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config.json

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+{
+  "_class_name": "HunyuanVideo_1_5_Pipeline",
+  "_diffusers_version": "0.35.0",
+  "byt5_max_length": 256,
+  "byt5_model": [
+    "transformers",
+    "T5Stack"
+  ],
+  "byt5_tokenizer": [
+    "transformers",
+    "ByT5Tokenizer"
+  ],
+  "default_negative_prompt": null,
+  "embedded_guidance_scale": null,
+  "flow_shift": 7.0,
+  "glyph_byT5_v2": true,
+  "guidance_scale": 6.0,
+  "scheduler": [
+    "hyvideo.schedulers.scheduling_flow_match_discrete",
+    "FlowMatchDiscreteScheduler"
+  ],
+  "text_encoder": [
+    "hyvideo.models.text_encoders",
+    "TextEncoder"
+  ],
+  "text_encoder_2": [
+    null,
+    null
+  ],
+  "transformer": [
+    "hyvideo.models.transformers.hunyuanvideo_1_5_transformer",
+    "HunyuanVideo_1_5_DiffusionTransformer"
+  ],
+  "vae": [
+    "hyvideo.models.autoencoders.hunyuanvideo_15_vae",
+    "AutoencoderKLConv3D"
+  ],
+  "vision_encoder": [
+    "hyvideo.models.vision_encoder",
+    "VisionEncoder"
+  ],
+  "vision_num_semantic_tokens": 729,
+  "vision_states_dim": 1152
+}

scheduler/scheduler_config.json

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+{
+  "_class_name": "FlowMatchDiscreteScheduler",
+  "_diffusers_version": "0.35.0",
+  "flux_base_shift": 0.5,
+  "flux_base_token": 256.0,
+  "flux_max_shift": 1.15,
+  "flux_max_token": 4096.0,
+  "flux_shift_factor": 1.0,
+  "n_tokens": null,
+  "num_train_timesteps": 1000,
+  "reverse": true,
+  "shift": 7.0,
+  "solver": "euler",
+  "use_flux_shift": false
+}

text_encoder/Glyph-SDXL-v2/.mv

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+Revision:master,CreatedAt:1718991679

text_encoder/Glyph-SDXL-v2/README.md

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+---
+language:
+- en
+library_name: glyph-byt5
+---
+
+# Glyph-ByT5-v2: A Strong Aesthetic Baseline for Accurate Multilingual Visual Text Rendering
+
+We introduce **Glyph-ByT5-v2**, a customized text encoder for accurate **multilingual** visual text rendering and improved aesthetics. 
+As an extension of **Glyph-SDXL**, our multilingual version supports visual text rendering for up to 10 different languages: English, Chinese, Japanese, Korean, French, German, Spanish, Italian, Portuguese and Russian. 
+Combined with SDXL, our proposed **Glyph-SDXL-v2** achieves accurate multilingual design image visual text rendering.
+
+
+> [**Glyph-ByT5-v2: A Strong Aesthetic Baseline for Accurate Multilingual Visual Text Rendering**](https://glyph-byt5-v2.github.io/)            
+> [Zeyu Liu](https://github.com/lzy-tony), [Weicong Liang](https://scholar.google.com/citations?user=QvHDIygAAAAJ&hl=zh-CN), [Yiming Zhao](https://scholar.google.com.hk/citations?user=_knPaYsAAAAJ&hl=zh-CN), [Bohan Chen](https://github.com/BHCHENGIT), [Ji Li](https://sites.google.com/a/usc.edu/jili/), [Yuhui Yuan](https://www.microsoft.com/en-us/research/people/yuyua/)            
+> Microsoft Research Asia; Tsinghua University; Peking University; University of Liverpool  
+> Preprint
+
+## Model Sources
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [https://github.com/AIGText/Glyph-ByT5]
+- **Paper:** [https://arxiv.org/abs/2406.10208]
+- **Project Page:** [https://glyph-byt5-v2.github.io/]
+
+
+## Model Description
+
+Please check our [paper](https://arxiv.org/abs/2406.10208) and [project page](https://glyph-byt5-v2.github.io/) for more details. Detail usage and inference code can be found [here](https://github.com/AIGText/Glyph-ByT5).
+
+## Visualization
+
+<table>
+  <tr>
+    <td><img src="assets/teaser/teaser_multilingual_1.webp" alt="example 1" width="200"/></td>
+    <td><img src="assets/teaser/teaser_multilingual_2.webp" alt="example 2" width="200"/></td>
+    <td><img src="assets/teaser/teaser_multilingual_3.webp" alt="example 3" width="200"/></td>
+    <td><img src="assets/teaser/teaser_multilingual_4.webp" alt="example 4" width="200"/></td>
+  </tr>
+</table>
+
+## Quick Usage
+
+```
+python inference_v2.py configs/glyph_sdxl_v2_albedo.py checkpoints examples/xiaoman.json --out_folder work_dirs/xiaoman --device cuda --sampler dpm
+```
+
+## More Configurations
+
+We list some more useful configurations for easy usage:
+
+| Argument/Config               | Place      | Default                             | Description                                                  |
+| ----------------------------- | ---------- | ----------------------------------- | ------------------------------------------------------------ |
+| cfg                           | argument   | 5.0                                 | Classifier-free guidance                                     |
+| sampler                       | argument   | dpm                                 | Sampler, provide support for dpm (DPM++ 2M Karras) and euler (EulerDiscreteScheduler) |
+| pretrained_model_name_or_path | config     | stablediffusionapi/albedobase-xl-20 | Base model                                                   |
+| seed                          | annotation | None                                | Seed for inference                                           |
+
+
+## Citation
+
+If you find our work useful in your research, please consider citing:
+
+```
+@misc{liu2024glyphbyt5v2,
+      title={Glyph-ByT5-v2: A Strong Aesthetic Baseline for Accurate Multilingual Visual Text Rendering}, 
+      author={Zeyu Liu and Weicong Liang and Yiming Zhao and Bohan Chen and Ji Li and Yuhui Yuan},
+      year={2024},
+      eprint={2406.10208},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```
+
+and
+
+```
+@misc{liu2024glyphbyt5,
+      title={Glyph-ByT5: A Customized Text Encoder for Accurate Visual Text Rendering}, 
+      author={Zeyu Liu and Weicong Liang and Zhanhao Liang and Chong Luo and Ji Li and Gao Huang and Yuhui Yuan},
+      year={2024},
+      eprint={2403.09622},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```

text_encoder/Glyph-SDXL-v2/assets/chinese_char.txt

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text_encoder/Glyph-SDXL-v2/assets/color_idx.json

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text_encoder/Glyph-SDXL-v2/assets/font_idx_512.json

@@ -0,0 +1 @@
+{"Montserrat-Regular": 0, "Poppins-Italic": 1, "GlacialIndifference-Regular": 2, "OpenSans-ExtraBoldItalic": 3, "Montserrat-Bold": 4, "Now-Regular": 5, "Garet-Regular": 6, "LeagueSpartan-Bold": 7, "DMSans-Regular": 8, "OpenSauceOne-Regular": 9, "OpenSans-ExtraBold": 10, "KGPrimaryPenmanship": 11, "Anton-Regular": 12, "Aileron-BlackItalic": 13, "Quicksand-Light": 14, "Roboto-BoldItalic": 15, "TheSeasons-It": 16, "Kollektif": 17, "Inter-BoldItalic": 18, "Poppins-Medium": 19, "Poppins-Light": 20, "RoxboroughCF-RegularItalic": 21, "PlayfairDisplay-SemiBold": 22, "Agrandir-Italic": 23, "Lato-Regular": 24, "MoreSugarRegular": 25, "CanvaSans-RegularItalic": 26, "PublicSans-Italic": 27, "CodePro-NormalLC": 28, "Belleza-Regular": 29, "JosefinSans-Bold": 30, "HKGrotesk-Bold": 31, "Telegraf-Medium": 32, "BrittanySignatureRegular": 33, "Raleway-ExtraBoldItalic": 34, "Mont-RegularItalic": 35, "Arimo-BoldItalic": 36, "Lora-Italic": 37, "ArchivoBlack-Regular": 38, "Poppins": 39, "Barlow-Black": 40, "CormorantGaramond-Bold": 41, "LibreBaskerville-Regular": 42, "CanvaSchoolFontRegular": 43, "BebasNeueBold": 44, "LazydogRegular": 45, "FredokaOne-Regular": 46, "Horizon-Bold": 47, "Nourd-Regular": 48, "Hatton-Regular": 49, "Nunito-ExtraBoldItalic": 50, "CerebriSans-Regular": 51, "Montserrat-Light": 52, "TenorSans": 53, "Norwester-Regular": 54, "ClearSans-Bold": 55, "Cardo-Regular": 56, "Alice-Regular": 57, "Oswald-Regular": 58, "Gaegu-Bold": 59, "Muli-Black": 60, "TAN-PEARL-Regular": 61, "CooperHewitt-Book": 62, "Agrandir-Grand": 63, "BlackMango-Thin": 64, "DMSerifDisplay-Regular": 65, "Antonio-Bold": 66, "Sniglet-Regular": 67, "BeVietnam-Regular": 68, "NunitoSans10pt-BlackItalic": 69, "AbhayaLibre-ExtraBold": 70, "Rubik-Regular": 71, "PPNeueMachina-Regular": 72, "TAN - MON CHERI-Regular": 73, "Jua-Regular": 74, "Playlist-Script": 75, "SourceSansPro-BoldItalic": 76, "MoonTime-Regular": 77, "Eczar-ExtraBold": 78, "Gatwick-Regular": 79, "MonumentExtended-Regular": 80, "BarlowSemiCondensed-Regular": 81, "BarlowCondensed-Regular": 82, "Alegreya-Regular": 83, "DreamAvenue": 84, "RobotoCondensed-Italic": 85, "BobbyJones-Regular": 86, "Garet-ExtraBold": 87, "YesevaOne-Regular": 88, "Dosis-ExtraBold": 89, "LeagueGothic-Regular": 90, "OpenSans-Italic": 91, "TANAEGEAN-Regular": 92, "Maharlika-Regular": 93, "MarykateRegular": 94, "Cinzel-Regular": 95, "Agrandir-Wide": 96, "Chewy-Regular": 97, "BodoniFLF-BoldItalic": 98, "Nunito-BlackItalic": 99, "LilitaOne": 100, "HandyCasualCondensed-Regular": 101, "Ovo": 102, "Livvic-Regular": 103, "Agrandir-Narrow": 104, "CrimsonPro-Italic": 105, "AnonymousPro-Bold": 106, "NF-OneLittleFont-Bold": 107, "RedHatDisplay-BoldItalic": 108, "CodecPro-Regular": 109, "HalimunRegular": 110, "LibreFranklin-Black": 111, "TeXGyreTermes-BoldItalic": 112, "Shrikhand-Regular": 113, "TTNormsPro-Italic": 114, "Gagalin-Regular": 115, "OpenSans-Bold": 116, "GreatVibes-Regular": 117, "Breathing": 118, "HeroLight-Regular": 119, "KGPrimaryDots": 120, "Quicksand-Bold": 121, "Brice-ExtraLightSemiExpanded": 122, "Lato-BoldItalic": 123, "Fraunces9pt-Italic": 124, "AbrilFatface-Regular": 125, "BerkshireSwash-Regular": 126, "Atma-Bold": 127, "HolidayRegular": 128, "BebasNeueCyrillic": 129, "IntroRust-Base": 130, "Gistesy": 131, "BDScript-Regular": 132, "ApricotsRegular": 133, "Prompt-Black": 134, "TAN MERINGUE": 135, "Sukar Regular": 136, "GentySans-Regular": 137, "NeueEinstellung-Normal": 138, "Garet-Bold": 139, "FiraSans-Black": 140, "BantayogLight": 141, "NotoSerifDisplay-Black": 142, "TTChocolates-Regular": 143, "Ubuntu-Regular": 144, "Assistant-Bold": 145, "ABeeZee-Regular": 146, "LexendDeca-Regular": 147, "KingredSerif": 148, "Radley-Regular": 149, "BrownSugar": 150, "MigraItalic-ExtraboldItalic": 151, "ChildosArabic-Regular": 152, "PeaceSans": 153, "LondrinaSolid-Black": 154, "SpaceMono-BoldItalic": 155, "RobotoMono-Light": 156, "CourierPrime-Regular": 157, "Alata-Regular": 158, "Amsterdam-One": 159, "IreneFlorentina-Regular": 160, "CatchyMager": 161, "Alta_regular": 162, "ArticulatCF-Regular": 163, "Raleway-Regular": 164, "BrasikaDisplay": 165, "TANAngleton-Italic": 166, "NotoSerifDisplay-ExtraCondensedItalic": 167, "Bryndan Write": 168, "TTCommonsPro-It": 169, "AlexBrush-Regular": 170, "Antic-Regular": 171, "TTHoves-Bold": 172, "DroidSerif": 173, "AblationRegular": 174, "Marcellus-Regular": 175, "Sanchez-Italic": 176, "JosefinSans": 177, "Afrah-Regular": 178, "PinyonScript": 179, "TTInterphases-BoldItalic": 180, "Yellowtail-Regular": 181, "Gliker-Regular": 182, "BobbyJonesSoft-Regular": 183, "IBMPlexSans": 184, "Amsterdam-Three": 185, "Amsterdam-FourSlant": 186, "TTFors-Regular": 187, "Quattrocento": 188, "Sifonn-Basic": 189, "AlegreyaSans-Black": 190, "Daydream": 191, "AristotelicaProTx-Rg": 192, "NotoSerif": 193, "EBGaramond-Italic": 194, "HammersmithOne-Regular": 195, "RobotoSlab-Regular": 196, "DO-Sans-Regular": 197, "KGPrimaryDotsLined": 198, "Blinker-Regular": 199, "TAN NIMBUS": 200, "Blueberry-Regular": 201, "Rosario-Regular": 202, "Forum": 203, "MistrullyRegular": 204, "SourceSerifPro-Regular": 205, "Bugaki-Regular": 206, "CMUSerif-Roman": 207, "GulfsDisplay-NormalItalic": 208, "PTSans-Bold": 209, "Sensei-Medium": 210, "SquadaOne-Regular": 211, "Arapey-Italic": 212, "Parisienne-Regular": 213, "Aleo-Italic": 214, "QuicheDisplay-Italic": 215, "RocaOne-It": 216, "Funtastic-Regular": 217, "PTSerif-BoldItalic": 218, "Muller-RegularItalic": 219, "ArgentCF-Regular": 220, "Brightwall-Italic": 221, "Knewave-Regular": 222, "TYSerif-D": 223, "Agrandir-Tight": 224, "AlfaSlabOne-Regular": 225, "TANTangkiwood-Display": 226, "Kief-Montaser-Regular": 227, "Gotham-Book": 228, "JuliusSansOne-Regular": 229, "CocoGothic-Italic": 230, "SairaCondensed-Regular": 231, "DellaRespira-Regular": 232, "Questrial-Regular": 233, "BukhariScript-Regular": 234, "HelveticaWorld-Bold": 235, "TANKINDRED-Display": 236, "CinzelDecorative-Regular": 237, "Vidaloka-Regular": 238, 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"Handyman-Regular": 278, "Aileron-BoldItalic": 279, "Bright-Italic": 280, "Solway-Regular": 281, "Higuen-Regular": 282, "WedgesItalic": 283, "TANASHFORD-BOLD": 284, "IBMPlexMono": 285, "RacingSansOne-Regular": 286, "RegularBrush": 287, "OpenSans-LightItalic": 288, "SpecialElite-Regular": 289, "FuturaLTPro-Medium": 290, "MaragsaDisplay": 291, "BigShouldersDisplay-Regular": 292, "BDSans-Regular": 293, "RasputinRegular": 294, "Yvesyvesdrawing-BoldItalic": 295, "Bitter-Regular": 296, "LuckiestGuy-Regular": 297, "CanvaSchoolFontDotted": 298, "TTFirsNeue-Italic": 299, "Sunday-Regular": 300, "HKGothic-MediumItalic": 301, "CaveatBrush-Regular": 302, "HeliosExt": 303, "ArchitectsDaughter-Regular": 304, "Angelina": 305, "Calistoga-Regular": 306, "ArchivoNarrow-Regular": 307, "ObjectSans-MediumSlanted": 308, "AyrLucidityCondensed-Regular": 309, "Nexa-RegularItalic": 310, "Lustria-Regular": 311, "Amsterdam-TwoSlant": 312, "Virtual-Regular": 313, "Brusher-Regular": 314, "NF-Lepetitcochon-Regular": 315, "TANTWINKLE": 316, "LeJour-Serif": 317, "Prata-Regular": 318, "PPWoodland-Regular": 319, "PlayfairDisplay-BoldItalic": 320, "AmaticSC-Regular": 321, "Cabin-Regular": 322, "Manjari-Bold": 323, "MrDafoe-Regular": 324, "TTRamillas-Italic": 325, "Luckybones-Bold": 326, "DarkerGrotesque-Light": 327, "BellabooRegular": 328, "CormorantSC-Bold": 329, "GochiHand-Regular": 330, "Atteron": 331, "RocaTwo-Lt": 332, "ZCOOLXiaoWei-Regular": 333, "TANSONGBIRD": 334, "HeadingNow-74Regular": 335, "Luthier-BoldItalic": 336, "Oregano-Regular": 337, "AyrTropikaIsland-Int": 338, "Mali-Regular": 339, "DidactGothic-Regular": 340, "Lovelace-Regular": 341, "BakerieSmooth-Regular": 342, "CarterOne": 343, "HussarBd": 344, "OldStandard-Italic": 345, "TAN-ASTORIA-Display": 346, "rugratssans-Regular": 347, "BMHANNA": 348, "BetterSaturday": 349, "AdigianaToybox": 350, "Sailors": 351, "PlayfairDisplaySC-Italic": 352, "Etna-Regular": 353, "Revive80Signature": 354, "CAGenerated": 355, "Poppins-Regular": 356, 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"Comfortaa-Bold": 437, "TraceFontRegular": 438, "RTL-AdamScript-Regular": 439, "EastmanGrotesque-Italic": 440, "Kalam-Bold": 441, "ChauPhilomeneOne-Regular": 442, "Coiny-Regular": 443, "Lovera": 444, "Gellatio": 445, "TitilliumWeb-Bold": 446, "OilvareBase-Italic": 447, "Catamaran-Black": 448, "Anteb-Italic": 449, "SueEllenFrancisco": 450, "SweetApricot": 451, "BrightSunshine": 452, "IM_FELL_Double_Pica_Italic": 453, "Granaina-limpia": 454, "TANPARFAIT": 455, "AcherusGrotesque-Regular": 456, "AwesomeLathusca-Italic": 457, "Signika-Bold": 458, "Andasia": 459, "DO-AllCaps-Slanted": 460, "Zenaida-Regular": 461, "Fahkwang-Regular": 462, "Play-Regular": 463, "BERNIERRegular-Regular": 464, "PlumaThin-Regular": 465, "SportsWorld": 466, "Garet-Black": 467, "CarolloPlayscript-BlackItalic": 468, "Cheque-Regular": 469, "SEGO": 470, "BobbyJones-Condensed": 471, "NexaSlab-RegularItalic": 472, "DancingScript-Regular": 473, "PaalalabasDisplayWideBETA": 474, "Magnolia-Script": 475, "OpunMai-400It": 476, "MadelynFill-Regular": 477, "ZingRust-Base": 478, "FingerPaint-Regular": 479, "BostonAngel-Light": 480, "Gliker-RegularExpanded": 481, "Ahsing": 482, "Engagement-Regular": 483, "EyesomeScript": 484, "LibraSerifModern-Regular": 485, "London-Regular": 486, "AtkinsonHyperlegible-Regular": 487, "StadioNow-TextItalic": 488, "Aniyah": 489, "ITCAvantGardePro-Bold": 490, "Comica-Regular": 491, "Coustard-Regular": 492, "Brice-BoldCondensed": 493, "TANNEWYORK-Bold": 494, "TANBUSTER-Bold": 495, "Alatsi-Regular": 496, "TYSerif-Book": 497, "Jingleberry": 498, "Rajdhani-Bold": 499, "LobsterTwo-BoldItalic": 500, "BestLight-Medium": 501, "Hitchcut-Regular": 502, "GermaniaOne-Regular": 503, "Emitha-Script": 504, "LemonTuesday": 505, "Cubao_Free_Regular": 506, "MonterchiSerif-Regular": 507, "AllertaStencil-Regular": 508, "RTL-Sondos-Regular": 509, "HomemadeApple-Regular": 510, "CosmicOcto-Medium": 511}

text_encoder/Glyph-SDXL-v2/assets/multi_fonts/cn.json

@@ -0,0 +1 @@
+[["cn-HelloFont-FangHuaTi", 0], ["cn-HelloFont-ID-DianFangSong-Bold", 1], ["cn-HelloFont-ID-DianFangSong", 2], ["cn-HelloFont-ID-DianHei-CEJ", 3], ["cn-HelloFont-ID-DianHei-DEJ", 4], ["cn-HelloFont-ID-DianHei-EEJ", 5], ["cn-HelloFont-ID-DianHei-FEJ", 6], ["cn-HelloFont-ID-DianHei-GEJ", 7], ["cn-HelloFont-ID-DianKai-Bold", 8], ["cn-HelloFont-ID-DianKai", 9], ["cn-HelloFont-WenYiHei", 10], ["cn-Hellofont-ID-ChenYanXingKai", 11], ["cn-Hellofont-ID-DaZiBao", 12], ["cn-Hellofont-ID-DaoCaoRen", 13], ["cn-Hellofont-ID-JianSong", 14], ["cn-Hellofont-ID-JiangHuZhaoPaiHei", 15], ["cn-Hellofont-ID-KeSong", 16], ["cn-Hellofont-ID-LeYuanTi", 17], ["cn-Hellofont-ID-Pinocchio", 18], ["cn-Hellofont-ID-QiMiaoTi", 19], ["cn-Hellofont-ID-QingHuaKai", 20], ["cn-Hellofont-ID-QingHuaXingKai", 21], ["cn-Hellofont-ID-ShanShuiXingKai", 22], ["cn-Hellofont-ID-ShouXieQiShu", 23], ["cn-Hellofont-ID-ShouXieTongZhenTi", 24], ["cn-Hellofont-ID-TengLingTi", 25], ["cn-Hellofont-ID-XiaoLiShu", 26], ["cn-Hellofont-ID-XuanZhenSong", 27], ["cn-Hellofont-ID-ZhongLingXingKai", 28], ["cn-HellofontIDJiaoTangTi", 29], ["cn-HellofontIDJiuZhuTi", 30], ["cn-HuXiaoBao-SaoBao", 31], ["cn-HuXiaoBo-NanShen", 32], ["cn-HuXiaoBo-ZhenShuai", 33], ["cn-SourceHanSansSC-Bold", 34], ["cn-SourceHanSansSC-ExtraLight", 35], ["cn-SourceHanSansSC-Heavy", 36], ["cn-SourceHanSansSC-Light", 37], ["cn-SourceHanSansSC-Medium", 38], ["cn-SourceHanSansSC-Normal", 39], ["cn-SourceHanSansSC-Regular", 40], ["cn-SourceHanSerifSC-Bold", 41], ["cn-SourceHanSerifSC-ExtraLight", 42], ["cn-SourceHanSerifSC-Heavy", 43], ["cn-SourceHanSerifSC-Light", 44], ["cn-SourceHanSerifSC-Medium", 45], ["cn-SourceHanSerifSC-Regular", 46], ["cn-SourceHanSerifSC-SemiBold", 47], ["cn-xiaowei", 48], ["cn-AaJianHaoTi", 49], ["cn-AlibabaPuHuiTi-Bold", 50], ["cn-AlibabaPuHuiTi-Heavy", 51], ["cn-AlibabaPuHuiTi-Light", 52], ["cn-AlibabaPuHuiTi-Medium", 53], ["cn-AlibabaPuHuiTi-Regular", 54], ["cn-CanvaAcidBoldSC", 55], ["cn-CanvaBreezeCN", 56], ["cn-CanvaBumperCropSC", 57], ["cn-CanvaCakeShopCN", 58], ["cn-CanvaEndeavorBlackSC", 59], ["cn-CanvaJoyHeiCN", 60], ["cn-CanvaLiCN", 61], ["cn-CanvaOrientalBrushCN", 62], ["cn-CanvaPoster", 63], ["cn-CanvaQinfuCalligraphyCN", 64], ["cn-CanvaSweetHeartCN", 65], ["cn-CanvaSwordLikeDreamCN", 66], ["cn-CanvaTangyuanHandwritingCN", 67], ["cn-CanvaWanderWorldCN", 68], ["cn-CanvaWenCN", 69], ["cn-DianZiChunYi", 70], ["cn-GenSekiGothicTW-H", 71], ["cn-GenWanMinTW-L", 72], ["cn-GenYoMinTW-B", 73], ["cn-GenYoMinTW-EL", 74], ["cn-GenYoMinTW-H", 75], ["cn-GenYoMinTW-M", 76], ["cn-GenYoMinTW-R", 77], ["cn-GenYoMinTW-SB", 78], ["cn-HYQiHei-AZEJ", 79], ["cn-HYQiHei-EES", 80], ["cn-HanaMinA", 81], ["cn-HappyZcool-2016", 82], ["cn-HelloFont ZJ KeKouKeAiTi", 83], ["cn-HelloFont-ID-BoBoTi", 84], ["cn-HelloFont-ID-FuGuHei-25", 85], ["cn-HelloFont-ID-FuGuHei-35", 86], ["cn-HelloFont-ID-FuGuHei-45", 87], ["cn-HelloFont-ID-FuGuHei-55", 88], ["cn-HelloFont-ID-FuGuHei-65", 89], ["cn-HelloFont-ID-FuGuHei-75", 90], ["cn-HelloFont-ID-FuGuHei-85", 91], ["cn-HelloFont-ID-HeiKa", 92], ["cn-HelloFont-ID-HeiTang", 93], ["cn-HelloFont-ID-JianSong-95", 94], ["cn-HelloFont-ID-JueJiangHei-50", 95], ["cn-HelloFont-ID-JueJiangHei-55", 96], ["cn-HelloFont-ID-JueJiangHei-60", 97], ["cn-HelloFont-ID-JueJiangHei-65", 98], ["cn-HelloFont-ID-JueJiangHei-70", 99], ["cn-HelloFont-ID-JueJiangHei-75", 100], ["cn-HelloFont-ID-JueJiangHei-80", 101], ["cn-HelloFont-ID-KuHeiTi", 102], ["cn-HelloFont-ID-LingDongTi", 103], ["cn-HelloFont-ID-LingLiTi", 104], ["cn-HelloFont-ID-MuFengTi", 105], ["cn-HelloFont-ID-NaiNaiJiangTi", 106], ["cn-HelloFont-ID-PangDu", 107], ["cn-HelloFont-ID-ReLieTi", 108], ["cn-HelloFont-ID-RouRun", 109], ["cn-HelloFont-ID-SaShuangShouXieTi", 110], ["cn-HelloFont-ID-WangZheFengFan", 111], ["cn-HelloFont-ID-YouQiTi", 112], ["cn-Hellofont-ID-XiaLeTi", 113], ["cn-Hellofont-ID-XianXiaTi", 114], ["cn-HuXiaoBoKuHei", 115], ["cn-IDDanMoXingKai", 116], ["cn-IDJueJiangHei", 117], ["cn-IDMeiLingTi", 118], ["cn-IDQQSugar", 119], ["cn-LiuJianMaoCao-Regular", 120], ["cn-LongCang-Regular", 121], ["cn-MaShanZheng-Regular", 122], ["cn-PangMenZhengDao-3", 123], ["cn-PangMenZhengDao-Cu", 124], ["cn-PangMenZhengDao", 125], ["cn-SentyCaramel", 126], ["cn-SourceHanSerifSC", 127], ["cn-WenCang-Regular", 128], ["cn-WenQuanYiMicroHei", 129], ["cn-XianErTi", 130], ["cn-YRDZSTJF", 131], ["cn-YS-HelloFont-BangBangTi", 132], ["cn-ZCOOLKuaiLe-Regular", 133], ["cn-ZCOOLQingKeHuangYou-Regular", 134], ["cn-ZCOOLXiaoWei-Regular", 135], ["cn-ZCOOL_KuHei", 136], ["cn-ZhiMangXing-Regular", 137], ["cn-baotuxiaobaiti", 138], ["cn-jiangxizhuokai-Regular", 139], ["cn-zcool-gdh", 140], ["cn-zcoolqingkehuangyouti-Regular", 141], ["cn-zcoolwenyiti", 142]]

text_encoder/Glyph-SDXL-v2/assets/multi_fonts/de.json

@@ -0,0 +1 @@
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text_encoder/Glyph-SDXL-v2/assets/multi_fonts/en.json

@@ -0,0 +1 @@
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text_encoder/Glyph-SDXL-v2/assets/multi_fonts/es.json

@@ -0,0 +1 @@
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text_encoder/Glyph-SDXL-v2/assets/multi_fonts/fr.json

@@ -0,0 +1 @@
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text_encoder/Glyph-SDXL-v2/assets/multi_fonts/it.json

@@ -0,0 +1 @@
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text_encoder/Glyph-SDXL-v2/assets/multi_fonts/jp.json

@@ -0,0 +1 @@
+[["jp-04KanjyukuGothic", 0], ["jp-07LightNovelPOP", 1], ["jp-07NikumaruFont", 2], ["jp-07YasashisaAntique", 3], ["jp-07YasashisaGothic", 4], ["jp-BokutachinoGothic2Bold", 5], ["jp-BokutachinoGothic2Regular", 6], ["jp-CHI_SpeedyRight_full_211128-Regular", 7], ["jp-CHI_SpeedyRight_italic_full_211127-Regular", 8], ["jp-CP-Font", 9], ["jp-Canva_CezanneProN-B", 10], ["jp-Canva_CezanneProN-M", 11], ["jp-Canva_ChiaroStd-B", 12], ["jp-Canva_CometStd-B", 13], ["jp-Canva_DotMincho16Std-M", 14], ["jp-Canva_GrecoStd-B", 15], ["jp-Canva_GrecoStd-M", 16], ["jp-Canva_LyraStd-DB", 17], ["jp-Canva_MatisseHatsuhiPro-B", 18], ["jp-Canva_MatisseHatsuhiPro-M", 19], ["jp-Canva_ModeMinAStd-B", 20], ["jp-Canva_NewCezanneProN-B", 21], ["jp-Canva_NewCezanneProN-M", 22], ["jp-Canva_PearlStd-L", 23], ["jp-Canva_RaglanStd-UB", 24], ["jp-Canva_RailwayStd-B", 25], ["jp-Canva_ReggaeStd-B", 26], ["jp-Canva_RocknRollStd-DB", 27], ["jp-Canva_RodinCattleyaPro-B", 28], ["jp-Canva_RodinCattleyaPro-M", 29], ["jp-Canva_RodinCattleyaPro-UB", 30], ["jp-Canva_RodinHimawariPro-B", 31], ["jp-Canva_RodinHimawariPro-M", 32], ["jp-Canva_RodinMariaPro-B", 33], ["jp-Canva_RodinMariaPro-DB", 34], ["jp-Canva_RodinProN-M", 35], ["jp-Canva_ShadowTLStd-B", 36], ["jp-Canva_StickStd-B", 37], ["jp-Canva_TsukuAOldMinPr6N-B", 38], ["jp-Canva_TsukuAOldMinPr6N-R", 39], ["jp-Canva_UtrilloPro-DB", 40], ["jp-Canva_UtrilloPro-M", 41], ["jp-Canva_YurukaStd-UB", 42], ["jp-FGUIGEN", 43], ["jp-GlowSansJ-Condensed-Heavy", 44], ["jp-GlowSansJ-Condensed-Light", 45], ["jp-GlowSansJ-Normal-Bold", 46], ["jp-GlowSansJ-Normal-Light", 47], ["jp-HannariMincho", 48], ["jp-HarenosoraMincho", 49], ["jp-Jiyucho", 50], ["jp-Kaiso-Makina-B", 51], ["jp-Kaisotai-Next-UP-B", 52], ["jp-KokoroMinchoutai", 53], ["jp-Mamelon-3-Hi-Regular", 54], ["jp-MotoyaAnemoneStd-W1", 55], ["jp-MotoyaAnemoneStd-W5", 56], ["jp-MotoyaAnticPro-W3", 57], ["jp-MotoyaCedarStd-W3", 58], ["jp-MotoyaCedarStd-W5", 59], ["jp-MotoyaGochikaStd-W4", 60], ["jp-MotoyaGochikaStd-W8", 61], ["jp-MotoyaGothicMiyabiStd-W6", 62], ["jp-MotoyaGothicStd-W3", 63], ["jp-MotoyaGothicStd-W5", 64], ["jp-MotoyaKoinStd-W3", 65], ["jp-MotoyaKyotaiStd-W2", 66], ["jp-MotoyaKyotaiStd-W4", 67], ["jp-MotoyaMaruStd-W3", 68], ["jp-MotoyaMaruStd-W5", 69], ["jp-MotoyaMinchoMiyabiStd-W4", 70], ["jp-MotoyaMinchoMiyabiStd-W6", 71], ["jp-MotoyaMinchoModernStd-W4", 72], ["jp-MotoyaMinchoModernStd-W6", 73], ["jp-MotoyaMinchoStd-W3", 74], ["jp-MotoyaMinchoStd-W5", 75], ["jp-MotoyaReisyoStd-W2", 76], ["jp-MotoyaReisyoStd-W6", 77], ["jp-MotoyaTohitsuStd-W4", 78], ["jp-MotoyaTohitsuStd-W6", 79], ["jp-MtySousyokuEmBcJis-W6", 80], ["jp-MtySousyokuLiBcJis-W6", 81], ["jp-Mushin", 82], ["jp-NotoSansJP-Bold", 83], ["jp-NotoSansJP-Regular", 84], ["jp-NudMotoyaAporoStd-W3", 85], ["jp-NudMotoyaAporoStd-W5", 86], ["jp-NudMotoyaCedarStd-W3", 87], ["jp-NudMotoyaCedarStd-W5", 88], ["jp-NudMotoyaMaruStd-W3", 89], ["jp-NudMotoyaMaruStd-W5", 90], ["jp-NudMotoyaMinchoStd-W5", 91], ["jp-Ounen-mouhitsu", 92], ["jp-Ronde-B-Square", 93], ["jp-SMotoyaGyosyoStd-W5", 94], ["jp-SMotoyaSinkaiStd-W3", 95], ["jp-SMotoyaSinkaiStd-W5", 96], ["jp-SourceHanSansJP-Bold", 97], ["jp-SourceHanSansJP-Regular", 98], ["jp-SourceHanSerifJP-Bold", 99], ["jp-SourceHanSerifJP-Regular", 100], ["jp-TazuganeGothicStdN-Bold", 101], ["jp-TazuganeGothicStdN-Regular", 102], ["jp-TelopMinProN-B", 103], ["jp-Togalite-Bold", 104], ["jp-Togalite-Regular", 105], ["jp-TsukuMinPr6N-E", 106], ["jp-TsukuMinPr6N-M", 107], ["jp-mikachan_o", 108], ["jp-nagayama_kai", 109], ["jp-07LogoTypeGothic7", 110], ["jp-07TetsubinGothic", 111], ["jp-851CHIKARA-DZUYOKU-KANA-A", 112], ["jp-ARMinchoJIS-Light", 113], ["jp-ARMinchoJIS-Ultra", 114], ["jp-ARPCrystalMinchoJIS-Medium", 115], ["jp-ARPCrystalRGothicJIS-Medium", 116], ["jp-ARShounanShinpitsuGyosyoJIS-Medium", 117], ["jp-AozoraMincho-bold", 118], ["jp-AozoraMinchoRegular", 119], ["jp-ArialUnicodeMS-Bold", 120], ["jp-ArialUnicodeMS", 121], ["jp-CanvaBreezeJP", 122], ["jp-CanvaLiCN", 123], ["jp-CanvaLiJP", 124], ["jp-CanvaOrientalBrushCN", 125], ["jp-CanvaQinfuCalligraphyJP", 126], ["jp-CanvaSweetHeartJP", 127], ["jp-CanvaWenJP", 128], ["jp-Corporate-Logo-Bold", 129], ["jp-DelaGothicOne-Regular", 130], ["jp-GN-Kin-iro_SansSerif", 131], ["jp-GN-Koharuiro_Sunray", 132], ["jp-GenEiGothicM-B", 133], ["jp-GenEiGothicM-R", 134], ["jp-GenJyuuGothic-Bold", 135], ["jp-GenRyuMinTW-B", 136], ["jp-GenRyuMinTW-R", 137], ["jp-GenSekiGothicTW-B", 138], ["jp-GenSekiGothicTW-R", 139], ["jp-GenSenRoundedTW-B", 140], ["jp-GenSenRoundedTW-R", 141], ["jp-GenShinGothic-Bold", 142], ["jp-GenShinGothic-Normal", 143], ["jp-GenWanMinTW-L", 144], ["jp-GenYoGothicTW-B", 145], ["jp-GenYoGothicTW-R", 146], ["jp-GenYoMinTW-B", 147], ["jp-GenYoMinTW-R", 148], ["jp-HGBouquet", 149], ["jp-HanaMinA", 150], ["jp-HanazomeFont", 151], ["jp-HinaMincho-Regular", 152], ["jp-Honoka-Antique-Maru", 153], ["jp-Honoka-Mincho", 154], ["jp-HuiFontP", 155], ["jp-IPAexMincho", 156], ["jp-JK-Gothic-L", 157], ["jp-JK-Gothic-M", 158], ["jp-JackeyFont", 159], ["jp-KaiseiTokumin-Bold", 160], ["jp-KaiseiTokumin-Regular", 161], ["jp-Keifont", 162], ["jp-KiwiMaru-Regular", 163], ["jp-Koku-Mincho-Regular", 164], ["jp-MotoyaLMaru-W3-90ms-RKSJ-H", 165], ["jp-NewTegomin-Regular", 166], ["jp-NicoKaku", 167], ["jp-NicoMoji+", 168], ["jp-Otsutome_font-Bold", 169], ["jp-PottaOne-Regular", 170], ["jp-RampartOne-Regular", 171], ["jp-Senobi-Gothic-Bold", 172], ["jp-Senobi-Gothic-Regular", 173], ["jp-SmartFontUI-Proportional", 174], ["jp-SoukouMincho", 175], ["jp-TEST_Klee-DB", 176], ["jp-TEST_Klee-M", 177], ["jp-TEST_UDMincho-B", 178], ["jp-TEST_UDMincho-L", 179], ["jp-TT_Akakane-EB", 180], ["jp-Tanuki-Permanent-Marker", 181], ["jp-TrainOne-Regular", 182], ["jp-TsunagiGothic-Black", 183], ["jp-Ume-Hy-Gothic", 184], ["jp-Ume-P-Mincho", 185], ["jp-WenQuanYiMicroHei", 186], ["jp-XANO-mincho-U32", 187], ["jp-YOzFontM90-Regular", 188], ["jp-Yomogi-Regular", 189], ["jp-YujiBoku-Regular", 190], ["jp-YujiSyuku-Regular", 191], ["jp-ZenKakuGothicNew-Bold", 192], ["jp-ZenKakuGothicNew-Regular", 193], ["jp-ZenKurenaido-Regular", 194], ["jp-ZenMaruGothic-Bold", 195], ["jp-ZenMaruGothic-Regular", 196], ["jp-darts-font", 197], ["jp-irohakakuC-Bold", 198], ["jp-irohakakuC-Medium", 199], ["jp-irohakakuC-Regular", 200], ["jp-katyou", 201], ["jp-mplus-1m-bold", 202], ["jp-mplus-1m-regular", 203], ["jp-mplus-1p-bold", 204], ["jp-mplus-1p-regular", 205], ["jp-rounded-mplus-1p-bold", 206], ["jp-rounded-mplus-1p-regular", 207], ["jp-timemachine-wa", 208], ["jp-ttf-GenEiLateMin-Medium", 209], ["jp-uzura_font", 210]]

text_encoder/Glyph-SDXL-v2/assets/multi_fonts/kr.json

@@ -0,0 +1 @@
+[["kr-Arita-buri-Bold_OTF", 0], ["kr-Arita-buri-HairLine_OTF", 1], ["kr-Arita-buri-Light_OTF", 2], ["kr-Arita-buri-Medium_OTF", 3], ["kr-Arita-buri-SemiBold_OTF", 4], ["kr-Canva_YDSunshineL", 5], ["kr-Canva_YDSunshineM", 6], ["kr-Canva_YoonGulimPro710", 7], ["kr-Canva_YoonGulimPro730", 8], ["kr-Canva_YoonGulimPro740", 9], ["kr-Canva_YoonGulimPro760", 10], ["kr-Canva_YoonGulimPro770", 11], ["kr-Canva_YoonGulimPro790", 12], ["kr-CreHappB", 13], ["kr-CreHappL", 14], ["kr-CreHappM", 15], ["kr-CreHappS", 16], ["kr-OTAuroraB", 17], ["kr-OTAuroraL", 18], ["kr-OTAuroraR", 19], ["kr-OTDoldamgilB", 20], ["kr-OTDoldamgilL", 21], ["kr-OTDoldamgilR", 22], ["kr-OTHamsterB", 23], ["kr-OTHamsterL", 24], ["kr-OTHamsterR", 25], ["kr-OTHapchangdanB", 26], ["kr-OTHapchangdanL", 27], ["kr-OTHapchangdanR", 28], ["kr-OTSupersizeBkBOX", 29], ["kr-SourceHanSansKR-Bold", 30], ["kr-SourceHanSansKR-ExtraLight", 31], ["kr-SourceHanSansKR-Heavy", 32], ["kr-SourceHanSansKR-Light", 33], ["kr-SourceHanSansKR-Medium", 34], ["kr-SourceHanSansKR-Normal", 35], ["kr-SourceHanSansKR-Regular", 36], ["kr-SourceHanSansSC-Bold", 37], ["kr-SourceHanSansSC-ExtraLight", 38], ["kr-SourceHanSansSC-Heavy", 39], ["kr-SourceHanSansSC-Light", 40], ["kr-SourceHanSansSC-Medium", 41], ["kr-SourceHanSansSC-Normal", 42], ["kr-SourceHanSansSC-Regular", 43], ["kr-SourceHanSerifSC-Bold", 44], ["kr-SourceHanSerifSC-SemiBold", 45], ["kr-TDTDBubbleBubbleOTF", 46], ["kr-TDTDConfusionOTF", 47], ["kr-TDTDCuteAndCuteOTF", 48], ["kr-TDTDEggTakOTF", 49], ["kr-TDTDEmotionalLetterOTF", 50], ["kr-TDTDGalapagosOTF", 51], ["kr-TDTDHappyHourOTF", 52], ["kr-TDTDLatteOTF", 53], ["kr-TDTDMoonLightOTF", 54], ["kr-TDTDParkForestOTF", 55], ["kr-TDTDPencilOTF", 56], ["kr-TDTDSmileOTF", 57], ["kr-TDTDSproutOTF", 58], ["kr-TDTDSunshineOTF", 59], ["kr-TDTDWaferOTF", 60], ["kr-777Chyaochyureu", 61], ["kr-ArialUnicodeMS-Bold", 62], ["kr-ArialUnicodeMS", 63], ["kr-BMHANNA", 64], ["kr-Baekmuk-Dotum", 65], ["kr-BagelFatOne-Regular", 66], ["kr-CoreBandi", 67], ["kr-CoreBandiFace", 68], ["kr-CoreBori", 69], ["kr-DoHyeon-Regular", 70], ["kr-Dokdo-Regular", 71], ["kr-Gaegu-Bold", 72], ["kr-Gaegu-Light", 73], ["kr-Gaegu-Regular", 74], ["kr-GamjaFlower-Regular", 75], ["kr-GasoekOne-Regular", 76], ["kr-GothicA1-Black", 77], ["kr-GothicA1-Bold", 78], ["kr-GothicA1-ExtraBold", 79], ["kr-GothicA1-ExtraLight", 80], ["kr-GothicA1-Light", 81], ["kr-GothicA1-Medium", 82], ["kr-GothicA1-Regular", 83], ["kr-GothicA1-SemiBold", 84], ["kr-GothicA1-Thin", 85], ["kr-Gugi-Regular", 86], ["kr-HiMelody-Regular", 87], ["kr-Jua-Regular", 88], ["kr-KirangHaerang-Regular", 89], ["kr-NanumBrush", 90], ["kr-NanumPen", 91], ["kr-NanumSquareRoundB", 92], ["kr-NanumSquareRoundEB", 93], ["kr-NanumSquareRoundL", 94], ["kr-NanumSquareRoundR", 95], ["kr-SeH-CB", 96], ["kr-SeH-CBL", 97], ["kr-SeH-CEB", 98], ["kr-SeH-CL", 99], ["kr-SeH-CM", 100], ["kr-SeN-CB", 101], ["kr-SeN-CBL", 102], ["kr-SeN-CEB", 103], ["kr-SeN-CL", 104], ["kr-SeN-CM", 105], ["kr-Sunflower-Bold", 106], ["kr-Sunflower-Light", 107], ["kr-Sunflower-Medium", 108], ["kr-TTClaytoyR", 109], ["kr-TTDalpangiR", 110], ["kr-TTMamablockR", 111], ["kr-TTNauidongmuR", 112], ["kr-TTOktapbangR", 113], ["kr-UhBeeMiMi", 114], ["kr-UhBeeMiMiBold", 115], ["kr-UhBeeSe_hyun", 116], ["kr-UhBeeSe_hyunBold", 117], ["kr-UhBeenamsoyoung", 118], ["kr-UhBeenamsoyoungBold", 119], ["kr-WenQuanYiMicroHei", 120], ["kr-YeonSung-Regular", 121]]

text_encoder/Glyph-SDXL-v2/assets/multi_fonts/pt.json

@@ -0,0 +1 @@
+[["en-Montserrat-Regular", 0], ["en-Poppins-Italic", 1], ["en-GlacialIndifference-Regular", 2], ["en-OpenSans-ExtraBoldItalic", 3], ["en-Montserrat-Bold", 4], ["en-Now-Regular", 5], ["en-Garet-Regular", 6], ["en-LeagueSpartan-Bold", 7], ["en-DMSans-Regular", 8], ["en-OpenSauceOne-Regular", 9], ["en-OpenSans-ExtraBold", 10], ["en-KGPrimaryPenmanship", 11], ["en-Anton-Regular", 12], ["en-Aileron-BlackItalic", 13], ["en-Quicksand-Light", 14], ["en-Roboto-BoldItalic", 15], ["en-TheSeasons-It", 16], ["en-Kollektif", 17], ["en-Inter-BoldItalic", 18], ["en-Poppins-Medium", 19], ["en-Poppins-Light", 20], ["en-RoxboroughCF-RegularItalic", 21], ["en-PlayfairDisplay-SemiBold", 22], ["en-Agrandir-Italic", 23], ["en-Lato-Regular", 24], ["en-MoreSugarRegular", 25], ["en-CanvaSans-RegularItalic", 26], ["en-PublicSans-Italic", 27], ["en-CodePro-NormalLC", 28], ["en-Belleza-Regular", 29], ["en-JosefinSans-Bold", 30], ["en-HKGrotesk-Bold", 31], ["en-Telegraf-Medium", 32], ["en-BrittanySignatureRegular", 33], ["en-Raleway-ExtraBoldItalic", 34], ["en-Mont-RegularItalic", 35], ["en-Arimo-BoldItalic", 36], ["en-Lora-Italic", 37], ["en-ArchivoBlack-Regular", 38], ["en-Poppins", 39], ["en-Barlow-Black", 40], ["en-CormorantGaramond-Bold", 41], ["en-LibreBaskerville-Regular", 42], ["en-BebasNeueBold", 44], ["en-LazydogRegular", 45], ["en-FredokaOne-Regular", 46], ["en-Horizon-Bold", 47], ["en-Nourd-Regular", 48], ["en-Hatton-Regular", 49], ["en-Nunito-ExtraBoldItalic", 50], ["en-CerebriSans-Regular", 51], ["en-Montserrat-Light", 52], ["en-TenorSans", 53], ["en-ClearSans-Bold", 55], ["en-Cardo-Regular", 56], ["en-Alice-Regular", 57], ["en-Oswald-Regular", 58], ["en-Muli-Black", 60], ["en-TAN-PEARL-Regular", 61], ["en-CooperHewitt-Book", 62], ["en-Agrandir-Grand", 63], ["en-BlackMango-Thin", 64], ["en-DMSerifDisplay-Regular", 65], ["en-Antonio-Bold", 66], ["en-Sniglet-Regular", 67], ["en-BeVietnam-Regular", 68], ["en-NunitoSans10pt-BlackItalic", 69], ["en-AbhayaLibre-ExtraBold", 70], ["en-Rubik-Regular", 71], ["en-PPNeueMachina-Regular", 72], ["en-TAN - MON CHERI-Regular", 73], ["en-Playlist-Script", 75], ["en-SourceSansPro-BoldItalic", 76], ["en-MoonTime-Regular", 77], ["en-Eczar-ExtraBold", 78], ["en-Gatwick-Regular", 79], ["en-MonumentExtended-Regular", 80], ["en-BarlowSemiCondensed-Regular", 81], ["en-BarlowCondensed-Regular", 82], ["en-Alegreya-Regular", 83], ["en-DreamAvenue", 84], ["en-RobotoCondensed-Italic", 85], ["en-BobbyJones-Regular", 86], ["en-Garet-ExtraBold", 87], ["en-YesevaOne-Regular", 88], ["en-Dosis-ExtraBold", 89], ["en-LeagueGothic-Regular", 90], ["en-OpenSans-Italic", 91], ["en-TANAEGEAN-Regular", 92], ["en-Maharlika-Regular", 93], ["en-MarykateRegular", 94], ["en-Cinzel-Regular", 95], ["en-Agrandir-Wide", 96], ["en-Chewy-Regular", 97], ["en-BodoniFLF-BoldItalic", 98], ["en-Nunito-BlackItalic", 99], ["en-LilitaOne", 100], ["en-HandyCasualCondensed-Regular", 101], ["en-Ovo", 102], ["en-Livvic-Regular", 103], ["en-Agrandir-Narrow", 104], ["en-CrimsonPro-Italic", 105], ["en-AnonymousPro-Bold", 106], ["en-NF-OneLittleFont-Bold", 107], ["en-RedHatDisplay-BoldItalic", 108], ["en-CodecPro-Regular", 109], ["en-HalimunRegular", 110], ["en-LibreFranklin-Black", 111], ["en-TeXGyreTermes-BoldItalic", 112], ["en-Shrikhand-Regular", 113], ["en-TTNormsPro-Italic", 114], ["en-Gagalin-Regular", 115], ["en-OpenSans-Bold", 116], ["en-GreatVibes-Regular", 117], ["en-Breathing", 118], ["en-HeroLight-Regular", 119], ["en-KGPrimaryDots", 120], ["en-Quicksand-Bold", 121], ["en-Brice-ExtraLightSemiExpanded", 122], ["en-Lato-BoldItalic", 123], ["en-Fraunces9pt-Italic", 124], ["en-AbrilFatface-Regular", 125], ["en-BerkshireSwash-Regular", 126], ["en-Atma-Bold", 127], ["en-HolidayRegular", 128], ["en-BebasNeueCyrillic", 129], ["en-IntroRust-Base", 130], ["en-Gistesy", 131], ["en-BDScript-Regular", 132], ["en-ApricotsRegular", 133], ["en-Prompt-Black", 134], ["en-TAN MERINGUE", 135], ["en-GentySans-Regular", 137], ["en-NeueEinstellung-Normal", 138], ["en-Garet-Bold", 139], ["en-FiraSans-Black", 140], ["en-BantayogLight", 141], ["en-NotoSerifDisplay-Black", 142], ["en-TTChocolates-Regular", 143], ["en-Ubuntu-Regular", 144], ["en-Assistant-Bold", 145], ["en-ABeeZee-Regular", 146], ["en-LexendDeca-Regular", 147], ["en-KingredSerif", 148], ["en-Radley-Regular", 149], ["en-BrownSugar", 150], ["en-MigraItalic-ExtraboldItalic", 151], ["en-ChildosArabic-Regular", 152], ["en-PeaceSans", 153], ["en-LondrinaSolid-Black", 154], ["en-SpaceMono-BoldItalic", 155], ["en-RobotoMono-Light", 156], ["en-CourierPrime-Regular", 157], ["en-Alata-Regular", 158], ["en-Amsterdam-One", 159], ["en-IreneFlorentina-Regular", 160], ["en-CatchyMager", 161], ["en-Alta_regular", 162], ["en-ArticulatCF-Regular", 163], ["en-Raleway-Regular", 164], ["en-BrasikaDisplay", 165], ["en-TANAngleton-Italic", 166], ["en-NotoSerifDisplay-ExtraCondensedItalic", 167], ["en-Bryndan Write", 168], ["en-TTCommonsPro-It", 169], ["en-AlexBrush-Regular", 170], ["en-Antic-Regular", 171], ["en-TTHoves-Bold", 172], ["en-DroidSerif", 173], ["en-AblationRegular", 174], ["en-Marcellus-Regular", 175], ["en-Sanchez-Italic", 176], ["en-JosefinSans", 177], ["en-Afrah-Regular", 178], ["en-PinyonScript", 179], ["en-TTInterphases-BoldItalic", 180], ["en-Yellowtail-Regular", 181], ["en-Gliker-Regular", 182], ["en-BobbyJonesSoft-Regular", 183], ["en-IBMPlexSans", 184], ["en-Amsterdam-Three", 185], ["en-Amsterdam-FourSlant", 186], ["en-TTFors-Regular", 187], ["en-Quattrocento", 188], ["en-Sifonn-Basic", 189], ["en-AlegreyaSans-Black", 190], ["en-Daydream", 191], ["en-AristotelicaProTx-Rg", 192], ["en-NotoSerif", 193], ["en-EBGaramond-Italic", 194], ["en-HammersmithOne-Regular", 195], ["en-RobotoSlab-Regular", 196], ["en-DO-Sans-Regular", 197], ["en-KGPrimaryDotsLined", 198], ["en-Blinker-Regular", 199], ["en-TAN NIMBUS", 200], ["en-Blueberry-Regular", 201], ["en-Rosario-Regular", 202], ["en-Forum", 203], ["en-MistrullyRegular", 204], ["en-SourceSerifPro-Regular", 205], ["en-Bugaki-Regular", 206], ["en-CMUSerif-Roman", 207], ["en-GulfsDisplay-NormalItalic", 208], ["en-PTSans-Bold", 209], ["en-SquadaOne-Regular", 211], ["en-Arapey-Italic", 212], ["en-Parisienne-Regular", 213], ["en-Aleo-Italic", 214], ["en-QuicheDisplay-Italic", 215], ["en-RocaOne-It", 216], ["en-Funtastic-Regular", 217], ["en-PTSerif-BoldItalic", 218], ["en-Muller-RegularItalic", 219], ["en-ArgentCF-Regular", 220], ["en-Brightwall-Italic", 221], ["en-Knewave-Regular", 222], ["en-TYSerif-D", 223], ["en-Agrandir-Tight", 224], ["en-AlfaSlabOne-Regular", 225], ["en-TANTangkiwood-Display", 226], ["en-Kief-Montaser-Regular", 227], ["en-Gotham-Book", 228], ["en-JuliusSansOne-Regular", 229], ["en-CocoGothic-Italic", 230], ["en-SairaCondensed-Regular", 231], ["en-DellaRespira-Regular", 232], ["en-Questrial-Regular", 233], ["en-BukhariScript-Regular", 234], ["en-HelveticaWorld-Bold", 235], ["en-TANKINDRED-Display", 236], 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text_encoder/Glyph-SDXL-v2/assets/multi_fonts/ru.json

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text_encoder/Glyph-SDXL-v2/assets/multilingual_10-lang_idx.json

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134, "jp-GenJyuuGothic-Bold": 135, "jp-GenRyuMinTW-B": 136, "jp-GenRyuMinTW-R": 137, "jp-GenSekiGothicTW-B": 138, "jp-GenSekiGothicTW-R": 139, "jp-GenSenRoundedTW-B": 140, "jp-GenSenRoundedTW-R": 141, "jp-GenShinGothic-Bold": 142, "jp-GenShinGothic-Normal": 143, "jp-GenWanMinTW-L": 144, "jp-GenYoGothicTW-B": 145, "jp-GenYoGothicTW-R": 146, "jp-GenYoMinTW-B": 147, "jp-GenYoMinTW-R": 148, "jp-HGBouquet": 149, "jp-HanaMinA": 150, "jp-HanazomeFont": 151, "jp-HinaMincho-Regular": 152, "jp-Honoka-Antique-Maru": 153, "jp-Honoka-Mincho": 154, "jp-HuiFontP": 155, "jp-IPAexMincho": 156, "jp-JK-Gothic-L": 157, "jp-JK-Gothic-M": 158, "jp-JackeyFont": 159, "jp-KaiseiTokumin-Bold": 160, "jp-KaiseiTokumin-Regular": 161, "jp-Keifont": 162, "jp-KiwiMaru-Regular": 163, "jp-Koku-Mincho-Regular": 164, "jp-MotoyaLMaru-W3-90ms-RKSJ-H": 165, "jp-NewTegomin-Regular": 166, "jp-NicoKaku": 167, "jp-NicoMoji+": 168, "jp-Otsutome_font-Bold": 169, "jp-PottaOne-Regular": 170, "jp-RampartOne-Regular": 171, "jp-Senobi-Gothic-Bold": 172, "jp-Senobi-Gothic-Regular": 173, "jp-SmartFontUI-Proportional": 174, "jp-SoukouMincho": 175, "jp-TEST_Klee-DB": 176, "jp-TEST_Klee-M": 177, "jp-TEST_UDMincho-B": 178, "jp-TEST_UDMincho-L": 179, "jp-TT_Akakane-EB": 180, "jp-Tanuki-Permanent-Marker": 181, "jp-TrainOne-Regular": 182, "jp-TsunagiGothic-Black": 183, "jp-Ume-Hy-Gothic": 184, "jp-Ume-P-Mincho": 185, "jp-WenQuanYiMicroHei": 186, "jp-XANO-mincho-U32": 187, "jp-YOzFontM90-Regular": 188, "jp-Yomogi-Regular": 189, "jp-YujiBoku-Regular": 190, "jp-YujiSyuku-Regular": 191, "jp-ZenKakuGothicNew-Bold": 192, "jp-ZenKakuGothicNew-Regular": 193, "jp-ZenKurenaido-Regular": 194, "jp-ZenMaruGothic-Bold": 195, "jp-ZenMaruGothic-Regular": 196, "jp-darts-font": 197, "jp-irohakakuC-Bold": 198, "jp-irohakakuC-Medium": 199, "jp-irohakakuC-Regular": 200, "jp-katyou": 201, "jp-mplus-1m-bold": 202, "jp-mplus-1m-regular": 203, "jp-mplus-1p-bold": 204, "jp-mplus-1p-regular": 205, "jp-rounded-mplus-1p-bold": 206, "jp-rounded-mplus-1p-regular": 207, "jp-timemachine-wa": 208, "jp-ttf-GenEiLateMin-Medium": 209, "jp-uzura_font": 210, "kr-Arita-buri-Bold_OTF": 0, "kr-Arita-buri-HairLine_OTF": 1, "kr-Arita-buri-Light_OTF": 2, "kr-Arita-buri-Medium_OTF": 3, "kr-Arita-buri-SemiBold_OTF": 4, "kr-Canva_YDSunshineL": 5, "kr-Canva_YDSunshineM": 6, "kr-Canva_YoonGulimPro710": 7, "kr-Canva_YoonGulimPro730": 8, "kr-Canva_YoonGulimPro740": 9, "kr-Canva_YoonGulimPro760": 10, "kr-Canva_YoonGulimPro770": 11, "kr-Canva_YoonGulimPro790": 12, "kr-CreHappB": 13, "kr-CreHappL": 14, "kr-CreHappM": 15, "kr-CreHappS": 16, "kr-OTAuroraB": 17, "kr-OTAuroraL": 18, "kr-OTAuroraR": 19, "kr-OTDoldamgilB": 20, "kr-OTDoldamgilL": 21, "kr-OTDoldamgilR": 22, "kr-OTHamsterB": 23, "kr-OTHamsterL": 24, "kr-OTHamsterR": 25, "kr-OTHapchangdanB": 26, "kr-OTHapchangdanL": 27, "kr-OTHapchangdanR": 28, "kr-OTSupersizeBkBOX": 29, "kr-SourceHanSansKR-Bold": 30, "kr-SourceHanSansKR-ExtraLight": 31, "kr-SourceHanSansKR-Heavy": 32, "kr-SourceHanSansKR-Light": 33, "kr-SourceHanSansKR-Medium": 34, "kr-SourceHanSansKR-Normal": 35, "kr-SourceHanSansKR-Regular": 36, "kr-SourceHanSansSC-Bold": 37, "kr-SourceHanSansSC-ExtraLight": 38, "kr-SourceHanSansSC-Heavy": 39, "kr-SourceHanSansSC-Light": 40, "kr-SourceHanSansSC-Medium": 41, "kr-SourceHanSansSC-Normal": 42, "kr-SourceHanSansSC-Regular": 43, "kr-SourceHanSerifSC-Bold": 44, "kr-SourceHanSerifSC-SemiBold": 45, "kr-TDTDBubbleBubbleOTF": 46, "kr-TDTDConfusionOTF": 47, "kr-TDTDCuteAndCuteOTF": 48, "kr-TDTDEggTakOTF": 49, "kr-TDTDEmotionalLetterOTF": 50, "kr-TDTDGalapagosOTF": 51, "kr-TDTDHappyHourOTF": 52, "kr-TDTDLatteOTF": 53, "kr-TDTDMoonLightOTF": 54, "kr-TDTDParkForestOTF": 55, "kr-TDTDPencilOTF": 56, "kr-TDTDSmileOTF": 57, "kr-TDTDSproutOTF": 58, "kr-TDTDSunshineOTF": 59, "kr-TDTDWaferOTF": 60, "kr-777Chyaochyureu": 61, "kr-ArialUnicodeMS-Bold": 62, "kr-ArialUnicodeMS": 63, "kr-BMHANNA": 64, "kr-Baekmuk-Dotum": 65, "kr-BagelFatOne-Regular": 66, "kr-CoreBandi": 67, "kr-CoreBandiFace": 68, "kr-CoreBori": 69, "kr-DoHyeon-Regular": 70, "kr-Dokdo-Regular": 71, "kr-Gaegu-Bold": 72, "kr-Gaegu-Light": 73, "kr-Gaegu-Regular": 74, "kr-GamjaFlower-Regular": 75, "kr-GasoekOne-Regular": 76, "kr-GothicA1-Black": 77, "kr-GothicA1-Bold": 78, "kr-GothicA1-ExtraBold": 79, "kr-GothicA1-ExtraLight": 80, "kr-GothicA1-Light": 81, "kr-GothicA1-Medium": 82, "kr-GothicA1-Regular": 83, "kr-GothicA1-SemiBold": 84, "kr-GothicA1-Thin": 85, "kr-Gugi-Regular": 86, "kr-HiMelody-Regular": 87, "kr-Jua-Regular": 88, "kr-KirangHaerang-Regular": 89, "kr-NanumBrush": 90, "kr-NanumPen": 91, "kr-NanumSquareRoundB": 92, "kr-NanumSquareRoundEB": 93, "kr-NanumSquareRoundL": 94, "kr-NanumSquareRoundR": 95, "kr-SeH-CB": 96, "kr-SeH-CBL": 97, "kr-SeH-CEB": 98, "kr-SeH-CL": 99, "kr-SeH-CM": 100, "kr-SeN-CB": 101, "kr-SeN-CBL": 102, "kr-SeN-CEB": 103, "kr-SeN-CL": 104, "kr-SeN-CM": 105, "kr-Sunflower-Bold": 106, "kr-Sunflower-Light": 107, "kr-Sunflower-Medium": 108, "kr-TTClaytoyR": 109, "kr-TTDalpangiR": 110, "kr-TTMamablockR": 111, "kr-TTNauidongmuR": 112, "kr-TTOktapbangR": 113, "kr-UhBeeMiMi": 114, "kr-UhBeeMiMiBold": 115, "kr-UhBeeSe_hyun": 116, "kr-UhBeeSe_hyunBold": 117, "kr-UhBeenamsoyoung": 118, "kr-UhBeenamsoyoungBold": 119, "kr-WenQuanYiMicroHei": 120, "kr-YeonSung-Regular": 121}

text_encoder/Glyph-SDXL-v2/configs/glyph_sdxl_multilingual_albedo.py

@@ -0,0 +1,96 @@
+#### Model Setting
+pretrained_model_name_or_path = 'stablediffusionapi/albedobase-xl-20'
+pretrained_vae_model_name_or_path = 'madebyollin/sdxl-vae-fp16-fix'
+revision = None
+
+byt5_max_length = 512
+byt5_mapper_type = 'T5EncoderBlockByT5Mapper'
+byt5_mapper_config = dict(
+    num_layers=4,
+    sdxl_channels=2048,
+)
+byt5_config = dict(
+    byt5_name='google/byt5-small', 
+    special_token=True, 
+    color_special_token=True,
+    font_special_token=True,
+    color_ann_path='assets/color_idx.json',
+    font_ann_path='assets/multilingual_10-lang_idx.json',
+    multilingual=True,
+)
+
+attn_block_to_modify = [
+    "down_blocks.1.attentions.0.transformer_blocks.0",
+    "down_blocks.1.attentions.0.transformer_blocks.1",
+    "down_blocks.1.attentions.1.transformer_blocks.0",
+    "down_blocks.1.attentions.1.transformer_blocks.1",
+    "down_blocks.2.attentions.0.transformer_blocks.0",
+    "down_blocks.2.attentions.0.transformer_blocks.1",
+    "down_blocks.2.attentions.0.transformer_blocks.2",
+    "down_blocks.2.attentions.0.transformer_blocks.3",
+    "down_blocks.2.attentions.0.transformer_blocks.4",
+    "down_blocks.2.attentions.0.transformer_blocks.5",
+    "down_blocks.2.attentions.0.transformer_blocks.6",
+    "down_blocks.2.attentions.0.transformer_blocks.7",
+    "down_blocks.2.attentions.0.transformer_blocks.8",
+    "down_blocks.2.attentions.0.transformer_blocks.9",
+    "down_blocks.2.attentions.1.transformer_blocks.0",
+    "down_blocks.2.attentions.1.transformer_blocks.1",
+    "down_blocks.2.attentions.1.transformer_blocks.2",
+    "down_blocks.2.attentions.1.transformer_blocks.3",
+    "down_blocks.2.attentions.1.transformer_blocks.4",
+    "down_blocks.2.attentions.1.transformer_blocks.5",
+    "down_blocks.2.attentions.1.transformer_blocks.6",
+    "down_blocks.2.attentions.1.transformer_blocks.7",
+    "down_blocks.2.attentions.1.transformer_blocks.8",
+    "down_blocks.2.attentions.1.transformer_blocks.9",
+    "up_blocks.0.attentions.0.transformer_blocks.0",
+    "up_blocks.0.attentions.0.transformer_blocks.1",
+    "up_blocks.0.attentions.0.transformer_blocks.2",
+    "up_blocks.0.attentions.0.transformer_blocks.3",
+    "up_blocks.0.attentions.0.transformer_blocks.4",
+    "up_blocks.0.attentions.0.transformer_blocks.5",
+    "up_blocks.0.attentions.0.transformer_blocks.6",
+    "up_blocks.0.attentions.0.transformer_blocks.7",
+    "up_blocks.0.attentions.0.transformer_blocks.8",
+    "up_blocks.0.attentions.0.transformer_blocks.9",
+    "up_blocks.0.attentions.1.transformer_blocks.0",
+    "up_blocks.0.attentions.1.transformer_blocks.1",
+    "up_blocks.0.attentions.1.transformer_blocks.2",
+    "up_blocks.0.attentions.1.transformer_blocks.3",
+    "up_blocks.0.attentions.1.transformer_blocks.4",
+    "up_blocks.0.attentions.1.transformer_blocks.5",
+    "up_blocks.0.attentions.1.transformer_blocks.6",
+    "up_blocks.0.attentions.1.transformer_blocks.7",
+    "up_blocks.0.attentions.1.transformer_blocks.8",
+    "up_blocks.0.attentions.1.transformer_blocks.9",
+    "up_blocks.0.attentions.2.transformer_blocks.0",
+    "up_blocks.0.attentions.2.transformer_blocks.1",
+    "up_blocks.0.attentions.2.transformer_blocks.2",
+    "up_blocks.0.attentions.2.transformer_blocks.3",
+    "up_blocks.0.attentions.2.transformer_blocks.4",
+    "up_blocks.0.attentions.2.transformer_blocks.5",
+    "up_blocks.0.attentions.2.transformer_blocks.6",
+    "up_blocks.0.attentions.2.transformer_blocks.7",
+    "up_blocks.0.attentions.2.transformer_blocks.8",
+    "up_blocks.0.attentions.2.transformer_blocks.9",
+    "up_blocks.1.attentions.0.transformer_blocks.0",
+    "up_blocks.1.attentions.0.transformer_blocks.1",
+    "up_blocks.1.attentions.1.transformer_blocks.0",
+    "up_blocks.1.attentions.1.transformer_blocks.1",
+    "up_blocks.1.attentions.2.transformer_blocks.0",
+    "up_blocks.1.attentions.2.transformer_blocks.1",
+    "mid_block.attentions.0.transformer_blocks.0",
+    "mid_block.attentions.0.transformer_blocks.1",
+    "mid_block.attentions.0.transformer_blocks.2",
+    "mid_block.attentions.0.transformer_blocks.3",
+    "mid_block.attentions.0.transformer_blocks.4",
+    "mid_block.attentions.0.transformer_blocks.5",
+    "mid_block.attentions.0.transformer_blocks.6",
+    "mid_block.attentions.0.transformer_blocks.7",
+    "mid_block.attentions.0.transformer_blocks.8",
+    "mid_block.attentions.0.transformer_blocks.9",
+]
+
+unet_lora_rank = 128
+inference_dtype = 'fp16'

text_encoder/Glyph-SDXL-v2/configuration.json

@@ -0,0 +1 @@
+{"framework": "pytorch", "task": "text-image", "allow_remote": true}

text_encoder/Glyph-SDXL-v2/examples/xiaoman.json

@@ -0,0 +1,65 @@
+{
+    "texts": [
+        "小满是二十四节气之一，夏季的第二个节气。该节气是指夏熟作物的籽粒开始灌浆饱满，但还未成熟，只是小满，还未大满。",
+        "2022.5.21",
+        "饱满的灵魂 无畏的生长 二十四节气之一",
+        "今日小满",
+        "Grain Buds"
+    ],
+    "styles": [
+        {
+            "color": "#427227",
+            "font-family": "cn-HYQiHei-AZEJ"
+        },
+        {
+            "font-family": "en-TAN MERINGUE",
+            "color": "#f89b2b"
+        },
+        {
+            "color": "#ffffff",
+            "font-family": "cn-SourceHanSansSC-ExtraLight"
+        },
+        {
+            "color": "#427227",
+            "font-family": "cn-AlibabaPuHuiTi-Bold"
+        },
+        {
+            "color": "#427227",
+            "font-family": "en-SairaCondensed-Regular"
+        }
+    ],
+    "bbox": [
+        [
+            0.09969604863221884,
+            0.4370820668693009,
+            0.31124620060790276,
+            0.2072948328267477
+        ],
+        [
+            0.10455927051671733,
+            0.09908814589665653,
+            0.22127659574468084,
+            0.034650455927051675
+        ],
+        [
+            0.09969604863221884,
+            0.9398176291793313,
+            0.7993920972644377,
+            0.026747720364741642
+        ],
+        [
+            0.09787234042553192,
+            0.17142857142857143,
+            0.4231003039513678,
+            0.10577507598784194
+        ],
+        [
+            0.10091185410334347,
+            0.3100303951367781,
+            0.2772036474164134,
+            0.053495440729483285
+        ]
+    ],
+    "bg_prompt": "The image portrays a young girl sitting on a large green leaf. The leaf is part of a plant with other green leaves. The girl is wearing a yellow dress and a straw hat. She is holding a small yellow flower in her hand. The background of the image is a light blue sky with a few clouds. The overall style of the image is a colorful, cartoon-like illustration.",
+    "seed": 0
+}

text_encoder/Glyph-SDXL-v2/inference_multilingual.py

@@ -0,0 +1,236 @@
+import argparse
+import os
+import json
+import copy
+import os.path as osp
+
+import torch
+from diffusers import UNet2DConditionModel, AutoencoderKL
+from diffusers.models.attention import BasicTransformerBlock
+from peft import LoraConfig
+from peft.utils import set_peft_model_state_dict
+from transformers import PretrainedConfig
+
+from diffusers import DPMSolverMultistepScheduler
+
+from glyph_sdxl.utils import (
+    parse_config,
+    UNET_CKPT_NAME,
+    huggingface_cache_dir,
+    load_byt5_and_byt5_tokenizer,
+    BYT5_MAPPER_CKPT_NAME,
+    INSERTED_ATTN_CKPT_NAME,
+    BYT5_CKPT_NAME,
+    MultilingualPromptFormat,
+)
+from glyph_sdxl.custom_diffusers import (
+    StableDiffusionGlyphXLPipeline,
+    CrossAttnInsertBasicTransformerBlock,
+)
+from glyph_sdxl.modules import T5EncoderBlockByT5Mapper
+
+byt5_mapper_dict = [T5EncoderBlockByT5Mapper]
+byt5_mapper_dict = {mapper.__name__: mapper for mapper in byt5_mapper_dict}
+
+
+def import_model_class_from_model_name_or_path(
+    pretrained_model_name_or_path: str, revision: str, subfolder: str = "text_encoder",
+):
+    text_encoder_config = PretrainedConfig.from_pretrained(
+        pretrained_model_name_or_path, 
+        subfolder=subfolder, 
+        revision=revision,
+    )
+    model_class = text_encoder_config.architectures[0]
+
+    if model_class == "CLIPTextModel":
+        from transformers import CLIPTextModel
+
+        return CLIPTextModel
+    elif model_class == "CLIPTextModelWithProjection":
+        from transformers import CLIPTextModelWithProjection
+
+        return CLIPTextModelWithProjection
+    else:
+        raise ValueError(f"{model_class} is not supported.")
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("config_dir", type=str)
+    parser.add_argument("ckpt_dir", type=str)
+    parser.add_argument("ann_path", type=str, default='examples/shower.json')
+    parser.add_argument("--out_folder", type=str, default='None')
+    parser.add_argument("--device", type=str, default='cuda')
+    parser.add_argument("--sampler", type=str, choices=['euler', 'dpm'])
+    parser.add_argument("--cfg", type=float, default=5.0)
+    args = parser.parse_args()
+    
+    config = parse_config(args.config_dir)
+        
+    text_encoder_cls_one = import_model_class_from_model_name_or_path(
+        config.pretrained_model_name_or_path, config.revision,
+    )
+    text_encoder_cls_two = import_model_class_from_model_name_or_path(
+        config.pretrained_model_name_or_path, config.revision, subfolder="text_encoder_2",
+    )
+    text_encoder_one = text_encoder_cls_one.from_pretrained(
+        config.pretrained_model_name_or_path, subfolder="text_encoder", revision=config.revision,
+        cache_dir=huggingface_cache_dir,
+    )
+    text_encoder_two = text_encoder_cls_two.from_pretrained(
+        config.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=config.revision,
+        cache_dir=huggingface_cache_dir,
+    )
+
+    unet = UNet2DConditionModel.from_pretrained(
+        config.pretrained_model_name_or_path, 
+        subfolder="unet", 
+        revision=config.revision,
+        cache_dir=huggingface_cache_dir,
+    )
+    
+    vae_path = (
+        config.pretrained_model_name_or_path
+        if config.pretrained_vae_model_name_or_path is None
+        else config.pretrained_vae_model_name_or_path
+    )
+    vae = AutoencoderKL.from_pretrained(
+        vae_path, subfolder="vae" if config.pretrained_vae_model_name_or_path is None else None, 
+        revision=config.revision,
+        cache_dir=huggingface_cache_dir,
+    )
+
+    byt5_model, byt5_tokenizer = load_byt5_and_byt5_tokenizer(
+        **config.byt5_config,
+        huggingface_cache_dir=huggingface_cache_dir,
+    )
+
+    inference_dtype = torch.float32
+    if config.inference_dtype == "fp16":
+        inference_dtype = torch.float16
+    elif config.inference_dtype == "bf16":
+        inference_dtype = torch.bfloat16
+
+    if config.pretrained_vae_model_name_or_path is None:
+        vae.to(args.device, dtype=torch.float32)
+    else:
+        vae.to(args.device, dtype=inference_dtype)
+    text_encoder_one.to(args.device, dtype=inference_dtype)
+    text_encoder_two.to(args.device, dtype=inference_dtype)
+    byt5_model.to(args.device)
+    unet.to(args.device, dtype=inference_dtype)
+
+    inserted_new_modules_para_set = set()
+    for name, module in unet.named_modules():
+        if isinstance(module, BasicTransformerBlock) and name in config.attn_block_to_modify:
+            parent_module = unet
+            for n in name.split(".")[:-1]:
+                parent_module = getattr(parent_module, n)
+            new_block = CrossAttnInsertBasicTransformerBlock.from_transformer_block(
+                module,
+                byt5_model.config.d_model if config.byt5_mapper_config.sdxl_channels is None else config.byt5_mapper_config.sdxl_channels,
+            )
+            new_block.requires_grad_(False)
+            for inserted_module_name, inserted_module in zip(
+                new_block.get_inserted_modules_names(), 
+                new_block.get_inserted_modules()
+            ):
+                inserted_module.requires_grad_(True)
+                for para_name, para in inserted_module.named_parameters():
+                    para_key = name + '.' + inserted_module_name + '.' + para_name
+                    assert para_key not in inserted_new_modules_para_set
+                    inserted_new_modules_para_set.add(para_key)
+            for origin_module in new_block.get_origin_modules():
+                origin_module.to(args.device, dtype=inference_dtype)
+            parent_module.register_module(name.split(".")[-1], new_block)
+            print(f"inserted cross attn block to {name}")
+
+    byt5_mapper = byt5_mapper_dict[config.byt5_mapper_type](
+        byt5_model.config,
+        **config.byt5_mapper_config,
+    )
+
+    unet_lora_target_modules = [
+        "attn1.to_k", "attn1.to_q", "attn1.to_v", "attn1.to_out.0",
+        "attn2.to_k", "attn2.to_q", "attn2.to_v", "attn2.to_out.0",
+    ]
+    unet_lora_config = LoraConfig(
+        r=config.unet_lora_rank,
+        lora_alpha=config.unet_lora_rank,
+        init_lora_weights="gaussian",
+        target_modules=unet_lora_target_modules,
+    )
+    unet.add_adapter(unet_lora_config)
+    
+    unet_lora_layers_para = torch.load(osp.join(args.ckpt_dir, UNET_CKPT_NAME), map_location='cpu')
+    incompatible_keys = set_peft_model_state_dict(unet, unet_lora_layers_para, adapter_name="default")
+    if getattr(incompatible_keys, 'unexpected_keys', []) == []:
+        print(f"loaded unet_lora_layers_para")
+    else:
+        print(f"unet_lora_layers has unexpected_keys: {getattr(incompatible_keys, 'unexpected_keys', None)}")
+    
+    inserted_attn_module_paras = torch.load(osp.join(args.ckpt_dir, INSERTED_ATTN_CKPT_NAME), map_location='cpu')
+    missing_keys, unexpected_keys = unet.load_state_dict(inserted_attn_module_paras, strict=False)
+    assert len(unexpected_keys) == 0, unexpected_keys
+    
+    byt5_mapper_para = torch.load(osp.join(args.ckpt_dir, BYT5_MAPPER_CKPT_NAME), map_location='cpu')
+    byt5_mapper.load_state_dict(byt5_mapper_para)
+    
+    byt5_model_para = torch.load(osp.join(args.ckpt_dir, BYT5_CKPT_NAME), map_location='cpu')
+    byt5_model.load_state_dict(byt5_model_para)
+
+    pipeline = StableDiffusionGlyphXLPipeline.from_pretrained(
+        config.pretrained_model_name_or_path, 
+        vae=vae, 
+        text_encoder=text_encoder_one,
+        text_encoder_2=text_encoder_two,
+        byt5_text_encoder=byt5_model,
+        byt5_tokenizer=byt5_tokenizer,
+        byt5_mapper=byt5_mapper,
+        unet=unet,
+        byt5_max_length=config.byt5_max_length,
+        revision=config.revision,
+        torch_dtype=inference_dtype,
+        safety_checker=None,
+        cache_dir=huggingface_cache_dir,
+    )
+
+    if args.sampler == 'dpm':
+        pipeline.scheduler = DPMSolverMultistepScheduler.from_pretrained(
+            config.pretrained_model_name_or_path,
+            subfolder="scheduler",
+            use_karras_sigmas=True,
+        )
+
+    pipeline = pipeline.to(args.device)
+
+    with open(args.ann_path, 'r') as f:
+        ann = json.load(f)
+    
+    os.makedirs(args.out_folder, exist_ok=True)
+
+    prompt_format = MultilingualPromptFormat()
+
+    texts = copy.deepcopy(ann['texts'])
+    bboxes = copy.deepcopy(ann['bbox'])
+    styles = copy.deepcopy(ann['styles'])
+
+    text_prompt = prompt_format.format_prompt(texts, styles)
+
+    if 'seed' not in ann:
+        generator = torch.Generator(device=args.device)
+    else: 
+        generator = torch.Generator(device=args.device).manual_seed(ann['seed'])
+
+    with torch.cuda.amp.autocast():
+        image = pipeline(
+            prompt=ann['bg_prompt'],
+            text_prompt=text_prompt,
+            texts=texts,
+            bboxes=bboxes,
+            num_inference_steps=50,
+            generator=generator,
+            text_attn_mask=None,
+            guidance_scale=args.cfg,
+        ).images[0]
+    image.save(f'{args.out_folder}/result.png')

text_encoder/Glyph-SDXL-v2/requirements.txt

@@ -0,0 +1,10 @@
+transformers==4.36.2
+diffusers==0.26.1
+mmengine
+accelerate
+torch==2.2.0
+torchvision==0.17.0
+deepspeed
+peft
+webcolors
+gradio

text_encoder/byt5-small/.gitattributes

@@ -0,0 +1,17 @@
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tar.gz filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text

text_encoder/byt5-small/README.md

@@ -0,0 +1,158 @@
+---
+language: 
+- multilingual
+- af
+- am
+- ar
+- az
+- be
+- bg
+- bn
+- ca
+- ceb
+- co
+- cs
+- cy
+- da
+- de
+- el
+- en
+- eo
+- es
+- et
+- eu
+- fa
+- fi
+- fil
+- fr
+- fy
+- ga
+- gd
+- gl
+- gu
+- ha
+- haw
+- hi
+- hmn
+- ht
+- hu
+- hy
+- ig
+- is
+- it
+- iw
+- ja
+- jv
+- ka
+- kk
+- km
+- kn
+- ko
+- ku
+- ky
+- la
+- lb
+- lo
+- lt
+- lv
+- mg
+- mi
+- mk
+- ml
+- mn
+- mr
+- ms
+- mt
+- my
+- ne
+- nl
+- no
+- ny
+- pa
+- pl
+- ps
+- pt
+- ro
+- ru
+- sd
+- si
+- sk
+- sl
+- sm
+- sn
+- so
+- sq
+- sr
+- st
+- su
+- sv
+- sw
+- ta
+- te
+- tg
+- th
+- tr
+- uk
+- und
+- ur
+- uz
+- vi
+- xh
+- yi
+- yo
+- zh
+- zu
+datasets:
+- mc4
+
+license: apache-2.0
+---
+
+# ByT5 - Small
+
+ByT5 is a tokenizer-free version of [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) and generally follows the architecture of [MT5](https://huggingface.co/google/mt5-small).
+
+ByT5 was only pre-trained on [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) excluding any supervised training with an average span-mask of 20 UTF-8 characters. Therefore, this model has to be fine-tuned before it is useable on a downstream task.
+
+ByT5 works especially well on noisy text data,*e.g.*, `google/byt5-small` significantly outperforms [mt5-small](https://huggingface.co/google/mt5-small) on [TweetQA](https://arxiv.org/abs/1907.06292).
+
+Paper: [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626)
+
+Authors: *Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel* 
+
+## Example Inference
+
+ByT5 works on raw UTF-8 bytes and can be used without a tokenizer:
+
+```python
+from transformers import T5ForConditionalGeneration
+import torch
+
+model = T5ForConditionalGeneration.from_pretrained('google/byt5-small')
+
+input_ids = torch.tensor([list("Life is like a box of chocolates.".encode("utf-8"))]) + 3  # add 3 for special tokens
+labels = torch.tensor([list("La vie est comme une boîte de chocolat.".encode("utf-8"))]) + 3  # add 3 for special tokens
+
+loss = model(input_ids, labels=labels).loss # forward pass
+```
+
+For batched inference & training it is however recommended using a tokenizer class for padding:
+
+```python
+from transformers import T5ForConditionalGeneration, AutoTokenizer
+
+model = T5ForConditionalGeneration.from_pretrained('google/byt5-small')
+tokenizer = AutoTokenizer.from_pretrained('google/byt5-small')
+
+model_inputs = tokenizer(["Life is like a box of chocolates.", "Today is Monday."], padding="longest", return_tensors="pt")
+labels = tokenizer(["La vie est comme une boîte de chocolat.", "Aujourd'hui c'est lundi."], padding="longest", return_tensors="pt").input_ids
+
+loss = model(**model_inputs, labels=labels).loss # forward pass
+```
+
+## Abstract
+
+Most widely-used pre-trained language models operate on sequences of tokens corresponding to word or subword units. Encoding text as a sequence of tokens requires a tokenizer, which is typically created as an independent artifact from the model. Token-free models that instead operate directly on raw text (bytes or characters) have many benefits: they can process text in any language out of the box, they are more robust to noise, and they minimize technical debt by removing complex and error-prone text preprocessing pipelines. Since byte or character sequences are longer than token sequences, past work on token-free models has often introduced new model architectures designed to amortize the cost of operating directly on raw text. In this paper, we show that a standard Transformer architecture can be used with minimal modifications to process byte sequences. We carefully characterize the trade-offs in terms of parameter count, training FLOPs, and inference speed, and show that byte-level models are competitive with their token-level counterparts. We also demonstrate that byte-level models are significantly more robust to noise and perform better on tasks that are sensitive to spelling and pronunciation. As part of our contribution, we release a new set of pre-trained byte-level Transformer models based on the T5 architecture, as well as all code and data used in our experiments.
+
+![model image](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/ByT5.png)
+

text_encoder/byt5-small/config.json

@@ -0,0 +1,28 @@
+{
+  "_name_or_path": "/home/patrick/t5/byt5-small",
+  "architectures": [
+    "T5ForConditionalGeneration"
+  ],
+  "d_ff": 3584,
+  "d_kv": 64,
+  "d_model": 1472,
+  "decoder_start_token_id": 0,
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "gated-gelu",
+  "gradient_checkpointing": false,
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "num_decoder_layers": 4,
+  "num_heads": 6,
+  "num_layers": 12,
+  "pad_token_id": 0,
+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": false,
+  "tokenizer_class": "ByT5Tokenizer",
+  "transformers_version": "4.7.0.dev0",
+  "use_cache": true,
+  "vocab_size": 384
+}

text_encoder/byt5-small/generation_config.json

@@ -0,0 +1,7 @@
+{
+  "_from_model_config": true,
+  "decoder_start_token_id": 0,
+  "eos_token_id": 1,
+  "pad_token_id": 0,
+  "transformers_version": "4.27.0.dev0"
+}

text_encoder/byt5-small/special_tokens_map.json

@@ -0,0 +1 @@
+{"eos_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "unk_token": {"content": "<unk>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "pad_token": {"content": "<pad>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "additional_special_tokens": ["<extra_id_0>", "<extra_id_1>", "<extra_id_2>", "<extra_id_3>", "<extra_id_4>", "<extra_id_5>", "<extra_id_6>", "<extra_id_7>", "<extra_id_8>", "<extra_id_9>", "<extra_id_10>", "<extra_id_11>", "<extra_id_12>", "<extra_id_13>", "<extra_id_14>", "<extra_id_15>", "<extra_id_16>", "<extra_id_17>", "<extra_id_18>", "<extra_id_19>", "<extra_id_20>", "<extra_id_21>", "<extra_id_22>", "<extra_id_23>", "<extra_id_24>", "<extra_id_25>", "<extra_id_26>", "<extra_id_27>", "<extra_id_28>", "<extra_id_29>", "<extra_id_30>", "<extra_id_31>", "<extra_id_32>", "<extra_id_33>", "<extra_id_34>", "<extra_id_35>", "<extra_id_36>", "<extra_id_37>", "<extra_id_38>", "<extra_id_39>", "<extra_id_40>", "<extra_id_41>", "<extra_id_42>", "<extra_id_43>", "<extra_id_44>", "<extra_id_45>", "<extra_id_46>", "<extra_id_47>", "<extra_id_48>", "<extra_id_49>", "<extra_id_50>", "<extra_id_51>", "<extra_id_52>", "<extra_id_53>", "<extra_id_54>", "<extra_id_55>", "<extra_id_56>", "<extra_id_57>", "<extra_id_58>", "<extra_id_59>", "<extra_id_60>", "<extra_id_61>", "<extra_id_62>", "<extra_id_63>", "<extra_id_64>", "<extra_id_65>", "<extra_id_66>", "<extra_id_67>", "<extra_id_68>", "<extra_id_69>", "<extra_id_70>", "<extra_id_71>", "<extra_id_72>", "<extra_id_73>", "<extra_id_74>", "<extra_id_75>", "<extra_id_76>", "<extra_id_77>", "<extra_id_78>", "<extra_id_79>", "<extra_id_80>", "<extra_id_81>", "<extra_id_82>", "<extra_id_83>", "<extra_id_84>", "<extra_id_85>", "<extra_id_86>", "<extra_id_87>", "<extra_id_88>", "<extra_id_89>", "<extra_id_90>", "<extra_id_91>", "<extra_id_92>", "<extra_id_93>", "<extra_id_94>", "<extra_id_95>", "<extra_id_96>", "<extra_id_97>", "<extra_id_98>", "<extra_id_99>", "<extra_id_100>", "<extra_id_101>", "<extra_id_102>", "<extra_id_103>", "<extra_id_104>", "<extra_id_105>", "<extra_id_106>", "<extra_id_107>", "<extra_id_108>", "<extra_id_109>", "<extra_id_110>", "<extra_id_111>", "<extra_id_112>", "<extra_id_113>", "<extra_id_114>", "<extra_id_115>", "<extra_id_116>", "<extra_id_117>", "<extra_id_118>", "<extra_id_119>", "<extra_id_120>", "<extra_id_121>", "<extra_id_122>", "<extra_id_123>", "<extra_id_124>"]}

text_encoder/byt5-small/tokenizer_config.json

@@ -0,0 +1 @@
+{"eos_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "unk_token": {"content": "<unk>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "pad_token": {"content": "<pad>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "extra_ids": 125, "additional_special_tokens": ["<extra_id_0>", "<extra_id_1>", "<extra_id_2>", "<extra_id_3>", "<extra_id_4>", "<extra_id_5>", "<extra_id_6>", "<extra_id_7>", "<extra_id_8>", "<extra_id_9>", "<extra_id_10>", "<extra_id_11>", "<extra_id_12>", "<extra_id_13>", "<extra_id_14>", "<extra_id_15>", "<extra_id_16>", "<extra_id_17>", "<extra_id_18>", "<extra_id_19>", "<extra_id_20>", "<extra_id_21>", "<extra_id_22>", "<extra_id_23>", "<extra_id_24>", "<extra_id_25>", "<extra_id_26>", "<extra_id_27>", "<extra_id_28>", "<extra_id_29>", "<extra_id_30>", "<extra_id_31>", "<extra_id_32>", "<extra_id_33>", "<extra_id_34>", "<extra_id_35>", "<extra_id_36>", "<extra_id_37>", "<extra_id_38>", "<extra_id_39>", "<extra_id_40>", "<extra_id_41>", "<extra_id_42>", "<extra_id_43>", "<extra_id_44>", "<extra_id_45>", "<extra_id_46>", "<extra_id_47>", "<extra_id_48>", "<extra_id_49>", "<extra_id_50>", "<extra_id_51>", "<extra_id_52>", "<extra_id_53>", "<extra_id_54>", "<extra_id_55>", "<extra_id_56>", "<extra_id_57>", "<extra_id_58>", "<extra_id_59>", "<extra_id_60>", "<extra_id_61>", "<extra_id_62>", "<extra_id_63>", "<extra_id_64>", "<extra_id_65>", "<extra_id_66>", "<extra_id_67>", "<extra_id_68>", "<extra_id_69>", "<extra_id_70>", "<extra_id_71>", "<extra_id_72>", "<extra_id_73>", "<extra_id_74>", "<extra_id_75>", "<extra_id_76>", "<extra_id_77>", "<extra_id_78>", "<extra_id_79>", "<extra_id_80>", "<extra_id_81>", "<extra_id_82>", "<extra_id_83>", "<extra_id_84>", "<extra_id_85>", "<extra_id_86>", "<extra_id_87>", "<extra_id_88>", "<extra_id_89>", "<extra_id_90>", "<extra_id_91>", "<extra_id_92>", "<extra_id_93>", "<extra_id_94>", "<extra_id_95>", "<extra_id_96>", "<extra_id_97>", "<extra_id_98>", "<extra_id_99>", "<extra_id_100>", "<extra_id_101>", "<extra_id_102>", "<extra_id_103>", "<extra_id_104>", "<extra_id_105>", "<extra_id_106>", "<extra_id_107>", "<extra_id_108>", "<extra_id_109>", "<extra_id_110>", "<extra_id_111>", "<extra_id_112>", "<extra_id_113>", "<extra_id_114>", "<extra_id_115>", "<extra_id_116>", "<extra_id_117>", "<extra_id_118>", "<extra_id_119>", "<extra_id_120>", "<extra_id_121>", "<extra_id_122>", "<extra_id_123>", "<extra_id_124>"]}

text_encoder/llm/.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
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+*.wasm filter=lfs diff=lfs merge=lfs -text
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+*tfevents* filter=lfs diff=lfs merge=lfs -text

text_encoder/llm/README.md

@@ -0,0 +1,528 @@
+
+---
+license: apache-2.0
+language:
+- en
+pipeline_tag: image-text-to-text
+tags:
+- multimodal
+library_name: transformers
+---
+
+# Qwen2.5-VL-7B-Instruct
+<a href="https://chat.qwenlm.ai/" target="_blank" style="margin: 2px;">
+    <img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
+</a>
+
+## Introduction
+
+In the past five months since Qwen2-VL’s release, numerous developers have built new models on the Qwen2-VL vision-language models, providing us with valuable feedback. During this period, we focused on building more useful vision-language models. Today, we are excited to introduce the latest addition to the Qwen family: Qwen2.5-VL.
+
+#### Key Enhancements:
+* **Understand things visually**: Qwen2.5-VL is not only proficient in recognizing common objects such as flowers, birds, fish, and insects, but it is highly capable of analyzing texts, charts, icons, graphics, and layouts within images.
+
+* **Being agentic**: Qwen2.5-VL directly plays as a visual agent that can reason and dynamically direct tools, which is capable of computer use and phone use.
+
+* **Understanding long videos and capturing events**: Qwen2.5-VL can comprehend videos of over 1 hour, and this time it has a new ability of cpaturing event by pinpointing the relevant video segments.
+
+* **Capable of visual localization in different formats**: Qwen2.5-VL can accurately localize objects in an image by generating bounding boxes or points, and it can provide stable JSON outputs for coordinates and attributes.
+
+* **Generating structured outputs**: for data like scans of invoices, forms, tables, etc. Qwen2.5-VL supports structured outputs of their contents, benefiting usages in finance, commerce, etc.
+
+
+#### Model Architecture Updates:
+
+* **Dynamic Resolution and Frame Rate Training for Video Understanding**:
+
+We extend dynamic resolution to the temporal dimension by adopting dynamic FPS sampling, enabling the model to comprehend videos at various sampling rates. Accordingly, we update mRoPE in the time dimension with IDs and absolute time alignment, enabling the model to learn temporal sequence and speed, and ultimately acquire the ability to pinpoint specific moments.
+
+<p align="center">
+    <img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2.5-VL/qwen2.5vl_arc.jpeg" width="80%"/>
+<p>
+
+
+* **Streamlined and Efficient Vision Encoder**
+
+We enhance both training and inference speeds by strategically implementing window attention into the ViT. The ViT architecture is further optimized with SwiGLU and RMSNorm, aligning it with the structure of the Qwen2.5 LLM.
+
+
+We have three models with 3, 7 and 72 billion parameters. This repo contains the instruction-tuned 7B Qwen2.5-VL model. For more information, visit our [Blog](https://qwenlm.github.io/blog/qwen2.5-vl/) and [GitHub](https://github.com/QwenLM/Qwen2.5-VL).
+
+
+
+## Evaluation
+
+### Image benchmark
+
+
+| Benchmark | InternVL2.5-8B | MiniCPM-o 2.6 | GPT-4o-mini | Qwen2-VL-7B |**Qwen2.5-VL-7B** |
+| :--- | :---: | :---: | :---: | :---: | :---: |
+| MMMU<sub>val</sub>  | 56 | 50.4 | **60**| 54.1 | 58.6|
+| MMMU-Pro<sub>val</sub>  | 34.3 | - | 37.6| 30.5 | 41.0|
+| DocVQA<sub>test</sub>  | 93 | 93 | - | 94.5 | **95.7** |
+| InfoVQA<sub>test</sub>  | 77.6 | - |  - |76.5 | **82.6** |
+| ChartQA<sub>test</sub>  | 84.8 | - |- | 83.0 |**87.3** |
+| TextVQA<sub>val</sub>  | 79.1 | 80.1 | -| 84.3 | **84.9**|
+| OCRBench | 822 | 852 | 785 | 845 | **864** |
+| CC_OCR | 57.7 |  | | 61.6 | **77.8**|
+| MMStar | 62.8| | |60.7| **63.9**|
+| MMBench-V1.1-En<sub>test</sub>  | 79.4 | 78.0 | 76.0| 80.7 | **82.6** |
+| MMT-Bench<sub>test</sub> | - | - | - |**63.7** |63.6 |
+| MMStar | **61.5** | 57.5 |  54.8 | 60.7 |63.9 |
+| MMVet<sub>GPT-4-Turbo</sub>  | 54.2 | 60.0 | 66.9 | 62.0 | **67.1**|
+| HallBench<sub>avg</sub>  | 45.2 | 48.1 | 46.1| 50.6 | **52.9**|
+| MathVista<sub>testmini</sub>  | 58.3 | 60.6 | 52.4 | 58.2 | **68.2**|
+| MathVision  | - | -  | - | 16.3 | **25.07** |
+
+### Video Benchmarks
+
+| Benchmark |  Qwen2-VL-7B | **Qwen2.5-VL-7B** |
+| :--- | :---: | :---: |
+| MVBench |  67.0 | **69.6** |
+| PerceptionTest<sub>test</sub>  | 66.9 | **70.5** |
+| Video-MME<sub>wo/w subs</sub>   | 63.3/69.0 | **65.1**/**71.6** |
+| LVBench  |  | 45.3 |
+| LongVideoBench  |  | 54.7 |
+| MMBench-Video | 1.44 | 1.79 |
+| TempCompass |  | 71.7 |
+| MLVU |  | 70.2 |
+| CharadesSTA/mIoU |  43.6|
+
+### Agent benchmark
+| Benchmarks              | Qwen2.5-VL-7B |
+|-------------------------|---------------|
+| ScreenSpot              |     84.7    |
+| ScreenSpot Pro          |     29.0    |
+| AITZ_EM                 |  	81.9    |
+| Android Control High_EM |    	60.1    |
+| Android Control Low_EM  |  	93.7    |
+| AndroidWorld_SR         | 	25.5  	|
+| MobileMiniWob++_SR      | 	91.4    |
+
+## Requirements
+The code of Qwen2.5-VL has been in the latest Hugging face transformers and we advise you to build from source with command:
+```
+pip install git+https://github.com/huggingface/transformers accelerate
+```
+or you might encounter the following error:
+```
+KeyError: 'qwen2_5_vl'
+```
+
+
+## Quickstart
+
+Below, we provide simple examples to show how to use Qwen2.5-VL with 🤖 ModelScope and 🤗 Transformers.
+
+The code of Qwen2.5-VL has been in the latest Hugging face transformers and we advise you to build from source with command:
+```
+pip install git+https://github.com/huggingface/transformers accelerate
+```
+or you might encounter the following error:
+```
+KeyError: 'qwen2_5_vl'
+```
+
+
+We offer a toolkit to help you handle various types of visual input more conveniently, as if you were using an API. This includes base64, URLs, and interleaved images and videos. You can install it using the following command:
+
+```bash
+# It's highly recommanded to use `[decord]` feature for faster video loading.
+pip install qwen-vl-utils[decord]==0.0.8
+```
+
+If you are not using Linux, you might not be able to install `decord` from PyPI. In that case, you can use `pip install qwen-vl-utils` which will fall back to using torchvision for video processing. However, you can still [install decord from source](https://github.com/dmlc/decord?tab=readme-ov-file#install-from-source) to get decord used when loading video.
+
+### Using 🤗  Transformers to Chat
+
+Here we show a code snippet to show you how to use the chat model with `transformers` and `qwen_vl_utils`:
+
+```python
+from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
+from qwen_vl_utils import process_vision_info
+
+# default: Load the model on the available device(s)
+model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    "Qwen/Qwen2.5-VL-7B-Instruct", torch_dtype="auto", device_map="auto"
+)
+
+# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
+# model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+#     "Qwen/Qwen2.5-VL-7B-Instruct",
+#     torch_dtype=torch.bfloat16,
+#     attn_implementation="flash_attention_2",
+#     device_map="auto",
+# )
+
+# default processer
+processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")
+
+# The default range for the number of visual tokens per image in the model is 4-16384.
+# You can set min_pixels and max_pixels according to your needs, such as a token range of 256-1280, to balance performance and cost.
+# min_pixels = 256*28*28
+# max_pixels = 1280*28*28
+# processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
+            },
+            {"type": "text", "text": "Describe this image."},
+        ],
+    }
+]
+
+# Preparation for inference
+text = processor.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)
+image_inputs, video_inputs = process_vision_info(messages)
+inputs = processor(
+    text=[text],
+    images=image_inputs,
+    videos=video_inputs,
+    padding=True,
+    return_tensors="pt",
+)
+inputs = inputs.to("cuda")
+
+# Inference: Generation of the output
+generated_ids = model.generate(**inputs, max_new_tokens=128)
+generated_ids_trimmed = [
+    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+]
+output_text = processor.batch_decode(
+    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+)
+print(output_text)
+```
+<details>
+<summary>Multi image inference</summary>
+
+```python
+# Messages containing multiple images and a text query
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "image": "file:///path/to/image1.jpg"},
+            {"type": "image", "image": "file:///path/to/image2.jpg"},
+            {"type": "text", "text": "Identify the similarities between these images."},
+        ],
+    }
+]
+
+# Preparation for inference
+text = processor.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)
+image_inputs, video_inputs = process_vision_info(messages)
+inputs = processor(
+    text=[text],
+    images=image_inputs,
+    videos=video_inputs,
+    padding=True,
+    return_tensors="pt",
+)
+inputs = inputs.to("cuda")
+
+# Inference
+generated_ids = model.generate(**inputs, max_new_tokens=128)
+generated_ids_trimmed = [
+    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+]
+output_text = processor.batch_decode(
+    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+)
+print(output_text)
+```
+</details>
+
+<details>
+<summary>Video inference</summary>
+
+```python
+# Messages containing a images list as a video and a text query
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "video",
+                "video": [
+                    "file:///path/to/frame1.jpg",
+                    "file:///path/to/frame2.jpg",
+                    "file:///path/to/frame3.jpg",
+                    "file:///path/to/frame4.jpg",
+                ],
+            },
+            {"type": "text", "text": "Describe this video."},
+        ],
+    }
+]
+
+# Messages containing a local video path and a text query
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "video",
+                "video": "file:///path/to/video1.mp4",
+                "max_pixels": 360 * 420,
+                "fps": 1.0,
+            },
+            {"type": "text", "text": "Describe this video."},
+        ],
+    }
+]
+
+# Messages containing a video url and a text query
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "video",
+                "video": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-VL/space_woaudio.mp4",
+            },
+            {"type": "text", "text": "Describe this video."},
+        ],
+    }
+]
+
+#In Qwen 2.5 VL, frame rate information is also input into the model to align with absolute time.
+# Preparation for inference
+text = processor.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)
+image_inputs, video_inputs, video_kwargs = process_vision_info(messages, return_video_kwargs=True)
+inputs = processor(
+    text=[text],
+    images=image_inputs,
+    videos=video_inputs,
+    fps=fps,
+    padding=True,
+    return_tensors="pt",
+    **video_kwargs,
+)
+inputs = inputs.to("cuda")
+
+# Inference
+generated_ids = model.generate(**inputs, max_new_tokens=128)
+generated_ids_trimmed = [
+    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+]
+output_text = processor.batch_decode(
+    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+)
+print(output_text)
+```
+
+Video URL compatibility largely depends on the third-party library version. The details are in the table below. change the backend by `FORCE_QWENVL_VIDEO_READER=torchvision` or `FORCE_QWENVL_VIDEO_READER=decord` if you prefer not to use the default one.
+
+| Backend     | HTTP | HTTPS |
+|-------------|------|-------|
+| torchvision >= 0.19.0 | ✅  | ✅   |
+| torchvision < 0.19.0  | ❌  | ❌   |
+| decord      | ✅  | ❌   |
+</details>
+
+<details>
+<summary>Batch inference</summary>
+
+```python
+# Sample messages for batch inference
+messages1 = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "image": "file:///path/to/image1.jpg"},
+            {"type": "image", "image": "file:///path/to/image2.jpg"},
+            {"type": "text", "text": "What are the common elements in these pictures?"},
+        ],
+    }
+]
+messages2 = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": "Who are you?"},
+]
+# Combine messages for batch processing
+messages = [messages1, messages2]
+
+# Preparation for batch inference
+texts = [
+    processor.apply_chat_template(msg, tokenize=False, add_generation_prompt=True)
+    for msg in messages
+]
+image_inputs, video_inputs = process_vision_info(messages)
+inputs = processor(
+    text=texts,
+    images=image_inputs,
+    videos=video_inputs,
+    padding=True,
+    return_tensors="pt",
+)
+inputs = inputs.to("cuda")
+
+# Batch Inference
+generated_ids = model.generate(**inputs, max_new_tokens=128)
+generated_ids_trimmed = [
+    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+]
+output_texts = processor.batch_decode(
+    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+)
+print(output_texts)
+```
+</details>
+
+### 🤖 ModelScope
+We strongly advise users especially those in mainland China to use ModelScope. `snapshot_download` can help you solve issues concerning downloading checkpoints.
+
+
+### More Usage Tips
+
+For input images, we support local files, base64, and URLs. For videos, we currently only support local files.
+
+```python
+# You can directly insert a local file path, a URL, or a base64-encoded image into the position where you want in the text.
+## Local file path
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "image": "file:///path/to/your/image.jpg"},
+            {"type": "text", "text": "Describe this image."},
+        ],
+    }
+]
+## Image URL
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "image": "http://path/to/your/image.jpg"},
+            {"type": "text", "text": "Describe this image."},
+        ],
+    }
+]
+## Base64 encoded image
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "image": "data:image;base64,/9j/..."},
+            {"type": "text", "text": "Describe this image."},
+        ],
+    }
+]
+```
+#### Image Resolution for performance boost
+
+The model supports a wide range of resolution inputs. By default, it uses the native resolution for input, but higher resolutions can enhance performance at the cost of more computation. Users can set the minimum and maximum number of pixels to achieve an optimal configuration for their needs, such as a token count range of 256-1280, to balance speed and memory usage.
+
+```python
+min_pixels = 256 * 28 * 28
+max_pixels = 1280 * 28 * 28
+processor = AutoProcessor.from_pretrained(
+    "Qwen/Qwen2.5-VL-7B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels
+)
+```
+
+Besides, We provide two methods for fine-grained control over the image size input to the model:
+
+1. Define min_pixels and max_pixels: Images will be resized to maintain their aspect ratio within the range of min_pixels and max_pixels.
+   
+2. Specify exact dimensions: Directly set `resized_height` and `resized_width`. These values will be rounded to the nearest multiple of 28.
+
+```python
+# min_pixels and max_pixels
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "image": "file:///path/to/your/image.jpg",
+                "resized_height": 280,
+                "resized_width": 420,
+            },
+            {"type": "text", "text": "Describe this image."},
+        ],
+    }
+]
+# resized_height and resized_width
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "image": "file:///path/to/your/image.jpg",
+                "min_pixels": 50176,
+                "max_pixels": 50176,
+            },
+            {"type": "text", "text": "Describe this image."},
+        ],
+    }
+]
+```
+
+### Processing Long Texts
+
+The current `config.json` is set for context length up to 32,768 tokens.
+To handle extensive inputs exceeding 32,768 tokens, we utilize [YaRN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts.
+
+For supported frameworks, you could add the following to `config.json` to enable YaRN:
+
+{
+	...,
+    "type": "yarn",
+    "mrope_section": [
+        16,
+        24,
+        24
+    ],
+    "factor": 4,
+    "original_max_position_embeddings": 32768
+}
+
+However, it should be noted that this method has a significant impact on the performance of temporal and spatial localization tasks, and is therefore not recommended for use.
+
+At the same time, for long video inputs, since MRoPE itself is more economical with ids, the max_position_embeddings can be directly modified to a larger value, such as 64k.
+
+
+
+
+## Citation
+
+If you find our work helpful, feel free to give us a cite.
+
+```
+@misc{qwen2.5-VL,
+    title = {Qwen2.5-VL},
+    url = {https://qwenlm.github.io/blog/qwen2.5-vl/},
+    author = {Qwen Team},
+    month = {January},
+    year = {2025}
+}
+
+@article{Qwen2VL,
+  title={Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution},
+  author={Wang, Peng and Bai, Shuai and Tan, Sinan and Wang, Shijie and Fan, Zhihao and Bai, Jinze and Chen, Keqin and Liu, Xuejing and Wang, Jialin and Ge, Wenbin and Fan, Yang and Dang, Kai and Du, Mengfei and Ren, Xuancheng and Men, Rui and Liu, Dayiheng and Zhou, Chang and Zhou, Jingren and Lin, Junyang},
+  journal={arXiv preprint arXiv:2409.12191},
+  year={2024}
+}
+
+@article{Qwen-VL,
+  title={Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond},
+  author={Bai, Jinze and Bai, Shuai and Yang, Shusheng and Wang, Shijie and Tan, Sinan and Wang, Peng and Lin, Junyang and Zhou, Chang and Zhou, Jingren},
+  journal={arXiv preprint arXiv:2308.12966},
+  year={2023}
+}
+```

text_encoder/llm/chat_template.json

@@ -0,0 +1,3 @@
+{
+    "chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
+}

text_encoder/llm/config.json

@@ -0,0 +1,61 @@
+{
+  "architectures": [
+    "Qwen2_5_VLForConditionalGeneration"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "vision_start_token_id": 151652,
+  "vision_end_token_id": 151653,
+  "vision_token_id": 151654,
+  "image_token_id": 151655,
+  "video_token_id": 151656,
+  "hidden_act": "silu",
+  "hidden_size": 3584,
+  "initializer_range": 0.02,
+  "intermediate_size": 18944,
+  "max_position_embeddings": 128000,
+  "max_window_layers": 28,
+  "model_type": "qwen2_5_vl",
+  "num_attention_heads": 28,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
+  "rms_norm_eps": 1e-06,
+  "rope_theta": 1000000.0,
+  "sliding_window": 32768,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.41.2",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vision_config": {
+    "depth": 32,
+    "hidden_act": "silu",
+    "hidden_size": 1280,
+    "intermediate_size": 3420,
+    "num_heads": 16,
+    "in_chans": 3,
+    "out_hidden_size": 3584,
+    "patch_size": 14,
+    "spatial_merge_size": 2,
+    "spatial_patch_size": 14,
+    "window_size": 112,
+    "fullatt_block_indexes": [
+      7,
+      15,
+      23,
+      31
+    ],
+    "tokens_per_second": 2,
+    "temporal_patch_size": 2
+  },
+  "rope_scaling": {
+    "type": "mrope",
+    "mrope_section": [
+      16,
+      24,
+      24
+    ]
+  },
+  "vocab_size": 152064
+}

text_encoder/llm/generation_config.json

@@ -0,0 +1,12 @@
+{
+  "bos_token_id": 151643,
+  "pad_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "repetition_penalty": 1.05,
+  "temperature": 0.000001,
+  "transformers_version": "4.37.0"
+}

text_encoder/llm/model.safetensors.index.json

@@ -0,0 +1,736 @@
+{
+  "metadata": {
+    "total_size": 16584333312
+  },
+  "weight_map": {
+    "lm_head.weight": "model-00005-of-00005.safetensors",
+    "model.embed_tokens.weight": "model-00001-of-00005.safetensors",
+    "model.layers.0.input_layernorm.weight": "model-00001-of-00005.safetensors",
+    "model.layers.0.mlp.down_proj.weight": "model-00001-of-00005.safetensors",
+    "model.layers.0.mlp.gate_proj.weight": "model-00001-of-00005.safetensors",
+    "model.layers.0.mlp.up_proj.weight": "model-00001-of-00005.safetensors",
+    "model.layers.0.post_attention_layernorm.weight": "model-00001-of-00005.safetensors",
+    "model.layers.0.self_attn.k_proj.bias": "model-00001-of-00005.safetensors",
+    "model.layers.0.self_attn.k_proj.weight": "model-00001-of-00005.safetensors",
+    "model.layers.0.self_attn.o_proj.weight": "model-00001-of-00005.safetensors",
+    "model.layers.0.self_attn.q_proj.bias": "model-00001-of-00005.safetensors",
+    "model.layers.0.self_attn.q_proj.weight": "model-00001-of-00005.safetensors",
+    "model.layers.0.self_attn.v_proj.bias": "model-00001-of-00005.safetensors",
+    "model.layers.0.self_attn.v_proj.weight": "model-00001-of-00005.safetensors",
+    "model.layers.1.input_layernorm.weight": "model-00001-of-00005.safetensors",
+    "model.layers.1.mlp.down_proj.weight": "model-00001-of-00005.safetensors",
+    "model.layers.1.mlp.gate_proj.weight": "model-00001-of-00005.safetensors",
+    "model.layers.1.mlp.up_proj.weight": "model-00001-of-00005.safetensors",
+    "model.layers.1.post_attention_layernorm.weight": "model-00001-of-00005.safetensors",
+    "model.layers.1.self_attn.k_proj.bias": "model-00001-of-00005.safetensors",
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+    "visual.patch_embed.proj.weight": "model-00001-of-00005.safetensors"
+  }
+}

text_encoder/llm/preprocessor_config.json

@@ -0,0 +1,19 @@
+{
+  "min_pixels": 3136,
+  "max_pixels": 12845056,
+  "patch_size": 14,
+  "temporal_patch_size": 2,
+  "merge_size": 2,
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "image_processor_type": "Qwen2VLImageProcessor",
+  "processor_class": "Qwen2_5_VLProcessor"
+}

text_encoder/llm/tokenizer_config.json

@@ -0,0 +1,207 @@
+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151644": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151645": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151646": {
+      "content": "<|object_ref_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151647": {
+      "content": "<|object_ref_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151648": {
+      "content": "<|box_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151649": {
+      "content": "<|box_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151650": {
+      "content": "<|quad_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151651": {
+      "content": "<|quad_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151652": {
+      "content": "<|vision_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151653": {
+      "content": "<|vision_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151654": {
+      "content": "<|vision_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151655": {
+      "content": "<|image_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151656": {
+      "content": "<|video_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151657": {
+      "content": "<tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151658": {
+      "content": "</tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151659": {
+      "content": "<|fim_prefix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151660": {
+      "content": "<|fim_middle|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151661": {
+      "content": "<|fim_suffix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151662": {
+      "content": "<|fim_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null,
+  "add_bos_token": false
+}

vae/config.json

@@ -0,0 +1,23 @@
+{
+  "_class_name": "AutoencoderKLConv3D",
+  "_diffusers_version": "0.35.0",
+  "block_out_channels": [
+    128,
+    256,
+    512,
+    1024,
+    1024
+  ],
+  "downsample_match_channel": true,
+  "ffactor_spatial": 16,
+  "ffactor_temporal": 4,
+  "in_channels": 3,
+  "latent_channels": 32,
+  "layers_per_block": 2,
+  "out_channels": 3,
+  "sample_size": 256,
+  "sample_tsize": 64,
+  "scaling_factor": 1.03682,
+  "shift_factor": null,
+  "upsample_match_channel": true
+}

vision_encoder/siglip/.gitattributes

@@ -0,0 +1,35 @@
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+*tfevents* filter=lfs diff=lfs merge=lfs -text

vision_encoder/siglip/LICENSE.md

@@ -0,0 +1,54 @@
+FLUX.1 [dev] Non-Commercial License v1.1.1
+
+Black Forest Labs Inc. (“we” or “our” or “Company”) is pleased to make available the weights, parameters and inference code for the FLUX.1 [dev] Model (as defined below) freely available for your non-commercial and non-production use as set forth in this FLUX.1 [dev] Non-Commercial License (“License”).  The “FLUX.1 [dev] Model” means the FLUX.1 [dev] AI models and models denoted as FLUX.1 [dev], including but not limited to FLUX.1 [dev], FLUX.1 Fill [dev], FLUX.1 Depth [dev], FLUX.1 Canny [dev], FLUX.1 Redux [dev], FLUX.1 Canny [dev] LoRA, FLUX.1 Depth [dev] LoRA, and FLUX.1 Kontext [dev], and their elements which includes algorithms, software, checkpoints, parameters, source code (inference code, evaluation code, and if applicable, fine-tuning code) and any other materials associated with the FLUX.1 [dev] AI models made available by Company under this License, including if any, the technical documentation, manuals and instructions for the use and operation thereof (collectively, “FLUX.1 [dev] Model”). Note that we may also make available certain elements of what is included in the definition of “FLUX.1 [dev] Model” under a separate license, such as the inference code, and nothing in this License will be deemed to restrict or limit any other licenses granted by us in such elements.
+
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+
+1. Definitions.
+- a. “Derivative”  means any (i) modified version of the FLUX.1 [dev] Model (including but not limited to any customized or fine-tuned version thereof), (ii) work based on the FLUX.1 [dev] Model, or (iii) any other derivative work thereof. For the avoidance of doubt, Outputs are not considered Derivatives under this License.
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+
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+
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+
+5. DISCLAIMERS.  THE FLUX.1 [dev] MODEL AND FLUX CONTENT FILTERS ARE PROVIDED “AS IS” AND “WITH ALL FAULTS” WITH NO WARRANTY OF ANY KIND, EXPRESS OR IMPLIED. COMPANY EXPRESSLY DISCLAIMS ALL REPRESENTATIONS AND WARRANTIES, EXPRESS OR IMPLIED, WHETHER BY STATUTE, CUSTOM, USAGE OR OTHERWISE AS TO ANY MATTERS RELATED TO THE FLUX.1 [dev] MODEL AND FLUX CONTENT FILTERS, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE, SATISFACTORY QUALITY, OR NON-INFRINGEMENT. COMPANY MAKES NO WARRANTIES OR REPRESENTATIONS THAT THE FLUX.1 [dev] MODEL AND FLUX CONTENT FILTERS WILL BE ERROR FREE OR FREE OF VIRUSES OR OTHER HARMFUL COMPONENTS, OR PRODUCE ANY PARTICULAR RESULTS.
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+6. LIMITATION OF LIABILITY.  TO THE FULLEST EXTENT PERMITTED BY LAW, IN NO EVENT WILL COMPANY BE LIABLE TO YOU OR YOUR EMPLOYEES, AFFILIATES, USERS, OFFICERS OR DIRECTORS (A) UNDER ANY THEORY OF LIABILITY, WHETHER BASED IN CONTRACT, TORT, NEGLIGENCE, STRICT LIABILITY, WARRANTY, OR OTHERWISE UNDER THIS LICENSE, OR (B) FOR ANY INDIRECT, CONSEQUENTIAL, EXEMPLARY, INCIDENTAL, PUNITIVE OR SPECIAL DAMAGES OR LOST PROFITS, EVEN IF COMPANY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. THE FLUX.1 [dev] MODEL, ITS CONSTITUENT COMPONENTS, FLUX CONTENT FILTERS, AND ANY OUTPUT (COLLECTIVELY, “MODEL MATERIALS”) ARE NOT DESIGNED OR INTENDED FOR USE IN ANY APPLICATION OR SITUATION WHERE FAILURE OR FAULT OF THE MODEL MATERIALS COULD REASONABLY BE ANTICIPATED TO LEAD TO SERIOUS INJURY OF ANY PERSON, INCLUDING POTENTIAL DISCRIMINATION OR VIOLATION OF AN INDIVIDUAL’S PRIVACY RIGHTS, OR TO SEVERE PHYSICAL, PROPERTY, OR ENVIRONMENTAL DAMAGE (EACH, A “HIGH-RISK USE”). IF YOU ELECT TO USE ANY OF THE MODEL MATERIALS FOR A HIGH-RISK USE, YOU DO SO AT YOUR OWN RISK. YOU AGREE TO DESIGN AND IMPLEMENT APPROPRIATE DECISION-MAKING AND RISK-MITIGATION PROCEDURES AND POLICIES IN CONNECTION WITH A HIGH-RISK USE SUCH THAT EVEN IF THERE IS A FAILURE OR FAULT IN ANY OF THE MODEL MATERIALS, THE SAFETY OF PERSONS OR PROPERTY AFFECTED BY THE ACTIVITY STAYS AT A LEVEL THAT IS REASONABLE, APPROPRIATE, AND LAWFUL FOR THE FIELD OF THE HIGH-RISK USE.
+
+7. INDEMNIFICATION. You will indemnify, defend and hold harmless Company and our subsidiaries and affiliates, and each of our respective shareholders, directors, officers, employees, agents, successors, and assigns (collectively, the “Company Parties”) from and against any losses, liabilities, damages, fines, penalties, and expenses (including reasonable attorneys’ fees) incurred by any Company Party in connection with any claim, demand, allegation, lawsuit, proceeding, or investigation (collectively, “Claims”) arising out of or related to  (a) your access to or use of the FLUX.1 [dev] Model (including in connection with any Output, results or data generated from such access or use, or from your access or use of any FLUX Content Filters), including any High-Risk Use; (b) your Content Filters, including your failure to implement any Content Filters where required by this License such as in Section 2(e); (c)  your violation of this License; or (d) your violation, misappropriation or infringement of any rights of another (including intellectual property or other proprietary rights and privacy rights). You will promptly notify the Company Parties of any such Claims, and cooperate with Company Parties in defending such Claims. You will also grant the Company Parties sole control of the defense or settlement, at Company’s sole option, of any Claims. This indemnity is in addition to, and not in lieu of, any other indemnities or remedies set forth in a written agreement between you and Company or the other Company Parties.
+
+8. Termination; Survival.
+- a. This License will automatically terminate upon any breach by you of the terms of this License.
+- b. We may terminate this License, in whole or in part, at any time upon notice (including electronic) to you.
+- c. If you initiate any legal action or proceedings against Company or any other entity (including a cross-claim or counterclaim in a lawsuit), alleging that the FLUX.1 [dev] Model, any Derivative, or FLUX Content Filters, or any part thereof, infringe upon intellectual property or other rights owned or licensable by you, then any licenses granted to you under this License will immediately terminate as of the date such legal action or claim is filed or initiated.
+- d. Upon termination of this License, you must cease all use, access or Distribution of the FLUX.1 [dev] Model, any Derivatives, and any FLUX Content Filters.  The following sections survive termination of this License  2(c), 2(d), 4-11.
+
+9. Third Party Materials. The FLUX.1 [dev] Model may contain third-party software or other components (including free and open source software) (all of the foregoing, “Third Party Materials”), which are subject to the license terms of the respective third-party licensors. Your dealings or correspondence with third parties and your use of or interaction with any Third Party Materials are solely between you and the third party. Company does not control or endorse, and makes no representations or warranties regarding, any Third Party Materials, and your access to and use of such Third Party Materials are at your own risk.
+
+10. Trademarks. You have not been granted any trademark license as part of this License and may not use any name, logo or trademark associated with Company without the prior written permission of Company, except to the extent necessary to make the reference required in the Attribution Notice as specified above or as is reasonably necessary in describing the FLUX.1 [dev] Model and its creators.
+
+11. General. This License will be governed and construed under the laws of the State of Delaware without regard to conflicts of law provisions. If any provision or part of a provision of this License is unlawful, void or unenforceable, that provision or part of the provision is deemed severed from this License, and will not affect the validity and enforceability of any remaining provisions. The failure of Company to exercise or enforce any right or provision of this License will not operate as a waiver of such right or provision. This License does not confer any third-party beneficiary rights upon any other person or entity. This License, together with the documentation, contains the entire understanding between you and Company regarding the subject matter of this License, and supersedes all other written or oral agreements and understandings between you and Company regarding such subject matter.

vision_encoder/siglip/README.md

@@ -0,0 +1,92 @@
+---
+language:
+- en
+license: other
+license_name: flux-1-dev-non-commercial-license
+license_link: LICENSE.md
+extra_gated_prompt: By clicking "Agree", you agree to the [FluxDev Non-Commercial License Agreement](https://huggingface.co/black-forest-labs/FLUX.1-Depth-dev/blob/main/LICENSE.md)
+  and acknowledge the [Acceptable Use Policy](https://huggingface.co/black-forest-labs/FLUX.1-Depth-dev/blob/main/POLICY.md).
+tags:
+- image-generation
+- flux
+- diffusion-single-file
+---
+
+![image/png](https://huggingface.co/black-forest-labs/FLUX.1-Redux-dev/resolve/main/redux.png)
+
+FLUX.1 Redux [dev] is an adapter for all FLUX.1 base models for image variation generation. 
+Given an input image, FLUX.1 Redux can reproduce the image with slight variation, allowing to refine a given image.
+It naturally integrates into more complex workflows unlocking image restyling. 
+Restyling via text is also available through our API by providing an image plus a language prompt. 
+For more information, please read our [blog post](https://blackforestlabs.ai/flux-1-tools/). 
+
+
+
+# Usage
+We provide a reference implementation of `FLUX.1 Redux [dev]`, as well as sampling code, in a dedicated [github repository](https://github.com/black-forest-labs/flux).
+
+## API Endpoints
+`FLUX.1 Redux [pro]` is available in our API [bfl.ml](https://docs.bfl.ml/). In addition to the `[dev]` adapter, the API endpoint allows users to modify an image given a textual description.
+The feature is supported in our latest model FLUX1.1 [pro] Ultra, allowing for combining input images and text prompts to create high-quality 4-megapixel outputs with flexible aspect ratios.
+
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64510d6304397681bcf9725b/P123igomKjAkmitOzot8T.png)
+
+## Diffusers
+
+To use `FLUX.1 Redux [pro]` with the 🧨 diffusers python library, first install or upgrade diffusers
+
+```shell
+pip install -U diffusers
+```
+
+Then you can use `FluxPriorReduxPipeline` along with `FluxPipeline` to generate images from images.
+
+```python
+import torch
+from diffusers import FluxPriorReduxPipeline, FluxPipeline
+from diffusers.utils import load_image
+
+pipe_prior_redux = FluxPriorReduxPipeline.from_pretrained("black-forest-labs/FLUX.1-Redux-dev", torch_dtype=torch.bfloat16).to("cuda")
+pipe = FluxPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-dev" , 
+    text_encoder=None,
+    text_encoder_2=None,
+    torch_dtype=torch.bfloat16
+).to("cuda")
+
+image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/robot.png")
+pipe_prior_output = pipe_prior_redux(image)
+images = pipe(
+    guidance_scale=2.5,
+    num_inference_steps=50,
+    generator=torch.Generator("cpu").manual_seed(0),
+    **pipe_prior_output,
+).images
+images[0].save("flux-dev-redux.png")
+```
+
+To learn more check out the [diffusers](https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux) documentation
+
+---
+
+# Limitations
+- This model is not intended or able to provide factual information.
+- As a statistical model this checkpoint might amplify existing societal biases.
+- The model may fail to generate output that matches the prompts.
+- Outputs are heavily influenced by the input image.
+
+# Out-of-Scope Use
+The model and its derivatives may not be used
+
+- In any way that violates any applicable national, federal, state, local or international law or regulation.
+- For the purpose of exploiting, harming or attempting to exploit or harm minors in any way; including but not limited to the solicitation, creation, acquisition, or dissemination of child exploitative content.
+- To generate or disseminate verifiably false information and/or content with the purpose of harming others.
+- To generate or disseminate personal identifiable information that can be used to harm an individual.
+- To harass, abuse, threaten, stalk, or bully individuals or groups of individuals.
+- To create non-consensual nudity or illegal pornographic content.
+- For fully automated decision making that adversely impacts an individual's legal rights or otherwise creates or modifies a binding, enforceable obligation.
+- Generating or facilitating large-scale disinformation campaigns.
+
+# License
+This model falls under the [`FLUX.1 [dev]` Non-Commercial License](https://huggingface.co/black-forest-labs/FLUX.1-Depth-dev/blob/main/LICENSE.md).

vision_encoder/siglip/feature_extractor/preprocessor_config.json

@@ -0,0 +1,24 @@
+{
+  "do_convert_rgb": null,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_processor_type": "SiglipImageProcessor",
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "processor_class": "SiglipProcessor",
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 384,
+    "width": 384
+  }
+}

vision_encoder/siglip/image_embedder/config.json

@@ -0,0 +1,6 @@
+{
+  "_class_name": "ReduxImageEncoder",
+  "_diffusers_version": "0.32.0.dev0",
+  "redux_dim": 1152,
+  "txt_in_features": 4096
+}

vision_encoder/siglip/image_encoder/config.json

@@ -0,0 +1,18 @@
+{
+  "architectures": [
+    "SiglipVisionModel"
+  ],
+  "attention_dropout": 0.0,
+  "hidden_act": "gelu_pytorch_tanh",
+  "hidden_size": 1152,
+  "image_size": 384,
+  "intermediate_size": 4304,
+  "layer_norm_eps": 1e-06,
+  "model_type": "siglip_vision_model",
+  "num_attention_heads": 16,
+  "num_channels": 3,
+  "num_hidden_layers": 27,
+  "patch_size": 14,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.45.2"
+}

vision_encoder/siglip/model_index.json

@@ -0,0 +1,16 @@
+{
+  "_class_name": "FluxPriorReduxPipeline",
+  "_diffusers_version": "0.32.0.dev0",
+  "feature_extractor": [
+    "transformers",
+    "SiglipImageProcessor"
+  ],
+  "image_embedder": [
+    "flux",
+    "ReduxImageEncoder"
+  ],
+  "image_encoder": [
+    "transformers",
+    "SiglipVisionModel"
+  ]
+}