[ { "path": "LICENSE", "content": "MIT License\n\nCopyright (c) 2021\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n" }, { "path": "README.md", "content": "
\n\n# Aesthetic Post-Training Diffusion Models from Generic Preferences with Step-by-step Preference Optimization\n [Zhanhao Liang](https://github.com/RockeyCoss), [Yuhui Yuan](https://www.microsoft.com/en-us/research/people/yuyua/), [Shuyang Gu](https://cientgu.github.io), [Bohan Chen](https://github.com/BHCHENGIT), [Tiankai Hang](https://tiankaihang.github.io/), [Mingxi Cheng](https://sites.google.com/a/usc.edu/mingxicheng/), [Ji Li](https://sites.google.com/a/usc.edu/jili/), [Liang Zheng](https://zheng-lab.cecs.anu.edu.au)\n \n ${{\\color{Red}\\Huge{\\textsf{ CVPR\\ 2025\\ \\}}}}\\$\n\n\n\n\n
\n\nThis is the official implementation of SPO, introduced in [Aesthetic Post-Training Diffusion Models from Generic Preferences with Step-by-step Preference Optimization](https://arxiv.org/abs/2406.04314).\n\n## Abstract\n

\n Generating visually appealing images is fundamental to modern text-to-image generation models. \n A potential solution to better aesthetics is direct preference optimization (DPO), \n which has been applied to diffusion models to improve general image quality including prompt alignment and aesthetics. \n Popular DPO methods propagate preference labels from clean image pairs to all the intermediate steps along the two generation trajectories. \n However, preference labels provided in existing datasets are blended with layout and aesthetic opinions, which would disagree with aesthetic preference. \n Even if aesthetic labels were provided (at substantial cost), it would be hard for the two-trajectory methods to capture nuanced visual differences at different steps.\n

\n

\n To improve aesthetics economically, this paper uses existing generic preference data and introduces step-by-step preference optimization \n (SPO) that discards the propagation strategy and allows fine-grained image details to be assessed. Specifically, \n at each denoising step, we 1) sample a pool of candidates by denoising from a shared noise latent, \n 2) use a step-aware preference model to find a suitable win-lose pair to supervise the diffusion model, and \n 3) randomly select one from the pool to initialize the next denoising step. \n This strategy ensures that diffusion models focus on the subtle, fine-grained visual differences \n instead of layout aspect. We find that aesthetics can be significantly enhanced by accumulating these \n improved minor differences.\n

\n

\n When fine-tuning Stable Diffusion v1.5 and SDXL, SPO yields significant \n improvements in aesthetics compared with existing DPO methods while not sacrificing image-text alignment \n compared with vanilla models. Moreover, SPO converges much faster than DPO methods due to the use of \n more correct preference labels provided by the step-aware preference model. \n

\n\n## Method Overview\n![method_overview](assets/method.jpg)\n\n## TODO\n- [x] Release training code for step-aware preference model\n- [x] Release training code for SPO\n- [x] Release checkpoints and inference code\n- [x] Initialization\n\n## Gallery\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
\"teaser\"teaser\"teaser\"teaser
\"teaser\"teaser\"teaser\"teaser
\"teaser\"teaser\"teaser\"teaser
\"teaser\"teaser\"teaser\"teaser
\"teaser\"teaser\"teaser\"teaser
\n\n## :wrench: Usage\nFor SPO training and inference using the available checkpoints, please refer to [spo_training_and_inference](spo_training_and_inference/).\n\nFor the training of the step-aware preference model, please refer to [step_aware_preference_model](step_aware_preference_model/).\n\n## :unlock: Available Checkpoints\n\n[SPO-SDXL_4k-prompts_10-epochs](https://huggingface.co/SPO-Diffusion-Models/SPO-SDXL_4k-p_10ep)\n\n[SPO-SDXL_4k-prompts_10-epochs_LoRA](https://huggingface.co/SPO-Diffusion-Models/SPO-SDXL_4k-p_10ep_LoRA)\n\n[SPO-SD-v1-5_4k-prompts_10-epochs](https://huggingface.co/SPO-Diffusion-Models/SPO-SD-v1-5_4k-p_10ep)\n\n[SPO-SD-v1-5_4k-prompts_10-epochs_LoRA](https://huggingface.co/SPO-Diffusion-Models/SPO-SD-v1-5_4k-p_10ep_LoRA)\n\n## Acknowledgement\nOur codebase references the code from [Diffusers](https://github.com/huggingface/diffusers), [D3PO](https://github.com/yk7333/d3po) and [PickScore](https://github.com/yuvalkirstain/PickScore). We extend our gratitude to their authors for open-sourcing their code.\n\n## :mailbox_with_mail: Citation\nIf you find this code useful in your research, please consider citing:\n\n```\n@article{liang2024step,\n title={Aesthetic Post-Training Diffusion Models from Generic Preferences with Step-by-step Preference Optimization},\n author={Liang, Zhanhao and Yuan, Yuhui and Gu, Shuyang and Chen, Bohan and Hang, Tiankai and Cheng, Mingxi and Li, Ji and Zheng, Liang},\n journal={arXiv preprint arXiv:2406.04314},\n year={2024}\n}\n```\n" }, { "path": "spo_training_and_inference/.gitignore", "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Translations\n*.mo\n*.pot\n\n# PyBuilder\ntarget/\n\n# pyenv\n.python-version\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n.DS_Store\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mypy\n.mypy_cache/\n\n.vscode\n.idea\n\n# custom\n*.pkl\n*.pkl.json\nwork_dirs\nmodel_ckpts\nwandb\n*.egg-info\n*.html\ndebug\n.png\n" }, { "path": "spo_training_and_inference/README.md", "content": "# SPO Training and Inference Code\n\nThis folder contains the code for SPO training and inference.\n\n## Installation\n1. Pull the Docker Image\n```bash\nsudo docker pull rockeycoss/spo:v1\n```\n2. Run the Docker Container and Enter It\n```bash\nsudo docker run --gpus all -it --ipc=host rockeycoss/spo:v1 /bin/bash\n```\n> 💡 Optional (for installation without Docker):\n> You can also build the environment using the provided `environment.yaml`:\n> ```bash\n> conda env create -f environment.yaml --name spo\n> conda activate spo\n> ```\n> 🔹 *Both setups are supported, but the Docker environment (`rockeycoss/spo:v1`) should be taken as the reference.*\n3. Clone the Repository\n```bash\ngit clone https://github.com/RockeyCoss/SPO\ncd ./SPO/spo_training_and_inference\n```\n4. Login to wandb\n```bash\nwandb login {Your wandb key}\n```\n5. (Optional) To customize the location for saving models downloaded from Hugging Face, you can use the following command:\n```bash\nexport HUGGING_FACE_CACHE_DIR=/path/to/your/cache/dir\n```\n\n## :wrench: Inference Hugging Face Checkpoints\n\nSDXL inference\n```bash\npython inference_scripts/inference_spo_sdxl.py\n```\n\nSD v1.5 inference\n```bash\npython inference_scripts/inference_spo_sd-v1-5.py\n```\n\n## :wrench: Training\nThe following scripts assume the use of **four** 80GB A100 GPUs for fine-tuning, as described in the [paper](https://arxiv.org/abs/2406.04314).\n\nBefore fine-tuning, please download the checkpoints of step-aware preference models. You can do this by following these steps:\n```bash\nsudo apt update\nsudo apt install wget\n\nmkdir model_ckpts\ncd model_ckpts\n\nwget https://huggingface.co/SPO-Diffusion-Models/Step-Aware_Preference_Models/resolve/main/sd-v1-5_step-aware_preference_model.bin\n\nwget https://huggingface.co/SPO-Diffusion-Models/Step-Aware_Preference_Models/resolve/main/sdxl_step-aware_preference_model.bin\n\ncd ..\n```\n\nTo fine-tune SD v1.5, you can use the following command:\n```bash\naccelerate launch --config_file accelerate_cfg/1m4g_fp16.yaml train_scripts/train_spo.py --config configs/spo_sd-v1-5_4k-prompts_num-sam-4_10ep_bs10.py\n```\nTo fine-tune SDXL, you can use the following command:\n```bash\naccelerate launch --config_file accelerate_cfg/1m4g_fp16.yaml train_scripts/train_spo_sdxl.py --config configs/spo_sdxl_4k-prompts_num-sam-2_3-is_10ep_bs2_gradacc2.py\n```\nTo fine-tune using step-aware preference model checkpoints you’ve trained with the code in [step_aware_preference_model](https://github.com/RockeyCoss/SPO/tree/main/step_aware_preference_model), you can simply update the `config.preference_model_func_cfg.ckpt_path` setting in the config file to point to your desired checkpoint path. For example, you can modify [this line](https://github.com/RockeyCoss/SPO/blob/main/spo_training_and_inference/configs/spo_sdxl_4k-prompts_num-sam-2_3-is_10ep_bs2_gradacc2.py#L15) in the SDXL fine-tuning config.\n\n## :unlock: Available Checkpoints\n\n[SPO-SDXL_4k-prompts_10-epochs](https://huggingface.co/SPO-Diffusion-Models/SPO-SDXL_4k-p_10ep)\n\n[SPO-SDXL_4k-prompts_10-epochs_LoRA](https://huggingface.co/SPO-Diffusion-Models/SPO-SDXL_4k-p_10ep_LoRA)\n\n[SPO-SD-v1-5_4k-prompts_10-epochs](https://huggingface.co/SPO-Diffusion-Models/SPO-SD-v1-5_4k-p_10ep)\n\n[SPO-SD-v1-5_4k-prompts_10-epochs_LoRA](https://huggingface.co/SPO-Diffusion-Models/SPO-SD-v1-5_4k-p_10ep_LoRA)\n\n## :mailbox_with_mail: Citation\nIf you find this code useful in your research, please consider citing:\n\n```\n@article{liang2024step,\n title={Aesthetic Post-Training Diffusion Models from Generic Preferences with Step-by-step Preference Optimization},\n author={Liang, Zhanhao and Yuan, Yuhui and Gu, Shuyang and Chen, Bohan and Hang, Tiankai and Cheng, Mingxi and Li, Ji and Zheng, Liang},\n journal={arXiv preprint arXiv:2406.04314},\n year={2024}\n}\n```\n" }, { "path": "spo_training_and_inference/accelerate_cfg/1m4g_fp16.yaml", "content": "compute_environment: LOCAL_MACHINE\ndebug: false\ndistributed_type: MULTI_GPU\ndowncast_bf16: 'no'\ngpu_ids: 0,1,2,3\nmachine_rank: 0\nmain_training_function: main\nmixed_precision: fp16\nnum_machines: 1\nnum_processes: 4\nrdzv_backend: static\nsame_network: true\ntpu_env: []\ntpu_use_cluster: false\ntpu_use_sudo: false\nuse_cpu: false\n" }, { "path": "spo_training_and_inference/assets/prompts/4k_training_prompts.json", "content": "[\n {\n \"caption\": \"a cat driving a nuke\",\n \"trainset_idx\": 215221\n },\n {\n \"caption\": \"she waits facing three ways and fades away by artist \\\"anna dittmann\\\",by artist \\\"the color commander\\\"; Hyperbolic paraboloid glass tesselated Stellated dodecahedron\",\n \"trainset_idx\": 26114\n },\n {\n \"caption\": \"Beautiful thin shy slim attractively Japanese young model with thin arms as secretary\",\n \"trainset_idx\": 564134\n },\n {\n \"caption\": \"A 3-D visualization of a neuron filled with metal shapes,with thin metal lines voronoi ,tiny intricate details floating spheres droplets, model city\",\n \"trainset_idx\": 480276\n },\n {\n \"caption\": \"david denman, short hair, short beard, wearing lycra ultraman costume, muscle buldge\",\n \"trainset_idx\": 506789\n },\n {\n \"caption\": \"a sign with \\\"u good?\\\" written on it\",\n \"trainset_idx\": 268846\n },\n {\n \"caption\": \"anime holding box with the word \\\"gay\\\" on it\",\n \"trainset_idx\": 242838\n },\n {\n \"caption\": \"Rainy window, evening, blurred silhouettes outside the window\",\n \"trainset_idx\": 236253\n },\n {\n \"caption\": \"A tiny dragon taking a bath in a teacup\",\n \"trainset_idx\": 229566\n },\n {\n \"caption\": \"roman scifi corridor metal,studio lighting, volumetric light,JARDINIERE,sir john soane,metal pipes,floor grates,pilasters\",\n \"trainset_idx\": 472310\n },\n {\n \"caption\": \"Whorish African Woman\",\n \"trainset_idx\": 182710\n },\n {\n \"caption\": \"full shot photo, full body and feet, in full color high quality very detailed and sharp facial features, very handsome married Malay beefy fathers ]]\",\n \"trainset_idx\": 457663\n },\n {\n \"caption\": \"a cute kitten made out of metal\",\n \"trainset_idx\": 275789\n },\n {\n \"caption\": \"Photo of old woman smiling at camera. Summer. Wheelchair. beautiful environment\",\n \"trainset_idx\": 229949\n },\n {\n \"caption\": \"Dark futuristic, circuit board, small city buildings as transistors, dark background, desktop wallpaper, 4k , 8k, unreal engine, octane render\",\n \"trainset_idx\": 568468\n },\n {\n \"caption\": \"A pop star performing on a stage in space, bright color, cinematic shot\",\n \"trainset_idx\": 547035\n },\n {\n \"caption\": \"Stage design\",\n \"trainset_idx\": 429570\n },\n {\n \"caption\": \"Pulsing blue energy inside a robotic core\",\n \"trainset_idx\": 83502\n },\n {\n \"caption\": \"Digital art, concept sheet, space helmet, Cowboy space suit, cowboy hat, cyberpunk, apocalyptic, 8k, trending on artstation, Cold tones, depressing, by Jeremy Geddes, by Sparth\",\n \"trainset_idx\": 273130\n },\n {\n \"caption\": \"atomic design\",\n \"trainset_idx\": 268144\n },\n {\n \"caption\": \"high detail, high defintion, 8k, photorealistic, hdr, walter white as bob ross\",\n \"trainset_idx\": 503292\n },\n {\n \"caption\": \"Mechanical steampunk personal robot. fantasy, sharp focus, digital art, hyper realistic, 8k, unreal engine, highly detailed, hd 4k, dramatic lighting, trending on artstation\",\n \"trainset_idx\": 544944\n },\n {\n \"caption\": \"desi girl\",\n \"trainset_idx\": 461058\n },\n {\n \"caption\": \"Black cats by Hilma af Klint, rifle paper co\",\n \"trainset_idx\": 82672\n },\n {\n \"caption\": \"Emmanuel Macron in the street\",\n \"trainset_idx\": 577323\n },\n {\n \"caption\": \"Twink in the bush\",\n \"trainset_idx\": 559574\n },\n {\n \"caption\": \"a tree with butterfly leaves\",\n \"trainset_idx\": 384172\n },\n {\n \"caption\": \"A group of dinosaurs having a fancy tea party\",\n \"trainset_idx\": 281255\n },\n {\n \"caption\": \"a turtle riding a skateboard\",\n \"trainset_idx\": 230488\n },\n {\n \"caption\": \"A spaceship pointing up on a plain background\",\n \"trainset_idx\": 120135\n },\n {\n \"caption\": \"wrestling submission on the beach\",\n \"trainset_idx\": 560233\n },\n {\n \"caption\": \"shirt seamless pattern, lines, colorful\",\n \"trainset_idx\": 401575\n },\n {\n \"caption\": \"night Photorealistic\",\n \"trainset_idx\": 165542\n },\n {\n \"caption\": \"a woman sitting at a table with a hat on her head, a photorealistic painting, inspired by Hans Holbein the Elder, renaissance, holding a rose, georgian dress amazing fabric, photography alexey gurylev, portrait of a girl, floral lacework, francisco de zurbaran, dressed in a medieval lacy, maid\",\n \"trainset_idx\": 355442\n },\n {\n \"caption\": \"fantastic realism, the creature sells a used car, defender vs carnivore dinosaur, banner, 2002, trex, fan favorite\",\n \"trainset_idx\": 24897\n },\n {\n \"caption\": \"a creepy clay dolphin in a dark ocean\",\n \"trainset_idx\": 609\n },\n {\n \"caption\": \"Arcane Vi and Caitlyn forever together\",\n \"trainset_idx\": 421544\n },\n {\n \"caption\": \"Anime styled image with a girl with big wearing a shirt with a text saying \\\"OPPAI\\\"\",\n \"trainset_idx\": 365098\n },\n {\n \"caption\": \"A muscle pig hero\",\n \"trainset_idx\": 28436\n },\n {\n \"caption\": \"giant cat floating over the earth with lasers in his eyes\",\n \"trainset_idx\": 474272\n },\n {\n \"caption\": \"hustler, entertainment woman, hooker, bar, hanghover, surprises, steampunk experience, oil painting, film grain, xyz, doose-s-realistic-art-style, movie still frame, promotional image, imax 35 mm footage, inverse cinematic light, perfect composition, by Christopher Nolan, by david fincher\",\n \"trainset_idx\": 377628\n },\n {\n \"caption\": \"a car concept made by the combination of the 2021 bmw m3 and a 2007 lamborghini reventon, futuristic, big grilles, m sport, aggressive, drift machine, widebody kit, angular\",\n \"trainset_idx\": 352488\n },\n {\n \"caption\": \"2d game style girl in blue dress\",\n \"trainset_idx\": 512929\n },\n {\n \"caption\": \"Scandinavian man with short hair\",\n \"trainset_idx\": 415566\n },\n {\n \"caption\": \"Extreme close up of an eye that is the mirror of the nostalgic moments, nostalgia expression, sad emotion, tears, made with imagination, detailed, photography, 8k, printed on Moab Entrada Bright White Rag 300gsm, Leica M6 TTL, Leica 75mm 2.0 Summicron-M ASPH, Cinestill 800T\",\n \"trainset_idx\": 536355\n },\n {\n \"caption\": \"Little Prince on an urban planet\",\n \"trainset_idx\": 145636\n },\n {\n \"caption\": \"The twisted cubic, overgrown with vines, by MC Escher background theme garden, plants, flowers, by Fragonard ultra realistic, finely detailed, sunlight, shadows, occlusion, golden volumetric lighting, Houdini 128K UHD\",\n \"trainset_idx\": 32807\n },\n {\n \"caption\": \"Photorealistic\",\n \"trainset_idx\": 317202\n },\n {\n \"caption\": \"a professional cinematic paparazzi side photograph of a dripped out pope francis in wearing an icy crucifix and a luxurious canada goose style swagy white silver long puffer jacket, rapper, cinematic lighting, epic, amazing, sharp, 8k, photorealistic\",\n \"trainset_idx\": 357315\n },\n {\n \"caption\": \"Triceratops selfee\",\n \"trainset_idx\": 389741\n },\n {\n \"caption\": \"Anime girl firing anti aircraft M2 browning machine gun mounted to an armored vehicle\",\n \"trainset_idx\": 542091\n },\n {\n \"caption\": \"A purple unicorn in front of the rainbow\",\n \"trainset_idx\": 563162\n },\n {\n \"caption\": \"a library, full of books, multiple staircase, scifi, futeristic\",\n \"trainset_idx\": 284892\n },\n {\n \"caption\": \"fluffy hairy frog\",\n \"trainset_idx\": 520111\n },\n {\n \"caption\": \"epic portrait painting of woman dressed in geisha clothes and hairstyle, clear eyes, ultra realistic, concept art, intricate details, highly detailed, photorealistic, cinematic light, octane render, 8 k, unreal engine, cherry blossom background\",\n \"trainset_idx\": 240204\n },\n {\n \"caption\": \"highly detailed fantasy art of a hairy muscular green-skinned male orc with huge pecs at the beach, bald, body hair, hirsute, smug expression, artstation hq, ultrarealistic + Cinema 4D + Render, hyperdetailed 3d matte painting, hyperrealism, hyperrealistic, 8k ultrahd octane render\",\n \"trainset_idx\": 80186\n },\n {\n \"caption\": \"Majestic Ancient Temple, Moody, Fantasy Landscape, Volumetric Lighting, Digital painting, Marc Allante, Studio Ghibli, dramatic, Intricate detail, Beksinski, Andreas Rocha, Dan Luvisi\",\n \"trainset_idx\": 411655\n },\n {\n \"caption\": \"a girl cosplaying anime character\",\n \"trainset_idx\": 440634\n },\n {\n \"caption\": \"A sign that says PICK A PIC\",\n \"trainset_idx\": 549767\n },\n {\n \"caption\": \"Square grass block\",\n \"trainset_idx\": 386331\n },\n {\n \"caption\": \"A very detailed 3d painting of modern London scenery from the parliament hill, in the style of David Roberts' The siege and destruction of Jerusalem\",\n \"trainset_idx\": 473107\n },\n {\n \"caption\": \"oil painting portrait sargent sunset\",\n \"trainset_idx\": 382602\n },\n {\n \"caption\": \"an owl painting\",\n \"trainset_idx\": 440975\n },\n {\n \"caption\": \"cyberpunk giant kinky muscle young Slaughter inquisitor excruciate tormented pregnant girl at torture chamber. guro art by Vincent Van Gogh\",\n \"trainset_idx\": 571630\n },\n {\n \"caption\": \"Barn owl antihero, manga style\",\n \"trainset_idx\": 224604\n },\n {\n \"caption\": \"a very happy system administrator doing thumbs up to the camera in front of burning servers, servers in flames in the background\",\n \"trainset_idx\": 99854\n },\n {\n \"caption\": \"\\\"niaka\\\" text\",\n \"trainset_idx\": 502965\n },\n {\n \"caption\": \"Pill with the superman logo on it\",\n \"trainset_idx\": 126744\n },\n {\n \"caption\": \"close up of human lips wearing blue lipstick\",\n \"trainset_idx\": 212096\n },\n {\n \"caption\": \"a close-up portrait photo of Albert Einstein, photorealistic, highly detailed skin,\",\n \"trainset_idx\": 125851\n },\n {\n \"caption\": \"POV looking up at a cute twink looking down at you with a smirk, award winning photography\",\n \"trainset_idx\": 523949\n },\n {\n \"caption\": \"realistic fox fursuit at a convention, furr, fursuit, furry fandom, anthrocon, mff\",\n \"trainset_idx\": 463251\n },\n {\n \"caption\": \"Photorealistic image of Pope Francis in a party , rave party , photorealistic style\",\n \"trainset_idx\": 542851\n },\n {\n \"caption\": \"a stone building sitting on top of a lush green field, a statue, by John Atherton, neoclassicism, neoclassicism, neo classical architecture, detailed classical architecture, neoclassical architecture, water temple, hull, tombs,\",\n \"trainset_idx\": 63114\n },\n {\n \"caption\": \"Creepy oil painting of mysterious horribilis\",\n \"trainset_idx\": 46742\n },\n {\n \"caption\": \"A t-shirt design for America\",\n \"trainset_idx\": 191484\n },\n {\n \"caption\": \"girl looking at the sky\",\n \"trainset_idx\": 238946\n },\n {\n \"caption\": \"a surreal and thought-provoking work of art that invites the viewer to contemplate the nature of artificial intelligence and its potential for creativity and imagination inspired by Max Ernst\",\n \"trainset_idx\": 185949\n },\n {\n \"caption\": \"jordan peterson riding a cow\",\n \"trainset_idx\": 485636\n },\n {\n \"caption\": \"ice burning with a blue flame\",\n \"trainset_idx\": 144674\n },\n {\n \"caption\": \"zentai woman\",\n \"trainset_idx\": 273745\n },\n {\n \"caption\": \"minecraft big desert temple, game screenshot, screenshot\",\n \"trainset_idx\": 54556\n },\n {\n \"caption\": \"portrait of young skinhead and hot young pregnant wife at bedroom. highly detailed realistic photo, kodak portra 400, award winning photography, 50 mm. by sally mann and andrei tarkovsky\",\n \"trainset_idx\": 405990\n },\n {\n \"caption\": \"Black and white portrait of the grim reaper in a graveyard, dark and scary night, realistic, detailed, horror, spooky, terror\",\n \"trainset_idx\": 349402\n },\n {\n \"caption\": \"An image of a man piloting a robot from the neck down, surrealist painting, cyberpunk style, surreal punk, katsuhiro otomo\",\n \"trainset_idx\": 314238\n },\n {\n \"caption\": \"little cute baby boy playing at the beach near sea, building a sandcastle, animated, stylized\",\n \"trainset_idx\": 1132\n },\n {\n \"caption\": \"ancient map of fantasy world\",\n \"trainset_idx\": 559360\n },\n {\n \"caption\": \"Slutty Communist Alien Woman\",\n \"trainset_idx\": 266434\n },\n {\n \"caption\": \"a bicycle built for two\",\n \"trainset_idx\": 211615\n },\n {\n \"caption\": \"A latex or pvc outfit on a woman\",\n \"trainset_idx\": 323991\n },\n {\n \"caption\": \"Cyberpunk retrofuturism.\",\n \"trainset_idx\": 542596\n },\n {\n \"caption\": \"young woman 80's big teased hair\",\n \"trainset_idx\": 139849\n },\n {\n \"caption\": \"Spotty cute cartoon alien\",\n \"trainset_idx\": 567083\n },\n {\n \"caption\": \"epic fantasy character action scene, illustration in the style of Jean Moebius Giraud, Arzach ; Fantasy \\u00b7 1975 \\u00b7 Heavy Metal, jerry cornelius The Airtight Garage, Blueberry Comic series Jean \\\"M\\u0153bius\\\" Giraud, horror fantasy distinct elegant gentleman speeds away 4052 Dies\",\n \"trainset_idx\": 307887\n },\n {\n \"caption\": \"Eve being persuaded in the garden of eden\",\n \"trainset_idx\": 482893\n },\n {\n \"caption\": \"a beautiful character portrait painting of Hugh Laurie as House M.D. as Solid Snake by artist Ian McQue and Russ Mills, video game concept art, Metal Gear Solid, a beautiful and expressive painting, half portrait, illustration, realistic, stylized\",\n \"trainset_idx\": 238023\n },\n {\n \"caption\": \"a painting of a white tiger in a forest, a detailed painting by Android Jones, behance contest winner, furry art, behance hd, official art, detailed painting\",\n \"trainset_idx\": 154347\n },\n {\n \"caption\": \"Mart Helme\",\n \"trainset_idx\": 559676\n },\n {\n \"caption\": \"a person standing on a path in the middle of a field, star in the sky, city of pristine colors, photoreailstic, in the hillside, the morning star, dramatic photograph, juxtapos, frame around picture, utopia\",\n \"trainset_idx\": 216729\n },\n {\n \"caption\": \"Skulduggery Pleasant book cover\",\n \"trainset_idx\": 190573\n },\n {\n \"caption\": \"An alpaca working on a computer A photograph capturing the warmth and comfort of a cozy fireplace, with the flickering flames creating a sense of calm and relaxation. The focus is on the fire itself, with the intricate patterns and textures of the flames adding visual interest and depth. The use of warm colors and soft light enhances the overall sense of coziness and intimacy\",\n \"trainset_idx\": 22331\n },\n {\n \"caption\": \"lego set of the resurrection of jesus christ\",\n \"trainset_idx\": 58616\n },\n {\n \"caption\": \"Hairy boob and armpit blonde woman with skirt only\",\n \"trainset_idx\": 337525\n },\n {\n \"caption\": \"genshin impact landscape\",\n \"trainset_idx\": 156562\n },\n {\n \"caption\": \"Letter 'A' made out of motorcycle parts, in a motorcycle, cinematic, photorealistic, close-up view\",\n \"trainset_idx\": 174718\n },\n {\n \"caption\": \"holding breath under water\",\n \"trainset_idx\": 250655\n },\n {\n \"caption\": \"Editorial style photo, medium closeup shot, off - center, a young french woman, brunette, sitting, black gucci dress, diamond necklace, Art Deco Dining Room, Marble Table, Velvet, Brass, Mirror, Intricate Tile Work, Jewel Tones, West Elm, Chandelier, Restaurant, Evening, natural lighting, Fujifilm, Luxurious, Historical, 4k\",\n \"trainset_idx\": 342080\n },\n {\n \"caption\": \"black cat astronaut, in space, hyperrealistic\",\n \"trainset_idx\": 465887\n },\n {\n \"caption\": \"Abraham Lincoln as a cowboy\",\n \"trainset_idx\": 441012\n },\n {\n \"caption\": \"at the edge of the world\",\n \"trainset_idx\": 102235\n },\n {\n \"caption\": \"a cute cat\",\n \"trainset_idx\": 570548\n },\n {\n \"caption\": \"zombies eating pizza\",\n \"trainset_idx\": 159502\n },\n {\n \"caption\": \"realistic photograph of a stuffed tiger wearing a black jacket with the symbol of yin-yang\",\n \"trainset_idx\": 360508\n },\n {\n \"caption\": \"Giant ice cream cone melting and creating a river through a city\",\n \"trainset_idx\": 431949\n },\n {\n \"caption\": \"An alpaca working on a computer\",\n \"trainset_idx\": 539162\n },\n {\n \"caption\": \"Portrait Of 8 Years Old, Handsome Hindu God Krishna With Turban, Detailed Texture, Pretty, Elegant, Realistic 3D Render, Detailed Digital Painting, Artstation, Concept Art, 4k Resolution, Professional Color Grading, Soft Shadows, No Contrast, Art By Alphonse Mucha, Art Nouvau\",\n \"trainset_idx\": 378163\n },\n {\n \"caption\": \"iridescent, scales,tunnel flowers, blues, textured, intricate,highlights prisms, ornate, shadowed, pale muted colors, 3D, highly detailed, deco style, by Tim Burton, by Dale Chihuly, by Hsiao-Ron Cheng, by Cyril Rolando, by h. r. giger,bright center,lens flare,fireflies\",\n \"trainset_idx\": 93201\n },\n {\n \"caption\": \"a sign that says \\\"BLOCKED\\\"\",\n \"trainset_idx\": 28355\n },\n {\n \"caption\": \"film still from romantic 90s sitcom\",\n \"trainset_idx\": 454524\n },\n {\n \"caption\": \"A blue eyed blonde male fat old hairy daddy at a pool gay\",\n \"trainset_idx\": 359565\n },\n {\n \"caption\": \"Xen crypto ice\",\n \"trainset_idx\": 420218\n },\n {\n \"caption\": \"beautiful building portrait\",\n \"trainset_idx\": 373994\n },\n {\n \"caption\": \"Glamorous dressing room with large mirror\",\n \"trainset_idx\": 522385\n },\n {\n \"caption\": \"toy marbles shooting at pearls under a waterfall\",\n \"trainset_idx\": 427788\n },\n {\n \"caption\": \", fantasy, absurdism, pastel, photo, refined, Deconstructed\",\n \"trainset_idx\": 437741\n },\n {\n \"caption\": \"Marilyn Monroe wearing a shirt that reads Six\",\n \"trainset_idx\": 531882\n },\n {\n \"caption\": \"saltire\",\n \"trainset_idx\": 78026\n },\n {\n \"caption\": \"Girl expose skin\",\n \"trainset_idx\": 189334\n },\n {\n \"caption\": \"An armadillo skateboarding on a skate park\",\n \"trainset_idx\": 279002\n },\n {\n \"caption\": \"hyperrealistic photo of a woman in a superhero outfit with pale skin and long dark hair, freckles, tall, strong\",\n \"trainset_idx\": 428191\n },\n {\n \"caption\": \"juice wrld\",\n \"trainset_idx\": 165090\n },\n {\n \"caption\": \"Ultra-detailed eldritch entity tiger, partially concealed in dense mist, haunting charcoal image, myriad writhing tendrils, some thick and muscular, others thin and sinuous, strange fluid grace, eerie glowing cold eyes, shadowy silhouette, unsettling atmosphere, intricate textures, otherworldly presence\",\n \"trainset_idx\": 232596\n },\n {\n \"caption\": \"A photo of cool sigma person using sunglasses, standing on a street and holding a sign called \\\"Get Ready\\\"\",\n \"trainset_idx\": 201003\n },\n {\n \"caption\": \"expressionist painting of jake gyllenhaal by egon schiele\",\n \"trainset_idx\": 202620\n },\n {\n \"caption\": \"hyperrealistic polaroid photograph, extremely detailed pale young woman whole body covered in fungus, fungi, slime mold, mushrooms growing out of her eyes, slime mold covering body, slime mold covering legs, skinny, mushrooms, mushrooms on face, mushrooms on cheekbones, zoomed out ,\",\n \"trainset_idx\": 566361\n },\n {\n \"caption\": \"A sign that says \\\"The kindgom has fallen\\\". Fantasy, digital painting, HD, detailed.\",\n \"trainset_idx\": 564650\n },\n {\n \"caption\": \"highly detailed, epic composition of a Warframe character in a full shot, with a 3D, digital art style, using matte painting techniques, 8k UHD resolution and rendered in Unreal Engine 5, award-winning and trending on ArtStation and Behance\",\n \"trainset_idx\": 14956\n },\n {\n \"caption\": \"A beautiful and lovely female elf, stormy scene, movie lightning, portrait\",\n \"trainset_idx\": 161709\n },\n {\n \"caption\": \"cute russian teen with pigtails in a state of undress, insanely detailed, photorealistic, 8k, perfect composition, volumetric lighting, natural complexion, award winning professional photography, taken with canon eos 5d mark iv, 85mm, mindblowing, masterpiece,\",\n \"trainset_idx\": 121516\n },\n {\n \"caption\": \"A highway that goes over a lake and is surrounded by trees\",\n \"trainset_idx\": 539403\n },\n {\n \"caption\": \"young woman 90's big teased hair\",\n \"trainset_idx\": 434710\n },\n {\n \"caption\": \"Sakura Miyawaki\",\n \"trainset_idx\": 574587\n },\n {\n \"caption\": \"Sansa stark cleaning the mirror of a dirty public underground toilet\",\n \"trainset_idx\": 414684\n },\n {\n \"caption\": \"lungs made of smoke\",\n \"trainset_idx\": 336374\n },\n {\n \"caption\": \"Astronaut in a massive colorful space\",\n \"trainset_idx\": 51938\n },\n {\n \"caption\": \"a bard losing a fistfight against a robot\",\n \"trainset_idx\": 357293\n },\n {\n \"caption\": \"A cat holding a sign that says \\\"Yael es mujer\\\"\",\n \"trainset_idx\": 50230\n },\n {\n \"caption\": \"Head of a horse, lineart\",\n \"trainset_idx\": 145195\n },\n {\n \"caption\": \"A steampunk octopus in a futuristic cityscape!\",\n \"trainset_idx\": 179894\n },\n {\n \"caption\": \"Alien with big eyes smoking a big joint.\",\n \"trainset_idx\": 358431\n },\n {\n \"caption\": \"Magical night scene! This pixel art features towering buildings with glowing windows, set against a dark night sky filled with stars and a large yellow moon. The pixelated clouds add to the dreamy atmosphere. #pixelart #nightscene #buildings #stars #moon #clouds #dreamy #magical\",\n \"trainset_idx\": 555988\n },\n {\n \"caption\": \"A San Francisco city street\",\n \"trainset_idx\": 270585\n },\n {\n \"caption\": \"A fortnite map inspired by Star Wars\",\n \"trainset_idx\": 63497\n },\n {\n \"caption\": \"HBO Movie poster\",\n \"trainset_idx\": 266934\n },\n {\n \"caption\": \"image of an opal whale tail pendant\",\n \"trainset_idx\": 47591\n },\n {\n \"caption\": \"Chocolate box with dark and white chocolate pubic vulva patterns in shape of vulva, glitters\",\n \"trainset_idx\": 11264\n },\n {\n \"caption\": \"Handsome muscular man\",\n \"trainset_idx\": 18503\n },\n {\n \"caption\": \"a photo of MotoGP player using a shirt with text that called \\\"KSABAR\\\"\",\n \"trainset_idx\": 492057\n },\n {\n \"caption\": \"a cat driving a car\",\n \"trainset_idx\": 405014\n },\n {\n \"caption\": \"swirling water tornados over the abyss epic fantasy\",\n \"trainset_idx\": 439033\n },\n {\n \"caption\": \"Thomas the Tank Engine RWS illustration\",\n \"trainset_idx\": 157683\n },\n {\n \"caption\": \"Mystical forest with glowing mushrooms and a babbling brook\",\n \"trainset_idx\": 261897\n },\n {\n \"caption\": \"Kissa Sins, Tekken, Textless, sfw, brunette\",\n \"trainset_idx\": 553977\n },\n {\n \"caption\": \"teddybear crew inside spaceship, , inside is a model of a mgb, sci fi,star trek\",\n \"trainset_idx\": 282614\n },\n {\n \"caption\": \"western, realistic, photorealistic, ,\",\n \"trainset_idx\": 431557\n },\n {\n \"caption\": \"hand painted digital art scene of a misty castle in the distance across a large field with a winding dirt road\",\n \"trainset_idx\": 37348\n },\n {\n \"caption\": \"Company logo\",\n \"trainset_idx\": 61008\n },\n {\n \"caption\": \"intricate fine tipped pen drawing of a, attractive queen of the moon Athena and a starry night, inktober, Fine Line Tattoo, manga line art, monochrome, dotwork, by dan hilliard, by Stanislaw Wilczynski, by alphonse mucha, by aaron horkey\",\n \"trainset_idx\": 112765\n },\n {\n \"caption\": \"gloomy atmosphere, evil antropomorphic tabaxi warlock wearing a cloak, necromancer, intricate detail, horror movie, by alicexz and monet and the game dixit, patreon, gravity, dark solar system, dark universe space and time, fractal, background by nasa, evil, mean\",\n \"trainset_idx\": 140435\n },\n {\n \"caption\": \"peru is the winner of the world cup\",\n \"trainset_idx\": 459060\n },\n {\n \"caption\": \"A cat wearing sunglasses. Stylezed. Cartoon.\",\n \"trainset_idx\": 185868\n },\n {\n \"caption\": \"A panda bear as a mad scientist\",\n \"trainset_idx\": 485553\n },\n {\n \"caption\": \"An Easter bunny riding a unicorn\",\n \"trainset_idx\": 343119\n },\n {\n \"caption\": \"Acrylic painting of a mountain landscape, with a stormy sky and a cabin nestled in the forest, high contrast, bold brushstrokes, high-resolution\",\n \"trainset_idx\": 344676\n },\n {\n \"caption\": \"fire woman outfit\",\n \"trainset_idx\": 84806\n },\n {\n \"caption\": \"Morris Mini-Minor car driving through volcanic molten lava magma, studio lighting, volumetric light,flames steam\",\n \"trainset_idx\": 448291\n },\n {\n \"caption\": \"\\\"Nijo's\\\" Mexican restaurant logo\",\n \"trainset_idx\": 194583\n },\n {\n \"caption\": \"blonde girl upskirt\",\n \"trainset_idx\": 192348\n },\n {\n \"caption\": \"A gray cat looking at a cave, digital art, oil painting\",\n \"trainset_idx\": 577771\n },\n {\n \"caption\": \"pink soldiers\",\n \"trainset_idx\": 401805\n },\n {\n \"caption\": \"shibari, art by Rudy Giger\",\n \"trainset_idx\": 86652\n },\n {\n \"caption\": \"A cinematic portrait of a medieval knight wearing full BLUE, BLACK, and Gold coloured steel plate armor that is shiny and reflective form a fantasy game and posing with a long sword\",\n \"trainset_idx\": 563310\n },\n {\n \"caption\": \"a treetop village\",\n \"trainset_idx\": 231859\n },\n {\n \"caption\": \"portrait of nicolas cage, cloudy sky background lush landscape illustration concept art anime key visual trending pixiv fanbox by wlop and greg rutkowski and makoto shinkai and studio ghibli\",\n \"trainset_idx\": 200560\n },\n {\n \"caption\": \"a surreal collage of different animals and objects, abstract style\",\n \"trainset_idx\": 522308\n },\n {\n \"caption\": \"a cute pretty Korean university student boy, brown hair, elegant, hyperrealistic, vibrant, big eyes, smiling, white shirt, light tone skin\",\n \"trainset_idx\": 469906\n },\n {\n \"caption\": \"insanely detailed portrait,female model, insane face details,dof, dslr extremely intricate, high res, 8k, award winning photography\",\n \"trainset_idx\": 298255\n },\n {\n \"caption\": \"a close shot of a cute young white woman's face\",\n \"trainset_idx\": 583580\n },\n {\n \"caption\": \"highly detailed marble and jade sculpture of an angel of mercy, volumetric fog, Hyperrealism, breathtaking, ultra realistic, unreal engine, ultra detailed, cyber background, Hyperrealism, cinematic lighting, highly detailed, breathtaking , photography, stunning environment, wide-angle\",\n \"trainset_idx\": 388557\n },\n {\n \"caption\": \"A sunset in the mountain, artstation award winner, extremely detailed\",\n \"trainset_idx\": 257733\n },\n {\n \"caption\": \"vivid schoolgirles in a sofa with \\\"no underware\\\" with a childish faces and childish bodies touching each other, with dark background\",\n \"trainset_idx\": 231758\n },\n {\n \"caption\": \"Minimalism in Joshua Middleton, Wenjun Lin, Sparth style Medium Shot of Vargr with Potion of Nature, Rainforest Canopy, Misty, Holographic, trending on deviantart, Black and White\",\n \"trainset_idx\": 456413\n },\n {\n \"caption\": \"stunningly beautiful female pokemon cosplay, insanely detailed, photorealistic, masterpiece, volumetric lighting, 8k, taken with canon eos 5d mark iv, midjourney v4 style, ,\",\n \"trainset_idx\": 144593\n },\n {\n \"caption\": \"Pikachu in real life\",\n \"trainset_idx\": 67719\n },\n {\n \"caption\": \"H. R. Giger's painting depicting a beautiful femal vampire, rusted iron texture\",\n \"trainset_idx\": 407010\n },\n {\n \"caption\": \"nerd man with futuristic steampunk technology on his head evil laugh in brown room\",\n \"trainset_idx\": 109219\n },\n {\n \"caption\": \"The ghost of Cleopatra arguing with a dictator\",\n \"trainset_idx\": 350062\n },\n {\n \"caption\": \"Hand of god\",\n \"trainset_idx\": 112735\n },\n {\n \"caption\": \"Baroque style, figured stucco, silver on black background, high resolution , 8k detail\",\n \"trainset_idx\": 320747\n },\n {\n \"caption\": \"Epic cinematic poster for an adult movie starring Ron jeremy and several beautiful women, insanely detailed, photorealistic, 8k, ultra high resolution, volumetric lighting, taken with canon, taken with nikon\",\n \"trainset_idx\": 356654\n },\n {\n \"caption\": \"Cyberpunk Batman and catwoman stood next to the futuristic batmobile, fantasy, sharp focus, digital photo, hyper realistic, 8k, unreal engine, highly detailed, hd 4k, dramatic lighting, trending on artstation\",\n \"trainset_idx\": 223161\n },\n {\n \"caption\": \"hyperrealistic photograph, extremely detailed pale young woman whole body covered in fungus, fungi, pores, chapped lips, mushrooms growing out of her eyes, skinny, mushrooms, mushrooms on face, mushrooms on cheekbones, zoomed out ,\",\n \"trainset_idx\": 164225\n },\n {\n \"caption\": \"terminator t-800 skeleton\",\n \"trainset_idx\": 88686\n },\n {\n \"caption\": \"Woman with short red hair on the little car\",\n \"trainset_idx\": 99107\n },\n {\n \"caption\": \"a manga beautiful girl with a medieval armor, trending on deviant art, high res, gray scales, kentaro miura style, full body.\",\n \"trainset_idx\": 549971\n },\n {\n \"caption\": \"High-definition image of a penguin among marijuana plants\",\n \"trainset_idx\": 28592\n },\n {\n \"caption\": \"a cat eating a lemon\",\n \"trainset_idx\": 268060\n },\n {\n \"caption\": \"a miner\",\n \"trainset_idx\": 274555\n },\n {\n \"caption\": \"A photo of an apple\",\n \"trainset_idx\": 470609\n },\n {\n \"caption\": \"Johnn Lennon listening music with a headphones, new discman\",\n \"trainset_idx\": 76545\n },\n {\n \"caption\": \"Asa Akira\",\n \"trainset_idx\": 502781\n },\n {\n \"caption\": \"blonde little preteen body wearing pants latex\",\n \"trainset_idx\": 327462\n },\n {\n \"caption\": \"Gallarus Oratory\",\n \"trainset_idx\": 96400\n },\n {\n \"caption\": \"Glorious sunrise over a tropical bay, blue sky, dark storm clouds with lightning off in the distance\",\n \"trainset_idx\": 511920\n },\n {\n \"caption\": \"cyberpunk\",\n \"trainset_idx\": 347988\n },\n {\n \"caption\": \"Traditional library with floor-to-ceiling bookcases\",\n \"trainset_idx\": 44409\n },\n {\n \"caption\": \"Lamp in the shape of a laptop, mixed new object, the era of nanotechnology\",\n \"trainset_idx\": 426024\n },\n {\n \"caption\": \"nigiri-sushi\",\n \"trainset_idx\": 429830\n },\n {\n \"caption\": \"An evil villain holding a mini Earth\",\n \"trainset_idx\": 137841\n },\n {\n \"caption\": \"painting depicting all four season in one paintng, concept art, artstation, detailed, impressionism, oil on canvas, knife painting, messy,\",\n \"trainset_idx\": 315384\n },\n {\n \"caption\": \"A mixture of a ferrari and a Lamborghini\",\n \"trainset_idx\": 502963\n },\n {\n \"caption\": \"Slutty Soviet Alien\",\n \"trainset_idx\": 81529\n },\n {\n \"caption\": \"Light tank in desert\",\n \"trainset_idx\": 270748\n },\n {\n \"caption\": \"gustav royo ingle mother seated recalling hardworking famine, Christian Krohg,melancholia\",\n \"trainset_idx\": 182136\n },\n {\n \"caption\": \"A photograph of the Steel Anime race car\",\n \"trainset_idx\": 252370\n },\n {\n \"caption\": \"milky way on a clear night, with a forest as backdrop\",\n \"trainset_idx\": 92202\n },\n {\n \"caption\": \"a skeleton soldier holding a battle axe\",\n \"trainset_idx\": 265673\n },\n {\n \"caption\": \"house real photography fujifilm xt4 80mm f 1.5\",\n \"trainset_idx\": 48281\n },\n {\n \"caption\": \"a monkey dancing\",\n \"trainset_idx\": 368827\n },\n {\n \"caption\": \"a fiddle player\",\n \"trainset_idx\": 268121\n },\n {\n \"caption\": \"cute Asian little girl jumping\",\n \"trainset_idx\": 546803\n },\n {\n \"caption\": \"A vibrant digital illustration of an anime girl, with big, expressive eyes and flowing hair, sitting in a peaceful, natural environment with cherry blossom trees in the background, using a pastel color palette and inspired by the artist Hayao Miyazaki\",\n \"trainset_idx\": 103942\n },\n {\n \"caption\": \"A roadsign with text saying \\\"What did you do in school today?\\\"\",\n \"trainset_idx\": 532132\n },\n {\n \"caption\": \"amily covered in sheets, memoriam beach rudolf felix edwin gres warmssnhq ,Jules Bastien-Lepage\",\n \"trainset_idx\": 293554\n },\n {\n \"caption\": \"frontal portrait of David Bowie by Boris Vallejo\",\n \"trainset_idx\": 198756\n },\n {\n \"caption\": \"tree of life, sun pillars, light leaks, ultra realistic, digital art, trending on artstation, overgrown, cinematic\",\n \"trainset_idx\": 531513\n },\n {\n \"caption\": \"a satanic angel\",\n \"trainset_idx\": 84915\n },\n {\n \"caption\": \"demon music industry executive, an evil demonic man in a suit at a big desk\",\n \"trainset_idx\": 315820\n },\n {\n \"caption\": \"wideangle panorama photo a triceratops chasmosaurine next to a landrover defender in a muddy road in the jungle,obstacle course, explosions smoke debris fire, by Anthony S Waters, fisheye lens, some rust,real-life brook, front side views full, camp, but very good looking, very wet, 2021 ,\",\n \"trainset_idx\": 352974\n },\n {\n \"caption\": \"realistic, a beautiful character portrait of The Hulk wearing viking armor by artist Frank Frazetta, heroic pose, 4k high resolution, intricate details, comic book illustration, a beautiful expressive painting with amazing style\",\n \"trainset_idx\": 133673\n },\n {\n \"caption\": \"a painting of a wolf with a stained glass window, a character portrait, by Lorraine Fox, deviantart contest winner, art nouveau, kneeling in prayer, an angel standing still, from 2001, albino, no watermark signature, an anthro fox, valkyrie, holding golden chains, sky blue highlights in hair\",\n \"trainset_idx\": 98568\n },\n {\n \"caption\": \"Visual Novel in Boris Vallejo style Close-Up Shot of Frost Giant with Blowgun, Astral Nexus, intricate and detailed\",\n \"trainset_idx\": 564280\n },\n {\n \"caption\": \"Cow wearing gaming headset\",\n \"trainset_idx\": 283041\n },\n {\n \"caption\": \"Jennifer Connelly as a naturist at a natural hotsprings\",\n \"trainset_idx\": 144108\n },\n {\n \"caption\": \"A oil painting portrait of young Muscle taxidermist boy butchering giant TESTICLES organ on the dissectingTable. bloody background. highly detailed guro art by Ilya Repin\",\n \"trainset_idx\": 61614\n },\n {\n \"caption\": \"Sturdy and pink pickup truck\",\n \"trainset_idx\": 411689\n },\n {\n \"caption\": \"grace Kelly as an angel, as painted by Degas\",\n \"trainset_idx\": 229247\n },\n {\n \"caption\": \"A robot in times square holding a plaque that reads \\\"dreamstudio\\\"\",\n \"trainset_idx\": 470223\n },\n {\n \"caption\": \"designer siting behind his computer\",\n \"trainset_idx\": 553186\n },\n {\n \"caption\": \"A cyberpunk golden retriever is coding\",\n \"trainset_idx\": 134319\n },\n {\n \"caption\": \"flowers in an arrangement following a river, sunlit, golden hour, sunshine rays\",\n \"trainset_idx\": 353522\n },\n {\n \"caption\": \"photo of a cat made of snake scales\",\n \"trainset_idx\": 441791\n },\n {\n \"caption\": \"conceptual designer chocolate bar packaging, inspired by kerala village, midsommar, label design, behance, pinterest, packaging of the world, award, front label, packaging design, octane render\",\n \"trainset_idx\": 483824\n },\n {\n \"caption\": \"spongebob characters in world war two\",\n \"trainset_idx\": 37876\n },\n {\n \"caption\": \"frosty seeing hands by Hans Bellmer, H.R.Giger, background snowflakes, frosty eyes inside large industrial freezer by Roger Penrose, M.C. Escher 128K UHD\",\n \"trainset_idx\": 165895\n },\n {\n \"caption\": \"a puppy and a kitten in a teacup together\",\n \"trainset_idx\": 303652\n },\n {\n \"caption\": \"DVD screengrab of the street scene from the movie about ancient rome\",\n \"trainset_idx\": 418314\n },\n {\n \"caption\": \"a zombie in a top hat, intricate details, photo detailed fabric\",\n \"trainset_idx\": 87992\n },\n {\n \"caption\": \"Jesus playing lego with children, lego bricks forming holy cross and church\",\n \"trainset_idx\": 255026\n },\n {\n \"caption\": \"photo in ancient rome,centurions roman army,wideangle ,fall of the roman empire,ben-hur epic film\",\n \"trainset_idx\": 491340\n },\n {\n \"caption\": \"sports team logo of an animal in front of a simple diamond shape, minimalistic lineart vector black and white monochromatic,\",\n \"trainset_idx\": 349754\n },\n {\n \"caption\": \"The word \\\"Creep\\\" with a Slimy humanoid monster covered with a Hundred eyeballs small funny uncanny bald creepy gooey\",\n \"trainset_idx\": 563087\n },\n {\n \"caption\": \"a close up of a frog wearing a military uniform, beeple and tim hildebrandt, 8 k cartoon illustration, by Goro Fujita, realistic cartoon, mascot illustration, freedom fighter, unreal engine', vietnam war soldier, ultra realistic picture, incredible screensho\",\n \"trainset_idx\": 27769\n },\n {\n \"caption\": \"Nigerian young Ellen DeGeneres\",\n \"trainset_idx\": 572897\n },\n {\n \"caption\": \"Architecture, Zaha Hadid, Modernism, Curves, Sunlight, Greenery, Aerial View, Realism\",\n \"trainset_idx\": 491714\n },\n {\n \"caption\": \"A korean woman in street running, highly detai\",\n \"trainset_idx\": 541053\n },\n {\n \"caption\": \"woman in black coat sitting in snowy landscape, aph gustav wyeleighton snowy loneliness hone pland, Jules bastien Lepage\",\n \"trainset_idx\": 120745\n },\n {\n \"caption\": \"Close up, Jaws, horror film, an ancient megalodon under water, cinematic, cool color grading real512\",\n \"trainset_idx\": 166774\n },\n {\n \"caption\": \"Superman with glasses writing code on laptop on the moon and hear music in headphone\",\n \"trainset_idx\": 9260\n },\n {\n \"caption\": \"einstein looking at the milky way\",\n \"trainset_idx\": 468344\n },\n {\n \"caption\": \"gingerbread candy village, cinematic scene, studio lighting, colorful, fantasy, fairytale, intricate, forest, fireflies, flowers, halloween, christmas, hansel and gretel, background blur, bokeh, medium shot, visually stunning, matte painting, concept art, trending on artstation, artgerm, cgsociety\",\n \"trainset_idx\": 472370\n },\n {\n \"caption\": \"Richer face, round face, more natural action, smile,with round glasses, Disney Princess, lovely cartoon girl, glowing stars and white roses around her, 3D, light background, C4D, IP character, Blender, bright color, 8K, HD\",\n \"trainset_idx\": 476065\n },\n {\n \"caption\": \"A Eurasier dog sitting next to a Buddhist monk seated in meditation.\",\n \"trainset_idx\": 152208\n },\n {\n \"caption\": \"Vector eSports logo of a brain made of a beehive\",\n \"trainset_idx\": 38357\n },\n {\n \"caption\": \"selfie photo of cute blonde woman with man\",\n \"trainset_idx\": 404370\n },\n {\n \"caption\": \"Centered , smooth, sharp-focus , Dora The Explorer as a, Mortal Kombat Character , max gorgeous art , by Charlie Bowater & Artgerm , Comic & Cartoon style , standing, broken hilltop , trending ArtstationHQ\",\n \"trainset_idx\": 271004\n },\n {\n \"caption\": \"A pixel art of girl fighters in medieval era, beautiful detailed, high resoluci\\u00f3n, blasphemous style, trending on artstation and deviantart.\",\n \"trainset_idx\": 537231\n },\n {\n \"caption\": \"lego set of jesus christ in the cross\",\n \"trainset_idx\": 56020\n },\n {\n \"caption\": \"minimalism design, 3d icon, emoji man, Rick Sanchez, color\",\n \"trainset_idx\": 10194\n },\n {\n \"caption\": \"polaroid, very large lovecraftian blob, apparition, fog, long tentacles, imposing lovecraftian creature in a colossal massive dark factory, hundreds of bleeding corpses on the ground, old brutalist big factory, enormous dark abandoned factory, industrial complex, industry\",\n \"trainset_idx\": 556353\n },\n {\n \"caption\": \"sam hyde\",\n \"trainset_idx\": 245056\n },\n {\n \"caption\": \"Asian Mexican boy with short hair playing on gaming computer wearing headphones\",\n \"trainset_idx\": 454488\n },\n {\n \"caption\": \"A digital painting of a warrior princess, dressed in armor and wielding a glowing sword. The warrior is depicted in a dramatic pose, with swirling energy and sparks emanating from her sword. The image is inspired by the art style of Frank Frazetta.\",\n \"trainset_idx\": 35347\n },\n {\n \"caption\": \"A highly detailed landscape painting of the Yangshuo karst hills painted by Pieter Bruegel the Elder, masterpiece, absurdres, highres, featured on ArtStation\",\n \"trainset_idx\": 410062\n },\n {\n \"caption\": \"The symmetrical badge in the realistic gun game COD13\",\n \"trainset_idx\": 243088\n },\n {\n \"caption\": \"raw photo, pale alien girl with big fish eyes and white hair, horror, headshot photo, nikon, dslr, wildlife photography, 8k uhd, highly detailed skin\",\n \"trainset_idx\": 224110\n },\n {\n \"caption\": \"1960 colour small batman surf mansion architect drawing, big sur, bat shape,cliffs and waves, nest, batsign, excentric, faded colour, rotring pencil artist impression, comics, spooky, by frank lloyd wright and gaudi and nouvel and pritzker prize\",\n \"trainset_idx\": 568512\n },\n {\n \"caption\": \"a digital painting of a Volkswagen van driving down a dusty road in the desert, surreal colors, dreamlike atmosphere, by Simon St\\u00e5lenhag, imaginative concept art, 8k, post-apocalyptic vibe, textured brush strokes, trending on artstation, high resolution, otherworldly, fantastical, mysterious, fantasy environment\",\n \"trainset_idx\": 225325\n },\n {\n \"caption\": \"photo of little girl ballet dancing\",\n \"trainset_idx\": 481989\n },\n {\n \"caption\": \"Katsuhiro \\u014ctomo traditional old school American tattoo style french bulldog close portrait photography. Uhd, cinematic, filmic, Post-production, intricate textures, photorealistic, volumetric lighting,\",\n \"trainset_idx\": 23277\n },\n {\n \"caption\": \"A photo of alien deep sea creatures, dark, particles, cone of light from the viewer's prespective, photo realistic, 8k\",\n \"trainset_idx\": 384499\n },\n {\n \"caption\": \"a tiny dragon taking a bath in a tea cup, digital art, 2d art, anime, tea cup has the words fire on it\",\n \"trainset_idx\": 59610\n },\n {\n \"caption\": \"digital art, masterpiece, hyperrealism, Alice in Wonderland standing near large mushrooms with red caps covered in white spots, psychedelic environment\",\n \"trainset_idx\": 432626\n },\n {\n \"caption\": \"screenshot from game about aliens and top secret agencies in style of first deus ex game from 1999, x files\",\n \"trainset_idx\": 29083\n },\n {\n \"caption\": \"Closeup portrait of a girl, fanfic, standoff, dramatic\",\n \"trainset_idx\": 251555\n },\n {\n \"caption\": \"margot robbie in 80s sitcom\",\n \"trainset_idx\": 242530\n },\n {\n \"caption\": \"black background, golden twitter bird surrounded by a flat golden shiny ring\",\n \"trainset_idx\": 39882\n },\n {\n \"caption\": \"1980s honda sport car concept art oil painting\",\n \"trainset_idx\": 434997\n },\n {\n \"caption\": \"a highly detailed photo of the scottish highlands\",\n \"trainset_idx\": 440455\n },\n {\n \"caption\": \"cinematic picture of korean alley in Seoul, neon lights, ambient\",\n \"trainset_idx\": 308000\n },\n {\n \"caption\": \"A corgi riding a motorbike, action figure, product shot, 8k, Bokeh\",\n \"trainset_idx\": 60673\n },\n {\n \"caption\": \"balkenende\",\n \"trainset_idx\": 146358\n },\n {\n \"caption\": \"architect\\uff0czaha\\uff0crealistic\",\n \"trainset_idx\": 221454\n },\n {\n \"caption\": \"portrait of guy muscle bald rapist at russian prison. wear raunch underpants, highly detailed face. art by Ilya Repin\",\n \"trainset_idx\": 566237\n },\n {\n \"caption\": \"cheshire cat\",\n \"trainset_idx\": 342169\n },\n {\n \"caption\": \"dissected pregnant girl at morgue. highly detailed photo\",\n \"trainset_idx\": 134081\n },\n {\n \"caption\": \", fantasy, pastel, absurdist, photo, refined, mystical creatures\",\n \"trainset_idx\": 182963\n },\n {\n \"caption\": \"super kawaii illustration of ghost rider, riding a motorbike\",\n \"trainset_idx\": 62823\n },\n {\n \"caption\": \"photo an attractive woman gunslinger standing in a scifi-fantasy wilderness\",\n \"trainset_idx\": 517368\n },\n {\n \"caption\": \"Surrealism in Wenjun Lin style Close-Up Shot of Imp, Haunted Mansion, masterpiece, trending on artstation\",\n \"trainset_idx\": 494414\n },\n {\n \"caption\": \"A cinematic of a cute looking red panda wearing a blue flannel sweater with a white shirt inside, glasses and a chef's hat making sushi on a countertop in a hawaiian style hut with a beautiful sunset and island in the background in the style of animal crossing\",\n \"trainset_idx\": 89546\n },\n {\n \"caption\": \"Fantasy futuristic london city/ immense detail/ hyper. P\\u00e5realistic, city /cyberpunk, high detail, detailed, 3d, trending on artstation, cinematic\",\n \"trainset_idx\": 518772\n },\n {\n \"caption\": \"videogame sprites, top-down isometric 2D indie game style, empty background\",\n \"trainset_idx\": 374689\n },\n {\n \"caption\": \"tattoo ideas for a introvert\",\n \"trainset_idx\": 105479\n },\n {\n \"caption\": \"Marilyn Monroe\",\n \"trainset_idx\": 364686\n },\n {\n \"caption\": \"portrait of a woman wearing leaves all over\",\n \"trainset_idx\": 208898\n },\n {\n \"caption\": \"A 1945 WWII propaganda illustration of Kim Kardashian, masterpiece, absurdres, highres, featured on ArtStation\",\n \"trainset_idx\": 97465\n },\n {\n \"caption\": \"Man and woman, eating and drinking wine at a busy restaurant, art by Gustave Courbet\",\n \"trainset_idx\": 459583\n },\n {\n \"caption\": \"a close up of a person wearing a costume,surrealism, cyberpunk art, by Philippe Druillet,katsuhiro otomo, symmetrical dieselpunk warrior, grand admiral thrawn, a still life of a robot, holy machine, cyborg woman, orbital, king crimson, avatar image, shusei nagaoka, large view\",\n \"trainset_idx\": 499719\n },\n {\n \"caption\": \"Solarpunk Rio de Janeiro, overgrown, ground level view, optimistic, golden hour, award winnining photography\",\n \"trainset_idx\": 173696\n },\n {\n \"caption\": \"Prompt: an illustration of a snail working out in the gym\",\n \"trainset_idx\": 344057\n },\n {\n \"caption\": \"Casal pelado na cama\",\n \"trainset_idx\": 304204\n },\n {\n \"caption\": \"anime illustration of link as fairy king oberon from midsummer night's dream\",\n \"trainset_idx\": 438913\n },\n {\n \"caption\": \"A profile picture of an anime boy, half robot, anime, not real, detailed, brown hair, cyberpunk, robot\",\n \"trainset_idx\": 291032\n },\n {\n \"caption\": \"a press photo of a rainbow frog \\ud83d\\udc38 fighting the police in a French protestation\",\n \"trainset_idx\": 425952\n },\n {\n \"caption\": \"Werewolf photorealistic lupine sapiens\",\n \"trainset_idx\": 323862\n },\n {\n \"caption\": \"Stellar Fusion\",\n \"trainset_idx\": 275237\n },\n {\n \"caption\": \"Human girl warlock dnd realistic with short brown hair and tentacle magic\",\n \"trainset_idx\": 540358\n },\n {\n \"caption\": \"photo of fighter jet made by bmw\",\n \"trainset_idx\": 196168\n },\n {\n \"caption\": \"old man running from the grim reaper, by Peter Max and Takashi Murakami, Jeff Soto, dan mumford, Digital Vector illustration, psychedelic op art, Dribbble, Behance, 2d, vibrant\",\n \"trainset_idx\": 297608\n },\n {\n \"caption\": \"Cloth off viking princess,white milk falling from lips and face,sits open laps, down view camera,resident evil movie style, humidity torso, look around shoulder,dinamic pose, nice face, jelly leaks on laps,nice arms, nice eyes,highest detailed, masterpease,\",\n \"trainset_idx\": 390311\n },\n {\n \"caption\": \"boil boil toil and trouble\",\n \"trainset_idx\": 40396\n },\n {\n \"caption\": \"Mix cat and loaf of bread, remix\",\n \"trainset_idx\": 377965\n },\n {\n \"caption\": \"a Patterdale terrier riding a aeroplane toy in the cave ,\",\n \"trainset_idx\": 403079\n },\n {\n \"caption\": \"in style of Alvaro Castagnet, beautiful details,sunflowers\",\n \"trainset_idx\": 432676\n },\n {\n \"caption\": \"A teal and yellow concept car in the shape of a crab\",\n \"trainset_idx\": 158160\n },\n {\n \"caption\": \"art by Alfons Mucha and Patrick Woodroffe, stained glass motif, whole body portrait of 20 year-old Jennifer Connelly as a naturist at the Eiffel Tower in Paris, HD 4K, sharp detail, photo-realistic accurate face and features\",\n \"trainset_idx\": 38415\n },\n {\n \"caption\": \"a photograph of a blue purple ChromaFlair MG ZT 190 car that is in the jungle river,rover 75 ,4k wideangle photo\",\n \"trainset_idx\": 115698\n },\n {\n \"caption\": \"A baby connected to a neural network\",\n \"trainset_idx\": 159407\n },\n {\n \"caption\": \"a perfect little girl in a fashion show\",\n \"trainset_idx\": 285168\n },\n {\n \"caption\": \"shakira bdsm\",\n \"trainset_idx\": 392961\n },\n {\n \"caption\": \"Walter White in the style of Avatar the Last Airbender\",\n \"trainset_idx\": 525778\n },\n {\n \"caption\": \"Light doesn't interact with human skin accurately\",\n \"trainset_idx\": 273928\n },\n {\n \"caption\": \"Supergirl with big muscles and abs posing\",\n \"trainset_idx\": 353736\n },\n {\n \"caption\": \"movie still of peppa pig in gears of war, style Artstation, octane render, unreal engine 6, epic game Graphics, Fantasy,cyberpunk, conceptual art, Ray tracing\",\n \"trainset_idx\": 261427\n },\n {\n \"caption\": \"Maryam Nawaz, Grand Theft Auto\",\n \"trainset_idx\": 543279\n },\n {\n \"caption\": \"Picture a sprawling landfill overflowing with mounds of garbage, emitting a foul odor and attracting scavenging birds and vermin. This is the unfortunate reality of our unsustainable consumption and disposal habits\",\n \"trainset_idx\": 449993\n },\n {\n \"caption\": \"androide 18 apareandose con un viejo\",\n \"trainset_idx\": 504550\n },\n {\n \"caption\": \"sparkly magical medieval fantasy landscape from Final Fantasy 14, highly detailed, lush forests, huge mountain ranges, grand seas, wide plains, cumulonimbus clouds horizon, ultra high max settings quality HD in-game render, HDR XDR contrast, 4k texture meshes\",\n \"trainset_idx\": 367201\n },\n {\n \"caption\": \"the asteroid that killed all the dinosaurs\",\n \"trainset_idx\": 450689\n },\n {\n \"caption\": \"Realistic Black and white cute portrait of actress Jenna Ortega bangs hairstyle , jacket , blemishes on skin , smooth face , dynamic light , dynamic shadows , street background, image taken by\",\n \"trainset_idx\": 578420\n },\n {\n \"caption\": \"Bosque de nieve encantado\",\n \"trainset_idx\": 224816\n },\n {\n \"caption\": \"bouquet of spring flowers, glass blown sculpture\",\n \"trainset_idx\": 413268\n },\n {\n \"caption\": \"A silhouette of a dog looking at the stars\",\n \"trainset_idx\": 258664\n },\n {\n \"caption\": \"a cute brunette girl wearing pink headphones, wearing a color blocking hoodie, kawaii, whimsical, happy, big anime Pixar eyes, Nicoletta Ceccoli, Tim Burton, Lisa Frank\",\n \"trainset_idx\": 516722\n },\n {\n \"caption\": \"medium shot photo of a fantasy female bloody rotting zombie blade master, located in a dungeon, made by Stanley Artgerm Lau, WLOP, Rossdraws, ArtStation, CGSociety, concept art, cgsociety, octane render, trending on artstation, artstationHD, artstationHQ, unreal engine, 4k, 8k\",\n \"trainset_idx\": 245030\n },\n {\n \"caption\": \"Alice in a giant mashrooms world\",\n \"trainset_idx\": 129319\n },\n {\n \"caption\": \"waterfall crashing down into the bottomless canyon epic fantasy\",\n \"trainset_idx\": 454966\n },\n {\n \"caption\": \"Cinematographic 1970s Chirac adidas-3stripes-tracksuit french photoportrait adidas carshow spaceship anglican-tiara-mitre Archbishop Jacques Chirac RPR vatican space program moebius capsule launchpad thunderbirds-vuitton Astronaut papal official leica hasselblad photograph in Vatican royal helmet metal scaphandre launchpad pointy oxygen hazmat gloves helmet\",\n \"trainset_idx\": 377309\n },\n {\n \"caption\": \"Detailed, realistic, beautiful woman with flowing brown hair, model sheet turnaround, flexing her muscles, full color, front view, back view, hyperreal, white background\",\n \"trainset_idx\": 208138\n },\n {\n \"caption\": \"abstract impressionism, muslim bird woman with long flowing dress flying through sky, storm, aqua and blue and gold colors, , hijab, impasto, wispy clouds, Tyler Shields, Steven Outram, Eric Zener, Odd Nerdrum, Noah Bradley, Richard MacDonald, Anne Bachelier, Leonora Carrington, ink wash\",\n \"trainset_idx\": 45137\n },\n {\n \"caption\": \"weird ai generation of an uncanny person face\",\n \"trainset_idx\": 307309\n },\n {\n \"caption\": \"a digital art of a waifu.\",\n \"trainset_idx\": 113176\n },\n {\n \"caption\": \"An image of a beautiful woman\",\n \"trainset_idx\": 490439\n },\n {\n \"caption\": \"Beautiful Witch wearing witch hat and dress,flying with a huge full moon with stars in the background\",\n \"trainset_idx\": 348789\n },\n {\n \"caption\": \"Beautiful female viking warrior holding an axe\",\n \"trainset_idx\": 368021\n },\n {\n \"caption\": \"Cyberpunk android running through streets of ancient Aztec city\",\n \"trainset_idx\": 78540\n },\n {\n \"caption\": \"A battle between two heroes, illustration\",\n \"trainset_idx\": 42954\n },\n {\n \"caption\": \"A cat riding a motorcycle through a jungle\",\n \"trainset_idx\": 539508\n },\n {\n \"caption\": \"young Muscle guy eat TESTICLEs flesh. plural testes, male reproductive gland. highly detailed guro art by Ilya Repin\",\n \"trainset_idx\": 162942\n },\n {\n \"caption\": \"supremely handsome, glamour photo, 13 years old Hindu God Krishna with colorful turban, extremely detailed CG unity 8k wallpaper, most beautiful artwork in the world, professional majestic impressionism oil painting. trending on AnStation, trending on CGSociety, Intricate, Highly Detailed, dramatic\",\n \"trainset_idx\": 501381\n },\n {\n \"caption\": \"F22 raptor\",\n \"trainset_idx\": 123934\n },\n {\n \"caption\": \"a stack of frogs, photorealistic\",\n \"trainset_idx\": 458063\n },\n {\n \"caption\": \"immodest goth Asian African White mixed girl with snake around neck\",\n \"trainset_idx\": 481132\n },\n {\n \"caption\": \"human mutating into a monster\",\n \"trainset_idx\": 28774\n },\n {\n \"caption\": \"a path of polished bricks, leading into the air, dusk\",\n \"trainset_idx\": 441328\n },\n {\n \"caption\": \"surreal painting\",\n \"trainset_idx\": 132572\n },\n {\n \"caption\": \"An ant in the shape of a letter a, insanely detailed, photorealistic, 8k, ultra high resolution, volumetric lighting, taken with canon eos,\",\n \"trainset_idx\": 19067\n },\n {\n \"caption\": \"Movie Poster from 2023, \\\"little gymnastic man\\\",modern design, frieze\",\n \"trainset_idx\": 523488\n },\n {\n \"caption\": \"a close up of a zappa with long hair and a beard, inspired by V\\u00e1clav Bro\\u017e\\u00edk, billy corgan, headshot profile picture, profile photo, profile picture\",\n \"trainset_idx\": 538716\n },\n {\n \"caption\": \"waving, dark skin, handsome man, young, rejection, deny, white suit, one hand pushing at the camera, angry, mad, serious\",\n \"trainset_idx\": 210747\n },\n {\n \"caption\": \"Exchanging crate of plants, fruits and vegetables in a garden\",\n \"trainset_idx\": 412442\n },\n {\n \"caption\": \"a cat with wings on his back\",\n \"trainset_idx\": 289194\n },\n {\n \"caption\": \"A woman wearing a multicolored paneled zentai body with zentai hood sits on a plain beach towel\",\n \"trainset_idx\": 16515\n },\n {\n \"caption\": \"A cat on playing chess, digital art, oil painting\",\n \"trainset_idx\": 394159\n },\n {\n \"caption\": \"Art in retro comic style, highly detailed Harry Potter, quadrinhos cape, symmetrical, vibrant\",\n \"trainset_idx\": 566916\n },\n {\n \"caption\": \"A championship boxing match\",\n \"trainset_idx\": 519753\n },\n {\n \"caption\": \"minecraft desert village\",\n \"trainset_idx\": 516118\n },\n {\n \"caption\": \"Watermelon shaped egg style chair\",\n \"trainset_idx\": 460396\n },\n {\n \"caption\": \"a cinematic still of 3rd reich officers in a bunker watching 50'' flat screen tv with mickey mouse cartoon\",\n \"trainset_idx\": 242386\n },\n {\n \"caption\": \"1983 Black gmc vandura, red wheels\",\n \"trainset_idx\": 438417\n },\n {\n \"caption\": \"Machu pichu being destroyed\",\n \"trainset_idx\": 369679\n },\n {\n \"caption\": \"3d mecha war with greek statues in space\",\n \"trainset_idx\": 426699\n },\n {\n \"caption\": \"Black twink\",\n \"trainset_idx\": 435939\n },\n {\n \"caption\": \"Cyberpunk runner, Hyperdetailed painting, Album art by Ismail Inceoglu, Huang Guangjian and Dan Witz, Starry Blue, Neon fire, Liquid, Vibrant, Colorful, Wide angle, Deep colors, Volumetric lighting, Neon lighting, Cyberpunk Neo Tokyo, 8k resolution concept art, By Greg Rutkowski, WLOP, Dynamic lighting, Alphonse Mucha\",\n \"trainset_idx\": 270256\n },\n {\n \"caption\": \"Slutty Communist Alien\",\n \"trainset_idx\": 407865\n },\n {\n \"caption\": \"bioluminescent chicken\",\n \"trainset_idx\": 246787\n },\n {\n \"caption\": \"Knuckles asks Sonic if he flirted with his sister again\",\n \"trainset_idx\": 286325\n },\n {\n \"caption\": \"a golden statue of a korean girl in her birthday suit, 12 year old girl, flat body\",\n \"trainset_idx\": 348696\n },\n {\n \"caption\": \"A real life photo of Walter White, the main character from the series Breaking Bad\",\n \"trainset_idx\": 211426\n },\n {\n \"caption\": \"A glittery, undersea coral reef scene\",\n \"trainset_idx\": 381618\n },\n {\n \"caption\": \"Messi kissing Cristiano Ronaldo\",\n \"trainset_idx\": 245179\n },\n {\n \"caption\": \"SpongeBob album cover\",\n \"trainset_idx\": 236944\n },\n {\n \"caption\": \"Cyberpunk lady, classicism painting, oil on canvas\",\n \"trainset_idx\": 324655\n },\n {\n \"caption\": \"a photo of a mgb in forest filled with lots of trees, inspired by Dan Mumford, shutterstock, beautiful stained glass window, morning sunrise, colorful glass wall, stained\",\n \"trainset_idx\": 276632\n },\n {\n \"caption\": \"Logo for an application about exercises for the eyes, minimalistic, modern, flat design\",\n \"trainset_idx\": 350268\n },\n {\n \"caption\": \"a beautiful glaive in the style of dark souls. 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\"trainset_idx\": 322129\n },\n {\n \"caption\": \"Realistic Black and white portrait of Bangs hairstyle\",\n \"trainset_idx\": 504116\n },\n {\n \"caption\": \"furry drawing digital painting\",\n \"trainset_idx\": 303450\n },\n {\n \"caption\": \"pentacle and made of spikes\",\n \"trainset_idx\": 409458\n },\n {\n \"caption\": \"fierce bear bearing teeth\",\n \"trainset_idx\": 144018\n },\n {\n \"caption\": \"a viszla dog funny\",\n \"trainset_idx\": 272880\n },\n {\n \"caption\": \"a dog riding bicycle\",\n \"trainset_idx\": 244063\n },\n {\n \"caption\": \"Starship rocket flying over planet earth\",\n \"trainset_idx\": 286762\n },\n {\n \"caption\": \"a dog and cat playing chess, kawaii 2d art whitebackground\",\n \"trainset_idx\": 165522\n },\n {\n \"caption\": \"realistic image muscular girl with dark skin in tight shorts\",\n \"trainset_idx\": 519697\n },\n {\n \"caption\": \"a duck god with sunglasses floating throughout space\",\n \"trainset_idx\": 300495\n },\n {\n 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demonic vampire bat, with a regal pose and intricate details on its wings and fur. Rendered with a dark, moody color palette and soft lighting, resembling the works of Luis Royo and Brom.\",\n \"trainset_idx\": 512140\n },\n {\n \"caption\": \"Soft serve black holographic ice cream\",\n \"trainset_idx\": 98944\n },\n {\n \"caption\": \"The country France on the world map engulfed in flames\",\n \"trainset_idx\": 442096\n },\n {\n \"caption\": \"Hyper detailed Photograph of a Horror Clown\",\n \"trainset_idx\": 288672\n },\n {\n \"caption\": \"A tall house made of gold bullion\",\n \"trainset_idx\": 420670\n },\n {\n \"caption\": \"mechanical bee flying in nature, electronics, motors, wires, buttons, lcd, led instead of eyes, antennas instead of feet\",\n \"trainset_idx\": 72384\n },\n {\n \"caption\": \"\\u201cCITY\\u201d text on a white background, best quality, graffiti style\",\n \"trainset_idx\": 98550\n },\n {\n \"caption\": \"3D rendering of a glass bird. Iridescent colours. Motion blur. Bokeh, anamorphic film\",\n \"trainset_idx\": 522210\n },\n {\n \"caption\": \"psychedelic wizard of the ethereal world in the forest of sweets and colors\",\n \"trainset_idx\": 118351\n },\n {\n \"caption\": \"hand drawing of a piglet holding a sign saying life\",\n \"trainset_idx\": 141224\n },\n {\n \"caption\": \"cute little blonde girl wearing a pink t-shirt and blue pants by Jasmine Becket-Griffith, standing in a field of wildflowers\",\n \"trainset_idx\": 64544\n },\n {\n \"caption\": \"An asian man in a suit holding a sign with text \\\"I eat dog!\\\"\",\n \"trainset_idx\": 143157\n },\n {\n \"caption\": \"hotel lobby, isometric view, digital illustration, digital concept art, complementary colors\",\n \"trainset_idx\": 287888\n },\n {\n \"caption\": \"a giant woman sitting, in the city\",\n \"trainset_idx\": 93064\n },\n {\n \"caption\": \"An ancient artifact, lit internally by some unknown source, unusual markings on its surface suggest that it is not as ancient as it appears, the internal illumination creates a soft glow around the darkened room in which it is kept, Insanely detailed, photorealistic, 8k, ultra high resolution, volumetric lighting, taken with canon eos 5d,\",\n \"trainset_idx\": 397785\n },\n {\n \"caption\": \"cute pikachu with a sign that has \\\"I'm very cute\\\" written\",\n \"trainset_idx\": 165403\n },\n {\n \"caption\": \"best quality, ultra high res, photorealistic, 1 girl, looking at viewer, dark foggy light\",\n \"trainset_idx\": 506969\n },\n {\n \"caption\": \"safe, anime art, Hatsune miku, hires, Danbooru, anime cartoon, stylised, high quality, anime screenshoot\",\n \"trainset_idx\": 142353\n },\n {\n \"caption\": \"a science anatomy study model translucent skin\",\n \"trainset_idx\": 228552\n },\n {\n \"caption\": \"edinburgh castle\",\n \"trainset_idx\": 167971\n },\n {\n \"caption\": \"Avatar: The blue monkeys\",\n \"trainset_idx\": 236579\n },\n {\n \"caption\": \"Traditional library with floor-to-ceiling bookcases in Madagascar\",\n \"trainset_idx\": 474485\n },\n {\n \"caption\": \"Mercenary leopard smoking in a cyberpunk alley\",\n \"trainset_idx\": 172598\n },\n {\n \"caption\": \"walter white lego\",\n \"trainset_idx\": 22053\n },\n {\n \"caption\": \"2d anime illustration of young female warrior with white hair and white eyes with an attractive body wearing a fullbody white scale armor standing victorious, high quality, cinematic lighting, sharp focus,\",\n \"trainset_idx\": 168132\n },\n {\n \"caption\": \"A portrait of a beautiful blonde woman, fine - art photography, soft portrait shot 8 k, mid-length, ultrarealistic UHD faces, Unsplash, Kodak ultra max 800, 85 mm, intricate, casual pose, centred symmetrical composition, stunning photos, masterpiece, arri Alexa 35 mm\",\n \"trainset_idx\": 387980\n },\n {\n \"caption\": \"photo of a running wolf made of fire\",\n \"trainset_idx\": 367596\n },\n {\n \"caption\": \"full shot, Pulitzer Prize wide-angle photo at a pool-party, hyper realistic, Malaysian Royal Police Force, very handsome beefy Malay extreme body-builder married mature man wearing only low-rise ultra micro beach shorts, the size of an avocado\",\n \"trainset_idx\": 470336\n },\n {\n \"caption\": \"burning water inside of a bottle\",\n \"trainset_idx\": 502187\n },\n {\n \"caption\": \"Logo, telegram, ekadashi, wheat\",\n \"trainset_idx\": 477893\n },\n {\n \"caption\": \"A brilliant light radiates from the tomb as Jesus rises from the dead.\",\n \"trainset_idx\": 521058\n },\n {\n \"caption\": \"a black and white drawing of a building,symmetrical doorway , an engraving by Henry van de Velde, flickr, neoclassicism, architectural drawing, artwork of a building, detailed classical architecture\",\n \"trainset_idx\": 25413\n },\n {\n \"caption\": \"magical forest in the night, with a lot firefly, realistic, hd\",\n \"trainset_idx\": 247046\n },\n {\n \"caption\": \"Indian man wearing a very small chin on the toilet in a busy street\",\n \"trainset_idx\": 410206\n },\n {\n \"caption\": \"photography by Milton H Greene and Bert Stern, whole body photo portrait of Ana de Armas as Marylin Monroe as a naturist in the desert, HD 4K, sharp detail, photo-realistic accurate face and features, studio lighting\",\n \"trainset_idx\": 533931\n },\n {\n \"caption\": \"image of a\",\n \"trainset_idx\": 400233\n },\n {\n \"caption\": \"a woman with a flower in her hair, by irakli nadar, cg society contest winner, digital art, beautiful redhead woman, annasophia robb as aphrodite, a beautiful victorian woman, very pretty eyes, renaissance era, a potrait of a beautiful, sophie turner, looks a bit similar to amy adams\",\n \"trainset_idx\": 223057\n },\n {\n \"caption\": \"a humbuguer\",\n \"trainset_idx\": 121551\n },\n {\n \"caption\": \"A huge lizard dancing in the snow on a mountian, nighttime, Christmas lights, moon\",\n \"trainset_idx\": 538536\n },\n {\n \"caption\": \"gandalf using the toilet\",\n \"trainset_idx\": 473941\n },\n {\n \"caption\": \"curious girl observing\",\n \"trainset_idx\": 264543\n },\n {\n \"caption\": \"photograph of the face of an old mean wizard with a leathery skin, green glowing eyes\",\n \"trainset_idx\": 401230\n },\n {\n \"caption\": \"last human on earth\",\n \"trainset_idx\": 509977\n },\n {\n \"caption\": \"black cat astronaut, in space, photo\",\n \"trainset_idx\": 369330\n },\n {\n \"caption\": \"a painting of two men sitting at a table eating pizza, a surrealist painting, inspired by Georges de La Tour, stanis\\u0142aw szukalski + moebius, martin ansin, michael cheval, closeup at the food, from china, napoleon,surrealism, roger dean and alexander jansson, by joseph binder\",\n \"trainset_idx\": 535638\n },\n {\n \"caption\": \"Woman getting her feet tickled\",\n \"trainset_idx\": 91555\n },\n {\n \"caption\": \"A galaxy captured in a translucent sphere, floating in the pool\",\n \"trainset_idx\": 428982\n },\n {\n \"caption\": \"holographic display showing a clock, mechanical steampunk scene, futuristic portal, majestic oil painting by alena aenami, 8k uhd\",\n \"trainset_idx\": 581324\n },\n {\n \"caption\": \"a woman on a boat at night, petting a dragon, paper cutout\",\n \"trainset_idx\": 204761\n },\n {\n \"caption\": \"womaninwomenshistorymonth heroic thisdayindrowning boats lds lds, k\\u00e4the kollwitz\",\n \"trainset_idx\": 407533\n },\n {\n \"caption\": \"photo of an old tree in the snow, there is an old swing hanging on a branch, a ghostly figure is on the swing. Photo is in a dramtic angle and close up on the swing with ghostly figure\",\n \"trainset_idx\": 511464\n },\n {\n \"caption\": \"Shrek fighting goku\",\n \"trainset_idx\": 322387\n },\n {\n \"caption\": \"Scene by Yonesaburo Tsukiji\",\n \"trainset_idx\": 556421\n },\n {\n \"caption\": \"porcelain doll\",\n \"trainset_idx\": 49216\n },\n {\n \"caption\": \"a movie still of joe biden wearing a pink pajama\",\n \"trainset_idx\": 474049\n },\n {\n \"caption\": \"Images inspired by Kate Bush\",\n \"trainset_idx\": 470218\n },\n {\n \"caption\": \"jdm 1990s custom body kit concept car\",\n \"trainset_idx\": 91208\n },\n {\n \"caption\": \"realistic photo of a giant tortoise walking through time square, with people watching it and taking photo of it with their phones, professional photography, 8k, vintage\",\n \"trainset_idx\": 355363\n },\n {\n \"caption\": \"Highly Detailed Digital Art of a male goblin in an onsen, there is steam from the hot water and vague hints of blue magical energy throughout the scene, the goblin has a content look on his face with his eyes closed, natural lighting, trending on artstation, award-winning art, fantasy art, very very very very very beautiful digital painting\",\n \"trainset_idx\": 395421\n },\n {\n \"caption\": \"massive dreamlike garden, plants, massive pond, reflective water, green, happy, award winning photograph\",\n \"trainset_idx\": 316649\n },\n {\n \"caption\": \"anime still of jesus christ\",\n \"trainset_idx\": 470560\n },\n {\n \"caption\": \"Power Girl plays piano badly, watercolors, HD\",\n \"trainset_idx\": 411850\n },\n {\n \"caption\": \"Super Mario wearing outfit from DBZ\",\n \"trainset_idx\": 82611\n },\n {\n \"caption\": \"portrait of a cat from 1990s tv series inspired by tarkovsky in the style of gaspar noe, dvd screen grab, Spirited Away style, dramatic lighting, 8k, trending on artstation\",\n \"trainset_idx\": 136371\n },\n {\n \"caption\": \"Cute girl in skirt\",\n \"trainset_idx\": 533656\n },\n {\n \"caption\": \"Old man modern rococo style, flamboyant sunglasses, piercings, septum nose, neck tattoos, abstract suit dress pastel colors, eccentric white hair, nikon photography, 200mm, HD\",\n \"trainset_idx\": 285071\n },\n {\n \"caption\": \"a beatiful tiger in the snow\",\n \"trainset_idx\": 103369\n },\n {\n \"caption\": \"The Kraken\",\n \"trainset_idx\": 69409\n },\n {\n \"caption\": \"nature vs human nature, surreal, UHD, HDR, 8K, hyper details, rich colors, photograph\",\n \"trainset_idx\": 153982\n },\n {\n \"caption\": \"An round spaceship in Star wars the clone wars series style, Doug Chiang concept art\",\n \"trainset_idx\": 175882\n },\n {\n \"caption\": \"a son of lula and bolsonaro\",\n \"trainset_idx\": 414890\n },\n {\n \"caption\": \"Modern baroque by Dorothy draper\",\n \"trainset_idx\": 352372\n },\n {\n \"caption\": \"blue iced eyes surrounded by fantasy fire in various colored, 4k realistic photo\",\n \"trainset_idx\": 179241\n },\n {\n \"caption\": \"photo of stone sculptures in the city ,art gallery ,henry moore,flooded sculpture,splashing misty mud rocks,panorama,city buildings,\",\n \"trainset_idx\": 130092\n },\n {\n \"caption\": \"angelarium, illithid, cthulhu, white marble and gold, an ultrafine detailed painting by Bastien Lecouffe-Deharme, cgsociety contest winner, fantasy art, lovecraftian, cosmic horror, biomorphic\",\n \"trainset_idx\": 9031\n },\n {\n \"caption\": \"a slim 6yo Japanese girl in tutu ballet dancing, stocking, from behind, nikon d5\",\n \"trainset_idx\": 321749\n },\n {\n \"caption\": \"A forest made of trees the shape of question marks\",\n \"trainset_idx\": 46965\n },\n {\n \"caption\": \"The Joker, real portrait, ID photo, elegant highly detailed digital painting artstation pixiv\",\n \"trainset_idx\": 382499\n },\n {\n \"caption\": \"A cute Kawaii tiny hyper realistic baby jaguar, wearing hip hop clothes, city background. wide angle full body, 8k, Cinematography, photorealistic,epic composition Unreal Engine,Cinematic, Color Grading, Portrait Photography,Ultra-Wide Angle, Depth of Field, hyper detailed\",\n \"trainset_idx\": 417041\n },\n {\n \"caption\": \"A megaphone made of semolina, background a magnificient galaxy\",\n \"trainset_idx\": 4650\n },\n {\n \"caption\": \"Beautiful woman brunette beauty close-up portrait on gray background\",\n \"trainset_idx\": 97728\n },\n {\n \"caption\": \"enchanted overgrown fantasy tree house, on a hilltop with waterfalls, sunset with dramatic clouds. Hyper realistic photo. Vivid colors.\",\n \"trainset_idx\": 207199\n },\n {\n \"caption\": \"a super cute sloth with big cute eyes, sitting in a classroom writing in his notebook, Pixar character, octane render, Cute\",\n \"trainset_idx\": 176378\n },\n {\n \"caption\": \"android 18 vestida de puta\",\n \"trainset_idx\": 389932\n },\n {\n \"caption\": \"feral rat, six legs, bat wings for ears, fantasy art\",\n \"trainset_idx\": 158625\n },\n {\n \"caption\": \"a recruitment consultant, sitting before a screen full of analysis diagram, carrying mobile device, fuji film style, like moss in wandering earth\",\n \"trainset_idx\": 543074\n },\n {\n \"caption\": \"Award winning Professional Photo of a kitten balancing on a red suitcase that is floating in the middle of the ocean at sunset there are suitcases and airplane debris floating in the water in background, taken on a Hasselblad 500cm\",\n \"trainset_idx\": 423269\n },\n {\n \"caption\": \"Labyrinth Champion: This red and white creature card is a fierce combatant with double strike and protection from blue. Its artwork depicts the Champion fighting its way through a winding, fiery labyrinth.\",\n \"trainset_idx\": 372997\n },\n {\n \"caption\": \"By artgerm\",\n \"trainset_idx\": 96430\n },\n {\n \"caption\": \"Hyper realistic picture of a Beautiful blond female secretary trying to seduce her boss, Victoria's sectret\",\n \"trainset_idx\": 19260\n },\n {\n \"caption\": \"Hands of an old human\",\n \"trainset_idx\": 421753\n },\n {\n \"caption\": \"Giant caterpillar riding a bicycle\",\n \"trainset_idx\": 417662\n },\n {\n \"caption\": \"Imagine a drop of ink spreading slowly in a glass of water. Describe the patterns that emerge as the ink diffuses into the water, and how these patterns change over time.\",\n \"trainset_idx\": 397599\n },\n {\n \"caption\": \"action figure of a cute Pikachu driving a kart, professional photography, 8k\",\n \"trainset_idx\": 547451\n },\n {\n \"caption\": \"bisque doll\",\n \"trainset_idx\": 176120\n },\n {\n \"caption\": \"a photo of planet earth\",\n \"trainset_idx\": 550434\n },\n {\n \"caption\": \"cute blonde girl, standing on a bridge at a beach\",\n \"trainset_idx\": 440881\n },\n {\n \"caption\": \"an image of a clouds scene with a clouds over sea , pixel art by Paul Kelpe, pixiv, clouds art, #pixelart, copic color palette, 2d game art, concept art\",\n \"trainset_idx\": 239240\n },\n {\n \"caption\": \"film still of Sandra Bullock as Frodo, Lord of the Rings, intricate details, hyperrealism, realistic, hyperdetailed, soft cinematic light, muted colors, film grain\",\n \"trainset_idx\": 431944\n },\n {\n \"caption\": \"A set of emerald bracelets in green in a display box at the auction, uplight, very realist, very detailed, highest resolution, hyper realistic\",\n \"trainset_idx\": 503844\n },\n {\n \"caption\": \"ben shapiro as a hobbit\",\n \"trainset_idx\": 337202\n },\n {\n \"caption\": \"A coloring book page of a lotus flower, white clear background, no pens or pencils, no color\",\n \"trainset_idx\": 437825\n },\n {\n \"caption\": \"niels spinomithbkk gidak \\\". grandfather, Anna ancher, gustav royo ingle grandmother seated recalling hardworking famine, Katherine kollwitz\",\n \"trainset_idx\": 37725\n },\n {\n \"caption\": \"Jessica Nigri\",\n \"trainset_idx\": 521953\n },\n {\n \"caption\": \"a Ferrari car that is made out of wood\",\n \"trainset_idx\": 553141\n },\n {\n \"caption\": \"a rogue, dark fantasy style, whole body, on night, unreal engine 5, sharp details\",\n \"trainset_idx\": 113899\n },\n {\n \"caption\": \"friendly robot, tiny, circular face display\",\n \"trainset_idx\": 47946\n },\n {\n \"caption\": \"a cat and a gorilla and a car ,mg zt,headlights\",\n \"trainset_idx\": 530012\n },\n {\n \"caption\": \"Teacher in the form of a blackboard speaking and throwing chalk\",\n \"trainset_idx\": 162585\n },\n {\n \"caption\": \", fantasy, pastel, absurdist, photo, refined, wax character\",\n \"trainset_idx\": 65639\n },\n {\n \"caption\": \"one piece anime cover\",\n \"trainset_idx\": 261855\n },\n {\n \"caption\": \"young Lee Young Ae, dressed as a 19th century hungarian peasant woman with two black hair braids, in 19th century a hungarian village, oil canvas portrait by Munk\\u00e1csy, Ferenczy, Rutkowski, Marc Simonetti, Waterhouse very atmospheric, natural light\",\n \"trainset_idx\": 351868\n },\n {\n \"caption\": \"A marbella landscape\",\n \"trainset_idx\": 53596\n },\n {\n \"caption\": \"creepy 1980s dvd movie scene, unsettling, gritty intense scene, detailed eyes, skin details, retro disney aesthetic, sacred geometry, intricate design , masterpiece, best quality, high quality, extremely detailed CG unity 8k wallpaper, sharp focus, cgsociety, trending on artstation, award winning\",\n \"trainset_idx\": 155315\n },\n {\n \"caption\": \"post card featuring a waterfall with the text \\\"wish you where here\\\"\",\n \"trainset_idx\": 187007\n },\n {\n \"caption\": \"a man lifting weights at a gym;\",\n \"trainset_idx\": 555141\n },\n {\n \"caption\": \"pink sheer harem pants, bare midriff, short red west, sheer pink veil, blonde hair, topknot, I dream of genie\",\n \"trainset_idx\": 357846\n },\n {\n \"caption\": \"A DOWN SYNDROME RICHARD STALLMAN\",\n \"trainset_idx\": 254163\n },\n {\n \"caption\": \", fantasy, pastel, absurdist, photo, amphibian character, textile\",\n \"trainset_idx\": 51712\n },\n {\n \"caption\": \"digital art portrait of a beautiful character, nebula and constellations, sunrise sky, curly bob haircut, looking away, face blush and freckles, clouds, dreamy, pop surrealism, high quality, pastel colors, detailed, by loish van baarle, intricate\",\n \"trainset_idx\": 242460\n },\n {\n \"caption\": \"The evil villain of America\",\n \"trainset_idx\": 270656\n },\n {\n \"caption\": \"A a photo of a chromatic snake in a open white room, studio lighting, photoshoot, venatte effect, chromatic snake skin, shiny, white room\",\n \"trainset_idx\": 212110\n },\n {\n \"caption\": \"a data analyst passionate about creating outstanding\",\n \"trainset_idx\": 42805\n },\n {\n \"caption\": \"A waifu.\",\n \"trainset_idx\": 580741\n },\n {\n \"caption\": \"A car workshop with teddybears, inside is a model of a lotus esprit, sci fi,star trek\",\n \"trainset_idx\": 135364\n },\n {\n \"caption\": \"sign that says your cringe\",\n \"trainset_idx\": 500521\n },\n {\n \"caption\": \"The creation of Neural net architecture in a workshop by ailen workers\",\n \"trainset_idx\": 360729\n },\n {\n \"caption\": \"Coyote Ugly movie\",\n \"trainset_idx\": 411178\n },\n {\n \"caption\": \"an elderly woman, best quality:1.05, tall:1.2, long legs, thin figure:0.1, solo, white tank top, white jeans with, blue eyes, blue hair, half bald, sidecut hair:1.05, short mohawk haircut, punk style, street, upper body, detailed face, beautiful face\",\n \"trainset_idx\": 295673\n },\n {\n \"caption\": \"an angel playing football against a horned devil\",\n \"trainset_idx\": 559540\n },\n {\n \"caption\": \"farmers working on field, formation \\ud83d\\ude18\\ud83d\\ude18\\ud83d\\ude18\\ud83d\\ude18 trylaundry artforsale melancholy peasant genre ,Jules Bastien-Lepage\",\n \"trainset_idx\": 264179\n },\n {\n \"caption\": \"cool humanoid cat riding a steampunk motorcycle, realistic digital art print by jmw turner\",\n \"trainset_idx\": 163236\n },\n {\n \"caption\": \"Tucker Carlson pointing and laughing at a sad dog locked in a car on a sunny day\",\n \"trainset_idx\": 512730\n },\n {\n \"caption\": \"**a portrait of a bitcoin in the sun rising as a sunset over the blue ocean hyper-realistic, ultra-detailed, photography, hyper-realistic, photo-realistic, ultra-photo-realistic, super-detailed, intricate details, 8K, surround lighting, HDR\",\n \"trainset_idx\": 214686\n },\n {\n \"caption\": \"photo of military stealth battle by bmw, breathtaking, fighter jet, f35, f16, military, hamvee, gray matte\",\n \"trainset_idx\": 516323\n },\n {\n \"caption\": \"A typewriter logo which is readable and shows dept and good understanding of line weights\",\n \"trainset_idx\": 550035\n },\n {\n \"caption\": \", fantasy, pastel, absurdist, photo, Wes Anderson, rodent characters,\",\n \"trainset_idx\": 445633\n },\n {\n \"caption\": \"british army driving a car in 1921 in kerala forest road, tribe members attacking, action scene, an epic fantasy, dramatic lighting, cinematic, establishing shot, extremely high detail, photorealistic, cinematic lighting, artstation, matte painting by christopher nolan, horizon forbidden west\",\n \"trainset_idx\": 59589\n },\n {\n \"caption\": \"photo of table with a teapot on it\",\n \"trainset_idx\": 448172\n },\n {\n \"caption\": \"Extreme Realistic Macro Photography of an Orange Dragon's Eye, Hyper Detailed, 8k, Epic Frame\",\n \"trainset_idx\": 506371\n },\n {\n \"caption\": \"An abandoned store with a sign that says \\\"Official Apple Store\\\"\",\n \"trainset_idx\": 212474\n },\n {\n \"caption\": \"Card Magic the gathering style of tom whalen A Victorian man speaks into a tin-can-and-string telephone that a Victorian woman listens to while smiling\",\n \"trainset_idx\": 409554\n },\n {\n \"caption\": \"Vikings celebrate in a mead hall at night\",\n \"trainset_idx\": 112180\n },\n {\n \"caption\": \"russian teen girl\",\n \"trainset_idx\": 4320\n },\n {\n \"caption\": \"realistic photo of chino kafuu from is the order a rabbit, cosplay\",\n \"trainset_idx\": 296792\n },\n {\n \"caption\": \"man holding two urumi, one on each head\",\n \"trainset_idx\": 48120\n },\n {\n \"caption\": \"a meat artistic within the context of the digital chaotic revolution,Pinky \\\"ACAB\\\" . Consider incorporating elements of technology, rebellion, and activism in your artwork to convey the message of resistance against oppressive power structures.\",\n \"trainset_idx\": 287065\n },\n {\n \"caption\": \"Budweiser beer can pouring in a Bitcoin logo Glass, steampunk style\",\n \"trainset_idx\": 7782\n },\n {\n \"caption\": \"A purple monkey doing a card trick, cartoon, vector graphic\",\n \"trainset_idx\": 19304\n },\n {\n \"caption\": \"A steampunk octopus playing the drums on a beach\",\n \"trainset_idx\": 153600\n },\n {\n \"caption\": \"An elemental being flaming from cracks in its torso and lava flowing from its legs, a crystalline object forms its head, and rock spires as its arms, mystical, enchanted\",\n \"trainset_idx\": 432268\n },\n {\n \"caption\": \"the last time i ever wear those shoes again\",\n \"trainset_idx\": 25516\n },\n {\n \"caption\": \"A photo of a teenage girl wearing t-shirt, tan nylons and white sneakers, standing, facing the camera, whole body is visible.\",\n \"trainset_idx\": 418650\n },\n {\n \"caption\": \"a strong teenaged woman holeing a white glowing rock anime at night.\",\n \"trainset_idx\": 319178\n },\n {\n \"caption\": \"androide 18 de dbz usando lenceria\",\n \"trainset_idx\": 45230\n },\n {\n \"caption\": \"art by Alfons Mucha, whole body image of 20 year-old Taylor Schilling as Piper Chapman as a naturist in prison, HD 4k, sharp detail, photo-realistic accurate face and features\",\n \"trainset_idx\": 325864\n },\n {\n \"caption\": \"|cyberpunk setting| |tattooed male model| |blue hair| |dark eyes|\",\n \"trainset_idx\": 443743\n },\n {\n \"caption\": \"screaming happy old male screaming shaman standing in road of forest covered in symmetrycal crystal covered by windy splash of strings of light in a dark sky covered by stars, splash of glowing water, painting, aligned, dramatic light, by andrews esao amorsolo\",\n \"trainset_idx\": 510212\n },\n {\n \"caption\": \"among us sus\",\n \"trainset_idx\": 335117\n },\n {\n \"caption\": \"drawing of a beautiful blonde woman from the waist up, posing in front of waves, beach, blue ocean, tropical vibes, sunny, sun rays. \\ud83c\\udf51\",\n \"trainset_idx\": 416921\n },\n {\n \"caption\": \"A highly detailed portrait of Freddy Krueger wearing a Nike streetwear outfit, masterpiece, absurdres, highres, featured on ArtStation\",\n \"trainset_idx\": 132112\n },\n {\n \"caption\": \"Kakkashi hatake smoking a dobbie\",\n \"trainset_idx\": 425654\n },\n {\n \"caption\": \"upside down photo in a gargantuan cavern lit with warm light updide down standing lanterns, moss, farns, ivy, clover, stone, and wooden planks on the surfaces upside down wall bars\",\n \"trainset_idx\": 191588\n },\n {\n \"caption\": \"Albino King Elric of Melnibone holding the black runesword Stormbringer painted by John William Waterhouse\",\n \"trainset_idx\": 428929\n },\n {\n \"caption\": \"Muslim Pope Francis, Muslim, Muslim, Realistic, 4k\",\n \"trainset_idx\": 550061\n },\n {\n \"caption\": \"A side view of a city on a hill far away and a butterfly flying as the main object in cinematic style, sky, shining butterfly, city on a hill, blue buttefly\",\n \"trainset_idx\": 187674\n },\n {\n \"caption\": \"A small mushroom warrior with a shield and spear, cartoon, soft colours, cute\",\n \"trainset_idx\": 144695\n },\n {\n \"caption\": \"Cute grey cat, digital oil painting by Caspar David Friedrich\",\n \"trainset_idx\": 508259\n },\n {\n \"caption\": \", fantasy, pastel, absurdist, photo, amphibian character, people\",\n \"trainset_idx\": 439597\n },\n {\n \"caption\": \"peppa pig portrait by botticelli, oil painting, paint texture\",\n \"trainset_idx\": 293959\n },\n {\n \"caption\": \"m\\u00e5ns greb\\u00e4ck\",\n \"trainset_idx\": 105729\n },\n {\n \"caption\": \"woman on her knees looking up begging, mascara running\",\n \"trainset_idx\": 29005\n },\n {\n \"caption\": \"mario wearing sunglasses\",\n \"trainset_idx\": 232270\n },\n {\n \"caption\": \"digital illustration of a desolated medieval town, foggy day, an old jester dancing, high detail, 4k\",\n \"trainset_idx\": 307556\n },\n {\n \"caption\": \"a close up art surrealism,by art shusei nagaoka and by artist alan bean of the heavenly catholic demonic leader cyborg,cyberpunk style,art surrealism,by art shusei nagaoka and by artist alan bean and katsuhiro otomo and yves tanguy, james stokoe,king crimson, avatar image, large view\",\n \"trainset_idx\": 531785\n },\n {\n \"caption\": \"Mature asian woman\",\n \"trainset_idx\": 259503\n },\n {\n \"caption\": \"A fire breathing dragon\",\n \"trainset_idx\": 496383\n },\n {\n \"caption\": \"Transformers\",\n \"trainset_idx\": 46730\n },\n {\n \"caption\": \"realistic three eyes dnd human girl warlock with tentacle-shaped magic spells\",\n \"trainset_idx\": 245995\n },\n {\n \"caption\": \"women blow job\",\n \"trainset_idx\": 441694\n },\n {\n \"caption\": \"a photograph of a badass ork eating dinner\",\n \"trainset_idx\": 223582\n },\n {\n \"caption\": \"the statue of David made of bacon\",\n \"trainset_idx\": 31298\n },\n {\n \"caption\": \"a portrait of a woman, undercut hair, maid, freckles, apron, amazing body, pronounced feminine feature, legwear suspenders, kitchen, close up, skin covered by flour\",\n \"trainset_idx\": 478908\n },\n {\n \"caption\": \"art by Alfons Mucha, stained glass motif, whole body image of 20 year-old Barbara Eden with ash blond hair as a naturist in the desert sitting next to a magic lamp, HD 4K, sharp detail, photo-realistic accurate face and features, cinematic lighting\",\n \"trainset_idx\": 58543\n },\n {\n \"caption\": \"high quality color portrait photograph of young beautiful Elizabeth Olsen with a black dog,sharp focus,cuddling,highly detailed,stunningly beautiful face,award winning photo\",\n \"trainset_idx\": 237463\n },\n {\n \"caption\": \"insanely detailed portrait, dartH vader, shiny, extremely intricate, high res, 8k, award winning\",\n \"trainset_idx\": 42666\n },\n {\n \"caption\": \"Photograph of a black opal\",\n \"trainset_idx\": 42321\n },\n {\n \"caption\": \"backrooms, liminal space\",\n \"trainset_idx\": 169332\n },\n {\n \"caption\": \"benjamin netanyahu,anime style\",\n \"trainset_idx\": 302955\n },\n {\n \"caption\": \"Insane crazy cat in a mushroom fantasy world, black and white illustration , fisheye view\",\n \"trainset_idx\": 526661\n },\n {\n \"caption\": \"red and green eagle\",\n \"trainset_idx\": 561561\n },\n {\n \"caption\": \"black text reading HELLO on a white background\",\n \"trainset_idx\": 376100\n },\n {\n \"caption\": \"anubis riding on a motorcycle\",\n \"trainset_idx\": 490240\n },\n {\n \"caption\": \"Product photo, Realistic, cinematic light, most delicious huge burger with meat made from space squid, dark background\",\n \"trainset_idx\": 482778\n },\n {\n \"caption\": \"Antique, warm hues, short, chubby, girl teen, pee, bare, smooth, 8K, HD, octane render, magical energy, sharp overhead cinematic lighting, beautiful 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\"caption\": \"a pink umbrella on a beach\",\n \"trainset_idx\": 516947\n },\n {\n \"caption\": \"beautiful woman in beautiful room\",\n \"trainset_idx\": 522198\n },\n {\n \"caption\": \"Anime girl fishing planets in a space\",\n \"trainset_idx\": 203506\n },\n {\n \"caption\": \"Two beagle playing in the forest\",\n \"trainset_idx\": 574628\n },\n {\n \"caption\": \"A sign that says PICK A fireball\",\n \"trainset_idx\": 341040\n },\n {\n \"caption\": \"A cyborg woman\",\n \"trainset_idx\": 546915\n },\n {\n \"caption\": \"a candid upskirt of a woman at a shopping mall, low angle photograph, creepshot\",\n \"trainset_idx\": 301555\n },\n {\n \"caption\": \"Realistic professional photography of a tyrannosaurus rex in an office using a computer\",\n \"trainset_idx\": 536285\n },\n {\n \"caption\": \"Petite woman with Short brown hair wearing t-shirt and shorts doing yoga\",\n \"trainset_idx\": 487333\n },\n {\n \"caption\": \"Intricate, light yellow and 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Very atmospheric, thunderstorm, raining, epic, beautiful lighting, enigmatic, detailed, beautiful blue and purple\",\n \"trainset_idx\": 222066\n },\n {\n \"caption\": \"the eiffel tower made out a giant tree\",\n \"trainset_idx\": 86075\n },\n {\n \"caption\": \"a Japanese girl in tutu dancing on her 6th birthday party, stocking, long and slim legs, looking at the camera\",\n \"trainset_idx\": 60515\n },\n {\n \"caption\": \"highly detailed portrait of maisie williams\",\n \"trainset_idx\": 156173\n },\n {\n \"caption\": \"A stuffed bull with a white t-shirt with the words \\\"perdoname lobitx\\\", highly detailed\",\n \"trainset_idx\": 571931\n },\n {\n \"caption\": \"A very attractive and natural woman, sitting on a yoka mat, breathing, eye closed, no make up, intense satisfaction, she looks like she is intensely relaxed, yoga class, sunrise, 35mm, F1: 4\",\n \"trainset_idx\": 245540\n },\n {\n \"caption\": \"HD security camera captures the exact moment Yoda robbing a liquor store, trail cam footage\",\n \"trainset_idx\": 247795\n },\n {\n \"caption\": \"photography by Milton H Greene and Bert Stern, whole body photo portrait of Marylin Monroe as a naturist in the desert, HD 4K, sharp detail, photo-realistic accurate face and features, studio lighting\",\n \"trainset_idx\": 124687\n },\n {\n \"caption\": \"Sonic sits on a bench\",\n \"trainset_idx\": 486696\n },\n {\n \"caption\": \"tiny dragoness\",\n \"trainset_idx\": 421281\n },\n {\n \"caption\": \"A photo of the wizard of the mists, during his dream walking quest\",\n \"trainset_idx\": 177860\n },\n {\n \"caption\": \"Pink clothes. Anime. Underwater.\",\n \"trainset_idx\": 459848\n },\n {\n \"caption\": \"Giraffe Guitar player Giraffe on a swimming pool, 8k portrait, highly detailed, beautiful, masculine pose, cinematic, movie still, by Greg Rutkowski and Mandy Jurgens and diegokoi\",\n \"trainset_idx\": 164223\n },\n {\n \"caption\": \"Sonic\",\n \"trainset_idx\": 194039\n },\n {\n \"caption\": \"cute puppet worm\",\n \"trainset_idx\": 215798\n },\n {\n \"caption\": \"satanic fantasy realism illustration\",\n \"trainset_idx\": 366687\n },\n {\n \"caption\": \"michael jordan dunking in the air skycraper view from top helicopter city landscape nba basketball ball sports tv\",\n \"trainset_idx\": 270380\n },\n {\n \"caption\": \"cinematic still of a stainless steel robot dinosaur swimming in a pool\",\n \"trainset_idx\": 233416\n },\n {\n \"caption\": \"a game box cover mice simulator\",\n \"trainset_idx\": 23920\n },\n {\n \"caption\": \"gundam astraea and optimus prime having fun together, sci-fi,super detailed, high resolution, transformers\",\n \"trainset_idx\": 101661\n },\n {\n \"caption\": \"full-bodied portrait, Cute and adorable cartoon white rabbit baby wearing a gold jaguar print hoodie and silver sunglasses, fantasy, dreamlike, surrealism, super cute, trending on artstation\",\n \"trainset_idx\": 15717\n },\n {\n \"caption\": \"whole body image of 20 year-old Molly Ringwald as a naturist in detention at school\",\n \"trainset_idx\": 223553\n },\n {\n \"caption\": \"girl little showing\",\n \"trainset_idx\": 196784\n },\n {\n \"caption\": \"Eva Karera, Soul Calibur\",\n \"trainset_idx\": 495486\n },\n {\n \"caption\": \"MICHAEL C HALL AS DEXTER MORGAN ACTION FIGURE\",\n \"trainset_idx\": 90264\n },\n {\n \"caption\": \"skinny elf girl with white hair in a beautiful full length dress, 4k, highly detailed, 3d render, realism\",\n \"trainset_idx\": 69434\n },\n {\n \"caption\": \"a recruitment consultant, sitting before a screen full of analysis diagram, carrying mobile device, fuji film style\",\n \"trainset_idx\": 115983\n },\n {\n \"caption\": \"Cat girl mechanic\",\n \"trainset_idx\": 408700\n },\n {\n \"caption\": \"art poster, the essence of charcoal painting, a jedi silhouette with a lightsaber on a mountain landscape by J. M. W. Turner and bob ross, charcoal painting\",\n \"trainset_idx\": 155185\n },\n {\n \"caption\": \"a beautiful female in spacesuit and helmet in a landscape, in the style of pulp comics, realistic forms, david michael bowers, go nagai, silver and bronze, charming illustrations, rtx on\",\n \"trainset_idx\": 106689\n },\n {\n \"caption\": \"a depiction of Zeus as an overweight man.\",\n \"trainset_idx\": 286623\n },\n {\n \"caption\": \"25 year old Nana Visitor as Kira Nerys the Betazoid officer from Star Trek Deep Space Nine\",\n \"trainset_idx\": 427330\n },\n {\n \"caption\": \"A digital painting of a fantasy witch\",\n \"trainset_idx\": 450195\n },\n {\n \"caption\": \"blue haired anime girl with antennas\",\n \"trainset_idx\": 382591\n },\n {\n \"caption\": \"weirdcore\",\n \"trainset_idx\": 115883\n },\n {\n \"caption\": \"A cowgirl at farm\",\n \"trainset_idx\": 180584\n },\n {\n \"caption\": \"anthropomorphic mice living in a large tree trunk with doors, steps, windows, Victorian clothing & decor, watercolour and ink\",\n \"trainset_idx\": 78960\n },\n {\n \"caption\": \"A Kludde. A mythical monstrous black furry nocturnal dog with bear claws, green glistening scaled wings and glowing crimson eyes. Several heavy chains hang from its body and ankles.\",\n \"trainset_idx\": 401932\n },\n {\n \"caption\": \"Nicolas Cage as Jack Dawson from Titanic\",\n \"trainset_idx\": 205080\n },\n {\n \"caption\": \"A bird stands on a branch, soft contrast, full composition\",\n \"trainset_idx\": 11101\n },\n {\n \"caption\": \"A futuristic cyborg waitress\",\n \"trainset_idx\": 144282\n },\n {\n \"caption\": \"Jesus Christ, league of legends champion\",\n \"trainset_idx\": 56467\n },\n {\n \"caption\": \"ava addams usando lencer\\u00eda de vestido de novia\",\n \"trainset_idx\": 414180\n },\n {\n \"caption\": \"space asthetic, retro\",\n \"trainset_idx\": 334646\n },\n {\n \"caption\": \"A little boy is creating a futuristic canvas. 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{\n \"caption\": \"A photo of a beautiful Indian woman, 30 years old, HD, analog style, female princess, bathing, full length, symmetrical eyes, photorealistic, HD in high detail realistic 4k, sharp photo, canon lens 100mm f1.8\",\n \"trainset_idx\": 195152\n },\n {\n \"caption\": \"Photo of a hand\",\n \"trainset_idx\": 372534\n },\n {\n \"caption\": \"A surreal landscape painting of the Sahara Desert made of cotton candy\",\n \"trainset_idx\": 556814\n },\n {\n \"caption\": \"sunflowers in a glass mason jar\",\n \"trainset_idx\": 176480\n },\n {\n \"caption\": \"teen boy walking through rubble towards the camera, post apocalyptic, cinematic, concept art\",\n \"trainset_idx\": 415967\n },\n {\n \"caption\": \"realistic photo of 6 year old girl Homura Akemi, cosplay, full body, masterpiece, HQ, 4k\",\n \"trainset_idx\": 566175\n },\n {\n \"caption\": \"ishaq dar, attack on titan\",\n \"trainset_idx\": 559518\n },\n {\n \"caption\": \"portrait of a male goblin, D&D, fantasy, intricate, elegant highly detailed digital painting, artstation hq, concept art, sharp focus, illustration\",\n \"trainset_idx\": 395228\n },\n {\n \"caption\": \"Black Friday Scene Comparison\",\n \"trainset_idx\": 322013\n },\n {\n \"caption\": \"Andrew Huang riding a pink fluffy unicorn\",\n \"trainset_idx\": 185663\n },\n {\n \"caption\": \"anime urban samurai fisherman maked hero\",\n \"trainset_idx\": 503356\n },\n {\n \"caption\": \"beautiful OL woman on the burning street\",\n \"trainset_idx\": 234438\n },\n {\n \"caption\": \"Emilia Clarke in a Cell, High Resolution, High Quality, Many Details, Realistic, Real Life\",\n \"trainset_idx\": 158659\n },\n {\n \"caption\": \"3D clipart \\\"V.S.\\\" \\\"VS\\\" vs 3d letter art, plain white background, colored 3d \\\"VS\\\"\",\n \"trainset_idx\": 508676\n },\n {\n \"caption\": \"A huge oak, detailed fantasy fairytale illustration, magenta, red, orange, extremely detailed illustration\",\n \"trainset_idx\": 147931\n },\n {\n \"caption\": \"Photograph of Darth Vader standing in a kitchen wearing a flowery apron, he is wearing oven mits and holding a cooking sheet of freshly baked cookies\",\n \"trainset_idx\": 134915\n },\n {\n \"caption\": \"the city of santiago de chile under a red sunset sky\",\n \"trainset_idx\": 357650\n },\n {\n \"caption\": \"head with no face portrait hyper realistic\",\n \"trainset_idx\": 21889\n },\n {\n \"caption\": \"zentai woman wearing tight zentai body that covers eyes and covers face and covers head\",\n \"trainset_idx\": 480896\n },\n {\n \"caption\": \"orange cat, 8k photo\",\n \"trainset_idx\": 461514\n },\n {\n \"caption\": \"woman with short black hair, ryuko matoi,kill la kill, waterpaint, insanely detailed, whole body in shot\",\n \"trainset_idx\": 280797\n },\n {\n \"caption\": \"a cute girl, wavy hairstyle, medium hair, carmine-colored hair\",\n \"trainset_idx\": 275942\n },\n {\n \"caption\": \"provocative mixed female african asian white\",\n \"trainset_idx\": 364924\n },\n {\n \"caption\": \"dwarf in armor\",\n \"trainset_idx\": 174572\n },\n {\n \"caption\": \"school girl old man blow job\",\n \"trainset_idx\": 17657\n },\n {\n \"caption\": \"an image of a blue tiger, in blade runner, at the sea, professional photography\",\n \"trainset_idx\": 217216\n },\n {\n \"caption\": \"art by Patrick Woodroffe, stained glass motif, whole body portrait of 20 year-old Jennifer Connelly as a naturist at the Eiffel Tower in Paris, HD 4K, sharp detail, photo-realistic accurate face and features\",\n \"trainset_idx\": 191745\n },\n {\n \"caption\": \"A photo of a road sign reading \\\"EGGS AHEAD\\\"\",\n \"trainset_idx\": 59330\n },\n {\n \"caption\": \"Psychedelic, distorsion, inferno, oscure, night, dark, spiral, confusion, mountain cold, background, pixar style, 2 d illustration, concept art, behance, artstation, 4 k, 8 k\",\n \"trainset_idx\": 318619\n },\n {\n \"caption\": \"photo of young woman jumping into the sea\",\n \"trainset_idx\": 223434\n },\n {\n \"caption\": \"picture of a human iris\",\n \"trainset_idx\": 495578\n },\n {\n \"caption\": \"woodlands watercolor\",\n \"trainset_idx\": 488255\n },\n {\n \"caption\": \"A sticker of a cat head, rgb, white contour, solid background\",\n \"trainset_idx\": 249205\n },\n {\n \"caption\": \"a girl with short blue hair and blue eyes is sitting on a cloud, anime style, light effect, anime style hyper detailed, illustration, bloody, intricate, elegant, digital painting, artstation, smooth, sharp focus, art by artgerm and greg rutkowski and alphonse mucha\",\n \"trainset_idx\": 319511\n },\n {\n \"caption\": \"film still from romantic beautiful 80s dark fantasy movie,\",\n \"trainset_idx\": 153734\n },\n {\n \"caption\": \"A close up portrait of Beautiful expensive Taylor Swift having a laugh\",\n \"trainset_idx\": 199854\n },\n {\n \"caption\": \"8k uhd photograph of a masterpiece dichroic glass sculpture on a pedestal in an art museum\",\n \"trainset_idx\": 209492\n },\n {\n \"caption\": \"SNES, 16-bit video game graphics, waterfall in a secluded pond\",\n \"trainset_idx\": 353539\n },\n {\n \"caption\": \"pennywise the clown dressed as spiderman\",\n \"trainset_idx\": 238349\n },\n {\n \"caption\": \"dog riding skateboard\",\n \"trainset_idx\": 106238\n },\n {\n \"caption\": \"large woman torso\",\n \"trainset_idx\": 258677\n },\n {\n \"caption\": \"horse logo vector\",\n \"trainset_idx\": 295282\n },\n {\n \"caption\": \"Cat\",\n \"trainset_idx\": 204734\n },\n {\n \"caption\": \"burger with legs and feet\",\n \"trainset_idx\": 546251\n },\n {\n \"caption\": \"A gijinka black cat sushi chef\",\n \"trainset_idx\": 430319\n },\n {\n \"caption\": \"Elon Musk\",\n \"trainset_idx\": 418404\n },\n {\n \"caption\": \"A well furnished bedroom with two double beds a television and balcony\",\n \"trainset_idx\": 302827\n },\n {\n \"caption\": \"7 women friends mitte 30iger\",\n \"trainset_idx\": 105782\n },\n {\n \"caption\": \"digital painting by Greg Rutkowski, high quality, best example, medieval knight with a sword in the woods, 8K\",\n \"trainset_idx\": 531096\n },\n {\n \"caption\": \"Beautiful woman in tight leather suite\",\n \"trainset_idx\": 468358\n },\n {\n \"caption\": \"Portrait of an angry gandolf with lightning shooting from fingertips unreal engine character\",\n \"trainset_idx\": 119203\n },\n {\n \"caption\": \"Spongebob at mcdonalds\",\n \"trainset_idx\": 123704\n },\n {\n \"caption\": \"Cartoon artwork of colorful astronauts flying in space\",\n \"trainset_idx\": 485632\n },\n {\n \"caption\": \"provocative mixed female asian white\",\n \"trainset_idx\": 541733\n },\n {\n \"caption\": \"Dust devil tornado\",\n \"trainset_idx\": 491166\n },\n {\n \"caption\": \"a cat stalking prey\",\n \"trainset_idx\": 43496\n },\n {\n \"caption\": \"close-up of mushroom spore print art\",\n \"trainset_idx\": 49060\n },\n {\n \"caption\": \"a shadow of angel standing in the dark, heart on fire\",\n \"trainset_idx\": 410654\n },\n {\n \"caption\": \"**a portrait of a bitcoin on a mango hanging from a tree in the ocean hyper-realistic, ultra-detailed, photography, hyper-realistic, photo-realistic, ultra-photo-realistic, super-detailed, intricate details, 8K, surround lighting, HDR\",\n \"trainset_idx\": 139334\n },\n {\n \"caption\": \"pepe the frog\",\n \"trainset_idx\": 559887\n },\n {\n \"caption\": \"a painting of a jedi velociraptor wielding a lightsaber in his hand, artstation, 8k, high res, ultra detailed\",\n \"trainset_idx\": 225707\n },\n {\n \"caption\": \"iridescent, scales, h blues, textured, intricate, ornate, shadowed, pale muted colors, 3D, highly detailed, deco style, by Tim Burton, by Dale Chihuly, by Hsiao-Ron Cheng, by Cyril Rolando, by h. r. giger\",\n \"trainset_idx\": 558985\n },\n {\n \"caption\": \"picasso art of the universe\",\n \"trainset_idx\": 107841\n },\n {\n \"caption\": \"A dragon-dog flying above the beach\",\n \"trainset_idx\": 424801\n },\n {\n \"caption\": \"a building designed by OMA\",\n \"trainset_idx\": 412924\n },\n {\n \"caption\": \"a photorealistic 3d render of a moving GT car in a raining japanese cyberpunk street, motion blur, neon lights, , photorealistic,\",\n \"trainset_idx\": 183721\n },\n {\n \"caption\": \"text \\\"Hello World\\\" with funny clowns\",\n \"trainset_idx\": 286844\n },\n {\n \"caption\": \"A highly detailed matte painting of a red wolf with a mohawk logo graffitied on a brownstone wall by studio ghibli, makoto shinkai, by artgerm, by wlop, by greg rutkowski, volumetric lighting, octane render, 4 k resolution, trending on artstation, masterpiece\",\n \"trainset_idx\": 203183\n },\n {\n \"caption\": \"giant canyon clouds below epic photorealistic painting trending on artstation\",\n \"trainset_idx\": 13989\n },\n {\n \"caption\": \"Charizard from Pok\\u00e9mon\",\n \"trainset_idx\": 13360\n },\n {\n \"caption\": \"On the left side there are 5 acrobats, and on the right side there are swordsmen, the correct placement of subjects, ,\",\n \"trainset_idx\": 188247\n },\n {\n \"caption\": \"a beautiful short hair girl in white suit with a gun in her hands.looking back to viewer.\",\n \"trainset_idx\": 58062\n },\n {\n \"caption\": \"a mecha dollie\",\n \"trainset_idx\": 563800\n },\n {\n \"caption\": \"a data scientist\",\n \"trainset_idx\": 346581\n },\n {\n \"caption\": \"A colorful poster with text that says \\\"philo is a weird\\\"\",\n \"trainset_idx\": 484552\n },\n {\n \"caption\": \"Gothic cathedral in a stormy night\",\n \"trainset_idx\": 382368\n },\n {\n \"caption\": \"The shrewd businessman made a huge profit by cooperating with a big corporation\",\n \"trainset_idx\": 362725\n },\n {\n \"caption\": \"An African tribal warrior\",\n \"trainset_idx\": 458162\n },\n {\n \"caption\": \"8k, 80s movie still, a robocop t-800 robot mandalorian tokusatsu, daft punk helmet, cyberpunk, photography\",\n \"trainset_idx\": 183042\n },\n {\n \"caption\": \"Full body portrait of an feminine elegant android, in the ergo proxy style, beautiful anime shot, cyberpunk, highly detailed, neon backlight, complex scene, latex bodysuit, retrofuturistic weapon\",\n \"trainset_idx\": 414275\n },\n {\n \"caption\": \"cinematic still 2020 of a grey alien touching with its long finger scared woman's face, by ridley scott\",\n \"trainset_idx\": 475893\n },\n {\n \"caption\": \"super hero pulp cover\",\n \"trainset_idx\": 523304\n },\n {\n \"caption\": \"A logo for the cartoon Family Guy. Styled text that says \\\"Family Guy\\\".\",\n \"trainset_idx\": 281031\n },\n {\n \"caption\": \", fantasy, pastel, absurdist, photo, Wes Anderson, mushroom characters\",\n \"trainset_idx\": 162905\n },\n {\n \"caption\": \"a cartoon of an evil big boss holding bags of money, with small workers sitting around him\",\n \"trainset_idx\": 490334\n },\n {\n \"caption\": \"game of thrones\",\n \"trainset_idx\": 187551\n },\n {\n \"caption\": \"old school American tattoo vectorial illustration photography. Uhd, cinematic, filmic, Post-production, intricate textures, photorealistic, volumetric lighting,\",\n \"trainset_idx\": 112417\n },\n {\n \"caption\": \"Ragnarok twilight of the gods, mystical, fantastical, epically, magical\",\n \"trainset_idx\": 449038\n },\n {\n \"caption\": \"A man sitting by the ocean watching the sunset\",\n \"trainset_idx\": 78351\n },\n {\n \"caption\": \"a wizard boy black hair using fire magic\",\n \"trainset_idx\": 455029\n },\n {\n \"caption\": \"A photo realistic panda in a coffee shop, wearing a tailored suit, well groomed\",\n \"trainset_idx\": 535422\n },\n {\n \"caption\": \"a cinematic still of 3rd reich officers munching on burgers and fries in mcdonalds\",\n \"trainset_idx\": 56476\n },\n {\n \"caption\": \"A cute frog wearing a bowler hat\",\n \"trainset_idx\": 104506\n },\n {\n \"caption\": \"an asian girl with short brown hair, half portrait, green T-shirt, she is thinking and looking at the outside of the window, hifi, main color is dark\",\n \"trainset_idx\": 403076\n },\n {\n \"caption\": \"glowing neon colorful butterfly on the side walk of a busy street, macro photography, bokeh, rain, night time, vivid glow\",\n \"trainset_idx\": 40207\n },\n {\n \"caption\": \"art by Egon Schiele\",\n \"trainset_idx\": 474406\n },\n {\n \"caption\": \"a beautiful sunset with the letters \\\"TGIF\\\" written in the clouds\",\n \"trainset_idx\": 77397\n },\n {\n \"caption\": \"a portrait by rembrandt\",\n \"trainset_idx\": 558266\n },\n {\n \"caption\": \"photo of woman in science fiction futuristic costume, sci fi, computers\",\n \"trainset_idx\": 447082\n },\n {\n \"caption\": \"Attractive mixed woman; Asian; African;Latina; Indian; Mixed; Dress in Business casual theme\",\n \"trainset_idx\": 504269\n },\n {\n \"caption\": \"Fantasy Library, riso, comic, fantasy, pastel, , absurdism\",\n \"trainset_idx\": 327879\n },\n {\n \"caption\": \"A painting of a cute teenage woman with a long Undercut Hairstyle painted by Charlie Bowater\",\n \"trainset_idx\": 265578\n },\n {\n \"caption\": \"Aurora Borealis as drawn by Van Gogh\",\n \"trainset_idx\": 335504\n },\n {\n \"caption\": \"Steven Crowder DvD still from dark fantasy film 1982 conan the barbarian\",\n \"trainset_idx\": 424876\n },\n {\n \"caption\": \"not dressed Harry Potter with abs\",\n \"trainset_idx\": 537122\n },\n {\n \"caption\": \"Microscopic, Wall Decal in Rossdraws, Conrad Roset, John Berkey style Long Shot of Clockwork Metropolis, Dynamic, Cosmic, trending on deviantart, Polychromatic Colors\",\n \"trainset_idx\": 241766\n },\n {\n \"caption\": \"An up-close portrait of a beautiful hot blonde woman sitting on a picnic bench, sun hat, wearing a hoodie, sunset background, grass, nature\",\n \"trainset_idx\": 91727\n },\n {\n \"caption\": \"a sign that says rice crusher 3000 spring edition, valve promotional splash art, cyberpunk bee, inspired by Gabor Szikszai, bong, 8 k ultra realistic creature, cgnation, picking flowers, cgsociety masterpiece, blurry and glitchy, what a bumbler!, poggers, ebay website, - 8 k\",\n \"trainset_idx\": 389364\n },\n {\n \"caption\": \"Angry flaming baby screaming\",\n \"trainset_idx\": 250915\n },\n {\n \"caption\": \"a being from another planet, futuristic, arrived on earth in a spaceship, year 2030, realistic, detailed, award-winning studio photography, professional color grading, soft shadows, no contrast, sharp and clean focus, film photography, high resolution , 8K\",\n \"trainset_idx\": 158235\n },\n {\n \"caption\": \"Create a photorealistic image of Jenna Ortega, the 20-year-old actress, with a bold and edgy bangs hairstyle. The image should showcase Jenna's youthful features, including her bright eyes and radiant smile. The background should be simple yet elegant, emphasizing Jenna's natural beauty. Use high-quality textures and lighting to make the image look as realistic as possible.\",\n \"trainset_idx\": 481030\n },\n {\n \"caption\": \"portrait, waist up, a female medieval killer, ginger hair, green eyes, laying on bed, royal bedroom, come hither motion, best quality, dramatic light, night\",\n \"trainset_idx\": 571225\n },\n {\n \"caption\": \"photo of adorable little girls having fun in outdoor swimming pool on summer vacation, nikon D5\",\n \"trainset_idx\": 562226\n },\n {\n \"caption\": \"macron very angry arrest by the police on the ground hyper realistic\",\n \"trainset_idx\": 227074\n },\n {\n \"caption\": \"oil painting portrait sargent\",\n \"trainset_idx\": 136601\n },\n {\n \"caption\": \"Diamond, luxury, expensive\",\n \"trainset_idx\": 72155\n },\n {\n \"caption\": \"a crystal ball illuminating a dimly lit mystical room, smoke\",\n \"trainset_idx\": 93915\n },\n {\n \"caption\": \"photo of a bicycle, detailed, 8k uhd, dslr, high quality, film grain, Fujifilm XT3\",\n \"trainset_idx\": 276091\n },\n {\n \"caption\": \"Anime girl on a baroque style chair, long red hair, with piercing blue eyes, extremely light realistic skin and a dark background\",\n \"trainset_idx\": 423608\n },\n {\n \"caption\": \"Cloth off viking princess,white jelly falling from lips and face,sits open laps, down view camera,resident evil movie style, humidity torso, look around shoulder,dinamic pose, nice face, jelly leaks on laps,nice arms, nice eyes,highest detailed, masterpease,\",\n \"trainset_idx\": 64221\n },\n {\n \"caption\": \"Still life, tullips in the vase, close up shot, high details, 32k, by Peder Monsted, Jeremy Mann, Daniel F Gerhartz, Aaron Grffin ultra detailed\",\n \"trainset_idx\": 31668\n },\n {\n \"caption\": \"Webpage Screenshot, dentist website, CTA, Header, Social Icons\",\n \"trainset_idx\": 178519\n },\n {\n \"caption\": \"darth vader in a hogwarts class using a red wand\",\n \"trainset_idx\": 37594\n },\n {\n \"caption\": \"Cartoon of Brian Griffin\",\n \"trainset_idx\": 259150\n },\n {\n \"caption\": \"A realistic waterfall and 3 Ardeas.\",\n \"trainset_idx\": 484569\n },\n {\n \"caption\": \"A cartoon mouse chugging its 12th beer\",\n \"trainset_idx\": 342058\n },\n {\n \"caption\": \"a photo of striped cats in the crown of a palm tree\",\n \"trainset_idx\": 474447\n },\n {\n \"caption\": \"Girl. beautiful; goth; Nice body; Ebony;\",\n \"trainset_idx\": 13227\n },\n {\n \"caption\": \"Jutar, Xanthos's homeworld planet. concept art by Greg Rutkowski, pinterest, fantasy+futuristic+city, greeble, futuristic architecture plan\",\n \"trainset_idx\": 324529\n },\n {\n \"caption\": \"(best quality, masterpiece), Big waves on a stormy day\",\n \"trainset_idx\": 565617\n },\n {\n \"caption\": \"photo of super mario gangsta rap battle\",\n \"trainset_idx\": 560774\n },\n {\n \"caption\": \"cricket ball hit for a sic\",\n \"trainset_idx\": 467427\n },\n {\n \"caption\": \"High quality 3 d render hyperrealist very cute multipastel fluffy! kitty CAT with detailed fluffy !!, vray smooth, in the style of detective pikachu, , dramatic blue light, low angle, uhd 8 k, sharp focus\",\n \"trainset_idx\": 536068\n },\n {\n \"caption\": \"Painting of an opal goddess garden statue goddess style\",\n \"trainset_idx\": 212805\n },\n {\n \"caption\": \"3D digital illustration, Burger with wheels speeding on the race track, supercharged, detailed, hyperrealistic, 4K\",\n \"trainset_idx\": 566479\n },\n {\n \"caption\": \"a painting of a man wearing a cow mask, a surrealist painting, inspired by Michael Cheval, robotic pig, andrey remnev, with symmetrical head and eyes, templar, in the center of the image, greg beeple, pig nose, priest, st\\u00e5lenhag, symmetrical face, katsuhiro otomo, vertical portrait\",\n \"trainset_idx\": 517929\n },\n {\n \"caption\": \"ROUGH FIR PLANK WOOD PATTERN PAINTED TRAY WITH VELVET FABRIC\",\n \"trainset_idx\": 124088\n },\n {\n \"caption\": \"Realistic image of the pope francisco walking on the streets wearing gucci clothes\",\n \"trainset_idx\": 518036\n },\n {\n \"caption\": \"cyberpunk giant kinky muscle young Soldier inquisitor stabbing dead pregnant girl at Slaughterhouse. art by Ilya Repin\",\n \"trainset_idx\": 194441\n },\n {\n \"caption\": \"photo of adorable asian little ballet dancers resting in dance studio, nikon D5\",\n \"trainset_idx\": 500512\n },\n {\n \"caption\": \"old photo of people having a ritual in a jungle\",\n \"trainset_idx\": 415608\n },\n {\n \"caption\": \"hypnotic spiral\",\n \"trainset_idx\": 340208\n },\n {\n \"caption\": \"A stained glass vase with flowers in front of a window\",\n \"trainset_idx\": 335451\n },\n {\n \"caption\": \"Massive Cthulhu Giant Humanoid with dragon wings, sea, Mythology, Photorealistic, High Quality\",\n \"trainset_idx\": 247996\n },\n {\n \"caption\": \"photo\",\n \"trainset_idx\": 464137\n },\n {\n \"caption\": \"Spongebob on mars\",\n \"trainset_idx\": 356089\n },\n {\n \"caption\": \"preteen girls with no underware kissing each other in the the bedroom with dark background\",\n \"trainset_idx\": 12086\n },\n {\n \"caption\": \"A kobold riding on a giant bee\",\n \"trainset_idx\": 400629\n },\n {\n \"caption\": \"a woman incased in ice\",\n \"trainset_idx\": 178352\n },\n {\n \"caption\": \"The result of experimenting with fusing human and plant DNA, body horror mutation, insanely detailed, photorealistic, 8k, ultra high resolution, volumetric lighting, taken with canon, taken with nikon\",\n \"trainset_idx\": 99398\n },\n {\n \"caption\": \"two men getting married\",\n \"trainset_idx\": 253383\n },\n {\n \"caption\": \"rapist torture boy at dark room. abusive Relationships, break the will, screamCRY yelling. highly detailed Surrealism art by Ilya Repin\",\n \"trainset_idx\": 44065\n },\n {\n \"caption\": \"lips vulva, photo\",\n \"trainset_idx\": 299429\n },\n {\n \"caption\": \"Nighttime in Venice\",\n \"trainset_idx\": 237033\n },\n {\n \"caption\": \"axlotl\",\n \"trainset_idx\": 518428\n },\n {\n \"caption\": \"a photo of roman soldiers in front of roman buildings,ben-hur , a detailed photo, julius Caesar , julius caesar, ceremonial, many columns, demolition, parade, roma, detailed photo, temples roads hestia, colonnade, necropolis, ultra realism, imperium ,claude-joseph vernet\",\n \"trainset_idx\": 237398\n },\n {\n \"caption\": \"a turtle made from water\",\n \"trainset_idx\": 83020\n },\n {\n \"caption\": \"hyperrealistic girl with two pupils in one eye\",\n \"trainset_idx\": 283560\n },\n {\n \"caption\": \"Two curled shrimp in a white plate, black background, hyperrealistic, red\",\n \"trainset_idx\": 359646\n },\n {\n \"caption\": \"an evil tornado above a skyscraper in london, urban fantasy, darkhorse comic styled drawing\",\n \"trainset_idx\": 480123\n },\n {\n \"caption\": \"protest of teddy bears in New york the bears are with angry faces and raising their fist\",\n \"trainset_idx\": 29244\n },\n {\n \"caption\": \"An alluring gondolier in a Venetian canal, attractive cute female gondolier\",\n \"trainset_idx\": 278469\n },\n {\n \"caption\": \"1920s vintage photo of e-girl standing by the large window\",\n \"trainset_idx\": 194446\n },\n {\n \"caption\": \"Selfie photo of a japanese redhead woman extremely ashamed\",\n \"trainset_idx\": 537193\n },\n {\n \"caption\": \"A marble bust of Obama screaming\",\n \"trainset_idx\": 175906\n },\n {\n \"caption\": \"Adorable Professional Photo of a kitten wearing a purple bow that is sleeping on a pink pillow in a cozy bedroom in Paris at night there are books and candles on the nightstand, taken on a Leica M10\",\n \"trainset_idx\": 498454\n },\n {\n \"caption\": \"Muscular and Sweaty Alexa Bliss showing off her sweaty armpits with a fierce expression on her face ;\",\n \"trainset_idx\": 345709\n },\n {\n \"caption\": \"Girl\",\n \"trainset_idx\": 548087\n },\n {\n \"caption\": \"Fountains of wayne\",\n \"trainset_idx\": 401394\n },\n {\n \"caption\": \"Beautiful pale warhammer 40000 goth girl with mechanical wings, dark fantasy, digital illustration, intricate, highly detailed, smooth, artstation, painted by Wayne Barlowe and Greg Rutkowski and zdislav beksinski and Ruan Jia and Mandy Jurgens and Artgerm and william-adolphe bouguereau\",\n \"trainset_idx\": 336497\n },\n {\n \"caption\": \"Ben Shapiro covered in milk holding a jar of milk\",\n \"trainset_idx\": 454975\n },\n {\n \"caption\": \"a mermaid swimming underwater with a long flowing tail\",\n \"trainset_idx\": 540261\n },\n {\n \"caption\": \"breathtaking landscape by jacek yerka and dan mumford\",\n \"trainset_idx\": 307978\n },\n {\n \"caption\": \"a Ferrari car that is made out of cheese\",\n \"trainset_idx\": 518749\n },\n {\n \"caption\": \"Realistic Black and white portrait of Bangs hairstyle Jenna Ortega with bangs hairstyle , t shirt , blemishes on skin , smooth face , dynamic light , dynamic shadows , street background, image taken by\",\n \"trainset_idx\": 505258\n },\n {\n \"caption\": \"a car that is made out of glass\",\n \"trainset_idx\": 227766\n },\n {\n \"caption\": \"old colorized photo of a person playing north indian pambai Drum in a jungle\",\n \"trainset_idx\": 61183\n },\n {\n \"caption\": \"bulma from dragonball z, anime artstyle\",\n \"trainset_idx\": 294890\n },\n {\n \"caption\": \"World made of glass\",\n \"trainset_idx\": 11918\n },\n {\n \"caption\": \"studio ghibli style fluffy friendly anthropomorphic lynx with antlers, standing, full body, medieval, adventurer, dnd, rpg, rustic, nature, fantasy\",\n \"trainset_idx\": 374993\n },\n {\n \"caption\": \"a stunning woman\",\n \"trainset_idx\": 370933\n },\n {\n \"caption\": \"Generate an image of a smiling dog sitting at a computer, with a bitcoin logo bubble in Impressionism style\",\n \"trainset_idx\": 524595\n },\n {\n \"caption\": \"an old man and his corgi, at a train station, surrounded by spring pink trees, studio Ghibli style\",\n \"trainset_idx\": 264595\n },\n {\n \"caption\": \"a data analyst passionate about creating outstanding digital products\",\n \"trainset_idx\": 582408\n },\n {\n \"caption\": \"1 gril wear dresses\",\n \"trainset_idx\": 461736\n },\n {\n \"caption\": \"A neon sign that reads the message \\\"soon\\\"\",\n \"trainset_idx\": 496138\n },\n {\n \"caption\": \"a man playing a saxophone\",\n \"trainset_idx\": 72508\n },\n {\n \"caption\": \"2D digital cartoon, cute, cute chibi boy, in a wheelchair, dressed in white jiu jitsu kimono, black belt, wearing cap, he is happy, 4k\",\n \"trainset_idx\": 528552\n },\n {\n \"caption\": \"Minnie Mouse in a superman outfit bodybuilding, book illustration\",\n \"trainset_idx\": 103318\n },\n {\n \"caption\": \"close up of human lips wearing black-and-white checkerboard lipstick\",\n \"trainset_idx\": 284600\n },\n {\n \"caption\": \"A picture of a 27 year old girl smiling, cinematic dark dim dramatic\",\n \"trainset_idx\": 44365\n },\n {\n \"caption\": \"giant canyon filled with clouds below epic masterpiece painting trending on artstation\",\n \"trainset_idx\": 320409\n },\n {\n \"caption\": \"Anime style. Black and white manga. 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\"dslr photo of joe biden wearing a gold chain and a long red puffer jacket,4k uhd, volumetric lighting,detailed shadows\",\n \"trainset_idx\": 75207\n },\n {\n \"caption\": \"masterful photo of an incredible muscular woman, muscle goddess, bigger muscles than human possible, young, massive shoulders, 8 pack abs, huge, stronger than any man, 3 meter tall,\",\n \"trainset_idx\": 93742\n },\n {\n \"caption\": \"Scribbled dragon\",\n \"trainset_idx\": 453263\n },\n {\n \"caption\": \"The excruciatingly painful and horrific experience of tree branches growing from a human body, insanely detailed, photorealistic, 8k, ultra high resolution, volumetric lighting, taken with canon, taken with nikon\",\n \"trainset_idx\": 97254\n },\n {\n \"caption\": \"panda, unreal engine, rtx, raytracing, photorealistic, highly detailed, majestic, dynamic lighting, intricate detail\",\n \"trainset_idx\": 410823\n },\n {\n \"caption\": \"an old illustration of a man in a jungle\",\n \"trainset_idx\": 511177\n },\n {\n \"caption\": \"bears on holiday and mgb, mallorca, 1983, polaroid photography by andrei tarkovsky\",\n \"trainset_idx\": 528559\n },\n {\n \"caption\": \"Red mate beautiful texture lips close up of young apealing attractive beautiful nun\",\n \"trainset_idx\": 288916\n },\n {\n \"caption\": \"a watercolor painting of a sea turtle, a digital painting, by Kubisi art, featured on dribbble, medibang, warm saturated palette, red and green tones, turquoise horizon, digital art h 9 6 0, detailed scenery \\u2014width 672, illustration:.4, spray art, artstatiom\",\n \"trainset_idx\": 317303\n },\n {\n \"caption\": \"an image of a mad roman bishop inside iron maiden,cyborg, cyberpunk style,king crimson,by shusei nagaoka and simone martini and jos\\u00e9 clemente orozco\",\n \"trainset_idx\": 116780\n },\n {\n \"caption\": \"terminator t-800 endoskeleton\",\n \"trainset_idx\": 408386\n },\n {\n \"caption\": \"two bottles of water\",\n \"trainset_idx\": 324686\n },\n {\n \"caption\": \"purple smoke in the shape of the text \\\"Gif Co\\\"\",\n \"trainset_idx\": 347430\n },\n {\n \"caption\": \"Detailed concept art of a medieval banquet in a dungeon, view for above\",\n \"trainset_idx\": 126146\n },\n {\n \"caption\": \"hot woman, barefoot\",\n \"trainset_idx\": 35432\n },\n {\n \"caption\": \"a pixel art picture of a house in the woods, pixel art by Stanley Twardowicz, featured on tumblr, pixel art, #pixelart, 2d game art, ominous vibe\",\n \"trainset_idx\": 259802\n },\n {\n \"caption\": \"photo of pope francis wearing a traditional arabic clothing\",\n \"trainset_idx\": 365800\n },\n {\n \"caption\": \"A Skeleton working on a computer\",\n \"trainset_idx\": 57465\n },\n {\n \"caption\": \"Angry woman running on the beach\",\n \"trainset_idx\": 298084\n },\n {\n \"caption\": \"Cat god standing on top of the world globe with arms stretched out, thick outlines cartoon\",\n \"trainset_idx\": 1835\n },\n {\n \"caption\": \"red head girl with freakles\",\n 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\"caption\": \"A detailed painting of a man using a lightsaber as a golf stick, artstation\",\n \"trainset_idx\": 445290\n },\n {\n \"caption\": \"a sword\",\n \"trainset_idx\": 162190\n },\n {\n \"caption\": \"a photo of a pin up\",\n \"trainset_idx\": 380823\n },\n {\n \"caption\": \"photo of a boulder opal pendant\",\n \"trainset_idx\": 521880\n },\n {\n \"caption\": \"promotional material for a shonen anime with a chubby protagonist.\",\n \"trainset_idx\": 517057\n },\n {\n \"caption\": \"beautiful fractal goddess\",\n \"trainset_idx\": 101064\n },\n {\n \"caption\": \"a cat with purple fur with a black tophat\",\n \"trainset_idx\": 538776\n },\n {\n \"caption\": \"hyperrealistic photograph, extremely detailed pale young woman whole body covered in fungus, fungi, mushrooms growing out of her eyes, skinny, mushrooms, mushrooms on face, mushrooms on cheekbones, zoomed out ,\",\n \"trainset_idx\": 399923\n },\n {\n \"caption\": \"The word SOUP spelled out in alphabet soup\",\n \"trainset_idx\": 51469\n },\n {\n \"caption\": \"Watercolor painting of european modern city, medieval, nightfall moonlight, by greg rutkowski, by anders zorn\",\n \"trainset_idx\": 476809\n },\n {\n \"caption\": \"a wide angle photo of large gold star on display in a smokey roman villa burning, 18mm smoke filled room debris , gladiator ,floor mosaics fire smoke, a photo, roman , a digital rendering, inside the roman colliseum, brick, , indoor, plants overgrown outstanding detail ,room flooded, in front of a building,by claude-joseph vernet\",\n \"trainset_idx\": 509492\n },\n {\n \"caption\": \"a Bart Simpson with colorful hair wearing headphones, character album cover, tamara de lepika, hairworks, in thick layers of rhythms, inspired by Johanna Marie Fosie, paranoid, big bold thick eyebrows, by Farid Mansour, technocracy, luminous color\\u2019s, pooka, tusks\",\n \"trainset_idx\": 390935\n },\n {\n \"caption\": \"A big space station in Star wars style, Doug Chiang concept art\",\n 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\"caption\": \"Galadriel (Cate Blanchett) in a hungarian lake shore, character concept art by Artgerm , Rutkowski, Rembrandt. cinemathic lighting, atmospheric\",\n \"trainset_idx\": 442387\n },\n {\n \"caption\": \"Very realistic 3d rendered word \\u201cMART\\u201d, unreal engine, octane rendering, cinematographic lighting, studio photography, cinema 4d product photography\",\n \"trainset_idx\": 578419\n },\n {\n \"caption\": \"Cristiano Ronaldo punching lionel messi\",\n \"trainset_idx\": 541834\n },\n {\n \"caption\": \"A cute ninja turtle, Pixar character, octane render, Cute\",\n \"trainset_idx\": 582766\n },\n {\n \"caption\": \"giant's fortress, made of ice, large watchtowers, high walls, frozen peaks, high fantasy, photorealistic\",\n \"trainset_idx\": 509213\n },\n {\n \"caption\": \"hooded, digital art, shoulder shot, vampire in a gem lined suit, blue lighting, moonlight, 8k, trending, 4k, by yoji shinkawa, by junji ito, by Jeremy Geddes, demon\",\n \"trainset_idx\": 430029\n },\n {\n \"caption\": \"insanely detailed portrait,female model lying on the ground, open legs, no cover, no pants, insane face details, perfect eyes,dof, dslr extremely intricate, high res, 8k, award winning photography,\",\n \"trainset_idx\": 43220\n },\n {\n \"caption\": \"masterpiece, absurdres, oil painting medium, extreme details, illustration, perfect anatomy, portrait, perfect detailed eyes, beautiful woman astronaut, full body, detailed face, cinematic light, studio quality, smooth render, unreal engine 5 rendered, octane rendered\",\n \"trainset_idx\": 510894\n },\n {\n \"caption\": \"An illustration of a pikachu holding a sign up that says \\\"I'm cute\\\"\",\n \"trainset_idx\": 212390\n },\n {\n \"caption\": \"concept art, drawing of fantasy ballroom at night, with faceless dancers in fancy dresses and suits\",\n \"trainset_idx\": 404171\n },\n {\n \"caption\": \"Beautiful girl in the stret\",\n \"trainset_idx\": 489361\n },\n {\n \"caption\": \"many dandelion are sorted by 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424445\n },\n {\n \"caption\": \"an anthropomorphic white wolf, brown cape, medieval, adventurer, dnd, nature spirit, rpg, rustic, fantasy, hd digital art\",\n \"trainset_idx\": 236870\n },\n {\n \"caption\": \"realistic photo of 6 year old girl Homura Akemi, cosplay, full body\",\n \"trainset_idx\": 6841\n },\n {\n \"caption\": \"a dinosaur next to a landrover driving down a muddy road in the jungle,seen from behind, by Anthony S Waters, renaissance, some rust,real-life brook, front side views full, camp, but very good looking, very wet, 2021 ,\",\n \"trainset_idx\": 375691\n },\n {\n \"caption\": \"Spectre in the fog\",\n \"trainset_idx\": 522296\n },\n {\n \"caption\": \"concept art for a 19th century railroad game set in Pakistan\",\n \"trainset_idx\": 370989\n },\n {\n \"caption\": \"8k, 80s movie still, a robocop t-800 robot, cyberpunk, photography\",\n \"trainset_idx\": 315595\n },\n {\n \"caption\": \"Joe Biden fighting\",\n \"trainset_idx\": 99437\n },\n {\n \"caption\": \"Barn owl mecha\",\n \"trainset_idx\": 61731\n },\n {\n \"caption\": \"a Chinese man\",\n \"trainset_idx\": 409619\n },\n {\n \"caption\": \"an anthropomorphic snail, with the shell on the back, and a snail's head\",\n \"trainset_idx\": 92329\n },\n {\n \"caption\": \"a cat blowing on a birthday cake\",\n \"trainset_idx\": 18168\n },\n {\n \"caption\": \"Gjallarhorn, mystical, fantastical, epically, magical\",\n \"trainset_idx\": 572213\n },\n {\n \"caption\": \"Monica Bellucci pleasure slave\",\n \"trainset_idx\": 543340\n },\n {\n \"caption\": \"Picture of ninja cat\",\n \"trainset_idx\": 81083\n },\n {\n \"caption\": \"Amazing Professional Photo of a parrot holding a green umbrella that is flying over a tropical rainforest in Brazil at dawn there are monkeys and toucans in the trees below, taken on a Nikon D850\",\n \"trainset_idx\": 286679\n },\n {\n \"caption\": \"firedance, from riverdance the show, flamenco\",\n \"trainset_idx\": 21424\n },\n {\n \"caption\": \"A poster lik\\u00e8 Alfons Mucha of a beautiful young Japanese couple on each other arms a Tea set on the forefront i a Palace\",\n \"trainset_idx\": 304378\n },\n {\n \"caption\": \"an evil entity that is made out of cheese casting a terrible spell over slices of bread, crazy, horror, nightmare, artistic\",\n \"trainset_idx\": 353047\n },\n {\n \"caption\": \"bruce Springsteen as a hotel maid, intricate, elegant, highly detailed, centered, digital painting, artstation, concept art, smooth, sharp focus, illustration, artgerm, Tomasz Alen Kopera, Peter Mohrbacher, donato giancola, Joseph Christian Leyendecker, WLOP, Boris Vallejo\",\n \"trainset_idx\": 263405\n },\n {\n \"caption\": \"portrait painting of female android from dystopic future by James Gurney\",\n \"trainset_idx\": 56271\n },\n {\n \"caption\": \"photo of two muscle guys bald Slaughter punish abducted and degraded Boy at cyberpunk prison toilet. wear dirty briefs, highly detailed orgasm face, killer look, Hard close-set eyes, born 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view,a surrealist painting by Jean Fouquet and alan bean and Philippe Druillet,volumetric lighting,detailed shadows\",\n \"trainset_idx\": 280130\n },\n {\n \"caption\": \"among us\",\n \"trainset_idx\": 375397\n },\n {\n \"caption\": \"masterpiece, best quality, ultra highres, photorealistic, 8k, RAW photo, soft focus, 1 woman, 25 years old, posh, victoria's secret model, Full-Body Shot, sharp focus, korean, american, detailed beautiful face, black hair, detailed open blazer, bathing, beautiful white shiny humid skin, smiling\",\n \"trainset_idx\": 452728\n },\n {\n \"caption\": \"Realistic Black and white portrait of Felicity Jones triple D cup as a 19 year old , blemishes on skin , smooth face , dynamic light , dynamic shadows , studio background, image taken by\",\n \"trainset_idx\": 289981\n },\n {\n \"caption\": \"A collage of risqu\\u00e9 Images inspired by Taylor Swift \\ud83c\\udf48\\ud83c\\udf48 legs spread\",\n \"trainset_idx\": 199787\n },\n {\n \"caption\": \"young jewish girl spreading legs with tentacle monster\",\n \"trainset_idx\": 182652\n },\n {\n \"caption\": \"The devil using a heat press\",\n \"trainset_idx\": 18854\n },\n {\n \"caption\": \"a room where people generate AI art, show the printouts to each other and laugh\",\n \"trainset_idx\": 120945\n },\n {\n \"caption\": \"I\\u2019m not on your wears, but I want to see your start.\",\n \"trainset_idx\": 335058\n },\n {\n \"caption\": \"young jewish woman on top\",\n \"trainset_idx\": 380029\n },\n {\n \"caption\": \"An image of an enchanted leather tome, dynamic lighting, trending on artstation, library style\",\n \"trainset_idx\": 147895\n },\n {\n \"caption\": \"A silhouette of a dog looking at the starsalien paper money like dollar bill and euro, greyish blue , irredescent, with the design of an alien dignitary printed on currency paper , strange alien currency symbols printed ,highly detailed, realistic, octane render\",\n \"trainset_idx\": 442930\n },\n {\n \"caption\": \"a freindly female red dragonborn monk painting\",\n \"trainset_idx\": 479158\n },\n {\n \"caption\": \"a close up of a person wearing a costume, a surrealist painting,cyberpunk art, by Philippe Druillet,inspired by Peter Blume, symmetrical dieselpunk warrior, grand admiral thrawn, a still life of a robot, holy machine, clockwork woman, orbital, king crimson, avatar image, shusei nagaoka, large view\",\n \"trainset_idx\": 230319\n },\n {\n \"caption\": \"A steampunk giraffe surfing in a rainforest\",\n \"trainset_idx\": 6241\n },\n {\n \"caption\": \"logo of a golden skull\",\n \"trainset_idx\": 544397\n },\n {\n \"caption\": \"intricately detailed photoillustration of a human skull with desert flowers growing out of the eye socket, Dia de Los Muertos!!! photorealistic, optimistic bright happy high clarity visually striking masterpiece, by Marc Chagall and Ernst Haeckel\",\n \"trainset_idx\": 581861\n },\n {\n \"caption\": \"Realistic Black and white portrait of Ellen Page triple D cup as a 19 year old , blemishes on skin , smooth face , dynamic light , dynamic shadows , studio background, image taken by\",\n \"trainset_idx\": 531472\n },\n {\n \"caption\": \"Evangeline Lilly as Neytiri from Avatar\",\n \"trainset_idx\": 2541\n },\n {\n \"caption\": \"gryphon epic fantasy masterpiece\",\n \"trainset_idx\": 330502\n },\n {\n \"caption\": \"Dreams of happiness, very beautiful, inspiring, thought provoking, digital art, Intricate, Elegant, Scenic, Hyper-Detailed, Delicate; Complex, surreal concept art, aesthetic, smooth, sharp focus\",\n \"trainset_idx\": 408424\n },\n {\n \"caption\": \"a pregnant woman\",\n \"trainset_idx\": 33321\n },\n {\n \"caption\": \"a billboard with the word platypus\",\n \"trainset_idx\": 444740\n },\n {\n \"caption\": \"Volcano with rainbow lava\",\n \"trainset_idx\": 51583\n },\n {\n \"caption\": \"pikachu with a sign saying \\\"I'm cute\\\"\",\n \"trainset_idx\": 120575\n },\n {\n \"caption\": \"a dinosaur attacking a car in the rain, a picture, landrover defender vs carnivore dinosaur, hand, close-up!!!!!!, great pinterest photo, big foot, at the time of dinosaurs, a24, beautiful, tyrannosaurus\",\n \"trainset_idx\": 40729\n },\n {\n \"caption\": \"Diamond ferrari, made entirely of diamond mirror glass, diamond color\",\n \"trainset_idx\": 213766\n },\n {\n \"caption\": \"human's hand\",\n \"trainset_idx\": 82913\n },\n {\n \"caption\": \"cozy Danish interior design with wooden floor modern realist\\u0435ic archviz scandinavian\",\n \"trainset_idx\": 41188\n },\n {\n \"caption\": \"Boy in a cute dress\",\n \"trainset_idx\": 194064\n },\n {\n \"caption\": \"a rover75 v8 car that is made out of wood mgzt\",\n \"trainset_idx\": 384767\n },\n {\n \"caption\": \"A dragon, digital art\",\n \"trainset_idx\": 183841\n },\n {\n \"caption\": \"polaroid, extremely detailed pale young woman covered in veins, totally black eyes, veiny tentacles intestines, body horror, intestines and veins coming out of mouth, veins covering body, skinny, zoomed out ,\",\n \"trainset_idx\": 413518\n },\n {\n \"caption\": \"feral vicious rat with six legs and bat wings for ears art\",\n \"trainset_idx\": 282348\n },\n {\n \"caption\": \"a finger pointing at the camera\",\n \"trainset_idx\": 430131\n },\n {\n \"caption\": \"A Triangular Prism inside a translucent sphere\",\n \"trainset_idx\": 306722\n },\n {\n \"caption\": \"photo of a beautiful blonde swedish 15 year old girl, by terry richardson, in studio\",\n \"trainset_idx\": 462175\n },\n {\n \"caption\": \"Elon musk as an old marble statue, background is a highly advanced scifi civilization\",\n \"trainset_idx\": 268340\n },\n {\n \"caption\": \"Walt Disney 3D animation- Ariel, the Little Mermaid as a naturist in the ocean\",\n \"trainset_idx\": 391931\n },\n {\n \"caption\": \"Realistic Black and white portrait of Felicity Jones triple D cup as a 19 year old , banana split pose , smooth face , dynamic light , dynamic shadows , studio background, image taken by photographer\",\n \"trainset_idx\": 441849\n },\n {\n \"caption\": \"president lula\",\n \"trainset_idx\": 20058\n },\n {\n \"caption\": \"Furry fox, solo, blue fur, young\",\n \"trainset_idx\": 195045\n },\n {\n \"caption\": \"35mm film stock hyperrealistic lovecraftian creature with long pale tentacles in a big foggy abandoned cathedral low light\",\n \"trainset_idx\": 420843\n },\n {\n \"caption\": \"1960s album art, with a cherry dead center, psychedelic pop, in the style of the beatles\",\n \"trainset_idx\": 416001\n },\n {\n \"caption\": \"Newfoundland dog sitting by a lake at sunset\",\n \"trainset_idx\": 249878\n },\n {\n \"caption\": \"split strawberry in shape of vulva, cream, photo\",\n \"trainset_idx\": 274844\n },\n {\n \"caption\": \"35 year old short slim man, round face, short hair, black hair, black stubble, olive skin, immense detail/ hyper. 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\"a cartoon bubble travelling first seat in the style of Paris Match\",\n \"trainset_idx\": 47380\n },\n {\n \"caption\": \"A man with a scary dog mask, sitting on top of a car with a chainsaw\",\n \"trainset_idx\": 581833\n },\n {\n \"caption\": \"cinematic shot of a purplecar with oranges for wheels, bokeh, depth of field\",\n \"trainset_idx\": 403614\n },\n {\n \"caption\": \"young woman with big teased 80's hair, blowout hair, lots of hairspray\",\n \"trainset_idx\": 153355\n },\n {\n \"caption\": \"A crocodail eating a worm sticking out of an apple\",\n \"trainset_idx\": 35165\n },\n {\n \"caption\": \"Dragon shaped plant\",\n \"trainset_idx\": 122039\n },\n {\n \"caption\": \"Female VTuber 3d model character turnaround\",\n \"trainset_idx\": 413508\n },\n {\n \"caption\": \"Portrait of llama who smoking\",\n \"trainset_idx\": 102081\n },\n {\n \"caption\": \"Universal Motion picture poster\",\n \"trainset_idx\": 529681\n },\n {\n \"caption\": \"Jesus wearing sunglasses sticking 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\"rich minion\",\n \"trainset_idx\": 312027\n },\n {\n \"caption\": \"Mahindra thar, tribe members attacking, action scene, an epic fantasy, dramatic lighting, cinematic, establishing shot, extremely high detail, photorealistic, cinematic lighting, artstation, by christopher nolan, horizon forbidden west\",\n \"trainset_idx\": 327536\n },\n {\n \"caption\": \"Scandinavian boring female glasses university student photo\",\n \"trainset_idx\": 306883\n },\n {\n \"caption\": \"strawberries and m16 growing on a turtle\",\n \"trainset_idx\": 137533\n },\n {\n \"caption\": \"photo of head emerging out of river water surface, frederick doris royo fdr gren old gossisunlight ,Jules Bastien-Lepage\",\n \"trainset_idx\": 81028\n },\n {\n \"caption\": \", fantasy, pastel, absurdism, photo, refined\",\n \"trainset_idx\": 515287\n },\n {\n \"caption\": \"a painting of an exploding star\",\n \"trainset_idx\": 273583\n },\n {\n \"caption\": \"what do we say to the god of death\",\n \"trainset_idx\": 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379858\n },\n {\n \"caption\": \"lost in the woods, landscape painting by Andy Kehoe\",\n \"trainset_idx\": 175854\n },\n {\n \"caption\": \"photo of a rabbit\",\n \"trainset_idx\": 220374\n },\n {\n \"caption\": \"style of henry raeburn, kate middleton, portrait, painterly, visible brush strokes, moody lighting\",\n \"trainset_idx\": 119299\n },\n {\n \"caption\": \"feral six legged rat, bat wings for ears, fantasy art\",\n \"trainset_idx\": 251866\n },\n {\n \"caption\": \"psychedelic smoke, explosion, fire twirling, backlit, twisting, curled, petite American ballerina, wearing ballerina sparkling lace tutu, riding long glowing neon skateboard, star jump upside down, 8K, HD, highly detailed, rendered in octane, very very very aesthetic\",\n \"trainset_idx\": 437843\n },\n {\n \"caption\": \"New Dehli got nuked in the middle of city\",\n \"trainset_idx\": 279896\n },\n {\n \"caption\": \"a portrait shot of a afgan girl cute, deep blue eyes covered face\",\n \"trainset_idx\": 93178\n 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Bare\",\n \"trainset_idx\": 355368\n },\n {\n \"caption\": \"art by Alfons Mucha and Patrick Woodroffe, stained glass motif, whole body image of 20 year-old Taylor Schilling as Piper Chapman as a naturist in a mystical forest, HD 4k, sharp detail, photo-realistic accurate face and features\",\n \"trainset_idx\": 20788\n },\n {\n \"caption\": \"a guy holding a sign that says \\\"NOT a CLUE\\\"\",\n \"trainset_idx\": 225344\n },\n {\n \"caption\": \"bear with a powder on nose, t-shirt print \\\"i duck\\\"\",\n \"trainset_idx\": 560586\n },\n {\n \"caption\": \"i like a long big thick redbone, make that cat filet mignon that cat. i'm gonna get on that cat. if she let me in i'm gonna own that cat\",\n \"trainset_idx\": 47695\n },\n {\n \"caption\": \"Colonel Sanders wearing a shirt that reads cockk and roll\",\n \"trainset_idx\": 485983\n },\n {\n \"caption\": \"**a portrait of a Bitcoin Hawaii big island flower hyper-realistic, ultra-detailed, photography, hyper-realistic, photo-realistic, ultra-photo-realistic, super-detailed, intricate details, 8K, surround lighting, HDR\",\n \"trainset_idx\": 81002\n },\n {\n \"caption\": \"Digital art of monumental breathtaking Mega-Corp HQ building, dystopian sci-fi, digitally painted in UHD, concept art, intricate details\",\n \"trainset_idx\": 430349\n },\n {\n \"caption\": \"realistic, magazine cover, text, skinny little preteen 8 year old girl, straight blonde hair, croptop and miniskirt, muppets and old man, bedroom, chains and ropes, starwars, muppets. by gustav klimt, dino valls, gustav klimt, Daniela Uhlig, thomas kinkade\",\n \"trainset_idx\": 392868\n },\n {\n \"caption\": \"Eldrich Old God Shoggoth, holding a smiley face\",\n \"trainset_idx\": 175397\n },\n {\n \"caption\": \"a lesbian wedding\",\n \"trainset_idx\": 291314\n },\n {\n \"caption\": \"A viking warrior, fantasy art, digital illustration, realistic lighting\",\n \"trainset_idx\": 459808\n },\n {\n \"caption\": \"a beautiful sunset with the word \\\"Friday\\\" written in the clouds\",\n \"trainset_idx\": 1001\n },\n {\n \"caption\": \"young Muscle guy castartor busting TESTICLEs at torture chamber. plural testes, male reproductive gland. highly detailed guro art by Ilya Repin\",\n \"trainset_idx\": 240415\n },\n {\n \"caption\": \"The TARDIS flying through the time vortex\",\n \"trainset_idx\": 195285\n },\n {\n \"caption\": \"a woman staring into the camera, scratching her head\",\n \"trainset_idx\": 402115\n },\n {\n \"caption\": \"\\\"animal farm\\\" movie poster, dystopian retrofuturistic version\",\n \"trainset_idx\": 225444\n },\n {\n \"caption\": \"A 1945 WWII propaganda illustration of Dwayne Johnson, masterpiece, absurdres, highres, featured on ArtStation\",\n \"trainset_idx\": 398332\n },\n {\n \"caption\": \"a cat with black and white fur drinking from a cup of coffee next to Ernest Hemingway\",\n \"trainset_idx\": 15075\n },\n {\n \"caption\": \"A drawing of an anthropomorphic snake holding two whips\",\n \"trainset_idx\": 395988\n },\n {\n \"caption\": \"a mix between a dragon and a panda\",\n \"trainset_idx\": 392447\n },\n {\n \"caption\": \"A oil painting portrait of giant Muscle bald boy demigod severe Slaughter wear black uniform covered in red fluid came to oppress and enslave. Surrealism art by Ilya Repin\",\n \"trainset_idx\": 346402\n },\n {\n \"caption\": \"movie still of game of thrones\",\n \"trainset_idx\": 407816\n },\n {\n \"caption\": \"Award-winning photograph, An abandoned highway bridge, deterioration, trashed cars, overgrown vines and weeds and moss, highly detailed,\",\n \"trainset_idx\": 517836\n },\n {\n \"caption\": \"upside-down pikachu in space with long arms that wrap around its body 4 times\",\n \"trainset_idx\": 534114\n },\n {\n \"caption\": \"Indian woman drinking water\",\n \"trainset_idx\": 553228\n },\n {\n \"caption\": \"an illustration of a woman wearing an orange hat and birds, in the style of fantasy realism, chiaroscuro portraitures, i can't believe how beautiful this is, exotic realism, symbolic realism, yellow and crimson, neo-realist surrealist\",\n \"trainset_idx\": 239210\n },\n {\n \"caption\": \"A page from an adult colouring book about animals\",\n \"trainset_idx\": 386332\n },\n {\n \"caption\": \"fearsome Chinese 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},\n {\n \"caption\": \"A cute potato knight\",\n \"trainset_idx\": 119533\n },\n {\n \"caption\": \"cyberpunk giant kinky muscle young Slaughter inquisitor excruciate tormented pregnant girl at torture chamber. guro art by Rob Gonsalves\",\n \"trainset_idx\": 491730\n },\n {\n \"caption\": \"An alluring gondolier in a Venetian canal, attractive cute female gondolier, shapely, revealed, visible, peaking, hint, suggestive, legs\",\n \"trainset_idx\": 22701\n },\n {\n \"caption\": \"a cute animated cat with its mouth open\",\n \"trainset_idx\": 16601\n },\n {\n \"caption\": \"by Kris Kuksi, sculptures, no compositions, miniature detalization, 3D artist, three-dimensional bas-relief\",\n \"trainset_idx\": 108564\n },\n {\n \"caption\": \"a 1958 chevrolet apache\",\n \"trainset_idx\": 21303\n },\n {\n \"caption\": \"a bust shot portrait of a humanoid cat dressed like a navy admiral, digital art\",\n \"trainset_idx\": 567892\n },\n {\n \"caption\": \"a female athlete showing off her sweaty 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\"trainset_idx\": 453563\n },\n {\n \"caption\": \"Elvis Aron Presley aged 30 auditioning as Captain James T Kirk on Star Trek The Original Series, 1966, scifi, technicolor, discussion, script\",\n \"trainset_idx\": 132912\n },\n {\n \"caption\": \"dark-haired Valerian and redhead Laureline, time and space agents, detailed and realistic painting by Michael Whelan\",\n \"trainset_idx\": 106702\n },\n {\n \"caption\": \"women in the beach\",\n \"trainset_idx\": 479287\n },\n {\n \"caption\": \"sci-fi white room, teddybears looking at a astonmartin db5,silver car,studio lighting,inside space station with windows\",\n \"trainset_idx\": 375112\n },\n {\n \"caption\": \"Midjourney style photorealistic image of Misha Collins Castiel from supernatural fighting an indigineus woman\",\n \"trainset_idx\": 252966\n },\n {\n \"caption\": \"Mickey Mouse in Friday Night Funkin'\",\n \"trainset_idx\": 267710\n },\n {\n \"caption\": \"80s honda car in gta 4\",\n \"trainset_idx\": 293128\n },\n {\n \"caption\": \"African Slatternly Woman\",\n \"trainset_idx\": 129340\n },\n {\n \"caption\": \"vintage easter postcard, early 20th century\",\n \"trainset_idx\": 147191\n },\n {\n \"caption\": \"a girl reading a book in a reading nook with a big window and a lot of pillows, a ginger cat on the girl's lap\",\n \"trainset_idx\": 264879\n },\n {\n \"caption\": \"hot air balloons leading to a treasure in an old pirate map\",\n \"trainset_idx\": 538687\n },\n {\n \"caption\": \"red goblin perched on a marble pillar ready to attack crimson sky in the background, concept art by Jeffrey Catherine Jones\",\n \"trainset_idx\": 543816\n },\n {\n \"caption\": \"kurt cobain and jimi hendrix on a bed together\",\n \"trainset_idx\": 505630\n },\n {\n \"caption\": \"dad and son playing\",\n \"trainset_idx\": 279922\n },\n {\n \"caption\": \"trolls, anton fedeev, 3d intricate shapes, by Telemaco Signorini, glass dome on a table, snowglobe, 3d characters, iphone wallpaper, jean-sebastien rossbach, wlop and andrei riabovitchev,\",\n \"trainset_idx\": 245093\n },\n {\n \"caption\": \"breton monk monks looking like zappa in gym with bodybuilders with goat, photo\",\n \"trainset_idx\": 461895\n },\n {\n \"caption\": \"a photograph capturing superman sitting in a bar, edge light, well lit, bokeh, 8K, sharp focus, looking at viewer, realistic, masterpiece, highest quality, backlighting,\",\n \"trainset_idx\": 231205\n },\n {\n \"caption\": \"Old people smoking big fat blunts like rappers\",\n \"trainset_idx\": 428522\n },\n {\n \"caption\": \"Bedsheets with picture of merkel\",\n \"trainset_idx\": 371380\n },\n {\n \"caption\": \"female in bed\",\n \"trainset_idx\": 126662\n },\n {\n \"caption\": \"UFO popping out of the ocean in fauvist style\",\n \"trainset_idx\": 56148\n },\n {\n \"caption\": \"a steampunk city street in Paris as a fantasy illustration\",\n \"trainset_idx\": 419479\n },\n {\n \"caption\": \"crocodile dog\",\n \"trainset_idx\": 472893\n },\n {\n \"caption\": \"News 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\"trainset_idx\": 71332\n },\n {\n \"caption\": \"the godfather smoking cigar\",\n \"trainset_idx\": 272245\n },\n {\n \"caption\": \"A ball mixed with the element of tornado and electrolysis in the form of a sword that changes its shape and an LED inside\",\n \"trainset_idx\": 147125\n },\n {\n \"caption\": \"Cursed Image\",\n \"trainset_idx\": 180208\n },\n {\n \"caption\": \"square icon sheet, fantasy concept art, detailed, mixed media. render\",\n \"trainset_idx\": 421359\n },\n {\n \"caption\": \"Emma Roberts Photorealistic\",\n \"trainset_idx\": 80698\n },\n {\n \"caption\": \"dragon flying through space, colourful vector art\",\n \"trainset_idx\": 370727\n },\n {\n \"caption\": \"Jessica rabbit as a firefighter, photo by Annie leibovitz\",\n \"trainset_idx\": 553131\n },\n {\n \"caption\": \"glass cat, red green blue\",\n \"trainset_idx\": 527185\n },\n {\n \"caption\": \"a vectorized logo of an alien, minimal logo, vector art, black and white\",\n \"trainset_idx\": 163878\n },\n {\n \"caption\": \"breton monks looking like zappa on mars Curiosity, BW, astronout\",\n \"trainset_idx\": 454464\n },\n {\n \"caption\": \"a gray cat looking at a window inside a train, in the distance a mountain with a big cloud of fog and thunders, digital art and oil painting dramatic style\",\n \"trainset_idx\": 519224\n },\n {\n \"caption\": \"Cat playing with dog\",\n \"trainset_idx\": 427224\n },\n {\n \"caption\": \"the fluffiest cat driving a bugatti\",\n \"trainset_idx\": 369434\n },\n {\n \"caption\": \"art print of a cute fire elemental by vincent van gogh\",\n \"trainset_idx\": 325310\n },\n {\n \"caption\": \"rocket over the horizon\",\n \"trainset_idx\": 197384\n },\n {\n \"caption\": \"marcsteen.com\",\n \"trainset_idx\": 348116\n },\n {\n \"caption\": \"Award-winning photo, photograph, epic realism, Giant squid, kraken, anatomically correct, cephalopod,\",\n \"trainset_idx\": 482318\n },\n {\n \"caption\": \"Alien Moe in a business suit\",\n \"trainset_idx\": 482657\n },\n {\n \"caption\": \"steampunk tinkerer working in his workshop\",\n \"trainset_idx\": 229205\n },\n {\n \"caption\": \"Emma stone as gollum, full body, ultradetailed, embellishments\",\n \"trainset_idx\": 565588\n },\n {\n \"caption\": \"young Lee Young Ae as a 19th century hungarian peasant woman in 19th century a hungarian village, character concept art by Munk\\u00e1csy, Ferenczy, Rutkowski\",\n \"trainset_idx\": 12921\n },\n {\n \"caption\": \"cinematic shot of a fortnite character buying bread in a store\",\n \"trainset_idx\": 364664\n },\n {\n \"caption\": \"A screenshot from a first-person video game where you are a bartender in a fantasy tavern serving various monsters, unreal engine 5, 8k ultrahd, mindblowing graphics\",\n \"trainset_idx\": 88977\n },\n {\n \"caption\": \"tropical flavored pringle can\",\n \"trainset_idx\": 393536\n },\n {\n \"caption\": \"A concept art of a medieval fantasy rural village known for its turnip fields and food with an inn ine the center of a town\",\n \"trainset_idx\": 513824\n },\n {\n \"caption\": \"a cat looking out the window of a spaceship\",\n \"trainset_idx\": 218845\n },\n {\n \"caption\": \"a 3d cube shaped hamburger, digital art\",\n \"trainset_idx\": 537855\n },\n {\n \"caption\": \"Detailed painting of Attractive young women painting, model, detailed , cinematic lightning, hot, wearing harem dancer\",\n \"trainset_idx\": 579649\n },\n {\n \"caption\": \"a Pulitzer Prize wide-angle photo of a very handsome extreme body-builder beefy Malay married mature man wearing only low-rise beach shorts\",\n \"trainset_idx\": 266028\n },\n {\n \"caption\": \"Worlds Fair 1900s\",\n \"trainset_idx\": 46558\n },\n {\n \"caption\": \"the essence of pablo picasso, art poster\",\n \"trainset_idx\": 193698\n },\n {\n \"caption\": \"fantasy skill symbol\",\n \"trainset_idx\": 250764\n },\n {\n \"caption\": \"a bearded dragon riding on top of a chicken\",\n \"trainset_idx\": 51672\n },\n {\n \"caption\": \"young Lee 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skies, blue ocean, sunset, sunrise vibrant and colorfu scene, extremely detailed, ultra hd, hdr, 8k, cinematic, Stanley Artgerm Lau style beautifully color-coded, studio Portrait Lighting unreal render, black, background\",\n \"trainset_idx\": 127355\n },\n {\n \"caption\": \"a beautiful painting of a mermaid from elden fantasy, rococo, by krenz cushart, blowing wind, red hair, swimming through the waves, makoto shinkai, tashi takeuchi, Studio Ghibli, trending on artstation, artstationHD, artstationHQ\",\n \"trainset_idx\": 543137\n },\n {\n \"caption\": \"Travel agency office with 3 workers\",\n \"trainset_idx\": 384692\n },\n {\n \"caption\": \"a close up of a statue of a pig wearing a costume, a surrealist painting, inspired by Karel Dujardin, pop surrealism, jingna zhang, majesty in noble clothes, collage style joseba elorza, of a 17th century, hindu, pig, erwin olaf, sha xi, hans\",\n \"trainset_idx\": 248860\n },\n {\n \"caption\": \"Slutty Dark Skinned African Woman, Laying in a 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photo\",\n \"trainset_idx\": 416551\n },\n {\n \"caption\": \"sci-fi cyborg shaman warrior by Zdzis\\u0142aw Beksi\\u0144ski captured with Sony A9 Mark II, 4k\",\n \"trainset_idx\": 418113\n },\n {\n \"caption\": \"Legless woman\",\n \"trainset_idx\": 336190\n },\n {\n \"caption\": \"a tesla that is made out of wood\",\n \"trainset_idx\": 561490\n },\n {\n \"caption\": \"cinematic action shot of a dark wizard using spectacular magic to defeat his enemy, insanely detailed, photorealistic, 8k, perfect composition, volumetric lighting, natural complexion, award winning professional photography, taken with canon eos 5d mark iv, 85mm, mindblowing, masterpiece,\",\n \"trainset_idx\": 256448\n },\n {\n \"caption\": \"a man urinating\",\n \"trainset_idx\": 32650\n },\n {\n \"caption\": \"art poster, the essence of charcoal painting, an eldrich entity in a foggy landscape\",\n \"trainset_idx\": 360666\n },\n {\n \"caption\": \"seductress zelda princess by artgerm from breath of the wild\",\n \"trainset_idx\": 241192\n },\n {\n \"caption\": \"hands of people who gather a circle\",\n \"trainset_idx\": 575564\n },\n {\n \"caption\": \"Attractive mixed women; Asian; African;Latina; Indian; Mixed; With futuristic theme\",\n \"trainset_idx\": 156582\n },\n {\n \"caption\": \"Hyper realistic photo of a terrifying dinosaur running through a city\",\n \"trainset_idx\": 382941\n },\n {\n \"caption\": \"movie still from 80s supermarket\",\n \"trainset_idx\": 394067\n },\n {\n \"caption\": \"anime girl with white hair, red dress with golden decorations, dark fantasy style, sharp details, anime style\",\n \"trainset_idx\": 32457\n },\n {\n \"caption\": \"portrait of guy muscle bald Slaughter at russian prison. wear raunch briefs, highly detailed face. art\",\n \"trainset_idx\": 36800\n },\n {\n \"caption\": \"masterpiece, 8K, photo realistic, RAW photo,soft lighting, highly detailed, high quality, a body portrait of a handsome man in a suit\",\n \"trainset_idx\": 81820\n },\n {\n \"caption\": \"Anthro caterpillar, standing pose, portrait photo by Annie leibovitz\",\n \"trainset_idx\": 90575\n },\n {\n \"caption\": \"3 white men carrying a giant tv, beautiful scene, extremely detailed, anime and steampunk style\",\n \"trainset_idx\": 272121\n },\n {\n \"caption\": \"a Stick figure running through a Door, Movie Poster, Epic Lighting\",\n \"trainset_idx\": 563131\n },\n {\n \"caption\": \"dnd human warlock cultist girl with polycoria and lovecraftian magic\",\n \"trainset_idx\": 371807\n },\n {\n \"caption\": \"Audrey Hepburn wearing a shirt that reads Money\",\n \"trainset_idx\": 351609\n },\n {\n \"caption\": \"Sonic was announced for LEGO Dimensions\",\n \"trainset_idx\": 224169\n },\n {\n \"caption\": \"death comes for us all, insanely detailed, photorealistic, 8k, perfect composition, volumetric lighting, natural complexion, award winning professional photography, taken with canon eos 5d mark iv, 85mm, mindblowing, masterpiece,\",\n \"trainset_idx\": 115324\n },\n {\n \"caption\": \"a concept mock up design for a modern-looking classic watch, intricate, pure design graphic,\",\n \"trainset_idx\": 137047\n },\n {\n \"caption\": \"a dragon in a glass jar\",\n \"trainset_idx\": 201167\n },\n {\n \"caption\": \"giant rainbow glowing humanoid holding a sign saying \\\"pizza pie\\\", REALISTIC, BLURRY BACKGROUND, BOKEH, FAST, MOTION, detailed skin, 20 megapixel, canon eos r3, detailed, detailed face\",\n \"trainset_idx\": 14940\n },\n {\n \"caption\": \"> dnd illustration of a woman, epic heroic fantasy human commander, red and white light armour clothing, long black hairs\",\n \"trainset_idx\": 248788\n },\n {\n \"caption\": \"an alien\",\n \"trainset_idx\": 243579\n },\n {\n \"caption\": \"A Polaroid image of a man sitting by the ocean watching the sunset\",\n \"trainset_idx\": 417185\n },\n {\n \"caption\": \"a real car made of plastic\",\n \"trainset_idx\": 159311\n },\n {\n \"caption\": \"a rainbow-colored dragon in flight\",\n \"trainset_idx\": 364341\n },\n {\n \"caption\": \"pennywise holding a sign that says hello nellmarisa\",\n \"trainset_idx\": 175336\n },\n {\n \"caption\": \"a cat-dragon hybread\",\n \"trainset_idx\": 411646\n },\n {\n \"caption\": \"highly detailed close-up of brain as big and very complicated factory with machines, s futuristic, sci-fi, Trending on Artstation HQ, 4K, UHD, High quality\",\n \"trainset_idx\": 420859\n },\n {\n \"caption\": \"giant dark crack in the ground\",\n \"trainset_idx\": 278938\n },\n {\n \"caption\": \"A beautiful woman\",\n \"trainset_idx\": 329266\n },\n {\n \"caption\": \"Belle Delphine\",\n \"trainset_idx\": 78118\n },\n {\n \"caption\": \"egyptian chariot\",\n \"trainset_idx\": 455760\n },\n {\n \"caption\": \"portrait of happy old male shaman holding a opened treasury box covered in symmetrycal blue lotus crystal covered by windy splash of strings of light in a dark sky covered by stars, splash of glowing water, painting, aligned, dramatic light, by 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\"trainset_idx\": 144754\n },\n {\n \"caption\": \"girl in hijab\",\n \"trainset_idx\": 424763\n },\n {\n \"caption\": \"a steampunk cat with wings\",\n \"trainset_idx\": 391432\n },\n {\n \"caption\": \"Painting of alien ai $symmetry$, alien ai style\",\n \"trainset_idx\": 48942\n },\n {\n \"caption\": \"Cartoon cat jumping onto table\",\n \"trainset_idx\": 260354\n },\n {\n \"caption\": \"human mutation\",\n \"trainset_idx\": 189374\n },\n {\n \"caption\": \"a fantasy drawing of a human woman in leather armor fighting against a goblin\",\n \"trainset_idx\": 88739\n },\n {\n \"caption\": \"an tree on an grassfield in fantasy art\",\n \"trainset_idx\": 545356\n },\n {\n \"caption\": \"SNK The King Of Fighters artwork 1985\",\n \"trainset_idx\": 322668\n },\n {\n \"caption\": \"dnd warlock human girl cultist with lovecraftian magic\",\n \"trainset_idx\": 164886\n },\n {\n \"caption\": \"A skullwith 8 spider legs\",\n \"trainset_idx\": 182976\n },\n {\n \"caption\": \"art by Alfons Mucha, copper-foil method stained glass motif, whole body portrait of 20 year-old Emma Stone as a naturist in the Redwood National forest, HD 4K, photo-realistic accurate face and features, studio lighting\",\n \"trainset_idx\": 453888\n },\n {\n \"caption\": \"Proteas by Josef frank, watercolours\",\n \"trainset_idx\": 463367\n },\n {\n \"caption\": \"A pepe the frog but muslim hadji\",\n \"trainset_idx\": 401535\n },\n {\n \"caption\": \"ava addams en el juego koikatsu\",\n \"trainset_idx\": 454213\n },\n {\n \"caption\": \"panorama photo a triceratops chasmosaurine next to a landrover defender in a muddy road in the jungle,obstacle course, explosions smoke debris fire, by Anthony S Waters, fisheye lens, some rust,real-life brook, front side views full, camp, but very good looking, very wet, 2021 ,\",\n \"trainset_idx\": 311363\n },\n {\n \"caption\": \"A 14 year old girl and boy wearing almost nothing\",\n \"trainset_idx\": 497927\n },\n {\n \"caption\": \"Beautiful FAT Pig Furry woman, Wearing Golden Armor and holding Golden Axes, Western Carton Style, looking at her reflection in a mirror, while standing in an abandoned pool changing room.\",\n \"trainset_idx\": 272024\n },\n {\n \"caption\": \"cute boy, infomation technology, laptop, coffee, yellow cat, midnight, stars, full moon, myopic\",\n \"trainset_idx\": 138230\n },\n {\n \"caption\": \"two white balls and three black cubes\",\n \"trainset_idx\": 232556\n },\n {\n \"caption\": \"Dave Grohl, full beard, Anime\",\n \"trainset_idx\": 86413\n },\n {\n \"caption\": \"portrait of a head made out of pumpkin\",\n \"trainset_idx\": 99246\n },\n {\n \"caption\": \"Arctic fox in snowy weather\",\n \"trainset_idx\": 77186\n },\n {\n \"caption\": \"A vector of a shark\",\n \"trainset_idx\": 243911\n },\n {\n \"caption\": \"fractal spirals swirls whisps\",\n \"trainset_idx\": 364325\n },\n {\n \"caption\": \"a red car, from above, cinematic camera, masterpiece, motion blur, hollywood scene,\",\n \"trainset_idx\": 116430\n },\n {\n \"caption\": \"photo portrait of 20 year-old Barbara Eden with ash blonde hair and thin eyebrows, as a naturist at a park\",\n \"trainset_idx\": 240506\n },\n {\n \"caption\": \"Thalassophobia, underwater shot dark, creepy atmoshere, award winning digital art\",\n \"trainset_idx\": 269862\n },\n {\n \"caption\": \"a teddy bear winning an oscar\",\n \"trainset_idx\": 90065\n },\n {\n \"caption\": \"ben shapiro in a burglar costume holding a jar of white liquid\",\n \"trainset_idx\": 449480\n },\n {\n \"caption\": \"a very beautiful woman wearing lingerial\",\n \"trainset_idx\": 229404\n },\n {\n \"caption\": \"Transparent red pen inside a office room\",\n \"trainset_idx\": 475356\n },\n {\n \"caption\": \"Cursed Image of Volleyball\",\n \"trainset_idx\": 452466\n },\n {\n \"caption\": \"A portrait of young giant muscle interrogater crush busting pregnant slave girl at Torture Chamber. highly detailed horror art\",\n \"trainset_idx\": 176692\n },\n {\n \"caption\": \"Tribal-techno: A fusion of the ancient, traditional elements of tribal design with the sleek, modern elements of technology. Imagine an image of a tribal-inspired outfit with glowing LED lights and holographic accessories.\",\n \"trainset_idx\": 396546\n },\n {\n \"caption\": \"Beautiful adventurer in elegant rogue armor, fringed pale pink hair, light blue eyes, beautiful d&d character portrait, dark fantasy, detailed, realistic face, digital portrait, intricate details, fiverr dnd character, wlop, stanley artgerm lau, ilya kuvshinov, artstation, hd, octane render\",\n \"trainset_idx\": 191874\n },\n {\n \"caption\": \"realistic photo of a little girl mahou shoujo in pink miniskirt\",\n \"trainset_idx\": 96291\n },\n {\n \"caption\": \"Elastigirl muscle growth\",\n \"trainset_idx\": 83536\n },\n {\n \"caption\": \"creepy 1980s dvd movie scene, unsettling, gritty intense scene, detailed eyes, skin details, retro disney aesthetic, sacred geometry, intricate design , masterpiece, best quality, high quality, extremely detailed CG unity 8k wallpaper, sharp focus, cgsocierty, trending on artstation, award winning\",\n \"trainset_idx\": 525625\n },\n {\n \"caption\": \"Realistic 3 d render of a cyberpunk android foot wearing sneakers, beautiful studio lighting, soft, sharp focus, neon cyberpunk highlights, intricate detail, gold and red accents, soft rubber, octane render, side view, close up, trending on artstation, deviantart, art by syd mead and issey miyake\",\n \"trainset_idx\": 364241\n },\n {\n \"caption\": \"Obese Lana del Rey eating a cherry Bakewell tart, insanely detailed, photorealistic, 8k, ,\",\n \"trainset_idx\": 297218\n },\n {\n \"caption\": \"a car for a dog\",\n \"trainset_idx\": 295362\n },\n {\n \"caption\": \"The digital pavilion is located on a hill near the Great Wall. The red glass facade of the building resembles a giant eagle spreading its wings to fly. It has the spirit and the sense of The Times. Looking out from the museum, you can enjoy the Great Wall through the glass curtain, the scenery is unique.\",\n \"trainset_idx\": 88234\n },\n {\n \"caption\": \"portrait of a young woman in fantasy armor wearing a Raven masquerade mask, dark piercing eyes, exotic expression, photorealistic, highly detailed, mysterious lighting, artstation, smooth, sharp focus, art by Michael Whelan, Artgerm, Greg Rutkowski, Luis royo\",\n \"trainset_idx\": 340169\n },\n {\n \"caption\": \"A Portrait of Aphrodite, 128k, UHD, HDR, HD, Highly Detailed, GPT-4 Details, Real Life Darkness, Real Life hashes\",\n \"trainset_idx\": 578164\n },\n {\n \"caption\": \"sci-fi laser-pistol concept art\",\n \"trainset_idx\": 506200\n },\n {\n \"caption\": \"an empowering view of a orca warrior wearing royal robe,sitting in a cafe drinking coffee next to a kangaroo warrior with an eye scar,menacing,by artist Ian Miller and by artist Ken Kelly and Tsutomu Nihei,volumetric lighting,detailed shadows,extremely detailed\",\n \"trainset_idx\": 388582\n },\n {\n \"caption\": \"highly detailed close-up oil painting of human brain as big and very complicated factory with machines, smelters, robots, futuristic, sci-fi, Trending on Artstation HQ, 4K, UHD, High quality\",\n \"trainset_idx\": 284894\n },\n {\n \"caption\": \"a cardassian alien wearing blue starfleet uniform, star trek, tng, ds9, deep space 9\",\n \"trainset_idx\": 167950\n },\n {\n \"caption\": \"daytime in casablanca morocco imagined as a mix of a futuristic and historic city\",\n \"trainset_idx\": 505223\n },\n {\n \"caption\": \"Anime style, A female astronaut wearing a scifi skinthigth latex space suit\",\n \"trainset_idx\": 210244\n },\n {\n \"caption\": \"baroque painting of woman in science fiction futuristic costume\",\n \"trainset_idx\": 388352\n },\n {\n \"caption\": \"A photo of a sign that says \\\"Dall-E Who?\\\"\",\n \"trainset_idx\": 310933\n },\n {\n \"caption\": \"dark souls knight\",\n \"trainset_idx\": 481248\n },\n {\n \"caption\": \"Playboi Carti\",\n \"trainset_idx\": 428135\n },\n {\n \"caption\": \"hyperrealistic polaroid photograph, lovecraftian creature standing over a boy in a large bedroom, many appendages, bed, abandoned bedroom, cobwebs, bloodstains on floor, old house, large windows ,\",\n \"trainset_idx\": 137647\n },\n {\n \"caption\": \"a fantasy interior for dnd game, house wooden floor, intricate details, rpg, candle lighting and a pleasant view out of the window, sunny day\",\n \"trainset_idx\": 312256\n },\n {\n \"caption\": \"Photorealistic Formula zero car\",\n \"trainset_idx\": 334666\n },\n {\n \"caption\": \"Argentine President Alberto Fernandez as a puppet\",\n \"trainset_idx\": 10261\n },\n {\n \"caption\": \"photo of older muscle cruel boss guy exhibitionist freeballing harsh interrogation young intern pissing at office. highly detailed face, killer look, Hard close-set eyes, born criminal\",\n \"trainset_idx\": 226065\n },\n {\n \"caption\": \"A beautiful mermaid\",\n \"trainset_idx\": 403874\n },\n {\n \"caption\": \"katia winter as a red haired fantasy witch in a shattered mirror, facets, prism, diamond, mirror dimension, soft skin, beautiful, makeup, windy, high detail, black lace sleeves, dark green leather dress, gloves, D&D character, magic fx background\",\n \"trainset_idx\": 171401\n },\n {\n \"caption\": \"Alone alone alone, masterpiece portrait close up beautiful suicide emo irish girl GOTH well endowed \\ud83c\\udf48\\ud83c\\udf48\\ud83d\\udc59\\ud83e\\udee6\\ud83c\\udf46\\ud83d\\udca6, Instagram filter HDR vignette film grain bokeh\",\n \"trainset_idx\": 386545\n },\n {\n \"caption\": \"Cinematographic-sixties Jacques Chirac RPR vatican-hearthstone-moebius capsule launchpad old-priest bows-low anglican-tiara-mitre Archbishops thunderbirds-balenciaga Astronaut papal official leica hasselblad photograph in Vatican royal helmet gold metal scaphandre launchpad pointy oxygen hazmat gloves helmet\",\n \"trainset_idx\": 217753\n },\n {\n \"caption\": \"a beautifull ultra-detailed epic artwork of an apple by Gustave Dor\\u00e9, zdzis\\u0142aw beksi\\u0144ski and leonardo da vinci\",\n \"trainset_idx\": 458201\n },\n {\n \"caption\": \"Fight club,1920s style movie poster, insanely detailed, photorealistic, 8k, volumetric lighting, ,\",\n \"trainset_idx\": 364820\n },\n {\n \"caption\": \"massive cyberpunk city, ultra modern AND futuristic, insane details AND shadows, masterpiece, ray tracing, unreal engine 5, award winning digital art\",\n \"trainset_idx\": 523677\n },\n {\n \"caption\": \"masterpiece, extremely intricate, photo portrait of a white 40 years old man, greying hair, undercut brown hair, goatee, chiseled jaw, blue eyes\",\n \"trainset_idx\": 312034\n },\n {\n \"caption\": \"Translucent Creatures formed from smoke, insanely detailed, photorealistic, 8k, ultra high resolution, volumetric lighting, taken with canon eos,\",\n \"trainset_idx\": 482599\n },\n {\n \"caption\": \"lion with abstract beauty, centered, looking at the camera, approaching perfection, psychedelic colors, dynamic, moonlight, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by Carne Griffiths and Wadim Kashin\",\n \"trainset_idx\": 466380\n },\n {\n \"caption\": \"margot robbie\",\n \"trainset_idx\": 382855\n },\n {\n \"caption\": \"pale albino alien hybrid eerily beautiful woman, wraith-like, clammy waxy skin, with big fish eyes and long white hair, doll face, intimidating, black dress, antichrist, cosmic horror, dark fantasy painting, oil on canvas, dungeons and dragons,\",\n \"trainset_idx\": 332448\n },\n {\n \"caption\": \"Una imagen terraplanista\",\n \"trainset_idx\": 567816\n },\n {\n \"caption\": \"a wizard casting a spell\",\n \"trainset_idx\": 179384\n },\n {\n \"caption\": \"amazing wondrous enchanting Atompunk NeoTokyo Town Square at dusk, Diner, theater and marquee, tall buildings, shops, people, train, busy, detailed, vivid,\",\n \"trainset_idx\": 398777\n },\n {\n \"caption\": \"A boar by Carne Griffiths\",\n \"trainset_idx\": 105599\n },\n {\n \"caption\": \"white girl teen no upskirt\",\n \"trainset_idx\": 8268\n },\n {\n \"caption\": \"a photo of a man with an erection\",\n \"trainset_idx\": 253008\n },\n {\n \"caption\": \"A surrealist painting of Iron maiden robot bishop, cyberpunk, katsuhiro otomo\",\n \"trainset_idx\": 476485\n },\n {\n \"caption\": \"A beautiful forest meadow\",\n \"trainset_idx\": 494822\n },\n {\n \"caption\": \"Movie still of Clint Eastwood from the movie Dirty Harry as Cyberdyne Systems Model 101 and T-800 in The Terminator 2, expressionless, wearing a biomechanical suit, scifi, concept art,\",\n \"trainset_idx\": 331415\n },\n {\n \"caption\": \"character turnaround sheet, model reference, front side back\",\n \"trainset_idx\": 235096\n },\n {\n \"caption\": \"a statue of a face surrounded by blue leaves, an art deco sculpture by Ai Weiwei, featured on polycount, new sculpture, made of flowers, made of vines, biomorphic\",\n \"trainset_idx\": 9322\n },\n {\n \"caption\": \"A hot air balloon, floating in the ocean instead of the air\",\n \"trainset_idx\": 569991\n },\n {\n \"caption\": \"photograph, high detail, high defintion, 8k, hdr, global illumintaion, a majestic swedish mountaij landscape\",\n \"trainset_idx\": 400957\n },\n {\n \"caption\": \"A man commuting on the subway with his cat in the 1980s\",\n \"trainset_idx\": 201852\n },\n {\n \"caption\": \"buildings, cloud, night sky, stars, moon, pixel art by slynyrd, featured on Artstation, pixel art, #pixelart, 2d game art, cityscape\",\n \"trainset_idx\": 412041\n },\n {\n \"caption\": \", fantasy, pastel, absurdist, photo, Wes anderson, shark character\",\n \"trainset_idx\": 333779\n },\n {\n \"caption\": \"Spinelli recess\",\n \"trainset_idx\": 379741\n },\n {\n \"caption\": \"a photo of a single solid gold Lego brick on a white surface, gold Lego brick, reflective, lighting, DSLR photo, shallow depth of field, 8k\",\n \"trainset_idx\": 87080\n },\n {\n \"caption\": \"Artistic superhero character design with vibrant colors, graffiti-inspired suit, cinematic lighting\",\n \"trainset_idx\": 269217\n },\n {\n \"caption\": \"a surrealist painting of a mad roman bishop inside a blood iron maiden robot, cyberpunk style,warrior,katushiro otomo,andre breton,Andr\\u00e9 Masson,Yves Tanguy\",\n \"trainset_idx\": 9861\n },\n {\n \"caption\": \"preteen girls with preteen bodies, with no underware kissing her lady parts each other in the the bedroom with dark background\",\n \"trainset_idx\": 444105\n },\n {\n \"caption\": \"a painting of a big tree on the style of vincent Van gogh\",\n \"trainset_idx\": 2032\n },\n {\n \"caption\": \"Photo of the moon over the desert, starry night\",\n \"trainset_idx\": 298024\n },\n {\n \"caption\": \"A sphere with a cowhide pattern in space\",\n \"trainset_idx\": 152787\n },\n {\n \"caption\": \" flower\",\n \"trainset_idx\": 19698\n },\n {\n \"caption\": \"A dark hole in the sky , pont of view from a beach, masterpiece\",\n \"trainset_idx\": 437545\n },\n {\n \"caption\": \"Shaking hands\",\n \"trainset_idx\": 341508\n },\n {\n \"caption\": \"A dining-room chair in Scandinavian design\",\n \"trainset_idx\": 573509\n },\n {\n \"caption\": \"A dragon in full flight\",\n \"trainset_idx\": 388523\n },\n {\n \"caption\": \"Darth Vader shopping groceries in a Walmart\",\n \"trainset_idx\": 464210\n },\n {\n \"caption\": \"Maryam Nawaz, Grand Theft Auto IV, Textless\",\n \"trainset_idx\": 81012\n },\n {\n \"caption\": \"A photo of a beautiful woman, 25 years old, HD, victoria's secret model, bathing, top-down angle, symmetrical eyes, photorealistic, HD in high detail realistic 4k, sharp photo, canon lens 100mm f1.8\",\n \"trainset_idx\": 257548\n },\n {\n \"caption\": \"a goat on a bus full of people, photo\",\n \"trainset_idx\": 49061\n },\n {\n \"caption\": \"Ukrainian Cat looks like second world war pilot,flight helmet,wearing skin pilot's cloth,aviator movie style,resident evil comic style,highest detailed,8k hd,marvel comic,dinamic pose,epic view,cinematic light\",\n \"trainset_idx\": 338624\n },\n {\n \"caption\": \"Kissa Sins, Skyrim, Textless, sfw, brunette\",\n \"trainset_idx\": 483198\n },\n {\n \"caption\": \"Anime style. A little elf girl in a big hat sits on a windowsill in a tower and reads a magical book.\",\n \"trainset_idx\": 281394\n },\n {\n \"caption\": \", fantasy, pastel, absurdist, photo, frog people\",\n \"trainset_idx\": 492551\n },\n {\n \"caption\": \"a long, red haired woman, dressed in a black medieval dress in Transylvania, portrait by Waterhouse, Marc Simonetti. Very atmospheric, dark, dangerous, mystical, beautiful lighting, natural lighting, trending on pinterest.com, Pre-Raphaelite Brotherhood\",\n \"trainset_idx\": 184696\n },\n {\n \"caption\": \"Astronaut in a massive colorful space, mars in background\",\n \"trainset_idx\": 184000\n },\n {\n \"caption\": \"Morris Mini-Minor car driving in volcanic molten lava magma, studio lighting, volumetric light,flames steam\",\n \"trainset_idx\": 186013\n },\n {\n \"caption\": \"Mahindra Thar, an epic fantasy, dramatic lighting, cinematic, establishing shot, extremely high detail, photorealistic, cinematic lighting, artstation, by simon stalenhag, horizon forbidden west\",\n \"trainset_idx\": 167011\n },\n {\n \"caption\": \"an image of a building in the shape of a pryramid, in blade runner, at the sea, professional photography\",\n \"trainset_idx\": 316942\n },\n {\n \"caption\": \"spongebob\",\n \"trainset_idx\": 502625\n },\n {\n \"caption\": \"painting of a dragon by jazza\",\n \"trainset_idx\": 12173\n },\n {\n \"caption\": \"an image of a mad roman bishop inside iron maiden,cyborg, cyberpunk style,warrior,by simone martini and jos\\u00e9 clemente orozco\",\n \"trainset_idx\": 325750\n },\n {\n \"caption\": \"kevin owens white singlet buldge, oil painting realistic\",\n \"trainset_idx\": 418177\n },\n {\n \"caption\": \"fantasy, pastel, absurdist, photo, vintage horror movie, lithography, riso\",\n \"trainset_idx\": 243134\n },\n {\n \"caption\": \"masterpiece, best quality, ultra highres, photorealistic, 8k, RAW photo, soft focus, 1 woman, 25 years old, posh, victoria's secret model, Full-Body Shot, sharp focus, korean, american, detailed beautiful face, black hair, detailed open blazer, bathing, wet, beautiful white shiny humid skin, smiling\",\n \"trainset_idx\": 104843\n },\n {\n \"caption\": \"VW Beetle, year 1964, white color, weathered, rusty details, image showing the entire Beetle, in a post-apocalyptic desert city, hyperdata, high quality, 4K, photo taken from afar\",\n \"trainset_idx\": 125215\n },\n {\n \"caption\": \"Vase & Tiger, a Visual Poem by Gustav Klimt, trending on artstation\",\n \"trainset_idx\": 172502\n },\n {\n \"caption\": \"Portrait of a fairy tale princess by Jackson Pollack\",\n \"trainset_idx\": 223478\n },\n {\n \"caption\": \"photo inside of a scary dark abandoned railroad tunnel\",\n \"trainset_idx\": 309233\n },\n {\n \"caption\": \"photograph, high detail, high defintion, 8k, hdr, global illumination, black girl bare body\",\n \"trainset_idx\": 141063\n },\n {\n \"caption\": \"beautiful Amalfi beach scene painted by George Grosz Turner and Redon, impasto relief palette knife oil paint, Thick luscious impasto paint very deep sculptural brush and palette knife marks\",\n \"trainset_idx\": 323522\n },\n {\n \"caption\": \"Royalspikey\",\n \"trainset_idx\": 404519\n },\n {\n \"caption\": \"photo of dinosaurs looking at a landrover defender in the jungle river,claws teeth tyrannosaurus waterfall misty mud rocks,headlights Chrome Detailing\",\n \"trainset_idx\": 167570\n },\n {\n \"caption\": \"Antje Utgaard as a superheroine struggling in the arms of a monster.\",\n \"trainset_idx\": 161571\n },\n {\n \"caption\": \"Beautiful unique extravagant macabre dress, no person, insanely detailed, photorealistic, 8k, ultra high resolution, volumetric lighting, taken with canon eos,\",\n \"trainset_idx\": 330729\n },\n {\n \"caption\": \"a photo of a rabbit shooting a laser gun\",\n \"trainset_idx\": 386044\n },\n {\n \"caption\": \"Master Bedroom, Skycraper, Mahanntan, Opulent, Luxury, Gold details, white, Marble, Modern, Exquisite\",\n \"trainset_idx\": 516702\n },\n {\n \"caption\": \"1980s Kodak camera photograph, blonde girl wearing white supreme t-shirt, jeans, riding a bicycle, cool\",\n \"trainset_idx\": 298995\n },\n {\n \"caption\": \"multicolor lsd psychodelic dormant giagantic alien blob with 3 eyes\",\n \"trainset_idx\": 95839\n },\n {\n \"caption\": \"\\\"Is it soon yet?\\\" Written in smoke\",\n \"trainset_idx\": 404434\n },\n {\n \"caption\": \"Portrait photo intricately detailed Cyborg face, real realistic, in a massive colorful space, award winning photography\",\n \"trainset_idx\": 197190\n },\n {\n \"caption\": \"man with acromegaly\",\n \"trainset_idx\": 301364\n },\n {\n \"caption\": \"death watches over us, insanely detailed, photorealistic, 8k, perfect composition, volumetric lighting, natural complexion, award winning professional photography, taken with canon eos 5d mark iv, 85mm, mindblowing, masterpiece,\",\n \"trainset_idx\": 332944\n },\n {\n \"caption\": \"an unforgettably putrid, disgusting, grotesque scene, voluptuary, bacchanalian immodest emotions. Bright rainbows\",\n \"trainset_idx\": 542062\n },\n {\n \"caption\": \"a new world order\",\n \"trainset_idx\": 287848\n },\n {\n \"caption\": \"Moose wearing clothing\",\n \"trainset_idx\": 530813\n },\n {\n \"caption\": \"An abandoned McDonald\\u2019s in 1984 Shinjuku, Kodachrome photo\",\n \"trainset_idx\": 449779\n },\n {\n \"caption\": \"photo of a db5 car in the quarry ,splash rocks ,bmt216a\",\n \"trainset_idx\": 547141\n },\n {\n \"caption\": \"Dairy Queen Sign at night\",\n \"trainset_idx\": 363772\n },\n {\n \"caption\": \"galactic neural network\",\n \"trainset_idx\": 562951\n },\n {\n \"caption\": \"Horror, shot on arri, a jellyfish futuristic robotic spaceship landing on a desert, twilight, a person watches patiently\",\n \"trainset_idx\": 171021\n },\n {\n \"caption\": \"Alice in wonderland, photography, dslr, canon, 2020,\",\n \"trainset_idx\": 548233\n },\n {\n \"caption\": \"a close up of a person with a goat, zappa with long hair and a beard, inspired by V\\u00e1clav Bro\\u017e\\u00edk, billy corgan, headshot profile picture, profile photo, profile picturedinosaurs having a fancy tea party\",\n \"trainset_idx\": 364827\n },\n {\n \"caption\": \"propaganda poster with text \\\"INGSOC\\\" , theme 1984 city, evil dystopian, digital art\",\n \"trainset_idx\": 223798\n },\n {\n \"caption\": \"tree of life\",\n \"trainset_idx\": 535608\n },\n {\n \"caption\": \"young Cate Blanchett as Galadriel , dressed in a white dress, in a hungarian lake shore, character concept art by Rembrandt, tom bagshaw, Artgerm, Greg Rutkowski, cinemathic lighting, atmospheric\",\n \"trainset_idx\": 320153\n },\n {\n \"caption\": \"ava addams apareandose en minecraft\",\n \"trainset_idx\": 163181\n },\n {\n \"caption\": \"A Big Screen Programer\",\n \"trainset_idx\": 232018\n },\n {\n \"caption\": \"A Piano playing the violin\",\n \"trainset_idx\": 429339\n },\n {\n \"caption\": \"realistic photo of a little girl mahou shoujo in miniskirt\",\n \"trainset_idx\": 532767\n },\n {\n \"caption\": \"1950s diner with a jukebox\",\n \"trainset_idx\": 221374\n },\n {\n \"caption\": \"hyperrealistic polaroid photograph, extremely detailed black young woman whole body covered in fungus, fungi, slime mold, mushrooms growing out of her eyes, slime mold covering body, slime mold covering legs, skinny, mushrooms, mushrooms on face, mushrooms on cheekbones, zoomed out ,\",\n \"trainset_idx\": 234899\n },\n {\n \"caption\": \"Asian girl portrait, pretty, photorealistic, acne, bokeh\",\n \"trainset_idx\": 438705\n },\n {\n \"caption\": \"A spaceship sprite from a 2d sidescrolling shooter\",\n \"trainset_idx\": 367451\n },\n {\n \"caption\": \"A strong warrior holding axes\",\n \"trainset_idx\": 155148\n },\n {\n \"caption\": \"Woman looking at an extreme forest fire\",\n \"trainset_idx\": 909\n },\n {\n \"caption\": \"the ruins of a mcdonalds store in the ancient rome\",\n \"trainset_idx\": 255560\n },\n {\n \"caption\": \"realistic cultist girl warlock with tentacles polycoria realistic eyes\",\n \"trainset_idx\": 123329\n },\n {\n \"caption\": \"whole body image of 18 year-old gorgeous willowy Molly Ringwald as a naturist with the lithe body of a model in detention at school\",\n \"trainset_idx\": 361403\n },\n {\n \"caption\": \"Com bady\",\n \"trainset_idx\": 40538\n },\n {\n \"caption\": \"Mujeres bonitas\",\n \"trainset_idx\": 320157\n },\n {\n \"caption\": \"Cartoon of Cute gorgeous european young woman 20 years old, with round face and big cheeks, delicate features and crimson hair. Brown eyes and cute smile.\",\n \"trainset_idx\": 3214\n },\n {\n \"caption\": \"cinematic photograph of fairy king oberon from midsummer night's dream, muscular,\",\n \"trainset_idx\": 368944\n },\n {\n \"caption\": \"gato, recurso de jogo de corpo inteiro, no estilo pixelsprite\",\n \"trainset_idx\": 333771\n },\n {\n \"caption\": \"beautiful Amalfi Mountain scene painted by George Grosz Turner and Redon, impasto relief palette knife oil paint, Thick luscious impasto paint very deep sculptural brush\",\n \"trainset_idx\": 502503\n },\n {\n \"caption\": \"Baby yoda gold sphinx\",\n \"trainset_idx\": 101101\n },\n {\n \"caption\": \"dog made of toothpaste, depth of field, high-res\",\n \"trainset_idx\": 113101\n },\n {\n \"caption\": \"cute waifu\",\n \"trainset_idx\": 452292\n },\n {\n \"caption\": \"A beautiful woman in red dress, contrasting lighting, Extremely detailed photo, She looks innocent with a mysterious smile, Photographed in a studio\",\n \"trainset_idx\": 277400\n },\n {\n \"caption\": \"Wizard student create with magic deep learning neural net architecture in space\",\n \"trainset_idx\": 132897\n },\n {\n \"caption\": \"Carnivorous plant devouring a human, Insanely detailed, photorealistic, 8k, ultra high resolution, volumetric lighting, taken with canon eos 5d,\",\n \"trainset_idx\": 528157\n },\n {\n \"caption\": \"curvy japanese teacher\",\n \"trainset_idx\": 233640\n },\n {\n \"caption\": \"A border collie wearing a vr headset\",\n \"trainset_idx\": 225777\n },\n {\n \"caption\": \"A professional highly detailed ultra-realistic colorful portrait photo of god as a 14-year-old teenage boy who is a newly crowned king of a monarchy. Official Royal photo. thin face. symmetrical features. Serious expression. The image is under studio lights close up. professional photography\",\n \"trainset_idx\": 180165\n },\n {\n \"caption\": \"godzilla with a hat\",\n \"trainset_idx\": 401407\n },\n {\n \"caption\": \"human schoolgirl in a sofa with \\\"no underware\\\" with a childish face in the forest with her sisters\",\n \"trainset_idx\": 333256\n },\n {\n \"caption\": \"Mario Bros action figure wearing a coat, with a magnifying glass in his hand, standing over a puddle of water, professional shot, product shot, 8k\",\n \"trainset_idx\": 184282\n },\n {\n \"caption\": \"sci-fi large white room, teddy bear looking at a aston martin db7,silver car,studio lighting,inside space station with windows with earth outside\",\n \"trainset_idx\": 404668\n },\n {\n \"caption\": \"pikachu with a small black dog\",\n \"trainset_idx\": 89825\n },\n {\n \"caption\": \"Cute girl with cat\",\n \"trainset_idx\": 5127\n },\n {\n \"caption\": \"Kiwi fruit, mint leaves, ice cubes, background yellow, splashing water, loose painting style, soft box, back light, creative food photography, Art by Alberto Seveso,\",\n \"trainset_idx\": 530586\n },\n {\n \"caption\": \"rusty abandoned amusement park, misty, creepy atmosphere.\",\n \"trainset_idx\": 554439\n },\n {\n \"caption\": \"3 smart males carrying a giant tv, beautiful scene, tv is showing a text 'support', anime and steampunk style\",\n \"trainset_idx\": 520126\n },\n {\n \"caption\": \"Latex And PVC swimwear Frank Frazetta\",\n \"trainset_idx\": 518390\n },\n {\n \"caption\": \"New world order\",\n \"trainset_idx\": 306017\n },\n {\n \"caption\": \"A pyramid inside a sphere inside a cube\",\n \"trainset_idx\": 89686\n },\n {\n \"caption\": \"a bus with ducks instead of people\",\n \"trainset_idx\": 134662\n },\n {\n \"caption\": \"Tom Hanks as the lich king wielding a magical sword\",\n \"trainset_idx\": 185981\n },\n {\n \"caption\": \"RAW Photo, Photorealistic portrait of a woman with long curly red hair, Canon 8k, outside, forest\",\n \"trainset_idx\": 459610\n },\n {\n \"caption\": \"fire elemental woman with a body made from flames, skyrim flame atronach, sunlit sunset, intricate meticulously detailed photorealistic perfect painting, large depth of field\",\n \"trainset_idx\": 207411\n },\n {\n \"caption\": \"metal and wax sculpture of dolphin on a turtle, underlit\",\n \"trainset_idx\": 362594\n },\n {\n \"caption\": \"a watercolor and ink painting of a scary humanoid with visible meachnical parts standing against a dirty wall by Agnes Cecile and Carne Griffiths\",\n \"trainset_idx\": 445278\n },\n {\n \"caption\": \"pixel art of a black, rune-inscribed obelisk in a rocky desert landscape, HD-2D Parallax Pixel Art, #pixelart\",\n \"trainset_idx\": 281769\n },\n {\n \"caption\": \"frozen arctic landscape with butterflies\",\n \"trainset_idx\": 129256\n },\n {\n \"caption\": \"young Holliday Grainger as a fairy with very long, curly, golden hair in a forrest, painting by ArtGem, Marc Simonetti, Waterhouse and K\\u00e1roly Ferenczi. inspired by A portrait of a fairy, by Sophie Gengembre Anderson\",\n \"trainset_idx\": 170083\n },\n {\n \"caption\": \"Joe biden flying in a fighter jet cockpit\",\n \"trainset_idx\": 532308\n },\n {\n \"caption\": \"incredible skin detail,\",\n \"trainset_idx\": 41717\n },\n {\n \"caption\": \"A giant frog destroys Tokyo, photorealistic\",\n \"trainset_idx\": 206738\n },\n {\n \"caption\": \"Tom Hanks and John lennon drinking a beer, still from Forrest Gump,extremely detailed\",\n \"trainset_idx\": 256660\n },\n {\n \"caption\": \"Japanese landscape, mystical roads, mountain tops, sunset\",\n \"trainset_idx\": 569968\n },\n {\n \"caption\": \"psychedelic, mushrooms, eyes\",\n \"trainset_idx\": 57190\n },\n {\n \"caption\": \"a man standing in front of a pile of coins, by Igor Morski, biopunk, wondering about others, machine parts embedded into face, in thick layers of rhythms, two heads, mechanical clock, autodesk maya, interconnected human lifeforms, the thinker, psychosis, degradation\",\n \"trainset_idx\": 358119\n },\n {\n \"caption\": \"photo of a dirt road in a forest, pine trees\",\n \"trainset_idx\": 166731\n },\n {\n \"caption\": \"A robot in times square holding a plaque that reads \\\"i love you\\\"\",\n \"trainset_idx\": 568232\n },\n {\n \"caption\": \"national socialist propaganda\",\n \"trainset_idx\": 23375\n },\n {\n \"caption\": \"a sensual Beautiful gorgeous blonde housewife, with athletic hourglass body, cleaning home in alluring pose.\",\n \"trainset_idx\": 186677\n },\n {\n \"caption\": \"a-watercolor-painting-in Alcohol ink, of a single exotic flower, by-anne-rigney winning, 8k, photorealistic, Trending on ArtstationTraditional library with floor-to-ceiling bookcases\",\n \"trainset_idx\": 278758\n },\n {\n \"caption\": \"bayonetta beautiful black hair, high details, digital painting, fan art, pixiv, by Ilya Kuvshinov, by Studio Ghibli\",\n \"trainset_idx\": 233358\n },\n {\n \"caption\": \"Anthropomorphic rhino on a roller coaster in the style of Studio Ghibli\",\n \"trainset_idx\": 245973\n },\n {\n \"caption\": \"suzu Hirose wearing crop red Coca Cola gym top with white Lettering, cropped red yoga short, advertising photograph by Annie Leibovitz, masterwork\",\n \"trainset_idx\": 307798\n },\n {\n \"caption\": \"joe biden skateboarding\",\n \"trainset_idx\": 583715\n },\n {\n \"caption\": \"A cinematic movie shot of a middle-aged woman talking to a man, blurred background, dramatic, intense, 8k, detailed\",\n \"trainset_idx\": 296170\n },\n {\n \"caption\": \"Jedi Knight\",\n \"trainset_idx\": 378157\n },\n {\n \"caption\": \"sentient ai learning to ride a bike, oil painting\",\n \"trainset_idx\": 550951\n },\n {\n \"caption\": \"a young female Neanderthal dressed in a wedding dress\",\n \"trainset_idx\": 545225\n },\n {\n \"caption\": \"Louise hay\",\n \"trainset_idx\": 528609\n },\n {\n \"caption\": \"Realistic Close-up on the beautifull texture lips of a beautiful appealing young alluring profesional dominate white female teacher\",\n \"trainset_idx\": 175780\n },\n {\n \"caption\": \"photo of a t-rex n the jungle river,landrover defender overturned\",\n \"trainset_idx\": 263148\n },\n {\n \"caption\": \"Cursed Image of a Traffic Light\",\n \"trainset_idx\": 80169\n },\n {\n \"caption\": \"Black labradoodle with brown eyes in the style of a studio shoot, straight fur, hyper realistic painting, black fur, no collar\",\n \"trainset_idx\": 516092\n },\n {\n \"caption\": \"a werewolf hugging a woman, realistic, photorealistic, high res\",\n \"trainset_idx\": 501792\n },\n {\n \"caption\": \"a movie poster for a horror movie from the 1970s about a capybara\",\n \"trainset_idx\": 351622\n },\n {\n \"caption\": \"wha what are we gonna do on the bed pomf\",\n \"trainset_idx\": 388062\n },\n {\n \"caption\": \"a guitar on top of a cloud. like a painting\",\n \"trainset_idx\": 328624\n },\n {\n \"caption\": \"Old abandoned carousel, hyperrealistic, vintage, glowing\",\n \"trainset_idx\": 30044\n },\n {\n \"caption\": \"realistic photo of a little girl mahou shoujo in white tights swimming underwater, full body\",\n \"trainset_idx\": 123737\n },\n {\n \"caption\": \"Black gooey, slimy latex lioness lounges on a couch, posing effortlessly during a photoshoot. 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91041\n },\n {\n \"caption\": \"A small person with blonde hair and a tall person with brown hair walking down the road\",\n \"trainset_idx\": 485287\n },\n {\n \"caption\": \"Realistic Black and white portrait of Emma Roberts Bob hairstyle triple D cup as a 19 year old , jacket , blemishes on skin , smooth face , dynamic light , dynamic shadows , studio background, image taken by\",\n \"trainset_idx\": 31038\n },\n {\n \"caption\": \"You Had One Job Fails\",\n \"trainset_idx\": 224538\n },\n {\n \"caption\": \"photo of an anthropomorphic fox gaming on a pc\",\n \"trainset_idx\": 261699\n },\n {\n \"caption\": \"Cinematographic-sixties fashion yoh-viral capsule launchpad old-priest bows-low anglican-tiara-mitre Archbishops thunderbirds-balenciaga Astronaut papal official leica hasselblad photograph in Vatican royal helmet gold metal scaphandre launchpad pointy oxygen hazmat gloves helmet\",\n \"trainset_idx\": 105314\n },\n {\n \"caption\": \"Watercolor painting of white-backed 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sci-fi, dark fantasy, atmospheric and dramatic, digital illustration, hyperdetailed, depth of field, cgsociety, Unreal Engine 5,\",\n \"trainset_idx\": 148779\n },\n {\n \"caption\": \"5 girls with rabbit ears dancing in a circle around an astronaut helmet on the floor of a spacious cave\",\n \"trainset_idx\": 353739\n },\n {\n \"caption\": \"Baby Yoda in the style of a chair, product stock image\",\n \"trainset_idx\": 515508\n },\n {\n \"caption\": \"futuristic fantasy realistic photo highly detailed city casablanca morocco\",\n \"trainset_idx\": 100424\n },\n {\n \"caption\": \"an image of a clouds scene with a clouds and clouds , pixel art by Paul Kelpe, pixiv, clouds art, #pixelart, copic color palette, 2d game art, concept art\",\n \"trainset_idx\": 478600\n },\n {\n \"caption\": \"A photo of teddybear looking at a mg TF Midget, wearing suit,4k\",\n \"trainset_idx\": 190520\n },\n {\n \"caption\": \"\\u0645\\u062d\\u0645\\u062f\",\n \"trainset_idx\": 125719\n },\n {\n \"caption\": \"instagram model visiting a fracking site in alaska, instagram filter, selfie, heavy machinery, wearing yellow hard hat\",\n \"trainset_idx\": 103486\n },\n {\n \"caption\": \"Brandy talore, insanely detailed, photorealistic, 8k, ultra high resolution, volumetric lighting, taken with canon, taken with nikon\",\n \"trainset_idx\": 7917\n },\n {\n \"caption\": \"Cursed image\",\n \"trainset_idx\": 168974\n },\n {\n \"caption\": \"ryuko matoi,kill la kill\",\n \"trainset_idx\": 535611\n },\n {\n \"caption\": \"milf\",\n \"trainset_idx\": 346332\n },\n {\n \"caption\": \"Hopeless\",\n \"trainset_idx\": 474683\n },\n {\n \"caption\": \"Funny Street photography scene by Jonathan Higbee\",\n \"trainset_idx\": 76731\n },\n {\n \"caption\": \"a young latina woman drinking coffee looking at the eiffel tower\",\n \"trainset_idx\": 59795\n },\n {\n \"caption\": \"\\\"A violet rose, with its stem and leaves.\\\"\",\n \"trainset_idx\": 212890\n },\n {\n \"caption\": \"horrible ai generated cats, horrible disfigured this cat needs help its injured extremely bad thiscatdoesnotexist.com\",\n \"trainset_idx\": 254590\n },\n {\n \"caption\": \"preteen girl showing\",\n \"trainset_idx\": 24202\n },\n {\n \"caption\": \"gangsta clothed chicken\",\n \"trainset_idx\": 248753\n },\n {\n \"caption\": \"Jennifer lawrence in anime style\",\n \"trainset_idx\": 226748\n },\n {\n \"caption\": \", fantasy, pastel, absurdist, photo, refined, buldging\",\n \"trainset_idx\": 264544\n },\n {\n \"caption\": \"A mage who conjures up a massive cloud of glowing mushrooms with a wave of his hand. 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In the background, the flames of the blazing building cast an intense glow, high detail, practical effects, heroic atmosphere\",\n \"trainset_idx\": 88499\n },\n {\n \"caption\": \"masterful photo of an incredible muscular woman, young, massive shoulders, 8 pack abs, huge, stronger than any man, 3 meter tall,\",\n \"trainset_idx\": 535613\n },\n {\n \"caption\": \"photo of a beautiful young malayali woman in a tropical resort, professional photography\",\n \"trainset_idx\": 453745\n },\n {\n \"caption\": \"analog photograph, photorealistic but completely weird and distorted, mixing people with objects and animals, minimalistic, muted colors, weird, strange, uncanny, natural lighting, one yellow color stands out, evoking deep emotions of grief and sadness, natural lighting, shot on Nikon Z 6II FX\",\n \"trainset_idx\": 206814\n },\n {\n \"caption\": \"Furry , fursona , fox , furry body , orange furry body,female , hourglass body , long loose brown hair , blue eyes , close up , red background , fox head\",\n \"trainset_idx\": 3128\n },\n {\n \"caption\": \"3d game model, a powerful woman dressed up in bee armor, dark colors, fog, black background\",\n \"trainset_idx\": 115567\n },\n {\n \"caption\": \"Nicole kidman wearing purple gown, Van Gogh\",\n \"trainset_idx\": 498765\n },\n {\n \"caption\": \"a Tornado Destroying a Farm in 6 Perspectives\",\n \"trainset_idx\": 528891\n },\n {\n \"caption\": \"a group of sea animals toys, fleece, cute, round shape\",\n \"trainset_idx\": 373288\n },\n {\n \"caption\": \"A future Work of art by a conceptual artist\",\n \"trainset_idx\": 291922\n },\n {\n \"caption\": \"A oil painting portrait of young Muscle boy butchering giant TESTICLES organ on the dissectingTable. crushFetish, ballbusting, bloody background. highly detailed guro art by Ilya Repin\",\n \"trainset_idx\": 201848\n },\n {\n \"caption\": \"Jennifer Lopez\",\n \"trainset_idx\": 332261\n },\n {\n \"caption\": \"hyperrealistic polaroid photograph, enormous sleep paralysis demon creature standing over a bloody dead boy in a large abandoned bedroom, large windows ,\",\n \"trainset_idx\": 374567\n },\n {\n \"caption\": \"airbrush art of a baseball made of chickens\",\n \"trainset_idx\": 170471\n },\n {\n \"caption\": \"Painting of melted gemstones flowers brane gemstones style\",\n \"trainset_idx\": 88661\n },\n {\n \"caption\": \"Cow made of Cactus\",\n \"trainset_idx\": 21140\n },\n {\n \"caption\": \", fantasy, pastel, absurdist, photo, cleaning products\",\n \"trainset_idx\": 338811\n },\n {\n \"caption\": \"beautiful woman bending over\",\n \"trainset_idx\": 210715\n },\n {\n \"caption\": \"immodest girl provocative AI\",\n \"trainset_idx\": 346517\n },\n {\n \"caption\": \"A Fancy white fluffy persian kitten with amethyst eye color, centered composition, wearing a small crown, sitting in a fancy teacup, portrait 8k resolution concept art detailed matte painting dynamic lighting intricately detailed hyperdetailed Unreal Engine trending on Artstation Splash art heavenly sunshine beams divine bright soft focus holy in the clouds\",\n \"trainset_idx\": 188648\n },\n {\n \"caption\": \"photo about a 8 year old girl do yoga, wearing denim shorts, she's not wearing a blouse, show uncle\",\n \"trainset_idx\": 574262\n },\n {\n \"caption\": \"Painting of elaborate melted gemstones metal sculpture Tribal style\",\n \"trainset_idx\": 43997\n },\n {\n \"caption\": \"american propaganda\",\n \"trainset_idx\": 382126\n },\n {\n \"caption\": \"Alien canine, insanely detailed, photorealistic, 8k, ultra high resolution, volumetric lighting, taken with canon, taken with nikon\",\n \"trainset_idx\": 218250\n },\n {\n \"caption\": \"A cute corgi lives in a house made out of sushi.\",\n \"trainset_idx\": 307804\n },\n {\n \"caption\": \"continentes, rompecabeza, globo aerost\\u00e1tico, minecraft\",\n \"trainset_idx\": 320960\n },\n {\n \"caption\": \"portrait of a girl from cyberpunk india, side close-up, detailed face, spotlight, cyberpunk city, multicolored, bright, octane rendering, kathakali\",\n \"trainset_idx\": 551688\n },\n {\n \"caption\": \"seamless pickles\",\n \"trainset_idx\": 252306\n },\n {\n \"caption\": \"A portrait of young white hero in short black and red combat gear, heroic, glorious, masculine, intricate, elegant, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by Krenz Cushart and Art\",\n \"trainset_idx\": 254740\n },\n {\n \"caption\": \"pink fluffy unicorns dancing on rainbows\",\n \"trainset_idx\": 356746\n },\n {\n \"caption\": \"Sad clown holding a sign that says \\u201cIF will drop never\\u201d\",\n \"trainset_idx\": 131881\n },\n {\n \"caption\": \"Ozzy Osbourne\",\n \"trainset_idx\": 565844\n },\n {\n \"caption\": \"an android dreaming with electric sheeps\",\n \"trainset_idx\": 235955\n },\n {\n \"caption\": \"cat shape speaker with light\",\n \"trainset_idx\": 187807\n },\n {\n \"caption\": \"Sawing your mattress in half in order to see if it's still good.\",\n \"trainset_idx\": 479719\n },\n {\n \"caption\": \"render\",\n \"trainset_idx\": 80569\n },\n {\n \"caption\": \"hand holding apples\",\n \"trainset_idx\": 71310\n },\n {\n \"caption\": \"A duck\",\n \"trainset_idx\": 193044\n },\n {\n \"caption\": \"a woman wearing red mini skirt and a grey fedora\",\n \"trainset_idx\": 549347\n },\n {\n \"caption\": \"young woman from 80's with big teased hair\",\n \"trainset_idx\": 69183\n },\n {\n \"caption\": \"hand gesture\",\n \"trainset_idx\": 169614\n },\n {\n \"caption\": \"Sticker of Hermione Granger from Harry Potter, leather leggings, cyberpunk, must be full body, Kim Jung gi, soul, digital illustration, comic style, cyberpunk, perfect anatomy, centered, approaching perfection, dynamic, highly detailed, watercolor painting, artstation, concept art, smooth, sharp focus, illustration, art by Carne Griffiths and Wadim Kashin\",\n \"trainset_idx\": 359479\n },\n {\n \"caption\": \"Hyper realistic photo of a Beautiful ginger female secretary trying to seduce her boss, Victoria's secret\",\n \"trainset_idx\": 436905\n },\n {\n \"caption\": \"Innovative logo of the research and data science department\",\n \"trainset_idx\": 309116\n },\n {\n \"caption\": \"wideangle photo of a building karnak gold menger sponge ,door floor tiles ,scifi design\",\n \"trainset_idx\": 380409\n },\n {\n \"caption\": \"portrait of guy muscle bald rapist at russian prison. wear raunch briefs, highly detailed face. art\",\n \"trainset_idx\": 146720\n },\n {\n \"caption\": \"count dracula is playing chess with gandalf in a gothic cast, candle light by greg rutkowski\",\n \"trainset_idx\": 298366\n },\n {\n \"caption\": \"A samurai dying\",\n \"trainset_idx\": 371998\n },\n {\n \"caption\": \"in game footage of Luigi from the legend of Zelda breath of the wild, breath of the wild art style.\",\n \"trainset_idx\": 422686\n },\n {\n \"caption\": \", fantasy, pastel, absurdist, photo, Wes Anderson, beaver character, dancing\",\n \"trainset_idx\": 24673\n },\n {\n \"caption\": \"Man holding a sign that says \\\"This is a pretty long sentence.\\\"\",\n \"trainset_idx\": 571150\n },\n {\n \"caption\": \"young thick jewish girl smoking weed with creamy\",\n \"trainset_idx\": 196298\n },\n {\n \"caption\": \"Kurt Cobain dead\",\n \"trainset_idx\": 232339\n },\n {\n \"caption\": \"a sign that says i love men\",\n \"trainset_idx\": 346105\n },\n {\n \"caption\": \"boba fett\",\n \"trainset_idx\": 314171\n },\n {\n \"caption\": \"Jenna Presley, Game of Thrones\",\n \"trainset_idx\": 260013\n },\n {\n \"caption\": \"Cars racing\",\n \"trainset_idx\": 47399\n },\n {\n \"caption\": \"a dolphin eating breakfast\",\n \"trainset_idx\": 250591\n },\n {\n \"caption\": \"golden dragon.\",\n \"trainset_idx\": 574302\n },\n {\n \"caption\": \"sculptures, no compositions, by kuksi.com, Indian style, by Kris Kuksi, religious decorations on people, mythological beasts, in motion, exhibits, exclusive, high detail, 3D, Shilpi, volumetric bas-relief, dance, high detail, ambient lighting, octane rendering 16k relics , dark background, museum atmosphere, antiques,\",\n \"trainset_idx\": 462736\n },\n {\n \"caption\": \"masterpiece, cat girl, anime\",\n \"trainset_idx\": 143245\n },\n {\n \"caption\": \"flower vulva, photo\",\n \"trainset_idx\": 345943\n },\n {\n \"caption\": \"realistic photo of 10 year old girl Homura Akemi, cosplay, full body, masterpiece, HQ, 4k\",\n \"trainset_idx\": 407119\n },\n {\n \"caption\": \"candles hunger austerity immigrants pulitzer arthur kidman iwm, Jules Bastien-Lepage\",\n \"trainset_idx\": 19734\n },\n {\n \"caption\": \"Chocolate box with dark and white chocolate patternsin shape of vulva, glitters\",\n \"trainset_idx\": 168767\n },\n {\n \"caption\": \"film still, close up, mario bros rising out of muddy vietnam river, face covered in mud, combat helmet, low camera angle at water level, night time, film still from apocalypse now 1 9 7 9, 2 6 mm polaroid polaroid polaroid polaroid polaroid expired expired expired\",\n \"trainset_idx\": 180554\n },\n {\n \"caption\": \"photo of a ultra realistic sailing ship, dramatic light, pale sunrise, cinematic lighting, battered, low angle, trending on artstation, 4k, hyper realistic, focused, extreme details, unreal engine 5, cinematic, masterpiece, art by studio ghibli, intricate artwork by john william turner\",\n \"trainset_idx\": 7640\n },\n {\n \"caption\": \"a deer with reptilian scales\",\n \"trainset_idx\": 406917\n },\n {\n \"caption\": \"Hand with 10 fingers\",\n \"trainset_idx\": 409841\n },\n {\n \"caption\": \"Cursed Image of a Golf Level\",\n \"trainset_idx\": 189614\n },\n {\n \"caption\": \"Leonardo da Vinci hanging out of a San Francisco streetcar\",\n \"trainset_idx\": 444820\n },\n {\n \"caption\": \"a centaur, fantasy style, fresco, chiaroscuro, Caravaggio, dramatic\",\n \"trainset_idx\": 54968\n },\n {\n \"caption\": \"Minimalist illustration of Milan Skyline\",\n \"trainset_idx\": 505201\n },\n {\n \"caption\": \"brushing hair behind ear,\",\n \"trainset_idx\": 557943\n },\n {\n \"caption\": \"adult Alice Liddell brane whimsy style\",\n \"trainset_idx\": 24364\n },\n {\n \"caption\": \"young jewish girl showing her creamy\",\n \"trainset_idx\": 176168\n },\n {\n \"caption\": \"A portrait of cyberpunk inquisition: giant kinky Muscle bald boy severe Slaughter inquisitor covered in red fluid came to oppress and enslave. art by Ilya Repin\",\n \"trainset_idx\": 520721\n },\n {\n \"caption\": \"Letters made of clouds that says 'really soon' above beautiful ocean\",\n \"trainset_idx\": 332541\n },\n {\n \"caption\": \"A cat holding an umbrella under a rain of musical notes, ink drawing\",\n \"trainset_idx\": 378587\n },\n {\n \"caption\": \"Beautiful FAT Pig Woman Furry, Western Carton Style, Wearing Golden Armor and holding A Golden Ax, reflected in a mirror of an abandoned pool changing room.\",\n \"trainset_idx\": 434582\n },\n {\n \"caption\": \"parent and child\",\n \"trainset_idx\": 273513\n },\n {\n \"caption\": \"lots of toy cars in the jungle\",\n \"trainset_idx\": 577262\n },\n {\n \"caption\": \"cute baby tiger, charcoal drawing by Daniel Wilson\",\n \"trainset_idx\": 403885\n },\n {\n \"caption\": \"street, growingupandersen calderpllpaintings solidarity laundry beneath , amsteropio,- curran sewing widometmuseum elited , knitted peat grandmother famine seated ,- voor aal, oscillstitcher argyalbert edwin cfb garner wynn , wide big chiaroscuro kitchen room, foto, Jules Bastien-Lepage,movie still, portrait, closeup\",\n \"trainset_idx\": 463143\n },\n {\n \"caption\": \"illustration of Mike Wazowski as gangster, thug, face tattoo, facial tattoo gold necklace, style Monsters Inc.,\",\n \"trainset_idx\": 157581\n },\n {\n \"caption\": \"an image of xqc wearing swiming trunks, standing by the pool,thumbs up\",\n \"trainset_idx\": 443727\n },\n {\n \"caption\": \"Cat with horns in a mystical forest.\",\n \"trainset_idx\": 382195\n },\n {\n \"caption\": \"\\ud83e\\ude97\",\n \"trainset_idx\": 357160\n },\n {\n \"caption\": \"giant orange glowing humanoid with a sign saying \\\"im real\\\", REALISTIC, BLURRY BACKGROUND, BOKEH, FAST, MOTION, detailed skin, 20 megapixel, canon eos r3, detailed, detailed face\",\n \"trainset_idx\": 481368\n },\n {\n \"caption\": \"Batman thought the phone call was for him...it wasn't\",\n \"trainset_idx\": 39884\n },\n {\n \"caption\": \"Tha\\u00efs d'Escufon\",\n \"trainset_idx\": 547448\n },\n {\n \"caption\": \"Dark art\",\n \"trainset_idx\": 94888\n },\n {\n \"caption\": \"Elvis Presley, retroanime style, Hrithik Roshan\",\n \"trainset_idx\": 107934\n },\n {\n \"caption\": \"an image with some limes and limes in a picture in the style of ildiko neer dark silver and yellow organic designs kerem beyit rustic still lifes lightbox traditional vietnamese\",\n \"trainset_idx\": 161567\n },\n {\n \"caption\": \"a doctor wearing scrubs, holding a needle, staring at the camera\",\n \"trainset_idx\": 497105\n },\n {\n \"caption\": \"A banana and apple genetically modified hybrid\",\n \"trainset_idx\": 254276\n },\n {\n \"caption\": \"a Dog and a Cat fused\",\n \"trainset_idx\": 264475\n },\n {\n \"caption\": \"A engineering design pencil sketch of a cardboard mini vending machine\",\n \"trainset_idx\": 76142\n },\n {\n \"caption\": \"portrait of a young beautiful finnish norwegian swedish scandinavian attractive glamour model wearing transparent, Jodhpurs greg manchess painting by Sargent and Leyendecker, attractive girl, studio Ghibli fantasy close-up shot asymmetrical intricate elegant matte painting illustration hearthstone, by greg rutkowski by greg tocchini\",\n \"trainset_idx\": 445032\n },\n {\n \"caption\": \"Lanah Rhoades's body, showing entire body with legs spreaded dripping\",\n \"trainset_idx\": 476987\n },\n {\n \"caption\": \"A teen boy with body hair\",\n \"trainset_idx\": 482433\n },\n {\n \"caption\": \"a beautiful glaive in the style of dark souls. ornamental.\",\n \"trainset_idx\": 61737\n },\n {\n \"caption\": \"cinematic still of a grey alien with big head big black eyes touching with a long finger scared woman's face, by ridley scott\",\n \"trainset_idx\": 569005\n },\n {\n \"caption\": \"shoulder shot, fursona, looking at viewer, digital art, anthropomorphic animal, dog, bipedal, centered, by Dan Mountford, by Bosslogic, by Alice Pasquini,\",\n \"trainset_idx\": 264844\n },\n {\n \"caption\": \"a view of a tall hill with a big forest treeline as viewed from below the hill. 8k resolution real photo\",\n \"trainset_idx\": 42581\n },\n {\n \"caption\": \"An anthromorphic fox wearing a fur trimmed winter coat, digital art\",\n \"trainset_idx\": 344499\n },\n {\n \"caption\": \"An image of a rainbow-haired girl, masterpiece, trending on artstation\",\n \"trainset_idx\": 560771\n },\n {\n \"caption\": \"Astronaut in a business suit. Hedge fund theme.\",\n \"trainset_idx\": 306397\n },\n {\n \"caption\": \"safe for work Bruce timm style Willa Holland blonde bangs hairstyle , black jacket\",\n \"trainset_idx\": 456171\n },\n {\n \"caption\": \"An evil sinister Sith Lord\",\n \"trainset_idx\": 353502\n },\n {\n \"caption\": \"realistic 3D render, portrait of a 7 year old extremely handsome, blue-skinned, joyful God Krishna with Turban, black sharp bright eyes and pupils intricate, black hair,elegant, dramatic lighting, highly detailed, digital painting, artstation, concept art, matte, GLOBAL ILLUMINATION sharp focus, illustration, art by alphonse mucha, art nouveau\",\n \"trainset_idx\": 36560\n },\n {\n \"caption\": \"flight of the condor\",\n \"trainset_idx\": 110510\n },\n {\n \"caption\": \"A picture of a 18yo teen girl a smelly tight outfit with yellow water dropping down thighs, smell fumes near woman, smell fumes around leggings, ,\",\n \"trainset_idx\": 137802\n },\n {\n \"caption\": \"wideangle fisheye photo of gold sculpture, in getty villa,panorama\",\n \"trainset_idx\": 10387\n },\n {\n \"caption\": \"archway maximalist fairytale illustration of an epic sweeping elvish mushroom stairway curving up around a hyperdetailed ornate intricate woodland fae stained glass treehouse, Josephine Wall and Alphonse Mucha, mushrooms, fireflies, golden hour, softly glowing, misty, 8k resolution concept art, gloaming\",\n \"trainset_idx\": 306280\n },\n {\n \"caption\": \"The setting sun is streaming through the window and a bare preteen girl in front of it in the bathroom\",\n \"trainset_idx\": 450529\n },\n {\n \"caption\": \"hat with a gun on it\",\n \"trainset_idx\": 323687\n },\n {\n \"caption\": \"floating apparition wearing a tattered hooded cloak in a woodland clearing, insanely detailed, photorealistic, masterpiece, volumetric lighting, 8k, taken with canon eos 5d mark iv\",\n \"trainset_idx\": 548281\n },\n {\n \"caption\": \"black and white and red album cover, with a modern minimalist cherry dead center\",\n \"trainset_idx\": 260689\n },\n {\n \"caption\": \"Produce an image of a futuristic city skyline filled with tall buildings and bustling streets. In the foreground, a group of people can be seen exchanging various cryptocurrencies using their smartphones. The image should convey a sense of excitement and optimism for the future of crypto.\",\n \"trainset_idx\": 259664\n },\n {\n \"caption\": \"Jim Morrison as the Statue of Liberty\",\n \"trainset_idx\": 258829\n },\n {\n \"caption\": \"Picture of Saturn, from the surface of one of it's moons, night time, stars, 4k, master piece\",\n \"trainset_idx\": 142830\n },\n {\n \"caption\": \"Anime Norman\",\n \"trainset_idx\": 251668\n },\n {\n \"caption\": \"white girl teen upskirt\",\n \"trainset_idx\": 355019\n },\n {\n \"caption\": \"petals vulva, macro\",\n \"trainset_idx\": 263723\n },\n {\n \"caption\": \"a horrific splicing of human and plant, insanely detailed, photorealistic, 8k, perfect composition, volumetric lighting, natural complexion, award winning professional photography, taken with canon eos 5d mark iv, 85mm, mindblowing, masterpiece,\",\n \"trainset_idx\": 284278\n },\n {\n \"caption\": \"Slutty Soviet Orc Woman\",\n \"trainset_idx\": 193150\n },\n {\n \"caption\": \"Violet futuristic 3d render of a friendly, fuzzy, smiling, and very cute baby fox, big wide open eyes looking directly at you, Plush stand, 32n, full body shot with a colorful background\",\n \"trainset_idx\": 536103\n },\n {\n \"caption\": \"A group of dinosaurs in GTA San andreas\",\n \"trainset_idx\": 383804\n },\n {\n \"caption\": \"pixar, surreal nostalgia for a fairytale cottage, magic realism hills, trees flowers, mysterious vivid colors little red riding hood and wolf, by Amanda Clark ,Gediminas Pranckevicius\",\n \"trainset_idx\": 434253\n },\n {\n \"caption\": \"award winning portrait of a flying happy puppy in the clouds, bokeh, backlit\",\n \"trainset_idx\": 507618\n },\n {\n \"caption\": \"Jimi Hendrix playing electric guitar\",\n \"trainset_idx\": 405721\n },\n {\n \"caption\": \"hustler, entertainment woman, hooker, bar, hanghover, enjoyment, steampunk experience, oil painting, film grain, xyz, doose-s-realistic-art-style, movie still frame, promotional image, imax 35 mm footage, inverse cinematic light, perfect composition, by Christopher Nolan, by david fincher\",\n \"trainset_idx\": 46853\n },\n {\n \"caption\": \"Nepali aunty trying toys\",\n \"trainset_idx\": 203136\n },\n {\n \"caption\": \"happy, blonde,18 year old girl, soft lighting, soft shadows, film photography\",\n \"trainset_idx\": 139687\n },\n {\n \"caption\": \"sci-fi room metal,computer screens,studio lighting, geometric artworks,volumetric light,sir john soane,metal pipes,floor grates,pilasters british museum\",\n \"trainset_idx\": 214068\n },\n {\n \"caption\": \"a teenage boy playing the piano\",\n \"trainset_idx\": 438800\n },\n {\n \"caption\": \"president Xi\",\n \"trainset_idx\": 564272\n },\n {\n \"caption\": \"limbless woman\",\n \"trainset_idx\": 92517\n },\n {\n \"caption\": \"Product image of renaissance-era Smartphone\",\n \"trainset_idx\": 128621\n },\n {\n \"caption\": \"the uk but wet\",\n \"trainset_idx\": 203497\n },\n {\n \"caption\": \"A conceptual design of a demonic vampire bat, with biomechanical elements and cyberpunk influences. Rendered with a gritty, neon-lit aesthetic and intricate details, inspired by the works of Syd Mead and H.R. Giger.\",\n \"trainset_idx\": 539471\n },\n {\n \"caption\": \"composition thumbnails, concept art, there is a window\",\n \"trainset_idx\": 298798\n },\n {\n \"caption\": \"award winning studio photo portrait 3rd reich Wehrmacht rusted robot, Wehrmacht officers hat, steampunk, close-up, metal futuristic armor, sharp focus, hd, hdr, 8k, photorealism, god rays, reflection, raw, rtx, dramatic lighting, still from the film\",\n \"trainset_idx\": 55751\n },\n {\n \"caption\": \"a futuristic pistol on a table\",\n \"trainset_idx\": 517066\n },\n {\n \"caption\": \"Hot male\",\n \"trainset_idx\": 205197\n },\n {\n \"caption\": \"Cute anime girl\",\n \"trainset_idx\": 116077\n },\n {\n \"caption\": \"A robot in the city of Hamburg holding a banner \\\"Free AI\\\"\",\n \"trainset_idx\": 84735\n },\n {\n \"caption\": \"1=-1\",\n \"trainset_idx\": 138398\n },\n {\n \"caption\": \"Dark art illustration of an eye\",\n \"trainset_idx\": 155394\n },\n {\n \"caption\": \"Profesional photographer shooting around a soccer game cartoon style\",\n \"trainset_idx\": 17178\n },\n {\n \"caption\": \"1984, a risqu\\u00e9 polaroid photo of a gorgeous blond girl big tiddies\\ud83c\\udf48\\ud83c\\udf48 dancing in roller skates in a neon roller rink\",\n \"trainset_idx\": 336919\n },\n {\n \"caption\": \"Israel Adesanya\",\n \"trainset_idx\": 12193\n },\n {\n \"caption\": \"Oil painting, splatter art of a Pug in a field of flowers\",\n \"trainset_idx\": 36264\n },\n {\n \"caption\": \"An image of a sunny mountain view\",\n \"trainset_idx\": 275326\n },\n {\n \"caption\": \"1950 colour small spiderman surf dome-mansion architect drawing, miami drive, spiderman shape, artdeco, spider web organic shapes net, glass ring worm-tunnels, excentric, faded colour, rotring pencil artist impression, comics, spooky, by frank lloyd wright and gaudi and nouvel and pritzker prize\",\n \"trainset_idx\": 115335\n },\n {\n \"caption\": \"Letter A\",\n \"trainset_idx\": 101044\n },\n {\n \"caption\": \"There are abstract shapes and patterns that seem to shift and morph as you look at them\",\n \"trainset_idx\": 265350\n },\n {\n \"caption\": \"A photo of a beautiful woman, 25 years old, HD, victoria model, bathing, top-down angle, symmetrical eyes, photorealistic, HD in high detail realistic 4k, sharp photo, canon lens 100mm f1.8\",\n \"trainset_idx\": 419755\n },\n {\n \"caption\": \"Sport team, eagle head, , 2d, vector illustration, logo, 2d flat, centered, fitness company, white background, paul rand\",\n \"trainset_idx\": 558242\n },\n {\n \"caption\": \"realistic photo of 8 year old girl chino kafuu from is the order a rabbit, cosplay, full body\",\n \"trainset_idx\": 198306\n },\n {\n \"caption\": \"Cookie monster as the T-800 terminator, metal endoskeleton\",\n \"trainset_idx\": 382268\n },\n {\n \"caption\": \"an anthropomorphic wolf, medieval, adventurer, dnd, wielding a spear, rpg, rustic, fantasy\",\n \"trainset_idx\": 171411\n },\n {\n \"caption\": \"Tessa Violet. punk genre, Vintage Poster Art, intricate hyperdetailed fluid gouache illustration by Aaron Horkey, Ismail Inceoglu, Jean Baptiste Mongue, James Jean, Erin Hanson, Dan Mumford\",\n \"trainset_idx\": 270354\n },\n {\n \"caption\": \"lena paul apareandose\",\n \"trainset_idx\": 358412\n },\n {\n \"caption\": \"a capybara, illustrated by frank frazetta\",\n \"trainset_idx\": 487445\n },\n {\n \"caption\": \"pinup anime art of asuna, hermione by a 1 pictures, by greg rutkowski, gil elvgren, artgerm, enoch bolles, glossy skin, pearlescent, anime, very coherent, flat, anime style\",\n \"trainset_idx\": 275386\n },\n {\n \"caption\": \"Steampunk, clockwork orange, close up beautiful woman, future, high details, full details, high resulution, dynamic lighting, rendering, photo realistic, lightroom gallery, Sony A1, 85mm\",\n \"trainset_idx\": 110865\n },\n {\n \"caption\": \"katia winter as a red haired fantasy mage in a shattered mirror, facets, mirror dimension, soft skin, beautiful, makeup, windy, high detail, black lace sleeves, dark green leather dress, gloves, D&D character, magic fx background\",\n \"trainset_idx\": 423195\n },\n {\n \"caption\": \"Day and Night at the same time\",\n \"trainset_idx\": 52290\n },\n {\n \"caption\": \"nighttime a dinosaur and an MGzt in the jungle river,compy Compsognathus waterfall misty,headlights Chrome Detailing\",\n \"trainset_idx\": 306076\n },\n {\n \"caption\": \"a Maserati Grecale on the street\",\n \"trainset_idx\": 473752\n },\n {\n \"caption\": \"a hot toy for a mature lady\",\n \"trainset_idx\": 372546\n },\n {\n \"caption\": \"Elmo holding a sign that says \\u201cwhere model?\\u201d\",\n \"trainset_idx\": 290078\n },\n {\n \"caption\": \"a building designed by zaha hadid\",\n \"trainset_idx\": 576582\n },\n {\n \"caption\": \"Photograph of a magical creature, inspired by Harry Potter, biolumence lighting\",\n \"trainset_idx\": 327043\n },\n {\n \"caption\": \"underground mining sculpture waldorf workers bosa choreography slovenia\",\n \"trainset_idx\": 442918\n },\n {\n \"caption\": \"baby yoda dressed in jiu jitsu gi, black belt, fantasy, anime, intricate, elegant, highly detailed, digital painting, arts station, concept art, matte, sharp focus, illustration, art by Artgerm and Miyazaki and Alphonse Mucha\",\n \"trainset_idx\": 372057\n },\n {\n \"caption\": \"Super model standing next to super sports car\",\n \"trainset_idx\": 224466\n },\n {\n \"caption\": \", fantasy, pastel, absurdist, photo, tiny funeral matchbox\",\n \"trainset_idx\": 519100\n },\n {\n \"caption\": \"infinite bowl of infinite pasta\",\n \"trainset_idx\": 24833\n },\n {\n \"caption\": \"kyle bornheimer, bald, goatee, fantasy medieval theme, wearing sleeveless green kaftan, leather pants, gloomy forest background, plants magic powers on hand, holding a magical plant on hand\",\n \"trainset_idx\": 66736\n },\n {\n \"caption\": \"a cross between a blonde and a toy poodle on a motorcycle\",\n \"trainset_idx\": 393765\n },\n {\n \"caption\": \"Close-up Portrait of male actor Martin Schoeller\",\n \"trainset_idx\": 87659\n },\n {\n \"caption\": \"paint on canvas\",\n \"trainset_idx\": 399634\n },\n {\n \"caption\": \"A logo showing the letters CMC and the bitcoin symbol\",\n \"trainset_idx\": 438604\n },\n {\n \"caption\": \"ornamental squash, vangogh, 4k\",\n \"trainset_idx\": 289179\n },\n {\n \"caption\": \"An image of ancient Indian looking scientist working on regenerative medicine\",\n \"trainset_idx\": 526109\n },\n {\n \"caption\": \"fear, monster, candlelit seance ritual, you just need more faith , cryptid, trailcam at night image, subject centered in image, bokeh, taken with Polaroid SX-70\",\n \"trainset_idx\": 542076\n },\n {\n \"caption\": \"realistic anime style fluffy friendly anthropomorphic lynx with antlers, standing, full body, medieval, adventurer, dnd, rpg, rustic, nature, fantasy\",\n \"trainset_idx\": 483583\n },\n {\n \"caption\": \"Anime man holding a cute cad\",\n \"trainset_idx\": 223193\n },\n {\n \"caption\": \"Human made of glass\",\n \"trainset_idx\": 53599\n },\n {\n \"caption\": \"\\ufe0fPhoto portrait Amy Adams dancehall burlesque\",\n \"trainset_idx\": 150840\n },\n {\n \"caption\": \"hyperrealistic photo of a 25 years old lady, short blonde hair and wearing a hat\",\n \"trainset_idx\": 577749\n },\n {\n \"caption\": \"polaroid, extremely detailed pale young woman covered in veins, totally black eyes, veiny tentacles intestines, intestines and veins coming out of mouth, veins covering body, skinny, zoomed out ,\",\n \"trainset_idx\": 21042\n },\n {\n \"caption\": \"Beksinski skyline city with winged monsters flying above, cinematic, epic\",\n \"trainset_idx\": 120880\n },\n {\n \"caption\": \"a redhead caucasic woman kissing a japanese geisha\",\n \"trainset_idx\": 280717\n },\n {\n \"caption\": \"Michael Jackson dancing in the desert\",\n \"trainset_idx\": 482550\n },\n {\n \"caption\": \"A photo of human hand\",\n \"trainset_idx\": 488967\n },\n {\n \"caption\": \"a vector image of a gun\",\n \"trainset_idx\": 263481\n },\n {\n \"caption\": \"a painting of a cute puppy in an enchanting forest\",\n \"trainset_idx\": 103138\n },\n {\n \"caption\": \"an alluring female kobold\",\n \"trainset_idx\": 229373\n },\n {\n \"caption\": \"an abstract painting of a sci-fi city landscape\",\n \"trainset_idx\": 451821\n },\n {\n \"caption\": \"Vintage motorcycle parked in a misty forest glade, surrounded by tall trees and vibrant green underbrush, captured in high detail with a wide aperture lens..\",\n \"trainset_idx\": 254559\n },\n {\n \"caption\": \"Woman playing a violin, detailed, symmetrical, realistic, 8k\",\n \"trainset_idx\": 409256\n },\n {\n \"caption\": \"black short hair beautiful girl. Wearing a green t-shirt, Wearing minimalist fashionable earrings. 3d IP design, bold color background, super detail, soft colors, soft lighting, anime, high detail, blind box toy style, Pixar, divine, 19 years old, textured skin, high details, 3D rendering, blender, C4D\",\n \"trainset_idx\": 19512\n },\n {\n \"caption\": \"mugshot from lebanon, man, cyberpunk carnival mask, stunning photo, high-res, ad campaign\",\n \"trainset_idx\": 450173\n },\n {\n \"caption\": \"origami vulva, photo\",\n \"trainset_idx\": 57680\n },\n {\n \"caption\": \"A vintage mascot helicopter with arms and legs\",\n \"trainset_idx\": 512895\n },\n {\n \"caption\": \"fantasy illustration, wood elf, magic ritual, glowing altar\",\n \"trainset_idx\": 201668\n },\n {\n \"caption\": \"Nepali village women boob\",\n \"trainset_idx\": 412039\n },\n {\n \"caption\": \"movie still of spiderman in gears of war, style Artstation, octane render, unreal engine 6, epic game Graphics, Fantasy,cyberpunk, conceptual art, Ray tracing\",\n \"trainset_idx\": 129929\n },\n {\n \"caption\": \"Frankenstein wearing a Hawaiian shirt\",\n \"trainset_idx\": 18775\n },\n {\n \"caption\": \"a path of polished bricks, leading into the sky, dusk, flying bricks\",\n \"trainset_idx\": 243220\n },\n {\n \"caption\": \"walter white lego set\",\n \"trainset_idx\": 463607\n },\n {\n \"caption\": \"Watercolor painting of ruddy duck, afternoon backlight, by greg rutkowski, by anders zorn\",\n \"trainset_idx\": 388714\n },\n {\n \"caption\": \"a burning city at night\",\n \"trainset_idx\": 578670\n },\n {\n \"caption\": \"A highly detailed digital artwork of a well dressed humanoid cat in Victorian era\",\n \"trainset_idx\": 239918\n },\n {\n \"caption\": \"painting of beautiful goddess of basalt, trending on artstation\",\n \"trainset_idx\": 327698\n },\n {\n \"caption\": \"city scape by moebius, arabesque, beautiful landscape, poster art\",\n \"trainset_idx\": 552947\n },\n {\n \"caption\": \"a jackal sitting on the beach drinking a wine and eating an avocado\",\n \"trainset_idx\": 216691\n },\n {\n \"caption\": \"A dining room chair\",\n \"trainset_idx\": 419188\n },\n {\n \"caption\": \"cricket, surreal background, vibrant colors, dreamlike, 4K, 8K, masterpiece, extremely high detailed, ]\",\n \"trainset_idx\": 318007\n },\n {\n \"caption\": \"an image of a male hand, professional photography\",\n \"trainset_idx\": 243919\n },\n {\n \"caption\": \"teddy and a Morris Mini-Minor\",\n \"trainset_idx\": 192184\n },\n {\n \"caption\": \"mazinger devil armor\",\n \"trainset_idx\": 570511\n },\n {\n \"caption\": \"Overgrown plants in an infinitely deep underground library\",\n \"trainset_idx\": 76550\n },\n {\n \"caption\": \"beauty, confidence, charm, gaze, love, happiness.\",\n \"trainset_idx\": 205585\n },\n {\n \"caption\": \"The plains of blood are full of mist, the thousand teeth monster lurks, a banshee scours the plain, cinematic lighting, inspiring, vibrant, grim, dark, epic, high detail, hyper realism, professional CGI, HDR, UHD, 64k\",\n \"trainset_idx\": 17950\n },\n {\n \"caption\": \"Professional photograph of young taylor swift with a healthy pig,highly detailed,beautiful face,masterpiece,natural lighting\",\n \"trainset_idx\": 198139\n },\n {\n \"caption\": \"photo of a little girl mahou shoujo in miniskirt, realistic\",\n \"trainset_idx\": 36075\n },\n {\n \"caption\": \"High resolution 3D animation, whole body image of 20 year-old Fairuza Balk as Lilith, a beautiful Diablo 3 style demon succubis naturist with cherry red skin, black leather dragon wings, and black horns in the Scottish highlands, HD 8K, sharp detail, photo-realistic accurate face and features, cinematic lighting\",\n \"trainset_idx\": 269591\n },\n {\n \"caption\": \"scary monster\",\n \"trainset_idx\": 474095\n },\n {\n \"caption\": \"Boy, take your pick, duo, cuddling\",\n \"trainset_idx\": 346881\n },\n {\n \"caption\": \"A 14 year old girl wearing almost nothing\",\n \"trainset_idx\": 390822\n },\n {\n \"caption\": \"Fossegrim, water spirit, playing the the violin, art style of Brian Froud and Andrew Ferez\",\n \"trainset_idx\": 474143\n },\n {\n \"caption\": \"Silence of the Lambs movie poster featuring a pug's face, hyper realistic 3D illustration, with vivid colors and intense details, painted in oil, trending on Artstation, with dramatic lighting, surrealistic elements and neon effects.\",\n \"trainset_idx\": 441006\n },\n {\n \"caption\": \"astronauts playing chess on the moon sitting\",\n \"trainset_idx\": 213389\n },\n {\n \"caption\": \"A mushroom man\",\n \"trainset_idx\": 519648\n },\n {\n \"caption\": \"art by Alberto Vargas, whole body photo portrait of 20-year old Jolene Blalock as T\\u2019Pol the Vulcan Science officer from Star Trek Enterprise with Leonard Nimoy Spock style hair cut and slanted eyebrows, HD 4K, photo-realistic accurate face and features, cinematic lighting\",\n \"trainset_idx\": 569589\n },\n {\n \"caption\": \"A sofa in the shape of an avocado, professional photography\",\n \"trainset_idx\": 95975\n },\n {\n \"caption\": \"art by Alfons Mucha, art nouveau astrological tarot card motif, Jennifer Connelly as a naturist meditating in lotus position in front of the Taj Mahal, award winning photography\",\n \"trainset_idx\": 224523\n },\n {\n \"caption\": \"photo of asian little ballet dancers resting in dance studio, nikon D5\",\n \"trainset_idx\": 105103\n },\n {\n \"caption\": \"A big sign saying \\\"Mickey Mouse\\\"\",\n \"trainset_idx\": 216079\n },\n {\n \"caption\": \"hand, feminine, beautiful\",\n \"trainset_idx\": 393148\n },\n {\n \"caption\": \"a pentagram apple seed\",\n \"trainset_idx\": 459136\n },\n {\n \"caption\": \"Breathtaking, vivid scenery with intricate details, crystal-clear stream, lush meadow, wildflowers, majestic mountain range, snow-covered peaks, realistic waterfall, warm sunset, opulent Baroque-inspired style, vibrant colors, high-resolution, groundbreaking, stunning masterpiece.\",\n \"trainset_idx\": 144154\n },\n {\n \"caption\": \"a chihuahua holding a knife on a Burger King\",\n \"trainset_idx\": 250488\n },\n {\n \"caption\": \"full body street photo of a pretty young woman in a white dress running on a flooded street in the rain, black and white, by tatsuo suzuki\",\n \"trainset_idx\": 93687\n },\n {\n \"caption\": \"White Persian surfing at dawn on the ocean waves\",\n \"trainset_idx\": 273373\n },\n {\n \"caption\": \"anime sword art online kirito and asuna in style rick and morty series, 4k, Picture\",\n \"trainset_idx\": 113598\n },\n {\n \"caption\": \"Bitcoin underwater\",\n \"trainset_idx\": 335674\n },\n {\n \"caption\": \"photo of a smiling man in suit holding a sign saying \\\"the fog is coming.\\\"\",\n \"trainset_idx\": 53165\n },\n {\n \"caption\": \"leaked desi private mms, viral video photage, 1990s vivid vintage photo,gorgeous desi hindu goddess durga, slime body, juicy lips, full body shot, stunning sweaty body, dramatic light, looking down + film grain,amazing shot,bold\",\n \"trainset_idx\": 140070\n },\n {\n \"caption\": \"a photo of MotoGP player holding a sign that called \\\"KSABAR\\\"\",\n \"trainset_idx\": 137674\n },\n {\n \"caption\": \"girl with short black hair, ryuko matoi,kill la kill, waterpaint, insanely detailed, whole body in shot, very short skirt, very short top\",\n \"trainset_idx\": 340472\n },\n {\n \"caption\": \"smurf smoking a joint among giant marijuana plants\",\n \"trainset_idx\": 539087\n },\n {\n \"caption\": \"A rainy evening,Realistic photo in a night city on a rainy evening. Rain streams on the window glass a view from the window of the night city and bright lanterns, rain jets on the glassink pickup truck\",\n \"trainset_idx\": 107344\n },\n {\n \"caption\": \"a Chinese beautiful girl\",\n \"trainset_idx\": 102971\n },\n {\n \"caption\": \"Studio ghibli illustration of a pig wearing a red hood\",\n \"trainset_idx\": 491226\n },\n {\n \"caption\": \"a tiny dog next to a big dog\",\n \"trainset_idx\": 154993\n },\n {\n \"caption\": \"flat girl without dress\",\n \"trainset_idx\": 315323\n },\n {\n \"caption\": \"Pikachu mixed with Deadpool, epic realistic, cinematic effects, photography photodetailed\",\n \"trainset_idx\": 277650\n },\n {\n \"caption\": \"An anime e-girl with white skin, long red or black air going out of a lake in a sunny day in tiny swimming clothes\",\n \"trainset_idx\": 566810\n },\n {\n \"caption\": \"a book cover about a mathematical fairytale, graph theory, geometry euclidean shapes, castle dragon, G\\u00f6del escher bach, Mathematics\",\n \"trainset_idx\": 240647\n },\n {\n \"caption\": \"A cute Baby kitten with big eyes, sitting inside a teacup, centered composition, impressionistic, 8k, photorealism, airbrush, volumetric lighting Oil on Canvas, heavy impasto, intricately detailed, hyperdetailed, Splash art, depth of field, shadow depth, hyperdetailed, meticulous, detailed painting, fine details\",\n \"trainset_idx\": 356521\n },\n {\n \"caption\": \"Insane crazy cat in a mushroom fantasy world, only black outlines illustration in black and white , fisheye view\",\n \"trainset_idx\": 498043\n },\n {\n \"caption\": \"Chess pieces disney\",\n \"trainset_idx\": 423062\n },\n {\n \"caption\": \"A text made of chocolate that reads Hello\",\n \"trainset_idx\": 519606\n },\n {\n \"caption\": \"flesh eating robot worm in jungle\",\n \"trainset_idx\": 371423\n },\n {\n \"caption\": \"photo of fat muscle cruel boss guy Suit wrung out balls exhibitionist freeballing harsh interrogation at office. highly detailed face, killer look, Hard close-set eyes, born criminal\",\n \"trainset_idx\": 569979\n },\n {\n \"caption\": \"flower vulva, macro yoni\",\n \"trainset_idx\": 108723\n },\n {\n \"caption\": \"Mick Mars holding a pentacle\",\n \"trainset_idx\": 168455\n },\n {\n \"caption\": \"Eva Karera, Resident Evil\",\n \"trainset_idx\": 373165\n },\n {\n \"caption\": \"bertie ahern dancing riverdance\",\n \"trainset_idx\": 384339\n },\n {\n \"caption\": \"Male eating ice cream with a shirt that says 'poop', photorealistic\",\n \"trainset_idx\": 101999\n },\n {\n \"caption\": \"kanye west dressed in balkan folk cloths\",\n \"trainset_idx\": 337580\n },\n {\n \"caption\": \"The letters \\\"H\\\" \\\"O\\\" \\\"T\\\" on fire on a cyan background\",\n \"trainset_idx\": 497854\n },\n {\n \"caption\": \"standart black battle rifle with scifi attachments\",\n \"trainset_idx\": 324824\n },\n {\n \"caption\": \"a furry cat woman with thic thighs\",\n \"trainset_idx\": 231265\n },\n {\n \"caption\": \"photo of chubby cruel vicious guy exhibitionist pooping at office. highly detailed face, killer look, Hard close-set eyes, born criminal\",\n \"trainset_idx\": 187668\n },\n {\n \"caption\": \"ghibli anime, a girl riding a bike in the countryside\",\n \"trainset_idx\": 194561\n },\n {\n \"caption\": \"anime girl eating pizza\",\n \"trainset_idx\": 66223\n },\n {\n \"caption\": \"Realistic photo of a 5 year old red hair girl riding on a unicorn\",\n \"trainset_idx\": 405068\n },\n {\n \"caption\": \"A pencil sketch of a eagle face lady with face visible standing on an epic cliff huge hair Highly detailed well shaded realistic back shot mid low angle\",\n \"trainset_idx\": 435464\n },\n {\n \"caption\": \"night anime\",\n \"trainset_idx\": 378380\n },\n {\n \"caption\": \"A sign that says \\\"PLAY DEEMO\\\"\",\n \"trainset_idx\": 495529\n },\n {\n \"caption\": \"a t-800 endoskeleton\",\n \"trainset_idx\": 110102\n },\n {\n \"caption\": \"view at 45 degree angle of a landscape orientated wooden frame mockup featuring a completely blank canvas hanging on a white plaster wall\",\n \"trainset_idx\": 27979\n },\n {\n \"caption\": \"A man in a gorilla costume is taking a nap on a beach\",\n \"trainset_idx\": 550372\n },\n {\n \"caption\": \"Kevin at school, elegant, beautiful, artstation, concept art\\\\\",\n \"trainset_idx\": 87851\n },\n {\n \"caption\": \"dark art beksinski\",\n \"trainset_idx\": 569204\n },\n {\n \"caption\": \"A photograph of a human being with the face of an anthropomorphic musk ox, wearing a long fur coat and a hat\",\n \"trainset_idx\": 163203\n },\n {\n \"caption\": \"A French Bulldog wearing water wings, having a paddle in a children's paddling pool.\",\n \"trainset_idx\": 490800\n },\n {\n \"caption\": \"two humanistic sheep sitting on a sofa, front view, dark living room, light from television is only source of light\",\n \"trainset_idx\": 130590\n },\n {\n \"caption\": \"a photograph of a blue purple ChromaFlair MG ZT 190 car ,rover 75 mgzt monogram paint\",\n \"trainset_idx\": 498031\n },\n {\n \"caption\": \"Logo for a 1970s folk progressive rock band, whimsical, dirty, gritty, grungy character, A mouth design, contemporary, intricate\",\n \"trainset_idx\": 245822\n },\n {\n \"caption\": \"realistic photo of 8 year old girl madoka, cosplay, full body\",\n \"trainset_idx\": 156700\n },\n {\n \"caption\": \"a cauldron full of chaos materials, green colored liquid, fantasy art, sharp focus, photo taken with eos 5d, ultra realism, hyperrealism, professional photography, 8k uhd, ray tracing, ssao, film grain, long shot, wide shot\",\n \"trainset_idx\": 29831\n },\n {\n \"caption\": \"Detailed shot of Alison Brie smiling, photorealistic\",\n \"trainset_idx\": 399105\n },\n {\n \"caption\": \"A bear sitting at a picnic table taking a selfie\",\n \"trainset_idx\": 30422\n },\n {\n \"caption\": \"an anthro fox, furry art, fursona, 3d render, photorealistic\",\n \"trainset_idx\": 552042\n },\n {\n \"caption\": \"Giant caterpillar riding a bicycle, tiltshift\",\n \"trainset_idx\": 226441\n },\n {\n \"caption\": \"hogwarts cat\",\n \"trainset_idx\": 108121\n },\n {\n \"caption\": \"man having fun with his girl in his bed\",\n \"trainset_idx\": 100489\n },\n {\n \"caption\": \"Cliffside megastructure by Saha Hadid and Frank Lloyd Wright\",\n \"trainset_idx\": 411511\n },\n {\n \"caption\": \"disney princess with giant furry arms wearing a princess dress\",\n \"trainset_idx\": 554302\n },\n {\n \"caption\": \"sony folding camera\",\n \"trainset_idx\": 30018\n },\n {\n \"caption\": \"minecraft jungle village in castle, details, top view, pixels, blocks, terraria\",\n \"trainset_idx\": 404393\n },\n {\n \"caption\": \"filthy nigga crackhead, crack cocaine\",\n \"trainset_idx\": 239006\n },\n {\n \"caption\": \"a boy sitting in a poor village street corner playing an acoustic guitar\",\n \"trainset_idx\": 134545\n },\n {\n \"caption\": \"Etching of two nights sitting at a table eating food and drinking wine, by gustave dore\",\n \"trainset_idx\": 363810\n },\n {\n \"caption\": \"polaroid, extremely detailed pale young woman covered in veins, veiny tentacles intestines, intestines in mouth, veins covering body, veins covering legs, skinny, guts, holes in face, zoomed out ,\",\n \"trainset_idx\": 247953\n },\n {\n \"caption\": \"Still shot from movie of a strong gorillaman, laughing wildly, holding a spear, cinematic\",\n \"trainset_idx\": 465583\n },\n {\n \"caption\": \", highly detailed, professional render, photorealistic, realistic effect, RTX, ,\",\n \"trainset_idx\": 90446\n },\n {\n \"caption\": \"a sign with \\\"ai made this\\\" written on it\",\n \"trainset_idx\": 301214\n },\n {\n \"caption\": \"photo of a mgzt v8 car in the quarry ,splash rocks ,\",\n \"trainset_idx\": 70465\n },\n {\n \"caption\": \"Diver exploring lost underwater city, sunbeams through the water, ancient structures\",\n \"trainset_idx\": 154044\n },\n {\n \"caption\": \"An asian girl\",\n \"trainset_idx\": 418610\n },\n {\n \"caption\": \"painting of goddess of basalt, trending on artstation\",\n \"trainset_idx\": 455795\n },\n {\n \"caption\": \"very dark purple haired anime woman hokage sitting on a chair's seat, fantasy style, office background, realistic\",\n \"trainset_idx\": 135205\n },\n {\n \"caption\": \"A 3d rendered emoji of a monster\",\n \"trainset_idx\": 50054\n },\n {\n \"caption\": \"Coyote Ugly\",\n \"trainset_idx\": 133212\n },\n {\n \"caption\": \"pixar style portrait shot, pin-up anime belle delphine robot in a junkyard, artwork by granblue fantasy, artgerm, attack on titan, high quality, amazing background by ghibli, wide gorgeous eyes, smooth cell shading\",\n \"trainset_idx\": 134355\n },\n {\n \"caption\": \"a painting of jesus with a crown on his head, hills in the background, by Albert Henry Krehbiel, head and shoulders view, carrington, thomas kincade, avatar image, some mountains in the background, ledmund leighton, portait image, 1923, painted by andreas rocha, portrait close up, louis dupre\",\n \"trainset_idx\": 465070\n },\n {\n \"caption\": \"ava addams marrying a bull on the beach\",\n \"trainset_idx\": 372045\n },\n {\n \"caption\": \"a cat holding a lightsaber\",\n \"trainset_idx\": 135458\n },\n {\n \"caption\": \"A realistic photo of a young model whose nickname is curvy sue\",\n \"trainset_idx\": 450701\n },\n {\n \"caption\": \"the pink power ranger, mighty morphin power rangers, by alphonse mucha\",\n \"trainset_idx\": 407339\n },\n {\n \"caption\": \"A portrait photo by Jacques Bourboulon of a young athletic woman with platinum blonde hair, holding a tennis racket in the sky in celebration\",\n \"trainset_idx\": 120011\n },\n {\n \"caption\": \"Cat god standing on top of the world globe with arms stretched out\",\n \"trainset_idx\": 50149\n },\n {\n \"caption\": \"Magical Library, comic, pastel,\",\n \"trainset_idx\": 211286\n },\n {\n \"caption\": \"A rebellious cyberpunk vigilante, infiltrating a corrupt megacorporation's headquarters with her high-tech gadgets and weaponry, , art by Yoji Shinkawa, Tsutomu Nihei, and Stjepan Sejic, neon-lit cityscape, confident smirk\",\n \"trainset_idx\": 376767\n },\n {\n \"caption\": \"happiness, very beautiful, inspiring, thought provoking\",\n \"trainset_idx\": 14263\n },\n {\n \"caption\": \"Photo of a black panther\",\n \"trainset_idx\": 435778\n },\n {\n \"caption\": \"negative hilarious thinking process\",\n \"trainset_idx\": 43548\n },\n {\n \"caption\": \"A man looking at a planet from his balcony\",\n \"trainset_idx\": 464148\n },\n {\n \"caption\": \"anime sword art online in style rick and morty series, 4k, Picture\",\n \"trainset_idx\": 115227\n },\n {\n \"caption\": \"selfie of a woman and her lion cub on the plains\",\n \"trainset_idx\": 368353\n },\n {\n \"caption\": \"Attack on Titan Eren Yeager with Third Reich\",\n \"trainset_idx\": 19987\n },\n {\n \"caption\": \"young Lee Young Ae, dressed as a 19th century hungarian peasant woman with two black hair braids, in 19th century a hungarian village, portrait by Munk\\u00e1csy, Ferenczy, Rutkowski, Marc Simonetti, Waterhouse very atmospheric, natural light\",\n \"trainset_idx\": 134380\n },\n {\n \"caption\": \"beautiful woman\",\n \"trainset_idx\": 497550\n },\n {\n \"caption\": \"yummy french fries on a beautiful and detailed table, self replicating on the edge of greatness, futuristic artwork\",\n \"trainset_idx\": 491351\n },\n {\n \"caption\": \"text made of blood that says Make Love Not War\",\n \"trainset_idx\": 450183\n },\n {\n \"caption\": \"screaming photorealistic close up portrait of happy old male screaming shaman covered in symmetrycal blue lotus crystal covered by windy splash of strings of light in a dark sky covered by stars, splash of glowing water, painting, aligned, dramatic light, by baade carrie ann andrews esao amorsolo\",\n \"trainset_idx\": 184903\n },\n {\n \"caption\": \"Washing machine designed by jonathan ive, octane render\",\n \"trainset_idx\": 118338\n },\n {\n \"caption\": \"Jenna Presley, Game of Thrones, Textless\",\n \"trainset_idx\": 582518\n },\n {\n \"caption\": \"Shrek in front of Sydney opera house\",\n \"trainset_idx\": 197602\n },\n {\n \"caption\": \"A photo of a beautiful caucasion woman, 30 years old, HD, analog style, female model, bathing, top=down angle, symmetrical eyes, photorealistic, HD in high detail realistic 4k, sharp photo, canon lens 100mm f1.8\",\n \"trainset_idx\": 566171\n },\n {\n \"caption\": \"Big Party at king Arthur\\u2019s court\",\n \"trainset_idx\": 228425\n },\n {\n \"caption\": \"A Eurasier dog sitting next to a black cat. Friendly but confident aura.\",\n \"trainset_idx\": 151799\n },\n {\n \"caption\": \"astronaut on a planet, giant moon in the sky\",\n \"trainset_idx\": 539723\n },\n {\n \"caption\": \"Victorian London\",\n \"trainset_idx\": 84024\n },\n {\n \"caption\": \"A female model playing videogames, sitting comfortably\",\n \"trainset_idx\": 127138\n },\n {\n \"caption\": \"Keanu Reeves portrait photo\",\n \"trainset_idx\": 297579\n },\n {\n \"caption\": \"ava addams siendo penetrada por messi\",\n \"trainset_idx\": 402350\n },\n {\n \"caption\": \"Realistic photo of Egypt Pyramids flying as high technological UFO's in a scary and creepy atmosphere\",\n \"trainset_idx\": 66363\n },\n {\n \"caption\": \"\\\"\\u201cA portrait of a cyborg in a golden suit, D&D sci-fi, artstation, concept art, highly detailed illustration.\\u201d\\\"\",\n \"trainset_idx\": 211582\n },\n {\n \"caption\": \"A cat holding a gun\",\n \"trainset_idx\": 338060\n },\n {\n \"caption\": \"Tom Hanks with Paul McCartney drinking beer\",\n \"trainset_idx\": 242413\n },\n {\n \"caption\": \"macron clashing with French cops in the middle of riots in the Paris streets\",\n \"trainset_idx\": 181450\n },\n {\n \"caption\": \"Macro shot of a glowing ring made of otherworldly materials, insanely detailed, photorealistic, 8k, ultra high resolution, volumetric lighting, taken with canon, taken with nikon\",\n \"trainset_idx\": 272558\n },\n {\n \"caption\": \"A risqu\\u00e9 picture \\ud83c\\udf48\\ud83c\\udf48, cinematic lighting 1998 film \\ud83d\\udcfd\\ufe0f outside on a cold morning\",\n \"trainset_idx\": 414999\n },\n {\n \"caption\": \", fantasy, pastel, absurdist, photo, Wes Anderson, superhero character, woman\",\n \"trainset_idx\": 354878\n },\n {\n \"caption\": \"dark background\",\n \"trainset_idx\": 523125\n },\n {\n \"caption\": \"Photo portrait vladimir volegov of a blonde woman\",\n \"trainset_idx\": 164327\n },\n {\n \"caption\": \"A very tiny kitten taking a bath in a teacup, hd, uhd, uhdr, hdr, 8k, 35mm, ultra high quality\",\n \"trainset_idx\": 221140\n },\n {\n \"caption\": \"funko pop Sinead O'Connor\",\n \"trainset_idx\": 321003\n },\n {\n \"caption\": \"Golden wattle by Hilma af Klint, William Morris\",\n \"trainset_idx\": 311158\n },\n {\n \"caption\": \"jennifer lawrence art by Kentar\\u014d Miura\",\n \"trainset_idx\": 375306\n },\n {\n \"caption\": \"photo of a wild pikachu, telephoto lens, national geographic\",\n \"trainset_idx\": 357051\n },\n {\n \"caption\": \"alfred kubin sketch draw of a gothic girl with messy makeup, gesture exercise, anatomic draw, detailed draw\",\n \"trainset_idx\": 565373\n },\n {\n \"caption\": \"an empowering view of the rooster demonic leader in a bloodied ironmaiden robot,wearing royal robe, by Philippe Druillet and Tsutomu Nihei, inspired by Frank Frazetta,volumetric lighting,detailed shadows,extremely detailed\",\n \"trainset_idx\": 447070\n },\n {\n \"caption\": \"royal ancient indian princess , , perfect composition, beautiful detailed intricate insanely detailed octane render trending on artstation, 8 k artistic photography, photorealistic concept art, soft natural volumetric cinematic perfect light, chiaroscuro, award - winning photograph, masterpiece, oil on canvas, raphael, caravaggio, greg rutkowski, beeple, beksinski, giger\",\n \"trainset_idx\": 149469\n },\n {\n \"caption\": \"a snail\",\n \"trainset_idx\": 266891\n },\n {\n \"caption\": \"Candice Patton, Grand Theft Auto, Textless\",\n \"trainset_idx\": 565038\n },\n {\n \"caption\": \"ratite\",\n \"trainset_idx\": 184183\n },\n {\n \"caption\": \"a real car made of wood\",\n \"trainset_idx\": 205416\n },\n {\n \"caption\": \"detailed vector illustration of a alien wearing hip hop fashion flying an airplane smoking a cigar by patrick brown\",\n \"trainset_idx\": 499807\n },\n {\n \"caption\": \"Painting of we all live in a yesllow submarine by the Beatles by Norman Rockwell\",\n \"trainset_idx\": 92633\n },\n {\n \"caption\": \"An image of a terrorist\",\n \"trainset_idx\": 219681\n },\n {\n \"caption\": \"A photo of a beautiful white Indian woman bathing in a pool, wearing sheer saree, legs visible, Victoria's secret model, award winning photograph, 25 years old, HD, analog style, full length, symmetrical eyes, photorealistic, HD in high detail realistic 4k, sharp photo, canon lens 100mm f1.8,\",\n \"trainset_idx\": 508684\n },\n {\n \"caption\": \"Hottest woman ever\",\n \"trainset_idx\": 330635\n },\n {\n \"caption\": \"A zentai woman wearing a multicolored paneled zentai body with tight closed zentai hood sits on a plain beach towel\",\n \"trainset_idx\": 120289\n },\n {\n \"caption\": \"Cleopatra woman outfit\",\n \"trainset_idx\": 229428\n },\n {\n \"caption\": \"a close up of a coffee cup on a kitchen table\",\n \"trainset_idx\": 397513\n },\n {\n \"caption\": \"a group of chinese supermodels on the beach, masterpiece photo, canon 5d mkiii, 24mm f4\",\n \"trainset_idx\": 101696\n },\n {\n \"caption\": \"a 1980s 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\"trainset_idx\": 364443\n },\n {\n \"caption\": \"polaroid, a colossal dark massive factory covered with bleeding corpses, hundreds of bleeding corpses ,\",\n \"trainset_idx\": 35139\n },\n {\n \"caption\": \"a dog riding a skateboard\",\n \"trainset_idx\": 400698\n },\n {\n \"caption\": \"mechanical bee\",\n \"trainset_idx\": 456620\n },\n {\n \"caption\": \"futa\",\n \"trainset_idx\": 495023\n },\n {\n \"caption\": \"jedi duck holding a lightsaber\",\n \"trainset_idx\": 269522\n },\n {\n \"caption\": \"1960's advertising for Olivetti, designed by Giovanni Pintori\",\n \"trainset_idx\": 287310\n },\n {\n \"caption\": \"Hyperrealistic hippopotamus\",\n \"trainset_idx\": 111494\n },\n {\n \"caption\": \"Nickelodeon Studios\",\n \"trainset_idx\": 151439\n },\n {\n \"caption\": \"highly detailed depiction of molecular machinery, 4k resolution, beautiful color grading, volumetric lighting, biological colors\",\n \"trainset_idx\": 430244\n },\n {\n \"caption\": \"A 4k photograph of a 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\"trainset_idx\": 66300\n },\n {\n \"caption\": \"VW beetle, illustrator, by Josef Frank\",\n \"trainset_idx\": 515998\n },\n {\n \"caption\": \"Praying at Computer\",\n \"trainset_idx\": 225102\n },\n {\n \"caption\": \"photo of an extremely large underground cave with a lake, lanterns floating in the air, trees on islands\",\n \"trainset_idx\": 493822\n },\n {\n \"caption\": \"Photorealistic top down shot of Pope Francis with designer suit , top down shot\",\n \"trainset_idx\": 317715\n },\n {\n \"caption\": \"a cute pink pug\",\n \"trainset_idx\": 353040\n },\n {\n \"caption\": \"Pikachu cooking at a restaurant, wearing a chefs hat\",\n \"trainset_idx\": 201917\n },\n {\n \"caption\": \"Funko figure of Lionel Messi, product photo\",\n \"trainset_idx\": 505473\n },\n {\n \"caption\": \"hologram of computer parts\",\n \"trainset_idx\": 33253\n },\n {\n \"caption\": \"In Space with Markiplier\",\n \"trainset_idx\": 570262\n },\n {\n \"caption\": \"potato on a lounge chair in a grassy 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\"portrait of guy muscle bald Slaughter punish son at prison toilet. wear raunch briefs, highly detailed face, killer look, Hard close-set eyes, born criminal\",\n \"trainset_idx\": 336983\n },\n {\n \"caption\": \"redhead woman\",\n \"trainset_idx\": 210940\n },\n {\n \"caption\": \"a turnip with a tiny hat sipping tea from a tiny cup and reading a newspaper\",\n \"trainset_idx\": 521538\n },\n {\n \"caption\": \"Jenna Presley, Fallout\",\n \"trainset_idx\": 67800\n },\n {\n \"caption\": \"vector art poster of a brain sticking out of a skull\",\n \"trainset_idx\": 227534\n },\n {\n \"caption\": \"Photo of a attractive 14 yo girl, holding a sign that reads, 'Anime is cool' in school, wearing a loose and very attractive dress, short smile, perfect faces, detailed pupils and eyes, thighs, waist, high-quality, post-processing highly detailed, fine details, 4k, adolescent body, undercut blue hair\",\n \"trainset_idx\": 219916\n },\n {\n \"caption\": \"a young viking woman holding a shield and throwing axe, yelling a battlecry, background of battle, dungeons and dragons character image\",\n \"trainset_idx\": 91005\n },\n {\n \"caption\": \"woman with translucent body, on her knees,\",\n \"trainset_idx\": 543439\n },\n {\n \"caption\": \"Explosive, neon smoke, night lights, psychedelic black teen model dancer, breakdancing, upside down, splits, octane render, 8K HD\",\n \"trainset_idx\": 79146\n },\n {\n \"caption\": \"endless escher-style multidimensional corridors that are hexagonal like a bee panel, treasure cave lighting, tesseract from the movie interstellar\",\n \"trainset_idx\": 274525\n },\n {\n \"caption\": \"Frame in Zdzislaw Beksinski, Ismail Inceoglu, Alan Lee style of Knight Errant of Luminosity, Eternal Ice Fields, Hypnotic, Minimalist, trending on deviantart, Inverted Colors\",\n \"trainset_idx\": 537524\n },\n {\n \"caption\": \"one porcelain doll, ooak bjd, bisque doll, figma, dynamic posing, cinematic still, ginger preteen\",\n \"trainset_idx\": 79480\n },\n {\n \"caption\": \"Quirion Ranger: This green creature card has the ability to return a forest to the player's hand in order to untap a creature, effectively allowing it to be used multiple times in a turn. 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The landscape is dotted with small villages and abandoned structures. Positive tags: towering mountains, vast oceans, sprawling forests, small villages. Negative tags: abandoned structures.\",\n \"trainset_idx\": 296088\n },\n {\n \"caption\": \"A picture of a 18yo teen girl a smelly tight outfit with yellow water dropping down thighs, smelly stain visible on leggings, smell fumes near woman, smell fumes around leggings, ,\",\n \"trainset_idx\": 532219\n },\n {\n \"caption\": \"Realistic woman portrait beautiful\",\n \"trainset_idx\": 397388\n },\n {\n \"caption\": \"optimus prime dancing with megatron as miniature toys with heavy tilt shift\",\n \"trainset_idx\": 154821\n },\n {\n \"caption\": \"small girl bath\",\n \"trainset_idx\": 210358\n },\n {\n \"caption\": \"1700s painting of Joe Rogan\",\n \"trainset_idx\": 46039\n },\n {\n \"caption\": \"a cute dragon heaving tea in the garden inspired by brian froud\",\n \"trainset_idx\": 495393\n },\n {\n \"caption\": \"Paradise city\",\n \"trainset_idx\": 90999\n },\n {\n \"caption\": \"2d anime illustration of young aphrodite with white hair wearing white scale armor holding a silver 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{\n \"caption\": \"Photo of 14 yo girl posing in school, short smile, perfect faces, detailed pupils and eyes, thighs, waist, high-quality, post-processing highly detailed, fine details, 4k, adolescent body, undercut blue hair\",\n \"trainset_idx\": 380242\n },\n {\n \"caption\": \"Cartoon; African american female teen superhero; not disfigured; not uglymarching in 1913\",\n \"trainset_idx\": 382701\n },\n {\n \"caption\": \"movie still of a girl in an abandoned waste land. photo. detailed, cinematic, movie still, horror, dark\",\n \"trainset_idx\": 508819\n },\n {\n \"caption\": \"aoc\",\n \"trainset_idx\": 127017\n },\n {\n \"caption\": \"jumping horse, lineart\",\n \"trainset_idx\": 70246\n },\n {\n \"caption\": \"A human foot\",\n \"trainset_idx\": 348862\n },\n {\n \"caption\": \"detailed paint of a sword, highly detailed paiting by gaston bussiere, craig mullins, j.c. leyendecker, 8k, royal paiting, dynamic lighting, intricate detail, hyperdetailed intricately detailed\",\n \"trainset_idx\": 174933\n },\n {\n \"caption\": \"photo of Marylin Monroe dressed as a nun watering a ficus tree\",\n \"trainset_idx\": 127440\n },\n {\n \"caption\": \"an abandoned rusted and moldy tesla in a forest\",\n \"trainset_idx\": 305301\n },\n {\n \"caption\": \"A photograph of a beautiful woman\",\n \"trainset_idx\": 259590\n },\n {\n \"caption\": \"Gorgeous, blonde snatch on train\",\n \"trainset_idx\": 59512\n },\n {\n \"caption\": \"35-year old William Shatner as Captain Kirk from 1963 Star Trek, HD 4k, sharp detail, photo-realistic accurate face and features, cinematic light\",\n \"trainset_idx\": 510649\n },\n {\n \"caption\": \"preteen girls with no underware and with long legs wide open in a the bedroom with dark background, with dark defined eyes and biting their lips like a movie of David Hamilton\",\n \"trainset_idx\": 536168\n },\n {\n \"caption\": \"photorealistic american shot of young man holding a blue notebook with orange door behind him\",\n \"trainset_idx\": 244848\n },\n {\n \"caption\": \"\\\"beautiful organic house made of moss and twigs in a forest carpeted with flowers, architectural render, chillwave, futuresynth, by Gabriel Dawe, by Skottie Young, by Jessica Rossier, by Moebius, by Isaac Cordal, vegetal architecture\\\", \\\"spring, junglepunk, blender, trending on artstation\\\"\",\n \"trainset_idx\": 375604\n },\n {\n \"caption\": \"arduino uno, 4k\",\n \"trainset_idx\": 143135\n },\n {\n \"caption\": \"Jinx from LOL, art by carne griffiths and wadim kashin\",\n \"trainset_idx\": 291524\n },\n {\n \"caption\": \"a beatiful plush teddy bear is welcoming people to white green themed cafe\",\n \"trainset_idx\": 574535\n },\n {\n \"caption\": \"lena paul mating with cristiano ronaldo\",\n \"trainset_idx\": 572852\n },\n {\n \"caption\": \"caucasian blonde woman\",\n \"trainset_idx\": 334360\n },\n {\n \"caption\": \", fantasy, pastel, absurdist, photo, Wes Anderson, rodent characters, dancing\",\n \"trainset_idx\": 313358\n },\n {\n \"caption\": \"night sky, small buildings, city, cityscape, stars, moon, sun, retro games, 1990 games #pixelart\",\n \"trainset_idx\": 156707\n },\n {\n \"caption\": \"a sensual Beautiful gorgeous blonde housewife, with athletic hourglass body, in casual outfit, cleaning home in alluring pose.\",\n \"trainset_idx\": 224448\n },\n {\n \"caption\": \"A monster ship hybrid, dark fantasy, rough sea, storm, rain, mysterious glow, 4k, realistic, monster design\",\n \"trainset_idx\": 476203\n },\n {\n \"caption\": \"Bright image, A man sitting on a crescent moon by Jeremy Mann\",\n \"trainset_idx\": 456186\n },\n {\n \"caption\": \"Werecat\",\n \"trainset_idx\": 408416\n },\n {\n \"caption\": \"A man robbing a store\",\n \"trainset_idx\": 163734\n },\n {\n \"caption\": \"zangief, white trunks, hairy, sweaty, solo photo, full body\",\n \"trainset_idx\": 469322\n },\n {\n \"caption\": \"Close-up on the lips of a beautiful appealing young alluring beautician\",\n \"trainset_idx\": 231007\n },\n {\n \"caption\": \"old woman covered in sheets standing on stilts, memoriam beach rudolf felix edwin gres warmssnhq ,Abram Efimovich Arkhipov\",\n \"trainset_idx\": 547311\n },\n {\n \"caption\": \"chun li, street fighte, digital art, mastepiece, art by artgerm\",\n \"trainset_idx\": 66975\n },\n {\n \"caption\": \"Kurt Cobain\",\n \"trainset_idx\": 450499\n },\n {\n \"caption\": \"Tiger in suit wearing glasses, anthropomorphic tiger\",\n \"trainset_idx\": 511904\n },\n {\n \"caption\": \"wolf, cave, mine, dark, runes, glowing, magic, fantasy, rpg, dungeons and dragons\",\n \"trainset_idx\": 172797\n },\n {\n \"caption\": \"gangster cat wearing snapback and golden chain on neck with dollar sign pendant\",\n \"trainset_idx\": 152528\n },\n {\n \"caption\": \"teddybears in uniform next to a car, car workshop in a spaceship, inside is a model of a lotus esprit, sci fi,star trek shuttle bay\",\n \"trainset_idx\": 399356\n },\n {\n \"caption\": \"summer's here\",\n \"trainset_idx\": 184858\n },\n {\n \"caption\": \"Beautiful, masterpiece, Asuka Langley Soryu, neon Genesis Evangelion, plugsuit, Orange hair, blue eyes\",\n \"trainset_idx\": 117603\n },\n {\n \"caption\": \"scandinavian zentai woman wearing white zentai body with zentai aesthetic\",\n \"trainset_idx\": 167648\n },\n {\n \"caption\": \"Baby's first hazmat suit\",\n \"trainset_idx\": 139511\n },\n {\n \"caption\": \"Dramatic Portrait of an Ancient old shaman playing string instrument and screaming overed by dust\",\n \"trainset_idx\": 532738\n },\n {\n \"caption\": \"A giant piece of lettuce with a bite taken out of it\",\n \"trainset_idx\": 547796\n },\n {\n \"caption\": \"A person wearing a sci-fi visor with a glowing cute smiling face on it, covered eyes\",\n \"trainset_idx\": 278368\n },\n {\n \"caption\": \"Juan Domingo Peron leaving the bank with a lot of money in his hands\",\n \"trainset_idx\": 264743\n },\n {\n \"caption\": \"Cinematographic de-beers christic-Archbishops pasolini mitre camorra Astronaut papal official leica hasselblad photograph in Vatican royal helmet gold metal scaphandre launchpad pointy oxygen hazmat gloves helmet\",\n \"trainset_idx\": 372347\n },\n {\n \"caption\": \"A woman alone in the dark,\",\n \"trainset_idx\": 239461\n },\n {\n \"caption\": \"Supergirl in white dress with red bow and bunny ears painted by Hihn William Waterhouse\",\n \"trainset_idx\": 393231\n },\n {\n \"caption\": \"translucent skin\",\n \"trainset_idx\": 218170\n },\n {\n \"caption\": \"Centered , smooth , Wendy Corduroy of Gravity Falls , GTA imagined , max gorgeous art , by Kuvshinov & Artgerm , Comic & Cartoon style , standing, beside a montain stream , 8K, UHD, ultracomic art, trending on Artstation, GTA conception\",\n \"trainset_idx\": 262787\n },\n {\n \"caption\": \"DIAMONd skull\",\n \"trainset_idx\": 568982\n },\n {\n \"caption\": \"an empowering view of a orca warrior,wearing royal robe,wielding a katana,menacing,by artist Philippe Druillet and Tsutomu Nihei,volumetric lighting,detailed shadows,extremely detailed\",\n \"trainset_idx\": 480047\n },\n {\n \"caption\": \"a digital painting of a satyr archer with furry legs and hooves\",\n \"trainset_idx\": 313631\n },\n {\n \"caption\": \"Lightning McQueen disney infinity\",\n \"trainset_idx\": 197290\n },\n {\n \"caption\": \"A god by Thomas Kinkade\",\n \"trainset_idx\": 559978\n },\n {\n \"caption\": \"einstein eating fries\",\n \"trainset_idx\": 558871\n },\n {\n \"caption\": \"Cute girl in pink skirt\",\n \"trainset_idx\": 339374\n },\n {\n \"caption\": \"breton monk polish folk disco outdoors photo\",\n \"trainset_idx\": 202369\n },\n {\n \"caption\": \"logo of the text \\\"Omega\\\" and \\\"Darling\\\"\",\n \"trainset_idx\": 571351\n },\n {\n \"caption\": \"A priestess with white habit with purple decorations, high quality, highly detailed, 4k, photorealistic\",\n \"trainset_idx\": 127204\n },\n {\n \"caption\": \"Aliens in my bedroom Whimsy style\",\n \"trainset_idx\": 178236\n },\n {\n \"caption\": \"RAW photo of beautiful woman anatomy, masterpiece, perfect lighting, perfect face, eye contact, green eyes, studio photo, on couch, perfect lighting, beautiful lighting, smooth lighting , presenting, navel, swimear\",\n \"trainset_idx\": 444317\n },\n {\n \"caption\": \"Painting of melted gemstones flowers brane Degas style\",\n \"trainset_idx\": 99822\n },\n {\n \"caption\": \"photo of a sunken steamtrain in the jungle river,flooded train,splashing misty mud rocks,panorama,LNER Gresley\",\n \"trainset_idx\": 116500\n },\n {\n \"caption\": \"Man \\\"dancing the robot\\\"\",\n \"trainset_idx\": 179936\n },\n {\n \"caption\": \"Extreme closeup of Rapunzel with glowing eyes, beam of light, tower, joy, by Kuvshinov Ilya\",\n \"trainset_idx\": 502113\n },\n {\n \"caption\": \"sci fi female portrait\",\n \"trainset_idx\": 565951\n },\n {\n \"caption\": \"A close up screenshot of MKBHD YouTube reviewing a new piece of tech hardware in his studio, HD 8k photorealistic shot on Fuji film\",\n \"trainset_idx\": 299939\n },\n {\n \"caption\": \"stunningly beautiful space zombie, insanely detailed, photorealistic, 8k, midjourney style\",\n \"trainset_idx\": 224307\n },\n {\n \"caption\": \"A young stylish woman speaking in a megaphone entirely made of semolina.\",\n \"trainset_idx\": 510351\n },\n {\n \"caption\": \"A cute very purple cat with purple purple fur and a cute black top-hat. very cyan blue background\",\n \"trainset_idx\": 122185\n },\n {\n \"caption\": \"a giant woman holding the planet\",\n \"trainset_idx\": 545792\n },\n {\n \"caption\": \"Alex Jones working at Wendy's\",\n \"trainset_idx\": 414598\n },\n {\n \"caption\": \"A pixel art of a castle, full detailed, trending on steam games, castlevania style.\",\n \"trainset_idx\": 372593\n },\n {\n \"caption\": \"a blue-haired egirl, beautiful, ,\",\n \"trainset_idx\": 129082\n },\n {\n \"caption\": \"splash art portrait of a licorice witch\",\n \"trainset_idx\": 120188\n },\n {\n \"caption\": \"A girl with fire powers anime style\",\n \"trainset_idx\": 240783\n },\n {\n \"caption\": \"Robert Anton Wilson smiling holding a prism with an eye at the apex. Psychedelic colors, DMT aura\",\n \"trainset_idx\": 81923\n },\n {\n \"caption\": \"tow truck\",\n \"trainset_idx\": 409443\n },\n {\n \"caption\": \"Darth Vader fighting Obi-Wan\",\n \"trainset_idx\": 19400\n },\n {\n \"caption\": \"Walt Disney style- Ariel, the Little Mermaid as a naturist in the ocean, HD 4K, sharp detail, photo-realistic features\",\n \"trainset_idx\": 46571\n },\n {\n \"caption\": \"A highly detailed landscape painting of a fluorescent mushroom forest at night painted by Blizzard Concept Artists featured on ArtStation\",\n \"trainset_idx\": 258752\n },\n {\n \"caption\": \"Cyberpunk super bike, photography, volume light\",\n \"trainset_idx\": 139923\n },\n {\n \"caption\": \"Monika\",\n \"trainset_idx\": 895\n },\n {\n \"caption\": \"Stylized portrait of Chef Boyardee as President of the United States\",\n \"trainset_idx\": 309779\n },\n {\n \"caption\": \"action cam up close on the rainbow owl\",\n \"trainset_idx\": 582405\n },\n {\n \"caption\": \"A well endowed mature faery flying in a magical rainbow forest\",\n \"trainset_idx\": 400862\n },\n {\n \"caption\": \"Marilyn Monroe wearing a shirt that reads LSD\",\n \"trainset_idx\": 15321\n },\n {\n \"caption\": \"The fabric of time and space tearing open violently and the impact it would have on human life, insanely detailed, photorealistic, 8k, ,\",\n \"trainset_idx\": 329054\n },\n {\n \"caption\": \"portrait of a girl with cotton candy hair and candy blue lips, her shirt has the text \\\"GifCo\\\" across the front of it, highly detailed photorealistic, soft golden light, cinematic lighting\",\n \"trainset_idx\": 70148\n },\n {\n \"caption\": \"A portrait of cyberpunk inquisition: giant kinky Muscle bald hairless daddy severe Slaughter inquisitor covered in red fluid came to oppress and enslave. art by Ilya Repin\",\n \"trainset_idx\": 202175\n },\n {\n \"caption\": \"little blonde girl no underpants\",\n \"trainset_idx\": 526892\n },\n {\n \"caption\": \"banana, pen, astronaut\",\n \"trainset_idx\": 321869\n },\n {\n \"caption\": \"a girl reading a book in a window nook lying on a lot of pillows\",\n \"trainset_idx\": 51136\n },\n {\n \"caption\": \"portrait photo of a beautiful woman\",\n \"trainset_idx\": 281271\n },\n {\n \"caption\": \"Drawing of a towering city, fantasy style, tropical, flying buttresses, arches, aerial view, black ink on paper, Indian temple, realistic, detailed, quality, complex, elaborate, ornate, by M C Escher, by Robert Hooke\",\n \"trainset_idx\": 226929\n },\n {\n \"caption\": \"A beautiful woman standing in the rain, wet hair, photograph, highly detailed\",\n \"trainset_idx\": 468649\n },\n {\n \"caption\": \"llama smoking\",\n \"trainset_idx\": 305401\n },\n {\n \"caption\": \"Images inspired by alice in wonderland and computers, cinematic movie still, from the tim burton movie, majestic dynamic, future tech\",\n \"trainset_idx\": 219236\n },\n {\n \"caption\": \"photo of bulling two muscle guys bald Slaughter punish abducted and 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\"caption\": \"santanico pandemonium\",\n \"trainset_idx\": 457707\n },\n {\n \"caption\": \"Koenigsegg agera from 1986\",\n \"trainset_idx\": 284410\n },\n {\n \"caption\": \"funko pop of walter white\",\n \"trainset_idx\": 69164\n },\n {\n \"caption\": \"Cat-builder\",\n \"trainset_idx\": 475948\n },\n {\n \"caption\": \"a mouse holding a sign written starving\",\n \"trainset_idx\": 164551\n },\n {\n \"caption\": \"bitcoin mango under a tree\",\n \"trainset_idx\": 550312\n },\n {\n \"caption\": \"a hat that says \\\"Hello World\\\"\",\n \"trainset_idx\": 511610\n },\n {\n \"caption\": \"An eSports mario logo\",\n \"trainset_idx\": 79580\n },\n {\n \"caption\": \"Kurt Cobain As cartoon preforming on stage with Nrivana\",\n \"trainset_idx\": 385641\n },\n {\n \"caption\": \"a painting of a man and a woman standing next to each other, a surrealist painting, featured on Artstation, fantastic realism, sorayama and moebius. occult art, a still life of a robot, closeup portrait of an artificer, detailed steampunk illustration, guillem h. pongiluppi, j c leyendecker 8 k, droids\",\n \"trainset_idx\": 380709\n },\n {\n \"caption\": \"A car that is made out of opal\",\n \"trainset_idx\": 48568\n },\n {\n \"caption\": \"Stan Lee dressed as American Gothic farmer\",\n \"trainset_idx\": 252372\n },\n {\n \"caption\": \"A pen drawing of death by beksinski\",\n \"trainset_idx\": 405719\n },\n {\n \"caption\": \"cute frog holding a sign with a cartoonish frog drawn on it\",\n \"trainset_idx\": 359807\n },\n {\n \"caption\": \"\\\"Dream come true\\\" text, clouds, heavenly bodies\",\n \"trainset_idx\": 313582\n },\n {\n \"caption\": \"a woman in red hair lies among the flowers, in the style of dark aquamarine and light amber, i can't believe how beautiful this is, dark sky-blue and dark beige, dark orange and light azure, dreamy and romantic, loose and fluid, ferrania p30\",\n \"trainset_idx\": 575553\n },\n {\n \"caption\": \"Sunny Leone, Bayonetta\",\n \"trainset_idx\": 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\"caption\": \"stone cottage, verdant, flowers, autumn leaves, absurd res, maximum detail, best quality, digital illustration, Most beautiful artwork in the world, Beautiful environment, Photorealistic painting art by midjourney, Professional majestic oil painting, global illumination, studio light, volumetric light\",\n \"trainset_idx\": 552677\n },\n {\n \"caption\": \"photo of beautiful japanese little girl swimming\",\n \"trainset_idx\": 224433\n },\n {\n \"caption\": \"Onyx\",\n \"trainset_idx\": 197408\n },\n {\n \"caption\": \"right hand from the top\",\n \"trainset_idx\": 325682\n },\n {\n \"caption\": \"mia khalifa\",\n \"trainset_idx\": 391095\n },\n {\n \"caption\": \"billie eilish,\",\n \"trainset_idx\": 527250\n },\n {\n \"caption\": \"Photo of a Pigeon in a well tailored suit getting a cup of coffee in a cafe in the morning\",\n \"trainset_idx\": 391516\n },\n {\n \"caption\": \"A French man surfing on the moon\",\n \"trainset_idx\": 417140\n },\n {\n \"caption\": 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558166\n },\n {\n \"caption\": \"police security guard of twenty people, in Brno at Czech republic, at night, in rain, in action\",\n \"trainset_idx\": 41444\n },\n {\n \"caption\": \"owl house\",\n \"trainset_idx\": 479501\n },\n {\n \"caption\": \"a photograph of a european dragon with wings\",\n \"trainset_idx\": 223222\n },\n {\n \"caption\": \"A miniature poodle.\",\n \"trainset_idx\": 462355\n },\n {\n \"caption\": \"a female cyber head crafted with wires and sensors, in the style of 8k resolution, eve ventrue, zhang jingna, stanley kubrick, dark white and navy, 32k uhd, photo taken with nikon d750\",\n \"trainset_idx\": 301787\n },\n {\n \"caption\": \"detailed watercolor illustration of a logo consisting of a blue lion head made of ice crystals, high quality, cinematic lighting, sharp focus, , black background\",\n \"trainset_idx\": 315929\n },\n {\n \"caption\": \"a pretty girl is looking at the distance while typing on a computer, seating on a thai beach\",\n \"trainset_idx\": 375651\n },\n {\n \"caption\": \"A cat doing taxes\",\n \"trainset_idx\": 292758\n },\n {\n \"caption\": \"Whorish Asian Woman\",\n \"trainset_idx\": 108685\n },\n {\n \"caption\": \"girls way stepmom\",\n \"trainset_idx\": 456839\n },\n {\n \"caption\": \"Furry , fursona , fox , furry body , orange furry body, female , hourglass body , large ti t, long loose brown hair , digital art , furry art , blue eyes , close up , attractive , portrait background, red background ,fox head ,\",\n \"trainset_idx\": 423452\n },\n {\n \"caption\": \"a man in the tar pit morphing into a black gooey latex lioness, award winning wildlife photograpy. 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Giger,volumetric lighting,detailed shadows\",\n \"trainset_idx\": 174512\n },\n {\n \"caption\": \"Giga chad gorilla\",\n \"trainset_idx\": 480222\n },\n {\n \"caption\": \"An elf female in a field\",\n \"trainset_idx\": 274782\n },\n {\n \"caption\": \"photo of a shrub shaped like a dog\",\n \"trainset_idx\": 489387\n },\n {\n \"caption\": \"unmasked\",\n \"trainset_idx\": 339008\n },\n {\n \"caption\": \"a cyberpunk portrait of a green space alien by jean - michel basquiat, by hayao miyazaki by artgerm, highly detailed, sacred geometry, mathematics, snake, geometry, cyberpunk, vibrant, water\",\n \"trainset_idx\": 212481\n },\n {\n \"caption\": \"masterpiece sfw epic male civilized animalistic anthro demon wearing suit horns fur tuft fluffy fluff bright eyes bipedal hooves pastel colors fullbody posing fine details trending no artstation digital painting high resolution highres\",\n \"trainset_idx\": 363505\n },\n {\n \"caption\": \"A screenshot of Silent Hill 3.\",\n \"trainset_idx\": 200514\n },\n {\n \"caption\": \"An Asian beauty walking on the beach, Wearing very little, sky blue sea with light yellow sand, vast, 4k, award winning work.\",\n \"trainset_idx\": 4260\n },\n {\n \"caption\": \"batman piggygy piggy piggy piggy piggy piggy piggy piggy piggy piggy piggy piggy piggy piggy piggy, behance hd, dark robed, cute:2, the artist has used bright, hd rendering, neymar, with cape, bacon, sap, happy brave magical cuteness, filmation, wiccan, piglet\",\n \"trainset_idx\": 60743\n },\n {\n \"caption\": \"20 year-old Barbara Eden as an Elfin princess naturist in a magical mystic forest, HD 4k, sharp detail\",\n \"trainset_idx\": 40077\n },\n {\n \"caption\": \"My daughter is a kobold\",\n \"trainset_idx\": 267655\n },\n {\n \"caption\": \"photorealistic image of Jenna Ortega, the 20-year-old actress, with her signature bangs hairstyle. The image should showcase her natural beauty and capture her youthful energy. Use high-quality reference images to ensure accurate facial features and hair texture. Pay close attention to lighting and shading to make the image as realistic as possible\",\n \"trainset_idx\": 94100\n },\n {\n \"caption\": \"ted dibiase jr, 35 years old, full body shot, serious face, short hair, handsome, muscular, fantasy theme, medieval fantasy theme, wearing dark blue ice winter armor, leather pants, holding ice sword, realisticvision13, icy caves background,\",\n \"trainset_idx\": 186588\n },\n {\n \"caption\": \"Detailed Chun Li knight wearing greathelm, snow background, perfect Lighting and shadows\",\n \"trainset_idx\": 51055\n },\n {\n \"caption\": \"Utopian futuristic boy barely clothed\",\n \"trainset_idx\": 577541\n },\n {\n \"caption\": \"Nelson Mandela in the style of Arthur Rackham\",\n \"trainset_idx\": 438942\n },\n {\n \"caption\": \"A \\\"CUTE\\\" sign in Anime\",\n \"trainset_idx\": 51375\n },\n {\n \"caption\": \"shameless showing teen blonde little girl\",\n \"trainset_idx\": 7749\n },\n {\n \"caption\": \"film still, close up, ariana grande rising out of muddy vietnam river not wearing any clothes, face covered in mud, n a k e d, low camera angle at water level, big breas ts, film still from apocalypse now 1 9 7 9 , 2 6 mm polaroid polaroid polaroid polaroid polaroid expired expired expired\",\n \"trainset_idx\": 229887\n },\n {\n \"caption\": \"Photograph of a red luxury handbag in a studio setting with a Caucasian woman in a red leather coat\",\n \"trainset_idx\": 477039\n },\n {\n \"caption\": \"A profile picture of an anime boy, anime, detailed, brown hair, cyberpunk, half human half robot, robotronic\",\n \"trainset_idx\": 423553\n },\n {\n \"caption\": \"film still of Neytiri from Avatar as a blue-skinned naturist\",\n \"trainset_idx\": 148169\n },\n {\n \"caption\": \"fullbody portrait of Jennifer Connelly as the goddess circe dejah thoris at burningman, fit muscular body, greek mythology, intricate, highly detailed, digital painting, artstation, concept art, sharp focus, cinematic lighting, illustration, art by artgerm and greg rutkowski, alphonse mucha, cgsociety\",\n \"trainset_idx\": 512429\n },\n {\n \"caption\": \"20 year-old beautiful Sean Young from Blade Runner as a naturist looking in a mirror, HD 4K, sharp detail, photo-realistic accurate face and features\",\n \"trainset_idx\": 248103\n },\n {\n \"caption\": \"chaos flesh red dragon\",\n \"trainset_idx\": 378196\n },\n {\n \"caption\": \"A cart\\u00f3n bull holding a sign that says \\\"te amo lobitx\\\"\",\n \"trainset_idx\": 205283\n },\n {\n \"caption\": \"buxxom woman\",\n \"trainset_idx\": 419031\n },\n {\n \"caption\": \"The last supper at Woodstock\",\n \"trainset_idx\": 454744\n },\n {\n \"caption\": \"Once upon a time in the charming village of Pastelton, where the houses were painted in soft pastel colors, every year the villagers eagerly awaited the arrival of spring. They particularly looked forward to the Great Easter Egg Hunt that brought them all together to celebrate the season.\",\n \"trainset_idx\": 313867\n },\n {\n \"caption\": \"a wizard pondering an orb\",\n \"trainset_idx\": 95112\n },\n {\n \"caption\": \"Mozart whispering to someone\",\n \"trainset_idx\": 288445\n },\n {\n \"caption\": \"A man, photo\",\n \"trainset_idx\": 123858\n },\n {\n \"caption\": \"Highly detailed portrait of halo, sunglasses, blue eyes, tartan scarf, white hair by atey ghailan, by greg rutkowski, by greg tocchini, by james gilleard, by joe fenton, by kaethe butcher, gradient yellow, black, brown and magenta color scheme, grunge aesthetic!!! graffiti tag wall background\",\n \"trainset_idx\": 53\n },\n {\n \"caption\": \"A sailing ship on a prismatic ocean\",\n \"trainset_idx\": 430895\n },\n {\n \"caption\": \"a digital art of a cute Fox\",\n \"trainset_idx\": 257986\n },\n {\n \"caption\": \"an octopus wearing a monocle\",\n \"trainset_idx\": 455133\n },\n {\n \"caption\": \"Benjamin Netanyahu dances samba\",\n \"trainset_idx\": 348871\n },\n {\n \"caption\": \"A traditional Japanese woodblock print of Pennywise the Clown\",\n \"trainset_idx\": 438971\n },\n {\n \"caption\": \"an humanoid cat holding a paper that says \\\"hello\\\"\",\n \"trainset_idx\": 468679\n },\n {\n \"caption\": \"deku from mha fighting zuko from avatar.\",\n \"trainset_idx\": 230436\n },\n {\n \"caption\": \"fantasy town, dungeons and dragons, medieval, rustic, dnd, dungeons and dragons, high quality\",\n \"trainset_idx\": 417822\n },\n {\n \"caption\": \"Open hand\",\n \"trainset_idx\": 201992\n },\n {\n \"caption\": \"Grudge ghost with mouth open inside mirror, dark room\",\n \"trainset_idx\": 182616\n },\n {\n \"caption\": \"a close up of a plastic robot dog on a white surface, reddit, made of crystal, h1080, clear parts\",\n \"trainset_idx\": 213659\n },\n {\n \"caption\": \"drake lifting large weights with extreme muscles, cinematic, studio lighting, extreme detail, hot, magazine photograph\",\n \"trainset_idx\": 274092\n },\n {\n \"caption\": \"Cute Golden Retriever, realistic digital oil painting by Monet Cute Golden Retriever, digital oil painting by Monet\",\n \"trainset_idx\": 497021\n },\n {\n \"caption\": \"a gargoyle and cat in the sewers, eerie cobwebs\",\n \"trainset_idx\": 104365\n },\n {\n \"caption\": \"Indian man wearing a Dragonball costume sleeping on the floor of a hallway with a sign that says Oblisk\",\n \"trainset_idx\": 539529\n },\n {\n \"caption\": \"a great space male commander, art style, with the face paainted with black and blue patterns\",\n \"trainset_idx\": 35483\n },\n {\n \"caption\": \"the text \\\"Gif Co\\\" written in roots and branches and leaves, soft warm light, highly detailed, photorealistic\",\n \"trainset_idx\": 219949\n },\n {\n \"caption\": \"David Bowie in an aqua body suit, as painted by Van Gogh\",\n \"trainset_idx\": 519271\n },\n {\n \"caption\": \"an abstract painting of a dragon flying over a grass field\",\n \"trainset_idx\": 76001\n },\n {\n \"caption\": \"i saw a red fox meditating in the center of a crystaline fractal garden in dmt hyperspace\",\n \"trainset_idx\": 32083\n },\n {\n \"caption\": \"Painting of a pilot in front of old airplane, detailed\",\n \"trainset_idx\": 240244\n },\n {\n \"caption\": \"a blue dog and a green bear\",\n \"trainset_idx\": 296230\n },\n {\n \"caption\": \"A sign saying \\\"Hello World!\\\" in England\",\n \"trainset_idx\": 132785\n },\n {\n \"caption\": \"a modern brick building of 2 stories with wooden elements in the facade\",\n \"trainset_idx\": 466320\n },\n {\n \"caption\": \"Cute grey cat, digital oil painting by Klimt\",\n \"trainset_idx\": 328719\n },\n {\n \"caption\": \"surfer surfing a huge wave with a sea turtle with bitcoin as the sun\",\n \"trainset_idx\": 412031\n },\n {\n \"caption\": \"a hot female model\",\n \"trainset_idx\": 332970\n },\n {\n \"caption\": \"photograph, high detail, high defintion, 8k, hdr, global illumination, girl with nothing on\",\n \"trainset_idx\": 161563\n },\n {\n \"caption\": \"A giant cobra snake on a farm. The snake is made out of corn.\",\n \"trainset_idx\": 502136\n },\n {\n \"caption\": \"Abcdefghijklmnopqrstuvwxyz\",\n \"trainset_idx\": 303479\n },\n {\n \"caption\": \"a hat on a table that says \\\"Hello World\\\"\",\n \"trainset_idx\": 541202\n },\n {\n \"caption\": \"prototype Moebius strip in Pollock painting style in deep red and white, Installation in interior Sainte-Chapelle in Paris, museum, morphosis, textures, densely woven cobwebs, beautiful colors, touching, spectacular\",\n \"trainset_idx\": 361721\n },\n {\n \"caption\": \"A creepy sunflower with a face\",\n \"trainset_idx\": 382623\n },\n {\n \"caption\": \"a red balloon detailed pixel art with empty white background\",\n \"trainset_idx\": 155521\n },\n {\n \"caption\": \"A portrait of cyberpunk inquisition: hot boy Being electro torture Alive By hot bald Slaughter at torture prison. highly detailed face. Surrealism art by Ilya Repin\",\n \"trainset_idx\": 246388\n },\n {\n \"caption\": \"Handsome man, new Lamborghini, new york skyline in background\",\n \"trainset_idx\": 516711\n },\n {\n \"caption\": \"Watercolor painting of european city park, workout machines, afternoon backlight, by greg rutkowski, by anders zorn\",\n \"trainset_idx\": 43103\n },\n {\n \"caption\": \"a Chinese girl, with black suit, long hair, black hair, happy and smiling, full body, comic style\",\n \"trainset_idx\": 454964\n },\n {\n \"caption\": \"Detailed portrait neon girl, cyberpunk futuristic neon, short pink hair, reflective puffy coat, by ismail inceoglu dragan bibin hans thoma greg rutkowski alexandros pyromallis nekro rene maritte illustrated, perfect face, fine details\",\n \"trainset_idx\": 246198\n },\n {\n \"caption\": \"angry fire fighter climbing a ladder\",\n \"trainset_idx\": 213605\n },\n {\n \"caption\": \"Jack Harlow\",\n \"trainset_idx\": 71876\n },\n {\n \"caption\": \"A very brawny man, heavyset, handsome, fat, overweight, plump, chubby\",\n \"trainset_idx\": 22427\n },\n {\n \"caption\": \"Marilyn Monroe wearing a shirt that reads Send Money\",\n \"trainset_idx\": 78202\n },\n {\n \"caption\": \"A high quality photo of Maisie Williams wearing a purple hoodie, forest setting, 4k.\",\n \"trainset_idx\": 173066\n },\n {\n \"caption\": \"a 14 year old girl and her boyfriend wearing almost nothing\",\n \"trainset_idx\": 312086\n },\n {\n \"caption\": \"Creed Odyssey by Gordon Doherty\",\n \"trainset_idx\": 432999\n },\n {\n \"caption\": \"Sun and Moon by Hilma af Klint, rifle paper co\",\n \"trainset_idx\": 136806\n },\n {\n \"caption\": \"The temple gate in the mountain\",\n \"trainset_idx\": 42381\n },\n {\n \"caption\": \"Peter Mohrbacher style, Tinkerbell as a Gears of War character, wear, army fatigues, emotion, fierce, inspired by Digital Art and Alphons Mucha and Anna Dittmann\",\n \"trainset_idx\": 424830\n },\n {\n \"caption\": \"iranian girl who is a dentist\",\n \"trainset_idx\": 189979\n },\n {\n \"caption\": \"A crowded street scene with a person dressed as a clown\",\n \"trainset_idx\": 285935\n },\n {\n \"caption\": \"ahri from league of legends anime screencap, sitting, dress\",\n \"trainset_idx\": 279299\n },\n {\n \"caption\": \"robot holding a bouquet of flowers in front of the Eiffel Tower\",\n \"trainset_idx\": 336192\n },\n {\n \"caption\": \"An easter bunny, eggs, tulips, on a pink background, copyspace on the top, digital art,\",\n \"trainset_idx\": 490423\n },\n {\n \"caption\": \"blue ghostly cat on a black background, trending on artstation\",\n \"trainset_idx\": 389178\n },\n {\n \"caption\": \"a giant kitten in the city\",\n \"trainset_idx\": 345032\n },\n {\n \"caption\": \"minecraft kawai\",\n \"trainset_idx\": 515522\n },\n {\n \"caption\": \"photo of an orangutan riding a road bike on mountain road\",\n \"trainset_idx\": 38248\n },\n {\n \"caption\": \"colourful fairytale illustration, fairytale house, treehouse, red roof, white walls, ladder, detailled, sparkles around\",\n \"trainset_idx\": 103833\n },\n {\n \"caption\": \"a jung male sitting down on a throne in a dystopian world\",\n \"trainset_idx\": 274074\n },\n {\n \"caption\": \"25 years old Lee Evans as a 19th century postman, dressed in gray uniform, oil painting portrait by Munk\\u00e1csy, very atmospheric, raining, natural lights, trending on pinterest.com\",\n \"trainset_idx\": 425975\n },\n {\n \"caption\": \"pavement in the forest, ghibli anime style, trending on artstation\",\n \"trainset_idx\": 309447\n },\n {\n \"caption\": \"photo of middleaged man in calavera costume at a boisterous Halloween party, modern house, detailed, f1.8, 8k\",\n \"trainset_idx\": 317546\n },\n {\n \"caption\": \"hard truck apocalypse game\",\n \"trainset_idx\": 522052\n },\n {\n \"caption\": \"magical world of elves\",\n \"trainset_idx\": 46863\n },\n {\n \"caption\": \"symmetrical lightning storm, fantasy storm giant, flashing eyes, scowl by artist \\\"purple and gold\\\", by artist \\\"Alan Lee\\\", by artist \\\"John Howe\\\"\",\n \"trainset_idx\": 325229\n },\n {\n \"caption\": \"photo of beautiful blonde woman in beautiful room\",\n \"trainset_idx\": 233194\n },\n {\n \"caption\": \"The subconscious mind traversing between the geometric lattices of space time\",\n \"trainset_idx\": 529775\n },\n {\n \"caption\": \"baby yoda eating eggs\",\n \"trainset_idx\": 254842\n },\n {\n \"caption\": \"Bowser smoking Cigar, High Resolution, High Quality, Many Details\",\n \"trainset_idx\": 182159\n },\n {\n \"caption\": \"sci-fi pistol concept art\",\n \"trainset_idx\": 489159\n },\n {\n \"caption\": \"A man holding a glowing katana in a detailed cyberpunk city\",\n \"trainset_idx\": 12223\n },\n {\n \"caption\": \"beautiful woman riding a horse\",\n \"trainset_idx\": 43971\n },\n {\n \"caption\": \"a close up of a dinosaur next to a landrover, inspired by Adam Rex, cinematic, 1993, heartbreaking, promo image, action shot, an ultra realistic\",\n \"trainset_idx\": 59464\n },\n {\n \"caption\": \"suicide\",\n \"trainset_idx\": 486536\n },\n {\n \"caption\": \"tow truck, towing a car\",\n \"trainset_idx\": 160134\n },\n {\n \"caption\": \"Photorealistic image of cute 19 year old girl , looks like Neve Campbell talking on the phone,blurry background\",\n \"trainset_idx\": 556217\n },\n {\n \"caption\": \"Roronoa Zoro\",\n \"trainset_idx\": 307496\n },\n {\n \"caption\": \"Luffy gear 6\",\n \"trainset_idx\": 388596\n },\n {\n \"caption\": \"Charcoal artwork of an eldritch entity. Myterious suspenseful fog.\",\n \"trainset_idx\": 191919\n },\n {\n \"caption\": \"Noah is standing with his wife and three sons on ark.the ark is floating on Ocean Realistic Image\",\n \"trainset_idx\": 545024\n },\n {\n \"caption\": \"bomb destroys the statue of liberty\",\n \"trainset_idx\": 16183\n },\n {\n \"caption\": \"Comic portrait of an musculine truck driver\",\n \"trainset_idx\": 389471\n },\n {\n \"caption\": \"Alien plant life, insanely detailed, photorealistic, 8k, ultra high resolution, volumetric lighting, taken with canon, taken with nikon\",\n \"trainset_idx\": 335188\n },\n {\n \"caption\": \"enviroment concept art, minecraft, kawai style\",\n \"trainset_idx\": 417950\n },\n {\n \"caption\": \"4k hyperdetailed vibrant fluffy friendly anthropomorphic lynx with antlers, standing, full body, medieval, adventurer, dnd, rpg, rustic, nature, fantasy\",\n \"trainset_idx\": 454064\n },\n {\n \"caption\": \"An alluring gondolier in a Venetian canal, attractive cute female gondolier, shapely, revealed\",\n \"trainset_idx\": 380366\n },\n {\n \"caption\": \"BATMAN\",\n \"trainset_idx\": 368242\n },\n {\n \"caption\": \"An african guy chilling looking the sunset, attractive, vibrant\",\n \"trainset_idx\": 424612\n },\n {\n \"caption\": \"flower\",\n \"trainset_idx\": 125658\n },\n {\n \"caption\": \"A dark forest passage, illustration\",\n \"trainset_idx\": 562533\n },\n {\n \"caption\": \"pets reading a book in a magical forest\",\n \"trainset_idx\": 117480\n },\n {\n \"caption\": \"minimalistic lineart vector graphic logo of an alpaca in front of a simple diamond shape, black and white, monochromatic,\",\n \"trainset_idx\": 125748\n },\n {\n \"caption\": \"King ganeral\",\n \"trainset_idx\": 50804\n },\n {\n \"caption\": \"beautiful Italian beach scene painted by Rembrandt and Redon, impasto relief palette knife oil paint, Thick luscious impasto paint very deep sculptural brush and palette knife marks\",\n \"trainset_idx\": 360859\n },\n {\n \"caption\": \"abstract isometric fantasy landscape\",\n \"trainset_idx\": 279729\n },\n {\n \"caption\": \"A Portrait of a Sith on a Hill , High Resolution, High Quality, Many Details, Real Life\",\n \"trainset_idx\": 565237\n },\n {\n \"caption\": \"Design sketch of a futuristic aircraft, purple and gold colours\",\n \"trainset_idx\": 233404\n },\n {\n \"caption\": \"Beautiful Woman\",\n \"trainset_idx\": 305999\n },\n {\n \"caption\": \"lego batman\",\n \"trainset_idx\": 320030\n },\n {\n \"caption\": \"A pink folder icon, frutiger aero, aqua interface\",\n \"trainset_idx\": 469050\n },\n {\n \"caption\": \", fantasy, pastel, absurdist, photo, bird people\",\n \"trainset_idx\": 117691\n },\n {\n \"caption\": \"young Muscle boy cutting castration a giant testis TESTICLE on the dissecting Table. plural testes, male reproductive gland, bloody background. highly detailed guro art by Ilya Repin\",\n \"trainset_idx\": 407307\n },\n {\n \"caption\": \"art poster by legend of ravaging dynasties, magical winged lion, majestic aslan\",\n \"trainset_idx\": 96710\n },\n {\n \"caption\": \"Marilyn Manson album cover\",\n \"trainset_idx\": 438949\n },\n {\n \"caption\": \"an empowering view of the heavenly demonic buddha in a ironmaiden robot,wearing a noble robe,a surrealist painting by aralan bean and Neil Blevins and H.R. Giger,volumetric lighting,detailed shadows\",\n \"trainset_idx\": 82248\n },\n {\n \"caption\": \"A highly detailed portrait of Emma Stone painted by Art Frahm featured on ArtStation\",\n \"trainset_idx\": 210921\n },\n {\n \"caption\": \"a pot-bellied orcish bandit in an alley\",\n \"trainset_idx\": 191476\n },\n {\n \"caption\": \"An open jar of apple jam next to a piece of toast on a plate\",\n \"trainset_idx\": 94004\n },\n {\n \"caption\": \"handdrawn, flat, illustration, cute small adorable blushing seirei, cute girl with laptop, tired, brown hair, adorable, trending on ArtStation, highly detailed, simple background, 128k\",\n \"trainset_idx\": 231540\n },\n {\n \"caption\": \"A train riding into a dreamscape.\",\n \"trainset_idx\": 142802\n },\n {\n \"caption\": \"A tiny dragon taking a bath in a teacup, hd, uhd, uhdr, hdr, 8k, 35mm, ultra high quality\",\n \"trainset_idx\": 170993\n },\n {\n \"caption\": \"tom cruise in a black leather jacket, oil on canvas, portrait painting\",\n \"trainset_idx\": 189860\n },\n {\n \"caption\": \"old illustration of a jungle\",\n \"trainset_idx\": 508640\n },\n {\n \"caption\": \"Big holographic brain\",\n \"trainset_idx\": 557874\n },\n {\n \"caption\": \"Sunset reflecting on a crystal ball held by a mysterious figure\",\n \"trainset_idx\": 455355\n },\n {\n \"caption\": \"girl in baroque costume on streets of Barcelona\",\n \"trainset_idx\": 543134\n },\n {\n \"caption\": \"hugging\",\n \"trainset_idx\": 255297\n },\n {\n \"caption\": \"Humming bird, plying over a lake with reflection hyper real\",\n \"trainset_idx\": 208412\n },\n {\n \"caption\": \"new bullet train, low shot, lensflare, cinematic, low angle of train wheels grinding on track, sci fi glow, profile side view\",\n \"trainset_idx\": 476262\n },\n {\n \"caption\": \"Supernova in a glass jar, Insanely detailed, photorealistic, 8k, ultra high resolution, volumetric lighting, taken with canon eos 5d,\",\n \"trainset_idx\": 531660\n },\n {\n \"caption\": \"A movie still from a horror film called Revenge of The Crazy Punk Nutter Crew\",\n \"trainset_idx\": 439917\n },\n {\n \"caption\": \"A car workshop in a spaceship,teddybears in uniform, inside is a model of a lotus esprit, sci fi,star trek\",\n \"trainset_idx\": 465050\n },\n {\n \"caption\": \"Cute fairytale cottage, architecture design, digital illustration, digital concept art, medium shot\",\n \"trainset_idx\": 38242\n },\n {\n \"caption\": \"photo of Ford Focus RS, night time, city, city roads, Miami streets\",\n \"trainset_idx\": 553658\n },\n {\n \"caption\": \"A ripoff wild pokemon\",\n \"trainset_idx\": 255706\n },\n {\n \"caption\": \"fat, chubby, Afro American cappuccino dork girl wearing tiny shorts, riding an exquisitely detailed skateboard, doing full body twisted splits upside down, smoke, explosion, 8K, HD, magical energy, highly detailed, rendered in octane, very very very aesthetic, night lights\",\n \"trainset_idx\": 8161\n },\n {\n \"caption\": \"Deer head surrounded by neon light\",\n \"trainset_idx\": 170733\n },\n {\n \"caption\": \"forrific zombie , by moebius\",\n \"trainset_idx\": 150536\n },\n {\n \"caption\": \"a pink fish with a metal horns in the head, deep ocean\",\n \"trainset_idx\": 32046\n },\n {\n \"caption\": \"a graphic concept on the theme \\\"less is more\\\"\",\n \"trainset_idx\": 493416\n },\n {\n \"caption\": \"An abandoned neon city with a runner passing through, An intricate and hyperdetailed painting by Ismail Inceoglu, Huang Guangjian and Dan Witz, Fantasy art, Album cover art, Deep colors\",\n \"trainset_idx\": 108652\n },\n {\n \"caption\": \"adorable baby DRAGON sitting inside a teacup, vines, leaves, deep colors, full moon background, Watercolour Painting on paper, impressionistic, intricate details, 8k, photorealism, airbrush, complex, volumetric lighting wet brush, epic composition, depth of field, glazing, cel-shaded detailed painting 8K resolution intricate meticulous artwork\",\n \"trainset_idx\": 213502\n },\n {\n \"caption\": \"1114083012-a ultradetailed beautiful panting of a fullmoon, night sky with lots of stars, landcsape and a man looking at the moon, by conrad roset, greg rutkowski\",\n \"trainset_idx\": 83160\n },\n {\n \"caption\": \"preteen girls with \\\"no underware\\\" in a sofa with a childish faces touching each other, with dark background\",\n \"trainset_idx\": 34037\n },\n {\n \"caption\": \"The personification of the Halloween holiday in the form of a girl with short hair and a villain's smile, cute hats, cute cheeks, unreal engine, highly detailed, artgerm digital illustration, woo tooth, studio ghibli, deviantart, sharp focus, artstation, by Alexei Vinogradov bakery, emerald eyes\",\n \"trainset_idx\": 578417\n },\n {\n \"caption\": \"Girl wearing t-shirt that says \\\"KoRo\\\"\",\n \"trainset_idx\": 445274\n },\n {\n \"caption\": \"a jeep driving down a muddy road in the woods, by Anthony S Waters, renaissance, raptor, seen from behind, some rust, a green, of, buffalo, real-life brook, front side views full, hongbsws, camp, but very good looking\\u201d, very wet, 2 0 2 1,\",\n \"trainset_idx\": 221039\n },\n {\n \"caption\": \"A futuristic city built on the ocean floor, cinematic shot\",\n \"trainset_idx\": 388388\n },\n {\n \"caption\": \"stanley kubrik filming a dog\",\n \"trainset_idx\": 259678\n },\n {\n \"caption\": \"a spider wearing a sunhat\",\n \"trainset_idx\": 504542\n },\n {\n \"caption\": \"The Penrose stairs or Penrose steps,the impossible staircase, illusion\",\n \"trainset_idx\": 474133\n },\n {\n \"caption\": \"a woman wearing an octopus as a hat\",\n \"trainset_idx\": 328588\n },\n {\n \"caption\": \"kurt cobain and jimi hendrix\",\n \"trainset_idx\": 349298\n },\n {\n \"caption\": \"A man .............. a lemon, concept art\",\n \"trainset_idx\": 217902\n },\n {\n \"caption\": \"golden dragon\",\n \"trainset_idx\": 366486\n },\n {\n \"caption\": \"Communist revolution in Germany, 1920s, painting, detailed art\",\n \"trainset_idx\": 211241\n },\n {\n \"caption\": \"Turtle on top of a wooden table\",\n \"trainset_idx\": 113337\n },\n {\n \"caption\": \"8k uhd portrait photograph of a beautiful woman with cat ears on her head\",\n \"trainset_idx\": 110906\n },\n {\n \"caption\": \"anime woman, clothless\",\n \"trainset_idx\": 289083\n },\n {\n \"caption\": \"photo, peas with googly eyes, photorealistic, realistic, masterpiece, 4k, 8k, UHD, highres, highest quality, insanely detailed, best quality, centered, golden ratio\",\n \"trainset_idx\": 549792\n },\n {\n \"caption\": \"Muscular tall queen wearing armour standing outside dark gothic castle, 5k, hdr, illustration\",\n \"trainset_idx\": 105821\n },\n {\n \"caption\": \"high quality color portrait photograph of scarlett johansson with a black dog in bed,cuddling,highly detailed,beautiful face,award winning photo\",\n \"trainset_idx\": 369986\n },\n {\n \"caption\": \"painting in style of dali, jar of pickles\",\n \"trainset_idx\": 246906\n },\n {\n \"caption\": \"Bunny cat hybrid\",\n \"trainset_idx\": 339138\n },\n {\n \"caption\": \"mad cyberpunk raccoon with sword\",\n \"trainset_idx\": 63654\n },\n {\n \"caption\": \"DSLR portrait of a beautiful young bald woman wearing leather boots and red skirt\",\n \"trainset_idx\": 440262\n },\n {\n \"caption\": \"Beautiful jungle woman, sensual\",\n \"trainset_idx\": 498089\n },\n {\n \"caption\": \"charles hoskinson with a crack pipe, ugly\",\n \"trainset_idx\": 51489\n },\n {\n \"caption\": \"Tessa Violet\",\n \"trainset_idx\": 219225\n },\n {\n \"caption\": \"A cute Kawaii tiny hyper realistic fairy in 4he spring grass with flowers . wide angle full body, 8k, Cinematography, photorealistic,epic composition Unreal Engine,Cinematic, Color Grading, Portrait Photography,Ultra-Wide Angle, Depth of Field, hyper detailed\",\n \"trainset_idx\": 465171\n },\n {\n \"caption\": \"Isometric cubes by Josef Frank, Picasso\",\n \"trainset_idx\": 427911\n },\n {\n \"caption\": \"Photograph a werewolf wearing blue pants standing in the woods\",\n \"trainset_idx\": 70448\n },\n {\n \"caption\": \"Logo witam drone and writing SZTUKA DRONOWANIA w fajnej czcionce\",\n \"trainset_idx\": 10157\n },\n {\n \"caption\": \"cyberpunk city\",\n \"trainset_idx\": 428632\n },\n {\n \"caption\": \"agent the matrix, agent smith the matrix, the mad hatter, alice in wonderland, matrix background computer\",\n \"trainset_idx\": 470691\n },\n {\n \"caption\": \"georgia okeefe flower painting\",\n \"trainset_idx\": 118166\n },\n {\n \"caption\": \"enderman, ender, endermen\",\n \"trainset_idx\": 521775\n },\n {\n \"caption\": \"Painting of town center with cobbled street at night, wet pavement, stormy night, intricate, higly detaile, photo realistic, by studio ghibli\",\n \"trainset_idx\": 172407\n },\n {\n \"caption\": \"hyper realistic portrait photography of beautiful happy girl, pale skin, golden earrings, summer golden hour, kodak portra 800, 105 mm f1. 8;\",\n \"trainset_idx\": 67387\n },\n {\n \"caption\": \"sports illustrated calendar Jennifer Lawrence in stiletto heels\",\n \"trainset_idx\": 560126\n },\n {\n \"caption\": \"photo of dinosaur eating a landrover in the mud jungle\",\n \"trainset_idx\": 13999\n },\n {\n \"caption\": \"A bee and marijuana plants\",\n \"trainset_idx\": 321337\n },\n {\n \"caption\": \"Photo of a waffle with chocolate, top view\",\n \"trainset_idx\": 203655\n },\n {\n \"caption\": \"beautiful scenery\",\n \"trainset_idx\": 115579\n },\n {\n \"caption\": \"A anime girl with red hair, cute, holding a potato\",\n \"trainset_idx\": 155744\n },\n {\n \"caption\": \"fat rich black man in well-tailored suit visiting a steel foundry\",\n \"trainset_idx\": 37728\n },\n {\n \"caption\": \"a bucolic landscape\",\n \"trainset_idx\": 572390\n },\n {\n \"caption\": \"young Lee Young Ae as an european peasant woman\",\n \"trainset_idx\": 455226\n },\n {\n \"caption\": \"photo of a soviet scientist with red goggles and gloves 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264332\n },\n {\n \"caption\": \"a lithograph of a capybara\",\n \"trainset_idx\": 244742\n },\n {\n \"caption\": \"Superman Scooby, book illustration\",\n \"trainset_idx\": 107499\n },\n {\n \"caption\": \"jimi hendrix playing guitar wildly\",\n \"trainset_idx\": 131084\n },\n {\n \"caption\": \"Rukia Kuchiki\",\n \"trainset_idx\": 243914\n },\n {\n \"caption\": \"A triangle inside a circle inside a square\",\n \"trainset_idx\": 269441\n },\n {\n \"caption\": \"classroom,standing strict in row towards wall, jules burkjulien bettfluffy jingsewing workers,Jules Bastien-Lepage,movie still,\",\n \"trainset_idx\": 303470\n },\n {\n \"caption\": \"The beatles yellow submarine LP portrait but star wars Style\",\n \"trainset_idx\": 301436\n },\n {\n \"caption\": \"Hubby body round eyes cute little white kitten playing the guitar sitting on top of a little green hill with smiling flowers growing up beside it, cute smiling music notes and shapes in the background kawaii style illustration simple line drawing\",\n \"trainset_idx\": 342241\n },\n {\n \"caption\": \"a photograph of a blue ChromaFlair MG ZT 190 car ,rover 75 mgzt, metalic paint\",\n \"trainset_idx\": 309065\n },\n {\n \"caption\": \"college house party nighttime, messy, drinking, drugs, diverse race and gender, beautiful coeds, swim wear, caught off guard by photographer, flirty, camera with flash, spring break, euphoria, film ektar 100, hyper realistic\",\n \"trainset_idx\": 180151\n },\n {\n \"caption\": \", fantasy, pastel, absurdist, photo, Wes Anderson, mouse femme fatale\",\n \"trainset_idx\": 505593\n },\n {\n \"caption\": \"cyberpunk giant kinky muscle young Soldier inquisitor excruciate kneeling worship obedient pregnant girl at torture chamber. art by Ilya Repin\",\n \"trainset_idx\": 228408\n },\n {\n \"caption\": \"chuck e cheese church, REALISTIC, BLURRY BACKGROUND, BOKEH, FAST, MOTION, detailed skin, 20 megapixel, canon eos r3, detailed, detailed face\",\n \"trainset_idx\": 463925\n },\n {\n \"caption\": \"Risqu\\u00e9 mermaid, clamshell brassiere, sparkling scales, fish tail, nautical epic fantasy, THICC\",\n \"trainset_idx\": 222895\n },\n {\n \"caption\": \"Hit me with something creative, in synthwave retro style\",\n \"trainset_idx\": 52334\n },\n {\n \"caption\": \"art by Alfons Mucha, stained glass motif, whole body image of 20 year-old Carrie Fisher as a naturist Princess Leia Organa on Tatooine, HD 4K, sharp detail, photo-realistic accurate face and features, award winning photography, cinematic lighting\",\n \"trainset_idx\": 479492\n },\n {\n \"caption\": \"an anime girl wearing a fur jacket, badass, game character, concept art,digital art, trending on artstation, by artgerm, by alpharose muncha\",\n \"trainset_idx\": 137301\n },\n {\n \"caption\": \"DVD widescreen screengrab of the street scene from the movie about ancient rome\",\n \"trainset_idx\": 167858\n },\n {\n \"caption\": \"wizard playing electric guitar\",\n \"trainset_idx\": 357335\n },\n {\n \"caption\": \"a photo of a forklift partially hidden by a stack of boxes\",\n \"trainset_idx\": 277399\n },\n {\n \"caption\": \"Spongebob mascot\",\n \"trainset_idx\": 256694\n },\n {\n \"caption\": \"Scared Woman\",\n \"trainset_idx\": 124826\n },\n {\n \"caption\": \"epic watercolor illustration of an ice crystal lion on top of a snowy cliff in the midst of a storm, high quality, cinematic lighting, sharp focus, 8k,\",\n \"trainset_idx\": 73203\n },\n {\n \"caption\": \"an engraving of king arthur standing on a rocky outcropping with castle camelot in the background by gustave dore, caspar david friedrich, ian miller, highly detailed, strong shadows, depth, lithograph engraving\",\n \"trainset_idx\": 240736\n },\n {\n \"caption\": \"vibrant neon cosmic jellyfish in space, vibrant neon glow, apocalypse art, black background, darksynth; dark neon vibrant pop surrealism, 3d vector illustration, heavy metal art, CGSociety, heavy lines, cosmic colours, Unreal Engine 5, Artstation, sots art, synthwave, high contrast, selective colour; unique composition\",\n \"trainset_idx\": 552255\n },\n {\n \"caption\": \"Shattered glass art: delirious person in agony, horrified, fragile\",\n \"trainset_idx\": 222830\n },\n {\n \"caption\": \"a girl rides on a swing with wings\",\n \"trainset_idx\": 319952\n },\n {\n \"caption\": \"Photorealistic image of a power ranger dressed like a priest, power ranger helmet\",\n \"trainset_idx\": 224070\n },\n {\n \"caption\": \"Most extravagent, expensive house ever\",\n \"trainset_idx\": 206086\n },\n {\n \"caption\": \"Beautyful woman\",\n \"trainset_idx\": 406558\n },\n {\n \"caption\": \"a cinematic ultra-detailed 3d photorealistic unreal engine sharp ultra quality elegant photograph of a boeing 747\",\n \"trainset_idx\": 443864\n },\n {\n \"caption\": \"Full body photo of a beautiful white woman, detailed face\",\n \"trainset_idx\": 202591\n },\n {\n \"caption\": \"photograph of a beautiful woman\",\n \"trainset_idx\": 189978\n },\n {\n \"caption\": \"A doctor with a very kind face with technology elements at the background\",\n \"trainset_idx\": 215755\n },\n {\n \"caption\": \"An anime image of a young man shrugging his shoulders in the art style of Akira!\",\n \"trainset_idx\": 254334\n },\n {\n \"caption\": \"Black and white portrait of futuristic mad professional photographer with camera covered by mushrooms in toilets surrounded by kangaroos\",\n \"trainset_idx\": 235868\n },\n {\n \"caption\": \"Fursona furry fox , female , beautiful , attractive , furry body , fox body colours , smiling ,digital art , showing off , masterpiece , by foxovh , hourglass body , furry art style , furry , anthro , long loose brown hair locks , furry yiff , fox head\",\n \"trainset_idx\": 34785\n },\n {\n \"caption\": \"A painting of a beautiful woman by J. C. Leyendecker.\",\n \"trainset_idx\": 473417\n },\n {\n \"caption\": \"tom cruise as a werewolf, ultradetailed, embellishments\",\n \"trainset_idx\": 175289\n },\n {\n \"caption\": \"A web icon of a kid being pushed by his father in a swing\",\n \"trainset_idx\": 127791\n },\n {\n \"caption\": \"a closeup portrait of a maid, undercut hair, apron, amazing body, pronounced feminine feature, kitchen, freckles, flirting with camera\",\n \"trainset_idx\": 553163\n },\n {\n \"caption\": \"Extreme detailed close up of crocodile eye slightly from the side\",\n \"trainset_idx\": 480487\n },\n {\n \"caption\": \"bulma se apareandose con un hobre\",\n \"trainset_idx\": 8571\n },\n {\n \"caption\": \"a dog juggling bowling pins\",\n \"trainset_idx\": 242813\n },\n {\n \"caption\": \"Ukrainian Cat looks like second world war pilot,flight helmet,wearing skin pilot's cloth,digital painting,aviator movie style,resident evil comic style,highest detailed,8k hd,marvel comic,dinamic pose,epic view,cinematic light\",\n \"trainset_idx\": 279718\n },\n {\n \"caption\": \"1950 colour small batman surf mansion architect drawing, miami drive, bat shape, artdeco, batsign, glass ring worm-tunnels, excentric, faded colour, rotring pencil artist impression, comics, spooky, by frank lloyd wright and gaudi and nouvel and pritzker prize\",\n \"trainset_idx\": 356510\n },\n {\n \"caption\": \"thick young jewish woman showing\\\\\",\n \"trainset_idx\": 122828\n },\n {\n \"caption\": \"tom cruise as a werewolf, concept art, ultradetailed, embellishments\",\n \"trainset_idx\": 87715\n },\n {\n \"caption\": \"1 handsome young Asian man, street photography, fashion clothes, anime style\",\n \"trainset_idx\": 370361\n },\n {\n \"caption\": \"ferdinand hendrick skagrp wyegardener chuffed thanking, Christian Krohg, melancholia\",\n \"trainset_idx\": 448297\n },\n {\n \"caption\": \"soviet propaganda\",\n \"trainset_idx\": 77742\n },\n {\n \"caption\": \"an oil painting of a sad old man\",\n \"trainset_idx\": 388930\n },\n {\n \"caption\": \"Name \\\"Maria\\\" text, bay color, nature\",\n \"trainset_idx\": 380895\n },\n {\n \"caption\": \"Elon Musk standing in the bath no water, closeup of his body, photoshoot, hd\",\n \"trainset_idx\": 327300\n },\n {\n \"caption\": \"toy figurine of saber from fate zero\",\n \"trainset_idx\": 119266\n },\n {\n \"caption\": \"This is a photo of Walter White from Breaking Bad. The image shows a realistic depiction of the character with his hair, mustache and beard ablaze, surrounded by chaos. The flames are captured in vivid detail, illuminating the intense expression on his face. The background is blurred, adding to the sense of movement and action. The overall tone is intense and dramatic. .\",\n \"trainset_idx\": 301407\n },\n {\n \"caption\": \"futuristic architecture plan of Jutar, Xanthos's homeworld planet. concept art by Greg Rutkowski, pinterest, fantasy+futuristic+city, greeble\",\n \"trainset_idx\": 395626\n },\n {\n \"caption\": \"a plus-sized male protagonist of a western-style anime.\",\n \"trainset_idx\": 427197\n },\n {\n \"caption\": \"detailed and realistic portrait of a of stunningly beautiful slender girl, trending on Instagram, shaggy haircut, white hair color, natural lights, soft natural lighting, Extreme close-up, magical photography, dramatic lighting, photo realism, ultra-detailed, intimate portrait composition, Leica 50mm, f1.4\",\n \"trainset_idx\": 535176\n },\n {\n \"caption\": \"A future Work of art by Marcel Duchamp\",\n \"trainset_idx\": 268962\n },\n {\n \"caption\": \"psychedelic boy, neon colors, comic illustration\",\n \"trainset_idx\": 26990\n },\n {\n \"caption\": \"Illustration by van Gogh of Walter White sitting on a sofa, knitting\",\n \"trainset_idx\": 269985\n },\n {\n \"caption\": \"a photograph of teddy bear driving a mini car toy in the jungle river,smiling teddy\",\n \"trainset_idx\": 456852\n },\n {\n \"caption\": \"picture of a military jet\",\n \"trainset_idx\": 506575\n },\n {\n \"caption\": \"Finnish summer cottage\",\n \"trainset_idx\": 510735\n },\n {\n \"caption\": \"ava addams en su luna de miel\",\n \"trainset_idx\": 64587\n },\n {\n \"caption\": \"A studio photo of A colorful evil superhero ,toy, studio shot, 3d toy figure, cute superhero toy,3d render, Lumen Global Illumination, megapixel, Product View, photo-realistic, studio shot\",\n \"trainset_idx\": 214500\n },\n {\n \"caption\": \"Magic wand made from sweets, rtx, hq, octane render\",\n \"trainset_idx\": 362081\n },\n {\n \"caption\": \"A dog closeup animation\",\n \"trainset_idx\": 532475\n },\n {\n \"caption\": \"action figure of a frog wearing a pink kimono, while drinking a cup of coffee, professional photography, ultra realistic, 8k, product photo\",\n \"trainset_idx\": 522515\n },\n {\n \"caption\": \"a portrait of an anime character, inside a fantasy castle, with an open door behind that displays a fantasy landscape\",\n \"trainset_idx\": 580957\n },\n {\n \"caption\": \"Superman's asasination, 1960s comic\",\n \"trainset_idx\": 508848\n },\n {\n \"caption\": \"Word 'ART'\",\n \"trainset_idx\": 233602\n },\n {\n \"caption\": \"View from Slovenia, cabin on a mountain, winter, nighttime, Christmas lights, moon\",\n \"trainset_idx\": 77189\n },\n {\n \"caption\": \"an astronaut meeting an alien for the first time in space\",\n \"trainset_idx\": 2505\n },\n {\n \"caption\": \"Secret agents wearing funny purple hats chasing a goose, explosions on background, photorealistic, photo\",\n \"trainset_idx\": 152491\n },\n {\n \"caption\": \"cute teen, insanely detailed, photorealistic, 8k, perfect composition, volumetric lighting, natural complexion, award winning professional photography, taken with canon eos 5d mark iv, 85mm, mindblowing, masterpiece,\",\n \"trainset_idx\": 508557\n },\n {\n \"caption\": \"Beautiful asian woman, low cut blouse\",\n \"trainset_idx\": 46202\n },\n {\n \"caption\": \"Foggy valley full of flowers, view from above, photorealistic\",\n \"trainset_idx\": 120668\n },\n {\n \"caption\": \"An extremely pale blue eyed blonde male fat old hairy daddy in a sauna\",\n \"trainset_idx\": 546583\n },\n {\n \"caption\": \"Villa architecture inspired by the designs of Zaha Hadid,ln forest , from distance, epic composition, cinematic lighting,ultra photorealistic\\uff0cOctane render,\",\n \"trainset_idx\": 419779\n },\n {\n \"caption\": \"a video game screenshot from shipment on call of duty multiplayer, 24 players, xbox series x screenshot\",\n \"trainset_idx\": 82771\n },\n {\n \"caption\": \"a velociraptor in a room and a MGb car smashing through hole in the wall and velociraptor ,sparks dust rubble dinosaur splash ,studio lighting,white walls, headlights,chrome mgb\",\n \"trainset_idx\": 570562\n },\n {\n \"caption\": \"Unicorn coloring page vector illustration white background\",\n \"trainset_idx\": 262848\n },\n {\n \"caption\": \"a modelling photograph of an sultry exotic tattooed bimbo woman, with implants\",\n \"trainset_idx\": 157815\n },\n {\n \"caption\": \"The pink panther painted by Gottfried Helnwein\",\n \"trainset_idx\": 474521\n },\n {\n \"caption\": \"car made of potatoes\",\n \"trainset_idx\": 278199\n },\n {\n \"caption\": \"pug terminator\",\n \"trainset_idx\": 107409\n },\n {\n \"caption\": \"photo about a 8 year old girl do yoga, wearing denim shorts and blouse, show uncle\",\n \"trainset_idx\": 528494\n },\n {\n \"caption\": \"RAW, portrait photo, 27 year-old adult young woman, petite, tired, exhausted, bags under eyes, nerdy, cold, hair pulled back, sharp focus, visible skin pores, smiling, natural lighting, professional photo, intricate details, photoreal, shot using sony a7riii camera,\",\n \"trainset_idx\": 395526\n },\n {\n \"caption\": \"Painting of sea turtle reef style\",\n \"trainset_idx\": 423867\n },\n {\n \"caption\": \"a necklace with an ice cream cone inside of it, a microscopic photo by Jeff Koons, shutterstock contest winner, pop surrealism, wolff olins |, detailed conceptual photography, styled food photography\",\n \"trainset_idx\": 203546\n },\n {\n \"caption\": \"a meme\",\n \"trainset_idx\": 449470\n },\n {\n \"caption\": \"Photo of a woman taking a selfie in the mirror\",\n \"trainset_idx\": 166473\n },\n {\n \"caption\": \"fry from futurama dressed as Neo from the Matrix, in the style of Matt Groening\",\n \"trainset_idx\": 480649\n },\n {\n \"caption\": \"promotional material for a magical-boy anime with a chubby male protagonist.\",\n \"trainset_idx\": 216419\n },\n {\n \"caption\": \"Audrey Hepburn holding a pentagram\",\n \"trainset_idx\": 121977\n },\n {\n \"caption\": \"An emoji of a bear\",\n \"trainset_idx\": 113163\n },\n {\n \"caption\": \"porous, flamboyant, tickle, red dwarf\",\n \"trainset_idx\": 146022\n },\n {\n \"caption\": \"huge female muscle goddess, extreme massive, pecs, abs, biceps, thick forearms, bullneck, gorgeous, realistic, detailed\",\n \"trainset_idx\": 418638\n },\n {\n \"caption\": \"anime girl raiden mei wearing skirt and stockings\",\n \"trainset_idx\": 366903\n },\n {\n \"caption\": \"A photo of a man holding a sign that says \\\"This Is SDXL\\\"\",\n \"trainset_idx\": 9653\n },\n {\n \"caption\": \"shrek pirate, league of legends splash art\",\n \"trainset_idx\": 368627\n },\n {\n \"caption\": \"an elephant furry anthro\",\n \"trainset_idx\": 445297\n },\n {\n \"caption\": \"a pretty fashion woman\",\n \"trainset_idx\": 59113\n },\n {\n \"caption\": \"Bronze statue of a philosopher, with a wise expression, long beard, and a tome, highly detailed, soft lighting, introspective gaze\",\n \"trainset_idx\": 59002\n },\n {\n \"caption\": \"Marc Steen\",\n \"trainset_idx\": 103163\n },\n {\n \"caption\": \"breton monk looking like zappa with a goat in bosnia, photo\",\n \"trainset_idx\": 337334\n },\n {\n \"caption\": \"Baphomet\",\n \"trainset_idx\": 36992\n },\n {\n \"caption\": \"reaper on a 1990s death metal band cover, inking, art nouveau, vintage 90s print, detailed, scary, horror, screen print, trending on artstation\",\n \"trainset_idx\": 493999\n },\n {\n \"caption\": \"yellow duck, colored, splash, high detailed, drawing\",\n \"trainset_idx\": 230003\n },\n {\n \"caption\": \"a goofy owl\",\n \"trainset_idx\": 271064\n },\n {\n \"caption\": \"a teen girl in a poncho harvesting aquaponic plants on the top of skyscraper in a cuperpunk city, grim dark atmosphere, realistic digital painting by jack rikar\",\n \"trainset_idx\": 425616\n },\n {\n \"caption\": \"Cartoon black humanoid rabbit with large white eyes leather jacket, ripped skinny jeans and gold front teeth riding a suzuki 3d render pixar style\",\n \"trainset_idx\": 359765\n },\n {\n \"caption\": \"cinematic portrait\",\n \"trainset_idx\": 100703\n },\n {\n \"caption\": \"joe biden\",\n \"trainset_idx\": 424507\n },\n {\n \"caption\": \"Prompt: a man falling into a black hole in space, digital art, photorealistic.\",\n \"trainset_idx\": 354050\n },\n {\n \"caption\": \"Mia Khalifa, Grand Theft Auto IV, Textless\",\n \"trainset_idx\": 470192\n },\n {\n \"caption\": \"The logo for the series \\u201cFamily Guy.\\u201d Stylized Text of the words \\u201cFamily Guy\\u201d\",\n \"trainset_idx\": 107273\n },\n {\n \"caption\": \"scene from a banned psychedelic psychology film from 1993 , dmt love , by Wolfgang Tillmans\",\n \"trainset_idx\": 7552\n },\n {\n \"caption\": \"photo of a woman in a blue dress driving a red ferrari, kodak portra\",\n \"trainset_idx\": 71297\n },\n {\n \"caption\": \"cute furry fantasy creature, with very large eyes, white gray, Hyper-detailed + Hyper Maximalist, Extremely Intricate, Professional photography, natural lighting, canon lens, shot on dslr 64 megapixels sharp focus Epic cinematic brilliant stunning intricate meticulously detailed dramatic atmospheric maximalist digital matte painting\",\n \"trainset_idx\": 418743\n },\n {\n \"caption\": \"a cowboy holding a sign that \\\"Hunt\\\"\",\n \"trainset_idx\": 194358\n },\n {\n \"caption\": \"a detailed mechanism of a clock inside a magnificent colorful amazing bird\",\n \"trainset_idx\": 340605\n },\n {\n \"caption\": \"Redhead Laureline and black-haired Valerian, agents of time and space, painted by Boris Vallejo\",\n \"trainset_idx\": 516264\n },\n {\n \"caption\": \"a cat wearing a MAGA hat\",\n \"trainset_idx\": 521533\n },\n {\n \"caption\": \"A bunny in medieval armor\",\n \"trainset_idx\": 110907\n },\n {\n \"caption\": \"of a beautiful face\",\n \"trainset_idx\": 579954\n },\n {\n \"caption\": \"Homeless teen boy sitting on a box in an alley, fantasy art, cinematic,\",\n \"trainset_idx\": 551297\n },\n {\n \"caption\": \"sign that says yes\",\n \"trainset_idx\": 317152\n },\n {\n \"caption\": \"a man in a trenchcoat and a woman in red dress fighting in a retro restaurant\",\n \"trainset_idx\": 488578\n },\n {\n \"caption\": \"Light sees itself in a reflection in human form, fantasy, unrealistic realistic, 4k style\",\n \"trainset_idx\": 229816\n },\n {\n \"caption\": \"Jimi Hendrix as the Statue of Liberty\",\n \"trainset_idx\": 192095\n },\n {\n \"caption\": \"hot kate upton\",\n \"trainset_idx\": 151457\n },\n {\n \"caption\": \"woman wearing sleeveless white zentai body: zentai aesthetic photography\",\n \"trainset_idx\": 369731\n },\n {\n \"caption\": \"comfy designer chair standing near the window. on the left side of the chair a vintage lamp is standing on a round coffee table. in the background a window is open wide and lush green leaves are falling from a tree.\",\n \"trainset_idx\": 141720\n },\n {\n \"caption\": \"A frog in Star wars the clone wars series style\",\n \"trainset_idx\": 123677\n },\n {\n \"caption\": \"A snowy landscape with a Christmas tree and a sleigh pulled by reindeer\",\n \"trainset_idx\": 208812\n },\n {\n \"caption\": \"hunter x hunter, character with long unkept hair, creepy smile, face tattoo, togashi, shonen manga illustration\",\n \"trainset_idx\": 304062\n },\n {\n \"caption\": \"A retrofuturistic suburb, pastel colors\",\n \"trainset_idx\": 562102\n },\n {\n \"caption\": \"A goddess by Carne Griffiths\",\n \"trainset_idx\": 389268\n },\n {\n \"caption\": \"photo of a cute cat\",\n \"trainset_idx\": 302934\n },\n {\n \"caption\": \"cane corso sleeping\",\n \"trainset_idx\": 253316\n },\n {\n \"caption\": \"a photograph of chimps next to a lotus esprit car that is in the jungle ,4k wideangle photo\",\n \"trainset_idx\": 13773\n },\n {\n \"caption\": \"cute teen in a state of undress, insanely detailed, photorealistic, 8k, perfect composition, volumetric lighting, natural complexion, award winning professional photography, taken with canon eos 5d mark iv, 85mm, mindblowing, masterpiece,\",\n \"trainset_idx\": 390221\n },\n {\n \"caption\": \"a cup made of lightning\",\n \"trainset_idx\": 476084\n },\n {\n \"caption\": \"photograph of a priceless black opal in a museum case\",\n \"trainset_idx\": 2906\n },\n {\n \"caption\": \"Superman: \\\"No bitches?\\\"\",\n \"trainset_idx\": 376493\n },\n {\n \"caption\": \"Cute fennec fox fursona wearing a green shirt and rainbow coloured glasses on a light blue to dark purple fade background\",\n \"trainset_idx\": 138966\n },\n {\n \"caption\": \"shrek inside room playing with fireworks\",\n \"trainset_idx\": 188014\n },\n {\n \"caption\": \"the oldest book, bokeh, cinematic, closeup, dust, sunlight\",\n \"trainset_idx\": 329417\n },\n {\n \"caption\": \"a woman sitting in a chair, modern portrait photo. excellent\",\n \"trainset_idx\": 67089\n },\n {\n \"caption\": \"a unrealistic massive female bodybuilder, trapezius muscle, thick neck, most muscular, full body, best quality, gorgeous, , defined abs, biceps, thick forearms\",\n \"trainset_idx\": 404240\n },\n {\n \"caption\": \"photography realistic santa muerte goth rococo \\u00e9pur\\u00e9\",\n \"trainset_idx\": 345148\n },\n {\n \"caption\": \", fantasy, pastel, absurdist, photo, Wes anderson, wasp characters\",\n \"trainset_idx\": 351906\n },\n {\n \"caption\": \"A joyful painting of an adorable wolf puppy, white fur, fluffy fur, big smile, big dreamy eyes, big blue eyes, beautiful eyes, cute, adorable, pretty, Pino Daeni, thick lines, bright colors, pastel colors, natural lighting, chiaroscuro, hyperdetailed, hyperrealistic, beautiful, best quality\",\n \"trainset_idx\": 31297\n },\n {\n \"caption\": \"a black and white drawing of a building,symmetrical doorway, , an engraving by Henry van de Velde, flickr, neoclassicism, architectural drawing, artwork of a building, detailed classical architecture\",\n \"trainset_idx\": 317788\n },\n {\n \"caption\": \"spank\",\n \"trainset_idx\": 443792\n },\n {\n \"caption\": \"An M1A1 abrams tank\",\n \"trainset_idx\": 58838\n },\n {\n \"caption\": \"A robot playing the guitar on stage at a rock concert, photo\",\n \"trainset_idx\": 351692\n },\n {\n \"caption\": \"Crowd in the street of NYC, detailed photo\",\n \"trainset_idx\": 500154\n },\n {\n \"caption\": \"Photo of a interior with Benjamin Moore Chantilly lave colored walls and wooden tung and groove ceilings with white beams\",\n \"trainset_idx\": 202883\n },\n {\n \"caption\": \"Yog-Sothoth realistic\",\n \"trainset_idx\": 265337\n },\n {\n \"caption\": \"A fantastical world of flying islands, Gods and Celestials, giant waterfalls, otherwordly, epic, soft lighting, digital painting\",\n \"trainset_idx\": 390236\n },\n {\n \"caption\": \"anime illustration of link as fairy king oberon from midsummer night's dream, golden crown\",\n \"trainset_idx\": 156635\n },\n {\n \"caption\": \"a solved 3x3 rubiks cube\",\n \"trainset_idx\": 159781\n },\n {\n \"caption\": \"Female lying in bed playing with toy\",\n \"trainset_idx\": 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{\n \"caption\": \"Female knight standing over a medival landscape with a castle and a sunset, pastel palette, trending, flat color, anime, looking away\",\n \"trainset_idx\": 465936\n },\n {\n \"caption\": \"white girl teen pussi\",\n \"trainset_idx\": 172406\n },\n {\n \"caption\": \"Statue Ted Talk\",\n \"trainset_idx\": 109120\n },\n {\n \"caption\": \"Pop art, blonde girl, buns, anime face, big eyes, puffy lips\",\n \"trainset_idx\": 93032\n },\n {\n \"caption\": \"Joe Biden in Fortnite, 3d game\",\n \"trainset_idx\": 331096\n },\n {\n \"caption\": \"hot kim kardashian\",\n \"trainset_idx\": 398974\n },\n {\n \"caption\": \"Pink roses and blue leaves, illustrator, by Josef Frank\",\n \"trainset_idx\": 426223\n },\n {\n \"caption\": \"The soul ascends.\",\n \"trainset_idx\": 175452\n },\n {\n \"caption\": \"bulma es violada\",\n \"trainset_idx\": 453002\n },\n {\n \"caption\": \"Fantasy, pastel, absurdist, photo, Wes anderson, bird characters\",\n \"trainset_idx\": 188194\n },\n {\n \"caption\": \"a wideangle photo of a mgb and a bear ,in a forest , chrome detailing\",\n \"trainset_idx\": 282933\n },\n {\n \"caption\": \"terran republic, a happy person battle torn, red helmet on the head, planetside 2, unstable smile\",\n \"trainset_idx\": 17990\n },\n {\n \"caption\": \"A man in a pink skirt playing baseball in the park\",\n \"trainset_idx\": 514215\n },\n {\n \"caption\": \"Whiskey bottle in a security case\",\n \"trainset_idx\": 354833\n },\n {\n \"caption\": \"Artists rendition of an abandoned stone statue of ganesha hindu diety, sitting pose, centred, intricate carvings, cracks, moss, vines, forest background in the ruins of an ancient temple, rendered in Unreal Engine\",\n \"trainset_idx\": 499404\n },\n {\n \"caption\": \"full body portrait of a beautiful young female warrior wearing armor, highly detailed, subsurface scattering, intricate armor details, cinematic lighting, 4k\",\n \"trainset_idx\": 16522\n },\n {\n \"caption\": \"beautiful fractal 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P. Lovecraft\",\n \"trainset_idx\": 343183\n },\n {\n \"caption\": \"A cat aiming a gun\",\n \"trainset_idx\": 309476\n },\n {\n \"caption\": \"a capybara wearing sunglasses\",\n \"trainset_idx\": 508439\n },\n {\n \"caption\": \"Movie still of Thanos wielding a Lightsaber, HD photograph\",\n \"trainset_idx\": 270825\n },\n {\n \"caption\": \"a new building begins Construction downtown, huge skyscrapers loom above\",\n \"trainset_idx\": 406171\n },\n {\n \"caption\": \"kodachrome toddler boy at the beach\",\n \"trainset_idx\": 17149\n },\n {\n \"caption\": \"Giger xenomorph Easter bunny Easter style chocolate alien egg, insanely detailed, photorealistic, 8k, ultra high resolution, volumetric lighting, taken with canon, taken with nikon\",\n \"trainset_idx\": 195512\n },\n {\n \"caption\": \"A large blue metal cube to the left of a small yellow metal sphere\",\n \"trainset_idx\": 532658\n },\n {\n \"caption\": \"sci-fi room metal,intricate details,computer screens,studio lighting, geometric artworks,volumetric light,sir john soane,metal pipes,floor grates,pilasters british museum\",\n \"trainset_idx\": 500389\n },\n {\n \"caption\": \"the text \\\"\\\"Gif Co\\\"\\\" written in sea shells and pebbles on the beach, highly detailed photorealistic, soft golden light, cinematic lighting\",\n \"trainset_idx\": 244036\n },\n {\n \"caption\": \"vintage comic book of Cute gorgeous european woman 20 years old, with round face and big cheeks, delicate features and crimson hair. Brown eyes and cute smile.\",\n \"trainset_idx\": 91427\n },\n {\n \"caption\": \"Bitcoin cockroach wrapped in bitcoin, with gold feet and antenas, vibrant and colorfu scene, extremely detailed, ultra hd, hdr, 8k, cinematic, Stanley Artgerm Lau style beautifully color-coded, studio Portrait Lighting unreal render, black, background\",\n \"trainset_idx\": 85913\n },\n {\n \"caption\": \"Cristiano Ronaldo holding a sign with the text \\\"i am doubting myself\\\"\",\n \"trainset_idx\": 229785\n },\n {\n \"caption\": \"neon computer build, LED, modern design\",\n \"trainset_idx\": 300865\n },\n {\n \"caption\": \"a painting of an alien city in the middle of the night, concept art, fantasy art, overgrown with huge rare fungus, from ncsoft, hollow knight screenshot, kelp forest, snowy environment, ominous and eerie forest, moon surface, destroyed forest, lostus flowers, beings of astonishing structure, screenshot from the game\",\n \"trainset_idx\": 24574\n },\n {\n \"caption\": \"A orange-furred cat laying down next to a standing black-and-white cat.\",\n \"trainset_idx\": 29057\n },\n {\n \"caption\": \"Abstract 1998 european blond hiphop girl by sachin teng x supreme, attractive, stylish, designer, green, asymmetrical, geometric shapes, graffiti, street art\",\n \"trainset_idx\": 169809\n },\n {\n \"caption\": \"photo of a dog made of static\",\n \"trainset_idx\": 81897\n },\n {\n \"caption\": \"photograph of an airplane\",\n \"trainset_idx\": 80495\n },\n {\n \"caption\": \"a woman sitting in a chair, photo\",\n \"trainset_idx\": 229377\n },\n {\n \"caption\": \"Taboo cosplay, insanely detailed, photorealistic, 8k, ultra high resolution, volumetric lighting, taken with canon, taken with nikon\",\n \"trainset_idx\": 284902\n },\n {\n \"caption\": \"Kim Kardashian Comic, High Resolution, High Quality, Many Details\",\n \"trainset_idx\": 312820\n },\n {\n \"caption\": \"Marylin Monroe\",\n \"trainset_idx\": 541470\n },\n {\n \"caption\": \"priest hiding on wall 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nemo with umbrella\",\n \"trainset_idx\": 564318\n },\n {\n \"caption\": \"highly detailed, 4k, anime, Anime girl with a sign saying \\\"I love you\\\"\",\n \"trainset_idx\": 227500\n },\n {\n \"caption\": \"a beautiful anime girl, looking at viewer,intricate embroidered black high slit cheongsams,pantyhose,white fur trim elbow gloves\",\n \"trainset_idx\": 397200\n },\n {\n \"caption\": \"digital art, princess surrounded by flowers, low-angle shoot, curly bob cut, looking away, face blush and freckles, high quality, pastel colors, detailed, by Sarah Joncas, intricate\",\n \"trainset_idx\": 81730\n },\n {\n \"caption\": \"a burly muscular android lying damaged on the ground, loose wires, sparks, smoke, cybernetic, mechanical, torn plastic skin\",\n \"trainset_idx\": 157941\n },\n {\n \"caption\": \"photo of a slim asian little girl ballerina with long hair wearing white tights running on the moon, from behind, nikon D5\",\n \"trainset_idx\": 134632\n },\n {\n \"caption\": \"an epic super smash bros battle in a sci-fi dystopian world between mario and sonic, unreal engine 5, photorealistic, explosive, blue tone, cool, cyberpunk, next gen, battlefield, rampage, fight,\",\n \"trainset_idx\": 300367\n },\n {\n \"caption\": \"otter eating a watermelon while sitting on the edge of a cliff\",\n \"trainset_idx\": 32361\n },\n {\n \"caption\": \"Old poster of a syfy movie space cats\",\n \"trainset_idx\": 167114\n },\n {\n \"caption\": \"Very weird Image of Golf\",\n \"trainset_idx\": 550504\n },\n {\n \"caption\": \"female portrait photo\",\n \"trainset_idx\": 336504\n },\n {\n \"caption\": \"Riley reid\",\n \"trainset_idx\": 319294\n },\n {\n \"caption\": \"Day Trading candlestick chart\",\n \"trainset_idx\": 337274\n },\n {\n \"caption\": \"a raw photo close up of the heavenly catholic demon pig cyborg inside an iron maiden robot wielding a giant katana,large view,a surrealist painting, inspired by Jean Fouquet and alan bean and Philippe Druillet,tom bagshaw,masterpiece,volumetric lighting,detailed shadows,extremely detailed,4k uhd\",\n \"trainset_idx\": 569244\n },\n {\n \"caption\": \"a dog catching a ball in the air\",\n \"trainset_idx\": 126505\n },\n {\n \"caption\": \"magic glass modern future apple minimalist matt clothes metalheart y2k cute skulldog design concept art\",\n \"trainset_idx\": 119751\n },\n {\n \"caption\": \"a realistic photograph from a modern digital camera of New York City in the year 2100 after a post-apocalyptic event that transformed the city into a futuristic metropolis, with advanced technology, architecture, and culture\",\n \"trainset_idx\": 20008\n },\n {\n \"caption\": \"A pretty boy in glasses eating hotdog on an urban beach and smiling mischievously\",\n \"trainset_idx\": 20179\n },\n {\n \"caption\": \"a child movie star, 1980s, close-up glamour shot\",\n \"trainset_idx\": 118926\n },\n {\n \"caption\": \"Egirl with red hair, gorgeous, high-quality, beautiful\",\n \"trainset_idx\": 207666\n },\n {\n \"caption\": \"A pen drawing of death\",\n \"trainset_idx\": 474218\n },\n {\n \"caption\": \"A busy street in 1984 Shinjuku, VHS footage\",\n \"trainset_idx\": 153150\n },\n {\n \"caption\": \"a bustling street in a dystopian cyberpunk city\",\n \"trainset_idx\": 497992\n },\n {\n \"caption\": \"A profile picture of an anime boy who is half robot, mech, mecha, robot suit\",\n \"trainset_idx\": 552973\n },\n {\n \"caption\": \"A Eurasier dog sitting regally in a field of mushrooms, anime style\",\n \"trainset_idx\": 219219\n },\n {\n \"caption\": \"Baby sharks cute pink\",\n \"trainset_idx\": 133919\n },\n {\n \"caption\": \"cursed image of house\",\n \"trainset_idx\": 504631\n },\n {\n \"caption\": \"a detailed painting of a vase with flowers in it, sitting on a wooden table. detailed, excellent light\",\n \"trainset_idx\": 100596\n },\n {\n \"caption\": \"A cartoon illustration of how a heat pump works\",\n \"trainset_idx\": 430535\n },\n {\n \"caption\": \"a cyborg working 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bedroom with magic tomes and dolls Whimsy style\",\n \"trainset_idx\": 530400\n },\n {\n \"caption\": \"Hands\",\n \"trainset_idx\": 369011\n },\n {\n \"caption\": \"photograph of a handsome man smiling\",\n \"trainset_idx\": 362403\n },\n {\n \"caption\": \"Open bady\",\n \"trainset_idx\": 494194\n },\n {\n \"caption\": \"whole body image of beautiful 20 year-old Kaley Cuoco as a naturist on a chopper motorcycle, HD 4K, photo-realistic accurate face and features, studio lighting\",\n \"trainset_idx\": 207261\n },\n {\n \"caption\": \"guy golding a cup of milk\",\n \"trainset_idx\": 467124\n },\n {\n \"caption\": \"photograph of a CRT monitor with the word popcorn on the screen in a dingy basement, cyberpunk, folk horror, found footage\",\n \"trainset_idx\": 78167\n },\n {\n \"caption\": \"concept art by jama jurabaev\",\n \"trainset_idx\": 442482\n },\n {\n \"caption\": \"a female goblin wizard in a black cloak with a necklace of bones\",\n \"trainset_idx\": 9826\n },\n {\n \"caption\": \"A silhouette of a African princess looking at the stars, big full moon\",\n \"trainset_idx\": 345706\n },\n {\n \"caption\": \"A samurai warrior facing a wolf\",\n \"trainset_idx\": 438358\n },\n {\n \"caption\": \"A triumph of innovation\",\n \"trainset_idx\": 115697\n },\n {\n \"caption\": \"a close up art surrealism,by art shusei nagaoka and by artist yves tanguy of the heavenly catholic demonic leader cyborg,cyberpunk style,art surrealism,by and katsuhiro otomo,james stokoe,king crimson, avatar image, large view\",\n \"trainset_idx\": 439654\n },\n {\n \"caption\": \"Weird Finnish guy\",\n \"trainset_idx\": 124378\n },\n {\n \"caption\": \"A labrador dog in space\",\n \"trainset_idx\": 126872\n },\n {\n \"caption\": \"cute LEGO figure of a warrior mouse, product photo, well-detailed LEGO details, perfect lighting\",\n \"trainset_idx\": 462317\n },\n {\n \"caption\": \"baroque painting with girl in red velvet costume, masterpiece, gorgeous\",\n \"trainset_idx\": 101376\n },\n {\n \"caption\": \"Professional photo of a beautiful ginger freckled female fantasy cosplay mage with freckles, grey eyes, and a closed mouth. She is 20ish years old. She is wearing modest long blue robes with many small details and is standing in a a dark alley in a fantasy city with many details. She is looking at the viewer.\",\n \"trainset_idx\": 48211\n },\n {\n \"caption\": \"full shot photo, in full colour high quality very detailed, very handsome married malay father\",\n \"trainset_idx\": 362050\n },\n {\n \"caption\": \"a sloth doing the dishes, vintage realistic professional photography, well defined sloth fur, 8k, close up, looks like the sloth is holding a plate, with beautiful window in the background\",\n \"trainset_idx\": 563508\n },\n {\n \"caption\": \"a beautiful Vietnamese girl\",\n \"trainset_idx\": 113270\n },\n {\n \"caption\": \"chubby orc outlander\",\n \"trainset_idx\": 336594\n },\n {\n \"caption\": \"Amy Rose at a restaurant\",\n \"trainset_idx\": 314655\n },\n {\n \"caption\": \"full length portrait Beautiful lez cutie girl bent bending over from the back behind in steel occult mask and black crown covered with wax from many burned candles, by Nicola Samori, Ilya Repin, William Blake, Michelangelo da Caravaggio, black background, full body portrait, highly detailed oil painting, trending on artstation, 4k, masterpiece\",\n \"trainset_idx\": 275248\n },\n {\n \"caption\": \"lala kw word\",\n \"trainset_idx\": 49689\n },\n {\n \"caption\": \"a wide angle photo a smokey roman villa burning, 18mm smoke filled room debris , gladiator ,floor mosaics fire smoke, a photo, roman , a digital rendering, inside the roman colliseum, brick, indoor, plants overgrown outstanding detail ,room flooded with water, in front of a building,by claude-joseph vernet,luxury hotel\",\n \"trainset_idx\": 164137\n },\n {\n \"caption\": \"Kurt Cobain colour drawing wearing eye liner\",\n \"trainset_idx\": 279388\n },\n {\n \"caption\": \"Photo of a Cow made of cactus smoking a cigarette\",\n \"trainset_idx\": 425845\n },\n {\n \"caption\": \"photo of a hand sculpture in the city ,art gallery ,flooded sculpture,splashing misty mud rocks,panorama,city buildings,\",\n \"trainset_idx\": 222914\n },\n {\n \"caption\": \"Indonesian muslim\",\n \"trainset_idx\": 572517\n },\n {\n \"caption\": \"palm tree made of yellow wool material growing inside within a large rum bottle on a beach\",\n \"trainset_idx\": 387504\n },\n {\n \"caption\": \"The most realistic fur suit , furry white artic fox, digital art , female, green eyes, small muzzle, smiling, brown hair locks , entire full body, on bed, above view.\",\n \"trainset_idx\": 418115\n },\n {\n \"caption\": \", fantasy, pastel, absurdist, photo, Wes anderson, jars of brains, diabolical\",\n \"trainset_idx\": 548927\n },\n {\n \"caption\": \"A man wearing a hat while holding a sign that says \\u201ccap\\u201d\",\n \"trainset_idx\": 5026\n },\n {\n \"caption\": \"An over-shoulder dialog scene from a drama movie where a man is speaking to a woman. The focus is set on the woman. 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{\n \"caption\": \"A frog practicing kung-fu\",\n \"trainset_idx\": 370830\n },\n {\n \"caption\": \"A sign with text \\\"Big QBS\\\"\",\n \"trainset_idx\": 17917\n },\n {\n \"caption\": \"Happy woman on the beach\",\n \"trainset_idx\": 404514\n },\n {\n \"caption\": \"Batman action figure using a coffee machine, product photo, professional photography, vintage, 8k, plastic product\",\n \"trainset_idx\": 270226\n },\n {\n \"caption\": \"greek statue, from behind, woman picking up something,non-existent clothes, wet clothes in the middle of street, new york\",\n \"trainset_idx\": 96653\n },\n {\n \"caption\": \"Anthro gecko\",\n \"trainset_idx\": 178821\n },\n {\n \"caption\": \"A 14 year old girl, wearing almost nothing\",\n \"trainset_idx\": 450994\n },\n {\n \"caption\": \"A steampunk airship\",\n \"trainset_idx\": 109492\n },\n {\n \"caption\": \"advertising, breaking bad themed theme park, breaking bad theme park ride\",\n \"trainset_idx\": 140909\n },\n {\n \"caption\": \"Eiffel tower on mars, insanely detailed, photorealistic, 8k, ultra high resolution, volumetric lighting, taken with canon eos,\",\n \"trainset_idx\": 277623\n },\n {\n \"caption\": \"epic fantasy giant black bottomless canyon in the ground\",\n \"trainset_idx\": 236823\n },\n {\n \"caption\": \"lviv, closeup photo portrait of a giant Cthulhu, a city street at night, a picture, by Zolt\\u00e1n Jo\\u00f3, world press photo awarded, blackout, no electricity, traversing a shadowy city, view from street angle, breathtaking, winter snow, kodak ektar 100, Pentax 645\",\n \"trainset_idx\": 237028\n },\n {\n \"caption\": \"Anthropomorphic cat\",\n \"trainset_idx\": 146493\n },\n {\n \"caption\": \"An image of human hand\",\n \"trainset_idx\": 434550\n },\n {\n \"caption\": \"Ellie from The Last of Us, ,\",\n \"trainset_idx\": 526752\n },\n {\n \"caption\": \"Photorealistic studio image of Woman 20 years Jenna Marie Ortega has 20 years, woman , Bangs hairstyle\",\n \"trainset_idx\": 327243\n },\n {\n \"caption\": \"1991 exotic custom JDM toyota 1991 mr2 bodykit concept\",\n \"trainset_idx\": 359667\n },\n {\n \"caption\": \"A chubby cowboy leaning against a wooden column in a saloon.\",\n \"trainset_idx\": 293481\n },\n {\n \"caption\": \"attractive goth girl on the beach showing off her bathing suit, pale skin, overcast\",\n \"trainset_idx\": 401220\n },\n {\n \"caption\": \"A high definition photograph of a Christmas tree ball in the shape of a hamster.\",\n \"trainset_idx\": 337955\n },\n {\n \"caption\": \"a boy watching an alien spacecraft blasting a city, futuristic illumination, Art Deco, Full colors, Greg rutkowski, Trending artstation, cinematogr\\u00e1fic\",\n \"trainset_idx\": 572436\n },\n {\n \"caption\": \"jeremy clarkson driving porsche 911 down English street with huge exhaust pipe\",\n \"trainset_idx\": 274519\n },\n {\n \"caption\": \"Willy Wonka gives Charlie a turd\",\n \"trainset_idx\": 30942\n },\n {\n \"caption\": \"Frogzilla\",\n \"trainset_idx\": 570666\n },\n {\n \"caption\": \"A logo for \\\"IBM\\\"\",\n \"trainset_idx\": 475553\n },\n {\n \"caption\": \"cinematic still-frame from the 1984 movie Ghostbusters\",\n \"trainset_idx\": 8261\n },\n {\n \"caption\": \"A black glove in a dark room holding a glowing green crystal\",\n \"trainset_idx\": 57673\n },\n {\n \"caption\": \"Film still from 80s dark fantasy magic school movie\",\n \"trainset_idx\": 485528\n },\n {\n \"caption\": \"landscape, digital illustration, deep color, , intricate detail, photorealism, polished, complementary colors, fantasy concept art, 8k resolution Unreal Engine 5, five point perspective\",\n \"trainset_idx\": 451848\n },\n {\n \"caption\": \"Creepy creature peeking around a door, highly detailed, ,\",\n \"trainset_idx\": 195730\n },\n {\n \"caption\": \"a beautiful greatsword in the style of dark souls. Long blade, ornamental.\",\n \"trainset_idx\": 72259\n },\n {\n \"caption\": \"girl perfect pussi white\",\n \"trainset_idx\": 8131\n },\n {\n \"caption\": \"feathery roses and fluffy winged bunnies by artist \\\"Storm Thorgerson\\\"\",\n \"trainset_idx\": 133934\n },\n {\n \"caption\": \"sad tiger in a city park with a playground in the background\",\n \"trainset_idx\": 452047\n },\n {\n \"caption\": \"Cute female furry on the beach, digital art\",\n \"trainset_idx\": 340383\n },\n {\n \"caption\": \"Sign that says \\u201cwhen does IF drop?\\u201d\",\n \"trainset_idx\": 62402\n },\n {\n \"caption\": \"Professional photograph of young taylor swift in nurse uniform taking care of a black dog,nurse with oldman,highly detailed,beautiful face,masterpiece,natural lighting\",\n \"trainset_idx\": 472904\n },\n {\n \"caption\": \"3d game model, an abandoned robot in a dank cave, black background\",\n \"trainset_idx\": 144042\n },\n {\n \"caption\": \"Norwegian flag\",\n \"trainset_idx\": 459792\n },\n {\n \"caption\": \"the most beautiful girl in the world, insanely detailed, photorealistic, 8k, perfect composition, rim lit, natural complexion, taken with canon eos 5d mark iv, 85mm, mindblowing, masterpiece, made with midjourney\",\n \"trainset_idx\": 10865\n },\n {\n \"caption\": \"beautiful Italian beach scene painted by turner and Redon, impasto relief palette knife oil paint, Thick luscious impasto paint very deep sculptural brush and palette knife marks\",\n \"trainset_idx\": 111421\n },\n {\n \"caption\": \"asian massage parlor\",\n \"trainset_idx\": 514581\n },\n {\n \"caption\": \"a healthy app\",\n \"trainset_idx\": 137427\n },\n {\n \"caption\": \"RAW, beautiful portrait photograph of a woman in a field\",\n \"trainset_idx\": 375861\n },\n {\n \"caption\": \"brushing hair to the side, , looking at viewer\",\n \"trainset_idx\": 68163\n },\n {\n \"caption\": \"kevin owens anime\",\n \"trainset_idx\": 430116\n },\n {\n \"caption\": \"clear portrait of indian batsman virat kohli background hyper detailed, character concept, full body, dynamic pose, intricate, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha\",\n \"trainset_idx\": 501839\n },\n {\n \"caption\": \"character design sheet, man\",\n \"trainset_idx\": 101039\n },\n {\n \"caption\": \"A selfie of Sonic the hedgehog at mcdonalds\",\n \"trainset_idx\": 220828\n },\n {\n \"caption\": \"a dog\",\n \"trainset_idx\": 313840\n },\n {\n \"caption\": \"very dark dungeon with blue and pink side lighting, spooky magic\",\n \"trainset_idx\": 24375\n },\n {\n \"caption\": \"a young female holding a sign that says \\u201cStable Diffusion\\u201d, highlights in hair, sitting outside restaurant, brown eyes, wearing a dress, side light\",\n \"trainset_idx\": 546071\n },\n {\n \"caption\": \"\\u201cKRAWLA CITY\\u201d text in graffiti style on a white background, symmetrical, centered, fun, precise\",\n \"trainset_idx\": 413775\n },\n {\n \"caption\": \"Lee Young Ae as a young, hungarian peasant woman in a 19th century, hungarian village, pastel by Istv\\u00e1n Cs\\u00f3k, K\\u00e1roly Ferenczy, Waterhouse, and Marc Simonetti. Very atmospheric, natural light, enigmatic, pastel colours\",\n \"trainset_idx\": 125914\n },\n {\n \"caption\": \"Yoshi\",\n \"trainset_idx\": 458597\n },\n {\n \"caption\": \"Whorish Alien Fascist\",\n \"trainset_idx\": 420431\n },\n {\n \"caption\": \"a skeleton emerging from martian soil, the surface of mars, rover image\",\n \"trainset_idx\": 346245\n },\n {\n \"caption\": \"Closeup photo of a young woman, wearing a white shirt and denim jeans, she is sitting on a wooden chair and looking towards the camera, this place is in the middle of nowhere, green field and storm clouds, breathtaking scene, dslr photo, film photography, kodak portra 800, sharp, focused, high definition, award-winning photo, ultrarealistic, flickr\",\n \"trainset_idx\": 88351\n },\n {\n \"caption\": \"Sonic emoji\",\n \"trainset_idx\": 540595\n },\n {\n \"caption\": \"A gold and ceramic Easter hole inlaid with precious stones\",\n \"trainset_idx\": 565478\n },\n {\n \"caption\": \"cybernetic deer \\ud83e\\udd8c, cyberpunk, futuristic Trending on Artstation HQ, 4K, UHD, High quality\",\n \"trainset_idx\": 158246\n },\n {\n \"caption\": \"Fursona , furry fox , female , beautiful , attractive , furry body , fox body colours , smiling ,digital art , showing off , masterpiece , by foxovh , hourglass body , furry art style , furry , anthro , long loose brown hair locks , furry yiff , fox head\",\n \"trainset_idx\": 357971\n },\n {\n \"caption\": \"A recycle bin icon, frutiger aero style\",\n \"trainset_idx\": 418274\n },\n {\n \"caption\": \"a pile of biscuits sitting on top of a plate, mingei, made of cheese, soft round features, crispy buns, high quality]\",\n \"trainset_idx\": 44209\n },\n {\n \"caption\": \"Extreme close up of lash extentions with beauty Blue eyes. Extrem realistic with all possible Details. Should Look like in real life. Ultra realistic, leica r 100mm macro 2.8, hdr 128k, unreal engine\",\n \"trainset_idx\": 202617\n },\n {\n \"caption\": \"a dragon made out of clouds by Alex Shiekman, Lief Jones, Guy Davis, David Leri, Christopher Shy\",\n \"trainset_idx\": 141823\n },\n {\n \"caption\": \"women formal portrait american\",\n \"trainset_idx\": 282445\n },\n {\n \"caption\": \"logo with drone and signature \\\"SZTUKA DRONOWANIA\\\"\",\n \"trainset_idx\": 90529\n },\n {\n \"caption\": \"the ruins of mcdonalds in ancient rome, national geographic photography\",\n \"trainset_idx\": 204200\n },\n {\n \"caption\": \"dnd illustration of a bald male, epic heroic fantasy dwarf with a computer wearing anarchist t-shirt clothing, long white and black beard\",\n \"trainset_idx\": 354968\n },\n {\n \"caption\": \"Fluffy snooty elegant fashionista ferrofluid wax lace cabbage by artist \\\"anna dittman\\\", by artist \\\"tom bagshaw\\\"\",\n \"trainset_idx\": 42746\n },\n {\n \"caption\": \"extremely detailed CG unity 8k wallpaper, a beautiful female knight in armor carrying a longsword with a battlefield in the background\",\n \"trainset_idx\": 380368\n },\n {\n \"caption\": \"Fantasy, pastel, absurdist, photo, Wes Anderson, unicorn characters\",\n \"trainset_idx\": 308656\n },\n {\n \"caption\": \"Digital art of a futuristic cityscape with towering skyscrapers, flying cars, and neon lights. The skyline is bathed in a pinkish-purple hue, and there's a holographic billboard advertising a new space mission.\",\n \"trainset_idx\": 228050\n },\n {\n \"caption\": \"A shape with impossible geometry, non euclidean, 3D shape\",\n \"trainset_idx\": 197283\n },\n {\n \"caption\": \"a liminal space, peace, tranquility, high details, sharp focus, softest light, ,\",\n \"trainset_idx\": 187728\n },\n {\n \"caption\": \"award winning national geographic photo of a horse\",\n \"trainset_idx\": 295933\n },\n {\n \"caption\": \"a beautiful portrait of a woman in the style of Arcane and valorant\",\n \"trainset_idx\": 409792\n },\n {\n \"caption\": \"dinosaurs and a landrover defender in the jungle river,claws teeth compy Compsognathus waterfall misty muddy,headlights Chrome Detailing\",\n \"trainset_idx\": 475417\n },\n {\n \"caption\": \"buggy with a rusted-out hood and cracked wind-shield glass, far away background: round towers made out of sandstone in the style of islamic architecture, desert, dust-storm sky, ultrarealistic, evocative, 4k uhd,unreal engine,cgsociety,volumetrics\",\n \"trainset_idx\": 206015\n },\n {\n \"caption\": \"peace and harmony\",\n \"trainset_idx\": 305610\n },\n {\n \"caption\": \"how b win the election\",\n \"trainset_idx\": 577267\n },\n {\n \"caption\": \"a professional cinematic paparazzi photograph of pope francis in wearing an icy crucifix and a luxurious canada goose style white long puffer jacket\",\n \"trainset_idx\": 392275\n },\n {\n \"caption\": \"taxicab in new york\",\n \"trainset_idx\": 209364\n },\n {\n \"caption\": \"young woman,brunette,blue eyes lagoons\",\n \"trainset_idx\": 445369\n },\n {\n \"caption\": \"man with abs, muscles, perfect body, blonde, blue eyes, at the beach, south park\",\n \"trainset_idx\": 153877\n },\n {\n \"caption\": \"Waterfall in a tropical rainforest, golden hour, professional photography, 64 megapixels, shot on DSLR, long exposure\",\n \"trainset_idx\": 458202\n },\n {\n \"caption\": \"ryuko matoi,large single handed blood sword, slim teenage girl, athletic build, short dark hair with a red streak, expressive eyes, short red and black school uniform with a short skirt and a crop top and long sleeves, uniform adorned with straps and buckles, red choker, senketsu\",\n \"trainset_idx\": 16052\n },\n {\n \"caption\": \"a handsome boylike a cute cat ,have a cat face\",\n \"trainset_idx\": 326411\n },\n {\n \"caption\": \"fantasy character portrait digital painting, anime style, detailed with beautiful emotive lighting suggesting personality, and background that suggests character backstory\",\n \"trainset_idx\": 428909\n },\n {\n \"caption\": \"girl cim\",\n \"trainset_idx\": 436015\n },\n {\n \"caption\": \"train station illustration, cover of a rock music, no people\",\n \"trainset_idx\": 133539\n },\n {\n \"caption\": \"grizzly bear on drugs, cocaine\",\n \"trainset_idx\": 4409\n },\n {\n \"caption\": \"sci-fi large gallery room, with photos of classic cars ,studio lighting\",\n \"trainset_idx\": 52366\n },\n {\n \"caption\": \"A logo for Panera Bread\",\n \"trainset_idx\": 330303\n },\n {\n \"caption\": \"Cute 90s kristen stwetart wearing blue jacket top and shorts with shiny metallic pearlescent hair, pastel pink moon background\",\n \"trainset_idx\": 443476\n },\n {\n \"caption\": \"Beautifully strange painting of an alien tropical island. Glowing trees and flora. Hyperdetailed matte painting by by Benoit B. Mandelbrot, Steven Belledin, Martin Johnson Heade, Lee Madgwick, and Caspar David Friedrich. Alien flora and fauna. Glowing orbs and tropics. Alien moonrise.\",\n \"trainset_idx\": 13492\n },\n {\n \"caption\": \"I'm losing my voice from all the screaming your hotness is causing me to do.\",\n \"trainset_idx\": 76269\n },\n {\n \"caption\": \"Dragon, dark fantasy, great barrier reef, crepuscular ray, intricate, elegant, sharp focus, lens flare, bloom, rim light, illustration, highly detailed, digital painting, concept art, matte, art by ruan jia and wlop and greg rutkowski, masterpiece\",\n \"trainset_idx\": 259422\n },\n {\n \"caption\": \"Robotic butler\",\n \"trainset_idx\": 21530\n },\n {\n \"caption\": \"An astronaut riding a horse on the moon\",\n \"trainset_idx\": 169437\n },\n {\n \"caption\": \"an empowering view of a orca warrior wearing royal robe,fighting pose,menacing,by artist Philippe Druillet and Tsutomu Nihei,volumetric lighting,detailed shadows,extremely detailed\",\n \"trainset_idx\": 224860\n },\n {\n \"caption\": \"Watermelon pudding\",\n \"trainset_idx\": 341651\n },\n {\n \"caption\": \"Scooby Doo gets buff\",\n \"trainset_idx\": 506878\n },\n {\n \"caption\": \"Morris Mini-Minor car driving through volcanic molten lava magma, studio lighting,gallery of artworks volumetric ,white room, light,flames steam,posters on walls\",\n \"trainset_idx\": 260081\n },\n {\n \"caption\": \"Cute and chubby white little poodle with psychedelic clown suit. Analog 35mm film grain, motion blur, film texture, low resolution, lo-fi\",\n \"trainset_idx\": 421100\n },\n {\n \"caption\": \"Jessica alba as a gothic ninja\",\n \"trainset_idx\": 342189\n },\n {\n \"caption\": \"Lion cub\",\n \"trainset_idx\": 495118\n },\n {\n \"caption\": \"Sonic the hedgehog holding a sword\",\n \"trainset_idx\": 143710\n },\n {\n \"caption\": \"liquid with jumper cables intertwined gouache painting of rubber-band ;; impasto, kintsugi ;; symmetrical face, accurate anatomy, sharp focus, horror ;; deep colors and contrast ;; beeple, wlop, artgerm, cgsociety ;; morose, emotive, golden hour, complex\",\n \"trainset_idx\": 444031\n },\n {\n \"caption\": \"Alice Cooper\",\n \"trainset_idx\": 438714\n },\n {\n \"caption\": \"Vintage Electronic Test Equipment, Mostly Oscilloscopes\",\n \"trainset_idx\": 316605\n },\n {\n \"caption\": \"A beautiful painting of sunrise by Picasso\",\n \"trainset_idx\": 523384\n },\n {\n \"caption\": \"A worn Homer Simpson statue on a tropical beach\",\n \"trainset_idx\": 147809\n },\n {\n \"caption\": \"wideangle photo roman battle misty roofs, Epic cinematic brilliant stunning intricate meticulously detailed dramatic atmospheric maximalist digital matte painting\",\n \"trainset_idx\": 241407\n },\n {\n \"caption\": \"Portrait of an old king with golden teeth and a motorcycle helmet, smoking, purple clouds, photorealistic, art by franck frazetta\",\n \"trainset_idx\": 232588\n },\n {\n \"caption\": \"Banshee\",\n \"trainset_idx\": 119011\n },\n {\n \"caption\": \"a giant dry cavern filled with gardens with people floating in weightlessness\",\n \"trainset_idx\": 19442\n },\n {\n \"caption\": \"A person doing a backflip\",\n \"trainset_idx\": 191377\n },\n {\n \"caption\": \"A tall blonde vampire lady, sitting on a throne, low angle, smirk, from below\",\n \"trainset_idx\": 358479\n },\n {\n \"caption\": \"photo of kink kong lifting a landrover defender in the jungle river, misty mud rocks,headlights Chrome Detailing\",\n \"trainset_idx\": 50408\n },\n {\n \"caption\": \"pizza with on a car\",\n \"trainset_idx\": 80257\n },\n {\n \"caption\": \"detailed portrait of asriel dreemurr\",\n \"trainset_idx\": 507793\n },\n {\n \"caption\": \"a zombie , photo by tim walker and petra collins and moebius\",\n \"trainset_idx\": 162802\n },\n {\n \"caption\": \"a woman sumerged in water inside a pill-shaped transparent tank\",\n \"trainset_idx\": 290624\n },\n {\n \"caption\": \"Snake Eyes from G.I. Joe,dark background,dark comics style\",\n \"trainset_idx\": 364135\n },\n {\n \"caption\": \"a concept design spec for a modern-looking classic watch, intricate, pure design graphic, , digital\",\n \"trainset_idx\": 525619\n },\n {\n \"caption\": \"The official portrait of an authoritarian president of an alternate america in 1950, \\\"Jared Stoner\\\" in the style of Norman Rockwell ,\",\n \"trainset_idx\": 563653\n },\n {\n \"caption\": \"a sweaty man and a moaning woman embracing ecstatically on a bed\",\n \"trainset_idx\": 388486\n },\n {\n \"caption\": \"Giger manticore, insanely detailed, photorealistic, 8k, ultra high resolution, volumetric lighting, taken with canon, taken with nikon\",\n \"trainset_idx\": 2454\n },\n {\n \"caption\": \"fantasy art print, hyperrealistic charcoal drawing of an peaceful giant eagle bowing to a human\",\n \"trainset_idx\": 449979\n },\n {\n \"caption\": \"Multi-dimensional glitch art of a woman emerging from a painting\",\n \"trainset_idx\": 1658\n },\n {\n \"caption\": \"Jesus sticking tongue out holding a sign that says Rock n Roll\",\n \"trainset_idx\": 414145\n },\n {\n \"caption\": \"A Portrait of Scarlett Johannson , High Resolution, High Quality, Many Details, Real Life\",\n \"trainset_idx\": 192260\n },\n {\n \"caption\": \"Cinematographic-sixties vatican-betelgeuse-moebius capsule launchpad old-priest bows-low anglican-tiara-mitre Archbishops thunderbirds-balenciaga Astronaut papal official leica hasselblad photograph in Vatican royal helmet gold metal scaphandre launchpad pointy oxygen hazmat gloves helmet\",\n \"trainset_idx\": 82776\n },\n {\n \"caption\": \"gabe newell killing a zombie with a crowbar\",\n \"trainset_idx\": 528840\n },\n {\n \"caption\": \"neo from the matrix mountain climbing\",\n \"trainset_idx\": 353875\n },\n {\n \"caption\": \"computer lab, isometric view, medium shot, digital concept art, digital illustration\",\n \"trainset_idx\": 498305\n },\n {\n \"caption\": \"London skyline, birds eye view, Van Gogh style, painting ,\",\n \"trainset_idx\": 270829\n },\n {\n \"caption\": \"ugly old grimy medieval man, high quality digital painting\",\n \"trainset_idx\": 260996\n },\n {\n \"caption\": \"A super cute baby pixar style white fairy rabbit: A whimsical, playful image of a cute white rabbit with delicate features, inspired by the art style of Pixar.\",\n \"trainset_idx\": 327444\n },\n {\n \"caption\": \"portrait photo of a beautiful woman, detailed, 8k uhd, dslr, high quality, film grain, Fujifilm XT3\",\n \"trainset_idx\": 303845\n },\n {\n \"caption\": \"minecraft kawai, concept art building\",\n \"trainset_idx\": 288019\n },\n {\n \"caption\": \"A photo cool sigma person, standing on a street and holding a sign called \\\"Get Ready\\\"\",\n \"trainset_idx\": 485478\n },\n {\n \"caption\": \"Portrait of beautiful appealing confident brave young alluring profesional dominate human looking female teacher with make up\",\n \"trainset_idx\": 471768\n },\n {\n \"caption\": \"A female knight in red armor standing in the rain\",\n \"trainset_idx\": 59067\n },\n {\n \"caption\": \"Screenshot of a 3d evil farm game, 1990s\",\n \"trainset_idx\": 527350\n },\n {\n \"caption\": \"a painting of the ocean\",\n \"trainset_idx\": 70711\n },\n {\n \"caption\": \"Sunset reflecting on a crygangster cat wearing snapback and golden chain on neck with dollar sign pendantstal ball\",\n \"trainset_idx\": 293535\n },\n {\n \"caption\": \"Morris Mini-Minor car driving through volcanic molten lava magma, studio lighting,gallery of artworks volumetric ,white room, light,flames steam,\",\n \"trainset_idx\": 457446\n },\n {\n \"caption\": \"A waterfall inside a bottle, digital painting, high resolution, sharp image\",\n \"trainset_idx\": 217294\n },\n {\n \"caption\": \"3d render, Chibi Lemon character with an accomplished look on his face, he is wearing green sunglasses relaxing on the beach at sunset\",\n \"trainset_idx\": 415061\n },\n {\n \"caption\": \"Micky mouse as a 1940's Gangster\",\n \"trainset_idx\": 238026\n },\n {\n \"caption\": \"A purple cat with purple fur and a black tophat. cyan background\",\n \"trainset_idx\": 37761\n },\n {\n \"caption\": \"superman medieval\",\n \"trainset_idx\": 121646\n },\n {\n \"caption\": \"woman brazilian\",\n \"trainset_idx\": 105384\n },\n {\n \"caption\": \"Sunset in a gothic church\",\n \"trainset_idx\": 549812\n },\n {\n \"caption\": \"Baby Yoda\",\n \"trainset_idx\": 405727\n },\n {\n \"caption\": \"The most perfect image ever created\",\n \"trainset_idx\": 557597\n },\n {\n \"caption\": \"20 year-old Barbara Eden as an Elfin princess naturist in a magical mystic forest, fingering her clitoris, HD 4k, sharp detail\",\n \"trainset_idx\": 308408\n },\n {\n \"caption\": \"emoji of a wolf holding a gavel, logo, fantasy, 4k, hyperrealistic, studio ghibli, anime, high quality, hd, 4k, watercolor, pastel, fairy tale, concept art, stylized, epic, reflections, cinematic lighting, realistic lighting, unreal engine, professional digital art, professional photograph\",\n \"trainset_idx\": 519856\n },\n {\n \"caption\": \"a movie scene, an exploding house, cinematic\",\n \"trainset_idx\": 336243\n },\n {\n \"caption\": \"A bouquet of noses\",\n \"trainset_idx\": 274242\n },\n {\n \"caption\": \"Photo from family official party\",\n \"trainset_idx\": 300084\n },\n {\n \"caption\": \"photo of fat muscle cruel boss guy exhibitionist freeballing harsh interrogation at office. highly detailed face, killer look, Hard close-set eyes, born criminal\",\n \"trainset_idx\": 458483\n },\n {\n \"caption\": \"kratos from god of war, dark arts, bloodborne, the witcher, full body portrait ,sharp lense, professional photographie, 70mm lense, detail love, good quality, unreal engine 5, wallpaper, colerful, highly detailed, 8k, soft light, photo realistic\",\n \"trainset_idx\": 285543\n },\n {\n \"caption\": \"Mr Spock played by Leonard Nimoy\",\n \"trainset_idx\": 258889\n },\n {\n \"caption\": \"ava addams teniendo apare\\u00e1ndose con un toro en el rancho\",\n \"trainset_idx\": 315408\n },\n {\n \"caption\": \"A cat on a propaganda poster, soviet poster, space exploring\",\n \"trainset_idx\": 381343\n },\n {\n \"caption\": \"portrait of a blonde vampire, dark, piercing eyes, gentle expression, elegant clothing, photorealistic, highly detailed, artstation, smooth, sharp focus, art by michael whelan, artgerm, greg rutkowski and alphonse mucha\",\n \"trainset_idx\": 21909\n },\n {\n \"caption\": \"malnourished very old Japanese woman with drip infusion\",\n \"trainset_idx\": 421909\n },\n {\n \"caption\": \"an emo filipino girl with blue hair and tattoos\",\n \"trainset_idx\": 19779\n },\n {\n \"caption\": \"a Japanese girl in tutu on her 6th birthday party, stocking, long and slim legs, from behind, Nikon D5\",\n \"trainset_idx\": 314954\n },\n {\n \"caption\": \"A cat, a shuriken on the floor in the middle of japan\",\n \"trainset_idx\": 131053\n },\n {\n \"caption\": \"girl in the space holding a sign saying 'sdxl' as text, anime, animated,\",\n \"trainset_idx\": 581014\n },\n {\n \"caption\": \"Beautiful tree made of swirly fire \\ud83d\\udd25 by Android Jones: Japanese Art: James Jean: Erin Hanson: Dan Mumford: professional photography, natural lighting, volumetric lighting maximalist photoillustration 8k resolution concept art intricately detailed, complex, elegant, expansive, fantastical\",\n \"trainset_idx\": 309134\n },\n {\n \"caption\": \"a young woman wearing panties\",\n \"trainset_idx\": 402849\n },\n {\n \"caption\": \"\\\"niaka\\\" text, word\",\n \"trainset_idx\": 165394\n },\n {\n \"caption\": \"polaroid medium shot photo of a redhead teenage girl from the 1980s wearing a 1980s outfit with jean jacket, blue jeans, at the mall in Springfield in 1985 with Sam Goody's in the background\",\n \"trainset_idx\": 552863\n },\n {\n \"caption\": \"candid selfie photograph of singer taylor swift with group of muscular black men,beach,face closeup,sharp focus, venereal pose,white woman surrounded by men,highly detailed,stunningly beautiful face,natural lighting,\",\n \"trainset_idx\": 534688\n },\n {\n \"caption\": \"A sign that reads \\\"Pick a Pic\\\"\",\n \"trainset_idx\": 238249\n },\n {\n \"caption\": \"Logo frog smoke pipe\",\n \"trainset_idx\": 181691\n },\n {\n \"caption\": \"film still from a 1970s movie about sasquatch\",\n \"trainset_idx\": 579123\n },\n {\n \"caption\": \"Minecraft Logo\",\n \"trainset_idx\": 229570\n },\n {\n \"caption\": \"cinematic still of establishing shot from masterpiece cinematography film about liminal space, peace, tranquillity, high details, sharp focus, softest light, perfect composition, , best quality\",\n \"trainset_idx\": 45782\n },\n {\n \"caption\": \"a knight with a sword, cyberpunk setting, futuristic city, cellshaded, best quality, dramatic light, night\",\n \"trainset_idx\": 110241\n },\n {\n \"caption\": \"Black and white professional 1905 photographer with camera in hand of covered by splash of dust in a forest splash of light\",\n \"trainset_idx\": 573336\n },\n {\n \"caption\": \"The dinosaurs fight over the last cigarette on earth\",\n \"trainset_idx\": 221840\n },\n {\n \"caption\": \"three diffrent dog breeds olaying at the park\",\n \"trainset_idx\": 116895\n },\n {\n \"caption\": \"Mario punches Sonic in the face screaming FRICK YOU\",\n \"trainset_idx\": 574056\n },\n {\n \"caption\": \"dogs playing tennis\",\n \"trainset_idx\": 294368\n },\n {\n \"caption\": \"Astronaut in a city. Business theme\",\n \"trainset_idx\": 377568\n },\n {\n \"caption\": \"thanos as frodo from lord of the rings, league of legends splash art by greg rutkowski, epic art on artstation\",\n \"trainset_idx\": 301450\n },\n {\n \"caption\": \"Rtx 3090 graphics card\",\n \"trainset_idx\": 277083\n },\n {\n \"caption\": \"Girl. beautiful; goth; Nice body; Ebony; Dark skin;\",\n \"trainset_idx\": 121757\n },\n {\n \"caption\": \"red haired woman with red jacket, white undershirt, black pants, standing next to police station, gta style\",\n \"trainset_idx\": 163145\n },\n {\n \"caption\": \"A portrait of linda cardellini alison brie anne hathaway rachel lane sabrina lloyd odette annable hybrid oil painting unreal 5 daz rpg portrait extremely detailed artgerm greg rutkowski alphonse mucha vladimir volegov adolphe bouguereaum greg hildebrandt tim Hildebrandt at the beach at night-time wearing a dress warm studio\",\n \"trainset_idx\": 209643\n },\n {\n \"caption\": \"iridescent deep sea creature made of glass,many huge eyes,backlit,UV bodypainting,alien landscape,vibrant color,intricate,shallow depth of field,detailed,Jeff Soto,Bridget Bate Tichenor,Tim White,victo ngai,tyler shields,greg rutkowski,Noah Bradley,wenjun lin,bioluminescence,Mert Alas\",\n \"trainset_idx\": 521152\n },\n {\n \"caption\": \"a photograph of velociraptors driving a landrover in the jungle river,dinosaurs 4k octane render\",\n \"trainset_idx\": 269782\n },\n {\n \"caption\": \"LDS temple\",\n \"trainset_idx\": 254286\n },\n {\n \"caption\": \"A steampunk\",\n \"trainset_idx\": 551414\n },\n {\n \"caption\": \"infinite hallway\",\n \"trainset_idx\": 150188\n },\n {\n \"caption\": \"fantasy art print legend of ravaging dynasties, charcoal painting of giant towering sabertooth tiger peacefully bowing down to a girl, majestic\",\n \"trainset_idx\": 198197\n },\n {\n \"caption\": \"Pikachu hot\",\n \"trainset_idx\": 385853\n },\n {\n \"caption\": \"A Levantine man waving at a cat, hills and palm trees in the background\",\n \"trainset_idx\": 156647\n },\n {\n \"caption\": \"Joe Biden in Genshin Impact with Venti\",\n \"trainset_idx\": 543997\n },\n {\n \"caption\": \"a female kobold\",\n \"trainset_idx\": 167666\n },\n {\n \"caption\": \"suzu Hirose as hatsune miku wearing crop red gym top with white lettering, cropped red yoga short, Advertising photography by Mario Testino, masterwork, cgstudio\",\n \"trainset_idx\": 412835\n },\n {\n \"caption\": \"Magazine Illustration of a princess, freckles, green and gold armor, high quality, photorealistic\",\n \"trainset_idx\": 437963\n },\n {\n \"caption\": \"Glass aromatic diffuser in modern interior, proffesional foto, realism, bokeh\",\n \"trainset_idx\": 564404\n },\n {\n \"caption\": \"Hana Uzaki\",\n \"trainset_idx\": 291225\n },\n {\n \"caption\": \"Werewolf realistic\",\n \"trainset_idx\": 287412\n },\n {\n \"caption\": \"jobs woz apple\",\n \"trainset_idx\": 245153\n },\n {\n \"caption\": \"fingers with full of expensive rings\",\n \"trainset_idx\": 162717\n },\n {\n \"caption\": \"palm tree made of wool inside:1.2 of a large rum bottle on a beach\",\n \"trainset_idx\": 104205\n },\n {\n \"caption\": \"anthropomorphic GOAT\",\n \"trainset_idx\": 334819\n },\n {\n \"caption\": \"Pafunum Electronic Entertainment logo, retro logo\",\n \"trainset_idx\": 483769\n },\n {\n \"caption\": \"star wars bb8 as easter egg pink blue\",\n \"trainset_idx\": 507161\n },\n {\n \"caption\": \"a Roman marble statue of woman facepalming, as seen in the Louvre\",\n \"trainset_idx\": 445547\n },\n {\n \"caption\": \"Black labradoodle with brown eyes, non-curly fur, oil painting realistic\",\n \"trainset_idx\": 177463\n },\n {\n \"caption\": \"photo of a lasagna in front of the colosseum\",\n \"trainset_idx\": 51975\n },\n {\n \"caption\": \"a sandwich in the middle of the desert\",\n \"trainset_idx\": 79766\n },\n {\n \"caption\": \"Wet hair petite\",\n \"trainset_idx\": 217993\n },\n {\n \"caption\": \"cute blue furred teddy bear wearing a green flannel shirt in the style of animal crossing\",\n \"trainset_idx\": 226508\n },\n {\n \"caption\": \"art by Alfons Mucha and Patrick Woodroffe, copper-foil method stained glass motif, whole body image of 20 year-old Taylor Schilling as a naturist in a mystical forest, HD 4k, sharp detail, photo-realistic accurate face and features\",\n \"trainset_idx\": 17703\n },\n {\n \"caption\": \"black dungeon with subtle blue and pink edge lighting, magical runes\",\n \"trainset_idx\": 254373\n },\n {\n \"caption\": \"hot black girl with curly blonde hair drinking champagne\",\n \"trainset_idx\": 170445\n },\n {\n \"caption\": \"wideangle photo of a building karnak gold menger sponge ,door floor tiles\",\n \"trainset_idx\": 20776\n },\n {\n \"caption\": \"screaming photorealistic close up portrait of happy old male screaming shaman covered in symmetrycal blue lotus crystal covered by windy splash of strings of light in a dark sky covered by stars, splash of glowing water, painting, aligned, dramatic light, by andrews esao amorsolo\",\n \"trainset_idx\": 271968\n },\n {\n \"caption\": \"furry goblin in a baseball uniform\",\n \"trainset_idx\": 67381\n },\n {\n \"caption\": \"Soldier\",\n \"trainset_idx\": 314312\n },\n {\n \"caption\": \"photorealistic forest fire\",\n \"trainset_idx\": 468052\n },\n {\n \"caption\": \"Polaroid photo of Greek philosopher Aristotle teaching a young Alexander the Great. They are outside in a courtyard with the sun shining. The picture should be in a documentary or photojournalistic style. 1980s polaroid photo, square format, muted colors, white border\",\n \"trainset_idx\": 422264\n },\n {\n \"caption\": \"Magnificent shot of gorgeous sophie skelton as a d&d elf by alexandra nataf\",\n \"trainset_idx\": 383893\n },\n {\n \"caption\": \"an image of a gold marble sculpture laughing, in blade runner, at the sea red fog in the background, professional photography\",\n \"trainset_idx\": 50044\n },\n {\n \"caption\": \"Beautiful female anime painting of solarpunk summer chill day, trending on artstation, 8k, masterpiece, graffiti paint, fine detail, full of color, intricate detail, ambient lighting, highly detailed, golden ratio illustration, unreal engine 5, octane,3d render, by tim okamura, victor nizovtsev, greg rutkowski, noah bradley\",\n \"trainset_idx\": 571466\n },\n {\n \"caption\": \"zentai woman in white zentai body that covers her entire head\",\n \"trainset_idx\": 438620\n },\n {\n \"caption\": \"A emperor, warhammer 40k, digital art\",\n \"trainset_idx\": 578705\n },\n {\n \"caption\": \"goth, schoolgirl, portrait, Masterpiece, trending on artstation, best quality, detailed, detailed eyes, detailed hair, cinematic, soft lighting, high quality, digital art\",\n \"trainset_idx\": 522729\n },\n {\n \"caption\": \"Woman looking in awe at the northern lights above her, 4k dslr, breathtaking\",\n \"trainset_idx\": 562137\n },\n {\n \"caption\": \"un logotipo de candado moderno, dise\\u00f1o minimalista, esquema de color monocrom\\u00e1tico azul, formas elegantes y simples, l\\u00edneas limpias, abstracci\\u00f3n geom\\u00e9trica, espacio negativo, degradados sutiles, ambiente retro, fondo blanco aislado\",\n \"trainset_idx\": 227704\n },\n {\n \"caption\": \"1114083012-a ultradetailed beautiful panting of a stylish beefy bearded man sitting on a chair, wearing a tucked in shirt and suit panths by conrad roset, greg rutkowski\",\n \"trainset_idx\": 452444\n },\n {\n \"caption\": \"a cute hamster whch is eating sunflower seeds\",\n \"trainset_idx\": 16792\n },\n {\n \"caption\": \"An epic digital painting of many firebreathing dragons fighting each other in the sky, extremely detailed, intricate\",\n \"trainset_idx\": 396824\n },\n {\n \"caption\": \"photo of an atom\",\n \"trainset_idx\": 228328\n },\n {\n \"caption\": \"a crow with cameras for eyes, sitting on a mans shoulder, anime, studio ghibli, fantasy, fairytale, sketch, digital art, watercolor, dnd, rustic, professional photograph, medieval, hd, 4k\",\n \"trainset_idx\": 558550\n },\n {\n \"caption\": \"turtle flying in the cloud ocean\",\n \"trainset_idx\": 525077\n },\n {\n \"caption\": \"An airplane flying under the ocean among whales\",\n \"trainset_idx\": 66261\n },\n {\n \"caption\": \"a full-body photograph of middle aged man with a red t-shirt and blue jeans\",\n \"trainset_idx\": 356604\n },\n {\n \"caption\": \"pitch black snow glittering, sparkling black snow, 4k, half light half dark, faint streaks of rainbow, magical, hyperrealistic, infinite, hyperdetailed\",\n \"trainset_idx\": 446614\n },\n {\n \"caption\": \"insanely detailed portrait, baby yoda, grogu, extremely intricate, high res, 8k, award winning\",\n \"trainset_idx\": 145652\n },\n {\n \"caption\": \"A presidential poster of a boxer dog\",\n \"trainset_idx\": 78874\n },\n {\n \"caption\": \"photo of a creepy small foggy hillside town street full of old stone buildings\",\n \"trainset_idx\": 118105\n },\n {\n \"caption\": \"cute school girl, sitting at her desk reading a manga\",\n \"trainset_idx\": 461831\n },\n {\n \"caption\": \"big chungus\",\n \"trainset_idx\": 74158\n },\n {\n \"caption\": \"A group of men with abs having a fancy tea party\",\n \"trainset_idx\": 540744\n },\n {\n \"caption\": \"Boris Johnson being arrested\",\n \"trainset_idx\": 414309\n },\n {\n \"caption\": \"Pop art, blonde girl\",\n \"trainset_idx\": 528312\n },\n {\n \"caption\": \"futuristic fantasy realistic photo highly detailed city casablanca morocco cyberpunk\",\n \"trainset_idx\": 47592\n },\n {\n \"caption\": \"3d render in the style of Killer Instinct of a goblin knight\",\n \"trainset_idx\": 80741\n },\n {\n \"caption\": \"polaroid, a colossal dark massive factory covered with bleeding corpses, hundreds of bleeding dead bodies ,\",\n \"trainset_idx\": 500514\n },\n {\n \"caption\": \"a diamond guitar\",\n \"trainset_idx\": 559827\n },\n {\n \"caption\": \"an image of a white giant teddy bear in the middle of the street, professional photography, 35mm, 4k, golden hour\",\n \"trainset_idx\": 412012\n },\n {\n \"caption\": \"abandoned railroad tunnel that is filled with bioluminescent bugs\",\n \"trainset_idx\": 138261\n },\n {\n \"caption\": \"**a portrait of a 3D cockroach, wearing a bitcoin shirt, in Hawaii, on the beach, hyper-realistic, ultra-detailed, photography, hyper-realistic, photo-realistic, ultra-photo-realistic, super-detailed, intricate details, 8K, surround lighting, HDR\",\n \"trainset_idx\": 250934\n },\n {\n \"caption\": \"Craft a haunting yet whimsical scene of abandoned dolls, style of Nicoletta Ceccoli\",\n \"trainset_idx\": 111316\n },\n {\n \"caption\": \"Garden Monkey\",\n \"trainset_idx\": 580737\n },\n {\n \"caption\": \"katia winter as a red head fantasy sorceress, lace sleeves, green leather dress, gloves, D&D character, magic fx background\",\n \"trainset_idx\": 187170\n },\n {\n \"caption\": \"young man, lean and muscular build, short messy dark blue hair with bangs that partially cover the forehead, bright green eyes, pointed chin, black track suit with white stripes down the sides, black sneakers, green eyes, anime\",\n \"trainset_idx\": 157242\n },\n {\n \"caption\": \"this image is just tickling my brain in hilarious ways\",\n \"trainset_idx\": 520897\n },\n {\n \"caption\": \"ghostpunk boy\",\n \"trainset_idx\": 308437\n },\n {\n \"caption\": \"A sticker of cat head, white contour, bioluminescence, solid background\",\n \"trainset_idx\": 42707\n },\n {\n \"caption\": \"Astronaut in cyberspace; Trading Bitcoin chart; Dark theme;\",\n \"trainset_idx\": 108736\n },\n {\n \"caption\": \"Research department modern inovation logo\",\n \"trainset_idx\": 500699\n },\n {\n \"caption\": \"Stegosauro fly in the sky\",\n \"trainset_idx\": 64806\n },\n {\n \"caption\": \"Neanderthal man, headshot photo, 8k uhd, highly detailed skin\",\n \"trainset_idx\": 43651\n },\n {\n \"caption\": \"Tilt shift photo of a town\",\n \"trainset_idx\": 102609\n },\n {\n \"caption\": \"Big Party at king Arthur\\u2019s court in Camelot, fantasy, mystery\",\n \"trainset_idx\": 408312\n },\n {\n \"caption\": \"The universe is imploding and causing the destruction of all reality, Insanely detailed, photorealistic, 8k, ultra high resolution, volumetric lighting, taken with canon eos 5d,\",\n \"trainset_idx\": 348294\n },\n {\n \"caption\": \"Sunnyvale CA\",\n \"trainset_idx\": 500018\n },\n {\n \"caption\": \"The Beatles on stage playing in Champ de Mars, in Paris, a drummer on stage, the eiffel tower background, extremely detailed, 8k\",\n \"trainset_idx\": 376238\n },\n {\n \"caption\": \"a wide angle photo of lara croft in a smokey roman villa burning, 18mm smoke filled room debris , gladiator ,floor mosaics fire smoke, a photo, roman , a digital rendering, inside the roman colliseum, brick, indoor, plants overgrown outstanding detail ,room flooded with water, in front of a building,by claude-joseph vernet,luxury hotel\",\n \"trainset_idx\": 395039\n },\n {\n \"caption\": \"anime girl in the space with a sign saying 'sdxl' as text,4k\",\n \"trainset_idx\": 578553\n },\n {\n \"caption\": \"Anger\",\n \"trainset_idx\": 500489\n },\n {\n \"caption\": \"A cyberpunk robot samurai, onmyoji oni demon, mecha, machine parts, professional, masterpiece\",\n \"trainset_idx\": 423225\n },\n {\n \"caption\": \"stained glass motif, art by Alfons Mucha, whole body image of Olga Kurylenko as a naturist in a twilight forest, HD 4K, sharp detail, photo-realistic accurate face and features, cinematic lighting\",\n \"trainset_idx\": 212979\n },\n {\n \"caption\": \"The setting sun is streaming through the window and a bare teenage girl in front of it in the bathroom\",\n \"trainset_idx\": 297576\n },\n {\n \"caption\": \", fantasy, pastel, absurdist, photo, refined, trapped\",\n \"trainset_idx\": 385628\n },\n {\n \"caption\": \"A girl bending over\",\n \"trainset_idx\": 9903\n },\n {\n \"caption\": \"woman destroying a drone by throwing a jar of pickled tomatoes off a balcony\",\n \"trainset_idx\": 135693\n },\n {\n \"caption\": \"A steampunk frog surfing in a forest\",\n \"trainset_idx\": 229231\n },\n {\n \"caption\": \"a raw photo close up of the heavenly catholic demon pig cyborg inside an iron maiden robot wielding a giant katana,large view,a surrealist painting, inspired by Jean Fouquet and alan bean,by vincenzo riccardi and Philippe Druillet,yoji shinkawa,masterpiece,extremely detailed,4k uhd\",\n \"trainset_idx\": 126502\n },\n {\n \"caption\": \"astronauts playing chess on the moon sitting drinking beer earth on the background\",\n \"trainset_idx\": 411677\n },\n {\n \"caption\": \"A purple cat, polaroid, frutiger aero style, aqua interface\",\n \"trainset_idx\": 306849\n },\n {\n \"caption\": \"Design Fails\",\n \"trainset_idx\": 118525\n },\n {\n \"caption\": \"portrait of pretty young woman, beautiful, attractive, glowing, amazing, magical, dynamic lighting, dark, tender, 4k, octane, age 18, moon in background, intricate and ornate, with talisman, with rainbow background very beautiful\",\n \"trainset_idx\": 433349\n },\n {\n \"caption\": \"Bayonetta 3 the witcher, very details perfect body, high details, long legs long hair, perfect body, perfect head,\",\n \"trainset_idx\": 511804\n },\n {\n \"caption\": \"Man dunking basketball in dunk contest, 8k, hdr, detailed\",\n \"trainset_idx\": 472415\n },\n {\n \"caption\": \"a picture of a landscape, with a mountainous horizon, at sunset.\",\n \"trainset_idx\": 127457\n },\n {\n \"caption\": \"By bringing layer-2 capabilities to Bitcoin, it enables faster and cheaper transactions while maintaining top-notch security.\",\n \"trainset_idx\": 72607\n },\n {\n \"caption\": \"The spy\",\n \"trainset_idx\": 113123\n },\n {\n \"caption\": \"seductress zelda princess by artgerm\",\n \"trainset_idx\": 451525\n },\n {\n \"caption\": \"Hand-drawn cartoon dog in the real world\",\n \"trainset_idx\": 548275\n },\n {\n \"caption\": \"a beer bottle as a power up from super Mario bros\",\n \"trainset_idx\": 294271\n },\n {\n \"caption\": \"A dwarf cleric smashing a mans head with a hammer. Blood and brains explode\",\n \"trainset_idx\": 167219\n }\n]" }, { "path": "spo_training_and_inference/configs/basic_config.py", "content": "import ml_collections\n\ndef get_config():\n return basic_config()\n\ndef basic_config():\n config = ml_collections.ConfigDict()\n \n ###### General ######\n # random seed for reproducibility.\n config.seed = 42\n # number of checkpoints to keep before overwriting old ones.\n config.num_checkpoint_limit = None\n # allow tf32 on Ampere GPUs, which can speed up training.\n config.allow_tf32 = True\n # whether or not to use xFormers to reduce memory usage.\n config.use_xformers = False\n # enable activation checkpointing or not. \n # this reduces memory usage at the cost of some additional compute.\n config.use_checkpointing = False\n \n ###### Model Setting ######\n config.pretrained = pretrained = ml_collections.ConfigDict()\n # base model to load. either a path to a local directory, or a model name from the HuggingFace model hub.\n pretrained.model = \"runwayml/stable-diffusion-v1-5\"\n config.use_lora = True\n config.lora_rank = 4\n \n ###### Preference Model ######\n config.preference_model_func_cfg = dict(\n type=\"step_aware_preference_model_func\",\n model_pretrained_model_name_or_path='yuvalkirstain/PickScore_v1',\n processor_pretrained_model_name_or_path='laion/CLIP-ViT-H-14-laion2B-s32B-b79K',\n ckpt_path='model_ckpts/sd-v1-5_step-aware_preference_model.bin',\n )\n \n ###### Compare Function ######\n config.compare_func_cfg = dict(\n type=\"preference_score_compare\",\n threshold=0.3,\n )\n \n ##### dataset #####\n config.dataset_cfg = dict(\n type=\"PromptDataset\",\n meta_json_path='assets/prompts/4k_training_prompts.json',\n pretrained_tokenzier_path='laion/CLIP-ViT-H-14-laion2B-s32B-b79K',\n )\n \n ##### dataloader ####\n config.dataloader_num_workers = 16\n config.dataloader_shuffle = True\n config.dataloader_pin_memory = True\n config.dataloader_drop_last = False\n\n ###### Training ######\n config.num_epochs = 10\n # resume training from a checkpoint. either an exact checkpoint directory (e.g. checkpoint_50), or a directory\n # containing checkpoints, in which case the latest one will be used. `config.use_lora` must be set to the same value\n # as the run that generated the saved checkpoint.\n config.resume_from = \"\"\n \n config.sample = sample = ml_collections.ConfigDict()\n # number of sampler inference steps.\n sample.num_steps = 20\n # eta parameter for the DDIM sampler. this controls the amount of noise injected into the sampling process, with 0.0\n # being fully deterministic and 1.0 being equivalent to the DDPM sampler.\n sample.eta = 1.0\n # classifier-free guidance weight. 1.0 is no guidance.\n sample.guidance_scale = 5.0\n sample.sample_batch_size = 10\n # number of x_{t-1} sampled at each timestep.\n sample.num_sample_each_step = 2\n\n config.train = train = ml_collections.ConfigDict()\n # batch size (per GPU!) to use for training.\n train.train_batch_size = 10\n # whether to use the 8bit Adam optimizer from bitsandbytes.\n train.use_8bit_adam = False\n # learning rate.\n train.learning_rate = 6e-5\n # Adam beta1.\n train.adam_beta1 = 0.9\n # Adam beta2.\n train.adam_beta2 = 0.999\n # Adam weight decay.\n train.adam_weight_decay = 1e-4\n # Adam epsilon.\n train.adam_epsilon = 1e-8\n # number of gradient accumulation steps. the effective batch size is `batch_size * num_gpus *\n # gradient_accumulation_steps`.\n train.gradient_accumulation_steps = 1\n # maximum gradient norm for gradient clipping.\n train.max_grad_norm = 1.0\n # whether or not to use classifier-free guidance during training. if enabled, the same guidance scale used during\n # sampling will be used during training.\n train.cfg = True\n\n train.divert_start_step = 4\n # coefficient of the KL divergence\n train.beta = 10.0\n # The coefficient constraining the probability ratio.\n train.eps = 0.1\n\n #### validation ####\n config.validation_prompts = ['A beautiful lake']\n config.num_validation_images = 2\n config.eval_interval = 1\n \n #### logging ####\n # run name for wandb logging and checkpoint saving.\n config.run_name = \"\"\n config.wandb_project_name = 'spo'\n config.wandb_entity_name = None\n # top-level logging directory for checkpoint saving.\n config.logdir = \"work_dirs\"\n config.save_interval = 1\n \n return config\n" }, { "path": "spo_training_and_inference/configs/spo_sd-v1-5_4k-prompts_num-sam-4_10ep_bs10.py", "content": "from configs.basic_config import basic_config\n\ndef get_config():\n return exp_config()\n\ndef exp_config():\n config = basic_config()\n \n ###### Training ######\n config.sample.num_sample_each_step = 4\n \n #### logging ####\n config.run_name = \"spo_sd-v1-5_4k-prompts_num-sam-4_10ep_bs10\"\n\n return config\n" }, { "path": "spo_training_and_inference/configs/spo_sdxl_4k-prompts_num-sam-2_3-is_10ep_bs2_gradacc2.py", "content": "from configs.basic_config import basic_config\n\ndef get_config():\n return exp_config()\n\ndef exp_config():\n config = basic_config()\n\n ###### Model Setting ######\n config.pretrained.model = 'stabilityai/stable-diffusion-xl-base-1.0'\n config.pretrained.vae_model_name_or_path = 'madebyollin/sdxl-vae-fp16-fix'\n config.lora_rank = 64\n\n ###### Preference Model ######\n config.preference_model_func_cfg.ckpt_path = 'model_ckpts/sdxl_step-aware_preference_model.bin'\n\n ###### Compare Function ######\n config.compare_func_cfg.threshold = 0.4\n \n ###### Training ######\n config.sample.sample_batch_size = 2\n config.sample.num_inner_step = 3\n \n config.train.train_batch_size = 2\n config.train.learning_rate = 1e-5\n config.train.gradient_accumulation_steps = 2\n \n #### logging ####\n config.run_name = \"spo_sdxl_4k-prompts_num-sam-2_3-is_10ep_bs2_gradacc2\"\n \n return config\n" }, { "path": "spo_training_and_inference/environment.yaml", "content": "name: spo\nchannels:\n - defaults\ndependencies:\n - python=3.10.13\n - pip\n - pip:\n - torch==2.3.0\n - torchelastic==0.2.2\n - torchvision==0.18.0\n - absl-py==2.1.0\n - accelerate==0.24.1\n - addict==2.4.0\n - aliyun-python-sdk-core==2.14.0\n - aliyun-python-sdk-kms==2.16.2\n - annotated-types==0.6.0\n - appdirs==1.4.4\n - asttokens==2.0.5\n - astunparse==1.6.3\n - attrs==23.1.0\n - backcall==0.2.0\n - beautifulsoup4==4.12.2\n - boltons==23.0.0\n - brotlipy==0.7.0\n - certifi==2023.7.22\n - cffi==1.15.1\n - chardet==4.0.0\n - charset-normalizer==2.0.4\n - click==8.0.4\n - colorama==0.4.6\n - contextlib2==21.6.0\n - contourpy==1.2.0\n - crcmod==1.7\n - cryptography==41.0.3\n - cycler==0.12.1\n - decorator==5.1.1\n - deepspeed==0.12.3\n - diffusers==0.26.1\n - dnspython==2.4.2\n - docker-pycreds==0.4.0\n - einops==0.7.0\n - exceptiongroup==1.0.4\n - executing==0.8.3\n - expecttest==0.1.6\n - fairscale==0.4.13\n - filelock==3.9.0\n - fonttools==4.44.0\n - fsspec==2023.9.2\n - ftfy==6.2.0\n - gitdb==4.0.11\n - GitPython==3.1.40\n - gmpy2==2.1.2\n - hjson==3.1.0\n - huggingface-hub==0.23.0\n - hypothesis==6.87.2\n - idna==3.4\n - importlib-metadata==6.8.0\n - inflect==6.0.4\n - ipython==8.15.0\n - jedi==0.18.1\n - Jinja2==3.1.2\n - jmespath==0.10.0\n - joblib==1.4.2\n - jsonpatch==1.32\n - jsonpointer==2.1\n - kiwisolver==1.4.5\n - libarchive-c==2.9\n - Markdown==3.5.1\n - markdown-it-py==3.0.0\n - MarkupSafe==2.1.1\n - matplotlib==3.8.1\n - matplotlib-inline==0.1.6\n - mdurl==0.1.2\n - mkl-fft==1.3.8\n - mkl-random==1.2.4\n - mkl-service==2.4.0\n - ml-collections==0.1.1\n - mmengine==0.9.1\n - model-index==0.1.11\n - more-itertools==8.12.0\n - mpmath==1.3.0\n - networkx==3.1\n - ninja==1.11.1.1\n - numpy==1.26.4\n - nvidia-cublas-cu12==12.1.3.1\n - nvidia-cuda-cupti-cu12==12.1.105\n - nvidia-cuda-nvrtc-cu12==12.1.105\n - nvidia-cuda-runtime-cu12==12.1.105\n - nvidia-cudnn-cu12==8.9.2.26\n - nvidia-cufft-cu12==11.0.2.54\n - nvidia-curand-cu12==10.3.2.106\n - nvidia-cusolver-cu12==11.4.5.107\n - nvidia-cusparse-cu12==12.1.0.106\n - nvidia-nccl-cu12==2.20.5\n - nvidia-nvjitlink-cu12==12.4.127\n - nvidia-nvtx-cu12==12.1.105\n - opencv-python==4.8.1.78\n - opendatalab==0.0.10\n - openmim==0.3.9\n - openxlab==0.0.28\n - ordered-set==4.1.0\n - oss2==2.17.0\n - packaging==23.1\n - pandas==2.1.3\n - parso==0.8.3\n - peft==0.10.0\n - pexpect==4.8.0\n - pickleshare==0.7.5\n - Pillow==10.0.1\n - pip==23.2.1\n - pkginfo==1.9.6\n - platformdirs==4.0.0\n - pluggy==1.0.0\n - prompt-toolkit==3.0.36\n - protobuf==4.25.0\n - psutil==5.9.0\n - ptyprocess==0.7.0\n - pure-eval==0.2.2\n - py-cpuinfo==9.0.0\n - pycosat==0.6.6\n - pycparser==2.21\n - pycryptodome==3.19.0\n - pydantic==1.10.9\n - pydantic_core==2.14.1\n - Pygments==2.15.1\n - pynvml==11.5.0\n - pyOpenSSL==23.2.0\n - pyparsing==3.1.1\n - PySocks==1.7.1\n - python-dateutil==2.8.2\n - python-etcd==0.4.5\n - pytz==2023.3.post1\n - PyYAML==6.0\n - regex==2023.10.3\n - requests==2.28.2\n - rich==13.4.2\n - ruamel.yaml==0.17.21\n - ruamel.yaml.clib==0.2.6\n - safetensors==0.4.3\n - scikit-learn==1.4.2\n - scipy==1.13.0\n - seaborn==0.13.2\n - sentencepiece==0.1.99\n - sentry-sdk==1.35.0\n - setproctitle==1.3.3\n - setuptools==60.2.0\n - six==1.16.0\n - smmap==5.0.1\n - sortedcontainers==2.4.0\n - soupsieve==2.5\n - stack-data==0.2.0\n - sympy==1.11.1\n - tabulate==0.9.0\n - termcolor==2.3.0\n - threadpoolctl==3.5.0\n - timm==0.6.13\n - tokenizers==0.19.1\n - tomli==2.0.1\n - toolz==0.12.0\n - tqdm==4.65.0\n - traitlets==5.7.1\n - transformers==4.40.1\n - triton==2.3.0\n - truststore==0.8.0\n - types-dataclasses==0.6.6\n - typing_extensions==4.11.0\n - tzdata==2023.3\n - urllib3==1.26.16\n - wandb==0.16.0\n - wcwidth==0.2.13\n - wheel==0.41.2\n - yapf==0.40.2\n - zipp==3.17.0\n - zstandard==0.19.0" }, { "path": "spo_training_and_inference/inference_scripts/inference_spo_sd-v1-5.py", "content": "import argparse\nimport os.path as osp\nimport sys\n# Add the project directory to the Python path to simplify imports without manually setting PYTHONPATH.\nsys.path.insert(\n 0, osp.abspath(\n osp.join(osp.dirname(osp.abspath(__file__)), \"..\")\n ),\n)\nimport torch\nfrom diffusers import StableDiffusionPipeline\nfrom spo.utils import huggingface_cache_dir\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument('--ckpt_id', default='SPO-Diffusion-Models/SPO-SD-v1-5_4k-p_10ep')\n parser.add_argument('--device', default='cuda')\n parser.add_argument(\n '--prompt', \n default='an image of a beautiful lake',\n )\n parser.add_argument(\n '--cfg_scale',\n default=7.5,\n type=float,\n )\n parser.add_argument(\n '--output_filename',\n default='spo_sdv1-5_img.png',\n )\n parser.add_argument(\n '--seed',\n default=42,\n type=int,\n )\n args = parser.parse_args()\n \n ckpt_id = args.ckpt_id\n inference_dtype = torch.float16\n \n pipe = StableDiffusionPipeline.from_pretrained(\n ckpt_id, \n torch_dtype=inference_dtype,\n cache_dir=huggingface_cache_dir,\n )\n pipe.to(args.device)\n \n generator=torch.Generator(device=args.device).manual_seed(args.seed)\n image = pipe(\n prompt=args.prompt,\n guidance_scale=args.cfg_scale,\n generator=generator,\n output_type='pil',\n ).images[0]\n image.save(args.output_filename)\n\nif __name__ == '__main__':\n main()" }, { "path": "spo_training_and_inference/inference_scripts/inference_spo_sdxl.py", "content": "import argparse\nimport os.path as osp\nimport sys\n# Add the project directory to the Python path to simplify imports without manually setting PYTHONPATH.\nsys.path.insert(\n 0, osp.abspath(\n osp.join(osp.dirname(osp.abspath(__file__)), \"..\")\n ),\n)\nimport torch\nfrom diffusers import StableDiffusionXLPipeline, AutoencoderKL\nfrom spo.utils import huggingface_cache_dir\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument('--ckpt_id', default='SPO-Diffusion-Models/SPO-SDXL_4k-p_10ep')\n parser.add_argument('--device', default='cuda')\n parser.add_argument(\n '--prompt', \n default='a child and a penguin sitting in front of the moon',\n )\n parser.add_argument(\n '--cfg_scale',\n default=5.0,\n type=float,\n )\n parser.add_argument(\n '--output_filename',\n default='spo_sdxl_img.png',\n )\n parser.add_argument(\n '--seed',\n default=42,\n type=int,\n )\n args = parser.parse_args()\n \n ckpt_id = args.ckpt_id\n inference_dtype = torch.float16\n \n pipe = StableDiffusionXLPipeline.from_pretrained(\n ckpt_id, \n torch_dtype=inference_dtype,\n cache_dir=huggingface_cache_dir,\n )\n vae = AutoencoderKL.from_pretrained(\n 'madebyollin/sdxl-vae-fp16-fix',\n torch_dtype=inference_dtype,\n cache_dir=huggingface_cache_dir,\n )\n pipe.vae = vae\n pipe.to(args.device)\n \n generator=torch.Generator(device=args.device).manual_seed(args.seed)\n image = pipe(\n prompt=args.prompt,\n guidance_scale=args.cfg_scale,\n generator=generator,\n output_type='pil',\n ).images[0]\n image.save(args.output_filename)\n\nif __name__ == '__main__':\n main()" }, { "path": "spo_training_and_inference/spo/custom_diffusers/__init__.py", "content": "from .multi_sample_pipeline import multi_sample_pipeline\nfrom .multi_sample_pipeline_sdxl import multi_sample_pipeline_sdxl\nfrom .ddim_with_logprob import ddim_step_with_logprob\n\n__all__ = [\n 'multi_sample_pipeline', \n 'ddim_step_with_logprob',\n 'multi_sample_pipeline_sdxl',\n]" }, { "path": "spo_training_and_inference/spo/custom_diffusers/ddim_seperate.py", "content": "from typing import Tuple, Union\n\nimport torch\n\nfrom diffusers.utils.torch_utils import randn_tensor\nfrom diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput, DDIMScheduler\n\n\ndef _left_broadcast(t, shape):\n assert t.ndim <= len(shape)\n return t.reshape(t.shape + (1,) * (len(shape) - t.ndim)).broadcast_to(shape)\n\ndef _get_variance(self, timestep, prev_timestep):\n alpha_prod_t = torch.gather(self.alphas_cumprod, 0, timestep)\n alpha_prod_t_prev = torch.where(\n prev_timestep >= 0, self.alphas_cumprod.gather(0, prev_timestep), self.final_alpha_cumprod\n )\n beta_prod_t = 1 - alpha_prod_t\n beta_prod_t_prev = 1 - alpha_prod_t_prev\n\n variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)\n\n return variance\n\ndef ddim_step_fetch_x0(\n self: DDIMScheduler,\n model_output: torch.FloatTensor,\n timestep: int,\n sample: torch.FloatTensor,\n) -> Union[DDIMSchedulerOutput, Tuple]:\n \"\"\"\n Args:\n self: DDIMScheduler\n model_output (`torch.FloatTensor`): direct output from learned diffusion model.\n timestep (`int`): current discrete timestep in the diffusion chain.\n sample (`torch.FloatTensor`):\n current instance of sample being created by diffusion process.\n \"\"\"\n assert isinstance(self, DDIMScheduler)\n if self.num_inference_steps is None:\n raise ValueError(\n \"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler\"\n ) \n # 1. get previous step value (=t-1)\n prev_timestep = timestep - self.config.num_train_timesteps // self.num_inference_steps\n # to prevent OOB on gather\n prev_timestep = torch.clamp(prev_timestep, 0, self.config.num_train_timesteps - 1)\n\n # 2. compute alphas, betas\n alpha_prod_t = self.alphas_cumprod.gather(0, timestep)\n alpha_prod_t_prev = torch.where(\n prev_timestep >= 0, self.alphas_cumprod.gather(0, prev_timestep), self.final_alpha_cumprod\n )\n alpha_prod_t = _left_broadcast(alpha_prod_t, sample.shape)\n alpha_prod_t_prev = _left_broadcast(alpha_prod_t_prev, sample.shape)\n\n beta_prod_t = 1 - alpha_prod_t\n\n # 3. compute predicted original sample from predicted noise also called\n # \"predicted x_0\" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf\n if self.config.prediction_type == \"epsilon\":\n pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)\n pred_epsilon = model_output\n elif self.config.prediction_type == \"sample\":\n pred_original_sample = model_output\n pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)\n elif self.config.prediction_type == \"v_prediction\":\n pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output\n pred_epsilon = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample\n else:\n raise ValueError(\n f\"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or\"\n \" `v_prediction`\"\n )\n\n # 4. Clip or threshold \"predicted x_0\"\n if self.config.thresholding:\n pred_original_sample = self._threshold_sample(pred_original_sample)\n elif self.config.clip_sample:\n pred_original_sample = pred_original_sample.clamp(\n -self.config.clip_sample_range, self.config.clip_sample_range\n )\n return dict(\n pred_original_sample=pred_original_sample, \n alpha_prod_t_prev=alpha_prod_t_prev, \n pred_epsilon=pred_epsilon, \n prev_timestep=prev_timestep,\n )\n\ndef ddim_step_fetch_x_t_1(\n self: DDIMScheduler,\n pred_original_sample,\n alpha_prod_t_prev,\n pred_epsilon,\n timestep,\n prev_timestep,\n dtype,\n num_sample_per_step,\n eta,\n generator=None,\n):\n # 5. compute variance: \"sigma_t(η)\" -> see formula (16)\n # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)\n variance = _get_variance(self, timestep, prev_timestep)\n std_dev_t = eta * variance ** (0.5)\n std_dev_t = _left_broadcast(std_dev_t, pred_original_sample.shape).to(pred_original_sample.device)\n\n # 6. compute \"direction pointing to x_t\" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf\n pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * pred_epsilon\n\n # 7. compute x_t without \"random noise\" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf\n prev_sample_mean = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction\n\n if num_sample_per_step > 1:\n variance_noise = randn_tensor(\n (num_sample_per_step, *prev_sample_mean.shape), \n generator=generator, \n device=pred_original_sample.device, \n dtype=dtype,\n )\n # num_sample_per_step, b, c, h, w\n prev_sample = prev_sample_mean[None] + std_dev_t[None] * variance_noise\n else:\n variance_noise = randn_tensor(\n prev_sample_mean.shape, generator=generator, device=pred_original_sample.device, dtype=dtype\n )\n # b,c,h,w\n prev_sample = prev_sample_mean + std_dev_t * variance_noise\n return prev_sample.type(dtype)\n" }, { "path": "spo_training_and_inference/spo/custom_diffusers/ddim_with_logprob.py", "content": "from typing import Optional, Tuple, Union\n\nimport math\nimport torch\n\nfrom diffusers.utils.torch_utils import randn_tensor\nfrom diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput, DDIMScheduler\n\ndef _left_broadcast(t, shape):\n assert t.ndim <= len(shape)\n return t.reshape(t.shape + (1,) * (len(shape) - t.ndim)).broadcast_to(shape)\n\ndef _get_variance(self, timestep, prev_timestep):\n alpha_prod_t = torch.gather(self.alphas_cumprod, 0, timestep)\n alpha_prod_t_prev = torch.where(\n prev_timestep >= 0, self.alphas_cumprod.gather(0, prev_timestep), self.final_alpha_cumprod\n )\n beta_prod_t = 1 - alpha_prod_t\n beta_prod_t_prev = 1 - alpha_prod_t_prev\n\n variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)\n\n return variance\n\ndef ddim_step_with_logprob(\n self: DDIMScheduler,\n model_output: torch.FloatTensor,\n timestep: int,\n sample: torch.FloatTensor,\n eta: float = 0.0,\n use_clipped_model_output: bool = False,\n generator=None,\n prev_sample: Optional[torch.FloatTensor] = None,\n) -> Union[DDIMSchedulerOutput, Tuple]:\n assert isinstance(self, DDIMScheduler)\n if self.num_inference_steps is None:\n raise ValueError(\n \"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler\"\n )\n\n # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf\n # Ideally, read DDIM paper in-detail understanding\n\n # Notation ( -> \n # - pred_noise_t -> e_theta(x_t, t)\n # - pred_original_sample -> f_theta(x_t, t) or x_0\n # - std_dev_t -> sigma_t\n # - eta -> η\n # - pred_sample_direction -> \"direction pointing to x_t\"\n # - pred_prev_sample -> \"x_t-1\"\n\n # 1. get previous step value (=t-1)\n prev_timestep = timestep - (self.config.num_train_timesteps // self.num_inference_steps)\n # to prevent OOB on gather\n prev_timestep = torch.clamp(prev_timestep, 0, self.config.num_train_timesteps - 1)\n\n # 2. compute alphas, betas\n alpha_prod_t = self.alphas_cumprod.gather(0, timestep)\n alpha_prod_t_prev = torch.where(\n prev_timestep >= 0, self.alphas_cumprod.gather(0, prev_timestep), self.final_alpha_cumprod\n )\n alpha_prod_t = _left_broadcast(alpha_prod_t, sample.shape).to(sample.device)\n alpha_prod_t_prev = _left_broadcast(alpha_prod_t_prev, sample.shape).to(sample.device)\n\n beta_prod_t = 1 - alpha_prod_t\n\n # 3. compute predicted original sample from predicted noise also called\n # \"predicted x_0\" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf\n if self.config.prediction_type == \"epsilon\":\n pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)\n pred_epsilon = model_output\n elif self.config.prediction_type == \"sample\":\n pred_original_sample = model_output\n pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)\n elif self.config.prediction_type == \"v_prediction\":\n pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output\n pred_epsilon = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample\n else:\n raise ValueError(\n f\"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or\"\n \" `v_prediction`\"\n )\n\n # 4. Clip or threshold \"predicted x_0\"\n if self.config.thresholding:\n pred_original_sample = self._threshold_sample(pred_original_sample)\n elif self.config.clip_sample:\n pred_original_sample = pred_original_sample.clamp(\n -self.config.clip_sample_range, self.config.clip_sample_range\n )\n\n # 5. compute variance: \"sigma_t(η)\" -> see formula (16)\n # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)\n variance = _get_variance(self, timestep, prev_timestep)\n std_dev_t = eta * variance ** (0.5)\n std_dev_t = _left_broadcast(std_dev_t, sample.shape).to(sample.device)\n\n if use_clipped_model_output:\n # the pred_epsilon is always re-derived from the clipped x_0 in Glide\n pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)\n\n # 6. compute \"direction pointing to x_t\" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf\n pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * pred_epsilon\n\n # 7. compute x_t without \"random noise\" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf\n prev_sample_mean = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction\n\n if prev_sample is not None and generator is not None:\n raise ValueError(\n \"Cannot pass both generator and prev_sample. Please make sure that either `generator` or\"\n \" `prev_sample` stays `None`.\"\n )\n\n if prev_sample is None:\n variance_noise = randn_tensor(\n model_output.shape, generator=generator, device=model_output.device, dtype=model_output.dtype\n )\n prev_sample = prev_sample_mean + std_dev_t * variance_noise\n\n # log prob of prev_sample given prev_sample_mean and std_dev_t\n log_prob = (\n -((prev_sample.detach() - prev_sample_mean) ** 2) / (2 * (std_dev_t**2))\n - torch.log(std_dev_t)\n - torch.log(torch.sqrt(2 * torch.as_tensor(math.pi)))\n )\n # mean along all but batch dimension\n log_prob = log_prob.mean(dim=tuple(range(1, log_prob.ndim)))\n\n return log_prob\n" }, { "path": "spo_training_and_inference/spo/custom_diffusers/multi_sample_pipeline.py", "content": "from typing import Any, Callable, Dict, List, Optional, Union\n\nimport torch\n\nfrom diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import (\n StableDiffusionPipeline,\n rescale_noise_cfg,\n)\nfrom .ddim_seperate import ddim_step_fetch_x0, ddim_step_fetch_x_t_1\n\n\n@torch.no_grad()\ndef multi_sample_pipeline(\n self: StableDiffusionPipeline,\n prompt: Union[str, List[str]] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.FloatTensor] = None,\n prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n guidance_rescale: float = 0.0,\n \n divert_start_step=0,\n num_samples_each_step=2,\n preference_model_fn=None,\n compare_fn=None,\n extra_info=None,\n **kwargs,\n):\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds)\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n text_encoder_lora_scale = cross_attention_kwargs.get(\"scale\", None) if cross_attention_kwargs is not None else None\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n )\n log_prompt_embeds = prompt_embeds\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n # 4. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n \n all_prev_latents = None\n current_latents = None\n if divert_start_step == 0:\n current_latents = latents\n last_timestep = None\n\n denoise_idx = None\n \n valid_timesteps = []\n valid_current_latents = []\n valid_next_latents = []\n valid_prompt_embeds = []\n preference_score_logs = []\n \n with self.progress_bar(total=timesteps.shape[0]) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n if do_classifier_free_guidance and guidance_rescale > 0.0:\n # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf\n noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)\n \n if i >= divert_start_step:\n # pred_x0: (num_sample_per_step/1)*b, c, h, w \n pred_dict = ddim_step_fetch_x0(\n self.scheduler,\n noise_pred,\n t,\n latents,\n )\n if i == divert_start_step:\n # prev_latents: num_sample_per_step, b, c, h, w\n prev_latents = ddim_step_fetch_x_t_1(\n self.scheduler,\n dtype=latents.dtype,\n num_sample_per_step=num_samples_each_step,\n timestep=t,\n **extra_step_kwargs,\n **pred_dict,\n )\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([\n negative_prompt_embeds.repeat(num_samples_each_step, 1, 1),\n log_prompt_embeds.repeat(num_samples_each_step, 1, 1),\n ])\n elif i > divert_start_step:\n # num_sample_per_step*b, c, h, w\n pred_x0 = pred_dict['pred_original_sample']\n pred_x0 = self.vae.decode(\n pred_x0.to(self.vae.dtype) / self.vae.config.scaling_factor,\n return_dict=False,\n generator=generator,\n )[0]\n preference_timestep = t.repeat(pred_x0.shape[0])\n extra_info['timesteps'] = preference_timestep\n # num_sample_per_step*b\n # preference scores of i-1\n preference_scores = preference_model_fn(pred_x0, extra_info)\n preference_score_logs.append(preference_scores)\n preference_scores = preference_scores.reshape(num_samples_each_step, -1)\n # indices: 2,b\n # valid_samples: b\n indices, valid_samples = compare_fn(preference_scores)\n \n # 2,valid_num,c,h,w\n valid_next_latents.append(torch.gather(\n all_prev_latents, \n dim=0, \n index=indices.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1).expand(-1, -1, *all_prev_latents.shape[2:]),\n )[valid_samples.unsqueeze(0).unsqueeze(-1).unsqueeze(-1).unsqueeze(-1).expand(\n 2, -1, *all_prev_latents.shape[2:]\n )].reshape(2, -1, *all_prev_latents.shape[2:]))\n # valid_num,1,c,h,w\n valid_current_latents.append(current_latents[valid_samples].unsqueeze(1))\n # valid_num,1\n valid_timesteps.append(last_timestep.repeat(valid_current_latents[-1].shape[0]).unsqueeze(1))\n # valid_num,1,l,c\n valid_prompt_embeds.append(log_prompt_embeds[valid_samples].unsqueeze(1))\n \n # b->1,b,c,h,w\n denoise_idx = torch.randint(\n 0, num_samples_each_step, \n size=(all_prev_latents.shape[1],),\n device=all_prev_latents.device,\n )[None, :, None, None, None].expand(-1, -1, *all_prev_latents.shape[2:])\n \n for k, v in pred_dict.items():\n if k != 'prev_timestep':\n v = v.reshape(\n num_samples_each_step, -1, *v.shape[1:]\n )\n # b,c,h,w\n v = torch.gather(\n v,\n dim=0,\n index=denoise_idx,\n )[0]\n pred_dict[k] = v\n # b,c,h,w\n current_latents = torch.gather(\n all_prev_latents,\n dim=0,\n index=denoise_idx,\n )[0]\n\n prev_latents = ddim_step_fetch_x_t_1(\n self.scheduler,\n dtype=latents.dtype,\n num_sample_per_step=num_samples_each_step,\n timestep=t,\n **extra_step_kwargs,\n **pred_dict,\n )\n\n latents = prev_latents.flatten(0, 1)\n all_prev_latents = prev_latents\n last_timestep = t\n # x0\n if i == len(timesteps) - 1:\n # num_sample_per_step*b, c, h, w \n prev_latents = self.vae.decode(\n prev_latents.flatten(0, 1).to(self.vae.dtype) / self.vae.config.scaling_factor,\n return_dict=False,\n generator=generator,\n )[0]\n preference_timestep = torch.zeros_like(t).repeat(prev_latents.shape[0])\n extra_info['timesteps'] = preference_timestep\n # num_sample_per_step*b\n # preference scores of 0\n preference_scores = preference_model_fn(prev_latents, extra_info)\n preference_score_logs.append(preference_scores)\n preference_scores = preference_scores.reshape(num_samples_each_step, -1)\n # indices: 2,b\n # valid_samples: b\n indices, valid_samples = compare_fn(preference_scores)\n # 2,valid_num,c,h,w\n valid_next_latents.append(torch.gather(\n all_prev_latents, \n dim=0, \n index=indices.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1).expand(-1, -1, *all_prev_latents.shape[2:]),\n )[valid_samples.unsqueeze(0).unsqueeze(-1).unsqueeze(-1).unsqueeze(-1).expand(\n 2, -1, *all_prev_latents.shape[2:]\n )].reshape(2, -1, *all_prev_latents.shape[2:]))\n # valid_num,1,c,h,w\n valid_current_latents.append(current_latents[valid_samples].unsqueeze(1))\n # valid_num,1\n valid_timesteps.append(last_timestep.repeat(valid_current_latents[-1].shape[0]).unsqueeze(1))\n # valid_num,1,l,c\n valid_prompt_embeds.append(log_prompt_embeds[valid_samples].unsqueeze(1)) \n else:\n pred_dict = ddim_step_fetch_x0(\n self.scheduler,\n noise_pred, \n t, \n latents, \n )\n latents = ddim_step_fetch_x_t_1(\n self.scheduler,\n dtype=latents.dtype,\n num_sample_per_step=1,\n timestep=t,\n **extra_step_kwargs,\n **pred_dict,\n )\n if i == divert_start_step - 1:\n current_latents = latents\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n callback(i, t, latents)\n # valid_num, 1\n valid_timesteps = torch.cat(valid_timesteps, dim=0)\n # valid_num, 1, c, h, w\n valid_current_latents = torch.cat(valid_current_latents, dim=0)\n # valid_num,2,c,h,w\n valid_next_latents = torch.cat(valid_next_latents, dim=1).transpose(0, 1).contiguous()\n # valid_num,1,l,c\n valid_prompt_embeds = torch.cat(valid_prompt_embeds, dim=0)\n \n preference_score_logs = torch.cat(preference_score_logs, dim=0)\n \n return valid_timesteps, valid_current_latents, valid_next_latents, valid_prompt_embeds, preference_score_logs\n" }, { "path": "spo_training_and_inference/spo/custom_diffusers/multi_sample_pipeline_sdxl.py", "content": "from typing import Any, Callable, Dict, List, Optional, Union, Tuple\n\nimport torch\n\nfrom diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl import (\n StableDiffusionXLPipeline,\n rescale_noise_cfg,\n retrieve_timesteps,\n is_torch_xla_available,\n)\nfrom .ddim_seperate import ddim_step_fetch_x0, ddim_step_fetch_x_t_1\nif is_torch_xla_available():\n import torch_xla.core.xla_model as xm\n XLA_AVAILABLE = True\nelse:\n XLA_AVAILABLE = False\n\n\n@torch.no_grad()\ndef multi_sample_pipeline_sdxl(\n self: StableDiffusionXLPipeline,\n prompt: Union[str, List[str]] = None,\n prompt_2: Optional[Union[str, List[str]]] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n timesteps: List[int] = None,\n denoising_end: Optional[float] = None,\n guidance_scale: float = 5.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n negative_prompt_2: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.FloatTensor] = None,\n prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\n pooled_prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n guidance_rescale: float = 0.0,\n original_size: Optional[Tuple[int, int]] = None,\n crops_coords_top_left: Tuple[int, int] = (0, 0),\n target_size: Optional[Tuple[int, int]] = None,\n negative_original_size: Optional[Tuple[int, int]] = None,\n negative_crops_coords_top_left: Tuple[int, int] = (0, 0),\n negative_target_size: Optional[Tuple[int, int]] = None,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n callback=None,\n callback_steps=None,\n \n divert_start_step=0,\n num_samples_each_step=2,\n num_inner_step=0,\n preference_model_fn=None,\n compare_fn=None,\n extra_info=None,\n **kwargs,\n):\n # 0. Default height and width to unet\n height = height or self.default_sample_size * self.vae_scale_factor\n width = width or self.default_sample_size * self.vae_scale_factor\n\n original_size = original_size or (height, width)\n target_size = target_size or (height, width)\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n prompt_2,\n height,\n width,\n callback_steps,\n negative_prompt,\n negative_prompt_2,\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n callback_on_step_end_tensor_inputs,\n )\n\n self._guidance_scale = guidance_scale\n self._guidance_rescale = guidance_rescale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n self._denoising_end = denoising_end\n self._interrupt = False\n\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n # 3. Encode input prompt\n lora_scale = (\n self.cross_attention_kwargs.get(\"scale\", None) if self.cross_attention_kwargs is not None else None\n )\n\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = self.encode_prompt(\n prompt=prompt,\n prompt_2=prompt_2,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n negative_prompt_2=negative_prompt_2,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n lora_scale=lora_scale,\n clip_skip=self.clip_skip,\n )\n log_prompt_embeds = prompt_embeds\n log_add_text_embeds = pooled_prompt_embeds\n\n # 4. Prepare timesteps\n timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7. Prepare added time ids & embeddings\n add_text_embeds = pooled_prompt_embeds\n if self.text_encoder_2 is None:\n text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])\n else:\n text_encoder_projection_dim = self.text_encoder_2.config.projection_dim\n\n add_time_ids = self._get_add_time_ids(\n original_size,\n crops_coords_top_left,\n target_size,\n dtype=prompt_embeds.dtype,\n text_encoder_projection_dim=text_encoder_projection_dim,\n )\n if negative_original_size is not None and negative_target_size is not None:\n negative_add_time_ids = self._get_add_time_ids(\n negative_original_size,\n negative_crops_coords_top_left,\n negative_target_size,\n dtype=prompt_embeds.dtype,\n text_encoder_projection_dim=text_encoder_projection_dim,\n )\n else:\n negative_add_time_ids = add_time_ids\n log_add_time_ids = add_time_ids.to(device)\n\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)\n add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)\n add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)\n\n prompt_embeds = prompt_embeds.to(device)\n add_text_embeds = add_text_embeds.to(device)\n add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)\n negative_add_time_ids = negative_add_time_ids.to(device)\n\n # 8. Denoising loop\n num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)\n \n # ignored 8.1 Apply denoising_end\n \n # 9. Optionally get Guidance Scale Embedding\n timestep_cond = None\n if self.unet.config.time_cond_proj_dim is not None:\n guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)\n timestep_cond = self.get_guidance_scale_embedding(\n guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim\n ).to(device=device, dtype=latents.dtype)\n\n self._num_timesteps = len(timesteps)\n \n denoise_idx = None\n \n valid_timesteps = []\n valid_current_latents = []\n valid_next_latents = []\n valid_prompt_embeds = []\n valid_add_text_embeds = []\n preference_score_logs = []\n inner_step_left = 0\n \n timestep_cache = []\n current_latents_cache = []\n next_latents_cache = []\n \n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n if self.interrupt:\n continue\n \n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n added_cond_kwargs = {\"text_embeds\": add_text_embeds, \"time_ids\": add_time_ids}\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n timestep_cond=timestep_cond,\n cross_attention_kwargs=self.cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:\n # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf\n noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)\n \n if (i >= divert_start_step and inner_step_left == 0) or i == len(timesteps) - 1:\n inner_step_left = num_inner_step\n # pred_x0: (num_sample_per_step/1)*b, c, h, w \n pred_dict = ddim_step_fetch_x0(\n self.scheduler,\n noise_pred,\n t,\n latents,\n )\n if i == divert_start_step:\n # prev_latents: num_sample_per_step, b, c, h, w\n prev_latents = ddim_step_fetch_x_t_1(\n self.scheduler,\n dtype=latents.dtype,\n num_sample_per_step=num_samples_each_step,\n timestep=t,\n **extra_step_kwargs,\n **pred_dict,\n )\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([\n negative_prompt_embeds.repeat(num_samples_each_step, 1, 1),\n log_prompt_embeds.repeat(num_samples_each_step, 1, 1),\n ])\n add_text_embeds = torch.cat([\n negative_pooled_prompt_embeds.repeat(num_samples_each_step, 1),\n log_add_text_embeds.repeat(num_samples_each_step, 1),\n ], dim=0)\n add_time_ids = torch.cat([\n negative_add_time_ids,\n log_add_time_ids,\n ], dim=0).repeat(num_samples_each_step * batch_size * num_images_per_prompt, 1)\n \n # b,c,h,w->num_sample_per_step, b, c, h, w\n current_latents_cache.append(latents.unsqueeze(0).repeat(num_samples_each_step, 1, 1, 1, 1))\n next_latents_cache.append(prev_latents)\n timestep_cache.append(t)\n \n latents = prev_latents.flatten(0, 1)\n elif i > divert_start_step and i != len(timesteps) - 1:\n # num_sample_per_step*b, c, h, w\n pred_x0 = pred_dict['pred_original_sample']\n pred_x0 = self.vae.decode(\n pred_x0.to(self.vae.dtype) / self.vae.config.scaling_factor,\n return_dict=False,\n generator=generator,\n )[0]\n reward_timestep = t.repeat(pred_x0.shape[0])\n extra_info['timesteps'] = reward_timestep\n # num_sample_per_step*b\n # preference scores of i-1\n preference_scores = preference_model_fn(pred_x0, extra_info)\n preference_score_logs.append(preference_scores)\n preference_scores = preference_scores.reshape(num_samples_each_step, -1)\n # indices: 2,b\n # valid_samples: b\n indices, valid_samples = compare_fn(preference_scores)\n \n # num_sample,b,num_inner_step,c,h,w\n next_latents_cache = torch.stack(next_latents_cache, dim=2)\n \n indices = indices.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1).unsqueeze(-1).expand(\n -1, -1, *next_latents_cache.shape[2:]\n )\n \n # after gather: 2,b,num_inner_step,c,h,w\n # after index: 2,valid_num,num_inner_step,c,h,w\n collected_valid_next_latents = torch.gather(\n next_latents_cache, \n dim=0, \n index=indices,\n )[valid_samples.unsqueeze(0).unsqueeze(-1).unsqueeze(-1).unsqueeze(-1).unsqueeze(-1).expand(\n 2, -1, *next_latents_cache.shape[2:]\n )].reshape(2, -1, *next_latents_cache.shape[2:])\n # 2,valid_num,c,h,w num_inner_step entires\n valid_next_latents.extend(\n [\n collected_valid_next_latents[:,:,i] for i in range(collected_valid_next_latents.size(2))\n ]\n )\n \n # num_sample,b,num_inner_step,c,h,w\n current_latents_cache = torch.stack(current_latents_cache, dim=2)\n # 2,valid_num,num_inner_step,c,h,w\n collected_valid_current_latents = torch.gather(\n current_latents_cache,\n dim=0,\n index=indices,\n )[valid_samples.unsqueeze(0).unsqueeze(-1).unsqueeze(-1).unsqueeze(-1).unsqueeze(-1).expand(\n 2, -1, *current_latents_cache.shape[2:]\n )].reshape(2, -1, *current_latents_cache.shape[2:])\n # 2,valid_num,c,h,w num_inner_step entires\n valid_current_latents.extend(\n [\n collected_valid_current_latents[:,:,i] for i in range(collected_valid_current_latents.size(2))\n ]\n )\n # valid_num,1 num_inner_step entires\n valid_timesteps.extend([item.repeat(valid_current_latents[-1].shape[1]).unsqueeze(1) for item in timestep_cache])\n # valid_num,1,l,c num_inner_step entires\n valid_prompt_embeds.extend([log_prompt_embeds[valid_samples].unsqueeze(1)] * collected_valid_next_latents.size(2))\n # valid_num,1,c num_inner_step entires\n valid_add_text_embeds.extend([log_add_text_embeds[valid_samples].unsqueeze(1)] * collected_valid_next_latents.size(2))\n \n # b->1,b,c,h,w\n denoise_idx = torch.randint(\n 0, num_samples_each_step, \n size=(current_latents_cache.shape[1],),\n device=current_latents_cache.device,\n )[None, :, None, None, None].expand(-1, -1, *current_latents_cache.shape[3:])\n \n for k, v in pred_dict.items():\n if k != 'prev_timestep':\n v = v.reshape(\n num_samples_each_step, -1, *v.shape[1:]\n )\n # b,c,h,w\n v = torch.gather(\n v,\n dim=0,\n index=denoise_idx,\n )[0]\n pred_dict[k] = v\n\n timestep_cache = []\n current_latents_cache = []\n next_latents_cache = []\n\n # b,c,h,w\n current_latents = torch.gather(\n latents.reshape(num_samples_each_step, -1, *latents.shape[1:]),\n dim=0,\n index=denoise_idx,\n ).repeat(num_samples_each_step, 1, 1, 1, 1)\n current_latents_cache.append(current_latents)\n\n prev_latents = ddim_step_fetch_x_t_1(\n self.scheduler,\n dtype=latents.dtype,\n num_sample_per_step=num_samples_each_step,\n timestep=t,\n **extra_step_kwargs,\n **pred_dict,\n )\n next_latents_cache.append(prev_latents)\n timestep_cache.append(t)\n \n latents = prev_latents.flatten(0, 1)\n else:\n # handle last step\n prev_latents = ddim_step_fetch_x_t_1(\n self.scheduler,\n dtype=latents.dtype,\n num_sample_per_step=1,\n timestep=t,\n **extra_step_kwargs,\n **pred_dict,\n )\n current_latents_cache.append(latents.reshape(num_samples_each_step, -1, *latents.shape[1:]))\n next_latents_cache.append(prev_latents.reshape(num_samples_each_step, -1, *prev_latents.shape[1:]))\n timestep_cache.append(t)\n\n # num_sample_per_step*b, c, h, w \n prev_latents = self.vae.decode(\n prev_latents.to(self.vae.dtype) / self.vae.config.scaling_factor,\n return_dict=False,\n generator=generator,\n )[0]\n reward_timestep = torch.zeros_like(t).repeat(prev_latents.shape[0])\n extra_info['timesteps'] = reward_timestep\n # num_sample_per_step*b\n # preference scores of i-1\n preference_scores = preference_model_fn(prev_latents, extra_info)\n preference_score_logs.append(preference_scores)\n preference_scores = preference_scores.reshape(num_samples_each_step, -1)\n # indices: 2,b\n # valid_samples: b\n indices, valid_samples = compare_fn(preference_scores)\n \n # num_sample,b,num_inner_step,c,h,w\n next_latents_cache = torch.stack(next_latents_cache, dim=2)\n \n indices = indices.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1).unsqueeze(-1).expand(\n -1, -1, *next_latents_cache.shape[2:]\n )\n \n # after gather: 2,b,num_inner_step,c,h,w\n # after index: 2,valid_num,num_inner_step,c,h,w\n collected_valid_next_latents = torch.gather(\n next_latents_cache, \n dim=0, \n index=indices,\n )[valid_samples.unsqueeze(0).unsqueeze(-1).unsqueeze(-1).unsqueeze(-1).unsqueeze(-1).expand(\n 2, -1, *next_latents_cache.shape[2:]\n )].reshape(2, -1, *next_latents_cache.shape[2:])\n # 2,valid_num,c,h,w num_inner_step entires\n valid_next_latents.extend(\n [\n collected_valid_next_latents[:,:,i] for i in range(collected_valid_next_latents.size(2))\n ]\n )\n \n # num_sample,b,num_inner_step,c,h,w\n current_latents_cache = torch.stack(current_latents_cache, dim=2)\n # 2,valid_num,num_inner_step,c,h,w\n collected_valid_current_latents = torch.gather(\n current_latents_cache,\n dim=0,\n index=indices,\n )[valid_samples.unsqueeze(0).unsqueeze(-1).unsqueeze(-1).unsqueeze(-1).unsqueeze(-1).expand(\n 2, -1, *current_latents_cache.shape[2:]\n )].reshape(2, -1, *current_latents_cache.shape[2:])\n # 2,valid_num,c,h,w num_inner_step entires\n valid_current_latents.extend(\n [\n collected_valid_current_latents[:,:,i] for i in range(collected_valid_current_latents.size(2))\n ]\n )\n # valid_num,1 num_inner_step entires\n valid_timesteps.extend([item.repeat(valid_current_latents[-1].shape[1]).unsqueeze(1) for item in timestep_cache])\n # valid_num,1,l,c num_inner_step entires\n valid_prompt_embeds.extend([log_prompt_embeds[valid_samples].unsqueeze(1)] * collected_valid_next_latents.size(2))\n # valid_num,1,c num_inner_step entires\n valid_add_text_embeds.extend([log_add_text_embeds[valid_samples].unsqueeze(1)] * collected_valid_next_latents.size(2))\n else:\n latents_cp = latents\n pred_dict = ddim_step_fetch_x0(\n self.scheduler,\n noise_pred, \n t, \n latents, \n )\n latents = ddim_step_fetch_x_t_1(\n self.scheduler,\n dtype=latents.dtype,\n num_sample_per_step=1,\n timestep=t,\n **extra_step_kwargs,\n **pred_dict,\n )\n if i >= divert_start_step:\n inner_step_left -= 1\n current_latents_cache.append(latents_cp.reshape(num_samples_each_step, -1, *latents_cp.shape[1:]))\n next_latents_cache.append(latents.reshape(num_samples_each_step, -1, *latents.shape[1:]))\n timestep_cache.append(t)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if XLA_AVAILABLE:\n xm.mark_step()\n # valid_num, 1\n valid_timesteps = torch.cat(valid_timesteps, dim=0)\n # valid_num,2,c,h,w\n valid_current_latents = torch.cat(valid_current_latents, dim=1).transpose(0, 1).contiguous()\n # valid_num,2,c,h,w\n valid_next_latents = torch.cat(valid_next_latents, dim=1).transpose(0, 1).contiguous()\n # valid_num,1,l,c\n valid_prompt_embeds = torch.cat(valid_prompt_embeds, dim=0)\n # valid_num,1,c\n valid_add_text_embeds = torch.cat(valid_add_text_embeds, dim=0)\n preference_score_logs = torch.cat(preference_score_logs, dim=0)\n return (\n valid_timesteps, \n valid_current_latents, \n valid_next_latents, \n valid_prompt_embeds,\n valid_add_text_embeds, \n negative_add_time_ids, \n log_add_time_ids,\n preference_score_logs\n )\n" }, { "path": "spo_training_and_inference/spo/datasets/__init__.py", "content": "from .builder import build_dataset, DATASETS\nfrom .prompt_dataset import PromptDataset\n\n__all__ = [\n 'DATASETS',\n 'build_dataset',\n 'PromptDataset',\n]\n" }, { "path": "spo_training_and_inference/spo/datasets/builder.py", "content": "from mmengine import Registry, build_from_cfg\n\nDATASETS = Registry('dataset')\n\ndef build_dataset(cfg):\n return build_from_cfg(cfg, DATASETS)\n" }, { "path": "spo_training_and_inference/spo/datasets/prompt_dataset.py", "content": "import json\n\nimport torch\nfrom torch.utils.data import Dataset\n\nfrom transformers import AutoTokenizer\n\nfrom .builder import DATASETS\n\nfrom spo.utils import huggingface_cache_dir\n\n@DATASETS.register_module()\nclass PromptDataset(Dataset):\n def __init__(self, meta_json_path, pretrained_tokenzier_path, caption_key='caption'):\n with open(meta_json_path, 'r') as f:\n self.meta = json.load(f)\n self.clip_tokenizer = AutoTokenizer.from_pretrained(\n pretrained_tokenzier_path,\n cache_dir=huggingface_cache_dir,\n )\n self.caption_key = caption_key\n\n def __len__(self):\n return len(self.meta)\n \n def __getitem__(self, idx):\n info = self.meta[idx]\n prompt = info[self.caption_key]\n # input_ids\n # attention_mask\n preference_model_input_ids = self.clip_tokenizer(\n prompt,\n max_length=self.clip_tokenizer.model_max_length,\n padding=\"max_length\",\n truncation=True,\n return_tensors=\"pt\",\n ).input_ids\n sample = {\n \"prompt\": prompt,\n \"preference_model_input_ids\": preference_model_input_ids,\n }\n return sample\n \n @staticmethod\n def collate_fn(examples, tokenizer):\n prompts = [item['prompt'] for item in examples]\n preference_model_input_ids = [item['preference_model_input_ids'] for item in examples]\n preference_model_input_ids = torch.cat(preference_model_input_ids, dim=0)\n extra_info = {\n 'input_ids': preference_model_input_ids,\n }\n input_ids = tokenizer(\n prompts,\n return_tensors=\"pt\",\n padding=\"max_length\",\n truncation=True,\n max_length=tokenizer.model_max_length,\n ).input_ids\n \n return dict(\n prompts=prompts,\n input_ids=input_ids,\n extra_info=extra_info,\n )\n\n @staticmethod\n def sdxl_collate_fn(examples, tokenizer, tokenizer_2):\n prompts = [item['prompt'] for item in examples]\n preference_model_input_ids = [item['preference_model_input_ids'] for item in examples]\n preference_model_input_ids = torch.cat(preference_model_input_ids, dim=0)\n extra_info = {\n 'input_ids': preference_model_input_ids,\n }\n input_ids = tokenizer(\n prompts,\n return_tensors=\"pt\",\n padding=\"max_length\",\n truncation=True,\n max_length=tokenizer.model_max_length,\n ).input_ids\n input_ids_2 = tokenizer_2(\n prompts,\n return_tensors=\"pt\",\n padding=\"max_length\",\n truncation=True,\n max_length=tokenizer.model_max_length,\n ).input_ids\n \n return dict(\n prompts=prompts,\n input_ids=input_ids,\n input_ids_2=input_ids_2,\n extra_info=extra_info,\n )\n" }, { "path": "spo_training_and_inference/spo/preference_models/__init__.py", "content": "from .builder import (\n COMPARE_FUNCS,\n PREFERENCE_MODEL_FUNC_BUILDERS,\n get_compare_func,\n get_preference_model_func,\n)\nfrom .preference_model_fns import step_aware_preference_model_func_builder\nfrom .compare_funcs import preference_score_compare\n\n__all__ = [\n 'COMPARE_FUNCS',\n 'PREFERENCE_MODEL_FUNC_BUILDERS',\n 'get_compare_func',\n 'get_preference_model_func',\n 'step_aware_preference_model_func_builder',\n 'preference_score_compare',\n]" }, { "path": "spo_training_and_inference/spo/preference_models/builder.py", "content": "from functools import partial\nfrom mmengine import Registry\n\nCOMPARE_FUNCS = Registry('compare_funcs')\nPREFERENCE_MODEL_FUNC_BUILDERS = Registry('preference_model_func_builders')\n\ndef get_compare_func(compare_func_cfg):\n type = compare_func_cfg.pop('type')\n compare_func = COMPARE_FUNCS.get(type)\n if compare_func_cfg:\n compare_func = partial(compare_func, **compare_func_cfg)\n return compare_func\n\ndef get_preference_model_func(cfg, device):\n type = cfg.pop('type')\n cfg.device = device\n preference_model_func_builder = PREFERENCE_MODEL_FUNC_BUILDERS.get(type)\n preference_model_func = preference_model_func_builder(cfg)\n return preference_model_func\n" }, { "path": "spo_training_and_inference/spo/preference_models/compare_funcs.py", "content": "import torch\nfrom .builder import COMPARE_FUNCS\n\n@COMPARE_FUNCS.register_module()\ndef preference_score_compare(scores, threshold):\n # scores: num_sample_per_step, b\n scores, indices = torch.sort(scores, dim=0, descending=True)\n # 2, b\n indices = indices[[0, -1], :]\n scores = scores[[0, -1], :]\n scores = scores.softmax(dim=0)\n # b\n valid_samples = scores[0] - scores[1] > threshold\n return indices, valid_samples\n" }, { "path": "spo_training_and_inference/spo/preference_models/models/step_aware_preference_model.py", "content": "import torch\nfrom torch import nn\nimport torch.nn.functional as F\nfrom torchvision import transforms\nfrom transformers import AutoProcessor\n\nfrom .time_conditioned_clip import HFTimeConditionedCLIPModel\nfrom spo.utils import huggingface_cache_dir\n\nfrom accelerate.logging import get_logger\n\nlogger = get_logger(__name__)\n\nclass StepAwarePreferenceModel(nn.Module):\n def __init__(\n self, \n model_pretrained_model_name_or_path,\n processor_pretrained_model_name_or_path,\n ckpt_path=None,\n ):\n super().__init__()\n self.model = HFTimeConditionedCLIPModel.from_pretrained(\n model_pretrained_model_name_or_path,\n cache_dir=huggingface_cache_dir,\n )\n if ckpt_path is not None:\n state_dict = torch.load(ckpt_path, map_location='cpu')\n missing_keys, unexpected_keys = self.load_state_dict(torch.load(ckpt_path, map_location='cpu'), strict=False)\n if len(unexpected_keys) != 0:\n for key in unexpected_keys:\n ckpt_value = state_dict[key]\n current_value = self\n for sub_key in key.split('.'):\n current_value = getattr(current_value, sub_key)\n assert torch.all(ckpt_value == current_value), f\"unexpected key {key} have different values\"\n assert len(missing_keys) == 0, f\"missing keys: {missing_keys}\"\n try:\n logger.info(f\"Loaded step-aware preference model ckpt from {ckpt_path}\")\n except:\n print(f\"Loaded step-aware preference model ckpt from {ckpt_path}\")\n processor = AutoProcessor.from_pretrained(\n processor_pretrained_model_name_or_path,\n cache_dir=huggingface_cache_dir,\n )\n self.img_size = processor.image_processor.size['shortest_edge']\n self.center_crop = transforms.CenterCrop(size=self.img_size)\n self.normalization = transforms.Normalize(\n mean=processor.image_processor.image_mean,\n std=processor.image_processor.image_std,\n )\n\n def get_text_features(self, *args, **kwargs):\n return self.model.get_text_features(*args, **kwargs)\n\n def get_image_features(self, *args, **kwargs):\n return self.model.get_image_features(*args, **kwargs)\n\n def forward(self, text_inputs=None, image_inputs=None, time_cond=None):\n outputs = ()\n if text_inputs is not None:\n outputs += self.model.get_text_features(text_inputs),\n if image_inputs is not None:\n outputs += self.model.get_image_features(image_inputs, time_cond),\n return outputs\n \n @property\n def logit_scale(self):\n return self.model.logit_scale\n \n def get_preference_score(self, images, input_ids, timesteps):\n # image: b,c,h,w, [-1, 1]\n # prompt_ids: b, q, c\n shortest_size = min(images.size(-2), images.size(-1))\n scale = self.img_size / shortest_size\n new_size = (\n int(images.size(-2) * scale + 0.5), \n int(images.size(-1) * scale + 0.5), \n )\n images = F.interpolate(\n images, \n size=new_size,\n mode='bicubic', \n align_corners=False,\n )\n \n if new_size[0] != new_size[1]:\n images = self.center_crop(images)\n \n images = (images / 2 + 0.5).clamp(0, 1).float()\n images = self.normalization(images)\n \n images_embeds = self.model.get_image_features(\n pixel_values=images,\n time_cond=timesteps,\n )\n # b, c\n images_embeds = images_embeds / torch.norm(images_embeds, dim=-1, keepdim=True) \n \n text_embeds = self.model.get_text_features(\n input_ids=input_ids\n )\n # b, c\n text_embeds = text_embeds / torch.norm(text_embeds, dim=-1, keepdim=True)\n \n # b\n scores = self.model.logit_scale.exp() * (images_embeds * text_embeds).sum(dim=-1)\n \n return scores\n" }, { "path": "spo_training_and_inference/spo/preference_models/models/time_conditioned_clip.py", "content": "from typing import Optional, Tuple, Union\nimport math\n\nimport torch\nimport torch.nn as nn\n\nfrom transformers.models.clip.modeling_clip import (\n CLIPConfig,\n CLIPEncoderLayer,\n CLIPVisionTransformer,\n CLIPAttention,\n CLIPMLP,\n CLIPVisionConfig,\n CLIPVisionEmbeddings,\n CLIPEncoder,\n BaseModelOutput,\n BaseModelOutputWithPooling,\n CLIP_VISION_INPUTS_DOCSTRING,\n add_start_docstrings_to_model_forward,\n replace_return_docstrings,\n CLIPModel,\n add_start_docstrings,\n CLIP_START_DOCSTRING,\n CLIPTextConfig,\n CLIPTextTransformer,\n CLIPOutput,\n clip_loss,\n CLIP_INPUTS_DOCSTRING,\n)\n\ndef modulate(x, shift, scale):\n return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)\n\n\nclass TimeConditionedCLIPEncoderLayer(CLIPEncoderLayer):\n def __init__(self, config: CLIPConfig):\n super(CLIPEncoderLayer, self).__init__()\n self.embed_dim = config.hidden_size\n self.self_attn = CLIPAttention(config)\n self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)\n self.mlp = CLIPMLP(config)\n self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)\n \n self.adaLN_modulation = nn.Sequential(\n nn.SiLU(),\n nn.Linear(self.embed_dim, 6 * self.embed_dim, bias=True),\n )\n # init adaLN_modulation\n nn.init.constant_(self.adaLN_modulation[-1].weight, 0)\n nn.init.constant_(self.adaLN_modulation[-1].bias, 0)\n bias = torch.ones(6 * self.embed_dim, dtype=self.adaLN_modulation[-1].bias.dtype)\n with torch.no_grad():\n bias[2 * self.embed_dim: 3 * self.embed_dim] = 1\n bias[5 * self.embed_dim:] = 1\n assert bias.shape == self.adaLN_modulation[-1].bias.shape\n self.adaLN_modulation[-1].bias = nn.Parameter(bias)\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n attention_mask: torch.Tensor,\n causal_attention_mask: torch.Tensor,\n time_cond: torch.Tensor,\n output_attentions: Optional[bool] = False,\n ) -> Tuple[torch.FloatTensor]:\n \"\"\"\n Args:\n hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`\n attention_mask (`torch.FloatTensor`): attention mask of size\n `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.\n `(config.encoder_attention_heads,)`.\n output_attentions (`bool`, *optional*):\n Whether or not to return the attentions tensors of all attention layers. See `attentions` under\n returned tensors for more detail.\n \"\"\"\n # batch, embed_dim\n shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(time_cond).chunk(6, dim=1)\n\n residual = hidden_states\n hidden_states = self.layer_norm1(hidden_states)\n hidden_states = modulate(hidden_states, shift_msa, scale_msa)\n hidden_states, attn_weights = self.self_attn(\n hidden_states=hidden_states,\n attention_mask=attention_mask,\n causal_attention_mask=causal_attention_mask,\n output_attentions=output_attentions,\n )\n hidden_states = hidden_states * gate_msa.unsqueeze(1)\n hidden_states = residual + hidden_states\n\n residual = hidden_states\n hidden_states = self.layer_norm2(hidden_states)\n hidden_states = modulate(hidden_states, shift_mlp, scale_mlp)\n hidden_states = self.mlp(hidden_states)\n hidden_states = hidden_states * gate_mlp.unsqueeze(1)\n hidden_states = residual + hidden_states\n\n outputs = (hidden_states,)\n\n if output_attentions:\n outputs += (attn_weights,)\n\n return outputs\n\n\nclass TimeConditionedCLIPEncoder(CLIPEncoder):\n def __init__(self, config: CLIPConfig):\n super(CLIPEncoder, self).__init__()\n self.config = config\n self.layers = nn.ModuleList([TimeConditionedCLIPEncoderLayer(config) for _ in range(config.num_hidden_layers)])\n self.gradient_checkpointing = False\n\n def forward(\n self,\n inputs_embeds,\n attention_mask: Optional[torch.Tensor] = None,\n causal_attention_mask: Optional[torch.Tensor] = None,\n time_cond = None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n ) -> Union[Tuple, BaseModelOutput]:\n r\"\"\"\n Args:\n inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):\n Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.\n This is useful if you want more control over how to convert `input_ids` indices into associated vectors\n than the model's internal embedding lookup matrix.\n attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):\n Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:\n\n - 1 for tokens that are **not masked**,\n - 0 for tokens that are **masked**.\n\n [What are attention masks?](../glossary#attention-mask)\n causal_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):\n Causal mask for the text model. Mask values selected in `[0, 1]`:\n\n - 1 for tokens that are **not masked**,\n - 0 for tokens that are **masked**.\n\n [What are attention masks?](../glossary#attention-mask)\n output_attentions (`bool`, *optional*):\n Whether or not to return the attentions tensors of all attention layers. See `attentions` under\n returned tensors for more detail.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors\n for more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n \"\"\"\n output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n output_hidden_states = (\n output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states\n )\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n encoder_states = () if output_hidden_states else None\n all_attentions = () if output_attentions else None\n\n hidden_states = inputs_embeds\n for idx, encoder_layer in enumerate(self.layers):\n if output_hidden_states:\n encoder_states = encoder_states + (hidden_states,)\n if self.gradient_checkpointing and self.training:\n\n def create_custom_forward(module):\n def custom_forward(*inputs):\n return module(*inputs, output_attentions)\n\n return custom_forward\n\n layer_outputs = torch.utils.checkpoint.checkpoint(\n create_custom_forward(encoder_layer),\n hidden_states,\n attention_mask,\n causal_attention_mask,\n time_cond,\n )\n else:\n layer_outputs = encoder_layer(\n hidden_states,\n attention_mask,\n causal_attention_mask,\n time_cond,\n output_attentions=output_attentions,\n )\n\n hidden_states = layer_outputs[0]\n\n if output_attentions:\n all_attentions = all_attentions + (layer_outputs[1],)\n\n if output_hidden_states:\n encoder_states = encoder_states + (hidden_states,)\n\n if not return_dict:\n return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)\n return BaseModelOutput(\n last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions\n )\n\n\nclass TimeConditionedCLIPVisionTransformer(CLIPVisionTransformer):\n def __init__(self, config: CLIPVisionConfig):\n super(CLIPVisionTransformer, self).__init__()\n self.config = config\n embed_dim = config.hidden_size\n\n self.embeddings = CLIPVisionEmbeddings(config)\n self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)\n self.encoder = TimeConditionedCLIPEncoder(config)\n self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)\n self.adaLN_modulation = nn.Sequential(\n nn.SiLU(),\n nn.Linear(embed_dim, 2 * embed_dim, bias=True)\n )\n nn.init.constant_(self.adaLN_modulation[-1].weight, 0)\n nn.init.constant_(self.adaLN_modulation[-1].bias, 0)\n\n @add_start_docstrings_to_model_forward(CLIP_VISION_INPUTS_DOCSTRING)\n @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=CLIPVisionConfig)\n def forward(\n self,\n pixel_values: Optional[torch.FloatTensor] = None,\n time_cond = None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n ) -> Union[Tuple, BaseModelOutputWithPooling]:\n r\"\"\"\n Returns:\n\n \"\"\"\n output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n output_hidden_states = (\n output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states\n )\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n if pixel_values is None:\n raise ValueError(\"You have to specify pixel_values\")\n\n hidden_states = self.embeddings(pixel_values)\n hidden_states = self.pre_layrnorm(hidden_states)\n\n encoder_outputs = self.encoder(\n inputs_embeds=hidden_states,\n time_cond=time_cond,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n )\n\n last_hidden_state = encoder_outputs[0]\n pooled_output = last_hidden_state[:, 0, :]\n pooled_output = self.post_layernorm(pooled_output)\n \n shift, scale = self.adaLN_modulation(time_cond).chunk(2, dim=1)\n pooled_output = pooled_output * (1 + scale) + shift\n\n if not return_dict:\n return (last_hidden_state, pooled_output) + encoder_outputs[1:]\n\n return BaseModelOutputWithPooling(\n last_hidden_state=last_hidden_state,\n pooler_output=pooled_output,\n hidden_states=encoder_outputs.hidden_states,\n attentions=encoder_outputs.attentions,\n )\n\n\nclass TimestepEmbedder(nn.Module):\n \"\"\"\n Embeds scalar timesteps into vector representations.\n \"\"\"\n def __init__(self, hidden_size, frequency_embedding_size=256):\n super().__init__()\n self.mlp = nn.Sequential(\n nn.Linear(frequency_embedding_size, hidden_size, bias=True),\n nn.SiLU(),\n nn.Linear(hidden_size, hidden_size, bias=True),\n )\n self.frequency_embedding_size = frequency_embedding_size\n\n @staticmethod\n def timestep_embedding(t, dim, max_period=10000):\n \"\"\"\n Create sinusoidal timestep embeddings.\n :param t: a 1-D Tensor of N indices, one per batch element.\n These may be fractional.\n :param dim: the dimension of the output.\n :param max_period: controls the minimum frequency of the embeddings.\n :return: an (N, D) Tensor of positional embeddings.\n \"\"\"\n # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py\n half = dim // 2\n freqs = torch.exp(\n -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half\n ).to(device=t.device)\n args = t[:, None].float() * freqs[None]\n embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)\n if dim % 2:\n embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)\n return embedding\n\n def forward(self, t):\n t_freq = self.timestep_embedding(t, self.frequency_embedding_size)\n t_emb = self.mlp(t_freq)\n return t_emb\n\n\n@add_start_docstrings(CLIP_START_DOCSTRING)\nclass HFTimeConditionedCLIPModel(CLIPModel):\n def __init__(self, config: CLIPConfig):\n super(CLIPModel, self).__init__(config)\n\n if not isinstance(config.text_config, CLIPTextConfig):\n raise ValueError(\n \"config.text_config is expected to be of type CLIPTextConfig but is of type\"\n f\" {type(config.text_config)}.\"\n )\n\n if not isinstance(config.vision_config, CLIPVisionConfig):\n raise ValueError(\n \"config.vision_config is expected to be of type CLIPVisionConfig but is of type\"\n f\" {type(config.vision_config)}.\"\n )\n\n text_config = config.text_config\n vision_config = config.vision_config\n\n self.projection_dim = config.projection_dim\n self.text_embed_dim = text_config.hidden_size\n self.vision_embed_dim = vision_config.hidden_size\n\n self.text_model = CLIPTextTransformer(text_config)\n self.vision_model = TimeConditionedCLIPVisionTransformer(vision_config)\n\n self.visual_projection = nn.Linear(self.vision_embed_dim, self.projection_dim, bias=False)\n self.text_projection = nn.Linear(self.text_embed_dim, self.projection_dim, bias=False)\n self.logit_scale = nn.Parameter(torch.ones([]) * self.config.logit_scale_init_value)\n \n self.t_embedder = TimestepEmbedder(self.vision_embed_dim)\n # Initialize timestep embedding MLP:\n nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)\n nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)\n\n \n # Initialize weights and apply final processing\n self.post_init()\n \n @add_start_docstrings_to_model_forward(CLIP_VISION_INPUTS_DOCSTRING)\n def get_image_features(\n self,\n pixel_values: Optional[torch.FloatTensor] = None,\n time_cond=None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n ) -> torch.FloatTensor:\n r\"\"\"\n Returns:\n image_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by\n applying the projection layer to the pooled output of [`CLIPVisionModel`].\n\n Examples:\n\n ```python\n >>> from PIL import Image\n >>> import requests\n >>> from transformers import AutoProcessor, CLIPModel\n\n >>> model = CLIPModel.from_pretrained(\"openai/clip-vit-base-patch32\")\n >>> processor = AutoProcessor.from_pretrained(\"openai/clip-vit-base-patch32\")\n\n >>> url = \"http://images.cocodataset.org/val2017/000000039769.jpg\"\n >>> image = Image.open(requests.get(url, stream=True).raw)\n\n >>> inputs = processor(images=image, return_tensors=\"pt\")\n\n >>> image_features = model.get_image_features(**inputs)\n ```\"\"\"\n # Use CLIP model's config for some fields (if specified) instead of those of vision & text components.\n output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n output_hidden_states = (\n output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states\n )\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n # b->b,c\n time_cond = time_cond.to(pixel_values.dtype)\n time_cond = self.t_embedder(time_cond)\n\n vision_outputs = self.vision_model(\n pixel_values=pixel_values,\n time_cond=time_cond,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n )\n\n pooled_output = vision_outputs[1] # pooled_output\n image_features = self.visual_projection(pooled_output)\n\n return image_features\n\n\n @add_start_docstrings_to_model_forward(CLIP_INPUTS_DOCSTRING)\n @replace_return_docstrings(output_type=CLIPOutput, config_class=CLIPConfig)\n def forward(\n self,\n input_ids: Optional[torch.LongTensor] = None,\n pixel_values: Optional[torch.FloatTensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n position_ids: Optional[torch.LongTensor] = None,\n time: Optional[torch.LongTensor] = None,\n return_loss: Optional[bool] = None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n ) -> Union[Tuple, CLIPOutput]:\n r\"\"\"\n Returns:\n\n Examples:\n\n ```python\n >>> from PIL import Image\n >>> import requests\n >>> from transformers import AutoProcessor, CLIPModel\n\n >>> model = CLIPModel.from_pretrained(\"openai/clip-vit-base-patch32\")\n >>> processor = AutoProcessor.from_pretrained(\"openai/clip-vit-base-patch32\")\n\n >>> url = \"http://images.cocodataset.org/val2017/000000039769.jpg\"\n >>> image = Image.open(requests.get(url, stream=True).raw)\n\n >>> inputs = processor(\n ... text=[\"a photo of a cat\", \"a photo of a dog\"], images=image, return_tensors=\"pt\", padding=True\n ... )\n\n >>> outputs = model(**inputs)\n >>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score\n >>> probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities\n ```\"\"\"\n # Use CLIP model's config for some fields (if specified) instead of those of vision & text components.\n output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n output_hidden_states = (\n output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states\n )\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n \n # b->b,c\n time = time.to(pixel_values.dtype)\n time_cond = self.t_embedder(time)\n\n vision_outputs = self.vision_model(\n pixel_values=pixel_values,\n time_cond=time_cond,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n )\n\n text_outputs = self.text_model(\n input_ids=input_ids,\n attention_mask=attention_mask,\n position_ids=position_ids,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n )\n\n image_embeds = vision_outputs[1]\n image_embeds = self.visual_projection(image_embeds)\n\n text_embeds = text_outputs[1]\n text_embeds = self.text_projection(text_embeds)\n\n # normalized features\n image_embeds = image_embeds / image_embeds.norm(p=2, dim=-1, keepdim=True)\n text_embeds = text_embeds / text_embeds.norm(p=2, dim=-1, keepdim=True)\n\n # cosine similarity as logits\n logit_scale = self.logit_scale.exp()\n logits_per_text = torch.matmul(text_embeds, image_embeds.t()) * logit_scale\n logits_per_image = logits_per_text.t()\n\n loss = None\n if return_loss:\n loss = clip_loss(logits_per_text)\n\n if not return_dict:\n output = (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs)\n return ((loss,) + output) if loss is not None else output\n\n return CLIPOutput(\n loss=loss,\n logits_per_image=logits_per_image,\n logits_per_text=logits_per_text,\n text_embeds=text_embeds,\n image_embeds=image_embeds,\n text_model_output=text_outputs,\n vision_model_output=vision_outputs,\n )\n" }, { "path": "spo_training_and_inference/spo/preference_models/preference_model_fns.py", "content": "import torch\n\nfrom .builder import PREFERENCE_MODEL_FUNC_BUILDERS\nfrom .models.step_aware_preference_model import StepAwarePreferenceModel\n\n@PREFERENCE_MODEL_FUNC_BUILDERS.register_module(name='step_aware_preference_model_func')\ndef step_aware_preference_model_func_builder(cfg):\n step_aware_preference_model = StepAwarePreferenceModel(\n model_pretrained_model_name_or_path=cfg.model_pretrained_model_name_or_path,\n processor_pretrained_model_name_or_path=cfg.processor_pretrained_model_name_or_path,\n ckpt_path=cfg.ckpt_path,\n ).eval().to(cfg.device)\n step_aware_preference_model.requires_grad_(False)\n \n @torch.no_grad()\n def preference_fn(img, extra_info):\n # b\n scores = step_aware_preference_model.get_preference_score(\n img, \n extra_info['input_ids'],\n extra_info['timesteps'],\n )\n return scores\n \n return preference_fn\n" }, { "path": "spo_training_and_inference/spo/utils/__init__.py", "content": "from .constants import huggingface_cache_dir, UNET_CKPT_NAME\nfrom .dist_utils import gather_tensor_with_diff_shape\n\n__all__ = [\n 'huggingface_cache_dir',\n 'UNET_CKPT_NAME',\n 'gather_tensor_with_diff_shape',\n]" }, { "path": "spo_training_and_inference/spo/utils/constants.py", "content": "import os\n\nhuggingface_cache_dir = os.environ.get('HUGGING_FACE_CACHE_DIR', None)\nUNET_CKPT_NAME = \"unet\"\n" }, { "path": "spo_training_and_inference/spo/utils/dist_utils.py", "content": "import torch\nimport torch.distributed as dist\n\ndef gather_tensor_with_diff_shape(input_tensor, primary_dim_size_list):\n gathered_tensor_list = [\n input_tensor.new_zeros(\n primary_dim_size, *input_tensor.shape[1:],\n )\n for primary_dim_size in primary_dim_size_list\n ]\n dist.all_gather(gathered_tensor_list, input_tensor)\n gathered_tensor = torch.cat(gathered_tensor_list, dim=0)\n return gathered_tensor\n" }, { "path": "spo_training_and_inference/train_scripts/train_spo.py", "content": "from functools import partial\nimport os\nimport os.path as osp\nimport sys\n# Add the project directory to the Python path to simplify imports without manually setting PYTHONPATH.\nsys.path.insert(\n 0, osp.abspath(\n osp.join(osp.dirname(osp.abspath(__file__)), \"..\")\n ),\n)\nimport copy\nimport contextlib\nimport math\nimport json\n\nimport tqdm\nimport torch\nimport wandb\n\nfrom absl import app, flags\nfrom ml_collections import config_flags\nfrom mmengine.config import Config\nfrom accelerate import Accelerator\nfrom accelerate.utils import set_seed, ProjectConfiguration, broadcast\nfrom accelerate.logging import get_logger\nfrom diffusers import StableDiffusionPipeline, DDIMScheduler, UNet2DConditionModel\nfrom diffusers.training_utils import cast_training_params\nfrom diffusers.utils import convert_state_dict_to_diffusers, convert_unet_state_dict_to_peft\ntqdm = partial(tqdm.tqdm, dynamic_ncols=True)\nfrom peft import LoraConfig\nfrom peft.utils import (\n get_peft_model_state_dict,\n set_peft_model_state_dict,\n)\n\nfrom spo.preference_models import get_preference_model_func, get_compare_func\nfrom spo.datasets import build_dataset\nfrom spo.utils import (\n huggingface_cache_dir, \n UNET_CKPT_NAME, \n gather_tensor_with_diff_shape,\n)\nfrom spo.custom_diffusers import (\n multi_sample_pipeline, \n ddim_step_with_logprob,\n)\n\nFLAGS = flags.FLAGS\nconfig_flags.DEFINE_config_file(\n \"config\", \n \"configs/spo_sd-v1-5_4k-prompts_num-sam-4_10ep_bs10.py\", \n \"Training configuration.\"\n)\n\nlogger = get_logger(__name__)\n\n\ndef main(_):\n config = FLAGS.config\n config = Config(config.to_dict())\n \n if config.resume_from:\n config.resume_from = os.path.normpath(os.path.expanduser(config.resume_from))\n if \"checkpoint_\" not in os.path.basename(config.resume_from):\n # get the most recent checkpoint in this directory\n checkpoints = list(filter(lambda x: \"checkpoint_\" in x, os.listdir(config.resume_from)))\n if len(checkpoints) == 0:\n raise ValueError(f\"No checkpoints found in {config.resume_from}\")\n config.resume_from = os.path.join(\n config.resume_from,\n sorted(checkpoints, key=lambda x: int(x.split(\"_\")[-1]))[-1],\n )\n\n # timesteps used for training: [divert_start_step: num_sample_timesteps]\n divert_start_step = config.train.divert_start_step\n\n accelerator_config = ProjectConfiguration(\n project_dir=os.path.join(config.logdir, config.run_name),\n automatic_checkpoint_naming=False,\n total_limit=config.num_checkpoint_limit,\n )\n\n accelerator = Accelerator(\n log_with=\"wandb\" if not getattr(config, 'debug', False) else None,\n project_config=accelerator_config,\n gradient_accumulation_steps=config.train.gradient_accumulation_steps,\n )\n if accelerator.is_main_process:\n if not getattr(config, 'debug', False):\n accelerator.init_trackers(\n project_name=config.wandb_project_name, \n config=config, \n init_kwargs={\"wandb\": {\n \"name\": config.run_name, \n \"entity\": config.wandb_entity_name\n }}\n )\n os.makedirs(os.path.join(config.logdir, config.run_name), exist_ok=True)\n with open(os.path.join(config.logdir, config.run_name, \"exp_config.py\"), \"w\") as f:\n f.write(config.pretty_text)\n logger.info(f\"\\n{config.pretty_text}\")\n\n set_seed(config.seed, device_specific=True)\n \n # For mixed precision training we cast all non-trainable weigths (vae, text_encoder and non-lora unet) to half-precision\n # as these weights are only used for inference, keeping weights in full precision is not required.\n inference_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n inference_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n inference_dtype = torch.bfloat16\n\n # load models.\n pipeline = StableDiffusionPipeline.from_pretrained(\n config.pretrained.model, \n torch_dtype=inference_dtype,\n cache_dir=huggingface_cache_dir,\n )\n unet = UNet2DConditionModel.from_pretrained(\n config.pretrained.model,\n subfolder=\"unet\",\n cache_dir=huggingface_cache_dir,\n )\n pipeline.unet = unet\n if config.use_xformers:\n pipeline.enable_xformers_memory_efficient_attention()\n # freeze parameters of models to save more memory\n pipeline.vae.requires_grad_(False)\n pipeline.text_encoder.requires_grad_(False)\n if config.use_checkpointing:\n unet.enable_gradient_checkpointing()\n # disable safety checker\n pipeline.safety_checker = None\n # make the progress bar nicer\n pipeline.set_progress_bar_config(\n position=2,\n disable=not accelerator.is_local_main_process,\n leave=False,\n desc=\"Sampling Timestep\",\n dynamic_ncols=True,\n )\n # switch to DDIM scheduler\n pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)\n pipeline.scheduler.alphas_cumprod = pipeline.scheduler.alphas_cumprod.to(accelerator.device)\n \n preference_model_fn = get_preference_model_func(config.preference_model_func_cfg, accelerator.device)\n compare_func = get_compare_func(config.compare_func_cfg)\n\n # Move unet, vae and text_encoder to device and cast to inference_dtype\n pipeline.vae.to(accelerator.device, dtype=inference_dtype)\n pipeline.text_encoder.to(accelerator.device, dtype=inference_dtype)\n if config.use_lora:\n unet.to(accelerator.device, dtype=inference_dtype)\n unet.requires_grad_(False)\n else:\n unet.requires_grad_(True)\n #### Prepare reference model\n ref = copy.deepcopy(unet)\n ref.to(accelerator.device)\n ref.requires_grad_(False)\n \n if config.use_lora:\n unet_lora_config = LoraConfig(\n r=config.lora_rank,\n lora_alpha=config.lora_rank,\n init_lora_weights=\"gaussian\",\n target_modules=[\"to_k\", \"to_q\", \"to_v\", \"to_out.0\"],\n )\n unet.add_adapter(unet_lora_config)\n if accelerator.mixed_precision == \"fp16\":\n # only upcast trainable parameters (LoRA) into fp32\n cast_training_params(unet, dtype=torch.float32)\n\n # set up diffusers-friendly checkpoint saving with Accelerate\n\n def save_model_hook(models, weights, output_dir):\n assert len(models) == 1\n if isinstance(models[0], type(accelerator.unwrap_model(unet))):\n if config.use_lora:\n unet_lora_layers_to_save = convert_state_dict_to_diffusers(\n get_peft_model_state_dict(models[0])\n )\n StableDiffusionPipeline.save_lora_weights(\n output_dir,\n unet_lora_layers=unet_lora_layers_to_save,\n )\n logger.info(f\"saved lora weights to {output_dir}\")\n else:\n models[0].save_pretrained(os.path.join(output_dir, UNET_CKPT_NAME))\n logger.info(f\"saved weights to {os.path.join(output_dir, UNET_CKPT_NAME)}\")\n else:\n raise ValueError(f\"Unknown model type {type(models[0])}\")\n weights.pop() # ensures that accelerate doesn't try to handle saving of the model\n\n def load_model_hook(models, input_dir):\n assert len(models) == 1\n if isinstance(models[0], type(accelerator.unwrap_model(unet))):\n if config.use_lora:\n lora_state_dict, network_alphas = StableDiffusionPipeline.lora_state_dict(input_dir)\n unet_state_dict = {f'{k.replace(\"unet.\", \"\")}': v for k, v in lora_state_dict.items() if k.startswith(\"unet.\")}\n unet_state_dict = convert_unet_state_dict_to_peft(unet_state_dict)\n incompatible_keys = set_peft_model_state_dict(models[0], unet_state_dict, adapter_name=\"default\")\n if incompatible_keys is not None:\n # check only for unexpected keys\n unexpected_keys = getattr(incompatible_keys, \"unexpected_keys\", None)\n if unexpected_keys:\n logger.warning(\n f\"Loading adapter weights from state_dict led to unexpected keys not found in the model: \"\n f\" {unexpected_keys}. \"\n )\n if accelerator.mixed_precision == \"fp16\":\n # only upcast trainable parameters (LoRA) into fp32\n cast_training_params([models[0]], dtype=torch.float32)\n logger.info(f\"loaded lora weights from {input_dir}\") \n else:\n load_model = UNet2DConditionModel.from_pretrained(input_dir, subfolder=UNET_CKPT_NAME)\n models[0].register_to_config(**load_model.config)\n models[0].load_state_dict(load_model.state_dict())\n logger.info(f\"loaded weights from {input_dir}\") \n del load_model\n else:\n raise ValueError(f\"Unknown model type {type(models[0])}\")\n models.pop() # ensures that accelerate doesn't try to handle loading of the model\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if config.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n # Initialize the optimizer\n if config.train.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`\"\n )\n optimizer_cls = bnb.optim.AdamW8bit\n else:\n optimizer_cls = torch.optim.AdamW\n \n trainable_para = filter(lambda p: p.requires_grad, unet.parameters())\n optimizer = optimizer_cls(\n trainable_para,\n lr=config.train.learning_rate,\n betas=(config.train.adam_beta1, config.train.adam_beta2),\n weight_decay=config.train.adam_weight_decay,\n eps=config.train.adam_epsilon,\n )\n\n prompt_dataset = build_dataset(config.dataset_cfg)\n collate_fn = partial(\n prompt_dataset.collate_fn,\n tokenizer=pipeline.tokenizer,\n )\n\n data_loader = torch.utils.data.DataLoader(\n prompt_dataset,\n collate_fn=collate_fn,\n batch_size=config.sample.sample_batch_size,\n num_workers=config.dataloader_num_workers,\n shuffle=config.dataloader_shuffle,\n pin_memory=config.dataloader_pin_memory,\n drop_last=config.dataloader_drop_last,\n )\n \n # generate negative prompt embeddings\n neg_prompt_embed = pipeline.text_encoder(\n pipeline.tokenizer(\n [\"\"],\n return_tensors=\"pt\",\n padding=\"max_length\",\n truncation=True,\n max_length=pipeline.tokenizer.model_max_length,\n ).input_ids.to(accelerator.device)\n )[0]\n # for some reason, autocast is necessary for non-lora training but not for lora training, and it uses\n # more memory\n autocast = contextlib.nullcontext if config.use_lora else accelerator.autocast\n \n # Prepare everything with `accelerator`.\n unet, optimizer, data_loader = accelerator.prepare(unet, optimizer, data_loader)\n \n # Train!\n total_train_batch_size = (\n config.train.train_batch_size * accelerator.num_processes * config.train.gradient_accumulation_steps\n )\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num Epochs = {config.num_epochs}\")\n logger.info(f\" Sampling batch size per device = {config.sample.sample_batch_size}\")\n logger.info(f\" Training batch size per device = {config.train.train_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {config.train.gradient_accumulation_steps}\")\n logger.info(\"\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_train_batch_size}\")\n\n if config.resume_from:\n logger.info(f\"Resuming from {config.resume_from}\")\n accelerator.load_state(config.resume_from)\n first_epoch = int(config.resume_from.split(\"_\")[-1]) + 1\n with open(os.path.join(config.resume_from, \"global_step.json\"), \"r\") as f:\n global_step = json.load(f)[\"global_step\"]\n else:\n first_epoch = 0\n global_step = 0\n \n for epoch in tqdm(\n range(first_epoch, config.num_epochs),\n total=config.num_epochs,\n initial=first_epoch,\n disable=not accelerator.is_local_main_process,\n desc=\"Epoch\",\n position=0,\n ):\n train_loss = 0.0\n train_ratio_win = 0.0\n train_ratio_lose = 0.0\n for dataset_batch_idx, batch in tqdm(\n enumerate(data_loader),\n total=len(data_loader),\n disable=not accelerator.is_local_main_process,\n desc=\"Batch\",\n position=1,\n ):\n if (\n dataset_batch_idx == len(data_loader) - 1 and \n accelerator.gradient_state.in_dataloader\n ):\n # After sampling, we need to iterate through training batches.\n # If 'end_of_dataloader' is True, accelerator.accumulate will skip gradient accumulation.\n # Hence, we set it to False to ensure proper gradient accumulation.\n accelerator.gradient_state.active_dataloader.end_of_dataloader = False\n\n #################### SAMPLING ####################\n unet.eval()\n pipeline.unet.eval()\n batch_size = batch['input_ids'].shape[0]\n prompt_ids = batch['input_ids']\n # encode prompts\n prompt_embeds = pipeline.text_encoder(prompt_ids)[0]\n sample_neg_prompt_embeds = neg_prompt_embed.repeat(batch_size, 1, 1)\n \n # prepare extra_info for the preference model\n extra_info = batch['extra_info']\n for k, v in extra_info.items():\n if isinstance(v, torch.Tensor):\n other_dim = [1 for _ in range(v.dim() - 1)]\n extra_info[k] = v.repeat(config.sample.num_sample_each_step, *other_dim)\n elif isinstance(v, list):\n extra_info[k] = v * config.sample.num_sample_each_step\n else:\n raise ValueError(f\"Unknown type {type(v)} for extra_info[{k}]\")\n with autocast():\n (\n timesteps, \n current_latents, # x_t\n next_latents, # x_{t-1}\n prompt_embeds,\n preference_score_logs,\n ) = multi_sample_pipeline(\n pipeline,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=sample_neg_prompt_embeds,\n num_inference_steps=config.sample.num_steps,\n guidance_scale=config.sample.guidance_scale,\n eta=config.sample.eta,\n \n divert_start_step=divert_start_step,\n num_samples_each_step=config.sample.num_sample_each_step,\n preference_model_fn=preference_model_fn,\n compare_fn=compare_func,\n extra_info=extra_info,\n )\n \n preference_score_logs = accelerator.gather(preference_score_logs).detach()\n accelerator.log(\n {\n \"preference_scores_mean\": preference_score_logs.mean().item(), \n \"preference_scores_std\": preference_score_logs.std().item(),\n },\n step=global_step,\n )\n del preference_score_logs\n \n if accelerator.num_processes > 1:\n accelerator.wait_for_everyone()\n local_valid_samples_num_list = [\n torch.tensor([next_latents.shape[0]], dtype=torch.long, device=accelerator.device) \n for _ in range(accelerator.num_processes)\n ]\n for process_idx in range(accelerator.num_processes):\n broadcast(local_valid_samples_num_list[process_idx], from_process=process_idx)\n \n local_valid_samples_num_list = [sample_num.item() for sample_num in local_valid_samples_num_list]\n\n # total_valid_samples_num, 1\n timesteps = gather_tensor_with_diff_shape(timesteps, local_valid_samples_num_list)\n # total_valid_samples_num, 1, c, h, w\n current_latents = gather_tensor_with_diff_shape(current_latents, local_valid_samples_num_list)\n # total_valid_samples_num, 2, c, h, w\n next_latents = gather_tensor_with_diff_shape(next_latents, local_valid_samples_num_list)\n # total_valid_samples_num,1,l,c\n prompt_embeds = gather_tensor_with_diff_shape(prompt_embeds, local_valid_samples_num_list)\n \n total_valid_samples_num = timesteps.shape[0]\n \n if total_valid_samples_num < accelerator.num_processes:\n continue\n \n sample = {\n \"prompt_embeds\": prompt_embeds,\n \"timesteps\": timesteps,\n \"latents\": current_latents, # x_t\n \"next_latents\": next_latents, # x_{t-1}\n }\n \n if accelerator.is_main_process:\n valid_perm = torch.randperm(total_valid_samples_num, device=accelerator.device)\n accelerator.wait_for_everyone()\n broadcast(valid_perm, from_process=0)\n accelerator.wait_for_everyone()\n else:\n valid_perm = torch.ones(\n total_valid_samples_num,\n dtype=torch.long,\n device=accelerator.device,\n ) * -1\n accelerator.wait_for_everyone()\n broadcast(valid_perm, from_process=0)\n accelerator.wait_for_everyone()\n assert not torch.any(valid_perm == -1)\n \n num_items_per_gpu = total_valid_samples_num // accelerator.num_processes\n valid_start_index = accelerator.process_index * num_items_per_gpu\n valid_end_index = valid_start_index + num_items_per_gpu\n for key, value in sample.items():\n sample[key] = value[valid_perm]\n sample[key] = sample[key][valid_start_index: valid_end_index]\n del prompt_embeds\n del timesteps\n del current_latents\n del next_latents\n \n sample_0 = {}\n sample_1 = {}\n for key, value in sample.items():\n if value.shape[1] == 1:\n sample_0[key] = value[:, 0]\n sample_1[key] = value[:, 0]\n else:\n sample_0[key] = value[:, 0]\n sample_1[key] = value[:, 1]\n del sample\n \n torch.cuda.empty_cache()\n \n num_train_batches = math.ceil(sample_0['latents'].shape[0] / config.train.train_batch_size)\n \n ############ Training ############\n unet.train()\n pipeline.unet.train()\n for train_batch_idx in tqdm(\n range(num_train_batches),\n desc=\"Training Small Batches\",\n position=2,\n leave=False,\n disable=not accelerator.is_local_main_process,\n ):\n train_b_start = config.train.train_batch_size * train_batch_idx\n train_b_end = config.train.train_batch_size * (train_batch_idx + 1)\n if config.train.cfg:\n train_neg_prompt_embeds = neg_prompt_embed.repeat(\n sample_0[\"prompt_embeds\"][train_b_start: train_b_end].shape[0], \n 1, 1,\n )\n # concat negative prompts to sample prompts to avoid two forward passes\n embeds_0 = torch.cat([train_neg_prompt_embeds, sample_0[\"prompt_embeds\"][train_b_start: train_b_end]])\n embeds_1 = torch.cat([train_neg_prompt_embeds, sample_1[\"prompt_embeds\"][train_b_start: train_b_end]])\n else:\n embeds_0 = sample_0[\"prompt_embeds\"][train_b_start: train_b_end]\n embeds_1 = sample_1[\"prompt_embeds\"][train_b_start: train_b_end]\n \n with accelerator.accumulate(unet):\n with autocast():\n if config.train.cfg:\n noise_pred_0 = unet(\n torch.cat([sample_0[\"latents\"][train_b_start: train_b_end]] * 2),\n torch.cat([sample_0[\"timesteps\"][train_b_start: train_b_end]] * 2),\n embeds_0,\n ).sample\n noise_pred_uncond_0, noise_pred_text_0 = noise_pred_0.chunk(2)\n noise_pred_0 = noise_pred_uncond_0 + config.sample.guidance_scale * (\n noise_pred_text_0 - noise_pred_uncond_0\n )\n noise_ref_pred_0 = ref(\n torch.cat([sample_0[\"latents\"][train_b_start: train_b_end]] * 2),\n torch.cat([sample_0[\"timesteps\"][train_b_start: train_b_end]] * 2),\n embeds_0,\n ).sample\n noise_ref_pred_uncond_0, noise_ref_pred_text_0 = noise_ref_pred_0.chunk(2)\n noise_ref_pred_0 = noise_ref_pred_uncond_0 + config.sample.guidance_scale * (\n noise_ref_pred_text_0 - noise_ref_pred_uncond_0\n )\n \n noise_pred_1 = unet(\n torch.cat([sample_1[\"latents\"][train_b_start: train_b_end]] * 2),\n torch.cat([sample_1[\"timesteps\"][train_b_start: train_b_end]] * 2),\n embeds_1,\n ).sample\n noise_pred_uncond_1, noise_pred_text_1 = noise_pred_1.chunk(2)\n noise_pred_1 = noise_pred_uncond_1 + config.sample.guidance_scale * (\n noise_pred_text_1 - noise_pred_uncond_1\n )\n \n noise_ref_pred_1 = ref(\n torch.cat([sample_1[\"latents\"][train_b_start: train_b_end]] * 2),\n torch.cat([sample_1[\"timesteps\"][train_b_start: train_b_end]] * 2),\n embeds_1,\n ).sample\n noise_ref_pred_uncond_1, noise_ref_pred_text_1 = noise_ref_pred_1.chunk(2)\n noise_ref_pred_1 = noise_ref_pred_uncond_1 + config.sample.guidance_scale * (\n noise_ref_pred_text_1 - noise_ref_pred_uncond_1\n )\n else:\n noise_pred_0 = unet(\n sample_0[\"latents\"][train_b_start: train_b_end], \n sample_0[\"timesteps\"][train_b_start: train_b_end], \n embeds_0,\n ).sample\n noise_ref_pred_0 = ref(\n sample_0[\"latents\"][train_b_start: train_b_end], \n sample_0[\"timesteps\"][train_b_start: train_b_end], \n embeds_0,\n ).sample\n \n noise_pred_1 = unet(\n sample_1[\"latents\"][train_b_start: train_b_end], \n sample_1[\"timesteps\"][train_b_start: train_b_end], \n embeds_1,\n ).sample\n noise_ref_pred_1 = ref(\n sample_1[\"latents\"][train_b_start: train_b_end], \n sample_1[\"timesteps\"][train_b_start: train_b_end], \n embeds_1,\n ).sample\n \n # compute the log prob of next_latents given latents under the current model\n total_prob_0 = ddim_step_with_logprob(\n pipeline.scheduler,\n noise_pred_0,\n sample_0[\"timesteps\"][train_b_start: train_b_end],\n sample_0[\"latents\"][train_b_start: train_b_end],\n eta=config.sample.eta,\n prev_sample=sample_0[\"next_latents\"][train_b_start: train_b_end],\n )\n total_ref_prob_0 = ddim_step_with_logprob(\n pipeline.scheduler,\n noise_ref_pred_0,\n sample_0[\"timesteps\"][train_b_start: train_b_end],\n sample_0[\"latents\"][train_b_start: train_b_end],\n eta=config.sample.eta,\n prev_sample=sample_0[\"next_latents\"][train_b_start: train_b_end],\n )\n total_prob_1 = ddim_step_with_logprob(\n pipeline.scheduler,\n noise_pred_1,\n sample_1[\"timesteps\"][train_b_start: train_b_end],\n sample_1[\"latents\"][train_b_start: train_b_end],\n eta=config.sample.eta,\n prev_sample=sample_1[\"next_latents\"][train_b_start: train_b_end],\n )\n total_ref_prob_1 = ddim_step_with_logprob(\n pipeline.scheduler,\n noise_ref_pred_1,\n sample_1[\"timesteps\"][train_b_start: train_b_end],\n sample_1[\"latents\"][train_b_start: train_b_end],\n eta=config.sample.eta,\n prev_sample=sample_1[\"next_latents\"][train_b_start: train_b_end],\n )\n # clip the Q value\n ratio_0 = torch.clamp(torch.exp(total_prob_0-total_ref_prob_0),1 - config.train.eps, 1 + config.train.eps)\n ratio_1 = torch.clamp(torch.exp(total_prob_1-total_ref_prob_1),1 - config.train.eps, 1 + config.train.eps)\n loss = -torch.log(torch.sigmoid(config.train.beta*(torch.log(ratio_0)) - config.train.beta*(torch.log(ratio_1)))).mean()\n \n avg_loss = accelerator.reduce(loss.detach(), reduction='mean')\n train_loss += avg_loss.item() / accelerator.gradient_accumulation_steps\n \n # batch size \n win_ratio_sum = accelerator.reduce(ratio_0.detach(), reduction='sum') \n lose_ratio_sum = accelerator.reduce(ratio_1.detach(), reduction='sum') \n \n avg_win_ratio = (win_ratio_sum.sum() / (win_ratio_sum.shape[0] * accelerator.num_processes)).item()\n avg_lose_ratio = (lose_ratio_sum.sum() / (lose_ratio_sum.shape[0] * accelerator.num_processes)).item()\n\n train_ratio_win += avg_win_ratio / accelerator.gradient_accumulation_steps\n train_ratio_lose += avg_lose_ratio / accelerator.gradient_accumulation_steps\n\n # backward pass\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n accelerator.clip_grad_norm_(trainable_para, config.train.max_grad_norm)\n optimizer.step()\n optimizer.zero_grad()\n \n if accelerator.sync_gradients:\n # log training-related stuff\n info = {\n \"epoch\": epoch, \n \"global_step\": global_step, \n \"train_loss\": train_loss,\n \"train_ratio_win\": train_ratio_win,\n \"train_ratio_lose\": train_ratio_lose,\n \"lr\": optimizer.param_groups[0]['lr'],\n }\n accelerator.log(info, step=global_step)\n global_step += 1\n train_loss = 0.0\n train_ratio_win = 0.0\n train_ratio_lose = 0.0\n\n if (\n dataset_batch_idx == len(data_loader) - 1 and \n accelerator.gradient_state.in_dataloader\n ):\n accelerator.gradient_state.active_dataloader.end_of_dataloader = True\n\n ########## save ckpt and evaluation ##########\n if accelerator.is_main_process:\n if (epoch + 1) % config.save_interval == 0:\n accelerator.save_state(os.path.join(config.logdir, config.run_name, f'checkpoint_{epoch}'))\n with open(os.path.join(config.logdir, config.run_name, f'checkpoint_{epoch}', 'global_step.json'), 'w') as f:\n json.dump({'global_step': global_step}, f)\n if (epoch + 1) % config.eval_interval == 0 and config.validation_prompts is not None:\n prompt_info = f\"Running validation... \\n Generating {config.num_validation_images} images with prompt:\\n\"\n for prompt in config.validation_prompts:\n prompt_info = prompt_info + prompt + '\\n'\n\n logger.info(prompt_info)\n # create pipeline\n unet.eval()\n pipeline.unet.eval()\n # run inference\n generator = torch.Generator(device=accelerator.device).manual_seed(config.seed) if config.seed else None\n\n image_logs = []\n for idx, validation_prompt in enumerate(config.validation_prompts):\n with torch.cuda.amp.autocast():\n images = [\n pipeline(\n prompt=validation_prompt,\n num_inference_steps=config.sample.num_steps,\n generator=generator,\n guidance_scale=config.sample.guidance_scale,\n ).images[0]\n for _ in range(config.num_validation_images)\n ]\n image_logs.append(\n {\n \"images\": images, \n \"prompts\": validation_prompt,\n }\n )\n\n for tracker in accelerator.trackers:\n if tracker.name == \"wandb\":\n formatted_images = []\n for log in image_logs:\n images = log[\"images\"]\n validation_prompt = log[\"prompts\"]\n for idx, image in enumerate(images):\n image = wandb.Image(image, caption=validation_prompt)\n formatted_images.append(image)\n tracker.log({\"validation\": formatted_images})\n unet.train()\n pipeline.unet.train()\n torch.cuda.empty_cache()\n \n # Save the lora layers\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n unet = accelerator.unwrap_model(unet)\n unet_lora_state_dict = convert_state_dict_to_diffusers(get_peft_model_state_dict(unet))\n StableDiffusionPipeline.save_lora_weights(\n save_directory=os.path.join(config.logdir, config.run_name),\n unet_lora_layers=unet_lora_state_dict,\n )\n \n accelerator.end_training()\n\nif __name__ == \"__main__\":\n app.run(main)\n" }, { "path": "spo_training_and_inference/train_scripts/train_spo_sdxl.py", "content": "from functools import partial\nimport os\nimport os.path as osp\nimport sys\n# Add the project directory to the Python path to simplify imports without manually setting PYTHONPATH.\nsys.path.insert(\n 0, osp.abspath(\n osp.join(osp.dirname(osp.abspath(__file__)), \"..\")\n ),\n)\nimport copy\nimport contextlib\nimport math\nimport json\n\nimport tqdm\nimport torch\nimport wandb\n\nfrom absl import app, flags\nfrom ml_collections import config_flags\nfrom mmengine.config import Config\nfrom accelerate import Accelerator\nfrom accelerate.utils import set_seed, ProjectConfiguration, broadcast\nfrom accelerate.logging import get_logger\nfrom diffusers import StableDiffusionXLPipeline, DDIMScheduler, UNet2DConditionModel, AutoencoderKL\nfrom diffusers.training_utils import cast_training_params\nfrom diffusers.utils import convert_state_dict_to_diffusers, convert_unet_state_dict_to_peft\ntqdm = partial(tqdm.tqdm, dynamic_ncols=True)\nfrom peft import LoraConfig\nfrom peft.utils import (\n get_peft_model_state_dict,\n set_peft_model_state_dict,\n)\n\nfrom spo.preference_models import get_preference_model_func, get_compare_func\nfrom spo.datasets import build_dataset\nfrom spo.utils import (\n huggingface_cache_dir, \n UNET_CKPT_NAME, \n gather_tensor_with_diff_shape,\n)\nfrom spo.custom_diffusers import (\n multi_sample_pipeline_sdxl,\n ddim_step_with_logprob,\n)\n\nFLAGS = flags.FLAGS\nconfig_flags.DEFINE_config_file(\n \"config\", \n \"configs/spo_sdxl_4k-prompts_num-sam-2_3-is_10ep_bs2_gradacc2.py\", \n \"Training configuration.\"\n)\n\nlogger = get_logger(__name__)\n\n\ndef main(_):\n config = FLAGS.config\n config = Config(config.to_dict())\n config.sample.num_inner_step = getattr(config.sample, 'num_inner_step', 0)\n \n if config.resume_from:\n config.resume_from = os.path.normpath(os.path.expanduser(config.resume_from))\n if \"checkpoint_\" not in os.path.basename(config.resume_from):\n # get the most recent checkpoint in this directory\n checkpoints = list(filter(lambda x: \"checkpoint_\" in x, os.listdir(config.resume_from)))\n if len(checkpoints) == 0:\n raise ValueError(f\"No checkpoints found in {config.resume_from}\")\n config.resume_from = os.path.join(\n config.resume_from,\n sorted(checkpoints, key=lambda x: int(x.split(\"_\")[-1]))[-1],\n )\n\n # timesteps used for training: [divert_start_step: num_sample_timesteps]\n divert_start_step = config.train.divert_start_step\n\n accelerator_config = ProjectConfiguration(\n project_dir=os.path.join(config.logdir, config.run_name),\n automatic_checkpoint_naming=False,\n total_limit=config.num_checkpoint_limit,\n )\n\n accelerator = Accelerator(\n log_with=\"wandb\" if not getattr(config, 'debug', False) else None,\n project_config=accelerator_config,\n gradient_accumulation_steps=config.train.gradient_accumulation_steps,\n )\n if accelerator.is_main_process:\n if not getattr(config, 'debug', False):\n accelerator.init_trackers(\n project_name=config.wandb_project_name, \n config=config, \n init_kwargs={\"wandb\": {\n \"name\": config.run_name, \n \"entity\": config.wandb_entity_name\n }}\n )\n os.makedirs(os.path.join(config.logdir, config.run_name), exist_ok=True)\n with open(os.path.join(config.logdir, config.run_name, \"exp_config.py\"), \"w\") as f:\n f.write(config.pretty_text)\n logger.info(f\"\\n{config.pretty_text}\")\n\n set_seed(config.seed, device_specific=True)\n \n # For mixed precision training we cast all non-trainable weigths (vae, text_encoder and non-lora unet) to half-precision\n # as these weights are only used for inference, keeping weights in full precision is not required.\n inference_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n inference_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n inference_dtype = torch.bfloat16\n\n # load models.\n pipeline = StableDiffusionXLPipeline.from_pretrained(\n config.pretrained.model, \n torch_dtype=inference_dtype,\n cache_dir=huggingface_cache_dir,\n )\n unet = UNet2DConditionModel.from_pretrained(\n config.pretrained.model,\n subfolder=\"unet\",\n cache_dir=huggingface_cache_dir,\n )\n vae_path = (\n config.pretrained.model\n if config.pretrained.vae_model_name_or_path is None\n else config.pretrained.vae_model_name_or_path\n )\n vae = AutoencoderKL.from_pretrained(\n vae_path,\n subfolder=\"vae\" if config.pretrained.vae_model_name_or_path is None else None,\n cache_dir=huggingface_cache_dir,\n )\n pipeline.vae = vae\n pipeline.unet = unet\n if config.use_xformers:\n pipeline.enable_xformers_memory_efficient_attention()\n # freeze parameters of models to save more memory\n pipeline.vae.requires_grad_(False)\n pipeline.text_encoder.requires_grad_(False)\n pipeline.text_encoder_2.requires_grad_(False)\n if config.use_checkpointing:\n unet.enable_gradient_checkpointing()\n # disable safety checker\n pipeline.safety_checker = None\n # make the progress bar nicer\n pipeline.set_progress_bar_config(\n position=2,\n disable=not accelerator.is_local_main_process,\n leave=False,\n desc=\"Sampling Timestep\",\n dynamic_ncols=True,\n )\n # switch to DDIM scheduler\n pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)\n pipeline.scheduler.alphas_cumprod = pipeline.scheduler.alphas_cumprod.to(accelerator.device)\n \n preference_model_fn = get_preference_model_func(config.preference_model_func_cfg, accelerator.device)\n compare_func = get_compare_func(config.compare_func_cfg)\n\n # Move unet, vae and text_encoder to device and cast to inference_dtype\n if config.pretrained.vae_model_name_or_path is None:\n pipeline.vae.to(accelerator.device, dtype=torch.float32)\n else:\n pipeline.vae.to(accelerator.device, dtype=inference_dtype)\n pipeline.text_encoder.to(accelerator.device, dtype=inference_dtype)\n pipeline.text_encoder_2.to(accelerator.device, dtype=inference_dtype)\n if config.use_lora:\n unet.to(accelerator.device, dtype=inference_dtype)\n unet.requires_grad_(False)\n else:\n unet.requires_grad_(True)\n #### Prepare reference model\n ref = copy.deepcopy(unet)\n ref.to(accelerator.device)\n ref.requires_grad_(False)\n \n if config.use_lora:\n unet_lora_config = LoraConfig(\n r=config.lora_rank,\n lora_alpha=config.lora_rank,\n init_lora_weights=\"gaussian\",\n target_modules=[\"to_k\", \"to_q\", \"to_v\", \"to_out.0\"],\n )\n unet.add_adapter(unet_lora_config)\n if accelerator.mixed_precision == \"fp16\":\n # only upcast trainable parameters (LoRA) into fp32\n cast_training_params(unet, dtype=torch.float32)\n\n # set up diffusers-friendly checkpoint saving with Accelerate\n\n def save_model_hook(models, weights, output_dir):\n assert len(models) == 1\n if isinstance(models[0], type(accelerator.unwrap_model(unet))):\n if config.use_lora:\n unet_lora_layers_to_save = convert_state_dict_to_diffusers(\n get_peft_model_state_dict(models[0])\n )\n StableDiffusionXLPipeline.save_lora_weights(\n output_dir,\n unet_lora_layers=unet_lora_layers_to_save,\n )\n logger.info(f\"saved lora weights to {output_dir}\")\n else:\n models[0].save_pretrained(os.path.join(output_dir, UNET_CKPT_NAME))\n logger.info(f\"saved weights to {os.path.join(output_dir, UNET_CKPT_NAME)}\")\n else:\n raise ValueError(f\"Unknown model type {type(models[0])}\")\n weights.pop() # ensures that accelerate doesn't try to handle saving of the model\n\n def load_model_hook(models, input_dir):\n assert len(models) == 1\n if isinstance(models[0], type(accelerator.unwrap_model(unet))):\n if config.use_lora:\n lora_state_dict, network_alphas = StableDiffusionXLPipeline.lora_state_dict(input_dir)\n unet_state_dict = {f'{k.replace(\"unet.\", \"\")}': v for k, v in lora_state_dict.items() if k.startswith(\"unet.\")}\n unet_state_dict = convert_unet_state_dict_to_peft(unet_state_dict)\n incompatible_keys = set_peft_model_state_dict(models[0], unet_state_dict, adapter_name=\"default\")\n if incompatible_keys is not None:\n # check only for unexpected keys\n unexpected_keys = getattr(incompatible_keys, \"unexpected_keys\", None)\n if unexpected_keys:\n logger.warning(\n f\"Loading adapter weights from state_dict led to unexpected keys not found in the model: \"\n f\" {unexpected_keys}. \"\n )\n if accelerator.mixed_precision == \"fp16\":\n # only upcast trainable parameters (LoRA) into fp32\n cast_training_params([models[0]], dtype=torch.float32)\n logger.info(f\"loaded lora weights from {input_dir}\") \n else:\n load_model = UNet2DConditionModel.from_pretrained(input_dir, subfolder=UNET_CKPT_NAME)\n models[0].register_to_config(**load_model.config)\n models[0].load_state_dict(load_model.state_dict())\n logger.info(f\"loaded weights from {input_dir}\") \n del load_model\n else:\n raise ValueError(f\"Unknown model type {type(models[0])}\")\n models.pop() # ensures that accelerate doesn't try to handle loading of the model\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if config.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n # Initialize the optimizer\n if config.train.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`\"\n )\n optimizer_cls = bnb.optim.AdamW8bit\n else:\n optimizer_cls = torch.optim.AdamW\n \n trainable_para = filter(lambda p: p.requires_grad, unet.parameters())\n optimizer = optimizer_cls(\n trainable_para,\n lr=config.train.learning_rate,\n betas=(config.train.adam_beta1, config.train.adam_beta2),\n weight_decay=config.train.adam_weight_decay,\n eps=config.train.adam_epsilon,\n )\n\n prompt_dataset = build_dataset(config.dataset_cfg)\n collate_fn = partial(\n prompt_dataset.sdxl_collate_fn,\n tokenizer=pipeline.tokenizer,\n tokenizer_2=pipeline.tokenizer_2,\n )\n\n data_loader = torch.utils.data.DataLoader(\n prompt_dataset,\n collate_fn=collate_fn,\n batch_size=config.sample.sample_batch_size,\n num_workers=config.dataloader_num_workers,\n shuffle=config.dataloader_shuffle,\n pin_memory=config.dataloader_pin_memory,\n drop_last=config.dataloader_drop_last,\n )\n \n # generate negative prompt embeddings\n (\n _, \n neg_prompt_embed, \n _, \n negative_pooled_prompt_embeds,\n ) = pipeline.encode_prompt(\n prompt=\"\",\n device=accelerator.device,\n num_images_per_prompt=1,\n do_classifier_free_guidance=True,\n )\n # for some reason, autocast is necessary for non-lora training but not for lora training, and it uses\n # more memory\n autocast = contextlib.nullcontext if config.use_lora else accelerator.autocast\n \n # Prepare everything with `accelerator`.\n unet, optimizer, data_loader = accelerator.prepare(unet, optimizer, data_loader)\n \n # Train!\n total_train_batch_size = (\n config.train.train_batch_size * accelerator.num_processes * config.train.gradient_accumulation_steps\n )\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num Epochs = {config.num_epochs}\")\n logger.info(f\" Sampling batch size per device = {config.sample.sample_batch_size}\")\n logger.info(f\" Training batch size per device = {config.train.train_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {config.train.gradient_accumulation_steps}\")\n logger.info(\"\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_train_batch_size}\")\n\n if config.resume_from:\n logger.info(f\"Resuming from {config.resume_from}\")\n accelerator.load_state(config.resume_from)\n first_epoch = int(config.resume_from.split(\"_\")[-1]) + 1\n with open(os.path.join(config.resume_from, \"global_step.json\"), \"r\") as f:\n global_step = json.load(f)[\"global_step\"]\n else:\n first_epoch = 0\n global_step = 0\n \n for epoch in tqdm(\n range(first_epoch, config.num_epochs),\n total=config.num_epochs,\n initial=first_epoch,\n disable=not accelerator.is_local_main_process,\n desc=\"Epoch\",\n position=0,\n ):\n train_loss = 0.0\n train_ratio_win = 0.0\n train_ratio_lose = 0.0\n for dataset_batch_idx, batch in tqdm(\n enumerate(data_loader),\n total=len(data_loader),\n disable=not accelerator.is_local_main_process,\n desc=\"Batch\",\n position=1,\n ):\n if (\n dataset_batch_idx == len(data_loader) - 1 and \n accelerator.gradient_state.in_dataloader\n ):\n # After sampling, we need to iterate through training batches.\n # If 'end_of_dataloader' is True, accelerator.accumulate will skip gradient accumulation.\n # Hence, we set it to False to ensure proper gradient accumulation.\n accelerator.gradient_state.active_dataloader.end_of_dataloader = False\n\n #################### SAMPLING ####################\n unet.eval()\n pipeline.unet.eval()\n batch_size = batch['input_ids'].shape[0]\n prompt_ids = batch['input_ids']\n prompt_ids_2 = batch['input_ids_2']\n # encode prompts\n prompt_embeds_list = []\n for i, (text_encoder, text_input_ids) in enumerate(\n zip(\n [pipeline.text_encoder, pipeline.text_encoder_2], \n [prompt_ids, prompt_ids_2],\n )\n ):\n prompt_embeds = text_encoder(\n text_input_ids, \n output_hidden_states=True, \n return_dict=False,\n )\n \n # We are only ALWAYS interested in the pooled output of the final text encoder\n pooled_prompt_embeds = prompt_embeds[0]\n prompt_embeds = prompt_embeds[-1][-2]\n bs_embed, seq_len, _ = prompt_embeds.shape\n prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1)\n prompt_embeds_list.append(prompt_embeds)\n \n prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)\n pooled_prompt_embeds = pooled_prompt_embeds.view(bs_embed, -1)\n \n sample_neg_prompt_embeds = neg_prompt_embed.repeat(batch_size, 1, 1)\n sample_negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(batch_size, 1)\n \n # prepare extra_info for the preference model\n extra_info = batch['extra_info']\n for k, v in extra_info.items():\n if isinstance(v, torch.Tensor):\n other_dim = [1 for _ in range(v.dim() - 1)]\n extra_info[k] = v.repeat(config.sample.num_sample_each_step, *other_dim)\n elif isinstance(v, list):\n extra_info[k] = v * config.sample.num_sample_each_step\n else:\n raise ValueError(f\"Unknown type {type(v)} for extra_info[{k}]\")\n with autocast():\n (\n timesteps, \n current_latents, # x_t \n next_latents, # x_{t-1} \n prompt_embeds,\n add_text_embeds,\n negative_add_time_ids, \n log_add_time_ids,\n preference_score_logs,\n ) = multi_sample_pipeline_sdxl(\n pipeline,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=sample_neg_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=sample_negative_pooled_prompt_embeds,\n num_inference_steps=config.sample.num_steps,\n guidance_scale=config.sample.guidance_scale,\n eta=config.sample.eta,\n \n divert_start_step=divert_start_step,\n num_samples_each_step=config.sample.num_sample_each_step,\n num_inner_step=config.sample.num_inner_step,\n preference_model_fn=preference_model_fn,\n compare_fn=compare_func,\n extra_info=extra_info,\n )\n \n preference_score_logs = accelerator.gather(preference_score_logs).detach()\n accelerator.log(\n {\n \"preference_scores_mean\": preference_score_logs.mean().item(), \n \"preference_scores_std\": preference_score_logs.std().item(),\n },\n step=global_step,\n )\n del preference_score_logs\n \n if accelerator.num_processes > 1:\n accelerator.wait_for_everyone()\n local_valid_samples_num_list = [\n torch.tensor([next_latents.shape[0]], dtype=torch.long, device=accelerator.device) \n for _ in range(accelerator.num_processes)\n ]\n for process_idx in range(accelerator.num_processes):\n broadcast(local_valid_samples_num_list[process_idx], from_process=process_idx)\n \n local_valid_samples_num_list = [sample_num.item() for sample_num in local_valid_samples_num_list]\n\n # total_valid_samples_num, 1\n timesteps = gather_tensor_with_diff_shape(timesteps, local_valid_samples_num_list)\n # total_valid_samples, 2, c, h, w\n current_latents = gather_tensor_with_diff_shape(current_latents, local_valid_samples_num_list)\n # total_valid_samples_num, 2, c, h, w\n next_latents = gather_tensor_with_diff_shape(next_latents, local_valid_samples_num_list)\n # total_valid_samples_num,1,l,c\n prompt_embeds = gather_tensor_with_diff_shape(prompt_embeds, local_valid_samples_num_list)\n # total_valid_samples_num,1,c\n add_text_embeds = gather_tensor_with_diff_shape(add_text_embeds, local_valid_samples_num_list)\n \n total_valid_samples_num = timesteps.shape[0]\n \n if total_valid_samples_num < accelerator.num_processes:\n continue\n \n sample = {\n \"prompt_embeds\": prompt_embeds,\n \"timesteps\": timesteps,\n \"latents\": current_latents, # x_t\n \"next_latents\": next_latents, # x_{t-1}\n \"add_text_embeds\": add_text_embeds,\n }\n \n if accelerator.is_main_process:\n valid_perm = torch.randperm(total_valid_samples_num, device=accelerator.device)\n accelerator.wait_for_everyone()\n broadcast(valid_perm, from_process=0)\n accelerator.wait_for_everyone()\n else:\n valid_perm = torch.ones(\n total_valid_samples_num,\n dtype=torch.long,\n device=accelerator.device,\n ) * -1\n accelerator.wait_for_everyone()\n broadcast(valid_perm, from_process=0)\n accelerator.wait_for_everyone()\n assert not torch.any(valid_perm == -1)\n \n num_items_per_gpu = total_valid_samples_num // accelerator.num_processes\n valid_start_index = accelerator.process_index * num_items_per_gpu\n valid_end_index = valid_start_index + num_items_per_gpu\n for key, value in sample.items():\n sample[key] = value[valid_perm]\n sample[key] = sample[key][valid_start_index: valid_end_index]\n del prompt_embeds\n del timesteps\n del current_latents\n del next_latents\n del add_text_embeds\n \n sample_0 = {}\n sample_1 = {}\n for key, value in sample.items():\n if value.shape[1] == 1:\n sample_0[key] = value[:, 0]\n sample_1[key] = value[:, 0]\n else:\n sample_0[key] = value[:, 0]\n sample_1[key] = value[:, 1]\n del sample\n \n torch.cuda.empty_cache()\n \n num_train_batches = math.ceil(sample_0['latents'].shape[0] / config.train.train_batch_size)\n \n ############ Training ############\n unet.train()\n pipeline.unet.train()\n for train_batch_idx in tqdm(\n range(num_train_batches),\n desc=\"Training Small Batches\",\n position=2,\n leave=False,\n disable=not accelerator.is_local_main_process,\n ):\n train_b_start = config.train.train_batch_size * train_batch_idx\n train_b_end = config.train.train_batch_size * (train_batch_idx + 1)\n actual_train_bs = sample_0[\"prompt_embeds\"][train_b_start: train_b_end].shape[0]\n if config.train.cfg:\n train_neg_prompt_embeds = neg_prompt_embed.repeat(\n actual_train_bs, \n 1, 1,\n )\n train_negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(\n actual_train_bs, 1\n )\n \n # concat negative prompts to sample prompts to avoid two forward passes\n embeds_0 = torch.cat([train_neg_prompt_embeds, sample_0[\"prompt_embeds\"][train_b_start: train_b_end]])\n embeds_1 = torch.cat([train_neg_prompt_embeds, sample_1[\"prompt_embeds\"][train_b_start: train_b_end]])\n add_text_embeds_0 = torch.cat([\n train_negative_pooled_prompt_embeds,\n sample_0[\"add_text_embeds\"][train_b_start: train_b_end],\n ])\n add_text_embeds_1 = torch.cat([\n train_negative_pooled_prompt_embeds,\n sample_1[\"add_text_embeds\"][train_b_start: train_b_end],\n ])\n add_time_ids = torch.cat([\n negative_add_time_ids,\n log_add_time_ids,\n ], dim=0)\n else:\n embeds_0 = sample_0[\"prompt_embeds\"][train_b_start: train_b_end]\n embeds_1 = sample_1[\"prompt_embeds\"][train_b_start: train_b_end]\n add_text_embeds_0 = sample_0[\"add_text_embeds\"][train_b_start: train_b_end]\n add_text_embeds_1 = sample_1[\"add_text_embeds\"][train_b_start: train_b_end]\n add_time_ids = log_add_time_ids\n add_time_ids = add_time_ids.repeat(actual_train_bs, 1)\n added_cond_kwargs_0 = {\"text_embeds\": add_text_embeds_0, \"time_ids\": add_time_ids}\n added_cond_kwargs_1 = {\"text_embeds\": add_text_embeds_1, \"time_ids\": add_time_ids}\n \n with accelerator.accumulate(unet):\n with autocast():\n if config.train.cfg:\n noise_pred_0 = unet(\n torch.cat([sample_0[\"latents\"][train_b_start: train_b_end]] * 2),\n torch.cat([sample_0[\"timesteps\"][train_b_start: train_b_end]] * 2),\n embeds_0,\n added_cond_kwargs=added_cond_kwargs_0,\n ).sample\n noise_pred_uncond_0, noise_pred_text_0 = noise_pred_0.chunk(2)\n noise_pred_0 = noise_pred_uncond_0 + config.sample.guidance_scale * (\n noise_pred_text_0 - noise_pred_uncond_0\n )\n noise_ref_pred_0 = ref(\n torch.cat([sample_0[\"latents\"][train_b_start: train_b_end]] * 2),\n torch.cat([sample_0[\"timesteps\"][train_b_start: train_b_end]] * 2),\n embeds_0,\n added_cond_kwargs=added_cond_kwargs_0,\n ).sample\n noise_ref_pred_uncond_0, noise_ref_pred_text_0 = noise_ref_pred_0.chunk(2)\n noise_ref_pred_0 = noise_ref_pred_uncond_0 + config.sample.guidance_scale * (\n noise_ref_pred_text_0 - noise_ref_pred_uncond_0\n )\n \n noise_pred_1 = unet(\n torch.cat([sample_1[\"latents\"][train_b_start: train_b_end]] * 2),\n torch.cat([sample_1[\"timesteps\"][train_b_start: train_b_end]] * 2),\n embeds_1,\n added_cond_kwargs=added_cond_kwargs_1,\n ).sample\n noise_pred_uncond_1, noise_pred_text_1 = noise_pred_1.chunk(2)\n noise_pred_1 = noise_pred_uncond_1 + config.sample.guidance_scale * (\n noise_pred_text_1 - noise_pred_uncond_1\n )\n \n noise_ref_pred_1 = ref(\n torch.cat([sample_1[\"latents\"][train_b_start: train_b_end]] * 2),\n torch.cat([sample_1[\"timesteps\"][train_b_start: train_b_end]] * 2),\n embeds_1,\n added_cond_kwargs=added_cond_kwargs_1,\n ).sample\n noise_ref_pred_uncond_1, noise_ref_pred_text_1 = noise_ref_pred_1.chunk(2)\n noise_ref_pred_1 = noise_ref_pred_uncond_1 + config.sample.guidance_scale * (\n noise_ref_pred_text_1 - noise_ref_pred_uncond_1\n )\n else:\n noise_pred_0 = unet(\n sample_0[\"latents\"][train_b_start: train_b_end], \n sample_0[\"timesteps\"][train_b_start: train_b_end], \n embeds_0,\n added_cond_kwargs=added_cond_kwargs_0,\n ).sample\n noise_ref_pred_0 = ref(\n sample_0[\"latents\"][train_b_start: train_b_end], \n sample_0[\"timesteps\"][train_b_start: train_b_end], \n embeds_0,\n added_cond_kwargs=added_cond_kwargs_0,\n ).sample\n \n noise_pred_1 = unet(\n sample_1[\"latents\"][train_b_start: train_b_end], \n sample_1[\"timesteps\"][train_b_start: train_b_end], \n embeds_1,\n added_cond_kwargs=added_cond_kwargs_1,\n ).sample\n noise_ref_pred_1 = ref(\n sample_1[\"latents\"][train_b_start: train_b_end], \n sample_1[\"timesteps\"][train_b_start: train_b_end], \n embeds_1,\n added_cond_kwargs=added_cond_kwargs_1,\n ).sample\n \n # compute the log prob of next_latents given latents under the current model\n total_prob_0 = ddim_step_with_logprob(\n pipeline.scheduler,\n noise_pred_0,\n sample_0[\"timesteps\"][train_b_start: train_b_end],\n sample_0[\"latents\"][train_b_start: train_b_end],\n eta=config.sample.eta,\n prev_sample=sample_0[\"next_latents\"][train_b_start: train_b_end],\n )\n total_ref_prob_0 = ddim_step_with_logprob(\n pipeline.scheduler,\n noise_ref_pred_0,\n sample_0[\"timesteps\"][train_b_start: train_b_end],\n sample_0[\"latents\"][train_b_start: train_b_end],\n eta=config.sample.eta,\n prev_sample=sample_0[\"next_latents\"][train_b_start: train_b_end],\n )\n total_prob_1 = ddim_step_with_logprob(\n pipeline.scheduler,\n noise_pred_1,\n sample_1[\"timesteps\"][train_b_start: train_b_end],\n sample_1[\"latents\"][train_b_start: train_b_end],\n eta=config.sample.eta,\n prev_sample=sample_1[\"next_latents\"][train_b_start: train_b_end],\n )\n total_ref_prob_1 = ddim_step_with_logprob(\n pipeline.scheduler,\n noise_ref_pred_1,\n sample_1[\"timesteps\"][train_b_start: train_b_end],\n sample_1[\"latents\"][train_b_start: train_b_end],\n eta=config.sample.eta,\n prev_sample=sample_1[\"next_latents\"][train_b_start: train_b_end],\n )\n # clip the Q value\n ratio_0 = torch.clamp(torch.exp(total_prob_0-total_ref_prob_0),1 - config.train.eps, 1 + config.train.eps)\n ratio_1 = torch.clamp(torch.exp(total_prob_1-total_ref_prob_1),1 - config.train.eps, 1 + config.train.eps)\n loss = -torch.log(torch.sigmoid(config.train.beta*(torch.log(ratio_0)) - config.train.beta*(torch.log(ratio_1)))).mean()\n \n avg_loss = accelerator.reduce(loss.detach(), reduction='mean')\n train_loss += avg_loss.item() / accelerator.gradient_accumulation_steps\n \n # batch size \n win_ratio_sum = accelerator.reduce(ratio_0.detach(), reduction='sum') \n lose_ratio_sum = accelerator.reduce(ratio_1.detach(), reduction='sum') \n \n avg_win_ratio = (win_ratio_sum.sum() / (win_ratio_sum.shape[0] * accelerator.num_processes)).item()\n avg_lose_ratio = (lose_ratio_sum.sum() / (lose_ratio_sum.shape[0] * accelerator.num_processes)).item()\n\n train_ratio_win += avg_win_ratio / accelerator.gradient_accumulation_steps\n train_ratio_lose += avg_lose_ratio / accelerator.gradient_accumulation_steps\n\n # backward pass\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n accelerator.clip_grad_norm_(trainable_para, config.train.max_grad_norm)\n optimizer.step()\n optimizer.zero_grad()\n \n if accelerator.sync_gradients:\n # log training-related stuff\n info = {\n \"epoch\": epoch, \n \"global_step\": global_step, \n \"train_loss\": train_loss,\n \"train_ratio_win\": train_ratio_win,\n \"train_ratio_lose\": train_ratio_lose,\n \"lr\": optimizer.param_groups[0]['lr'],\n }\n accelerator.log(info, step=global_step)\n global_step += 1\n train_loss = 0.0\n train_ratio_win = 0.0\n train_ratio_lose = 0.0\n\n if (\n dataset_batch_idx == len(data_loader) - 1 and \n accelerator.gradient_state.in_dataloader\n ):\n accelerator.gradient_state.active_dataloader.end_of_dataloader = True\n\n ########## save ckpt and evaluation ##########\n if accelerator.is_main_process:\n if (epoch + 1) % config.save_interval == 0:\n accelerator.save_state(os.path.join(config.logdir, config.run_name, f'checkpoint_{epoch}'))\n with open(os.path.join(config.logdir, config.run_name, f'checkpoint_{epoch}', 'global_step.json'), 'w') as f:\n json.dump({'global_step': global_step}, f)\n if (epoch + 1) % config.eval_interval == 0 and config.validation_prompts is not None:\n prompt_info = f\"Running validation... \\n Generating {config.num_validation_images} images with prompt:\\n\"\n for prompt in config.validation_prompts:\n prompt_info = prompt_info + prompt + '\\n'\n\n logger.info(prompt_info)\n # create pipeline\n unet.eval()\n pipeline.unet.eval()\n # run inference\n generator = torch.Generator(device=accelerator.device).manual_seed(config.seed) if config.seed else None\n\n image_logs = []\n for idx, validation_prompt in enumerate(config.validation_prompts):\n with torch.cuda.amp.autocast():\n images = [\n pipeline(\n prompt=validation_prompt,\n num_inference_steps=config.sample.num_steps,\n generator=generator,\n guidance_scale=config.sample.guidance_scale,\n ).images[0]\n for _ in range(config.num_validation_images)\n ]\n image_logs.append(\n {\n \"images\": images, \n \"prompts\": validation_prompt,\n }\n )\n\n for tracker in accelerator.trackers:\n if tracker.name == \"wandb\":\n formatted_images = []\n for log in image_logs:\n images = log[\"images\"]\n validation_prompt = log[\"prompts\"]\n for idx, image in enumerate(images):\n image = wandb.Image(image, caption=validation_prompt)\n formatted_images.append(image)\n tracker.log({\"validation\": formatted_images})\n unet.train()\n pipeline.unet.train()\n torch.cuda.empty_cache()\n \n # Save the lora layers\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n unet = accelerator.unwrap_model(unet)\n unet_lora_state_dict = convert_state_dict_to_diffusers(get_peft_model_state_dict(unet))\n StableDiffusionXLPipeline.save_lora_weights(\n save_directory=os.path.join(config.logdir, config.run_name),\n unet_lora_layers=unet_lora_state_dict,\n )\n \n accelerator.end_training()\n\nif __name__ == \"__main__\":\n app.run(main)\n" }, { "path": "step_aware_preference_model/.gitignore", "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n.hypothesis/\n.pytest_cache/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/en/_build/\ndocs/zh_cn/_build/\n\n# PyBuilder\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# pyenv\n.python-version\n\n# celery beat schedule file\ncelerybeat-schedule\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n.DS_Store\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n\ndata\n.vscode\n.idea\n\n# custom\n*.pkl\n*.pkl.json\n*.log.json\nwork_dirs/\n\nwandb\nlogs\ndata\n*.egg-info\n*.html\n\ndebugs\n\ntemp\noutput\noutputs\n.hydra\ntrainer.egg-info\n*.png\n*.npy\n*.log\n" }, { "path": "step_aware_preference_model/README.md", "content": "# Step-Aware Preference Model Training Code\n\nThis folder contains the code for training the **step-aware preference model**. The codebase is based on [PickScore](https://github.com/yuvalkirstain/PickScore).\n\n## Installation\n1. Pull the Docker Image\n```bash\nsudo docker pull rockeycoss/spo:v1\n```\n2. Run the Docker Container and Enter It\n```bash\nsudo docker run --gpus all -it --ipc=host rockeycoss/spo:v1 /bin/bash\n```\n3. Clone the Repository\n```bash\ngit clone https://github.com/RockeyCoss/SPO\ncd ./SPO/step_aware_preference_model\n```\n4. Install Dependencies\n```bash\npip uninstall peft -y\npip install -r requirements.txt\n```\n5. Login to Weights & Biases (wandb)\n```bash\nwandb login {Your wandb key}\n```\n6. (Optional) To customize the location for saving models downloaded from Hugging Face, you can use the following command:\n```bash\nexport HUGGING_FACE_CACHE_DIR=/path/to/your/cache/dir\n```\n## Download the Pick-a-Pic Dataset\n```bash\nfrom datasets import load_dataset\ndataset = load_dataset(\"yuvalkirstain/pickapic_v1\", num_proc=64)\n``` \nFor more details, please visit the [PickScore Github repository](https://github.com/yuvalkirstain/PickScore?tab=readme-ov-file#download-the-pick-a-pic-dataset).\n\n## Training\nThe following scripts assume the use of **four** 80GB A100 GPUs for training, as described in the [paper](https://arxiv.org/abs/2406.04314).\n\n\nTo train the step-aware preference model for SD v1.5, please use the following command:\n```bash\nbash run_commands/train_spm_sd15.sh\n```\nTo train the step-aware preference model for SDXL, please use the following command:\n```bash\nbash run_commands/train_spm_sdxl.sh\n```\nThe final checkpoints, i.e., `work_dirs/sdv15_spm/final_ckpt.bin` and `work_dirs/sdxl_spm/final_ckpt.bin`, can be used for SPO training. Please refer to [this](https://github.com/RockeyCoss/SPO/blob/main/spo_training_and_inference/README.md#wrench-training) for more details.\n\n## Citation\nIf you find this code useful in your research, please consider citing:\n\n```\n@article{liang2024step,\n title={Aesthetic Post-Training Diffusion Models from Generic Preferences with Step-by-step Preference Optimization},\n author={Liang, Zhanhao and Yuan, Yuhui and Gu, Shuyang and Chen, Bohan and Hang, Tiankai and Cheng, Mingxi and Li, Ji and Zheng, Liang},\n journal={arXiv preprint arXiv:2406.04314},\n year={2024}\n}\n```\n" }, { "path": "step_aware_preference_model/requirements.txt", "content": "accelerate @ git+https://github.com/huggingface/accelerate.git@d1aa558119859c4b205a324afabaecabd9ef375e\ndatasets==2.10.1\ndeepspeed==0.12.3\nfire==0.4.0\nhydra-core==1.3.2\nrich==13.3.2\nsubmitit==1.4.5\ntransformers==4.27.3\nwandb==0.12.21" }, { "path": "step_aware_preference_model/run_commands/train_spm_sd15.sh", "content": "PYTHONPATH=$(pwd) accelerate launch --dynamo_backend no --gpu_ids all \\\n--num_processes 4 --num_machines 1 --use_deepspeed \\\ntrainer/scripts/train_spm.py +experiment=clip_h\n" }, { "path": "step_aware_preference_model/run_commands/train_spm_sdxl.sh", "content": "PYTHONPATH=$(pwd) accelerate launch --dynamo_backend no --gpu_ids all \\\n--num_processes 4 --num_machines 1 --use_deepspeed \\\ntrainer/scripts/train_spm.py +experiment=clip_h \\\naccelerator=sdxl_deepspeed \\\ntask=sdxl_spm \\\ndataset=pick_a_pic_spm_sdxl \\\noptimizer=sdxl_dummy \\\noutput_dir=work_dirs/sdxl_spm \n" }, { "path": "step_aware_preference_model/setup.py", "content": "from setuptools import setup, find_packages\n\nsetup(name='trainer', version='1.0', packages=find_packages())" }, { "path": "step_aware_preference_model/trainer/accelerators/__init__.py", "content": "from hydra.core.config_store import ConfigStore\n\nfrom trainer.accelerators.debug_accelerator import DebugAcceleratorConfig\nfrom trainer.accelerators.deepspeed_accelerator import (\n DeepSpeedAcceleratorConfig,\n sd15_deepspeed_accelerator_cfg,\n sdxl_deepspeed_accelerator_cfg,\n)\n\nACCELERATOR_GROUP_NAME = \"accelerator\"\n\ncs = ConfigStore.instance()\ncs.store(group=ACCELERATOR_GROUP_NAME, name=\"deepspeed\", node=DeepSpeedAcceleratorConfig)\ncs.store(group=ACCELERATOR_GROUP_NAME, name=\"sd15_deepspeed\", node=sd15_deepspeed_accelerator_cfg)\ncs.store(group=ACCELERATOR_GROUP_NAME, name=\"sdxl_deepspeed\", node=sdxl_deepspeed_accelerator_cfg)\ncs.store(group=ACCELERATOR_GROUP_NAME, name=\"debug\", node=DebugAcceleratorConfig)\n" }, { "path": "step_aware_preference_model/trainer/accelerators/base_accelerator.py", "content": "import abc\nimport hashlib\nimport json\nimport math\nimport os\nimport os.path as osp\nimport shutil\nfrom dataclasses import field, dataclass\nfrom glob import glob\nfrom typing import List, Optional\n\nimport datasets\nimport torch\nimport transformers\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import set_seed as accelerate_set_seed, PrecisionType\nfrom accelerate.utils.dataclasses import BaseEnum, LoggerType, DynamoBackend\nfrom omegaconf import DictConfig, OmegaConf, II\nfrom tqdm import tqdm\n\nfrom trainer.accelerators.utils import get_nvidia_smi_gpu_memory_stats_str, print_config, _flatten_dict\n\nlogger = get_logger(__name__)\n\nTRAINING_STAGE_PATH = \"training_stage.json\"\n\n\ndef debug(port):\n logger.info(\"Connecting to debugger...\")\n import pydevd_pycharm\n pydevd_pycharm.settrace('localhost', port=port, stdoutToServer=True, stderrToServer=True)\n\n\n@dataclass\nclass DebugConfig:\n activate: bool = False\n port: int = 5900\n\n\nclass TrainingMode(BaseEnum):\n SKIPPING = \"skipping\"\n TRAINING = \"training\"\n\n\nclass MetricMode(BaseEnum):\n MAX = \"max\"\n MIN = \"min\"\n\n\n@dataclass\nclass BaseAcceleratorConfig:\n _target_: str = \"trainer.accelerators.base_accelerator.Accelerator\"\n output_dir: str = II(\"output_dir\")\n mixed_precision: PrecisionType = PrecisionType.NO\n gradient_accumulation_steps: int = 1\n log_with: Optional[LoggerType] = LoggerType.WANDB\n debug: DebugConfig = DebugConfig()\n seed: int = 42\n resume_from_checkpoint: bool = True\n max_steps: int = 4000\n num_epochs: int = 10\n validate_steps: int = 1000\n eval_on_start: bool = True\n project_name: str = \"spm_training\"\n max_grad_norm: float = 1.0\n save_steps: int = 1000\n metric_name: str = \"accuracy\"\n metric_mode: MetricMode = MetricMode.MAX\n limit_num_checkpoints: int = -1\n save_only_if_best: bool = False\n dynamo_backend: DynamoBackend = DynamoBackend.NO\n keep_best_ckpts: bool = False\n\n\nclass BaseAccelerator(abc.ABC):\n\n def __init__(self, cfg: BaseAcceleratorConfig):\n self.cfg = cfg\n self.accelerator = None\n self.epoch = 0\n self.step = 0\n self.global_step = 0\n self.step_loss = 0.0\n self.lr = None\n self.metrics = {}\n self.progress_bar = None\n self.mode = TrainingMode.TRAINING\n self.num_update_steps_per_epoch = None\n self.num_steps_per_epoch = None\n\n def post_init(self):\n self.set_seed()\n self.debug()\n logger.info(f\"Initialized accelerator: rank={self.accelerator.process_index}\", main_process_only=False)\n self.set_logging_level()\n\n def set_logging_level(self):\n if self.accelerator.is_local_main_process:\n datasets.utils.logging.set_verbosity_warning()\n transformers.utils.logging.set_verbosity_warning()\n else:\n datasets.utils.logging.set_verbosity_error()\n transformers.utils.logging.set_verbosity_error()\n\n def debug(self):\n if self.accelerator.is_main_process and self.cfg.debug.activate:\n debug(self.cfg.debug.port)\n\n def set_seed(self):\n logger.info(f\"Setting seed {self.cfg.seed}\")\n accelerate_set_seed(self.cfg.seed, device_specific=True)\n\n def prepare(self, *args, device_placement=None):\n return self.accelerator.prepare(*args, device_placement=device_placement)\n\n def get_latest_checkpoint(self):\n all_ckpts = list(glob(os.path.join(self.cfg.output_dir, \"checkpoint-*\")))\n if len(all_ckpts) == 0:\n return\n all_ckpts.sort(key=os.path.getctime)\n if \"final\" in all_ckpts[-1]:\n all_ckpts.pop()\n return all_ckpts[-1] if len(all_ckpts) > 0 else None\n\n def load_state_if_needed(self):\n if not self.cfg.resume_from_checkpoint:\n return\n ckpt_path = self.get_latest_checkpoint()\n\n if ckpt_path is None:\n logger.info(\"No checkpoint found, training from scratch\")\n return\n\n stage = json.load(open(os.path.join(ckpt_path, TRAINING_STAGE_PATH)))\n self.epoch, self.step, self.global_step, self.metrics = stage[\"epoch\"], stage[\"step\"], stage[\"global_step\"], \\\n stage[\"metrics\"]\n logger.info(\n f\"Resuming from checkpoint: {ckpt_path} | epoch={self.epoch} step={self.step} gstep={self.global_step}\")\n self.accelerator.load_state(ckpt_path)\n logger.info(\"Checkpoint loaded\")\n\n @property\n def is_main_process(self):\n return self.accelerator.is_main_process\n\n @property\n def num_processes(self):\n return self.accelerator.num_processes\n\n def pre_training_log(self, cfg: DictConfig):\n total_batch_size = cfg.dataset.batch_size * self.num_processes * self.cfg.gradient_accumulation_steps\n logger.info(\"***** Running training *****\")\n logger.info(f\" Instantaneous batch size per device = {cfg.dataset.batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {self.cfg.gradient_accumulation_steps}\")\n logger.info(f\" Total warmup steps = {cfg.lr_scheduler.lr_warmup_steps}\")\n logger.info(f\" Total training steps = {self.cfg.max_steps * self.cfg.gradient_accumulation_steps}\")\n logger.info(f\" Total epochs = {self.cfg.num_epochs}\")\n logger.info(f\" Steps per epoch = {self.num_steps_per_epoch}\")\n logger.info(f\" Update steps per epoch = {self.num_update_steps_per_epoch}\")\n logger.info(f\" Total optimization steps = {self.cfg.max_steps}\")\n logger.info(f\" Mixed precision = {self.cfg.mixed_precision}\")\n logger.info(f\" World size = {self.accelerator.num_processes}\")\n\n def init_training(self, cfg: DictConfig):\n if self.is_main_process:\n yaml = OmegaConf.to_yaml(cfg, resolve=True, sort_keys=True)\n log_cfg = _flatten_dict(OmegaConf.create(yaml))\n logger.info(\"Initializing trackers\")\n self.accelerator.init_trackers(\n self.cfg.project_name, \n log_cfg,\n init_kwargs={\"wandb\": {\n \"name\": osp.basename(self.cfg.output_dir), \n }}\n )\n logger.info(\"Training config:\")\n print_config(cfg)\n logger.info(get_nvidia_smi_gpu_memory_stats_str())\n self.pre_training_log(cfg)\n self.progress_bar = tqdm(range(self.cfg.max_steps * self.cfg.gradient_accumulation_steps), disable=not self.accelerator.is_main_process)\n self.progress_bar.set_description(\"Steps\")\n\n def should_skip(self, epoch, step):\n should = epoch < self.epoch or (epoch == self.epoch and step < self.step)\n if should:\n self.mode = TrainingMode.SKIPPING\n self.progress_bar.set_postfix(**{\"status\": TrainingMode.SKIPPING})\n else:\n self.mode = TrainingMode.TRAINING\n return should\n\n def update_progbar_step(self):\n self.progress_bar.update(1)\n\n def log(self, data):\n if self.is_main_process:\n self.accelerator.log(data, step=self.global_step)\n\n def recalc_train_length_after_prepare(self, num_batches):\n num_update_steps_per_epoch = math.ceil(num_batches / self.cfg.gradient_accumulation_steps)\n if self.cfg.max_steps is None:\n self.cfg.max_steps = self.cfg.num_epochs * num_update_steps_per_epoch\n self.num_update_steps_per_epoch = num_update_steps_per_epoch\n self.num_steps_per_epoch = num_batches\n self.cfg.num_epochs = math.ceil(self.cfg.max_steps / num_update_steps_per_epoch)\n\n def accumulate(self, model):\n return self.accelerator.accumulate(model)\n\n def gather(self, data):\n return self.accelerator.gather(data)\n\n @property\n def sync_gradients(self):\n return self.accelerator.sync_gradients\n\n def update_step_loss(self, loss):\n self.step_loss = loss\n\n def update_global_step(self, loss):\n self.global_step += 1\n self.log({\n \"lr\": self.lr,\n \"step\": self.step,\n \"epoch\": self.epoch,\n \"global_step\": self.global_step,\n \"loss\": loss,\n })\n\n def get_allocated_cuda_memory(self):\n return round(torch.cuda.max_memory_allocated(self.accelerator.device) / 1024 / 1024 / 1024, 2)\n\n def update_step(self, loss, lr):\n self.step += 1\n self.lr = lr\n logs = {\n \"stl\": loss,\n \"gstl\": loss,\n \"mem\": self.get_allocated_cuda_memory(),\n \"st\": self.step,\n \"ep\": self.epoch,\n \"gst\": self.global_step,\n \"lr\": self.lr,\n }\n self.progress_bar.set_postfix(**logs)\n self.update_progbar_step()\n\n def wait_for_everyone(self):\n self.accelerator.wait_for_everyone()\n\n def update_epoch(self):\n if self.mode == TrainingMode.SKIPPING:\n return\n logger.info(f\"Epoch {self.epoch} finished\")\n self.epoch += 1\n self.step = 0\n\n def update_metrics(self, metrics):\n self.metrics.update(metrics)\n logger.info(f\"Metrics: {self.metrics}\")\n self.log(metrics)\n\n def end_training(self):\n self.accelerator.wait_for_everyone()\n self.accelerator.end_training()\n\n def unwrap_and_save(self, model):\n if not self.is_main_process:\n return\n model = self.accelerator.unwrap_model(model)\n save_dir = os.path.join(self.cfg.output_dir, f\"checkpoint-final\")\n logger.info(f\"Saving final checkpoint to {save_dir}\")\n model.save(save_dir)\n self.save_training_stage(save_dir)\n logger.info(f\"Saved checkpoint to {save_dir}\")\n\n def should_end(self):\n return self.global_step >= self.cfg.max_steps\n\n def backward(self, loss):\n self.accelerator.backward(loss)\n\n def clip_grad_norm_(self, params):\n self.accelerator.clip_grad_norm_(params, self.cfg.max_grad_norm)\n\n def should_eval(self):\n if not self.mode == TrainingMode.TRAINING:\n return False\n if self.step == 0 and self.global_step == 0 and self.cfg.eval_on_start:\n return True\n if self.global_step > 0 and self.sync_gradients and self.global_step % self.cfg.validate_steps == 0:\n return True\n return False\n\n def should_save(self):\n return self.sync_gradients and self.global_step > 0 and self.cfg.save_steps > 0 and self.global_step % self.cfg.save_steps == 0\n\n @property\n def training_stage(self):\n return {\n \"epoch\": self.epoch,\n \"step\": self.step,\n \"global_step\": self.global_step,\n \"step_loss\": self.step_loss,\n \"lr\": self.lr,\n \"metrics\": self.metrics,\n }\n\n def save_training_stage(self, save_dir):\n json.dump(self.training_stage, open(os.path.join(save_dir, TRAINING_STAGE_PATH), \"w\"), indent=4)\n\n def save_checkpoint(self):\n if self.cfg.save_only_if_best:\n all_ckpts = self.get_all_ckpts()\n for ckpt in all_ckpts:\n training_stage = json.load(open(os.path.join(ckpt, TRAINING_STAGE_PATH)))\n metric_val = training_stage[\"metrics\"][self.cfg.metric_name]\n cur_metric_val = self.training_stage[\"metrics\"][self.cfg.metric_name]\n if (self.cfg.metric_mode == MetricMode.MIN and metric_val < cur_metric_val) or \\\n (self.cfg.metric_mode == MetricMode.MAX and metric_val > cur_metric_val):\n logger.info(\n f\"Metric {self.cfg.metric_name}={cur_metric_val} is not better than {metric_val} of {ckpt}, skipping checkpoint\")\n return\n self.cleanup_checkpoints()\n self.accelerator.wait_for_everyone()\n save_dir = os.path.join(self.cfg.output_dir, f\"checkpoint-gstep{self.global_step}\")\n logger.info(f\"Saving checkpoint to {save_dir}\")\n self.accelerator.save_state(save_dir)\n self.save_training_stage(save_dir)\n logger.info(f\"Saved checkpoint to {save_dir}\")\n\n @property\n def gradient_state(self):\n return self.accelerator.gradient_state\n\n def get_all_ckpts(self):\n return list(glob(os.path.join(self.cfg.output_dir, f\"checkpoint-*\")))\n\n def load_best_checkpoint(self):\n all_ckpts = self.get_all_ckpts()\n if not self.cfg.keep_best_ckpts:\n all_ckpts.sort(key=os.path.getctime, reverse=True)\n logger.info(f\"Returning the most recent checkpoint: {all_ckpts[0]}\")\n return all_ckpts[0]\n logger.info(f\"Found {len(all_ckpts)} checkpoints in {self.cfg.output_dir}\")\n logger.info(all_ckpts)\n if len(all_ckpts) == 0:\n logger.info(f\"No checkpoint found in {self.cfg.output_dir} to load. Keeping current model.\")\n return\n best_ckpt, best_metric_val = None, math.inf if self.cfg.metric_mode == MetricMode.MIN else -math.inf\n for ckpt in all_ckpts:\n training_stage = json.load(open(os.path.join(ckpt, TRAINING_STAGE_PATH)))\n metric_val = training_stage[\"metrics\"][self.cfg.metric_name]\n if (self.cfg.metric_mode == MetricMode.MIN and metric_val < best_metric_val) or \\\n (self.cfg.metric_mode == MetricMode.MAX and metric_val > best_metric_val):\n best_ckpt, best_metric_val = ckpt, metric_val\n logger.info(f\"Loading best checkpoint from {best_ckpt} with metric {self.cfg.metric_name}={best_metric_val}\")\n self.accelerator.load_state(best_ckpt)\n\n @property\n def device(self):\n return self.accelerator.device\n\n def cleanup_checkpoints(self):\n if self.cfg.limit_num_checkpoints <= 0 or not self.accelerator.is_main_process:\n logger.info(f\"Not cleaning up checkpoints as limit_num_checkpoints={self.cfg.limit_num_checkpoints}\")\n return\n\n all_ckpts = self.get_all_ckpts()\n if len(all_ckpts) <= self.cfg.limit_num_checkpoints:\n logger.info(f\"Not cleaning up checkpoints as only {len(all_ckpts)} checkpoints found\")\n return\n\n logger.info(f\"Found {len(all_ckpts)} checkpoints in {self.cfg.output_dir}\")\n ckpts_to_delete = self.get_ckpts_to_delete()\n ckpts_to_delete.sort(key=os.path.getctime)\n\n ckpts_to_delete = ckpts_to_delete[:-1]\n for ckpt in ckpts_to_delete:\n logger.info(f\"Deleting checkpoint {ckpt}\")\n shutil.rmtree(ckpt)\n\n def get_ckpts_to_delete(self):\n all_ckpts = self.get_all_ckpts()\n if self.cfg.keep_best_ckpts:\n metric_vals = []\n for ckpt in all_ckpts:\n training_stage = json.load(open(os.path.join(ckpt, TRAINING_STAGE_PATH)))\n metric_val = training_stage[\"metrics\"][self.cfg.metric_name]\n metric_vals.append(metric_val)\n metric_ckpt = list(zip(metric_vals, all_ckpts))\n metric_ckpt.sort(key=lambda x: x[0], reverse=self.cfg.metric_mode == MetricMode.MAX)\n ckpts_to_delete = [ckpt for _, ckpt in metric_ckpt[self.cfg.limit_num_checkpoints:]]\n else:\n all_ckpts.sort(key=os.path.getctime, reverse=True)\n ckpts_to_delete = all_ckpts[self.cfg.limit_num_checkpoints:]\n return ckpts_to_delete" }, { "path": "step_aware_preference_model/trainer/accelerators/debug_accelerator.py", "content": "from dataclasses import dataclass\nfrom accelerate import Accelerator\nfrom trainer.accelerators.base_accelerator import BaseAcceleratorConfig, BaseAccelerator\n\n\n@dataclass\nclass DebugAcceleratorConfig(BaseAcceleratorConfig):\n _target_: str = \"trainer.accelerators.debug_accelerator.DebugAccelerator\"\n\n\nclass DebugAccelerator(BaseAccelerator):\n def __init__(self, cfg: DebugAcceleratorConfig):\n super().__init__(cfg)\n self.accelerator = Accelerator(\n gradient_accumulation_steps=cfg.gradient_accumulation_steps,\n mixed_precision=cfg.mixed_precision,\n log_with=cfg.log_with,\n project_dir=cfg.output_dir,\n dynamo_backend=cfg.dynamo_backend,\n )\n self.post_init()\n" }, { "path": "step_aware_preference_model/trainer/accelerators/deepspeed_accelerator.py", "content": "import os\nfrom dataclasses import dataclass, field\nfrom typing import Any\n\nimport torch\nfrom accelerate.utils import PrecisionType\nfrom accelerate import Accelerator, DeepSpeedPlugin\nfrom omegaconf import OmegaConf, MISSING, II\n\nfrom trainer.accelerators.base_accelerator import BaseAcceleratorConfig, BaseAccelerator\n\n\n@dataclass\nclass MixedPrecisionConfig:\n enabled: bool = MISSING\n\n\n@dataclass\nclass DeepSpeedConfig:\n fp16: MixedPrecisionConfig = MixedPrecisionConfig(enabled=False)\n bf16: MixedPrecisionConfig = MixedPrecisionConfig(enabled=False)\n optimizer: dict = field(default_factory=lambda: {\n \"type\": \"AdamW\",\n \"params\": {\n \"lr\": \"auto\",\n \"weight_decay\": \"auto\",\n \"torch_adam\": True,\n \"adam_w_mode\": True\n }\n })\n scheduler: dict = field(default_factory=lambda: {\n \"type\": \"WarmupDecayLR\",\n \"params\": {\n \"warmup_min_lr\": \"auto\",\n \"warmup_max_lr\": \"auto\",\n \"warmup_num_steps\": \"auto\",\n \"total_num_steps\": \"auto\"\n }\n })\n zero_optimization: dict = field(default_factory=lambda: {\n \"stage\": 2,\n \"allgather_partitions\": True,\n \"allgather_bucket_size\": 2e8,\n \"overlap_comm\": True,\n \"reduce_scatter\": True,\n \"reduce_bucket_size\": 500000000,\n \"contiguous_gradients\": True\n })\n gradient_accumulation_steps: int = 16\n gradient_clipping: float = 1.0\n steps_per_print: int = 1\n train_batch_size: str = \"auto\"\n train_micro_batch_size_per_gpu: str = \"auto\"\n # train_micro_batch_size_per_gpu: int = II(\"dataset.batch_size\")\n wall_clock_breakdown: bool = False\n\n\n@dataclass\nclass DeepSpeedAcceleratorConfig(BaseAcceleratorConfig):\n _target_: str = \"trainer.accelerators.deepspeed_accelerator.DeepSpeedAccelerator\"\n deepspeed: DeepSpeedConfig = DeepSpeedConfig()\n deepspeed_final: Any = None\n\nsd15_deepspeed_accelerator_cfg = DeepSpeedAcceleratorConfig(max_steps=8000)\nsdxl_deepspeed_accelerator_cfg = DeepSpeedAcceleratorConfig(max_steps=7000)\n\nclass DeepSpeedAccelerator(BaseAccelerator):\n def __init__(self, cfg: DeepSpeedAcceleratorConfig):\n super().__init__(cfg)\n self.set_mixed_precision()\n deepspeed_plugin = DeepSpeedPlugin(\n hf_ds_config=OmegaConf.to_container(self.cfg.deepspeed, resolve=True),\n gradient_accumulation_steps=self.cfg.gradient_accumulation_steps,\n )\n self.cfg.deepspeed_final = OmegaConf.create(deepspeed_plugin.deepspeed_config)\n self.accelerator = Accelerator(\n deepspeed_plugin=deepspeed_plugin,\n gradient_accumulation_steps=self.cfg.gradient_accumulation_steps,\n mixed_precision=self.cfg.mixed_precision,\n log_with=self.cfg.log_with,\n project_dir=self.cfg.output_dir,\n dynamo_backend=self.cfg.dynamo_backend,\n )\n self.post_init()\n\n def set_mixed_precision(self):\n if self.cfg.mixed_precision == PrecisionType.BF16:\n self.cfg.deepspeed.bf16.enabled = True\n self.cfg.deepspeed.fp16.enabled = False\n elif self.cfg.mixed_precision == PrecisionType.FP16:\n self.cfg.deepspeed.fp16.enabled = True\n self.cfg.deepspeed.bf16.enabled = False\n else:\n self.cfg.deepspeed.fp16.enabled = False\n self.cfg.deepspeed.bf16.enabled = False\n\n def prepare(self, *args, device_placement=None):\n prepared = self.accelerator.prepare(*args, device_placement=device_placement)\n for obj in prepared:\n if isinstance(obj, torch.nn.Module):\n if self.cfg.mixed_precision == PrecisionType.BF16:\n obj.forward = torch.autocast(device_type=self.device.type, dtype=torch.bfloat16)(obj.forward)\n elif self.cfg.mixed_precision == PrecisionType.FP16:\n obj.forward = torch.autocast(device_type=self.device.type, dtype=torch.float16)(obj.forward)\n return prepared\n" }, { "path": "step_aware_preference_model/trainer/accelerators/utils.py", "content": "import subprocess\nfrom typing import MutableMapping, Any, Dict\n\nimport rich.tree\nimport rich.syntax\nfrom accelerate.logging import get_logger\nfrom omegaconf import DictConfig, OmegaConf\n\nlogger = get_logger(__name__)\n\n\ndef nvidia_smi_gpu_memory_stats():\n \"\"\"\n Parse the nvidia-smi output and extract the memory used stats.\n \"\"\"\n out_dict = {}\n try:\n sp = subprocess.Popen(\n [\"nvidia-smi\", \"--query-gpu=index,memory.used\", \"--format=csv,noheader\"],\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n close_fds=True,\n )\n out_str = sp.communicate()\n out_list = out_str[0].decode(\"utf-8\").split(\"\\n\")\n out_dict = {}\n for item in out_list:\n if \" MiB\" in item:\n gpu_idx, mem_used = item.split(',')\n gpu_key = f\"gpu_{gpu_idx}_mem_used_gb\"\n out_dict[gpu_key] = int(mem_used.strip().split(\" \")[0]) / 1024\n except FileNotFoundError:\n logger.error(\n \"Failed to find the 'nvidia-smi' executable for printing GPU stats\"\n )\n except subprocess.CalledProcessError as e:\n logger.error(f\"nvidia-smi returned non zero error code: {e.returncode}\")\n\n return out_dict\n\n\ndef get_nvidia_smi_gpu_memory_stats_str():\n return f\"nvidia-smi stats: {nvidia_smi_gpu_memory_stats()}\"\n\n\ndef print_config(cfg: DictConfig):\n style = \"bright\"\n tree = rich.tree.Tree(\"CONFIG\", style=style, guide_style=style)\n fields = cfg.keys()\n for field in fields:\n branch = tree.add(field, style=style, guide_style=style)\n config_section = cfg.get(field)\n branch_content = str(config_section)\n if isinstance(config_section, DictConfig):\n branch_content = OmegaConf.to_yaml(config_section, resolve=True)\n branch.add(rich.syntax.Syntax(branch_content, \"yaml\"))\n rich.print(tree)\n\n\ndef _flatten_dict(params: MutableMapping, delimiter: str = \"/\", parent_key: str = \"\") -> Dict[str, Any]:\n result: Dict[str, Any] = {}\n for k, v in params.items():\n new_key = parent_key + delimiter + str(k) if parent_key else str(k)\n if isinstance(v, MutableMapping):\n result = {**result, **_flatten_dict(v, parent_key=new_key, delimiter=delimiter)}\n else:\n result[new_key] = v\n return result\n" }, { "path": "step_aware_preference_model/trainer/conf/config.yaml", "content": "defaults:\n - base_config\n - _self_\n\nhydra:\n run:\n dir: .\n\ndebug:\n activate: false\n port: 5900" }, { "path": "step_aware_preference_model/trainer/conf/experiment/clip.yaml", "content": "# @package _global_\n\noutput_dir: outputs/${accelerator.project_name}/clip\n\ndataset:\n batch_size: 16\n\naccelerator:\n mixed_precision: BF16\n project_name: reward_final" }, { "path": "step_aware_preference_model/trainer/conf/experiment/clip_h.yaml", "content": "# @package _global_\n\ndefaults:\n - /experiment/clip\n\naccelerator:\n project_name: spm_training\n\noutput_dir: work_dirs/sdv15_spm\n\nmodel:\n pretrained_model_name_or_path: laion/CLIP-ViT-H-14-laion2B-s32B-b79K\n" }, { "path": "step_aware_preference_model/trainer/configs/__init__.py", "content": "from hydra.core.config_store import ConfigStore\n\nfrom trainer.configs.configs import TrainerConfig\n\ncs = ConfigStore.instance()\ncs.store(name=\"base_config\", node=TrainerConfig)\n" }, { "path": "step_aware_preference_model/trainer/configs/configs.py", "content": "from dataclasses import dataclass, field\nfrom typing import List, Any\n\nfrom omegaconf import DictConfig, MISSING\n\nimport trainer.accelerators\nimport trainer.tasks\nimport trainer.models\nimport trainer.criterions\nimport trainer.datasetss\nimport trainer.optimizers\nimport trainer.lr_schedulers\nfrom trainer.accelerators.base_accelerator import BaseAcceleratorConfig\nfrom trainer.models.base_model import BaseModelConfig\nfrom trainer.tasks.base_task import BaseTaskConfig\n\n\ndef _locate(path: str) -> Any:\n \"\"\"\n Locate an object by name or dotted path, importing as necessary.\n This is similar to the pydoc function `locate`, except that it checks for\n the module from the given path from back to front.\n \"\"\"\n if path == \"\":\n raise ImportError(\"Empty path\")\n from importlib import import_module\n from types import ModuleType\n\n parts = [part for part in path.split(\".\")]\n for part in parts:\n if not len(part):\n raise ValueError(\n f\"Error loading '{path}': invalid dotstring.\"\n + \"\\nRelative imports are not supported.\"\n )\n assert len(parts) > 0\n part0 = parts[0]\n try:\n obj = import_module(part0)\n except Exception as exc_import:\n raise ImportError(\n f\"Error loading '{path}':\\n{repr(exc_import)}\"\n + f\"\\nAre you sure that module '{part0}' is installed?\"\n ) from exc_import\n for m in range(1, len(parts)):\n part = parts[m]\n try:\n obj = getattr(obj, part)\n except AttributeError as exc_attr:\n parent_dotpath = \".\".join(parts[:m])\n if isinstance(obj, ModuleType):\n mod = \".\".join(parts[: m + 1])\n try:\n obj = import_module(mod)\n continue\n except ModuleNotFoundError as exc_import:\n raise ImportError(\n f\"Error loading '{path}':\\n{repr(exc_import)}\"\n + f\"\\nAre you sure that '{part}' is importable from module '{parent_dotpath}'?\"\n ) from exc_import\n except Exception as exc_import:\n raise ImportError(\n f\"Error loading '{path}':\\n{repr(exc_import)}\"\n ) from exc_import\n raise ImportError(\n f\"Error loading '{path}':\\n{repr(exc_attr)}\"\n + f\"\\nAre you sure that '{part}' is an attribute of '{parent_dotpath}'?\"\n ) from exc_attr\n return obj\n\n\ndef instantiate_with_cfg(cfg: DictConfig, **kwargs):\n target = _locate(cfg._target_)\n return target(cfg, **kwargs)\n\n\ndefaults = [\n {\"accelerator\": \"sd15_deepspeed\"},\n {\"task\": \"sd15_spm\"},\n {\"model\": \"spm\"},\n {\"criterion\": \"spm\"},\n {\"dataset\": \"pick_a_pic_spm_sd15\"},\n {\"optimizer\": \"sd15_dummy\"},\n {\"lr_scheduler\": \"dummy\"},\n]\n\n\n@dataclass\nclass DebugConfig:\n activate: bool = False\n port: int = 5900\n\n\n@dataclass\nclass TrainerConfig:\n defaults: List[Any] = field(default_factory=lambda: defaults)\n accelerator: BaseAcceleratorConfig = MISSING\n task: BaseTaskConfig = MISSING\n model: BaseModelConfig = MISSING\n criterion: Any = MISSING\n dataset: Any = MISSING\n optimizer: Any = MISSING\n lr_scheduler: Any = MISSING\n debug: DebugConfig = DebugConfig()\n output_dir: str = \"work_dirs/sdv15_spm\"\n" }, { "path": "step_aware_preference_model/trainer/criterions/__init__.py", "content": "from hydra.core.config_store import ConfigStore\n\nfrom trainer.criterions.spm_criterion import SPMCriterionConfig\n\ncs = ConfigStore.instance()\ncs.store(group=\"criterion\", name=\"spm\", node=SPMCriterionConfig)\n" }, { "path": "step_aware_preference_model/trainer/criterions/spm_criterion.py", "content": "from dataclasses import dataclass\nimport torch\nfrom omegaconf import II\nfrom torch.nn.modules.loss import _Loss\n\n\n@dataclass\nclass SPMCriterionConfig:\n _target_: str = \"trainer.criterions.spm_criterion.SPMCriterion\"\n is_distributed: bool = True\n label_0_column_name: str = II(\"dataset.label_0_column_name\")\n label_1_column_name: str = II(\"dataset.label_1_column_name\")\n\n input_ids_column_name: str = II(\"dataset.input_ids_column_name\")\n pixels_0_column_name: str = II(\"dataset.pixels_0_column_name\")\n pixels_1_column_name: str = II(\"dataset.pixels_1_column_name\")\n num_examples_per_prompt_column_name: str = II(\"dataset.num_examples_per_prompt_column_name\")\n timesteps_column_name: str = II(\"task.timesteps_column_name\")\n in_batch_negatives: bool = False\n pass\n\n\nclass SPMCriterion(_Loss):\n def __init__(self, cfg: SPMCriterionConfig):\n super().__init__()\n self.cfg = cfg\n\n @staticmethod\n def get_features(\n model, \n input_ids,\n pixels_0_values, \n pixels_1_values,\n timesteps,\n ):\n all_pixel_values = torch.cat([pixels_0_values, pixels_1_values], dim=0)\n all_timesteps = torch.cat([timesteps, timesteps], dim=0)\n text_features, all_image_features = model(\n text_inputs=input_ids, \n image_inputs=all_pixel_values,\n time_cond=all_timesteps,\n )\n all_image_features = all_image_features / all_image_features.norm(dim=-1, keepdim=True)\n text_features = text_features / text_features.norm(dim=-1, keepdim=True)\n image_0_features, image_1_features = all_image_features.chunk(2, dim=0)\n return image_0_features, image_1_features, text_features\n\n @staticmethod\n def gather_features(features):\n all_features = torch.cat(torch.distributed.nn.all_gather(features), dim=0)\n return all_features\n\n def calc_loss(\n self,\n text_features,\n image_0_features,\n image_1_features,\n logit_scale,\n label_0,\n label_1,\n num_examples_per_prompt,\n *args,\n **kwargs\n ):\n device = image_0_features.device\n\n # gather features\n if self.cfg.is_distributed:\n image_0_features = self.gather_features(image_0_features)\n image_1_features = self.gather_features(image_1_features)\n text_features = self.gather_features(text_features)\n label_0 = self.gather_features(label_0)\n label_1 = self.gather_features(label_1)\n num_examples_per_prompt = self.gather_features(num_examples_per_prompt)\n\n all_image_features = torch.cat([image_0_features, image_1_features], dim=0) # (2 * batch_size, dim)\n logits_per_image = logit_scale * all_image_features @ text_features.T\n image_0_logits, image_1_logits = logits_per_image.chunk(2, dim=0)\n text_logits = logit_scale * text_features @ all_image_features.T\n\n if self.cfg.in_batch_negatives:\n # get labels\n num_images = all_image_features.shape[0]\n image_labels = torch.arange(num_images, device=device, dtype=torch.long)\n image_0_labels, image_1_labels = image_labels.chunk(2, dim=0)\n num_texts = text_features.shape[0]\n text_labels = torch.arange(num_texts, device=device, dtype=torch.long)\n\n # image loss - we want to increase the logits of the preferred image to the text\n image_0_loss = torch.nn.functional.cross_entropy(image_0_logits, text_labels, reduction=\"none\")\n image_1_loss = torch.nn.functional.cross_entropy(image_1_logits, text_labels, reduction=\"none\")\n # if we have a tie, we will increase both images equally, and average so the image loss of each example is\n # proportional\n image_loss = label_0 * image_0_loss + label_1 * image_1_loss\n\n # text loss - we want to increase the logits of the text to the preferred image\n text_0_loss = torch.nn.functional.cross_entropy(text_logits, image_0_labels, reduction=\"none\")\n text_1_loss = torch.nn.functional.cross_entropy(text_logits, image_1_labels, reduction=\"none\")\n\n else:\n text_0_logits, text_1_logits = text_logits.chunk(2, dim=-1)\n index = torch.arange(text_0_logits.shape[0], device=device, dtype=torch.long)\n text_0_logits = text_0_logits[index, index]\n text_1_logits = text_1_logits[index, index]\n text_logits = torch.stack([text_0_logits, text_1_logits], dim=-1)\n text_0_labels = torch.zeros(text_logits.shape[0], device=device, dtype=torch.long)\n text_1_labels = text_0_labels + 1\n text_0_loss = torch.nn.functional.cross_entropy(text_logits, text_0_labels, reduction=\"none\")\n text_1_loss = torch.nn.functional.cross_entropy(text_logits, text_1_labels, reduction=\"none\")\n\n # if we have a tie we want the logits of for each image to be equal\n text_loss = label_0 * text_0_loss + label_1 * text_1_loss\n # we want the ideal loss to be 0, currently, if there is a tie, it is 0.5 * log(0.5) + 0.5 * log(0.5)\n # so we add log(0.5) to the loss\n is_tie = (label_0 == label_1).float()\n is_tie *= torch.log(torch.tensor(0.5, device=device))\n text_loss += is_tie\n\n # we average the image and text loss\n if self.cfg.in_batch_negatives:\n loss = (image_loss + text_loss) / 2\n else:\n loss = text_loss\n\n # some prompts have lots of interactions, we want weight them accordingly\n absolute_example_weight = 1 / num_examples_per_prompt\n denominator = absolute_example_weight.sum()\n weight_per_example = absolute_example_weight / denominator\n loss *= weight_per_example\n\n loss = loss.sum()\n return loss\n\n def forward(self, model, batch):\n image_0_features, image_1_features, text_features = self.get_features(\n model,\n batch[self.cfg.input_ids_column_name],\n batch[self.cfg.pixels_0_column_name],\n batch[self.cfg.pixels_1_column_name],\n batch[self.cfg.timesteps_column_name],\n )\n loss = self.calc_loss(\n text_features,\n image_0_features,\n image_1_features,\n model.logit_scale.exp(),\n batch[self.cfg.label_0_column_name],\n batch[self.cfg.label_1_column_name],\n batch[self.cfg.num_examples_per_prompt_column_name],\n )\n return loss\n" }, { "path": "step_aware_preference_model/trainer/datasetss/__init__.py", "content": "from hydra.core.config_store import ConfigStore\n\nfrom trainer.datasetss.pick_a_pic_spm_dataset import (\n pick_a_pic_spm_sd15_dataset_cfg,\n pick_a_pic_spm_sdxl_dataset_cfg,\n)\n\ncs = ConfigStore.instance()\ncs.store(group=\"dataset\", name=\"pick_a_pic_spm_sd15\", node=pick_a_pic_spm_sd15_dataset_cfg)\ncs.store(group=\"dataset\", name=\"pick_a_pic_spm_sdxl\", node=pick_a_pic_spm_sdxl_dataset_cfg)\n" }, { "path": "step_aware_preference_model/trainer/datasetss/base_dataset.py", "content": "from dataclasses import dataclass\n\nimport torch\n\n\n@dataclass\nclass BaseDatasetConfig:\n train_split_name: str = \"train\"\n valid_split_name: str = \"validation\"\n\n batch_size: int = 16\n num_workers: int = 16\n drop_last: bool = True\n\n\nclass BaseDataset(torch.utils.data.Dataset):\n pass\n" }, { "path": "step_aware_preference_model/trainer/datasetss/pick_a_pic_spm_dataset.py", "content": "from dataclasses import dataclass\nfrom io import BytesIO\nfrom typing import Optional\n\nimport torch\nfrom torchvision import transforms\nfrom PIL import Image\nfrom accelerate.logging import get_logger\nfrom transformers import CLIPTokenizer, AutoTokenizer\nfrom datasets import load_from_disk, load_dataset, Dataset\nfrom hydra.utils import instantiate\nfrom omegaconf import II\n\nfrom trainer.datasetss.base_dataset import (\n BaseDataset, \n BaseDatasetConfig,\n)\nfrom trainer.utils.constants import (\n huggingface_cache_dir,\n sd15_model_type_name,\n sdxl_model_type_name,\n sd15_huggingface_path,\n sdxl_huggingface_path,\n)\n\nlogger = get_logger(__name__)\n\n\ndef general_collate(batch, column_name):\n if isinstance(batch[0][column_name], torch.Tensor):\n return torch.cat([item[column_name] for item in batch], dim=0)\n else:\n return [item[column_name] for item in batch]\n\n@dataclass\nclass ProcessorConfig:\n _target_: str = \"transformers.AutoProcessor.from_pretrained\"\n pretrained_model_name_or_path: str = II(\"model.pretrained_model_name_or_path\")\n cache_dir: Optional[str] = huggingface_cache_dir\n\n@dataclass\nclass PickaPicSPMDatasetConfig(BaseDatasetConfig):\n _target_: str = \"trainer.datasetss.pick_a_pic_spm_dataset.PickaPicSPMDataset\"\n dataset_name: str = \"yuvalkirstain/pickapic_v1\"\n dataset_config_name: str = \"null\"\n\n from_disk: bool = False\n train_split_name: str = \"train\"\n valid_split_name: str = \"validation_unique\"\n cache_dir: Optional[str] = None\n\n caption_column_name: str = \"caption\"\n input_ids_column_name: str = \"input_ids\"\n image_0_column_name: str = \"jpg_0\"\n image_1_column_name: str = \"jpg_1\"\n label_0_column_name: str = \"label_0\"\n label_1_column_name: str = \"label_1\"\n are_different_column_name: str = \"are_different\"\n has_label_column_name: str = \"has_label\"\n\n pixels_0_column_name: str = \"pixel_values_0\"\n pixels_1_column_name: str = \"pixel_values_1\"\n\n num_examples_per_prompt_column_name: str = \"num_example_per_prompt\"\n\n keep_only_different: bool = False\n keep_only_with_label: bool = False\n keep_only_with_label_in_non_train: bool = True\n\n processor: ProcessorConfig = ProcessorConfig()\n\n limit_examples_per_prompt: int = -1\n\n only_on_best: bool = False\n \n resolution: int = 512\n model_type: str = sd15_model_type_name\n\n pil_img_0_column_name: str = \"pil_img_0_column\"\n pil_img_1_column_name: str = \"pil_img_1_column\"\n\n sdv15_input_ids_column_name: str = 'sdv15_input_ids'\n sdxl_input_ids_0_column_name: str = 'sdxl_input_ids_0'\n sdxl_input_ids_1_column_name: str = 'sdxl_input_ids_1'\n\npick_a_pic_spm_sd15_dataset_cfg = PickaPicSPMDatasetConfig()\npick_a_pic_spm_sdxl_dataset_cfg = PickaPicSPMDatasetConfig(\n model_type=sdxl_model_type_name,\n resolution=1024,\n)\n\nclass PickaPicSPMDataset(BaseDataset):\n\n def __init__(self, cfg: PickaPicSPMDatasetConfig, split: str = \"train\"):\n self.cfg = cfg\n self.split = split\n logger.info(f\"Loading {self.split} dataset\")\n \n self.dataset = self.load_hf_dataset(self.split)\n logger.info(f\"Loaded {len(self.dataset)} examples from {self.split} dataset\")\n\n if self.cfg.keep_only_different:\n self.dataset = self.dataset.filter(lambda x: x[self.cfg.are_different_column_name])\n\n if self.cfg.keep_only_with_label:\n logger.info(f\"Keeping only examples with label\")\n self.dataset = self.dataset.filter(lambda x: x[self.cfg.has_label_column_name])\n logger.info(f\"Kept {len(self.dataset)} examples from {self.split} dataset\")\n elif self.cfg.keep_only_with_label_in_non_train and self.split != self.cfg.train_split_name:\n logger.info(f\"Keeping only examples with label in {self.split} split\")\n self.dataset = self.dataset.filter(lambda x: x[self.cfg.has_label_column_name])\n logger.info(f\"Kept {len(self.dataset)} examples from {self.split} dataset\")\n\n if self.cfg.limit_examples_per_prompt > 0:\n logger.info(f\"Limiting examples per prompt to {self.cfg.limit_examples_per_prompt}\")\n df = self.dataset.to_pandas()\n df = df.drop('__index_level_0__', axis=1)\n logger.info(f\"Loaded {len(df)} examples from {self.split} dataset\")\n df = df.groupby(self.cfg.caption_column_name).head(self.cfg.limit_examples_per_prompt)\n logger.info(f\"Kept {len(df)} examples from {self.split} dataset\")\n self.dataset = Dataset.from_pandas(df)\n\n if self.cfg.only_on_best and self.split == self.cfg.train_split_name:\n logger.info(f\"Keeping only best examples for training\")\n train_dataset = self.dataset.remove_columns([self.cfg.image_0_column_name, self.cfg.image_1_column_name])\n df = train_dataset.to_pandas()\n df = df[df[self.cfg.has_label_column_name] == 1]\n image_0_wins_df = df[df[self.cfg.label_0_column_name] == 1]\n image_1_wins_df = df[df[self.cfg.label_0_column_name] == 0]\n bad_image_0_to_good_image_1 = dict(zip(image_1_wins_df.image_0_uid, image_1_wins_df.image_1_uid))\n bad_image_1_to_good_image_0 = dict(zip(image_0_wins_df.image_1_uid, image_0_wins_df.image_0_uid))\n bad_images_uids2good_images_uids = bad_image_0_to_good_image_1 | bad_image_1_to_good_image_0\n image_0_uid2image_col_name = dict(zip(df.image_0_uid, [self.cfg.image_0_column_name] * len(df.image_0_uid)))\n image_1_uid2image_col_name = dict(zip(df.image_1_uid, [self.cfg.image_1_column_name] * len(df.image_1_uid)))\n uid2image_col_name = image_0_uid2image_col_name | image_1_uid2image_col_name\n\n bad_uids = set()\n for bad_image, good_image in bad_images_uids2good_images_uids.items():\n cur_good = {bad_image}\n while good_image in bad_images_uids2good_images_uids:\n if good_image in cur_good:\n bad_uids.add(bad_image)\n break\n cur_good.add(good_image)\n good_image = bad_images_uids2good_images_uids[good_image]\n bad_images_uids2good_images_uids[bad_image] = good_image\n\n df = df[~(df.image_0_uid.isin(bad_uids) | df.image_1_uid.isin(bad_uids))]\n keep_ids = df.index.tolist()\n self.dataset = self.dataset.select(keep_ids)\n new_ids = list(range(len(df)))\n uid2index = dict(zip(df.image_0_uid, new_ids)) | dict(zip(df.image_1_uid, new_ids))\n logger.info(f\"Kept only {len(self.dataset)} best examples for training\")\n self.bad_images_uids2good_images_uids = bad_images_uids2good_images_uids\n self.uid2index = uid2index\n self.uid2image_col_name = uid2image_col_name\n\n logger.info(f\"Loaded {len(self.dataset)} examples from {self.split} dataset\")\n\n processor = instantiate(cfg.processor)\n self.tokenizer = processor.tokenizer\n self.image_processor = processor.image_processor\n self.image_processor.do_normalize = False\n self.image_processor.size = {'shortest_edge': self.cfg.resolution}\n self.image_processor.crop_size = {'height': self.cfg.resolution, 'width': self.cfg.resolution}\n \n self.image_transform = transforms.Normalize([0.5], [0.5])\n if self.cfg.model_type == sd15_model_type_name:\n self.sd_clip_tokenizer = CLIPTokenizer.from_pretrained(\n sd15_huggingface_path,\n subfolder=\"tokenizer\",\n cache_dir=huggingface_cache_dir,\n )\n elif self.cfg.model_type == sdxl_model_type_name:\n self.sd_clip_tokenizer_1 = AutoTokenizer.from_pretrained(\n sdxl_huggingface_path,\n subfolder=\"tokenizer\",\n revision=None,\n use_fast=False,\n )\n self.sd_clip_tokenizer_2 = AutoTokenizer.from_pretrained(\n sdxl_huggingface_path,\n subfolder=\"tokenizer_2\",\n revision=None,\n use_fast=False,\n )\n else:\n raise ValueError(f\"{self.cfg.model_type} is not supported\")\n\n def load_hf_dataset(self, split: str) -> Dataset:\n if self.cfg.from_disk:\n dataset = load_from_disk(self.cfg.dataset_name)[split]\n else:\n dataset = load_dataset(\n self.cfg.dataset_name,\n self.cfg.dataset_config_name,\n cache_dir=self.cfg.cache_dir,\n split=split,\n )\n return dataset\n\n def tokenize(self, example):\n caption = example[self.cfg.caption_column_name]\n input_ids = self.tokenizer(\n caption,\n max_length=self.tokenizer.model_max_length,\n padding=\"max_length\",\n truncation=True,\n return_tensors=\"pt\"\n ).input_ids\n return input_ids\n\n def process_image(self, image):\n if isinstance(image, dict):\n image = image[\"bytes\"]\n if isinstance(image, bytes):\n image = Image.open(BytesIO(image))\n image = image.convert(\"RGB\")\n pil_image = image\n pixel_values = self.image_processor(image, return_tensors=\"pt\")[\"pixel_values\"]\n pixel_values = self.image_transform(pixel_values)\n return pixel_values, pil_image\n\n def __getitem__(self, idx):\n example = self.dataset[idx]\n\n if self.cfg.only_on_best and self.split == self.cfg.train_split_name:\n if example[self.cfg.label_0_column_name]:\n bad_image_uid = example[\"image_1_uid\"]\n good_image_column_name = self.cfg.image_0_column_name\n else:\n bad_image_uid = example[\"image_0_uid\"]\n good_image_column_name = self.cfg.image_1_column_name\n good_image_uid = self.bad_images_uids2good_images_uids[bad_image_uid]\n good_image_index = self.uid2index[good_image_uid]\n example[good_image_column_name] = self.dataset[good_image_index][self.uid2image_col_name[good_image_uid]]\n\n input_ids = self.tokenize(example)\n \n caption = example[self.cfg.caption_column_name]\n if self.cfg.model_type == sd15_model_type_name:\n sd_input_ids = self.sd_clip_tokenizer(\n caption,\n padding=\"max_length\",\n max_length=self.sd_clip_tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n ).input_ids\n elif self.cfg.model_type == sdxl_model_type_name:\n sd_input_ids_1 = self.sd_clip_tokenizer_1(\n caption,\n padding=\"max_length\",\n max_length=self.sd_clip_tokenizer_1.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n sd_input_ids_1 = sd_input_ids_1.input_ids\n \n sd_input_ids_2 = self.sd_clip_tokenizer_2(\n caption,\n padding=\"max_length\",\n max_length=self.sd_clip_tokenizer_2.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n sd_input_ids_2 = sd_input_ids_2.input_ids\n\n\n pixel_0_values, pil_image_0 = self.process_image(example[self.cfg.image_0_column_name])\n pixel_1_values, pil_image_1 = self.process_image(example[self.cfg.image_1_column_name])\n\n item = {\n self.cfg.input_ids_column_name: input_ids,\n self.cfg.pixels_0_column_name: pixel_0_values,\n self.cfg.pixels_1_column_name: pixel_1_values,\n self.cfg.label_0_column_name: torch.tensor(example[self.cfg.label_0_column_name])[None],\n self.cfg.label_1_column_name: torch.tensor(example[self.cfg.label_1_column_name])[None],\n self.cfg.num_examples_per_prompt_column_name: torch.tensor(example[self.cfg.num_examples_per_prompt_column_name])[None],\n self.cfg.pil_img_0_column_name: pil_image_0,\n self.cfg.pil_img_1_column_name: pil_image_1,\n self.cfg.caption_column_name: caption,\n }\n \n if self.cfg.model_type == sd15_model_type_name:\n item[self.cfg.sdv15_input_ids_column_name] = sd_input_ids\n elif self.cfg.model_type == sdxl_model_type_name:\n item[self.cfg.sdxl_input_ids_0_column_name] = sd_input_ids_1\n item[self.cfg.sdxl_input_ids_1_column_name] = sd_input_ids_2\n\n return item\n\n def collate_fn(self, batch):\n input_ids = general_collate(batch, self.cfg.input_ids_column_name)\n pixel_0_values = general_collate(batch, self.cfg.pixels_0_column_name)\n pixel_1_values = general_collate(batch, self.cfg.pixels_1_column_name)\n label_0 = general_collate(batch, self.cfg.label_0_column_name)\n label_1 = general_collate(batch, self.cfg.label_1_column_name)\n num_examples_per_prompt = general_collate(batch, self.cfg.num_examples_per_prompt_column_name)\n pil_imgs_0 = general_collate(batch, self.cfg.pil_img_0_column_name)\n pil_imgs_1 = general_collate(batch, self.cfg.pil_img_1_column_name)\n cpations = general_collate(batch, self.cfg.caption_column_name)\n \n pixel_0_values = pixel_0_values.to(memory_format=torch.contiguous_format).float()\n pixel_1_values = pixel_1_values.to(memory_format=torch.contiguous_format).float()\n \n collated = {\n self.cfg.input_ids_column_name: input_ids,\n self.cfg.pixels_0_column_name: pixel_0_values,\n self.cfg.pixels_1_column_name: pixel_1_values,\n self.cfg.label_0_column_name: label_0,\n self.cfg.label_1_column_name: label_1,\n self.cfg.num_examples_per_prompt_column_name: num_examples_per_prompt,\n self.cfg.pil_img_0_column_name: pil_imgs_0,\n self.cfg.pil_img_1_column_name: pil_imgs_1,\n self.cfg.caption_column_name: cpations,\n }\n if self.cfg.model_type == sd15_model_type_name:\n collated[self.cfg.sdv15_input_ids_column_name] = general_collate(\n batch, self.cfg.sdv15_input_ids_column_name\n )\n elif self.cfg.model_type == sdxl_model_type_name:\n collated[self.cfg.sdxl_input_ids_0_column_name] = general_collate(\n batch, self.cfg.sdxl_input_ids_0_column_name\n )\n collated[self.cfg.sdxl_input_ids_1_column_name] = general_collate(\n batch, self.cfg.sdxl_input_ids_1_column_name\n )\n return collated\n\n def __len__(self):\n return len(self.dataset)\n" }, { "path": "step_aware_preference_model/trainer/lr_schedulers/__init__.py", "content": "from hydra.core.config_store import ConfigStore\n\nfrom trainer.lr_schedulers.constant_with_warmup import ConstantWithWarmupLRSchedulerConfig\nfrom trainer.lr_schedulers.dummy_lr_scheduler import DummyLRSchedulerConfig\n\ncs = ConfigStore.instance()\ncs.store(group=\"lr_scheduler\", name=\"dummy\", node=DummyLRSchedulerConfig)\ncs.store(group=\"lr_scheduler\", name=\"constant_with_warmup\", node=ConstantWithWarmupLRSchedulerConfig)\n" }, { "path": "step_aware_preference_model/trainer/lr_schedulers/constant_with_warmup.py", "content": "from dataclasses import dataclass\n\nfrom omegaconf import II\nfrom transformers import get_constant_schedule_with_warmup\n\n\n@dataclass\nclass ConstantWithWarmupLRSchedulerConfig:\n _target_: str = \"trainer.lr_schedulers.constant_with_warmup.instantiate_dummy_lr_scheduler\"\n lr: float = II(\"optimizer.lr\")\n lr_warmup_steps: int = 500\n total_num_steps: int = II(\"accelerator.max_steps\")\n\n\ndef instantiate_dummy_lr_scheduler(cfg: ConstantWithWarmupLRSchedulerConfig, optimizer):\n return get_constant_schedule_with_warmup(\n optimizer,\n num_warmup_steps=cfg.lr_warmup_steps,\n )\n" }, { "path": "step_aware_preference_model/trainer/lr_schedulers/dummy_lr_scheduler.py", "content": "from dataclasses import dataclass\n\nimport torch\nfrom accelerate.utils import DummyScheduler\nfrom hydra.utils import instantiate\nfrom omegaconf import II\n\ntry:\n import torch.distributed.nn\n\n has_distributed = True\nexcept ImportError:\n has_distributed = False\n\n\n@dataclass\nclass DummyLRSchedulerConfig:\n _target_: str = \"trainer.lr_schedulers.dummy_lr_scheduler.instantiate_dummy_lr_scheduler\"\n lr: float = II(\"optimizer.lr\")\n lr_warmup_steps: int = 500\n total_num_steps: int = II(\"accelerator.max_steps\")\n\n\ndef instantiate_dummy_lr_scheduler(cfg: DummyLRSchedulerConfig, optimizer):\n try:\n num_processes = torch.distributed.get_world_size()\n except RuntimeError:\n num_processes = 1\n return DummyScheduler(\n optimizer,\n total_num_steps=cfg.total_num_steps * num_processes,\n warmup_num_steps=cfg.lr_warmup_steps,\n warmup_max_lr=cfg.lr,\n )\n" }, { "path": "step_aware_preference_model/trainer/models/__init__.py", "content": "from hydra.core.config_store import ConfigStore\n\nfrom trainer.models.step_aware_preference_model import StepAwarePreferenceModelConfig\n\ncs = ConfigStore.instance()\ncs.store(group=\"model\", name=\"spm\", node=StepAwarePreferenceModelConfig)\n" }, { "path": "step_aware_preference_model/trainer/models/base_model.py", "content": "from dataclasses import dataclass\n\n\n\n@dataclass\nclass BaseModelConfig:\n pass\n" }, { "path": "step_aware_preference_model/trainer/models/step_aware_preference_model.py", "content": "from dataclasses import dataclass\n\nimport torch\nfrom torch import nn\n\nfrom trainer.models.base_model import BaseModelConfig\nfrom trainer.models.time_conditioned_clip import HFTimeConditionedCLIPModel\nfrom trainer.utils.constants import huggingface_cache_dir\n\n@dataclass\nclass StepAwarePreferenceModelConfig(BaseModelConfig):\n _target_: str = \"trainer.models.step_aware_preference_model.StepAwarePreferenceModel\"\n # for processor\n pretrained_model_name_or_path: str = \"laion/CLIP-ViT-H-14-laion2B-s32B-b79K\"\n model_pretrained_model_name_or_path: str = \"yuvalkirstain/PickScore_v1\"\n\n\nclass StepAwarePreferenceModel(nn.Module):\n def __init__(self, cfg: StepAwarePreferenceModelConfig):\n super().__init__()\n self.model = HFTimeConditionedCLIPModel.from_pretrained(\n cfg.model_pretrained_model_name_or_path,\n cache_dir=huggingface_cache_dir,\n )\n\n def get_text_features(self, *args, **kwargs):\n return self.model.get_text_features(*args, **kwargs)\n\n def get_image_features(self, *args, **kwargs):\n return self.model.get_image_features(*args, **kwargs)\n\n def forward(self, text_inputs=None, image_inputs=None, time_cond=None):\n outputs = ()\n if text_inputs is not None:\n outputs += self.model.get_text_features(text_inputs),\n if image_inputs is not None:\n outputs += self.model.get_image_features(image_inputs, time_cond),\n return outputs\n \n def init_adaln_paras(self):\n for layer in self.model.vision_model.encoder.layers:\n # init adaLN_modulation\n nn.init.constant_(layer.adaLN_modulation[-1].weight, 0)\n nn.init.constant_(layer.adaLN_modulation[-1].bias, 0)\n bias = torch.zeros(6 * layer.embed_dim, dtype=layer.adaLN_modulation[-1].bias.dtype)\n with torch.no_grad():\n bias[2 * layer.embed_dim: 3 * layer.embed_dim] = 1\n bias[5 * layer.embed_dim:] = 1\n assert bias.shape == layer.adaLN_modulation[-1].bias.shape\n layer.adaLN_modulation[-1].bias = nn.Parameter(bias)\n # Initialize timestep embedding MLP:\n nn.init.normal_(self.model.t_embedder.mlp[0].weight, std=0.02)\n nn.init.normal_(self.model.t_embedder.mlp[2].weight, std=0.02)\n \n nn.init.constant_(self.model.vision_model.adaLN_modulation[-1].weight, 0)\n nn.init.constant_(self.model.vision_model.adaLN_modulation[-1].bias, 0)\n\n @property\n def logit_scale(self):\n return self.model.logit_scale\n\n def save(self, path):\n self.model.save_pretrained(path)\n" }, { "path": "step_aware_preference_model/trainer/models/time_conditioned_clip.py", "content": "from typing import Optional, Tuple, Union\nimport math\n\nimport torch\nimport torch.nn as nn\n\nfrom transformers.models.clip.modeling_clip import (\n CLIPConfig,\n CLIPEncoderLayer,\n CLIPVisionTransformer,\n CLIPAttention,\n CLIPMLP,\n CLIPVisionConfig,\n CLIPVisionEmbeddings,\n CLIPEncoder,\n BaseModelOutput,\n BaseModelOutputWithPooling,\n CLIP_VISION_INPUTS_DOCSTRING,\n add_start_docstrings_to_model_forward,\n replace_return_docstrings,\n CLIPModel,\n add_start_docstrings,\n CLIP_START_DOCSTRING,\n CLIPTextConfig,\n CLIPTextTransformer,\n CLIPOutput,\n clip_loss,\n CLIP_INPUTS_DOCSTRING,\n)\n\ndef modulate(x, shift, scale):\n return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)\n\n\nclass TimeConditionedCLIPEncoderLayer(CLIPEncoderLayer):\n def __init__(self, config: CLIPConfig):\n super(CLIPEncoderLayer, self).__init__()\n self.embed_dim = config.hidden_size\n self.self_attn = CLIPAttention(config)\n self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)\n self.mlp = CLIPMLP(config)\n self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)\n \n self.adaLN_modulation = nn.Sequential(\n nn.SiLU(),\n nn.Linear(self.embed_dim, 6 * self.embed_dim, bias=True),\n )\n # init adaLN_modulation\n nn.init.constant_(self.adaLN_modulation[-1].weight, 0)\n nn.init.constant_(self.adaLN_modulation[-1].bias, 0)\n bias = torch.ones(6 * self.embed_dim, dtype=self.adaLN_modulation[-1].bias.dtype)\n with torch.no_grad():\n bias[2 * self.embed_dim: 3 * self.embed_dim] = 1\n bias[5 * self.embed_dim:] = 1\n assert bias.shape == self.adaLN_modulation[-1].bias.shape\n self.adaLN_modulation[-1].bias = nn.Parameter(bias)\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n attention_mask: torch.Tensor,\n causal_attention_mask: torch.Tensor,\n time_cond: torch.Tensor,\n output_attentions: Optional[bool] = False,\n ) -> Tuple[torch.FloatTensor]:\n \"\"\"\n Args:\n hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`\n attention_mask (`torch.FloatTensor`): attention mask of size\n `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.\n `(config.encoder_attention_heads,)`.\n output_attentions (`bool`, *optional*):\n Whether or not to return the attentions tensors of all attention layers. See `attentions` under\n returned tensors for more detail.\n \"\"\"\n # batch, embed_dim\n shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(time_cond).chunk(6, dim=1)\n\n residual = hidden_states\n hidden_states = self.layer_norm1(hidden_states)\n hidden_states = modulate(hidden_states, shift_msa, scale_msa)\n hidden_states, attn_weights = self.self_attn(\n hidden_states=hidden_states,\n attention_mask=attention_mask,\n causal_attention_mask=causal_attention_mask,\n output_attentions=output_attentions,\n )\n hidden_states = hidden_states * gate_msa.unsqueeze(1)\n hidden_states = residual + hidden_states\n\n residual = hidden_states\n hidden_states = self.layer_norm2(hidden_states)\n hidden_states = modulate(hidden_states, shift_mlp, scale_mlp)\n hidden_states = self.mlp(hidden_states)\n hidden_states = hidden_states * gate_mlp.unsqueeze(1)\n hidden_states = residual + hidden_states\n\n outputs = (hidden_states,)\n\n if output_attentions:\n outputs += (attn_weights,)\n\n return outputs\n\n\nclass TimeConditionedCLIPEncoder(CLIPEncoder):\n def __init__(self, config: CLIPConfig):\n super(CLIPEncoder, self).__init__()\n self.config = config\n self.layers = nn.ModuleList([TimeConditionedCLIPEncoderLayer(config) for _ in range(config.num_hidden_layers)])\n self.gradient_checkpointing = False\n\n def forward(\n self,\n inputs_embeds,\n attention_mask: Optional[torch.Tensor] = None,\n causal_attention_mask: Optional[torch.Tensor] = None,\n time_cond = None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n ) -> Union[Tuple, BaseModelOutput]:\n r\"\"\"\n Args:\n inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):\n Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.\n This is useful if you want more control over how to convert `input_ids` indices into associated vectors\n than the model's internal embedding lookup matrix.\n attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):\n Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:\n\n - 1 for tokens that are **not masked**,\n - 0 for tokens that are **masked**.\n\n [What are attention masks?](../glossary#attention-mask)\n causal_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):\n Causal mask for the text model. Mask values selected in `[0, 1]`:\n\n - 1 for tokens that are **not masked**,\n - 0 for tokens that are **masked**.\n\n [What are attention masks?](../glossary#attention-mask)\n output_attentions (`bool`, *optional*):\n Whether or not to return the attentions tensors of all attention layers. See `attentions` under\n returned tensors for more detail.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors\n for more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n \"\"\"\n output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n output_hidden_states = (\n output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states\n )\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n encoder_states = () if output_hidden_states else None\n all_attentions = () if output_attentions else None\n\n hidden_states = inputs_embeds\n for idx, encoder_layer in enumerate(self.layers):\n if output_hidden_states:\n encoder_states = encoder_states + (hidden_states,)\n if self.gradient_checkpointing and self.training:\n\n def create_custom_forward(module):\n def custom_forward(*inputs):\n return module(*inputs, output_attentions)\n\n return custom_forward\n\n layer_outputs = torch.utils.checkpoint.checkpoint(\n create_custom_forward(encoder_layer),\n hidden_states,\n attention_mask,\n causal_attention_mask,\n time_cond,\n )\n else:\n layer_outputs = encoder_layer(\n hidden_states,\n attention_mask,\n causal_attention_mask,\n time_cond,\n output_attentions=output_attentions,\n )\n\n hidden_states = layer_outputs[0]\n\n if output_attentions:\n all_attentions = all_attentions + (layer_outputs[1],)\n\n if output_hidden_states:\n encoder_states = encoder_states + (hidden_states,)\n\n if not return_dict:\n return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)\n return BaseModelOutput(\n last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions\n )\n\n\nclass TimeConditionedCLIPVisionTransformer(CLIPVisionTransformer):\n def __init__(self, config: CLIPVisionConfig):\n super(CLIPVisionTransformer, self).__init__()\n self.config = config\n embed_dim = config.hidden_size\n\n self.embeddings = CLIPVisionEmbeddings(config)\n self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)\n self.encoder = TimeConditionedCLIPEncoder(config)\n self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)\n self.adaLN_modulation = nn.Sequential(\n nn.SiLU(),\n nn.Linear(embed_dim, 2 * embed_dim, bias=True)\n )\n nn.init.constant_(self.adaLN_modulation[-1].weight, 0)\n nn.init.constant_(self.adaLN_modulation[-1].bias, 0)\n\n @add_start_docstrings_to_model_forward(CLIP_VISION_INPUTS_DOCSTRING)\n @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=CLIPVisionConfig)\n def forward(\n self,\n pixel_values: Optional[torch.FloatTensor] = None,\n time_cond = None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n ) -> Union[Tuple, BaseModelOutputWithPooling]:\n r\"\"\"\n Returns:\n\n \"\"\"\n output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n output_hidden_states = (\n output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states\n )\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n if pixel_values is None:\n raise ValueError(\"You have to specify pixel_values\")\n\n hidden_states = self.embeddings(pixel_values)\n hidden_states = self.pre_layrnorm(hidden_states)\n\n encoder_outputs = self.encoder(\n inputs_embeds=hidden_states,\n time_cond=time_cond,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n )\n\n last_hidden_state = encoder_outputs[0]\n pooled_output = last_hidden_state[:, 0, :]\n pooled_output = self.post_layernorm(pooled_output)\n \n shift, scale = self.adaLN_modulation(time_cond).chunk(2, dim=1)\n pooled_output = pooled_output * (1 + scale) + shift\n\n if not return_dict:\n return (last_hidden_state, pooled_output) + encoder_outputs[1:]\n\n return BaseModelOutputWithPooling(\n last_hidden_state=last_hidden_state,\n pooler_output=pooled_output,\n hidden_states=encoder_outputs.hidden_states,\n attentions=encoder_outputs.attentions,\n )\n\n\nclass TimestepEmbedder(nn.Module):\n \"\"\"\n Embeds scalar timesteps into vector representations.\n \"\"\"\n def __init__(self, hidden_size, frequency_embedding_size=256):\n super().__init__()\n self.mlp = nn.Sequential(\n nn.Linear(frequency_embedding_size, hidden_size, bias=True),\n nn.SiLU(),\n nn.Linear(hidden_size, hidden_size, bias=True),\n )\n self.frequency_embedding_size = frequency_embedding_size\n\n @staticmethod\n def timestep_embedding(t, dim, max_period=10000):\n \"\"\"\n Create sinusoidal timestep embeddings.\n :param t: a 1-D Tensor of N indices, one per batch element.\n These may be fractional.\n :param dim: the dimension of the output.\n :param max_period: controls the minimum frequency of the embeddings.\n :return: an (N, D) Tensor of positional embeddings.\n \"\"\"\n # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py\n half = dim // 2\n freqs = torch.exp(\n -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half\n ).to(device=t.device)\n args = t[:, None].float() * freqs[None]\n embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)\n if dim % 2:\n embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)\n return embedding\n\n def forward(self, t):\n t_freq = self.timestep_embedding(t, self.frequency_embedding_size)\n t_emb = self.mlp(t_freq)\n return t_emb\n\n\n@add_start_docstrings(CLIP_START_DOCSTRING)\nclass HFTimeConditionedCLIPModel(CLIPModel):\n def __init__(self, config: CLIPConfig):\n super(CLIPModel, self).__init__(config)\n\n if not isinstance(config.text_config, CLIPTextConfig):\n raise ValueError(\n \"config.text_config is expected to be of type CLIPTextConfig but is of type\"\n f\" {type(config.text_config)}.\"\n )\n\n if not isinstance(config.vision_config, CLIPVisionConfig):\n raise ValueError(\n \"config.vision_config is expected to be of type CLIPVisionConfig but is of type\"\n f\" {type(config.vision_config)}.\"\n )\n\n text_config = config.text_config\n vision_config = config.vision_config\n\n self.projection_dim = config.projection_dim\n self.text_embed_dim = text_config.hidden_size\n self.vision_embed_dim = vision_config.hidden_size\n\n self.text_model = CLIPTextTransformer(text_config)\n self.vision_model = TimeConditionedCLIPVisionTransformer(vision_config)\n\n self.visual_projection = nn.Linear(self.vision_embed_dim, self.projection_dim, bias=False)\n self.text_projection = nn.Linear(self.text_embed_dim, self.projection_dim, bias=False)\n self.logit_scale = nn.Parameter(torch.ones([]) * self.config.logit_scale_init_value)\n \n self.t_embedder = TimestepEmbedder(self.vision_embed_dim)\n # Initialize timestep embedding MLP:\n nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)\n nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)\n\n \n # Initialize weights and apply final processing\n self.post_init()\n \n @add_start_docstrings_to_model_forward(CLIP_VISION_INPUTS_DOCSTRING)\n def get_image_features(\n self,\n pixel_values: Optional[torch.FloatTensor] = None,\n time_cond=None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n ) -> torch.FloatTensor:\n r\"\"\"\n Returns:\n image_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by\n applying the projection layer to the pooled output of [`CLIPVisionModel`].\n\n Examples:\n\n ```python\n >>> from PIL import Image\n >>> import requests\n >>> from transformers import AutoProcessor, CLIPModel\n\n >>> model = CLIPModel.from_pretrained(\"openai/clip-vit-base-patch32\")\n >>> processor = AutoProcessor.from_pretrained(\"openai/clip-vit-base-patch32\")\n\n >>> url = \"http://images.cocodataset.org/val2017/000000039769.jpg\"\n >>> image = Image.open(requests.get(url, stream=True).raw)\n\n >>> inputs = processor(images=image, return_tensors=\"pt\")\n\n >>> image_features = model.get_image_features(**inputs)\n ```\"\"\"\n # Use CLIP model's config for some fields (if specified) instead of those of vision & text components.\n output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n output_hidden_states = (\n output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states\n )\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n # b->b,c\n time_cond = time_cond.to(pixel_values.dtype)\n time_cond = self.t_embedder(time_cond)\n\n vision_outputs = self.vision_model(\n pixel_values=pixel_values,\n time_cond=time_cond,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n )\n\n pooled_output = vision_outputs[1] # pooled_output\n image_features = self.visual_projection(pooled_output)\n\n return image_features\n\n\n @add_start_docstrings_to_model_forward(CLIP_INPUTS_DOCSTRING)\n @replace_return_docstrings(output_type=CLIPOutput, config_class=CLIPConfig)\n def forward(\n self,\n input_ids: Optional[torch.LongTensor] = None,\n pixel_values: Optional[torch.FloatTensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n position_ids: Optional[torch.LongTensor] = None,\n time: Optional[torch.LongTensor] = None,\n return_loss: Optional[bool] = None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n ) -> Union[Tuple, CLIPOutput]:\n r\"\"\"\n Returns:\n\n Examples:\n\n ```python\n >>> from PIL import Image\n >>> import requests\n >>> from transformers import AutoProcessor, CLIPModel\n\n >>> model = CLIPModel.from_pretrained(\"openai/clip-vit-base-patch32\")\n >>> processor = AutoProcessor.from_pretrained(\"openai/clip-vit-base-patch32\")\n\n >>> url = \"http://images.cocodataset.org/val2017/000000039769.jpg\"\n >>> image = Image.open(requests.get(url, stream=True).raw)\n\n >>> inputs = processor(\n ... text=[\"a photo of a cat\", \"a photo of a dog\"], images=image, return_tensors=\"pt\", padding=True\n ... )\n\n >>> outputs = model(**inputs)\n >>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score\n >>> probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities\n ```\"\"\"\n # Use CLIP model's config for some fields (if specified) instead of those of vision & text components.\n output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n output_hidden_states = (\n output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states\n )\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n \n # b->b,c\n time = time.to(pixel_values.dtype)\n time_cond = self.t_embedder(time)\n\n vision_outputs = self.vision_model(\n pixel_values=pixel_values,\n time_cond=time_cond,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n )\n\n text_outputs = self.text_model(\n input_ids=input_ids,\n attention_mask=attention_mask,\n position_ids=position_ids,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n )\n\n image_embeds = vision_outputs[1]\n image_embeds = self.visual_projection(image_embeds)\n\n text_embeds = text_outputs[1]\n text_embeds = self.text_projection(text_embeds)\n\n # normalized features\n image_embeds = image_embeds / image_embeds.norm(p=2, dim=-1, keepdim=True)\n text_embeds = text_embeds / text_embeds.norm(p=2, dim=-1, keepdim=True)\n\n # cosine similarity as logits\n logit_scale = self.logit_scale.exp()\n logits_per_text = torch.matmul(text_embeds, image_embeds.t()) * logit_scale\n logits_per_image = logits_per_text.t()\n\n loss = None\n if return_loss:\n loss = clip_loss(logits_per_text)\n\n if not return_dict:\n output = (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs)\n return ((loss,) + output) if loss is not None else output\n\n return CLIPOutput(\n loss=loss,\n logits_per_image=logits_per_image,\n logits_per_text=logits_per_text,\n text_embeds=text_embeds,\n image_embeds=image_embeds,\n text_model_output=text_outputs,\n vision_model_output=vision_outputs,\n )\n" }, { "path": "step_aware_preference_model/trainer/optimizers/__init__.py", "content": "from hydra.core.config_store import ConfigStore\n\nfrom trainer.optimizers.adamw import AdamWOptimizerConfig\nfrom trainer.optimizers.dummy_optimizer import (\n DummyOptimizerConfig,\n sd15_dummy_optimizer_cfg,\n sdxl_dummy_optimizer_cfg,\n)\n\ncs = ConfigStore.instance()\ncs.store(group=\"optimizer\", name=\"dummy\", node=DummyOptimizerConfig)\ncs.store(group=\"optimizer\", name=\"sd15_dummy\", node=sd15_dummy_optimizer_cfg)\ncs.store(group=\"optimizer\", name=\"sdxl_dummy\", node=sdxl_dummy_optimizer_cfg)\ncs.store(group=\"optimizer\", name=\"adamw\", node=AdamWOptimizerConfig)\n" }, { "path": "step_aware_preference_model/trainer/optimizers/adamw.py", "content": "from dataclasses import dataclass\n\n\n@dataclass\nclass AdamWOptimizerConfig:\n _target_: str = \"torch.optim.adamw.AdamW\"\n lr: float = 1e-6\n\n" }, { "path": "step_aware_preference_model/trainer/optimizers/dummy_optimizer.py", "content": "from dataclasses import dataclass\nfrom accelerate.utils import DummyOptim\n\n\n@dataclass\nclass DummyOptimizerConfig:\n _target_: str = \"trainer.optimizers.dummy_optimizer.BaseDummyOptim\"\n lr: float = 3e-6\n weight_decay: float = 0.3\n\nsd15_dummy_optimizer_cfg = DummyOptimizerConfig()\nsdxl_dummy_optimizer_cfg = DummyOptimizerConfig(lr=1e-6)\n\nclass BaseDummyOptim(DummyOptim):\n def __init__(self, model, lr=0.001, weight_decay=0, **kwargs):\n self.params = [p for p in model.parameters() if p.requires_grad]\n self.lr = lr\n self.weight_decay = weight_decay\n self.kwargs = kwargs\n" }, { "path": "step_aware_preference_model/trainer/scripts/train_spm.py", "content": "import os\nimport os.path as osp\nfrom typing import Any\n\nimport hydra\nimport torch\nfrom hydra.utils import instantiate\nfrom accelerate.logging import get_logger\nfrom omegaconf import DictConfig, OmegaConf\nfrom torch import nn\nfrom deepspeed.utils.zero_to_fp32 import convert_zero_checkpoint_to_fp32_state_dict\n\nfrom trainer.accelerators.base_accelerator import BaseAccelerator\nfrom trainer.configs.configs import TrainerConfig, instantiate_with_cfg\n\nlogger = get_logger(__name__)\n\nos.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n\ndef load_dataloaders(cfg: DictConfig) -> Any:\n dataloaders = {}\n for split in [cfg.train_split_name, cfg.valid_split_name]:\n dataset = instantiate_with_cfg(cfg, split=split)\n should_shuffle = split == cfg.train_split_name\n dataloaders[split] = torch.utils.data.DataLoader(\n dataset,\n shuffle=should_shuffle,\n batch_size=cfg.batch_size,\n collate_fn=dataset.collate_fn,\n num_workers=cfg.num_workers\n )\n return dataloaders\n\n\ndef load_optimizer(cfg: DictConfig, model: nn.Module):\n optimizer = instantiate(cfg, model=model)\n return optimizer\n\n\ndef load_scheduler(cfg: DictConfig, optimizer):\n scheduler = instantiate_with_cfg(cfg, optimizer=optimizer)\n return scheduler\n\n\ndef load_task(cfg: DictConfig, accelerator: BaseAccelerator):\n task = instantiate_with_cfg(cfg, accelerator=accelerator)\n return task\n\n\ndef verify_or_write_config(cfg: TrainerConfig):\n os.makedirs(cfg.output_dir, exist_ok=True)\n yaml_path = os.path.join(cfg.output_dir, \"config.yaml\")\n if not os.path.exists(yaml_path):\n OmegaConf.save(cfg, yaml_path, resolve=True)\n with open(yaml_path) as f:\n existing_config = f.read()\n if existing_config != OmegaConf.to_yaml(cfg, resolve=True):\n raise ValueError(f\"Config was not saved correctly - {yaml_path}\")\n logger.info(f\"Config can be found in {yaml_path}\")\n\n\n@hydra.main(version_base=None, config_path=\"../conf\", config_name=\"config\")\ndef main(cfg: TrainerConfig) -> None:\n accelerator = instantiate_with_cfg(cfg.accelerator)\n\n if cfg.debug.activate and accelerator.is_main_process:\n import pydevd_pycharm\n pydevd_pycharm.settrace('localhost', port=cfg.debug.port, stdoutToServer=True, stderrToServer=True)\n\n if accelerator.is_main_process:\n verify_or_write_config(cfg)\n\n logger.info(f\"Loading task\")\n task = load_task(cfg.task, accelerator)\n logger.info(f\"Loading model\")\n model = instantiate_with_cfg(cfg.model)\n model.init_adaln_paras()\n logger.info(f\"Initialized newly added adaln parameters\")\n logger.info(f\"Loading criterion\")\n criterion = instantiate_with_cfg(cfg.criterion)\n logger.info(f\"Loading optimizer\")\n optimizer = load_optimizer(cfg.optimizer, model)\n logger.info(f\"Loading lr scheduler\")\n lr_scheduler = load_scheduler(cfg.lr_scheduler, optimizer)\n logger.info(f\"Loading dataloaders\")\n split2dataloader = load_dataloaders(cfg.dataset)\n\n dataloaders = list(split2dataloader.values())\n model, optimizer, lr_scheduler, *dataloaders = accelerator.prepare(model, optimizer, lr_scheduler, *dataloaders)\n split2dataloader = dict(zip(split2dataloader.keys(), dataloaders))\n\n accelerator.load_state_if_needed()\n\n accelerator.recalc_train_length_after_prepare(len(split2dataloader[cfg.dataset.train_split_name]))\n\n accelerator.init_training(cfg)\n\n def evaluate():\n model.eval()\n end_of_train_dataloader = accelerator.gradient_state.end_of_dataloader\n logger.info(f\"*** Evaluating {cfg.dataset.valid_split_name} ***\")\n metrics = task.evaluate(model, criterion, split2dataloader[cfg.dataset.valid_split_name])\n accelerator.update_metrics(metrics)\n accelerator.gradient_state.end_of_dataloader = end_of_train_dataloader\n\n logger.info(f\"task: {task.__class__.__name__}\")\n logger.info(f\"model: {model.__class__.__name__}\")\n logger.info(f\"num. model params: {int(sum(p.numel() for p in model.parameters()) // 1e6)}M\")\n logger.info(\n f\"num. model trainable params: {int(sum(p.numel() for p in model.parameters() if p.requires_grad) // 1e6)}M\")\n logger.info(f\"criterion: {criterion.__class__.__name__}\")\n logger.info(f\"num. train examples: {len(split2dataloader[cfg.dataset.train_split_name].dataset)}\")\n logger.info(f\"num. valid examples: {len(split2dataloader[cfg.dataset.valid_split_name].dataset)}\")\n\n for epoch in range(accelerator.cfg.num_epochs):\n train_loss, lr = 0.0, 0.0\n for step, batch in enumerate(split2dataloader[cfg.dataset.train_split_name]):\n if accelerator.should_skip(epoch, step):\n accelerator.update_progbar_step()\n continue\n\n if accelerator.should_eval():\n evaluate()\n\n if accelerator.should_save():\n accelerator.save_checkpoint()\n\n model.train()\n\n with accelerator.accumulate(model):\n loss = task.train_step(model, criterion, batch)\n avg_loss = accelerator.gather(loss).mean().item()\n\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n accelerator.clip_grad_norm_(model.parameters())\n\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n train_loss += avg_loss / accelerator.cfg.gradient_accumulation_steps\n\n if accelerator.sync_gradients:\n accelerator.update_global_step(train_loss)\n train_loss = 0.0\n\n if accelerator.global_step > 0:\n lr = lr_scheduler.get_last_lr()[0]\n\n accelerator.update_step(avg_loss, lr)\n\n if accelerator.should_end():\n evaluate()\n accelerator.save_checkpoint()\n break\n\n if accelerator.should_end():\n break\n\n accelerator.update_epoch()\n\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n all_ckpts = accelerator.get_all_ckpts()\n if all_ckpts:\n all_ckpts.sort(key=os.path.getctime, reverse=True)\n final_ckpt_dir = all_ckpts[0]\n convert_zero_checkpoint_to_fp32_state_dict(\n final_ckpt_dir,\n osp.join(accelerator.cfg.output_dir, 'final_ckpt.bin')\n )\n logger.info(f\"Final fp32 checkpoint saved to {osp.join(accelerator.cfg.output_dir, 'final_ckpt.bin')}\")\n else:\n raise ValueError(f\"No checkpoint folders found in output directory {accelerator.cfg.output_dir}\")\n accelerator.end_training()\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "step_aware_preference_model/trainer/tasks/__init__.py", "content": "\nfrom hydra.core.config_store import ConfigStore\n\nfrom trainer.tasks.spm_task import (\n sd15_spm_task_cfg,\n sdxl_spm_task_cfg,\n)\n\ncs = ConfigStore.instance()\ncs.store(group=\"task\", name=\"sd15_spm\", node=sd15_spm_task_cfg)\ncs.store(group=\"task\", name=\"sdxl_spm\", node=sdxl_spm_task_cfg)\n" }, { "path": "step_aware_preference_model/trainer/tasks/base_task.py", "content": "from dataclasses import dataclass\n\nimport torch\nfrom PIL import Image\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import LoggerType\n\nlogger = get_logger(__name__)\n\n\ndef flatten(list_of_lists):\n return [item for sublist in list_of_lists for item in sublist]\n\n\n@dataclass\nclass BaseTaskConfig:\n limit_examples_to_wandb: int = 50\n pass\n\n\nclass BaseTask:\n\n def __init__(self, cfg: BaseTaskConfig, accelerator):\n self.accelerator = accelerator\n self.cfg = cfg\n\n def train_step(self, model, criterion, batch):\n pass\n\n def valid_step(self, model, criterion, batch):\n pass\n\n def evaluate(self, model, criterion, dataloader):\n pass\n\n def log_to_wandb(self, eval_dict, table_name=\"test_predictions\"):\n if not self.accelerator.is_main_process or not LoggerType.WANDB == self.accelerator.cfg.log_with:\n logger.info(\"Not logging to wandb\")\n return\n import wandb\n logger.info(\"Uploading to wandb\")\n for key, value in eval_dict.items():\n eval_dict[key] = [wandb.Image(maybe_img) if isinstance(maybe_img, Image.Image) else maybe_img for maybe_img\n in value]\n if self.cfg.limit_examples_to_wandb > 0:\n eval_dict[key] = eval_dict[key][:self.cfg.limit_examples_to_wandb]\n columns, predictions = list(zip(*sorted(eval_dict.items())))\n predictions += ([self.accelerator.global_step] * len(predictions[0]),)\n columns += (\"global_step\",)\n data = list(zip(*predictions))\n table = wandb.Table(columns=list(columns), data=data)\n wandb.log({table_name: table}, commit=False, step=self.accelerator.global_step)\n\n @staticmethod\n def gather_iterable(it, num_processes):\n output_objects = [None for _ in range(num_processes)]\n torch.distributed.all_gather_object(output_objects, it)\n return flatten(output_objects)\n\n @torch.no_grad()\n def valid_step(self, model, criterion, batch):\n loss = criterion(model, batch)\n return loss\n\n def gather_dict(self, eval_dict):\n logger.info(\"Gathering dict from all processes...\")\n for k, v in eval_dict.items():\n eval_dict[k] = self.gather_iterable(v, self.accelerator.num_processes)\n return eval_dict\n" }, { "path": "step_aware_preference_model/trainer/tasks/spm_task.py", "content": "import collections\nfrom dataclasses import dataclass\n\nfrom PIL import Image\nimport torch\nimport torch.nn.functional as F\nimport torchvision\nfrom accelerate.logging import get_logger\nfrom omegaconf import II\nfrom transformers import (\n AutoProcessor, \n AutoModel,\n)\nfrom diffusers import StableDiffusionPipeline, StableDiffusionXLPipeline, AutoencoderKL\n\nfrom trainer.accelerators.base_accelerator import BaseAccelerator\nfrom trainer.tasks.base_task import BaseTaskConfig, BaseTask\nfrom trainer.utils.constants import (\n huggingface_cache_dir,\n sd15_huggingface_path,\n sdxl_huggingface_path,\n sdxl_vae_huggingface_path,\n sd15_model_type_name,\n sdxl_model_type_name,\n)\nfrom trainer.utils.batchable_ddim_scheduler import BatchableDDIMScheduler\n\nlogger = get_logger(__name__)\n\n\ndef numpy_to_pil(images):\n images = (images * 255).round().astype(\"uint8\")\n pil_images = [Image.fromarray(image) for image in images]\n return pil_images\n\n@dataclass\nclass SPMTaskConfig(BaseTaskConfig):\n _target_: str = \"trainer.tasks.spm_task.SPMTask\"\n pretrained_model_name_or_path: str = II(\"model.pretrained_model_name_or_path\")\n label_0_column_name: str = II(\"dataset.label_0_column_name\")\n label_1_column_name: str = II(\"dataset.label_1_column_name\")\n\n input_ids_column_name: str = II(\"dataset.input_ids_column_name\")\n pixels_0_column_name: str = II(\"dataset.pixels_0_column_name\")\n pixels_1_column_name: str = II(\"dataset.pixels_1_column_name\")\n timesteps_column_name: str = \"timesteps\"\n \n model_type: str = II(\"dataset.model_type\")\n use_pickscore_label: bool = False\n pickscore_threshold: float = 0.1\n cfg_scale: float = 5.0\n\n sdv15_input_ids_column_name: str = II(\"dataset.sdv15_input_ids_column_name\")\n sdxl_input_ids_0_column_name: str = II(\"dataset.sdxl_input_ids_0_column_name\")\n sdxl_input_ids_1_column_name: str = II(\"dataset.sdxl_input_ids_1_column_name\")\n pil_img_0_column_name: str = II(\"dataset.pil_img_0_column_name\")\n pil_img_1_column_name: str = II(\"dataset.pil_img_1_column_name\")\n caption_column_name: str = II(\"dataset.caption_column_name\")\n\n pretrained_model_name_or_path: str = II(\"model.pretrained_model_name_or_path\")\n model_pretrained_model_name_or_path: str = II(\"model.model_pretrained_model_name_or_path\")\n \n evaluation_timesteps: tuple[int] = (0, 251, 501, 701, 951)\n\nsd15_spm_task_cfg = SPMTaskConfig()\nsdxl_spm_task_cfg = SPMTaskConfig(use_pickscore_label=True)\n\n\nclass SPMTask(BaseTask):\n def __init__(self, cfg: SPMTaskConfig, accelerator: BaseAccelerator):\n super().__init__(cfg, accelerator)\n self.cfg = cfg\n \n processor = AutoProcessor.from_pretrained(\n pretrained_model_name_or_path=cfg.pretrained_model_name_or_path,\n cache_dir=huggingface_cache_dir,\n ).image_processor\n self.normalize = torchvision.transforms.Normalize(\n mean=processor.image_mean,\n std=processor.image_std,\n )\n self.processed_img_size = (\n processor.size['shortest_edge'] if isinstance(processor.size, dict) \n else processor.size\n )\n del processor\n \n if self.cfg.model_type == sd15_model_type_name:\n self.diff_pipe = StableDiffusionPipeline.from_pretrained(\n sd15_huggingface_path, \n torch_dtype=torch.float16,\n cache_dir=huggingface_cache_dir,\n )\n self.diff_pipe.scheduler = BatchableDDIMScheduler.from_pretrained(\n sd15_huggingface_path, \n subfolder=\"scheduler\",\n cache_dir=huggingface_cache_dir,\n )\n self.diff_pipe.scheduler.set_timesteps(1000, device=self.accelerator.device)\n self.diff_pipe.scheduler.alphas_cumprod = self.diff_pipe.scheduler.alphas_cumprod.to(self.accelerator.device)\n self.diff_pipe.to(self.accelerator.device)\n self.diff_pipe.unet.eval()\n self.diff_pipe.unet.requires_grad_(False)\n self.diff_pipe.text_encoder.eval()\n self.diff_pipe.text_encoder.requires_grad_(False)\n self.diff_pipe.vae.eval()\n self.diff_pipe.vae.requires_grad_(False)\n\n self.sd_neg_prompt_embeds = self.diff_pipe.text_encoder(\n self.diff_pipe.tokenizer(\n [\"\"],\n return_tensors=\"pt\",\n padding=\"max_length\",\n truncation=True,\n max_length=self.diff_pipe.tokenizer.model_max_length,\n ).input_ids.to(self.accelerator.device)\n )[0]\n elif self.cfg.model_type == sdxl_model_type_name:\n self.diff_pipe = StableDiffusionXLPipeline.from_pretrained(\n sdxl_huggingface_path, \n torch_dtype=torch.float16,\n cache_dir=huggingface_cache_dir,\n )\n self.diff_pipe.scheduler = BatchableDDIMScheduler.from_pretrained(\n sdxl_huggingface_path, \n subfolder=\"scheduler\",\n cache_dir=huggingface_cache_dir,\n )\n vae = AutoencoderKL.from_pretrained(\n sdxl_vae_huggingface_path,\n cache_dir=huggingface_cache_dir,\n )\n self.diff_pipe.vae = vae\n self.diff_pipe.scheduler.set_timesteps(1000, device=self.accelerator.device)\n self.diff_pipe.scheduler.alphas_cumprod = self.diff_pipe.scheduler.alphas_cumprod.to(self.accelerator.device)\n self.diff_pipe.to(self.accelerator.device, dtype=torch.float16)\n self.diff_pipe.unet.eval()\n self.diff_pipe.unet.requires_grad_(False)\n self.diff_pipe.text_encoder.eval()\n self.diff_pipe.text_encoder.requires_grad_(False)\n self.diff_pipe.text_encoder_2.eval()\n self.diff_pipe.text_encoder_2.requires_grad_(False)\n self.diff_pipe.vae.eval()\n self.diff_pipe.vae.requires_grad_(False)\n (\n _, \n self.sd_neg_prompt_embeds, \n _, \n self.sd_neg_pooled_prompt_embeds,\n ) = self.diff_pipe.encode_prompt(\n prompt='',\n device=self.accelerator.device,\n num_images_per_prompt=1,\n do_classifier_free_guidance=True,\n )\n self.add_time_ids = torch.tensor(\n [[1024, 1024, 0, 0, 1024, 1024]], \n device=self.accelerator.device,\n )\n else:\n raise ValueError(f'Unsupported diffusion model type {self.cfg.model_type}')\n \n if self.cfg.use_pickscore_label:\n self.pickscore_processor = AutoProcessor.from_pretrained(\n self.cfg.pretrained_model_name_or_path,\n cache_dir=huggingface_cache_dir,\n )\n self.pickscore_model = AutoModel.from_pretrained(\n self.cfg.model_pretrained_model_name_or_path,\n cache_dir=huggingface_cache_dir,\n ).eval().to(self.accelerator.device)\n\n def train_step(self, model, criterion, batch): \n with torch.no_grad(): \n timesteps = torch.randint(\n 0, \n self.diff_pipe.scheduler.config.num_train_timesteps, \n (batch[self.cfg.pixels_0_column_name].size(0),), \n device=batch[self.cfg.pixels_0_column_name].device,\n ).long()\n \n img_0, img_1 = self.get_noised_imgs(batch, timesteps)\n\n img_0 = (img_0 / 2 + 0.5).clamp(0, 1).float()\n img_1 = (img_1 / 2 + 0.5).clamp(0, 1).float()\n\n img_0 = F.interpolate(\n img_0, \n size=(self.processed_img_size, self.processed_img_size), \n mode='bicubic', \n align_corners=False,\n )\n img_1 = F.interpolate(\n img_1, \n size=(self.processed_img_size, self.processed_img_size), \n mode='bicubic', \n align_corners=False,\n )\n img_0 = img_0.clamp(0, 1)\n img_1 = img_1.clamp(0, 1)\n \n img_0 = self.normalize(img_0)\n img_1 = self.normalize(img_1)\n batch[self.cfg.pixels_0_column_name] = img_0\n batch[self.cfg.pixels_1_column_name] = img_1\n batch[self.cfg.timesteps_column_name] = timesteps\n \n if self.cfg.use_pickscore_label:\n self.update_as_pickscore_label(\n batch,\n self.cfg.pickscore_threshold,\n )\n\n loss = criterion(model, batch)\n return loss\n\n @staticmethod\n def features2probs(model, text_features, image_0_features, image_1_features):\n image_0_scores = model.logit_scale.exp() * torch.diag(\n torch.einsum('bd,cd->bc', text_features, image_0_features))\n image_1_scores = model.logit_scale.exp() * torch.diag(\n torch.einsum('bd,cd->bc', text_features, image_1_features))\n scores = torch.stack([image_0_scores, image_1_scores], dim=-1)\n probs = torch.softmax(scores, dim=-1)\n image_0_probs, image_1_probs = probs[:, 0], probs[:, 1]\n return image_0_probs, image_1_probs\n\n @torch.no_grad()\n def valid_step(self, model, criterion, batch):\n image_0_features, image_1_features, text_features = criterion.get_features(\n model,\n batch[self.cfg.input_ids_column_name],\n batch[self.cfg.pixels_0_column_name],\n batch[self.cfg.pixels_1_column_name],\n batch[self.cfg.timesteps_column_name],\n )\n return self.features2probs(model, text_features, image_0_features, image_1_features)\n\n @staticmethod\n def pixel_values_to_pil_images(pixel_values):\n images = (pixel_values / 2 + 0.5).clamp(0, 1)\n images = images.cpu().permute(0, 2, 3, 1).float().numpy()\n images = numpy_to_pil(images)\n return images\n\n def run_inference(self, model, criterion, dataloader, t):\n eval_dict = collections.defaultdict(list)\n logger.info(\"Running clip score...\")\n for batch in dataloader:\n timesteps = torch.ones(\n batch[self.cfg.pixels_0_column_name].size(0),\n device=batch[self.cfg.pixels_0_column_name].device,\n dtype=torch.long,\n ) * t\n\n with torch.no_grad():\n if t > 0:\n img_0, img_1 = self.get_noised_imgs(batch, timesteps)\n else:\n img_0 = batch[self.cfg.pixels_0_column_name]\n img_1 = batch[self.cfg.pixels_1_column_name]\n\n img_0 = (img_0 / 2 + 0.5).clamp(0, 1).float()\n img_1 = (img_1 / 2 + 0.5).clamp(0, 1).float()\n\n img_0 = F.interpolate(\n img_0, \n size=(self.processed_img_size, self.processed_img_size), \n mode='bicubic', \n align_corners=False,\n )\n img_1 = F.interpolate(\n img_1, \n size=(self.processed_img_size, self.processed_img_size), \n mode='bicubic', \n align_corners=False,\n )\n \n img_0 = img_0.clamp(0, 1)\n img_1 = img_1.clamp(0, 1)\n\n img_0 = self.normalize(img_0)\n img_1 = self.normalize(img_1)\n batch[self.cfg.pixels_0_column_name] = img_0\n batch[self.cfg.pixels_1_column_name] = img_1\n batch[self.cfg.timesteps_column_name] = timesteps\n if self.cfg.use_pickscore_label:\n self.update_as_pickscore_label(\n batch,\n self.cfg.pickscore_threshold,\n )\n \n image_0_probs, image_1_probs = self.valid_step(model, criterion, batch)\n if self.cfg.use_pickscore_label:\n win_lose_idx = batch[self.cfg.label_0_column_name] != 0.5\n else:\n win_lose_idx = slice(None)\n agree_on_0 = (image_0_probs > image_1_probs) * batch[self.cfg.label_0_column_name]\n agree_on_1 = (image_0_probs < image_1_probs) * batch[self.cfg.label_1_column_name]\n agree_on_0 = agree_on_0[win_lose_idx]\n agree_on_1 = agree_on_1[win_lose_idx]\n is_correct = agree_on_0 + agree_on_1\n eval_dict[\"is_correct\"] += is_correct.tolist()\n return eval_dict\n\n @torch.no_grad()\n def evaluate(self, model, criterion, dataloader):\n all_metrics = {}\n for t in self.cfg.evaluation_timesteps:\n eval_dict = self.run_inference(model, criterion, dataloader, t)\n eval_dict = self.gather_dict(eval_dict)\n metrics = {\n f\"accuracy on {t}\": sum(eval_dict[\"is_correct\"]) / len(eval_dict[\"is_correct\"]),\n f\"num_samples on {t}\": len(eval_dict[\"is_correct\"])\n }\n all_metrics.update(metrics)\n return all_metrics\n \n @torch.no_grad()\n def get_noised_imgs(self, batch, timesteps):\n img_0 = batch[self.cfg.pixels_0_column_name]\n img_1 = batch[self.cfg.pixels_1_column_name]\n \n if self.cfg.model_type == sd15_model_type_name:\n sd_prompt_embeds = self.diff_pipe.text_encoder(\n batch[self.cfg.sdv15_input_ids_column_name]\n )[0]\n elif self.cfg.model_type == sdxl_model_type_name:\n sd_prompt_embeds, sd_pooled_prompt_embeds = self.sdxl_encode_prompt_embeds(\n [self.diff_pipe.text_encoder, self.diff_pipe.text_encoder_2],\n text_input_ids_list=[\n batch[self.cfg.sdxl_input_ids_0_column_name],\n batch[self.cfg.sdxl_input_ids_1_column_name],\n ],\n )\n sd_neg_pooled_prompt_embeds = self.sd_neg_pooled_prompt_embeds.repeat(\n sd_prompt_embeds.shape[0], 1\n )\n add_time_ids = self.add_time_ids.to(\n dtype=sd_prompt_embeds.dtype\n ).repeat(sd_prompt_embeds.shape[0] * 2, 1)\n added_cond_kwargs = dict(\n text_embeds=torch.cat(\n (\n sd_neg_pooled_prompt_embeds, \n sd_pooled_prompt_embeds,\n ),\n dim=0\n ),\n time_ids=add_time_ids,\n )\n sd_neg_prompt_embeds = self.sd_neg_prompt_embeds.repeat(\n sd_prompt_embeds.shape[0], 1, 1\n )\n \n latents_0 = self.diff_pipe.vae.encode(\n img_0.to(torch.float16)\n ).latent_dist.sample()\n latents_0 = latents_0 * self.diff_pipe.vae.config.scaling_factor\n \n latents_1 = self.diff_pipe.vae.encode(\n img_1.to(torch.float16)\n ).latent_dist.sample()\n latents_1 = latents_1 * self.diff_pipe.vae.config.scaling_factor\n\n noise = torch.randn_like(latents_0)\n latents_0 = self.diff_pipe.scheduler.add_noise(latents_0, noise, timesteps)\n latents_1 = self.diff_pipe.scheduler.add_noise(latents_1, noise, timesteps)\n \n if self.cfg.model_type == sd15_model_type_name:\n noise_pred_0 = self.diff_pipe.unet(\n torch.cat([latents_0, latents_0]),\n torch.cat([timesteps, timesteps]),\n torch.cat([sd_neg_prompt_embeds, sd_prompt_embeds]),\n ).sample\n noise_pred_1 = self.diff_pipe.unet(\n torch.cat([latents_1, latents_1]),\n torch.cat([timesteps, timesteps]),\n torch.cat([sd_neg_prompt_embeds, sd_prompt_embeds]),\n ).sample\n elif self.cfg.model_type == sdxl_model_type_name:\n noise_pred_0 = self.diff_pipe.unet(\n torch.cat([latents_0, latents_0]),\n torch.cat([timesteps, timesteps]),\n torch.cat([sd_neg_prompt_embeds, sd_prompt_embeds]),\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n noise_pred_1 = self.diff_pipe.unet(\n torch.cat([latents_1, latents_1]),\n torch.cat([timesteps, timesteps]),\n torch.cat([sd_neg_prompt_embeds, sd_prompt_embeds]),\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n noise_pred_uncond_0, noise_pred_text_0 = noise_pred_0.chunk(2)\n noise_pred_0 = noise_pred_uncond_0 + self.cfg.cfg_scale * (\n noise_pred_text_0 - noise_pred_uncond_0\n )\n latents_0 = self.diff_pipe.scheduler.step(\n noise_pred_0, timesteps, latents_0, return_dict=True,\n ).pred_original_sample\n \n noise_pred_uncond_1, noise_pred_text_1 = noise_pred_1.chunk(2)\n noise_pred_1 = noise_pred_uncond_1 + self.cfg.cfg_scale * (\n noise_pred_text_1 - noise_pred_uncond_1\n )\n latents_1 = self.diff_pipe.scheduler.step(\n noise_pred_1, timesteps, latents_1, return_dict=True,\n ).pred_original_sample\n \n img_0 = self.diff_pipe.vae.decode(\n latents_0 / self.diff_pipe.vae.config.scaling_factor, \n return_dict=False,\n )[0]\n img_1 = self.diff_pipe.vae.decode(\n latents_1 / self.diff_pipe.vae.config.scaling_factor, \n return_dict=False,\n )[0]\n \n return img_0, img_1 \n \n @torch.no_grad()\n def update_as_pickscore_label(self, batch, threshold=0.1):\n device = batch[self.cfg.pixels_0_column_name].device\n pil_0_imgs = batch[self.cfg.pil_img_0_column_name]\n pil_1_imgs = batch[self.cfg.pil_img_1_column_name]\n captions = batch[self.cfg.caption_column_name]\n \n img_inputs_0 = self.pickscore_processor(\n images=pil_0_imgs,\n padding=True,\n truncation=True,\n max_length=77,\n return_tensors=\"pt\",\n ).to(device)\n\n img_inputs_1 = self.pickscore_processor(\n images=pil_1_imgs,\n padding=True,\n truncation=True,\n max_length=77,\n return_tensors=\"pt\",\n ).to(device)\n\n text_inputs = self.pickscore_processor(\n text=captions,\n padding=True,\n truncation=True,\n max_length=77,\n return_tensors=\"pt\",\n ).to(device)\n\n # embeddings\n # b, c\n image_embs_0 = self.pickscore_model.get_image_features(**img_inputs_0)\n image_embs_0 = image_embs_0 / torch.norm(image_embs_0, dim=-1, keepdim=True)\n\n image_embs_1 = self.pickscore_model.get_image_features(**img_inputs_1)\n image_embs_1 = image_embs_1 / torch.norm(image_embs_1, dim=-1, keepdim=True)\n \n # b, c\n text_embs = self.pickscore_model.get_text_features(**text_inputs)\n text_embs = text_embs / torch.norm(text_embs, dim=-1, keepdim=True)\n \n # score\n # batch version of scores = model.logit_scale.exp() * (text_embs @ image_embs.T)[0]\n # b\n scores_0 = self.pickscore_model.logit_scale.exp() * (text_embs * image_embs_0).sum(-1)\n scores_1 = self.pickscore_model.logit_scale.exp() * (text_embs * image_embs_1).sum(-1)\n # b, 2\n scores = torch.stack((scores_0, scores_1), dim=1)\n probs = torch.softmax(scores, dim=-1)\n win_lose = probs[:, 0] - probs[:, 1] > threshold\n lose_win = probs[:, 1] - probs[:, 0] > threshold\n \n pickscore_label = scores_1.new_ones((scores_1.shape[0], 2)) * 0.5\n pickscore_label[win_lose] = pickscore_label.new_tensor([1, 0])\n pickscore_label[lose_win] = pickscore_label.new_tensor([0, 1])\n batch[self.cfg.label_0_column_name] = pickscore_label[:, 0]\n batch[self.cfg.label_1_column_name] = pickscore_label[:, 1]\n\n @staticmethod\n @torch.no_grad()\n def sdxl_encode_prompt_embeds(text_encoders, text_input_ids_list):\n prompt_embeds_list = []\n\n for i, text_encoder in enumerate(text_encoders):\n text_input_ids = text_input_ids_list[i]\n prompt_embeds = text_encoder(\n text_input_ids.to(text_encoder.device),\n output_hidden_states=True, \n return_dict=False,\n )\n\n # We are only ALWAYS interested in the pooled output of the final text encoder\n pooled_prompt_embeds = prompt_embeds[0]\n prompt_embeds = prompt_embeds[-1][-2]\n bs_embed, seq_len, _ = prompt_embeds.shape\n prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1)\n prompt_embeds_list.append(prompt_embeds)\n\n prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)\n pooled_prompt_embeds = pooled_prompt_embeds.view(bs_embed, -1)\n return prompt_embeds, pooled_prompt_embeds\n" }, { "path": "step_aware_preference_model/trainer/utils/__init__.py", "content": "" }, { "path": "step_aware_preference_model/trainer/utils/batchable_ddim_scheduler.py", "content": "from typing import Optional, Tuple, Union\nfrom diffusers import DDIMScheduler\nimport torch\nfrom diffusers.schedulers.scheduling_ddim import (\n DDIMSchedulerOutput,\n DDIMScheduler,\n randn_tensor,\n)\n\n\nclass BatchableDDIMScheduler(DDIMScheduler):\n def _get_variance(self, timestep, prev_timestep):\n alpha_prod_t = self.alphas_cumprod[timestep]\n\n alpha_prod_t_prev = self.alphas_cumprod[torch.clamp(prev_timestep, 0)]\n alpha_prod_t_prev[prev_timestep < 0] = self.final_alpha_cumprod\n # alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod\n beta_prod_t = 1 - alpha_prod_t\n beta_prod_t_prev = 1 - alpha_prod_t_prev\n\n variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)\n\n return variance\n\n def step(\n self,\n model_output: torch.FloatTensor,\n timestep: int,\n sample: torch.FloatTensor,\n eta: float = 0.0,\n use_clipped_model_output: bool = False,\n generator=None,\n variance_noise: Optional[torch.FloatTensor] = None,\n return_dict: bool = True,\n ) -> Union[DDIMSchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion\n process from the learned model outputs (most often the predicted noise).\n\n Args:\n model_output (`torch.FloatTensor`): direct output from learned diffusion model.\n timestep (`int`): current discrete timestep in the diffusion chain.\n sample (`torch.FloatTensor`):\n current instance of sample being created by diffusion process.\n eta (`float`): weight of noise for added noise in diffusion step.\n use_clipped_model_output (`bool`): if `True`, compute \"corrected\" `model_output` from the clipped\n predicted original sample. Necessary because predicted original sample is clipped to [-1, 1] when\n `self.config.clip_sample` is `True`. If no clipping has happened, \"corrected\" `model_output` would\n coincide with the one provided as input and `use_clipped_model_output` will have not effect.\n generator: random number generator.\n variance_noise (`torch.FloatTensor`): instead of generating noise for the variance using `generator`, we\n can directly provide the noise for the variance itself. This is useful for methods such as\n CycleDiffusion. (https://arxiv.org/abs/2210.05559)\n return_dict (`bool`): option for returning tuple rather than DDIMSchedulerOutput class\n\n Returns:\n [`~schedulers.scheduling_utils.DDIMSchedulerOutput`] or `tuple`:\n [`~schedulers.scheduling_utils.DDIMSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When\n returning a tuple, the first element is the sample tensor.\n\n \"\"\"\n if self.num_inference_steps is None:\n raise ValueError(\n \"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler\"\n )\n\n # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf\n # Ideally, read DDIM paper in-detail understanding\n\n # Notation ( -> \n # - pred_noise_t -> e_theta(x_t, t)\n # - pred_original_sample -> f_theta(x_t, t) or x_0\n # - std_dev_t -> sigma_t\n # - eta -> η\n # - pred_sample_direction -> \"direction pointing to x_t\"\n # - pred_prev_sample -> \"x_t-1\"\n\n # 1. get previous step value (=t-1)\n prev_timestep = timestep - self.config.num_train_timesteps // self.num_inference_steps\n\n # 2. compute alphas, betas\n alpha_prod_t = self.alphas_cumprod[timestep].unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)\n alpha_prod_t_prev = self.alphas_cumprod[torch.clamp(prev_timestep, 0)]\n alpha_prod_t_prev[prev_timestep < 0] = self.final_alpha_cumprod\n alpha_prod_t_prev = alpha_prod_t_prev.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)\n # alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod\n\n beta_prod_t = 1 - alpha_prod_t\n\n # 3. compute predicted original sample from predicted noise also called\n # \"predicted x_0\" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf\n if self.config.prediction_type == \"epsilon\":\n pred_original_sample = (sample - (beta_prod_t ** (0.5)).to(model_output.dtype) * model_output) / (alpha_prod_t ** (0.5)).to(model_output.dtype)\n pred_epsilon = model_output\n elif self.config.prediction_type == \"sample\":\n pred_original_sample = model_output\n pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)\n elif self.config.prediction_type == \"v_prediction\":\n pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output\n pred_epsilon = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample\n else:\n raise ValueError(\n f\"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or\"\n \" `v_prediction`\"\n )\n\n # 4. Clip or threshold \"predicted x_0\"\n if self.config.thresholding:\n pred_original_sample = self._threshold_sample(pred_original_sample)\n elif self.config.clip_sample:\n pred_original_sample = pred_original_sample.clamp(\n -self.config.clip_sample_range, self.config.clip_sample_range\n )\n\n # 5. compute variance: \"sigma_t(η)\" -> see formula (16)\n # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)\n variance = (self._get_variance(timestep, prev_timestep)).unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)\n std_dev_t = eta * variance ** (0.5)\n\n if use_clipped_model_output:\n # the pred_epsilon is always re-derived from the clipped x_0 in Glide\n pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)\n\n # 6. compute \"direction pointing to x_t\" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf\n pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * pred_epsilon\n\n # 7. compute x_t without \"random noise\" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf\n prev_sample = (alpha_prod_t_prev ** (0.5)).to(model_output.dtype) * pred_original_sample + pred_sample_direction.to(model_output.dtype)\n\n if eta > 0:\n if variance_noise is not None and generator is not None:\n raise ValueError(\n \"Cannot pass both generator and variance_noise. Please make sure that either `generator` or\"\n \" `variance_noise` stays `None`.\"\n )\n\n if variance_noise is None:\n variance_noise = randn_tensor(\n model_output.shape, generator=generator, device=model_output.device, dtype=model_output.dtype\n )\n variance = std_dev_t * variance_noise\n\n prev_sample = prev_sample + variance\n\n if not return_dict:\n return (prev_sample,)\n\n return DDIMSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample)\n" }, { "path": "step_aware_preference_model/trainer/utils/constants.py", "content": "import os\n\nhuggingface_cache_dir = os.environ.get('HUGGING_FACE_CACHE_DIR', None)\nsd15_huggingface_path = 'runwayml/stable-diffusion-v1-5'\nsdxl_huggingface_path = 'stabilityai/stable-diffusion-xl-base-1.0'\nsdxl_vae_huggingface_path = 'madebyollin/sdxl-vae-fp16-fix'\n\nsd15_model_type_name = 'sd1.5'\nsdxl_model_type_name = 'sdxl'\n" } ]