Merge branch 'main' into feature/generatemode

.config/hakari.toml

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

.gitignore

@@ -32,9 +32,6 @@ target/
 crates/lib/database_queries/target/
 crates/lib/sqlite_queries/target/
 
-# Javascript build directories
-node_modules/
-
 # Vim noise
 *.swo
 *.swp

Cargo.toml

@@ -1,8 +1,6 @@
 [workspace]
 resolver = "2"
 
-# This block defines the workspace members.
-# When we add a library or binary crate, it needs an entry here.
 members = [
   # Types: common strongly-typed tokens, ids, payloads, etc.
   "crates/_types/composite_identifiers",
@@ -24,8 +22,9 @@ members = [
   "crates/cli/production/migration-tool",
 
   # Desktop apps
-  "crates/desktop/tauri-4o",
-  "crates/desktop/tauri-realtime",
+  "crates/desktop/src-tauri",
+  "crates/desktop/ml_models",
+  "crates/desktop/ml_weights_registry",
 
   # Shared libraries
   "crates/lib/actix_cors_configs",
@@ -33,7 +32,6 @@ members = [
   "crates/lib/api_clients/cloud_storage",
   "crates/lib/api_clients/google_drive_common",
   "crates/lib/api_clients/google_sign_in",
-  "crates/lib/api_clients/openai_sora_client",
   "crates/lib/api_clients/thumbnail_generator",
   "crates/lib/bootstrap",
   "crates/lib/bucket_paths",
@@ -60,20 +58,16 @@ 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",
@@ -121,40 +115,5 @@ members = [
 
   # cargo-hakari workspace hack (compilation speedup hack)
   # see usage: https://crates.io/crates/cargo-hakari
-  "crates/workspace-build-acceleration",
+  "crates/my-workspace-hack",
 ]
-
-[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" }

crates/_types/composite_identifiers/Cargo.toml

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

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

@@ -40,10 +40,6 @@ pub enum InferenceJobType {
   #[serde(rename = "studio_gen2")]
   StudioGen2,
 
-  /// Sora GPT 4o image gen
-  #[serde(rename = "image_gen_api")]
-  ImageGenApi,
-
   /// A job that turns "FBX" game engine files into "GLTF" files (Bevy-compatible).
   #[serde(rename = "convert_fbx_gltf")]
   ConvertFbxToGltf,
@@ -112,7 +108,6 @@ impl InferenceJobType {
       Self::GptSovits => "gpt_sovits",
       Self::ComfyUi => "comfy_ui",
       Self::StudioGen2 => "studio_gen2",
-      Self::ImageGenApi => "image_gen_api",
       Self::ConvertFbxToGltf => "convert_fbx_gltf",
       Self::MocapNet => "mocap_net",
       Self::RvcV2 => "rvc_v2",
@@ -137,7 +132,6 @@ impl InferenceJobType {
       "gpt_sovits" => Ok(Self::GptSovits),
       "comfy_ui" => Ok(Self::ComfyUi),
       "studio_gen2" => Ok(Self::StudioGen2),
-      "image_gen_api" => Ok(Self::ImageGenApi),
       "convert_fbx_gltf" => Ok(Self::ConvertFbxToGltf),
       "mocap_net" => Ok(Self::MocapNet),
       "rvc_v2" => Ok(Self::RvcV2),
@@ -165,7 +159,6 @@ impl InferenceJobType {
       Self::GptSovits,
       Self::ComfyUi,
       Self::StudioGen2,
-      Self::ImageGenApi,
       Self::ConvertFbxToGltf,
       Self::MocapNet,
       Self::RvcV2,
@@ -204,7 +197,6 @@ mod tests {
       assert_serialization(InferenceJobType::GptSovits, "gpt_sovits");
       assert_serialization(InferenceJobType::ComfyUi, "comfy_ui");
       assert_serialization(InferenceJobType::StudioGen2, "studio_gen2");
-      assert_serialization(InferenceJobType::ImageGenApi, "image_gen_api");
       assert_serialization(InferenceJobType::ConvertFbxToGltf, "convert_fbx_gltf");
       assert_serialization(InferenceJobType::MocapNet, "mocap_net");
       assert_serialization(InferenceJobType::RvcV2, "rvc_v2");
@@ -228,7 +220,6 @@ mod tests {
       assert_eq!(InferenceJobType::GptSovits.to_str(), "gpt_sovits");
       assert_eq!(InferenceJobType::ComfyUi.to_str(), "comfy_ui");
       assert_eq!(InferenceJobType::StudioGen2.to_str(), "studio_gen2");
-      assert_eq!(InferenceJobType::ImageGenApi.to_str(), "image_gen_api");
       assert_eq!(InferenceJobType::ConvertFbxToGltf.to_str(), "convert_fbx_gltf");
       assert_eq!(InferenceJobType::MocapNet.to_str(), "mocap_net");
       assert_eq!(InferenceJobType::RvcV2.to_str(), "rvc_v2");
@@ -252,7 +243,6 @@ mod tests {
       assert_eq!(InferenceJobType::from_str("gpt_sovits").unwrap(), InferenceJobType::GptSovits);
       assert_eq!(InferenceJobType::from_str("comfy_ui").unwrap(), InferenceJobType::ComfyUi);
       assert_eq!(InferenceJobType::from_str("studio_gen2").unwrap(), InferenceJobType::StudioGen2);
-      assert_eq!(InferenceJobType::from_str("image_gen_api").unwrap(), InferenceJobType::ImageGenApi);
       assert_eq!(InferenceJobType::from_str("convert_fbx_gltf").unwrap(), InferenceJobType::ConvertFbxToGltf);
       assert_eq!(InferenceJobType::from_str("mocap_net").unwrap(), InferenceJobType::MocapNet);
       assert_eq!(InferenceJobType::from_str("rvc_v2").unwrap(), InferenceJobType::RvcV2);
@@ -271,7 +261,7 @@ mod tests {
     fn all_variants() {
       // Static check
       let mut variants = InferenceJobType::all_variants();
-      assert_eq!(variants.len(), 17);
+      assert_eq!(variants.len(), 16);
       assert_eq!(variants.pop_first(), Some(InferenceJobType::VideoRender));
       assert_eq!(variants.pop_first(), Some(InferenceJobType::LivePortrait));
       assert_eq!(variants.pop_first(), Some(InferenceJobType::FaceFusion));
@@ -279,7 +269,6 @@ mod tests {
       assert_eq!(variants.pop_first(), Some(InferenceJobType::GptSovits));
       assert_eq!(variants.pop_first(), Some(InferenceJobType::ComfyUi));
       assert_eq!(variants.pop_first(), Some(InferenceJobType::StudioGen2));
-      assert_eq!(variants.pop_first(), Some(InferenceJobType::ImageGenApi));
       assert_eq!(variants.pop_first(), Some(InferenceJobType::ConvertFbxToGltf));
       assert_eq!(variants.pop_first(), Some(InferenceJobType::MocapNet));
       assert_eq!(variants.pop_first(), Some(InferenceJobType::RvcV2));

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

@@ -33,10 +33,6 @@ pub enum InferenceModelType {
   #[serde(rename = "seed_vc")]
   SeedVc,
 
-  /// NB: This is for Sora GPT 4o image gen
-  #[serde(rename = "image_gen_api")]
-  ImageGenApi,
-
   #[serde(rename = "tacotron2")]
   Tacotron2,
   #[serde(rename = "vits")]
@@ -74,7 +70,6 @@ impl InferenceModelType {
       Self::VallEX => "vall_e_x",
       Self::RerenderAVideo => "rerender_a_video",
       Self::StableDiffusion => "stable_diffusion",
-      Self::ImageGenApi => "image_gen_api",
       Self::SeedVc => "seed_vc",
       Self::MocapNet => "mocap_net",
       Self::StyleTTS2 => "styletts2",
@@ -95,7 +90,6 @@ impl InferenceModelType {
       "vall_e_x" => Ok(Self::VallEX),
       "rerender_a_video" => Ok(Self::RerenderAVideo),
       "stable_diffusion" => Ok(Self::StableDiffusion),
-      "image_gen_api" => Ok(Self::ImageGenApi),
       "mocap_net" => Ok(Self::MocapNet),
       "styletts2" => Ok(Self::StyleTTS2),
       "comfy_ui" => Ok(Self::ComfyUi),
@@ -119,7 +113,6 @@ impl InferenceModelType {
       Self::VallEX,
       Self::RerenderAVideo,
       Self::StableDiffusion,
-      Self::ImageGenApi,
       Self::MocapNet,
       Self::StyleTTS2,
       Self::ConvertFbxToGltf,
@@ -147,7 +140,6 @@ mod tests {
       assert_serialization(InferenceModelType::VallEX, "vall_e_x");
       assert_serialization(InferenceModelType::RerenderAVideo, "rerender_a_video");
       assert_serialization(InferenceModelType::StableDiffusion, "stable_diffusion");
-      assert_serialization(InferenceModelType::ImageGenApi, "image_gen_api");
       assert_serialization(InferenceModelType::MocapNet, "mocap_net");
       assert_serialization(InferenceModelType::ComfyUi, "comfy_ui");
       assert_serialization(InferenceModelType::StyleTTS2, "styletts2");
@@ -167,7 +159,6 @@ mod tests {
       assert_eq!(InferenceModelType::VallEX.to_str(), "vall_e_x");
       assert_eq!(InferenceModelType::RerenderAVideo.to_str(), "rerender_a_video");
       assert_eq!(InferenceModelType::StableDiffusion.to_str(), "stable_diffusion");
-      assert_eq!(InferenceModelType::ImageGenApi.to_str(), "image_gen_api");
       assert_eq!(InferenceModelType::MocapNet.to_str(), "mocap_net");
       assert_eq!(InferenceModelType::StyleTTS2.to_str(), "styletts2");
       assert_eq!(InferenceModelType::ConvertFbxToGltf.to_str(), "convert_fbx_gltf");
@@ -185,7 +176,6 @@ mod tests {
       assert_eq!(InferenceModelType::from_str("vall_e_x").unwrap(), InferenceModelType::VallEX);
       assert_eq!(InferenceModelType::from_str("rerender_a_video").unwrap(), InferenceModelType::RerenderAVideo);
       assert_eq!(InferenceModelType::from_str("stable_diffusion").unwrap(), InferenceModelType::StableDiffusion);
-      assert_eq!(InferenceModelType::from_str("image_gen_api").unwrap(), InferenceModelType::ImageGenApi);
       assert_eq!(InferenceModelType::from_str("mocap_net").unwrap(), InferenceModelType::MocapNet);
       assert_eq!(InferenceModelType::from_str("styletts2").unwrap(), InferenceModelType::StyleTTS2);
       assert_eq!(InferenceModelType::from_str("comfy_ui").unwrap(), InferenceModelType::ComfyUi);
@@ -197,18 +187,17 @@ mod tests {
     fn all_variants() {
       // Static check
       let mut variants = InferenceModelType::all_variants();
-      assert_eq!(variants.len(), 15);
+      assert_eq!(variants.len(), 13);
       assert_eq!(variants.pop_first(), Some(InferenceModelType::ComfyUi));
       assert_eq!(variants.pop_first(), Some(InferenceModelType::RvcV2));
       assert_eq!(variants.pop_first(), Some(InferenceModelType::SadTalker));
-      assert_eq!(variants.pop_first(), Some(InferenceModelType::SoVitsSvc));
       assert_eq!(variants.pop_first(), Some(InferenceModelType::SeedVc));
+      assert_eq!(variants.pop_first(), Some(InferenceModelType::SoVitsSvc));
       assert_eq!(variants.pop_first(), Some(InferenceModelType::Tacotron2));
       assert_eq!(variants.pop_first(), Some(InferenceModelType::Vits));
       assert_eq!(variants.pop_first(), Some(InferenceModelType::VallEX));
       assert_eq!(variants.pop_first(), Some(InferenceModelType::RerenderAVideo));
       assert_eq!(variants.pop_first(), Some(InferenceModelType::StableDiffusion));
-      assert_eq!(variants.pop_first(), Some(InferenceModelType::ImageGenApi));
       assert_eq!(variants.pop_first(), Some(InferenceModelType::MocapNet));
       assert_eq!(variants.pop_first(), Some(InferenceModelType::StyleTTS2));
       assert_eq!(variants.pop_first(), Some(InferenceModelType::ConvertFbxToGltf));

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

@@ -57,9 +57,6 @@ pub enum MediaFileOriginModelType {
   #[serde(rename = "studio")]
   StorytellerStudio,
 
-  #[serde(rename = "studio_ig")]
-  StorytellerStudioImageGen,
-
   #[serde(rename = "vst")]
   VideoStyleTransfer,
 
@@ -96,7 +93,6 @@ 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",
@@ -119,7 +115,6 @@ 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),
@@ -147,7 +142,6 @@ 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 = ["workspace-build-acceleration"]
+normal = ["my-workspace-hack"]
 
 [dependencies]
 # Workspace hack (faster compile times)
-workspace-build-acceleration = { version = "0.1", path = "../../workspace-build-acceleration" }
+my-workspace-hack = { version = "0.1", path = "../../my-workspace-hack" }
 
 # Internal
 enums = { path = "../enums" }

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

@@ -91,10 +91,6 @@ pub enum PublicInferenceJobType {
   /// Re-render a video is a video style transfer algorithm. We developed code
   /// around it, but chose to develop AnimateDiff / ComfyUI support instead.
   RerenderAVideo,
-
-  /// Sora GPT 4o image gen
-  #[serde(rename = "image_gen_api")]
-  ImageGenApi,
 }
 
 /// NB: Legacy API for older code.
@@ -123,7 +119,6 @@ impl PublicInferenceJobType {
       InferenceJobType::Unknown => Self::Unknown,
       InferenceJobType::BevyToWorkflow => Self::BevyToWorkflow,
       InferenceJobType::RerenderAVideo => Self::RerenderAVideo,
-      InferenceJobType::ImageGenApi => Self::ImageGenApi,
     }
   }
   
@@ -145,7 +140,6 @@ impl PublicInferenceJobType {
       Self::SeedVc => InferenceJobType::SeedVc,
       Self::SoVitsSvc => InferenceJobType::SoVitsSvc,
       Self::StableDiffusion => InferenceJobType::StableDiffusion,
-      Self::ImageGenApi => InferenceJobType::ImageGenApi,
       Self::StyleTTS2 => InferenceJobType::StyleTTS2,
       Self::Tacotron2 => InferenceJobType::Tacotron2,
       Self::Unknown => InferenceJobType::Unknown,

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

@@ -64,10 +64,6 @@ pub enum PublicMediaFileModelType {
   #[serde(rename = "studio")]
   StorytellerStudio,
 
-  /// NB: This is GPT4o image generation
-  #[serde(rename = "studio_ig")]
-  StorytellerStudioImageGen,
-
   #[serde(rename = "vst")]
   VideoStyleTransfer,
 
@@ -93,7 +89,6 @@ 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,
@@ -118,7 +113,6 @@ 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 = ["workspace-build-acceleration"]
+normal = ["my-workspace-hack"]
 
 [dependencies]
 
@@ -21,7 +21,7 @@ normal = ["workspace-build-acceleration"]
 # (none)
 
 # Workspace hack (faster compile times)
-workspace-build-acceleration = { version = "0.1", path = "../../workspace-build-acceleration" }
+my-workspace-hack = { version = "0.1", path = "../../my-workspace-hack" }
 
 # 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 = ["workspace-build-acceleration"]
+normal = ["my-workspace-hack"]
 
 [dependencies]
 
@@ -21,7 +21,7 @@ normal = ["workspace-build-acceleration"]
 # (none)
 
 # Workspace hack (faster compile times)
-workspace-build-acceleration = { version = "0.1", path = "../../workspace-build-acceleration" }
+my-workspace-hack = { version = "0.1", path = "../../my-workspace-hack" }
 
 # 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 = ["workspace-build-acceleration"]
+normal = ["my-workspace-hack"]
 
 [dependencies]
 # Internal
@@ -32,7 +32,7 @@ storyteller_root = { path = "../../../lib/storyteller_root" }
 tokens = { path = "../../../_types/tokens" }
 
 # Workspace hack (faster compile times)
-workspace-build-acceleration = { version = "0.1", path = "../../../workspace-build-acceleration" }
+my-workspace-hack = { version = "0.1", path = "../../../my-workspace-hack" }
 
 # 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 = ["workspace-build-acceleration"]
+normal = ["my-workspace-hack"]
 
 [dependencies]
 # Internal
@@ -29,7 +29,7 @@ mysql_queries = { path = "../../../lib/mysql_queries" }
 tokens = { path = "../../../_types/tokens" }
 
 # Workspace hack (faster compile times)
-workspace-build-acceleration = { version = "0.1", path = "../../../workspace-build-acceleration" }
+my-workspace-hack = { version = "0.1", path = "../../../my-workspace-hack" }
 
 # 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 = ["workspace-build-acceleration"]
+normal = ["my-workspace-hack"]
 
 [dependencies]
 
@@ -22,7 +22,7 @@ errors = { path = "../../../lib/errors" }
 web_scrapers = { path = "../../../lib/web_scrapers" }
 
 # Workspace hack (faster compile times)
-workspace-build-acceleration = { version = "0.1", path = "../../../workspace-build-acceleration" }
+my-workspace-hack = { version = "0.1", path = "../../../my-workspace-hack" }
 
 # 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 = ["workspace-build-acceleration"]
+normal = ["my-workspace-hack"]
 
 [dependencies]
 # Workspace hack (faster compile times)
-workspace-build-acceleration = { version = "0.1", path = "../../../workspace-build-acceleration" }
+my-workspace-hack = { version = "0.1", path = "../../../my-workspace-hack" }
 
 # 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 = ["workspace-build-acceleration"]
+normal = ["my-workspace-hack"]
 
 [dependencies]
 # Internal
@@ -21,7 +21,7 @@ easyenv = { path = "../../../lib/easyenv" }
 errors = { path = "../../../lib/errors" }
 
 # Workspace hack (faster compile times)
-workspace-build-acceleration = { version = "0.1", path = "../../../workspace-build-acceleration" }
+my-workspace-hack = { version = "0.1", path = "../../../my-workspace-hack" }
 
 # 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 = ["workspace-build-acceleration"]
+normal = ["my-workspace-hack"]
 
 [dependencies]
 # Internal
@@ -26,7 +26,7 @@ storyteller_root = { path = "../../../lib/storyteller_root" }
 tokens = { path = "../../../_types/tokens" }
 
 # Workspace hack (faster compile times)
-workspace-build-acceleration = { version = "0.1", path = "../../../workspace-build-acceleration" }
+my-workspace-hack = { version = "0.1", path = "../../../my-workspace-hack" }
 
 # 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 = ["workspace-build-acceleration"]
+normal = ["my-workspace-hack"]
 
 [dependencies]
 # Internal
@@ -29,7 +29,7 @@ storyteller_root = { path = "../../../lib/storyteller_root" }
 tokens = { path = "../../../_types/tokens" }
 
 # Workspace hack (faster compile times)
-workspace-build-acceleration = { version = "0.1", path = "../../../workspace-build-acceleration" }
+my-workspace-hack = { version = "0.1", path = "../../../my-workspace-hack" }
 
 # 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 = ["workspace-build-acceleration"]
+normal = ["my-workspace-hack"]
 
 [dependencies]
 # Internal
@@ -22,7 +22,7 @@ easyenv = { path = "../../../lib/easyenv" }
 errors = { path = "../../../lib/errors" }
 
 # Workspace hack (faster compile yytimes)
-workspace-build-acceleration = { version = "0.1", path = "../../../workspace-build-acceleration" }
+my-workspace-hack = { version = "0.1", path = "../../../my-workspace-hack" }
 
 # 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 = ["workspace-build-acceleration"]
+normal = ["my-workspace-hack"]
 
 [dependencies]
 # Internal
@@ -21,7 +21,7 @@ errors = { path = "../../../lib/errors" }
 logging = { path = "../../../lib/logging" }
 
 # Workspace hack (faster compile times)
-workspace-build-acceleration = { version = "0.1", path = "../../../workspace-build-acceleration" }
+my-workspace-hack = { version = "0.1", path = "../../../my-workspace-hack" }
 
 # 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 = ["workspace-build-acceleration"]
+normal = ["my-workspace-hack"]
 
 [dependencies]
 # Internal
@@ -30,7 +30,7 @@ storyteller_root = { path = "../../../lib/storyteller_root" }
 tokens = { path = "../../../_types/tokens" }
 
 # Workspace hack (faster compile times)
-workspace-build-acceleration = { version = "0.1", path = "../../../workspace-build-acceleration" }
+my-workspace-hack = { version = "0.1", path = "../../../my-workspace-hack" }
 
 # External
 clap = "4.4.8"

crates/desktop/ml_models/Cargo.toml

@@ -14,19 +14,15 @@ path = "src/lib.rs"
 crate-type = ["staticlib", "cdylib", "rlib"]
 
 [package.metadata.cargo-udeps.ignore]
-normal = ["workspace-build-acceleration"]
+normal = ["my-workspace-hack"]
 
 [dependencies]
 
-# Workspace hack (faster compile times)
-workspace-build-acceleration = { workspace =  true }
-
 # Internal
-filesys = { path = "../../files/filesys" }
-image_algorithms = { path = "../../image_algorithms" }
-memory_caching = { path = "../../caching/memory_caching" }
+filesys = { path = "../../lib/files/filesys" }
+memory_caching = { path = "../../lib/caching/memory_caching" }
 ml_weights_registry = { path = "../ml_weights_registry" }
-primitives = { path = "../../data/primitives" }
+primitives = { path = "../../lib/data/primitives" }
 
 # Bypass cudarc issues on Windows https://github.com/coreylowman/cudarc/issues/332
 cudarc = "0.13.7"
@@ -35,9 +31,9 @@ cudarc = "0.13.7"
 anyhow = "1.0"
 base64 = "0.22.1"
 bytes = "1.10"
-candle-core = { path = "../../../vendor/candle/candle-core", features = ["cuda"] }
-candle-nn = { path = "../../../vendor/candle/candle-nn", features = ["cuda"] }
-candle-transformers = { path = "../../../vendor/candle/candle-transformers", features = ["cuda"] }
+candle-core = { path = "../../vendor/candle/candle-core", features = ["cuda"] }
+candle-nn = { path = "../../vendor/candle/candle-nn", features = ["cuda"] }
+candle-transformers = { path = "../../vendor/candle/candle-transformers", features = ["cuda"] }
 const_format = "0.2.34"
 directories = "6.0.0"
 dirs = "6.0.0"

crates/desktop/ml_models/src/ml/background_removal/remove_image_background.rs

@@ -3,7 +3,6 @@ use image::{imageops, DynamicImage};
 use ndarray::{Array, CowArray};
 use ort::Value;
 use std::path::Path;
-use image_algorithms::remove_image_border::remove_image_border;
 
 pub async fn remove_image_background<P: AsRef<Path>>(model_path: P, image: DynamicImage) -> anyhow::Result<DynamicImage> {
   // NB: Real time needed cuDNN 9, but this uses cuDNN 8:
@@ -12,10 +11,6 @@ pub async fn remove_image_background<P: AsRef<Path>>(model_path: P, image: Dynam
   // update-alternatives: using /usr/include/x86_64-linux-gnu/cudnn_v8.h to provide /usr/include/cudnn.h (libcudnn) in manual mode
   let session = onnx_session(model_path.as_ref())?;
   let image = process_dynamic_image(&session, image)?;
-  
-  // Crop transparent border cruft away.
-  let image = remove_image_border(&image)?;
-  
   Ok(image)
 }
 

crates/desktop/ml_models/src/ml/mod.rs

@@ -1,5 +1,4 @@
 pub mod background_removal;
-pub mod flux;
 pub mod image;
 pub mod model_cache;
 pub mod model_config;

crates/desktop/ml_models/src/ml/stable_diffusion/lcm_pipeline.rs

@@ -20,14 +20,12 @@ use crate::ml::stable_diffusion::get_vae_scale::get_vae_scale;
 use crate::ml::stable_diffusion::infer_clip_text_embeddings::{infer_clip_text_embeddings, ClipArgs};
 use crate::ml::stable_diffusion::remap_lcm_strength_range::remap_lcm_strength_range;
 use image::DynamicImage;
-use log::{debug, error, info};
+use log::info;
 use ml_weights_registry::weights_registry::weights::{LYKON_DEAMSHAPER_7_TEXT_ENCODER_FP16, LYKON_DEAMSHAPER_7_VAE, SIMIANLUO_LCM_DREAMSHAPER_V7_UNET};
 use rand::Rng;
 
 const DEFAULT_STRENGTH : f64 = 75.0;
 
-const DEFAULT_SEED : u64 = 42;
-
 pub struct Args<'a, P1: AsRef<Path>, P2: AsRef<Path>, P3: AsRef<Path>, P4: AsRef<Path>> {
   pub image: &'a DynamicImage,
   pub prompt: String,
@@ -87,9 +85,10 @@ pub fn lcm_pipeline<P1, P2, P3, P4>(args: Args<'_, P1, P2, P3, P4>) -> Result<Rg
   // Use LCM Scheduler instead of Euler Ancestral for better speed and quality
   let mut scheduler = LCMScheduler::new(scheduler_steps, img2img_strength, LCMSchedulerConfig::default())?;
 
-  //let seed = maybe_seed.unwrap_or_else(|| rand::thread_rng().gen());
-  let seed = maybe_seed.unwrap_or(DEFAULT_SEED);
+  let seed = maybe_seed.unwrap_or_else(|| rand::thread_rng().gen());
 
+  let seed = 42;
+  
   device.set_seed(seed)?;
 
   let guidance_scale = match cfg_scale {
@@ -130,13 +129,13 @@ pub fn lcm_pipeline<P1, P2, P3, P4>(args: Args<'_, P1, P2, P3, P4>) -> Result<Rg
     tensor
   };
 
-  debug!("Text embeddings shape: {:?}", text_embeddings.shape());
+  info!("Text embeddings shape: {:?}", text_embeddings.shape());
 
-  debug!("Loading input image into tensor...");
+  info!("Loading input image into tensor...");
 
   let input_image = dynamic_image_to_tensor(image, &device, dtype)?;
 
-  debug!("Reference image shape: {:?}", input_image.shape());
+  info!("Reference image shape: {:?}", input_image.shape());
 
   let vae_scale = get_vae_scale(sd_version);
 
@@ -162,7 +161,7 @@ pub fn lcm_pipeline<P1, P2, P3, P4>(args: Args<'_, P1, P2, P3, P4>) -> Result<Rg
   let unet = match maybe_unet {
     Some(unet) => unet,
     None => {
-      info!("Building UNET model from file {:?}...", unet_path.as_ref());
+      info!("No unet found in cache; loading...");
 
       let in_channels = match sd_version {
         StableDiffusionVersion::XlInpaint | StableDiffusionVersion::V2Inpaint | StableDiffusionVersion::V1_5Inpaint => 9,
@@ -218,17 +217,16 @@ pub fn lcm_pipeline<P1, P2, P3, P4>(args: Args<'_, P1, P2, P3, P4>) -> Result<Rg
   let guidance_scale_embedding = scheduler.get_guidance_scale_embedding(guidance_scale, embedding_dim, &device, dtype)?;
 
   // Generate latents from input image using the LCM approach
-  debug!("Generating latents from input image...");
+  info!("Generating latents from input image...");
   let init_latents = (init_latent_dist.sample()? * vae_scale)?;
-
-  debug!("Initial latents shape: {:?}", init_latents.shape());
+  info!("Initial latents shape: {:?}", init_latents.shape());
 
   // Calculate timesteps for LCM with proper img2img handling
   let timesteps = LCMScheduler::get_timesteps_for_steps(scheduler_steps, img2img_strength);
   let t_start = timesteps[0]; // First denoising step
 
   // Add noise at the appropriate timestep for img2img
-  debug!("Adding noise to latents for t_start={}", t_start);
+  info!("Adding noise to latents for t_start={}", t_start);
   let noise = init_latents.randn_like(0f64, 1f64)?;
   let latents = scheduler.add_noise(&init_latents, noise, t_start)?;
 
@@ -246,7 +244,7 @@ pub fn lcm_pipeline<P1, P2, P3, P4>(args: Args<'_, P1, P2, P3, P4>) -> Result<Rg
     let mut noise_pred = match unet.forward_with_guidance(&latent_model_input, timestep as f64, &text_embeddings, Some(&guidance_scale_embedding)) {
       Ok(pred) => pred,
       Err(e) => {
-        error!("UNet inference failed with error: {}", e);
+        info!("UNet inference failed with error: {}", e);
         return Err(anyhow::anyhow!("UNet inference failed: {}", e));
       },
     };
@@ -261,19 +259,21 @@ pub fn lcm_pipeline<P1, P2, P3, P4>(args: Args<'_, P1, P2, P3, P4>) -> Result<Rg
     latents = scheduler.step(&noise_pred, timestep, &latents, timestep_index, scheduler_steps)?;
   }
 
-  debug!("Diffusion process completed, decoding image...");
+  info!("Diffusion process completed, decoding image...");
   let image = vae.decode(&(latents / vae_scale)?)?;
 
-  debug!("Scaling image back");
+  info!("VAE decode completed");
+
+  info!("Scaling image back");
   let image = ((image / 2.)? + 0.5)?;
 
-  debug!("Normalized image values");
+  info!("Normalized image values");
   let image = (image.clamp(0f32, 1.)? * 255.)?;
 
-  debug!("Convert to int8");
+  info!("Convert to int8");
   let image = image.to_dtype(DType::U8)?;
 
-  debug!("Converted to 8-bit format");
+  info!("Converted to 8-bit format");
 
   let image = tensor_to_image_buffer(&image.i(0)?)?;
 

crates/desktop/ml_models/src/ml/stable_diffusion/stable_diffusion_pipeline.rs

@@ -59,6 +59,14 @@ pub fn stable_diffusion_pipeline<P1, P2, P3, P4>(args: Args<'_, P1, P2, P3, P4>)
         clip_weights_path,
     } = args;
     
+    println!("Starting image generation with the following configuration:");
+    println!("  Model: {:?}", sd_version);
+    println!("  Prompt: {}", prompt);
+    println!("  Steps: {}", scheduler_steps);
+    println!("  Device: {:?}", device);
+
+    println!("Model dimensions: {}x{}", sd_config.width, sd_config.height);
+
     // TODO(bt,2025-02-18): The scheduler is `EulerAncestralDiscreteScheduler`, but we may want to port an LCM scheduler.
     //  This is a target for performance improvement
     //  See: https://github.com/huggingface/candle/issues/1331

crates/desktop/ml_weights_registry/src/weights_registry/mod.rs

@@ -1,4 +1,3 @@
 mod weight_descriptor_builder;
-pub mod flux_weights;
 pub mod weight_descriptor;
 pub mod weights;

crates/desktop/ml_weights_registry/src/weights_registry/weight_descriptor.rs

@@ -5,7 +5,6 @@
 
 #[derive(Clone, Copy)]
 pub enum WeightFunction {
-  ImageGeneration,
   ImageSegmentation,
   TextEncoder,
   TextTokenizer,
@@ -30,7 +29,6 @@ pub struct WeightDescriptor {
 impl WeightFunction {
   pub fn as_descriptive_str(&self) -> &'static str {
     match self {
-      WeightFunction::ImageGeneration => "image generation",
       WeightFunction::ImageSegmentation => "image segmentation",
       WeightFunction::TextEncoder => "text encoder",
       WeightFunction::TextTokenizer => "text tokenizer",

crates/desktop/src-tauri/Cargo.toml

@@ -1,5 +1,5 @@
 [package]
-name = "tauri-realtime"
+name = "app"
 version = "0.1.0"
 description = "A Tauri App"
 authors = ["you"]
@@ -16,30 +16,25 @@ name = "app_lib"
 crate-type = ["staticlib", "cdylib", "rlib"]
 
 [build-dependencies]
-tauri-build = { version = "2.1.1", features = [] }
+tauri-build = { version = "2.0.4", 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" }
+ml_weights_registry = { path = "../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"
@@ -48,14 +43,14 @@ half = { version = "2.3.1", features = ["num-traits", "use-intrinsics", "rand_di
 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"
+# NB(bt,2025-03-10): default-features = false on Windows while I debug openssl / rustls
 reqwest = { version = "0.12.12", features = ["stream"], default-features = false }
 serde = { version = "1.0", features = ["derive"] }
 serde_json = "1.0"
 serde_yaml = "0.9"
-tauri = { version = "2.4.1", features = [] }
-tauri-plugin-log = "2.3.1"
+tauri = { version = "2.2.4", features = [] }
+tauri-plugin-log = "2"
 tempdir = "0.3.7"
 tempfile = "3.18.0"
 tokio = { version = "1.42", features = ["rt", "rt-multi-thread", "fs"] }
@@ -64,7 +59,7 @@ 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" }
+ml_models = { path = "../ml_models" }
 
 [dev-dependencies]
 libloading = "0.8.6"

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

@@ -0,0 +1,24 @@
+use crate::ml::model_cache::ModelCache;
+use crate::ml::prompt_cache::PromptCache;
+use crate::state::app_config::AppConfig;
+use crate::state::app_dir::AppDataRoot;
+use image::{ImageReader, RgbImage};
+use log::info;
+use tauri::{AppHandle, State};
+
+
+#[tauri::command]
+pub async fn inpaint_image(
+  image: &str,
+  mask: &str,
+  prompt: String,
+  model_config: State<'_, AppConfig>,
+  model_cache: State<'_, ModelCache>,
+  prompt_cache: State<'_, PromptCache>,
+  app_data_root: State<'_, AppDataRoot>,
+  app: AppHandle,
+) -> Result<String, String> {
+  info!("inpaint_endpoint endpoint called.");
+
+  Ok(mask.to_string())
+}

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

@@ -3,5 +3,4 @@ pub mod flip_image;
 pub mod inpaint_image_endpoint;
 pub mod realtime_image_endpoint;
 pub mod remove_background_endpoint;
-pub mod save_image_endpoint;
 pub mod text_to_image_endpoint;

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

@@ -31,7 +31,6 @@ pub async fn infer_image(
   image: &str,
   prompt: Option<String>,
   strength: Option<f64>,
-  seed: Option<u64>,
   model_config: State<'_, AppConfig>,
   model_cache: State<'_, ModelCache>,
   prompt_cache: State<'_, PromptCache>,
@@ -50,15 +49,12 @@ pub async fn infer_image(
   // TODO(bt,2025-02-17): Running out of vram with full image buffer size
   let mut image = image.resize(512, 512, FilterType::CatmullRom);
 
-  let seed = seed.or(model_config.seed);
-
   #[cfg(not(target_os = "macos"))]
   {
     let result = infer_image_impl(
       &prompt,
       image,
       strength,
-      seed,
       &model_config,
       &model_cache,
       prompt_cache,
@@ -87,7 +83,6 @@ async fn infer_image_impl(
   prompt: &str,
   image: DynamicImage,
   strength: Option<f64>,
-  seed: Option<u64>,
   config: &AppConfig,
   model_cache: &ModelCache,
   prompt_cache: State<'_, PromptCache>,
@@ -111,7 +106,7 @@ async fn infer_image_impl(
     cfg_scale: config.cfg_scale,
     use_flash_attn: true,
     model_config: &config.model_config,
-    maybe_seed: seed,
+    maybe_seed: None,
     scheduler_steps: config.scheduler_steps,
     vae_path: &vae_path,
     unet_path: &unet_path,

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

@@ -1,20 +1,22 @@
 #[cfg(not(target_os = "macos"))]
-use ml_models::ml::flux::flux_pipeline::{flux_pipeline, FluxModel, FluxPipelineArgs};
+use ml_models::ml::stable_diffusion::lcm_pipeline::{lcm_pipeline, Args};
 
 use crate::state::app_config::AppConfig;
-use crate::state::app_dir::{AppDataRoot, AppWeightsDir};
+use crate::state::app_dir::AppDataRoot;
 use crate::stubs::model_cache::ModelCache;
 use crate::stubs::prompt_cache::PromptCache;
 use crate::utils::image::encode_dynamic_image_base64_png::encode_dynamic_image_base64_png;
 use image::imageops::FilterType;
 use image::{DynamicImage, ImageReader, RgbImage};
 use log::{error, info};
+use ml_weights_registry::weights_registry::weights::{CLIP_JSON, LYKON_DEAMSHAPER_7_VAE, SDXL_TURBO_CLIP_TEXT_ENCODER, SIMIANLUO_LCM_DREAMSHAPER_V7_UNET};
 
-use ml_weights_registry::weights_registry::flux_weights::{FLUX_DEV, FLUX_SCHNELL, FLUX_SCHNELL_AUTOENCODER, GOOGLE_T5_V1_1_XXL_CONFIG, GOOGLE_T5_V1_1_XXL_MODEL, LMZ_CANDLE_FLUX_SCHNELL_QUANTIZED_GGUF, LMZ_T5_TOKENIZER_JSON, OPENAI_CLIP_VIT_P14_MODEL, OPENAI_CLIP_VIT_P14_TOKENIZER_JSON};
 use once_cell::sync::Lazy;
 use std::io::Cursor;
 use tauri::{AppHandle, State};
 
+const RANDOM_SEED: u32 = 42;
+
 const STRENGTH: f64 = 100.0;
 const PNG_BYTES : &[u8] = include_bytes!("../../binary_includes/1024.png");
 
@@ -33,8 +35,8 @@ pub async fn text_to_image(
   model_config: State<'_, AppConfig>,
   model_cache: State<'_, ModelCache>,
   prompt_cache: State<'_, PromptCache>,
-  app: AppHandle,
   app_data_root: State<'_, AppDataRoot>,
+  app: AppHandle,
 ) -> Result<String, String> {
   info!("text_to_image endpoint called.");
 
@@ -49,8 +51,8 @@ pub async fn text_to_image(
       &model_config,
       &model_cache,
       prompt_cache,
-      app_data_root.weights_dir(),
       app,
+      &app_data_root
     ).await;
 
     image = match result {
@@ -78,36 +80,35 @@ async fn text_to_image_impl(
   config: &AppConfig,
   model_cache: &ModelCache,
   prompt_cache: State<'_, PromptCache>,
-  weights_dir: &AppWeightsDir,
   app: AppHandle,
+  app_data_root: &AppDataRoot,
 ) -> Result<RgbImage, String> {
   
-  let args = FluxPipelineArgs {
+  let weights_dir = app_data_root.weights_dir();
+  let vae_path = weights_dir.weight_path(&LYKON_DEAMSHAPER_7_VAE);
+  let unet_path = weights_dir.weight_path(&SIMIANLUO_LCM_DREAMSHAPER_V7_UNET);
+  let clip_json_path = weights_dir.weight_path(&CLIP_JSON);
+  let clip_weights_path= weights_dir.weight_path(&SDXL_TURBO_CLIP_TEXT_ENCODER);
+
+  let args = Args {
     image: &image,
     prompt: prompt.to_string(),
+    uncond_prompt: "".to_string(),
     model_cache,
     prompt_cache: &prompt_cache,
-    app: &app,
-    flux_model: FluxModel::Schnell,
-    active_blocks: None,
-    use_cuda_stream: Some(true),
-    prefetch_next_batch: Some(false),
-    use_full_gpu: Some(true),
     img2img_strength: strength,
-    seed: None,
-    use_quantized_model: Some(true),
+    cfg_scale: config.cfg_scale,
+    use_flash_attn: true,
     model_config: &config.model_config,
-    flux_dev_or_schnell_path: &weights_dir.weight_path(&FLUX_SCHNELL),
-    flux_schnell_quantized_gguf: &weights_dir.weight_path(&LMZ_CANDLE_FLUX_SCHNELL_QUANTIZED_GGUF),
-    flux_autoencoder_path: &weights_dir.weight_path(&FLUX_SCHNELL_AUTOENCODER),
-    google_t5_model_path: &weights_dir.weight_path(&GOOGLE_T5_V1_1_XXL_MODEL),
-    google_t5_config_path: &weights_dir.weight_path(&GOOGLE_T5_V1_1_XXL_CONFIG),
-    lmz_t5_tokenizer_path: &weights_dir.weight_path(&LMZ_T5_TOKENIZER_JSON),
-    openai_clip_model_path: &weights_dir.weight_path(&OPENAI_CLIP_VIT_P14_MODEL),
-    openai_clip_tokenizer_json_path: &weights_dir.weight_path(&OPENAI_CLIP_VIT_P14_TOKENIZER_JSON),
+    maybe_seed: None,
+    scheduler_steps: config.scheduler_steps,
+    vae_path: &vae_path,
+    unet_path: &unet_path,
+    clip_json_path: &clip_json_path,
+    clip_weights_path: &clip_weights_path,
   };
 
-  let image = flux_pipeline(args)
+  let image = lcm_pipeline(args)
     .map_err(|err| format!("failure to encode image: {:?}", err))?;
 
   Ok(image)

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

@@ -8,9 +8,6 @@ pub enum NotificationModelType {
   TextTokenizer,
   Unet,
   Vae,
-  Json,
-  ClipEncoder,
-  FluxModel,
 }
 
 #[derive(Clone, Serialize)]
@@ -29,17 +26,5 @@ pub enum NotificationEvent<'a> {
   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/src-tauri/src/lib.rs

@@ -7,11 +7,10 @@ pub mod transfer;
 pub mod utils;
 
 use crate::endpoints::download_models::download_models;
-use crate::endpoints::flip_image::flip_image;
 use crate::endpoints::inpaint_image_endpoint::inpaint_image;
+use crate::endpoints::flip_image::flip_image;
 use crate::endpoints::realtime_image_endpoint::infer_image;
 use crate::endpoints::remove_background_endpoint::remove_background;
-use crate::endpoints::save_image_endpoint::save_image;
 use crate::endpoints::text_to_image_endpoint::text_to_image;
 use crate::state::app_config::AppConfig;
 use crate::stubs::model_cache::ModelCache;
@@ -30,6 +29,7 @@ pub fn run() {
   let config = AppConfig::init()
     .expect("config should load");
 
+
   let prompt_cache = PromptCache::with_capacity(8)
     .expect("prompt cache should load");
 
@@ -45,10 +45,7 @@ pub fn run() {
   tauri::Builder::default()
     .plugin(tauri_plugin_log::Builder::new()
       .level(log::LevelFilter::Info)
-      .targets(vec![
-        Target::new(TargetKind::Stdout),
-        Target::new(TargetKind::LogDir { file_name: Some(app_data_root.log_file_name_str().to_string()) }),
-      ])
+      .targets(vec![Target::new(TargetKind::Stdout)])
       .build())
     .setup(|app| {
       // TODO(bt): This is broken on windows
@@ -77,8 +74,6 @@ pub fn run() {
       infer_image,
       inpaint_image,
       remove_background,
-      save_image,
-      text_to_image,
       text_to_image,
     ])
     .run(tauri::generate_context!())

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

@@ -4,7 +4,6 @@ use anyhow::anyhow;
 use directories::UserDirs;
 use ml_weights_registry::weights_registry::weight_descriptor::WeightDescriptor;
 use std::path::{Path, PathBuf};
-use chrono::{DateTime, Local};
 use tempdir::TempDir;
 use tempfile::{Builder, NamedTempFile};
 
@@ -14,9 +13,6 @@ 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 {
@@ -24,8 +20,6 @@ pub struct AppDataRoot {
   assets_dir: AppAssetsDir,
   weights_dir: AppWeightsDir,
   temp_dir: TemporaryDir,
-  log_file_name: PathBuf,
-  log_file_name_string: String,
 }
 
 #[derive(Clone)]
@@ -76,19 +70,12 @@ impl AppDataRoot {
     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,
     })
   }
   
@@ -107,14 +94,6 @@ impl AppDataRoot {
   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
-  }
 }
 
 impl AppAssetsDir {
@@ -138,18 +117,6 @@ impl AppAssetsDir {
   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 {

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

@@ -13,7 +13,6 @@ use std::sync::{Arc, Mutex};
 use tauri::{AppHandle, Emitter};
 use tempfile::NamedTempFile;
 use tokio::task::JoinHandle;
-use ml_weights_registry::weights_registry::flux_weights::{FLUX_SCHNELL, FLUX_SCHNELL_AUTOENCODER, GOOGLE_T5_V1_1_XXL_CONFIG, GOOGLE_T5_V1_1_XXL_MODEL, LMZ_CANDLE_FLUX_SCHNELL_QUANTIZED_GGUF, LMZ_T5_TOKENIZER_JSON, OPENAI_CLIP_VIT_P14_MODEL, OPENAI_CLIP_VIT_P14_TOKENIZER_JSON};
 
 const NOTIFICATION_CHANNEL_NAME : &str = "notification";
 const MAX_FILES : usize = 8;
@@ -51,16 +50,6 @@ pub async fn downloader_thread(app_data_root: AppDataRoot, app: AppHandle) -> !
   //download_queue.push_back(SDXL_TURBO_VAE); // TODO(bt): REMOVE
   //download_queue.push_back(SDXL_TURBO_UNET); // TODO(bt): REMOVE
 
-  // Flux:
-  download_queue.push_back(FLUX_SCHNELL);
-  download_queue.push_back(FLUX_SCHNELL_AUTOENCODER);
-  download_queue.push_back(LMZ_CANDLE_FLUX_SCHNELL_QUANTIZED_GGUF);
-  download_queue.push_back(GOOGLE_T5_V1_1_XXL_MODEL);
-  download_queue.push_back(GOOGLE_T5_V1_1_XXL_CONFIG);
-  download_queue.push_back(LMZ_T5_TOKENIZER_JSON);
-  download_queue.push_back(OPENAI_CLIP_VIT_P14_MODEL);
-  download_queue.push_back(OPENAI_CLIP_VIT_P14_TOKENIZER_JSON);
-
   let mut handles = FuturesUnordered::new();
 
   while let Some(model) = download_queue.pop_front() {
@@ -93,7 +82,6 @@ pub async fn downloader_thread(app_data_root: AppDataRoot, app: AppHandle) -> !
       WeightFunction::Unet => NotificationModelType::Unet,
       WeightFunction::Vae => NotificationModelType::Vae,
       WeightFunction::ImageSegmentation => NotificationModelType::ImageSegmentation,
-      WeightFunction::ImageGeneration => NotificationModelType::ImageSegmentation, // TODO FIX
     };
 
     let result = app.emit(NOTIFICATION_CHANNEL_NAME, NotificationEvent::ModelDownloadStarted {