Merge branch 'main' into feature/generatemode

cleanup
can't trigger default features; switch to platform switches
2026-11-04 05:20:58 -05:00 · 2025-03-25 01:38:48 -04:00 · 2025-03-24 23:08:07 -04:00 · 2025-03-24 23:00:29 -04:00 · 2025-03-24 22:31:58 -04:00 · 2025-03-24 21:46:25 -04:00
155 changed files with 6430 additions and 2769 deletions
@@ -66,3 +66,7 @@ scraps/
 # These should be built by the platform
 windows/sqlite/sqlite3.exp
 windows/sqlite/sqlite3.lib
+
+# ArtCraft Windows DLLs
+crates/desktop/src-tauri/*.dll
+windows_dlls/
@@ -2,6 +2,7 @@
  <dictionary name="bt">
    <words>
      <w>arpabet</w>
+      <w>artcraft</w>
      <w>backfill</w>
      <w>backfilling</w>
      <w>backfills</w>
@@ -658,28 +658,36 @@ name = "app"
 version = "0.1.0"
 dependencies = [
 "anyhow",
+ "assertor",
 "base64 0.22.1",
 "bytes 1.10.0",
- "candle-core",
- "candle-nn",
- "candle-transformers",
- "cudarc",
+ "const_format",
+ "directories 6.0.0",
+ "dirs 6.0.0",
 "filesys",
- "hf-hub 0.3.2",
+ "futures 0.3.29",
+ "half",
 "image",
 "libloading 0.8.6",
 "log",
 "memory_caching",
+ "ml_models",
+ "ml_weights_registry",
 "once_cell",
+ "primitives",
 "rand 0.8.5",
 "reqwest 0.12.12",
 "serde",
 "serde_json",
 "serde_yaml",
+ "serial_test",
 "tauri",
 "tauri-build",
 "tauri-plugin-log",
- "tokenizers 0.15.2",
+ "tempdir",
+ "tempfile",
+ "tokio",
+ "tokio-util",
 ]

 [[package]]
@@ -846,6 +854,15 @@ 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"
@@ -1816,6 +1833,7 @@ dependencies = [
 "candle-flash-attn",
 "candle-nn",
 "fancy-regex",
+ "half",
 "intel-mkl-src",
 "num-traits",
 "rand 0.8.5",
@@ -1859,6 +1877,18 @@ 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"
@@ -2036,9 +2066,9 @@ dependencies = [

 [[package]]
 name = "clap"
-version = "4.4.8"
+version = "4.5.13"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "2275f18819641850fa26c89acc84d465c1bf91ce57bc2748b28c420473352f64"
+checksum = "0fbb260a053428790f3de475e304ff84cdbc4face759ea7a3e64c1edd938a7fc"
 dependencies = [
 "clap_builder",
 "clap_derive",
@@ -2046,23 +2076,23 @@ dependencies = [

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

 [[package]]
 name = "clap_derive"
-version = "4.4.7"
+version = "4.5.13"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "cf9804afaaf59a91e75b022a30fb7229a7901f60c755489cc61c9b423b836442"
+checksum = "501d359d5f3dcaf6ecdeee48833ae73ec6e42723a1e52419c79abf9507eec0a0"
 dependencies = [
- "heck 0.4.1",
+ "heck 0.5.0",
 "proc-macro2",
 "quote",
 "syn 2.0.87",
@@ -2070,9 +2100,9 @@ dependencies = [

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

 [[package]]
 name = "cloud_storage"
@@ -2165,6 +2195,16 @@ 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"
@@ -2290,6 +2330,26 @@ 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"
@@ -2833,19 +2893,6 @@ dependencies = [
 "syn 2.0.87",
 ]

-[[package]]
-name = "dashmap"
-version = "5.5.3"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "978747c1d849a7d2ee5e8adc0159961c48fb7e5db2f06af6723b80123bb53856"
-dependencies = [
- "cfg-if 1.0.0",
- "hashbrown 0.14.5",
- "lock_api 0.4.12",
- "once_cell",
- "parking_lot_core 0.9.10",
-]
-
 [[package]]
 name = "dasp_sample"
 version = "0.11.0"
@@ -3188,6 +3235,15 @@ dependencies = [
 "dirs-sys 0.4.1",
 ]

+[[package]]
+name = "directories"
+version = "6.0.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "16f5094c54661b38d03bd7e50df373292118db60b585c08a411c6d840017fe7d"
+dependencies = [
+ "dirs-sys 0.5.0",
+]
+
 [[package]]
 name = "dirs"
 version = "5.0.1"
@@ -3763,12 +3819,12 @@ dependencies = [

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

 [[package]]
@@ -3887,9 +3943,9 @@ dependencies = [

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

 [[package]]
 name = "fdeflate"
@@ -4617,6 +4673,18 @@ dependencies = [
 "wasm-bindgen",
 ]

+[[package]]
+name = "getrandom"
+version = "0.3.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "43a49c392881ce6d5c3b8cb70f98717b7c07aabbdff06687b9030dbfbe2725f8"
+dependencies = [
+ "cfg-if 1.0.0",
+ "libc",
+ "wasi 0.13.3+wasi-0.2.2",
+ "windows-targets 0.52.6",
+]
+
 [[package]]
 name = "getset"
 version = "0.1.4"
@@ -5971,7 +6039,7 @@ dependencies = [
 "chumsky",
 "email-encoding",
 "email_address",
- "fastrand 2.1.0",
+ "fastrand 2.3.0",
 "futures-util",
 "hostname 0.3.1",
 "httpdate",
@@ -6189,6 +6257,12 @@ version = "0.4.14"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "78b3ae25bc7c8c38cec158d1f2757ee79e9b3740fbc7ccf0e59e4b08d793fa89"

+[[package]]
+name = "linux-raw-sys"
+version = "0.9.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "6db9c683daf087dc577b7506e9695b3d556a9f3849903fa28186283afd6809e9"
+
 [[package]]
 name = "local-channel"
 version = "0.1.5"
@@ -6723,6 +6797,58 @@ 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",
+ "ml_weights_registry",
+ "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 = "ml_weights_registry"
+version = "0.0.1"
+dependencies = [
+ "anyhow",
+ "const_format",
+]
+
 [[package]]
 name = "mockall"
 version = "0.12.1"
@@ -6987,6 +7113,19 @@ 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"
@@ -7264,6 +7403,23 @@ 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"
@@ -7557,7 +7713,7 @@ dependencies = [
 "base16ct",
 "base64 0.22.1",
 "chrono",
- "directories",
+ "directories 5.0.1",
 "flate2",
 "lazy_static",
 "log",
@@ -7769,6 +7925,26 @@ 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"
@@ -8336,6 +8512,7 @@ dependencies = [
 name = "primitives"
 version = "0.0.1"
 dependencies = [
+ "assertor",
 "my-workspace-hack",
 "testing",
 ]
@@ -9591,10 +9768,23 @@ dependencies = [
 "bitflags 2.5.0",
 "errno",
 "libc",
- "linux-raw-sys",
+ "linux-raw-sys 0.4.14",
 "windows-sys 0.52.0",
 ]

+[[package]]
+name = "rustix"
+version = "1.0.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "f7178faa4b75a30e269c71e61c353ce2748cf3d76f0c44c393f4e60abf49b825"
+dependencies = [
+ "bitflags 2.5.0",
+ "errno",
+ "libc",
+ "linux-raw-sys 0.9.2",
+ "windows-sys 0.59.0",
+]
+
 [[package]]
 name = "rustls"
 version = "0.21.12"
@@ -9726,6 +9916,15 @@ dependencies = [
 "winapi-util",
 ]

+[[package]]
+name = "scc"
+version = "2.3.3"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "ea091f6cac2595aa38993f04f4ee692ed43757035c36e67c180b6828356385b1"
+dependencies = [
+ "sdd",
+]
+
 [[package]]
 name = "schannel"
 version = "0.1.23"
@@ -9815,6 +10014,12 @@ dependencies = [
 "untrusted",
 ]

+[[package]]
+name = "sdd"
+version = "3.0.8"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "584e070911c7017da6cb2eb0788d09f43d789029b5877d3e5ecc8acf86ceee21"
+
 [[package]]
 name = "seahash"
 version = "4.1.0"
@@ -10135,23 +10340,23 @@ dependencies = [

 [[package]]
 name = "serial_test"
-version = "3.0.0"
+version = "3.2.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "953ad9342b3aaca7cb43c45c097dd008d4907070394bd0751a0aa8817e5a018d"
+checksum = "1b258109f244e1d6891bf1053a55d63a5cd4f8f4c30cf9a1280989f80e7a1fa9"
 dependencies = [
- "dashmap",
 "futures 0.3.29",
- "lazy_static",
 "log",
+ "once_cell",
 "parking_lot 0.12.2",
+ "scc",
 "serial_test_derive",
 ]

 [[package]]
 name = "serial_test_derive"
-version = "3.0.0"
+version = "3.2.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "b93fb4adc70021ac1b47f7d45e8cc4169baaa7ea58483bc5b721d19a26202212"
+checksum = "5d69265a08751de7844521fd15003ae0a888e035773ba05695c5c759a6f89eef"
 dependencies = [
 "proc-macro2",
 "quote",
@@ -10330,6 +10535,17 @@ 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"
@@ -11826,14 +12042,16 @@ dependencies = [

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

 [[package]]
@@ -11858,6 +12076,15 @@ 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"
@@ -12519,9 +12746,9 @@ dependencies = [

 [[package]]
 name = "tracing-subscriber"
-version = "0.3.19"
+version = "0.3.18"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e8189decb5ac0fa7bc8b96b7cb9b2701d60d48805aca84a238004d665fcc4008"
+checksum = "ad0f048c97dbd9faa9b7df56362b8ebcaa52adb06b498c050d2f4e32f90a7a8b"
 dependencies = [
 "nu-ansi-term",
 "sharded-slab",
@@ -12830,6 +13057,12 @@ 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"
@@ -13206,6 +13439,15 @@ version = "0.11.0+wasi-snapshot-preview1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "9c8d87e72b64a3b4db28d11ce29237c246188f4f51057d65a7eab63b7987e423"

+[[package]]
+name = "wasi"
+version = "0.13.3+wasi-0.2.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "26816d2e1a4a36a2940b96c5296ce403917633dff8f3440e9b236ed6f6bacad2"
+dependencies = [
+ "wit-bindgen-rt",
+]
+
 [[package]]
 name = "wasite"
 version = "0.1.0"
@@ -13975,6 +14217,15 @@ dependencies = [
 "windows-sys 0.48.0",
 ]

+[[package]]
+name = "wit-bindgen-rt"
+version = "0.33.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "3268f3d866458b787f390cf61f4bbb563b922d091359f9608842999eaee3943c"
+dependencies = [
+ "bitflags 2.5.0",
+]
+
 [[package]]
 name = "wry"
 version = "0.48.1"
@@ -14065,8 +14316,8 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "e105d177a3871454f754b33bb0ee637ecaaac997446375fd3e5d43a2ed00c909"
 dependencies = [
 "libc",
- "linux-raw-sys",
- "rustix",
+ "linux-raw-sys 0.4.14",
+ "rustix 0.38.34",
 ]

 [[package]]
@@ -23,6 +23,8 @@ members = [

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

  # Shared libraries
  "crates/lib/actix_cors_configs",
@@ -1,7 +1,8 @@
-image_width: 512
-image_height: 512
+image_width: 1024
+image_height: 1024
 #scheduler_steps: 15
 # NB(bt): It looks like we can get down to 2 sampling steps, perhaps ?
-scheduler_steps: 2
+scheduler_steps: 8
 scheduler_samples: 1
-cfg_scale: 1.1
+cfg_scale: 15.0
+seed: 42
@@ -0,0 +1,79 @@
+[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" }
+ml_weights_registry = { path = "../ml_weights_registry" }
+primitives = { path = "../../lib/data/primitives" }
+
+# Bypass cudarc issues on Windows https://github.com/coreylowman/cudarc/issues/332
+cudarc = "0.13.7"
+
+# External
+anyhow = "1.0"
+base64 = "0.22.1"
+bytes = "1.10"
+candle-core = { path = "../../vendor/candle/candle-core", features = ["cuda"] }
+candle-nn = { path = "../../vendor/candle/candle-nn", features = ["cuda"] }
+candle-transformers = { path = "../../vendor/candle/candle-transformers", features = ["cuda"] }
+const_format = "0.2.34"
+directories = "6.0.0"
+dirs = "6.0.0"
+futures = "0.3"
+half = { version = "2.3.1", features = ["num-traits", "use-intrinsics", "rand_distr"] }
+hf-hub = "0.3.2"
+image = "0.25.5"
+log = "0.4"
+ndarray = "0.15.6"
+nvidia-checker = "0.1.2"
+once_cell = "1.20.3"
+ort = { version = "1.16.2", features = ["cuda"] }
+rand = "0.8.5"
+# NB(bt,2025-03-10): default-features = false on Windows while I debug openssl / rustls
+reqwest = { version = "0.12.12", features = ["stream"], default-features = false }
+serde = { version = "1.0", features = ["derive"] }
+serde_json = "1.0"
+serde_yaml = "0.9"
+tauri = { version = "2.2.4", features = [] }
+tauri-plugin-log = "2"
+tempdir = "0.3.7"
+tempfile = "3.18.0"
+tokenizers = "0.15.2"
+tokio = { version = "1.42", features = ["rt", "rt-multi-thread", "fs"] }
+tokio-util = { version = "0.7", features = ["codec"] }
+
+# tracing-subscriber = { version = "0.3", features = [ "env-filter", "fmt" ] }
+
+[features]
+default = ["cuda"]
+cuda = [
+    "candle-core/cuda",
+    "candle-nn/cuda",
+    "candle-transformers/cuda",
+    # NB(bt,2025-03-10): Building without Flash attention on Windows for now to bypass linker issues
+    "candle-transformers/flash-attn",
+    "candle-core/cudnn"
+]
+
+[dev-dependencies]
+libloading = "0.8.6"
+serial_test = "3.2.0"
+assertor = "0.0.3"
@@ -0,0 +1 @@
+pub mod ml;
@@ -0,0 +1,2 @@
+pub mod onnx_session;
+pub mod remove_image_background;
@@ -0,0 +1,19 @@
+//! 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)
+}
@@ -0,0 +1,61 @@
+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))
+}
@@ -0,0 +1,7 @@
+pub mod background_removal;
+pub mod image;
+pub mod model_cache;
+pub mod model_config;
+pub mod model_file;
+pub mod prompt_cache;
+pub mod stable_diffusion;
@@ -1,15 +1,14 @@
-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<UNetModel>>>,
+  unet2: RwLock<Option<Arc<UNet2DConditionModel>>>,
 }

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

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

-  pub fn get_unet(&self) -> anyhow::Result<Option<Arc<UNetModel>>> {
+  pub fn get_unet(&self) -> anyhow::Result<Option<Arc<UNet2DConditionModel>>> {
    match self.unet2.read() {
      Err(err) => Err(anyhow!("{}", err)),
      Ok(ref lock) => {
@@ -0,0 +1,61 @@
+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,
+    })
+  }
+}
@@ -0,0 +1,180 @@
+#[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)
+            }
+        }
+    }
+}
@@ -0,0 +1,158 @@
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+use candle_transformers::models::stable_diffusion;
+use std::path::{Path, PathBuf};
+
+use crate::ml::model_file::{ModelFile, StableDiffusionVersion};
+use ml_weights_registry::weights_registry::weights::{CLIP_JSON, SDXL_TURBO_CLIP_TEXT_ENCODER, SDXL_TURBO_CLIP_TEXT_ENCODER_2};
+use anyhow::Error as E;
+use candle_core::{DType, Device, Module, Tensor, D};
+use log::info;
+use tokenizers::Tokenizer;
+
+
+pub struct ClipArgs<'a, P: AsRef<Path>,  Q: AsRef<Path>> {
+  pub prompt: &'a str, 
+  pub uncond_prompt: &'a str, 
+  pub tokenizer: Option<String>, 
+  pub clip_weights: Option<String>, 
+  pub clip2_weights: Option<String>, 
+  pub sd_version: StableDiffusionVersion, 
+  pub sd_config: &'a stable_diffusion::StableDiffusionConfig, 
+  pub use_f16: bool, 
+  pub device: &'a Device, 
+  pub dtype: DType, 
+  pub use_guide_scale: bool, 
+  pub clip_json_path: P,
+  pub clip_weights_path: Q,
+}
+
+#[allow(clippy::too_many_arguments)]
+pub fn infer_clip_text_embeddings<P: AsRef<Path>, Q: AsRef<Path>>(args: ClipArgs<'_, P, Q>) -> anyhow::Result<Tensor> {
+  let ClipArgs {
+    prompt, 
+    uncond_prompt, 
+    tokenizer, 
+    clip_weights, 
+    clip2_weights, 
+    sd_version, 
+    sd_config, 
+    use_f16, 
+    device, 
+    dtype, 
+    use_guide_scale, 
+    clip_json_path,
+    clip_weights_path,
+  } = args;
+
+  info!("Preparing text embeddings... for {:?}", sd_version);
+
+  let which = match sd_version {
+    StableDiffusionVersion::Xl | StableDiffusionVersion::XlInpaint | StableDiffusionVersion::Turbo => vec![true, false],
+    _ => vec![true],
+  };
+
+  let text_embeddings = which
+    .iter()
+    .map(|first| {
+      do_infer_clip_text_embeddings(
+        &prompt,
+        &uncond_prompt,
+        None, // tokenizer
+        clip_weights.clone(),
+        None, // clip2_weights
+        sd_version,
+        &sd_config,
+        false, // use_f16
+        &device,
+        dtype,
+        use_guide_scale,
+        *first,
+        clip_json_path.as_ref(),
+        clip_weights_path.as_ref(),
+      )
+    })
+    .collect::<anyhow::Result<Vec<_>>>()?;
+
+  let text_embeddings = Tensor::cat(&text_embeddings, D::Minus1)?;
+
+  Ok(text_embeddings)
+}
+
+#[allow(clippy::too_many_arguments)]
+fn do_infer_clip_text_embeddings(
+  prompt: &str, 
+  uncond_prompt: &str, 
+  tokenizer: Option<String>, 
+  clip_weights: Option<String>, 
+  clip2_weights: Option<String>, 
+  sd_version: StableDiffusionVersion, 
+  sd_config: &stable_diffusion::StableDiffusionConfig, 
+  use_f16: bool, 
+  device: &Device, 
+  dtype: DType, 
+  use_guide_scale: bool, 
+  first: bool, 
+  clip_json_path: &Path,
+  clip_weights_path: &Path,
+) -> anyhow::Result<Tensor> {
+  info!("do_infer_clip_text_embeddings called with args {:?}", (prompt, uncond_prompt, tokenizer.clone().unwrap_or_else(|| "None".to_string()), clip_weights.clone().unwrap_or_else(|| "None".to_string()), clip2_weights.clone().unwrap_or_else(|| "None".to_string()), sd_version, sd_config, use_f16, device, dtype, use_guide_scale, first));
+
+  let tokenizer = clip_json_path;
+
+  info!("Loading Clip Tokenizer path: {:?}", tokenizer);
+
+  let tokenizer = Tokenizer::from_file(tokenizer).map_err(E::msg)?;
+
+  let pad_id = match &sd_config.clip.pad_with {
+    Some(padding) => *tokenizer.get_vocab(true).get(padding.as_str()).unwrap(),
+    None => *tokenizer.get_vocab(true).get("<|endoftext|>").unwrap(),
+  };
+
+  println!("Running with prompt \"{prompt}\".");
+  let mut tokens = tokenizer.encode(prompt, true).map_err(E::msg)?.get_ids().to_vec();
+  if tokens.len() > sd_config.clip.max_position_embeddings {
+    anyhow::bail!("the prompt is too long, {} > max-tokens ({})", tokens.len(), sd_config.clip.max_position_embeddings)
+  }
+  while tokens.len() < sd_config.clip.max_position_embeddings {
+    tokens.push(pad_id)
+  }
+  let tokens = Tensor::new(tokens.as_slice(), device)?.unsqueeze(0)?;
+
+  println!("Building the Clip transformer.");
+  
+  // TODO(bt,2025-03-23): These used to be two different clip models. We should only need one.
+  let clip_weights = if first {
+    println!(">>>>> CLIP 1");
+    clip_weights_path
+  } else {
+    println!(">>>>> CLIP 2");
+    clip_weights_path
+  };
+  
+  let clip_config = if first { &sd_config.clip } else { sd_config.clip2.as_ref().unwrap() };
+  let text_model = stable_diffusion::build_clip_transformer(clip_config, clip_weights, device, DType::F32)?;
+
+  let text_embeddings = text_model.forward(&tokens)?;
+
+  let text_embeddings = if use_guide_scale {
+    let mut uncond_tokens = tokenizer.encode(uncond_prompt, true).map_err(E::msg)?.get_ids().to_vec();
+    if uncond_tokens.len() > sd_config.clip.max_position_embeddings {
+      anyhow::bail!("the negative prompt is too long, {} > max-tokens ({})", uncond_tokens.len(), sd_config.clip.max_position_embeddings)
+    }
+    while uncond_tokens.len() < sd_config.clip.max_position_embeddings {
+      uncond_tokens.push(pad_id)
+    }
+
+    let uncond_tokens = Tensor::new(uncond_tokens.as_slice(), device)?.unsqueeze(0)?;
+    let uncond_embeddings = text_model.forward(&uncond_tokens)?;
+
+    Tensor::cat(&[uncond_embeddings, text_embeddings], 0)?.to_dtype(dtype)?
+  } else {
+    text_embeddings.to_dtype(dtype)?
+  };
+  Ok(text_embeddings)
+}
@@ -0,0 +1,281 @@
+use std::path::{Path, PathBuf};
+use std::sync::{Arc, RwLock, RwLockReadGuard, RwLockWriteGuard};
+
+use anyhow::{anyhow, Error as E, Result};
+use candle_core::{DType, Device, IndexOp, Tensor, D};
+use candle_nn::VarBuilder;
+use candle_transformers::models::stable_diffusion::StableDiffusionConfig;
+use candle_transformers::models::stable_diffusion::lcm::{LCMScheduler, LCMSchedulerConfig};
+use candle_transformers::models::stable_diffusion::unet_2d::BlockConfig;
+use candle_transformers::models::stable_diffusion::unet_2d::UNet2DConditionModel;
+use candle_transformers::models::stable_diffusion::unet_2d::UNet2DConditionModelConfig;
+use candle_transformers::models::stable_diffusion::vae::{AutoEncoderKL, DiagonalGaussianDistribution};
+use crate::ml::image::dynamic_image_to_tensor::dynamic_image_to_tensor;
+use crate::ml::image::tensor_to_image_buffer::{tensor_to_image_buffer, RgbImage};
+use crate::ml::model_cache::ModelCache;
+use crate::ml::model_config::ModelConfig;
+use crate::ml::model_file::{ModelFile, StableDiffusionVersion};
+use crate::ml::prompt_cache::PromptCache;
+use crate::ml::stable_diffusion::get_vae_scale::get_vae_scale;
+use crate::ml::stable_diffusion::infer_clip_text_embeddings::{infer_clip_text_embeddings, ClipArgs};
+use crate::ml::stable_diffusion::remap_lcm_strength_range::remap_lcm_strength_range;
+use image::DynamicImage;
+use log::info;
+use ml_weights_registry::weights_registry::weights::{LYKON_DEAMSHAPER_7_TEXT_ENCODER_FP16, LYKON_DEAMSHAPER_7_VAE, SIMIANLUO_LCM_DREAMSHAPER_V7_UNET};
+use rand::Rng;
+
+const DEFAULT_STRENGTH : f64 = 75.0;
+
+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)
+}
@@ -0,0 +1,5 @@
+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;
@@ -0,0 +1,54 @@
+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);
+  }
+}
@@ -1,71 +1,86 @@
+use std::path::Path;
 use std::sync::{Arc, RwLock, RwLockReadGuard, RwLockWriteGuard};

-use crate::events::notification_event::{ModelType, 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_registry::ModelRegistry;
-use crate::ml::models::unet_model::UNetModel;
 use crate::ml::prompt_cache::PromptCache;
 use crate::ml::stable_diffusion::get_vae_scale::get_vae_scale;
-use crate::ml::stable_diffusion::infer_clip_text_embeddings::infer_clip_text_embeddings;
-use crate::state::app_config::AppConfig;
+use crate::ml::stable_diffusion::infer_clip_text_embeddings::{infer_clip_text_embeddings, ClipArgs};
 use anyhow::{anyhow, Error as E, Result};
-use candle_core::{DType, IndexOp, Tensor, D};
+use candle_core::{DType, Device, 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};

-pub struct Args<'a> {
+pub struct Args<'a, P: AsRef<Path>, Q: AsRef<Path>, R: AsRef<Path>, S: AsRef<Path>> {
    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: &'a AppHandle,
+    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 fn stable_diffusion_pipeline(args: Args<'_>) -> Result<RgbImage> {
+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>
+{
    let Args { 
        prompt, 
        uncond_prompt, 
        cfg_scale, 
        i2i_strength, 
-        configs, 
        model_cache, 
        prompt_cache, 
-        app, 
-        image,  
+        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,
    } = args;
    
    println!("Starting image generation with the following configuration:");
-    println!("  Model: {:?}", configs.sd_version);
+    println!("  Model: {:?}", sd_version);
    println!("  Prompt: {}", prompt);
-    println!("  Steps: {}", configs.scheduler_steps);
-    println!("  Device: {:?}", configs.device);
+    println!("  Steps: {}", scheduler_steps);
+    println!("  Device: {:?}", device);

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

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

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

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

    let guidance_scale = match cfg_scale {
        Some(guidance_scale) => guidance_scale,
-        None => match configs.sd_version {
+        None => match sd_version {
            StableDiffusionVersion::V1_5
            | StableDiffusionVersion::V2_1
            | StableDiffusionVersion::Xl => 7.5,
@@ -86,19 +101,21 @@ pub fn stable_diffusion_pipeline(args: Args<'_>) -> Result<RgbImage> {
    } 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,
+        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,
            use_guide_scale,
-        )?;
+            clip_json_path: clip_json_path.as_ref(),
+            clip_weights_path: clip_weights_path.as_ref(),
+        })?;
        prompt_cache.store_copy(&prompt, &tensor)?;
        tensor
    };
@@ -107,14 +124,11 @@ pub fn stable_diffusion_pipeline(args: Args<'_>) -> Result<RgbImage> {

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

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

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

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

    let maybe_vae = model_cache.get_vae()?;
    
@@ -123,40 +137,14 @@ pub fn stable_diffusion_pipeline(args: Args<'_>) -> Result<RgbImage> {
        None => {
            info!("No vae found in cache; loading...");
            
-            let repo = configs.sd_version.repo();
-
-            println!("Building VAE model from : {:?} ... (3)", repo);
-
-            let vae_file = configs.hf_api.model(repo.to_string())
-              .get("vae/diffusion_pytorch_model.safetensors")?;
-
-            println!("Building VAE model from file {:?}...", &vae_file);
-
-            let mut notify_download_complete = false;
-            //if !vae_file.exists() {
-            if true {
-                notify_download_complete = true;
-                app.emit("notification", NotificationEvent::ModelDownloadStarted {
-                    model_name: repo,
-                    model_type: ModelType::Vae,
-                })?;
-            }
+            let vae_file = sdxl_turbo_vae_path.as_ref();
            
-            let vae = configs
-              .sd_config
-              .build_vae(vae_file, &configs.device, configs.dtype)?;
+            let vae = sd_config.build_vae(vae_file, &device, dtype)?;

            let vae = Arc::new(vae);
            
            model_cache.set_vae(vae.clone())?;
-            
-            if notify_download_complete {
-                app.emit("notification", NotificationEvent::ModelDownloadComplete {
-                    model_name: repo,
-                    model_type: ModelType::Vae,
-                })?;
-            }
-            
+
            vae
        }
    };
@@ -168,32 +156,15 @@ pub fn stable_diffusion_pipeline(args: Args<'_>) -> Result<RgbImage> {
        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: ModelType::Unet,
-                })?;
-            }
-            
-            let unet_file = ModelRegistry::SdxlTurboUnet.get_filename();
+            let unet_weights_file = sdxl_turbo_unet_path.as_ref();

-            let unet = UNetModel::new(&configs.sd_config, unet_file, &configs.device, configs.dtype)
+            let unet = sd_config.build_unet(unet_weights_file, &device, 4, true, 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: ModelType::Unet,
-                })?;
-            }
-
            unet
        }
    };
@@ -217,9 +188,9 @@ pub fn stable_diffusion_pipeline(args: Args<'_>) -> Result<RgbImage> {
    };

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

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

@@ -258,7 +229,7 @@ pub fn stable_diffusion_pipeline(args: Args<'_>) -> Result<RgbImage> {

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

-        let mut noise_pred = match unet.inference(&latent_model_input, timestep as f64, &text_embeddings) {
+        let mut noise_pred = match unet.forward(&latent_model_input, timestep as f64, &text_embeddings) {
            Ok(pred) => pred,
            Err(e) => {
                println!("UNet inference failed with error: {}", e);
@@ -0,0 +1,18 @@
+[package]
+name = "ml_weights_registry"
+edition = "2021"
+version = "0.0.1"
+authors = ["storyteller"]
+publish = false
+description = ""
+license = ""
+repository = ""
+
+[lib]
+name = "ml_weights_registry"
+path = "src/lib.rs"
+
+# External
+[dependencies]
+anyhow = "1.0"
+const_format = "0.2.34"
@@ -0,0 +1 @@
+pub mod weights_registry;
@@ -0,0 +1,3 @@
+mod weight_descriptor_builder;
+pub mod weight_descriptor;
+pub mod weights;
@@ -0,0 +1,45 @@
+
+// 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())
+  }
+}
@@ -0,0 +1,17 @@
+pub (super) const R2_BUCKET_URL : &str = "https://pub-bc5e2bc0cdee4bb5ae8fca9d641ca0d6.r2.dev/";
+
+// TODO(bt,2025-03-13): Try to use keyword args from the call site.
+// NB: We're using a macro because we can't use a builder pattern and then concat string literals
+//  and variables (even if they're known to be &'static) and keep allocations static.
+macro_rules! weight {
+    ($name: literal, $file_name:literal, $weight_function:expr) => {
+      $crate::weights_registry::weight_descriptor::WeightDescriptor {
+        name: $name,
+        filename: $file_name,
+        r2_download_url: const_format::concatcp!(crate::weights_registry::weight_descriptor_builder::R2_BUCKET_URL, $file_name),
+        function: $weight_function,
+      }
+    };
+}
+
+pub(crate) use weight;
@@ -0,0 +1,65 @@
+use crate::weights_registry::weight_descriptor::{WeightDescriptor, WeightFunction};
+use crate::weights_registry::weight_descriptor_builder::weight;
+
+pub const CLIP_JSON : WeightDescriptor = weight!(
+  "CLIP",
+  "clip_vit_bigg_14_lion2b_39b_b160k.tokenizer.json",
+  WeightFunction::TextTokenizer
+);
+
+/// For LCM-based image generation
+pub const LYKON_DEAMSHAPER_7_TEXT_ENCODER_FP16 : WeightDescriptor = weight!(
+  "Lykon Dreamshaper 7",
+  "lykon_dreamshaper_7_text_encoder.fp16.safetensors",
+  WeightFunction::TextEncoder
+);
+
+/// For LCM-based image generation
+pub const LYKON_DEAMSHAPER_7_VAE : WeightDescriptor = weight!(
+  "Lykon Dreamshaper 7",
+  "lykon_dreamshaper_7_vae.safetensors",
+  WeightFunction::Vae
+);
+
+/// For LCM-based image generation
+pub const SIMIANLUO_LCM_DREAMSHAPER_V7_UNET : WeightDescriptor = weight!(
+  "SimianLuo LCM Dreamshaper v7",
+  "simianluo_lcm_dreamshaper_v7_unet.safetensors",
+  WeightFunction::Unet
+);
+
+/// Image segmentation and background removal
+/// dis_medium.onnx
+pub const DIS_MEDIUM_ONNX : WeightDescriptor = weight!(
+  "Dichotomous Image Segmentation Medium",
+  "dis_medium.onnx",
+  WeightFunction::ImageSegmentation
+);
+
+#[deprecated]
+pub const SDXL_TURBO_CLIP_TEXT_ENCODER : WeightDescriptor = weight!(
+  "SDXL Turbo CLIP",
+  "clip_text_encoder.safetensors",
+  WeightFunction::TextEncoder
+);
+
+#[deprecated]
+pub const SDXL_TURBO_CLIP_TEXT_ENCODER_2 : WeightDescriptor = weight!(
+  "SDXL Turbo CLIP #2",
+  "clip_text_encoder_2.safetensors",
+  WeightFunction::TextEncoder
+);
+
+#[deprecated]
+pub const SDXL_TURBO_VAE : WeightDescriptor = weight!(
+  "SDXL Turbo",
+  "diffusion_pytorch_model.vae.safetensors",
+  WeightFunction::Vae
+);
+
+#[deprecated]
+pub const SDXL_TURBO_UNET : WeightDescriptor = weight!(
+  "SDXL Turbo",
+  "diffusion_pytorch_model.unet.safetensors",
+  WeightFunction::Unet
+);
@@ -0,0 +1,4 @@
+# Don't restart the dev server on Rust changes
+crates/
+Cargo.toml
+Cargo.lock
@@ -7,6 +7,7 @@ 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

@@ -22,36 +23,52 @@ tauri-build = { version = "2.0.4", features = [] }
 # Internal
 filesys = { path = "../../lib/files/filesys" }
 memory_caching = { path = "../../lib/caching/memory_caching" }
+ml_weights_registry = { path = "../ml_weights_registry" }
+primitives = { path = "../../lib/data/primitives" }

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

 # External
+#openssl = { version = "0.10", features = ["vendored"] }
 anyhow = "1.0"
-bytes = "1.10"
 base64 = "0.22.1"
-candle-core = { path = "../../vendor/candle/candle-core", features = ["cuda"] }
-candle-nn = { path = "../../vendor/candle/candle-nn", features = ["cuda"] }
-candle-transformers = { path = "../../vendor/candle/candle-transformers", features = ["cuda"] }
-hf-hub = "0.3.2"
+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"] }
 image = "0.25.5"
-serde_yaml = "0.9"
 log = "0.4"
 once_cell = "1.20.3"
-rand = "0.8"
-reqwest = "0.12.12"
+rand = "0.8.5"
+# NB(bt,2025-03-10): default-features = false on Windows while I debug openssl / rustls
+reqwest = { version = "0.12.12", features = ["stream"], default-features = false }
 serde = { version = "1.0", features = ["derive"] }
 serde_json = "1.0"
+serde_yaml = "0.9"
 tauri = { version = "2.2.4", features = [] }
 tauri-plugin-log = "2"
-tokenizers = "0.15.2"
+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"] }

-[features]
-default = ["cuda"]
-cuda = ["candle-core/cuda", "candle-nn/cuda", "candle-transformers/cuda"]
+# tracing-subscriber = { version = "0.3", features = [ "env-filter", "fmt" ] }
+
+[target.'cfg(not(target_os = "macos"))'.dependencies]
+ml_models = { path = "../ml_models" }

 [dev-dependencies]
 libloading = "0.8.6"
+serial_test = "3.2.0"
+assertor = "0.0.3"
+
+#[target.'cfg(target_os = "macos")'.features]
+#default  = []
+#ml_models = ["dep:ml_models"]

 [profile.dev]
 incremental = true # Compile your binary in smaller steps.
@@ -1,17 +1,12 @@
-use crate::ml::downloads::download_all_models::download_all_models;
-use crate::state::app_config::AppConfig;
-use tauri::{AppHandle, State};
+use crate::state::app_dir::AppDataRoot;
+use tauri::State;

 #[tauri::command]
 pub async fn download_models(
-  model_config: State<'_, AppConfig>,
-  app: AppHandle,
+  _app_data_root: State<'_, AppDataRoot>,
 ) -> Result<String, String> {
-  
-  download_all_models().await
-    .map_err(|err| {
-      "error downloading models".to_string()
-    })?;
-  
+
+  // NB: This endpoint has been removed for now as we now download models in the app lifecycle.
+
  Ok(("downloaded".to_string()))
 }
@@ -1,130 +0,0 @@
-use crate::ml::model_cache::ModelCache;
-use crate::ml::prompt_cache::PromptCache;
-use crate::ml::stable_diffusion::stable_diffusion_pipeline::{stable_diffusion_pipeline, Args};
-use crate::state::app_config::AppConfig;
-use base64::{engine::general_purpose::STANDARD as BASE64_STANDARD, Engine};
-use bytes::BytesMut;
-use image::imageops::FilterType;
-use image::{DynamicImage, ImageFormat, ImageReader};
-use log::{error, info};
-use std::io::Cursor;
-use std::path::PathBuf;
-use tauri::{AppHandle, State};
-use crate::ml::downloads::download_all_models::download_all_models;
-
-const PROMPT_FILENAME : &str = "prompt.txt";
-const PROMPT: &str = "A beautiful landscape with mountains and a lake";
-
-const NEGATIVE_PROMPT: &str = "bad quality, bad faces, poor quality, blurry faces, watermark";
-
-// TODO: Mark async https://github.com/tauri-apps/tauri/discussions/7737
-
-
-/// This handler takes an image (as a base64 encoded string) and a prompt and returns
-/// an image (as a base64-encoded string).
-#[tauri::command]
-pub async fn infer_image(
-  image: &str,
-  prompt: Option<String>,
-  strength: Option<u8>,
-  model_config: State<'_, AppConfig>,
-  model_cache: State<'_, ModelCache>,
-  prompt_cache: State<'_, PromptCache>,
-  app: AppHandle,
-) -> Result<String, String> {
-
-  let prompt = get_prompt_or_fallback(prompt);
-
-  info!("Prompt: {}", prompt);
-  info!("Strength: {:?}", strength);
-
-  let image = hydrate_base64_image(image)
-    .map_err(|err| format!("Couldn't hydrate image from base64: {}", err))?;
-
-  // TODO(bt): Move this to another endpoint
-  download_all_models().await
-    .map_err(|err| {
-      error!("couldn't download models: {:?}", err);
-      "Couldn't download models".to_string()
-    })?;
-
-  let result = do_infer_image(&prompt, image, strength, &model_config, &model_cache, prompt_cache, app).await;
-  
-  if let Err(err) = result.as_deref() {
-    error!("There was an error: {:?}", err);
-  }
-  
-  result
-}
-
-async fn do_infer_image(
-  prompt: &str,
-  image: DynamicImage,
-  strength: Option<u8>,
-  config: &AppConfig,
-  model_cache: &ModelCache,
-  prompt_cache: State<'_, PromptCache>,
-  app: AppHandle,
-) -> Result<String, String> {
-
-  let args = Args {
-    image: &image,
-    prompt: prompt.to_string(),
-    uncond_prompt: NEGATIVE_PROMPT.to_string(),
-    model_cache,
-    configs: config,
-    prompt_cache: &prompt_cache,
-    i2i_strength: strength,
-    cfg_scale: config.cfg_scale,
-    app: &app,
-  };
-
-  match stable_diffusion_pipeline(args) {
-    Ok(image) => {
-      let mut bytes = Vec::with_capacity(1024*1024);
-      
-      image.write_to(&mut Cursor::new(&mut bytes), ImageFormat::Png)
-        .map_err(|err| format!("failure to encode image: {:?}", err))?;
-
-      let bytes = BASE64_STANDARD.encode(bytes);
-
-      Ok(bytes)
-    },
-    Err(e) => Err(format!("Failed to generate image: {}", e)),
-  }
-}
-
-fn get_prompt_or_fallback(user_prompt: Option<String>) -> String {
-  let user_prompt = user_prompt.map(|prompt| prompt.trim().to_string())
-    .filter(|prompt| !prompt.is_empty());
-
-  if let Some(prompt) = user_prompt {
-    return prompt;
-  }
-
-  let prompt_file = PathBuf::from(PROMPT_FILENAME)
-    .canonicalize()
-    .unwrap_or_else(|_| PathBuf::from(PROMPT_FILENAME));
-
-  std::fs::read_to_string(&prompt_file)
-    .map_err(|err| format!("Failed to read prompt file: {}", err))
-    .unwrap_or_else(|_| {
-      error!("Failed to read prompt file: {:?}", prompt_file);
-      PROMPT.to_string()
-    })
-    .trim()
-    .to_string()
-}
-
-fn hydrate_base64_image(base64_image: &str) -> anyhow::Result<DynamicImage> {
-  let bytes = BASE64_STANDARD.decode(base64_image)?;
-
-  let image = ImageReader::new(Cursor::new(bytes))
-    .with_guessed_format()?
-    .decode()?;
-
-  // TODO(bt,2025-02-17): Running out of vram with full image buffer size
-  let image = image.resize(512, 512, FilterType::CatmullRom);
-
-  Ok(image)
-}
@@ -0,0 +1,24 @@
+use crate::ml::model_cache::ModelCache;
+use crate::ml::prompt_cache::PromptCache;
+use crate::state::app_config::AppConfig;
+use crate::state::app_dir::AppDataRoot;
+use image::{ImageReader, RgbImage};
+use log::info;
+use tauri::{AppHandle, State};
+
+
+#[tauri::command]
+pub async fn inpaint_image(
+  image: &str,
+  mask: &str,
+  prompt: String,
+  model_config: State<'_, AppConfig>,
+  model_cache: State<'_, ModelCache>,
+  prompt_cache: State<'_, PromptCache>,
+  app_data_root: State<'_, AppDataRoot>,
+  app: AppHandle,
+) -> Result<String, String> {
+  info!("inpaint_endpoint endpoint called.");
+
+  Ok(mask.to_string())
+}
@@ -1,4 +1,6 @@
 pub mod download_models;
 pub mod flip_image;
-pub mod image_endpoint;
-pub mod test_counter;
+pub mod inpaint_image_endpoint;
+pub mod realtime_image_endpoint;
+pub mod remove_background_endpoint;
+pub mod text_to_image_endpoint;
@@ -0,0 +1,143 @@
+#[cfg(not(target_os = "macos"))]
+use ml_models::ml::stable_diffusion::lcm_pipeline::{lcm_pipeline, Args};
+
+use crate::state::app_config::AppConfig;
+use crate::state::app_dir::AppDataRoot;
+use crate::stubs::model_cache::ModelCache;
+use crate::stubs::prompt_cache::PromptCache;
+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 crate::utils::image::encode_rgb_image_base64_png::encode_rgb_image_base64_png;
+use image::imageops::FilterType;
+use image::{DynamicImage, RgbImage};
+use log::{error, info};
+use ml_weights_registry::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::path::PathBuf;
+use tauri::{AppHandle, State};
+
+
+const PROMPT_FILENAME : &str = "prompt.txt";
+const PROMPT: &str = "shiba inu, cute dog, detailed, walking in a wooded forest, photorealistic, 8k";
+
+const NEGATIVE_PROMPT: &str = "bad quality, bad faces, poor quality, blurry faces, watermark";
+
+const RANDOM_SEED: u32 = 42;
+
+
+/// This handler takes an image (as a base64 encoded string) and a prompt and returns
+/// an image (as a base64-encoded string).
+#[tauri::command]
+pub async fn infer_image(
+  image: &str,
+  prompt: Option<String>,
+  strength: Option<f64>,
+  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);
+
+  let image = decode_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 mut image = image.resize(512, 512, FilterType::CatmullRom);
+
+  #[cfg(not(target_os = "macos"))]
+  {
+    let result = infer_image_impl(
+      &prompt,
+      image,
+      strength,
+      &model_config,
+      &model_cache,
+      prompt_cache,
+      &app_data_root
+    ).await;
+
+    image = match result {
+      Ok(img) => DynamicImage::ImageRgb8(img),
+      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_dynamic_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)
+}
+
+#[cfg(not(target_os = "macos"))]
+async fn infer_image_impl(
+  prompt: &str,
+  image: DynamicImage,
+  strength: Option<f64>,
+  config: &AppConfig,
+  model_cache: &ModelCache,
+  prompt_cache: State<'_, PromptCache>,
+  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);
+
+  let args = Args {
+    image: &image,
+    prompt: prompt.to_string(),
+    uncond_prompt: NEGATIVE_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)
+}
+
+fn get_prompt_or_fallback(user_prompt: Option<String>) -> String {
+  let user_prompt = user_prompt.map(|prompt| prompt.trim().to_string())
+    .filter(|prompt| !prompt.is_empty());
+
+  if let Some(prompt) = user_prompt {
+    return prompt;
+  }
+
+  let prompt_file = PathBuf::from(PROMPT_FILENAME)
+    .canonicalize()
+    .unwrap_or_else(|_| PathBuf::from(PROMPT_FILENAME));
+
+  std::fs::read_to_string(&prompt_file)
+    .map_err(|err| format!("Failed to read prompt file: {}", err))
+    .unwrap_or_else(|_| {
+      error!("Failed to read prompt file: {:?}", prompt_file);
+      PROMPT.to_string()
+    })
+    .trim()
+    .to_string()
+}
@@ -0,0 +1,45 @@
+//! Adapted from https://github.com/dnanhkhoa/rust-background-removal
+
+#[cfg(not(target_os = "macos"))]
+use ml_models::ml::background_removal::remove_image_background::remove_image_background;
+
+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::info;
+use ml_weights_registry::weights_registry::weights::DIS_MEDIUM_ONNX;
+use tauri::State;
+
+/// This handler removes the background from an image.
+#[tauri::command]
+pub async fn remove_background(
+  image: &str,
+  app_data_root: State<'_, AppDataRoot>,
+) -> Result<String, String> {
+  info!("remove_background endpoint called.");
+
+  let mut image = decode_base64_image(image)
+      .map_err(|err| format!("Couldn't hydrate image from base64: {}", err))?;
+
+  #[cfg(not(target_os = "macos"))]
+  {
+    image = remove_background_impl(&app_data_root, image).await?;
+  }
+
+  let image = encode_dynamic_image_base64_png(image)
+    .map_err(|err| format!("failure to encode image: {:?}", err))?;
+
+  Ok(image)
+}
+
+#[cfg(not(target_os = "macos"))]
+async fn remove_background_impl(app_data_root: &AppDataRoot, image: DynamicImage) -> Result<DynamicImage, String> {
+  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))?;
+
+  Ok(image)
+}
@@ -1,24 +0,0 @@
-use std::sync::{Arc, RwLock};
-use once_cell::sync::Lazy;
-
-static COUNTER : Lazy<Arc<RwLock<u64>>> = Lazy::new(|| Arc::new(RwLock::new(0)));
-
-#[tauri::command]
-pub fn test_counter() -> u64 {
-  let value : u64;
-  {
-    match COUNTER.write() {
-      Ok(mut counter) => {
-        *counter += 1;
-        value = *counter;
-      },
-      Err(_e) => {
-        value = 0;
-      }
-    }
-  }
-
-  println!("I was invoked from JavaScript! {:?}", value);
-
-  value
-}
@@ -0,0 +1,115 @@
+#[cfg(not(target_os = "macos"))]
+use ml_models::ml::stable_diffusion::lcm_pipeline::{lcm_pipeline, Args};
+
+use crate::state::app_config::AppConfig;
+use crate::state::app_dir::AppDataRoot;
+use crate::stubs::model_cache::ModelCache;
+use crate::stubs::prompt_cache::PromptCache;
+use crate::utils::image::encode_dynamic_image_base64_png::encode_dynamic_image_base64_png;
+use image::imageops::FilterType;
+use image::{DynamicImage, ImageReader, RgbImage};
+use log::{error, info};
+use ml_weights_registry::weights_registry::weights::{CLIP_JSON, LYKON_DEAMSHAPER_7_VAE, SDXL_TURBO_CLIP_TEXT_ENCODER, SIMIANLUO_LCM_DREAMSHAPER_V7_UNET};
+
+use once_cell::sync::Lazy;
+use std::io::Cursor;
+use tauri::{AppHandle, State};
+
+const RANDOM_SEED: u32 = 42;
+
+const STRENGTH: f64 = 100.0;
+const PNG_BYTES : &[u8] = include_bytes!("../../binary_includes/1024.png");
+
+// 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 mut image = IMAGE.resize(512, 512, FilterType::CatmullRom);
+
+  #[cfg(not(target_os = "macos"))]
+  {
+    let result = text_to_image_impl(
+      &prompt,
+      image,
+      Some(STRENGTH),
+      &model_config,
+      &model_cache,
+      prompt_cache,
+      app,
+      &app_data_root
+    ).await;
+
+    image = match result {
+      Ok(img) => DynamicImage::ImageRgb8(img),
+      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_dynamic_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)
+}
+
+#[cfg(not(target_os = "macos"))]
+async fn text_to_image_impl(
+  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)
+}
@@ -1,9 +1,11 @@
 use serde::Serialize;

-
 #[derive(Clone, Copy, Serialize)]
 #[serde(rename_all = "snake_case")]
-pub enum ModelType {
+pub enum NotificationModelType {
+  ImageSegmentation,
+  TextEncoder,
+  TextTokenizer,
  Unet,
  Vae,
 }
@@ -13,10 +15,16 @@ pub enum ModelType {
 pub enum NotificationEvent<'a> {
  ModelDownloadStarted {
    model_name: &'a str, 
-    model_type: ModelType,
+    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: ModelType,
+    model_type: NotificationModelType,
  }
 }
@@ -1,14 +1,22 @@
 pub mod endpoints;
 pub mod events;
-pub mod ml;
 pub mod state;
+pub mod stubs;
+pub mod threads;
+pub mod transfer;
+pub mod utils;

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

@@ -21,12 +29,16 @@ pub fn run() {
  let config = AppConfig::init()
    .expect("config should load");

+
  let prompt_cache = PromptCache::with_capacity(8)
    .expect("prompt cache should load");

  println!("Creating model cache...");
  
  let model_cache = ModelCache::new();
+  
+  let app_data_root = config.app_data_root.clone();
+  let app_data_root2 = config.app_data_root.clone();

  println!("Initializing backend runtime...");

@@ -36,6 +48,9 @@ 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()
@@ -43,15 +58,23 @@ pub fn run() {
      //      .build(),
      //  )?;
      //}
+      let app = app.handle().clone();
+
+      tauri::async_runtime::spawn(downloader_thread(app_data_root2, app));
+
      Ok(())
    })
    .manage(config)
    .manage(prompt_cache)
    .manage(model_cache)
+    .manage(app_data_root)
    .invoke_handler(tauri::generate_handler![
-      test_counter, 
-      infer_image,
      download_models,
+      flip_image,
+      infer_image,
+      inpaint_image,
+      remove_background,
+      text_to_image,
    ])
    .run(tauri::generate_context!())
    .expect("error while running tauri application");
@@ -1,9 +0,0 @@
-use crate::ml::downloads::download_model_registry_file::download_model_registry_file;
-use crate::ml::model_registry::ModelRegistry;
-
-pub async fn download_all_models() -> anyhow::Result<()> {
-  download_model_registry_file(ModelRegistry::Clip).await?;
-  download_model_registry_file(ModelRegistry::SdxlTurboUnet).await?;
-
-  Ok(())
-}
@@ -1,24 +0,0 @@
-use std::fs::File;
-use std::path::{Path, PathBuf};
-use log::info;
-use crate::ml::model_registry::ModelRegistry;
-
-pub async fn download_model_registry_file(model_type: ModelRegistry) -> anyhow::Result<PathBuf> {
-  info!("downloading model: {:?}", model_type);
-  let path = PathBuf::from(model_type.get_filename());
-  if path.exists() {
-    info!("model already exists: {:?}", path);
-    return Ok(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)
-}
@@ -1,2 +0,0 @@
-pub mod download_model_registry_file;
-pub mod download_all_models;
@@ -1,8 +0,0 @@
-pub mod image;
-pub mod model_cache;
-pub mod model_file;
-pub mod models;
-pub mod prompt_cache;
-pub mod stable_diffusion;
-pub mod model_registry;
-pub mod downloads;
@@ -1,83 +0,0 @@
-#[cfg(feature = "accelerate")]
-extern crate accelerate_src;
-#[cfg(feature = "mkl")]
-extern crate intel_mkl_src;
-
-use candle_transformers::models::stable_diffusion;
-
-#[derive(Debug, Clone, Copy, PartialEq, Eq)]
-pub enum ModelFile {
-  Tokenizer,
-  Tokenizer2,
-  Clip,
-  Clip2,
-  Unet,
-  Vae,
-}
-
-#[derive(Debug, Clone, Copy, PartialEq, Eq)]
-pub enum StableDiffusionVersion {
-  V1_5,
-  V1_5Inpaint,
-  V2_1,
-  V2Inpaint,
-  Xl,
-  XlInpaint,
-  Turbo,
-}
-
-impl StableDiffusionVersion {
-  pub fn repo(&self) -> &'static str {
-    match self {
-      Self::XlInpaint => "diffusers/stable-diffusion-xl-1.0-inpainting-0.1",
-      Self::Xl => "stabilityai/stable-diffusion-xl-base-1.0",
-      Self::V2Inpaint => "stabilityai/stable-diffusion-2-inpainting",
-      Self::V2_1 => "stabilityai/stable-diffusion-2-1",
-      Self::V1_5 => "runwayml/stable-diffusion-v1-5",
-      Self::V1_5Inpaint => "stable-diffusion-v1-5/stable-diffusion-inpainting",
-      Self::Turbo => "stabilityai/sdxl-turbo",
-    }
-  }
-}
-
-impl ModelFile {
-  pub fn get(
-    &self,
-    filename: Option<String>,
-    version: StableDiffusionVersion,
-    use_f16: bool,
-  ) -> anyhow::Result<std::path::PathBuf> {
-    use hf_hub::api::sync::Api;
-    match filename {
-      Some(filename) => Ok(std::path::PathBuf::from(filename)),
-      None => {
-        let (repo, path) = match self {
-          Self::Tokenizer => {
-            let tokenizer_repo = match version {
-              StableDiffusionVersion::V1_5
-              | StableDiffusionVersion::V2_1
-              | StableDiffusionVersion::V1_5Inpaint
-              | StableDiffusionVersion::V2Inpaint => "openai/clip-vit-base-patch32",
-              StableDiffusionVersion::Xl
-              | StableDiffusionVersion::XlInpaint
-              | StableDiffusionVersion::Turbo => {
-                "openai/clip-vit-large-patch14"
-              }
-            };
-            (tokenizer_repo, "tokenizer.json")
-          }
-          Self::Tokenizer2 => {
-            ("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", "tokenizer.json")
-          }
-          Self::Clip => (version.repo(), "text_encoder/model.safetensors"),
-          Self::Clip2 => (version.repo(), "text_encoder_2/model.safetensors"),
-          Self::Unet => (version.repo(), "unet/diffusion_pytorch_model.safetensors"),
-          Self::Vae => (version.repo(), "vae/diffusion_pytorch_model.safetensors"),
-        };
-        let filename = Api::new()?.model(repo.to_string()).get(path)?;
-        Ok(filename)
-      }
-    }
-  }
-}
-
@@ -1,29 +0,0 @@
-
-#[derive(Copy, Clone, Debug)]
-pub enum ModelRegistry {
-  Clip,
-  SdxlTurboUnet,
-}
-
-impl ModelRegistry {
-  pub fn get_hf_id(&self) -> Option<&'static str> {
-    match self {
-      Self::Clip => Some("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k"),
-      Self::SdxlTurboUnet => Some("stabilityai/sdxl-turbo"),
-    }
-  }
-
-  pub fn get_download_url(&self) -> &'static str {
-    match self {
-      Self::Clip => "https://pub-bc5e2bc0cdee4bb5ae8fca9d641ca0d6.r2.dev/tokenizer.json",
-      Self::SdxlTurboUnet => "https://pub-bc5e2bc0cdee4bb5ae8fca9d641ca0d6.r2.dev/diffusion_pytorch_model.unet.safetensors",
-    }
-  }
-
-  pub fn get_filename(&self) -> &'static str {
-    match self {
-      Self::Clip => "clip_vit_bigg_14_lion2b_39b_b160k.tokenizer.json",
-      Self::SdxlTurboUnet => "diffusion_pytorch_model.unet.safetensors",
-    }
-  }
-}
@@ -1,130 +0,0 @@
-use crate::ml::model_file::StableDiffusionVersion;
-use anyhow::anyhow;
-use candle_core::{DType, Device, Tensor};
-use candle_transformers::models::stable_diffusion::vae::{AutoEncoderKL, DiagonalGaussianDistribution};
-use candle_transformers::models::stable_diffusion::StableDiffusionConfig;
-use hf_hub::api::sync::Api;
-use std::sync::{Arc, RwLock};
-
-pub struct LazyLoadVaeModel {
-  lazy_model: Arc<RwLock<Option<AutoEncoderKL>>>,
-  model_configs: VaeConfigs,
-}
-
-// TODO: Maybe we pass these via a central config read at startup, passed as an Arc<ConfigT>
-pub struct VaeConfigs {
-  sd_config: StableDiffusionConfig,
-  sd_version: StableDiffusionVersion,
-  device: Device,
-  dtype: DType,
-  hf_api: Api,
-}
-
-impl LazyLoadVaeModel {
-  pub fn new(
-    sd_config: &StableDiffusionConfig,
-    sd_version: &StableDiffusionVersion,
-    hf_api: &Api,
-    device: &Device,
-    dtype: DType,
-  ) -> anyhow::Result<Self> {
-    println!("building vae model... (1)");
-    
-    Ok(Self {
-      lazy_model: Arc::new(RwLock::new(None)),
-      model_configs: VaeConfigs {
-        sd_config: sd_config.clone(),
-        sd_version: sd_version.clone(),
-        device: device.clone(),
-        hf_api: hf_api.clone(),
-        dtype
-      }
-    })
-  }
-
-  pub fn encode(&self, xs: &Tensor) -> anyhow::Result<DiagonalGaussianDistribution> {
-    println!("vae encode...");
-    match self.try_encode(xs) {
-      Err(err) => Err(err),
-      Ok(Some(result)) => Ok(result),
-      Ok(None) => {
-        // Model wasn't previously loaded
-        self.load_model()?;
-        self.try_encode(xs)?
-          .ok_or(anyhow!("model was not previously loaded"))
-      }
-    }
-  }
-  
-  pub fn decode(&self, xs: &Tensor) -> anyhow::Result<Tensor> {
-    println!("vae decode...");
-    match self.try_decode(xs) {
-      Err(err) => Err(err),
-      Ok(Some(result)) => Ok(result),
-      Ok(None) => {
-        // Model wasn't previously loaded
-        self.load_model()?;
-        self.try_decode(xs)?
-          .ok_or(anyhow!("model was not previously loaded"))
-      }
-    }
-  }
-
-  fn try_encode(&self, xs: &Tensor) -> anyhow::Result<Option<DiagonalGaussianDistribution>> {
-    match self.lazy_model.read() {
-      Err(err) => Err(anyhow!("lock error: {:?}", err)),
-      Ok(model) => {
-        match &*model {
-          None => Ok(None), // Model is not yet loaded
-          Some(model) => {
-            Ok(Some(model.encode(xs)?))
-          }
-        }
-      }
-    }
-  }
-
-  fn try_decode(&self, xs: &Tensor) -> anyhow::Result<Option<Tensor>> {
-    match self.lazy_model.read() {
-      Err(err) => Err(anyhow!("lock error: {:?}", err)),
-      Ok(model) => {
-        match &*model {
-          None => Ok(None), // Model is not yet loaded
-          Some(model) => {
-            Ok(Some(model.decode(xs)?))
-          }
-        }
-      }
-    }
-  }
-
-  fn load_model(&self) -> anyhow::Result<()> {
-    println!("load vae");
-    match self.lazy_model.write() {
-      Err(err) => return Err(anyhow!("lock error: {:?}", err)),
-      Ok(mut maybe_model) => {
-        match &*maybe_model {
-          Some(_model) => {} // Already loaded; fall through
-          None => {
-            let repo = self.model_configs.sd_version.repo();
-
-            println!("Building VAE model from : {:?} ... (3)", repo);
-            
-            let vae_file = self.model_configs.hf_api.model(repo.to_string())
-              .get("vae/diffusion_pytorch_model.safetensors")?;
-
-            println!("Building VAE model from file {:?}...", &vae_file);
-            let vae = self.model_configs
-              .sd_config
-              .build_vae(vae_file, &self.model_configs.device, self.model_configs.dtype)?;
-
-            println!("Built VAE...");
-            *maybe_model = Some(vae);
-          }
-        }
-      }
-    }
-    Ok(())
-  }
- 
-}
@@ -1,2 +0,0 @@
-pub mod unet_model;
-pub mod lazy_load_vae_model;
@@ -1,35 +0,0 @@
-use candle_core::{DType, Device, Tensor};
-use candle_transformers::models::stable_diffusion::unet_2d::UNet2DConditionModel;
-use candle_transformers::models::stable_diffusion::StableDiffusionConfig;
-use std::path::Path;
-
-pub struct UNetModel {
-  model: UNet2DConditionModel,
-}
-
-impl UNetModel {
-  pub fn new<P: AsRef<Path>>(
-    sd_config: &StableDiffusionConfig,
-    unet_weights_file: P,
-    device: &Device,
-    dtype: DType,
-  ) -> anyhow::Result<Self> {
-    println!("building unet model... (1)");
-    
-    let model = sd_config.build_unet(
-      unet_weights_file, 
-      &device, 
-      4, 
-      false, 
-      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)?)
-  }
-}
@@ -1,164 +0,0 @@
-#[cfg(feature = "accelerate")]
-extern crate accelerate_src;
-#[cfg(feature = "mkl")]
-extern crate intel_mkl_src;
-
-use std::path::PathBuf;
-use candle_transformers::models::stable_diffusion;
-
-use crate::ml::model_file::{ModelFile, StableDiffusionVersion};
-use anyhow::Error as E;
-use candle_core::{DType, Device, Module, Tensor, D};
-use log::info;
-use tokenizers::Tokenizer;
-use crate::ml::model_registry::ModelRegistry;
-
-#[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,
-) -> 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,
-      )
-    })
-    .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,
-  ) -> 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 = PathBuf::from(ModelRegistry::Clip.get_filename());
-  
-  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)?
-  } else {
-    clip_weights_file.get(clip2_weights, sd_version, use_f16)?
-  };
-  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)
-}
@@ -1,3 +0,0 @@
-pub mod get_vae_scale;
-pub mod infer_clip_text_embeddings;
-pub mod stable_diffusion_pipeline;
@@ -1,31 +1,14 @@
-use crate::ml::model_file::StableDiffusionVersion;
+use crate::state::app_dir::AppDataRoot;
+use crate::state::os_platform::OsPlatform;
 use crate::state::yaml_config::YamlConfig;
-use candle_core::{DType, Device};
-use candle_transformers::models::stable_diffusion::StableDiffusionConfig;
-use filesys::recursively_find_file_by_name::recursively_find_file_by_name;
-use hf_hub::api::sync::Api;
-use log::info;
+use crate::stubs::model_config::ModelConfig;

-const CONFIG_FILENAME : &str = "app_config.yaml";
+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;

 /// 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,
@@ -35,62 +18,45 @@ 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 {
  
  pub fn init() -> anyhow::Result<Self> {
-    println!("Configuration filename: {}", CONFIG_FILENAME);
-    let yaml_configs = load_yaml_configs();
+    let yaml_configs = YamlConfig::load_from_config_file_recursive();
    
    println!("Configs: {:?}", yaml_configs);
    
-    let device = Device::new_cuda(0)
-      .unwrap_or_else(|e| {
-          info!("CUDA not available ({}), falling back to CPU", e);
-          Device::Cpu
-        });
-    
-    let sd_config = match SD_VERSION {
-      StableDiffusionVersion::Turbo => {
-        StableDiffusionConfig::sdxl_turbo(None, Some(DEFAULT_SD_IMAGE_HEIGHT), Some(DEFAULT_SD_IMAGE_WIDTH))
-      }
-      _ => {
-        StableDiffusionConfig::v2_1(None, Some(DEFAULT_SD_IMAGE_HEIGHT), Some(DEFAULT_SD_IMAGE_WIDTH))
-      }
+    let os_platform = OsPlatform::get();
+
+    let default_data_root = match os_platform {
+      OsPlatform::Linux => yaml_configs.linux_default_data_path.as_deref(),
+      OsPlatform::MacOs => yaml_configs.mac_default_data_path.as_deref(),
+      OsPlatform::Windows => yaml_configs.windows_default_data_path.as_deref(),
    };
    
-    let hf_api = Api::new()?;
-    
+    println!("Possible user-defined data root: {:?}", default_data_root);
+
+    let app_data_root = match default_data_root {
+      None => AppDataRoot::create_default()?,
+      Some(path) => AppDataRoot::create_existing(path)?,
+    };
+
+    println!("App data root: {:?}", app_data_root.path());
+
    Ok(Self {
-      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(1),
+      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()?,
    })
  }
 }
-
-fn load_yaml_configs() -> YamlConfig {
-  let maybe_path = recursively_find_file_by_name(CONFIG_FILENAME, "../../../", 5)
-    .ok()
-    .flatten();
-  if let Some(path) = maybe_path {
-    let path = path.canonicalize().unwrap_or(path);
-    println!("Attempting to load configs from: {:?}", &path);
-    if let Ok(config) = YamlConfig::read_from_file(path) {
-      return config;
-    }
-  }
-  println!("Loading default configs.");
-  YamlConfig::default()
-}
@@ -0,0 +1,187 @@
+use crate::state::expanduser::expanduser;
+use crate::state::os_platform::OsPlatform;
+use anyhow::anyhow;
+use directories::UserDirs;
+use ml_weights_registry::weights_registry::weight_descriptor::WeightDescriptor;
+use std::path::{Path, PathBuf};
+use tempdir::TempDir;
+use tempfile::{Builder, NamedTempFile};
+
+const DEFAULT_DATA_DIR : &str = "artcraft";
+const ASSETS_SUBDIRECTORY : &str = "assets";
+const WEIGHTS_SUBDIRECTORY : &str = "weights";
+
+const TEMPORARY_SUBDIRECTORY : &str = "temp";
+
+/// The path to the application data directory, which includes "asset" and "weights" data.
+#[derive(Clone)]
+pub struct AppDataRoot {
+  path: PathBuf,
+  assets_dir: AppAssetsDir,
+  weights_dir: AppWeightsDir,
+  temp_dir: TemporaryDir,
+}
+
+#[derive(Clone)]
+pub struct AppAssetsDir {
+  path: PathBuf,
+}
+
+#[derive(Clone)]
+pub struct AppWeightsDir {
+  path: PathBuf,
+}
+
+#[derive(Clone)]
+pub struct TemporaryDir {
+  path: PathBuf,
+}
+
+impl AppDataRoot {
+  pub fn create_default() -> anyhow::Result<Self> {
+    let directory = get_default_data_dir()?;
+    Self::create_existing(directory)
+  }
+
+  pub fn create_existing<P: AsRef<Path>>(dir: P) -> anyhow::Result<Self> {
+    let mut dir = dir.as_ref().to_path_buf();
+    
+    match OsPlatform::get() {
+      OsPlatform::Linux | OsPlatform::MacOs => {
+        if let Some(d) = dir.as_os_str().to_str() {
+          dir = expanduser(d)?;
+        }
+      },
+      OsPlatform::Windows => {}
+    }
+    
+    if !dir.is_dir() {
+      println!("Creating directory {:?}", dir);
+      std::fs::create_dir_all(&dir)?;
+    }
+
+    match dir.canonicalize() {
+      Ok(d) => dir = d,
+      Err(err) => {
+        println!("Error canonicalizing {:?}: {}", dir, err);
+      }
+    }
+    
+    let assets_dir = AppAssetsDir::create_existing(dir.join(ASSETS_SUBDIRECTORY))?;
+    let weights_dir = AppWeightsDir::create_existing(dir.join(WEIGHTS_SUBDIRECTORY))?;
+    let temp_dir = TemporaryDir::create_existing(dir.join(TEMPORARY_SUBDIRECTORY))?;
+
+    Ok(Self {
+      path: dir,
+      assets_dir,
+      weights_dir,
+      temp_dir,
+    })
+  }
+  
+  pub fn assets_dir(&self) -> &AppAssetsDir {
+    &self.assets_dir
+  }
+  
+  pub fn weights_dir(&self) -> &AppWeightsDir {
+    &self.weights_dir
+  }
+
+  pub fn temp_dir(&self) -> &TemporaryDir {
+    &self.temp_dir
+  }
+
+  pub fn path(&self) -> &Path {
+    &self.path
+  }
+}
+
+impl AppAssetsDir {
+  pub fn create_existing<P: AsRef<Path>>(dir: P) -> anyhow::Result<Self> {
+    let mut dir = dir.as_ref().to_path_buf();
+    match dir.canonicalize() {
+      Ok(d) => dir = d,
+      Err(err) => {
+        println!("Error canonicalizing {:?}: {}", dir, err);
+      }
+    }
+    if !dir.exists() {
+      println!("Creating directory {:?}", dir);
+      std::fs::create_dir(&dir)?;
+    }
+    Ok(Self {
+      path: dir,
+    })
+  }
+
+  pub fn path(&self) -> &Path {
+    &self.path
+  }
+}
+
+impl AppWeightsDir {
+  pub fn create_existing<P: AsRef<Path>>(dir: P) -> anyhow::Result<Self> {
+    let mut dir = dir.as_ref().to_path_buf();
+    match dir.canonicalize() {
+      Ok(d) => dir = d,
+      Err(err) => {
+        println!("Error canonicalizing {:?}: {}", dir, err);
+      }
+    }
+    if !dir.exists() {
+      println!("Creating directory {:?}", dir);
+      std::fs::create_dir(&dir)?;
+    }
+    Ok(Self {
+      path: dir,
+    })
+  }
+
+  pub fn path(&self) -> &Path {
+    &self.path
+  }
+
+  pub fn weight_path(&self, descriptor: &WeightDescriptor) -> PathBuf {
+    self.path.join(descriptor.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))
+}
@@ -0,0 +1,73 @@
+use once_cell::sync::Lazy;
+use std::io;
+use std::path::{PathBuf, MAIN_SEPARATOR};
+
+static PREFIX: Lazy<String> = Lazy::new(|| format!("~{}", MAIN_SEPARATOR));
+
+/// The "expanduser" crate doesn't compile on Windows, so we replace its functionality slightly
+pub fn expanduser<P: AsRef<str>>(path: P) -> io::Result<PathBuf> {
+
+   Ok(match path.as_ref() {
+       // matches an exact "~"
+       s if s == "~" => {
+           home_dir()?
+       },
+       // matches paths that start with `~/`
+       s if s.starts_with(&*PREFIX) => {
+           let home = home_dir()?;
+           home.join(&s[2..])
+       },
+       // // matches paths that start with `~` but not `~/`, might be a `~username/` path
+       // s if s.starts_with("~") => {
+       //     let mut parts = s[1..].splitn(2, MAIN_SEPARATOR);
+       //     let user = parts.next()
+       //         .ok_or_else(|| io::Error::new(io::ErrorKind::Other, "malformed path"))?;
+       //     let user = Passwd::from_name(&user)
+       //         .map_err(|_| io::Error::new(io::ErrorKind::Other, "error searching for user"))?
+       //         .ok_or_else(|| io::Error::new(io::ErrorKind::Other, format!("user '{}', does not exist", &user)))?;
+       //     if let Some(ref path) = parts.next() {
+       //         PathBuf::from(user.dir).join(&path)
+       //     } else {
+       //         PathBuf::from(user.dir)
+       //     }
+       // },
+       // nothing to expand, just make a PathBuf
+       s => PathBuf::from(s)
+   })
+}
+
+pub fn home_dir() -> io::Result<PathBuf> {
+    dirs::home_dir()
+        .ok_or_else(|| io::Error::new(io::ErrorKind::Other, "no home directory is set"))
+}
+
+#[cfg(test)]
+mod tests {
+    use crate::state::expanduser::expanduser;
+    use std::env;
+    use std::path::PathBuf;
+
+    #[test]
+    #[serial_test::serial]
+    fn test_success() {
+        let old_home = env::var("HOME").expect("no home dir set");
+        let new_home = "/home/foo";
+        env::set_var("HOME", new_home);
+        let path = expanduser("~/path/to/directory");
+        env::set_var("HOME", old_home);
+        assert_eq!(path.expect("io error"), PathBuf::from("/home/foo/path/to/directory"));
+    }
+
+    #[test]
+    #[serial_test::serial]
+    fn test_only_tilde() {
+        let old_home = env::var("HOME").expect("no home dir set");
+        let new_home = "/home/foo";
+        env::set_var("HOME", new_home);
+        let pathstr = "~";
+        let path = expanduser(pathstr);
+        env::set_var("HOME", old_home);
+        assert_eq!(path.expect("io error"), PathBuf::from("/home/foo"));
+    }
+
+}
@@ -1,2 +1,5 @@
+pub mod app_config;
+pub mod app_dir;
+pub mod os_platform;
 pub mod yaml_config;
-pub mod app_config;
+pub mod expanduser;
@@ -0,0 +1,20 @@
+
+#[derive(Copy,Clone,Eq,PartialEq,Debug)]
+pub enum OsPlatform {
+  Linux,
+  MacOs,
+  Windows,
+}
+
+impl OsPlatform {
+  pub fn get() -> OsPlatform {
+    // Totally scientific.
+    // https://doc.rust-lang.org/std/env/consts/constant.OS.html
+    match std::env::consts::OS {
+      "linux" => OsPlatform::Linux,
+      "windows" => OsPlatform::Windows,
+      "macos" | "apple" => OsPlatform::MacOs,
+      _ => OsPlatform::Linux,
+    }
+  }
+}
@@ -1,7 +1,10 @@
+use filesys::recursively_find_file_by_name::recursively_find_file_by_name;
 use std::fs::File;
 use std::io::BufReader;
 use std::path::Path;

+const CONFIG_FILENAME : &str = "app_config.yaml";
+
 /// Configuration read or derived from a YAML config file at startup
 /// This is not meant to contain any runtime config values.
 /// This may not be exhaustive.
@@ -13,6 +16,15 @@ pub struct YamlConfig {
  pub scheduler_samples: Option<usize>,
  pub seed: Option<u64>,
  pub cfg_scale: Option<f64>,
+
+  /// Override the default location to store data on Linux
+  pub linux_default_data_path: Option<String>,
+
+  /// Override the default location to store data on Windows
+  pub windows_default_data_path: Option<String>,
+  
+  /// Override the default location to store data on Mac
+  pub mac_default_data_path: Option<String>,
 }

 impl YamlConfig {
@@ -22,4 +34,19 @@ impl YamlConfig {
    let config = serde_yaml::from_reader(reader)?;
    Ok(config)
  }
+
+  pub fn load_from_config_file_recursive() -> YamlConfig {
+    let maybe_path = recursively_find_file_by_name(CONFIG_FILENAME, "../../../", 5)
+      .ok()
+      .flatten();
+    if let Some(path) = maybe_path {
+      let path = path.canonicalize().unwrap_or(path);
+      println!("Attempting to load configs from: {:?}", &path);
+      if let Ok(config) = YamlConfig::read_from_file(path) {
+        return config;
+      }
+    }
+    println!("Couldn't load config file. Using default configs.");
+    YamlConfig::default()
+  }
 }
@@ -0,0 +1,3 @@
+pub mod model_cache;
+pub mod model_config;
+pub mod prompt_cache;
@@ -0,0 +1,14 @@
+#[cfg(not(target_os = "macos"))]
+pub use ml_models::ml::model_cache::ModelCache;
+
+#[cfg(not(not(target_os = "macos")))]
+pub struct ModelCache {
+  // Intentionally left blank
+}
+
+#[cfg(not(not(target_os = "macos")))]
+impl ModelCache {
+  pub fn new() -> Self {
+    Self {}
+  }
+}
@@ -0,0 +1,14 @@
+#[cfg(not(target_os = "macos"))]
+pub use ml_models::ml::model_config::ModelConfig;
+
+#[cfg(not(not(target_os = "macos")))]
+pub struct ModelConfig {
+  // Intentionally left blank
+}
+
+#[cfg(not(not(target_os = "macos")))]
+impl ModelConfig {
+  pub fn init() -> anyhow::Result<Self> {
+    Ok(Self {})
+  }
+}
@@ -0,0 +1,15 @@
+
+#[cfg(not(target_os = "macos"))]
+pub use ml_models::ml::prompt_cache::PromptCache;
+
+#[cfg(not(not(target_os = "macos")))]
+pub struct PromptCache {
+  // Intentionally left blank
+}
+
+#[cfg(not(not(target_os = "macos")))]
+impl PromptCache {
+  pub fn with_capacity(capacity: usize) -> anyhow::Result<Self> {
+    Ok(Self {})
+  }
+}
@@ -0,0 +1,181 @@
+use crate::events::notification_event::{NotificationEvent, NotificationModelType};
+use crate::state::app_dir::AppDataRoot;
+use crate::transfer::download::{download_async, ProgressUpdate};
+use futures::StreamExt;
+use futures::stream::FuturesUnordered;
+use log::{debug, error, info};
+use ml_weights_registry::weights_registry::weight_descriptor::WeightFunction;
+use ml_weights_registry::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>,
+}
+
+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;
+    
+    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;
+  }
+}
@@ -0,0 +1 @@
+pub mod downloader_thread;
@@ -0,0 +1,241 @@
+//! Implemented from https://github.com/huggingface/hf_transfer/blob/main/src/lib.rs
+
+use crate::transfer::error::Error;
+use crate::transfer::util::{exponential_backoff, BASE_WAIT_TIME, MAX_WAIT_TIME};
+use futures::stream::FuturesUnordered;
+use futures::StreamExt;
+use reqwest::header::{
+  HeaderMap, HeaderName, HeaderValue, AUTHORIZATION, CONTENT_RANGE,
+  RANGE,
+};
+use reqwest::Url;
+use std::collections::HashMap;
+use std::fmt::Display;
+use std::fs::remove_file;
+use std::io::SeekFrom;
+use std::path::Path;
+use std::sync::Arc;
+use std::sync::atomic::{AtomicUsize, Ordering};
+use std::sync::mpsc::Sender;
+use std::time::Duration;
+use log::info;
+use tempfile::NamedTempFile;
+use tokio::fs::OpenOptions;
+use tokio::io::AsyncWriteExt;
+use tokio::io::AsyncSeekExt;
+use tokio::sync::Semaphore;
+use tokio::time::sleep;
+
+pub struct ProgressUpdate {
+  pub complete: usize,
+  pub total_length: usize,
+}
+
+/// max_files: Number of open file handles, which determines the maximum number of parallel downloads
+/// parallel_failures:  Number of maximum failures of different chunks in parallel (cannot exceed max_files)
+/// max_retries: Number of maximum attempts per chunk. (Retries are exponentially backed off + jitter)
+///
+/// The number of threads can be tuned by the environment variable `TOKIO_WORKER_THREADS` as documented in
+/// https://docs.rs/tokio/latest/tokio/runtime/struct.Builder.html#method.worker_threads
+#[allow(clippy::too_many_arguments)]
+pub async fn download_async<P: AsRef<Path>>(
+  url: String,
+  filename: NamedTempFile,
+  final_filename: P,
+  max_files: usize,
+  chunk_size: usize,
+  parallel_failures: usize,
+  max_retries: usize,
+  input_headers: Option<HashMap<String, String>>,
+  maybe_progress_sender: Option<Sender<ProgressUpdate>>,
+) -> Result<(), Error> {
+  info!(">>> Downloading: {}", filename.as_ref().display());
+
+  let client = reqwest::Client::builder()
+    // https://github.com/hyperium/hyper/issues/2136#issuecomment-589488526
+    .http2_keep_alive_timeout(Duration::from_secs(15))
+    .build()?;
+
+  let mut headers = HeaderMap::new();
+  let mut auth_token = None;
+  if let Some(input_headers) = input_headers {
+    headers.reserve(input_headers.len());
+    for (k, v) in input_headers {
+      let name: HeaderName = k
+        .try_into()
+        .map_err(|err| Error::new_err(format!("Invalid header: {err}")))?;
+      let value: HeaderValue = AsRef::<str>::as_ref(&v)
+        .try_into()
+        .map_err(|err| Error::new_err(format!("Invalid header value: {err}")))?;
+      if name == AUTHORIZATION {
+        auth_token = Some(value);
+      } else {
+        headers.insert(name, value);
+      }
+    }
+  };
+
+  let response = if let Some(token) = auth_token.as_ref() {
+    client.get(&url).header(AUTHORIZATION, token)
+  } else {
+    client.get(&url)
+  }
+    .headers(headers.clone())
+    .header(RANGE, "bytes=0-0")
+    .send()
+    .await
+    .map_err(|err| Error::new_err(format!("Error while downloading: {err}")))?
+    .error_for_status()
+    .map_err(|err| Error::new_err(err.to_string()))?;
+
+  // Only call the final redirect URL to avoid overloading the Hub with requests and also
+  // altering the download count
+  let redirected_url = response.url();
+  if Url::parse(&url)
+    .map_err(|err| Error::new_err(format!("failed to parse url: {err}")))?
+    .host()
+    == redirected_url.host()
+  {
+    if let Some(token) = auth_token {
+      headers.insert(AUTHORIZATION, token);
+    }
+  }
+
+  let content_range = response
+    .headers()
+    .get(CONTENT_RANGE)
+    .ok_or(Error::new_err("No content length".to_string()))?
+    .to_str()
+    .map_err(|err| Error::new_err(format!("Error while downloading: {err}")))?;
+
+  let size: Vec<&str> = content_range.split('/').collect();
+  // Content-Range: bytes 0-0/702517648
+  // https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Content-Range
+  let length: usize = size
+    .last()
+    .ok_or(Error::new_err(
+      "Error while downloading: No size was detected".to_string(),
+    ))?
+    .parse()
+    .map_err(|err| Error::new_err(format!("Error while downloading: {err}")))?;
+
+
+  info!("File {:?} is {:?} length", filename.as_ref(), length);
+
+  let mut handles = FuturesUnordered::new();
+  let semaphore = Arc::new(Semaphore::new(max_files));
+  let parallel_failures_semaphore = Arc::new(Semaphore::new(parallel_failures));
+  let max_chunk_complete = Arc::new(AtomicUsize::new(0));
+
+  for start in (0..length).step_by(chunk_size) {
+    let url = redirected_url.to_string();
+    let filename = filename.as_ref().to_owned();
+    let client = client.clone();
+    let headers = headers.clone();
+
+    let stop = std::cmp::min(start + chunk_size - 1, length);
+    let semaphore = semaphore.clone();
+    let parallel_failures_semaphore = parallel_failures_semaphore.clone();
+    let maybe_progress = maybe_progress_sender.clone();
+    let max_chunk_complete = max_chunk_complete.clone();
+    handles.push(tokio::spawn(async move {
+      let permit = semaphore
+        .acquire_owned()
+        .await
+        .map_err(|err| Error::new_err(format!("Error while downloading: {err}")))?;
+      let mut chunk = download_chunk(&client, &url, &filename, start, stop, headers.clone()).await;
+      let mut i = 0;
+      if parallel_failures > 0 {
+        while let Err(dlerr) = chunk {
+          if i >= max_retries {
+            return Err(Error::new_err(format!(
+              "Failed after too many retries ({max_retries}): {dlerr}"
+            )));
+          }
+          let parallel_failure_permit = parallel_failures_semaphore.clone().try_acquire_owned().map_err(|err| {
+            Error::new_err(format!(
+              "Failed too many failures in parallel ({parallel_failures}): {dlerr} ({err})"
+            ))
+          })?;
+
+          let wait_time = exponential_backoff(BASE_WAIT_TIME, i, MAX_WAIT_TIME);
+          sleep(Duration::from_millis(wait_time as u64)).await;
+
+          chunk = download_chunk(&client, &url, &filename, start, stop, headers.clone()).await;
+          i += 1;
+          drop(parallel_failure_permit);
+          
+          if let Some(sender) = maybe_progress.as_ref() {
+            // NB: Calling fetch_max twice. First call will return the lower value.
+            let _old = max_chunk_complete.fetch_max(stop, Ordering::SeqCst);
+            let max_chunk = max_chunk_complete.fetch_max(stop, Ordering::SeqCst);
+            let progress_update = ProgressUpdate { 
+              complete: max_chunk,
+              total_length: length,
+            };
+            if let Err(_err) = sender.send(progress_update) {
+              // Fail silently, not essential to issue progress updates
+            }
+          }
+        }
+      }
+      drop(permit);
+      chunk.map_err(|e| Error::new_err(format!("Downloading error {e}"))).and(Ok(stop - start))
+    }));
+  }
+
+  // Output the chained result
+  while let Some(result) = handles.next().await {
+    match result {
+      Ok(Ok(size)) => {
+        //if let Some(ref callback) = callback {
+        //  callback.call((size,), None)?;
+        //}
+      }
+      Ok(Err(py_err)) => {
+        return Err(py_err);
+      }
+      Err(err) => {
+        return Err(Error::new_err(format!(
+          "Error while downloading: {err}"
+        )));
+      }
+    }
+  }
+
+  let file = filename.persist(final_filename)
+    .map_err(|e| Error::new_err(format!("Could not rename to final filename: {:?}", e)))?;
+
+  file.sync_all()?;
+
+  Ok(())
+}
+
+async fn download_chunk<P: AsRef<Path>>(
+  client: &reqwest::Client,
+  url: &str,
+  filename: P,
+  start: usize,
+  stop: usize,
+  headers: HeaderMap,
+) -> Result<(), Error> {
+  // Process each socket concurrently.
+  let range = format!("bytes={start}-{stop}");
+  let mut file = OpenOptions::new()
+    .write(true)
+    .truncate(false)
+    .create(true)
+    .open(filename)
+    .await?;
+  file.seek(SeekFrom::Start(start as u64)).await?;
+  let response = client
+    .get(url)
+    .headers(headers)
+    .header(RANGE, range)
+    .send()
+    .await?
+    .error_for_status()?;
+  let content = response.bytes().await?;
+  file.write_all(&content).await?;
+  Ok(())
+}
@@ -0,0 +1,49 @@
+//! Implemented from https://github.com/huggingface/hf_transfer/blob/main/src/lib.rs
+
+use std::fmt::Display;
+use reqwest::header::ToStrError;
+
+#[derive(Debug)]
+pub enum Error {
+  Io(std::io::Error),
+  Request(reqwest::Error),
+  ToStrError(ToStrError),
+  Misc(String),
+}
+
+impl From<std::io::Error> for Error {
+  fn from(value: std::io::Error) -> Self {
+    Self::Io(value)
+  }
+}
+
+impl From<reqwest::Error> for Error {
+  fn from(value: reqwest::Error) -> Self {
+    Self::Request(value)
+  }
+}
+
+impl From<ToStrError> for Error {
+  fn from(value: ToStrError) -> Self {
+    Self::ToStrError(value)
+  }
+}
+
+impl Display for Error {
+  fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+    match self {
+      Self::Io(io) => write!(f, "Io: {io}"),
+      Self::Request(req) => write!(f, "Request: {req}"),
+      Self::ToStrError(req) => write!(f, "Response non ascii: {req}"),
+      Self::Misc(err) => write!(f, "Misc error: {err}"),
+    }
+  }
+}
+
+impl std::error::Error for Error {}
+
+impl Error {
+  pub fn new_err(reason: String) -> Self {
+    Self::Misc(reason)
+  }
+}
@@ -0,0 +1,4 @@
+pub mod download;
+pub mod upload;
+pub mod util;
+pub mod error;
@@ -0,0 +1,189 @@
+//! Implemented from https://github.com/huggingface/hf_transfer/blob/main/src/lib.rs
+
+use crate::transfer::error::Error;
+use crate::transfer::util::{exponential_backoff, BASE_WAIT_TIME, MAX_WAIT_TIME};
+use futures::stream::FuturesUnordered;
+use futures::StreamExt;
+use reqwest::header::CONTENT_LENGTH;
+use std::collections::HashMap;
+use std::fmt::Display;
+use std::io::SeekFrom;
+use std::sync::Arc;
+use std::time::Duration;
+use tokio::fs::OpenOptions;
+use tokio::io::{AsyncReadExt, AsyncSeekExt};
+use tokio::sync::Semaphore;
+use tokio::time::sleep;
+use tokio_util::codec::{BytesCodec, FramedRead};
+
+/// parts_urls: Dictionary consisting of part numbers as keys and the associated url as values
+/// completion_url: The url that should be called when the upload is finished
+/// max_files: Number of open file handles, which determines the maximum number of parallel uploads
+/// parallel_failures:  Number of maximum failures of different chunks in parallel (cannot exceed max_files)
+/// max_retries: Number of maximum attempts per chunk. (Retries are exponentially backed off + jitter)
+///
+/// The number of threads can be tuned by the environment variable `TOKIO_WORKER_THREADS` as documented in
+/// https://docs.rs/tokio/latest/tokio/runtime/struct.Builder.html#method.worker_threads
+///
+/// See https://docs.aws.amazon.com/AmazonS3/latest/userguide/mpuoverview.html for more information
+/// on the multipart upload
+#[allow(clippy::too_many_arguments)]
+fn multipart_upload(
+  file_path: String,
+  parts_urls: Vec<String>,
+  chunk_size: u64,
+  max_files: usize,
+  parallel_failures: usize,
+  max_retries: usize,
+) -> Result<Vec<HashMap<String, String>>, Error> {
+  if parallel_failures > max_files {
+    return Err(Error::new_err(
+      "Error parallel_failures cannot be > max_files".to_string(),
+    ));
+  }
+  if (parallel_failures == 0) != (max_retries == 0) {
+    return Err(Error::new_err(
+      "For retry mechanism you need to set both `parallel_failures` and `max_retries`"
+        .to_string(),
+    ));
+  }
+
+  tokio::runtime::Builder::new_multi_thread()
+    .enable_all()
+    .build()?
+    .block_on(async {
+      upload_async(
+        file_path,
+        parts_urls,
+        chunk_size,
+        max_files,
+        parallel_failures,
+        max_retries,
+      )
+        .await
+    })
+}
+
+
+
+#[allow(clippy::too_many_arguments)]
+async fn upload_async(
+  file_path: String,
+  parts_urls: Vec<String>,
+  chunk_size: u64,
+  max_files: usize,
+  parallel_failures: usize,
+  max_retries: usize,
+) -> Result<Vec<HashMap<String, String>>, Error> {
+  let client = reqwest::Client::new();
+
+  let mut handles = FuturesUnordered::new();
+  let semaphore = Arc::new(Semaphore::new(max_files));
+  let parallel_failures_semaphore = Arc::new(Semaphore::new(parallel_failures));
+
+  for (part_number, part_url) in parts_urls.iter().enumerate() {
+    let url = part_url.to_string();
+    let path = file_path.to_owned();
+    let client = client.clone();
+
+    let start = (part_number as u64) * chunk_size;
+    let semaphore = semaphore.clone();
+    let parallel_failures_semaphore = parallel_failures_semaphore.clone();
+    handles.push(tokio::spawn(async move {
+      let permit = semaphore
+        .clone()
+        .acquire_owned()
+        .await
+        .map_err(|err| Error::new_err(format!("Error acquiring semaphore: {err}")))?;
+      let mut chunk = upload_chunk(&client, &url, &path, start, chunk_size).await;
+      let mut i = 0;
+      if parallel_failures > 0 {
+        while let Err(ul_err) = chunk {
+          if i >= max_retries {
+            return Err(Error::new_err(format!(
+              "Failed after too many retries ({max_retries}): {ul_err}"
+            )));
+          }
+
+          let parallel_failure_permit = parallel_failures_semaphore.clone().try_acquire_owned().map_err(|err| {
+            Error::new_err(format!(
+              "Failed too many failures in parallel ({parallel_failures}): {ul_err} ({err})"
+            ))
+          })?;
+
+          let wait_time = exponential_backoff(BASE_WAIT_TIME, i, MAX_WAIT_TIME);
+          sleep(Duration::from_millis(wait_time as u64)).await;
+
+          chunk = upload_chunk(&client, &url, &path, start, chunk_size).await;
+          i += 1;
+          drop(parallel_failure_permit);
+        }
+      }
+      drop(permit);
+      chunk.map_err(|e|{
+        //match e {
+        //  Error::Io(io) => Error::new_err(format!("Io error {io}")),
+        //  Error::Request(req) => Error::new_err(format!("Error while sending chunk {req}")),
+        //  Error::ToStrError(req) => Error::new_err(format!("Response header contains non ASCII chars: {req}")),
+        //}
+        e
+      }).map(|chunk| (part_number, chunk, chunk_size))
+    }));
+  }
+
+  let mut results: Vec<HashMap<String, String>> = vec![HashMap::default(); parts_urls.len()];
+
+  while let Some(result) = handles.next().await {
+    match result {
+      Ok(Ok((part_number, headers, size))) => {
+        //if let Some(ref callback) = callback {
+        //  callback.call((size,), None)?;
+        //}
+        results[part_number] = headers;
+      }
+      Ok(Err(py_err)) => {
+        return Err(py_err);
+      }
+      Err(err) => {
+        return Err(Error::new_err(format!(
+          "Error occurred while uploading: {err}"
+        )));
+      }
+    }
+  }
+
+  Ok(results)
+}
+
+async fn upload_chunk(
+  client: &reqwest::Client,
+  url: &str,
+  path: &str,
+  start: u64,
+  chunk_size: u64,
+) -> Result<HashMap<String, String>, Error> {
+  let mut options = OpenOptions::new();
+  let mut file = options.read(true).open(path).await?;
+  let file_size = file.metadata().await?.len();
+  let bytes_transferred = std::cmp::min(file_size - start, chunk_size);
+
+  file.seek(SeekFrom::Start(start)).await?;
+  let chunk = file.take(chunk_size);
+
+  let response = client
+    .put(url)
+    .header(CONTENT_LENGTH, bytes_transferred)
+    .body(reqwest::Body::wrap_stream(FramedRead::new(
+      chunk,
+      BytesCodec::new(),
+    )))
+    .send()
+    .await?;
+  let response = response.error_for_status()?;
+  let mut headers = HashMap::new();
+  for (name, value) in response.headers().into_iter() {
+    headers.insert(name.to_string(), value.to_str()?.to_owned());
+  }
+  Ok(headers)
+}
+
@@ -0,0 +1,15 @@
+//! Implemented from https://github.com/huggingface/hf_transfer/blob/main/src/lib.rs
+
+use rand::{thread_rng, Rng};
+use std::fmt::Display;
+
+pub const BASE_WAIT_TIME: usize = 300;
+pub const MAX_WAIT_TIME: usize = 10_000;
+
+fn jitter() -> usize {
+  thread_rng().gen_range(0..=500)
+}
+
+pub fn exponential_backoff(base_wait_time: usize, n: usize, max: usize) -> usize {
+  (base_wait_time + n.pow(2) + jitter()).min(max)
+}
@@ -0,0 +1,14 @@
+use base64::prelude::BASE64_STANDARD;
+use base64::Engine;
+use image::{DynamicImage, ImageReader};
+use std::io::Cursor;
+
+pub fn decode_base64_image(base64_image: &str) -> anyhow::Result<DynamicImage> {
+  let bytes = BASE64_STANDARD.decode(base64_image)?;
+
+  let image = ImageReader::new(Cursor::new(bytes))
+    .with_guessed_format()?
+    .decode()?;
+
+  Ok(image)
+}
@@ -0,0 +1,14 @@
+use base64::prelude::BASE64_STANDARD;
+use base64::Engine;
+use image::{DynamicImage, ImageFormat};
+use std::io::Cursor;
+
+pub fn encode_dynamic_image_base64_png(image: DynamicImage) -> anyhow::Result<String> {
+  let mut bytes = Vec::with_capacity(1024*1024);
+
+  image.write_to(&mut Cursor::new(&mut bytes), ImageFormat::Png)?;
+
+  let bytes = BASE64_STANDARD.encode(bytes);
+
+  Ok(bytes)
+}
@@ -0,0 +1,16 @@
+use base64::prelude::BASE64_STANDARD;
+use base64::Engine;
+use image::{ImageFormat, RgbImage};
+use std::io::Cursor;
+
+pub fn encode_rgb_image_base64_png(
+  image: RgbImage
+) -> anyhow::Result<String> {
+  let mut bytes = Vec::with_capacity(1024*1024);
+
+  image.write_to(&mut Cursor::new(&mut bytes), ImageFormat::Png)?;
+
+  let bytes = BASE64_STANDARD.encode(bytes);
+
+  Ok(bytes)
+}
@@ -0,0 +1,3 @@
+pub mod decode_base64_image;
+pub mod encode_dynamic_image_base64_png;
+pub mod encode_rgb_image_base64_png;
@@ -0,0 +1,11 @@
+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));
+}
@@ -0,0 +1,2 @@
+pub mod image;
+pub mod log_environment_details;
@@ -1,6 +1,6 @@
 {
  "$schema": "https://schema.tauri.app/config/2.0.0-rc",
-  "productName": "Realtime Desktop App",
+  "productName": "ArtCraft",
  "version": "0.1.0",
  "identifier": "com.unique.value.thing.why",
  "build": {
@@ -12,9 +12,9 @@
  "app": {
    "windows": [
      {
-        "title": "Realtime Desktop App Title Goes Here",
-        "width": 1200,
-        "height": 800,
+        "title": "ArtCraft",
+        "width": 2400,
+        "height": 1300,
        "resizable": true,
        "fullscreen": false
      }
@@ -32,3 +32,4 @@ my-workspace-hack = { version = "0.1", path = "../../../my-workspace-hack" }

 [dev-dependencies]
 testing = { path = "../../testing" }
+assertor = "0.0.3"
@@ -1,5 +1,6 @@
 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;
@@ -0,0 +1,131 @@
+
+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);
+  }
+}
@@ -118,24 +118,40 @@ pub static DEVELOPMENT_PREMIUM_PLANS: Lazy<HashSet<Plan>> = Lazy::new(|| {

 /// All paid-for production premium plans in the system.
 pub static PRODUCTION_PREMIUM_PLANS: Lazy<HashSet<Plan>> = Lazy::new(|| {
-    let mut plans = HashSet::new();
+  let mut plans = HashSet::new();

-    // ========== All-locale plans ==========
+  // ========== All-locale plans ==========

-    plans.insert(PlanBuilder::new("fakeyou_plus")
-        .plan_category(PlanCategory::Paid)
-        .stripe_product_id("prod_MoLv23HrxPiY7D")
-        .stripe_price_id("price_1M4jDCEU5se17MekOaJ92HYX")
-        .cost_per_month_dollars(7)
-        .tts_base_priority_level(20)
-        .tts_max_character_length(2048)
-        .tts_max_duration_seconds(30)
-        .web_vc_base_priority_level(20)
-        .web_vc_requires_frontend_keepalive(false)
-        .lipsync_requires_frontend_keepalive(false)
-        .build());
+  // Old "plus" price
+  plans.insert(PlanBuilder::new("fakeyou_plus")
+      .plan_category(PlanCategory::Paid)
+      .stripe_product_id("prod_MoLv23HrxPiY7D")
+      .stripe_price_id("price_1M4jDCEU5se17MekOaJ92HYX")
+      .cost_per_month_dollars(7)
+      .tts_base_priority_level(20)
+      .tts_max_character_length(2048)
+      .tts_max_duration_seconds(30)
+      .web_vc_base_priority_level(20)
+      .web_vc_requires_frontend_keepalive(false)
+      .lipsync_requires_frontend_keepalive(false)
+      .build());

-    plans.insert(PlanBuilder::new("fakeyou_pro")
+  // New "plus" price
+  plans.insert(PlanBuilder::new("fakeyou_plus_plan")
+      .plan_category(PlanCategory::Paid)
+      .stripe_product_id("prod_RvC5qUgLTSLGSD")
+      .stripe_price_id("price_1R1Lh8EU5se17MekaUNtWc59")
+      .cost_per_month_dollars(12)
+      .tts_base_priority_level(20)
+      .tts_max_character_length(2048)
+      .tts_max_duration_seconds(30)
+      .web_vc_base_priority_level(20)
+      .web_vc_requires_frontend_keepalive(false)
+      .lipsync_requires_frontend_keepalive(false)
+      .build());
+
+  // Old "pro" price
+  plans.insert(PlanBuilder::new("fakeyou_pro")
        .plan_category(PlanCategory::Paid)
        .stripe_product_id("prod_MoLw8nA6eFxHzc")
        .stripe_price_id("price_1M4jEQEU5se17MeksNfA0EKm")
@@ -149,7 +165,23 @@ pub static PRODUCTION_PREMIUM_PLANS: Lazy<HashSet<Plan>> = Lazy::new(|| {
        .can_remove_visual_watermarks(true)
        .build());

-    plans.insert(PlanBuilder::new("fakeyou_elite")
+  // New "pro" price
+  plans.insert(PlanBuilder::new("fakeyou_pro_plan")
+      .plan_category(PlanCategory::Paid)
+      .stripe_product_id("prod_RvC6nB9rw6yAsI")
+      .stripe_price_id("price_1R1LhzEU5se17Mek3JLwN3Gg")
+      .cost_per_month_dollars(25)
+      .tts_base_priority_level(30)
+      .tts_max_character_length(3072)
+      .tts_max_duration_seconds(60 * 2)
+      .web_vc_base_priority_level(30)
+      .web_vc_requires_frontend_keepalive(false)
+      .lipsync_requires_frontend_keepalive(false)
+      .can_remove_visual_watermarks(true)
+      .build());
+
+  // Old "elite" price
+  plans.insert(PlanBuilder::new("fakeyou_elite")
        .plan_category(PlanCategory::Paid)
        .stripe_product_id("prod_MoLxQmLA4R24fv")
        .stripe_price_id("price_1M4jFREU5se17Mekc8pQaSKB")
@@ -163,6 +195,21 @@ pub static PRODUCTION_PREMIUM_PLANS: Lazy<HashSet<Plan>> = Lazy::new(|| {
        .can_remove_visual_watermarks(true)
        .build());

+  // New "elite" price
+  plans.insert(PlanBuilder::new("fakeyou_elite_plan")
+      .plan_category(PlanCategory::Paid)
+      .stripe_product_id("prod_RvC6i84YiAYStd")
+      .stripe_price_id("price_1R1LiQEU5se17MekiJdBcsHQ")
+      .cost_per_month_dollars(40)
+      .tts_base_priority_level(40)
+      .tts_max_character_length(4096)
+      .tts_max_duration_seconds(60 * 5)
+      .web_vc_base_priority_level(40)
+      .web_vc_requires_frontend_keepalive(false)
+      .lipsync_requires_frontend_keepalive(false)
+      .can_remove_visual_watermarks(true)
+      .build());
+
    // ========== Spanish plans ==========

    plans.insert(PlanBuilder::new("fakeyou_basic")
@@ -293,7 +340,7 @@ mod test {
    // making ourselves check twice when we make additions, removals, or other changes.
    #[test]
    fn test_number_of_plans_is_expected() {
-        assert_eq!(11, ALL_PLANS_BY_SLUG.len());
+        assert_eq!(14, ALL_PLANS_BY_SLUG.len());
    }

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

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

        assert_eq!(expected, production_plans.len());
        assert_eq!(expected, PRODUCTION_PREMIUM_PLANS.len());
@@ -346,6 +393,9 @@ mod test {
            "prod_MoLw8nA6eFxHzc".to_string(), // NB: Production
            "prod_MoLxQmLA4R24fv".to_string(), // NB: Production
            "prod_MoLyt8qMDmscjr".to_string(), // NB: Production
+            "prod_RvC5qUgLTSLGSD".to_string(), // NB: Production - new "plus" product
+            "prod_RvC6nB9rw6yAsI".to_string(), // NB: Production - new "pro" product
+            "prod_RvC6i84YiAYStd".to_string(), // NB: Production - new "elite" product
        ];

        assert_that(&product_ids).contains_all_of(&expected_product_ids.iter());
@@ -366,6 +416,9 @@ mod test {
            "price_1M4jEQEU5se17MeksNfA0EKm".to_string(), // NB: Production
            "price_1M4jFREU5se17Mekc8pQaSKB".to_string(), // NB: Production
            "price_1M4jGPEU5se17Mek2FiztNE5".to_string(), // NB: Production
+            "price_1R1Lh8EU5se17MekaUNtWc59".to_string(), // NB: Production - new "plus" price
+            "price_1R1LhzEU5se17Mek3JLwN3Gg".to_string(), // NB: Production - new "pro" price
+            "price_1R1LiQEU5se17MekiJdBcsHQ".to_string(), // NB: Production - new "elite" price
        ];

        assert_that(&price_ids).contains_all_of(&expected_price_ids.iter());
@@ -86,6 +86,8 @@ fn main() -> Result<()> {
        .arg("--expt-relaxed-constexpr")
        .arg("--expt-extended-lambda")
        .arg("--use_fast_math")
+        .arg("--compiler-options")
+        .arg("-fPIC")
        .arg("--verbose");

    if let Ok(target) = std::env::var("TARGET") {
@@ -100,7 +102,12 @@ fn main() -> Result<()> {
    println!("cargo:rustc-link-search={}", build_dir.display());
    println!("cargo:rustc-link-lib=flashattention");
    println!("cargo:rustc-link-lib=dylib=cudart");
-    println!("cargo:rustc-link-lib=dylib=stdc++");
+
+
+    // TODO(bt,2025-03-12): Fix builds on windows
+    //println!("cargo:rustc-link-lib=dylib=stdc++");
+
+

    Ok(())
 }
@@ -32,3 +32,4 @@ metal = ["candle/metal", "dep:candle-metal-kernels", "dep:metal"]
 [[bench]]
 name = "bench_main"
 harness = false
+
@@ -17,6 +17,7 @@ serde = { version = "1.0.171", features = ["derive"] }
 serde_json = "1.0.99"
 serde_plain = "1.0.2"
 tracing = "0.1.37"
+half = { version = "2.3.1", features = ["num-traits", "use-intrinsics", "rand_distr"] }

 [features]
 default = []
@@ -84,6 +84,8 @@ impl StableDiffusionConfig {
        };
        // https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/unet/config.json
        let unet = unet_2d::UNet2DConditionModelConfig {
+            time_embed_dim: None,
+            time_cond_proj_dim: None,
            blocks: vec![
                bc(320, Some(1), 8),
                bc(640, Some(1), 8),
@@ -153,6 +155,8 @@ impl StableDiffusionConfig {
        };
        // https://huggingface.co/stabilityai/stable-diffusion-2-1/blob/main/unet/config.json
        let unet = unet_2d::UNet2DConditionModelConfig {
+            time_cond_proj_dim: None,
+            time_embed_dim: None,
            blocks: vec![
                bc(320, Some(1), 5),
                bc(640, Some(1), 10),
@@ -237,6 +241,8 @@ impl StableDiffusionConfig {
        };
        // https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/unet/config.json
        let unet = unet_2d::UNet2DConditionModelConfig {
+            time_cond_proj_dim: None,
+            time_embed_dim: None,
            blocks: vec![
                bc(320, None, 5),
                bc(640, Some(2), 10),
@@ -306,6 +312,8 @@ impl StableDiffusionConfig {
        };
        // https://huggingface.co/stabilityai/sdxl-turbo/blob/main/unet/config.json
        let unet = unet_2d::UNet2DConditionModelConfig {
+            time_cond_proj_dim: None,
+            time_embed_dim: None,
            blocks: vec![
                bc(320, None, 5),
                bc(640, Some(2), 10),
@@ -405,6 +413,8 @@ impl StableDiffusionConfig {
        };
        // https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/unet/config.json
        let unet = unet_2d::UNet2DConditionModelConfig {
+            time_cond_proj_dim: None,
+            time_embed_dim: None,
            blocks: vec![
                bc(320, None, 5),
                bc(640, Some(2), 10),
@@ -10,6 +10,10 @@ use candle_nn as nn;
 use candle_nn::Module;
 use std::collections::HashMap;

+// fn save_tensor_as_numpy(tensor: &Tensor, filename: &str) -> Result<()> {
+//     // Use Candle's built-in NPY serialization
+//     tensor.write_npy(filename).map_err(|e| anyhow::anyhow!("{}", e))
+// }
 #[derive(Debug, Clone, Copy)]
 pub struct BlockConfig {
  pub out_channels: usize,
@@ -33,11 +37,13 @@ pub struct UNet2DConditionModelConfig {
  pub cross_attention_dim: usize,
  pub sliced_attention_size: Option<usize>,
  pub use_linear_projection: bool,
+  pub time_cond_proj_dim: Option<usize>,
+  pub time_embed_dim: Option<usize>,
 }

 impl Default for UNet2DConditionModelConfig {
  fn default() -> Self {
-    Self { center_input_sample: false, flip_sin_to_cos: true, freq_shift: 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., norm_num_groups: 32, norm_eps: 1e-5, cross_attention_dim: 1280, sliced_attention_size: None, use_linear_projection: false }
+    Self { center_input_sample: false, flip_sin_to_cos: true, freq_shift: 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., norm_num_groups: 32, norm_eps: 1e-5, cross_attention_dim: 1280, sliced_attention_size: None, use_linear_projection: false, time_cond_proj_dim: Some(256), time_embed_dim: None }
  }
 }

@@ -73,8 +79,16 @@ impl UNet2DConditionModel {
    let b_channels = config.blocks[0].out_channels;
    let bl_channels = config.blocks.last().unwrap().out_channels;
    let bl_attention_head_dim = config.blocks.last().unwrap().attention_head_dim;
-    let time_embed_dim = b_channels * 4;
+    let time_embed_dim = config.time_embed_dim.unwrap_or(b_channels * 4);
    let conv_cfg = nn::Conv2dConfig { padding: 1, ..Default::default() };
+
+    // Log important configuration details
+    println!("Creating UNet2DConditionModel with {} blocks", n_blocks);
+    if let Some(proj_dim) = config.time_cond_proj_dim {
+      println!("UNet configured with time_cond_proj_dim: {}", proj_dim);
+      println!("This means the model expects guidance embeddings of dimension {}", proj_dim);
+    }
+
    let conv_in = conv2d(in_channels, b_channels, 3, conv_cfg, vs.pp("conv_in"))?;

    let time_proj = Timesteps::new(b_channels, config.flip_sin_to_cos, config.freq_shift);
@@ -241,18 +255,138 @@ impl UNet2DConditionModel {

    // Add guidance embedding if provided
    if let Some(guidance_emb) = guidance_embedding {
-      // Print shapes for debugging
-      println!("Guidance embedding shape: {:?}", guidance_emb.shape());
-      println!("Time embedding shape: {:?}", time_emb.shape());
+      // Get dimensions of both embeddings
+      let (batch_size, time_emb_dim) = time_emb.dims2()?;
+      let (guidance_batch, guidance_emb_dim) = guidance_emb.dims2()?;

-      // Get the batch size and embedding dimension
-      let (batch_size, emb_dim) = time_emb.dims2()?;
+      println!("==== Guidance Embedding Integration ====");
+      println!("Time embedding dims: [{}, {}]", batch_size, time_emb_dim);
+      println!("Guidance embedding dims: [{}, {}]", guidance_batch, guidance_emb_dim);

-      // Reshape guidance embedding to match time embedding dimensions
-      let guidance_emb = guidance_emb.reshape(&[1, emb_dim])?.expand(&[batch_size, emb_dim])?;
+      // Use time_cond_proj_dim from config to determine expected guidance embedding dimension
+      let expected_guidance_dim = self.config.time_cond_proj_dim.unwrap_or(time_emb_dim);
+      println!("Expected guidance dimension from config: {}", expected_guidance_dim);
+
+      // Check if dimensions match the expected projection dimension
+      let processed_guidance_emb = if guidance_emb_dim != time_emb_dim {
+        // Convert guidance_emb to f32 for calculations
+        let guidance_emb_f32 = guidance_emb.to_dtype(DType::F32)?;
+        println!("Converting guidance embedding to F32 for processing");
+
+        // If the guidance embedding dimension matches the expected projection dimension,
+        // we need to project it to match the time embedding dimension
+        if guidance_emb_dim == expected_guidance_dim {
+          println!("Projecting guidance embedding from {} to {}", guidance_emb_dim, time_emb_dim);
+
+          // Use linear interpolation
+          let src_positions: Vec<f32> = (0..guidance_emb_dim).map(|i| i as f32 / (guidance_emb_dim - 1) as f32).collect();
+          let dst_positions: Vec<f32> = (0..time_emb_dim).map(|i| i as f32 / (time_emb_dim - 1) as f32).collect();
+
+          // Get guidance embedding data (now f32)
+          let guidance_data = guidance_emb_f32.reshape((guidance_emb_dim,))?.to_vec1::<f32>()?;
+          println!("Reshaping guidance data to vector of length {}", guidance_data.len());
+
+          // Perform linear interpolation
+          let mut interpolated_data = Vec::with_capacity(time_emb_dim);
+          for &t in &dst_positions {
+            // Find the two source positions that surround the target position
+            let mut i = 0;
+            while i < src_positions.len() - 1 && src_positions[i + 1] < t {
+              i += 1;
+            }
+
+            if i == src_positions.len() - 1 {
+              // At or beyond the last position
+              interpolated_data.push(guidance_data[i]);
+            } else if t <= src_positions[0] {
+              // At or before the first position
+              interpolated_data.push(guidance_data[0]);
+            } else {
+              // Linear interpolation between two points
+              let t_norm = (t - src_positions[i]) / (src_positions[i + 1] - src_positions[i]);
+              let value = guidance_data[i] * (1.0 - t_norm) + guidance_data[i + 1] * t_norm;
+              interpolated_data.push(value);
+            }
+          }
+
+          println!("Created interpolated data of length {}", interpolated_data.len());
+
+          // Create tensor and convert to time_emb's dtype
+          let tensor = Tensor::from_vec(interpolated_data, (1, time_emb_dim), device)?;
+          let result = tensor.to_dtype(time_emb.dtype())?;
+          println!("Final projected guidance embedding shape: {:?}", result.shape());
+          result
+        } else {
+          // Unusual case - dimensions don't match expected pattern
+          println!("Warning: Guidance embedding dimension ({}) doesn't match expected projection dimension ({})", guidance_emb_dim, expected_guidance_dim);
+
+          // Fallback to generic interpolation
+          let guidance_data = guidance_emb_f32.reshape((guidance_emb_dim,))?.to_vec1::<f32>()?;
+
+          let src_positions: Vec<f32> = (0..guidance_emb_dim).map(|i| i as f32 / (guidance_emb_dim - 1) as f32).collect();
+          let dst_positions: Vec<f32> = (0..time_emb_dim).map(|i| i as f32 / (time_emb_dim - 1) as f32).collect();
+
+          let mut interpolated_data = Vec::with_capacity(time_emb_dim);
+          for &t in &dst_positions {
+            let mut i = 0;
+            while i < src_positions.len() - 1 && src_positions[i + 1] < t {
+              i += 1;
+            }
+
+            if i == src_positions.len() - 1 {
+              interpolated_data.push(guidance_data[i]);
+            } else if t <= src_positions[0] {
+              interpolated_data.push(guidance_data[0]);
+            } else {
+              let t_norm = (t - src_positions[i]) / (src_positions[i + 1] - src_positions[i]);
+              let value = guidance_data[i] * (1.0 - t_norm) + guidance_data[i + 1] * t_norm;
+              interpolated_data.push(value);
+            }
+          }
+
+          println!("Created interpolated data of length {}", interpolated_data.len());
+          let tensor = Tensor::from_vec(interpolated_data, (1, time_emb_dim), device)?;
+          let result = tensor.to_dtype(time_emb.dtype())?;
+          println!("Final fallback guidance embedding shape: {:?}", result.shape());
+          result
+        }
+      } else {
+        // Dimensions match, no processing needed
+        println!("Guidance dimension already matches time embedding dimension, no interpolation needed");
+        let result = guidance_emb.reshape((1, time_emb_dim))?.to_dtype(time_emb.dtype())?;
+        println!("Reshaped guidance embedding shape: {:?}", result.shape());
+        result
+      };
+
+      // Expand to match batch size if needed
+      let expanded_guidance = processed_guidance_emb.broadcast_as(time_emb.shape())?;
+      println!("Expanded guidance embedding shape: {:?}", expanded_guidance.shape());
+
+      // Save the processed and expanded guidance embeddings to disk if requested
+      if let Ok(save_dir) = std::env::var("CANDLE_SAVE_TENSORS_DIR") {
+        // Save pre-expansion (original projection)
+        let proc_path = format!("{}/unet_processed_guidance_embedding.npy", save_dir);
+        println!("Saving processed guidance embedding to {}", proc_path);
+        if let Err(e) = processed_guidance_emb.write_npy(&proc_path) {
+          println!("Error saving processed guidance embedding: {}", e);
+        } else {
+          println!("Successfully saved processed guidance embedding to {}", proc_path);
+        }
+
+        // Save post-expansion (final tensor used for addition to time_emb)
+        let exp_path = format!("{}/unet_expanded_guidance_embedding.npy", save_dir);
+        println!("Saving expanded guidance embedding to {}", exp_path);
+        if let Err(e) = expanded_guidance.write_npy(&exp_path) {
+          println!("Error saving expanded guidance embedding: {}", e);
+        } else {
+          println!("Successfully saved expanded guidance embedding to {}", exp_path);
+        }
+      }

      // Add to time embeddings
-      time_emb = (time_emb + guidance_emb)?;
+      time_emb = (time_emb + expanded_guidance)?;
+      println!("Final time embedding with guidance shape: {:?}", time_emb.shape());
+      println!("==== End Guidance Integration ====");
    }

    // 2. pre-process
@@ -8,6 +8,7 @@
      "name": "storyteller-board",
      "version": "0.0.0",
      "dependencies": {
+        "@devnomic/marquee": "^1.0.3",
        "@fortawesome/free-brands-svg-icons": "^6.6.0",
        "@headlessui/react": "^2.1.3",
        "@preact/signals-react": "^2.2.0",
@@ -381,6 +382,21 @@
        "node": ">=6.9.0"
      }
    },
+    "node_modules/@devnomic/marquee": {
+      "version": "1.0.3",
+      "resolved": "https://registry.npmjs.org/@devnomic/marquee/-/marquee-1.0.3.tgz",
+      "integrity": "sha512-q8QUtNO8ff+UvfrKJsHBtxh4fbOfhGk6SSdK0Uu/XZFdEzrmiGJ/l0FuVHRYsPAmzgnhVVnz1dGNB5CWGLMVvw==",
+      "dependencies": {
+        "clsx": "^2.1.0",
+        "tailwind-merge": "^2.2.0"
+      },
+      "peerDependencies": {
+        "@types/react": "^18.2.47",
+        "@types/react-dom": "^18.2.18",
+        "react": "^18.2.0",
+        "react-dom": "^18.2.0"
+      }
+    },
    "node_modules/@emotion/is-prop-valid": {
      "version": "1.3.0",
      "resolved": "https://registry.npmjs.org/@emotion/is-prop-valid/-/is-prop-valid-1.3.0.tgz",
@@ -1510,14 +1526,12 @@
    "node_modules/@types/prop-types": {
      "version": "15.7.12",
      "resolved": "https://registry.npmjs.org/@types/prop-types/-/prop-types-15.7.12.tgz",
-      "integrity": "sha512-5zvhXYtRNRluoE/jAp4GVsSduVUzNWKkOZrCDBWYtE7biZywwdC2AcEzg+cSMLFRfVgeAFqpfNabiPjxFddV1Q==",
-      "dev": true
+      "integrity": "sha512-5zvhXYtRNRluoE/jAp4GVsSduVUzNWKkOZrCDBWYtE7biZywwdC2AcEzg+cSMLFRfVgeAFqpfNabiPjxFddV1Q=="
    },
    "node_modules/@types/react": {
      "version": "18.3.4",
      "resolved": "https://registry.npmjs.org/@types/react/-/react-18.3.4.tgz",
      "integrity": "sha512-J7W30FTdfCxDDjmfRM+/JqLHBIyl7xUIp9kwK637FGmY7+mkSFSe6L4jpZzhj5QMfLssSDP4/i75AKkrdC7/Jw==",
-      "dev": true,
      "dependencies": {
        "@types/prop-types": "*",
        "csstype": "^3.0.2"
@@ -1527,7 +1541,6 @@
      "version": "18.3.0",
      "resolved": "https://registry.npmjs.org/@types/react-dom/-/react-dom-18.3.0.tgz",
      "integrity": "sha512-EhwApuTmMBmXuFOikhQLIBUn6uFg81SwLMOAUgodJF14SOBOCMdU04gDoYi0WOJJHD144TL32z4yDqCW3dnkQg==",
-      "dev": true,
      "dependencies": {
        "@types/react": "*"
      }
@@ -2364,8 +2377,7 @@
    "node_modules/csstype": {
      "version": "3.1.3",
      "resolved": "https://registry.npmjs.org/csstype/-/csstype-3.1.3.tgz",
-      "integrity": "sha512-M1uQkMl8rQK/szD0LNhtqxIPLpimGm8sOBwU7lLnCpSbTyY3yeU1Vc7l4KT5zT4s/yOxHH5O7tIuuLOCnLADRw==",
-      "dev": true
+      "integrity": "sha512-M1uQkMl8rQK/szD0LNhtqxIPLpimGm8sOBwU7lLnCpSbTyY3yeU1Vc7l4KT5zT4s/yOxHH5O7tIuuLOCnLADRw=="
    },
    "node_modules/data-view-buffer": {
      "version": "1.0.1",
@@ -11,6 +11,7 @@
    "preview": "vite preview"
  },
  "dependencies": {
+    "@devnomic/marquee": "^1.0.3",
    "@fortawesome/free-brands-svg-icons": "^6.6.0",
    "@headlessui/react": "^2.1.3",
    "@preact/signals-react": "^2.2.0",
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Brandon Thomas	14feaccb91	Merge branch 'main' into feature/generatemode	2025-03-25 01:38:48 -04:00
Brandon Thomas	81c256e4f8	cleanup	2025-03-24 23:08:07 -04:00
Brandon Thomas	8d51d5b8ab	can't trigger default features; switch to platform switches	2025-03-24 23:00:29 -04:00
Brandon Thomas	456bfd7bb9	trigger with --feature ml_models (need to make default)	2025-03-24 22:31:58 -04:00
Brandon Thomas	cd3c864eff	runs on mac	2025-03-24 21:46:25 -04:00
Brandon Thomas	990b7ff3e5	package split for mac (part one) (#459 )	2025-03-24 20:18:57 -04:00
Brandon Thomas	e1dbf4f2fe	add inpaint demo/test endpoint	2025-03-22 03:16:48 -04:00
Tanish Manku	d826db599d	Remove the cancel generation button for now	2025-03-22 00:08:06 -07:00
Tanish Manku	a642732c59	Clip overflow to have top rounded corners on image	2025-03-22 00:02:21 -07:00
Brandon Thomas	435c5aa0b4	fix strength bug	2025-03-22 02:57:06 -04:00
Tanish Manku	90b4aff846	Fix: Multiple invokes of a prompt after changing mode	2025-03-21 23:56:50 -07:00
Tanish Manku	4d3f097432	Hook up the TTI UI to invoke call	2025-03-21 23:38:25 -07:00
Tanish Manku	e5de0e4731	Merge branch 'main' into feature/generatemode	2025-03-21 23:11:07 -07:00
Tanish Manku	04498b0457	Add initial Edit mode files	2025-03-21 23:02:17 -07:00
Brandon Thomas	e3245dc593	fix on windows	2025-03-22 00:58:04 -04:00
bflatastic	fbfc1e15f2	gallery gap fix	2025-03-21 22:46:30 +07:00
bflatastic	4a65317729	rename image to realtime	2025-03-21 20:13:13 +07:00
Tanish Manku	e7ed0693a3	Add generation engine and signals	2025-03-21 00:44:44 -07:00
Tanish Manku	99ac8faf33	Merge branch 'main' into feature/generatemode	2025-03-20 23:41:19 -07:00
Brandon Thomas	08590d0c61	log environment details	2025-03-21 01:11:34 -04:00
Brandon Thomas	32cb39a643	text_to_image endpoint	2025-03-20 23:28:59 -04:00
bflatastic	f0213d4a63	cancel button	2025-03-21 00:03:15 +07:00
Brandon Thomas	5fdb4d1024	disable msi builds due to 2.0 gb limit for 32bit	2025-03-20 11:59:43 -04:00
bflatastic	58f684b1e0	tab disabled prop and disabled message to block tab change when generating	2025-03-20 22:42:36 +07:00
bflatastic	e83389a874	background gallery + many other styling and animation transition updates	2025-03-20 22:11:12 +07:00
Brandon Thomas	d11945b685	download DIS model	2025-03-20 11:07:34 -04:00
Brandon Thomas	f3babbcb19	if statements -> switch statements	2025-03-20 10:53:34 -04:00
Tanish Manku	3602f8fc2b	Add generation signals	2025-03-20 02:10:27 -07:00
Tanish Manku	115c4fc60c	Add a generation mode UI component	2025-03-19 23:58:53 -07:00
Brandon Thomas	f37011ffc6	remove background: frontend UX (#458 )	2025-03-20 01:41:43 -04:00
Brandon Thomas	de683b750a	remove background from images	2025-03-18 22:10:18 -04:00
Brandon Thomas	4603c65d2a	fix strength slider range	2025-03-18 18:21:39 -04:00
Brandon Thomas	c5b0d837a9	fix bug with float strengths from frontend	2025-03-18 01:48:54 -04:00
Brandon Thomas	652785224a	easier windows builds. put dlls in the windows_dlls folder	2025-03-14 04:22:18 -04:00
Brandon Thomas	8d3ed947d1	change default prompt, log end of endpoint logic	2025-03-14 03:20:46 -04:00
Brandon Thomas	243583f0b7	kill more dead code	2025-03-13 23:39:04 -04:00
Brandon Thomas	150223c48c	refactor weights (#455 )	2025-03-13 23:29:20 -04:00
bflatastic	ddf6a7f54e	top center ui bug fix and padding	2025-03-13 22:28:26 +07:00
Brandon Thomas	17c6e549b5	ignore windows dlls	2025-03-13 00:38:10 -04:00
Brandon Thomas	031d9d8f20	Download models from Cloudflare instead of HF (#454 )	2025-03-13 00:32:43 -04:00
Brandon Thomas	43df6c9181	app default size	2025-03-12 21:58:27 -04:00
Tanish Manku	a8e029497f	Fix context menu showing up after transforming (#453 )	2025-03-12 15:18:00 -07:00
Brandon Thomas	4936662ddd	correct default path on windows	2025-03-12 18:08:23 -04:00
BFlat	883e32220c	tabs and app mode signals (#450 ) Usage example - Conditional Rendering Based on App Mode: ```typescript import { appMode } from "~/signals"; function ConditionalComponent() { return ( <div className="app-container"> {/* Show/hide components based on current mode */} {appMode.value === 'image' && ( <div className="image-tools"> <h3>Image Mode Tools</h3> </div> )} {appMode.value === 'edit' && ( <div className="edit-tools"> <h3>Edit Mode Tools</h3> </div> )} ); } ```	2025-03-12 13:26:10 -04:00
Brandon Thomas	727eead5a7	fix windows search for missing stdc++.lib during build	2025-03-12 01:27:27 -04:00
Kasisnu	b4cc5b84cf	f16	2025-03-11 22:30:02 +05:30
Brandon Thomas	d7e77775ca	Add new premium plan identifiers	2025-03-11 02:15:46 -04:00
Brandon Thomas	b75472c434	fix windows builds by disabling flash attn/cutlass, openssl, etc.	2025-03-11 00:49:30 -04:00
Brandon Thomas	a4f876daa9	Multithreaded downloader + notifications (#447 )	2025-03-10 21:51:07 -04:00
Tanish Manku	4b309a5dd8	Render shapes as vectors and revamp rendering logic (#442 )	2025-03-07 14:41:11 -08:00
Tanish Manku	08384955e4	Remove 'React-Eyedrop' and unused imports (#443 ) As simple as it sounds	2025-03-06 23:05:23 -08:00
Brandon Thomas	31037b83a3	downloader thread	2025-03-06 22:48:48 -05:00
Brandon Thomas	53d93cb1ac	change name of app	2025-03-06 21:45:01 -05:00
Brandon Thomas	a0de8fc603	load all models from model registry	2025-03-06 21:39:19 -05:00
Brandon Thomas	ef3527dd85	weights directory, assets directory	2025-03-06 21:06:58 -05:00
Brandon Thomas	1a887d7414	don't download anything from huggingface	2025-03-06 19:27:22 -05:00
Brandon Thomas	48abc0d731	no longer download clip encoders from huggingface	2025-03-06 15:22:10 -05:00
CrossPr0duct	0233105393	Merge branch 'main' of github.com:storytold/storyteller-rust	2025-03-06 15:12:28 -05:00
CrossPr0duct	8f94f81db4	Background Hack Implemented without engine refactors.	2025-03-06 15:12:04 -05:00
Kasisnu	3f25aefc3f	starting point (#440 )	2025-03-06 10:41:08 -05:00
BFlat	c2929cfdb6	color picker ux and make paint cursor visible on both dark/light bg (#441 ) ux updates to color picker and paint cursor	2025-03-06 20:52:25 +07:00
Brandon Thomas	a859bd427b	download clip models (not using them yet)	2025-03-06 08:42:44 -05:00