-[4/5] w-full overflow-hidden rounded-lg shadow-lg transition-all duration-700 ease-out ${
+              isLoaded
+                ? "translate-y-0 opacity-100"
+                : "translate-y-12 opacity-0"
+            }`}
+            style={{
+              transitionDelay: `${delay + i * 50}ms`,
+            }}
+          >
+            <img
+              src={image.src}
+              alt={image.alt}
+              className="w-full object-cover"
+            />
+          </div>
+        ))}
+      </Marquee>
+    </div>
+  );
+}
+
+export default function GalleryBackground() {
+  const [images, _setImages] = useState(() => getRandomImages(20));
+  const [isLoaded, setIsLoaded] = useState(false);
+
+  useEffect(() => {
+    // delay for smooth animation
+    const timer = setTimeout(() => {
+      setIsLoaded(true);
+    }, 100);
+
+    return () => clearTimeout(timer);
+  }, []);
+
+  // Columns for masonry layout
+  const columns = {
+    col1: images.filter((_, i) => i % 5 === 0),
+    col2: images.filter((_, i) => i % 5 === 1),
+    col3: images.filter((_, i) => i % 5 === 2),
+    col4: images.filter((_, i) => i % 5 === 3),
+    col5: images.filter((_, i) => i % 5 === 4),
+  };
+
+  return (
+    <>
+      <div className="fixed inset-0 z-[1] overflow-hidden bg-[radial-gradient(50%_50%_at_50%_50%,rgba(0,0,0,0.00)_49%,rgba(0,0,0,0.60)_100%)]" />
+      <div className="fixed inset-0 z-0 overflow-hidden">
+        <div
+          className={`h-full w-full transition-opacity duration-1000 ${isLoaded ? "opacity-50" : "opacity-0"}`}
+        >
+          <div className="absolute inset-0 grid grid-cols-5 gap-6 px-6 opacity-35">
+            <ImageColumn
+              images={columns.col1}
+              delay={0}
+              isLoaded={isLoaded}
+              duration={100}
+            />
+            <ImageColumn
+              images={columns.col2}
+              delay={columns.col1.length * 50}
+              isLoaded={isLoaded}
+              reverse={true}
+              duration={100}
+            />
+            <ImageColumn
+              images={columns.col3}
+              delay={(columns.col1.length + columns.col2.length) * 50}
+              isLoaded={isLoaded}
+              duration={125}
+            />
+            <ImageColumn
+              images={columns.col4}
+              delay={
+                (columns.col1.length +
+                  columns.col2.length +
+                  columns.col3.length) *
+                50
+              }
+              isLoaded={isLoaded}
+              reverse={true}
+              duration={160}
+            />
+            <ImageColumn
+              images={columns.col5}
+              delay={
+                (columns.col1.length +
+                  columns.col2.length +
+                  columns.col3.length +
+                  columns.col4.length) *
+                50
+              }
+              isLoaded={isLoaded}
+              duration={110}
+            />
+          </div>
+        </div>
+      </div>
+    </>
+  );
+}
@@ -0,0 +1,196 @@
+import { faSparkles } from "@fortawesome/pro-solid-svg-icons";
+import { useSignals } from "@preact/signals-react/runtime";
+import { Button, LoadingSpinner } from "~/components/ui";
+import { useRenderCounter } from "~/hooks/useRenderCounter";
+import { GenerationLoadingState, generationSignal } from "~/signals";
+import { useRef, useState } from "react";
+import BackgroundGallery from "./BackgroundGallery";
+import { twMerge } from "tailwind-merge";
+import { Transition } from "@headlessui/react";
+import { GenerationEngine } from "~/KonvaApp/GenerationEngine";
+import { ensureBase64Prefix } from "~/KonvaApp/EngineUtitlities/Base64Helpers";
+
+export const GenerationRootComponent = ({
+  generationEngineRef,
+}: {
+  generationEngineRef: React.MutableRefObject<GenerationEngine | null>;
+}) => {
+  // This is a hook that will log the number of times the component has rerendered
+  // Let's make sure we only log once
+  useRenderCounter("GenerationRootComponent");
+  useSignals();
+
+  const generationState = generationSignal.value;
+  const inputRef = useRef<HTMLTextAreaElement>(null);
+  const [isInputFocused, setIsInputFocused] = useState(false);
+
+  if (generationEngineRef.current === null) {
+    generationEngineRef.current = new GenerationEngine();
+  }
+
+  const handleGenerate = () => {
+    if (!inputRef.current) {
+      return;
+    }
+
+    const prompt = inputRef.current.value;
+    generationSignal.value = {
+      loadingState: GenerationLoadingState.GENERATING,
+      prompt,
+    };
+  };
+
+  let contentElement;
+  switch (generationState.loadingState) {
+    case GenerationLoadingState.INIT:
+      contentElement = null;
+      break;
+    case GenerationLoadingState.GENERATING:
+    case GenerationLoadingState.GENERATED:
+      contentElement = (
+        <GenerationContent
+          loadingState={generationState.loadingState}
+          imageB64={generationState.imageB64}
+          prompt={generationState.prompt}
+        />
+      );
+      break;
+  }
+
+  // const isButtonDisabled = generationState.loadingState === GenerationLoadingState.GENERATING;
+  const isButtonDisabled = false;
+
+  return (
+    <>
+      <div className="fixed z-10 flex h-full w-full flex-col items-center justify-center transition-all duration-500">
+        <Transition
+          show={generationState.loadingState !== GenerationLoadingState.INIT}
+          enter="transition-opacity duration-500"
+          enterFrom="opacity-0"
+          enterTo="opacity-100"
+          leave="transition-opacity duration-500"
+          leaveFrom="opacity-100"
+          leaveTo="opacity-0"
+          as="div"
+        >
+          {contentElement}
+        </Transition>
+        <div
+          className={twMerge(
+            "absolute flex w-full flex-col items-center justify-center gap-16 transition-all duration-500",
+            generationState.loadingState === GenerationLoadingState.INIT
+              ? "bottom-1/2 translate-y-1/2 transform"
+              : "bottom-8",
+          )}
+        >
+          <Transition
+            show={generationState.loadingState === GenerationLoadingState.INIT}
+            enter="transition-opacity duration-200"
+            enterFrom="opacity-0"
+            enterTo="opacity-100"
+            leave="transition-opacity duration-200"
+            leaveFrom="opacity-100"
+            leaveTo="opacity-0"
+            as="div"
+          >
+            <div className="flex flex-col items-center justify-center text-center drop-shadow-xl">
+              <span className="text-8xl font-bold">Generate Image</span>
+              <span className="pt-2 text-2xl opacity-80">
+                Imagine, Describe, Generate.
+              </span>
+            </div>
+          </Transition>
+          <div
+            className={twMerge(
+              "glass flex min-h-[56px] w-full max-w-[900px] items-end rounded-xl border-2 p-3 shadow-xl transition-all duration-[400ms] ease-in-out",
+              isInputFocused ? "border-primary-400/60" : "border-ui-panel",
+            )}
+          >
+            <textarea
+              ref={inputRef}
+              rows={1}
+              placeholder="Describe what you want to see..."
+              className="max-h-[120px] min-h-[40px] flex-1 resize-none overflow-y-auto rounded-md bg-transparent px-3 py-2 text-lg leading-normal focus:outline-none"
+              style={{ lineHeight: "24px" }}
+              onInput={(e) => {
+                const target = e.target as HTMLTextAreaElement;
+                target.style.height = "auto";
+                target.style.height = `${target.scrollHeight}px`;
+              }}
+              autoFocus={true}
+              onFocus={() => setIsInputFocused(true)}
+              onBlur={() => setIsInputFocused(false)}
+            />
+            <Button
+              icon={faSparkles}
+              variant="primary"
+              className="text-md ml-2 self-end"
+              onClick={handleGenerate}
+              disabled={isButtonDisabled}
+              loading={
+                generationState.loadingState ===
+                GenerationLoadingState.GENERATING
+              }
+            >
+              Generate
+            </Button>
+          </div>
+        </div>
+      </div>
+
+      <Transition
+        show={generationState.loadingState === GenerationLoadingState.INIT}
+        enter="transition-opacity duration-700"
+        enterFrom="opacity-0"
+        enterTo="opacity-100"
+        leave="transition-opacity duration-700"
+        leaveFrom="opacity-100"
+        leaveTo="opacity-0"
+        as="div"
+      >
+        <BackgroundGallery />
+      </Transition>
+    </>
+  );
+};
+
+const GenerationContent = (generationState: {
+  loadingState: GenerationLoadingState;
+  imageB64?: string;
+  prompt: string;
+}) => {
+  let imgBoxContent;
+  if (generationState.loadingState === GenerationLoadingState.GENERATING) {
+    imgBoxContent = (
+      <div className="flex h-full w-full flex-col items-center justify-center gap-7">
+        <LoadingSpinner isShowing={true} message="Generating image..." />
+      </div>
+    );
+  } else if (
+    generationState.loadingState === GenerationLoadingState.GENERATED &&
+    generationState.imageB64
+  ) {
+    imgBoxContent = (
+      <img
+        src={ensureBase64Prefix(generationState.imageB64)}
+        alt="Generated Image"
+        className="h-full w-full object-contain"
+      />
+    );
+  }
+
+  return (
+    <>
+      <div className="absolute inset-0 flex h-full w-full flex-col items-center justify-center">
+        <div className="aspect-[1/1] w-[min(80vw,68vh)] overflow-clip rounded-t-lg bg-[#29292D]/60">
+          {imgBoxContent}
+        </div>
+        <div className="mb-16 flex w-[min(80vw,68vh)] rounded-b-lg bg-[#29292D]">
+          <span className="p-3.5 text-sm opacity-50">
+            {generationState.prompt || "No prompt provided"}
+          </span>
+        </div>
+      </div>
+    </>
+  );
+};
@@ -0,0 +1,365 @@
+import Konva from "konva";
+
+import { uiAccess } from "~/signals";
+import { CommandManager, MatteBox } from "./EngineUtitlities";
+import { ImageNode } from "./Nodes";
+import { PaintNode } from "./Nodes/PaintNode";
+import {
+  NodesManager,
+  NodesTranslationEventDetails,
+  NodeTransformationEventDetails,
+  NodeTransformer,
+  SelectionManager,
+  SelectionManagerEvents,
+  SelectorSquare,
+} from "./NodesManagers";
+import { UndoStackManager } from "./UndoRedo";
+
+import { ToolbarMainButtonNames } from "~/components/features/ToolbarMain/enum";
+import { EditModes, VideoResolutions } from "./constants";
+
+import { ToolbarNodeButtonNames } from "~/components/features/ToolbarNode/enums";
+
+import { RealTimeDrawEngine } from "./RenderingPrimitives/RealTimeDrawEngine";
+
+export interface RenderingOptions {
+  artstyle: string;
+  positivePrompt: string;
+  negativePrompt: string;
+  cinematic: boolean;
+  enginePreProcessing: boolean;
+  faceDetail: boolean;
+  lipSync: boolean;
+  upscale: boolean;
+  styleStrength: number;
+}
+
+export class EditEngine {
+  private editMode: EditModes = EditModes.SELECT;
+  private boardCanvasRef: HTMLDivElement;
+  private stage: Konva.Stage;
+  private bgLayer: Konva.Layer;
+  private mediaLayer: Konva.Layer;
+  private nodeIsolationLayer: Konva.Layer;
+  private uiLayer: Konva.Layer;
+  private offScreenCanvas: OffscreenCanvas;
+  private realTimeDrawEngine: RealTimeDrawEngine;
+  private nodesManager: NodesManager;
+  private nodeTransformer: NodeTransformer;
+  private selectionManager: SelectionManager;
+  private selectorSquare: SelectorSquare;
+  private matteBox: MatteBox;
+
+  private undoStackManager: UndoStackManager;
+  private commandManager: CommandManager;
+
+  public segmentationButtonCanBePressed: boolean = true;
+  // signal reference
+  constructor(boardCanvasRef: HTMLDivElement) {
+    if (import.meta.env.DEV) {
+      console.log("Engine Created");
+    }
+    this.editMode = EditModes.SELECT;
+
+    this.boardCanvasRef = boardCanvasRef;
+    this.stage = new Konva.Stage({
+      container: this.boardCanvasRef,
+      width: window.innerWidth,
+      height: window.innerHeight,
+    });
+    this.bgLayer = new Konva.Layer();
+
+    this.mediaLayer = new Konva.Layer();
+
+    this.nodeIsolationLayer = new Konva.Layer();
+    this.uiLayer = new Konva.Layer();
+
+    this.stage.add(this.bgLayer);
+    this.stage.add(this.mediaLayer);
+    this.stage.add(this.nodeIsolationLayer);
+    this.stage.add(this.uiLayer);
+
+    // Konva Transformer
+    this.nodeTransformer = new NodeTransformer();
+    this.uiLayer.add(this.nodeTransformer.getKonvaNode());
+
+    // core layer for all the work done.
+    this.offScreenCanvas = new OffscreenCanvas(0, 0);
+
+    // Collection of all Nodes
+    this.nodesManager = new NodesManager();
+    // Partial Collection of selected Nodes
+    this.selectionManager = new SelectionManager({
+      nodeTransformerRef: this.nodeTransformer,
+      mediaLayerRef: this.mediaLayer,
+    });
+
+    this.realTimeDrawEngine = new RealTimeDrawEngine({
+      width: VideoResolutions.SQUARE_1024.width,
+      height: VideoResolutions.SQUARE_1024.height,
+      mediaLayerRef: this.mediaLayer,
+      offScreenCanvas: this.offScreenCanvas,
+      onDraw: async (canvas, lineBounds) => {
+        await this.addPaintNode(canvas, lineBounds);
+      },
+    });
+
+    // Selector Square to select Nodes
+    this.selectorSquare = new SelectorSquare({
+      captureCanvasRef: this.realTimeDrawEngine.captureCanvas,
+      mediaLayerRef: this.mediaLayer,
+      nodesManagerRef: this.nodesManager,
+      selectionManagerRef: this.selectionManager,
+      stageRef: this.stage,
+    });
+    this.uiLayer.add(this.selectorSquare.getKonvaNode());
+
+    //Collection of commands for undo-redo
+    this.undoStackManager = new UndoStackManager(() => {
+      this.realTimeDrawEngine.render();
+    });
+    this.commandManager = new CommandManager({
+      mediaLayerRef: this.mediaLayer,
+      nodesManagerRef: this.nodesManager,
+      nodeTransformerRef: this.nodeTransformer,
+      selectionManagerRef: this.selectionManager,
+      renderEngineRef: this.realTimeDrawEngine,
+      undoStackManagerRef: this.undoStackManager,
+    });
+
+    this.matteBox = new MatteBox({
+      boardCanvasSize: {
+        width: this.boardCanvasRef.clientWidth,
+        height: this.boardCanvasRef.clientHeight,
+      },
+      captureCanvasSize: this.realTimeDrawEngine.captureCanvas.getSize(),
+      uiLayerRef: this.uiLayer,
+    });
+
+    // some of the managers has events
+    // hence, lastly, setup these events
+    this.setupEventSystem();
+    this.setEditMode(this.editMode);
+  }
+
+  private setEditMode(newEditMode: EditModes) {
+    this.editMode = newEditMode;
+    switch (this.editMode) {
+      case EditModes.SELECT:
+      default: {
+        console.log("EDITMODE: SELECT");
+        this.realTimeDrawEngine.disablePaintMode();
+
+        this.selectorSquare.enable();
+        this.selectionManager.enable();
+        uiAccess.toolbarMain.enable();
+        uiAccess.toolbarMain.changeButtonState(ToolbarMainButtonNames.SELECT, {
+          active: true,
+        });
+        uiAccess.toolbarMain.changeButtonState(ToolbarMainButtonNames.PAINT, {
+          active: false,
+        });
+        this.matteBox.disable();
+        document.body.style.cursor = "default";
+        return;
+      }
+      case EditModes.EDIT: {
+        console.log("EDITMODE: EDIT");
+        this.realTimeDrawEngine.enablePaintMode();
+        this.selectorSquare.disable();
+        this.selectionManager.disable();
+      }
+    }
+  }
+  private setupEventSystem() {
+    // Listen to changes in container size
+    const resizeObserver = new ResizeObserver(() => {
+      this.onBoardCanvasResize();
+    });
+    resizeObserver.observe(this.boardCanvasRef);
+    this.onBoardCanvasResize();
+
+    // Listen to Nodes
+    this.selectionManager.eventTarget.addEventListener(
+      SelectionManagerEvents.NODES_TRANSLATIONS,
+      ((event: CustomEvent<NodesTranslationEventDetails>) => {
+        //console.log("Event: SelectionManager -> Engine", event);
+        this.commandManager.translateNodes(event.detail);
+      }) as EventListener,
+    );
+    this.selectionManager.eventTarget.addEventListener(
+      SelectionManagerEvents.NODES_TRANSFORMATION,
+      ((event: CustomEvent<NodeTransformationEventDetails>) => {
+        //console.log("Event: SelectionManager -> Engine", event);
+        this.commandManager.transformNodes(event.detail);
+      }) as EventListener,
+    );
+
+    // Listen to Toolbar Main
+    // uiEvents.toolbarMain.UNDO.onClick(() => {
+    //   this.undoStackManager.undo();
+    // });
+    // uiEvents.toolbarMain.REDO.onClick(() => {
+    //   this.undoStackManager.redo();
+    //   this.realTimeDrawEngine.render();
+    // });
+
+    // uiEvents.toolbarMain.SAVE.onClick(async (/*event*/) => {
+    //   await this.realTimeDrawEngine.saveOutput();
+    // });
+
+    // uiEvents.toolbarMain.SELECT.onClick(() => {
+    //   console.log("Toolbar Main >> Select");
+    //   this.setAppMode(AppModes.SELECT);
+    // });
+
+    // uiEvents.toolbarMain.PAINT.onClick(() => {
+    //   console.log("Toolbar Main >> Paint");
+    //   this.setAppMode(AppModes.PAINT);
+    // });
+  }
+
+  disableAllButtons() {
+    const buttonNames = Object.values(ToolbarNodeButtonNames);
+    for (const name of buttonNames) {
+      uiAccess.toolbarNode.changeButtonState(name, { disabled: true });
+    }
+  }
+
+  async enableAllButtons() {
+    const buttonNames = Object.values(ToolbarNodeButtonNames);
+    for (const name of buttonNames) {
+      await uiAccess.toolbarNode.changeButtonState(name, { disabled: false });
+    }
+  }
+
+  sleep(ms: number): Promise<void> {
+    return new Promise((resolve) => setTimeout(resolve, ms));
+  }
+
+  private onBoardCanvasResize() {
+    this.realTimeDrawEngine.updateCaptureCanvas(undefined, undefined);
+    this.matteBox.updateSize({
+      boardCanvasSize: {
+        width: this.boardCanvasRef.offsetWidth,
+        height: this.boardCanvasRef.offsetHeight,
+      },
+    });
+    this.uiLayer.draw();
+    this.stage.width(this.boardCanvasRef.offsetWidth);
+    this.stage.height(this.boardCanvasRef.offsetHeight);
+    this.stage.draw(); // Redraw the canvas
+  }
+
+  // Sandbox is quickly a way to test your idea.
+  public async sandbox() {}
+
+  public onMessage(event: MessageEvent) {
+    console.log("Message From Shared Worker");
+    console.log(event);
+  }
+
+  public initializeStage() {
+    // load canvas that was originaly saved TODO Save manager for resharing.
+    uiAccess.toolbarNode.hide();
+    uiAccess.loadingBar.hide();
+    this.setupStage();
+  }
+
+  public isInitialized() {
+    return this.stage !== null;
+  }
+
+  public async setupStage() {
+    // Frame rate inicator
+    const textNode = new Konva.Text({
+      x: 10,
+      y: 80,
+      text: "",
+      fontSize: 18,
+      fontFamily: "Source Sans 3",
+      fill: "white",
+    });
+    const anim = new Konva.Animation((frame) => {
+      if (frame && import.meta.env.DEV) {
+        const timeDiff = frame.timeDiff;
+        const frameRate = frame.frameRate;
+        textNode.setText(
+          `FrameTime: ${timeDiff.toFixed(0)} ms\nFrameRate: ${frameRate.toFixed(0)} fps`,
+        );
+      }
+    }, this.mediaLayer);
+
+    anim.start();
+    this.uiLayer.add(textNode);
+    this.addKeyboardShortcuts();
+  }
+
+  public addImage(imageFile: File) {
+    const imageNode = new ImageNode({
+      mediaLayerRef: this.mediaLayer,
+      canvasPosition: this.realTimeDrawEngine.captureCanvas.position(),
+      canvasSize: this.realTimeDrawEngine.captureCanvas.size(),
+      imageFile: imageFile,
+      selectionManagerRef: this.selectionManager,
+      loaded: async () => {
+        await this.realTimeDrawEngine.render();
+        this.setEditMode(EditModes.SELECT);
+      },
+    });
+
+    this.commandManager.createNode(imageNode);
+  }
+
+  public addImageFromImageBitmap(imageBitmap: ImageBitmap) {
+    const imageNode = new ImageNode({
+      mediaLayerRef: this.mediaLayer,
+      canvasPosition: this.realTimeDrawEngine.captureCanvas.position(),
+      canvasSize: this.realTimeDrawEngine.captureCanvas.size(),
+      imageBitmap: imageBitmap,
+      selectionManagerRef: this.selectionManager,
+      loaded: async () => {
+        await this.realTimeDrawEngine.render();
+        this.setEditMode(EditModes.SELECT);
+      },
+    });
+
+    this.commandManager.createNode(imageNode);
+  }
+
+  public addPaintNode(
+    canvas: HTMLCanvasElement,
+    lineBounds: {
+      width: number;
+      height: number;
+      x: number;
+      y: number;
+    },
+  ) {
+    var node = new PaintNode({
+      canvasElement: canvas,
+      lineBounds: lineBounds,
+      mediaLayerRef: this.mediaLayer,
+      selectionManagerRef: this.selectionManager,
+      loaded: async () => {
+        await this.realTimeDrawEngine.render();
+      },
+    });
+    this.commandManager.createNode(node);
+  }
+  // Events for Undo and Redo
+  private addKeyboardShortcuts() {
+    window.addEventListener("keydown", (event) => {
+      if (event.ctrlKey && event.key === "z") {
+        this.undoStackManager.undo();
+      } else if (
+        (event.ctrlKey && event.key === "y") ||
+        (event.ctrlKey && event.shiftKey && event.key === "Z")
+      ) {
+        this.undoStackManager.redo();
+      } else if (event.key === "Delete") {
+        this.commandManager.deleteNodes();
+      }
+    });
+  }
+}
@@ -1,35 +1,36 @@
 import Konva from "konva";

-import { uiAccess, uiEvents } from "~/signals";
-import { ShapeNode } from "./Nodes";
-import { PaintNode } from "./Nodes/PaintNode";
-import { PreviewCopyNode } from "./Nodes/PreviewCopy";
-import { UndoStackManager } from "./UndoRedo";
-import { CommandManager, MatteBox, SceneManager } from "./EngineUtitlities";
+import {uiAccess, uiEvents} from "~/signals";
+import {ImageNode, ShapeNode, ShapeType, TextNode, VideoNode} from "./Nodes";
+import {PaintNode} from "./Nodes/PaintNode";
+import {PreviewCopyNode} from "./Nodes/PreviewCopy";
+import {UndoStackManager} from "./UndoRedo";
+import {CommandManager, MatteBox, SceneManager} from "./EngineUtitlities";
 import {
-  NodesManager,
  NodeIsolator,
-  NodeTransformer,
+  NodesManager,
  NodesTranslationEventDetails,
  NodeTransformationEventDetails,
+  NodeTransformer,
  SelectionManager,
  SelectionManagerEvents,
  SelectorSquare,
 } from "./NodesManagers";
-import { ImageNode, VideoNode, TextNode } from "./Nodes";
-import { EngineOptions, TextNodeData, VideoNodeData } from "./types";
+import {EngineOptions, TextNodeData, VideoNodeData} from "./types";

-import { AppModes, VideoResolutions } from "./constants";
-import { ToolbarMainButtonNames } from "~/components/features/ToolbarMain/enum";
+import {AppModes, VideoResolutions} from "./constants";
+import {ToolbarMainButtonNames} from "~/components/features/ToolbarMain/enum";

-import { ToolbarNodeButtonNames } from "~/components/features/ToolbarNode/enums";
-import { NavigateFunction } from "react-router-dom";
-import { LoadingVideosProvider } from "./EngineUtitlities/LoadingVideosProvider";
+import {ToolbarNodeButtonNames} from "~/components/features/ToolbarNode/enums";
+import {NavigateFunction} from "react-router-dom";
+import {LoadingVideosProvider} from "./EngineUtitlities/LoadingVideosProvider";

-import { VideoExtractionHandler } from "./EngineUtitlities/VideoExtractionHandler/VideoExtractionHandler";
-import { RealTimeDrawEngine } from "./RenderingPrimitives/RealTimeDrawEngine";
-import { NodeColor } from "~/signals/uiEvents/toolbarNode";
-import { ShapeType } from "./Nodes";
+import {VideoExtractionHandler} from "./EngineUtitlities/VideoExtractionHandler/VideoExtractionHandler";
+import {RealTimeDrawEngine} from "./RenderingPrimitives/RealTimeDrawEngine";
+import {NodeColor} from "~/signals/uiEvents/toolbarNode";
+import {invoke} from "@tauri-apps/api/core";
+import {DecodeBase64ToImage} from "~/utilities/DecodeBase64ToImage.ts";
+import {EncodeImageBitmapToBase64} from "~/utilities/EncodeImageBitmapToBase64.ts";


 export interface RenderingOptions {
@@ -326,6 +327,9 @@ export class Engine {
    uiEvents.toolbarNode.MOVE_LAYER_UP.onClick(() =>
      this.commandManager.moveNodesUp(),
    );
+    uiEvents.toolbarNode.REMOVE_BACKGROUND.onClick(() =>
+      this.removeBackground(),
+    );

    // Listen to Toolbar Main
    uiEvents.toolbarMain.UNDO.onClick(() => {
@@ -445,6 +449,38 @@ export class Engine {
    });
  }

+  removeBackground() {
+    const selectedNodeSet = this.selectionManager.getSelectedNodes();
+
+    if (selectedNodeSet.size !== 1) {
+      return; // Can only remove background from a single image at a time.
+    }
+
+    const [ selectedNode ] = selectedNodeSet;
+
+    const removeBgInvocation
+        = async (selectedNode: ImageNode, callback: (existingImageNode: ImageNode, createdImage: ImageBitmap) => void) => {
+
+      const image : HTMLImageElement = selectedNode.kNode.image();
+      const base64Bitmap = await EncodeImageBitmapToBase64(image);
+      const base64BitmapResponse = await invoke("remove_background", {
+        image: base64Bitmap,
+      });
+
+      const createdImage = await DecodeBase64ToImage(base64BitmapResponse as string);
+
+      callback(selectedNode, createdImage);
+    };
+
+    const _promise = removeBgInvocation(selectedNode, (selectedNode: ImageNode, createdImage: ImageBitmap) => {
+      if (!this.nodesManager.hasNode(selectedNode)) {
+        return;
+      }
+      this.commandManager.deleteSpecificNodes([selectedNode]);
+      this.addImageFromImageBitmap(createdImage);
+    })
+  }
+
  disableAllButtons() {
    const buttonNames = Object.values(ToolbarNodeButtonNames);
    for (const name of buttonNames) {
@@ -624,6 +660,23 @@ export class Engine {

    this.commandManager.createNode(imageNode);
  }
+
+  public addImageFromImageBitmap(imageBitmap: ImageBitmap) {
+    const imageNode = new ImageNode({
+      mediaLayerRef: this.mediaLayer,
+      canvasPosition: this.realTimeDrawEngine.captureCanvas.position(),
+      canvasSize: this.realTimeDrawEngine.captureCanvas.size(),
+      imageBitmap: imageBitmap,
+      selectionManagerRef: this.selectionManager,
+      loaded: async () => {
+        await this.realTimeDrawEngine.render();
+        this.setAppMode(AppModes.SELECT);
+      },
+    });
+
+    this.commandManager.createNode(imageNode);
+  }
+
  public addPaintNode(
    canvas: HTMLCanvasElement,
    lineBounds: {
@@ -0,0 +1,7 @@
+const ensureBase64Prefix = (base64String: string) => {
+    return base64String.startsWith("data:")
+      ? base64String
+      : `data:image/png;base64,${base64String}`;
+}
+
+export { ensureBase64Prefix };
@@ -78,6 +78,20 @@ export class CommandManager {
    this.undoStackManagerRef.executeCommand(command);
    this.renderEngineRef.render();
  }
+
+  deleteSpecificNodes(nodes: MediaNode[]) {
+    const command = new DeleteCommand({
+      nodes: nodes,
+      mediaLayerRef: this.mediaLayerRef,
+      nodesManagerRef: this.nodesManagerRef,
+      nodeTransformerRef: this.nodeTransformerRef,
+      selectionManagerRef: this.selectionManagerRef,
+      renderEngineRef: this.renderEngineRef,
+    });
+    this.undoStackManagerRef.executeCommand(command);
+    this.renderEngineRef.render();
+  }
+
  toggleLockNodes() {
    const nodes = this.selectionManagerRef.getSelectedNodes();
    const node = nodes.values().next().value;
@@ -0,0 +1,46 @@
+import { invoke } from "@tauri-apps/api/core";
+import {
+  cancelGeneration,
+  finishGeneration,
+  onStartGeneration,
+} from "~/signals/generation/generationSignals";
+
+export class GenerationEngine {
+  private isGenerating = false;
+
+  constructor() {
+    console.log("GenerationEngine constructor");
+    this.attachEventListeners();
+  }
+
+  private attachEventListeners() {
+    onStartGeneration((prompt) => {
+      this.invokeGeneration(prompt);
+    });
+  }
+
+  private async invokeGeneration(prompt: string) {
+    if (this.isGenerating) {
+      return;
+    }
+
+    this.isGenerating = true;
+    console.log("Invoking generation for prompt:", prompt);
+
+    try {
+      const base64BitmapResponse = await invoke("text_to_image", {
+        prompt: prompt,
+      });
+
+      console.log("Generation finished with response:", base64BitmapResponse);
+
+      const base64string = base64BitmapResponse as string;
+      finishGeneration(base64string);
+    } catch (error) {
+      console.error("Error during image processing:", error);
+      cancelGeneration();
+    } finally {
+      this.isGenerating = false;
+    }
+  }
+}
@@ -12,6 +12,7 @@ interface ImageNodeContructor {
  canvasPosition: Position;
  canvasSize: Size;
  imageFile?: File;
+  imageBitmap?: ImageBitmap;
  mediaFileToken?: string;
  mediaFileUrl?: string;
  transform?: TransformationData;
@@ -34,6 +35,7 @@ export class ImageNode extends NetworkedNode {
    mediaLayerRef,
    selectionManagerRef,
    loaded,
+    imageBitmap,
  }: ImageNodeContructor) {
    // kNodes need to be created first to guaruntee
    // that it is not undefined in parent's context
@@ -73,6 +75,14 @@ export class ImageNode extends NetworkedNode {
      this.loadImageFromUrl(mediaFileUrl);
      return;
    }
+    if (imageBitmap) {
+      this.loadImageFromImageBitmap({
+        imageBitmap: imageBitmap,
+        maxSize: canvasSize,
+        refPosition: canvasPosition,
+      });
+      return;
+    }
    console.log("image node creation is fucked");
  }
  private loadImageFromFile({
@@ -136,6 +146,40 @@ export class ImageNode extends NetworkedNode {
    this.listenToBaseKNode();
  }

+  private loadImageFromImageBitmap({
+    imageBitmap,
+    maxSize,
+    refPosition,
+  }: {
+    imageBitmap: ImageBitmap;
+    maxSize: Size;
+    refPosition: Position;
+  }) {
+    this.imageSize = {
+      width: imageBitmap.width,
+      height: imageBitmap.height,
+    };
+    const adjustedSize = NodeUtilities.adjustNodeSizeToCanvas({
+      componentSize: this.imageSize,
+      maxSize: maxSize, // TODO
+    });
+    const centerPosition = NodeUtilities.positionNodeOnCanvasCenter({
+      canvasOffset: refPosition, // TODO
+      componentSize: adjustedSize, // TODO
+      maxSize: maxSize, // TODO
+    });
+    this.kNode.image(imageBitmap);
+    this.kNode.setSize(adjustedSize);
+    this.kNode.setPosition(centerPosition);
+    this.kNode.moveToTop();
+    this.kNode.fill(transparent);
+    this.listenToBaseKNode();
+    this.mediaLayerRef.draw();
+    //this.uploadImage(imageFile);
+    this.loaded();
+    this.listenToBaseKNode();
+  }
+
  private async uploadImage(imageFile: File) {
    //this.setProgress({ progress: 10, status: UploadStatus.UPLOADING });

@@ -44,6 +44,10 @@ export class NodesManager {
    // this.printNodesAndZIndices();
  }

+  public hasNode(node: MediaNode): boolean {
+    return this.allNodes.has(node);
+  }
+
  /*************************
   *  For Debugging use
   */
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Brandon Thomas	14feaccb91	Merge branch 'main' into feature/generatemode	2025-03-25 01:38:48 -04:00
Brandon Thomas	81c256e4f8	cleanup	2025-03-24 23:08:07 -04:00
Brandon Thomas	8d51d5b8ab	can't trigger default features; switch to platform switches	2025-03-24 23:00:29 -04:00
Brandon Thomas	456bfd7bb9	trigger with --feature ml_models (need to make default)	2025-03-24 22:31:58 -04:00
Brandon Thomas	cd3c864eff	runs on mac	2025-03-24 21:46:25 -04:00
Brandon Thomas	990b7ff3e5	package split for mac (part one) (#459 )	2025-03-24 20:18:57 -04:00
Brandon Thomas	e1dbf4f2fe	add inpaint demo/test endpoint	2025-03-22 03:16:48 -04:00
Tanish Manku	d826db599d	Remove the cancel generation button for now	2025-03-22 00:08:06 -07:00
Tanish Manku	a642732c59	Clip overflow to have top rounded corners on image	2025-03-22 00:02:21 -07:00
Brandon Thomas	435c5aa0b4	fix strength bug	2025-03-22 02:57:06 -04:00
Tanish Manku	90b4aff846	Fix: Multiple invokes of a prompt after changing mode	2025-03-21 23:56:50 -07:00
Tanish Manku	4d3f097432	Hook up the TTI UI to invoke call	2025-03-21 23:38:25 -07:00
Tanish Manku	e5de0e4731	Merge branch 'main' into feature/generatemode	2025-03-21 23:11:07 -07:00
Tanish Manku	04498b0457	Add initial Edit mode files	2025-03-21 23:02:17 -07:00
Brandon Thomas	e3245dc593	fix on windows	2025-03-22 00:58:04 -04:00
bflatastic	fbfc1e15f2	gallery gap fix	2025-03-21 22:46:30 +07:00
bflatastic	4a65317729	rename image to realtime	2025-03-21 20:13:13 +07:00
Tanish Manku	e7ed0693a3	Add generation engine and signals	2025-03-21 00:44:44 -07:00
Tanish Manku	99ac8faf33	Merge branch 'main' into feature/generatemode	2025-03-20 23:41:19 -07:00
Brandon Thomas	08590d0c61	log environment details	2025-03-21 01:11:34 -04:00
Brandon Thomas	32cb39a643	text_to_image endpoint	2025-03-20 23:28:59 -04:00
bflatastic	f0213d4a63	cancel button	2025-03-21 00:03:15 +07:00
Brandon Thomas	5fdb4d1024	disable msi builds due to 2.0 gb limit for 32bit	2025-03-20 11:59:43 -04:00
bflatastic	58f684b1e0	tab disabled prop and disabled message to block tab change when generating	2025-03-20 22:42:36 +07:00
bflatastic	e83389a874	background gallery + many other styling and animation transition updates	2025-03-20 22:11:12 +07:00
Brandon Thomas	d11945b685	download DIS model	2025-03-20 11:07:34 -04:00
Brandon Thomas	f3babbcb19	if statements -> switch statements	2025-03-20 10:53:34 -04:00
Tanish Manku	3602f8fc2b	Add generation signals	2025-03-20 02:10:27 -07:00
Tanish Manku	115c4fc60c	Add a generation mode UI component	2025-03-19 23:58:53 -07:00
Brandon Thomas	f37011ffc6	remove background: frontend UX (#458 )	2025-03-20 01:41:43 -04:00
Brandon Thomas	de683b750a	remove background from images	2025-03-18 22:10:18 -04:00
Brandon Thomas	4603c65d2a	fix strength slider range	2025-03-18 18:21:39 -04:00
Brandon Thomas	c5b0d837a9	fix bug with float strengths from frontend	2025-03-18 01:48:54 -04:00
Brandon Thomas	652785224a	easier windows builds. put dlls in the windows_dlls folder	2025-03-14 04:22:18 -04:00
Brandon Thomas	8d3ed947d1	change default prompt, log end of endpoint logic	2025-03-14 03:20:46 -04:00
Brandon Thomas	243583f0b7	kill more dead code	2025-03-13 23:39:04 -04:00
Brandon Thomas	150223c48c	refactor weights (#455 )	2025-03-13 23:29:20 -04:00
bflatastic	ddf6a7f54e	top center ui bug fix and padding	2025-03-13 22:28:26 +07:00
Brandon Thomas	17c6e549b5	ignore windows dlls	2025-03-13 00:38:10 -04:00
Brandon Thomas	031d9d8f20	Download models from Cloudflare instead of HF (#454 )	2025-03-13 00:32:43 -04:00