Fix Shape size jumping after recreation (on color change)

Merge branch 'main' into bugfix/shape-changes
RealTimeDrawEngine: Change RenderFrame logic to use stage clone instead of OffscreenCanvas
2026-10-21 09:22:41 -04:00 · 2025-03-07 00:15:54 -08:00 · 2025-03-06 22:58:33 -08:00 · 2025-03-06 22:53:46 -08:00 · 2025-03-06 22:47:09 -08:00 · 2025-03-06 22:46:40 -08:00
3469 changed files with 3434 additions and 168814 deletions
@@ -66,7 +66,3 @@ 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/
@@ -1,8 +1,6 @@
 [workspace]
 resolver = "2"

-# This block defines the workspace members.
-# When we add a library or binary crate, it needs an entry here.
 members = [
  # Types: common strongly-typed tokens, ids, payloads, etc.
  "crates/_types/composite_identifiers",
@@ -24,10 +22,7 @@ members = [
  "crates/cli/production/migration-tool",

  # Desktop apps
-  "crates/desktop/ml_models",
-  "crates/desktop/ml_weights_registry",
-  "crates/desktop/tauri-4o",
-  "crates/desktop/tauri-realtime",
+  "crates/desktop/src-tauri",

  # Shared libraries
  "crates/lib/actix_cors_configs",
@@ -35,7 +30,6 @@ members = [
  "crates/lib/api_clients/cloud_storage",
  "crates/lib/api_clients/google_drive_common",
  "crates/lib/api_clients/google_sign_in",
-  "crates/lib/api_clients/openai_sora_client",
  "crates/lib/api_clients/thumbnail_generator",
  "crates/lib/bootstrap",
  "crates/lib/bucket_paths",
@@ -62,7 +56,6 @@ members = [
  "crates/lib/files/mimetypes",
  "crates/lib/files/videos",
  "crates/lib/idempotency",
-  "crates/lib/image_algorithms",
  "crates/lib/jobs_common",
  "crates/lib/logging",
  "crates/lib/markdown",
@@ -122,35 +115,3 @@ members = [
  # see usage: https://crates.io/crates/cargo-hakari
  "crates/my-workspace-hack",
 ]
-
-[workspace.package]
-# Common inheritable settings for monorepo packages
-publish = false
-
-[workspace.dependencies]
-# Hakari workspace hack (faster compile times)
-my-workspace-hack = { path = "crates/my-workspace-hack" }
-
-# External libs
-# Centrally pinned versions so that we can uniformly upgrade/downgrade versions across the workspace
-# If a crate needs to use an older/newer version, the dependency should be managed in that crate's own Cargo.toml
-anyhow = "1.0.97"
-chrono = "0.4.40"
-log = "0.4.27"
-once_cell = "1.21.3"
-rand = "0.9.0"
-reqwest = { version = "0.12.15", features = ["json", "cookies"] }
-serde = {  version = "1.0.219", features = ["derive"] }
-serde_derive = "1.0.219"
-serde_json = "1.0.140"
-serde_yaml = "0.9.1"
-toml = "0.7.4"
-
-# Internal libs
-crockford = { path = "crates/lib/data/crockford" }
-errors = { path = "crates/lib/errors" }
-filesys = { path = "crates/lib/files/filesys" }
-memory_caching = { path = "crates/lib/caching/memory_caching" }
-openai_sora_client = { path = "crates/lib/api_clients/openai_sora_client" }
-primitives = { path = "crates/lib/data/primitives" }
-subprocess_common = { path = "crates/lib/subprocess_common" }
@@ -1,8 +1,7 @@
-image_width: 1024
-image_height: 1024
+image_width: 512
+image_height: 512
 #scheduler_steps: 15
 # NB(bt): It looks like we can get down to 2 sampling steps, perhaps ?
-scheduler_steps: 8
+scheduler_steps: 4
 scheduler_samples: 1
-cfg_scale: 15.0
-seed: 42
+cfg_scale: 1.1
@@ -40,10 +40,6 @@ pub enum InferenceJobType {
  #[serde(rename = "studio_gen2")]
  StudioGen2,

-  /// Sora GPT 4o image gen
-  #[serde(rename = "image_gen_api")]
-  ImageGenApi,
-
  /// A job that turns "FBX" game engine files into "GLTF" files (Bevy-compatible).
  #[serde(rename = "convert_fbx_gltf")]
  ConvertFbxToGltf,
@@ -112,7 +108,6 @@ impl InferenceJobType {
      Self::GptSovits => "gpt_sovits",
      Self::ComfyUi => "comfy_ui",
      Self::StudioGen2 => "studio_gen2",
-      Self::ImageGenApi => "image_gen_api",
      Self::ConvertFbxToGltf => "convert_fbx_gltf",
      Self::MocapNet => "mocap_net",
      Self::RvcV2 => "rvc_v2",
@@ -137,7 +132,6 @@ impl InferenceJobType {
      "gpt_sovits" => Ok(Self::GptSovits),
      "comfy_ui" => Ok(Self::ComfyUi),
      "studio_gen2" => Ok(Self::StudioGen2),
-      "image_gen_api" => Ok(Self::ImageGenApi),
      "convert_fbx_gltf" => Ok(Self::ConvertFbxToGltf),
      "mocap_net" => Ok(Self::MocapNet),
      "rvc_v2" => Ok(Self::RvcV2),
@@ -165,7 +159,6 @@ impl InferenceJobType {
      Self::GptSovits,
      Self::ComfyUi,
      Self::StudioGen2,
-      Self::ImageGenApi,
      Self::ConvertFbxToGltf,
      Self::MocapNet,
      Self::RvcV2,
@@ -204,7 +197,6 @@ mod tests {
      assert_serialization(InferenceJobType::GptSovits, "gpt_sovits");
      assert_serialization(InferenceJobType::ComfyUi, "comfy_ui");
      assert_serialization(InferenceJobType::StudioGen2, "studio_gen2");
-      assert_serialization(InferenceJobType::ImageGenApi, "image_gen_api");
      assert_serialization(InferenceJobType::ConvertFbxToGltf, "convert_fbx_gltf");
      assert_serialization(InferenceJobType::MocapNet, "mocap_net");
      assert_serialization(InferenceJobType::RvcV2, "rvc_v2");
@@ -228,7 +220,6 @@ mod tests {
      assert_eq!(InferenceJobType::GptSovits.to_str(), "gpt_sovits");
      assert_eq!(InferenceJobType::ComfyUi.to_str(), "comfy_ui");
      assert_eq!(InferenceJobType::StudioGen2.to_str(), "studio_gen2");
-      assert_eq!(InferenceJobType::ImageGenApi.to_str(), "image_gen_api");
      assert_eq!(InferenceJobType::ConvertFbxToGltf.to_str(), "convert_fbx_gltf");
      assert_eq!(InferenceJobType::MocapNet.to_str(), "mocap_net");
      assert_eq!(InferenceJobType::RvcV2.to_str(), "rvc_v2");
@@ -252,7 +243,6 @@ mod tests {
      assert_eq!(InferenceJobType::from_str("gpt_sovits").unwrap(), InferenceJobType::GptSovits);
      assert_eq!(InferenceJobType::from_str("comfy_ui").unwrap(), InferenceJobType::ComfyUi);
      assert_eq!(InferenceJobType::from_str("studio_gen2").unwrap(), InferenceJobType::StudioGen2);
-      assert_eq!(InferenceJobType::from_str("image_gen_api").unwrap(), InferenceJobType::ImageGenApi);
      assert_eq!(InferenceJobType::from_str("convert_fbx_gltf").unwrap(), InferenceJobType::ConvertFbxToGltf);
      assert_eq!(InferenceJobType::from_str("mocap_net").unwrap(), InferenceJobType::MocapNet);
      assert_eq!(InferenceJobType::from_str("rvc_v2").unwrap(), InferenceJobType::RvcV2);
@@ -271,7 +261,7 @@ mod tests {
    fn all_variants() {
      // Static check
      let mut variants = InferenceJobType::all_variants();
-      assert_eq!(variants.len(), 17);
+      assert_eq!(variants.len(), 16);
      assert_eq!(variants.pop_first(), Some(InferenceJobType::VideoRender));
      assert_eq!(variants.pop_first(), Some(InferenceJobType::LivePortrait));
      assert_eq!(variants.pop_first(), Some(InferenceJobType::FaceFusion));
@@ -279,7 +269,6 @@ mod tests {
      assert_eq!(variants.pop_first(), Some(InferenceJobType::GptSovits));
      assert_eq!(variants.pop_first(), Some(InferenceJobType::ComfyUi));
      assert_eq!(variants.pop_first(), Some(InferenceJobType::StudioGen2));
-      assert_eq!(variants.pop_first(), Some(InferenceJobType::ImageGenApi));
      assert_eq!(variants.pop_first(), Some(InferenceJobType::ConvertFbxToGltf));
      assert_eq!(variants.pop_first(), Some(InferenceJobType::MocapNet));
      assert_eq!(variants.pop_first(), Some(InferenceJobType::RvcV2));
@@ -33,10 +33,6 @@ pub enum InferenceModelType {
  #[serde(rename = "seed_vc")]
  SeedVc,

-  /// NB: This is for Sora GPT 4o image gen
-  #[serde(rename = "image_gen_api")]
-  ImageGenApi,
-
  #[serde(rename = "tacotron2")]
  Tacotron2,
  #[serde(rename = "vits")]
@@ -74,7 +70,6 @@ impl InferenceModelType {
      Self::VallEX => "vall_e_x",
      Self::RerenderAVideo => "rerender_a_video",
      Self::StableDiffusion => "stable_diffusion",
-      Self::ImageGenApi => "image_gen_api",
      Self::SeedVc => "seed_vc",
      Self::MocapNet => "mocap_net",
      Self::StyleTTS2 => "styletts2",
@@ -95,7 +90,6 @@ impl InferenceModelType {
      "vall_e_x" => Ok(Self::VallEX),
      "rerender_a_video" => Ok(Self::RerenderAVideo),
      "stable_diffusion" => Ok(Self::StableDiffusion),
-      "image_gen_api" => Ok(Self::ImageGenApi),
      "mocap_net" => Ok(Self::MocapNet),
      "styletts2" => Ok(Self::StyleTTS2),
      "comfy_ui" => Ok(Self::ComfyUi),
@@ -119,7 +113,6 @@ impl InferenceModelType {
      Self::VallEX,
      Self::RerenderAVideo,
      Self::StableDiffusion,
-      Self::ImageGenApi,
      Self::MocapNet,
      Self::StyleTTS2,
      Self::ConvertFbxToGltf,
@@ -147,7 +140,6 @@ mod tests {
      assert_serialization(InferenceModelType::VallEX, "vall_e_x");
      assert_serialization(InferenceModelType::RerenderAVideo, "rerender_a_video");
      assert_serialization(InferenceModelType::StableDiffusion, "stable_diffusion");
-      assert_serialization(InferenceModelType::ImageGenApi, "image_gen_api");
      assert_serialization(InferenceModelType::MocapNet, "mocap_net");
      assert_serialization(InferenceModelType::ComfyUi, "comfy_ui");
      assert_serialization(InferenceModelType::StyleTTS2, "styletts2");
@@ -167,7 +159,6 @@ mod tests {
      assert_eq!(InferenceModelType::VallEX.to_str(), "vall_e_x");
      assert_eq!(InferenceModelType::RerenderAVideo.to_str(), "rerender_a_video");
      assert_eq!(InferenceModelType::StableDiffusion.to_str(), "stable_diffusion");
-      assert_eq!(InferenceModelType::ImageGenApi.to_str(), "image_gen_api");
      assert_eq!(InferenceModelType::MocapNet.to_str(), "mocap_net");
      assert_eq!(InferenceModelType::StyleTTS2.to_str(), "styletts2");
      assert_eq!(InferenceModelType::ConvertFbxToGltf.to_str(), "convert_fbx_gltf");
@@ -185,7 +176,6 @@ mod tests {
      assert_eq!(InferenceModelType::from_str("vall_e_x").unwrap(), InferenceModelType::VallEX);
      assert_eq!(InferenceModelType::from_str("rerender_a_video").unwrap(), InferenceModelType::RerenderAVideo);
      assert_eq!(InferenceModelType::from_str("stable_diffusion").unwrap(), InferenceModelType::StableDiffusion);
-      assert_eq!(InferenceModelType::from_str("image_gen_api").unwrap(), InferenceModelType::ImageGenApi);
      assert_eq!(InferenceModelType::from_str("mocap_net").unwrap(), InferenceModelType::MocapNet);
      assert_eq!(InferenceModelType::from_str("styletts2").unwrap(), InferenceModelType::StyleTTS2);
      assert_eq!(InferenceModelType::from_str("comfy_ui").unwrap(), InferenceModelType::ComfyUi);
@@ -197,18 +187,17 @@ mod tests {
    fn all_variants() {
      // Static check
      let mut variants = InferenceModelType::all_variants();
-      assert_eq!(variants.len(), 15);
+      assert_eq!(variants.len(), 13);
      assert_eq!(variants.pop_first(), Some(InferenceModelType::ComfyUi));
      assert_eq!(variants.pop_first(), Some(InferenceModelType::RvcV2));
      assert_eq!(variants.pop_first(), Some(InferenceModelType::SadTalker));
-      assert_eq!(variants.pop_first(), Some(InferenceModelType::SoVitsSvc));
      assert_eq!(variants.pop_first(), Some(InferenceModelType::SeedVc));
+      assert_eq!(variants.pop_first(), Some(InferenceModelType::SoVitsSvc));
      assert_eq!(variants.pop_first(), Some(InferenceModelType::Tacotron2));
      assert_eq!(variants.pop_first(), Some(InferenceModelType::Vits));
      assert_eq!(variants.pop_first(), Some(InferenceModelType::VallEX));
      assert_eq!(variants.pop_first(), Some(InferenceModelType::RerenderAVideo));
      assert_eq!(variants.pop_first(), Some(InferenceModelType::StableDiffusion));
-      assert_eq!(variants.pop_first(), Some(InferenceModelType::ImageGenApi));
      assert_eq!(variants.pop_first(), Some(InferenceModelType::MocapNet));
      assert_eq!(variants.pop_first(), Some(InferenceModelType::StyleTTS2));
      assert_eq!(variants.pop_first(), Some(InferenceModelType::ConvertFbxToGltf));
@@ -91,10 +91,6 @@ pub enum PublicInferenceJobType {
  /// Re-render a video is a video style transfer algorithm. We developed code
  /// around it, but chose to develop AnimateDiff / ComfyUI support instead.
  RerenderAVideo,
-
-  /// Sora GPT 4o image gen
-  #[serde(rename = "image_gen_api")]
-  ImageGenApi,
 }

 /// NB: Legacy API for older code.
@@ -123,7 +119,6 @@ impl PublicInferenceJobType {
      InferenceJobType::Unknown => Self::Unknown,
      InferenceJobType::BevyToWorkflow => Self::BevyToWorkflow,
      InferenceJobType::RerenderAVideo => Self::RerenderAVideo,
-      InferenceJobType::ImageGenApi => Self::ImageGenApi,
    }
  }
  
@@ -145,7 +140,6 @@ impl PublicInferenceJobType {
      Self::SeedVc => InferenceJobType::SeedVc,
      Self::SoVitsSvc => InferenceJobType::SoVitsSvc,
      Self::StableDiffusion => InferenceJobType::StableDiffusion,
-      Self::ImageGenApi => InferenceJobType::ImageGenApi,
      Self::StyleTTS2 => InferenceJobType::StyleTTS2,
      Self::Tacotron2 => InferenceJobType::Tacotron2,
      Self::Unknown => InferenceJobType::Unknown,
@@ -1,80 +0,0 @@
-[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" }
-image_algorithms = { path = "../../lib/image_algorithms" }
-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"
@@ -1 +0,0 @@
-pub mod ml;
@@ -1,2 +0,0 @@
-pub mod onnx_session;
-pub mod remove_image_background;
@@ -1,19 +0,0 @@
-//! 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)
-}
@@ -1,66 +0,0 @@
-use crate::ml::background_removal::onnx_session::onnx_session;
-use image::{imageops, DynamicImage};
-use ndarray::{Array, CowArray};
-use ort::Value;
-use std::path::Path;
-use image_algorithms::remove_image_border::remove_image_border;
-
-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)?;
-  
-  // Crop transparent border cruft away.
-  let image = remove_image_border(&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))
-}
@@ -1,297 +0,0 @@
-use std::path::Path;
-use std::sync::{Arc, RwLock, RwLockReadGuard, RwLockWriteGuard};
-
-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::prompt_cache::PromptCache;
-use candle_nn::VarBuilder;
-use crate::ml::model_config::ModelConfig;
-use anyhow::{anyhow, Error as E, Result};
-use candle_core::{DType, IndexOp, Tensor, D, Device, Module};
-use candle_transformers::models::stable_diffusion::vae::{AutoEncoderKL, DiagonalGaussianDistribution};
-use candle_transformers::models::stable_diffusion::lcm::LCMScheduler;
-use image::DynamicImage;
-use log::{debug, info};
-use rand::Rng;
-use tauri::{AppHandle, Emitter};
-use candle_transformers::models::{clip, t5, flux};
-use tokenizers::Tokenizer;
-
-#[derive(Debug, Clone, Copy, PartialEq, Eq)]
-pub enum FluxModel {
-  Schnell,
-  Dev,
-}
-
-pub struct FluxPipelineArgs<'a> {
-  pub image: &'a DynamicImage,
-  pub prompt: String,
-  pub model_cache: &'a ModelCache,
-  pub prompt_cache: &'a PromptCache,
-  pub app: &'a AppHandle,
-  pub flux_model: FluxModel,
-  pub active_blocks: Option<usize>,
-  pub use_cuda_stream: Option<bool>,
-  pub prefetch_next_batch: Option<bool>,
-  pub use_full_gpu: Option<bool>,
-  pub img2img_strength: Option<f64>,
-  pub seed: Option<u64>,
-  pub use_quantized_model: Option<bool>,
-  pub model_config: &'a ModelConfig,
-  pub flux_dev_or_schnell_path: &'a Path,
-  pub flux_schnell_quantized_gguf: &'a Path,
-  pub flux_autoencoder_path: &'a Path,
-  pub google_t5_model_path: &'a Path,
-  pub google_t5_config_path: &'a Path,
-  pub lmz_t5_tokenizer_path: &'a Path,
-  pub openai_clip_model_path: &'a Path,
-  pub openai_clip_tokenizer_json_path: &'a Path,
-}
-
-pub fn flux_pipeline(args: FluxPipelineArgs<'_>) -> Result<RgbImage> {
-  let FluxPipelineArgs { 
-    prompt, 
-    model_cache, 
-    prompt_cache, 
-    app, 
-    image, 
-    flux_model, 
-    active_blocks, 
-    use_cuda_stream, 
-    prefetch_next_batch, 
-    use_full_gpu, 
-    img2img_strength, 
-    seed, 
-    use_quantized_model, 
-    model_config ,
-    flux_dev_or_schnell_path,
-    flux_schnell_quantized_gguf,
-    flux_autoencoder_path,
-    google_t5_model_path,
-    google_t5_config_path,
-    lmz_t5_tokenizer_path,
-    openai_clip_model_path,
-    openai_clip_tokenizer_json_path,
-  } = args;
-
-  if use_quantized_model.unwrap_or(false) {
-    #[cfg(feature = "cuda")]
-    candle_core::quantized::cuda::set_force_dmmv(false);
-  }
-
-  let height = 1024;
-  let width = 1024;
-
-  let quantized = use_quantized_model.unwrap_or(false);
-  let device = model_config.device.clone();
-  let dtype = device.bf16_default_to_f32();
-
-  info!("Starting image generation with FLUX model:");
-  info!("  Model: {:?}", flux_model);
-  info!("  Prompt: {}", prompt);
-  info!("  Dimensions: {}x{}", width, height);
-  info!("  Device: {:?}", model_config.device);
-
-  let seed_value = seed.unwrap_or_else(|| rand::thread_rng().gen());
-  device.set_seed(seed_value)?;
-  
-  info!("Using seed: {}", seed_value);
-
-  // Setup FLUX model configuration
-  let flux_config = match flux_model {
-    FluxModel::Dev => flux::model::Config::dev(),
-    FluxModel::Schnell => flux::model::Config::schnell(),
-  };
-
-  // Get T5 embeddings
-  let t5_emb = {
-    info!("Generating T5 embeddings for prompt...");
-    let vb = unsafe { VarBuilder::from_mmaped_safetensors(&[google_t5_model_path], dtype , &device)? };
-    let config = std::fs::read_to_string(google_t5_config_path)?;
-    let config: t5::Config = serde_json::from_str(&config)?;
-
-    let mut model = t5::T5EncoderModel::load(vb, &config)?;
-    let tokenizer = Tokenizer::from_file(lmz_t5_tokenizer_path).map_err(E::msg)?;
-
-    let mut tokens = tokenizer.encode(prompt.as_str(), true).map_err(E::msg)?.get_ids().to_vec();
-    tokens.resize(256, 0);
-    let input_token_ids = Tensor::new(&tokens[..], &device)?.unsqueeze(0)?;
-
-    model.forward(&input_token_ids)?
-  };
-  
-  info!("T5 embeddings generated");
-
-  // Get CLIP embeddings
-  let clip_emb = {
-    info!("Generating CLIP embeddings for prompt...");
-    let vb =
-        unsafe { VarBuilder::from_mmaped_safetensors(&[openai_clip_model_path], dtype, &device)? };
-    // https://huggingface.co/openai/clip-vit-large-patch14/blob/main/config.json
-    let config = clip::text_model::ClipTextConfig {
-        vocab_size: 49408,
-        projection_dim: 768,
-        activation: clip::text_model::Activation::QuickGelu,
-        intermediate_size: 3072,
-        embed_dim: 768,
-        max_position_embeddings: 77,
-        pad_with: None,
-        num_hidden_layers: 12,
-        num_attention_heads: 12,
-    };
-    let model =
-        clip::text_model::ClipTextTransformer::new(vb.pp("text_model"), &config)?;
-    let tokenizer = Tokenizer::from_file(openai_clip_tokenizer_json_path).map_err(E::msg)?;
-    let tokens = tokenizer
-        .encode(prompt.as_str(), true)
-        .map_err(E::msg)?
-        .get_ids()
-        .to_vec();
-    let input_token_ids = Tensor::new(&tokens[..], &device)?.unsqueeze(0)?;
-    model.forward(&input_token_ids)?
-  };
-  info!("CLIP embeddings generated");
-
-  // Process input image to tensor
-  info!("Converting input image to tensor...");
-  let img_tensor = dynamic_image_to_tensor(image, &device, dtype)?;
-  info!("Image tensor shape: {:?}", img_tensor.shape());
-
-  // Create initial noise - this is important to match the expected format
-  info!("Generating initial noise for FLUX...");
-  let initial_tensor = flux::sampling::get_noise(1, height, width, &device)?.to_dtype(dtype)?;
-  info!("Initial tensor shape: {:?}", initial_tensor.shape());
-  
-  // Blend initial noise with input image if img2img_strength is specified
-  let initial_tensor = if let Some(strength) = img2img_strength {
-    if strength > 0.0 && strength < 1.0 {
-      info!("Using img2img with strength: {}", strength);
-      // Reshape the input tensor to match the noise tensor dimensions
-      let adjusted_img = if img_tensor.shape() != initial_tensor.shape() {
-        info!("Reshaping input image to match noise dimensions...");
-        // You may need more complex reshaping logic here depending on your use case
-        let (_, c, h, w) = initial_tensor.dims4()?;
-        img_tensor.upsample_nearest2d(h, w)?
-      } else {
-        img_tensor
-      };
-      
-      // Blend the noise and the image according to strength
-      let strength_tensor = Tensor::new(strength as f32, &device)?;
-      let inverse_strength = Tensor::new(1.0 - strength as f32, &device)?;
-      let weighted_noise = (&initial_tensor * &strength_tensor)?;
-      let weighted_img = (&adjusted_img * &inverse_strength)?;
-      (&weighted_noise + &weighted_img)?
-    } else {
-      initial_tensor
-    }
-  } else {
-    initial_tensor
-  };
-  info!("Final initial tensor shape: {:?}", initial_tensor.shape());
-
-  // Create FLUX state
-  info!("Creating FLUX state...");
-  let state = if quantized {
-    flux::sampling::State::new(
-        &t5_emb.to_dtype(DType::F32)?,
-        &clip_emb.to_dtype(DType::F32)?,
-        &initial_tensor.to_dtype(DType::F32)?,
-    )?
-  } else {
-    flux::sampling::State::new(&t5_emb, &clip_emb, &initial_tensor)?
-  };
-  info!("{state:?}");
-
-  // Set appropriate timesteps based on model
-  let timesteps = match flux_model {
-    FluxModel::Dev => flux::sampling::get_schedule(28, Some((state.img.dim(1)?, 0.5, 1.15))),
-    FluxModel::Schnell => flux::sampling::get_schedule(4, None),
-  };
-
-  info!("Starting FLUX denoising with timesteps: {:?}", timesteps);
-
-  // Actual model loading and inference
-  let img_latent = if quantized {
-    // Quantized model path
-    info!("Loading quantized FLUX model...");
-    let model_file = match flux_model {
-      FluxModel::Schnell => flux_schnell_quantized_gguf,
-      FluxModel::Dev => return Err(anyhow!("Quantized model not available for FLUX.1-dev")),
-    };
-
-    info!("Loading quantized model from {}", model_file.to_str().unwrap());
-
-    let vb = candle_transformers::quantized_var_builder::VarBuilder::from_gguf(model_file, &device)?;
-    let mut model = flux::quantized_model::Flux::new(&flux_config, vb)?;
-    info!("Quantized model loaded on {:?}", device);
-
-    // Run denoising
-    info!("Running denoising...");
-    let img_latent = flux::sampling::denoise(&mut model, &state.img, &state.img_ids, &state.txt, &state.txt_ids, &state.vec, &timesteps, 4.)?.to_dtype(dtype)?;
-    info!("Denoising completed");
-    img_latent
-  } else {
-    // Full precision model
-    info!("Loading FLUX model...");
-
-    let mut model = if use_full_gpu.unwrap_or(false) {
-      // Load entire model on GPU
-      info!("Loading entire model directly to GPU...");
-      let gpu_vb = unsafe { VarBuilder::from_mmaped_safetensors(&[flux_dev_or_schnell_path], dtype, &device)? };
-      let mut model = flux::model::Flux::new(&flux_config, gpu_vb)?;
-      model.set_use_device_management(false);
-      model
-    } else {
-      // Use memory-efficient approach with blocks on CPU
-      info!("Using memory-efficient approach with blocks on CPU...");
-      let cpu_device = candle_core::Device::Cpu;
-      let cpu_vb = unsafe { VarBuilder::from_mmaped_safetensors(&[flux_dev_or_schnell_path], dtype, &cpu_device)? };
-      let mut model = flux::model::Flux::new_with_gpu_core(&flux_config, cpu_vb, &device)?;
-
-      // Set active blocks
-      let active_blocks_count = active_blocks.unwrap_or(1);
-      model.set_max_active_blocks(active_blocks_count);
-      info!("Using {} active blocks on GPU", active_blocks_count);
-
-      // Enable prefetching if requested
-      if prefetch_next_batch.unwrap_or(false) {
-        model.set_prefetch_next_batch(true);
-        info!("Prefetching enabled - will overlap transfers with computation");
-      }
-
-      model
-    };
-
-    // Run denoising
-    flux::sampling::denoise(&mut model, &state.img, &state.img_ids, &state.txt, &state.txt_ids, &state.vec, &timesteps, 4.)?
-  };
-
-  info!("FLUX denoising completed, unpacking latent image...");
-  let img_unpacked = flux::sampling::unpack(&img_latent, height, width)?;
-
-  // Load autoencoder for final decoding
-  info!("Loading autoencoder for final decoding...");
-  
-  let ae_config = match flux_model {
-    FluxModel::Dev => flux::autoencoder::Config::dev(),
-    FluxModel::Schnell => flux::autoencoder::Config::schnell(),
-  };
-
-  let vb = unsafe { VarBuilder::from_mmaped_safetensors(&[flux_autoencoder_path], dtype, &device)? };
-  let ae_model = flux::autoencoder::AutoEncoder::new(&ae_config, vb)?;
-
-  info!("Decoding final image...");
-  let img = ae_model.decode(&img_unpacked)?;
-
-  // Convert tensor to image
-  info!("Converting tensor to image...");
-  let img = ((img.clamp(-1f32, 1f32)? + 1.0)? * 127.5)?.to_dtype(candle_core::DType::U8)?;
-
-  // Convert to RGB image
-  let image = tensor_to_image_buffer(&img.i(0)?)?;
-
-  info!("FLUX pipeline completed successfully");
-  Ok(image)
-}
@@ -1 +0,0 @@
-pub mod flux_pipeline;
@@ -1,8 +0,0 @@
-pub mod background_removal;
-pub mod flux;
-pub mod image;
-pub mod model_cache;
-pub mod model_config;
-pub mod model_file;
-pub mod prompt_cache;
-pub mod stable_diffusion;
@@ -1,61 +0,0 @@
-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,
-    })
-  }
-}
@@ -1,180 +0,0 @@
-#[cfg(feature = "accelerate")]
-extern crate accelerate_src;
-#[cfg(feature = "mkl")]
-extern crate intel_mkl_src;
-
-use candle_transformers::models::stable_diffusion;
-use anyhow::{Error as E, Result};
-use hf_hub::api::sync::Api;
-#[derive(Debug, Clone, Copy, PartialEq, Eq)]
-pub enum StableDiffusionVersion {
-    V1_5,
-    V1_5Inpaint,
-    V2_1,
-    V2Inpaint,
-    Xl,
-    XlInpaint,
-    Turbo,
-}
-
-#[derive(Debug, Clone, Copy, PartialEq, Eq)]
-pub enum ModelFile {
-    Tokenizer,
-    Tokenizer2,
-    Clip,
-    Clip2,
-    Unet,
-    UnetLcm,
-    Vae,
-}
-
-impl StableDiffusionVersion {
-    pub fn repo(&self) -> &'static str {
-        match self {
-            Self::XlInpaint => "diffusers/stable-diffusion-xl-1.0-inpainting-0.1",
-            Self::Xl => "stabilityai/stable-diffusion-xl-base-1.0",
-            Self::V2Inpaint => "stabilityai/stable-diffusion-2-inpainting",
-            Self::V2_1 => "stabilityai/stable-diffusion-2-1",
-            Self::V1_5 => "Lykon/dreamshaper-7",
-            Self::V1_5Inpaint => "stable-diffusion-v1-5/stable-diffusion-inpainting",
-            Self::Turbo => "Lykon/dreamshaper-7",
-        }
-    }
-
-    fn unet_file(&self, use_f16: bool) -> &'static str {
-        match self {
-            Self::V1_5
-            | Self::V1_5Inpaint
-            | Self::V2_1
-            | Self::V2Inpaint
-            | Self::Xl
-            | Self::XlInpaint
-            | Self::Turbo => {
-                if use_f16 {
-                    "unet/diffusion_pytorch_model.fp16.safetensors"
-                } else {
-                    "unet/diffusion_pytorch_model.safetensors"
-                }
-            }
-        }
-    }
-
-    fn vae_file(&self, use_f16: bool) -> &'static str {
-        match self {
-            Self::V1_5
-            | Self::V1_5Inpaint
-            | Self::V2_1
-            | Self::V2Inpaint
-            | Self::Xl
-            | Self::XlInpaint
-            | Self::Turbo => {
-                if use_f16 {
-                    "vae/diffusion_pytorch_model.fp16.safetensors"
-                } else {
-                    "vae/diffusion_pytorch_model.safetensors"
-                }
-            }
-        }
-    }
-
-    fn clip_file(&self, use_f16: bool) -> &'static str {
-        match self {
-            Self::V1_5
-            | Self::V1_5Inpaint
-            | Self::V2_1
-            | Self::V2Inpaint
-            | Self::Xl
-            | Self::XlInpaint
-            | Self::Turbo => {
-                if use_f16 {
-                    "text_encoder/model.fp16.safetensors"
-                } else {
-                    "text_encoder/model.safetensors"
-                }
-            }
-        }
-    }
-
-    fn clip2_file(&self, use_f16: bool) -> &'static str {
-        match self {
-            Self::V1_5
-            | Self::V1_5Inpaint
-            | Self::V2_1
-            | Self::V2Inpaint
-            | Self::Xl
-            | Self::XlInpaint
-            | Self::Turbo => {
-                if use_f16 {
-                    "text_encoder_2/model.fp16.safetensors"
-                } else {
-                    "text_encoder_2/model.safetensors"
-                }
-            }
-        }
-    }
-}
-
-impl ModelFile {
-    pub fn get(
-        &self,
-        filename: Option<String>,
-        version: StableDiffusionVersion,
-        use_f16: bool,
-    ) -> Result<std::path::PathBuf> {
-        match filename {
-            Some(filename) => Ok(std::path::PathBuf::from(filename)),
-            None => {
-                let (repo, path) = match self {
-                    Self::Tokenizer => {
-                        let tokenizer_repo = match version {
-                            StableDiffusionVersion::V1_5
-                            | StableDiffusionVersion::V2_1
-                            | StableDiffusionVersion::V1_5Inpaint
-                            | StableDiffusionVersion::V2Inpaint => "openai/clip-vit-base-patch32",
-                            StableDiffusionVersion::Xl
-                            | StableDiffusionVersion::XlInpaint
-                            | StableDiffusionVersion::Turbo => {
-                                // This seems similar to the patch32 version except some very small
-                                // difference in the split regex.
-                                "openai/clip-vit-large-patch14"
-                            }
-                        };
-                        (tokenizer_repo, "tokenizer.json")
-                    }
-                    Self::Tokenizer2 => {
-                        ("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", "tokenizer.json")
-                    }
-                    Self::Clip => (version.repo(), version.clip_file(use_f16)),
-                    Self::Clip2 => (version.repo(), version.clip2_file(use_f16)),
-                    Self::UnetLcm => (
-                        "SimianLuo/LCM_Dreamshaper_v7",
-                        if use_f16 {
-                            "unet/diffusion_pytorch_model.fp16.safetensors"
-                        } else {
-                            "unet/diffusion_pytorch_model.safetensors"
-                        },
-                    ),
-                    Self::Unet => (version.repo(), version.unet_file(use_f16)),
-                    Self::Vae => {
-                        // Override for SDXL when using f16 weights.
-                        // See https://github.com/huggingface/candle/issues/1060
-                        if matches!(
-                            version,
-                            StableDiffusionVersion::Xl | StableDiffusionVersion::Turbo,
-                        ) && use_f16
-                        {
-                            (
-                                "madebyollin/sdxl-vae-fp16-fix",
-                                "diffusion_pytorch_model.safetensors",
-                            )
-                        } else {
-                            (version.repo(), version.vae_file(use_f16))
-                        }
-                    }
-                };
-                let filename = Api::new()?.model(repo.to_string()).get(path)?;
-                Ok(filename)
-            }
-        }
-    }
-}
@@ -1,158 +0,0 @@
-#[cfg(feature = "accelerate")]
-extern crate accelerate_src;
-#[cfg(feature = "mkl")]
-extern crate intel_mkl_src;
-
-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;
-
-
-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<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 {
-    StableDiffusionVersion::Xl | StableDiffusionVersion::XlInpaint | StableDiffusionVersion::Turbo => vec![true, false],
-    _ => vec![true],
-  };
-
-  let text_embeddings = which
-    .iter()
-    .map(|first| {
-      do_infer_clip_text_embeddings(
-        &prompt,
-        &uncond_prompt,
-        None, // tokenizer
-        clip_weights.clone(),
-        None, // clip2_weights
-        sd_version,
-        &sd_config,
-        false, // use_f16
-        &device,
-        dtype,
-        use_guide_scale,
-        *first,
-        clip_json_path.as_ref(),
-        clip_weights_path.as_ref(),
-      )
-    })
-    .collect::<anyhow::Result<Vec<_>>>()?;
-
-  let text_embeddings = Tensor::cat(&text_embeddings, D::Minus1)?;
-
-  Ok(text_embeddings)
-}
-
-#[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, 
-  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 = clip_json_path;
-
-  info!("Loading Clip Tokenizer path: {:?}", tokenizer);
-
-  let tokenizer = Tokenizer::from_file(tokenizer).map_err(E::msg)?;
-
-  let pad_id = match &sd_config.clip.pad_with {
-    Some(padding) => *tokenizer.get_vocab(true).get(padding.as_str()).unwrap(),
-    None => *tokenizer.get_vocab(true).get("<|endoftext|>").unwrap(),
-  };
-
-  println!("Running with prompt \"{prompt}\".");
-  let mut tokens = tokenizer.encode(prompt, true).map_err(E::msg)?.get_ids().to_vec();
-  if tokens.len() > sd_config.clip.max_position_embeddings {
-    anyhow::bail!("the prompt is too long, {} > max-tokens ({})", tokens.len(), sd_config.clip.max_position_embeddings)
-  }
-  while tokens.len() < sd_config.clip.max_position_embeddings {
-    tokens.push(pad_id)
-  }
-  let tokens = Tensor::new(tokens.as_slice(), device)?.unsqueeze(0)?;
-
-  println!("Building the Clip transformer.");
-  
-  // TODO(bt,2025-03-23): These used to be two different clip models. We should only need one.
-  let clip_weights = if first {
-    println!(">>>>> CLIP 1");
-    clip_weights_path
-  } else {
-    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)?;
-
-  let text_embeddings = text_model.forward(&tokens)?;
-
-  let text_embeddings = if use_guide_scale {
-    let mut uncond_tokens = tokenizer.encode(uncond_prompt, true).map_err(E::msg)?.get_ids().to_vec();
-    if uncond_tokens.len() > sd_config.clip.max_position_embeddings {
-      anyhow::bail!("the negative prompt is too long, {} > max-tokens ({})", uncond_tokens.len(), sd_config.clip.max_position_embeddings)
-    }
-    while uncond_tokens.len() < sd_config.clip.max_position_embeddings {
-      uncond_tokens.push(pad_id)
-    }
-
-    let uncond_tokens = Tensor::new(uncond_tokens.as_slice(), device)?.unsqueeze(0)?;
-    let uncond_embeddings = text_model.forward(&uncond_tokens)?;
-
-    Tensor::cat(&[uncond_embeddings, text_embeddings], 0)?.to_dtype(dtype)?
-  } else {
-    text_embeddings.to_dtype(dtype)?
-  };
-  Ok(text_embeddings)
-}
@@ -1,281 +0,0 @@
-use std::path::{Path, PathBuf};
-use std::sync::{Arc, RwLock, RwLockReadGuard, RwLockWriteGuard};
-
-use anyhow::{anyhow, Error as E, Result};
-use candle_core::{DType, Device, IndexOp, Tensor, D};
-use candle_nn::VarBuilder;
-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::{debug, error, 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;
-
-const DEFAULT_STRENGTH : f64 = 75.0;
-
-const DEFAULT_SEED : u64 = 42;
-
-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 img2img_strength: Option<f64>,
-  pub model_cache: &'a ModelCache,
-  pub prompt_cache: &'a PromptCache,
-  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<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 img2img_strength = img2img_strength
-    .map(|s| remap_lcm_strength_range(s))
-    .unwrap_or(DEFAULT_STRENGTH);
-  
-  let img2img_strength = img2img_strength / 100.0;
-
-  let use_f16 = true;
-
-  // Use LCM Scheduler instead of Euler Ancestral for better speed and quality
-  let mut scheduler = LCMScheduler::new(scheduler_steps, img2img_strength, LCMSchedulerConfig::default())?;
-
-  //let seed = maybe_seed.unwrap_or_else(|| rand::thread_rng().gen());
-  let seed = maybe_seed.unwrap_or(DEFAULT_SEED);
-
-  device.set_seed(seed)?;
-
-  let guidance_scale = match cfg_scale {
-    Some(guidance_scale) => guidance_scale,
-    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"
-    },
-  };
-
-  // let use_guide_scale = guidance_scale > 1.0;
-  let use_guide_scale = false;
-
-  let maybe_cached = prompt_cache.get_copy(&prompt)?;
-
-  let mut text_embeddings = if let Some(tensor) = maybe_cached {
-    tensor
-  } else {
-    info!("Prompt is NOT cached! Calculating embedding...");
-    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
-      device,
-      dtype,
-      use_guide_scale,
-      clip_json_path,
-      clip_weights_path,
-    })?;
-
-    prompt_cache.store_copy(&prompt, &tensor)?;
-    tensor
-  };
-
-  debug!("Text embeddings shape: {:?}", text_embeddings.shape());
-
-  debug!("Loading input image into tensor...");
-
-  let input_image = dynamic_image_to_tensor(image, &device, dtype)?;
-
-  debug!("Reference image shape: {:?}", input_image.shape());
-
-  let vae_scale = get_vae_scale(sd_version);
-
-  let maybe_vae = model_cache.get_vae()?;
-
-  let vae = match maybe_vae {
-    Some(vae) => vae,
-    None => {
-      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);
-
-      model_cache.set_vae(vae.clone())?;
-
-      vae
-    }
-  };
-
-  let maybe_unet = model_cache.get_unet()?;
-
-  let unet = match maybe_unet {
-    Some(unet) => unet,
-    None => {
-      info!("Building UNET model from file {:?}...", unet_path.as_ref());
-
-      let in_channels = match sd_version {
-        StableDiffusionVersion::XlInpaint | StableDiffusionVersion::V2Inpaint | StableDiffusionVersion::V1_5Inpaint => 9,
-        _ => 4,
-      };
-
-      // For LCM models, ensure that:
-      // 1. We're using the correct prediction type (epsilon)
-      // 2. The cross-attention dimension is correct (768 for Dreamshaper)
-      // 3. The in_channels matches the expected input (4 for standard, 9 for inpainting)
-      let unet_config = UNet2DConditionModelConfig {
-        // LCM specific config values
-        cross_attention_dim: 768,      // Dreamshaper v7 uses 768
-        time_cond_proj_dim: Some(256), // LCM models use 256-dimensional guidance embeddings
-        // Rest of configuration...
-        center_input_sample: false,
-        flip_sin_to_cos: true,
-        freq_shift: 0.0,
-        blocks: vec![
-          BlockConfig { out_channels: 320, use_cross_attn: Some(1), attention_head_dim: 8 },
-          BlockConfig { out_channels: 640, use_cross_attn: Some(1), attention_head_dim: 8 },
-          BlockConfig { out_channels: 1280, use_cross_attn: Some(1), attention_head_dim: 8 },
-          BlockConfig { out_channels: 1280, use_cross_attn: None, attention_head_dim: 8 }
-        ],
-        layers_per_block: 2,
-        downsample_padding: 1,
-        mid_block_scale_factor: 1.0,
-        norm_num_groups: 32,
-        norm_eps: 1e-5,
-        sliced_attention_size: None,
-        use_linear_projection: false,
-        time_embed_dim: Some(1280),
-        // time_embed_dim: Some(256),
-      };
-
-      // Load the model directly from the safetensors file
-      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 = Arc::new(unet);
-
-      model_cache.set_unet(unet.clone())?;
-
-      unet
-    },
-  };
-
-  let init_latent_dist: DiagonalGaussianDistribution = vae.encode(&input_image)?;
-
-  // Create LCM guidance scale embeddings
-  let embedding_dim = 1280;
-  let guidance_scale_embedding = scheduler.get_guidance_scale_embedding(guidance_scale, embedding_dim, &device, dtype)?;
-
-  // Generate latents from input image using the LCM approach
-  debug!("Generating latents from input image...");
-  let init_latents = (init_latent_dist.sample()? * vae_scale)?;
-
-  debug!("Initial latents shape: {:?}", init_latents.shape());
-
-  // Calculate timesteps for LCM with proper img2img handling
-  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
-  debug!("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)?;
-
-  let mut latents = latents;
-  let timesteps: Vec<_> = scheduler.timesteps().iter().copied().collect();
-
-  for (timestep_index, &timestep) in timesteps.iter().enumerate() {
-    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, Some(&guidance_scale_embedding)) {
-      Ok(pred) => pred,
-      Err(e) => {
-        error!("UNet inference failed with error: {}", e);
-        return Err(anyhow::anyhow!("UNet inference failed: {}", e));
-      },
-    };
-
-    if use_guide_scale {
-      let chunks = noise_pred.chunk(2, 0)?;
-      let (noise_pred_uncond, noise_pred_text) = (&chunks[0], &chunks[1]);
-      noise_pred = (noise_pred_uncond + (guidance_scale * (noise_pred_text - noise_pred_uncond)?)?)?;
-    }
-
-    // Apply the LCM scheduler step
-    latents = scheduler.step(&noise_pred, timestep, &latents, timestep_index, scheduler_steps)?;
-  }
-
-  debug!("Diffusion process completed, decoding image...");
-  let image = vae.decode(&(latents / vae_scale)?)?;
-
-  debug!("Scaling image back");
-  let image = ((image / 2.)? + 0.5)?;
-
-  debug!("Normalized image values");
-  let image = (image.clamp(0f32, 1.)? * 255.)?;
-
-  debug!("Convert to int8");
-  let image = image.to_dtype(DType::U8)?;
-
-  debug!("Converted to 8-bit format");
-
-  let image = tensor_to_image_buffer(&image.i(0)?)?;
-
-  Ok(image)
-}
@@ -1,54 +0,0 @@
-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);
-  }
-}
@@ -1,18 +0,0 @@
-[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"
@@ -1 +0,0 @@
-pub mod weights_registry;
@@ -1,80 +0,0 @@
-use crate::weights_registry::weight_descriptor::{WeightDescriptor, WeightFunction};
-use crate::weights_registry::weight_descriptor_builder::weight;
-
-
-/// https://huggingface.co/black-forest-labs/FLUX.1-dev
-pub const FLUX_DEV : WeightDescriptor = weight!(
-  "Flux-1 Dev",
-  "flux1-dev.safetensors",
-  WeightFunction::ImageGeneration
-);
-
-// TODO(bt): I think the autoencoder for Dev and Schnell are the same.
-//  They have the same checksum unless I mixed up the files when downloading.
-
-/// https://huggingface.co/black-forest-labs/FLUX.1-dev
-pub const FLUX_DEV_AUTOENCODER : WeightDescriptor = weight!(
-  "Flux-1 Dev (Autoencoder)",
-  "flux1-dev.ae.safetensors",
-  WeightFunction::ImageGeneration
-);
-
-/// https://huggingface.co/black-forest-labs/FLUX.1-schnell
-pub const FLUX_SCHNELL : WeightDescriptor = weight!(
-  "Flux-1 Schnell",
-  "flux1-schnell.safetensors",
-  WeightFunction::ImageGeneration
-);
-
-/// https://huggingface.co/black-forest-labs/FLUX.1-schnell
-pub const FLUX_SCHNELL_AUTOENCODER : WeightDescriptor = weight!(
-  "Flux-1 Schnell (Autoencoder)",
-  "flux1-schnell.ae.safetensors",
-  WeightFunction::ImageGeneration
-);
-
-/// https://huggingface.co/lmz/candle-flux
-pub const LMZ_CANDLE_FLUX_SCHNELL_QUANTIZED_GGUF : WeightDescriptor = weight!(
-  "LMZ Flux Schnell Quantized",
-  "lmz_candle_flux_flux1-schnell.gguf",
-  WeightFunction::ImageGeneration
-);
-
-/// https://huggingface.co/google/t5-v1_1-xxl/tree/main
-/// NB: I couldn't find the safetensors file here
-/// NB: This should live under "refs/pr/2"
-pub const GOOGLE_T5_V1_1_XXL_MODEL : WeightDescriptor = weight!(
-  "Google T5 v1.1 (XXL)",
-  "google_t5_v1_1_xxl_model.safetensors",
-  WeightFunction::TextEncoder
-);
-
-/// https://huggingface.co/google/t5-v1_1-xxl/tree/main
-/// NB: I couldn't find the safetensors file here
-/// NB: This should live under "refs/pr/2"
-pub const GOOGLE_T5_V1_1_XXL_CONFIG : WeightDescriptor = weight!(
-  "Google T5 v1.1 (XXL) Config",
-  "google_t5_v1_1_xxl_config.json",
-  WeightFunction::TextEncoder
-);
-
-/// https://huggingface.co/lmz/mt5-tokenizers
-pub const LMZ_T5_TOKENIZER_JSON : WeightDescriptor = weight!(
-  "LMZ T5 Tokenizer",
-  "lmz_mt5_tokenizers_t5-v1_1-xxl.tokenizer.json",
-  WeightFunction::TextTokenizer
-);
-
-/// https://huggingface.co/openai/clip-vit-large-patch14/tree/main
-pub const OPENAI_CLIP_VIT_P14_MODEL : WeightDescriptor = weight!(
-  "OpenAI CLIP VIT (patch 14) model",
-  "openai_clip-vit-large-patch14.model.safetensors",
-  WeightFunction::TextTokenizer
-);
-
-/// https://huggingface.co/openai/clip-vit-large-patch14/tree/main
-pub const OPENAI_CLIP_VIT_P14_TOKENIZER_JSON: WeightDescriptor = weight!(
-  "OpenAI CLIP VIT (patch 14) tokenizer",
-  "openai_clip-vit-large-patch14.tokenizer.json",
-  WeightFunction::TextTokenizer
-);
@@ -1,4 +0,0 @@
-mod weight_descriptor_builder;
-pub mod flux_weights;
-pub mod weight_descriptor;
-pub mod weights;
@@ -1,47 +0,0 @@
-
-// 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 {
-  ImageGeneration,
-  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::ImageGeneration => "image generation",
-      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())
-  }
-}
@@ -1,17 +0,0 @@
-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;
@@ -1,65 +0,0 @@
-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
-);
@@ -0,0 +1,65 @@
+[package]
+name = "app"
+version = "0.1.0"
+description = "A Tauri App"
+authors = ["you"]
+license = ""
+repository = ""
+edition = "2021"
+rust-version = "1.77.2"
+
+# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
+
+[lib]
+name = "app_lib"
+crate-type = ["staticlib", "cdylib", "rlib"]
+
+[build-dependencies]
+tauri-build = { version = "2.0.4", features = [] }
+
+[dependencies]
+
+# Internal
+filesys = { path = "../../lib/files/filesys" }
+memory_caching = { path = "../../lib/caching/memory_caching" }
+
+# Bypass cudarc issues on Windows https://github.com/coreylowman/cudarc/issues/332
+cudarc = "0.13.7"
+
+# External
+anyhow = "1.0"
+bytes = "1.10"
+base64 = "0.22.1"
+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"] }
+hf-hub = "0.3.2"
+image = "0.25.5"
+serde_yaml = "0.9"
+log = "0.4"
+once_cell = "1.20.3"
+expanduser = "1.2.2"
+rand = "0.8"
+reqwest = "0.12.12"
+serde = { version = "1.0", features = ["derive"] }
+serde_json = "1.0"
+tauri = { version = "2.2.4", features = [] }
+tauri-plugin-log = "2"
+tokenizers = "0.15.2"
+
+[features]
+default = ["cuda"]
+cuda = ["candle-core/cuda", "candle-nn/cuda", "candle-transformers/cuda", "candle-transformers/flash-attn", "candle-core/cudnn"]
+
+[dev-dependencies]
+libloading = "0.8.6"
+
+[profile.dev]
+incremental = true # Compile your binary in smaller steps.
+
+[profile.release]
+codegen-units = 1 # Allows LLVM to perform better optimization.
+lto = true # Enables link-time-optimizations.
+opt-level = 3 # `s` prioritizes small binary size. Use `3` if you prefer speed.
+panic = "abort" # Higher performance by disabling panic handlers.
+strip = true # Ensures debug symbols are removed.
@@ -0,0 +1,16 @@
+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>,
+) -> Result<String, String> {
+  
+  download_all_models(&app_data_root).await
+    .map_err(|err| {
+      "error downloading models".to_string()
+    })?;
+  
+  Ok(("downloaded".to_string()))
+}
@@ -0,0 +1,143 @@
+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 = "A beautiful landscape with mountains and a lake";
+
+const NEGATIVE_PROMPT: &str = "bad quality, bad faces, poor quality, blurry faces, watermark";
+
+// 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,
+  };
+
+  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 +1,4 @@
+pub mod download_models;
+pub mod flip_image;
+pub mod image_endpoint;
+pub mod test_counter;
@@ -0,0 +1,24 @@
+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 +1,22 @@
+use serde::Serialize;
+
+
+#[derive(Clone, Copy, Serialize)]
+#[serde(rename_all = "snake_case")]
+pub enum NotificationModelType {
+  Unet,
+  Vae,
+}
+
+#[derive(Clone, Serialize)]
+#[serde(rename_all = "snake_case")]
+pub enum NotificationEvent<'a> {
+  ModelDownloadStarted {
+    model_name: &'a str, 
+    model_type: NotificationModelType,
+  },
+  ModelDownloadComplete {
+    model_name: &'a str,
+    model_type: NotificationModelType,
+  }
+}
@@ -1,23 +1,16 @@
 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::flip_image::flip_image;
-use crate::endpoints::inpaint_image_endpoint::inpaint_image;
-use crate::endpoints::realtime_image_endpoint::infer_image;
-use crate::endpoints::remove_background_endpoint::remove_background;
-use crate::endpoints::save_image_endpoint::save_image;
-use crate::endpoints::text_to_image_endpoint::text_to_image;
+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::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;

@@ -45,15 +38,9 @@ pub fn run() {
  tauri::Builder::default()
    .plugin(tauri_plugin_log::Builder::new()
      .level(log::LevelFilter::Info)
-      .targets(vec![
-        Target::new(TargetKind::Stdout),
-        Target::new(TargetKind::LogDir { file_name: Some(app_data_root.log_file_name_str().to_string()) }),
-      ])
+      .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()
@@ -72,14 +59,9 @@ pub fn run() {
    .manage(model_cache)
    .manage(app_data_root)
    .invoke_handler(tauri::generate_handler![
-      download_models,
-      flip_image,
+      test_counter, 
      infer_image,
-      inpaint_image,
-      remove_background,
-      save_image,
-      text_to_image,
-      text_to_image,
+      download_models,
    ])
    .run(tauri::generate_context!())
    .expect("error while running tauri application");
@@ -0,0 +1,15 @@
+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(())
+}
@@ -0,0 +1,30 @@
+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)
+}
@@ -0,0 +1,2 @@
+pub mod download_model_registry_file;
+pub mod download_all_models;
@@ -0,0 +1,8 @@
+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;
@@ -1,14 +1,15 @@
+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<UNet2DConditionModel>>>,
+  unet2: RwLock<Option<Arc<UNetModel>>>,
 }

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

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

-  pub fn get_unet(&self) -> anyhow::Result<Option<Arc<UNet2DConditionModel>>> {
+  pub fn get_unet(&self) -> anyhow::Result<Option<Arc<UNetModel>>> {
    match self.unet2.read() {
      Err(err) => Err(anyhow!("{}", err)),
      Ok(ref lock) => {
@@ -0,0 +1,83 @@
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+use candle_transformers::models::stable_diffusion;
+
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+pub enum ModelFile {
+  Tokenizer,
+  Tokenizer2,
+  Clip,
+  Clip2,
+  Unet,
+  Vae,
+}
+
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+pub enum StableDiffusionVersion {
+  V1_5,
+  V1_5Inpaint,
+  V2_1,
+  V2Inpaint,
+  Xl,
+  XlInpaint,
+  Turbo,
+}
+
+impl StableDiffusionVersion {
+  pub fn repo(&self) -> &'static str {
+    match self {
+      Self::XlInpaint => "diffusers/stable-diffusion-xl-1.0-inpainting-0.1",
+      Self::Xl => "stabilityai/stable-diffusion-xl-base-1.0",
+      Self::V2Inpaint => "stabilityai/stable-diffusion-2-inpainting",
+      Self::V2_1 => "stabilityai/stable-diffusion-2-1",
+      Self::V1_5 => "runwayml/stable-diffusion-v1-5",
+      Self::V1_5Inpaint => "stable-diffusion-v1-5/stable-diffusion-inpainting",
+      Self::Turbo => "stabilityai/sdxl-turbo",
+    }
+  }
+}
+
+impl ModelFile {
+  pub fn get(
+    &self,
+    filename: Option<String>,
+    version: StableDiffusionVersion,
+    use_f16: bool,
+  ) -> anyhow::Result<std::path::PathBuf> {
+    use hf_hub::api::sync::Api;
+    match filename {
+      Some(filename) => Ok(std::path::PathBuf::from(filename)),
+      None => {
+        let (repo, path) = match self {
+          Self::Tokenizer => {
+            let tokenizer_repo = match version {
+              StableDiffusionVersion::V1_5
+              | StableDiffusionVersion::V2_1
+              | StableDiffusionVersion::V1_5Inpaint
+              | StableDiffusionVersion::V2Inpaint => "openai/clip-vit-base-patch32",
+              StableDiffusionVersion::Xl
+              | StableDiffusionVersion::XlInpaint
+              | StableDiffusionVersion::Turbo => {
+                "openai/clip-vit-large-patch14"
+              }
+            };
+            (tokenizer_repo, "tokenizer.json")
+          }
+          Self::Tokenizer2 => {
+            ("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", "tokenizer.json")
+          }
+          Self::Clip => (version.repo(), "text_encoder/model.safetensors"),
+          Self::Clip2 => (version.repo(), "text_encoder_2/model.safetensors"),
+          Self::Unet => (version.repo(), "unet/diffusion_pytorch_model.safetensors"),
+          Self::Vae => (version.repo(), "vae/diffusion_pytorch_model.safetensors"),
+        };
+        let filename = Api::new()?.model(repo.to_string()).get(path)?;
+        Ok(filename)
+      }
+    }
+  }
+}
+
@@ -0,0 +1,47 @@
+use std::path::PathBuf;
+use crate::state::app_dir::AppWeightsDir;
+
+#[derive(Copy, Clone, Debug)]
+pub enum ModelType {
+  ClipJson,
+  SdxlTurboUnet,
+  SdxlTurboVae,
+  SdxlTurboClipEncoder,
+  SdxlTurboClipEncoder2,
+}
+
+impl ModelType {
+  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"),
+    }
+  }
+
+  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",
+    }
+  }
+
+  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",
+    }
+  }
+  
+  pub fn get_path(&self, weights_dir: &AppWeightsDir) -> PathBuf {
+    weights_dir.model_path(self)
+  }
+}
@@ -0,0 +1,130 @@
+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(())
+  }
+ 
+}
@@ -0,0 +1,2 @@
+pub mod unet_model;
+pub mod lazy_load_vae_model;
@@ -0,0 +1,26 @@
+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 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))?)
+  }
+}
@@ -0,0 +1,170 @@
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+use std::path::PathBuf;
+use candle_transformers::models::stable_diffusion;
+
+use crate::ml::model_file::{ModelFile, StableDiffusionVersion};
+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;
+
+#[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> {
+  info!("Preparing text embeddings...");
+
+  let which = match sd_version {
+    StableDiffusionVersion::Xl
+    | StableDiffusionVersion::XlInpaint
+    | StableDiffusionVersion::Turbo => vec![true, false],
+    _ => vec![true],
+  };
+
+  let text_embeddings = which
+    .iter()
+    .map(|first| {
+      do_infer_clip_text_embeddings(
+        &prompt,
+        &uncond_prompt,
+        None, // tokenizer
+        None, // clip_weights
+        None, // clip2_weights
+        sd_version,
+        &sd_config,
+        false, // use_f16
+        &device,
+        dtype,
+        use_guide_scale,
+        *first,
+        weights_dir,
+      )
+    })
+    .collect::<anyhow::Result<Vec<_>>>()?;
+
+  let text_embeddings = Tensor::cat(&text_embeddings, D::Minus1)?;
+  
+  Ok(text_embeddings)
+}
+
+#[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> {
+  
+  let tokenizer_file = if first {
+    info!("ModelFile::Tokenizer");
+    ModelFile::Tokenizer
+  } else {
+    info!("ModelFile::Tokenizer2");
+    ModelFile::Tokenizer2
+  };
+  
+  info!("Loading clip from download");
+  //let tokenizer = tokenizer_file.get(tokenizer, sd_version, use_f16)?;
+  let tokenizer = weights_dir.model_path(&ModelType::ClipJson);
+  
+  info!("Tokenizer path: {:?}", tokenizer);
+  
+  let tokenizer = Tokenizer::from_file(tokenizer).map_err(E::msg)?;
+  
+  let pad_id = match &sd_config.clip.pad_with {
+    Some(padding) => *tokenizer.get_vocab(true).get(padding.as_str()).unwrap(),
+    None => *tokenizer.get_vocab(true).get("<|endoftext|>").unwrap(),
+  };
+
+  println!("Running with prompt \"{prompt}\".");
+  let mut tokens = tokenizer
+    .encode(prompt, true)
+    .map_err(E::msg)?
+    .get_ids()
+    .to_vec();
+  if tokens.len() > sd_config.clip.max_position_embeddings {
+    anyhow::bail!(
+            "the prompt is too long, {} > max-tokens ({})",
+            tokens.len(),
+            sd_config.clip.max_position_embeddings
+        )
+  }
+  while tokens.len() < sd_config.clip.max_position_embeddings {
+    tokens.push(pad_id)
+  }
+  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
+  };
+  let clip_weights = if first {
+    //clip_weights_file.get(clip_weights, sd_version, use_f16)?
+    weights_dir.model_path(&ModelType::SdxlTurboClipEncoder)
+  } else {
+    //clip_weights_file.get(clip2_weights, sd_version, use_f16)?
+    weights_dir.model_path(&ModelType::SdxlTurboClipEncoder2)
+  };
+  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)?;
+
+  let text_embeddings = text_model.forward(&tokens)?;
+
+  let text_embeddings = if use_guide_scale {
+    let mut uncond_tokens = tokenizer
+      .encode(uncond_prompt, true)
+      .map_err(E::msg)?
+      .get_ids()
+      .to_vec();
+    if uncond_tokens.len() > sd_config.clip.max_position_embeddings {
+      anyhow::bail!(
+                "the negative prompt is too long, {} > max-tokens ({})",
+                uncond_tokens.len(),
+                sd_config.clip.max_position_embeddings
+            )
+    }
+    while uncond_tokens.len() < sd_config.clip.max_position_embeddings {
+      uncond_tokens.push(pad_id)
+    }
+
+    let uncond_tokens = Tensor::new(uncond_tokens.as_slice(), device)?.unsqueeze(0)?;
+    let uncond_embeddings = text_model.forward(&uncond_tokens)?;
+
+    Tensor::cat(&[uncond_embeddings, text_embeddings], 0)?.to_dtype(dtype)?
+  } else {
+    text_embeddings.to_dtype(dtype)?
+  };
+  Ok(text_embeddings)
+}
@@ -0,0 +1,293 @@
+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 anyhow::{anyhow, Error as E, Result};
+use candle_core::{DType, IndexOp, Tensor, D};
+use candle_transformers::models::stable_diffusion::vae::{AutoEncoderKL, DiagonalGaussianDistribution};
+use candle_transformers::models::stable_diffusion::lcm::LCMScheduler;
+use image::DynamicImage;
+use log::info;
+use rand::Rng;
+use tauri::{AppHandle, Emitter};
+use crate::state::app_dir::AppDataRoot;
+
+pub struct Args<'a> {
+    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 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,
+    } = args;
+
+    let weights_dir = app_data_root.weights_dir();
+    
+    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,
+        candle_transformers::models::stable_diffusion::lcm::LCMSchedulerConfig::default(),
+    )?;
+
+    let seed = configs.seed.unwrap_or_else(|| rand::thread_rng().gen());
+    configs.device.set_seed(seed)?;
+
+    info!("Using seed: {}", seed);
+
+    let guidance_scale = match cfg_scale {
+        Some(guidance_scale) => guidance_scale,
+        None => match configs.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"
+        },
+    };
+
+    let use_guide_scale = guidance_scale > 1.0;
+
+    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!("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,
+            use_guide_scale,
+            weights_dir,
+        )?;
+        prompt_cache.store_copy(&prompt, &tensor)?;
+        tensor
+    };
+
+    println!("Text embeddings shape: {:?}", text_embeddings.shape());
+
+    println!("Loading input image into tensor...");
+
+    let input_image = dynamic_image_to_tensor(
+        image,
+        &configs.device,
+        configs.dtype)?;
+
+    println!("Reference image shape: {:?}", input_image.shape());
+
+    let vae_scale = get_vae_scale(configs.sd_version);
+
+    let maybe_vae = model_cache.get_vae()?;
+    
+    let vae = match maybe_vae {
+        Some(vae) => vae,
+        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 = weights_dir.model_path(&ModelType::SdxlTurboVae);
+            
+            let vae = configs
+              .sd_config
+              .build_vae(vae_file, &configs.device, configs.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
+        }
+    };
+
+    let maybe_unet = model_cache.get_unet()?;
+
+    let unet = match maybe_unet {
+        Some(unet) => unet,
+        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 = UNetModel::new(&configs.sd_config, unet_file, &configs.device, configs.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
+        }
+    };
+
+    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 = 256;
+    let guidance_scale_embedding = scheduler.get_guidance_scale_embedding(
+        guidance_scale, 
+        embedding_dim, 
+        &configs.device, 
+        configs.dtype
+    )?;
+
+    // Generate latents from input image using the LCM approach
+    println!("Generating latents from input image...");
+    let init_latents = (init_latent_dist.sample()? * vae_scale)?;
+    println!("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 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);
+    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);
+
+        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) {
+            Ok(pred) => pred,
+            Err(e) => {
+                println!("UNet inference failed with error: {}", e);
+                return Err(anyhow::anyhow!("UNet inference failed: {}", e));
+            },
+        };
+
+        if use_guide_scale {
+            let chunks = noise_pred.chunk(2, 0)?;
+            let (noise_pred_uncond, noise_pred_text) = (&chunks[0], &chunks[1]);
+            noise_pred = (noise_pred_uncond + (guidance_scale * (noise_pred_text - noise_pred_uncond)?)?)?;
+        }
+
+        // Apply the LCM scheduler step
+        latents = scheduler.step(&noise_pred, timestep, &latents, timestep_index, configs.scheduler_steps)?;
+    }
+
+    println!("Diffusion process completed, decoding image...");
+    let image = vae.decode(&(latents / vae_scale)?)?;
+    
+    println!("VAE decode completed");
+
+    println!("Scaling image back");
+    let image = ((image / 2.)? + 0.5)?;
+    
+    println!("Normalized image values");
+    let image = (image.clamp(0f32, 1.)? * 255.)?;
+
+    println!("Convert to int8");
+    let image = image.to_dtype(DType::U8)?;
+    
+    println!("Converted to 8-bit format");
+
+    let image = tensor_to_image_buffer(&image.i(0)?)?;
+
+    Ok(image)
+}
@@ -1,5 +1,4 @@
 pub mod get_vae_scale;
 pub mod infer_clip_text_embeddings;
 pub mod stable_diffusion_pipeline;
-pub mod lcm_pipeline;
-mod remap_lcm_strength_range;
+pub mod lcm_pipeline;
@@ -1,78 +1,76 @@
-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, ClipArgs};
+use crate::ml::stable_diffusion::infer_clip_text_embeddings::infer_clip_text_embeddings;
+use crate::state::app_config::AppConfig;
 use anyhow::{anyhow, Error as E, Result};
-use candle_core::{DType, Device, IndexOp, Tensor, D};
+use candle_core::{DType, 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, P: AsRef<Path>, Q: AsRef<Path>, R: AsRef<Path>, S: AsRef<Path>> {
+pub struct Args<'a> {
    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 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 app_data_root: &'a AppDataRoot,
+    pub app: &'a AppHandle,
 }

-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>
-{
+pub fn stable_diffusion_pipeline(args: Args<'_>) -> Result<RgbImage> {
    let Args { 
        prompt, 
        uncond_prompt, 
        cfg_scale, 
        i2i_strength, 
+        configs, 
        model_cache, 
        prompt_cache, 
+        app, 
        image,
-        sd_version,
-        sd_config,
-        device,
-        dtype,
-        maybe_seed,
-        scheduler_steps,
-        sdxl_turbo_vae_path,
-        sdxl_turbo_unet_path,
-        clip_json_path,
-        clip_weights_path,
+        app_data_root,
    } = args;
    
+    let weights_dir = app_data_root.weights_dir();
+    
+    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);
+
    // 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 = sd_config.build_scheduler(scheduler_steps)?;
+    let mut scheduler = configs.sd_config.build_scheduler(
+        configs.scheduler_steps)?;

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

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

    let guidance_scale = match cfg_scale {
        Some(guidance_scale) => guidance_scale,
-        None => match sd_version {
+        None => match configs.sd_version {
            StableDiffusionVersion::V1_5
            | StableDiffusionVersion::V2_1
            | StableDiffusionVersion::Xl => 7.5,
@@ -93,21 +91,20 @@ pub fn stable_diffusion_pipeline<P1, P2, P3, P4>(args: Args<'_, P1, P2, P3, P4>)
    } else {
        
        info!("Prompt is NOT cached! Calculating embedding...");
-        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,
+        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,
            use_guide_scale,
-            clip_json_path: clip_json_path.as_ref(),
-            clip_weights_path: clip_weights_path.as_ref(),
-        })?;
+            weights_dir,
+        )?;
        prompt_cache.store_copy(&prompt, &tensor)?;
        tensor
    };
@@ -116,11 +113,14 @@ pub fn stable_diffusion_pipeline<P1, P2, P3, P4>(args: Args<'_, P1, P2, P3, P4>)

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

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

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

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

    let maybe_vae = model_cache.get_vae()?;
    
@@ -129,14 +129,33 @@ pub fn stable_diffusion_pipeline<P1, P2, P3, P4>(args: Args<'_, P1, P2, P3, P4>)
        None => {
            info!("No vae found in cache; loading...");
            
-            let vae_file = sdxl_turbo_vae_path.as_ref();
+            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 = sd_config.build_vae(vae_file, &device, dtype)?;
+            let vae_file = weights_dir.model_path(&ModelType::SdxlTurboVae);
+            
+            let vae = configs
+              .sd_config
+              .build_vae(vae_file, &configs.device, configs.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
        }
    };
@@ -148,15 +167,32 @@ pub fn stable_diffusion_pipeline<P1, P2, P3, P4>(args: Args<'_, P1, P2, P3, P4>)
        None => {
            info!("No unet found in cache; loading...");

-            let unet_weights_file = sdxl_turbo_unet_path.as_ref();
+            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 = sd_config.build_unet(unet_weights_file, &device, 4, true, dtype)
+            let unet = UNetModel::new(&configs.sd_config, unet_file, &configs.device, configs.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
        }
    };
@@ -180,9 +216,9 @@ pub fn stable_diffusion_pipeline<P1, P2, P3, P4>(args: Args<'_, P1, P2, P3, P4>)
    };

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

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

@@ -221,7 +257,7 @@ pub fn stable_diffusion_pipeline<P1, P2, P3, P4>(args: Args<'_, P1, P2, P3, P4>)

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

-        let mut noise_pred = match unet.forward(&latent_model_input, timestep as f64, &text_embeddings) {
+        let mut noise_pred = match unet.inference(&latent_model_input, timestep as f64, &text_embeddings) {
            Ok(pred) => pred,
            Err(e) => {
                println!("UNet inference failed with error: {}", e);
@@ -0,0 +1,108 @@
+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 expanduser::expanduser;
+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;
+
+const DEFAULT_SD_IMAGE_WIDTH: usize = 512;
+const DEFAULT_SD_IMAGE_HEIGHT: usize = 512;
+
+const DEFAULT_SD_NUMERIC_DATATYPE: DType = DType::F32;
+const SD_VERSION: StableDiffusionVersion = StableDiffusionVersion::Turbo;
+
+const DEFAULT_DATA_DIR_UNIX : &str = "~/artcraft";
+
+/// 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,
+  pub scheduler_samples: usize,
+  
+  pub seed: Option<u64>,
+
+  pub cfg_scale: Option<f64>,
+  
+  /// Probably shouldn't be here.
+  pub hf_api: Api,
+  
+  /// Root location for application data.
+  pub app_data_root: AppDataRoot,
+}
+
+impl AppConfig {
+  
+  pub fn init() -> anyhow::Result<Self> {
+    let yaml_configs = YamlConfig::load_from_config_file_recursive();
+    
+    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))
+      }
+      _ => {
+        StableDiffusionConfig::v2_1(None, Some(DEFAULT_SD_IMAGE_HEIGHT), Some(DEFAULT_SD_IMAGE_WIDTH))
+      }
+    };
+    
+    let hf_api = Api::new()?;
+    
+    let os_platform = OsPlatform::get();
+
+    let default_data_path = match os_platform {
+      OsPlatform::Linux => yaml_configs.linux_default_data_path.as_deref(),
+      OsPlatform::MacOs => yaml_configs.mac_default_data_path.as_deref(),
+      OsPlatform::Windows => yaml_configs.windows_default_data_path.as_deref(),
+    };
+    
+    let data_root_path = match default_data_path {
+      Some(default_data_path) => default_data_path.to_string(),
+      None => match os_platform { 
+        OsPlatform::Linux | OsPlatform::MacOs => DEFAULT_DATA_DIR_UNIX.to_string(),
+        OsPlatform::Windows => ".".to_string(),
+      }
+    };
+      
+    println!("Using data root: {}", data_root_path);
+
+    let app_data_root = AppDataRoot::create_existing(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,
+    })
+  }
+}
@@ -0,0 +1,123 @@
+use crate::state::os_platform::OsPlatform;
+use expanduser::expanduser;
+use std::path::{Path, PathBuf};
+use crate::ml::model_type::ModelType;
+
+const ASSETS_SUBDIRECTORY : &str = "assets";
+const WEIGHTS_SUBDIRECTORY : &str = "weights";
+
+/// The path to the application data directory, which includes "asset" and "weights" data.
+#[derive(Clone)]
+pub struct AppDataRoot {
+  path: PathBuf,
+  assets_dir: AppAssetsDir,
+  weights_dir: AppWeightsDir,
+}
+
+#[derive(Clone)]
+pub struct AppAssetsDir {
+  path: PathBuf,
+}
+
+#[derive(Clone)]
+pub struct AppWeightsDir {
+  path: PathBuf,
+}
+
+impl AppDataRoot {
+  pub fn create_existing<P: AsRef<Path>>(dir: P) -> anyhow::Result<Self> {
+    let mut dir = dir.as_ref().to_path_buf();
+    
+    match OsPlatform::get() {
+      OsPlatform::Linux | OsPlatform::MacOs => {
+        if let Some(d) = dir.as_os_str().to_str() {
+          dir = expanduser(d)?;
+        }
+      },
+      OsPlatform::Windows => {}
+    }
+    
+    if !dir.is_dir() {
+      println!("Creating directory {:?}", dir);
+      std::fs::create_dir_all(&dir)?;
+    }
+
+    match dir.canonicalize() {
+      Ok(d) => dir = d,
+      Err(err) => {
+        println!("Error canonicalizing {:?}: {}", dir, err);
+      }
+    }
+    
+    let assets_dir = AppAssetsDir::create_existing(dir.join(ASSETS_SUBDIRECTORY))?;
+    let weights_dir = AppWeightsDir::create_existing(dir.join(WEIGHTS_SUBDIRECTORY))?;
+    
+    Ok(Self {
+      path: dir,
+      assets_dir,
+      weights_dir,
+    })
+  }
+  
+  pub fn assets_dir(&self) -> &AppAssetsDir {
+    &self.assets_dir
+  }
+  
+  pub fn weights_dir(&self) -> &AppWeightsDir {
+    &self.weights_dir
+  }
+  
+  pub fn path(&self) -> &Path {
+    &self.path
+  }
+}
+
+impl AppAssetsDir {
+  pub fn create_existing<P: AsRef<Path>>(dir: P) -> anyhow::Result<Self> {
+    let mut dir = dir.as_ref().to_path_buf();
+    match dir.canonicalize() {
+      Ok(d) => dir = d,
+      Err(err) => {
+        println!("Error canonicalizing {:?}: {}", dir, err);
+      }
+    }
+    if !dir.exists() {
+      println!("Creating directory {:?}", dir);
+      std::fs::create_dir(&dir)?;
+    }
+    Ok(Self {
+      path: dir,
+    })
+  }
+
+  pub fn path(&self) -> &Path {
+    &self.path
+  }
+}
+
+impl AppWeightsDir {
+  pub fn create_existing<P: AsRef<Path>>(dir: P) -> anyhow::Result<Self> {
+    let mut dir = dir.as_ref().to_path_buf();
+    match dir.canonicalize() {
+      Ok(d) => dir = d,
+      Err(err) => {
+        println!("Error canonicalizing {:?}: {}", dir, err);
+      }
+    }
+    if !dir.exists() {
+      println!("Creating directory {:?}", dir);
+      std::fs::create_dir(&dir)?;
+    }
+    Ok(Self {
+      path: dir,
+    })
+  }
+
+  pub fn path(&self) -> &Path {
+    &self.path
+  }
+  
+  pub fn model_path(&self, model_registry: &ModelType) -> PathBuf {
+    self.path.join(model_registry.get_filename())
+  }
+}
@@ -1,5 +1,4 @@
 pub mod app_config;
 pub mod app_dir;
 pub mod os_platform;
-pub mod yaml_config;
-pub mod expanduser;
+pub mod yaml_config;
@@ -0,0 +1,11 @@
+use tauri::{AppHandle, Emitter};
+use crate::state::app_dir::AppDataRoot;
+
+pub async fn downloader_thread(app_data_root: AppDataRoot, app: AppHandle) -> ! {
+  loop {
+    app.emit("testing", 1);
+    
+    println!(">>> Testing: {:?}", app_data_root.path());
+    std::thread::sleep(std::time::Duration::from_millis(500));
+  }
+}
@@ -13,8 +13,8 @@
    "windows": [
      {
        "title": "ArtCraft",
-        "width": 2400,
-        "height": 1300,
+        "width": 1200,
+        "height": 800,
        "resizable": true,
        "fullscreen": false
      }
@@ -1,4 +0,0 @@
-# Don't restart the dev server on Rust changes
-crates/
-Cargo.toml
-Cargo.lock
@@ -1,74 +0,0 @@
-[package]
-name = "artcraft"
-version = "0.1.0"
-description = "Artcraft"
-authors = ["you"]
-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
-
-[lib]
-name = "app_lib"
-crate-type = ["staticlib", "cdylib", "rlib"]
-
-[build-dependencies]
-tauri-build = { version = "2.1.1", features = [] }
-
-[dependencies]
-# Workspace hack (faster compile times)
-my-workspace-hack = { workspace =  true }
-
-# Internal
-crockford = { workspace = true }
-errors = { workspace = true }
-filesys = { workspace = true }
-memory_caching = { workspace = true }
-openai_sora_client = { workspace = true }
-primitives = { workspace = true }
-subprocess_common = { workspace = true }
-
-# External
-#openssl = { version = "0.10", features = ["vendored"] }
-# NB(bt,2025-03-10): default-features = false on Windows while I debug openssl / rustls
-anyhow = { workspace = true }
-base64 = "0.22.1"
-bytes = "1.10"
-chrono = { workspace = true }
-const_format = "0.2.34"
-directories = "6.0.0"
-dirs = "6.0.0"
-futures = "0.3"
-image = "0.25.5"
-log = { workspace = true }
-once_cell = { workspace = true }
-rand = { workspace = true }
-reqwest = { workspace = true, features = ["multipart", "stream"], default-features = false }
-serde = { workspace = true }
-serde_derive = { workspace = true }
-serde_json = { workspace = true }
-serde_yaml = { workspace = true }
-tauri = { version = "2.4.1", features = ["unstable"] }
-tauri-plugin-log = "2.3.1"
-tempdir = "0.3.7"
-tempfile = "3.18.0"
-tokio = { version = "1.42", features = ["rt", "rt-multi-thread", "fs"] }
-tokio-util = { version = "0.7", features = ["codec"] }
-
-[dev-dependencies]
-libloading = "0.8.6"
-serial_test = "3.2.0"
-assertor = "0.0.3"
-
-[profile.dev]
-incremental = true # Compile your binary in smaller steps.
-
-[profile.release]
-codegen-units = 1 # Allows LLVM to perform better optimization.
-lto = true # Enables link-time-optimizations.
-opt-level = 3 # `s` prioritizes small binary size. Use `3` if you prefer speed.
-panic = "abort" # Higher performance by disabling panic handlers.
-strip = true # Ensures debug symbols are removed.
@@ -1,90 +0,0 @@
-use std::fs::read_to_string;
-use base64::prelude::BASE64_STANDARD;
-use errors::AnyhowResult;
-use base64::Engine;
-use image::codecs::png::{CompressionType, FilterType, PngEncoder};
-use image::{DynamicImage, ImageReader};
-use std::io::Cursor;
-use log::{error, info};
-use tauri::{AppHandle, Manager, State};
-use openai_sora_client::credentials::SoraCredentials;
-use openai_sora_client::image_gen::common::{ImageSize, NumImages};
-use openai_sora_client::image_gen::sora_image_gen_remix::{sora_image_gen_remix, SoraImageGenRemixRequest};
-use openai_sora_client::upload::upload_media_from_bytes::sora_media_upload_from_bytes;
-use openai_sora_client::upload::upload_media_from_file::SoraMediaUploadRequest;
-use crate::state::app_dir::AppDataRoot;
-
-#[tauri::command]
-pub async fn image_generation_command(
-  image: &str,
-  prompt: &str,
-  app: AppHandle,
-  app_data_root: State<'_, AppDataRoot>,
-) -> Result<String, String> {
-  info!("image_generation_command called; processing image...");
-
-  let bytes = BASE64_STANDARD.decode(image)
-    .map_err(|err| format!("Base64 decode error: {}", err))?;
-
-  generate_image(bytes, prompt, &app_data_root)
-    .await
-    .map_err(|err| {
-      error!("error: {:?}", err);
-      "there was an error".to_string()
-    })?;
-
-  Ok("success".to_string())
-}
-
-pub async fn generate_image(file_bytes: Vec<u8>, prompt: &str, app_data_root: &AppDataRoot) -> AnyhowResult<()> {
-  let sora_credentials = get_credentials(app_data_root)?;
-
-  let filename = "image.png".to_string();
-
-  let response= sora_media_upload_from_bytes(file_bytes, filename, &sora_credentials)
-      .await
-      .map_err(|err| {
-        error!("Failed to upload image to Sora: {:?}", err);
-        err
-      })?;
-
-  let sora_media_tokens = vec![response.id];
-
-  let response = sora_image_gen_remix(SoraImageGenRemixRequest {
-    prompt: prompt.to_string(),
-    num_images: NumImages::One,
-    image_size: ImageSize::Square,
-    sora_media_tokens: sora_media_tokens.clone(),
-    credentials: &sora_credentials,
-  }).await
-      .map_err(|err| {
-        error!("Failed to call Sora image generation: {:?}", err);
-        err
-      })?;
-
-  println!(">> TASK ID: {:?} ", response.task_id);
-
-  Ok(())
-}
-
-fn get_credentials(app_data_root: &AppDataRoot) -> AnyhowResult<SoraCredentials> {
-  let cookie_file = app_data_root.get_sora_cookie_file_path();
-
-  let bearer = read_to_string("/Users/bt/dev/storyteller/storyteller-rust/test_data/temp/bearer.txt")?
-      .trim()
-      .to_string();
-
-  let cookie= read_to_string(cookie_file)?
-      .trim()
-      .to_string();
-
-  let sentinel = read_to_string("/Users/bt/dev/storyteller/storyteller-rust/test_data/temp/sentinel.txt")?
-      .trim()
-      .to_string();
-
-  Ok(SoraCredentials {
-    bearer_token: bearer,
-    cookie: cookie,
-    sentinel: Some(sentinel),
-  })
-}
@@ -1,3 +0,0 @@
-pub mod flip_image;
-pub mod image_generation_command;
-pub mod open_login_command;
@@ -1,72 +0,0 @@
-use std::time::Duration;
-use log::{error, info};
-use once_cell::sync::Lazy;
-use tauri::{AppHandle, Manager, Url, WebviewUrl, WebviewWindowBuilder, WindowBuilder};
-use tokio::time::sleep_until;
-use errors::AnyhowResult;
-use crate::threads::login_thread::{LOGIN_WINDOW_NAME, SORA_LOGIN_URL};
-use crate::utils::clear_all_webview_cookies::clear_all_webview_cookies;
-
-pub const START_URL_STR: &str = "https://storyteller.ai/";
-
-pub static START_URL: Lazy<Url> = Lazy::new(|| {
-  Url::parse(START_URL_STR).expect("URL should parse")
-});
-
-pub const SORA_HOMEPAGE_URL_STR: &str = "https://sora.com/";
-
-pub static SORA_HOMEPAGE_URL : Lazy<Url> = Lazy::new(|| {
-  Url::parse(SORA_HOMEPAGE_URL_STR).expect("URL should parse")
-});
-
-#[tauri::command]
-pub async fn open_login_command(
-  app: AppHandle,
-) -> Result<String, String> {
-  info!("open_login_command called");
-
-  do_open_login(&app)
-    .await
-    .map_err(|err| {
-      error!("Error opening login: {:?}", err);
-      format!("Error opening login: {:?}", err)
-    })?;
-
-  Ok("result".to_string())
-}
-
-async fn do_open_login(app: &AppHandle) -> AnyhowResult<()> {
-  info!("Building login window...");
-
-  let url = WebviewUrl::External(START_URL.clone());
-
-  let window = WebviewWindowBuilder::new(app, LOGIN_WINDOW_NAME, url)
-      //.user_agent(openai_sora_client::credentials::USER_AGENT)
-      .always_on_top(true)
-      .title("Login to OpenAI")
-      .center()
-      .resizable(true)
-      .visible(true)
-      .closable(true)
-      .focused(true)
-      .devtools(true)
-      .build()?;
-
-  for (name, webview) in window.webviews() {
-    if name != LOGIN_WINDOW_NAME {
-      continue;
-    }
-
-    clear_all_webview_cookies(&webview)?;
-
-    // NB: We're starting to get Cloudflare protection screens. Let's try to avoid.
-    webview.navigate(SORA_HOMEPAGE_URL.clone())?;
-    tokio::time::sleep(Duration::from_millis(100)).await;
-
-    webview.navigate(SORA_LOGIN_URL.clone())?;
-    break;
-  }
-
-  info!("Done.");
-  Ok(())
-}
@@ -1,45 +0,0 @@
-use serde::Serialize;
-
-#[derive(Clone, Copy, Serialize)]
-#[serde(rename_all = "snake_case")]
-pub enum NotificationModelType {
-  ImageSegmentation,
-  TextEncoder,
-  TextTokenizer,
-  Unet,
-  Vae,
-  Json,
-  ClipEncoder,
-  FluxModel,
-}
-
-#[derive(Clone, Serialize)]
-#[serde(rename_all = "snake_case")]
-pub enum NotificationEvent<'a> {
-  ModelDownloadStarted {
-    model_name: &'a str, 
-    model_type: NotificationModelType,
-  },
-  ModelDownloadProgress {
-    model_name: &'a str,
-    model_type: NotificationModelType,
-    currently_downloaded_bytes: usize,
-    total_file_bytes: usize,
-  },
-  ModelDownloadComplete {
-    model_name: &'a str,
-    model_type: NotificationModelType,
-  },
-  ModelLoading {
-    model_name: String,
-    model_type: NotificationModelType,
-  },
-  ModelInference {
-    model_name: String,
-    model_type: NotificationModelType,
-  },
-  ModelInferenceComplete {
-    model_name: String,
-    model_type: NotificationModelType,
-  },
-}
@@ -1,65 +0,0 @@
-pub mod commands;
-pub mod events;
-pub mod state;
-pub mod threads;
-pub mod transfer;
-pub mod utils;
-
-use crate::commands::flip_image::flip_image;
-use crate::commands::image_generation_command::image_generation_command;
-use crate::commands::open_login_command::open_login_command;
-use crate::state::app_config::AppConfig;
-use crate::threads::login_thread::login_thread;
-
-use tauri_plugin_log::Target;
-use tauri_plugin_log::TargetKind;
-
-#[cfg_attr(mobile, tauri::mobile_entry_point)]
-pub fn run() {
-  // NB: Tauri wants to install the logger itself, so we can't rely on the logger crate 
-  // until the tauri runtime begins.
-  println!("Loading model config...");
-
-  let config = AppConfig::init()
-    .expect("config should load");
-
-  let app_data_root = config.app_data_root.clone();
-  let app_data_root2 = config.app_data_root.clone();
-
-  println!("Initializing backend runtime...");
-
-  tauri::Builder::default()
-    .plugin(tauri_plugin_log::Builder::new()
-      .level(log::LevelFilter::Info)
-      .targets(vec![
-        Target::new(TargetKind::Stdout),
-        Target::new(TargetKind::LogDir { file_name: Some(app_data_root.log_file_name_str().to_string()) }),
-      ])
-      .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()
-      //      .level(log::LevelFilter::Info)
-      //      .build(),
-      //  )?;
-      //}
-      let app = app.handle().clone();
-
-      tauri::async_runtime::spawn(login_thread(app, app_data_root2));
-
-      Ok(())
-    })
-    .manage(config)
-    .manage(app_data_root)
-    .invoke_handler(tauri::generate_handler![
-      flip_image,
-      image_generation_command,
-      open_login_command,
-    ])
-    .run(tauri::generate_context!("tauri.conf.json"))
-    .expect("error while running tauri application");
-}
@@ -1,6 +0,0 @@
-// Prevents additional console window on Windows in release, DO NOT REMOVE!!
-#![cfg_attr(not(debug_assertions), windows_subsystem = "windows")]
-
-fn main() {
-  app_lib::run();
-}
@@ -1,58 +0,0 @@
-use crate::state::app_dir::AppDataRoot;
-use crate::state::os_platform::OsPlatform;
-use crate::state::yaml_config::YamlConfig;
-
-const DEFAULT_SD_IMAGE_WIDTH: usize = 1024;
-const DEFAULT_SD_IMAGE_HEIGHT: usize = 1024;
-
-
-/// A central place to configure app-wide details.
-pub struct AppConfig {
-  pub image_height: usize,
-  pub image_width: usize,
-  pub scheduler_steps: usize,
-  pub scheduler_samples: usize,
-  
-  pub seed: Option<u64>,
-
-  pub cfg_scale: Option<f64>,
-  
-  /// Root location for application data.
-  pub app_data_root: AppDataRoot,
-}
-
-impl AppConfig {
-  
-  pub fn init() -> anyhow::Result<Self> {
-    let yaml_configs = YamlConfig::load_from_config_file_recursive();
-    
-    println!("Configs: {:?}", yaml_configs);
-    
-    let os_platform = OsPlatform::get();
-
-    let default_data_root = match os_platform {
-      OsPlatform::Linux => yaml_configs.linux_default_data_path.as_deref(),
-      OsPlatform::MacOs => yaml_configs.mac_default_data_path.as_deref(),
-      OsPlatform::Windows => yaml_configs.windows_default_data_path.as_deref(),
-    };
-    
-    println!("Possible user-defined data root: {:?}", default_data_root);
-
-    let app_data_root = match default_data_root {
-      None => AppDataRoot::create_default()?,
-      Some(path) => AppDataRoot::create_existing(path)?,
-    };
-
-    println!("App data root: {:?}", app_data_root.path());
-
-    Ok(Self {
-      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,
-      app_data_root,
-    })
-  }
-}
@@ -1,219 +0,0 @@
-use crate::state::expanduser::expanduser;
-use crate::state::os_platform::OsPlatform;
-use anyhow::anyhow;
-use directories::UserDirs;
-use std::path::{Path, PathBuf};
-use chrono::{DateTime, Local};
-use tempdir::TempDir;
-use tempfile::{Builder, NamedTempFile};
-
-const DEFAULT_DATA_DIR : &str = "artcraft";
-const ASSETS_SUBDIRECTORY : &str = "assets";
-const WEIGHTS_SUBDIRECTORY : &str = "weights";
-
-const TEMPORARY_SUBDIRECTORY : &str = "temp";
-
-/// Note: Tauri appends ".log" to the end of the filename.
-const LOG_FILE_NAME : &str = "application_debug";
-
-/// The path to the application data directory, which includes "asset" and "weights" data.
-#[derive(Clone)]
-pub struct AppDataRoot {
-  path: PathBuf,
-  assets_dir: AppAssetsDir,
-  weights_dir: AppWeightsDir,
-  temp_dir: TemporaryDir,
-  log_file_name: PathBuf,
-  log_file_name_string: String,
-}
-
-#[derive(Clone)]
-pub struct AppAssetsDir {
-  path: PathBuf,
-}
-
-#[derive(Clone)]
-pub struct AppWeightsDir {
-  path: PathBuf,
-}
-
-#[derive(Clone)]
-pub struct TemporaryDir {
-  path: PathBuf,
-}
-
-impl AppDataRoot {
-  pub fn create_default() -> anyhow::Result<Self> {
-    let directory = get_default_data_dir()?;
-    Self::create_existing(directory)
-  }
-
-  pub fn create_existing<P: AsRef<Path>>(dir: P) -> anyhow::Result<Self> {
-    let mut dir = dir.as_ref().to_path_buf();
-    
-    match OsPlatform::get() {
-      OsPlatform::Linux | OsPlatform::MacOs => {
-        if let Some(d) = dir.as_os_str().to_str() {
-          dir = expanduser(d)?;
-        }
-      },
-      OsPlatform::Windows => {}
-    }
-    
-    if !dir.is_dir() {
-      println!("Creating directory {:?}", dir);
-      std::fs::create_dir_all(&dir)?;
-    }
-
-    match dir.canonicalize() {
-      Ok(d) => dir = d,
-      Err(err) => {
-        println!("Error canonicalizing {:?}: {}", dir, err);
-      }
-    }
-    
-    let assets_dir = AppAssetsDir::create_existing(dir.join(ASSETS_SUBDIRECTORY))?;
-    let weights_dir = AppWeightsDir::create_existing(dir.join(WEIGHTS_SUBDIRECTORY))?;
-    let temp_dir = TemporaryDir::create_existing(dir.join(TEMPORARY_SUBDIRECTORY))?;
-    let log_file_name = dir.join(LOG_FILE_NAME);
-    let log_file_name_string = log_file_name
-        .to_str()
-        .ok_or(anyhow!("couldn't convert log path to str"))?
-        .to_string();
-
-    Ok(Self {
-      path: dir,
-      assets_dir,
-      weights_dir,
-      temp_dir,
-      log_file_name,
-      log_file_name_string,
-    })
-  }
-  
-  pub fn assets_dir(&self) -> &AppAssetsDir {
-    &self.assets_dir
-  }
-  
-  pub fn weights_dir(&self) -> &AppWeightsDir {
-    &self.weights_dir
-  }
-
-  pub fn temp_dir(&self) -> &TemporaryDir {
-    &self.temp_dir
-  }
-
-  pub fn path(&self) -> &Path {
-    &self.path
-  }
-
-  pub fn log_file_name(&self) -> &Path {
-    &self.log_file_name
-  }
-
-  pub fn log_file_name_str(&self) -> &str {
-    &self.log_file_name_string
-  }
-
-  pub fn get_sora_cookie_file_path(&self) -> PathBuf {
-    self.path.join("sora_cookies.txt")
-  }
-}
-
-impl AppAssetsDir {
-  pub fn create_existing<P: AsRef<Path>>(dir: P) -> anyhow::Result<Self> {
-    let mut dir = dir.as_ref().to_path_buf();
-    match dir.canonicalize() {
-      Ok(d) => dir = d,
-      Err(err) => {
-        println!("Error canonicalizing {:?}: {}", dir, err);
-      }
-    }
-    if !dir.exists() {
-      println!("Creating directory {:?}", dir);
-      std::fs::create_dir(&dir)?;
-    }
-    Ok(Self {
-      path: dir,
-    })
-  }
-
-  pub fn path(&self) -> &Path {
-    &self.path
-  }
-  
-  /// Return the current date asset directory
-  /// If it doesn't exist, create it.
-  pub fn make_or_get_current_date_dir(&self) -> anyhow::Result<PathBuf> {
-    let format: DateTime<Local> = Local::now();
-    let date_directory_name = format.format("%Y-%d-%m").to_string();
-    let full_path = self.path.join(date_directory_name);
-    if !full_path.exists() {
-      std::fs::create_dir(&full_path)?;
-    }
-    Ok(full_path)
-  }
-}
-
-impl AppWeightsDir {
-  pub fn create_existing<P: AsRef<Path>>(dir: P) -> anyhow::Result<Self> {
-    let mut dir = dir.as_ref().to_path_buf();
-    match dir.canonicalize() {
-      Ok(d) => dir = d,
-      Err(err) => {
-        println!("Error canonicalizing {:?}: {}", dir, err);
-      }
-    }
-    if !dir.exists() {
-      println!("Creating directory {:?}", dir);
-      std::fs::create_dir(&dir)?;
-    }
-    Ok(Self {
-      path: dir,
-    })
-  }
-
-  pub fn path(&self) -> &Path {
-    &self.path
-  }
-}
-
-impl TemporaryDir {
-  pub fn create_existing<P: AsRef<Path>>(dir: P) -> anyhow::Result<Self> {
-    let mut dir = dir.as_ref().to_path_buf();
-    match dir.canonicalize() {
-      Ok(d) => dir = d,
-      Err(err) => {
-        println!("Error canonicalizing {:?}: {}", dir, err);
-      }
-    }
-    if !dir.exists() {
-      println!("Creating directory {:?}", dir);
-      std::fs::create_dir(&dir)?;
-    }
-    Ok(Self {
-      path: dir,
-    })
-  }
-
-  pub fn with_prefix(&self, prefix: &str) -> anyhow::Result<TempDir> {
-    let tempdir = TempDir::new_in(&self.path, prefix)?;
-    Ok(tempdir)
-  }
-
-  pub fn new_named_temp_file(&self) -> anyhow::Result<NamedTempFile> {
-    let tempfile = Builder::new()
-      .prefix("temp_")
-      .suffix(".bin")
-      .tempfile_in(&self.path)?;
-    Ok(tempfile)
-  }
-}
-
-// eg. /home/bob/artcraft, /Users/bob/artcraft, or C:\Users\Alice\artcraft
-fn get_default_data_dir() -> anyhow::Result<PathBuf> {
-  Ok(UserDirs::new()
-      .ok_or_else(|| anyhow!("could not determine user home directory"))?
-      .home_dir()
-      .join(DEFAULT_DATA_DIR))
-}
@@ -1,73 +0,0 @@
-use once_cell::sync::Lazy;
-use std::io;
-use std::path::{PathBuf, MAIN_SEPARATOR};
-
-static PREFIX: Lazy<String> = Lazy::new(|| format!("~{}", MAIN_SEPARATOR));
-
-/// The "expanduser" crate doesn't compile on Windows, so we replace its functionality slightly
-pub fn expanduser<P: AsRef<str>>(path: P) -> io::Result<PathBuf> {
-
-   Ok(match path.as_ref() {
-       // matches an exact "~"
-       s if s == "~" => {
-           home_dir()?
-       },
-       // matches paths that start with `~/`
-       s if s.starts_with(&*PREFIX) => {
-           let home = home_dir()?;
-           home.join(&s[2..])
-       },
-       // // matches paths that start with `~` but not `~/`, might be a `~username/` path
-       // s if s.starts_with("~") => {
-       //     let mut parts = s[1..].splitn(2, MAIN_SEPARATOR);
-       //     let user = parts.next()
-       //         .ok_or_else(|| io::Error::new(io::ErrorKind::Other, "malformed path"))?;
-       //     let user = Passwd::from_name(&user)
-       //         .map_err(|_| io::Error::new(io::ErrorKind::Other, "error searching for user"))?
-       //         .ok_or_else(|| io::Error::new(io::ErrorKind::Other, format!("user '{}', does not exist", &user)))?;
-       //     if let Some(ref path) = parts.next() {
-       //         PathBuf::from(user.dir).join(&path)
-       //     } else {
-       //         PathBuf::from(user.dir)
-       //     }
-       // },
-       // nothing to expand, just make a PathBuf
-       s => PathBuf::from(s)
-   })
-}
-
-pub fn home_dir() -> io::Result<PathBuf> {
-    dirs::home_dir()
-        .ok_or_else(|| io::Error::new(io::ErrorKind::Other, "no home directory is set"))
-}
-
-#[cfg(test)]
-mod tests {
-    use crate::state::expanduser::expanduser;
-    use std::env;
-    use std::path::PathBuf;
-
-    #[test]
-    #[serial_test::serial]
-    fn test_success() {
-        let old_home = env::var("HOME").expect("no home dir set");
-        let new_home = "/home/foo";
-        env::set_var("HOME", new_home);
-        let path = expanduser("~/path/to/directory");
-        env::set_var("HOME", old_home);
-        assert_eq!(path.expect("io error"), PathBuf::from("/home/foo/path/to/directory"));
-    }
-
-    #[test]
-    #[serial_test::serial]
-    fn test_only_tilde() {
-        let old_home = env::var("HOME").expect("no home dir set");
-        let new_home = "/home/foo";
-        env::set_var("HOME", new_home);
-        let pathstr = "~";
-        let path = expanduser(pathstr);
-        env::set_var("HOME", old_home);
-        assert_eq!(path.expect("io error"), PathBuf::from("/home/foo"));
-    }
-
-}
@@ -1,103 +0,0 @@
-use std::fs::OpenOptions;
-use std::io::Write;
-use anyhow::anyhow;
-use log::{error, info};
-use once_cell::sync::Lazy;
-use reqwest::Url;
-use tauri::{AppHandle, Manager, Webview};
-use errors::AnyhowResult;
-use crate::state::app_dir::AppDataRoot;
-use crate::utils::sora_webview_cookies::get_all_sora_cookies_as_string;
-
-pub const LOGIN_WINDOW_NAME: &str = "login_window";
-
-pub const SORA_LOGIN_URL_STR: &str = "https://sora.com/auth/login?callback_path=/";
-
-pub static SORA_LOGIN_URL: Lazy<Url> = Lazy::new(|| {
-  Url::parse(SORA_LOGIN_URL_STR).expect("URL should parse")
-});
-
-pub const SORA_ROOT_URL_STR: &str = "https://sora.com/";
-
-pub static SORA_ROOT_URL: Lazy<Url> = Lazy::new(|| {
-  Url::parse(SORA_ROOT_URL_STR).expect("URL should parse")
-});
-
-pub async fn login_thread(app: AppHandle, app_data_root: AppDataRoot) -> ! {
-  loop {
-    for (window_name, webview) in app.webviews() {
-      if window_name == LOGIN_WINDOW_NAME {
-        let result = check_login_window(&webview, &app_data_root).await;
-        if let Err(err) = result {
-          error!("Error checking login window: {:?}", err);
-        }
-        break;
-      }
-    }
-    tokio::time::sleep(std::time::Duration::from_millis(2_000)).await;
-  }
-}
-
-async fn check_login_window(webview: &Webview, app_data_root: &AppDataRoot) -> AnyhowResult<()> {
-  clear_browsing_data_on_test_domain(webview)?;
-  //keep_on_task(webview)?;
-  extract_cookies_to_file(webview, app_data_root)?;
-  // TODO: Other maintenance tasks ...
-  Ok(())
-}
-
-fn keep_on_task(webview: &Webview) -> AnyhowResult<()> {
-  let url = webview.url()?;
-  let hostname= url.host()
-      .ok_or(anyhow!("no host in url"))?
-      .to_string();
-  match hostname.as_str() {
-    "auth.openai.com" => {},
-    "openai.com" => {},
-    "sora.com" => {},
-    // Third party SSO
-    "accounts.google.com" => {},
-    "accounts.youtube.com" => {},
-    "login.live.com" => {},
-    "appleid.apple.com" => {},
-    _ => {
-      info!("Non login hostname: {}", hostname);
-      webview.navigate(SORA_LOGIN_URL.clone())?;
-    }
-  }
-  Ok(())
-}
-
-/// This is just so we have a way to clear browsing data.
-fn clear_browsing_data_on_test_domain(webview: &Webview) -> AnyhowResult<()> {
-  let url = webview.url()?;
-  let hostname= url.host()
-      .ok_or(anyhow!("no host in url"))?
-      .to_string();
-  match hostname.as_str() {
-    "storyteller.ai" => {
-      info!("Clearing all browsing data...");
-      webview.clear_all_browsing_data()?;
-    }
-    _ => {}
-  }
-  Ok(())
-}
-
-// TODO(bt,2025-04-07): Heuristic to detect when logged in. Only write when logged in.
-fn extract_cookies_to_file(webview: &Webview, app_data_root: &AppDataRoot) -> AnyhowResult<()> {
-  let cookies = get_all_sora_cookies_as_string(webview)?;
-
-  let cookie_file = app_data_root.get_sora_cookie_file_path();
-
-  let mut file = OpenOptions::new()
-      .create(true)
-      .write(true)
-      .truncate(true)
-      .open(cookie_file)?;
-
-  file.write_all(cookies.as_bytes())?;
-  file.flush()?;
-
-  Ok(())
-}
@@ -1 +0,0 @@
-pub mod login_thread;
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Tanish Manku	becbfa06f5	Fix Shape size jumping after recreation (on color change)	2025-03-07 00:15:54 -08:00
Tanish Manku	018a77d113	Merge branch 'main' into bugfix/shape-changes	2025-03-06 22:58:33 -08:00
Tanish Manku	d5df7dbca1	RealTimeDrawEngine: Change RenderFrame logic to use stage clone instead of OffscreenCanvas	2025-03-06 22:53:46 -08:00
Tanish Manku	a605a2e51d	ShapeNode: Use actual shapes instead of raster, write custom triangle logic	2025-03-06 22:47:09 -08:00
Tanish Manku	3e2016a7af	NetworkedNode doesn't only have to be Image node	2025-03-06 22:46:40 -08:00
Tanish Manku	a77f9c3eec	Enable stroke highlighting even for Konva Shapes	2025-03-06 22:46:20 -08:00