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Runtime error
Runtime error
| import os | |
| from collections import OrderedDict | |
| import gradio as gr | |
| import shutil | |
| import uuid | |
| import torch | |
| from pathlib import Path | |
| from lib.utils.iimage import IImage | |
| from PIL import Image | |
| from lib import models | |
| from lib.methods import rasg, sd, sr | |
| from lib.utils import poisson_blend, image_from_url_text | |
| TMP_DIR = 'gradio_tmp' | |
| if Path(TMP_DIR).exists(): | |
| shutil.rmtree(TMP_DIR) | |
| Path(TMP_DIR).mkdir(exist_ok=True, parents=True) | |
| os.environ['GRADIO_TEMP_DIR'] = TMP_DIR | |
| on_huggingspace = os.environ.get("SPACE_AUTHOR_NAME") == "PAIR" | |
| negative_prompt_str = "text, bad anatomy, bad proportions, blurry, cropped, deformed, disfigured, duplicate, error, extra limbs, gross proportions, jpeg artifacts, long neck, low quality, lowres, malformed, morbid, mutated, mutilated, out of frame, ugly, worst quality" | |
| positive_prompt_str = "Full HD, 4K, high quality, high resolution" | |
| example_inputs = [ | |
| ['assets/examples/images/a40.jpg', 'medieval castle'], | |
| ['assets/examples/images/a4.jpg', 'parrot'], | |
| ['assets/examples/images/a65.jpg', 'hoodie'], | |
| ['assets/examples/images/a54.jpg', 'salad'], | |
| ['assets/examples/images/a51.jpg', 'space helmet'], | |
| ['assets/examples/images/a46.jpg', 'teddy bear'], | |
| ['assets/examples/images/a19.jpg', 'antique greek vase'], | |
| ['assets/examples/images/a2.jpg', 'sunglasses'], | |
| ] | |
| thumbnails = [ | |
| 'https://lh3.googleusercontent.com/fife/AK0iWDxaRlJeZGIBuB3_oGKKhd5buKaL3kJ6moPp7r6svDYFkehrv5XKyF6mj_Pqy3yV-qDQQZj_n8CMpuYH_iDy5717rPL-qpXf-prcIv2pET4LDjFInFQVLoxuurB3_7fugCUogt5ZYIGlTgSbirJkHDqN5min3riiRJLd0ZuGN-ETDDCs5e0wohdX_Wl_Kv5RAdYjZqWFGfKcmzCF-1ny6bjCab-1hcDzIaokggkl3INTG23nhSLWhNB8EdeCdfkQmoF5fROfCPe0Lsvk6RwlAr-ZQ9jaszJ355oXOz4Y-IQLfWvnyfdyxQ02anJF7DiBZbfhH4WcA6faK7Pbjo7RIt-nTN6THwTGlEmxHEzrO-1iuy3j4UfMxPB7r-RjDrn7M9KwLuWSybIJ3dgSx_DF9OqIYkHeG_TSvs1Vi-ugbq9E0K20HNXkFlhe6ty7Ee5xr0nNqhD6lVr-6wbBvI1SJUQg03KoJkduYbTTQ-ibGIwCu7J24kdlo-_d1xBWC01zeW2MfqPjpdNHUtnPAF2IuldEsWMJMHEpWjmXwYfM1D1BmIcOuRLvdNEA6IyPD6VCgLVD27MdLWxdKHgpSvTZ1beTcMM_QuHV2vMJnBT4H7faPmLBe9yMTKlIe7Yf8fGiehOrfEgXZwNyRzmbOyGKfNRKiSVuqeqJt3h1Vze0UKFzAx3rYnibzc--58atwasvdKY_dj8f0_QKR0l2vzWogqh3NdhO9m2r77ni5JXaik25QQzF-BPe70ikVgEomHa4xySlf-Gr3Z1v_HWDa4kYg2KE3P0WjqUD_cmdylbZ45TIOkGAU1qmiEcTCs_wOkOIfCC58Z9P6Lff_BRxc_lhut8hp-Pe8tfRXhITYFFRthXforXyDuqzPmWBBz2EnUHqxa1aYOo4WeQc7KTXK2kF36qAzPwm2QFDreqV3QmS02Gev8MCz2U3bQQa9H8VSB4ZGhrzNWIwG1R8YU_G1Xb4BAgjYEZ5qX6WJaQrjuD6_Zw_pDxRew8t0mjj_tjCrmoZjpxjHsgtudH4IBah5xag0bGdUSThnszYJPM5g1weldimKE63HqaQTG-IN_N51nBken4K_0-liw73ABzUiA6EJzqCQKEoT2pejNTN88N9RFXXB5ZJ9x0NvtuMcy_JsrsVArfA5b7m4OGbwF6b5wN3Ag3XNQ3d58hVJ_Hw99HNIvrjTVCVmU0-DsYIu_njIfyjdqps1cyv6_f23F0X-q4ZsbooPoNg2lc3oeFtC2K58Dgr8JsBjt33Lnmra3YG2nBh3lkIycCvrUOS69xo8Aq9z2ODklCd9soUUQVNa2XKKsMofRi8ESGwuiWYKWdSI7XAXi4dbzFQhWwMRfsSAk3KGMpnUnlOd8Jx68fiGMwTKCCsgIPgo8mFZHSaN29ipzNoACG1bd0ZFX2qxa7UfMnnRobl-AcFLzTtOYD2T2PcIKKjnxy1-gG1Ff0mgGS-BCDl9TtCscAoKcXqTYWCSY5otnfVPcjgEFw_0KEmIbmrf6Rpyr7YGJFPTQfDRah5Ro5LhBIbXhFAkyyvWQZCecGWi3lRAh-pSm2zuGl0nVdykgMqzVwiat8_lhnHMBNRg9xWyLWrgw3bWpdf64Bjgshr0V1XV7kikgRDpyPMJPDNzX1jVASiL5S3dmWDnd49tdnaKBUXGAIWmTyUWfs1bDFZZoOfrmLrnLLUT9R11lMO0EgkZ48_UU7CMJKwgq6hegh-ErsV6S6_SLa2tQYqYlSNUESO7jw1w4_T9KTkM2635QlPH-A81yvgrSsRo0lq3uPtFEHP35-dfGT63yFd=w3385-h1364', | |
| 'https://lh3.googleusercontent.com/fife/AK0iWDw7CKs6uhv5cJnJ48zW_5UW3IjrXwWqvemhbJGzB_xNhHgNyt4NIP0xPkCK_nyGFEsbb-4iaeWnoJbbeQxU4yBnnuckhD5d-t1qgOuFke8NRVaGCDgtDimbwNY7Jkkc9pj3pMd33P7UhBg7rVueTL7hmAyhtT_wLD9B5a50VfM_T9ErEeYUgsiEYPoE-msuALr2Sl5t0jXttYNt8R2JHdXG8Y6EegiQaEdZjZbJxdwA5S175pCmrOvVVBH334NwarLF_HDr8ESIVEkYxNtLDwb0QPDZSDeci-JS2WZcvEQUmClbxuk3hjtOR0Rm6VX_wXo0-nczybNDdTiZFsBc1Fj1L_08VLYP1OIz1fbcRF7iv2luyGmgUijGaJLr0wtW_C65eXK9aU-NJ5KVt67JT-TncumCqBpvn1-msbzbcL8krOmYqiZthOcvnrIQHeW1jIUVD_zMZADmuRdpjxO-Dn3yC3uev0ve0u4b3vEHR4iiLx-4Jf5DrMvpfdHHIL76verfqhLkiz3gtZENg1jsRTcjH35AWspo3-lMJnZqMP9wzvw7ubBBP1QrblSwfflz4aAIzmh-WisQ5UWMSltQ9DwpT0UggiXe0JbbtloWkbo2_VpaaMJh7VhBQbvRbFUxCm__UPVfglTfdiNv1m2777oGwyDbv682I_qGDW5nG91D22pqo9-enRGLvCnN-STKtWTnJQ5Qod2QYRAEI-1IR9_h-UWtCyBLLpqcGKxkHaLZvjpDmTdiPhVkoG1irracCLbPJGvrrclorr1k0nqTIhvVVH8pNZdx-yCK6KFFGNAz3aSsmxGJURWEt4TQEVDLfet8iFzuLfD2Tg9gJ4kozB88G8PrfLnGopwlPO4y6GkcfJggtshQr7yuo3xnxtci1FlLhOJNkMKCg0xhL-tVDIfWsMCzSIk08XtGgitU7DK8CE7DlkA7chCuq2BHUTeFCEALF-5DLFxa-3ru30gtNsbd71sNH01S8qhTVegWM8yQwwXXhIVqoWM-e-SSUyQldgklvatev1V6iDxLh9u2nbhoxxCcVZvyKx7rUyhSEEQzXF6nYNQn5HaAmp4jW1rCoVVvVMNP_wfvY76vamAPkw22z_nylCpbKW0BQmTBZStyoB4u_lZANVkL0fbyWoxyfZHKOoDgyTKdzDWmbeiIPmr7nvF8QWYsq160pKzIKIhj9ccF2NqyXHqjaDzmqVfZ6EUg0sJ8CeiCUskbSnyxZHVeM5GgG7cj9xMncUUsdvd0WTw3g0aqstb2MAxF3ucO0EXP8dAWqU2XrJ6E0rA51Jdn16CW9KV5HMGEcB5Y6bN-Md8-FT1r_0NQq8ZOJ5nmTzg5JNSTEw-FOBwSvukEMDmbf6ADs1emocozcqk9KiYv1ii3niD58XPIhQWfBcgyYBsqbJQ5x-UqijqcihN7i3RgQMOVJem954MKU8D-DzSet2FUcGWseyzF6Sr4GJQn5g0rOXtFP2HwT2kTXy_pqad1ukQkovfQG30gbRvIRAzMmigVJskadvadasF7Lc5eo5Pm7CPPZQ-ZJUlZuowbagWm0Kz_T-PIreVNJ5WxxQ4w_HH27QqKxe_LZvHJ9y74O8oVCywVJeKQirxDc-yKuUNJ7vagJ5a-paB_cXf6RECpu7caFM6U3g43xVMtxDf-3d0r6G7zs0oPHb4uDbovgLzqooAnjNY3_RGWldXUks0pmCqFbEhLgl8JJd4Lyv5mt34WTDqPggRnraAV5AEtZ8NvGNDWk-ItblCZQ-neXYVj0z1p1rWSJiG1XaDa8ro7G9-7XJtwfJDnFZsf=w3385-h1364', | |
| 'https://lh3.googleusercontent.com/fife/AK0iWDxK7tdzr1tx6g49uS-pyR-7BdPJfk0_wWoErGJiQPMRbggXquguwth2I35go_GFsW8TUoj17p-jtCKCi0ryGH2gTGNvZEki8xfPn1aroIxOXRt7Ucl64Lu2cyixNSBqyJUBQEOL6LzY6DlwXJ5SVxRRvJkj1Vz-yJYz4mNA12YdDFPj7UkLs_7PEDRanydfEVFhIoiMrYitDW8fGsqn53PHaB8GoxoQHAXFyf82HPfhqqgKNRUR9yz1LAN5q72ERzxg5h4apStr16aLzijuuJF7wmtltf3HdTIMwAAd8Blsgk0d88rNZzItVZMdCeHgAyvoD7JKUCTWWJbu-uXf1PRd6mIg11OzweG04c-uloZFvJdF0pdfcfocibniKt-filYASFN43KYtO7Eyzc-YUHJn1qm73eDr0RkntHy1kQaDPmlRvlZrtbDgZIEau_FsMa-BRNq_jHlTDkyRR_8dyBf__na0I45lcChJWZCOfinbQwzQfiryK13yBB1bQDGk47JM5PTIEBK2JvsOEwezA9geZWhi3oIM6N4b1YXzvWbDYbN-cQvrBd6cuXoecUL-i_Qqda2-xGByMP4BUI0J1qF3mKwzTLvzZAk1f-zCUNHpOrzF2-WsXCUoL4R_t7MOZ2OeT7a5pCAnyiFX8Vdq3x9QzrJjjTqvttS5ElXVUGi5NBVeRV0hbXI5XLMKmiu-lWV3625eosYK0FE6hmFZk9ZAeSkrLDjkh9k1XwpB2x83kcpC_tkk25G9czy6utoNhC2YHc1uVvnUjZ0DjK2d0naLVOCKOCVRcTSvN3kt7PaLASArW5vOzZX0_LcjuhHSzK63mok6XvXXZ5AOiF2fDhUNUl1W8TaDz7aIssbibuLXxjpluone6WJglRrxAkpwtIw2rwk73icupAeVG6Frx2QctrHC0vLG4PKSfrIrZRiJC52Oxpp2dAMnhj76CIwX9wwbj86uSyIuxeIviiKumwTolZjbOKhPrHY-ZOydVDn_ZUsln4mOfDZXwUl9p1CanzpLCUsZTXcq12zTbHJRPwNw6SH9srQc3cTYYsWpBu77VmS5zVkIndUPItXWmUqds_2AI8LCUSWE9NVHiCRSw-B8J8j3SkqOD95-np2cMxDxoVX4nD11CwBr2W0xWS1kqc4mZL4aHdwhKdDcSKk6EGG4kmY1Eq1RsYxc0I08TJK-_nrbWTgA4NDTjh-oJpFFiF11ZHbEksKlSWBhH-MXF-0zxmiar-EIhAQe4hQX_suDty4GXxMwzcF84JthDAvWC0tGLJV5WQGLkLvBkTODythSNoN7l5HjnzoJf2JKI5AF8W3HJJWgntD0F2iFe5K0Ik_huovMbzGjkgPZDvWzrYpA2V6VZyPI2q08axvAfaevPrCd7G8Jy_gliK3hj3qxjIvqJXBUSO2puqum-TlUaSYgbjhWUCLXKE1BH8RRDt0brLklVvNwGpHxG6Eg9vmzp00fV3qhjoJqokTYOAxcAumtfUpyJFVk3cvpgm4826o4kKUM6eXRz8ke1L34YI9Rtf7Ppk2864hIBIy5xA6ajIpI_rIXWG8ogrhEp9GMbXGkLGiLMfd4t7P50JynPc9hYWfachaTlQfQWuz1NtV3ZGdTaEe5y_SpEwWG-UQ-_MglvY8p0EYNjGc6yhu4oEx9D0RN5H4QA2e0YjmVybG05Hfm7LwEBMBvHb8GHnpHGTxL-WDlZtU8DqDmGmhv5l4npcLnuVmSE4JcEtRJ651ZYn88wD2ghTAJtcEuyFW3nzZ2Na3z0LW4Y8Y42YdWb3hmgGsJAYBIa9YC_Cmh=w3385-h1364', | |
| 'https://lh3.googleusercontent.com/fife/AK0iWDwPG9L-g7hX4NKVZ45PuqnRkQUV3XZlMEWZxXASV2zYVytOhbhds--yBA0ZXUzxeEAbpGa3qCl8sXlu-of8ZjVVNSdSen-zgpoN0BT-R7JGmTqjT9aEdhnysy85Gqr4e4A5KPrInLLvCCkFSHolBEhu-hO1u8kEZ7aDuk8FsPvchP1SvqjnuWtY_OCYa2FpjHH_i24cITjs58nlDxNTFdQQNCnX4KLJQAzh5cKxtG_7pqoBfBzpXBvVDwoxPafbFn0X0u8oJ7V-VOh7faO0JtJrsfcdS06tvw_J29RbDdWojpUS_pRUtA8w9Z6flNlbShj4Ib8X1V6veQLrEYWi3SojO5tfntpl5bKXPGgWhsT8mykvfhg4Tq-Ti9kyPBDPQqEqf0ll9-wFHYoAWSMsmCvZ7kakuDMH1766rOk8QgqYbDr6kMuJw_OFBR2qX7DaQw9XnJqGv7J3guCj-yU5vXes4sOtZ3n1IOheXnlvJL79KhXbsLgznYBjC9uv0fDLuqKaFL34YisLY5xY_2zi_uzTc5BXmtoAFH8otrUVUTVyt6sEPaCtjyPzG2uoSq094QFY_FxagW0E6OYPqskBUwPzg_Wc2eBwazaGy4MXEoDzgvK3PId5N4MWqU232uHsUrEEaCUUKm8-KX43c0C6O2daqjwsh-bxKOIic3pHqDoAOcq-QA83qB7pPyWwGddsaOWRIdzf-QLrB55YuvTeTmOEL84m2YDtxNEHWdYcnXYlZEXAex1xMOfqkbCQGM8jSgC2di_794HKMpsNtKwYH31WoI-Pl73t4THuq9CX1pWYdjhH0ss1j3PUMJ4hELE187E2m6fhQLHNNSHRajfesIwPVP1FgP0W5o-AKLaC7o53R0XrOZ7SqYjOua5TX0RXyJE6ceZATyiZ_2tzUpc4baqRGJb88vr8dhfECh_1J7O_8ufMvYL47HWhVNqltcIGjujtIXp64XEqShlut8TkqtB1-3OqE7gNwfiP4859pPkk0q-kfIdaLCjqB6PeqNFNgcX0dfK_-weXpM-LjrgNM_aVlQEBKwTDjJteUHY3pFTrEU8FoPnPeIjbU_rGIcwA3Lf_NE0CJUhKG1gzKYWG00CCg0srvQRipJpVrW-4SkUrxtW28iq6kVRMER83sN2RnVi7ugEuZ3S-OPdgXkUglk3bIz9ehDcFfQrll0iCabIGQxrN9-7GfKSpi3j2nU0aLeZ6DxgQ7x9f9f-hUgxM97i_90SX-S_M6Lo6bA28RB515HqqXc8FN72Wsp_XlFp3oTb4cJtOI5SSUYkdxGtrn0AQWXuE0Rp1DEaUDEdVbu757FQsct4Us0jLByauEfiZcQS_lGTjzGQxud-2NMKIeIYOWAxBGz08eTWv4b2k_IsnekNnXAHzP5WCYFoNtDiGbgURj7QWz6Qh8HbTsFJCrI3mPYkTN2jMUwviTUKpeElUwqDeA_DhT5tiHa7ldtdzncNzpxH-J75asams5J_O2W5dJMN4PYUxGmVw5mhWEClFo2stMJOfPkrmaga4D1OXc_C3Utf6OWB5CBBHGNjfAekE3QWm7ibEtwC91g1pIujyCUEYVs8YiFi0RWcMsmhWG2yrghA9Hu3kERWuVT0nHHfLRx8L0_PjlQBkNijUjK_gI-C66729qLNsmdwZxym1JCFgV4xRT7Vu9EQGL3tyhbOLRWYlHAjBE6itM-DM5T7idIyWamFBb9Nt6ZFehCpslKzEHy2VtEyiRlZ2z9kH-IEIuZ2qCu3kiGL8m6yQTyboTCI7LNLYbSn497VR8h3WkcqWqlVlWOzGUikL=w3385-h1364', | |
| 'https://lh3.googleusercontent.com/fife/AK0iWDyoheia4yOtefRVFWnA5EHolnS-xa4pPy0yL4Wb3uIr1-mSHxQ54i0wKr3lk6uhAm5qjJ9UVqwut3UqR54log5XVnaeNu_3y9Gsn44quU_HsGAY0-84HygWkr9Ld0_Dt1JEefD_f82Ijp1TXQf1CS6IbruUOnrljFOraQ2Bu-1To3p2Pk-T3tU8xVCxU3pr6zvFz9UYHFTss8_Xw70ZtLRMT4x4suHtOaSPI42VTq_T8HEnm04Ie_0Yh0Ri2_P-qsaP2ysz-Wnw4Ykbj9nc2VIDqtvCRwti36mlNheyg_8xOLD9sMNWDu3PXoRtn7aBUpw50GMCqeGUMAUMPKhJuZoTDdQK2SVHJ8QwNhYcC8mhAmRFvt85hvuT6NyZ00SeYwyj9_rux77vZThx5ioDoUAH3CQBcwgH82xahatReym7ehL3DXm3JLHDdQLbRM6xvsE0X0MsMXkuNlx8wn6HyteW_yq8fK3wQJ_3XLh-gK5YOFdvd08A7IIK6qi__-o8nvEK_hfHgMMS-O9eT9acfa2Sr0rGGNvoUlpljVOyONyQftP2nGD92R51K2Xcq9oV2sjSu8TDDel2t2DYr5gMB3FsDfgEhQEWE-O9fRLkzIZnOTTAcUDoS-b3R_kB485Ry56FzKFbz3w3tvHhwJ3W-sqvygb8LDoF3qjURWrf7Pau6UMjTSPH6FTjbVzZeWITKsSnA14xA2wj6xi9Bp3JkCOsT0qOfXrPkK7uT3H2U1M8uqFjpNTj6u3tZyF8GgueprmH13rJjYst9d_vevhpXpXIhSDvAbJuA3xG-YNr_SG8BMXpi7N3NC0VHmBXhl_wDBVUnAD6VVqqNtXzB-6NdZzjZKnxApDdi5SGp8C9kDd6bkaXUmwG__BRNVqdbchMw3H1re5t9VxiqWTelfGl6UqAX1W8RzQR21bgu1x7EAGbVsC_UpMxeDeJq9PprMF9cCRC9ziT_H2-ubctN7O9qPpADPT0nqWN28vH-9CqB4jPeBqYwi658twIpLwRFvEukajsrvb-OqmeesnT9QpCPEpL4G0HrjB9rkRX7g-T6q3kqbGfvnqgW4Q8ilOUnkEsFK3qLCIVxwp6B3t2yg8XSOsM3tnzsA1Ua8MofFKqvmwaq7QbIBcMOHa50I1fxRMs4YEVLgu89fSZxRrKSr_8oUXcRWiqSgF-pLLU37GYMrn3yXUJxxO4bXiEifeK8id5H1khO-8ZEBXzwuZBQBYLXbCkCou6enZu98tfPA-prr_NpKfSZM4e0clZWhjo1761-BmJSlIG0JrTo2N2cKhVz-WM5BZjVr1FPYOri5fIjORUL17_RbqMw5MefYN6tLPnpVrOSUmKW9bVgdFdOVpj9Wg5lZxxswAM5qK-wOzEjfdBCW0xjKzxD97zhszCKxc7Rj2uoaJzk9CeauU83LYcihkyMHn5IKhLeAou2yKEwgqXkU0LUObdUxqjavnVgVMcVYRds_j7zpmpKNT_KOV9s2jus8aptJl8sXZ_Gzw0vi7wC_AuAGfmNCsZBEFhn_b_ZgONqdaR9EKwP0hVRDNw4dZkYn9MGqeiX77I40eEbnwVbKaUZvK2Nrt7ukjkgSP6FdvZFfs-aVIUFMc6rBAknAFDFHPzFYcy9ANPDgVAlms8fO5GGuid8kgpxtjSoneUG_A9Az6JY-suY8zFr6mDtJC1LuY9ftIKW37ZaMtutqhbBX5b5w4DLnO32Uv_ZtK1nbV8E4T0KY6qHm2kZiIYuYCsHysbvNW4MODUihrpDh1UfILdMEsae6zGpCTQ7gnKlrB7QJ5Ig28y96Om=w3385-h1364', | |
| 'https://lh3.googleusercontent.com/fife/AK0iWDxriikbWcV-sJ5xBcy8xcJHsRC9EEBmCimXzsrBiSOy-UnRwSoGhdXNqnB92vMZk1LiOn0h3KbBMgbFr0I0SmQG5TwG__bM8AvtMrXA-DGAaNTktO1JcSPb-wVr_OepLK6P1hHyGYSvcGDdF03pIFNcKxN6QLCZB6rgFLdaWd72z3Dx8eB8QLtU8P_4G4sT2oJ0hAUlz9mKz9lTxWDWl0-1ufKUctmvWfU8EjNuQQzckJA0YwvV2jDl4ZA1r42UHDss6dy6hkjmPoDZN-p2UW_3Ju4vOVAdv0Pf73demNu-L1LALuK0rq4d7OHUaAuP0bubXJAH-wsuVervwPQDEsmBwR-FdW8jfdppKxy4MC2ISf-eyLmsTYR9dLPIlKkOAHh_84vdLeWdGtxs9gES-jhrqOiW-brFtIZoKbH_nR1yLeq9IJ7Z7-GJk3PVi_Ex7gT9WJyIuySNi6s7GH6AnDFf9wfHkzyJ1qLDKMddrNi4GfEyO89S0yScxZFW7hERAH7T2_1YqkeMv48ik9dMA0RcpJK0LYA8GDD5_MQaycXUjeoCO5tvlGQUEE8Dt815Ev3xh9dlKJiKJf_cClK_kL7iBICasjcVSNxP606Zn1fBTc_hF3QymDP8Q_Xl-g9p-ufobo_x2x6nOOdfiq1q37ik_3kPZCPPCnI2OtM3EYZ_yVlFmwWbtqxp2Rz3jaWo6t1TL8PWkFZno-aqJ8YEm4ppZVyX7ne6GTFKzFpW1SKnAnjqu67LjS0DCFhFATAcYhWRmdb5cMXve72eU5DNLgKZOaM0THDa5dnQPaRmu-c-7HlB3WISebcJHj0vIDw7DLwxaCnqqLgybvqW7O-Rt82bat5Lc-jIyMjjkZOutnc3OoYTPRN5PrLZ6HXGFBTq_s5fCimxpXvlw0bzNHqOzovgP4NC6UChXwn9CxSrbLou8vuqeD4YqyjBhh5Do3l7KcGMZtYUUMUhVf7fUrZIJcLb-ZBrg6UdiPRc3h9jcubLjXcPIrzxZeqgQ2ccJRljZqq4CBoX8WU6AiEe51Hbp0C96693G7rVomhzxa8JCMCCL3sy8v8nl1tqfqQ3kE53XnvzuqNMzJCNIfZg-GehqMBmJZp-Vaup9DOLWYWnzRqYsO9pC9r37Ajhh-wkxZyV0XhXD6EecYGcXBX8TOQRF9OMV9BYRDYVnpnGfTBGyxI7lkCU3prZQL0H4JlS48k38oWj9fE1F-PnGysDXLAxYqEERP-AXIwgsPyoKYrq7mG5UzBcEn6qminlmT2wM-EnInAb7F9e2aMxFwWv17RG2fWkO28p4t9hiMEyvAQfYyWKhGui1yqPnHnytNe3BJspV0uekBUBauvvHHLF0_tgHTJwcU03KlOLto7iIOUuwLgBT3z-_diWu_w9WTiOqZppBYKHUVfQlR1JsFn0j0Tg-kk5NbRfRU5BB4nMtQlkpW3vjTrQW88SrSKNOr_vepd1F39EqxBNG4tIyR6lvSCSGefWzUxwHBoHou8MtGPvcdxInB7imHdpwCFtoVy0wGh2kXy36CyxXqV82VehVvmfc8AnNtgVWTPJ8vvs2AQ2WL7xd7lE-DCfy-la9d31t6iLxVAkq_S067Z3iyTiJg508a_BGt_UF41VW2m2dW2v8KRjgXks4Wz7kAvO7rLLhL2CMdh-W7bBP4bfVa9BfcKVKGxaa6kCQB4rXtFs2gEbWx4o6iXf6KIUgl-RAdAgYYjIsavlkyihtgQ28-1--7JjaeO61d3wLYgJ5_t_POL8OjmFJfPeO1m9HTzDJf30C5hVP348dRhrkxSTPlSo=w3385-h1364', | |
| 'https://lh3.googleusercontent.com/fife/AK0iWDzcrCgKc1IMiriL10KOqvN79VCXca9U5g657RP5HU1zsUNVcZPyefBpRCtevRE0k6FkGxUa4yKW5ELtqaSmVRE8R2jwZPHmpd0xeBvIHYeoySmntJD3wJl_iC9Ma20qubbH3OFIxNFFLCXMJDPY9cJ2D1xFzjD5t6jQi7tJKXc0B5W9vGTyQ3dzHlZuLEfTp4D58WszOFBLPsOq8zeve0ej-2wCEMkCrT8kTwfKTnsi_GXRpK1xFRZjczbA9kSwIxZ_x1iWNWW_XY8aw5Kgn5Du-4r3rBqpHr4_fzv5ehY20T7Cq6Rf15zCBnBad1HGxLLnrJrXujKwxHELaRfqXPItfQAmuoIfL-tgK6giSSNKDDT2Ynn7AXxHz5vzD3m4ALYWql9Qpe1Jw69AxGZt3vyuSC76LPhMmNIJzVIXVMLnw55hHCLH28GVg2WxLTLyUXoX7o-PHhFjGoBbx3X58yQjyrEQPbfmY3gtuijG_vrjoz3SPcoG4eXB6d_NnEM3b84Ml9eqE8BjlACMEJriwtqLEstPBhIsJygDZKB822bY63pG2hYzYi5-bWS19NRwun-jdNraiye8D9AIqxrXaknLvbCnWbgbKDJzlRnt9Nz8GJKU__oZ1wQgzq2DOQqEoJxPwpocJxwYjnzIb-dFqhcLkR9SQVx7rAEyJ_u23WBSp4AVTw6c7sYxd597C5EFBvzOv4qYtJp1G-hBrvHYFvYD13oDtgcFDO_F0nXg7XwmCJ7aQ28Hp79dWewQiSq0nBcgWSOic1Q4feWLFEL21Dw0pFmSVF9f3C67YNA7ZXukOAnv6xUN5RNVoBl58-KDOFSQZYysT8FGYaaY8bFRvYvI-VXcBtMuAw1VYMKLKHrhg2mSvvdPCxsGH_rQWijgGUm9pvqbmyrPzYRd-Tr_i6pKH7EMcBYgcvZNMk9_B2EMj6NUWLVkHEUTDiovgFQJDrYAuiUcKZgp_MXJcTDO79qJEsiP52C83C8Vg91w4t_Q0dPklY8EjjpbMZGvsr5NC-TlQraNFszsrUA_JsNdnMW6Te2GMnXHDaHjEDCdX2kY3XC1Ltzvl3f4tHY12OMOUzMKHFWHWyBNYNKlHnwPdxYNxvHKa-9p_okvxw547oBfsXwUrRpQQVxljmLZJbbGpxkfbEW6Rg70MRKHdbEUg0h0FS7F90kuD7pR1zUFv92fPoI5BUNUn2XjQb-DZZaC1oF5VtRMgO8RSFM_Dolrx_c6ZrPLy7bllakKTDj48CNvafL3UxaD6x9FFu4dNQVHKidcVS__EZ1SYMSZoDkD2sna5Pzpjl0Qz61U4KVAz5lSCJbF1stdiwO4jMwzmAgMNV5-fnJA3kkf9dDaIzqk2diKUh-WGSOwEwwizH54Y-e2EASTqCzGK7FGoVRVr1d1PdN7wcd8MuRMXqBfonrIGmf5cSiuvOL6odbSChO2WFOKkRbBHeV1pO46uaBVeLKGjcbAALymYsv23_veIW-RQdHnaVlvB2HE7wD1afI-3LaRKCjCUD_X5QJuTXff_EQayDwtW46_0hYRI76LGXjQuOc_LUdIi2QHlO4kt2kR8eLBPm09gKl0GJrZoS9HxKS0sERHCjUvugFcvVmgt7idjFKfi0AuUz9XmqYCoSRiMYj9z612Ot5L2D00SLTOcTLV6nlx-PeBpAyiu2ia0ehvVjzLUn9ai0_XOg1bU-ab8fvzrRJIrao5ZxdRp9wF41lJpkanwvurynNJXXu0uk2SoA1_soIeLshOsONxT4DG_PitVFMYjxY8rgx7hfNBJmAJW4GZPsnx1_P5Ojip=w3385-h1364', | |
| 'https://lh3.googleusercontent.com/fife/AK0iWDziEIX1beA1lUdgcqMcnVcmRuODfH0IHpIkZW5YYZzhQRcRmYCfi9N7-vRWfcfMTuR28ZWDA5EngVjUpwIbVxRBF2DaIb_lJhd9zG3arGxRbi7CwmWdhAXeODEvniYR-IxtWB9lYpNd9hJ8wdleTP_ai10Xscy7iFeXOmFZ76dnr0r690LnZULOd7iyGv9EWZmhKhw4wtEJsqi3e1Yu4CXRsVrM5KLYKG-EWdRW_-m9H3o0G2W7KvOwDvqAewIz5zApPBHvuEE6x-XaUOuF_FuYQVhsKpfI_1Y70SCjOCphpbn51Bv8idg0tgTDn8oL8hkvSl50VqgQqjCLNxmCHlQE88xmjK_4NMI3kbIBLWfiPGCURr95dZt8eniqVi7yu8LNgkaMixAdRBCrQF_z56EsIvkozenXBdS2FiaUEh7LqIHLBcOa6ZaxV6_3t5Q83wgJTaM4cNkvH5_nCeQ9wkwKjf7zcBxFusa5LvhM-qSm4BJz3WzE1zgqTLVnDeh-EFNPMilPhevOdBuNfTY_VF8chvWS5Nwwcxlls8xSdVVqblYGw8YBlzWdi_X5PqynTKn6aWE0IiWOzA_O0hg2q1FtAHRT-PaINo4wjIbBar6fiNNwcZIeTKJrijHcpkIhnI8PHxrUtO3s0c2pfLFvuxCCRMSEfxpcwt0rz-ODEWIZkALajwE2SyFV6Qioc4fH_xWnI-jgRvzHjDf2c14vx3bXjM_gy-25mrECLQYcSWWZVINUbvKf6_YQDwwzAKL9zhMpyGa4EToTBhMSmroGi-NwIPxh8gAfdBCAh8TFAdg2aA7D3N_KpAv4Eh5bkovhCiALFYkGLch6KogZcn7NU3OX8qyn_wJ3oGO2CFmfKkMtLHqmjQpnLtM1U9BPnRELir7pNyG9bTNzs-Vz7-Hzu3vJavGeRhypl0JCoGfO08be-ee_7EnUcKSdepfd3dG39Gc1eulLgIVCRb82Ga5mAkNp_SDSa2BGI24--uOyAUwTBazpQjJ25W0wsHpLRF4obk8Tygl8Fgt2F-VPXYz1-q0x3_KZVWf-PJmKjYD6t3ICuBMoFeJtQUxp88WlSKC7KvhEZYdEaHmEabNNK7j-VTAgi0BeBaw_dTTO0tad9rXbCW9Co3Tc1YXv53oz96VURj-FAKHk_PKPRSV7-NO-BWAk1DOTq3ZDnlKUTA5-x6k4IR5HyNzW9C7rIPGzd_PRA9ddSiRxOjSiBru_P8xS0zQn6p75V48ZkoNsLPWEWCKhANJOaOB7Y01pg3wjjnftuxkp0KpokrlCZVUn2eKPmB0Oee6TP_6DVFhgM6ksqLHO-sNxpehUjWDx84znkN0MihGRgl6TK-6xnWzD9tjvIOsK0mBzk_XY3Vuvb5OEZvLzDJ5POqNHjLcAFaDtX7gsAUtEWk20qmRbpGBnHiZv2kLOUWCy6ICkc3yFv5uUMx7pxgfc_YO95ybO8-FTDG7m1yaoz-WdLV3tHao4_MfFaRXGKtV0_7xnlyXEZ3tMYwKu4hRx2lIOsL4Aff_O8-H0jmJId0llt__iOdVDkuypQWQDOKGGP9B1_gfLkV-ymEP0Bl59jQWNnAqE-jUpTeRRcUB6FkcH8XBPKL7F9N0sq-6XeOmPPpsecmm3SflF6zJ1YV8Uv6H4_9_uQLVBB8wXSvtcQuwgzYnrtpjpMQwFqSvJDhcCPGRfRCR6H7oa-T_ACYAMcICpl8felwVUOQs4O03ywLHNrZBY05hS13cj-_aYw69kw9TdetT-GbvTKC6eY5uwBTq4ytb4eeJQJc4zBlB2Dw1vKmcgIFfZ=w3385-h1364', | |
| ] | |
| example_previews = [ | |
| [thumbnails[0], 'Prompt: medieval castle'], | |
| [thumbnails[1], 'Prompt: parrot'], | |
| [thumbnails[2], 'Prompt: hoodie'], | |
| [thumbnails[3], 'Prompt: salad'], | |
| [thumbnails[4], 'Prompt: space helmet'], | |
| [thumbnails[5], 'Prompt: laptop'], | |
| [thumbnails[6], 'Prompt: antique greek vase'], | |
| [thumbnails[7], 'Prompt: sunglasses'], | |
| ] | |
| # Load models | |
| inpainting_models = OrderedDict([ | |
| ("Dreamshaper Inpainting V8", models.ds_inp.load_model()), | |
| ("Stable-Inpainting 2.0", models.sd2_inp.load_model()), | |
| ("Stable-Inpainting 1.5", models.sd15_inp.load_model()) | |
| ]) | |
| sr_model = models.sd2_sr.load_model(device='cuda:1') | |
| sam_predictor = models.sam.load_model(device='cuda:0') | |
| inp_model = None | |
| cached_inp_model_name = '' | |
| def remove_cached_inpainting_model(): | |
| global inp_model | |
| global cached_inp_model_name | |
| del inp_model | |
| inp_model = None | |
| cached_inp_model_name = '' | |
| torch.cuda.empty_cache() | |
| def set_model_from_name(inp_model_name): | |
| global cached_inp_model_name | |
| global inp_model | |
| if inp_model_name == cached_inp_model_name: | |
| print (f"Activating Cached Inpaintng Model: {inp_model_name}") | |
| return | |
| print (f"Activating Inpaintng Model: {inp_model_name}") | |
| inp_model = inpainting_models[inp_model_name] | |
| cached_inp_model_name = inp_model_name | |
| def rasg_run(use_painta, prompt, input, seed, eta, negative_prompt, positive_prompt, ddim_steps, | |
| guidance_scale=7.5, batch_size=4): | |
| torch.cuda.empty_cache() | |
| seed = int(seed) | |
| batch_size = max(1, min(int(batch_size), 4)) | |
| image = IImage(input['image']).resize(512) | |
| mask = IImage(input['mask']).rgb().resize(512) | |
| method = ['rasg'] | |
| if use_painta: method.append('painta') | |
| inpainted_images = [] | |
| blended_images = [] | |
| for i in range(batch_size): | |
| inpainted_image = rasg.run( | |
| ddim = inp_model, | |
| method = '-'.join(method), | |
| prompt = prompt, | |
| image = image.padx(64), | |
| mask = mask.alpha().padx(64), | |
| seed = seed+i*1000, | |
| eta = eta, | |
| prefix = '{}', | |
| negative_prompt = negative_prompt, | |
| positive_prompt = f', {positive_prompt}', | |
| dt = 1000 // ddim_steps, | |
| guidance_scale = guidance_scale | |
| ).crop(image.size) | |
| blended_image = poisson_blend(orig_img = image.data[0], fake_img = inpainted_image.data[0], | |
| mask = mask.data[0], dilation = 12) | |
| blended_images.append(blended_image) | |
| inpainted_images.append(inpainted_image.numpy()[0]) | |
| return blended_images, inpainted_images | |
| def sd_run(use_painta, prompt, input, seed, eta, negative_prompt, positive_prompt, ddim_steps, | |
| guidance_scale=7.5, batch_size=4): | |
| torch.cuda.empty_cache() | |
| seed = int(seed) | |
| batch_size = max(1, min(int(batch_size), 4)) | |
| image = IImage(input['image']).resize(512) | |
| mask = IImage(input['mask']).rgb().resize(512) | |
| method = ['default'] | |
| if use_painta: method.append('painta') | |
| inpainted_images = [] | |
| blended_images = [] | |
| for i in range(batch_size): | |
| inpainted_image = sd.run( | |
| ddim = inp_model, | |
| method = '-'.join(method), | |
| prompt = prompt, | |
| image = image.padx(64), | |
| mask = mask.alpha().padx(64), | |
| seed = seed+i*1000, | |
| eta = eta, | |
| prefix = '{}', | |
| negative_prompt = negative_prompt, | |
| positive_prompt = f', {positive_prompt}', | |
| dt = 1000 // ddim_steps, | |
| guidance_scale = guidance_scale | |
| ).crop(image.size) | |
| blended_image = poisson_blend(orig_img = image.data[0], fake_img = inpainted_image.data[0], | |
| mask = mask.data[0], dilation = 12) | |
| blended_images.append(blended_image) | |
| inpainted_images.append(inpainted_image.numpy()[0]) | |
| return blended_images, inpainted_images | |
| def upscale_run( | |
| prompt, input, ddim_steps, seed, use_sam_mask, gallery, img_index, | |
| negative_prompt='', positive_prompt=', high resolution professional photo'): | |
| torch.cuda.empty_cache() | |
| # Load SR model and SAM predictor | |
| # sr_model = models.sd2_sr.load_model() | |
| # sam_predictor = None | |
| # if use_sam_mask: | |
| # sam_predictor = models.sam.load_model() | |
| seed = int(seed) | |
| img_index = int(img_index) | |
| img_index = 0 if img_index < 0 else img_index | |
| img_index = len(gallery) - 1 if img_index >= len(gallery) else img_index | |
| img_info = gallery[img_index if img_index >= 0 else 0] | |
| inpainted_image = image_from_url_text(img_info) | |
| lr_image = IImage(inpainted_image) | |
| hr_image = IImage(input['image']).resize(2048) | |
| hr_mask = IImage(input['mask']).resize(2048) | |
| output_image = sr.run(sr_model, sam_predictor, lr_image, hr_image, hr_mask, prompt=prompt + positive_prompt, | |
| noise_level=0, blend_trick=True, blend_output=True, negative_prompt=negative_prompt, | |
| seed=seed, use_sam_mask=use_sam_mask) | |
| return output_image.numpy()[0], output_image.numpy()[0] | |
| def switch_run(use_rasg, model_name, *args): | |
| set_model_from_name(model_name) | |
| if use_rasg: | |
| return rasg_run(*args) | |
| return sd_run(*args) | |
| with gr.Blocks(css='style.css') as demo: | |
| gr.HTML( | |
| """ | |
| <div style="text-align: center; max-width: 1200px; margin: 20px auto;"> | |
| <h1 style="font-weight: 900; font-size: 3rem; margin-bottom: 0.5rem"> | |
| π§βπ¨ HD-Painter Demo | |
| </h1> | |
| <h2 style="font-weight: 450; font-size: 1rem; margin: 0rem"> | |
| Hayk Manukyan<sup>1*</sup>, Andranik Sargsyan<sup>1*</sup>, Barsegh Atanyan<sup>1</sup>, Zhangyang Wang<sup>1,2</sup>, Shant Navasardyan<sup>1</sup> | |
| and <a href="https://www.humphreyshi.com/home">Humphrey Shi</a><sup>1,3</sup> | |
| </h2> | |
| <h2 style="font-weight: 450; font-size: 1rem; margin: 0rem"> | |
| <sup>1</sup>Picsart AI Resarch (PAIR), <sup>2</sup>UT Austin, <sup>3</sup>Georgia Tech | |
| </h2> | |
| <h2 style="font-weight: 450; font-size: 1rem; margin: 0rem"> | |
| [<a href="https://arxiv.org/abs/2312.14091" style="color:blue;">arXiv</a>] | |
| [<a href="https://github.com/Picsart-AI-Research/HD-Painter" style="color:blue;">GitHub</a>] | |
| </h2> | |
| <h2 style="font-weight: 450; font-size: 1rem; margin: 0.7rem auto; max-width: 1000px"> | |
| <b>HD-Painter</b> enables prompt-faithfull and high resolution (up to 2k) image inpainting upon any diffusion-based image inpainting method. | |
| </h2> | |
| </div> | |
| """) | |
| if on_huggingspace: | |
| gr.HTML(""" | |
| <p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. | |
| <br/> | |
| <a href="https://huggingface.co/spaces/PAIR/HD-Painter?duplicate=true"> | |
| <img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a> | |
| </p>""") | |
| with open('script.js', 'r') as f: | |
| js_str = f.read() | |
| demo.load(_js=js_str) | |
| with gr.Row(): | |
| with gr.Column(): | |
| model_picker = gr.Dropdown( | |
| list(inpainting_models.keys()), | |
| value=0, | |
| label = "Please select a model!", | |
| ) | |
| with gr.Column(): | |
| use_painta = gr.Checkbox(value = True, label = "Use PAIntA") | |
| use_rasg = gr.Checkbox(value = True, label = "Use RASG") | |
| prompt = gr.Textbox(label = "Inpainting Prompt") | |
| with gr.Row(): | |
| with gr.Column(): | |
| input = gr.ImageMask(label = "Input Image", brush_color='#ff0000', elem_id="inputmask") | |
| with gr.Row(): | |
| inpaint_btn = gr.Button("Inpaint", scale = 0) | |
| with gr.Accordion('Advanced options', open=False): | |
| guidance_scale = gr.Slider(minimum = 0, maximum = 30, value = 7.5, label = "Guidance Scale") | |
| eta = gr.Slider(minimum = 0, maximum = 1, value = 0.1, label = "eta") | |
| ddim_steps = gr.Slider(minimum = 10, maximum = 100, value = 50, step = 1, label = 'Number of diffusion steps') | |
| with gr.Row(): | |
| seed = gr.Number(value = 49123, label = "Seed") | |
| batch_size = gr.Number(value = 1, label = "Batch size", minimum=1, maximum=4) | |
| negative_prompt = gr.Textbox(value=negative_prompt_str, label = "Negative prompt", lines=3) | |
| positive_prompt = gr.Textbox(value=positive_prompt_str, label = "Positive prompt", lines=1) | |
| with gr.Column(): | |
| with gr.Row(): | |
| output_gallery = gr.Gallery( | |
| [], | |
| columns = 4, | |
| preview = True, | |
| allow_preview = True, | |
| object_fit='scale-down', | |
| elem_id='outputgallery' | |
| ) | |
| with gr.Row(): | |
| upscale_btn = gr.Button("Send to Inpainting-Specialized Super-Resolution (x4)", scale = 1) | |
| with gr.Row(): | |
| use_sam_mask = gr.Checkbox(value = False, label = "Use SAM mask for background preservation (for SR only, experimental feature)") | |
| with gr.Row(): | |
| hires_image = gr.Image(label = "Hi-res Image") | |
| label = gr.Markdown("## High-Resolution Generation Samples (2048px large side)") | |
| with gr.Column(): | |
| example_container = gr.Gallery( | |
| example_previews, | |
| columns = 4, | |
| preview = True, | |
| allow_preview = True, | |
| object_fit='scale-down' | |
| ) | |
| gr.Examples( | |
| [ | |
| example_inputs[i] + [[example_previews[i]]] | |
| for i in range(len(example_previews)) | |
| ], | |
| [input, prompt, example_container] | |
| ) | |
| mock_output_gallery = gr.Gallery([], columns = 4, visible=False) | |
| mock_hires = gr.Image(label = "__MHRO__", visible = False) | |
| html_info = gr.HTML(elem_id=f'html_info', elem_classes="infotext") | |
| inpaint_btn.click( | |
| fn=switch_run, | |
| inputs=[ | |
| use_rasg, | |
| model_picker, | |
| use_painta, | |
| prompt, | |
| input, | |
| seed, | |
| eta, | |
| negative_prompt, | |
| positive_prompt, | |
| ddim_steps, | |
| guidance_scale, | |
| batch_size | |
| ], | |
| outputs=[output_gallery, mock_output_gallery], | |
| api_name="inpaint" | |
| ) | |
| upscale_btn.click( | |
| fn=upscale_run, | |
| inputs=[ | |
| prompt, | |
| input, | |
| ddim_steps, | |
| seed, | |
| use_sam_mask, | |
| mock_output_gallery, | |
| html_info | |
| ], | |
| outputs=[hires_image, mock_hires], | |
| api_name="upscale", | |
| _js="function(a, b, c, d, e, f, g){ return [a, b, c, d, e, f, selected_gallery_index()] }", | |
| ) | |
| demo.queue() | |
| demo.launch(share=True, allowed_paths=[TMP_DIR]) |