focal length value to show on top when dragging slider

- replaces sidebar with asset modal button on left side
- editor selection outline color change

.config/hakari.toml

@@ -1,4 +1,4 @@
-hakari-package = "my-workspace-hack"
+hakari-package = "workspace-build-acceleration"
 
 resolver = "1"
 
@@ -11,4 +11,4 @@ platforms = [
     "x86_64-pc-windows-msvc",
 ]
 
-exact-versions = true
+exact-versions = true

.gitignore

@@ -32,6 +32,9 @@ target/
 crates/lib/database_queries/target/
 crates/lib/sqlite_queries/target/
 
+# Javascript build directories
+node_modules/
+
 # Vim noise
 *.swo
 *.swp
@@ -66,3 +69,7 @@ scraps/
 # These should be built by the platform
 windows/sqlite/sqlite3.exp
 windows/sqlite/sqlite3.lib
+
+# ArtCraft Windows DLLs
+crates/desktop/src-tauri/*.dll
+windows_dlls/

Cargo.toml

@@ -1,6 +1,8 @@
 [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",
@@ -22,7 +24,8 @@ members = [
   "crates/cli/production/migration-tool",
 
   # Desktop apps
-  "crates/desktop/src-tauri",
+  "crates/desktop/tauri-4o",
+  "crates/desktop/tauri-realtime",
 
   # Shared libraries
   "crates/lib/actix_cors_configs",
@@ -30,6 +33,7 @@ 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",
@@ -56,16 +60,20 @@ 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/machine_learning/ml_models",
+  "crates/lib/machine_learning/ml_weights_registry",
+  "crates/lib/machine_learning/prompt_engineering",
   "crates/lib/markdown",
   "crates/lib/migration",
   "crates/lib/mysql_queries",
   "crates/lib/password",
-  "crates/lib/prompt_engineering",
   "crates/lib/redis_common",
   "crates/lib/redis_schema",
   "crates/lib/server_environment",
+  "crates/lib/shared_service_components",
   "crates/lib/sqlx_mysql_helpers",
   "crates/lib/storyteller_root",
   "crates/lib/subprocess_common",
@@ -113,5 +121,40 @@ members = [
 
   # cargo-hakari workspace hack (compilation speedup hack)
   # see usage: https://crates.io/crates/cargo-hakari
-  "crates/my-workspace-hack",
+  "crates/workspace-build-acceleration",
 ]
+
+[workspace.package]
+# Common inheritable settings for monorepo packages
+publish = false
+
+[workspace.dependencies]
+# Hakari workspace hack (faster compile times)
+workspace-build-acceleration = { path = "crates/workspace-build-acceleration" }
+
+# 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 = { version = "0.4.40", features = ["serde"] }
+log = "0.4.27"
+once_cell = "1.21.3"
+rand = "0.9.0"
+regex = "1.11.1"
+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"
+url = "2.5.4"
+
+# 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" }
+shared_service_components = { path = "crates/lib/shared_service_components" }
+openai_sora_client = { path = "crates/lib/api_clients/openai_sora_client" }
+primitives = { path = "crates/lib/data/primitives" }
+subprocess_common = { path = "crates/lib/subprocess_common" }

app_config.yaml

@@ -1,7 +1,8 @@
-image_width: 512
-image_height: 512
+image_width: 1024
+image_height: 1024
 #scheduler_steps: 15
 # NB(bt): It looks like we can get down to 2 sampling steps, perhaps ?
-scheduler_steps: 4
+scheduler_steps: 8
 scheduler_samples: 1
-cfg_scale: 1.1
+cfg_scale: 15.0
+seed: 42

crates/_types/composite_identifiers/Cargo.toml

@@ -13,7 +13,7 @@ name = "composite_identifiers"
 path = "src/lib.rs"
 
 [package.metadata.cargo-udeps.ignore]
-normal = ["my-workspace-hack"]
+normal = ["workspace-build-acceleration"]
 
 [dependencies]
 # Internal
@@ -21,7 +21,7 @@ enums = { path = "../enums" }
 tokens = { path = "../tokens" }
 
 # Workspace hack (faster compile times)
-my-workspace-hack = { version = "0.1", path = "../../my-workspace-hack" }
+workspace-build-acceleration = { version = "0.1", path = "../../workspace-build-acceleration" }
 
 # Serialization
 serde = "1.0.125"

crates/_types/enums/Cargo.toml

@@ -13,13 +13,13 @@ name = "enums"
 path = "src/lib.rs"
 
 [package.metadata.cargo-udeps.ignore]
-normal = ["my-workspace-hack"]
+normal = ["workspace-build-acceleration"]
 
 [dependencies]
 # Internal
 
 # Workspace hack (faster compile times)
-my-workspace-hack = { version = "0.1", path = "../../my-workspace-hack" }
+workspace-build-acceleration = { version = "0.1", path = "../../workspace-build-acceleration" }
 
 # Serialization
 serde = "1.0.125"

crates/_types/enums/src/by_table/generic_inference_jobs/inference_job_type.rs

@@ -40,6 +40,10 @@ 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,
@@ -108,6 +112,7 @@ 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",
@@ -132,6 +137,7 @@ 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),
@@ -159,6 +165,7 @@ impl InferenceJobType {
       Self::GptSovits,
       Self::ComfyUi,
       Self::StudioGen2,
+      Self::ImageGenApi,
       Self::ConvertFbxToGltf,
       Self::MocapNet,
       Self::RvcV2,
@@ -197,6 +204,7 @@ 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");
@@ -220,6 +228,7 @@ 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");
@@ -243,6 +252,7 @@ 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);
@@ -261,7 +271,7 @@ mod tests {
     fn all_variants() {
       // Static check
       let mut variants = InferenceJobType::all_variants();
-      assert_eq!(variants.len(), 16);
+      assert_eq!(variants.len(), 17);
       assert_eq!(variants.pop_first(), Some(InferenceJobType::VideoRender));
       assert_eq!(variants.pop_first(), Some(InferenceJobType::LivePortrait));
       assert_eq!(variants.pop_first(), Some(InferenceJobType::FaceFusion));
@@ -269,6 +279,7 @@ 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));

crates/_types/enums/src/by_table/generic_inference_jobs/inference_model_type.rs

@@ -33,6 +33,10 @@ 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")]
@@ -70,6 +74,7 @@ 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",
@@ -90,6 +95,7 @@ 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),
@@ -113,6 +119,7 @@ impl InferenceModelType {
       Self::VallEX,
       Self::RerenderAVideo,
       Self::StableDiffusion,
+      Self::ImageGenApi,
       Self::MocapNet,
       Self::StyleTTS2,
       Self::ConvertFbxToGltf,
@@ -140,6 +147,7 @@ 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");
@@ -159,6 +167,7 @@ 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");
@@ -176,6 +185,7 @@ 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);
@@ -187,17 +197,18 @@ mod tests {
     fn all_variants() {
       // Static check
       let mut variants = InferenceModelType::all_variants();
-      assert_eq!(variants.len(), 13);
+      assert_eq!(variants.len(), 15);
       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::SeedVc));
       assert_eq!(variants.pop_first(), Some(InferenceModelType::SoVitsSvc));
+      assert_eq!(variants.pop_first(), Some(InferenceModelType::SeedVc));
       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));

crates/_types/enums/src/by_table/media_files/media_file_origin_model_type.rs

@@ -57,6 +57,9 @@ pub enum MediaFileOriginModelType {
   #[serde(rename = "studio")]
   StorytellerStudio,
 
+  #[serde(rename = "studio_ig")]
+  StorytellerStudioImageGen,
+
   #[serde(rename = "vst")]
   VideoStyleTransfer,
 
@@ -93,6 +96,7 @@ impl MediaFileOriginModelType {
       Self::MocapNet => "mocap_net",
       Self::StyleTTS2 => "styletts2",
       Self::StableDiffusion15 => "stable_diffusion_1_5",
+      Self::StorytellerStudioImageGen => "studio_ig",
       Self::GptSovits => "gpt_sovits",
       Self::StorytellerStudio => "studio",
       Self::VideoStyleTransfer => "vst",
@@ -115,6 +119,7 @@ impl MediaFileOriginModelType {
       "mocap_net" => Ok(Self::MocapNet),
       "styletts2" => Ok(Self::StyleTTS2),
       "stable_diffusion_1_5" => Ok(Self::StableDiffusion15),
+      "studio_ig" => Ok(Self::StorytellerStudioImageGen),
       "gpt_sovits" => Ok(Self::GptSovits),
       "studio" => Ok(Self::StorytellerStudio),
       "vst" => Ok(Self::VideoStyleTransfer),
@@ -142,6 +147,7 @@ impl MediaFileOriginModelType {
       Self::StableDiffusion15,
       Self::GptSovits,
       Self::StorytellerStudio,
+      Self::StorytellerStudioImageGen,
       Self::VideoStyleTransfer,
       Self::ComfyUi,
       Self::VallEX,

crates/_types/enums_public/Cargo.toml

@@ -13,11 +13,11 @@ name = "enums_public"
 path = "src/lib.rs"
 
 [package.metadata.cargo-udeps.ignore]
-normal = ["my-workspace-hack"]
+normal = ["workspace-build-acceleration"]
 
 [dependencies]
 # Workspace hack (faster compile times)
-my-workspace-hack = { version = "0.1", path = "../../my-workspace-hack" }
+workspace-build-acceleration = { version = "0.1", path = "../../workspace-build-acceleration" }
 
 # Internal
 enums = { path = "../enums" }

crates/_types/enums_public/src/by_table/generic_inference_jobs/public_inference_job_type.rs

@@ -91,6 +91,10 @@ 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.
@@ -119,6 +123,7 @@ impl PublicInferenceJobType {
       InferenceJobType::Unknown => Self::Unknown,
       InferenceJobType::BevyToWorkflow => Self::BevyToWorkflow,
       InferenceJobType::RerenderAVideo => Self::RerenderAVideo,
+      InferenceJobType::ImageGenApi => Self::ImageGenApi,
     }
   }
   
@@ -140,6 +145,7 @@ 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,

crates/_types/enums_public/src/by_table/media_files/public_media_file_model_type.rs

@@ -64,6 +64,10 @@ pub enum PublicMediaFileModelType {
   #[serde(rename = "studio")]
   StorytellerStudio,
 
+  /// NB: This is GPT4o image generation
+  #[serde(rename = "studio_ig")]
+  StorytellerStudioImageGen,
+
   #[serde(rename = "vst")]
   VideoStyleTransfer,
 
@@ -89,6 +93,7 @@ impl PublicMediaFileModelType {
       MediaFileOriginModelType::LivePortrait => Self::FaceMirror,
       MediaFileOriginModelType::SadTalker => Self::FaceAnimator,
       MediaFileOriginModelType::StyleTTS2 => Self::VoiceDesigner,
+      MediaFileOriginModelType::StorytellerStudioImageGen => Self::StorytellerStudioImageGen,
       // Conserved variants
       MediaFileOriginModelType::RvcV2 => Self::RvcV2,
       MediaFileOriginModelType::SoVitsSvc => Self::SoVitsSvc,
@@ -113,6 +118,7 @@ impl PublicMediaFileModelType {
       Self::FaceAnimator => MediaFileOriginModelType::SadTalker,
       Self::Lipsync => MediaFileOriginModelType::FaceFusion,
       Self::VoiceDesigner => MediaFileOriginModelType::StyleTTS2,
+      Self::StorytellerStudioImageGen => MediaFileOriginModelType::StorytellerStudioImageGen,
       // Conserved variants
       Self::RvcV2 => MediaFileOriginModelType::RvcV2,
       Self::SoVitsSvc => MediaFileOriginModelType::SoVitsSvc,

crates/_types/reusable_types/Cargo.toml

@@ -13,7 +13,7 @@ name = "reusable_types"
 path = "src/lib.rs"
 
 [package.metadata.cargo-udeps.ignore]
-normal = ["my-workspace-hack"]
+normal = ["workspace-build-acceleration"]
 
 [dependencies]
 
@@ -21,7 +21,7 @@ normal = ["my-workspace-hack"]
 # (none)
 
 # Workspace hack (faster compile times)
-my-workspace-hack = { version = "0.1", path = "../../my-workspace-hack" }
+workspace-build-acceleration = { version = "0.1", path = "../../workspace-build-acceleration" }
 
 # External
 chrono = { version = "0.4.22", features = ["serde"] }

crates/_types/tokens/Cargo.toml

@@ -13,7 +13,7 @@ name = "tokens"
 path = "src/lib.rs"
 
 [package.metadata.cargo-udeps.ignore]
-normal = ["my-workspace-hack"]
+normal = ["workspace-build-acceleration"]
 
 [dependencies]
 
@@ -21,7 +21,7 @@ normal = ["my-workspace-hack"]
 # (none)
 
 # Workspace hack (faster compile times)
-my-workspace-hack = { version = "0.1", path = "../../my-workspace-hack" }
+workspace-build-acceleration = { version = "0.1", path = "../../workspace-build-acceleration" }
 
 # Serialization
 serde = "1.0.125"

crates/cli/devenv/dev-database-seed/Cargo.toml

@@ -13,7 +13,7 @@ name = "dev-database-seed"
 path = "src/main.rs"
 
 [package.metadata.cargo-udeps.ignore]
-normal = ["my-workspace-hack"]
+normal = ["workspace-build-acceleration"]
 
 [dependencies]
 # Internal
@@ -32,7 +32,7 @@ storyteller_root = { path = "../../../lib/storyteller_root" }
 tokens = { path = "../../../_types/tokens" }
 
 # Workspace hack (faster compile times)
-my-workspace-hack = { version = "0.1", path = "../../../my-workspace-hack" }
+workspace-build-acceleration = { version = "0.1", path = "../../../workspace-build-acceleration" }
 
 # External
 log = "0.4.14"

crates/cli/devenv/dev-upload-media-file/Cargo.toml

@@ -13,7 +13,7 @@ name = "dev-upload-media-file"
 path = "src/main.rs"
 
 [package.metadata.cargo-udeps.ignore]
-normal = ["my-workspace-hack"]
+normal = ["workspace-build-acceleration"]
 
 [dependencies]
 # Internal
@@ -29,7 +29,7 @@ mysql_queries = { path = "../../../lib/mysql_queries" }
 tokens = { path = "../../../_types/tokens" }
 
 # Workspace hack (faster compile times)
-my-workspace-hack = { version = "0.1", path = "../../../my-workspace-hack" }
+workspace-build-acceleration = { version = "0.1", path = "../../../workspace-build-acceleration" }
 
 # External
 clap = "4.4.8"

crates/cli/devenv/scrape-test/Cargo.toml

@@ -13,7 +13,7 @@ name = "scrape-test"
 path = "src/main.rs"
 
 [package.metadata.cargo-udeps.ignore]
-normal = ["my-workspace-hack"]
+normal = ["workspace-build-acceleration"]
 
 [dependencies]
 
@@ -22,7 +22,7 @@ errors = { path = "../../../lib/errors" }
 web_scrapers = { path = "../../../lib/web_scrapers" }
 
 # Workspace hack (faster compile times)
-my-workspace-hack = { version = "0.1", path = "../../../my-workspace-hack" }
+workspace-build-acceleration = { version = "0.1", path = "../../../workspace-build-acceleration" }
 
 # External
 clap = "4.4.8"

crates/cli/production/db-backfill/Cargo.toml

@@ -13,11 +13,11 @@ name = "db-backfill"
 path = "src/main.rs"
 
 [package.metadata.cargo-udeps.ignore]
-normal = ["my-workspace-hack"]
+normal = ["workspace-build-acceleration"]
 
 [dependencies]
 # Workspace hack (faster compile times)
-my-workspace-hack = { version = "0.1", path = "../../../my-workspace-hack" }
+workspace-build-acceleration = { version = "0.1", path = "../../../workspace-build-acceleration" }
 
 # Internal
 datetimes = { path = "../../../lib/data/datetimes" }

crates/cli/production/db-backup/Cargo.toml

@@ -13,7 +13,7 @@ name = "db-backup"
 path = "src/main.rs"
 
 [package.metadata.cargo-udeps.ignore]
-normal = ["my-workspace-hack"]
+normal = ["workspace-build-acceleration"]
 
 [dependencies]
 # Internal
@@ -21,7 +21,7 @@ easyenv = { path = "../../../lib/easyenv" }
 errors = { path = "../../../lib/errors" }
 
 # Workspace hack (faster compile times)
-my-workspace-hack = { version = "0.1", path = "../../../my-workspace-hack" }
+workspace-build-acceleration = { version = "0.1", path = "../../../workspace-build-acceleration" }
 
 # External
 anyhow = "1.0.86"

crates/cli/production/db-cleanup/Cargo.toml

@@ -13,7 +13,7 @@ name = "db-cleanup"
 path = "src/main.rs"
 
 [package.metadata.cargo-udeps.ignore]
-normal = ["my-workspace-hack"]
+normal = ["workspace-build-acceleration"]
 
 [dependencies]
 # Internal
@@ -26,7 +26,7 @@ storyteller_root = { path = "../../../lib/storyteller_root" }
 tokens = { path = "../../../_types/tokens" }
 
 # Workspace hack (faster compile times)
-my-workspace-hack = { version = "0.1", path = "../../../my-workspace-hack" }
+workspace-build-acceleration = { version = "0.1", path = "../../../workspace-build-acceleration" }
 
 # External
 clap = "4.4.8"

crates/cli/production/elasticsearch-cli/Cargo.toml

@@ -13,7 +13,7 @@ name = "elasticsearch-cli"
 path = "src/main.rs"
 
 [package.metadata.cargo-udeps.ignore]
-normal = ["my-workspace-hack"]
+normal = ["workspace-build-acceleration"]
 
 [dependencies]
 # Internal
@@ -29,7 +29,7 @@ storyteller_root = { path = "../../../lib/storyteller_root" }
 tokens = { path = "../../../_types/tokens" }
 
 # Workspace hack (faster compile times)
-my-workspace-hack = { version = "0.1", path = "../../../my-workspace-hack" }
+workspace-build-acceleration = { version = "0.1", path = "../../../workspace-build-acceleration" }
 
 # External
 clap = "4.4.8"

crates/cli/production/kubectl-pod-cleanup/Cargo.toml

@@ -13,7 +13,7 @@ name = "kubectl-pod-cleanup"
 path = "src/main.rs"
 
 [package.metadata.cargo-udeps.ignore]
-normal = ["my-workspace-hack"]
+normal = ["workspace-build-acceleration"]
 
 [dependencies]
 # Internal
@@ -22,7 +22,7 @@ easyenv = { path = "../../../lib/easyenv" }
 errors = { path = "../../../lib/errors" }
 
 # Workspace hack (faster compile yytimes)
-my-workspace-hack = { version = "0.1", path = "../../../my-workspace-hack" }
+workspace-build-acceleration = { version = "0.1", path = "../../../workspace-build-acceleration" }
 
 # External
 log = "0.4.20"

crates/cli/production/lru-delete-models/Cargo.toml

@@ -13,7 +13,7 @@ name = "lru-delete-models"
 path = "src/main.rs"
 
 [package.metadata.cargo-udeps.ignore]
-normal = ["my-workspace-hack"]
+normal = ["workspace-build-acceleration"]
 
 [dependencies]
 # Internal
@@ -21,7 +21,7 @@ errors = { path = "../../../lib/errors" }
 logging = { path = "../../../lib/logging" }
 
 # Workspace hack (faster compile times)
-my-workspace-hack = { version = "0.1", path = "../../../my-workspace-hack" }
+workspace-build-acceleration = { version = "0.1", path = "../../../workspace-build-acceleration" }
 
 # External
 clap = { version = "4.4.8", features = ["derive"] }

crates/cli/production/migration-tool/Cargo.toml

@@ -13,7 +13,7 @@ name = "migration-tool"
 path = "src/main.rs"
 
 [package.metadata.cargo-udeps.ignore]
-normal = ["my-workspace-hack"]
+normal = ["workspace-build-acceleration"]
 
 [dependencies]
 # Internal
@@ -30,7 +30,7 @@ storyteller_root = { path = "../../../lib/storyteller_root" }
 tokens = { path = "../../../_types/tokens" }
 
 # Workspace hack (faster compile times)
-my-workspace-hack = { version = "0.1", path = "../../../my-workspace-hack" }
+workspace-build-acceleration = { version = "0.1", path = "../../../workspace-build-acceleration" }
 
 # External
 clap = "4.4.8"

crates/desktop/src-tauri/Cargo.toml

@@ -1,65 +0,0 @@
-[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.

crates/desktop/src-tauri/src/endpoints/download_models.rs

@@ -1,16 +0,0 @@
-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()))
-}

crates/desktop/src-tauri/src/endpoints/image_endpoint.rs

@@ -1,143 +0,0 @@
-use crate::ml::downloads::download_all_models::download_all_models;
-use crate::ml::model_cache::ModelCache;
-use crate::ml::prompt_cache::PromptCache;
-use crate::ml::stable_diffusion::lcm_pipeline::{lcm_pipeline, Args};
-use crate::state::app_config::AppConfig;
-use base64::{engine::general_purpose::STANDARD as BASE64_STANDARD, Engine};
-use bytes::BytesMut;
-use image::imageops::FilterType;
-use image::{DynamicImage, ImageFormat, ImageReader};
-use log::{error, info};
-use std::io::Cursor;
-use std::path::PathBuf;
-use tauri::{AppHandle, State};
-use crate::state::app_dir::AppDataRoot;
-
-const PROMPT_FILENAME : &str = "prompt.txt";
-const PROMPT: &str = "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)
-}

crates/desktop/src-tauri/src/endpoints/mod.rs

@@ -1,4 +0,0 @@
-pub mod download_models;
-pub mod flip_image;
-pub mod image_endpoint;
-pub mod test_counter;

crates/desktop/src-tauri/src/endpoints/test_counter.rs

@@ -1,24 +0,0 @@
-use std::sync::{Arc, RwLock};
-use once_cell::sync::Lazy;
-
-static COUNTER : Lazy<Arc<RwLock<u64>>> = Lazy::new(|| Arc::new(RwLock::new(0)));
-
-#[tauri::command]
-pub fn test_counter() -> u64 {
-  let value : u64;
-  {
-    match COUNTER.write() {
-      Ok(mut counter) => {
-        *counter += 1;
-        value = *counter;
-      },
-      Err(_e) => {
-        value = 0;
-      }
-    }
-  }
-
-  println!("I was invoked from JavaScript! {:?}", value);
-
-  value
-}

crates/desktop/src-tauri/src/events/notification_event.rs

@@ -1,22 +0,0 @@
-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,
-  }
-}

crates/desktop/src-tauri/src/ml/downloads/download_all_models.rs

@@ -1,15 +0,0 @@
-use crate::ml::downloads::download_model_registry_file::download_model_registry_file;
-use crate::ml::model_type::ModelType;
-use crate::state::app_dir::AppDataRoot;
-
-pub async fn download_all_models(app_data_root: &AppDataRoot) -> anyhow::Result<()> {
-  let weights_dir = app_data_root.weights_dir();
-  
-  download_model_registry_file(ModelType::ClipJson, weights_dir).await?;
-  download_model_registry_file(ModelType::SdxlTurboUnet, weights_dir).await?;
-  download_model_registry_file(ModelType::SdxlTurboVae, weights_dir).await?;
-  download_model_registry_file(ModelType::SdxlTurboClipEncoder, weights_dir).await?;
-  download_model_registry_file(ModelType::SdxlTurboClipEncoder2, weights_dir).await?;
-  
-  Ok(())
-}

crates/desktop/src-tauri/src/ml/downloads/download_model_registry_file.rs

@@ -1,30 +0,0 @@
-use crate::ml::model_type::ModelType;
-use crate::state::app_dir::AppWeightsDir;
-use log::info;
-use std::fs::File;
-use std::path::{Path, PathBuf};
-
-pub async fn download_model_registry_file(
-  model_type: ModelType,
-  weights_dir: &AppWeightsDir,
-) -> anyhow::Result<PathBuf> {
-  
-  let path = weights_dir.model_path(&model_type);
-
-  if path.exists() {
-    return Ok(path);
-  }
-
-  info!("downloading model: {:?} to {:?}", model_type, path);
-
-  let body = reqwest::get(model_type.get_download_url())
-    .await?
-    .bytes()
-    .await?;
-  
-  let _file = File::create(&path)?;
-  
-  std::fs::write(&path, body.as_ref())?;
-  
-  Ok(path)
-}

crates/desktop/src-tauri/src/ml/downloads/mod.rs

@@ -1,2 +0,0 @@
-pub mod download_model_registry_file;
-pub mod download_all_models;

crates/desktop/src-tauri/src/ml/mod.rs

@@ -1,8 +0,0 @@
-pub mod image;
-pub mod model_cache;
-pub mod model_file;
-pub mod models;
-pub mod prompt_cache;
-pub mod stable_diffusion;
-pub mod model_type;
-pub mod downloads;

crates/desktop/src-tauri/src/ml/model_file.rs

@@ -1,83 +0,0 @@
-#[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)
-      }
-    }
-  }
-}
-

crates/desktop/src-tauri/src/ml/model_type.rs

@@ -1,47 +0,0 @@
-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)
-  }
-}

crates/desktop/src-tauri/src/ml/models/lazy_load_vae_model.rs

@@ -1,130 +0,0 @@
-use crate::ml::model_file::StableDiffusionVersion;
-use anyhow::anyhow;
-use candle_core::{DType, Device, Tensor};
-use candle_transformers::models::stable_diffusion::vae::{AutoEncoderKL, DiagonalGaussianDistribution};
-use candle_transformers::models::stable_diffusion::StableDiffusionConfig;
-use hf_hub::api::sync::Api;
-use std::sync::{Arc, RwLock};
-
-pub struct LazyLoadVaeModel {
-  lazy_model: Arc<RwLock<Option<AutoEncoderKL>>>,
-  model_configs: VaeConfigs,
-}
-
-// TODO: Maybe we pass these via a central config read at startup, passed as an Arc<ConfigT>
-pub struct VaeConfigs {
-  sd_config: StableDiffusionConfig,
-  sd_version: StableDiffusionVersion,
-  device: Device,
-  dtype: DType,
-  hf_api: Api,
-}
-
-impl LazyLoadVaeModel {
-  pub fn new(
-    sd_config: &StableDiffusionConfig,
-    sd_version: &StableDiffusionVersion,
-    hf_api: &Api,
-    device: &Device,
-    dtype: DType,
-  ) -> anyhow::Result<Self> {
-    println!("building vae model... (1)");
-    
-    Ok(Self {
-      lazy_model: Arc::new(RwLock::new(None)),
-      model_configs: VaeConfigs {
-        sd_config: sd_config.clone(),
-        sd_version: sd_version.clone(),
-        device: device.clone(),
-        hf_api: hf_api.clone(),
-        dtype
-      }
-    })
-  }
-
-  pub fn encode(&self, xs: &Tensor) -> anyhow::Result<DiagonalGaussianDistribution> {
-    println!("vae encode...");
-    match self.try_encode(xs) {
-      Err(err) => Err(err),
-      Ok(Some(result)) => Ok(result),
-      Ok(None) => {
-        // Model wasn't previously loaded
-        self.load_model()?;
-        self.try_encode(xs)?
-          .ok_or(anyhow!("model was not previously loaded"))
-      }
-    }
-  }
-  
-  pub fn decode(&self, xs: &Tensor) -> anyhow::Result<Tensor> {
-    println!("vae decode...");
-    match self.try_decode(xs) {
-      Err(err) => Err(err),
-      Ok(Some(result)) => Ok(result),
-      Ok(None) => {
-        // Model wasn't previously loaded
-        self.load_model()?;
-        self.try_decode(xs)?
-          .ok_or(anyhow!("model was not previously loaded"))
-      }
-    }
-  }
-
-  fn try_encode(&self, xs: &Tensor) -> anyhow::Result<Option<DiagonalGaussianDistribution>> {
-    match self.lazy_model.read() {
-      Err(err) => Err(anyhow!("lock error: {:?}", err)),
-      Ok(model) => {
-        match &*model {
-          None => Ok(None), // Model is not yet loaded
-          Some(model) => {
-            Ok(Some(model.encode(xs)?))
-          }
-        }
-      }
-    }
-  }
-
-  fn try_decode(&self, xs: &Tensor) -> anyhow::Result<Option<Tensor>> {
-    match self.lazy_model.read() {
-      Err(err) => Err(anyhow!("lock error: {:?}", err)),
-      Ok(model) => {
-        match &*model {
-          None => Ok(None), // Model is not yet loaded
-          Some(model) => {
-            Ok(Some(model.decode(xs)?))
-          }
-        }
-      }
-    }
-  }
-
-  fn load_model(&self) -> anyhow::Result<()> {
-    println!("load vae");
-    match self.lazy_model.write() {
-      Err(err) => return Err(anyhow!("lock error: {:?}", err)),
-      Ok(mut maybe_model) => {
-        match &*maybe_model {
-          Some(_model) => {} // Already loaded; fall through
-          None => {
-            let repo = self.model_configs.sd_version.repo();
-
-            println!("Building VAE model from : {:?} ... (3)", repo);
-            
-            let vae_file = self.model_configs.hf_api.model(repo.to_string())
-              .get("vae/diffusion_pytorch_model.safetensors")?;
-
-            println!("Building VAE model from file {:?}...", &vae_file);
-            let vae = self.model_configs
-              .sd_config
-              .build_vae(vae_file, &self.model_configs.device, self.model_configs.dtype)?;
-
-            println!("Built VAE...");
-            *maybe_model = Some(vae);
-          }
-        }
-      }
-    }
-    Ok(())
-  }
- 
-}

crates/desktop/src-tauri/src/ml/models/mod.rs

@@ -1,2 +0,0 @@
-pub mod unet_model;
-pub mod lazy_load_vae_model;

crates/desktop/src-tauri/src/ml/models/unet_model.rs

@@ -1,26 +0,0 @@
-use candle_core::{DType, Device, Tensor};
-use candle_transformers::models::stable_diffusion::unet_2d::UNet2DConditionModel;
-use candle_transformers::models::stable_diffusion::StableDiffusionConfig;
-use std::path::Path;
-
-pub struct UNetModel {
-  model: UNet2DConditionModel,
-}
-
-impl UNetModel {
-  pub fn new<P: AsRef<Path>>(sd_config: &StableDiffusionConfig, unet_weights_file: P, device: &Device, dtype: DType) -> anyhow::Result<Self> {
-    println!("building unet model... (1)");
-
-    let model = sd_config.build_unet(unet_weights_file, &device, 4, true, dtype)?;
-
-    Ok(Self { model })
-  }
-
-  pub fn 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))?)
-  }
-}

crates/desktop/src-tauri/src/ml/stable_diffusion/infer_clip_text_embeddings.rs

@@ -1,170 +0,0 @@
-#[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)
-}

crates/desktop/src-tauri/src/ml/stable_diffusion/lcm_pipeline.rs

@@ -1,293 +0,0 @@
-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)
-}

crates/desktop/src-tauri/src/state/app_config.rs

@@ -1,108 +0,0 @@
-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,
-    })
-  }
-}

crates/desktop/src-tauri/src/state/app_dir.rs

@@ -1,123 +0,0 @@
-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())
-  }
-}

crates/desktop/src-tauri/src/threads/downloader_thread.rs

@@ -1,11 +0,0 @@
-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));
-  }
-}

crates/desktop/tauri-4o/.taurignore

@@ -0,0 +1,4 @@
+# Don't restart the dev server on Rust changes
+crates/
+Cargo.toml
+Cargo.lock

crates/desktop/tauri-4o/Cargo.toml

@@ -0,0 +1,74 @@
+[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)
+workspace-build-acceleration = { 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.

crates/desktop/tauri-4o/src/commands/image_generation_command.rs

@@ -0,0 +1,90 @@
+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),
+  })
+}

crates/desktop/tauri-4o/src/commands/mod.rs

@@ -0,0 +1,3 @@
+pub mod flip_image;
+pub mod image_generation_command;
+pub mod open_login_command;

crates/desktop/tauri-4o/src/commands/open_login_command.rs

@@ -0,0 +1,72 @@
+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(())
+}

crates/desktop/tauri-4o/src/events/notification_event.rs

@@ -0,0 +1,45 @@
+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,
+  },
+}

crates/desktop/tauri-4o/src/lib.rs

@@ -0,0 +1,65 @@
+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");
+}

crates/desktop/tauri-4o/src/main.rs

@@ -0,0 +1,6 @@
+// Prevents additional console window on Windows in release, DO NOT REMOVE!!
+#![cfg_attr(not(debug_assertions), windows_subsystem = "windows")]
+
+fn main() {
+  app_lib::run();
+}

crates/desktop/tauri-4o/src/state/app_config.rs

@@ -0,0 +1,58 @@
+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,
+    })
+  }
+}

crates/desktop/tauri-4o/src/state/app_dir.rs

@@ -0,0 +1,219 @@
+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))
+}

crates/desktop/tauri-4o/src/state/expanduser.rs

@@ -0,0 +1,73 @@
+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"));
+    }
+
+}

crates/desktop/tauri-4o/src/state/mod.rs

@@ -1,4 +1,5 @@
 pub mod app_config;
 pub mod app_dir;
 pub mod os_platform;
-pub mod yaml_config;
+pub mod yaml_config;
+pub mod expanduser;

crates/desktop/tauri-4o/src/threads/login_thread.rs

@@ -0,0 +1,103 @@
+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(())
+}

crates/desktop/tauri-4o/src/threads/mod.rs

@@ -0,0 +1 @@
+pub mod login_thread;

crates/desktop/tauri-4o/src/transfer/download.rs

@@ -0,0 +1,241 @@
+//! Implemented from https://github.com/huggingface/hf_transfer/blob/main/src/lib.rs
+
+use crate::transfer::error::Error;
+use crate::transfer::util::{exponential_backoff, BASE_WAIT_TIME, MAX_WAIT_TIME};
+use futures::stream::FuturesUnordered;
+use futures::StreamExt;
+use reqwest::header::{
+  HeaderMap, HeaderName, HeaderValue, AUTHORIZATION, CONTENT_RANGE,
+  RANGE,
+};
+use reqwest::Url;
+use std::collections::HashMap;
+use std::fmt::Display;
+use std::fs::remove_file;
+use std::io::SeekFrom;
+use std::path::Path;
+use std::sync::Arc;
+use std::sync::atomic::{AtomicUsize, Ordering};
+use std::sync::mpsc::Sender;
+use std::time::Duration;
+use log::info;
+use tempfile::NamedTempFile;
+use tokio::fs::OpenOptions;
+use tokio::io::AsyncWriteExt;
+use tokio::io::AsyncSeekExt;
+use tokio::sync::Semaphore;
+use tokio::time::sleep;
+
+pub struct ProgressUpdate {
+  pub complete: usize,
+  pub total_length: usize,
+}
+
+/// max_files: Number of open file handles, which determines the maximum number of parallel downloads
+/// parallel_failures:  Number of maximum failures of different chunks in parallel (cannot exceed max_files)
+/// max_retries: Number of maximum attempts per chunk. (Retries are exponentially backed off + jitter)
+///
+/// The number of threads can be tuned by the environment variable `TOKIO_WORKER_THREADS` as documented in
+/// https://docs.rs/tokio/latest/tokio/runtime/struct.Builder.html#method.worker_threads
+#[allow(clippy::too_many_arguments)]
+pub async fn download_async<P: AsRef<Path>>(
+  url: String,
+  filename: NamedTempFile,
+  final_filename: P,
+  max_files: usize,
+  chunk_size: usize,
+  parallel_failures: usize,
+  max_retries: usize,
+  input_headers: Option<HashMap<String, String>>,
+  maybe_progress_sender: Option<Sender<ProgressUpdate>>,
+) -> Result<(), Error> {
+  info!(">>> Downloading: {}", filename.as_ref().display());
+
+  let client = reqwest::Client::builder()
+    // https://github.com/hyperium/hyper/issues/2136#issuecomment-589488526
+    .http2_keep_alive_timeout(Duration::from_secs(15))
+    .build()?;
+
+  let mut headers = HeaderMap::new();
+  let mut auth_token = None;
+  if let Some(input_headers) = input_headers {
+    headers.reserve(input_headers.len());
+    for (k, v) in input_headers {
+      let name: HeaderName = k
+        .try_into()
+        .map_err(|err| Error::new_err(format!("Invalid header: {err}")))?;
+      let value: HeaderValue = AsRef::<str>::as_ref(&v)
+        .try_into()
+        .map_err(|err| Error::new_err(format!("Invalid header value: {err}")))?;
+      if name == AUTHORIZATION {
+        auth_token = Some(value);
+      } else {
+        headers.insert(name, value);
+      }
+    }
+  };
+
+  let response = if let Some(token) = auth_token.as_ref() {
+    client.get(&url).header(AUTHORIZATION, token)
+  } else {
+    client.get(&url)
+  }
+    .headers(headers.clone())
+    .header(RANGE, "bytes=0-0")
+    .send()
+    .await
+    .map_err(|err| Error::new_err(format!("Error while downloading: {err}")))?
+    .error_for_status()
+    .map_err(|err| Error::new_err(err.to_string()))?;
+
+  // Only call the final redirect URL to avoid overloading the Hub with requests and also
+  // altering the download count
+  let redirected_url = response.url();
+  if Url::parse(&url)
+    .map_err(|err| Error::new_err(format!("failed to parse url: {err}")))?
+    .host()
+    == redirected_url.host()
+  {
+    if let Some(token) = auth_token {
+      headers.insert(AUTHORIZATION, token);
+    }
+  }
+
+  let content_range = response
+    .headers()
+    .get(CONTENT_RANGE)
+    .ok_or(Error::new_err("No content length".to_string()))?
+    .to_str()
+    .map_err(|err| Error::new_err(format!("Error while downloading: {err}")))?;
+
+  let size: Vec<&str> = content_range.split('/').collect();
+  // Content-Range: bytes 0-0/702517648
+  // https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Content-Range
+  let length: usize = size
+    .last()
+    .ok_or(Error::new_err(
+      "Error while downloading: No size was detected".to_string(),
+    ))?
+    .parse()
+    .map_err(|err| Error::new_err(format!("Error while downloading: {err}")))?;
+
+
+  info!("File {:?} is {:?} length", filename.as_ref(), length);
+
+  let mut handles = FuturesUnordered::new();
+  let semaphore = Arc::new(Semaphore::new(max_files));
+  let parallel_failures_semaphore = Arc::new(Semaphore::new(parallel_failures));
+  let max_chunk_complete = Arc::new(AtomicUsize::new(0));
+
+  for start in (0..length).step_by(chunk_size) {
+    let url = redirected_url.to_string();
+    let filename = filename.as_ref().to_owned();
+    let client = client.clone();
+    let headers = headers.clone();
+
+    let stop = std::cmp::min(start + chunk_size - 1, length);
+    let semaphore = semaphore.clone();
+    let parallel_failures_semaphore = parallel_failures_semaphore.clone();
+    let maybe_progress = maybe_progress_sender.clone();
+    let max_chunk_complete = max_chunk_complete.clone();
+    handles.push(tokio::spawn(async move {
+      let permit = semaphore
+        .acquire_owned()
+        .await
+        .map_err(|err| Error::new_err(format!("Error while downloading: {err}")))?;
+      let mut chunk = download_chunk(&client, &url, &filename, start, stop, headers.clone()).await;
+      let mut i = 0;
+      if parallel_failures > 0 {
+        while let Err(dlerr) = chunk {
+          if i >= max_retries {
+            return Err(Error::new_err(format!(
+              "Failed after too many retries ({max_retries}): {dlerr}"
+            )));
+          }
+          let parallel_failure_permit = parallel_failures_semaphore.clone().try_acquire_owned().map_err(|err| {
+            Error::new_err(format!(
+              "Failed too many failures in parallel ({parallel_failures}): {dlerr} ({err})"
+            ))
+          })?;
+
+          let wait_time = exponential_backoff(BASE_WAIT_TIME, i, MAX_WAIT_TIME);
+          sleep(Duration::from_millis(wait_time as u64)).await;
+
+          chunk = download_chunk(&client, &url, &filename, start, stop, headers.clone()).await;
+          i += 1;
+          drop(parallel_failure_permit);
+          
+          if let Some(sender) = maybe_progress.as_ref() {
+            // NB: Calling fetch_max twice. First call will return the lower value.
+            let _old = max_chunk_complete.fetch_max(stop, Ordering::SeqCst);
+            let max_chunk = max_chunk_complete.fetch_max(stop, Ordering::SeqCst);
+            let progress_update = ProgressUpdate { 
+              complete: max_chunk,
+              total_length: length,
+            };
+            if let Err(_err) = sender.send(progress_update) {
+              // Fail silently, not essential to issue progress updates
+            }
+          }
+        }
+      }
+      drop(permit);
+      chunk.map_err(|e| Error::new_err(format!("Downloading error {e}"))).and(Ok(stop - start))
+    }));
+  }
+
+  // Output the chained result
+  while let Some(result) = handles.next().await {
+    match result {
+      Ok(Ok(size)) => {
+        //if let Some(ref callback) = callback {
+        //  callback.call((size,), None)?;
+        //}
+      }
+      Ok(Err(py_err)) => {
+        return Err(py_err);
+      }
+      Err(err) => {
+        return Err(Error::new_err(format!(
+          "Error while downloading: {err}"
+        )));
+      }
+    }
+  }
+
+  let file = filename.persist(final_filename)
+    .map_err(|e| Error::new_err(format!("Could not rename to final filename: {:?}", e)))?;
+
+  file.sync_all()?;
+
+  Ok(())
+}
+
+async fn download_chunk<P: AsRef<Path>>(
+  client: &reqwest::Client,
+  url: &str,
+  filename: P,
+  start: usize,
+  stop: usize,
+  headers: HeaderMap,
+) -> Result<(), Error> {
+  // Process each socket concurrently.
+  let range = format!("bytes={start}-{stop}");
+  let mut file = OpenOptions::new()
+    .write(true)
+    .truncate(false)
+    .create(true)
+    .open(filename)
+    .await?;
+  file.seek(SeekFrom::Start(start as u64)).await?;
+  let response = client
+    .get(url)
+    .headers(headers)
+    .header(RANGE, range)
+    .send()
+    .await?
+    .error_for_status()?;
+  let content = response.bytes().await?;
+  file.write_all(&content).await?;
+  Ok(())
+}

crates/desktop/tauri-4o/src/transfer/error.rs

@@ -0,0 +1,49 @@
+//! Implemented from https://github.com/huggingface/hf_transfer/blob/main/src/lib.rs
+
+use std::fmt::Display;
+use reqwest::header::ToStrError;
+
+#[derive(Debug)]
+pub enum Error {
+  Io(std::io::Error),
+  Request(reqwest::Error),
+  ToStrError(ToStrError),
+  Misc(String),
+}
+
+impl From<std::io::Error> for Error {
+  fn from(value: std::io::Error) -> Self {
+    Self::Io(value)
+  }
+}
+
+impl From<reqwest::Error> for Error {
+  fn from(value: reqwest::Error) -> Self {
+    Self::Request(value)
+  }
+}
+
+impl From<ToStrError> for Error {
+  fn from(value: ToStrError) -> Self {
+    Self::ToStrError(value)
+  }
+}
+
+impl Display for Error {
+  fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+    match self {
+      Self::Io(io) => write!(f, "Io: {io}"),
+      Self::Request(req) => write!(f, "Request: {req}"),
+      Self::ToStrError(req) => write!(f, "Response non ascii: {req}"),
+      Self::Misc(err) => write!(f, "Misc error: {err}"),
+    }
+  }
+}
+
+impl std::error::Error for Error {}
+
+impl Error {
+  pub fn new_err(reason: String) -> Self {
+    Self::Misc(reason)
+  }
+}

crates/desktop/tauri-4o/src/transfer/mod.rs

@@ -0,0 +1,4 @@
+pub mod download;
+pub mod upload;
+pub mod util;
+pub mod error;

crates/desktop/tauri-4o/src/transfer/upload.rs

@@ -0,0 +1,189 @@
+//! Implemented from https://github.com/huggingface/hf_transfer/blob/main/src/lib.rs
+
+use crate::transfer::error::Error;
+use crate::transfer::util::{exponential_backoff, BASE_WAIT_TIME, MAX_WAIT_TIME};
+use futures::stream::FuturesUnordered;
+use futures::StreamExt;
+use reqwest::header::CONTENT_LENGTH;
+use std::collections::HashMap;
+use std::fmt::Display;
+use std::io::SeekFrom;
+use std::sync::Arc;
+use std::time::Duration;
+use tokio::fs::OpenOptions;
+use tokio::io::{AsyncReadExt, AsyncSeekExt};
+use tokio::sync::Semaphore;
+use tokio::time::sleep;
+use tokio_util::codec::{BytesCodec, FramedRead};
+
+/// parts_urls: Dictionary consisting of part numbers as keys and the associated url as values
+/// completion_url: The url that should be called when the upload is finished
+/// max_files: Number of open file handles, which determines the maximum number of parallel uploads
+/// parallel_failures:  Number of maximum failures of different chunks in parallel (cannot exceed max_files)
+/// max_retries: Number of maximum attempts per chunk. (Retries are exponentially backed off + jitter)
+///
+/// The number of threads can be tuned by the environment variable `TOKIO_WORKER_THREADS` as documented in
+/// https://docs.rs/tokio/latest/tokio/runtime/struct.Builder.html#method.worker_threads
+///
+/// See https://docs.aws.amazon.com/AmazonS3/latest/userguide/mpuoverview.html for more information
+/// on the multipart upload
+#[allow(clippy::too_many_arguments)]
+fn multipart_upload(
+  file_path: String,
+  parts_urls: Vec<String>,
+  chunk_size: u64,
+  max_files: usize,
+  parallel_failures: usize,
+  max_retries: usize,
+) -> Result<Vec<HashMap<String, String>>, Error> {
+  if parallel_failures > max_files {
+    return Err(Error::new_err(
+      "Error parallel_failures cannot be > max_files".to_string(),
+    ));
+  }
+  if (parallel_failures == 0) != (max_retries == 0) {
+    return Err(Error::new_err(
+      "For retry mechanism you need to set both `parallel_failures` and `max_retries`"
+        .to_string(),
+    ));
+  }
+
+  tokio::runtime::Builder::new_multi_thread()
+    .enable_all()
+    .build()?
+    .block_on(async {
+      upload_async(
+        file_path,
+        parts_urls,
+        chunk_size,
+        max_files,
+        parallel_failures,
+        max_retries,
+      )
+        .await
+    })
+}
+
+
+
+#[allow(clippy::too_many_arguments)]
+async fn upload_async(
+  file_path: String,
+  parts_urls: Vec<String>,
+  chunk_size: u64,
+  max_files: usize,
+  parallel_failures: usize,
+  max_retries: usize,
+) -> Result<Vec<HashMap<String, String>>, Error> {
+  let client = reqwest::Client::new();
+
+  let mut handles = FuturesUnordered::new();
+  let semaphore = Arc::new(Semaphore::new(max_files));
+  let parallel_failures_semaphore = Arc::new(Semaphore::new(parallel_failures));
+
+  for (part_number, part_url) in parts_urls.iter().enumerate() {
+    let url = part_url.to_string();
+    let path = file_path.to_owned();
+    let client = client.clone();
+
+    let start = (part_number as u64) * chunk_size;
+    let semaphore = semaphore.clone();
+    let parallel_failures_semaphore = parallel_failures_semaphore.clone();
+    handles.push(tokio::spawn(async move {
+      let permit = semaphore
+        .clone()
+        .acquire_owned()
+        .await
+        .map_err(|err| Error::new_err(format!("Error acquiring semaphore: {err}")))?;
+      let mut chunk = upload_chunk(&client, &url, &path, start, chunk_size).await;
+      let mut i = 0;
+      if parallel_failures > 0 {
+        while let Err(ul_err) = chunk {
+          if i >= max_retries {
+            return Err(Error::new_err(format!(
+              "Failed after too many retries ({max_retries}): {ul_err}"
+            )));
+          }
+
+          let parallel_failure_permit = parallel_failures_semaphore.clone().try_acquire_owned().map_err(|err| {
+            Error::new_err(format!(
+              "Failed too many failures in parallel ({parallel_failures}): {ul_err} ({err})"
+            ))
+          })?;
+
+          let wait_time = exponential_backoff(BASE_WAIT_TIME, i, MAX_WAIT_TIME);
+          sleep(Duration::from_millis(wait_time as u64)).await;
+
+          chunk = upload_chunk(&client, &url, &path, start, chunk_size).await;
+          i += 1;
+          drop(parallel_failure_permit);
+        }
+      }
+      drop(permit);
+      chunk.map_err(|e|{
+        //match e {
+        //  Error::Io(io) => Error::new_err(format!("Io error {io}")),
+        //  Error::Request(req) => Error::new_err(format!("Error while sending chunk {req}")),
+        //  Error::ToStrError(req) => Error::new_err(format!("Response header contains non ASCII chars: {req}")),
+        //}
+        e
+      }).map(|chunk| (part_number, chunk, chunk_size))
+    }));
+  }
+
+  let mut results: Vec<HashMap<String, String>> = vec![HashMap::default(); parts_urls.len()];
+
+  while let Some(result) = handles.next().await {
+    match result {
+      Ok(Ok((part_number, headers, size))) => {
+        //if let Some(ref callback) = callback {
+        //  callback.call((size,), None)?;
+        //}
+        results[part_number] = headers;
+      }
+      Ok(Err(py_err)) => {
+        return Err(py_err);
+      }
+      Err(err) => {
+        return Err(Error::new_err(format!(
+          "Error occurred while uploading: {err}"
+        )));
+      }
+    }
+  }
+
+  Ok(results)
+}
+
+async fn upload_chunk(
+  client: &reqwest::Client,
+  url: &str,
+  path: &str,
+  start: u64,
+  chunk_size: u64,
+) -> Result<HashMap<String, String>, Error> {
+  let mut options = OpenOptions::new();
+  let mut file = options.read(true).open(path).await?;
+  let file_size = file.metadata().await?.len();
+  let bytes_transferred = std::cmp::min(file_size - start, chunk_size);
+
+  file.seek(SeekFrom::Start(start)).await?;
+  let chunk = file.take(chunk_size);
+
+  let response = client
+    .put(url)
+    .header(CONTENT_LENGTH, bytes_transferred)
+    .body(reqwest::Body::wrap_stream(FramedRead::new(
+      chunk,
+      BytesCodec::new(),
+    )))
+    .send()
+    .await?;
+  let response = response.error_for_status()?;
+  let mut headers = HashMap::new();
+  for (name, value) in response.headers().into_iter() {
+    headers.insert(name.to_string(), value.to_str()?.to_owned());
+  }
+  Ok(headers)
+}
+

crates/desktop/tauri-4o/src/transfer/util.rs

@@ -0,0 +1,15 @@
+//! Implemented from https://github.com/huggingface/hf_transfer/blob/main/src/lib.rs
+
+use rand::{thread_rng, Rng};
+use std::fmt::Display;
+
+pub const BASE_WAIT_TIME: usize = 300;
+pub const MAX_WAIT_TIME: usize = 10_000;
+
+fn jitter() -> usize {
+  thread_rng().gen_range(0..=500)
+}
+
+pub fn exponential_backoff(base_wait_time: usize, n: usize, max: usize) -> usize {
+  (base_wait_time + n.pow(2) + jitter()).min(max)
+}

crates/desktop/tauri-4o/src/utils/clear_all_webview_cookies.rs

@@ -0,0 +1,7 @@
+use tauri::Webview;
+use errors::AnyhowResult;
+
+pub fn clear_all_webview_cookies(webview: &Webview) -> AnyhowResult<()> {
+  webview.clear_all_browsing_data()?;
+  Ok(())
+}

crates/desktop/tauri-4o/src/utils/image/decode_base64_image.rs

@@ -0,0 +1,14 @@
+use base64::prelude::BASE64_STANDARD;
+use base64::Engine;
+use image::{DynamicImage, ImageReader};
+use std::io::Cursor;
+
+pub fn decode_base64_image(base64_image: &str) -> anyhow::Result<DynamicImage> {
+  let bytes = BASE64_STANDARD.decode(base64_image)?;
+
+  let image = ImageReader::new(Cursor::new(bytes))
+    .with_guessed_format()?
+    .decode()?;
+
+  Ok(image)
+}

crates/desktop/tauri-4o/src/utils/image/encode_dynamic_image_base64_png.rs

@@ -0,0 +1,14 @@
+use base64::prelude::BASE64_STANDARD;
+use base64::Engine;
+use image::{DynamicImage, ImageFormat};
+use std::io::Cursor;
+
+pub fn encode_dynamic_image_base64_png(image: DynamicImage) -> anyhow::Result<String> {
+  let mut bytes = Vec::with_capacity(1024*1024);
+
+  image.write_to(&mut Cursor::new(&mut bytes), ImageFormat::Png)?;
+
+  let bytes = BASE64_STANDARD.encode(bytes);
+
+  Ok(bytes)
+}

crates/desktop/tauri-4o/src/utils/image/encode_rgb_image_base64_png.rs

@@ -0,0 +1,16 @@
+use base64::prelude::BASE64_STANDARD;
+use base64::Engine;
+use image::{ImageFormat, RgbImage};
+use std::io::Cursor;
+
+pub fn encode_rgb_image_base64_png(
+  image: RgbImage
+) -> anyhow::Result<String> {
+  let mut bytes = Vec::with_capacity(1024*1024);
+
+  image.write_to(&mut Cursor::new(&mut bytes), ImageFormat::Png)?;
+
+  let bytes = BASE64_STANDARD.encode(bytes);
+
+  Ok(bytes)
+}

crates/desktop/tauri-4o/src/utils/image/mod.rs

@@ -0,0 +1,3 @@
+pub mod decode_base64_image;
+pub mod encode_dynamic_image_base64_png;
+pub mod encode_rgb_image_base64_png;

crates/desktop/tauri-4o/src/utils/mod.rs

@@ -0,0 +1,3 @@
+pub mod clear_all_webview_cookies;
+pub mod image;
+pub mod sora_webview_cookies;

crates/desktop/tauri-4o/src/utils/sora_webview_cookies.rs

@@ -0,0 +1,26 @@
+use once_cell::sync::Lazy;
+use reqwest::Url;
+use tauri::Webview;
+use tauri::webview::Cookie;
+use errors::AnyhowResult;
+
+const SORA_ROOT_COOKIE_URL_STR: &str = "https://sora.com/";
+
+static SORA_ROOT_COOKIE_URL: Lazy<Url> = Lazy::new(|| {
+  Url::parse(SORA_ROOT_COOKIE_URL_STR).expect("URL should parse")
+});
+
+pub fn get_all_sora_cookies(webview: &Webview) -> AnyhowResult<Vec<Cookie>> {
+  let cookies = webview.cookies_for_url(SORA_ROOT_COOKIE_URL.clone())?;
+  Ok(cookies)
+}
+
+pub fn get_all_sora_cookies_as_string(webview: &Webview) -> AnyhowResult<String> {
+  let cookies = get_all_sora_cookies(webview)?;
+  let cookie_string = cookies
+    .iter()
+    .map(|cookie| format!("{}={}", cookie.name(), cookie.value()))
+    .collect::<Vec<String>>()
+    .join("; ");
+  Ok(cookie_string)
+}

crates/desktop/tauri-4o/tauri.conf.json

@@ -0,0 +1,37 @@
+{
+  "$schema": "https://schema.tauri.app/config/2.0.0-rc",
+  "productName": "ArtCraft",
+  "version": "0.1.0",
+  "identifier": "com.unique.value.thing.why",
+  "build": {
+    "frontendDist": "../../../frontend/realtime/dist",
+    "devUrl": "http://localhost:5174",
+    "beforeDevCommand": "npm run dev",
+    "beforeBuildCommand": "npm run build2"
+  },
+  "app": {
+    "windows": [
+      {
+        "title": "ArtCraft",
+        "width": 2400,
+        "height": 1300,
+        "resizable": true,
+        "fullscreen": false
+      }
+    ],
+    "security": {
+      "csp": null
+    }
+  },
+  "bundle": {
+    "active": true,
+    "targets": "all",
+    "icon": [
+      "icons/32x32.png",
+      "icons/128x128.png",
+      "icons/128x128@2x.png",
+      "icons/icon.icns",
+      "icons/icon.ico"
+    ]
+  }
+}

crates/desktop/tauri-realtime/.gitignore

@@ -0,0 +1,4 @@
+# Generated by Cargo
+# will have compiled files and executables
+/target/
+/gen/schemas

crates/desktop/tauri-realtime/.taurignore

@@ -0,0 +1,4 @@
+# Don't restart the dev server on Rust changes
+crates/
+Cargo.toml
+Cargo.lock

crates/desktop/tauri-realtime/Cargo.toml

@@ -0,0 +1,86 @@
+[package]
+name = "tauri-realtime"
+version = "0.1.0"
+description = "A Tauri App"
+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]
+
+# Internal
+crockford = { path = "../../lib/data/crockford" }
+filesys = { path = "../../lib/files/filesys" }
+memory_caching = { path = "../../lib/caching/memory_caching" }
+ml_weights_registry = { path = "../../lib/machine_learning/ml_weights_registry" }
+primitives = { path = "../../lib/data/primitives" }
+
+# Internal ML
+# TODO: Only on Windows and Linux
+#ml_models = { path = "../ml_models", optional = true }
+# Bypass cudarc issues on Windows https://github.com/coreylowman/cudarc/issues/332
+cudarc = { version = "0.13.5", features = ["std", "cublas", "cublaslt", "curand", "driver", "nvrtc", "f16", "cuda-version-from-build-system", "dynamic-linking"], default-features=false }
+
+# External
+#openssl = { version = "0.10", features = ["vendored"] }
+# NB(bt,2025-03-10): default-features = false on Windows while I debug openssl / rustls
+anyhow = "1.0"
+base64 = "0.22.1"
+bytes = "1.10"
+chrono = "0.4.40"
+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"] }
+image = "0.25.5"
+log = "0.4"
+once_cell = "1.20.3"
+ort = { version = "1.16.2", features = ["cuda", "load-dynamic"] }
+rand = "0.8.5"
+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 = "2.4.1", features = [] }
+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"] }
+
+# tracing-subscriber = { version = "0.3", features = [ "env-filter", "fmt" ] }
+
+[target.'cfg(not(target_os = "macos"))'.dependencies]
+ml_models = { path = "../../lib/machine_learning/ml_models" }
+
+[dev-dependencies]
+libloading = "0.8.6"
+serial_test = "3.2.0"
+assertor = "0.0.3"
+
+#[target.'cfg(target_os = "macos")'.features]
+#default  = []
+#ml_models = ["dep:ml_models"]
+
+[profile.dev]
+incremental = true # Compile your binary in smaller steps.
+
+[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.