Fix Shape size jumping after recreation (on color change)

Merge branch 'main' into bugfix/shape-changes
RealTimeDrawEngine: Change RenderFrame logic to use stage clone instead of OffscreenCanvas
2026-10-21 17:28:24 -04:00 · 2025-03-07 00:15:54 -08:00 · 2025-03-06 22:58:33 -08:00 · 2025-03-06 22:53:46 -08:00 · 2025-03-06 22:47:09 -08:00 · 2025-03-06 22:46:40 -08:00
93 changed files with 1389 additions and 3233 deletions
@@ -66,7 +66,3 @@ scraps/
 # These should be built by the platform
 windows/sqlite/sqlite3.exp
 windows/sqlite/sqlite3.lib
-
-# ArtCraft Windows DLLs
-crates/desktop/src-tauri/*.dll
-windows_dlls/
@@ -658,35 +658,29 @@ name = "app"
 version = "0.1.0"
 dependencies = [
 "anyhow",
- "assertor",
 "base64 0.22.1",
 "bytes 1.10.0",
- "const_format",
- "directories 6.0.0",
- "dirs 6.0.0",
+ "candle-core",
+ "candle-nn",
+ "candle-transformers",
+ "cudarc",
+ "expanduser",
 "filesys",
- "futures 0.3.29",
- "half",
+ "hf-hub 0.3.2",
 "image",
 "libloading 0.8.6",
 "log",
 "memory_caching",
- "ml_models",
 "once_cell",
- "primitives",
 "rand 0.8.5",
 "reqwest 0.12.12",
 "serde",
 "serde_json",
 "serde_yaml",
- "serial_test",
 "tauri",
 "tauri-build",
 "tauri-plugin-log",
- "tempdir",
- "tempfile",
- "tokio",
- "tokio-util",
+ "tokenizers 0.15.2",
 ]

 [[package]]
@@ -739,6 +733,12 @@ 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"
@@ -853,15 +853,6 @@ version = "1.1.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "10d2119f741b79fe9907f5396d19bffcb46568cfcc315e78677d731972ac7085"

-[[package]]
-name = "assertor"
-version = "0.0.3"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "4b02f2af4042fbbed4d5732c517666629512c574c8e2edb5cfe2cf8a86564d52"
-dependencies = [
- "num-traits",
-]
-
 [[package]]
 name = "async-channel"
 version = "1.9.0"
@@ -1031,7 +1022,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "6678909d8c5d46a42abcf571271e15fdbc0a225e3646cf23762cd415046c78bf"
 dependencies = [
 "anyhow",
- "arrayvec",
+ "arrayvec 0.7.6",
 "log",
 "nom",
 "num-rational",
@@ -1044,7 +1035,7 @@ version = "0.8.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "e335041290c43101ca215eed6f43ec437eb5a42125573f600fc3fa42b9bddd62"
 dependencies = [
- "arrayvec",
+ "arrayvec 0.7.6",
 ]

 [[package]]
@@ -1314,6 +1305,17 @@ 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"
@@ -1321,7 +1323,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "23285ad32269793932e830392f2fe2f83e26488fd3ec778883a93c8323735780"
 dependencies = [
 "arrayref",
- "arrayvec",
+ "arrayvec 0.7.6",
 "constant_time_eq 0.3.0",
 ]

@@ -1832,7 +1834,6 @@ dependencies = [
 "candle-flash-attn",
 "candle-nn",
 "fancy-regex",
- "half",
 "intel-mkl-src",
 "num-traits",
 "rand 0.8.5",
@@ -1876,18 +1877,6 @@ dependencies = [
 "toml 0.8.14",
 ]

-[[package]]
-name = "carrot_utils"
-version = "0.1.7"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "3f34277d609ff503187aa8e8604913b103e2eb8f893222a4948748f22527252e"
-dependencies = [
- "chrono",
- "colored",
- "log",
- "simplelog",
-]
-
 [[package]]
 name = "cast"
 version = "0.3.0"
@@ -2065,9 +2054,9 @@ dependencies = [

 [[package]]
 name = "clap"
-version = "4.5.13"
+version = "4.4.8"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "0fbb260a053428790f3de475e304ff84cdbc4face759ea7a3e64c1edd938a7fc"
+checksum = "2275f18819641850fa26c89acc84d465c1bf91ce57bc2748b28c420473352f64"
 dependencies = [
 "clap_builder",
 "clap_derive",
@@ -2075,23 +2064,23 @@ dependencies = [

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

 [[package]]
 name = "clap_derive"
-version = "4.5.13"
+version = "4.4.7"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "501d359d5f3dcaf6ecdeee48833ae73ec6e42723a1e52419c79abf9507eec0a0"
+checksum = "cf9804afaaf59a91e75b022a30fb7229a7901f60c755489cc61c9b423b836442"
 dependencies = [
- "heck 0.5.0",
+ "heck 0.4.1",
 "proc-macro2",
 "quote",
 "syn 2.0.87",
@@ -2099,9 +2088,9 @@ dependencies = [

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

 [[package]]
 name = "cloud_storage"
@@ -2194,16 +2183,6 @@ version = "1.0.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "0b6a852b24ab71dffc585bcb46eaf7959d175cb865a7152e35b348d1b2960422"

-[[package]]
-name = "colored"
-version = "2.2.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "117725a109d387c937a1533ce01b450cbde6b88abceea8473c4d7a85853cda3c"
-dependencies = [
- "lazy_static",
- "windows-sys 0.59.0",
-]
-
 [[package]]
 name = "combine"
 version = "3.8.1"
@@ -2329,26 +2308,6 @@ dependencies = [
 "tiny-keccak",
 ]

-[[package]]
-name = "const_format"
-version = "0.2.34"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "126f97965c8ad46d6d9163268ff28432e8f6a1196a55578867832e3049df63dd"
-dependencies = [
- "const_format_proc_macros",
-]
-
-[[package]]
-name = "const_format_proc_macros"
-version = "0.2.34"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "1d57c2eccfb16dbac1f4e61e206105db5820c9d26c3c472bc17c774259ef7744"
-dependencies = [
- "proc-macro2",
- "quote",
- "unicode-xid",
-]
-
 [[package]]
 name = "constant_time_eq"
 version = "0.1.5"
@@ -2892,6 +2851,19 @@ 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"
@@ -3235,12 +3207,14 @@ dependencies = [
 ]

 [[package]]
-name = "directories"
-version = "6.0.0"
+name = "dirs"
+version = "1.0.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "16f5094c54661b38d03bd7e50df373292118db60b585c08a411c6d840017fe7d"
+checksum = "3fd78930633bd1c6e35c4b42b1df7b0cbc6bc191146e512bb3bedf243fcc3901"
 dependencies = [
- "dirs-sys 0.5.0",
+ "libc",
+ "redox_users 0.3.5",
+ "winapi 0.3.9",
 ]

 [[package]]
@@ -3818,12 +3792,12 @@ dependencies = [

 [[package]]
 name = "errno"
-version = "0.3.10"
+version = "0.3.9"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "33d852cb9b869c2a9b3df2f71a3074817f01e1844f839a144f5fcef059a4eb5d"
+checksum = "534c5cf6194dfab3db3242765c03bbe257cf92f22b38f6bc0c58d59108a820ba"
 dependencies = [
 "libc",
- "windows-sys 0.59.0",
+ "windows-sys 0.52.0",
 ]

 [[package]]
@@ -3893,6 +3867,17 @@ 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"
@@ -3942,9 +3927,9 @@ dependencies = [

 [[package]]
 name = "fastrand"
-version = "2.3.0"
+version = "2.1.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "37909eebbb50d72f9059c3b6d82c0463f2ff062c9e95845c43a6c9c0355411be"
+checksum = "9fc0510504f03c51ada170672ac806f1f105a88aa97a5281117e1ddc3368e51a"

 [[package]]
 name = "fdeflate"
@@ -4672,18 +4657,6 @@ 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"
@@ -5927,7 +5900,7 @@ checksum = "50ae7e0018905a795d6f2a60ac32a547490abdd8df509906a8c6171e6d861711"
 dependencies = [
 "anyhow",
 "binstring",
- "blake2b_simd",
+ "blake2b_simd 1.0.2",
 "coarsetime",
 "ct-codecs",
 "ed25519-compact",
@@ -6038,7 +6011,7 @@ dependencies = [
 "chumsky",
 "email-encoding",
 "email_address",
- "fastrand 2.3.0",
+ "fastrand 2.1.0",
 "futures-util",
 "hostname 0.3.1",
 "httpdate",
@@ -6256,12 +6229,6 @@ 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"
@@ -6796,49 +6763,6 @@ dependencies = [
 "ws2_32-sys",
 ]

-[[package]]
-name = "ml_models"
-version = "0.0.1"
-dependencies = [
- "anyhow",
- "assertor",
- "base64 0.22.1",
- "bytes 1.10.0",
- "candle-core",
- "candle-nn",
- "candle-transformers",
- "const_format",
- "cudarc",
- "directories 6.0.0",
- "dirs 6.0.0",
- "filesys",
- "futures 0.3.29",
- "half",
- "hf-hub 0.3.2",
- "image",
- "libloading 0.8.6",
- "log",
- "memory_caching",
- "ndarray",
- "nvidia-checker",
- "once_cell",
- "ort",
- "primitives",
- "rand 0.8.5",
- "reqwest 0.12.12",
- "serde",
- "serde_json",
- "serde_yaml",
- "serial_test",
- "tauri",
- "tauri-plugin-log",
- "tempdir",
- "tempfile",
- "tokenizers 0.15.2",
- "tokio",
- "tokio-util",
-]
-
 [[package]]
 name = "mockall"
 version = "0.12.1"
@@ -6935,7 +6859,7 @@ dependencies = [
 "actix-rt",
 "ahash 0.8.11",
 "anstream",
- "arrayvec",
+ "arrayvec 0.7.6",
 "base64 0.21.7",
 "base64 0.22.1",
 "base64ct",
@@ -7103,19 +7027,6 @@ dependencies = [
 "tempfile",
 ]

-[[package]]
-name = "ndarray"
-version = "0.15.6"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "adb12d4e967ec485a5f71c6311fe28158e9d6f4bc4a447b474184d0f91a8fa32"
-dependencies = [
- "matrixmultiply",
- "num-complex",
- "num-integer",
- "num-traits",
- "rawpointer",
-]
-
 [[package]]
 name = "ndk"
 version = "0.8.0"
@@ -7393,23 +7304,6 @@ version = "0.4.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "830b246a0e5f20af87141b25c173cd1b609bd7779a4617d6ec582abaf90870f3"

-[[package]]
-name = "nvidia-checker"
-version = "0.1.3"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "c8b584e4b0be14d4e3f8b779d79477aa0f36658d7a535c42abc725013d0c8951"
-dependencies = [
- "carrot_utils",
- "chrono",
- "clap",
- "colored",
- "dirs 5.0.1",
- "log",
- "serde",
- "serde_derive",
- "toml 0.8.14",
-]
-
 [[package]]
 name = "objc"
 version = "0.2.7"
@@ -7703,7 +7597,7 @@ dependencies = [
 "base16ct",
 "base64 0.22.1",
 "chrono",
- "directories 5.0.1",
+ "directories",
 "flate2",
 "lazy_static",
 "log",
@@ -7915,26 +7809,6 @@ dependencies = [
 "hashbrown 0.14.5",
 ]

-[[package]]
-name = "ort"
-version = "1.16.3"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "889dca4c98efa21b1ba54ddb2bde44fd4920d910f492b618351f839d8428d79d"
-dependencies = [
- "flate2",
- "half",
- "lazy_static",
- "libc",
- "ndarray",
- "tar",
- "thiserror 1.0.63",
- "tracing",
- "ureq",
- "vswhom",
- "winapi 0.3.9",
- "zip 0.6.6",
-]
-
 [[package]]
 name = "overload"
 version = "0.1.1"
@@ -8502,7 +8376,6 @@ dependencies = [
 name = "primitives"
 version = "0.0.1"
 dependencies = [
- "assertor",
 "my-workspace-hack",
 "testing",
 ]
@@ -8763,6 +8636,16 @@ 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"
@@ -9088,7 +8971,7 @@ checksum = "cd87ce80a7665b1cce111f8a16c1f3929f6547ce91ade6addf4ec86a8dda5ce9"
 dependencies = [
 "arbitrary",
 "arg_enum_proc_macro",
- "arrayvec",
+ "arrayvec 0.7.6",
 "av1-grain",
 "bitstream-io",
 "built",
@@ -9317,6 +9200,17 @@ 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"
@@ -9603,6 +9497,18 @@ 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"
@@ -9688,7 +9594,7 @@ version = "1.36.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "b082d80e3e3cc52b2ed634388d436fe1f4de6af5786cc2de9ba9737527bdf555"
 dependencies = [
- "arrayvec",
+ "arrayvec 0.7.6",
 "borsh",
 "bytes 1.10.0",
 "num-traits",
@@ -9758,23 +9664,10 @@ dependencies = [
 "bitflags 2.5.0",
 "errno",
 "libc",
- "linux-raw-sys 0.4.14",
+ "linux-raw-sys",
 "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"
@@ -9906,15 +9799,6 @@ 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"
@@ -10004,12 +9888,6 @@ 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"
@@ -10330,23 +10208,23 @@ dependencies = [

 [[package]]
 name = "serial_test"
-version = "3.2.0"
+version = "3.0.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "1b258109f244e1d6891bf1053a55d63a5cd4f8f4c30cf9a1280989f80e7a1fa9"
+checksum = "953ad9342b3aaca7cb43c45c097dd008d4907070394bd0751a0aa8817e5a018d"
 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 = "3.2.0"
+version = "3.0.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "5d69265a08751de7844521fd15003ae0a888e035773ba05695c5c759a6f89eef"
+checksum = "b93fb4adc70021ac1b47f7d45e8cc4169baaa7ea58483bc5b721d19a26202212"
 dependencies = [
 "proc-macro2",
 "quote",
@@ -10525,17 +10403,6 @@ version = "0.1.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "e3a9fe34e3e7a50316060351f37187a3f546bce95496156754b601a5fa71b76e"

-[[package]]
-name = "simplelog"
-version = "0.12.2"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "16257adbfaef1ee58b1363bdc0664c9b8e1e30aed86049635fb5f147d065a9c0"
-dependencies = [
- "log",
- "termcolor",
- "time",
-]
-
 [[package]]
 name = "siphasher"
 version = "0.3.11"
@@ -11405,7 +11272,7 @@ name = "symphonia-core"
 version = "0.5.0"
 source = "git+https://github.com/pdeljanov/Symphonia.git?rev=e1a70095c4409478c1ded499fb8dd4afa540fb90#e1a70095c4409478c1ded499fb8dd4afa540fb90"
 dependencies = [
- "arrayvec",
+ "arrayvec 0.7.6",
 "bitflags 1.3.2",
 "bytemuck",
 "lazy_static",
@@ -11418,7 +11285,7 @@ version = "0.5.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "798306779e3dc7d5231bd5691f5a813496dc79d3f56bf82e25789f2094e022c3"
 dependencies = [
- "arrayvec",
+ "arrayvec 0.7.6",
 "bitflags 1.3.2",
 "bytemuck",
 "lazy_static",
@@ -12032,16 +11899,14 @@ dependencies = [

 [[package]]
 name = "tempfile"
-version = "3.18.0"
+version = "3.10.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "2c317e0a526ee6120d8dabad239c8dadca62b24b6f168914bbbc8e2fb1f0e567"
+checksum = "85b77fafb263dd9d05cbeac119526425676db3784113aa9295c88498cbf8bff1"
 dependencies = [
 "cfg-if 1.0.0",
- "fastrand 2.3.0",
- "getrandom 0.3.1",
- "once_cell",
- "rustix 1.0.2",
- "windows-sys 0.59.0",
+ "fastrand 2.1.0",
+ "rustix",
+ "windows-sys 0.52.0",
 ]

 [[package]]
@@ -12066,15 +11931,6 @@ dependencies = [
 "safetensors",
 ]

-[[package]]
-name = "termcolor"
-version = "1.4.1"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "06794f8f6c5c898b3275aebefa6b8a1cb24cd2c6c79397ab15774837a0bc5755"
-dependencies = [
- "winapi-util",
-]
-
 [[package]]
 name = "termtree"
 version = "0.4.1"
@@ -12736,9 +12592,9 @@ dependencies = [

 [[package]]
 name = "tracing-subscriber"
-version = "0.3.18"
+version = "0.3.19"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "ad0f048c97dbd9faa9b7df56362b8ebcaa52adb06b498c050d2f4e32f90a7a8b"
+checksum = "e8189decb5ac0fa7bc8b96b7cb9b2701d60d48805aca84a238004d665fcc4008"
 dependencies = [
 "nu-ansi-term",
 "sharded-slab",
@@ -13047,12 +12903,6 @@ version = "0.2.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "1fc81956842c57dac11422a97c3b8195a1ff727f06e85c84ed2e8aa277c9a0fd"

-[[package]]
-name = "unicode-xid"
-version = "0.2.6"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "ebc1c04c71510c7f702b52b7c350734c9ff1295c464a03335b00bb84fc54f853"
-
 [[package]]
 name = "unicode_categories"
 version = "0.1.1"
@@ -13429,15 +13279,6 @@ 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"
@@ -14207,15 +14048,6 @@ 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"
@@ -14306,8 +14138,8 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "e105d177a3871454f754b33bb0ee637ecaaac997446375fd3e5d43a2ed00c909"
 dependencies = [
 "libc",
- "linux-raw-sys 0.4.14",
- "rustix 0.38.34",
+ "linux-raw-sys",
+ "rustix",
 ]

 [[package]]
@@ -1,8 +1,7 @@
-image_width: 1024
-image_height: 1024
+image_width: 512
+image_height: 512
 #scheduler_steps: 15
 # NB(bt): It looks like we can get down to 2 sampling steps, perhaps ?
-scheduler_steps: 8
+scheduler_steps: 4
 scheduler_samples: 1
-cfg_scale: 15.0
-seed: 42
+cfg_scale: 1.1
@@ -1,78 +0,0 @@
-[package]
-name = "ml_models"
-edition = "2021"
-version = "0.0.1"
-authors = ["storyteller"]
-publish = false
-description = ""
-license = ""
-repository = ""
-
-[lib]
-name = "ml_models"
-path = "src/lib.rs"
-crate-type = ["staticlib", "cdylib", "rlib"]
-
-[package.metadata.cargo-udeps.ignore]
-normal = ["my-workspace-hack"]
-
-[dependencies]
-
-# Internal
-filesys = { path = "../../lib/files/filesys" }
-memory_caching = { path = "../../lib/caching/memory_caching" }
-primitives = { path = "../../lib/data/primitives" }
-
-# Bypass cudarc issues on Windows https://github.com/coreylowman/cudarc/issues/332
-cudarc = "0.13.7"
-
-# External
-anyhow = "1.0"
-base64 = "0.22.1"
-bytes = "1.10"
-candle-core = { path = "../../vendor/candle/candle-core", features = ["cuda"] }
-candle-nn = { path = "../../vendor/candle/candle-nn", features = ["cuda"] }
-candle-transformers = { path = "../../vendor/candle/candle-transformers", features = ["cuda"] }
-const_format = "0.2.34"
-directories = "6.0.0"
-dirs = "6.0.0"
-futures = "0.3"
-half = { version = "2.3.1", features = ["num-traits", "use-intrinsics", "rand_distr"] }
-hf-hub = "0.3.2"
-image = "0.25.5"
-log = "0.4"
-ndarray = "0.15.6"
-nvidia-checker = "0.1.2"
-once_cell = "1.20.3"
-ort = { version = "1.16.2", features = ["cuda"] }
-rand = "0.8.5"
-# NB(bt,2025-03-10): default-features = false on Windows while I debug openssl / rustls
-reqwest = { version = "0.12.12", features = ["stream"], default-features = false }
-serde = { version = "1.0", features = ["derive"] }
-serde_json = "1.0"
-serde_yaml = "0.9"
-tauri = { version = "2.2.4", features = [] }
-tauri-plugin-log = "2"
-tempdir = "0.3.7"
-tempfile = "3.18.0"
-tokenizers = "0.15.2"
-tokio = { version = "1.42", features = ["rt", "rt-multi-thread", "fs"] }
-tokio-util = { version = "0.7", features = ["codec"] }
-
-# tracing-subscriber = { version = "0.3", features = [ "env-filter", "fmt" ] }
-
-[features]
-default = ["cuda"]
-cuda = [
-    "candle-core/cuda",
-    "candle-nn/cuda",
-    "candle-transformers/cuda",
-    # NB(bt,2025-03-10): Building without Flash attention on Windows for now to bypass linker issues
-    "candle-transformers/flash-attn",
-    "candle-core/cudnn"
-]
-
-[dev-dependencies]
-libloading = "0.8.6"
-serial_test = "3.2.0"
-assertor = "0.0.3"
@@ -1 +0,0 @@
-pub mod ml;
@@ -1,2 +0,0 @@
-pub mod onnx_session;
-pub mod remove_image_background;
@@ -1,19 +0,0 @@
-//! Adapted from https://github.com/dnanhkhoa/rust-background-removal/blob/main/src/onnx.rs
-
-use std::path::Path;
-use ort::{Environment, ExecutionProvider, GraphOptimizationLevel, SessionBuilder};
-
-pub fn onnx_session(onnx_model_file: &Path) -> Result<ort::Session, anyhow::Error> {
-  let environment = Environment::default().into_arc();
-  let session = SessionBuilder::new(&environment)?
-    .with_optimization_level(GraphOptimizationLevel::Level1)? // Configure model optimization level
-    .with_intra_threads(1)? // Configure the number of threads used for inference
-    .with_execution_providers([
-      // Configure execution providers (e.g., CUDA, CoreML, CPU)
-      ExecutionProvider::CUDA(Default::default()),
-      ExecutionProvider::CoreML(Default::default()),
-      ExecutionProvider::CPU(Default::default()),
-    ])?
-    .with_model_from_file(onnx_model_file)?;
-  Ok(session)
-}
@@ -1,61 +0,0 @@
-use crate::ml::background_removal::onnx_session::onnx_session;
-use image::{imageops, DynamicImage};
-use ndarray::{Array, CowArray};
-use ort::Value;
-use std::path::Path;
-
-pub async fn remove_image_background<P: AsRef<Path>>(model_path: P, image: DynamicImage) -> anyhow::Result<DynamicImage> {
-  // NB: Real time needed cuDNN 9, but this uses cuDNN 8:
-  // sudo apt-get install libcudnn8-dev libcudnn8
-  // update-alternatives: warning: forcing reinstallation of alternative /usr/include/x86_64-linux-gnu/cudnn_v9.h because link group libcudnn is broken
-  // update-alternatives: using /usr/include/x86_64-linux-gnu/cudnn_v8.h to provide /usr/include/cudnn.h (libcudnn) in manual mode
-  let session = onnx_session(model_path.as_ref())?;
-  let image = process_dynamic_image(&session, image)?;
-  Ok(image)
-}
-
-fn process_dynamic_image(
-  session: &ort::Session,
-  dynamic_img: DynamicImage,
-) -> Result<DynamicImage, anyhow::Error> {
-  let input_shape = session.inputs[0]
-    .dimensions()
-    .map(|dim| dim.unwrap())
-    .collect::<Vec<usize>>();
-  let input_img = dynamic_img.into_rgba8();
-  let scaling_factor = f32::min(
-    1., // Avoid upscaling
-    f32::min(
-      input_shape[3] as f32 / input_img.width() as f32, // Width ratio
-      input_shape[2] as f32 / input_img.height() as f32, // Height ratio
-    ),
-  );
-  let mut resized_img = imageops::resize(
-    &input_img,
-    input_shape[3] as u32,
-    input_shape[2] as u32,
-    imageops::FilterType::Triangle,
-  );
-  let input_tensor = CowArray::from(
-    Array::from_shape_fn(input_shape, |indices| {
-      let mean = 128.;
-      let std = 256.;
-
-      (resized_img[(indices[3] as u32, indices[2] as u32)][indices[1]] as f32 - mean) / std
-    })
-      .into_dyn(),
-  );
-  let inputs = vec![Value::from_array(session.allocator(), &input_tensor)?];
-  let outputs = session.run(inputs)?;
-  let output_tensor = outputs[0].try_extract::<f32>()?;
-  for (indices, alpha) in output_tensor.view().indexed_iter() {
-    resized_img[(indices[3] as u32, indices[2] as u32)][3] = (alpha * 255.) as u8;
-  }
-  let output_img = imageops::resize(
-    &resized_img,
-    (input_img.width() as f32 * scaling_factor) as u32,
-    (input_img.height() as f32 * scaling_factor) as u32,
-    imageops::FilterType::Triangle,
-  );
-  Ok(DynamicImage::ImageRgba8(output_img))
-}
@@ -1,61 +0,0 @@
-use crate::ml::model_file::StableDiffusionVersion;
-use candle_core::{DType, Device};
-use candle_transformers::models::stable_diffusion::StableDiffusionConfig;
-use hf_hub::api::sync::Api;
-use log::info;
-
-const DEFAULT_SD_IMAGE_WIDTH: usize = 1024;
-const DEFAULT_SD_IMAGE_HEIGHT: usize = 1024;
-const DEFAULT_SD_NUMERIC_DATATYPE: DType = DType::F32;
-const SD_VERSION: StableDiffusionVersion = StableDiffusionVersion::V1_5;
-
-pub struct ModelConfig {
-
-  /// We only support one device type currently, and we can't multiplex
-  /// over several devices yet.
-  pub device: Device,
-
-  /// The core numeric type to use for models.
-  pub dtype: DType,
-
-  pub sd_version: StableDiffusionVersion,
-  pub sd_config: StableDiffusionConfig,
-
-  /// Probably shouldn't be here.
-  pub hf_api: Api,
-}
-
-impl ModelConfig {
-  pub fn init() -> anyhow::Result<Self> {
-    let device = Device::new_cuda(0)
-      .unwrap_or_else(|e| {
-          info!("CUDA not available ({}), falling back to CPU", e);
-          Device::Cpu
-        });
-
-    let sd_config = match SD_VERSION {
-      StableDiffusionVersion::Turbo => {
-        StableDiffusionConfig::sdxl_turbo(None, Some(DEFAULT_SD_IMAGE_HEIGHT), Some(DEFAULT_SD_IMAGE_WIDTH))
-      }
-      StableDiffusionVersion::V1_5 | StableDiffusionVersion::V1_5Inpaint => {
-        StableDiffusionConfig::v1_5(None, Some(DEFAULT_SD_IMAGE_HEIGHT), Some(DEFAULT_SD_IMAGE_WIDTH))
-      }
-      StableDiffusionVersion::V2_1 | StableDiffusionVersion::V2Inpaint => {
-        StableDiffusionConfig::v2_1(None, Some(DEFAULT_SD_IMAGE_HEIGHT), Some(DEFAULT_SD_IMAGE_WIDTH))
-      }
-      StableDiffusionVersion::Xl | StableDiffusionVersion::XlInpaint => {
-        StableDiffusionConfig::sdxl(None, Some(DEFAULT_SD_IMAGE_HEIGHT), Some(DEFAULT_SD_IMAGE_WIDTH))
-      }
-    };
-
-    let hf_api = Api::new()?;
-
-    Ok(Self {
-      device,
-      dtype: DEFAULT_SD_NUMERIC_DATATYPE,
-      sd_version: SD_VERSION,
-      sd_config,
-      hf_api,
-    })
-  }
-}
@@ -1,180 +0,0 @@
-#[cfg(feature = "accelerate")]
-extern crate accelerate_src;
-#[cfg(feature = "mkl")]
-extern crate intel_mkl_src;
-
-use candle_transformers::models::stable_diffusion;
-use anyhow::{Error as E, Result};
-use hf_hub::api::sync::Api;
-#[derive(Debug, Clone, Copy, PartialEq, Eq)]
-pub enum StableDiffusionVersion {
-    V1_5,
-    V1_5Inpaint,
-    V2_1,
-    V2Inpaint,
-    Xl,
-    XlInpaint,
-    Turbo,
-}
-
-#[derive(Debug, Clone, Copy, PartialEq, Eq)]
-pub enum ModelFile {
-    Tokenizer,
-    Tokenizer2,
-    Clip,
-    Clip2,
-    Unet,
-    UnetLcm,
-    Vae,
-}
-
-impl StableDiffusionVersion {
-    pub fn repo(&self) -> &'static str {
-        match self {
-            Self::XlInpaint => "diffusers/stable-diffusion-xl-1.0-inpainting-0.1",
-            Self::Xl => "stabilityai/stable-diffusion-xl-base-1.0",
-            Self::V2Inpaint => "stabilityai/stable-diffusion-2-inpainting",
-            Self::V2_1 => "stabilityai/stable-diffusion-2-1",
-            Self::V1_5 => "Lykon/dreamshaper-7",
-            Self::V1_5Inpaint => "stable-diffusion-v1-5/stable-diffusion-inpainting",
-            Self::Turbo => "Lykon/dreamshaper-7",
-        }
-    }
-
-    fn unet_file(&self, use_f16: bool) -> &'static str {
-        match self {
-            Self::V1_5
-            | Self::V1_5Inpaint
-            | Self::V2_1
-            | Self::V2Inpaint
-            | Self::Xl
-            | Self::XlInpaint
-            | Self::Turbo => {
-                if use_f16 {
-                    "unet/diffusion_pytorch_model.fp16.safetensors"
-                } else {
-                    "unet/diffusion_pytorch_model.safetensors"
-                }
-            }
-        }
-    }
-
-    fn vae_file(&self, use_f16: bool) -> &'static str {
-        match self {
-            Self::V1_5
-            | Self::V1_5Inpaint
-            | Self::V2_1
-            | Self::V2Inpaint
-            | Self::Xl
-            | Self::XlInpaint
-            | Self::Turbo => {
-                if use_f16 {
-                    "vae/diffusion_pytorch_model.fp16.safetensors"
-                } else {
-                    "vae/diffusion_pytorch_model.safetensors"
-                }
-            }
-        }
-    }
-
-    fn clip_file(&self, use_f16: bool) -> &'static str {
-        match self {
-            Self::V1_5
-            | Self::V1_5Inpaint
-            | Self::V2_1
-            | Self::V2Inpaint
-            | Self::Xl
-            | Self::XlInpaint
-            | Self::Turbo => {
-                if use_f16 {
-                    "text_encoder/model.fp16.safetensors"
-                } else {
-                    "text_encoder/model.safetensors"
-                }
-            }
-        }
-    }
-
-    fn clip2_file(&self, use_f16: bool) -> &'static str {
-        match self {
-            Self::V1_5
-            | Self::V1_5Inpaint
-            | Self::V2_1
-            | Self::V2Inpaint
-            | Self::Xl
-            | Self::XlInpaint
-            | Self::Turbo => {
-                if use_f16 {
-                    "text_encoder_2/model.fp16.safetensors"
-                } else {
-                    "text_encoder_2/model.safetensors"
-                }
-            }
-        }
-    }
-}
-
-impl ModelFile {
-    pub fn get(
-        &self,
-        filename: Option<String>,
-        version: StableDiffusionVersion,
-        use_f16: bool,
-    ) -> Result<std::path::PathBuf> {
-        match filename {
-            Some(filename) => Ok(std::path::PathBuf::from(filename)),
-            None => {
-                let (repo, path) = match self {
-                    Self::Tokenizer => {
-                        let tokenizer_repo = match version {
-                            StableDiffusionVersion::V1_5
-                            | StableDiffusionVersion::V2_1
-                            | StableDiffusionVersion::V1_5Inpaint
-                            | StableDiffusionVersion::V2Inpaint => "openai/clip-vit-base-patch32",
-                            StableDiffusionVersion::Xl
-                            | StableDiffusionVersion::XlInpaint
-                            | StableDiffusionVersion::Turbo => {
-                                // This seems similar to the patch32 version except some very small
-                                // difference in the split regex.
-                                "openai/clip-vit-large-patch14"
-                            }
-                        };
-                        (tokenizer_repo, "tokenizer.json")
-                    }
-                    Self::Tokenizer2 => {
-                        ("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", "tokenizer.json")
-                    }
-                    Self::Clip => (version.repo(), version.clip_file(use_f16)),
-                    Self::Clip2 => (version.repo(), version.clip2_file(use_f16)),
-                    Self::UnetLcm => (
-                        "SimianLuo/LCM_Dreamshaper_v7",
-                        if use_f16 {
-                            "unet/diffusion_pytorch_model.fp16.safetensors"
-                        } else {
-                            "unet/diffusion_pytorch_model.safetensors"
-                        },
-                    ),
-                    Self::Unet => (version.repo(), version.unet_file(use_f16)),
-                    Self::Vae => {
-                        // Override for SDXL when using f16 weights.
-                        // See https://github.com/huggingface/candle/issues/1060
-                        if matches!(
-                            version,
-                            StableDiffusionVersion::Xl | StableDiffusionVersion::Turbo,
-                        ) && use_f16
-                        {
-                            (
-                                "madebyollin/sdxl-vae-fp16-fix",
-                                "diffusion_pytorch_model.safetensors",
-                            )
-                        } else {
-                            (version.repo(), version.vae_file(use_f16))
-                        }
-                    }
-                };
-                let filename = Api::new()?.model(repo.to_string()).get(path)?;
-                Ok(filename)
-            }
-        }
-    }
-}
@@ -1,158 +0,0 @@
-#[cfg(feature = "accelerate")]
-extern crate accelerate_src;
-#[cfg(feature = "mkl")]
-extern crate intel_mkl_src;
-
-use candle_transformers::models::stable_diffusion;
-use std::path::{Path, PathBuf};
-
-use crate::ml::model_file::{ModelFile, StableDiffusionVersion};
-use crate::ml::weights_registry::weights::{CLIP_JSON, SDXL_TURBO_CLIP_TEXT_ENCODER, SDXL_TURBO_CLIP_TEXT_ENCODER_2};
-use anyhow::Error as E;
-use candle_core::{DType, Device, Module, Tensor, D};
-use log::info;
-use tokenizers::Tokenizer;
-
-
-pub struct ClipArgs<'a, P: AsRef<Path>,  Q: AsRef<Path>> {
-  pub prompt: &'a str, 
-  pub uncond_prompt: &'a str, 
-  pub tokenizer: Option<String>, 
-  pub clip_weights: Option<String>, 
-  pub clip2_weights: Option<String>, 
-  pub sd_version: StableDiffusionVersion, 
-  pub sd_config: &'a stable_diffusion::StableDiffusionConfig, 
-  pub use_f16: bool, 
-  pub device: &'a Device, 
-  pub dtype: DType, 
-  pub use_guide_scale: bool, 
-  pub clip_json_path: P,
-  pub clip_weights_path: Q,
-}
-
-#[allow(clippy::too_many_arguments)]
-pub fn infer_clip_text_embeddings<P: AsRef<Path>, Q: AsRef<Path>>(args: ClipArgs<'_, P, Q>) -> anyhow::Result<Tensor> {
-  let ClipArgs {
-    prompt, 
-    uncond_prompt, 
-    tokenizer, 
-    clip_weights, 
-    clip2_weights, 
-    sd_version, 
-    sd_config, 
-    use_f16, 
-    device, 
-    dtype, 
-    use_guide_scale, 
-    clip_json_path,
-    clip_weights_path,
-  } = args;
-
-  info!("Preparing text embeddings... for {:?}", sd_version);
-
-  let which = match sd_version {
-    StableDiffusionVersion::Xl | StableDiffusionVersion::XlInpaint | StableDiffusionVersion::Turbo => vec![true, false],
-    _ => vec![true],
-  };
-
-  let text_embeddings = which
-    .iter()
-    .map(|first| {
-      do_infer_clip_text_embeddings(
-        &prompt,
-        &uncond_prompt,
-        None, // tokenizer
-        clip_weights.clone(),
-        None, // clip2_weights
-        sd_version,
-        &sd_config,
-        false, // use_f16
-        &device,
-        dtype,
-        use_guide_scale,
-        *first,
-        clip_json_path.as_ref(),
-        clip_weights_path.as_ref(),
-      )
-    })
-    .collect::<anyhow::Result<Vec<_>>>()?;
-
-  let text_embeddings = Tensor::cat(&text_embeddings, D::Minus1)?;
-
-  Ok(text_embeddings)
-}
-
-#[allow(clippy::too_many_arguments)]
-fn do_infer_clip_text_embeddings(
-  prompt: &str, 
-  uncond_prompt: &str, 
-  tokenizer: Option<String>, 
-  clip_weights: Option<String>, 
-  clip2_weights: Option<String>, 
-  sd_version: StableDiffusionVersion, 
-  sd_config: &stable_diffusion::StableDiffusionConfig, 
-  use_f16: bool, 
-  device: &Device, 
-  dtype: DType, 
-  use_guide_scale: bool, 
-  first: bool, 
-  clip_json_path: &Path,
-  clip_weights_path: &Path,
-) -> anyhow::Result<Tensor> {
-  info!("do_infer_clip_text_embeddings called with args {:?}", (prompt, uncond_prompt, tokenizer.clone().unwrap_or_else(|| "None".to_string()), clip_weights.clone().unwrap_or_else(|| "None".to_string()), clip2_weights.clone().unwrap_or_else(|| "None".to_string()), sd_version, sd_config, use_f16, device, dtype, use_guide_scale, first));
-
-  let tokenizer = clip_json_path;
-
-  info!("Loading Clip Tokenizer path: {:?}", tokenizer);
-
-  let tokenizer = Tokenizer::from_file(tokenizer).map_err(E::msg)?;
-
-  let pad_id = match &sd_config.clip.pad_with {
-    Some(padding) => *tokenizer.get_vocab(true).get(padding.as_str()).unwrap(),
-    None => *tokenizer.get_vocab(true).get("<|endoftext|>").unwrap(),
-  };
-
-  println!("Running with prompt \"{prompt}\".");
-  let mut tokens = tokenizer.encode(prompt, true).map_err(E::msg)?.get_ids().to_vec();
-  if tokens.len() > sd_config.clip.max_position_embeddings {
-    anyhow::bail!("the prompt is too long, {} > max-tokens ({})", tokens.len(), sd_config.clip.max_position_embeddings)
-  }
-  while tokens.len() < sd_config.clip.max_position_embeddings {
-    tokens.push(pad_id)
-  }
-  let tokens = Tensor::new(tokens.as_slice(), device)?.unsqueeze(0)?;
-
-  println!("Building the Clip transformer.");
-  
-  // TODO(bt,2025-03-23): These used to be two different clip models. We should only need one.
-  let clip_weights = if first {
-    println!(">>>>> CLIP 1");
-    clip_weights_path
-  } else {
-    println!(">>>>> CLIP 2");
-    clip_weights_path
-  };
-  
-  let clip_config = if first { &sd_config.clip } else { sd_config.clip2.as_ref().unwrap() };
-  let text_model = stable_diffusion::build_clip_transformer(clip_config, clip_weights, device, DType::F32)?;
-
-  let text_embeddings = text_model.forward(&tokens)?;
-
-  let text_embeddings = if use_guide_scale {
-    let mut uncond_tokens = tokenizer.encode(uncond_prompt, true).map_err(E::msg)?.get_ids().to_vec();
-    if uncond_tokens.len() > sd_config.clip.max_position_embeddings {
-      anyhow::bail!("the negative prompt is too long, {} > max-tokens ({})", uncond_tokens.len(), sd_config.clip.max_position_embeddings)
-    }
-    while uncond_tokens.len() < sd_config.clip.max_position_embeddings {
-      uncond_tokens.push(pad_id)
-    }
-
-    let uncond_tokens = Tensor::new(uncond_tokens.as_slice(), device)?.unsqueeze(0)?;
-    let uncond_embeddings = text_model.forward(&uncond_tokens)?;
-
-    Tensor::cat(&[uncond_embeddings, text_embeddings], 0)?.to_dtype(dtype)?
-  } else {
-    text_embeddings.to_dtype(dtype)?
-  };
-  Ok(text_embeddings)
-}
@@ -1,281 +0,0 @@
-use std::path::{Path, PathBuf};
-use std::sync::{Arc, RwLock, RwLockReadGuard, RwLockWriteGuard};
-
-use crate::ml::image::dynamic_image_to_tensor::dynamic_image_to_tensor;
-use crate::ml::image::tensor_to_image_buffer::{tensor_to_image_buffer, RgbImage};
-use crate::ml::model_cache::ModelCache;
-use crate::ml::model_config::ModelConfig;
-use crate::ml::model_file::{ModelFile, StableDiffusionVersion};
-use crate::ml::prompt_cache::PromptCache;
-use crate::ml::stable_diffusion::get_vae_scale::get_vae_scale;
-use crate::ml::stable_diffusion::infer_clip_text_embeddings::{infer_clip_text_embeddings, ClipArgs};
-use crate::ml::stable_diffusion::remap_lcm_strength_range::remap_lcm_strength_range;
-use crate::ml::weights_registry::weights::{LYKON_DEAMSHAPER_7_TEXT_ENCODER_FP16, LYKON_DEAMSHAPER_7_VAE, SIMIANLUO_LCM_DREAMSHAPER_V7_UNET};
-use anyhow::{anyhow, Error as E, Result};
-use candle_core::{DType, Device, IndexOp, Tensor, D};
-use candle_nn::VarBuilder;
-use candle_transformers::models::stable_diffusion::lcm::{LCMScheduler, LCMSchedulerConfig};
-use candle_transformers::models::stable_diffusion::unet_2d::BlockConfig;
-use candle_transformers::models::stable_diffusion::unet_2d::UNet2DConditionModel;
-use candle_transformers::models::stable_diffusion::unet_2d::UNet2DConditionModelConfig;
-use candle_transformers::models::stable_diffusion::vae::{AutoEncoderKL, DiagonalGaussianDistribution};
-use candle_transformers::models::stable_diffusion::StableDiffusionConfig;
-use image::DynamicImage;
-use log::info;
-use rand::Rng;
-
-const DEFAULT_STRENGTH : f64 = 75.0;
-
-pub struct Args<'a, P1: AsRef<Path>, P2: AsRef<Path>, P3: AsRef<Path>, P4: AsRef<Path>> {
-  pub image: &'a DynamicImage,
-  pub prompt: String,
-  pub uncond_prompt: String,
-  pub cfg_scale: Option<f64>,
-  pub img2img_strength: Option<f64>,
-  pub model_cache: &'a ModelCache,
-  pub prompt_cache: &'a PromptCache,
-  pub use_flash_attn: bool,
-  pub model_config: &'a ModelConfig,
-  pub maybe_seed: Option<u64>,
-  pub scheduler_steps: usize,
-  pub vae_path: P1,
-  pub unet_path: P2,
-  pub clip_json_path: P3,
-  pub clip_weights_path: P4,
-}
-
-pub fn lcm_pipeline<P1, P2, P3, P4>(args: Args<'_, P1, P2, P3, P4>) -> Result<RgbImage> 
-  where P1: AsRef<Path>, P2: AsRef<Path>, P3: AsRef<Path>, P4: AsRef<Path>
-{
-  let Args { 
-    prompt, 
-    uncond_prompt, 
-    cfg_scale,
-    img2img_strength, 
-    model_cache, 
-    prompt_cache, 
-    image, 
-    use_flash_attn ,
-    model_config: ModelConfig {
-      device,
-      dtype,
-      sd_version,
-      sd_config,
-      hf_api,
-    },
-    maybe_seed,
-    scheduler_steps,
-    vae_path,
-    unet_path,
-    clip_json_path,
-    clip_weights_path,
-  } = args;
-  
-  let dtype = *dtype;
-  let sd_version = *sd_version;
-
-  let img2img_strength = img2img_strength
-    .map(|s| remap_lcm_strength_range(s))
-    .unwrap_or(DEFAULT_STRENGTH);
-  
-  let img2img_strength = img2img_strength / 100.0;
-
-  let use_f16 = true;
-
-  // Use LCM Scheduler instead of Euler Ancestral for better speed and quality
-  let mut scheduler = LCMScheduler::new(scheduler_steps, img2img_strength, LCMSchedulerConfig::default())?;
-
-  let seed = maybe_seed.unwrap_or_else(|| rand::thread_rng().gen());
-
-  let seed = 42;
-  
-  device.set_seed(seed)?;
-
-  let guidance_scale = match cfg_scale {
-    Some(guidance_scale) => guidance_scale,
-    None => match sd_version {
-      StableDiffusionVersion::V1_5 | StableDiffusionVersion::V2_1 | StableDiffusionVersion::Xl => 7.5,
-      StableDiffusionVersion::Turbo => 0.,
-      _ => 0., // NB: Not sure what the other model families should use, so sticking with "0"
-    },
-  };
-
-  // let use_guide_scale = guidance_scale > 1.0;
-  let use_guide_scale = false;
-
-  let maybe_cached = prompt_cache.get_copy(&prompt)?;
-
-  let mut text_embeddings = if let Some(tensor) = maybe_cached {
-    tensor
-  } else {
-    info!("Prompt is NOT cached! Calculating embedding...");
-    let tensor = infer_clip_text_embeddings(ClipArgs {
-      prompt: & prompt,
-      uncond_prompt: &uncond_prompt,
-      tokenizer: None, // tokenizer
-      clip_weights: None,
-      clip2_weights: None, // clip2_weights
-      sd_version,
-      sd_config: &sd_config,
-      use_f16, // use_f16
-      device,
-      dtype,
-      use_guide_scale,
-      clip_json_path,
-      clip_weights_path,
-    })?;
-
-    prompt_cache.store_copy(&prompt, &tensor)?;
-    tensor
-  };
-
-  info!("Text embeddings shape: {:?}", text_embeddings.shape());
-
-  info!("Loading input image into tensor...");
-
-  let input_image = dynamic_image_to_tensor(image, &device, dtype)?;
-
-  info!("Reference image shape: {:?}", input_image.shape());
-
-  let vae_scale = get_vae_scale(sd_version);
-
-  let maybe_vae = model_cache.get_vae()?;
-
-  let vae = match maybe_vae {
-    Some(vae) => vae,
-    None => {
-      info!("Building VAE model from file {:?}...", vae_path.as_ref());
-      
-      let vae = sd_config.build_vae(vae_path, &device, dtype)?;
-
-      let vae = Arc::new(vae);
-
-      model_cache.set_vae(vae.clone())?;
-
-      vae
-    }
-  };
-
-  let maybe_unet = model_cache.get_unet()?;
-
-  let unet = match maybe_unet {
-    Some(unet) => unet,
-    None => {
-      info!("No unet found in cache; loading...");
-
-      let in_channels = match sd_version {
-        StableDiffusionVersion::XlInpaint | StableDiffusionVersion::V2Inpaint | StableDiffusionVersion::V1_5Inpaint => 9,
-        _ => 4,
-      };
-
-      // For LCM models, ensure that:
-      // 1. We're using the correct prediction type (epsilon)
-      // 2. The cross-attention dimension is correct (768 for Dreamshaper)
-      // 3. The in_channels matches the expected input (4 for standard, 9 for inpainting)
-      let unet_config = UNet2DConditionModelConfig {
-        // LCM specific config values
-        cross_attention_dim: 768,      // Dreamshaper v7 uses 768
-        time_cond_proj_dim: Some(256), // LCM models use 256-dimensional guidance embeddings
-        // Rest of configuration...
-        center_input_sample: false,
-        flip_sin_to_cos: true,
-        freq_shift: 0.0,
-        blocks: vec![
-          BlockConfig { out_channels: 320, use_cross_attn: Some(1), attention_head_dim: 8 },
-          BlockConfig { out_channels: 640, use_cross_attn: Some(1), attention_head_dim: 8 },
-          BlockConfig { out_channels: 1280, use_cross_attn: Some(1), attention_head_dim: 8 },
-          BlockConfig { out_channels: 1280, use_cross_attn: None, attention_head_dim: 8 }
-        ],
-        layers_per_block: 2,
-        downsample_padding: 1,
-        mid_block_scale_factor: 1.0,
-        norm_num_groups: 32,
-        norm_eps: 1e-5,
-        sliced_attention_size: None,
-        use_linear_projection: false,
-        time_embed_dim: Some(1280),
-        // time_embed_dim: Some(256),
-      };
-
-      // Load the model directly from the safetensors file
-      let vs_unet = unsafe { VarBuilder::from_mmaped_safetensors(&[unet_path], dtype, &device)? };
-
-      let unet = UNet2DConditionModel::new(vs_unet, in_channels, 4, use_flash_attn, unet_config)?;
-
-      let unet = Arc::new(unet);
-
-      model_cache.set_unet(unet.clone())?;
-
-      unet
-    },
-  };
-
-  let init_latent_dist: DiagonalGaussianDistribution = vae.encode(&input_image)?;
-
-  // Create LCM guidance scale embeddings
-  let embedding_dim = 1280;
-  let guidance_scale_embedding = scheduler.get_guidance_scale_embedding(guidance_scale, embedding_dim, &device, dtype)?;
-
-  // Generate latents from input image using the LCM approach
-  info!("Generating latents from input image...");
-  let init_latents = (init_latent_dist.sample()? * vae_scale)?;
-  info!("Initial latents shape: {:?}", init_latents.shape());
-
-  // Calculate timesteps for LCM with proper img2img handling
-  let timesteps = LCMScheduler::get_timesteps_for_steps(scheduler_steps, img2img_strength);
-  let t_start = timesteps[0]; // First denoising step
-
-  // Add noise at the appropriate timestep for img2img
-  info!("Adding noise to latents for t_start={}", t_start);
-  let noise = init_latents.randn_like(0f64, 1f64)?;
-  let latents = scheduler.add_noise(&init_latents, noise, t_start)?;
-
-  let mut latents = latents;
-  let timesteps: Vec<_> = scheduler.timesteps().iter().copied().collect();
-
-  for (timestep_index, &timestep) in timesteps.iter().enumerate() {
-    info!("Processing step {}/{} @ timestamp = {}", timestep_index + 1, timesteps.len(), timestep);
-
-    let latent_model_input = if use_guide_scale { Tensor::cat(&[&latents, &latents], 0)? } else { latents.clone() };
-
-    let latent_model_input = scheduler.scale_model_input(latent_model_input, timestep);
-
-    // Use the guidance scale embedding in the UNet inference with the proper method name
-    let mut noise_pred = match unet.forward_with_guidance(&latent_model_input, timestep as f64, &text_embeddings, Some(&guidance_scale_embedding)) {
-      Ok(pred) => pred,
-      Err(e) => {
-        info!("UNet inference failed with error: {}", e);
-        return Err(anyhow::anyhow!("UNet inference failed: {}", e));
-      },
-    };
-
-    if use_guide_scale {
-      let chunks = noise_pred.chunk(2, 0)?;
-      let (noise_pred_uncond, noise_pred_text) = (&chunks[0], &chunks[1]);
-      noise_pred = (noise_pred_uncond + (guidance_scale * (noise_pred_text - noise_pred_uncond)?)?)?;
-    }
-
-    // Apply the LCM scheduler step
-    latents = scheduler.step(&noise_pred, timestep, &latents, timestep_index, scheduler_steps)?;
-  }
-
-  info!("Diffusion process completed, decoding image...");
-  let image = vae.decode(&(latents / vae_scale)?)?;
-
-  info!("VAE decode completed");
-
-  info!("Scaling image back");
-  let image = ((image / 2.)? + 0.5)?;
-
-  info!("Normalized image values");
-  let image = (image.clamp(0f32, 1.)? * 255.)?;
-
-  info!("Convert to int8");
-  let image = image.to_dtype(DType::U8)?;
-
-  info!("Converted to 8-bit format");
-
-  let image = tensor_to_image_buffer(&image.i(0)?)?;
-
-  Ok(image)
-}
@@ -1,54 +0,0 @@
-use primitives::numerics::remap_range_f64::{remap_range_f64, Args};
-
-/// Our LCM implementation uses strength [13, 100]. Strength below 13 will take 8 steps, 
-/// so we need to limit ourselves to this range from an input range of [0, 100].
-pub fn remap_lcm_strength_range(value: f64) -> f64 {
-  remap_range_f64(Args {
-    old_min: 0.0,
-    old_max: 100.0,
-    new_min: 13.0,
-    new_max: 100.0,
-    value,
-  })
-}
-
-#[cfg(test)]
-mod test {
-  use crate::ml::stable_diffusion::remap_lcm_strength_range::remap_lcm_strength_range;
-  use assertor::{assert_that, FloatAssertion};
-
-  #[test]
-  fn old_max() {
-    assert_eq!(remap_lcm_strength_range(100.0), 100.0);
-  }
-  
-  #[test]
-  fn old_min() {
-    assert_eq!(remap_lcm_strength_range(0.0), 13.0);
-  }
-
-  #[test]
-  fn under_bounds() {
-    assert_eq!(remap_lcm_strength_range(-100.0), 13.0);
-  }
-  
-  #[test]
-  fn over_bounds() {
-    assert_eq!(remap_lcm_strength_range(200.0), 100.0);
-  }
-
-  #[test]
-  fn other_values() {
-    assert_that!(remap_lcm_strength_range(0.0)).with_abs_tol(0.001).is_approx_equal_to(13.000);
-    assert_that!(remap_lcm_strength_range(5.0)).with_abs_tol(0.001).is_approx_equal_to(17.350);
-    assert_that!(remap_lcm_strength_range(11.0)).with_abs_tol(0.001).is_approx_equal_to(22.570);
-    assert_that!(remap_lcm_strength_range(13.0)).with_abs_tol(0.001).is_approx_equal_to(24.310);
-    assert_that!(remap_lcm_strength_range(14.0)).with_abs_tol(0.001).is_approx_equal_to(25.180);
-    assert_that!(remap_lcm_strength_range(20.0)).with_abs_tol(0.001).is_approx_equal_to(30.400);
-    assert_that!(remap_lcm_strength_range(25.0)).with_abs_tol(0.001).is_approx_equal_to(34.750);
-    assert_that!(remap_lcm_strength_range(50.0)).with_abs_tol(0.001).is_approx_equal_to(56.500);
-    assert_that!(remap_lcm_strength_range(75.0)).with_abs_tol(0.001).is_approx_equal_to(78.250);
-    assert_that!(remap_lcm_strength_range(99.0)).with_abs_tol(0.001).is_approx_equal_to(99.130);
-    assert_that!(remap_lcm_strength_range(100.0)).with_abs_tol(0.001).is_approx_equal_to(100.000);
-  }
-}
@@ -1,3 +0,0 @@
-mod weight_descriptor_builder;
-pub mod weight_descriptor;
-pub mod weights;
@@ -1,45 +0,0 @@
-
-// TODO(bt,2025-03-13): When it's time for dynamic, non-const allocation, refer to
-//  http::header::name::HeaderName: https://github.com/hyperium/http/blob/master/src/header/name.rs
-//  as a pattern that will support both compile-time constants and dynamically allocated String-based variants.
-
-#[derive(Clone, Copy)]
-pub enum WeightFunction {
-  ImageSegmentation,
-  TextEncoder,
-  TextTokenizer,
-  Unet,
-  Vae,
-}
-
-pub struct WeightDescriptor {
-  /// Name of the weight, eg. "Lykon Dreamshaper 7"
-  pub name: &'static str,
-
-  /// Name of the file on the filesystem, eg. lykon_dreamshaper_7_text_encoder.fp16.safetensors
-  pub filename: &'static str,
-
-  /// URL of the file in the R2 bucket,
-  /// eg. https://pub-bc5e2bc0cdee4bb5ae8fca9d641ca0d6.r2.dev/lykon_dreamshaper_7_text_encoder.fp16.safetensors
-  pub r2_download_url: &'static str,
-
-  pub function: WeightFunction,
-}
-
-impl WeightFunction {
-  pub fn as_descriptive_str(&self) -> &'static str {
-    match self {
-      WeightFunction::ImageSegmentation => "image segmentation",
-      WeightFunction::TextEncoder => "text encoder",
-      WeightFunction::TextTokenizer => "text tokenizer",
-      WeightFunction::Unet => "UNET",
-      WeightFunction::Vae => "VAE",
-    }
-  }
-}
-
-impl WeightDescriptor {
-  pub fn to_descriptive_name(&self) -> String {
-    format!("{} ({})", self.name, self.function.as_descriptive_str())
-  }
-}
@@ -1,17 +0,0 @@
-pub (super) const R2_BUCKET_URL : &str = "https://pub-bc5e2bc0cdee4bb5ae8fca9d641ca0d6.r2.dev/";
-
-// TODO(bt,2025-03-13): Try to use keyword args from the call site.
-// NB: We're using a macro because we can't use a builder pattern and then concat string literals
-//  and variables (even if they're known to be &'static) and keep allocations static.
-macro_rules! weight {
-    ($name: literal, $file_name:literal, $weight_function:expr) => {
-      $crate::ml::weights_registry::weight_descriptor::WeightDescriptor {
-        name: $name,
-        filename: $file_name,
-        r2_download_url: const_format::concatcp!(crate::ml::weights_registry::weight_descriptor_builder::R2_BUCKET_URL, $file_name),
-        function: $weight_function,
-      }
-    };
-}
-
-pub(crate) use weight;
@@ -1,65 +0,0 @@
-use crate::ml::weights_registry::weight_descriptor::{WeightDescriptor, WeightFunction};
-use crate::ml::weights_registry::weight_descriptor_builder::weight;
-
-pub const CLIP_JSON : WeightDescriptor = weight!(
-  "CLIP",
-  "clip_vit_bigg_14_lion2b_39b_b160k.tokenizer.json",
-  WeightFunction::TextTokenizer
-);
-
-/// For LCM-based image generation
-pub const LYKON_DEAMSHAPER_7_TEXT_ENCODER_FP16 : WeightDescriptor = weight!(
-  "Lykon Dreamshaper 7",
-  "lykon_dreamshaper_7_text_encoder.fp16.safetensors",
-  WeightFunction::TextEncoder
-);
-
-/// For LCM-based image generation
-pub const LYKON_DEAMSHAPER_7_VAE : WeightDescriptor = weight!(
-  "Lykon Dreamshaper 7",
-  "lykon_dreamshaper_7_vae.safetensors",
-  WeightFunction::Vae
-);
-
-/// For LCM-based image generation
-pub const SIMIANLUO_LCM_DREAMSHAPER_V7_UNET : WeightDescriptor = weight!(
-  "SimianLuo LCM Dreamshaper v7",
-  "simianluo_lcm_dreamshaper_v7_unet.safetensors",
-  WeightFunction::Unet
-);
-
-/// Image segmentation and background removal
-/// dis_medium.onnx
-pub const DIS_MEDIUM_ONNX : WeightDescriptor = weight!(
-  "Dichotomous Image Segmentation Medium",
-  "dis_medium.onnx",
-  WeightFunction::ImageSegmentation
-);
-
-#[deprecated]
-pub const SDXL_TURBO_CLIP_TEXT_ENCODER : WeightDescriptor = weight!(
-  "SDXL Turbo CLIP",
-  "clip_text_encoder.safetensors",
-  WeightFunction::TextEncoder
-);
-
-#[deprecated]
-pub const SDXL_TURBO_CLIP_TEXT_ENCODER_2 : WeightDescriptor = weight!(
-  "SDXL Turbo CLIP #2",
-  "clip_text_encoder_2.safetensors",
-  WeightFunction::TextEncoder
-);
-
-#[deprecated]
-pub const SDXL_TURBO_VAE : WeightDescriptor = weight!(
-  "SDXL Turbo",
-  "diffusion_pytorch_model.vae.safetensors",
-  WeightFunction::Vae
-);
-
-#[deprecated]
-pub const SDXL_TURBO_UNET : WeightDescriptor = weight!(
-  "SDXL Turbo",
-  "diffusion_pytorch_model.unet.safetensors",
-  WeightFunction::Unet
-);
@@ -1,4 +0,0 @@
-# Don't restart the dev server on Rust changes
-crates/
-Cargo.toml
-Cargo.lock
@@ -7,7 +7,6 @@ license = ""
 repository = ""
 edition = "2021"
 rust-version = "1.77.2"
-publish = false

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

@@ -23,44 +22,37 @@ tauri-build = { version = "2.0.4", features = [] }
 # Internal
 filesys = { path = "../../lib/files/filesys" }
 memory_caching = { path = "../../lib/caching/memory_caching" }
-primitives = { path = "../../lib/data/primitives" }

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

 # External
-#openssl = { version = "0.10", features = ["vendored"] }
 anyhow = "1.0"
-base64 = "0.22.1"
 bytes = "1.10"
-const_format = "0.2.34"
-directories = "6.0.0"
-dirs = "6.0.0"
-futures = "0.3"
-half = { version = "2.3.1", features = ["num-traits", "use-intrinsics", "rand_distr"] }
+base64 = "0.22.1"
+candle-core = { path = "../../vendor/candle/candle-core", features = ["cuda"] }
+candle-nn = { path = "../../vendor/candle/candle-nn", features = ["cuda"] }
+candle-transformers = { path = "../../vendor/candle/candle-transformers", features = ["cuda"] }
+hf-hub = "0.3.2"
 image = "0.25.5"
+serde_yaml = "0.9"
 log = "0.4"
 once_cell = "1.20.3"
-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 }
+expanduser = "1.2.2"
+rand = "0.8"
+reqwest = "0.12.12"
 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"
-tokio = { version = "1.42", features = ["rt", "rt-multi-thread", "fs"] }
-tokio-util = { version = "0.7", features = ["codec"] }
+tokenizers = "0.15.2"

-# tracing-subscriber = { version = "0.3", features = [ "env-filter", "fmt" ] }
+[features]
+default = ["cuda"]
+cuda = ["candle-core/cuda", "candle-nn/cuda", "candle-transformers/cuda", "candle-transformers/flash-attn", "candle-core/cudnn"]

 [dev-dependencies]
 libloading = "0.8.6"
-serial_test = "3.2.0"
-assertor = "0.0.3"

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

 #[tauri::command]
 pub async fn download_models(
-  _app_data_root: State<'_, AppDataRoot>,
+  app_data_root: State<'_, AppDataRoot>,
 ) -> Result<String, String> {
-
-  // NB: This endpoint has been removed for now as we now download models in the app lifecycle.
-
+  
+  download_all_models(&app_data_root).await
+    .map_err(|err| {
+      "error downloading models".to_string()
+    })?;
+  
  Ok(("downloaded".to_string()))
 }
@@ -1,23 +1,24 @@
+use crate::ml::downloads::download_all_models::download_all_models;
+use crate::ml::model_cache::ModelCache;
+use crate::ml::prompt_cache::PromptCache;
+use crate::ml::stable_diffusion::lcm_pipeline::{lcm_pipeline, Args};
 use crate::state::app_config::AppConfig;
-use crate::state::app_dir::AppDataRoot;
-use crate::utils::image::decode_base64_image::decode_base64_image;
-use crate::utils::image::encode_rgb_image_base64_png::encode_rgb_image_base64_png;
+use base64::{engine::general_purpose::STANDARD as BASE64_STANDARD, Engine};
+use bytes::BytesMut;
 use image::imageops::FilterType;
-use image::{DynamicImage, RgbImage};
+use image::{DynamicImage, ImageFormat, ImageReader};
 use log::{error, info};
-use ml_models::ml::model_cache::ModelCache;
-use ml_models::ml::prompt_cache::PromptCache;
-use ml_models::ml::stable_diffusion::lcm_pipeline::{lcm_pipeline, Args};
-use ml_models::ml::weights_registry::weights::{CLIP_JSON, LYKON_DEAMSHAPER_7_TEXT_ENCODER_FP16, LYKON_DEAMSHAPER_7_VAE, SDXL_TURBO_CLIP_TEXT_ENCODER, SIMIANLUO_LCM_DREAMSHAPER_V7_UNET};
+use std::io::Cursor;
 use std::path::PathBuf;
 use tauri::{AppHandle, State};
+use crate::state::app_dir::AppDataRoot;

 const PROMPT_FILENAME : &str = "prompt.txt";
-const PROMPT: &str = "shiba inu, cute dog, detailed, walking in a wooded forest, photorealistic, 8k";
+const PROMPT: &str = "A beautiful landscape with mountains and a lake";

 const NEGATIVE_PROMPT: &str = "bad quality, bad faces, poor quality, blurry faces, watermark";

-const RANDOM_SEED: u32 = 42;
+// TODO: Mark async https://github.com/tauri-apps/tauri/discussions/7737


 /// This handler takes an image (as a base64 encoded string) and a prompt and returns
@@ -26,24 +27,28 @@ const RANDOM_SEED: u32 = 42;
 pub async fn infer_image(
  image: &str,
  prompt: Option<String>,
-  strength: Option<f64>,
+  strength: Option<u8>,
  model_config: State<'_, AppConfig>,
  model_cache: State<'_, ModelCache>,
  prompt_cache: State<'_, PromptCache>,
  app_data_root: State<'_, AppDataRoot>,
  app: AppHandle,
 ) -> Result<String, String> {
-  info!("infer_image endpoint called.");

  let prompt = get_prompt_or_fallback(prompt);

-  info!("Strength: {:?}; Prompt: {}", strength, prompt);
+  info!("Prompt: {}", prompt);
+  info!("Strength: {:?}", strength);

-  let image = decode_base64_image(image)
+  let image = hydrate_base64_image(image)
    .map_err(|err| format!("Couldn't hydrate image from base64: {}", err))?;
-  
-  // TODO(bt,2025-02-17): Running out of vram with full image buffer size
-  let image = image.resize(512, 512, FilterType::CatmullRom);
+
+  // TODO(bt): Move this to another endpoint
+  download_all_models(&app_data_root).await
+    .map_err(|err| {
+      error!("couldn't download models: {:?}", err);
+      "Couldn't download models".to_string()
+    })?;

  let result = do_infer_image(
    &prompt, 
@@ -52,64 +57,54 @@ pub async fn infer_image(
    &model_config, 
    &model_cache, 
    prompt_cache, 
+    app, 
    &app_data_root
  ).await;
  
-  let image = match result {
-    Ok(image) => image,
-    Err(err) => {
-      error!("There was an error generating the image: {:?}", err);
-      return Err(format!("There was an error generating the image: {}", err));
-    }
-  };
-
-  let bytes = encode_rgb_image_base64_png(image)
-    .map_err(|err| format!("failure to encode image: {:?}", err))?;
-
-  info!("Inference successful; image converted to base64, serving back to browser...");
-
-  Ok(bytes)
+  if let Err(err) = result.as_deref() {
+    error!("There was an error: {:?}", err);
+  }
+  
+  result
 }

 async fn do_infer_image(
  prompt: &str,
  image: DynamicImage,
-  strength: Option<f64>,
+  strength: Option<u8>,
  config: &AppConfig,
  model_cache: &ModelCache,
  prompt_cache: State<'_, PromptCache>,
+  app: AppHandle,
  app_data_root: &AppDataRoot,
-) -> Result<RgbImage, String> {
-  
-  let weights_dir = app_data_root.weights_dir();
-  let vae_path = weights_dir.weight_path(&LYKON_DEAMSHAPER_7_VAE);
-  let unet_path = weights_dir.weight_path(&SIMIANLUO_LCM_DREAMSHAPER_V7_UNET);
-  let clip_json_path = weights_dir.weight_path(&CLIP_JSON);
-  //let clip_weights_path= weights_dir.weight_path(&LYKON_DEAMSHAPER_7_TEXT_ENCODER_FP16);
-  let clip_weights_path= weights_dir.weight_path(&SDXL_TURBO_CLIP_TEXT_ENCODER);
+) -> Result<String, String> {

  let args = Args {
    image: &image,
    prompt: prompt.to_string(),
    uncond_prompt: NEGATIVE_PROMPT.to_string(),
    model_cache,
+    configs: config,
    prompt_cache: &prompt_cache,
-    img2img_strength: strength,
+    i2i_strength: strength,
    cfg_scale: config.cfg_scale,
-    use_flash_attn: true,
-    model_config: &config.model_config,
-    maybe_seed: None,
-    scheduler_steps: config.scheduler_steps,
-    vae_path: &vae_path,
-    unet_path: &unet_path,
-    clip_json_path: &clip_json_path,
-    clip_weights_path: &clip_weights_path,
+    app: &app,
+    app_data_root,
  };

-  let image = lcm_pipeline(args)
-    .map_err(|err| format!("failure to encode image: {:?}", err))?;
+  match lcm_pipeline(args) {
+    Ok(image) => {
+      let mut bytes = Vec::with_capacity(1024*1024);
+      
+      image.write_to(&mut Cursor::new(&mut bytes), ImageFormat::Png)
+        .map_err(|err| format!("failure to encode image: {:?}", err))?;

-  Ok(image)
+      let bytes = BASE64_STANDARD.encode(bytes);
+
+      Ok(bytes)
+    },
+    Err(e) => Err(format!("Failed to generate image: {}", e)),
+  }
 }

 fn get_prompt_or_fallback(user_prompt: Option<String>) -> String {
@@ -133,3 +128,16 @@ fn get_prompt_or_fallback(user_prompt: Option<String>) -> String {
    .trim()
    .to_string()
 }
+
+fn hydrate_base64_image(base64_image: &str) -> anyhow::Result<DynamicImage> {
+  let bytes = BASE64_STANDARD.decode(base64_image)?;
+
+  let image = ImageReader::new(Cursor::new(bytes))
+    .with_guessed_format()?
+    .decode()?;
+
+  // TODO(bt,2025-02-17): Running out of vram with full image buffer size
+  let image = image.resize(512, 512, FilterType::CatmullRom);
+
+  Ok(image)
+}
@@ -1,6 +1,4 @@
 pub mod download_models;
 pub mod flip_image;
-pub mod realtime_image_endpoint;
-pub mod remove_background_endpoint;
+pub mod image_endpoint;
 pub mod test_counter;
-pub mod text_to_image_endpoint;
@@ -1,37 +0,0 @@
-//! Adapted from https://github.com/dnanhkhoa/rust-background-removal
-
-use crate::state::app_dir::AppDataRoot;
-use crate::utils::image::decode_base64_image::decode_base64_image;
-use crate::utils::image::encode_dynamic_image_base64_png::encode_dynamic_image_base64_png;
-use image::{imageops, DynamicImage};
-use log::{error, info};
-use ml_models::ml::background_removal::onnx_session::onnx_session;
-use ml_models::ml::background_removal::remove_image_background::remove_image_background;
-use ml_models::ml::model_cache::ModelCache;
-use ml_models::ml::weights_registry::weights::{DIS_MEDIUM_ONNX, SIMIANLUO_LCM_DREAMSHAPER_V7_UNET};
-use tauri::{AppHandle, State};
-
-/// This handler removes the background from an image.
-#[tauri::command]
-pub async fn remove_background(
-  image: &str,
-  model_cache: State<'_, ModelCache>,
-  app_data_root: State<'_, AppDataRoot>,
-  app: AppHandle,
-) -> Result<String, String> {
-  info!("remove_background endpoint called.");
-
-  let image = decode_base64_image(image)
-    .map_err(|err| format!("Couldn't hydrate image from base64: {}", err))?;
-
-  let model_path = app_data_root.weights_dir().weight_path(&DIS_MEDIUM_ONNX);
-  
-  let image = remove_image_background(model_path, image)
-    .await
-    .map_err(|err| format!("Couldn't remove image from image: {}", err))?;
-
-  let image = encode_dynamic_image_base64_png(image)
-    .map_err(|err| format!("failure to encode image: {:?}", err))?;
-
-  Ok(image)
-}
@@ -1,110 +0,0 @@
-use crate::state::app_config::AppConfig;
-use crate::state::app_dir::AppDataRoot;
-use crate::utils::image::decode_base64_image::decode_base64_image;
-use crate::utils::image::encode_rgb_image_base64_png::encode_rgb_image_base64_png;
-use image::imageops::FilterType;
-use image::{DynamicImage, ImageReader, RgbImage};
-use log::{error, info};
-use ml_models::ml::model_cache::ModelCache;
-use ml_models::ml::prompt_cache::PromptCache;
-use ml_models::ml::stable_diffusion::lcm_pipeline::{lcm_pipeline, Args};
-use once_cell::sync::Lazy;
-use std::io::Cursor;
-use std::path::PathBuf;
-use tauri::{AppHandle, State};
-use ml_models::ml::weights_registry::weights::{CLIP_JSON, LYKON_DEAMSHAPER_7_TEXT_ENCODER_FP16, LYKON_DEAMSHAPER_7_VAE, SDXL_TURBO_CLIP_TEXT_ENCODER, SIMIANLUO_LCM_DREAMSHAPER_V7_UNET};
-
-const RANDOM_SEED: u32 = 42;
-
-const STRENGTH: f64 = 1.0;
-const PNG_BYTES : &[u8] = include_bytes!("../../binary_includes/1024.png");
-
-// NB: We're just hacking a temporary "text to image" test endpoint leveraging the existing image to image modality.
-static IMAGE : Lazy<DynamicImage> = Lazy::new(||{
-  ImageReader::new(Cursor::new(PNG_BYTES))
-    .with_guessed_format()
-    .expect("failed to detect png format")
-    .decode()
-    .expect("failed to decode image")
-});
-
-#[tauri::command]
-pub async fn text_to_image(
-  prompt: String,
-  model_config: State<'_, AppConfig>,
-  model_cache: State<'_, ModelCache>,
-  prompt_cache: State<'_, PromptCache>,
-  app_data_root: State<'_, AppDataRoot>,
-  app: AppHandle,
-) -> Result<String, String> {
-  info!("text_to_image endpoint called.");
-
-  let image = IMAGE.resize(512, 512, FilterType::CatmullRom);
-
-  let result = do_infer_image(
-    &prompt, 
-    image, 
-    Some(STRENGTH), 
-    &model_config, 
-    &model_cache, 
-    prompt_cache, 
-    app, 
-    &app_data_root
-  ).await;
-  
-  let image = match result {
-    Ok(image) => image,
-    Err(err) => {
-      error!("There was an error generating the image: {:?}", err);
-      return Err(format!("There was an error generating the image: {}", err));
-    }
-  };
-
-  let bytes = encode_rgb_image_base64_png(image)
-    .map_err(|err| format!("failure to encode image: {:?}", err))?;
-
-  info!("Inference successful; image converted to base64, serving back to browser...");
-
-  Ok(bytes)
-}
-
-async fn do_infer_image(
-  prompt: &str,
-  image: DynamicImage,
-  strength: Option<f64>,
-  config: &AppConfig,
-  model_cache: &ModelCache,
-  prompt_cache: State<'_, PromptCache>,
-  app: AppHandle,
-  app_data_root: &AppDataRoot,
-) -> Result<RgbImage, String> {
-  
-  let weights_dir = app_data_root.weights_dir();
-  let vae_path = weights_dir.weight_path(&LYKON_DEAMSHAPER_7_VAE);
-  let unet_path = weights_dir.weight_path(&SIMIANLUO_LCM_DREAMSHAPER_V7_UNET);
-  let clip_json_path = weights_dir.weight_path(&CLIP_JSON);
-  let clip_weights_path= weights_dir.weight_path(&SDXL_TURBO_CLIP_TEXT_ENCODER);
-
-  let args = Args {
-    image: &image,
-    prompt: prompt.to_string(),
-    uncond_prompt: "".to_string(),
-    model_cache,
-    prompt_cache: &prompt_cache,
-    img2img_strength: strength,
-    cfg_scale: config.cfg_scale,
-    use_flash_attn: true,
-    model_config: &config.model_config,
-    maybe_seed: None,
-    scheduler_steps: config.scheduler_steps,
-    vae_path: &vae_path,
-    unet_path: &unet_path,
-    clip_json_path: &clip_json_path,
-    clip_weights_path: &clip_weights_path,
-  };
-
-  let image = lcm_pipeline(args)
-    .map_err(|err| format!("failure to encode image: {:?}", err))?;
-
-  Ok(image)
-}
@@ -1,11 +1,9 @@
 use serde::Serialize;

+
 #[derive(Clone, Copy, Serialize)]
 #[serde(rename_all = "snake_case")]
 pub enum NotificationModelType {
-  ImageSegmentation,
-  TextEncoder,
-  TextTokenizer,
  Unet,
  Vae,
 }
@@ -17,12 +15,6 @@ 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,
@@ -1,20 +1,16 @@
 pub mod endpoints;
 pub mod events;
+pub mod ml;
 pub mod state;
 pub mod threads;
-pub mod transfer;
-pub mod utils;

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

@@ -45,9 +41,6 @@ pub fn run() {
      .targets(vec![Target::new(TargetKind::Stdout)])
      .build())
    .setup(|app| {
-      // TODO(bt): This is broken on windows
-      // log_environment_details();
-
      //if cfg!(debug_assertions) {
      //  app.handle().plugin(
      //    tauri_plugin_log::Builder::default()
@@ -66,11 +59,9 @@ pub fn run() {
    .manage(model_cache)
    .manage(app_data_root)
    .invoke_handler(tauri::generate_handler![
-      download_models,
+      test_counter, 
      infer_image,
-      remove_background,
-      test_counter,
-      text_to_image,
+      download_models,
    ])
    .run(tauri::generate_context!())
    .expect("error while running tauri application");
@@ -0,0 +1,15 @@
+use crate::ml::downloads::download_model_registry_file::download_model_registry_file;
+use crate::ml::model_type::ModelType;
+use crate::state::app_dir::AppDataRoot;
+
+pub async fn download_all_models(app_data_root: &AppDataRoot) -> anyhow::Result<()> {
+  let weights_dir = app_data_root.weights_dir();
+  
+  download_model_registry_file(ModelType::ClipJson, weights_dir).await?;
+  download_model_registry_file(ModelType::SdxlTurboUnet, weights_dir).await?;
+  download_model_registry_file(ModelType::SdxlTurboVae, weights_dir).await?;
+  download_model_registry_file(ModelType::SdxlTurboClipEncoder, weights_dir).await?;
+  download_model_registry_file(ModelType::SdxlTurboClipEncoder2, weights_dir).await?;
+  
+  Ok(())
+}
@@ -0,0 +1,30 @@
+use crate::ml::model_type::ModelType;
+use crate::state::app_dir::AppWeightsDir;
+use log::info;
+use std::fs::File;
+use std::path::{Path, PathBuf};
+
+pub async fn download_model_registry_file(
+  model_type: ModelType,
+  weights_dir: &AppWeightsDir,
+) -> anyhow::Result<PathBuf> {
+  
+  let path = weights_dir.model_path(&model_type);
+
+  if path.exists() {
+    return Ok(path);
+  }
+
+  info!("downloading model: {:?} to {:?}", model_type, path);
+
+  let body = reqwest::get(model_type.get_download_url())
+    .await?
+    .bytes()
+    .await?;
+  
+  let _file = File::create(&path)?;
+  
+  std::fs::write(&path, body.as_ref())?;
+  
+  Ok(path)
+}
@@ -0,0 +1,2 @@
+pub mod download_model_registry_file;
+pub mod download_all_models;
@@ -1,8 +1,8 @@
-pub mod background_removal;
 pub mod image;
 pub mod model_cache;
-pub mod model_config;
 pub mod model_file;
+pub mod models;
 pub mod prompt_cache;
 pub mod stable_diffusion;
-pub mod weights_registry;
+pub mod model_type;
+pub mod downloads;
@@ -1,14 +1,15 @@
+use crate::ml::models::unet_model::UNetModel;
 use anyhow::anyhow;
-use candle_transformers::models::stable_diffusion::unet_2d::UNet2DConditionModel;
 use candle_transformers::models::stable_diffusion::vae::AutoEncoderKL;
 use std::sync::{Arc, RwLock};
+
 // TODO: This data structure is gross. Generalize the locking and loading semantics for reuse.

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

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

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

-  pub fn get_unet(&self) -> anyhow::Result<Option<Arc<UNet2DConditionModel>>> {
+  pub fn get_unet(&self) -> anyhow::Result<Option<Arc<UNetModel>>> {
    match self.unet2.read() {
      Err(err) => Err(anyhow!("{}", err)),
      Ok(ref lock) => {
@@ -0,0 +1,83 @@
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+use candle_transformers::models::stable_diffusion;
+
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+pub enum ModelFile {
+  Tokenizer,
+  Tokenizer2,
+  Clip,
+  Clip2,
+  Unet,
+  Vae,
+}
+
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+pub enum StableDiffusionVersion {
+  V1_5,
+  V1_5Inpaint,
+  V2_1,
+  V2Inpaint,
+  Xl,
+  XlInpaint,
+  Turbo,
+}
+
+impl StableDiffusionVersion {
+  pub fn repo(&self) -> &'static str {
+    match self {
+      Self::XlInpaint => "diffusers/stable-diffusion-xl-1.0-inpainting-0.1",
+      Self::Xl => "stabilityai/stable-diffusion-xl-base-1.0",
+      Self::V2Inpaint => "stabilityai/stable-diffusion-2-inpainting",
+      Self::V2_1 => "stabilityai/stable-diffusion-2-1",
+      Self::V1_5 => "runwayml/stable-diffusion-v1-5",
+      Self::V1_5Inpaint => "stable-diffusion-v1-5/stable-diffusion-inpainting",
+      Self::Turbo => "stabilityai/sdxl-turbo",
+    }
+  }
+}
+
+impl ModelFile {
+  pub fn get(
+    &self,
+    filename: Option<String>,
+    version: StableDiffusionVersion,
+    use_f16: bool,
+  ) -> anyhow::Result<std::path::PathBuf> {
+    use hf_hub::api::sync::Api;
+    match filename {
+      Some(filename) => Ok(std::path::PathBuf::from(filename)),
+      None => {
+        let (repo, path) = match self {
+          Self::Tokenizer => {
+            let tokenizer_repo = match version {
+              StableDiffusionVersion::V1_5
+              | StableDiffusionVersion::V2_1
+              | StableDiffusionVersion::V1_5Inpaint
+              | StableDiffusionVersion::V2Inpaint => "openai/clip-vit-base-patch32",
+              StableDiffusionVersion::Xl
+              | StableDiffusionVersion::XlInpaint
+              | StableDiffusionVersion::Turbo => {
+                "openai/clip-vit-large-patch14"
+              }
+            };
+            (tokenizer_repo, "tokenizer.json")
+          }
+          Self::Tokenizer2 => {
+            ("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", "tokenizer.json")
+          }
+          Self::Clip => (version.repo(), "text_encoder/model.safetensors"),
+          Self::Clip2 => (version.repo(), "text_encoder_2/model.safetensors"),
+          Self::Unet => (version.repo(), "unet/diffusion_pytorch_model.safetensors"),
+          Self::Vae => (version.repo(), "vae/diffusion_pytorch_model.safetensors"),
+        };
+        let filename = Api::new()?.model(repo.to_string()).get(path)?;
+        Ok(filename)
+      }
+    }
+  }
+}
+
@@ -0,0 +1,47 @@
+use std::path::PathBuf;
+use crate::state::app_dir::AppWeightsDir;
+
+#[derive(Copy, Clone, Debug)]
+pub enum ModelType {
+  ClipJson,
+  SdxlTurboUnet,
+  SdxlTurboVae,
+  SdxlTurboClipEncoder,
+  SdxlTurboClipEncoder2,
+}
+
+impl ModelType {
+  pub fn get_hf_id(&self) -> Option<&'static str> {
+    match self {
+      Self::ClipJson => Some("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k"),
+      Self::SdxlTurboUnet 
+      | Self::SdxlTurboVae
+      | Self::SdxlTurboClipEncoder
+      | Self::SdxlTurboClipEncoder2 => Some("stabilityai/sdxl-turbo"),
+    }
+  }
+
+  pub fn get_download_url(&self) -> &'static str {
+    match self {
+      Self::ClipJson => "https://pub-bc5e2bc0cdee4bb5ae8fca9d641ca0d6.r2.dev/tokenizer.json",
+      Self::SdxlTurboUnet => "https://pub-bc5e2bc0cdee4bb5ae8fca9d641ca0d6.r2.dev/diffusion_pytorch_model.unet.safetensors",
+      Self::SdxlTurboVae => "https://pub-bc5e2bc0cdee4bb5ae8fca9d641ca0d6.r2.dev/diffusion_pytorch_model.vae.safetensors",
+      Self::SdxlTurboClipEncoder => "https://pub-bc5e2bc0cdee4bb5ae8fca9d641ca0d6.r2.dev/clip_text_encoder.safetensors",
+      Self::SdxlTurboClipEncoder2 => "https://pub-bc5e2bc0cdee4bb5ae8fca9d641ca0d6.r2.dev/clip_text_encoder_2.safetensors",
+    }
+  }
+
+  pub fn get_filename(&self) -> &'static str {
+    match self {
+      Self::ClipJson => "clip_vit_bigg_14_lion2b_39b_b160k.tokenizer.json",
+      Self::SdxlTurboUnet => "diffusion_pytorch_model.unet.safetensors",
+      Self::SdxlTurboVae => "diffusion_pytorch_model.vae.safetensors",
+      Self::SdxlTurboClipEncoder => "clip_text_encoder.safetensors",
+      Self::SdxlTurboClipEncoder2 => "clip_text_encoder_2.safetensors",
+    }
+  }
+  
+  pub fn get_path(&self, weights_dir: &AppWeightsDir) -> PathBuf {
+    weights_dir.model_path(self)
+  }
+}
@@ -0,0 +1,130 @@
+use crate::ml::model_file::StableDiffusionVersion;
+use anyhow::anyhow;
+use candle_core::{DType, Device, Tensor};
+use candle_transformers::models::stable_diffusion::vae::{AutoEncoderKL, DiagonalGaussianDistribution};
+use candle_transformers::models::stable_diffusion::StableDiffusionConfig;
+use hf_hub::api::sync::Api;
+use std::sync::{Arc, RwLock};
+
+pub struct LazyLoadVaeModel {
+  lazy_model: Arc<RwLock<Option<AutoEncoderKL>>>,
+  model_configs: VaeConfigs,
+}
+
+// TODO: Maybe we pass these via a central config read at startup, passed as an Arc<ConfigT>
+pub struct VaeConfigs {
+  sd_config: StableDiffusionConfig,
+  sd_version: StableDiffusionVersion,
+  device: Device,
+  dtype: DType,
+  hf_api: Api,
+}
+
+impl LazyLoadVaeModel {
+  pub fn new(
+    sd_config: &StableDiffusionConfig,
+    sd_version: &StableDiffusionVersion,
+    hf_api: &Api,
+    device: &Device,
+    dtype: DType,
+  ) -> anyhow::Result<Self> {
+    println!("building vae model... (1)");
+    
+    Ok(Self {
+      lazy_model: Arc::new(RwLock::new(None)),
+      model_configs: VaeConfigs {
+        sd_config: sd_config.clone(),
+        sd_version: sd_version.clone(),
+        device: device.clone(),
+        hf_api: hf_api.clone(),
+        dtype
+      }
+    })
+  }
+
+  pub fn encode(&self, xs: &Tensor) -> anyhow::Result<DiagonalGaussianDistribution> {
+    println!("vae encode...");
+    match self.try_encode(xs) {
+      Err(err) => Err(err),
+      Ok(Some(result)) => Ok(result),
+      Ok(None) => {
+        // Model wasn't previously loaded
+        self.load_model()?;
+        self.try_encode(xs)?
+          .ok_or(anyhow!("model was not previously loaded"))
+      }
+    }
+  }
+  
+  pub fn decode(&self, xs: &Tensor) -> anyhow::Result<Tensor> {
+    println!("vae decode...");
+    match self.try_decode(xs) {
+      Err(err) => Err(err),
+      Ok(Some(result)) => Ok(result),
+      Ok(None) => {
+        // Model wasn't previously loaded
+        self.load_model()?;
+        self.try_decode(xs)?
+          .ok_or(anyhow!("model was not previously loaded"))
+      }
+    }
+  }
+
+  fn try_encode(&self, xs: &Tensor) -> anyhow::Result<Option<DiagonalGaussianDistribution>> {
+    match self.lazy_model.read() {
+      Err(err) => Err(anyhow!("lock error: {:?}", err)),
+      Ok(model) => {
+        match &*model {
+          None => Ok(None), // Model is not yet loaded
+          Some(model) => {
+            Ok(Some(model.encode(xs)?))
+          }
+        }
+      }
+    }
+  }
+
+  fn try_decode(&self, xs: &Tensor) -> anyhow::Result<Option<Tensor>> {
+    match self.lazy_model.read() {
+      Err(err) => Err(anyhow!("lock error: {:?}", err)),
+      Ok(model) => {
+        match &*model {
+          None => Ok(None), // Model is not yet loaded
+          Some(model) => {
+            Ok(Some(model.decode(xs)?))
+          }
+        }
+      }
+    }
+  }
+
+  fn load_model(&self) -> anyhow::Result<()> {
+    println!("load vae");
+    match self.lazy_model.write() {
+      Err(err) => return Err(anyhow!("lock error: {:?}", err)),
+      Ok(mut maybe_model) => {
+        match &*maybe_model {
+          Some(_model) => {} // Already loaded; fall through
+          None => {
+            let repo = self.model_configs.sd_version.repo();
+
+            println!("Building VAE model from : {:?} ... (3)", repo);
+            
+            let vae_file = self.model_configs.hf_api.model(repo.to_string())
+              .get("vae/diffusion_pytorch_model.safetensors")?;
+
+            println!("Building VAE model from file {:?}...", &vae_file);
+            let vae = self.model_configs
+              .sd_config
+              .build_vae(vae_file, &self.model_configs.device, self.model_configs.dtype)?;
+
+            println!("Built VAE...");
+            *maybe_model = Some(vae);
+          }
+        }
+      }
+    }
+    Ok(())
+  }
+ 
+}
@@ -0,0 +1,2 @@
+pub mod unet_model;
+pub mod lazy_load_vae_model;
@@ -0,0 +1,26 @@
+use candle_core::{DType, Device, Tensor};
+use candle_transformers::models::stable_diffusion::unet_2d::UNet2DConditionModel;
+use candle_transformers::models::stable_diffusion::StableDiffusionConfig;
+use std::path::Path;
+
+pub struct UNetModel {
+  model: UNet2DConditionModel,
+}
+
+impl UNetModel {
+  pub fn new<P: AsRef<Path>>(sd_config: &StableDiffusionConfig, unet_weights_file: P, device: &Device, dtype: DType) -> anyhow::Result<Self> {
+    println!("building unet model... (1)");
+
+    let model = sd_config.build_unet(unet_weights_file, &device, 4, true, dtype)?;
+
+    Ok(Self { model })
+  }
+
+  pub fn inference(&self, latent_model_input: &Tensor, timestep: f64, text_embeddings: &Tensor) -> anyhow::Result<Tensor> {
+    Ok(self.model.forward(&latent_model_input, timestep, &text_embeddings)?)
+  }
+
+  pub fn forward_with_guidance(&self, latent_model_input: &Tensor, timestep: f64, text_embeddings: &Tensor, guidance_scale_embedding: &Tensor) -> anyhow::Result<Tensor> {
+    Ok(self.model.forward_with_guidance(&latent_model_input, timestep, &text_embeddings, Some(guidance_scale_embedding))?)
+  }
+}
@@ -0,0 +1,170 @@
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+use std::path::PathBuf;
+use candle_transformers::models::stable_diffusion;
+
+use crate::ml::model_file::{ModelFile, StableDiffusionVersion};
+use anyhow::Error as E;
+use candle_core::{DType, Device, Module, Tensor, D};
+use log::info;
+use tokenizers::Tokenizer;
+use crate::ml::model_type::ModelType;
+use crate::state::app_dir::AppWeightsDir;
+
+#[allow(clippy::too_many_arguments)]
+pub fn infer_clip_text_embeddings(
+  prompt: &str,
+  uncond_prompt: &str,
+  tokenizer: Option<String>,
+  clip_weights: Option<String>,
+  clip2_weights: Option<String>,
+  sd_version: StableDiffusionVersion,
+  sd_config: &stable_diffusion::StableDiffusionConfig,
+  use_f16: bool,
+  device: &Device,
+  dtype: DType,
+  use_guide_scale: bool,
+  weights_dir: &AppWeightsDir,
+) -> anyhow::Result<Tensor> {
+  info!("Preparing text embeddings...");
+
+  let which = match sd_version {
+    StableDiffusionVersion::Xl
+    | StableDiffusionVersion::XlInpaint
+    | StableDiffusionVersion::Turbo => vec![true, false],
+    _ => vec![true],
+  };
+
+  let text_embeddings = which
+    .iter()
+    .map(|first| {
+      do_infer_clip_text_embeddings(
+        &prompt,
+        &uncond_prompt,
+        None, // tokenizer
+        None, // clip_weights
+        None, // clip2_weights
+        sd_version,
+        &sd_config,
+        false, // use_f16
+        &device,
+        dtype,
+        use_guide_scale,
+        *first,
+        weights_dir,
+      )
+    })
+    .collect::<anyhow::Result<Vec<_>>>()?;
+
+  let text_embeddings = Tensor::cat(&text_embeddings, D::Minus1)?;
+  
+  Ok(text_embeddings)
+}
+
+#[allow(clippy::too_many_arguments)]
+fn do_infer_clip_text_embeddings(
+    prompt: &str,
+    uncond_prompt: &str,
+    tokenizer: Option<String>,
+    clip_weights: Option<String>,
+    clip2_weights: Option<String>,
+    sd_version: StableDiffusionVersion,
+    sd_config: &stable_diffusion::StableDiffusionConfig,
+    use_f16: bool,
+    device: &Device,
+    dtype: DType,
+    use_guide_scale: bool,
+    first: bool,
+    weights_dir: &AppWeightsDir,
+  ) -> anyhow::Result<Tensor> {
+  
+  let tokenizer_file = if first {
+    info!("ModelFile::Tokenizer");
+    ModelFile::Tokenizer
+  } else {
+    info!("ModelFile::Tokenizer2");
+    ModelFile::Tokenizer2
+  };
+  
+  info!("Loading clip from download");
+  //let tokenizer = tokenizer_file.get(tokenizer, sd_version, use_f16)?;
+  let tokenizer = weights_dir.model_path(&ModelType::ClipJson);
+  
+  info!("Tokenizer path: {:?}", tokenizer);
+  
+  let tokenizer = Tokenizer::from_file(tokenizer).map_err(E::msg)?;
+  
+  let pad_id = match &sd_config.clip.pad_with {
+    Some(padding) => *tokenizer.get_vocab(true).get(padding.as_str()).unwrap(),
+    None => *tokenizer.get_vocab(true).get("<|endoftext|>").unwrap(),
+  };
+
+  println!("Running with prompt \"{prompt}\".");
+  let mut tokens = tokenizer
+    .encode(prompt, true)
+    .map_err(E::msg)?
+    .get_ids()
+    .to_vec();
+  if tokens.len() > sd_config.clip.max_position_embeddings {
+    anyhow::bail!(
+            "the prompt is too long, {} > max-tokens ({})",
+            tokens.len(),
+            sd_config.clip.max_position_embeddings
+        )
+  }
+  while tokens.len() < sd_config.clip.max_position_embeddings {
+    tokens.push(pad_id)
+  }
+  let tokens = Tensor::new(tokens.as_slice(), device)?.unsqueeze(0)?;
+
+  println!("Building the Clip transformer.");
+  let clip_weights_file = if first {
+    ModelFile::Clip
+  } else {
+    ModelFile::Clip2
+  };
+  let clip_weights = if first {
+    //clip_weights_file.get(clip_weights, sd_version, use_f16)?
+    weights_dir.model_path(&ModelType::SdxlTurboClipEncoder)
+  } else {
+    //clip_weights_file.get(clip2_weights, sd_version, use_f16)?
+    weights_dir.model_path(&ModelType::SdxlTurboClipEncoder2)
+  };
+  let clip_config = if first {
+    &sd_config.clip
+  } else {
+    sd_config.clip2.as_ref().unwrap()
+  };
+  let text_model = stable_diffusion::build_clip_transformer(clip_config, clip_weights, device, DType::F32)?;
+
+  let text_embeddings = text_model.forward(&tokens)?;
+
+  let text_embeddings = if use_guide_scale {
+    let mut uncond_tokens = tokenizer
+      .encode(uncond_prompt, true)
+      .map_err(E::msg)?
+      .get_ids()
+      .to_vec();
+    if uncond_tokens.len() > sd_config.clip.max_position_embeddings {
+      anyhow::bail!(
+                "the negative prompt is too long, {} > max-tokens ({})",
+                uncond_tokens.len(),
+                sd_config.clip.max_position_embeddings
+            )
+    }
+    while uncond_tokens.len() < sd_config.clip.max_position_embeddings {
+      uncond_tokens.push(pad_id)
+    }
+
+    let uncond_tokens = Tensor::new(uncond_tokens.as_slice(), device)?.unsqueeze(0)?;
+    let uncond_embeddings = text_model.forward(&uncond_tokens)?;
+
+    Tensor::cat(&[uncond_embeddings, text_embeddings], 0)?.to_dtype(dtype)?
+  } else {
+    text_embeddings.to_dtype(dtype)?
+  };
+  Ok(text_embeddings)
+}
@@ -0,0 +1,293 @@
+use std::sync::{Arc, RwLock, RwLockReadGuard, RwLockWriteGuard};
+
+use crate::events::notification_event::{NotificationModelType, NotificationEvent};
+use crate::ml::image::dynamic_image_to_tensor::dynamic_image_to_tensor;
+use crate::ml::image::tensor_to_image_buffer::{tensor_to_image_buffer, RgbImage};
+use crate::ml::model_cache::ModelCache;
+use crate::ml::model_file::StableDiffusionVersion;
+use crate::ml::model_type::ModelType;
+use crate::ml::models::unet_model::UNetModel;
+use crate::ml::prompt_cache::PromptCache;
+use crate::ml::stable_diffusion::get_vae_scale::get_vae_scale;
+use crate::ml::stable_diffusion::infer_clip_text_embeddings::infer_clip_text_embeddings;
+use crate::state::app_config::AppConfig;
+use anyhow::{anyhow, Error as E, Result};
+use candle_core::{DType, IndexOp, Tensor, D};
+use candle_transformers::models::stable_diffusion::vae::{AutoEncoderKL, DiagonalGaussianDistribution};
+use candle_transformers::models::stable_diffusion::lcm::LCMScheduler;
+use image::DynamicImage;
+use log::info;
+use rand::Rng;
+use tauri::{AppHandle, Emitter};
+use crate::state::app_dir::AppDataRoot;
+
+pub struct Args<'a> {
+    pub image: &'a DynamicImage,
+    pub prompt: String,
+    pub uncond_prompt: String,
+    pub cfg_scale: Option<f64>,
+    pub i2i_strength: Option<u8>,
+    pub configs: &'a AppConfig,
+    pub model_cache: &'a ModelCache,
+    pub prompt_cache: &'a PromptCache,
+    pub app_data_root: &'a AppDataRoot,
+    pub app: &'a AppHandle,
+}
+
+pub fn lcm_pipeline(args: Args<'_>) -> Result<RgbImage> {
+    let Args { 
+        prompt, 
+        uncond_prompt, 
+        cfg_scale, 
+        i2i_strength, 
+        configs, 
+        model_cache, 
+        prompt_cache, 
+        app, 
+        image,  
+        app_data_root,
+    } = args;
+
+    let weights_dir = app_data_root.weights_dir();
+    
+    println!("Starting image generation with the following configuration:");
+    println!("  Model: {:?}", configs.sd_version);
+    println!("  Prompt: {}", prompt);
+    println!("  Steps: {}", configs.scheduler_steps);
+    println!("  Device: {:?}", configs.device);
+
+    println!("Model dimensions: {}x{}", configs.sd_config.width, configs.sd_config.height);
+
+    // Use LCM Scheduler instead of Euler Ancestral for better speed and quality
+    let mut scheduler = LCMScheduler::new(
+        configs.scheduler_steps,
+        candle_transformers::models::stable_diffusion::lcm::LCMSchedulerConfig::default(),
+    )?;
+
+    let seed = configs.seed.unwrap_or_else(|| rand::thread_rng().gen());
+    configs.device.set_seed(seed)?;
+
+    info!("Using seed: {}", seed);
+
+    let guidance_scale = match cfg_scale {
+        Some(guidance_scale) => guidance_scale,
+        None => match configs.sd_version {
+            StableDiffusionVersion::V1_5
+            | StableDiffusionVersion::V2_1
+            | StableDiffusionVersion::Xl => 7.5,
+            StableDiffusionVersion::Turbo => 0.,
+            _ => 0., // NB: Not sure what the other model families should use, so sticking with "0"
+        },
+    };
+
+    let use_guide_scale = guidance_scale > 1.0;
+
+    info!("Using guide scale: {}", use_guide_scale);
+
+    info!("Checking if prompt is cached");
+    let maybe_cached = prompt_cache.get_copy(&prompt)?;
+
+    let mut text_embeddings = if let Some(tensor) = maybe_cached {
+        tensor
+    } else {
+        
+        info!("Prompt is NOT cached! Calculating embedding...");
+        let tensor = infer_clip_text_embeddings(
+            &prompt,
+            &uncond_prompt,
+            None, // tokenizer
+            None, // clip_weights
+            None, // clip2_weights
+            configs.sd_version,
+            &configs.sd_config,
+            false, // use_f16
+            &configs.device,
+            configs.dtype,
+            use_guide_scale,
+            weights_dir,
+        )?;
+        prompt_cache.store_copy(&prompt, &tensor)?;
+        tensor
+    };
+
+    println!("Text embeddings shape: {:?}", text_embeddings.shape());
+
+    println!("Loading input image into tensor...");
+
+    let input_image = dynamic_image_to_tensor(
+        image,
+        &configs.device,
+        configs.dtype)?;
+
+    println!("Reference image shape: {:?}", input_image.shape());
+
+    let vae_scale = get_vae_scale(configs.sd_version);
+
+    let maybe_vae = model_cache.get_vae()?;
+    
+    let vae = match maybe_vae {
+        Some(vae) => vae,
+        None => {
+            info!("No vae found in cache; loading...");
+            
+            let mut notify_download_complete = false;
+            //if !vae_file.exists() {
+            if true {
+                notify_download_complete = true;
+                app.emit("notification", NotificationEvent::ModelDownloadStarted {
+                    model_name: "sdxl-turbo-vae",
+                    model_type: NotificationModelType::Vae,
+                })?;
+            }
+
+            let vae_file = weights_dir.model_path(&ModelType::SdxlTurboVae);
+            
+            let vae = configs
+              .sd_config
+              .build_vae(vae_file, &configs.device, configs.dtype)?;
+
+            let vae = Arc::new(vae);
+            
+            model_cache.set_vae(vae.clone())?;
+            
+            if notify_download_complete {
+                app.emit("notification", NotificationEvent::ModelDownloadComplete {
+                    model_name: "sdxl-turbo-vae",
+                    model_type: NotificationModelType::Vae,
+                })?;
+            }
+            
+            vae
+        }
+    };
+
+    let maybe_unet = model_cache.get_unet()?;
+
+    let unet = match maybe_unet {
+        Some(unet) => unet,
+        None => {
+            info!("No unet found in cache; loading...");
+
+            let mut notify_download_complete = false;
+            //if !unet_file.exists() {
+            if true {
+                notify_download_complete = true;
+                app.emit("notification", NotificationEvent::ModelDownloadStarted {
+                    model_name: "sdxl-turbo-unet",
+                    model_type: NotificationModelType::Unet,
+                })?;
+            }
+            
+            let unet_file = weights_dir.model_path(&ModelType::SdxlTurboUnet);
+
+            let unet = UNetModel::new(&configs.sd_config, unet_file, &configs.device, configs.dtype)
+              .map_err(|err| anyhow!("error initializing unet model: {:?}", err))?;
+            
+            let unet = Arc::new(unet);
+
+            model_cache.set_unet(unet.clone())?;
+
+            if notify_download_complete {
+                app.emit("notification", NotificationEvent::ModelDownloadComplete {
+                    model_name: "sdxl-turbo-unet",
+                    model_type: NotificationModelType::Unet,
+                })?;
+            }
+
+            unet
+        }
+    };
+
+    let init_latent_dist : DiagonalGaussianDistribution = vae.encode(&input_image)?;
+
+    // Parse and calculate the img2img strength
+    let img2img_strength = match i2i_strength {
+        None => 0.75f64,
+        Some(strength) => (strength as f64) / 100.0f64,
+    };
+
+    // Create LCM guidance scale embeddings
+    let embedding_dim = 256;
+    let guidance_scale_embedding = scheduler.get_guidance_scale_embedding(
+        guidance_scale, 
+        embedding_dim, 
+        &configs.device, 
+        configs.dtype
+    )?;
+
+    // Generate latents from input image using the LCM approach
+    println!("Generating latents from input image...");
+    let init_latents = (init_latent_dist.sample()? * vae_scale)?;
+    println!("Initial latents shape: {:?}", init_latents.shape());
+
+    // Calculate timesteps for LCM with proper img2img handling
+    let timesteps = LCMScheduler::get_timesteps_for_steps(configs.scheduler_steps, img2img_strength);
+    let t_start = timesteps[0]; // First denoising step
+
+    // Add noise at the appropriate timestep for img2img
+    println!("Adding noise to latents for t_start={}", t_start);
+    let noise = init_latents.randn_like(0f64, 1f64)?;
+    let latents = scheduler.add_noise(&init_latents, noise, t_start)?;
+
+    println!("Latents initialized successfully");
+    println!("Initial noise shape: {:?}", latents.shape());
+
+    println!("Starting diffusion process...");
+    
+    let mut latents = latents;
+    let timesteps: Vec<_> = scheduler.timesteps().iter().copied().collect();
+
+    for (timestep_index, &timestep) in timesteps.iter().enumerate() {
+        println!("Processing step {}/{} @ timestamp = {}", timestep_index + 1, timesteps.len(), timestep);
+
+        let latent_model_input = if use_guide_scale {
+            Tensor::cat(&[&latents, &latents], 0)?
+        } else {
+            latents.clone()
+        };
+
+        let latent_model_input = scheduler.scale_model_input(latent_model_input, timestep);
+
+        // Use the guidance scale embedding in the UNet inference with the proper method name
+        let mut noise_pred = match unet.forward_with_guidance(
+            &latent_model_input, 
+            timestep as f64, 
+            &text_embeddings,
+            &guidance_scale_embedding) {
+            Ok(pred) => pred,
+            Err(e) => {
+                println!("UNet inference failed with error: {}", e);
+                return Err(anyhow::anyhow!("UNet inference failed: {}", e));
+            },
+        };
+
+        if use_guide_scale {
+            let chunks = noise_pred.chunk(2, 0)?;
+            let (noise_pred_uncond, noise_pred_text) = (&chunks[0], &chunks[1]);
+            noise_pred = (noise_pred_uncond + (guidance_scale * (noise_pred_text - noise_pred_uncond)?)?)?;
+        }
+
+        // Apply the LCM scheduler step
+        latents = scheduler.step(&noise_pred, timestep, &latents, timestep_index, configs.scheduler_steps)?;
+    }
+
+    println!("Diffusion process completed, decoding image...");
+    let image = vae.decode(&(latents / vae_scale)?)?;
+    
+    println!("VAE decode completed");
+
+    println!("Scaling image back");
+    let image = ((image / 2.)? + 0.5)?;
+    
+    println!("Normalized image values");
+    let image = (image.clamp(0f32, 1.)? * 255.)?;
+
+    println!("Convert to int8");
+    let image = image.to_dtype(DType::U8)?;
+    
+    println!("Converted to 8-bit format");
+
+    let image = tensor_to_image_buffer(&image.i(0)?)?;
+
+    Ok(image)
+}
@@ -1,5 +1,4 @@
 pub mod get_vae_scale;
 pub mod infer_clip_text_embeddings;
 pub mod stable_diffusion_pipeline;
-pub mod lcm_pipeline;
-mod remap_lcm_strength_range;
+pub mod lcm_pipeline;
@@ -1,86 +1,76 @@
-use std::path::Path;
 use std::sync::{Arc, RwLock, RwLockReadGuard, RwLockWriteGuard};

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

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

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

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

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

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

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

    let guidance_scale = match cfg_scale {
        Some(guidance_scale) => guidance_scale,
-        None => match sd_version {
+        None => match configs.sd_version {
            StableDiffusionVersion::V1_5
            | StableDiffusionVersion::V2_1
            | StableDiffusionVersion::Xl => 7.5,
@@ -101,21 +91,20 @@ pub fn stable_diffusion_pipeline<P1, P2, P3, P4>(args: Args<'_, P1, P2, P3, P4>)
    } else {
        
        info!("Prompt is NOT cached! Calculating embedding...");
-        let tensor = infer_clip_text_embeddings(ClipArgs {
-            prompt: &prompt,
-            uncond_prompt: &uncond_prompt,
-            tokenizer: None, // tokenizer
-            clip_weights: None, // clip_weights
-            clip2_weights: None, // clip2_weights
-            sd_version: sd_version,
-            sd_config: &sd_config,
-            use_f16: false, // use_f16
-            device: device,
-            dtype: dtype,
+        let tensor = infer_clip_text_embeddings(
+            &prompt,
+            &uncond_prompt,
+            None, // tokenizer
+            None, // clip_weights
+            None, // clip2_weights
+            configs.sd_version,
+            &configs.sd_config,
+            false, // use_f16
+            &configs.device,
+            configs.dtype,
            use_guide_scale,
-            clip_json_path: clip_json_path.as_ref(),
-            clip_weights_path: clip_weights_path.as_ref(),
-        })?;
+            weights_dir,
+        )?;
        prompt_cache.store_copy(&prompt, &tensor)?;
        tensor
    };
@@ -124,11 +113,14 @@ pub fn stable_diffusion_pipeline<P1, P2, P3, P4>(args: Args<'_, P1, P2, P3, P4>)

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

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

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

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

    let maybe_vae = model_cache.get_vae()?;
    
@@ -137,14 +129,33 @@ pub fn stable_diffusion_pipeline<P1, P2, P3, P4>(args: Args<'_, P1, P2, P3, P4>)
        None => {
            info!("No vae found in cache; loading...");
            
-            let vae_file = sdxl_turbo_vae_path.as_ref();
+            let mut notify_download_complete = false;
+            //if !vae_file.exists() {
+            if true {
+                notify_download_complete = true;
+                app.emit("notification", NotificationEvent::ModelDownloadStarted {
+                    model_name: "sdxl-turbo-vae",
+                    model_type: NotificationModelType::Vae,
+                })?;
+            }
            
-            let vae = sd_config.build_vae(vae_file, &device, dtype)?;
+            let vae_file = weights_dir.model_path(&ModelType::SdxlTurboVae);
+            
+            let vae = configs
+              .sd_config
+              .build_vae(vae_file, &configs.device, configs.dtype)?;

            let vae = Arc::new(vae);
            
            model_cache.set_vae(vae.clone())?;
-
+            
+            if notify_download_complete {
+                app.emit("notification", NotificationEvent::ModelDownloadComplete {
+                    model_name: "sdxl-turbo-vae",
+                    model_type: NotificationModelType::Vae,
+                })?;
+            }
+            
            vae
        }
    };
@@ -156,15 +167,32 @@ pub fn stable_diffusion_pipeline<P1, P2, P3, P4>(args: Args<'_, P1, P2, P3, P4>)
        None => {
            info!("No unet found in cache; loading...");

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

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

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

+            if notify_download_complete {
+                app.emit("notification", NotificationEvent::ModelDownloadComplete {
+                    model_name: "sdxl-turbo-unet",
+                    model_type: NotificationModelType::Unet,
+                })?;
+            }
+
            unet
        }
    };
@@ -188,9 +216,9 @@ pub fn stable_diffusion_pipeline<P1, P2, P3, P4>(args: Args<'_, P1, P2, P3, P4>)
    };

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

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

@@ -229,7 +257,7 @@ pub fn stable_diffusion_pipeline<P1, P2, P3, P4>(args: Args<'_, P1, P2, P3, P4>)

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

-        let mut noise_pred = match unet.forward(&latent_model_input, timestep as f64, &text_embeddings) {
+        let mut noise_pred = match unet.inference(&latent_model_input, timestep as f64, &text_embeddings) {
            Ok(pred) => pred,
            Err(e) => {
                println!("UNet inference failed with error: {}", e);
@@ -1,14 +1,35 @@
+use crate::ml::model_file::StableDiffusionVersion;
 use crate::state::app_dir::AppDataRoot;
 use crate::state::os_platform::OsPlatform;
 use crate::state::yaml_config::YamlConfig;
-use ml_models::ml::model_config::ModelConfig;
+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 = 1024;
-const DEFAULT_SD_IMAGE_HEIGHT: usize = 1024;
+const DEFAULT_SD_IMAGE_WIDTH: usize = 512;
+const DEFAULT_SD_IMAGE_HEIGHT: usize = 512;

+const DEFAULT_SD_NUMERIC_DATATYPE: DType = DType::F32;
+const SD_VERSION: StableDiffusionVersion = StableDiffusionVersion::Turbo;
+
+const DEFAULT_DATA_DIR_UNIX : &str = "~/artcraft";

 /// A central place to configure app-wide details.
 pub struct AppConfig {
+  /// We only support one device type currently, and we can't multiplex 
+  /// over several devices yet.
+  pub device: Device,
+  
+  /// The core numeric type to use for models.
+  pub dtype: DType,
+  
+  pub sd_version: StableDiffusionVersion,
+  pub sd_config: StableDiffusionConfig,
+
  pub image_height: usize,
  pub image_width: usize,
  pub scheduler_steps: usize,
@@ -18,10 +39,11 @@ pub struct AppConfig {

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

 impl AppConfig {
@@ -31,32 +53,56 @@ impl AppConfig {
    
    println!("Configs: {:?}", yaml_configs);
    
+    let device = Device::new_cuda(0)
+      .unwrap_or_else(|e| {
+          info!("CUDA not available ({}), falling back to CPU", e);
+          Device::Cpu
+        });
+    
+    let sd_config = match SD_VERSION {
+      StableDiffusionVersion::Turbo => {
+        StableDiffusionConfig::sdxl_turbo(None, Some(DEFAULT_SD_IMAGE_HEIGHT), Some(DEFAULT_SD_IMAGE_WIDTH))
+      }
+      _ => {
+        StableDiffusionConfig::v2_1(None, Some(DEFAULT_SD_IMAGE_HEIGHT), Some(DEFAULT_SD_IMAGE_WIDTH))
+      }
+    };
+    
+    let hf_api = Api::new()?;
+    
    let os_platform = OsPlatform::get();

-    let default_data_root = match os_platform {
+    let default_data_path = match os_platform {
      OsPlatform::Linux => yaml_configs.linux_default_data_path.as_deref(),
      OsPlatform::MacOs => yaml_configs.mac_default_data_path.as_deref(),
      OsPlatform::Windows => yaml_configs.windows_default_data_path.as_deref(),
    };
    
-    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)?,
+    let data_root_path = match default_data_path {
+      Some(default_data_path) => default_data_path.to_string(),
+      None => match os_platform { 
+        OsPlatform::Linux | OsPlatform::MacOs => DEFAULT_DATA_DIR_UNIX.to_string(),
+        OsPlatform::Windows => ".".to_string(),
+      }
    };
+      
+    println!("Using data root: {}", data_root_path);

-    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,
+      sd_version: SD_VERSION,
+      sd_config,
      image_height: yaml_configs.image_height.unwrap_or(DEFAULT_SD_IMAGE_HEIGHT),
      image_width: yaml_configs.image_width.unwrap_or(DEFAULT_SD_IMAGE_WIDTH),
      scheduler_steps: yaml_configs.scheduler_steps.unwrap_or(4),
      scheduler_samples: yaml_configs.scheduler_samples.unwrap_or(15),
      seed: yaml_configs.seed,
      cfg_scale: yaml_configs.cfg_scale,
+      hf_api,
      app_data_root,
-      model_config: ModelConfig::init()?,
    })
  }
 }
@@ -1,25 +1,17 @@
-use crate::state::expanduser::expanduser;
 use crate::state::os_platform::OsPlatform;
-use anyhow::anyhow;
-use directories::UserDirs;
-use ml_models::ml::weights_registry::weight_descriptor::WeightDescriptor;
+use expanduser::expanduser;
 use std::path::{Path, PathBuf};
-use tempdir::TempDir;
-use tempfile::{Builder, NamedTempFile};
+use crate::ml::model_type::ModelType;

-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)]
@@ -32,17 +24,7 @@ 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();
    
@@ -69,13 +51,11 @@ 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,
    })
  }
  
@@ -86,11 +66,7 @@ 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
  }
@@ -140,48 +116,8 @@ impl AppWeightsDir {
  pub fn path(&self) -> &Path {
    &self.path
  }
-
-  pub fn weight_path(&self, descriptor: &WeightDescriptor) -> PathBuf {
-    self.path.join(descriptor.filename)
+  
+  pub fn model_path(&self, model_registry: &ModelType) -> PathBuf {
+    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))
-}
@@ -1,73 +0,0 @@
-use once_cell::sync::Lazy;
-use std::io;
-use std::path::{PathBuf, MAIN_SEPARATOR};
-
-static PREFIX: Lazy<String> = Lazy::new(|| format!("~{}", MAIN_SEPARATOR));
-
-/// The "expanduser" crate doesn't compile on Windows, so we replace its functionality slightly
-pub fn expanduser<P: AsRef<str>>(path: P) -> io::Result<PathBuf> {
-
-   Ok(match path.as_ref() {
-       // matches an exact "~"
-       s if s == "~" => {
-           home_dir()?
-       },
-       // matches paths that start with `~/`
-       s if s.starts_with(&*PREFIX) => {
-           let home = home_dir()?;
-           home.join(&s[2..])
-       },
-       // // matches paths that start with `~` but not `~/`, might be a `~username/` path
-       // s if s.starts_with("~") => {
-       //     let mut parts = s[1..].splitn(2, MAIN_SEPARATOR);
-       //     let user = parts.next()
-       //         .ok_or_else(|| io::Error::new(io::ErrorKind::Other, "malformed path"))?;
-       //     let user = Passwd::from_name(&user)
-       //         .map_err(|_| io::Error::new(io::ErrorKind::Other, "error searching for user"))?
-       //         .ok_or_else(|| io::Error::new(io::ErrorKind::Other, format!("user '{}', does not exist", &user)))?;
-       //     if let Some(ref path) = parts.next() {
-       //         PathBuf::from(user.dir).join(&path)
-       //     } else {
-       //         PathBuf::from(user.dir)
-       //     }
-       // },
-       // nothing to expand, just make a PathBuf
-       s => PathBuf::from(s)
-   })
-}
-
-pub fn home_dir() -> io::Result<PathBuf> {
-    dirs::home_dir()
-        .ok_or_else(|| io::Error::new(io::ErrorKind::Other, "no home directory is set"))
-}
-
-#[cfg(test)]
-mod tests {
-    use crate::state::expanduser::expanduser;
-    use std::env;
-    use std::path::PathBuf;
-
-    #[test]
-    #[serial_test::serial]
-    fn test_success() {
-        let old_home = env::var("HOME").expect("no home dir set");
-        let new_home = "/home/foo";
-        env::set_var("HOME", new_home);
-        let path = expanduser("~/path/to/directory");
-        env::set_var("HOME", old_home);
-        assert_eq!(path.expect("io error"), PathBuf::from("/home/foo/path/to/directory"));
-    }
-
-    #[test]
-    #[serial_test::serial]
-    fn test_only_tilde() {
-        let old_home = env::var("HOME").expect("no home dir set");
-        let new_home = "/home/foo";
-        env::set_var("HOME", new_home);
-        let pathstr = "~";
-        let path = expanduser(pathstr);
-        env::set_var("HOME", old_home);
-        assert_eq!(path.expect("io error"), PathBuf::from("/home/foo"));
-    }
-
-}
@@ -1,5 +1,4 @@
 pub mod app_config;
 pub mod app_dir;
 pub mod os_platform;
-pub mod yaml_config;
-pub mod expanduser;
+pub mod yaml_config;
@@ -1,181 +1,11 @@
-use crate::events::notification_event::{NotificationEvent, NotificationModelType};
-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 ml_models::ml::weights_registry::weight_descriptor::WeightFunction;
-use ml_models::ml::weights_registry::weights::{CLIP_JSON, DIS_MEDIUM_ONNX, LYKON_DEAMSHAPER_7_TEXT_ENCODER_FP16, LYKON_DEAMSHAPER_7_VAE, SDXL_TURBO_CLIP_TEXT_ENCODER, SDXL_TURBO_CLIP_TEXT_ENCODER_2, SDXL_TURBO_UNET, SDXL_TURBO_VAE, SIMIANLUO_LCM_DREAMSHAPER_V7_UNET};
-use std::collections::VecDeque;
-use std::path::PathBuf;
-use std::sync::mpsc::Receiver;
-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>,
-}
+use crate::state::app_dir::AppDataRoot;

 pub async fn downloader_thread(app_data_root: AppDataRoot, app: AppHandle) -> ! {
-
-  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();
-
-  download_queue.push_back(CLIP_JSON);
-  download_queue.push_back(LYKON_DEAMSHAPER_7_TEXT_ENCODER_FP16);
-  download_queue.push_back(LYKON_DEAMSHAPER_7_VAE);
-  download_queue.push_back(SIMIANLUO_LCM_DREAMSHAPER_V7_UNET);
-  download_queue.push_back(SDXL_TURBO_CLIP_TEXT_ENCODER); // TODO(bt): Why is this still needed?
-  download_queue.push_back(DIS_MEDIUM_ONNX);
-  //download_queue.push_back(SDXL_TURBO_CLIP_TEXT_ENCODER_2); // TODO(bt): REMOVE
-  //download_queue.push_back(SDXL_TURBO_VAE); // TODO(bt): REMOVE
-  //download_queue.push_back(SDXL_TURBO_UNET); // TODO(bt): REMOVE
-
-  let mut handles = FuturesUnordered::new();
-
-  while let Some(model) = download_queue.pop_front() {
-    let url = model.r2_download_url.to_string();
-
-    let filename = app_data_root.weights_dir().weight_path(&model);
-
-    if filename.exists() {
-      info!("File already exists: {:?}", &filename);
-      continue;
-    }
-
-    let headers = None;
+  loop {
+    app.emit("testing", 1);
    
-    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 model_type = match model.function {
-      WeightFunction::TextEncoder => NotificationModelType::TextEncoder,
-      WeightFunction::TextTokenizer => NotificationModelType::TextTokenizer,
-      WeightFunction::Unet => NotificationModelType::Unet,
-      WeightFunction::Vae => NotificationModelType::Vae,
-      WeightFunction::ImageSegmentation => NotificationModelType::ImageSegmentation,
-    };
-
-    let result = app.emit(NOTIFICATION_CHANNEL_NAME, NotificationEvent::ModelDownloadStarted {
-      model_name: model.name,
-      model_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.to_descriptive_name(),
-          model_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;
+    println!(">>> Testing: {:?}", app_data_root.path());
+    std::thread::sleep(std::time::Duration::from_millis(500));
  }
 }
@@ -1,241 +0,0 @@
-//! 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(())
-}
@@ -1,49 +0,0 @@
-//! 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)
-  }
-}
@@ -1,4 +0,0 @@
-pub mod download;
-pub mod upload;
-pub mod util;
-pub mod error;
@@ -1,189 +0,0 @@
-//! 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)
-}
-
@@ -1,15 +0,0 @@
-//! 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)
-}
@@ -1,14 +0,0 @@
-use base64::prelude::BASE64_STANDARD;
-use base64::Engine;
-use image::{DynamicImage, ImageReader};
-use std::io::Cursor;
-
-pub fn decode_base64_image(base64_image: &str) -> anyhow::Result<DynamicImage> {
-  let bytes = BASE64_STANDARD.decode(base64_image)?;
-
-  let image = ImageReader::new(Cursor::new(bytes))
-    .with_guessed_format()?
-    .decode()?;
-
-  Ok(image)
-}
@@ -1,14 +0,0 @@
-use base64::prelude::BASE64_STANDARD;
-use base64::Engine;
-use image::{DynamicImage, ImageFormat};
-use std::io::Cursor;
-
-pub fn encode_dynamic_image_base64_png(image: DynamicImage) -> anyhow::Result<String> {
-  let mut bytes = Vec::with_capacity(1024*1024);
-
-  image.write_to(&mut Cursor::new(&mut bytes), ImageFormat::Png)?;
-
-  let bytes = BASE64_STANDARD.encode(bytes);
-
-  Ok(bytes)
-}
@@ -1,16 +0,0 @@
-use base64::prelude::BASE64_STANDARD;
-use base64::Engine;
-use image::{ImageFormat, RgbImage};
-use std::io::Cursor;
-
-pub fn encode_rgb_image_base64_png(
-  image: RgbImage
-) -> anyhow::Result<String> {
-  let mut bytes = Vec::with_capacity(1024*1024);
-
-  image.write_to(&mut Cursor::new(&mut bytes), ImageFormat::Png)?;
-
-  let bytes = BASE64_STANDARD.encode(bytes);
-
-  Ok(bytes)
-}
@@ -1,3 +0,0 @@
-pub mod decode_base64_image;
-pub mod encode_dynamic_image_base64_png;
-pub mod encode_rgb_image_base64_png;
@@ -1,11 +0,0 @@
-use log::info;
-//use nvidia_checker::{get_cuda_version, get_cudnn_version, get_kernel_version, get_nvidia_driver_version, get_os_version, get_tensorrt_version};
-
-pub fn log_environment_details() {
-  //info!("NVIDIA driver version: {}", get_nvidia_driver_version(false));
-  //info!("CUDA version: {}", get_cuda_version(false));
-  //info!("cuDNN version: {}", get_cudnn_version(false));
-  //info!("TensorRT version: {}", get_tensorrt_version(false));
-  //info!("OS version: {}", get_os_version(false));
-  //info!("Kernel version: {}", get_kernel_version(false));
-}
@@ -1,2 +0,0 @@
-pub mod image;
-pub mod log_environment_details;
@@ -13,8 +13,8 @@
    "windows": [
      {
        "title": "ArtCraft",
-        "width": 2400,
-        "height": 1300,
+        "width": 1200,
+        "height": 800,
        "resizable": true,
        "fullscreen": false
      }
@@ -32,4 +32,3 @@ my-workspace-hack = { version = "0.1", path = "../../../my-workspace-hack" }

 [dev-dependencies]
 testing = { path = "../../testing" }
-assertor = "0.0.3"
@@ -1,6 +1,5 @@
 pub mod i32_to_u32_zero_clamped;
 pub mod i64_to_u64_zero_clamped;
-pub mod remap_range_f64;
 pub mod u64_to_i32_saturating;
 pub mod u64_to_i64_saturating;
 pub mod u64_to_u32_saturating;
@@ -1,131 +0,0 @@
-
-pub struct Args {
-  pub old_min: f64,
-  pub old_max: f64,
-  pub new_min: f64,
-  pub new_max: f64,
-  
-  /// The value to remap to the new range.
-  pub value: f64,
-}
-
-pub fn remap_range_f64(args: Args) -> f64 {
-  let old_range = args.old_max - args.old_min;
-  let new_range = args.new_max -  args.new_min;
-
-  let new_value = (args.value -  args.old_min) * new_range / old_range + args.new_min;
-  
-  if new_value > args.new_max {
-    args.new_max
-  } else if new_value < args.new_min { 
-    args.new_min
-  } else {
-    new_value
-  }
-}
-
-#[cfg(test)]
-mod tests {
-  use crate::numerics::remap_range_f64::{remap_range_f64, Args};
-  use assertor::{assert_that, FloatAssertion};
-
-  #[test]
-  fn test_base_cases() {
-    assert_that!(remap_range_f64(Args {
-      old_min: 0.0,
-      old_max: 1.0,
-      new_min: 0.0,
-      new_max: 1.0,
-      value: 0.0,
-    })).with_abs_tol(0.01).is_approx_equal_to(0.0);
-    
-    assert_that!(remap_range_f64(Args {
-      old_min: 0.0,
-      old_max: 1.0,
-      new_min: 0.0,
-      new_max: 1.0,
-      value: 1.0,
-    })).with_abs_tol(0.01).is_approx_equal_to(1.0);
-  }
-
-  #[test]
-  fn test_maxima() {
-    assert_that!(remap_range_f64(Args {
-      old_min: 0.0,
-      old_max: 100.0,
-      new_min: 0.0,
-      new_max: 1.0,
-      value: 100.0,
-    })).with_abs_tol(0.01).is_approx_equal_to(1.0);
-
-    assert_that!(remap_range_f64(Args {
-      old_min: 0.0,
-      old_max: 1.0,
-      new_min: 0.0,
-      new_max: 50.0,
-      value: 1.0,
-    })).with_abs_tol(0.01).is_approx_equal_to(50.0);
-
-    assert_that!(remap_range_f64(Args {
-      old_min: 0.0,
-      old_max: 1.0,
-      new_min: -100.0,
-      new_max: 100.0,
-      value: 1.0,
-    })).with_abs_tol(0.01).is_approx_equal_to(100.0);
-  }
-
-  #[test]
-  fn test_minima() {
-    assert_that!(remap_range_f64(Args {
-      old_min: 0.0,
-      old_max: 100.0,
-      new_min: 0.0,
-      new_max: 1.0,
-      value: 0.0,
-    })).with_abs_tol(0.01).is_approx_equal_to(0.0);
-
-    assert_that!(remap_range_f64(Args {
-      old_min: 0.0,
-      old_max: 1.0,
-      new_min: 0.0,
-      new_max: 50.0,
-      value: 0.0,
-    })).with_abs_tol(0.01).is_approx_equal_to(0.0);
-
-    assert_that!(remap_range_f64(Args {
-      old_min: 0.0,
-      old_max: 1.0,
-      new_min: -100.0,
-      new_max: 100.0,
-      value: 0.0,
-    })).with_abs_tol(0.01).is_approx_equal_to(-100.0);
-  }
-
-  #[test]
-  fn test_other_values() {
-    assert_that!(remap_range_f64(Args {
-      old_min: 0.0,
-      old_max: 100.0,
-      new_min: 0.0,
-      new_max: 1.0,
-      value: 50.0,
-    })).with_abs_tol(0.01).is_approx_equal_to(0.50);
-
-    assert_that!(remap_range_f64(Args {
-      old_min: 0.0,
-      old_max: 1.0,
-      new_min: 0.0,
-      new_max: 50.0,
-      value: 0.10,
-    })).with_abs_tol(0.01).is_approx_equal_to(5.0);
-
-    assert_that!(remap_range_f64(Args {
-      old_min: 0.0,
-      old_max: 1.0,
-      new_min: -100.0,
-      new_max: 100.0,
-      value: 0.25,
-    })).with_abs_tol(0.01).is_approx_equal_to(-50.0);
-  }
-}
@@ -118,40 +118,24 @@ 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 ==========

-  // 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_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());

-  // 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")
+    plans.insert(PlanBuilder::new("fakeyou_pro")
        .plan_category(PlanCategory::Paid)
        .stripe_product_id("prod_MoLw8nA6eFxHzc")
        .stripe_price_id("price_1M4jEQEU5se17MeksNfA0EKm")
@@ -165,23 +149,7 @@ pub static PRODUCTION_PREMIUM_PLANS: Lazy<HashSet<Plan>> = Lazy::new(|| {
        .can_remove_visual_watermarks(true)
        .build());

-  // 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")
+    plans.insert(PlanBuilder::new("fakeyou_elite")
        .plan_category(PlanCategory::Paid)
        .stripe_product_id("prod_MoLxQmLA4R24fv")
        .stripe_price_id("price_1M4jFREU5se17Mekc8pQaSKB")
@@ -195,21 +163,6 @@ 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")
@@ -340,7 +293,7 @@ mod test {
    // making ourselves check twice when we make additions, removals, or other changes.
    #[test]
    fn test_number_of_plans_is_expected() {
-        assert_eq!(14, ALL_PLANS_BY_SLUG.len());
+        assert_eq!(11, ALL_PLANS_BY_SLUG.len());
    }

    #[test]
@@ -350,7 +303,7 @@ mod test {
            .filter(|plan| !plan.is_synthetic_plan()) // NB: Synthetic plans are production!
            .collect::<Vec<&Plan>>();

-        let expected = 7;
+        let expected = 4;

        assert_eq!(expected, production_plans.len());
        assert_eq!(expected, PRODUCTION_PREMIUM_PLANS.len());
@@ -393,9 +346,6 @@ 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());
@@ -416,9 +366,6 @@ 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());
@@ -623,7 +623,6 @@ fn run(args: Args) -> Result<()> {

    let mut scheduler = stable_diffusion::lcm::LCMScheduler::new(
        n_steps,
-        strength,
        stable_diffusion::lcm::LCMSchedulerConfig::default(),
    )?;

@@ -632,8 +631,7 @@ 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 = false;
+    let use_guide_scale = guidance_scale > 1.0;

    let which = match sd_version {
        StableDiffusionVersion::Xl
@@ -986,7 +984,7 @@ fn run(args: Args) -> Result<()> {
        println!("Starting LCM sampling");
        let start_time = std::time::Instant::now();

-        let embedding_dim = 1280;
+        let embedding_dim = 256;
        let guidance_scale_embedding = scheduler.get_guidance_scale_embedding(guidance_scale, embedding_dim, &device, dtype)?;

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

    Ok(())
 }
@@ -17,7 +17,6 @@ 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 = []
@@ -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, strength: f64, config: LCMSchedulerConfig) -> Result<Self> {
+    pub fn new(inference_steps: usize, config: LCMSchedulerConfig) -> Result<Self> {
        let betas = match config.beta_schedule {
            BetaSchedule::ScaledLinear => super::utils::linspace(
                config.beta_start.sqrt(),
@@ -84,6 +84,7 @@ 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;

@@ -334,7 +335,7 @@ impl LCMScheduler {

        let alpha_prod_t = self.alphas_cumprod[timestep];
        let alpha_prod_t_prev = if prev_timestep == 0 {
-            FINAL_ALPHA_CUMPROD
+            1.0
        } else {
            self.alphas_cumprod[prev_timestep]
        };
@@ -354,7 +355,7 @@ impl LCMScheduler {
            }
        };

-        let denoised = ((c_out * pred_original_sample )? + (c_skip * sample)?)?;
+        let denoised = ((pred_original_sample * c_out)? + (sample * c_skip)?)?;

        let pred_prev_sample = if step_index != self.timesteps.len() - 1 {
            println!("Adding noise at intermediate step {}", timestep);
@@ -469,7 +470,6 @@ 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 {
@@ -1,36 +1,35 @@
 import Konva from "konva";

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

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

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

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


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

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

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

    this.commandManager.createNode(imageNode);
  }
-
-  public addImageFromImageBitmap(imageBitmap: ImageBitmap) {
-    const imageNode = new ImageNode({
-      mediaLayerRef: this.mediaLayer,
-      canvasPosition: this.realTimeDrawEngine.captureCanvas.position(),
-      canvasSize: this.realTimeDrawEngine.captureCanvas.size(),
-      imageBitmap: imageBitmap,
-      selectionManagerRef: this.selectionManager,
-      loaded: async () => {
-        await this.realTimeDrawEngine.render();
-        this.setAppMode(AppModes.SELECT);
-      },
-    });
-
-    this.commandManager.createNode(imageNode);
-  }
-
  public addPaintNode(
    canvas: HTMLCanvasElement,
    lineBounds: {
@@ -78,20 +78,6 @@ export class CommandManager {
    this.undoStackManagerRef.executeCommand(command);
    this.renderEngineRef.render();
  }
-
-  deleteSpecificNodes(nodes: MediaNode[]) {
-    const command = new DeleteCommand({
-      nodes: nodes,
-      mediaLayerRef: this.mediaLayerRef,
-      nodesManagerRef: this.nodesManagerRef,
-      nodeTransformerRef: this.nodeTransformerRef,
-      selectionManagerRef: this.selectionManagerRef,
-      renderEngineRef: this.renderEngineRef,
-    });
-    this.undoStackManagerRef.executeCommand(command);
-    this.renderEngineRef.render();
-  }
-
  toggleLockNodes() {
    const nodes = this.selectionManagerRef.getSelectedNodes();
    const node = nodes.values().next().value;
@@ -12,7 +12,6 @@ interface ImageNodeContructor {
  canvasPosition: Position;
  canvasSize: Size;
  imageFile?: File;
-  imageBitmap?: ImageBitmap;
  mediaFileToken?: string;
  mediaFileUrl?: string;
  transform?: TransformationData;
@@ -35,7 +34,6 @@ export class ImageNode extends NetworkedNode {
    mediaLayerRef,
    selectionManagerRef,
    loaded,
-    imageBitmap,
  }: ImageNodeContructor) {
    // kNodes need to be created first to guaruntee
    // that it is not undefined in parent's context
@@ -75,14 +73,6 @@ export class ImageNode extends NetworkedNode {
      this.loadImageFromUrl(mediaFileUrl);
      return;
    }
-    if (imageBitmap) {
-      this.loadImageFromImageBitmap({
-        imageBitmap: imageBitmap,
-        maxSize: canvasSize,
-        refPosition: canvasPosition,
-      });
-      return;
-    }
    console.log("image node creation is fucked");
  }
  private loadImageFromFile({
@@ -146,40 +136,6 @@ export class ImageNode extends NetworkedNode {
    this.listenToBaseKNode();
  }

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

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

-  public hasNode(node: MediaNode): boolean {
-    return this.allNodes.has(node);
-  }
-
  /*************************
   *  For Debugging use
   */
@@ -252,6 +252,8 @@ export class SelectionManager {
        },
      ),
    );
+    this.updateContextComponents();
+    this.showContextComponents();
  }

  public updateNodeTransformer() {
@@ -46,29 +46,29 @@ export function getImageNodeButtonStates(
        return buttonStates;
      }

-      switch (buttonName) {
-        case ButtonNames.AI_STYLIZE:
-        case ButtonNames.SEGMENTATION:
-        case ButtonNames.DOWNLOAD:
-        case ButtonNames.CHROMA:
-        case ButtonNames.COLOR:
-          // hidden buttons
-          buttonStates[buttonName] = {
-            disabled: true,
-            hidden: true,
-            active: false,
-          };
-          return buttonStates;
-
-        default:
-          // all other buttons
-          buttonStates[buttonName] = {
-            disabled: false,
-            hidden: false,
-            active: false,
-          };
-          return buttonStates;
+      // hidden buttons
+      if (
+        buttonName === ButtonNames.AI_STYLIZE ||
+        buttonName === ButtonNames.SEGMENTATION ||
+        buttonName === ButtonNames.DOWNLOAD ||
+        buttonName === ButtonNames.CHROMA ||
+        buttonName === ButtonNames.COLOR
+      ) {
+        buttonStates[buttonName] = {
+          disabled: true,
+          hidden: true,
+          active: false,
+        };
+        return buttonStates;
      }
+
+      // all other buttons
+      buttonStates[buttonName] = {
+        disabled: false,
+        hidden: false,
+        active: false,
+      };
+      return buttonStates;
    },
    {} as ContextualToolbarProps["buttonStates"],
  );
@@ -89,29 +89,28 @@ export function getTextNodeButtonStates(
        return buttonStates;
      }

-      switch (buttonName) {
-        case ButtonNames.AI_STYLIZE:
-        case ButtonNames.SEGMENTATION:
-        case ButtonNames.DOWNLOAD:
-        case ButtonNames.CHROMA:
-        case ButtonNames.REMOVE_BACKGROUND:
-          // hidden buttons
-          buttonStates[buttonName] = {
-            disabled: true,
-            hidden: true,
-            active: false,
-          };
-          return buttonStates;
-
-        default:
-          // all other buttons
-          buttonStates[buttonName] = {
-            disabled: false,
-            hidden: false,
-            active: false,
-          };
-          return buttonStates;
+      // hidden buttons
+      if (
+        buttonName === ButtonNames.AI_STYLIZE ||
+        buttonName === ButtonNames.SEGMENTATION ||
+        buttonName === ButtonNames.DOWNLOAD ||
+        buttonName === ButtonNames.CHROMA
+      ) {
+        buttonStates[buttonName] = {
+          disabled: true,
+          hidden: true,
+          active: false,
+        };
+        return buttonStates;
      }
+
+      // all other buttons
+      buttonStates[buttonName] = {
+        disabled: false,
+        hidden: false,
+        active: false,
+      };
+      return buttonStates;
    },
    {} as ContextualToolbarProps["buttonStates"],
  );
@@ -131,26 +130,24 @@ export function getVideoNodeButtonStates(
        };
        return buttonStates;
      }
-      switch (buttonName) {
-        case ButtonNames.AI_STYLIZE:
-        case ButtonNames.COLOR:
-        case ButtonNames.REMOVE_BACKGROUND:
-          // soon to come feature is disabled
-          buttonStates[buttonName] = {
-              disabled: true,
-              hidden: true,
-              active: false,
-          };
-          return buttonStates;
-        default:
-          // all other buttons
-          buttonStates[buttonName] = {
-              disabled: false,
-              hidden: false,
-              active: false,
-          };
-          return buttonStates;
+      // soon to come feature is disabled
+      if (buttonName === ButtonNames.AI_STYLIZE ||
+        buttonName === ButtonNames.COLOR
+      ) {
+        buttonStates[buttonName] = {
+          disabled: true,
+          hidden: true,
+          active: false,
+        };
+        return buttonStates;
      }
+      // all other buttons
+      buttonStates[buttonName] = {
+        disabled: false,
+        hidden: false,
+        active: false,
+      };
+      return buttonStates;
    },
    {} as ContextualToolbarProps["buttonStates"],
  );
@@ -172,28 +169,28 @@ export function getShapeNodeButtonStates(
        return buttonStates;
      }

-      switch (buttonName) {
-        case ButtonNames.AI_STYLIZE:
-        case ButtonNames.SEGMENTATION:
-        case ButtonNames.DOWNLOAD:
-        case ButtonNames.CHROMA:
-        case ButtonNames.REMOVE_BACKGROUND:
-          // hidden buttons
-          buttonStates[buttonName] = {
-              disabled: true,
-              hidden: true,
-              active: false,
-          };
-          return buttonStates;
-        default:
-          // all other buttons
-          buttonStates[buttonName] = {
-              disabled: false,
-              hidden: false,
-              active: false,
-          };
-          return buttonStates;
+      // hidden buttons
+      if (
+        buttonName === ButtonNames.AI_STYLIZE ||
+        buttonName === ButtonNames.SEGMENTATION ||
+        buttonName === ButtonNames.DOWNLOAD ||
+        buttonName === ButtonNames.CHROMA
+      ) {
+        buttonStates[buttonName] = {
+          disabled: true,
+          hidden: true,
+          active: false,
+        };
+        return buttonStates;
      }
+
+      // all other buttons
+      buttonStates[buttonName] = {
+        disabled: false,
+        hidden: false,
+        active: false,
+      };
+      return buttonStates;
    },
    {} as ContextualToolbarProps["buttonStates"],
  );
@@ -215,29 +212,29 @@ export function getMultiSelectButtonStates(
        return buttonStates;
      }

-      switch (buttonName) {
-        case ButtonNames.AI_STYLIZE:
-        case ButtonNames.SEGMENTATION:
-        case ButtonNames.DOWNLOAD:
-        case ButtonNames.CHROMA:
-        case ButtonNames.COLOR:
-        case ButtonNames.REMOVE_BACKGROUND:
-          // hidden buttons
-          buttonStates[buttonName] = {
-              disabled: true,
-              hidden: true,
-              active: false,
-          };
-          return buttonStates;
-        default:
-          // all other buttons
-          buttonStates[buttonName] = {
-              disabled: false,
-              hidden: false,
-              active: false,
-          };
-          return buttonStates;
+      // hidden buttons
+      if (
+        buttonName === ButtonNames.AI_STYLIZE ||
+        buttonName === ButtonNames.SEGMENTATION ||
+        buttonName === ButtonNames.DOWNLOAD ||
+        buttonName === ButtonNames.CHROMA ||
+        buttonName === ButtonNames.COLOR
+      ) {
+        buttonStates[buttonName] = {
+          disabled: true,
+          hidden: true,
+          active: false,
+        };
+        return buttonStates;
      }
+
+      // all other buttons
+      buttonStates[buttonName] = {
+        disabled: false,
+        hidden: false,
+        active: false,
+      };
+      return buttonStates;
    },
    {} as ContextualToolbarProps["buttonStates"],
  );
@@ -25,7 +25,6 @@ import {
  isLoadingVisible,
  loadingProgress,
 } from "~/signals/uiEvents/loadingIndicator";
-import {DecodeBase64ToImage} from "~/utilities/DecodeBase64ToImage.ts";

 interface ServerSettings {
  model_path: string;
@@ -215,7 +214,7 @@ export class RealTimeDrawEngine {
    // Add mouse events for preview canvas copying
    this.previewCopyListener();
    
-    this.listenToServerEvents();
+    //this.listenToServerEvents();
   
  }

@@ -286,7 +285,7 @@ export class RealTimeDrawEngine {
      this.client.onResult(async (response) => {
        try {
          // Convert the base64 image to ImageBitmap and update the preview
-          const bitmap = await DecodeBase64ToImage(response.image);
+          const bitmap = await this.base64ToImageBitmap(response.image);
          this.outputBitmap = bitmap;
          this.previewCanvas.image(bitmap);
          this.mediaLayerRef.batchDraw();
@@ -896,6 +895,35 @@ export class RealTimeDrawEngine {
    return base64String.split(",")[1];
  }

+  private async base64ToImageBitmap(
+    base64String: string,
+  ): Promise<ImageBitmap> {
+    // Create an image element
+    const img = document.createElement("img");
+
+    // Convert base64 to data URL if it doesn't include the prefix
+    const dataUrl = base64String.startsWith("data:")
+      ? base64String
+      : `data:image/png;base64,${base64String}`;
+
+    // Create a promise to handle the image loading
+    return new Promise((resolve, reject) => {
+      img.onload = async () => {
+        try {
+          const bitmap = await createImageBitmap(img);
+          resolve(bitmap);
+        } catch (error) {
+          reject(error);
+        }
+      };
+
+      img.onerror = () => reject(new Error("Failed to load image"));
+
+      // Set the source to trigger loading
+      img.src = dataUrl;
+    });
+  }
+
  public async saveOutput() {
    if (!this.outputBitmap) {
      console.error("No output bitmap available to save");
@@ -979,7 +1007,7 @@ export class RealTimeDrawEngine {
      });

      //console.log(base64BitmapResponse);
-      const decoded = await DecodeBase64ToImage(
+      const decoded = await this.base64ToImageBitmap(
        base64BitmapResponse as string,
      );

@@ -22,11 +22,6 @@ export const ToolbarNodeButtonData = [
    icon: faPalette,
    tooltip: "Color",
  },
-  {
-    name: ButtonNames.REMOVE_BACKGROUND,
-    icon: faScalpelLineDashed,
-    tooltip: "Remove Background",
-  },
  {
    name: ButtonNames.CHROMA,
    icon: faTransporter,
@@ -8,5 +8,4 @@ export enum ToolbarNodeButtonNames {
  MOVE_LAYER_DOWN = "MOVE_LAYER_DOWN",
  DOWNLOAD = "DOWNLOAD",
  COLOR = "COLOR",
-  REMOVE_BACKGROUND = "REMOVE_BACKGROUND",
 }
@@ -3,7 +3,7 @@ import { SignaledModelButtons } from "~/KonvaRootComponent/SignaledModelButtons"

 export const ToolbarTopCenter = () => {
  return (
-    <div className="flex flex-col items-center gap-3">
+    <div className="flex flex-col gap-3">
      {/* Model selection buttons (only shown in dev mode for now) */}
      {import.meta.env.DEV && <SignaledModelButtons />}

@@ -1,45 +1,17 @@
 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-8 pl-1 pr-4")}
-    >
+    <div className={twMerge("z-20 flex h-fit w-fit items-center gap-1")}>
      <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>
@@ -1,82 +0,0 @@
-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-200 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-0.5 text-center font-semibold transition-all duration-200 ease-in-out",
-                selected ? "text-white" : "text-gray-300 hover:text-white",
-              )
-            }
-          >
-            {tab.label}
-          </Tab>
-        ))}
-      </TabList>
-    </TabGroup>
-  );
-};
@@ -11,9 +11,17 @@ 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 { HexEyedropPicker } from "../HexEyedropPicker";
 import { Input } from "../Input";
+import { Slider } from "../Slider";
+import { EyeDropper } from "react-eyedrop";
+import { HexEyedropPicker } from "../HexEyedropPicker";

 export const BackgroundMenu = ({
  color: prevColor,
@@ -8,7 +8,6 @@ export * from "./PanelRight";
 export * from "./Slider";
 export * from "./Spinner";
 export * from "./Switches";
-export * from "./TabSelector";
 export * from "./TextArea";
 export * from "./TextEditor";
 export * from "./Tooltip";
@@ -1,38 +0,0 @@
-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);
-      }
-    };
-  },
-};
@@ -36,7 +36,6 @@ import {
  dispatchers as modelSelectionDispatchers,
  events as modelSelectionEvents,
 } from "./modelSelection";
-import { appMode, changeAppMode, appModeEvents } from "./appMode";

 export const uiEvents = {
  ...addMediaEvents,
@@ -50,7 +49,6 @@ export const uiEvents = {
  promptEvents: promptEvents,
  loadingIndicator: loadingIndicatorEvents,
  modelSelection: modelSelectionEvents,
-  appMode: appModeEvents,
 };
 export const dispatchUiEvents = {
  ...addMediaDispatchers,
@@ -64,7 +62,4 @@ export const dispatchUiEvents = {
  promptSettings: promptDispatchers,
  loadingIndicator: loadingIndicatorDispatchers,
  modelSelection: modelSelectionDispatchers,
-  changeAppMode,
 };
-
-export { appMode };
@@ -1,30 +0,0 @@
-/**
- * This decodes Base64 encoded PNG images into an image element.
- * @param base64String a standard (not "web safe") base64-encoded string
- */
-export const DecodeBase64ToImage = async (base64String: string) : Promise<ImageBitmap> => {
-  // Create an image element
-  const img = document.createElement("img");
-
-  // Convert base64 to data URL if it doesn't include the prefix
-  const dataUrl = base64String.startsWith("data:")
-    ? base64String
-    : `data:image/png;base64,${base64String}`;
-
-  // Create a promise to handle the image loading
-  return new Promise((resolve, reject) => {
-    img.onload = async () => {
-      try {
-        const bitmap = await createImageBitmap(img);
-        resolve(bitmap);
-      } catch (error) {
-        reject(error);
-      }
-    };
-
-    img.onerror = () => reject(new Error("Failed to load image"));
-
-    // Set the source to trigger loading
-    img.src = dataUrl;
-  });
-}
@@ -1,22 +0,0 @@
-
-export const EncodeImageBitmapToBase64 = async (imageBitmap: ImageBitmap): Promise<string> => {
-  // Create a temporary canvas
-  const canvas = document.createElement("canvas");
-  canvas.width = imageBitmap.width;
-  canvas.height = imageBitmap.height;
-
-  // Draw the ImageBitmap onto the canvas
-  const ctx = canvas.getContext("2d");
-  if (!ctx) throw new Error("Failed to get 2D context");
-
-  ctx.drawImage(imageBitmap, 0, 0);
-
-  // Convert to base64
-  const base64String = canvas.toDataURL("image/png");
-
-  // Clean up
-  canvas.remove();
-
-  // Remove the data:image/png;base64, prefix if you want just the base64 string
-  return base64String.split(",")[1];
-}
Author	SHA1	Message	Date
Tanish Manku	becbfa06f5	Fix Shape size jumping after recreation (on color change)	2025-03-07 00:15:54 -08:00
Tanish Manku	018a77d113	Merge branch 'main' into bugfix/shape-changes	2025-03-06 22:58:33 -08:00
Tanish Manku	d5df7dbca1	RealTimeDrawEngine: Change RenderFrame logic to use stage clone instead of OffscreenCanvas	2025-03-06 22:53:46 -08:00
Tanish Manku	a605a2e51d	ShapeNode: Use actual shapes instead of raster, write custom triangle logic	2025-03-06 22:47:09 -08:00
Tanish Manku	3e2016a7af	NetworkedNode doesn't only have to be Image node	2025-03-06 22:46:40 -08:00
Tanish Manku	a77f9c3eec	Enable stroke highlighting even for Konva Shapes	2025-03-06 22:46:20 -08:00