don't download sdxl anymore

Usage example - Conditional Rendering Based on App Mode:
```typescript
import { appMode } from "~/signals";

function ConditionalComponent() {
  return (
    <div className="app-container">
      {/* Show/hide components based on current mode */}
      {appMode.value === 'image' && (
        <div className="image-tools">
          <h3>Image Mode Tools</h3>
        </div>
      )}
      
      {appMode.value === 'edit' && (
        <div className="edit-tools">
          <h3>Edit Mode Tools</h3>
        </div>
      )}
  );
}
```

Cargo.lock

@@ -664,8 +664,11 @@ dependencies = [
  "candle-nn",
  "candle-transformers",
  "cudarc",
- "expanduser",
+ "directories 6.0.0",
+ "dirs 6.0.0",
  "filesys",
+ "futures 0.3.29",
+ "half",
  "hf-hub 0.3.2",
  "image",
  "libloading 0.8.6",
@@ -677,10 +680,15 @@ dependencies = [
  "serde",
  "serde_json",
  "serde_yaml",
+ "serial_test",
  "tauri",
  "tauri-build",
  "tauri-plugin-log",
+ "tempdir",
+ "tempfile",
  "tokenizers 0.15.2",
+ "tokio",
+ "tokio-util",
 ]
 
 [[package]]
@@ -733,12 +741,6 @@ version = "0.3.9"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "76a2e8124351fda1ef8aaaa3bbd7ebbcb486bbcd4225aca0aa0d84bb2db8fecb"
 
-[[package]]
-name = "arrayvec"
-version = "0.5.2"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "23b62fc65de8e4e7f52534fb52b0f3ed04746ae267519eef2a83941e8085068b"
-
 [[package]]
 name = "arrayvec"
 version = "0.7.6"
@@ -1022,7 +1024,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "6678909d8c5d46a42abcf571271e15fdbc0a225e3646cf23762cd415046c78bf"
 dependencies = [
  "anyhow",
- "arrayvec 0.7.6",
+ "arrayvec",
  "log",
  "nom",
  "num-rational",
@@ -1035,7 +1037,7 @@ version = "0.8.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "e335041290c43101ca215eed6f43ec437eb5a42125573f600fc3fa42b9bddd62"
 dependencies = [
- "arrayvec 0.7.6",
+ "arrayvec",
 ]
 
 [[package]]
@@ -1305,17 +1307,6 @@ dependencies = [
  "wyz",
 ]
 
-[[package]]
-name = "blake2b_simd"
-version = "0.5.11"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "afa748e348ad3be8263be728124b24a24f268266f6f5d58af9d75f6a40b5c587"
-dependencies = [
- "arrayref",
- "arrayvec 0.5.2",
- "constant_time_eq 0.1.5",
-]
-
 [[package]]
 name = "blake2b_simd"
 version = "1.0.2"
@@ -1323,7 +1314,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "23285ad32269793932e830392f2fe2f83e26488fd3ec778883a93c8323735780"
 dependencies = [
  "arrayref",
- "arrayvec 0.7.6",
+ "arrayvec",
  "constant_time_eq 0.3.0",
 ]
 
@@ -1834,6 +1825,7 @@ dependencies = [
  "candle-flash-attn",
  "candle-nn",
  "fancy-regex",
+ "half",
  "intel-mkl-src",
  "num-traits",
  "rand 0.8.5",
@@ -2851,19 +2843,6 @@ dependencies = [
  "syn 2.0.87",
 ]
 
-[[package]]
-name = "dashmap"
-version = "5.5.3"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "978747c1d849a7d2ee5e8adc0159961c48fb7e5db2f06af6723b80123bb53856"
-dependencies = [
- "cfg-if 1.0.0",
- "hashbrown 0.14.5",
- "lock_api 0.4.12",
- "once_cell",
- "parking_lot_core 0.9.10",
-]
-
 [[package]]
 name = "dasp_sample"
 version = "0.11.0"
@@ -3207,14 +3186,12 @@ dependencies = [
 ]
 
 [[package]]
-name = "dirs"
-version = "1.0.5"
+name = "directories"
+version = "6.0.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "3fd78930633bd1c6e35c4b42b1df7b0cbc6bc191146e512bb3bedf243fcc3901"
+checksum = "16f5094c54661b38d03bd7e50df373292118db60b585c08a411c6d840017fe7d"
 dependencies = [
- "libc",
- "redox_users 0.3.5",
- "winapi 0.3.9",
+ "dirs-sys 0.5.0",
 ]
 
 [[package]]
@@ -3792,12 +3769,12 @@ dependencies = [
 
 [[package]]
 name = "errno"
-version = "0.3.9"
+version = "0.3.10"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "534c5cf6194dfab3db3242765c03bbe257cf92f22b38f6bc0c58d59108a820ba"
+checksum = "33d852cb9b869c2a9b3df2f71a3074817f01e1844f839a144f5fcef059a4eb5d"
 dependencies = [
  "libc",
- "windows-sys 0.52.0",
+ "windows-sys 0.59.0",
 ]
 
 [[package]]
@@ -3867,17 +3844,6 @@ version = "2.5.3"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "0206175f82b8d6bf6652ff7d71a1e27fd2e4efde587fd368662814d6ec1d9ce0"
 
-[[package]]
-name = "expanduser"
-version = "1.2.2"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "14e0b79235da57db6b6c2beed9af6e5de867d63a973ae3e91910ddc33ba40bc0"
-dependencies = [
- "dirs 1.0.5",
- "lazy_static",
- "pwd",
-]
-
 [[package]]
 name = "exr"
 version = "1.73.0"
@@ -3927,9 +3893,9 @@ dependencies = [
 
 [[package]]
 name = "fastrand"
-version = "2.1.0"
+version = "2.3.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "9fc0510504f03c51ada170672ac806f1f105a88aa97a5281117e1ddc3368e51a"
+checksum = "37909eebbb50d72f9059c3b6d82c0463f2ff062c9e95845c43a6c9c0355411be"
 
 [[package]]
 name = "fdeflate"
@@ -4657,6 +4623,18 @@ dependencies = [
  "wasm-bindgen",
 ]
 
+[[package]]
+name = "getrandom"
+version = "0.3.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "43a49c392881ce6d5c3b8cb70f98717b7c07aabbdff06687b9030dbfbe2725f8"
+dependencies = [
+ "cfg-if 1.0.0",
+ "libc",
+ "wasi 0.13.3+wasi-0.2.2",
+ "windows-targets 0.52.6",
+]
+
 [[package]]
 name = "getset"
 version = "0.1.4"
@@ -5900,7 +5878,7 @@ checksum = "50ae7e0018905a795d6f2a60ac32a547490abdd8df509906a8c6171e6d861711"
 dependencies = [
  "anyhow",
  "binstring",
- "blake2b_simd 1.0.2",
+ "blake2b_simd",
  "coarsetime",
  "ct-codecs",
  "ed25519-compact",
@@ -6011,7 +5989,7 @@ dependencies = [
  "chumsky",
  "email-encoding",
  "email_address",
- "fastrand 2.1.0",
+ "fastrand 2.3.0",
  "futures-util",
  "hostname 0.3.1",
  "httpdate",
@@ -6229,6 +6207,12 @@ version = "0.4.14"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "78b3ae25bc7c8c38cec158d1f2757ee79e9b3740fbc7ccf0e59e4b08d793fa89"
 
+[[package]]
+name = "linux-raw-sys"
+version = "0.9.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "6db9c683daf087dc577b7506e9695b3d556a9f3849903fa28186283afd6809e9"
+
 [[package]]
 name = "local-channel"
 version = "0.1.5"
@@ -6859,7 +6843,7 @@ dependencies = [
  "actix-rt",
  "ahash 0.8.11",
  "anstream",
- "arrayvec 0.7.6",
+ "arrayvec",
  "base64 0.21.7",
  "base64 0.22.1",
  "base64ct",
@@ -7597,7 +7581,7 @@ dependencies = [
  "base16ct",
  "base64 0.22.1",
  "chrono",
- "directories",
+ "directories 5.0.1",
  "flate2",
  "lazy_static",
  "log",
@@ -8636,16 +8620,6 @@ dependencies = [
  "version_check",
 ]
 
-[[package]]
-name = "pwd"
-version = "1.4.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "72c71c0c79b9701efe4e1e4b563b2016dd4ee789eb99badcb09d61ac4b92e4a2"
-dependencies = [
- "libc",
- "thiserror 1.0.63",
-]
-
 [[package]]
 name = "pyo3"
 version = "0.22.6"
@@ -8971,7 +8945,7 @@ checksum = "cd87ce80a7665b1cce111f8a16c1f3929f6547ce91ade6addf4ec86a8dda5ce9"
 dependencies = [
  "arbitrary",
  "arg_enum_proc_macro",
- "arrayvec 0.7.6",
+ "arrayvec",
  "av1-grain",
  "bitstream-io",
  "built",
@@ -9200,17 +9174,6 @@ dependencies = [
  "bitflags 2.5.0",
 ]
 
-[[package]]
-name = "redox_users"
-version = "0.3.5"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "de0737333e7a9502c789a36d7c7fa6092a49895d4faa31ca5df163857ded2e9d"
-dependencies = [
- "getrandom 0.1.16",
- "redox_syscall 0.1.57",
- "rust-argon2",
-]
-
 [[package]]
 name = "redox_users"
 version = "0.4.6"
@@ -9497,18 +9460,6 @@ dependencies = [
  "realfft",
 ]
 
-[[package]]
-name = "rust-argon2"
-version = "0.8.3"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "4b18820d944b33caa75a71378964ac46f58517c92b6ae5f762636247c09e78fb"
-dependencies = [
- "base64 0.13.1",
- "blake2b_simd 0.5.11",
- "constant_time_eq 0.1.5",
- "crossbeam-utils 0.8.20",
-]
-
 [[package]]
 name = "rust-embed"
 version = "8.0.0"
@@ -9594,7 +9545,7 @@ version = "1.36.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "b082d80e3e3cc52b2ed634388d436fe1f4de6af5786cc2de9ba9737527bdf555"
 dependencies = [
- "arrayvec 0.7.6",
+ "arrayvec",
  "borsh",
  "bytes 1.10.0",
  "num-traits",
@@ -9664,10 +9615,23 @@ dependencies = [
  "bitflags 2.5.0",
  "errno",
  "libc",
- "linux-raw-sys",
+ "linux-raw-sys 0.4.14",
  "windows-sys 0.52.0",
 ]
 
+[[package]]
+name = "rustix"
+version = "1.0.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "f7178faa4b75a30e269c71e61c353ce2748cf3d76f0c44c393f4e60abf49b825"
+dependencies = [
+ "bitflags 2.5.0",
+ "errno",
+ "libc",
+ "linux-raw-sys 0.9.2",
+ "windows-sys 0.59.0",
+]
+
 [[package]]
 name = "rustls"
 version = "0.21.12"
@@ -9799,6 +9763,15 @@ dependencies = [
  "winapi-util",
 ]
 
+[[package]]
+name = "scc"
+version = "2.3.3"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "ea091f6cac2595aa38993f04f4ee692ed43757035c36e67c180b6828356385b1"
+dependencies = [
+ "sdd",
+]
+
 [[package]]
 name = "schannel"
 version = "0.1.23"
@@ -9888,6 +9861,12 @@ dependencies = [
  "untrusted",
 ]
 
+[[package]]
+name = "sdd"
+version = "3.0.8"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "584e070911c7017da6cb2eb0788d09f43d789029b5877d3e5ecc8acf86ceee21"
+
 [[package]]
 name = "seahash"
 version = "4.1.0"
@@ -10208,23 +10187,23 @@ dependencies = [
 
 [[package]]
 name = "serial_test"
-version = "3.0.0"
+version = "3.2.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "953ad9342b3aaca7cb43c45c097dd008d4907070394bd0751a0aa8817e5a018d"
+checksum = "1b258109f244e1d6891bf1053a55d63a5cd4f8f4c30cf9a1280989f80e7a1fa9"
 dependencies = [
- "dashmap",
  "futures 0.3.29",
- "lazy_static",
  "log",
+ "once_cell",
  "parking_lot 0.12.2",
+ "scc",
  "serial_test_derive",
 ]
 
 [[package]]
 name = "serial_test_derive"
-version = "3.0.0"
+version = "3.2.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "b93fb4adc70021ac1b47f7d45e8cc4169baaa7ea58483bc5b721d19a26202212"
+checksum = "5d69265a08751de7844521fd15003ae0a888e035773ba05695c5c759a6f89eef"
 dependencies = [
  "proc-macro2",
  "quote",
@@ -11272,7 +11251,7 @@ name = "symphonia-core"
 version = "0.5.0"
 source = "git+https://github.com/pdeljanov/Symphonia.git?rev=e1a70095c4409478c1ded499fb8dd4afa540fb90#e1a70095c4409478c1ded499fb8dd4afa540fb90"
 dependencies = [
- "arrayvec 0.7.6",
+ "arrayvec",
  "bitflags 1.3.2",
  "bytemuck",
  "lazy_static",
@@ -11285,7 +11264,7 @@ version = "0.5.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "798306779e3dc7d5231bd5691f5a813496dc79d3f56bf82e25789f2094e022c3"
 dependencies = [
- "arrayvec 0.7.6",
+ "arrayvec",
  "bitflags 1.3.2",
  "bytemuck",
  "lazy_static",
@@ -11899,14 +11878,16 @@ dependencies = [
 
 [[package]]
 name = "tempfile"
-version = "3.10.1"
+version = "3.18.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "85b77fafb263dd9d05cbeac119526425676db3784113aa9295c88498cbf8bff1"
+checksum = "2c317e0a526ee6120d8dabad239c8dadca62b24b6f168914bbbc8e2fb1f0e567"
 dependencies = [
  "cfg-if 1.0.0",
- "fastrand 2.1.0",
- "rustix",
- "windows-sys 0.52.0",
+ "fastrand 2.3.0",
+ "getrandom 0.3.1",
+ "once_cell",
+ "rustix 1.0.2",
+ "windows-sys 0.59.0",
 ]
 
 [[package]]
@@ -13279,6 +13260,15 @@ version = "0.11.0+wasi-snapshot-preview1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "9c8d87e72b64a3b4db28d11ce29237c246188f4f51057d65a7eab63b7987e423"
 
+[[package]]
+name = "wasi"
+version = "0.13.3+wasi-0.2.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "26816d2e1a4a36a2940b96c5296ce403917633dff8f3440e9b236ed6f6bacad2"
+dependencies = [
+ "wit-bindgen-rt",
+]
+
 [[package]]
 name = "wasite"
 version = "0.1.0"
@@ -14048,6 +14038,15 @@ dependencies = [
  "windows-sys 0.48.0",
 ]
 
+[[package]]
+name = "wit-bindgen-rt"
+version = "0.33.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "3268f3d866458b787f390cf61f4bbb563b922d091359f9608842999eaee3943c"
+dependencies = [
+ "bitflags 2.5.0",
+]
+
 [[package]]
 name = "wry"
 version = "0.48.1"
@@ -14138,8 +14137,8 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "e105d177a3871454f754b33bb0ee637ecaaac997446375fd3e5d43a2ed00c909"
 dependencies = [
  "libc",
- "linux-raw-sys",
- "rustix",
+ "linux-raw-sys 0.4.14",
+ "rustix 0.38.34",
 ]
 
 [[package]]

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/desktop/src-tauri/.taurignore

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

crates/desktop/src-tauri/Cargo.toml

@@ -27,32 +27,49 @@ memory_caching = { path = "../../lib/caching/memory_caching" }
 cudarc = "0.13.7"
 
 # External
+#openssl = { version = "0.10", features = ["vendored"] }
 anyhow = "1.0"
-bytes = "1.10"
 base64 = "0.22.1"
+bytes = "1.10"
 candle-core = { path = "../../vendor/candle/candle-core", features = ["cuda"] }
 candle-nn = { path = "../../vendor/candle/candle-nn", features = ["cuda"] }
 candle-transformers = { path = "../../vendor/candle/candle-transformers", features = ["cuda"] }
+dirs = "6.0.0"
+directories = "6.0.0"
+futures = "0.3"
 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"
+rand = "0.8.5"
+# NB(bt,2025-03-10): default-features = false on Windows while I debug openssl / rustls
+reqwest = { version = "0.12.12", features = ["stream"], default-features = false }
 serde = { version = "1.0", features = ["derive"] }
 serde_json = "1.0"
+serde_yaml = "0.9"
 tauri = { version = "2.2.4", features = [] }
 tauri-plugin-log = "2"
+tempdir = "0.3.7"
+tempfile = "3.18.0"
 tokenizers = "0.15.2"
+tokio = { version = "1.42", features = ["rt", "rt-multi-thread", "fs"] }
+tokio-util = { version = "0.7", features = ["codec"] }
+half = { version = "2.3.1", features = ["num-traits", "use-intrinsics", "rand_distr"] }
 
 [features]
 default = ["cuda"]
-cuda = ["candle-core/cuda", "candle-nn/cuda", "candle-transformers/cuda", "candle-transformers/flash-attn", "candle-core/cudnn"]
+cuda = [
+    "candle-core/cuda",
+    "candle-nn/cuda",
+    "candle-transformers/cuda",
+# NB(bt,2025-03-10): Building without Flash attention on Windows for now to bypass linker issues
+    "candle-transformers/flash-attn",
+    "candle-core/cudnn"
+]
 
 [dev-dependencies]
 libloading = "0.8.6"
+serial_test = "3.2.0"
 
 [profile.dev]
 incremental = true # Compile your binary in smaller steps.

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

@@ -14,10 +14,12 @@ 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 PROMPT: &str = "green goblin, blood dripping from lips, detailed, photorealistic, 8k";
 
 const NEGATIVE_PROMPT: &str = "bad quality, bad faces, poor quality, blurry faces, watermark";
 
+const RANDOM_SEED: u32 = 42;
+
 // TODO: Mark async https://github.com/tauri-apps/tauri/discussions/7737
 
 
@@ -90,6 +92,7 @@ async fn do_infer_image(
     cfg_scale: config.cfg_scale,
     app: &app,
     app_data_root,
+    use_flash_attn: true,
   };
 
   match lcm_pipeline(args) {

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

@@ -6,6 +6,8 @@ use serde::Serialize;
 pub enum NotificationModelType {
   Unet,
   Vae,
+  Json,
+  ClipEncoder,
 }
 
 #[derive(Clone, Serialize)]
@@ -15,6 +17,12 @@ pub enum NotificationEvent<'a> {
     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,

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

@@ -3,6 +3,7 @@ pub mod events;
 pub mod ml;
 pub mod state;
 pub mod threads;
+pub mod transfer;
 
 use crate::endpoints::download_models::download_models;
 use crate::endpoints::image_endpoint::infer_image;

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

@@ -5,11 +5,11 @@ 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?;
+  //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/model_file.rs

@@ -4,80 +4,177 @@ extern crate accelerate_src;
 extern crate intel_mkl_src;
 
 use candle_transformers::models::stable_diffusion;
+use anyhow::{Error as E, Result};
+use hf_hub::api::sync::Api;
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+pub enum StableDiffusionVersion {
+    V1_5,
+    V1_5Inpaint,
+    V2_1,
+    V2Inpaint,
+    Xl,
+    XlInpaint,
+    Turbo,
+}
 
 #[derive(Debug, Clone, Copy, PartialEq, Eq)]
 pub enum ModelFile {
-  Tokenizer,
-  Tokenizer2,
-  Clip,
-  Clip2,
-  Unet,
-  Vae,
-}
-
-#[derive(Debug, Clone, Copy, PartialEq, Eq)]
-pub enum StableDiffusionVersion {
-  V1_5,
-  V1_5Inpaint,
-  V2_1,
-  V2Inpaint,
-  Xl,
-  XlInpaint,
-  Turbo,
+    Tokenizer,
+    Tokenizer2,
+    Clip,
+    Clip2,
+    Unet,
+    UnetLcm,
+    Vae,
 }
 
 impl StableDiffusionVersion {
-  pub fn repo(&self) -> &'static str {
-    match self {
-      Self::XlInpaint => "diffusers/stable-diffusion-xl-1.0-inpainting-0.1",
-      Self::Xl => "stabilityai/stable-diffusion-xl-base-1.0",
-      Self::V2Inpaint => "stabilityai/stable-diffusion-2-inpainting",
-      Self::V2_1 => "stabilityai/stable-diffusion-2-1",
-      Self::V1_5 => "runwayml/stable-diffusion-v1-5",
-      Self::V1_5Inpaint => "stable-diffusion-v1-5/stable-diffusion-inpainting",
-      Self::Turbo => "stabilityai/sdxl-turbo",
+    pub fn repo(&self) -> &'static str {
+        match self {
+            Self::XlInpaint => "diffusers/stable-diffusion-xl-1.0-inpainting-0.1",
+            Self::Xl => "stabilityai/stable-diffusion-xl-base-1.0",
+            Self::V2Inpaint => "stabilityai/stable-diffusion-2-inpainting",
+            Self::V2_1 => "stabilityai/stable-diffusion-2-1",
+            Self::V1_5 => "Lykon/dreamshaper-7",
+            Self::V1_5Inpaint => "stable-diffusion-v1-5/stable-diffusion-inpainting",
+            Self::Turbo => "Lykon/dreamshaper-7",
+        }
+    }
+
+    fn unet_file(&self, use_f16: bool) -> &'static str {
+        match self {
+            Self::V1_5
+            | Self::V1_5Inpaint
+            | Self::V2_1
+            | Self::V2Inpaint
+            | Self::Xl
+            | Self::XlInpaint
+            | Self::Turbo => {
+                if use_f16 {
+                    "unet/diffusion_pytorch_model.fp16.safetensors"
+                } else {
+                    "unet/diffusion_pytorch_model.safetensors"
+                }
+            }
+        }
+    }
+
+    fn vae_file(&self, use_f16: bool) -> &'static str {
+        match self {
+            Self::V1_5
+            | Self::V1_5Inpaint
+            | Self::V2_1
+            | Self::V2Inpaint
+            | Self::Xl
+            | Self::XlInpaint
+            | Self::Turbo => {
+                if use_f16 {
+                    "vae/diffusion_pytorch_model.fp16.safetensors"
+                } else {
+                    "vae/diffusion_pytorch_model.safetensors"
+                }
+            }
+        }
+    }
+
+    fn clip_file(&self, use_f16: bool) -> &'static str {
+        match self {
+            Self::V1_5
+            | Self::V1_5Inpaint
+            | Self::V2_1
+            | Self::V2Inpaint
+            | Self::Xl
+            | Self::XlInpaint
+            | Self::Turbo => {
+                if use_f16 {
+                    "text_encoder/model.fp16.safetensors"
+                } else {
+                    "text_encoder/model.safetensors"
+                }
+            }
+        }
+    }
+
+    fn clip2_file(&self, use_f16: bool) -> &'static str {
+        match self {
+            Self::V1_5
+            | Self::V1_5Inpaint
+            | Self::V2_1
+            | Self::V2Inpaint
+            | Self::Xl
+            | Self::XlInpaint
+            | Self::Turbo => {
+                if use_f16 {
+                    "text_encoder_2/model.fp16.safetensors"
+                } else {
+                    "text_encoder_2/model.safetensors"
+                }
+            }
+        }
     }
-  }
 }
 
 impl ModelFile {
-  pub fn get(
-    &self,
-    filename: Option<String>,
-    version: StableDiffusionVersion,
-    use_f16: bool,
-  ) -> 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)
-      }
+    pub fn get(
+        &self,
+        filename: Option<String>,
+        version: StableDiffusionVersion,
+        use_f16: bool,
+    ) -> Result<std::path::PathBuf> {
+        match filename {
+            Some(filename) => Ok(std::path::PathBuf::from(filename)),
+            None => {
+                let (repo, path) = match self {
+                    Self::Tokenizer => {
+                        let tokenizer_repo = match version {
+                            StableDiffusionVersion::V1_5
+                            | StableDiffusionVersion::V2_1
+                            | StableDiffusionVersion::V1_5Inpaint
+                            | StableDiffusionVersion::V2Inpaint => "openai/clip-vit-base-patch32",
+                            StableDiffusionVersion::Xl
+                            | StableDiffusionVersion::XlInpaint
+                            | StableDiffusionVersion::Turbo => {
+                                // This seems similar to the patch32 version except some very small
+                                // difference in the split regex.
+                                "openai/clip-vit-large-patch14"
+                            }
+                        };
+                        (tokenizer_repo, "tokenizer.json")
+                    }
+                    Self::Tokenizer2 => {
+                        ("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", "tokenizer.json")
+                    }
+                    Self::Clip => (version.repo(), version.clip_file(use_f16)),
+                    Self::Clip2 => (version.repo(), version.clip2_file(use_f16)),
+                    Self::UnetLcm => (
+                        "SimianLuo/LCM_Dreamshaper_v7",
+                        if use_f16 {
+                            "unet/diffusion_pytorch_model.fp16.safetensors"
+                        } else {
+                            "unet/diffusion_pytorch_model.safetensors"
+                        },
+                    ),
+                    Self::Unet => (version.repo(), version.unet_file(use_f16)),
+                    Self::Vae => {
+                        // Override for SDXL when using f16 weights.
+                        // See https://github.com/huggingface/candle/issues/1060
+                        if matches!(
+                            version,
+                            StableDiffusionVersion::Xl | StableDiffusionVersion::Turbo,
+                        ) && use_f16
+                        {
+                            (
+                                "madebyollin/sdxl-vae-fp16-fix",
+                                "diffusion_pytorch_model.safetensors",
+                            )
+                        } else {
+                            (version.repo(), version.vae_file(use_f16))
+                        }
+                    }
+                };
+                let filename = Api::new()?.model(repo.to_string()).get(path)?;
+                Ok(filename)
+            }
+        }
     }
-  }
 }
-

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

@@ -1,5 +1,6 @@
-use std::path::PathBuf;
+use crate::events::notification_event::NotificationModelType;
 use crate::state::app_dir::AppWeightsDir;
+use std::path::PathBuf;
 
 #[derive(Copy, Clone, Debug)]
 pub enum ModelType {
@@ -8,9 +9,38 @@ pub enum ModelType {
   SdxlTurboVae,
   SdxlTurboClipEncoder,
   SdxlTurboClipEncoder2,
+  SimianLuoLcmDreamshaperV7Unet,
+  LykonDreamshaper7Vae,
+  LykonDreamshaper7TextEncoderFp16,
 }
 
 impl ModelType {
+  pub fn get_name(&self) -> &'static str {
+    match self {
+      Self::ClipJson => "CLIP (JSON)",
+      Self::SdxlTurboUnet => "SDXL Turbo UNET",
+      Self::SdxlTurboVae => "SDXL Turbo VAE",
+      Self::SdxlTurboClipEncoder => "Clip Encoder #1",
+      Self::SdxlTurboClipEncoder2 => "Clip Encoder #2",
+      Self::SimianLuoLcmDreamshaperV7Unet => "SimianLuo LCM Dreamshaper v7 UNET",
+      Self::LykonDreamshaper7Vae => "Lykon Dreamshaper 7 VAE",
+      Self::LykonDreamshaper7TextEncoderFp16 => "Lykon Dreamshaper Text Encoder (fp16)",
+    }
+  }
+
+  pub fn get_notification_type(&self) -> NotificationModelType {
+    match self {
+      Self::ClipJson => NotificationModelType::Json,
+      Self::SdxlTurboUnet => NotificationModelType::Unet,
+      Self::SdxlTurboVae => NotificationModelType::Vae,
+      Self::SdxlTurboClipEncoder => NotificationModelType::ClipEncoder,
+      Self::SdxlTurboClipEncoder2 => NotificationModelType::ClipEncoder,
+      Self::SimianLuoLcmDreamshaperV7Unet => NotificationModelType::Unet,
+      Self::LykonDreamshaper7Vae => NotificationModelType::Vae,
+      Self::LykonDreamshaper7TextEncoderFp16 => NotificationModelType::ClipEncoder,
+    }
+  }
+
   pub fn get_hf_id(&self) -> Option<&'static str> {
     match self {
       Self::ClipJson => Some("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k"),
@@ -18,6 +48,9 @@ impl ModelType {
       | Self::SdxlTurboVae
       | Self::SdxlTurboClipEncoder
       | Self::SdxlTurboClipEncoder2 => Some("stabilityai/sdxl-turbo"),
+      Self::SimianLuoLcmDreamshaperV7Unet => Some("SimianLuo/LCM_Dreamshaper_v7"),
+      Self::LykonDreamshaper7Vae
+      | Self::LykonDreamshaper7TextEncoderFp16 => Some("Lykon/dreamshaper-7"),
     }
   }
 
@@ -28,6 +61,9 @@ impl ModelType {
       Self::SdxlTurboVae => "https://pub-bc5e2bc0cdee4bb5ae8fca9d641ca0d6.r2.dev/diffusion_pytorch_model.vae.safetensors",
       Self::SdxlTurboClipEncoder => "https://pub-bc5e2bc0cdee4bb5ae8fca9d641ca0d6.r2.dev/clip_text_encoder.safetensors",
       Self::SdxlTurboClipEncoder2 => "https://pub-bc5e2bc0cdee4bb5ae8fca9d641ca0d6.r2.dev/clip_text_encoder_2.safetensors",
+      Self::SimianLuoLcmDreamshaperV7Unet => "https://pub-bc5e2bc0cdee4bb5ae8fca9d641ca0d6.r2.dev/simianluo_lcm_dreamshaper_v7_unet.safetensors",
+      Self::LykonDreamshaper7Vae => "https://pub-bc5e2bc0cdee4bb5ae8fca9d641ca0d6.r2.dev/lykon_dreamshaper_7_vae.safetensors",
+      Self::LykonDreamshaper7TextEncoderFp16 => "https://pub-bc5e2bc0cdee4bb5ae8fca9d641ca0d6.r2.dev/lykon_dreamshaper_7_text_encoder.fp16.safetensors",
     }
   }
 
@@ -38,6 +74,9 @@ impl ModelType {
       Self::SdxlTurboVae => "diffusion_pytorch_model.vae.safetensors",
       Self::SdxlTurboClipEncoder => "clip_text_encoder.safetensors",
       Self::SdxlTurboClipEncoder2 => "clip_text_encoder_2.safetensors",
+      Self::SimianLuoLcmDreamshaperV7Unet => "simianluo_lcm_dreamshaper_v7_unet.safetensors",
+      Self::LykonDreamshaper7Vae => "lykon_dreamshaper_7_vae.safetensors",
+      Self::LykonDreamshaper7TextEncoderFp16 => "lykon_dreamshaper_7_text_encoder.fp16.safetensors",
     }
   }
   

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

@@ -16,6 +16,10 @@ impl UNetModel {
     Ok(Self { model })
   }
 
+  pub fn new_with_inner(model: UNet2DConditionModel) -> Self {
+    Self { model }
+  }
+
   pub fn inference(&self, latent_model_input: &Tensor, timestep: f64, text_embeddings: &Tensor) -> anyhow::Result<Tensor> {
     Ok(self.model.forward(&latent_model_input, timestep, &text_embeddings)?)
   }

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

@@ -15,26 +15,11 @@ 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...");
+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... for {:?}", sd_version);
 
   let which = match sd_version {
-    StableDiffusionVersion::Xl
-    | StableDiffusionVersion::XlInpaint
-    | StableDiffusionVersion::Turbo => vec![true, false],
+    StableDiffusionVersion::Xl | StableDiffusionVersion::XlInpaint | StableDiffusionVersion::Turbo => vec![true, false],
     _ => vec![true],
   };
 
@@ -45,7 +30,7 @@ pub fn infer_clip_text_embeddings(
         &prompt,
         &uncond_prompt,
         None, // tokenizer
-        None, // clip_weights
+        clip_weights.clone(),
         None, // clip2_weights
         sd_version,
         &sd_config,
@@ -60,60 +45,39 @@ pub fn infer_clip_text_embeddings(
     .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");
+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> {
+  info!("do_infer_clip_text_embeddings called with args {:?}", (prompt, uncond_prompt, tokenizer.clone().unwrap_or_else(|| "None".to_string()), clip_weights.clone().unwrap_or_else(|| "None".to_string()), clip2_weights.clone().unwrap_or_else(|| "None".to_string()), sd_version, sd_config, use_f16, device, dtype, use_guide_scale, first));
+
+  //let tokenizer_file = if first {
+  //  info!("ModelFile::Tokenizer");
+  //  ModelFile::Tokenizer
+  //} else {
+  //  info!("ModelFile::Tokenizer2");
+  //  ModelFile::Tokenizer2
+  //};
+
   //let tokenizer = tokenizer_file.get(tokenizer, sd_version, use_f16)?;
+
   let tokenizer = weights_dir.model_path(&ModelType::ClipJson);
-  
-  info!("Tokenizer path: {:?}", tokenizer);
-  
+
+  info!("Loading Clip Tokenizer path: {:?}", tokenizer);
+
   let tokenizer = Tokenizer::from_file(tokenizer).map_err(E::msg)?;
-  
+
   let pad_id = match &sd_config.clip.pad_with {
     Some(padding) => *tokenizer.get_vocab(true).get(padding.as_str()).unwrap(),
     None => *tokenizer.get_vocab(true).get("<|endoftext|>").unwrap(),
   };
 
   println!("Running with prompt \"{prompt}\".");
-  let mut tokens = tokenizer
-    .encode(prompt, true)
-    .map_err(E::msg)?
-    .get_ids()
-    .to_vec();
+  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
-        )
+    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)
@@ -121,11 +85,7 @@ fn do_infer_clip_text_embeddings(
   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_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)
@@ -133,27 +93,15 @@ fn do_infer_clip_text_embeddings(
     //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 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();
+    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
-            )
+      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)

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

@@ -1,293 +1,291 @@
+use std::path::PathBuf;
 use std::sync::{Arc, RwLock, RwLockReadGuard, RwLockWriteGuard};
 
-use crate::events::notification_event::{NotificationModelType, NotificationEvent};
+use crate::events::notification_event::{NotificationEvent, NotificationModelType};
 use crate::ml::image::dynamic_image_to_tensor::dynamic_image_to_tensor;
 use crate::ml::image::tensor_to_image_buffer::{tensor_to_image_buffer, RgbImage};
 use crate::ml::model_cache::ModelCache;
-use crate::ml::model_file::StableDiffusionVersion;
+use crate::ml::model_file::{ModelFile, StableDiffusionVersion};
 use crate::ml::model_type::ModelType;
 use crate::ml::models::unet_model::UNetModel;
 use crate::ml::prompt_cache::PromptCache;
 use crate::ml::stable_diffusion::get_vae_scale::get_vae_scale;
 use crate::ml::stable_diffusion::infer_clip_text_embeddings::infer_clip_text_embeddings;
 use crate::state::app_config::AppConfig;
+use crate::state::app_dir::AppDataRoot;
 use anyhow::{anyhow, Error as E, Result};
 use candle_core::{DType, IndexOp, Tensor, D};
-use candle_transformers::models::stable_diffusion::vae::{AutoEncoderKL, DiagonalGaussianDistribution};
+use candle_nn::VarBuilder;
 use candle_transformers::models::stable_diffusion::lcm::LCMScheduler;
+use candle_transformers::models::stable_diffusion::unet_2d::BlockConfig;
+use candle_transformers::models::stable_diffusion::unet_2d::UNet2DConditionModel;
+use candle_transformers::models::stable_diffusion::unet_2d::UNet2DConditionModelConfig;
+use candle_transformers::models::stable_diffusion::vae::{AutoEncoderKL, DiagonalGaussianDistribution};
 use 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 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 use_flash_attn: bool,
 }
 
 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 Args { prompt, uncond_prompt, cfg_scale, i2i_strength, configs, model_cache, prompt_cache, app, image, app_data_root, use_flash_attn } = 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);
+  let weights_dir = app_data_root.weights_dir();
 
-    println!("Model dimensions: {}x{}", configs.sd_config.width, configs.sd_config.height);
+  let img2img_strength = args.i2i_strength.unwrap_or(75) as f64 / 100.0;
+  let use_f16 = true;
 
-    // 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(),
+  //println!("Starting image generation with the following configuration:");
+  //println!("  Model: {:?}", configs.sd_version);
+  //println!("  Prompt: {}", prompt);
+  //println!("  Steps: {}", configs.scheduler_steps);
+  //println!("  Device: {:?}", configs.device);
+
+  //println!("Model dimensions: {}x{}", configs.sd_config.width, configs.sd_config.height);
+
+  // Use LCM Scheduler instead of Euler Ancestral for better speed and quality
+  let mut scheduler = LCMScheduler::new(configs.scheduler_steps, img2img_strength, candle_transformers::models::stable_diffusion::lcm::LCMSchedulerConfig::default())?;
+
+  let 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;
+  let use_guide_scale = false;
+
+  //info!("Using guide scale: {}", use_guide_scale);
+
+  //info!("Checking if prompt is cached");
+  let maybe_cached = prompt_cache.get_copy(&prompt)?;
+
+  let mut text_embeddings = if let Some(tensor) = maybe_cached {
+    tensor
+  } else {
+    info!("Loading clip weights...");
+    //println!("Loading clip weights for {:?} with model {:?}", configs.sd_version, configs.sd_version.repo());
+    //let api = configs.hf_api.clone();
+
+    //let clip_path = api.model(configs.sd_version.repo().to_string()).get(if use_f16 { "text_encoder/model.fp16.safetensors" } else { "text_encoder/model.safetensors" })?;
+
+    //println!(">>>> CLIP REPO = {:?}", configs.sd_version.repo());
+    //println!(">>>> CLIP PATH = {:?}", clip_path);
+    //println!(">>>> USE_FP16 = {:?}", use_f16);
+    //let clip_weights = Some(clip_path.to_string_lossy().to_string());
+
+    let clip_weights = weights_dir.model_path(&ModelType::LykonDreamshaper7TextEncoderFp16);
+    let clip_weights = Some(clip_weights.to_string_lossy().to_string());
+
+    info!("Prompt is NOT cached! Calculating embedding...");
+    let tensor = infer_clip_text_embeddings(
+      &prompt,
+      &uncond_prompt,
+      None, // tokenizer
+      clip_weights,
+      None, // clip2_weights
+      configs.sd_version,
+      &configs.sd_config,
+      use_f16, // use_f16
+      &configs.device,
+      configs.dtype,
+      use_guide_scale,
+      weights_dir,
     )?;
+    prompt_cache.store_copy(&prompt, &tensor)?;
+    tensor
+  };
 
-    let seed = configs.seed.unwrap_or_else(|| rand::thread_rng().gen());
-    configs.device.set_seed(seed)?;
+  println!("Text embeddings shape: {:?}", text_embeddings.shape());
 
-    info!("Using seed: {}", seed);
+  println!("Loading input image into tensor...");
 
-    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 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 => {
+      //let repo = configs.sd_version.repo();
+      //println!(">>>>> VAE REPO = {:?}", repo);
+      //println!("Building VAE model from : {:?} ... (3)", repo);
+      //let vae_file = configs.hf_api.model(repo.to_string()).get("vae/diffusion_pytorch_model.safetensors")?;
+
+      let vae_file = weights_dir.model_path(&ModelType::LykonDreamshaper7Vae);
+
+      println!("Building VAE model from file {:?}...", &vae_file);
+
+      let vae = configs.sd_config.build_vae(vae_file, &configs.device, configs.dtype)?;
+
+      let vae = Arc::new(vae);
+
+      model_cache.set_vae(vae.clone())?;
+
+      vae
+    }
+  };
+
+  let maybe_unet = model_cache.get_unet()?;
+
+  let unet = match maybe_unet {
+    Some(unet) => unet,
+    None => {
+      info!("No unet found in cache; loading...");
+
+      let unet_weights = weights_dir.model_path(&ModelType::SimianLuoLcmDreamshaperV7Unet);
+
+      let in_channels = match configs.sd_version {
+        StableDiffusionVersion::XlInpaint | StableDiffusionVersion::V2Inpaint | StableDiffusionVersion::V1_5Inpaint => 9,
+        _ => 4,
+      };
+
+      // For LCM models, ensure that:
+      // 1. We're using the correct prediction type (epsilon)
+      // 2. The cross-attention dimension is correct (768 for Dreamshaper)
+      // 3. The in_channels matches the expected input (4 for standard, 9 for inpainting)
+      let unet_config = UNet2DConditionModelConfig {
+        // LCM specific config values
+        cross_attention_dim: 768,      // Dreamshaper v7 uses 768
+        time_cond_proj_dim: Some(256), // LCM models use 256-dimensional guidance embeddings
+        // Rest of configuration...
+        center_input_sample: false,
+        flip_sin_to_cos: true,
+        freq_shift: 0.0,
+        blocks: vec![
+          BlockConfig { out_channels: 320, use_cross_attn: Some(1), attention_head_dim: 8 },
+          BlockConfig { out_channels: 640, use_cross_attn: Some(1), attention_head_dim: 8 },
+          BlockConfig { out_channels: 1280, use_cross_attn: Some(1), attention_head_dim: 8 },
+          BlockConfig { out_channels: 1280, use_cross_attn: None, attention_head_dim: 8 }
+        ],
+        layers_per_block: 2,
+        downsample_padding: 1,
+        mid_block_scale_factor: 1.0,
+        norm_num_groups: 32,
+        norm_eps: 1e-5,
+        sliced_attention_size: None,
+        use_linear_projection: false,
+        time_embed_dim: Some(1280),
+        // time_embed_dim: Some(256),
+      };
+
+      // Load the model directly from the safetensors file
+      let vs_unet = unsafe { VarBuilder::from_mmaped_safetensors(&[unet_weights], configs.dtype, &configs.device)? };
+
+      let unet = UNet2DConditionModel::new(vs_unet, in_channels, 4, use_flash_attn, unet_config)?;
+
+      let unet = UNetModel::new_with_inner(unet);
+
+      let unet = Arc::new(unet);
+
+      model_cache.set_unet(unet.clone())?;
+
+      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 = 1280;
+  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));
+      },
     };
 
-    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)?;
+    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)?)?)?;
     }
 
-    println!("Diffusion process completed, decoding image...");
-    let image = vae.decode(&(latents / vae_scale)?)?;
-    
-    println!("VAE decode completed");
+    // Apply the LCM scheduler step
+    latents = scheduler.step(&noise_pred, timestep, &latents, timestep_index, configs.scheduler_steps)?;
+  }
 
-    println!("Scaling image back");
-    let image = ((image / 2.)? + 0.5)?;
-    
-    println!("Normalized image values");
-    let image = (image.clamp(0f32, 1.)? * 255.)?;
+  println!("Diffusion process completed, decoding image...");
+  let image = vae.decode(&(latents / vae_scale)?)?;
 
-    println!("Convert to int8");
-    let image = image.to_dtype(DType::U8)?;
-    
-    println!("Converted to 8-bit format");
+  println!("VAE decode completed");
 
-    let image = tensor_to_image_buffer(&image.i(0)?)?;
+  println!("Scaling image back");
+  let image = ((image / 2.)? + 0.5)?;
 
-    Ok(image)
+  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

@@ -4,19 +4,16 @@ 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_IMAGE_WIDTH: usize = 1024;
+const DEFAULT_SD_IMAGE_HEIGHT: usize = 1024;
 
 const DEFAULT_SD_NUMERIC_DATATYPE: DType = DType::F32;
-const SD_VERSION: StableDiffusionVersion = StableDiffusionVersion::Turbo;
-
-const DEFAULT_DATA_DIR_UNIX : &str = "~/artcraft";
+const SD_VERSION: StableDiffusionVersion = StableDiffusionVersion::V1_5;
 
 /// A central place to configure app-wide details.
 pub struct AppConfig {
@@ -58,38 +55,42 @@ impl AppConfig {
           info!("CUDA not available ({}), falling back to CPU", e);
           Device::Cpu
         });
+
     
     let sd_config = match SD_VERSION {
       StableDiffusionVersion::Turbo => {
         StableDiffusionConfig::sdxl_turbo(None, Some(DEFAULT_SD_IMAGE_HEIGHT), Some(DEFAULT_SD_IMAGE_WIDTH))
       }
-      _ => {
+      StableDiffusionVersion::V1_5 | StableDiffusionVersion::V1_5Inpaint => {
+        StableDiffusionConfig::v1_5(None, Some(DEFAULT_SD_IMAGE_HEIGHT), Some(DEFAULT_SD_IMAGE_WIDTH))
+      }
+      StableDiffusionVersion::V2_1 | StableDiffusionVersion::V2Inpaint => {
         StableDiffusionConfig::v2_1(None, Some(DEFAULT_SD_IMAGE_HEIGHT), Some(DEFAULT_SD_IMAGE_WIDTH))
       }
+      StableDiffusionVersion::Xl | StableDiffusionVersion::XlInpaint => {
+        StableDiffusionConfig::sdxl(None, Some(DEFAULT_SD_IMAGE_HEIGHT), Some(DEFAULT_SD_IMAGE_WIDTH))
+      }
     };
     
     let hf_api = Api::new()?;
     
     let os_platform = OsPlatform::get();
 
-    let default_data_path = match os_platform {
+    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(),
     };
     
-    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!("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!("Using data root: {}", data_root_path);
+
+    println!("App data root: {:?}", app_data_root.path());
 
-    let app_data_root = AppDataRoot::create_existing(data_root_path)?;
-    
     Ok(Self {
       device,
       dtype: DEFAULT_SD_NUMERIC_DATATYPE,

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

@@ -1,17 +1,24 @@
-use crate::state::os_platform::OsPlatform;
-use expanduser::expanduser;
-use std::path::{Path, PathBuf};
 use crate::ml::model_type::ModelType;
-
+use crate::state::expanduser::expanduser;
+use crate::state::os_platform::OsPlatform;
+use anyhow::anyhow;
+use directories::UserDirs;
+use std::path::{Path, PathBuf};
+use tempdir::TempDir;
+use tempfile::{Builder, NamedTempFile};
+const DEFAULT_DATA_DIR : &str = "artcraft";
 const ASSETS_SUBDIRECTORY : &str = "assets";
 const WEIGHTS_SUBDIRECTORY : &str = "weights";
 
+const TEMPORARY_SUBDIRECTORY : &str = "temp";
+
 /// 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,
 }
 
 #[derive(Clone)]
@@ -24,7 +31,17 @@ 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();
     
@@ -51,11 +68,13 @@ 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))?;
+
     Ok(Self {
       path: dir,
       assets_dir,
       weights_dir,
+      temp_dir,
     })
   }
   
@@ -66,7 +85,11 @@ impl AppDataRoot {
   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
   }
@@ -121,3 +144,43 @@ impl AppWeightsDir {
     self.path.join(model_registry.get_filename())
   }
 }
+
+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/src-tauri/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/src-tauri/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/src-tauri/src/threads/downloader_thread.rs

@@ -1,11 +1,172 @@
-use tauri::{AppHandle, Emitter};
+use crate::events::notification_event::{NotificationEvent, NotificationModelType};
+use crate::ml::model_type::ModelType;
 use crate::state::app_dir::AppDataRoot;
+use crate::transfer::download::{download_async, ProgressUpdate};
+use futures::stream::FuturesUnordered;
+use futures::StreamExt;
+use log::{debug, error, info};
+use std::collections::VecDeque;
+use std::path::PathBuf;
+use std::sync::mpsc::Receiver;
+use std::sync::{Arc, Mutex};
+use tauri::{AppHandle, Emitter};
+use tempfile::NamedTempFile;
+use tokio::task::JoinHandle;
+
+const NOTIFICATION_CHANNEL_NAME : &str = "notification";
+const MAX_FILES : usize = 8;
+const CHUNK_SIZE : usize = 1024 * 1024;
+const PARALLEL_FAILURES : usize = 8;
+const MAX_RETRIES : usize = 30;
+
+struct ModelDownloadStatusChannel {
+  pub model_name: String,
+  pub model_type: NotificationModelType,
+  pub final_file_path: PathBuf,
+  pub receiver: Receiver<ProgressUpdate>,
+}
 
 pub async fn downloader_thread(app_data_root: AppDataRoot, app: AppHandle) -> ! {
-  loop {
-    app.emit("testing", 1);
+
+  let mut status_check_queue : Arc<Mutex<Vec<ModelDownloadStatusChannel>>> = Arc::new(Mutex::new(Vec::new()));
+
+  {
+    let status_check_queue2 = status_check_queue.clone();
+    let app2 = app.clone();
+
+    tokio::spawn(download_notify_thread(status_check_queue2, app2));
+  }
+
+  let mut download_queue = VecDeque::new();
+
+  // TODO: Automatic enqueue of known important models + enqueue new models on-demand
+  download_queue.push_back(ModelType::ClipJson);
+  //download_queue.push_back(ModelType::SdxlTurboUnet);
+  //download_queue.push_back(ModelType::SdxlTurboVae);
+  download_queue.push_back(ModelType::SdxlTurboClipEncoder); // TODO(bt): Why is this still needed?
+  //download_queue.push_back(ModelType::SdxlTurboClipEncoder2);
+  download_queue.push_back(ModelType::SimianLuoLcmDreamshaperV7Unet);
+  download_queue.push_back(ModelType::LykonDreamshaper7Vae);
+  download_queue.push_back(ModelType::LykonDreamshaper7TextEncoderFp16);
+
+  let mut handles = FuturesUnordered::new();
+
+  while let Some(model) = download_queue.pop_front() {
+    let url = model.get_download_url().to_string();
+
+    let filename = app_data_root.weights_dir().model_path(&model);
+
+    if filename.exists() {
+      info!("File already exists: {:?}", &filename);
+      continue;
+    }
+
+    let headers = None;
     
-    println!(">>> Testing: {:?}", app_data_root.path());
-    std::thread::sleep(std::time::Duration::from_millis(500));
+    let (tx, rx) = std::sync::mpsc::channel();
+
+    info!("Download: {:?}", filename);
+
+    let temp_file = match app_data_root.temp_dir().new_named_temp_file() {
+      Ok(file) => file,
+      Err(err) => {
+        error!("Couldn't create a temporary file: {:?}", err);
+        continue;
+      }
+    };
+
+    let result = app.emit(NOTIFICATION_CHANNEL_NAME, NotificationEvent::ModelDownloadStarted {
+      model_name: model.get_name(),
+      model_type: model.get_notification_type(),
+    });
+
+    if let Err(err) = result {
+      error!("Could not emit event: {}", err);
+    }
+
+    let task = tokio::spawn(download_async(
+      url,
+      temp_file,
+      filename.clone(),
+      MAX_FILES,
+      CHUNK_SIZE,
+      PARALLEL_FAILURES,
+      MAX_RETRIES,
+      headers,
+      Some(tx),
+    ));
+
+    match status_check_queue.lock() {
+      Err(err) => error!("Could not unlock: {:?}", err),
+      Ok(mut lock) => {
+        lock.push(ModelDownloadStatusChannel {
+          receiver: rx,
+          final_file_path: filename.clone(),
+          model_name: model.get_name().to_string(),
+          model_type: model.get_notification_type(),
+        })
+      }
+    }
+
+    handles.push(task);
+  }
+
+  while let Some(result) = handles.next().await {
+    match result {
+      Err(err) => error!("Error downloading: {:?}", err),
+      Ok(_) => {},
+    }
+  }
+
+  loop {
+    // TODO(bt,2025-03-10): Queue management for new files entering the queue.
+    tokio::time::sleep(std::time::Duration::from_millis(60_000)).await;
+  }
+}
+
+pub async fn download_notify_thread(queue: Arc<Mutex<Vec<ModelDownloadStatusChannel>>>, app: AppHandle) -> ! {
+  loop {
+    match queue.lock() {
+      Err(err) => error!("Could not unlock: {:?}", err),
+      Ok(mut lock) => {
+        debug!(">>>>> Notifier has {} items", lock.len());
+
+        let mut reenqueue = Vec::with_capacity(lock.len());
+
+        for item in (*lock).drain(..) {
+          if let Ok(update) = item.receiver.try_recv() {
+            let result = app.emit(NOTIFICATION_CHANNEL_NAME, NotificationEvent::ModelDownloadProgress {
+              model_name: &item.model_name,
+              model_type: item.model_type,
+              currently_downloaded_bytes: update.complete,
+              total_file_bytes: update.total_length,
+            });
+
+            if let Err(err) = result {
+              error!("Could not emit event: {}", err);
+            }
+          }
+
+          if item.final_file_path.exists() {
+            debug!("No longer sending notifications for {:?}", &item.final_file_path);
+
+            let result = app.emit(NOTIFICATION_CHANNEL_NAME, NotificationEvent::ModelDownloadComplete {
+              model_name: &item.model_name,
+              model_type: item.model_type,
+            });
+
+            if let Err(err) = result {
+              error!("Could not emit event: {}", err);
+            }
+          } else {
+            reenqueue.push(item);
+          }
+        }
+
+        lock.extend(reenqueue);
+      }
+    }
+
+    tokio::time::sleep(std::time::Duration::from_millis(500)).await;
   }
 }

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

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

crates/desktop/src-tauri/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/src-tauri/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/src-tauri/tauri.conf.json

@@ -13,8 +13,8 @@
     "windows": [
       {
         "title": "ArtCraft",
-        "width": 1200,
-        "height": 800,
+        "width": 2400,
+        "height": 1300,
         "resizable": true,
         "fullscreen": false
       }

crates/service/web/storyteller_web/src/configs/plans/plan_list.rs

@@ -118,24 +118,40 @@ pub static DEVELOPMENT_PREMIUM_PLANS: Lazy<HashSet<Plan>> = Lazy::new(|| {
 
 /// All paid-for production premium plans in the system.
 pub static PRODUCTION_PREMIUM_PLANS: Lazy<HashSet<Plan>> = Lazy::new(|| {
-    let mut plans = HashSet::new();
+  let mut plans = HashSet::new();
 
-    // ========== All-locale plans ==========
+  // ========== All-locale plans ==========
 
-    plans.insert(PlanBuilder::new("fakeyou_plus")
-        .plan_category(PlanCategory::Paid)
-        .stripe_product_id("prod_MoLv23HrxPiY7D")
-        .stripe_price_id("price_1M4jDCEU5se17MekOaJ92HYX")
-        .cost_per_month_dollars(7)
-        .tts_base_priority_level(20)
-        .tts_max_character_length(2048)
-        .tts_max_duration_seconds(30)
-        .web_vc_base_priority_level(20)
-        .web_vc_requires_frontend_keepalive(false)
-        .lipsync_requires_frontend_keepalive(false)
-        .build());
+  // Old "plus" price
+  plans.insert(PlanBuilder::new("fakeyou_plus")
+      .plan_category(PlanCategory::Paid)
+      .stripe_product_id("prod_MoLv23HrxPiY7D")
+      .stripe_price_id("price_1M4jDCEU5se17MekOaJ92HYX")
+      .cost_per_month_dollars(7)
+      .tts_base_priority_level(20)
+      .tts_max_character_length(2048)
+      .tts_max_duration_seconds(30)
+      .web_vc_base_priority_level(20)
+      .web_vc_requires_frontend_keepalive(false)
+      .lipsync_requires_frontend_keepalive(false)
+      .build());
 
-    plans.insert(PlanBuilder::new("fakeyou_pro")
+  // New "plus" price
+  plans.insert(PlanBuilder::new("fakeyou_plus_plan")
+      .plan_category(PlanCategory::Paid)
+      .stripe_product_id("prod_RvC5qUgLTSLGSD")
+      .stripe_price_id("price_1R1Lh8EU5se17MekaUNtWc59")
+      .cost_per_month_dollars(12)
+      .tts_base_priority_level(20)
+      .tts_max_character_length(2048)
+      .tts_max_duration_seconds(30)
+      .web_vc_base_priority_level(20)
+      .web_vc_requires_frontend_keepalive(false)
+      .lipsync_requires_frontend_keepalive(false)
+      .build());
+
+  // Old "pro" price
+  plans.insert(PlanBuilder::new("fakeyou_pro")
         .plan_category(PlanCategory::Paid)
         .stripe_product_id("prod_MoLw8nA6eFxHzc")
         .stripe_price_id("price_1M4jEQEU5se17MeksNfA0EKm")
@@ -149,7 +165,23 @@ pub static PRODUCTION_PREMIUM_PLANS: Lazy<HashSet<Plan>> = Lazy::new(|| {
         .can_remove_visual_watermarks(true)
         .build());
 
-    plans.insert(PlanBuilder::new("fakeyou_elite")
+  // New "pro" price
+  plans.insert(PlanBuilder::new("fakeyou_pro_plan")
+      .plan_category(PlanCategory::Paid)
+      .stripe_product_id("prod_RvC6nB9rw6yAsI")
+      .stripe_price_id("price_1R1LhzEU5se17Mek3JLwN3Gg")
+      .cost_per_month_dollars(25)
+      .tts_base_priority_level(30)
+      .tts_max_character_length(3072)
+      .tts_max_duration_seconds(60 * 2)
+      .web_vc_base_priority_level(30)
+      .web_vc_requires_frontend_keepalive(false)
+      .lipsync_requires_frontend_keepalive(false)
+      .can_remove_visual_watermarks(true)
+      .build());
+
+  // Old "elite" price
+  plans.insert(PlanBuilder::new("fakeyou_elite")
         .plan_category(PlanCategory::Paid)
         .stripe_product_id("prod_MoLxQmLA4R24fv")
         .stripe_price_id("price_1M4jFREU5se17Mekc8pQaSKB")
@@ -163,6 +195,21 @@ pub static PRODUCTION_PREMIUM_PLANS: Lazy<HashSet<Plan>> = Lazy::new(|| {
         .can_remove_visual_watermarks(true)
         .build());
 
+  // New "elite" price
+  plans.insert(PlanBuilder::new("fakeyou_elite_plan")
+      .plan_category(PlanCategory::Paid)
+      .stripe_product_id("prod_RvC6i84YiAYStd")
+      .stripe_price_id("price_1R1LiQEU5se17MekiJdBcsHQ")
+      .cost_per_month_dollars(40)
+      .tts_base_priority_level(40)
+      .tts_max_character_length(4096)
+      .tts_max_duration_seconds(60 * 5)
+      .web_vc_base_priority_level(40)
+      .web_vc_requires_frontend_keepalive(false)
+      .lipsync_requires_frontend_keepalive(false)
+      .can_remove_visual_watermarks(true)
+      .build());
+
     // ========== Spanish plans ==========
 
     plans.insert(PlanBuilder::new("fakeyou_basic")
@@ -293,7 +340,7 @@ mod test {
     // making ourselves check twice when we make additions, removals, or other changes.
     #[test]
     fn test_number_of_plans_is_expected() {
-        assert_eq!(11, ALL_PLANS_BY_SLUG.len());
+        assert_eq!(14, ALL_PLANS_BY_SLUG.len());
     }
 
     #[test]
@@ -303,7 +350,7 @@ mod test {
             .filter(|plan| !plan.is_synthetic_plan()) // NB: Synthetic plans are production!
             .collect::<Vec<&Plan>>();
 
-        let expected = 4;
+        let expected = 7;
 
         assert_eq!(expected, production_plans.len());
         assert_eq!(expected, PRODUCTION_PREMIUM_PLANS.len());
@@ -346,6 +393,9 @@ mod test {
             "prod_MoLw8nA6eFxHzc".to_string(), // NB: Production
             "prod_MoLxQmLA4R24fv".to_string(), // NB: Production
             "prod_MoLyt8qMDmscjr".to_string(), // NB: Production
+            "prod_RvC5qUgLTSLGSD".to_string(), // NB: Production - new "plus" product
+            "prod_RvC6nB9rw6yAsI".to_string(), // NB: Production - new "pro" product
+            "prod_RvC6i84YiAYStd".to_string(), // NB: Production - new "elite" product
         ];
 
         assert_that(&product_ids).contains_all_of(&expected_product_ids.iter());
@@ -366,6 +416,9 @@ mod test {
             "price_1M4jEQEU5se17MeksNfA0EKm".to_string(), // NB: Production
             "price_1M4jFREU5se17Mekc8pQaSKB".to_string(), // NB: Production
             "price_1M4jGPEU5se17Mek2FiztNE5".to_string(), // NB: Production
+            "price_1R1Lh8EU5se17MekaUNtWc59".to_string(), // NB: Production - new "plus" price
+            "price_1R1LhzEU5se17Mek3JLwN3Gg".to_string(), // NB: Production - new "pro" price
+            "price_1R1LiQEU5se17MekiJdBcsHQ".to_string(), // NB: Production - new "elite" price
         ];
 
         assert_that(&price_ids).contains_all_of(&expected_price_ids.iter());

crates/vendor/candle/candle-examples/examples/lcm/main.rs

@@ -623,6 +623,7 @@ fn run(args: Args) -> Result<()> {
 
     let mut scheduler = stable_diffusion::lcm::LCMScheduler::new(
         n_steps,
+        strength,
         stable_diffusion::lcm::LCMSchedulerConfig::default(),
     )?;
 
@@ -631,7 +632,8 @@ fn run(args: Args) -> Result<()> {
     let seed = seed.unwrap_or(rand::thread_rng().gen_range(0u64..u64::MAX));
     println!("Using seed {seed}");
     device.set_seed(seed)?;
-    let use_guide_scale = guidance_scale > 1.0;
+    // let use_guide_scale = guidance_scale > 1.0;
+    let use_guide_scale = false;
 
     let which = match sd_version {
         StableDiffusionVersion::Xl
@@ -984,7 +986,7 @@ fn run(args: Args) -> Result<()> {
         println!("Starting LCM sampling");
         let start_time = std::time::Instant::now();
 
-        let embedding_dim = 256;
+        let embedding_dim = 1280;
         let guidance_scale_embedding = scheduler.get_guidance_scale_embedding(guidance_scale, embedding_dim, &device, dtype)?;
 
         for (timestep_index, &timestep) in timesteps.iter().enumerate() {

crates/vendor/candle/candle-flash-attn/build.rs

@@ -102,7 +102,12 @@ fn main() -> Result<()> {
     println!("cargo:rustc-link-search={}", build_dir.display());
     println!("cargo:rustc-link-lib=flashattention");
     println!("cargo:rustc-link-lib=dylib=cudart");
-    println!("cargo:rustc-link-lib=dylib=stdc++");
+
+
+    // TODO(bt,2025-03-12): Fix builds on windows
+    //println!("cargo:rustc-link-lib=dylib=stdc++");
+
+
 
     Ok(())
 }

crates/vendor/candle/candle-transformers/Cargo.toml

@@ -17,6 +17,7 @@ serde = { version = "1.0.171", features = ["derive"] }
 serde_json = "1.0.99"
 serde_plain = "1.0.2"
 tracing = "0.1.37"
+half = { version = "2.3.1", features = ["num-traits", "use-intrinsics", "rand_distr"] }
 
 [features]
 default = []

crates/vendor/candle/candle-transformers/src/models/stable_diffusion/lcm.rs

@@ -1,6 +1,6 @@
 use super::schedulers::{betas_for_alpha_bar, BetaSchedule, PredictionType};
 use candle::{DType, Device, IndexOp, Result, Tensor};
-
+const FINAL_ALPHA_CUMPROD: f64 = 0.9991499781608582;
 #[derive(Debug, Clone, PartialEq, Eq)]
 pub enum LCMVarianceType {
     FixedSmall,
@@ -60,7 +60,7 @@ pub struct LCMScheduler {
 }
 
 impl LCMScheduler {
-    pub fn new(inference_steps: usize, config: LCMSchedulerConfig) -> Result<Self> {
+    pub fn new(inference_steps: usize, strength: f64, config: LCMSchedulerConfig) -> Result<Self> {
         let betas = match config.beta_schedule {
             BetaSchedule::ScaledLinear => super::utils::linspace(
                 config.beta_start.sqrt(),
@@ -84,7 +84,6 @@ impl LCMScheduler {
         }
 
         // Create proper LCM timesteps to match diffusers
-        let strength = 0.8;
         let timesteps = LCMScheduler::get_timesteps_for_steps(inference_steps, strength);
         let step_ratio = config.train_timesteps / inference_steps;
 
@@ -335,7 +334,7 @@ impl LCMScheduler {
 
         let alpha_prod_t = self.alphas_cumprod[timestep];
         let alpha_prod_t_prev = if prev_timestep == 0 {
-            1.0
+            FINAL_ALPHA_CUMPROD
         } else {
             self.alphas_cumprod[prev_timestep]
         };
@@ -355,7 +354,7 @@ impl LCMScheduler {
             }
         };
 
-        let denoised = ((pred_original_sample * c_out)? + (sample * c_skip)?)?;
+        let denoised = ((c_out * pred_original_sample )? + (c_skip * sample)?)?;
 
         let pred_prev_sample = if step_index != self.timesteps.len() - 1 {
             println!("Adding noise at intermediate step {}", timestep);
@@ -470,6 +469,7 @@ impl LCMScheduler {
     // }
 
     pub fn add_noise(&self, original: &Tensor, noise: Tensor, timestep: usize) -> Result<Tensor> {
+        println!("Adding noise at timestep {}", timestep);
         let timestep = if timestep >= self.alphas_cumprod.len() {
             self.alphas_cumprod.len() - 1 // Ensure valid range
         } else {

frontend/realtime/src/KonvaApp/NodesManagers/SelectionManager.ts

@@ -252,8 +252,6 @@ export class SelectionManager {
         },
       ),
     );
-    this.updateContextComponents();
-    this.showContextComponents();
   }
 
   public updateNodeTransformer() {

frontend/realtime/src/KonvaApp/RenderingPrimitives/RealTimeDrawEngine.ts

@@ -214,7 +214,7 @@ export class RealTimeDrawEngine {
     // Add mouse events for preview canvas copying
     this.previewCopyListener();
     
-    //this.listenToServerEvents();
+    this.listenToServerEvents();
    
   }
 

frontend/realtime/src/components/features/ToolbarTopLeft/index.tsx

@@ -1,17 +1,43 @@
 import { twMerge } from "tailwind-merge";
+import { TabSelector, TabItem } from "~/components/ui/TabSelector";
+import { useComputed } from "@preact/signals-react";
+import { appMode, dispatchUiEvents } from "~/signals";
 
 // import { paperWrapperStyles } from "~/components/styles";
 // import { faPlus, faQuestion } from "@fortawesome/pro-solid-svg-icons";
 // import { ToolbarButton } from "~/components/features/ToolbarButton";
 
 export const ToolbarTopLeft = () => {
+  // Use the appMode signal directly with useComputed for reactivity - active tab sets the app mode
+  const activeTab = useComputed(() => appMode.value);
+
+  const tabs: TabItem[] = [
+    { id: "image", label: "Image" },
+    { id: "edit", label: "Edit" },
+    // { id: "video", label: "Video" },
+  ];
+
+  const handleTabChange = (tabId: string) => {
+    // Update the global signal
+    dispatchUiEvents.changeAppMode(tabId as "image" | "edit");
+  };
+
+  console.log(appMode.value);
+
   return (
-    <div className={twMerge("z-20 flex h-fit w-fit items-center gap-1")}>
+    <div className={twMerge("z-20 flex h-fit w-fit items-center gap-8 px-4")}>
       <img
         src="/brand/mira-logo.png"
         alt="logo"
         className="h-[30px] select-none pb-1"
       />
+
+      <TabSelector
+        tabs={tabs}
+        activeTab={activeTab.value}
+        onTabChange={handleTabChange}
+      />
+
       {/* <ToolbarButton icon={faPlus}>
         <span className="font-semibold">New Board</span>
       </ToolbarButton>

frontend/realtime/src/components/ui/TabSelector.tsx

@@ -0,0 +1,82 @@
+import React, { useRef, useEffect, useState } from "react";
+import { Tab, TabGroup, TabList } from "@headlessui/react";
+import { twMerge } from "tailwind-merge";
+
+export interface TabItem {
+  id: string;
+  label: string;
+}
+
+interface TabSelectorProps {
+  tabs: TabItem[];
+  activeTab: string;
+  onTabChange: (tabId: string) => void;
+  className?: string;
+}
+
+export const TabSelector: React.FC<TabSelectorProps> = ({
+  tabs,
+  activeTab,
+  onTabChange,
+  className,
+}) => {
+  // Find the index of the active tab
+  const selectedIndex = tabs.findIndex((tab) => tab.id === activeTab);
+  const tabsRef = useRef<(HTMLElement | null)[]>([]);
+  const [indicatorStyle, setIndicatorStyle] = useState({
+    left: 0,
+    width: 0,
+  });
+
+  // Handle tab change
+  const handleTabChange = (index: number) => {
+    onTabChange(tabs[index].id);
+  };
+
+  // Update the indicator position when the selected tab changes
+  useEffect(() => {
+    if (selectedIndex >= 0 && tabsRef.current[selectedIndex]) {
+      const currentTab = tabsRef.current[selectedIndex];
+      if (currentTab) {
+        setIndicatorStyle({
+          left: currentTab.offsetLeft,
+          width: currentTab.offsetWidth,
+        });
+      }
+    }
+  }, [selectedIndex, tabs]);
+
+  return (
+    <TabGroup
+      selectedIndex={selectedIndex}
+      onChange={handleTabChange}
+      className={twMerge("w-full", className)}
+    >
+      <TabList className="glass relative inline-flex min-w-fit overflow-x-auto rounded-lg p-1 shadow-lg">
+        {/* Animated indicator */}
+        <div
+          className="absolute top-1 z-10 h-[calc(100%-8px)] rounded-md border-2 border-primary bg-primary/30 transition-all duration-300 ease-in-out"
+          style={{
+            left: indicatorStyle.left,
+            width: indicatorStyle.width,
+          }}
+        />
+
+        {tabs.map((tab, index) => (
+          <Tab
+            key={tab.id}
+            ref={(el) => (tabsRef.current[index] = el)}
+            className={({ selected }) =>
+              twMerge(
+                "relative z-20 mx-0.5 min-w-max rounded-md border-2 border-transparent px-4 py-1 text-center font-semibold transition-all duration-300 ease-in-out",
+                selected ? "text-white" : "text-gray-300 hover:text-white",
+              )
+            }
+          >
+            {tab.label}
+          </Tab>
+        ))}
+      </TabList>
+    </TabGroup>
+  );
+};

frontend/realtime/src/components/ui/ToolbarMain/BackgroundMenu.tsx

@@ -11,17 +11,9 @@ import { twMerge } from "tailwind-merge";
 
 import { useSignals } from "@preact/signals-react/runtime";
 import { paperWrapperStyles } from "~/components/styles";
-import {
-  BRUSH_MAX_SIZE,
-  BRUSH_MIN_SIZE,
-  paintBrushSize,
-  setPaintBrushSize,
-} from "~/signals/uiEvents/toolbarMain/paintMode";
 import { Button } from "../Button";
-import { Input } from "../Input";
-import { Slider } from "../Slider";
-import { EyeDropper } from "react-eyedrop";
 import { HexEyedropPicker } from "../HexEyedropPicker";
+import { Input } from "../Input";
 
 export const BackgroundMenu = ({
   color: prevColor,

frontend/realtime/src/components/ui/index.ts

@@ -8,6 +8,7 @@ export * from "./PanelRight";
 export * from "./Slider";
 export * from "./Spinner";
 export * from "./Switches";
+export * from "./TabSelector";
 export * from "./TextArea";
 export * from "./TextEditor";
 export * from "./Tooltip";

frontend/realtime/src/signals/uiEvents/appMode.ts

@@ -0,0 +1,38 @@
+import { signal, effect } from "@preact/signals-core";
+
+// this is to control the app mode - BFlat
+
+// Define the app mode types
+export type AppModeType = "image" | "edit";
+
+// Create a signal for the current app mode
+export const appMode = signal<AppModeType>("image");
+
+// Create a function to change the app mode
+export const changeAppMode = (mode: AppModeType) => {
+  appMode.value = mode;
+};
+
+// Store callbacks for mode changes
+const callbacks: ((mode: AppModeType) => void)[] = [];
+
+// Set up an effect to notify callbacks when the mode changes
+effect(() => {
+  const currentMode = appMode.value;
+  callbacks.forEach((callback) => callback(currentMode));
+});
+
+// Export an event handler for components to use
+export const appModeEvents = {
+  onChange: (callback: (mode: AppModeType) => void) => {
+    callbacks.push(callback);
+
+    // Return a function to unsubscribe
+    return () => {
+      const index = callbacks.indexOf(callback);
+      if (index !== -1) {
+        callbacks.splice(index, 1);
+      }
+    };
+  },
+};

frontend/realtime/src/signals/uiEvents/index.ts

@@ -36,6 +36,7 @@ import {
   dispatchers as modelSelectionDispatchers,
   events as modelSelectionEvents,
 } from "./modelSelection";
+import { appMode, changeAppMode, appModeEvents } from "./appMode";
 
 export const uiEvents = {
   ...addMediaEvents,
@@ -49,6 +50,7 @@ export const uiEvents = {
   promptEvents: promptEvents,
   loadingIndicator: loadingIndicatorEvents,
   modelSelection: modelSelectionEvents,
+  appMode: appModeEvents,
 };
 export const dispatchUiEvents = {
   ...addMediaDispatchers,
@@ -62,4 +64,7 @@ export const dispatchUiEvents = {
   promptSettings: promptDispatchers,
   loadingIndicator: loadingIndicatorDispatchers,
   modelSelection: modelSelectionDispatchers,
+  changeAppMode,
 };
+
+export { appMode };