├── .gitignore
├── files
    ├── image
    │   ├── bee.jpg
    │   ├── cat.jpg
    │   ├── dog.jpeg
    │   ├── arc.jpeg
    │   ├── berries.jpeg
    │   ├── concert.jpeg
    │   ├── einstein.jpg
    │   ├── food.jpeg
    │   ├── glasses.jpeg
    │   ├── house.jpg
    │   ├── lake.jpeg
    │   ├── marigold.jpeg
    │   ├── puzzle.jpeg
    │   ├── road.jpg
    │   ├── surfer.jpeg
    │   ├── swings.jpg
    │   ├── teamwork.jpeg
    │   ├── wave.jpeg
    │   ├── butterfly.jpeg
    │   ├── doughnuts.jpeg
    │   ├── portrait_1.jpeg
    │   ├── portrait_2.jpeg
    │   ├── pumpkins.jpg
    │   ├── scientists.jpg
    │   ├── surfboards.jpeg
    │   └── switzerland.jpeg
    ├── video
    │   ├── cab.mp4
    │   ├── elephant.mp4
    │   └── obama.mp4
    └── basrelief
    │   ├── coin.jpg
    │   ├── einstein.jpg
    │   └── food.jpeg
├── marigold_logo_square.jpg
├── gradio_cached_examples
    ├── 18
    │   ├── Depth outputs
    │   │   ├── 299ab3ef0144ff335248
    │   │   │   └── cat_depth_fp32.npy
    │   │   ├── 9a9aecb6bc451b000cc3
    │   │   │   └── arc_depth_fp32.npy
    │   │   ├── c99982e80e8d9c0b50a9
    │   │   │   └── dog_depth_fp32.npy
    │   │   ├── 08a115340ea524934177
    │   │   │   └── einstein_depth_fp32.npy
    │   │   ├── 1112b1ca9f107132c5ff
    │   │   │   └── berries_depth_fp32.npy
    │   │   ├── 13cfd7cd0acfd90c3608
    │   │   │   └── glasses_depth_fp32.npy
    │   │   ├── 2456fcca77a17345e260
    │   │   │   └── road_depth_colored.png
    │   │   ├── 26b0eacc0203a3b721be
    │   │   │   └── surfer_depth_colored.png
    │   │   ├── 2847b2df4b1c235f7651
    │   │   │   └── dog_depth_colored.png
    │   │   ├── 2a35fc317380d4737290
    │   │   │   └── glasses_depth_16bit.png
    │   │   ├── 2de7e7ec84f177bb092d
    │   │   │   └── food_depth_fp32.npy
    │   │   ├── 2f5832324077dab185ef
    │   │   │   └── swings_depth_colored.png
    │   │   ├── 357b353412dd9759ad6a
    │   │   │   └── lake_depth_colored.png
    │   │   ├── 3b9f1c0a73b1eb66b3dc
    │   │   │   └── puzzle_depth_colored.png
    │   │   ├── 3c6ba7efdd300e7b7764
    │   │   │   └── lake_depth_fp32.npy
    │   │   ├── 3d61175e3f24234c9eca
    │   │   │   └── arc_depth_colored.png
    │   │   ├── 4cb13fd8b9ec9608e19f
    │   │   │   └── swings_depth_16bit.png
    │   │   ├── 550e81d9c69c40fa22b0
    │   │   │   └── wave_depth_16bit.png
    │   │   ├── 565217320fe1057733c9
    │   │   │   └── road_depth_fp32.npy
    │   │   ├── 58f4bc0302114565854f
    │   │   │   └── concert_depth_fp32.npy
    │   │   ├── 5ef3eed2059133180232
    │   │   │   └── lake_depth_16bit.png
    │   │   ├── 6a0860f054b0406feef5
    │   │   │   └── wave_depth_fp32.npy
    │   │   ├── 6d5d27b6ae03d7a40b7c
    │   │   │   └── surfer_depth_16bit.png
    │   │   ├── 8c492010216806b7c84c
    │   │   │   └── teamwork_depth_fp32.npy
    │   │   ├── 8e97f11903b9b898c2f3
    │   │   │   └── arc_depth_16bit.png
    │   │   ├── 9faf8f227f90f70bbdaa
    │   │   │   └── marigold_depth_fp32.npy
    │   │   ├── a4dcdec4a501e723b211
    │   │   │   └── dog_depth_16bit.png
    │   │   ├── a639dbc49c03860c86a7
    │   │   │   └── food_depth_colored.png
    │   │   ├── b264a159bd653f1548e3
    │   │   │   └── berries_depth_16bit.png
    │   │   ├── b42b9d9b1dd973751204
    │   │   │   └── surfer_depth_fp32.npy
    │   │   ├── b58afb3c3ab61448a7dd
    │   │   │   └── puzzle_depth_fp32.npy
    │   │   ├── ca3ffeaccc005c6fd5ee
    │   │   │   └── cat_depth_16bit.png
    │   │   ├── d0a4e40b24fa717ffabb
    │   │   │   └── road_depth_16bit.png
    │   │   ├── de994c47587fcbb3e990
    │   │   │   └── house_depth_fp32.npy
    │   │   ├── e0911d2e7a4a5debfbab
    │   │   │   └── food_depth_16bit.png
    │   │   ├── e29c660ea20526461142
    │   │   │   └── cat_depth_colored.png
    │   │   ├── e747489c970c86200120
    │   │   │   └── puzzle_depth_16bit.png
    │   │   ├── ed486427beaead8e6d4d
    │   │   │   └── house_depth_16bit.png
    │   │   ├── ee9d905b3a5337c2db21
    │   │   │   └── concert_depth_16bit.png
    │   │   ├── ef56552b1f18d14a2c19
    │   │   │   └── swings_depth_fp32.npy
    │   │   ├── ef827c6d4ffc7d48c85a
    │   │   │   └── wave_depth_colored.png
    │   │   ├── f53efcc50ebfeaac3b8f
    │   │   │   └── house_depth_colored.png
    │   │   ├── 068c389756186ef0ec21
    │   │   │   └── portrait_1_depth_colored.png
    │   │   ├── 099bb138bf851d8de112
    │   │   │   └── scientists_depth_colored.png
    │   │   ├── 0fb09ff06177b86b75cc
    │   │   │   └── portrait_2_depth_colored.png
    │   │   ├── 115d14d955de41ae1e33
    │   │   │   └── switzerland_depth_colored.png
    │   │   ├── 1ab135c405036a016085
    │   │   │   └── teamwork_depth_16bit.png
    │   │   ├── 2b8e100af28995f679f3
    │   │   │   └── switzerland_depth_fp32.npy
    │   │   ├── 3fea61e86ad657759143
    │   │   │   └── doughnuts_depth_colored.png
    │   │   ├── 4256ccd86bcb885f93a6
    │   │   │   └── surfboards_depth_colored.png
    │   │   ├── 4758b54d8928c9860970
    │   │   │   └── butterfly_depth_colored.png
    │   │   ├── 534e159c4967aec56bcd
    │   │   │   └── portrait_1_depth_fp32.npy
    │   │   ├── 67a09b9fd46c50fb1a30
    │   │   │   └── scientists_depth_16bit.png
    │   │   ├── 6b7fa86ff307ce280458
    │   │   │   └── glasses_depth_colored.png
    │   │   ├── 6eeb531b1c22dd97fae4
    │   │   │   └── portrait_2_depth_16bit.png
    │   │   ├── 70a6ecd79155c8fb213b
    │   │   │   └── einstein_depth_colored.png
    │   │   ├── 743f28023ee74ec7ba8a
    │   │   │   └── butterfly_depth_fp32.npy
    │   │   ├── 7b1d5a7cecd449b13a5e
    │   │   │   └── berries_depth_colored.png
    │   │   ├── 7fce16f173010012f6b6
    │   │   │   └── portrait_2_depth_fp32.npy
    │   │   ├── 8c1f452606cac27a66c0
    │   │   │   └── scientists_depth_fp32.npy
    │   │   ├── 8d6952f2bb813c899fb0
    │   │   │   └── marigold_depth_16bit.png
    │   │   ├── a20cbbb5b772a7b1763e
    │   │   │   └── pumpkins_depth_colored.png
    │   │   ├── a5db29c9ded284668775
    │   │   │   └── einstein_depth_16bit.png
    │   │   ├── a7486302ab044e374c04
    │   │   │   └── butterfly_depth_16bit.png
    │   │   ├── af5de30ca8186879928b
    │   │   │   └── teamwork_depth_colored.png
    │   │   ├── bf0a2b7b9d7d20d3fba4
    │   │   │   └── portrait_1_depth_16bit.png
    │   │   ├── c28a0efd69ada6c1de17
    │   │   │   └── doughnuts_depth_16bit.png
    │   │   ├── c9d2b99fd4995c01c932
    │   │   │   └── concert_depth_colored.png
    │   │   ├── d8b07ff54e7a206832a0
    │   │   │   └── switzerland_depth_16bit.png
    │   │   ├── dbc8049901349d03b961
    │   │   │   └── pumpkins_depth_16bit.png
    │   │   ├── eb7676ae3737abdd8b33
    │   │   │   └── surfboards_depth_fp32.npy
    │   │   ├── ed2db910fdec870cb513
    │   │   │   └── marigold_depth_colored.png
    │   │   ├── f024df95b9f9d82cd10b
    │   │   │   └── pumpkins_depth_fp32.npy
    │   │   ├── f6b17b1b9adbe5433e03
    │   │   │   └── surfboards_depth_16bit.png
    │   │   └── fd79bd61b90eb3c8750e
    │   │   │   └── doughnuts_depth_fp32.npy
    │   ├── Predicted depth red-near blue-far
    │   │   ├── 04f54e1ce435ff6459b9
    │   │   │   └── arc_depth_colored.png
    │   │   ├── 19eb29bd0e44aa61bd6f
    │   │   │   └── road_depth_colored.png
    │   │   ├── 295edcd8c90a1d58bff5
    │   │   │   └── cat_depth_16bit.png
    │   │   ├── 313712cc25c7afeec5e2
    │   │   │   └── swings_depth_16bit.png
    │   │   ├── 432db43f24ab254cdc77
    │   │   │   └── glasses_depth_16bit.png
    │   │   ├── 45e324d0b847afbbc4c7
    │   │   │   └── arc_depth_16bit.png
    │   │   ├── 478e837f6ca2f7737dc5
    │   │   │   └── wave_depth_16bit.png
    │   │   ├── 71ea41371688664fd898
    │   │   │   └── house_depth_colored.png
    │   │   ├── 7b90e60f0490de180bc9
    │   │   │   └── wave_depth_colored.png
    │   │   ├── 879cfd69d9c554f83d96
    │   │   │   └── dog_depth_16bit.png
    │   │   ├── 983c2e26ef549272c897
    │   │   │   └── swings_depth_colored.png
    │   │   ├── 9c492b4a05b0c27418e7
    │   │   │   └── concert_depth_16bit.png
    │   │   ├── a1e0b4145b283feacb69
    │   │   │   └── food_depth_16bit.png
    │   │   ├── b56ad8cd93517abd7a57
    │   │   │   └── food_depth_colored.png
    │   │   ├── b82a8e2c9968943eaf37
    │   │   │   └── surfer_depth_16bit.png
    │   │   ├── bf130ca9410ab692b150
    │   │   │   └── road_depth_16bit.png
    │   │   ├── c80da87c39169644afa1
    │   │   │   └── house_depth_16bit.png
    │   │   ├── cab7bd5be8a912541996
    │   │   │   └── berries_depth_16bit.png
    │   │   ├── ccbf8e60d9ee20e4e70b
    │   │   │   └── cat_depth_colored.png
    │   │   ├── d22944415f5ee905e9e0
    │   │   │   └── puzzle_depth_16bit.png
    │   │   ├── d28b5d4dcd878e6c5daf
    │   │   │   └── lake_depth_colored.png
    │   │   ├── db15ec0fec292ce702f3
    │   │   │   └── puzzle_depth_colored.png
    │   │   ├── edb434ab1e157595210a
    │   │   │   └── surfer_depth_colored.png
    │   │   ├── f10d9f52ef10b6ce2819
    │   │   │   └── dog_depth_colored.png
    │   │   ├── f8bc9d03bcb924e64306
    │   │   │   └── lake_depth_16bit.png
    │   │   ├── 0968bcc549dace8031f8
    │   │   │   └── butterfly_depth_colored.png
    │   │   ├── 18e6abaa75c6b4266122
    │   │   │   └── marigold_depth_colored.png
    │   │   ├── 1cb720d1f47690df2bf2
    │   │   │   └── surfboards_depth_colored.png
    │   │   ├── 2345a66ea597f0724ac6
    │   │   │   └── teamwork_depth_16bit.png
    │   │   ├── 24ec8180e34432fcb5d3
    │   │   │   └── marigold_depth_16bit.png
    │   │   ├── 25947e3f3a07b767d2e3
    │   │   │   └── doughnuts_depth_16bit.png
    │   │   ├── 2e503f2592bb798b7d75
    │   │   │   └── portrait_2_depth_16bit.png
    │   │   ├── 37a744be12464174a9fe
    │   │   │   └── pumpkins_depth_colored.png
    │   │   ├── 3b7e23ac2f10ff0acd40
    │   │   │   └── portrait_1_depth_colored.png
    │   │   ├── 66dedd2f9b9a56204b8a
    │   │   │   └── pumpkins_depth_16bit.png
    │   │   ├── 6d576b810915984be5d9
    │   │   │   └── butterfly_depth_16bit.png
    │   │   ├── 88870588e2c8198c46db
    │   │   │   └── concert_depth_colored.png
    │   │   ├── 8d5b5a9cc5bde83ad215
    │   │   │   └── scientists_depth_16bit.png
    │   │   ├── 9f9ad00dda4a8b76a9d3
    │   │   │   └── doughnuts_depth_colored.png
    │   │   ├── ace9fc12e651074b9ffe
    │   │   │   └── berries_depth_colored.png
    │   │   ├── adf7d8a23971611c0c2c
    │   │   │   └── surfboards_depth_16bit.png
    │   │   ├── b019af76a770991b9f05
    │   │   │   └── switzerland_depth_16bit.png
    │   │   ├── bb1b89e55f54f6ad11a4
    │   │   │   └── glasses_depth_colored.png
    │   │   ├── d3d1aea16f58fa4e9c34
    │   │   │   └── teamwork_depth_colored.png
    │   │   ├── dfced8fb6c3b5b3cdd85
    │   │   │   └── switzerland_depth_colored.png
    │   │   ├── e14a6d59396caef161db
    │   │   │   └── portrait_1_depth_16bit.png
    │   │   ├── e58db2a94f058c3cbda5
    │   │   │   └── portrait_2_depth_colored.png
    │   │   ├── e9c962c7d4300842fbd2
    │   │   │   └── einstein_depth_colored.png
    │   │   ├── f74d13a60bda9b080432
    │   │   │   └── einstein_depth_16bit.png
    │   │   └── f9fb6b3768c97d11bf63
    │   │   │   └── scientists_depth_colored.png
    │   └── log.csv
    ├── 29
    │   ├── Depth outputs
    │   │   ├── 5f96f60f0b5af752e271
    │   │   │   └── cab_depth_colored.mp4
    │   │   ├── 63df77111c5982e3d501
    │   │   │   └── cab_depth_16bit.zip
    │   │   ├── 8209c6d80711c2b193b6
    │   │   │   └── obama_depth_colored.mp4
    │   │   ├── d7825cd99c31975cb780
    │   │   │   └── obama_depth_16bit.zip
    │   │   ├── bce9827dbf145c2cd2e5
    │   │   │   └── elephant_depth_colored.mp4
    │   │   └── d49d367de8bff332fa2e
    │   │   │   └── elephant_depth_16bit.zip
    │   ├── Output video depth red-near blue-far
    │   │   ├── b1a647fc7fb469eb9c90
    │   │   │   └── cab_depth_colored.mp4
    │   │   ├── 315c7361216ee7b38070
    │   │   │   └── elephant_depth_colored.mp4
    │   │   └── ee3bed949e6cdc9413a9
    │   │   │   └── obama_depth_colored.mp4
    │   └── log.csv
    └── 58
    │   ├── 3D model outputs high-res
    │       ├── 409aac8c714a19097c19
    │       │   └── coin_depth_512.glb
    │       ├── 789054453156abfa3dac
    │       │   └── coin_depth_512.stl
    │       ├── a4a4773a0122b3417297
    │       │   └── food_depth_512.glb
    │       ├── c56ef4018e4584940192
    │       │   └── food_depth_512.stl
    │       ├── 1b5111a9ba7be9778d37
    │       │   └── einstein_depth_512.glb
    │       └── bee443acba581914a3e5
    │       │   └── einstein_depth_512.stl
    │   ├── 3D preview low-res relief highlight
    │       ├── 4237fcd0dad8ff3298f3
    │       │   └── coin_depth_256.glb
    │       ├── 4756f7e8f07d8445b1df
    │       │   └── food_depth_256.glb
    │       └── 9cfc51ac897aa0a455f2
    │       │   └── einstein_depth_256.glb
    │   └── log.csv
├── requirements.txt
├── README.md
├── .gitattributes
├── extrude.py
├── marigold_depth_estimation_lcm.py
└── app.py


/.gitignore:
--------------------------------------------------------------------------------
1 | .idea
2 | .DS_Store
3 | __pycache__
4 | 


--------------------------------------------------------------------------------
/files/image/bee.jpg:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:7643ccdbc9550e2bf6ebdd5c768db5bc829ef719b0d1a91b4f6f9184b52f4751
3 | size 146201
4 | 


--------------------------------------------------------------------------------
/files/image/cat.jpg:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:794796a86e56a4b372287661dc934daa2d15e988d01afe88afc50b32644c007a
3 | size 236008
4 | 


--------------------------------------------------------------------------------
/files/image/dog.jpeg:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:c932a965dfe63c8c6dbc1bb48f7ea245a6a6dd2fb40fd243545e908b3aa7aa62
3 | size 672127
4 | 


--------------------------------------------------------------------------------
/files/video/cab.mp4:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:e7857328de30257e2985e0218e18e35f0dbc6ca9dd9f89b28687881f13ca0a4a
3 | size 3268179
4 | 


--------------------------------------------------------------------------------
/files/basrelief/coin.jpg:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:d5295c5cb301ef73099e3dd91f80916e7b013f6b04d75759df57081b16a18adc
3 | size 632342
4 | 


--------------------------------------------------------------------------------
/files/image/arc.jpeg:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:f888e3770134e2073459026f58c568f7cf30524dd26a9182413c84b709e1b63e
3 | size 1010127
4 | 


--------------------------------------------------------------------------------
/files/image/berries.jpeg:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:dac1411ea48cf83b7a59c6424032f95b2ff496b3a98cdccf168bbed1c8f0aed4
3 | size 940276
4 | 


--------------------------------------------------------------------------------
/files/image/concert.jpeg:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:fc746e234cb8a3e483999ee4c4f4d22b4e6c48cb2655eaa47c0936f3a37b61dc
3 | size 420401
4 | 


--------------------------------------------------------------------------------
/files/image/einstein.jpg:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:d4a4543c0fffb2ca5ea3c17e23e88fcfcf66eae8b487173fbc5c25d0d614bdb6
3 | size 366986
4 | 


--------------------------------------------------------------------------------
/files/image/food.jpeg:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:a26151050a574b0dc0014e9c4806da3d6f6bc1297ee1035a16b9ace007a179af
3 | size 1041165
4 | 


--------------------------------------------------------------------------------
/files/image/glasses.jpeg:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:de8c0c20adb7c187357c21e467d3f178888574962027cdd366c390b63913ffec
3 | size 677382
4 | 


--------------------------------------------------------------------------------
/files/image/house.jpg:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:4087027e84a6323099fc839fd0b6816fd614814e92d12df21051cff3ed472819
3 | size 14934360
4 | 


--------------------------------------------------------------------------------
/files/image/lake.jpeg:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:181dc0f684f0f3b94bc4bec829becd3dec817f69032731edf55ee8370c6898f0
3 | size 1026470
4 | 


--------------------------------------------------------------------------------
/files/image/marigold.jpeg:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:575c1a7bc1199d86b5ec305b4efc12286842dee4a189e8699dcf8a6d0276807c
3 | size 416411
4 | 


--------------------------------------------------------------------------------
/files/image/puzzle.jpeg:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:60b66432124a0936c6143301a9f9b793af4184bc9340c567d11fdd5a22cc98cc
3 | size 373654
4 | 


--------------------------------------------------------------------------------
/files/image/road.jpg:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:58bb01aea37f6e1206260eddb6d003589d779e8b3fb3ef0a0f1e2e38a8fa3925
3 | size 13088652
4 | 


--------------------------------------------------------------------------------
/files/image/surfer.jpeg:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:52827abf2c3951b752d4e58c88fff7ab907672c58fda70b813df3922650c7495
3 | size 1007262
4 | 


--------------------------------------------------------------------------------
/files/image/swings.jpg:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:cae2ac669c948313eae8aca53017f10b64b42f87c53b9c34639962b218fdf1f1
3 | size 353138
4 | 


--------------------------------------------------------------------------------
/files/image/teamwork.jpeg:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:3cd48af8f3db4d89760cd6f40f2716570e697ae74a9bd88ed1ba36c0e68326b3
3 | size 699825
4 | 


--------------------------------------------------------------------------------
/files/image/wave.jpeg:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:7f14e77f7990d75104d6e3447077eb176d6437c58f5fb0fffcdb6015193b2d03
3 | size 1073936
4 | 


--------------------------------------------------------------------------------
/files/video/elephant.mp4:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:7d198ec2e3e5a308c5eeb18c9f3a882f6c5812d329d9e8497e1bf79ff466dd84
3 | size 3078416
4 | 


--------------------------------------------------------------------------------
/files/video/obama.mp4:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:4aa0ac19460e0966139247cc180f98398fb11a35e3ca5c90cb70f0c4704904de
3 | size 955458
4 | 


--------------------------------------------------------------------------------
/marigold_logo_square.jpg:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:bd5f1e527678fc913aee17ab69831551cfdb2934f673e9e97a7f011103b63c9e
3 | size 76006
4 | 


--------------------------------------------------------------------------------
/files/basrelief/einstein.jpg:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:d4a4543c0fffb2ca5ea3c17e23e88fcfcf66eae8b487173fbc5c25d0d614bdb6
3 | size 366986
4 | 


--------------------------------------------------------------------------------
/files/basrelief/food.jpeg:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:a26151050a574b0dc0014e9c4806da3d6f6bc1297ee1035a16b9ace007a179af
3 | size 1041165
4 | 


--------------------------------------------------------------------------------
/files/image/butterfly.jpeg:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:e0364b8eec31d2c113c15c2b6c892754130765e8e2c960adc87d51ca5c0ea8f9
3 | size 877657
4 | 


--------------------------------------------------------------------------------
/files/image/doughnuts.jpeg:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:2ede4170b4a17f0c076c1a336eb4d3c03d64688997a986e3a8101972016b799a
3 | size 606606
4 | 


--------------------------------------------------------------------------------
/files/image/portrait_1.jpeg:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:76e3ad74311975f0db43cdebd4202d1464e19b6950cc3e7c5aa0a160f95493c3
3 | size 506148
4 | 


--------------------------------------------------------------------------------
/files/image/portrait_2.jpeg:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:805ad1127b0d9d09068df70e3ab7aa7450ff802fa5464db8430787dfee1ec6a0
3 | size 525467
4 | 


--------------------------------------------------------------------------------
/files/image/pumpkins.jpg:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:92f03bc05dc882231bce735f2afb8c27eb9d0616166abe3794b39ff24314fd0a
3 | size 11260012
4 | 


--------------------------------------------------------------------------------
/files/image/scientists.jpg:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:7b164dfbc4ab6e491ce81972b8c0e076fdc4af622289d0aa3cb43ee3c2be4030
3 | size 444241
4 | 


--------------------------------------------------------------------------------
/files/image/surfboards.jpeg:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:326f9ffd3b85b29b971205eb87c2d0c9b5e4409b496be1eb961b46d5f7c5d6c6
3 | size 1158169
4 | 


--------------------------------------------------------------------------------
/files/image/switzerland.jpeg:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:81e35ba90f7736167ea3e8a0a58f932ecded07b00b012a5bd7df5dabbe0eb3ce
3 | size 846710
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/299ab3ef0144ff335248/cat_depth_fp32.npy:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:c044f1c9887648e376fcecba0ab7c842bc6883a7b3153f1ca6c46e05254b44e6
3 | size 5237888
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/9a9aecb6bc451b000cc3/arc_depth_fp32.npy:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:32c608321e04a95cd7ab56712041cf4f62baf4952764e1a1f3ad3660a3503cd4
3 | size 4377728
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/c99982e80e8d9c0b50a9/dog_depth_fp32.npy:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:108268dd0ecc12630ada4ea195937be23111106c6dbed7c80cc14a36c991d206
3 | size 6291584
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/08a115340ea524934177/einstein_depth_fp32.npy:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:47312a4edd16d84c631982bbdc7fe8d666146d0df6c70ee09285dde07e8bc036
3 | size 4194432
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/1112b1ca9f107132c5ff/berries_depth_fp32.npy:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:7c4a044cb2730b35833967b4963b52c0e8f66de9cf76209206f937409b1d49a2
3 | size 6291584
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/13cfd7cd0acfd90c3608/glasses_depth_fp32.npy:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:0bd3ee73cdf541e63e6e2913f01474fa9cbec5000c20e149b409bc1b480956f9
3 | size 6283392
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/2456fcca77a17345e260/road_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:a60241a8bde8ddcbc35add52633d1e0068c799971160092946825c04b0284984
3 | size 4683563
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/26b0eacc0203a3b721be/surfer_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:838d1f2b6b476f04f7817da84290eea83f0d635283519b79f78243bab9a9cc07
3 | size 622703
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/2847b2df4b1c235f7651/dog_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:aff95907eb238fcb609ca68d7dfffd45e882ffd5bd8761f82a09a6c9e0ea28af
3 | size 842692
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/2a35fc317380d4737290/glasses_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:cbfb1d3c3353501f18e1eeb68265e7d9740b0a49149c24251a65d75ea2cc6452
3 | size 1703574
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/2de7e7ec84f177bb092d/food_depth_fp32.npy:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:de2a4248f8a5c26591846ce1372aa8dfa997b4ec1a2f075785cda9cac689f52f
3 | size 6291584
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/2f5832324077dab185ef/swings_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:bd47a9bb15a77f551edff4c4f2731e3422b5a07be845e7e7b8d4d69265f5ba25
3 | size 291481
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/357b353412dd9759ad6a/lake_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:78337d31f1c119dd60027a0cb96ba19eccf12566a2fba0a69f2b3e02b132ff61
3 | size 550204
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/3b9f1c0a73b1eb66b3dc/puzzle_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:74567b099a5a40da030739d8ceb5e273cdb3179612257a80ec202f732a3a22ea
3 | size 405008
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/3c6ba7efdd300e7b7764/lake_depth_fp32.npy:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:8f114e79de81a27a4ab8cf16819459cb4e1fafe7267af4868c9757011e25ca71
3 | size 6291584
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/3d61175e3f24234c9eca/arc_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:97c1f5eb53d82ca407ed9e0d00651baaaa850c3db9aec2854738fb8e6b9ae0fc
3 | size 756097
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/4cb13fd8b9ec9608e19f/swings_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:f6e9f6922669d429fd4ad1db192fd0d14171f10f8b9631e35b2a107bbe34e05b
3 | size 520701
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/550e81d9c69c40fa22b0/wave_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:e8bb3cdcf1d3778ce59c9a0b107fe52278ba13ebfefab872cfbbb9bc833e9415
3 | size 1790462
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/565217320fe1057733c9/road_depth_fp32.npy:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:fa3d9094f6127c390009dbe02062925e03d52eb50dfa6a1c0597c69e5e51eef0
3 | size 41809728
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/58f4bc0302114565854f/concert_depth_fp32.npy:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:65e88d7d193b732218f442c4cdd4f07f096c993cc10934068f12432278a0cc2a
3 | size 6291584
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/5ef3eed2059133180232/lake_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:239419b77e2265b59e545b176442c1d854b3322644b6c7b86a8c9e0d3a6a557c
3 | size 1896565
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/6a0860f054b0406feef5/wave_depth_fp32.npy:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:a8ed7fbba0a80c0b96817c98e23adf1f8f5733cd60915575f9f6c2864eacd9f6
3 | size 6291584
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/6d5d27b6ae03d7a40b7c/surfer_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:bc73a374fd33a86fa071422bce34a9193bac71344247274cf166620d2020f0d9
3 | size 2080093
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/8c492010216806b7c84c/teamwork_depth_fp32.npy:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:99b330b19ded761a4e11b56794baac487ca8137d46035dbb01270a23c00cc4c7
3 | size 8388736
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/8e97f11903b9b898c2f3/arc_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:efa3f99a2f3eb941782122205879b814a0931b7aff0501f2de51cf7414ab79a7
3 | size 1490869
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/9faf8f227f90f70bbdaa/marigold_depth_fp32.npy:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:903fc45c106d2468008012090663908e1fa0f3dcfde2795a6e85750d231a4de5
3 | size 4616740
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/a4dcdec4a501e723b211/dog_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:5ed7e4976f6cdd1323a0f808797e283ff557f287c0ff1fa7a01e4a28b0874d85
3 | size 2190077
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/a639dbc49c03860c86a7/food_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:68e8aae96f126f6f5e67048ca1bf631d87466f60209a132899c2cea0be078e5a
3 | size 617833
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/b264a159bd653f1548e3/berries_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:3a0709360a684682620b1d576c10bc0ec92e6530e22ff47442a727b0d35f510e
3 | size 2150087
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/b42b9d9b1dd973751204/surfer_depth_fp32.npy:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:18774c86e3ac46bf5d8ac751c595ab5c504cf35c764b104d98cbb4bf26017d2f
3 | size 6291584
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/b58afb3c3ab61448a7dd/puzzle_depth_fp32.npy:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:9ce74482958e46658d2026f1d13598aa85f081bdba984933d0b147f4e104845e
3 | size 6291584
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/ca3ffeaccc005c6fd5ee/cat_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:f8f99b74628f236d2201d2637e62d773689fa4491522b58d58467df81e093108
3 | size 1736687
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/d0a4e40b24fa717ffabb/road_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:da76dedb7ac05812682a23ecfa52f5f830ea3528983f68f9988038eee8cdc858
3 | size 11871381
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/de994c47587fcbb3e990/house_depth_fp32.npy:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:065f1c4391ac8b5c80f5dae0092c0d8b5d86972cc3b8965f594b4d7defd64e92
3 | size 57509536
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/e0911d2e7a4a5debfbab/food_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:088282ff1b355aeb57a4d77a1ca3f483e9a7d7ba4fe721437a086a580b10b6e8
3 | size 2009101
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/e29c660ea20526461142/cat_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:2bdafad5960fe85f771470dd04f3da901481cf94014027ccda26493f555b7f5c
3 | size 540541
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/e747489c970c86200120/puzzle_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:df59abe9d997b2a003b1df44b91901a3fd53b83e1bb143f7acc43c613bd5d01c
3 | size 1746321
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/ed486427beaead8e6d4d/house_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:e5297467fccdb41c6a7bc45cc41a912fb558a4aa4eee231a1a3c76873ed3128b
3 | size 16604424
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/ee9d905b3a5337c2db21/concert_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:70ae0247810df3b870013a13005f8a2100ca1c8a9027cde39e1b9ea2bbfbe18e
3 | size 1725191
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/ef56552b1f18d14a2c19/swings_depth_fp32.npy:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:073205298320622b01de97e23bc99e84e869b7a0d052d5b1b74cca6f63edda11
3 | size 1398912
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/ef827c6d4ffc7d48c85a/wave_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:455466d3c556cde893b07d91e68cb44c7a1ff3e69cc64a3e5a961941b3da3c46
3 | size 353870
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/f53efcc50ebfeaac3b8f/house_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:0a5fba37ae8f6ef2ec704d5ea036fcf2c4c40f2ec9305ffbfd455212a1332a74
3 | size 3921236
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/29/Depth outputs/5f96f60f0b5af752e271/cab_depth_colored.mp4:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:81b3d4916e5b594174a49a7b064e4490b39dbed443f9d8ecea347c433284ff54
3 | size 473925
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/29/Depth outputs/63df77111c5982e3d501/cab_depth_16bit.zip:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:efda0f0ba879b63e0f8b7dc60a2eaabef9f0b85e203e0df9cd598dad3ee371d1
3 | size 39185578
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/29/Depth outputs/8209c6d80711c2b193b6/obama_depth_colored.mp4:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:86a007b675d5e294a7e8b036dea45a1034a0580f387353feec599920eb560ba7
3 | size 430863
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/29/Depth outputs/d7825cd99c31975cb780/obama_depth_16bit.zip:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:e58cae1afa395b8d260814ad23b59c7dab9a659e39ee27e05b4fc48c4203e7f0
3 | size 38156752
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/068c389756186ef0ec21/portrait_1_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:d5f4c46c08d27c87c61fdf0383189e2ee6b570e48f154474d114cb18f615dd95
3 | size 499699
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/099bb138bf851d8de112/scientists_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:24f43de7775ac97a9485d0b018fa27be5557dc6bd4d6b1a873456791910a8f46
3 | size 631879
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/0fb09ff06177b86b75cc/portrait_2_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:f4d3caec2a68ca8b5fbedb42441bf12d27da401ae53629f102548f07680db5e2
3 | size 304975
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/115d14d955de41ae1e33/switzerland_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:a599aebb989cfa5d5b9ffb900dd0e221fe94f10a99d06a7b1b1b08c2d855ecc7
3 | size 296596
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/1ab135c405036a016085/teamwork_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:49febeb62c3cd72df6d2173c4225483838be90f89a13db4f1e751ed0d4e76bfc
3 | size 2243230
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/2b8e100af28995f679f3/switzerland_depth_fp32.npy:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:c61928f8e05758e93b08cec04a0e5a0f077ba347ae6a179975ff72a74e0b43af
3 | size 4593728
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/3fea61e86ad657759143/doughnuts_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:af3ab93de2dd66ec5ac25d59d8095411bdd243f67eba8bf3bf1cda99d915c85f
3 | size 347441
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/4256ccd86bcb885f93a6/surfboards_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:729848e7097fd3d92d2c8cd646185883948b8b42e554bedc73f36b9c93512708
3 | size 1098012
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/4758b54d8928c9860970/butterfly_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:446e540807ab6a0f88b3904f3c56899fc7167378564577647da8269ea291083b
3 | size 1089045
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/534e159c4967aec56bcd/portrait_1_depth_fp32.npy:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:e88991beac17c8f27448f31f702bef7d02f7ac5556324855bb1025a87d435b0b
3 | size 4194432
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/67a09b9fd46c50fb1a30/scientists_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:37d4a6e3e9ad9b931b7210cdd96ce3504c25ed7c1382361990a6be51ea0fea2f
3 | size 3187383
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/6b7fa86ff307ce280458/glasses_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:6fa2fb4c244664f3efc759f742f11c51a1c82f091eb8ec30b88a1b32af9a5c01
3 | size 332572
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/6eeb531b1c22dd97fae4/portrait_2_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:e7e4adbe7c0e4e075b548f8d546b4c81c4c9a7c4413f316536a81eec6dfc369c
3 | size 1248383
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/70a6ecd79155c8fb213b/einstein_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:8e12862876cc4752e18cb2b5f3b4bd38f2fe62bf72267275f06681aa2ea1abd8
3 | size 693251
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/743f28023ee74ec7ba8a/butterfly_depth_fp32.npy:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:91df242eb97d167b481310c18ffaf2d6dfa28a3f51c053674c2718a85c16b456
3 | size 6701184
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/7b1d5a7cecd449b13a5e/berries_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:9915cb2dfe8a586e030e115181b3b29292819b59743cd4a64a894c33388a8585
3 | size 880971
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/7fce16f173010012f6b6/portrait_2_depth_fp32.npy:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:573b944207767549ca2bc29e8bffca084b895c505608ce670cfbf2c080a18809
3 | size 4194432
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/8c1f452606cac27a66c0/scientists_depth_fp32.npy:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:527e0cdb078addb324360119faea09135c7f13708febe8ae3eb9a70aa4ae2fd7
3 | size 12441480
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/8d6952f2bb813c899fb0/marigold_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:71afa70070c58ce421c17bc5fc98ed86443abfc5654502fc88a6ec049d5aafc6
3 | size 1413951
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/a20cbbb5b772a7b1763e/pumpkins_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:f2c1d352923415414fdb1f729f412779f4c9972c8a270d00400751f820d91dc8
3 | size 5383025
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/a5db29c9ded284668775/einstein_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:410ff615dbbadf27f9679a495a8e747009a4958b86aa10559470af816075b016
3 | size 1492165
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/a7486302ab044e374c04/butterfly_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:9def44f3b150223741a599037024f7580751e958509ee600c0bc17233f8ffe67
3 | size 2182986
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/af5de30ca8186879928b/teamwork_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:67ce7be73b140701112ee47ac229ea33aa6d27de7ac9dbd2ea26993fdd909aea
3 | size 659822
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/bf0a2b7b9d7d20d3fba4/portrait_1_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:9d5a4bd282660e4e900b23f7de06fdce00f35516507c201e6bbf44b865cd419a
3 | size 1368255
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/c28a0efd69ada6c1de17/doughnuts_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:4643d2b313796d995bf39a712cb7a0a48c458c320c6c4a0b90b1d926db6477cd
3 | size 1768092
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/c9d2b99fd4995c01c932/concert_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:3491aebbd2191ec11f1dffe5cc85c419011ba1860ad41a4c04284fb021d3bbdd
3 | size 544306
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/d8b07ff54e7a206832a0/switzerland_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:fb145e24cb8d87aff79086d7000d02c9280b553f47d694802d0bc8dd1ba62dde
3 | size 1344029
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/dbc8049901349d03b961/pumpkins_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:4fd195e60914b4d948dda5957c233b12f81941c4f9c4ec9f6b931018968c3381
3 | size 16880885
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/eb7676ae3737abdd8b33/surfboards_depth_fp32.npy:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:5a8e23a17fb50b8939357cc5c2452114a0f0bdedb62a0771e31cf0354bd60df6
3 | size 6291584
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/ed2db910fdec870cb513/marigold_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:878e1dd4ee12be5185c62c8feded1fed766ccc75f50afe63b365bf5de47de528
3 | size 335290
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/f024df95b9f9d82cd10b/pumpkins_depth_fp32.npy:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:5282193bd7064bce759ba0f2b2f7edfe5217e358c16ffe602b1c94277f42f7d2
3 | size 63701120
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/f6b17b1b9adbe5433e03/surfboards_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:411ead23a9fbfe0853a43605ca4074210464bc9f788e4088c0fd22e8bfe1bad0
3 | size 2196782
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Depth outputs/fd79bd61b90eb3c8750e/doughnuts_depth_fp32.npy:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:876ad2bd67faf6f605cef2943d1aaad166dc86417cd7094224648d35c27bebd3
3 | size 6291584
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/29/Depth outputs/bce9827dbf145c2cd2e5/elephant_depth_colored.mp4:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:9dfb3689c97f04cabf39b4aa8a06ad4ce8fb424a353bf30d1641fb28f79c4d48
3 | size 755285
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/29/Depth outputs/d49d367de8bff332fa2e/elephant_depth_16bit.zip:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:ee959afea87d7a2e184ca9749fead1fbf820af579eb70b3b579d190a71afa4e2
3 | size 41877515
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/58/3D model outputs high-res/409aac8c714a19097c19/coin_depth_512.glb:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:119ddcac09a8be5bf2015bad1b0549a774649f2910479e88b0dbae19911850da
3 | size 10465944
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/58/3D model outputs high-res/789054453156abfa3dac/coin_depth_512.stl:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:0c8a12b4ee58679b68119ebfdbd55e02fce399019df42728fa5ddfdca7a0ded9
3 | size 26162484
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/58/3D model outputs high-res/a4a4773a0122b3417297/food_depth_512.glb:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:614cffc305abd442a3e540a42d4d71c2da61d5d9f44217a23726f49af50fc642
3 | size 7053036
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/58/3D model outputs high-res/c56ef4018e4584940192/food_depth_512.stl:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:6cd19b87a5ff3377295dc22d0021a8946ef0e3cf7905f3d99b0f09b92bbd5cf1
3 | size 17630084
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/58/3D model outputs high-res/1b5111a9ba7be9778d37/einstein_depth_512.glb:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:c2b798467b1bbd8e3d82400cb43441edc5c0a8275705a138bd6ab309c0705298
3 | size 10568768
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/58/3D model outputs high-res/bee443acba581914a3e5/einstein_depth_512.stl:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:345d0881a93dba3e5b5768405399fa1578d30da549fcc937c78267706acdc8ac
3 | size 26419484
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/04f54e1ce435ff6459b9/arc_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:97c1f5eb53d82ca407ed9e0d00651baaaa850c3db9aec2854738fb8e6b9ae0fc
3 | size 756097
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/19eb29bd0e44aa61bd6f/road_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:a60241a8bde8ddcbc35add52633d1e0068c799971160092946825c04b0284984
3 | size 4683563
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/295edcd8c90a1d58bff5/cat_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:f8f99b74628f236d2201d2637e62d773689fa4491522b58d58467df81e093108
3 | size 1736687
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/313712cc25c7afeec5e2/swings_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:f6e9f6922669d429fd4ad1db192fd0d14171f10f8b9631e35b2a107bbe34e05b
3 | size 520701
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/432db43f24ab254cdc77/glasses_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:cbfb1d3c3353501f18e1eeb68265e7d9740b0a49149c24251a65d75ea2cc6452
3 | size 1703574
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/45e324d0b847afbbc4c7/arc_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:efa3f99a2f3eb941782122205879b814a0931b7aff0501f2de51cf7414ab79a7
3 | size 1490869
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/478e837f6ca2f7737dc5/wave_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:e8bb3cdcf1d3778ce59c9a0b107fe52278ba13ebfefab872cfbbb9bc833e9415
3 | size 1790462
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/71ea41371688664fd898/house_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:0a5fba37ae8f6ef2ec704d5ea036fcf2c4c40f2ec9305ffbfd455212a1332a74
3 | size 3921236
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/7b90e60f0490de180bc9/wave_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:455466d3c556cde893b07d91e68cb44c7a1ff3e69cc64a3e5a961941b3da3c46
3 | size 353870
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/879cfd69d9c554f83d96/dog_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:5ed7e4976f6cdd1323a0f808797e283ff557f287c0ff1fa7a01e4a28b0874d85
3 | size 2190077
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/983c2e26ef549272c897/swings_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:bd47a9bb15a77f551edff4c4f2731e3422b5a07be845e7e7b8d4d69265f5ba25
3 | size 291481
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/9c492b4a05b0c27418e7/concert_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:70ae0247810df3b870013a13005f8a2100ca1c8a9027cde39e1b9ea2bbfbe18e
3 | size 1725191
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/a1e0b4145b283feacb69/food_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:088282ff1b355aeb57a4d77a1ca3f483e9a7d7ba4fe721437a086a580b10b6e8
3 | size 2009101
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/b56ad8cd93517abd7a57/food_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:68e8aae96f126f6f5e67048ca1bf631d87466f60209a132899c2cea0be078e5a
3 | size 617833
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/b82a8e2c9968943eaf37/surfer_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:bc73a374fd33a86fa071422bce34a9193bac71344247274cf166620d2020f0d9
3 | size 2080093
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/bf130ca9410ab692b150/road_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:da76dedb7ac05812682a23ecfa52f5f830ea3528983f68f9988038eee8cdc858
3 | size 11871381
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/c80da87c39169644afa1/house_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:e5297467fccdb41c6a7bc45cc41a912fb558a4aa4eee231a1a3c76873ed3128b
3 | size 16604424
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/cab7bd5be8a912541996/berries_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:3a0709360a684682620b1d576c10bc0ec92e6530e22ff47442a727b0d35f510e
3 | size 2150087
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/ccbf8e60d9ee20e4e70b/cat_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:2bdafad5960fe85f771470dd04f3da901481cf94014027ccda26493f555b7f5c
3 | size 540541
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/d22944415f5ee905e9e0/puzzle_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:df59abe9d997b2a003b1df44b91901a3fd53b83e1bb143f7acc43c613bd5d01c
3 | size 1746321
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/d28b5d4dcd878e6c5daf/lake_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:78337d31f1c119dd60027a0cb96ba19eccf12566a2fba0a69f2b3e02b132ff61
3 | size 550204
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/db15ec0fec292ce702f3/puzzle_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:74567b099a5a40da030739d8ceb5e273cdb3179612257a80ec202f732a3a22ea
3 | size 405008
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/edb434ab1e157595210a/surfer_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:838d1f2b6b476f04f7817da84290eea83f0d635283519b79f78243bab9a9cc07
3 | size 622703
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/f10d9f52ef10b6ce2819/dog_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:aff95907eb238fcb609ca68d7dfffd45e882ffd5bd8761f82a09a6c9e0ea28af
3 | size 842692
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/f8bc9d03bcb924e64306/lake_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:239419b77e2265b59e545b176442c1d854b3322644b6c7b86a8c9e0d3a6a557c
3 | size 1896565
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/29/Output video depth red-near blue-far/b1a647fc7fb469eb9c90/cab_depth_colored.mp4:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:81b3d4916e5b594174a49a7b064e4490b39dbed443f9d8ecea347c433284ff54
3 | size 473925
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/58/3D preview low-res relief highlight/4237fcd0dad8ff3298f3/coin_depth_256.glb:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:ddc7b500a38b5841e5e183261b98cb6b275e26163578b4483929ced3105f1388
3 | size 2369860
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/58/3D preview low-res relief highlight/4756f7e8f07d8445b1df/food_depth_256.glb:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:f6a0e0654315d7288795f90c6c02fb0da6f3cf440dcbf11e5581fd92f30fe005
3 | size 1598964
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/0968bcc549dace8031f8/butterfly_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:446e540807ab6a0f88b3904f3c56899fc7167378564577647da8269ea291083b
3 | size 1089045
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/18e6abaa75c6b4266122/marigold_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:878e1dd4ee12be5185c62c8feded1fed766ccc75f50afe63b365bf5de47de528
3 | size 335290
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/1cb720d1f47690df2bf2/surfboards_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:729848e7097fd3d92d2c8cd646185883948b8b42e554bedc73f36b9c93512708
3 | size 1098012
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/2345a66ea597f0724ac6/teamwork_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:49febeb62c3cd72df6d2173c4225483838be90f89a13db4f1e751ed0d4e76bfc
3 | size 2243230
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/24ec8180e34432fcb5d3/marigold_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:71afa70070c58ce421c17bc5fc98ed86443abfc5654502fc88a6ec049d5aafc6
3 | size 1413951
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/25947e3f3a07b767d2e3/doughnuts_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:4643d2b313796d995bf39a712cb7a0a48c458c320c6c4a0b90b1d926db6477cd
3 | size 1768092
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/2e503f2592bb798b7d75/portrait_2_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:e7e4adbe7c0e4e075b548f8d546b4c81c4c9a7c4413f316536a81eec6dfc369c
3 | size 1248383
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/37a744be12464174a9fe/pumpkins_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:f2c1d352923415414fdb1f729f412779f4c9972c8a270d00400751f820d91dc8
3 | size 5383025
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/3b7e23ac2f10ff0acd40/portrait_1_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:d5f4c46c08d27c87c61fdf0383189e2ee6b570e48f154474d114cb18f615dd95
3 | size 499699
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/66dedd2f9b9a56204b8a/pumpkins_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:4fd195e60914b4d948dda5957c233b12f81941c4f9c4ec9f6b931018968c3381
3 | size 16880885
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/6d576b810915984be5d9/butterfly_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:9def44f3b150223741a599037024f7580751e958509ee600c0bc17233f8ffe67
3 | size 2182986
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/88870588e2c8198c46db/concert_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:3491aebbd2191ec11f1dffe5cc85c419011ba1860ad41a4c04284fb021d3bbdd
3 | size 544306
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/8d5b5a9cc5bde83ad215/scientists_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:37d4a6e3e9ad9b931b7210cdd96ce3504c25ed7c1382361990a6be51ea0fea2f
3 | size 3187383
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/9f9ad00dda4a8b76a9d3/doughnuts_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:af3ab93de2dd66ec5ac25d59d8095411bdd243f67eba8bf3bf1cda99d915c85f
3 | size 347441
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/ace9fc12e651074b9ffe/berries_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:9915cb2dfe8a586e030e115181b3b29292819b59743cd4a64a894c33388a8585
3 | size 880971
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/adf7d8a23971611c0c2c/surfboards_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:411ead23a9fbfe0853a43605ca4074210464bc9f788e4088c0fd22e8bfe1bad0
3 | size 2196782
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/b019af76a770991b9f05/switzerland_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:fb145e24cb8d87aff79086d7000d02c9280b553f47d694802d0bc8dd1ba62dde
3 | size 1344029
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/bb1b89e55f54f6ad11a4/glasses_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:6fa2fb4c244664f3efc759f742f11c51a1c82f091eb8ec30b88a1b32af9a5c01
3 | size 332572
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/d3d1aea16f58fa4e9c34/teamwork_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:67ce7be73b140701112ee47ac229ea33aa6d27de7ac9dbd2ea26993fdd909aea
3 | size 659822
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/dfced8fb6c3b5b3cdd85/switzerland_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:a599aebb989cfa5d5b9ffb900dd0e221fe94f10a99d06a7b1b1b08c2d855ecc7
3 | size 296596
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/e14a6d59396caef161db/portrait_1_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:9d5a4bd282660e4e900b23f7de06fdce00f35516507c201e6bbf44b865cd419a
3 | size 1368255
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/e58db2a94f058c3cbda5/portrait_2_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:f4d3caec2a68ca8b5fbedb42441bf12d27da401ae53629f102548f07680db5e2
3 | size 304975
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/e9c962c7d4300842fbd2/einstein_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:8e12862876cc4752e18cb2b5f3b4bd38f2fe62bf72267275f06681aa2ea1abd8
3 | size 693251
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/f74d13a60bda9b080432/einstein_depth_16bit.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:410ff615dbbadf27f9679a495a8e747009a4958b86aa10559470af816075b016
3 | size 1492165
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/Predicted depth red-near blue-far/f9fb6b3768c97d11bf63/scientists_depth_colored.png:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:24f43de7775ac97a9485d0b018fa27be5557dc6bd4d6b1a873456791910a8f46
3 | size 631879
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/29/Output video depth red-near blue-far/315c7361216ee7b38070/elephant_depth_colored.mp4:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:9dfb3689c97f04cabf39b4aa8a06ad4ce8fb424a353bf30d1641fb28f79c4d48
3 | size 755285
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/29/Output video depth red-near blue-far/ee3bed949e6cdc9413a9/obama_depth_colored.mp4:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:86a007b675d5e294a7e8b036dea45a1034a0580f387353feec599920eb560ba7
3 | size 430863
4 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/58/3D preview low-res relief highlight/9cfc51ac897aa0a455f2/einstein_depth_256.glb:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:fcb511d580a765ace266bfb5a0591803dbe9b6524cddba487db3d8a8859f99b6
3 | size 2397704
4 | 


--------------------------------------------------------------------------------
/requirements.txt:
--------------------------------------------------------------------------------
 1 | gradio==4.22.0
 2 | gradio-imageslider==0.0.16
 3 | pygltflib==1.16.1
 4 | trimesh==4.0.5
 5 | imageio
 6 | imageio-ffmpeg
 7 | Pillow
 8 | 
 9 | accelerate==0.28.0
10 | diffusers==0.27.2
11 | matplotlib==3.8.2
12 | scipy==1.11.4
13 | torch==2.0.1
14 | transformers==4.39.1
15 | xformers==0.0.21
16 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | ---
 2 | title: Marigold-LCM Depth Estimation
 3 | emoji: 🏵️
 4 | colorFrom: blue
 5 | colorTo: red
 6 | sdk: gradio
 7 | sdk_version: 4.22.0
 8 | app_file: app.py
 9 | pinned: true
10 | license: cc-by-sa-4.0
11 | models:
12 | - prs-eth/marigold-v1-0
13 | - prs-eth/marigold-lcm-v1-0
14 | ---
15 | 
16 | This is a demo of Marigold-LCM, the state-of-the-art depth estimator for images in the wild.
17 | It combines the power of the original Marigold 10-step estimator and the Latent Consistency Models, delivering high-quality results in as little as one step.
18 | Find out more in our paper titled ["Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation"](https://arxiv.org/abs/2312.02145)
19 | 
20 | ```
21 | @misc{ke2023repurposing,
22 |       title={Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation}, 
23 |       author={Bingxin Ke and Anton Obukhov and Shengyu Huang and Nando Metzger and Rodrigo Caye Daudt and Konrad Schindler},
24 |       year={2023},
25 |       eprint={2312.02145},
26 |       archivePrefix={arXiv},
27 |       primaryClass={cs.CV}
28 | }
29 | ```
30 | 


--------------------------------------------------------------------------------
/.gitattributes:
--------------------------------------------------------------------------------
 1 | *.7z filter=lfs diff=lfs merge=lfs -text
 2 | *.arrow filter=lfs diff=lfs merge=lfs -text
 3 | *.bin filter=lfs diff=lfs merge=lfs -text
 4 | *.bz2 filter=lfs diff=lfs merge=lfs -text
 5 | *.ckpt filter=lfs diff=lfs merge=lfs -text
 6 | *.ftz filter=lfs diff=lfs merge=lfs -text
 7 | *.gz filter=lfs diff=lfs merge=lfs -text
 8 | *.h5 filter=lfs diff=lfs merge=lfs -text
 9 | *.joblib filter=lfs diff=lfs merge=lfs -text
10 | *.lfs.* filter=lfs diff=lfs merge=lfs -text
11 | *.mlmodel filter=lfs diff=lfs merge=lfs -text
12 | *.model filter=lfs diff=lfs merge=lfs -text
13 | *.msgpack filter=lfs diff=lfs merge=lfs -text
14 | *.npy filter=lfs diff=lfs merge=lfs -text
15 | *.npz filter=lfs diff=lfs merge=lfs -text
16 | *.onnx filter=lfs diff=lfs merge=lfs -text
17 | *.ot filter=lfs diff=lfs merge=lfs -text
18 | *.parquet filter=lfs diff=lfs merge=lfs -text
19 | *.pb filter=lfs diff=lfs merge=lfs -text
20 | *.pickle filter=lfs diff=lfs merge=lfs -text
21 | *.pkl filter=lfs diff=lfs merge=lfs -text
22 | *.pt filter=lfs diff=lfs merge=lfs -text
23 | *.pth filter=lfs diff=lfs merge=lfs -text
24 | *.rar filter=lfs diff=lfs merge=lfs -text
25 | *.safetensors filter=lfs diff=lfs merge=lfs -text
26 | saved_model/**/* filter=lfs diff=lfs merge=lfs -text
27 | *.tar.* filter=lfs diff=lfs merge=lfs -text
28 | *.tar filter=lfs diff=lfs merge=lfs -text
29 | *.tflite filter=lfs diff=lfs merge=lfs -text
30 | *.tgz filter=lfs diff=lfs merge=lfs -text
31 | *.wasm filter=lfs diff=lfs merge=lfs -text
32 | *.xz filter=lfs diff=lfs merge=lfs -text
33 | *.zip filter=lfs diff=lfs merge=lfs -text
34 | *.zst filter=lfs diff=lfs merge=lfs -text
35 | *tfevents* filter=lfs diff=lfs merge=lfs -text
36 | *.stl filter=lfs diff=lfs merge=lfs -text
37 | *.glb filter=lfs diff=lfs merge=lfs -text
38 | *.jpg filter=lfs diff=lfs merge=lfs -text
39 | *.jpeg filter=lfs diff=lfs merge=lfs -text
40 | *.png filter=lfs diff=lfs merge=lfs -text
41 | *.mp4 filter=lfs diff=lfs merge=lfs -text
42 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/58/log.csv:
--------------------------------------------------------------------------------
1 | "3D preview (low-res, relief highlight)",3D model outputs (high-res),flag,username,timestamp
2 | "{""path"": ""gradio_cached_examples/58/3D preview low-res relief highlight/4237fcd0dad8ff3298f3/coin_depth_256.glb"", ""url"": ""/file=/tmp/gradio/867147083b9e6dbc7da5aae5aa65a25eda49c925/coin_output/coin_depth_256.glb"", ""size"": null, ""orig_name"": ""coin_depth_256.glb"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}","[{""path"": ""gradio_cached_examples/58/3D model outputs high-res/409aac8c714a19097c19/coin_depth_512.glb"", ""url"": ""/file=/tmp/gradio/867147083b9e6dbc7da5aae5aa65a25eda49c925/coin_output/coin_depth_512.glb"", ""size"": 10465944, ""orig_name"": ""coin_depth_512.glb"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/58/3D model outputs high-res/789054453156abfa3dac/coin_depth_512.stl"", ""url"": ""/file=/tmp/gradio/867147083b9e6dbc7da5aae5aa65a25eda49c925/coin_output/coin_depth_512.stl"", ""size"": 26162484, ""orig_name"": ""coin_depth_512.stl"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]",,,2024-03-22 11:03:44.094538
3 | "{""path"": ""gradio_cached_examples/58/3D preview low-res relief highlight/9cfc51ac897aa0a455f2/einstein_depth_256.glb"", ""url"": ""/file=/tmp/gradio/ea36dd96d126ddf35c2b3652ba451b133ce44d8c/einstein_output/einstein_depth_256.glb"", ""size"": null, ""orig_name"": ""einstein_depth_256.glb"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}","[{""path"": ""gradio_cached_examples/58/3D model outputs high-res/1b5111a9ba7be9778d37/einstein_depth_512.glb"", ""url"": ""/file=/tmp/gradio/ea36dd96d126ddf35c2b3652ba451b133ce44d8c/einstein_output/einstein_depth_512.glb"", ""size"": 10568768, ""orig_name"": ""einstein_depth_512.glb"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/58/3D model outputs high-res/bee443acba581914a3e5/einstein_depth_512.stl"", ""url"": ""/file=/tmp/gradio/ea36dd96d126ddf35c2b3652ba451b133ce44d8c/einstein_output/einstein_depth_512.stl"", ""size"": 26419484, ""orig_name"": ""einstein_depth_512.stl"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]",,,2024-03-22 11:03:46.478981
4 | "{""path"": ""gradio_cached_examples/58/3D preview low-res relief highlight/4756f7e8f07d8445b1df/food_depth_256.glb"", ""url"": ""/file=/tmp/gradio/a69b5239bc9b0d955739849b83ae54ed33bb7b4d/food_output/food_depth_256.glb"", ""size"": null, ""orig_name"": ""food_depth_256.glb"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}","[{""path"": ""gradio_cached_examples/58/3D model outputs high-res/a4a4773a0122b3417297/food_depth_512.glb"", ""url"": ""/file=/tmp/gradio/a69b5239bc9b0d955739849b83ae54ed33bb7b4d/food_output/food_depth_512.glb"", ""size"": 7053036, ""orig_name"": ""food_depth_512.glb"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/58/3D model outputs high-res/c56ef4018e4584940192/food_depth_512.stl"", ""url"": ""/file=/tmp/gradio/a69b5239bc9b0d955739849b83ae54ed33bb7b4d/food_output/food_depth_512.stl"", ""size"": 17630084, ""orig_name"": ""food_depth_512.stl"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]",,,2024-03-22 11:03:49.626119
5 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/29/log.csv:
--------------------------------------------------------------------------------
1 | "Output video depth (red-near, blue-far)",Depth outputs,flag,username,timestamp
2 | "{""video"": {""path"": ""gradio_cached_examples/29/Output video depth red-near blue-far/b1a647fc7fb469eb9c90/cab_depth_colored.mp4"", ""url"": ""/file=/tmp/gradio/dcb7ac3b468a001ba8a3deaf15f49765331b0746/cab_output/cab_depth_colored.mp4"", ""size"": null, ""orig_name"": ""cab_depth_colored.mp4"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, ""subtitles"": null}","[{""path"": ""gradio_cached_examples/29/Depth outputs/5f96f60f0b5af752e271/cab_depth_colored.mp4"", ""url"": ""/file=/tmp/gradio/dcb7ac3b468a001ba8a3deaf15f49765331b0746/cab_output/cab_depth_colored.mp4"", ""size"": 473925, ""orig_name"": ""cab_depth_colored.mp4"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/29/Depth outputs/63df77111c5982e3d501/cab_depth_16bit.zip"", ""url"": ""/file=/tmp/gradio/dcb7ac3b468a001ba8a3deaf15f49765331b0746/cab_output/cab_depth_16bit.zip"", ""size"": 39185578, ""orig_name"": ""cab_depth_16bit.zip"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]",,,2024-03-22 11:02:13.904822
3 | "{""video"": {""path"": ""gradio_cached_examples/29/Output video depth red-near blue-far/315c7361216ee7b38070/elephant_depth_colored.mp4"", ""url"": ""/file=/tmp/gradio/fc2aed651bf530fc1d0b9cc911f81c5eb63ed149/elephant_output/elephant_depth_colored.mp4"", ""size"": null, ""orig_name"": ""elephant_depth_colored.mp4"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, ""subtitles"": null}","[{""path"": ""gradio_cached_examples/29/Depth outputs/bce9827dbf145c2cd2e5/elephant_depth_colored.mp4"", ""url"": ""/file=/tmp/gradio/fc2aed651bf530fc1d0b9cc911f81c5eb63ed149/elephant_output/elephant_depth_colored.mp4"", ""size"": 755285, ""orig_name"": ""elephant_depth_colored.mp4"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/29/Depth outputs/d49d367de8bff332fa2e/elephant_depth_16bit.zip"", ""url"": ""/file=/tmp/gradio/fc2aed651bf530fc1d0b9cc911f81c5eb63ed149/elephant_output/elephant_depth_16bit.zip"", ""size"": 41877515, ""orig_name"": ""elephant_depth_16bit.zip"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]",,,2024-03-22 11:02:53.650304
4 | "{""video"": {""path"": ""gradio_cached_examples/29/Output video depth red-near blue-far/ee3bed949e6cdc9413a9/obama_depth_colored.mp4"", ""url"": ""/file=/tmp/gradio/3a35ca3cac43e275139bd3e94c2aa0da9ce82011/obama_output/obama_depth_colored.mp4"", ""size"": null, ""orig_name"": ""obama_depth_colored.mp4"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, ""subtitles"": null}","[{""path"": ""gradio_cached_examples/29/Depth outputs/8209c6d80711c2b193b6/obama_depth_colored.mp4"", ""url"": ""/file=/tmp/gradio/3a35ca3cac43e275139bd3e94c2aa0da9ce82011/obama_output/obama_depth_colored.mp4"", ""size"": 430863, ""orig_name"": ""obama_depth_colored.mp4"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/29/Depth outputs/d7825cd99c31975cb780/obama_depth_16bit.zip"", ""url"": ""/file=/tmp/gradio/3a35ca3cac43e275139bd3e94c2aa0da9ce82011/obama_output/obama_depth_16bit.zip"", ""size"": 38156752, ""orig_name"": ""obama_depth_16bit.zip"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]",,,2024-03-22 11:03:38.092138
5 | 


--------------------------------------------------------------------------------
/extrude.py:
--------------------------------------------------------------------------------
  1 | import math
  2 | import os
  3 | 
  4 | import numpy as np
  5 | import pygltflib
  6 | import trimesh
  7 | from PIL import Image, ImageFilter
  8 | 
  9 | 
 10 | def quaternion_multiply(q1, q2):
 11 |     x1, y1, z1, w1 = q1
 12 |     x2, y2, z2, w2 = q2
 13 |     return [
 14 |         w1 * x2 + x1 * w2 + y1 * z2 - z1 * y2,
 15 |         w1 * y2 - x1 * z2 + y1 * w2 + z1 * x2,
 16 |         w1 * z2 + x1 * y2 - y1 * x2 + z1 * w2,
 17 |         w1 * w2 - x1 * x2 - y1 * y2 - z1 * z2,
 18 |     ]
 19 | 
 20 | 
 21 | def glb_add_lights(path_input, path_output):
 22 |     """
 23 |     Adds directional lights in the horizontal plane to the glb file.
 24 |     :param path_input: path to input glb
 25 |     :param path_output: path to output glb
 26 |     :return: None
 27 |     """
 28 |     glb = pygltflib.GLTF2().load(path_input)
 29 | 
 30 |     N = 3  # default max num lights in Babylon.js is 4
 31 |     angle_step = 2 * math.pi / N
 32 |     elevation_angle = math.radians(75)
 33 | 
 34 |     light_colors = [
 35 |         [1.0, 0.0, 0.0],
 36 |         [0.0, 1.0, 0.0],
 37 |         [0.0, 0.0, 1.0],
 38 |     ]
 39 | 
 40 |     lights_extension = {
 41 |         "lights": [
 42 |             {"type": "directional", "color": light_colors[i], "intensity": 2.0}
 43 |             for i in range(N)
 44 |         ]
 45 |     }
 46 | 
 47 |     if "KHR_lights_punctual" not in glb.extensionsUsed:
 48 |         glb.extensionsUsed.append("KHR_lights_punctual")
 49 |     glb.extensions["KHR_lights_punctual"] = lights_extension
 50 | 
 51 |     light_nodes = []
 52 |     for i in range(N):
 53 |         angle = i * angle_step
 54 | 
 55 |         pos_rot = [0.0, 0.0, math.sin(angle / 2), math.cos(angle / 2)]
 56 |         elev_rot = [
 57 |             math.sin(elevation_angle / 2),
 58 |             0.0,
 59 |             0.0,
 60 |             math.cos(elevation_angle / 2),
 61 |         ]
 62 |         rotation = quaternion_multiply(pos_rot, elev_rot)
 63 | 
 64 |         node = {
 65 |             "rotation": rotation,
 66 |             "extensions": {"KHR_lights_punctual": {"light": i}},
 67 |         }
 68 |         light_nodes.append(node)
 69 | 
 70 |     light_node_indices = list(range(len(glb.nodes), len(glb.nodes) + N))
 71 |     glb.nodes.extend(light_nodes)
 72 | 
 73 |     root_node_index = glb.scenes[glb.scene].nodes[0]
 74 |     root_node = glb.nodes[root_node_index]
 75 |     if hasattr(root_node, "children"):
 76 |         root_node.children.extend(light_node_indices)
 77 |     else:
 78 |         root_node.children = light_node_indices
 79 | 
 80 |     glb.save(path_output)
 81 | 
 82 | 
 83 | def extrude_depth_3d(
 84 |     path_rgb,
 85 |     path_depth,
 86 |     output_model_scale=100,
 87 |     filter_size=3,
 88 |     coef_near=0.0,
 89 |     coef_far=1.0,
 90 |     emboss=0.3,
 91 |     f_thic=0.05,
 92 |     f_near=-0.15,
 93 |     f_back=0.01,
 94 |     vertex_colors=True,
 95 |     scene_lights=True,
 96 |     prepare_for_3d_printing=False,
 97 | ):
 98 |     f_far_inner = -emboss
 99 |     f_far_outer = f_far_inner - f_back
100 | 
101 |     f_near = max(f_near, f_far_inner)
102 | 
103 |     depth_image = Image.open(path_depth)
104 |     assert depth_image.mode == "I", depth_image.mode
105 |     depth_image = depth_image.filter(ImageFilter.MedianFilter(size=filter_size))
106 | 
107 |     w, h = depth_image.size
108 |     d_max = max(w, h)
109 |     depth_image = np.array(depth_image).astype(np.double)
110 |     z_min, z_max = np.min(depth_image), np.max(depth_image)
111 |     depth_image = (depth_image.astype(np.double) - z_min) / (z_max - z_min)
112 |     depth_image[depth_image < coef_near] = coef_near
113 |     depth_image[depth_image > coef_far] = coef_far
114 |     depth_image = emboss * (depth_image - coef_near) / (coef_far - coef_near)
115 |     rgb_image = np.array(
116 |         Image.open(path_rgb).convert("RGB").resize((w, h), Image.Resampling.LANCZOS)
117 |     )
118 | 
119 |     w_norm = w / float(d_max - 1)
120 |     h_norm = h / float(d_max - 1)
121 |     w_half = w_norm / 2
122 |     h_half = h_norm / 2
123 | 
124 |     x, y = np.meshgrid(np.arange(w), np.arange(h))
125 |     x = x / float(d_max - 1) - w_half  # [-w_half, w_half]
126 |     y = -y / float(d_max - 1) + h_half  # [-h_half, h_half]
127 |     z = -depth_image  # -depth_emboss (far) - 0 (near)
128 |     vertices_2d = np.stack((x, y, z), axis=-1)
129 |     vertices = vertices_2d.reshape(-1, 3)
130 |     colors = rgb_image[:, :, :3].reshape(-1, 3) / 255.0
131 | 
132 |     faces = []
133 |     for y in range(h - 1):
134 |         for x in range(w - 1):
135 |             idx = y * w + x
136 |             faces.append([idx, idx + w, idx + 1])
137 |             faces.append([idx + 1, idx + w, idx + 1 + w])
138 | 
139 |     # OUTER frame
140 | 
141 |     nv = len(vertices)
142 |     vertices = np.append(
143 |         vertices,
144 |         [
145 |             [-w_half - f_thic, -h_half - f_thic, f_near],  # 00
146 |             [-w_half - f_thic, -h_half - f_thic, f_far_outer],  # 01
147 |             [w_half + f_thic, -h_half - f_thic, f_near],  # 02
148 |             [w_half + f_thic, -h_half - f_thic, f_far_outer],  # 03
149 |             [w_half + f_thic, h_half + f_thic, f_near],  # 04
150 |             [w_half + f_thic, h_half + f_thic, f_far_outer],  # 05
151 |             [-w_half - f_thic, h_half + f_thic, f_near],  # 06
152 |             [-w_half - f_thic, h_half + f_thic, f_far_outer],  # 07
153 |         ],
154 |         axis=0,
155 |     )
156 |     faces.extend(
157 |         [
158 |             [nv + 0, nv + 1, nv + 2],
159 |             [nv + 2, nv + 1, nv + 3],
160 |             [nv + 2, nv + 3, nv + 4],
161 |             [nv + 4, nv + 3, nv + 5],
162 |             [nv + 4, nv + 5, nv + 6],
163 |             [nv + 6, nv + 5, nv + 7],
164 |             [nv + 6, nv + 7, nv + 0],
165 |             [nv + 0, nv + 7, nv + 1],
166 |         ]
167 |     )
168 |     colors = np.append(colors, [[0.5, 0.5, 0.5]] * 8, axis=0)
169 | 
170 |     # INNER frame
171 | 
172 |     nv = len(vertices)
173 |     vertices_left_data = vertices_2d[:, 0]  # H x 3
174 |     vertices_left_frame = vertices_2d[:, 0].copy()  # H x 3
175 |     vertices_left_frame[:, 2] = f_near
176 |     vertices = np.append(vertices, vertices_left_data, axis=0)
177 |     vertices = np.append(vertices, vertices_left_frame, axis=0)
178 |     colors = np.append(colors, [[0.5, 0.5, 0.5]] * (2 * h), axis=0)
179 |     for i in range(h - 1):
180 |         nvi_d = nv + i
181 |         nvi_f = nvi_d + h
182 |         faces.append([nvi_d, nvi_f, nvi_d + 1])
183 |         faces.append([nvi_d + 1, nvi_f, nvi_f + 1])
184 | 
185 |     nv = len(vertices)
186 |     vertices_right_data = vertices_2d[:, -1]  # H x 3
187 |     vertices_right_frame = vertices_2d[:, -1].copy()  # H x 3
188 |     vertices_right_frame[:, 2] = f_near
189 |     vertices = np.append(vertices, vertices_right_data, axis=0)
190 |     vertices = np.append(vertices, vertices_right_frame, axis=0)
191 |     colors = np.append(colors, [[0.5, 0.5, 0.5]] * (2 * h), axis=0)
192 |     for i in range(h - 1):
193 |         nvi_d = nv + i
194 |         nvi_f = nvi_d + h
195 |         faces.append([nvi_d, nvi_d + 1, nvi_f])
196 |         faces.append([nvi_d + 1, nvi_f + 1, nvi_f])
197 | 
198 |     nv = len(vertices)
199 |     vertices_top_data = vertices_2d[0, :]  # H x 3
200 |     vertices_top_frame = vertices_2d[0, :].copy()  # H x 3
201 |     vertices_top_frame[:, 2] = f_near
202 |     vertices = np.append(vertices, vertices_top_data, axis=0)
203 |     vertices = np.append(vertices, vertices_top_frame, axis=0)
204 |     colors = np.append(colors, [[0.5, 0.5, 0.5]] * (2 * w), axis=0)
205 |     for i in range(w - 1):
206 |         nvi_d = nv + i
207 |         nvi_f = nvi_d + w
208 |         faces.append([nvi_d, nvi_d + 1, nvi_f])
209 |         faces.append([nvi_d + 1, nvi_f + 1, nvi_f])
210 | 
211 |     nv = len(vertices)
212 |     vertices_bottom_data = vertices_2d[-1, :]  # H x 3
213 |     vertices_bottom_frame = vertices_2d[-1, :].copy()  # H x 3
214 |     vertices_bottom_frame[:, 2] = f_near
215 |     vertices = np.append(vertices, vertices_bottom_data, axis=0)
216 |     vertices = np.append(vertices, vertices_bottom_frame, axis=0)
217 |     colors = np.append(colors, [[0.5, 0.5, 0.5]] * (2 * w), axis=0)
218 |     for i in range(w - 1):
219 |         nvi_d = nv + i
220 |         nvi_f = nvi_d + w
221 |         faces.append([nvi_d, nvi_f, nvi_d + 1])
222 |         faces.append([nvi_d + 1, nvi_f, nvi_f + 1])
223 | 
224 |     # FRONT frame
225 | 
226 |     nv = len(vertices)
227 |     vertices = np.append(
228 |         vertices,
229 |         [
230 |             [-w_half - f_thic, -h_half - f_thic, f_near],
231 |             [-w_half - f_thic, h_half + f_thic, f_near],
232 |         ],
233 |         axis=0,
234 |     )
235 |     vertices = np.append(vertices, vertices_left_frame, axis=0)
236 |     colors = np.append(colors, [[0.5, 0.5, 0.5]] * (2 + h), axis=0)
237 |     for i in range(h - 1):
238 |         faces.append([nv, nv + 2 + i + 1, nv + 2 + i])
239 |     faces.append([nv, nv + 2, nv + 1])
240 | 
241 |     nv = len(vertices)
242 |     vertices = np.append(
243 |         vertices,
244 |         [
245 |             [w_half + f_thic, h_half + f_thic, f_near],
246 |             [w_half + f_thic, -h_half - f_thic, f_near],
247 |         ],
248 |         axis=0,
249 |     )
250 |     vertices = np.append(vertices, vertices_right_frame, axis=0)
251 |     colors = np.append(colors, [[0.5, 0.5, 0.5]] * (2 + h), axis=0)
252 |     for i in range(h - 1):
253 |         faces.append([nv, nv + 2 + i, nv + 2 + i + 1])
254 |     faces.append([nv, nv + h + 1, nv + 1])
255 | 
256 |     nv = len(vertices)
257 |     vertices = np.append(
258 |         vertices,
259 |         [
260 |             [w_half + f_thic, h_half + f_thic, f_near],
261 |             [-w_half - f_thic, h_half + f_thic, f_near],
262 |         ],
263 |         axis=0,
264 |     )
265 |     vertices = np.append(vertices, vertices_top_frame, axis=0)
266 |     colors = np.append(colors, [[0.5, 0.5, 0.5]] * (2 + w), axis=0)
267 |     for i in range(w - 1):
268 |         faces.append([nv, nv + 2 + i, nv + 2 + i + 1])
269 |     faces.append([nv, nv + 1, nv + 2])
270 | 
271 |     nv = len(vertices)
272 |     vertices = np.append(
273 |         vertices,
274 |         [
275 |             [-w_half - f_thic, -h_half - f_thic, f_near],
276 |             [w_half + f_thic, -h_half - f_thic, f_near],
277 |         ],
278 |         axis=0,
279 |     )
280 |     vertices = np.append(vertices, vertices_bottom_frame, axis=0)
281 |     colors = np.append(colors, [[0.5, 0.5, 0.5]] * (2 + w), axis=0)
282 |     for i in range(w - 1):
283 |         faces.append([nv, nv + 2 + i + 1, nv + 2 + i])
284 |     faces.append([nv, nv + 1, nv + w + 1])
285 | 
286 |     # BACK frame
287 | 
288 |     nv = len(vertices)
289 |     vertices = np.append(
290 |         vertices,
291 |         [
292 |             [-w_half - f_thic, -h_half - f_thic, f_far_outer],  # 00
293 |             [w_half + f_thic, -h_half - f_thic, f_far_outer],  # 01
294 |             [w_half + f_thic, h_half + f_thic, f_far_outer],  # 02
295 |             [-w_half - f_thic, h_half + f_thic, f_far_outer],  # 03
296 |         ],
297 |         axis=0,
298 |     )
299 |     faces.extend(
300 |         [
301 |             [nv + 0, nv + 2, nv + 1],
302 |             [nv + 2, nv + 0, nv + 3],
303 |         ]
304 |     )
305 |     colors = np.append(colors, [[0.5, 0.5, 0.5]] * 4, axis=0)
306 | 
307 |     trimesh_kwargs = {}
308 |     if vertex_colors:
309 |         trimesh_kwargs["vertex_colors"] = colors
310 |     mesh = trimesh.Trimesh(vertices=vertices, faces=faces, **trimesh_kwargs)
311 | 
312 |     mesh.merge_vertices()
313 | 
314 |     current_max_dimension = max(mesh.extents)
315 |     scaling_factor = output_model_scale / current_max_dimension
316 |     mesh.apply_scale(scaling_factor)
317 | 
318 |     if prepare_for_3d_printing:
319 |         rotation_mat = trimesh.transformations.rotation_matrix(np.radians(90), [-1, 0, 0])
320 |         mesh.apply_transform(rotation_mat)
321 | 
322 |     path_out_base = os.path.splitext(path_depth)[0].replace("_16bit", "")
323 |     path_out_glb = path_out_base + ".glb"
324 |     path_out_stl = path_out_base + ".stl"
325 | 
326 |     mesh.export(path_out_glb, file_type="glb")
327 |     if scene_lights:
328 |         glb_add_lights(path_out_glb, path_out_glb)
329 | 
330 |     mesh.export(path_out_stl, file_type="stl")
331 | 
332 |     return path_out_glb, path_out_stl
333 | 


--------------------------------------------------------------------------------
/marigold_depth_estimation_lcm.py:
--------------------------------------------------------------------------------
  1 | # Copyright 2024 Anton Obukhov, Bingxin Ke, ETH Zurich and The HuggingFace Team. All rights reserved.
  2 | #
  3 | # Licensed under the Apache License, Version 2.0 (the "License");
  4 | # you may not use this file except in compliance with the License.
  5 | # You may obtain a copy of the License at
  6 | #
  7 | #     http://www.apache.org/licenses/LICENSE-2.0
  8 | #
  9 | # Unless required by applicable law or agreed to in writing, software
 10 | # distributed under the License is distributed on an "AS IS" BASIS,
 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 12 | # See the License for the specific language governing permissions and
 13 | # limitations under the License.
 14 | # --------------------------------------------------------------------------
 15 | # If you find this code useful, we kindly ask you to cite our paper in your work.
 16 | # Please find bibtex at: https://github.com/prs-eth/Marigold#-citation
 17 | # More information about the method can be found at https://marigoldmonodepth.github.io
 18 | # --------------------------------------------------------------------------
 19 | 
 20 | 
 21 | import math
 22 | from typing import Dict, Union, Tuple
 23 | 
 24 | import matplotlib
 25 | import numpy as np
 26 | import torch
 27 | from PIL import Image
 28 | from scipy.optimize import minimize
 29 | from torch.utils.data import DataLoader, TensorDataset
 30 | from tqdm.auto import tqdm
 31 | from transformers import CLIPTextModel, CLIPTokenizer
 32 | 
 33 | from diffusers import (
 34 |     AutoencoderKL,
 35 |     DDIMScheduler,
 36 |     DiffusionPipeline,
 37 |     UNet2DConditionModel,
 38 | )
 39 | from diffusers.utils import BaseOutput, check_min_version
 40 | 
 41 | 
 42 | # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
 43 | check_min_version("0.27.0.dev0")
 44 | 
 45 | 
 46 | class MarigoldDepthConsistencyOutput(BaseOutput):
 47 |     """
 48 |     Output class for Marigold monocular depth prediction pipeline.
 49 | 
 50 |     Args:
 51 |         depth_np (`np.ndarray`):
 52 |             Predicted depth map, with depth values in the range of [0, 1].
 53 |         depth_colored (`None` or `PIL.Image.Image`):
 54 |             Colorized depth map, with the shape of [3, H, W] and values in [0, 1].
 55 |         depth_latent (`torch.Tensor`):
 56 |             Depth map's latent, with the shape of [4, h, w].
 57 |         uncertainty (`None` or `np.ndarray`):
 58 |             Uncalibrated uncertainty(MAD, median absolute deviation) coming from ensembling.
 59 |     """
 60 | 
 61 |     depth_np: np.ndarray
 62 |     depth_colored: Union[None, Image.Image]
 63 |     depth_latent: torch.Tensor
 64 |     uncertainty: Union[None, np.ndarray]
 65 | 
 66 | 
 67 | class MarigoldDepthConsistencyPipeline(DiffusionPipeline):
 68 |     """
 69 |     Pipeline for monocular depth estimation using Marigold: https://marigoldmonodepth.github.io.
 70 | 
 71 |     This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
 72 |     library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
 73 | 
 74 |     Args:
 75 |         unet (`UNet2DConditionModel`):
 76 |             Conditional U-Net to denoise the depth latent, conditioned on image latent.
 77 |         vae (`AutoencoderKL`):
 78 |             Variational Auto-Encoder (VAE) Model to encode and decode images and depth maps
 79 |             to and from latent representations.
 80 |         scheduler (`DDIMScheduler`):
 81 |             A scheduler to be used in combination with `unet` to denoise the encoded image latents.
 82 |         text_encoder (`CLIPTextModel`):
 83 |             Text-encoder, for empty text embedding.
 84 |         tokenizer (`CLIPTokenizer`):
 85 |             CLIP tokenizer.
 86 |     """
 87 | 
 88 |     rgb_latent_scale_factor = 0.18215
 89 |     depth_latent_scale_factor = 0.18215
 90 | 
 91 |     def __init__(
 92 |         self,
 93 |         unet: UNet2DConditionModel,
 94 |         vae: AutoencoderKL,
 95 |         scheduler: DDIMScheduler,
 96 |         text_encoder: CLIPTextModel,
 97 |         tokenizer: CLIPTokenizer,
 98 |     ):
 99 |         super().__init__()
100 | 
101 |         self.register_modules(
102 |             unet=unet,
103 |             vae=vae,
104 |             scheduler=scheduler,
105 |             text_encoder=text_encoder,
106 |             tokenizer=tokenizer,
107 |         )
108 | 
109 |         self.empty_text_embed = None
110 | 
111 |     @torch.no_grad()
112 |     def __call__(
113 |         self,
114 |         input_image: Image,
115 |         denoising_steps: int = 1,
116 |         ensemble_size: int = 1,
117 |         processing_res: int = 768,
118 |         match_input_res: bool = True,
119 |         batch_size: int = 0,
120 |         depth_latent_init: torch.Tensor = None,
121 |         depth_latent_init_strength: float = 0.1,
122 |         seed: int = None,
123 |         color_map: str = "Spectral",
124 |         show_progress_bar: bool = True,
125 |         ensemble_kwargs: Dict = None,
126 |     ) -> MarigoldDepthConsistencyOutput:
127 |         """
128 |         Function invoked when calling the pipeline.
129 | 
130 |         Args:
131 |             input_image (`Image`):
132 |                 Input RGB (or gray-scale) image.
133 |             processing_res (`int`, *optional*, defaults to `768`):
134 |                 Maximum resolution of processing.
135 |                 If set to 0: will not resize at all.
136 |             match_input_res (`bool`, *optional*, defaults to `True`):
137 |                 Resize depth prediction to match input resolution.
138 |                 Only valid if `limit_input_res` is not None.
139 |             denoising_steps (`int`, *optional*, defaults to `1`):
140 |                 Number of diffusion denoising steps (consistency) during inference.
141 |             ensemble_size (`int`, *optional*, defaults to `1`):
142 |                 Number of predictions to be ensembled.
143 |             batch_size (`int`, *optional*, defaults to `0`):
144 |                 Inference batch size, no bigger than `num_ensemble`.
145 |                 If set to 0, the script will automatically decide the proper batch size.
146 |             depth_latent_init (`torch.Tensor`, *optional*, defaults to `None`):
147 |                 Initial depth map latent for better temporal consistency.
148 |             depth_latent_init_strength (`float`, *optional*, defaults to `0.1`)
149 |                 Degree of initial depth latent influence, must be between 0 and 1.
150 |             seed (`int`, *optional*, defaults to `None`)
151 |                 Reproducibility seed.
152 |             show_progress_bar (`bool`, *optional*, defaults to `True`):
153 |                 Display a progress bar of diffusion denoising.
154 |             color_map (`str`, *optional*, defaults to `"Spectral"`, pass `None` to skip colorized depth map generation):
155 |                 Colormap used to colorize the depth map.
156 |             ensemble_kwargs (`dict`, *optional*, defaults to `None`):
157 |                 Arguments for detailed ensembling settings.
158 |         Returns:
159 |             `MarigoldDepthConsistencyOutput`: Output class for Marigold monocular depth prediction pipeline, including:
160 |             - **depth_np** (`np.ndarray`) Predicted depth map, with depth values in the range of [0, 1]
161 |             - **depth_colored** (`None` or `PIL.Image.Image`) Colorized depth map, with the shape of [3, H, W] and
162 |                     values in [0, 1]. None if `color_map` is `None`
163 |             - **depth_latent** (`torch.Tensor`) Predicted depth map latent
164 |             - **uncertainty** (`None` or `np.ndarray`) Uncalibrated uncertainty(MAD, median absolute deviation)
165 |                     coming from ensembling. None if `ensemble_size = 1`
166 |         """
167 | 
168 |         device = self.device
169 |         input_size = input_image.size
170 | 
171 |         if not match_input_res:
172 |             assert (
173 |                 processing_res is not None
174 |             ), "Value error: `resize_output_back` is only valid with "
175 |         assert processing_res >= 0, "Value error: `processing_res` must be non-negative"
176 |         assert (
177 |             1 <= denoising_steps <= 10
178 |         ), "Value error: This model degrades with large number of steps"
179 |         assert ensemble_size >= 1
180 | 
181 |         # ----------------- Image Preprocess -----------------
182 |         # Resize image
183 |         if processing_res > 0:
184 |             input_image = self.resize_max_res(
185 |                 input_image, max_edge_resolution=processing_res
186 |             )
187 |         # Convert the image to RGB, to 1.remove the alpha channel 2.convert B&W to 3-channel
188 |         input_image = input_image.convert("RGB")
189 |         image = np.asarray(input_image)
190 | 
191 |         # Normalize rgb values
192 |         rgb = np.transpose(image, (2, 0, 1))  # [H, W, rgb] -> [rgb, H, W]
193 |         rgb_norm = rgb / 255.0 * 2.0 - 1.0  # [0, 255] -> [-1, 1]
194 |         rgb_norm = torch.from_numpy(rgb_norm).to(self.dtype)
195 |         rgb_norm = rgb_norm.to(device)
196 |         assert rgb_norm.min() >= -1.0 and rgb_norm.max() <= 1.0
197 | 
198 |         # ----------------- Predicting depth -----------------
199 |         # Batch repeated input image
200 |         duplicated_rgb = torch.stack([rgb_norm] * ensemble_size)
201 |         batch_dataset = TensorDataset(duplicated_rgb)
202 |         if batch_size > 0:
203 |             _bs = batch_size
204 |         else:
205 |             _bs = self._find_batch_size(
206 |                 ensemble_size=ensemble_size,
207 |                 input_res=max(duplicated_rgb.shape[-2:]),
208 |                 dtype=self.dtype,
209 |             )
210 | 
211 |         batch_loader = DataLoader(batch_dataset, batch_size=_bs, shuffle=False)
212 | 
213 |         # Predict depth maps (batched)
214 |         depth_pred_ls = []
215 |         if show_progress_bar:
216 |             iterable = tqdm(
217 |                 batch_loader, desc=" " * 2 + "Inference batches", leave=False
218 |             )
219 |         else:
220 |             iterable = batch_loader
221 |         depth_latent = None
222 |         for batch in iterable:
223 |             (batched_img,) = batch
224 |             depth_pred_raw, depth_latent = self.single_infer(
225 |                 rgb_in=batched_img,
226 |                 num_inference_steps=denoising_steps,
227 |                 depth_latent_init=depth_latent_init,
228 |                 depth_latent_init_strength=depth_latent_init_strength,
229 |                 seed=seed,
230 |                 show_pbar=show_progress_bar,
231 |             )
232 |             depth_pred_ls.append(depth_pred_raw.detach())
233 |         depth_preds = torch.concat(depth_pred_ls, dim=0).squeeze()
234 |         torch.cuda.empty_cache()  # clear vram cache for ensembling
235 | 
236 |         # ----------------- Test-time ensembling -----------------
237 |         if ensemble_size > 1:
238 |             depth_pred, pred_uncert = self.ensemble_depths(
239 |                 depth_preds, **(ensemble_kwargs or {})
240 |             )
241 |         else:
242 |             depth_pred = depth_preds
243 |             pred_uncert = None
244 | 
245 |         # ----------------- Post processing -----------------
246 |         # Scale prediction to [0, 1]
247 |         min_d = torch.min(depth_pred)
248 |         max_d = torch.max(depth_pred)
249 |         depth_pred = (depth_pred - min_d) / (max_d - min_d)
250 |         if ensemble_size > 1:
251 |             depth_latent = self._encode_depth(2 * depth_pred - 1)
252 | 
253 |         # Convert to numpy
254 |         depth_pred = depth_pred.cpu().numpy().astype(np.float32)
255 | 
256 |         # Resize back to original resolution
257 |         if match_input_res:
258 |             pred_img = Image.fromarray(depth_pred)
259 |             pred_img = pred_img.resize(input_size)
260 |             depth_pred = np.asarray(pred_img)
261 | 
262 |         # Clip output range
263 |         depth_pred = depth_pred.clip(0, 1)
264 | 
265 |         # Colorize
266 |         if color_map is not None:
267 |             depth_colored = self.colorize_depth_maps(
268 |                 depth_pred, 0, 1, cmap=color_map
269 |             ).squeeze()  # [3, H, W], value in (0, 1)
270 |             depth_colored = (depth_colored * 255).astype(np.uint8)
271 |             depth_colored_hwc = self.chw2hwc(depth_colored)
272 |             depth_colored_img = Image.fromarray(depth_colored_hwc)
273 |         else:
274 |             depth_colored_img = None
275 |         return MarigoldDepthConsistencyOutput(
276 |             depth_np=depth_pred,
277 |             depth_colored=depth_colored_img,
278 |             depth_latent=depth_latent,
279 |             uncertainty=pred_uncert,
280 |         )
281 | 
282 |     def _encode_empty_text(self):
283 |         """
284 |         Encode text embedding for empty prompt.
285 |         """
286 |         prompt = ""
287 |         text_inputs = self.tokenizer(
288 |             prompt,
289 |             padding="do_not_pad",
290 |             max_length=self.tokenizer.model_max_length,
291 |             truncation=True,
292 |             return_tensors="pt",
293 |         )
294 |         text_input_ids = text_inputs.input_ids.to(self.text_encoder.device)
295 |         self.empty_text_embed = self.text_encoder(text_input_ids)[0].to(self.dtype)
296 | 
297 |     @torch.no_grad()
298 |     def single_infer(
299 |         self,
300 |         rgb_in: torch.Tensor,
301 |         num_inference_steps: int,
302 |         depth_latent_init: torch.Tensor,
303 |         depth_latent_init_strength: float,
304 |         seed: int,
305 |         show_pbar: bool,
306 |     ) -> Tuple[torch.Tensor, torch.Tensor]:
307 |         """
308 |         Perform an individual depth prediction without ensembling.
309 | 
310 |         Args:
311 |             rgb_in (`torch.Tensor`):
312 |                 Input RGB image.
313 |             num_inference_steps (`int`):
314 |                 Number of diffusion denoisign steps (DDIM) during inference.
315 |             depth_latent_init (`torch.Tensor`, `optional`):
316 |                 Initial depth latent
317 |             depth_latent_init_strength (`float`, `optional`):
318 |                 Degree of initial depth latent influence, must be between 0 and 1
319 |             seed (`int`, *optional*, defaults to `None`)
320 |                 Reproducibility seed.
321 |             show_pbar (`bool`):
322 |                 Display a progress bar of diffusion denoising.
323 |         Returns:
324 |             `torch.Tensor`: Predicted depth map.
325 |         """
326 |         device = rgb_in.device
327 | 
328 |         # Set timesteps
329 |         self.scheduler.set_timesteps(num_inference_steps, device=device)
330 |         timesteps = self.scheduler.timesteps  # [T]
331 | 
332 |         # Encode image
333 |         rgb_latent = self._encode_rgb(rgb_in)
334 | 
335 |         # Initial depth map (noise)
336 |         if seed is None:
337 |             rng = None
338 |         else:
339 |             rng = torch.Generator(device=device)
340 |             rng.manual_seed(seed)
341 |         depth_latent = torch.randn(
342 |             rgb_latent.shape, device=device, dtype=self.dtype, generator=rng
343 |         )  # [B, 4, h, w]
344 | 
345 |         if depth_latent_init is not None:
346 |             assert 0.0 <= depth_latent_init_strength <= 1.0
347 |             assert (
348 |                 depth_latent_init.dim() == 4
349 |                 and depth_latent.dim() == 4
350 |                 and depth_latent_init.shape[0] == 1
351 |             )
352 |             if depth_latent.shape[0] != 1:
353 |                 depth_latent_init = depth_latent_init.repeat(
354 |                     depth_latent.shape[0], 1, 1, 1
355 |                 )
356 |             depth_latent *= 1.0 - depth_latent_init_strength
357 |             depth_latent = depth_latent + depth_latent_init * depth_latent_init_strength
358 | 
359 |         # Batched empty text embedding
360 |         if self.empty_text_embed is None:
361 |             self._encode_empty_text()
362 |         batch_empty_text_embed = self.empty_text_embed.repeat(
363 |             (rgb_latent.shape[0], 1, 1)
364 |         )  # [B, 2, 1024]
365 | 
366 |         # Denoising loop
367 |         if show_pbar:
368 |             iterable = tqdm(
369 |                 enumerate(timesteps),
370 |                 total=len(timesteps),
371 |                 leave=False,
372 |                 desc=" " * 4 + "Diffusion denoising",
373 |             )
374 |         else:
375 |             iterable = enumerate(timesteps)
376 | 
377 |         for i, t in iterable:
378 |             unet_input = torch.cat(
379 |                 [rgb_latent, depth_latent], dim=1
380 |             )  # this order is important
381 | 
382 |             # predict the noise residual
383 |             noise_pred = self.unet(
384 |                 unet_input, t, encoder_hidden_states=batch_empty_text_embed
385 |             ).sample  # [B, 4, h, w]
386 | 
387 |             # compute the previous noisy sample x_t -> x_t-1
388 |             depth_latent = self.scheduler.step(noise_pred, t, depth_latent).prev_sample
389 | 
390 |         depth = self._decode_depth(depth_latent)
391 | 
392 |         # clip prediction
393 |         depth = torch.clip(depth, -1.0, 1.0)
394 |         # shift to [0, 1]
395 |         depth = (depth + 1.0) / 2.0
396 | 
397 |         return depth, depth_latent
398 | 
399 |     def _encode_depth(self, depth_in: torch.Tensor) -> torch.Tensor:
400 |         """
401 |         Encode depth image into latent.
402 | 
403 |         Args:
404 |             depth_in (`torch.Tensor`):
405 |                 Input Depth image to be encoded.
406 | 
407 |         Returns:
408 |             `torch.Tensor`: Depth latent.
409 |         """
410 |         # encode
411 |         dims = depth_in.squeeze().shape
412 |         h = self.vae.encoder(depth_in.reshape(1, 1, *dims).repeat(1, 3, 1, 1))
413 |         moments = self.vae.quant_conv(h)
414 |         mean, _ = torch.chunk(moments, 2, dim=1)
415 |         depth_latent = mean * self.depth_latent_scale_factor
416 |         return depth_latent
417 | 
418 |     def _encode_rgb(self, rgb_in: torch.Tensor) -> torch.Tensor:
419 |         """
420 |         Encode RGB image into latent.
421 | 
422 |         Args:
423 |             rgb_in (`torch.Tensor`):
424 |                 Input RGB image to be encoded.
425 | 
426 |         Returns:
427 |             `torch.Tensor`: Image latent.
428 |         """
429 |         # encode
430 |         h = self.vae.encoder(rgb_in)
431 |         moments = self.vae.quant_conv(h)
432 |         mean, logvar = torch.chunk(moments, 2, dim=1)
433 |         # scale latent
434 |         rgb_latent = mean * self.rgb_latent_scale_factor
435 |         return rgb_latent
436 | 
437 |     def _decode_depth(self, depth_latent: torch.Tensor) -> torch.Tensor:
438 |         """
439 |         Decode depth latent into depth map.
440 | 
441 |         Args:
442 |             depth_latent (`torch.Tensor`):
443 |                 Depth latent to be decoded.
444 | 
445 |         Returns:
446 |             `torch.Tensor`: Decoded depth map.
447 |         """
448 |         # scale latent
449 |         depth_latent = depth_latent / self.depth_latent_scale_factor
450 |         # decode
451 |         z = self.vae.post_quant_conv(depth_latent)
452 |         stacked = self.vae.decoder(z)
453 |         # mean of output channels
454 |         depth_mean = stacked.mean(dim=1, keepdim=True)
455 |         return depth_mean
456 | 
457 |     @staticmethod
458 |     def resize_max_res(img: Image.Image, max_edge_resolution: int) -> Image.Image:
459 |         """
460 |         Resize image to limit maximum edge length while keeping aspect ratio.
461 | 
462 |         Args:
463 |             img (`Image.Image`):
464 |                 Image to be resized.
465 |             max_edge_resolution (`int`):
466 |                 Maximum edge length (pixel).
467 | 
468 |         Returns:
469 |             `Image.Image`: Resized image.
470 |         """
471 |         original_width, original_height = img.size
472 |         downscale_factor = min(
473 |             max_edge_resolution / original_width, max_edge_resolution / original_height
474 |         )
475 | 
476 |         new_width = int(original_width * downscale_factor)
477 |         new_height = int(original_height * downscale_factor)
478 | 
479 |         resized_img = img.resize((new_width, new_height))
480 |         return resized_img
481 | 
482 |     @staticmethod
483 |     def colorize_depth_maps(
484 |         depth_map, min_depth, max_depth, cmap="Spectral", valid_mask=None
485 |     ):
486 |         """
487 |         Colorize depth maps.
488 |         """
489 |         assert len(depth_map.shape) >= 2, "Invalid dimension"
490 | 
491 |         if isinstance(depth_map, torch.Tensor):
492 |             depth = depth_map.detach().squeeze().numpy()
493 |         elif isinstance(depth_map, np.ndarray):
494 |             depth = depth_map.copy().squeeze()
495 |         # reshape to [ (B,) H, W ]
496 |         if depth.ndim < 3:
497 |             depth = depth[np.newaxis, :, :]
498 | 
499 |         # colorize
500 |         cm = matplotlib.colormaps[cmap]
501 |         depth = ((depth - min_depth) / (max_depth - min_depth)).clip(0, 1)
502 |         img_colored_np = cm(depth, bytes=False)[:, :, :, 0:3]  # value from 0 to 1
503 |         img_colored_np = np.rollaxis(img_colored_np, 3, 1)
504 | 
505 |         if valid_mask is not None:
506 |             if isinstance(depth_map, torch.Tensor):
507 |                 valid_mask = valid_mask.detach().numpy()
508 |             valid_mask = valid_mask.squeeze()  # [H, W] or [B, H, W]
509 |             if valid_mask.ndim < 3:
510 |                 valid_mask = valid_mask[np.newaxis, np.newaxis, :, :]
511 |             else:
512 |                 valid_mask = valid_mask[:, np.newaxis, :, :]
513 |             valid_mask = np.repeat(valid_mask, 3, axis=1)
514 |             img_colored_np[~valid_mask] = 0
515 | 
516 |         if isinstance(depth_map, torch.Tensor):
517 |             img_colored = torch.from_numpy(img_colored_np).float()
518 |         elif isinstance(depth_map, np.ndarray):
519 |             img_colored = img_colored_np
520 | 
521 |         return img_colored
522 | 
523 |     @staticmethod
524 |     def chw2hwc(chw):
525 |         assert 3 == len(chw.shape)
526 |         if isinstance(chw, torch.Tensor):
527 |             hwc = torch.permute(chw, (1, 2, 0))
528 |         elif isinstance(chw, np.ndarray):
529 |             hwc = np.moveaxis(chw, 0, -1)
530 |         return hwc
531 | 
532 |     @staticmethod
533 |     def _find_batch_size(ensemble_size: int, input_res: int, dtype: torch.dtype) -> int:
534 |         """
535 |         Automatically search for suitable operating batch size.
536 | 
537 |         Args:
538 |             ensemble_size (`int`):
539 |                 Number of predictions to be ensembled.
540 |             input_res (`int`):
541 |                 Operating resolution of the input image.
542 | 
543 |         Returns:
544 |             `int`: Operating batch size.
545 |         """
546 |         # Search table for suggested max. inference batch size
547 |         bs_search_table = [
548 |             # tested on A100-PCIE-80GB
549 |             {"res": 768, "total_vram": 79, "bs": 35, "dtype": torch.float32},
550 |             {"res": 1024, "total_vram": 79, "bs": 20, "dtype": torch.float32},
551 |             # tested on A100-PCIE-40GB
552 |             {"res": 768, "total_vram": 39, "bs": 15, "dtype": torch.float32},
553 |             {"res": 1024, "total_vram": 39, "bs": 8, "dtype": torch.float32},
554 |             {"res": 768, "total_vram": 39, "bs": 30, "dtype": torch.float16},
555 |             {"res": 1024, "total_vram": 39, "bs": 15, "dtype": torch.float16},
556 |             # tested on RTX3090, RTX4090
557 |             {"res": 512, "total_vram": 23, "bs": 20, "dtype": torch.float32},
558 |             {"res": 768, "total_vram": 23, "bs": 7, "dtype": torch.float32},
559 |             {"res": 1024, "total_vram": 23, "bs": 3, "dtype": torch.float32},
560 |             {"res": 512, "total_vram": 23, "bs": 40, "dtype": torch.float16},
561 |             {"res": 768, "total_vram": 23, "bs": 18, "dtype": torch.float16},
562 |             {"res": 1024, "total_vram": 23, "bs": 10, "dtype": torch.float16},
563 |             # tested on GTX1080Ti
564 |             {"res": 512, "total_vram": 10, "bs": 5, "dtype": torch.float32},
565 |             {"res": 768, "total_vram": 10, "bs": 2, "dtype": torch.float32},
566 |             {"res": 512, "total_vram": 10, "bs": 10, "dtype": torch.float16},
567 |             {"res": 768, "total_vram": 10, "bs": 5, "dtype": torch.float16},
568 |             {"res": 1024, "total_vram": 10, "bs": 3, "dtype": torch.float16},
569 |         ]
570 | 
571 |         if not torch.cuda.is_available():
572 |             return 1
573 | 
574 |         total_vram = torch.cuda.mem_get_info()[1] / 1024.0**3
575 |         filtered_bs_search_table = [s for s in bs_search_table if s["dtype"] == dtype]
576 |         for settings in sorted(
577 |             filtered_bs_search_table,
578 |             key=lambda k: (k["res"], -k["total_vram"]),
579 |         ):
580 |             if input_res <= settings["res"] and total_vram >= settings["total_vram"]:
581 |                 bs = settings["bs"]
582 |                 if bs > ensemble_size:
583 |                     bs = ensemble_size
584 |                 elif bs > math.ceil(ensemble_size / 2) and bs < ensemble_size:
585 |                     bs = math.ceil(ensemble_size / 2)
586 |                 return bs
587 | 
588 |         return 1
589 | 
590 |     @staticmethod
591 |     def ensemble_depths(
592 |         input_images: torch.Tensor,
593 |         regularizer_strength: float = 0.02,
594 |         max_iter: int = 2,
595 |         tol: float = 1e-3,
596 |         reduction: str = "median",
597 |         max_res: int = None,
598 |     ):
599 |         """
600 |         To ensemble multiple affine-invariant depth images (up to scale and shift),
601 |             by aligning estimating the scale and shift
602 |         """
603 | 
604 |         def inter_distances(tensors: torch.Tensor):
605 |             """
606 |             To calculate the distance between each two depth maps.
607 |             """
608 |             distances = []
609 |             for i, j in torch.combinations(torch.arange(tensors.shape[0])):
610 |                 arr1 = tensors[i : i + 1]
611 |                 arr2 = tensors[j : j + 1]
612 |                 distances.append(arr1 - arr2)
613 |             dist = torch.concatenate(distances, dim=0)
614 |             return dist
615 | 
616 |         device = input_images.device
617 |         dtype = input_images.dtype
618 |         np_dtype = np.float32
619 | 
620 |         original_input = input_images.clone()
621 |         n_img = input_images.shape[0]
622 |         ori_shape = input_images.shape
623 | 
624 |         if max_res is not None:
625 |             scale_factor = torch.min(max_res / torch.tensor(ori_shape[-2:]))
626 |             if scale_factor < 1:
627 |                 downscaler = torch.nn.Upsample(
628 |                     scale_factor=scale_factor, mode="nearest"
629 |                 )
630 |                 input_images = downscaler(torch.from_numpy(input_images)).numpy()
631 | 
632 |         # init guess
633 |         _min = np.min(input_images.reshape((n_img, -1)).cpu().numpy(), axis=1)
634 |         _max = np.max(input_images.reshape((n_img, -1)).cpu().numpy(), axis=1)
635 |         s_init = 1.0 / (_max - _min).reshape((-1, 1, 1))
636 |         t_init = (-1 * s_init.flatten() * _min.flatten()).reshape((-1, 1, 1))
637 |         x = np.concatenate([s_init, t_init]).reshape(-1).astype(np_dtype)
638 | 
639 |         input_images = input_images.to(device)
640 | 
641 |         # objective function
642 |         def closure(x):
643 |             l = len(x)
644 |             s = x[: int(l / 2)]
645 |             t = x[int(l / 2) :]
646 |             s = torch.from_numpy(s).to(dtype=dtype).to(device)
647 |             t = torch.from_numpy(t).to(dtype=dtype).to(device)
648 | 
649 |             transformed_arrays = input_images * s.view((-1, 1, 1)) + t.view((-1, 1, 1))
650 |             dists = inter_distances(transformed_arrays)
651 |             sqrt_dist = torch.sqrt(torch.mean(dists**2))
652 | 
653 |             if "mean" == reduction:
654 |                 pred = torch.mean(transformed_arrays, dim=0)
655 |             elif "median" == reduction:
656 |                 pred = torch.median(transformed_arrays, dim=0).values
657 |             else:
658 |                 raise ValueError
659 | 
660 |             near_err = torch.sqrt((0 - torch.min(pred)) ** 2)
661 |             far_err = torch.sqrt((1 - torch.max(pred)) ** 2)
662 | 
663 |             err = sqrt_dist + (near_err + far_err) * regularizer_strength
664 |             err = err.detach().cpu().numpy().astype(np_dtype)
665 |             return err
666 | 
667 |         res = minimize(
668 |             closure,
669 |             x,
670 |             method="BFGS",
671 |             tol=tol,
672 |             options={"maxiter": max_iter, "disp": False},
673 |         )
674 |         x = res.x
675 |         l = len(x)
676 |         s = x[: int(l / 2)]
677 |         t = x[int(l / 2) :]
678 | 
679 |         # Prediction
680 |         s = torch.from_numpy(s).to(dtype=dtype).to(device)
681 |         t = torch.from_numpy(t).to(dtype=dtype).to(device)
682 |         transformed_arrays = original_input * s.view(-1, 1, 1) + t.view(-1, 1, 1)
683 |         if "mean" == reduction:
684 |             aligned_images = torch.mean(transformed_arrays, dim=0)
685 |             std = torch.std(transformed_arrays, dim=0)
686 |             uncertainty = std
687 |         elif "median" == reduction:
688 |             aligned_images = torch.median(transformed_arrays, dim=0).values
689 |             # MAD (median absolute deviation) as uncertainty indicator
690 |             abs_dev = torch.abs(transformed_arrays - aligned_images)
691 |             mad = torch.median(abs_dev, dim=0).values
692 |             uncertainty = mad
693 |         else:
694 |             raise ValueError(f"Unknown reduction method: {reduction}")
695 | 
696 |         # Scale and shift to [0, 1]
697 |         _min = torch.min(aligned_images)
698 |         _max = torch.max(aligned_images)
699 |         aligned_images = (aligned_images - _min) / (_max - _min)
700 |         uncertainty /= _max - _min
701 | 
702 |         return aligned_images, uncertainty
703 | 


--------------------------------------------------------------------------------
/app.py:
--------------------------------------------------------------------------------
  1 | import functools
  2 | import os
  3 | import shutil
  4 | import zipfile
  5 | from io import BytesIO
  6 | 
  7 | import spaces
  8 | import gradio as gr
  9 | import imageio as imageio
 10 | import numpy as np
 11 | import torch as torch
 12 | from PIL import Image
 13 | from diffusers import UNet2DConditionModel, LCMScheduler
 14 | from gradio_imageslider import ImageSlider
 15 | from huggingface_hub import login
 16 | from tqdm import tqdm
 17 | 
 18 | from extrude import extrude_depth_3d
 19 | from marigold_depth_estimation_lcm import MarigoldDepthConsistencyPipeline
 20 | 
 21 | default_seed = 2024
 22 | 
 23 | default_image_denoise_steps = 4
 24 | default_image_ensemble_size = 1
 25 | default_image_processing_res = 768
 26 | default_image_reproducuble = True
 27 | 
 28 | default_video_depth_latent_init_strength = 0.1
 29 | default_video_denoise_steps = 1
 30 | default_video_ensemble_size = 1
 31 | default_video_processing_res = 768
 32 | default_video_out_fps = 15
 33 | default_video_out_max_frames = 100
 34 | 
 35 | default_bas_plane_near = 0.0
 36 | default_bas_plane_far = 1.0
 37 | default_bas_embossing = 20
 38 | default_bas_denoise_steps = 4
 39 | default_bas_ensemble_size = 1
 40 | default_bas_processing_res = 768
 41 | default_bas_size_longest_px = 512
 42 | default_bas_size_longest_cm = 10
 43 | default_bas_filter_size = 3
 44 | default_bas_frame_thickness = 5
 45 | default_bas_frame_near = 1
 46 | default_bas_frame_far = 1
 47 | 
 48 | 
 49 | @spaces.GPU
 50 | def process_image(
 51 |     pipe,
 52 |     path_input,
 53 |     denoise_steps=default_image_denoise_steps,
 54 |     ensemble_size=default_image_ensemble_size,
 55 |     processing_res=default_image_processing_res,
 56 |     reproducible=default_image_reproducuble,
 57 | ):
 58 |     input_image = Image.open(path_input)
 59 | 
 60 |     pipe_out = pipe(
 61 |         input_image,
 62 |         denoising_steps=denoise_steps,
 63 |         ensemble_size=ensemble_size,
 64 |         processing_res=processing_res,
 65 |         batch_size=1 if processing_res == 0 else 0,
 66 |         seed=default_seed if reproducible else None,
 67 |         show_progress_bar=False,
 68 |     )
 69 | 
 70 |     depth_pred = pipe_out.depth_np
 71 |     depth_colored = pipe_out.depth_colored
 72 |     depth_16bit = (depth_pred * 65535.0).astype(np.uint16)
 73 | 
 74 |     path_output_dir = os.path.splitext(path_input)[0] + "_output"
 75 |     os.makedirs(path_output_dir, exist_ok=True)
 76 | 
 77 |     name_base = os.path.splitext(os.path.basename(path_input))[0]
 78 |     path_out_fp32 = os.path.join(path_output_dir, f"{name_base}_depth_fp32.npy")
 79 |     path_out_16bit = os.path.join(path_output_dir, f"{name_base}_depth_16bit.png")
 80 |     path_out_vis = os.path.join(path_output_dir, f"{name_base}_depth_colored.png")
 81 | 
 82 |     np.save(path_out_fp32, depth_pred)
 83 |     Image.fromarray(depth_16bit).save(path_out_16bit, mode="I;16")
 84 |     depth_colored.save(path_out_vis)
 85 | 
 86 |     return (
 87 |         [path_out_16bit, path_out_vis],
 88 |         [path_out_16bit, path_out_fp32, path_out_vis],
 89 |     )
 90 | 
 91 | 
 92 | @spaces.GPU
 93 | def process_video(
 94 |     pipe,
 95 |     path_input,
 96 |     depth_latent_init_strength=default_video_depth_latent_init_strength,
 97 |     denoise_steps=default_video_denoise_steps,
 98 |     ensemble_size=default_video_ensemble_size,
 99 |     processing_res=default_video_processing_res,
100 |     out_fps=default_video_out_fps,
101 |     out_max_frames=default_video_out_max_frames,
102 |     progress=gr.Progress(),
103 | ):
104 |     path_output_dir = os.path.splitext(path_input)[0] + "_output"
105 |     os.makedirs(path_output_dir, exist_ok=True)
106 | 
107 |     name_base = os.path.splitext(os.path.basename(path_input))[0]
108 |     path_out_vis = os.path.join(path_output_dir, f"{name_base}_depth_colored.mp4")
109 |     path_out_16bit = os.path.join(path_output_dir, f"{name_base}_depth_16bit.zip")
110 | 
111 |     reader = imageio.get_reader(path_input)
112 | 
113 |     meta_data = reader.get_meta_data()
114 |     fps = meta_data["fps"]
115 |     size = meta_data["size"]
116 |     duration_sec = meta_data["duration"]
117 | 
118 |     if fps <= out_fps:
119 |         frame_interval, out_fps = 1, fps
120 |     else:
121 |         frame_interval = round(fps / out_fps)
122 |         out_fps = fps / frame_interval
123 | 
124 |     out_duration_sec = out_max_frames / out_fps
125 |     if duration_sec > out_duration_sec:
126 |         gr.Warning(
127 |             f"Only the first ~{int(out_duration_sec)} seconds will be processed; "
128 |             f"use alternative setups for full processing"
129 |         )
130 | 
131 |     writer = imageio.get_writer(path_out_vis, fps=out_fps)
132 |     zipf = zipfile.ZipFile(path_out_16bit, "w", zipfile.ZIP_DEFLATED)
133 |     prev_depth_latent = None
134 | 
135 |     pbar = tqdm(desc="Processing Video", total=out_max_frames)
136 | 
137 |     out_frame_id = 0
138 |     for frame_id, frame in enumerate(reader):
139 |         if not (frame_id % frame_interval == 0):
140 |             continue
141 |         out_frame_id += 1
142 |         pbar.update(1)
143 |         if out_frame_id > out_max_frames:
144 |             break
145 | 
146 |         frame_pil = Image.fromarray(frame)
147 | 
148 |         pipe_out = pipe(
149 |             frame_pil,
150 |             denoising_steps=denoise_steps,
151 |             ensemble_size=ensemble_size,
152 |             processing_res=processing_res,
153 |             match_input_res=False,
154 |             batch_size=0,
155 |             depth_latent_init=prev_depth_latent,
156 |             depth_latent_init_strength=depth_latent_init_strength,
157 |             seed=default_seed,
158 |             show_progress_bar=False,
159 |         )
160 | 
161 |         prev_depth_latent = pipe_out.depth_latent
162 | 
163 |         processed_frame = pipe_out.depth_colored
164 |         processed_frame = imageio.core.util.Array(np.array(processed_frame))
165 |         writer.append_data(processed_frame)
166 | 
167 |         processed_frame = (65535 * np.clip(pipe_out.depth_np, 0.0, 1.0)).astype(
168 |             np.uint16
169 |         )
170 |         processed_frame = Image.fromarray(processed_frame, mode="I;16")
171 | 
172 |         archive_path = os.path.join(
173 |             f"{name_base}_depth_16bit", f"{out_frame_id:05d}.png"
174 |         )
175 |         img_byte_arr = BytesIO()
176 |         processed_frame.save(img_byte_arr, format="png")
177 |         img_byte_arr.seek(0)
178 |         zipf.writestr(archive_path, img_byte_arr.read())
179 | 
180 |     reader.close()
181 |     writer.close()
182 |     zipf.close()
183 | 
184 |     return (
185 |         path_out_vis,
186 |         [path_out_vis, path_out_16bit],
187 |     )
188 | 
189 | 
190 | @spaces.GPU
191 | def process_bas(
192 |     pipe,
193 |     path_input,
194 |     plane_near=default_bas_plane_near,
195 |     plane_far=default_bas_plane_far,
196 |     embossing=default_bas_embossing,
197 |     denoise_steps=default_bas_denoise_steps,
198 |     ensemble_size=default_bas_ensemble_size,
199 |     processing_res=default_bas_processing_res,
200 |     size_longest_px=default_bas_size_longest_px,
201 |     size_longest_cm=default_bas_size_longest_cm,
202 |     filter_size=default_bas_filter_size,
203 |     frame_thickness=default_bas_frame_thickness,
204 |     frame_near=default_bas_frame_near,
205 |     frame_far=default_bas_frame_far,
206 | ):
207 |     if plane_near >= plane_far:
208 |         raise gr.Error("NEAR plane must have a value smaller than the FAR plane")
209 | 
210 |     path_output_dir = os.path.splitext(path_input)[0] + "_output"
211 |     os.makedirs(path_output_dir, exist_ok=True)
212 | 
213 |     name_base, name_ext = os.path.splitext(os.path.basename(path_input))
214 | 
215 |     input_image = Image.open(path_input)
216 | 
217 |     pipe_out = pipe(
218 |         input_image,
219 |         denoising_steps=denoise_steps,
220 |         ensemble_size=ensemble_size,
221 |         processing_res=processing_res,
222 |         seed=default_seed,
223 |         show_progress_bar=False,
224 |     )
225 | 
226 |     depth_pred = pipe_out.depth_np * 65535
227 | 
228 |     def _process_3d(
229 |         size_longest_px,
230 |         filter_size,
231 |         vertex_colors,
232 |         scene_lights,
233 |         output_model_scale=None,
234 |         prepare_for_3d_printing=False,
235 |     ):
236 |         image_rgb_w, image_rgb_h = input_image.width, input_image.height
237 |         image_rgb_d = max(image_rgb_w, image_rgb_h)
238 |         image_new_w = size_longest_px * image_rgb_w // image_rgb_d
239 |         image_new_h = size_longest_px * image_rgb_h // image_rgb_d
240 | 
241 |         image_rgb_new = os.path.join(
242 |             path_output_dir, f"{name_base}_rgb_{size_longest_px}{name_ext}"
243 |         )
244 |         image_depth_new = os.path.join(
245 |             path_output_dir, f"{name_base}_depth_{size_longest_px}.png"
246 |         )
247 |         input_image.resize((image_new_w, image_new_h), Image.LANCZOS).save(
248 |             image_rgb_new
249 |         )
250 |         Image.fromarray(depth_pred).convert(mode="F").resize(
251 |             (image_new_w, image_new_h), Image.BILINEAR
252 |         ).convert("I").save(image_depth_new)
253 | 
254 |         path_glb, path_stl = extrude_depth_3d(
255 |             image_rgb_new,
256 |             image_depth_new,
257 |             output_model_scale=size_longest_cm * 10
258 |             if output_model_scale is None
259 |             else output_model_scale,
260 |             filter_size=filter_size,
261 |             coef_near=plane_near,
262 |             coef_far=plane_far,
263 |             emboss=embossing / 100,
264 |             f_thic=frame_thickness / 100,
265 |             f_near=frame_near / 100,
266 |             f_back=frame_far / 100,
267 |             vertex_colors=vertex_colors,
268 |             scene_lights=scene_lights,
269 |             prepare_for_3d_printing=prepare_for_3d_printing,
270 |         )
271 | 
272 |         return path_glb, path_stl
273 | 
274 |     path_viewer_glb, _ = _process_3d(
275 |         256, filter_size, vertex_colors=False, scene_lights=True, output_model_scale=1
276 |     )
277 |     path_files_glb, path_files_stl = _process_3d(
278 |         size_longest_px, filter_size, vertex_colors=True, scene_lights=False, prepare_for_3d_printing=True
279 |     )
280 | 
281 |     return path_viewer_glb, [path_files_glb, path_files_stl]
282 | 
283 | 
284 | def run_demo_server(pipe):
285 |     process_pipe_image = functools.partial(process_image, pipe)
286 |     process_pipe_video = functools.partial(process_video, pipe)
287 |     process_pipe_bas = functools.partial(process_bas, pipe)
288 |     os.environ["GRADIO_ALLOW_FLAGGING"] = "never"
289 | 
290 |     gradio_theme = gr.themes.Default()
291 | 
292 |     with gr.Blocks(
293 |         theme=gradio_theme,
294 |         title="Marigold-LCM Depth Estimation",
295 |         css="""
296 |             #download {
297 |                 height: 118px;
298 |             }
299 |             .slider .inner {
300 |                 width: 5px;
301 |                 background: #FFF;
302 |             }
303 |             .viewport {
304 |                 aspect-ratio: 4/3;
305 |             }
306 |             .tabs button.selected {
307 |                 font-size: 20px !important;
308 |                 color: crimson !important;
309 |             }
310 |         """,
311 |         head="""
312 |             <script async src="https://www.googletagmanager.com/gtag/js?id=G-1FWSVCGZTG"></script>
313 |             <script>
314 |                 window.dataLayer = window.dataLayer || [];
315 |                 function gtag() {dataLayer.push(arguments);}
316 |                 gtag('js', new Date());
317 |                 gtag('config', 'G-1FWSVCGZTG');
318 |             </script>
319 |         """,
320 |     ) as demo:
321 |         gr.Markdown(
322 |             """
323 |             <h1 align="center">Marigold-LCM Depth Estimation</h1>
324 |             <p align="center">
325 |             <a title="Website" href="https://marigoldmonodepth.github.io/" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
326 |                 <img src="https://www.obukhov.ai/img/badges/badge-website.svg">
327 |             </a>
328 |             <a title="arXiv" href="https://arxiv.org/abs/2312.02145" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
329 |                 <img src="https://www.obukhov.ai/img/badges/badge-pdf.svg">
330 |             </a>
331 |             <a title="Github" href="https://github.com/prs-eth/marigold" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
332 |                 <img src="https://img.shields.io/github/stars/prs-eth/marigold?label=GitHub%20%E2%98%85&logo=github&color=C8C" alt="badge-github-stars">
333 |             </a>
334 |             <a title="Social" href="https://twitter.com/antonobukhov1" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
335 |                 <img src="https://www.obukhov.ai/img/badges/badge-social.svg" alt="social">
336 |             </a>
337 |             </p>
338 |             <p align="justify">
339 |                 Marigold-LCM is the fast version of Marigold, the state-of-the-art depth estimator for images in the wild.
340 |                 It combines the power of the original Marigold 10-step estimator and the Latent Consistency Models, delivering high-quality results in as little as <b>one step</b>.
341 |                 We provide three functions in this demo: Image, Video, and Bas-relief 3D processing — <b>see the tabs below</b>. 
342 |                 Upload your content into the <b>left</b> side, or click any of the <b>examples</b> below.
343 |                 Wait a second (for images and 3D) or a minute (for videos), and interact with the result in the <b>right</b> side.
344 |                 To avoid queuing, fork the demo into your profile.
345 |             </p>
346 |         """
347 |         )
348 | 
349 |         with gr.Tabs(elem_classes=["tabs"]):
350 |             with gr.Tab("Image"):
351 |                 with gr.Row():
352 |                     with gr.Column():
353 |                         image_input = gr.Image(
354 |                             label="Input Image",
355 |                             type="filepath",
356 |                         )
357 |                         with gr.Row():
358 |                             image_submit_btn = gr.Button(
359 |                                 value="Compute Depth", variant="primary"
360 |                             )
361 |                             image_reset_btn = gr.Button(value="Reset")
362 |                         with gr.Accordion("Advanced options", open=False):
363 |                             image_denoise_steps = gr.Slider(
364 |                                 label="Number of denoising steps",
365 |                                 minimum=1,
366 |                                 maximum=4,
367 |                                 step=1,
368 |                                 value=default_image_denoise_steps,
369 |                             )
370 |                             image_ensemble_size = gr.Slider(
371 |                                 label="Ensemble size",
372 |                                 minimum=1,
373 |                                 maximum=10,
374 |                                 step=1,
375 |                                 value=default_image_ensemble_size,
376 |                             )
377 |                             image_processing_res = gr.Radio(
378 |                                 [
379 |                                     ("Native", 0),
380 |                                     ("Recommended", 768),
381 |                                 ],
382 |                                 label="Processing resolution",
383 |                                 value=default_image_processing_res,
384 |                             )
385 |                     with gr.Column():
386 |                         image_output_slider = ImageSlider(
387 |                             label="Predicted depth (red-near, blue-far)",
388 |                             type="filepath",
389 |                             show_download_button=True,
390 |                             show_share_button=True,
391 |                             interactive=False,
392 |                             elem_classes="slider",
393 |                             position=0.25,
394 |                         )
395 |                         image_output_files = gr.Files(
396 |                             label="Depth outputs",
397 |                             elem_id="download",
398 |                             interactive=False,
399 |                         )
400 |                 gr.Examples(
401 |                     fn=process_pipe_image,
402 |                     examples=[
403 |                         os.path.join("files", "image", name)
404 |                         for name in [
405 |                             "arc.jpeg",
406 |                             "berries.jpeg",
407 |                             "butterfly.jpeg",
408 |                             "cat.jpg",
409 |                             "concert.jpeg",
410 |                             "dog.jpeg",
411 |                             "doughnuts.jpeg",
412 |                             "einstein.jpg",
413 |                             "food.jpeg",
414 |                             "glasses.jpeg",
415 |                             "house.jpg",
416 |                             "lake.jpeg",
417 |                             "marigold.jpeg",
418 |                             "portrait_1.jpeg",
419 |                             "portrait_2.jpeg",
420 |                             "pumpkins.jpg",
421 |                             "puzzle.jpeg",
422 |                             "road.jpg",
423 |                             "scientists.jpg",
424 |                             "surfboards.jpeg",
425 |                             "surfer.jpeg",
426 |                             "swings.jpg",
427 |                             "switzerland.jpeg",
428 |                             "teamwork.jpeg",
429 |                             "wave.jpeg",
430 |                         ]
431 |                     ],
432 |                     inputs=[image_input],
433 |                     outputs=[image_output_slider, image_output_files],
434 |                     cache_examples=True,
435 |                 )
436 | 
437 |             with gr.Tab("Video"):
438 |                 with gr.Row():
439 |                     with gr.Column():
440 |                         video_input = gr.Video(
441 |                             label="Input Video",
442 |                             sources=["upload"],
443 |                         )
444 |                         with gr.Row():
445 |                             video_submit_btn = gr.Button(
446 |                                 value="Compute Depth", variant="primary"
447 |                             )
448 |                             video_reset_btn = gr.Button(value="Reset")
449 |                     with gr.Column():
450 |                         video_output_video = gr.Video(
451 |                             label="Output video depth (red-near, blue-far)",
452 |                             interactive=False,
453 |                         )
454 |                         video_output_files = gr.Files(
455 |                             label="Depth outputs",
456 |                             elem_id="download",
457 |                             interactive=False,
458 |                         )
459 |                 gr.Examples(
460 |                     fn=process_pipe_video,
461 |                     examples=[
462 |                         os.path.join("files", "video", name)
463 |                         for name in [
464 |                             "cab.mp4",
465 |                             "elephant.mp4",
466 |                             "obama.mp4",
467 |                         ]
468 |                     ],
469 |                     inputs=[video_input],
470 |                     outputs=[video_output_video, video_output_files],
471 |                     cache_examples=True,
472 |                 )
473 | 
474 |             with gr.Tab("Bas-relief (3D)"):
475 |                 gr.Markdown(
476 |                     """
477 |                     <p align="justify">
478 |                         This part of the demo uses Marigold-LCM to create a bas-relief model. 
479 |                         The models are watertight, with correct normals, and exported in the STL format, which makes them <b>3D-printable</b>.
480 |                         Start by uploading the image and click "Create" with the default parameters. 
481 |                         To improve the result, click "Clear", adjust the geometry sliders below, and click "Create" again.
482 |                     </p>
483 |                     """,
484 |                 )
485 |                 with gr.Row():
486 |                     with gr.Column():
487 |                         bas_input = gr.Image(
488 |                             label="Input Image",
489 |                             type="filepath",
490 |                         )
491 |                         with gr.Row():
492 |                             bas_submit_btn = gr.Button(value="Create 3D", variant="primary")
493 |                             bas_clear_btn = gr.Button(value="Clear")
494 |                             bas_reset_btn = gr.Button(value="Reset")
495 |                         with gr.Accordion("3D printing demo: Main options", open=True):
496 |                             bas_plane_near = gr.Slider(
497 |                                 label="Relative position of the near plane (between 0 and 1)",
498 |                                 minimum=0.0,
499 |                                 maximum=1.0,
500 |                                 step=0.001,
501 |                                 value=default_bas_plane_near,
502 |                             )
503 |                             bas_plane_far = gr.Slider(
504 |                                 label="Relative position of the far plane (between near and 1)",
505 |                                 minimum=0.0,
506 |                                 maximum=1.0,
507 |                                 step=0.001,
508 |                                 value=default_bas_plane_far,
509 |                             )
510 |                             bas_embossing = gr.Slider(
511 |                                 label="Embossing level",
512 |                                 minimum=0,
513 |                                 maximum=100,
514 |                                 step=1,
515 |                                 value=default_bas_embossing,
516 |                             )
517 |                         with gr.Accordion("3D printing demo: Advanced options", open=False):
518 |                             bas_denoise_steps = gr.Slider(
519 |                                 label="Number of denoising steps",
520 |                                 minimum=1,
521 |                                 maximum=4,
522 |                                 step=1,
523 |                                 value=default_bas_denoise_steps,
524 |                             )
525 |                             bas_ensemble_size = gr.Slider(
526 |                                 label="Ensemble size",
527 |                                 minimum=1,
528 |                                 maximum=10,
529 |                                 step=1,
530 |                                 value=default_bas_ensemble_size,
531 |                             )
532 |                             bas_processing_res = gr.Radio(
533 |                                 [
534 |                                     ("Native", 0),
535 |                                     ("Recommended", 768),
536 |                                 ],
537 |                                 label="Processing resolution",
538 |                                 value=default_bas_processing_res,
539 |                             )
540 |                             bas_size_longest_px = gr.Slider(
541 |                                 label="Size (px) of the longest side",
542 |                                 minimum=256,
543 |                                 maximum=1024,
544 |                                 step=256,
545 |                                 value=default_bas_size_longest_px,
546 |                             )
547 |                             bas_size_longest_cm = gr.Slider(
548 |                                 label="Size (cm) of the longest side",
549 |                                 minimum=1,
550 |                                 maximum=100,
551 |                                 step=1,
552 |                                 value=default_bas_size_longest_cm,
553 |                             )
554 |                             bas_filter_size = gr.Slider(
555 |                                 label="Size (px) of the smoothing filter",
556 |                                 minimum=1,
557 |                                 maximum=5,
558 |                                 step=2,
559 |                                 value=default_bas_filter_size,
560 |                             )
561 |                             bas_frame_thickness = gr.Slider(
562 |                                 label="Frame thickness",
563 |                                 minimum=0,
564 |                                 maximum=100,
565 |                                 step=1,
566 |                                 value=default_bas_frame_thickness,
567 |                             )
568 |                             bas_frame_near = gr.Slider(
569 |                                 label="Frame's near plane offset",
570 |                                 minimum=-100,
571 |                                 maximum=100,
572 |                                 step=1,
573 |                                 value=default_bas_frame_near,
574 |                             )
575 |                             bas_frame_far = gr.Slider(
576 |                                 label="Frame's far plane offset",
577 |                                 minimum=1,
578 |                                 maximum=10,
579 |                                 step=1,
580 |                                 value=default_bas_frame_far,
581 |                             )
582 |                     with gr.Column():
583 |                         bas_output_viewer = gr.Model3D(
584 |                             camera_position=(75.0, 90.0, 1.25),
585 |                             elem_classes="viewport",
586 |                             label="3D preview (low-res, relief highlight)",
587 |                             interactive=False,
588 |                         )
589 |                         bas_output_files = gr.Files(
590 |                             label="3D model outputs (high-res)",
591 |                             elem_id="download",
592 |                             interactive=False,
593 |                         )
594 |                 gr.Examples(
595 |                     fn=process_pipe_bas,
596 |                     examples=[
597 |                         [
598 |                             "files/basrelief/coin.jpg",  # input
599 |                             0.0,  # plane_near
600 |                             0.66,  # plane_far
601 |                             15,  # embossing
602 |                             4,  # denoise_steps
603 |                             4,  # ensemble_size
604 |                             768,  # processing_res
605 |                             512,  # size_longest_px
606 |                             10,  # size_longest_cm
607 |                             3,  # filter_size
608 |                             5,  # frame_thickness
609 |                             0,  # frame_near
610 |                             1,  # frame_far
611 |                         ],
612 |                         [
613 |                             "files/basrelief/einstein.jpg",  # input
614 |                             0.0,  # plane_near
615 |                             0.5,  # plane_far
616 |                             50,  # embossing
617 |                             2,  # denoise_steps
618 |                             1,  # ensemble_size
619 |                             768,  # processing_res
620 |                             512,  # size_longest_px
621 |                             10,  # size_longest_cm
622 |                             3,  # filter_size
623 |                             5,  # frame_thickness
624 |                             -15,  # frame_near
625 |                             1,  # frame_far
626 |                         ],
627 |                         [
628 |                             "files/basrelief/food.jpeg",  # input
629 |                             0.0,  # plane_near
630 |                             1.0,  # plane_far
631 |                             20,  # embossing
632 |                             2,  # denoise_steps
633 |                             4,  # ensemble_size
634 |                             768,  # processing_res
635 |                             512,  # size_longest_px
636 |                             10,  # size_longest_cm
637 |                             3,  # filter_size
638 |                             5,  # frame_thickness
639 |                             -5,  # frame_near
640 |                             1,  # frame_far
641 |                         ],
642 |                     ],
643 |                     inputs=[
644 |                         bas_input,
645 |                         bas_plane_near,
646 |                         bas_plane_far,
647 |                         bas_embossing,
648 |                         bas_denoise_steps,
649 |                         bas_ensemble_size,
650 |                         bas_processing_res,
651 |                         bas_size_longest_px,
652 |                         bas_size_longest_cm,
653 |                         bas_filter_size,
654 |                         bas_frame_thickness,
655 |                         bas_frame_near,
656 |                         bas_frame_far,
657 |                     ],
658 |                     outputs=[bas_output_viewer, bas_output_files],
659 |                     cache_examples=True,
660 |                 )
661 | 
662 |         image_submit_btn.click(
663 |             fn=process_pipe_image,
664 |             inputs=[
665 |                 image_input,
666 |                 image_denoise_steps,
667 |                 image_ensemble_size,
668 |                 image_processing_res,
669 |             ],
670 |             outputs=[image_output_slider, image_output_files],
671 |             concurrency_limit=1,
672 |         )
673 | 
674 |         image_reset_btn.click(
675 |             fn=lambda: (
676 |                 None,
677 |                 None,
678 |                 None,
679 |                 default_image_ensemble_size,
680 |                 default_image_denoise_steps,
681 |                 default_image_processing_res,
682 |             ),
683 |             inputs=[],
684 |             outputs=[
685 |                 image_input,
686 |                 image_output_slider,
687 |                 image_output_files,
688 |                 image_ensemble_size,
689 |                 image_denoise_steps,
690 |                 image_processing_res,
691 |             ],
692 |             concurrency_limit=1,
693 |         )
694 | 
695 |         video_submit_btn.click(
696 |             fn=process_pipe_video,
697 |             inputs=[video_input],
698 |             outputs=[video_output_video, video_output_files],
699 |             concurrency_limit=1,
700 |         )
701 | 
702 |         video_reset_btn.click(
703 |             fn=lambda: (None, None, None),
704 |             inputs=[],
705 |             outputs=[video_input, video_output_video, video_output_files],
706 |             concurrency_limit=1,
707 |         )
708 | 
709 |         def wrapper_process_pipe_bas(*args, **kwargs):
710 |             out = list(process_pipe_bas(*args, **kwargs))
711 |             out = [gr.Button(interactive=False), gr.Image(interactive=False)] + out
712 |             return out
713 | 
714 |         bas_submit_btn.click(
715 |             fn=wrapper_process_pipe_bas,
716 |             inputs=[
717 |                 bas_input,
718 |                 bas_plane_near,
719 |                 bas_plane_far,
720 |                 bas_embossing,
721 |                 bas_denoise_steps,
722 |                 bas_ensemble_size,
723 |                 bas_processing_res,
724 |                 bas_size_longest_px,
725 |                 bas_size_longest_cm,
726 |                 bas_filter_size,
727 |                 bas_frame_thickness,
728 |                 bas_frame_near,
729 |                 bas_frame_far,
730 |             ],
731 |             outputs=[bas_submit_btn, bas_input, bas_output_viewer, bas_output_files],
732 |             concurrency_limit=1,
733 |         )
734 | 
735 |         bas_clear_btn.click(
736 |             fn=lambda: (gr.Button(interactive=True), None, None),
737 |             inputs=[],
738 |             outputs=[
739 |                 bas_submit_btn,
740 |                 bas_output_viewer,
741 |                 bas_output_files,
742 |             ],
743 |             concurrency_limit=1,
744 |         )
745 | 
746 |         bas_reset_btn.click(
747 |             fn=lambda: (
748 |                 gr.Button(interactive=True),
749 |                 None,
750 |                 None,
751 |                 None,
752 |                 default_bas_plane_near,
753 |                 default_bas_plane_far,
754 |                 default_bas_embossing,
755 |                 default_bas_denoise_steps,
756 |                 default_bas_ensemble_size,
757 |                 default_bas_processing_res,
758 |                 default_bas_size_longest_px,
759 |                 default_bas_size_longest_cm,
760 |                 default_bas_filter_size,
761 |                 default_bas_frame_thickness,
762 |                 default_bas_frame_near,
763 |                 default_bas_frame_far,
764 |             ),
765 |             inputs=[],
766 |             outputs=[
767 |                 bas_submit_btn,
768 |                 bas_input,
769 |                 bas_output_viewer,
770 |                 bas_output_files,
771 |                 bas_plane_near,
772 |                 bas_plane_far,
773 |                 bas_embossing,
774 |                 bas_denoise_steps,
775 |                 bas_ensemble_size,
776 |                 bas_processing_res,
777 |                 bas_size_longest_px,
778 |                 bas_size_longest_cm,
779 |                 bas_filter_size,
780 |                 bas_frame_thickness,
781 |                 bas_frame_near,
782 |                 bas_frame_far,
783 |             ],
784 |             concurrency_limit=1,
785 |         )
786 | 
787 |         demo.queue(
788 |             api_open=False,
789 |         ).launch(
790 |             server_name="0.0.0.0",
791 |             server_port=7860,
792 |         )
793 | 
794 | 
795 | def prefetch_hf_cache(pipe):
796 |     process_image(pipe, "files/image/bee.jpg", 1, 1, 64)
797 |     shutil.rmtree("files/image/bee_output")
798 | 
799 | 
800 | def main():
801 |     CHECKPOINT = "prs-eth/marigold-v1-0"
802 |     CHECKPOINT_UNET_LCM = "prs-eth/marigold-lcm-v1-0"
803 | 
804 |     if "HF_TOKEN_LOGIN" in os.environ:
805 |         login(token=os.environ["HF_TOKEN_LOGIN"])
806 | 
807 |     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
808 | 
809 |     pipe = MarigoldDepthConsistencyPipeline.from_pretrained(
810 |         CHECKPOINT,
811 |         unet=UNet2DConditionModel.from_pretrained(
812 |             CHECKPOINT_UNET_LCM, subfolder="unet", use_auth_token=True
813 |         ),
814 |     )
815 |     pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
816 |     try:
817 |         import xformers
818 | 
819 |         pipe.enable_xformers_memory_efficient_attention()
820 |     except:
821 |         pass  # run without xformers
822 | 
823 |     pipe = pipe.to(device)
824 |     prefetch_hf_cache(pipe)
825 |     run_demo_server(pipe)
826 | 
827 | 
828 | if __name__ == "__main__":
829 |     main()
830 | 


--------------------------------------------------------------------------------
/gradio_cached_examples/18/log.csv:
--------------------------------------------------------------------------------
 1 | "Predicted depth (red-near, blue-far)",Depth outputs,flag,username,timestamp
 2 | "[{""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/45e324d0b847afbbc4c7/arc_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/46a79bd0c5a0464662a5b7a52e59e9da300fbada/arc_output/arc_depth_16bit.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/04f54e1ce435ff6459b9/arc_depth_colored.png"", ""url"": ""/file=/tmp/gradio/46a79bd0c5a0464662a5b7a52e59e9da300fbada/arc_output/arc_depth_colored.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]","[{""path"": ""gradio_cached_examples/18/Depth outputs/8e97f11903b9b898c2f3/arc_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/46a79bd0c5a0464662a5b7a52e59e9da300fbada/arc_output/arc_depth_16bit.png"", ""size"": 1490869, ""orig_name"": ""arc_depth_16bit.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/9a9aecb6bc451b000cc3/arc_depth_fp32.npy"", ""url"": ""/file=/tmp/gradio/46a79bd0c5a0464662a5b7a52e59e9da300fbada/arc_output/arc_depth_fp32.npy"", ""size"": 4377728, ""orig_name"": ""arc_depth_fp32.npy"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/3d61175e3f24234c9eca/arc_depth_colored.png"", ""url"": ""/file=/tmp/gradio/46a79bd0c5a0464662a5b7a52e59e9da300fbada/arc_output/arc_depth_colored.png"", ""size"": 756097, ""orig_name"": ""arc_depth_colored.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]",,,2024-03-22 11:00:44.101614
 3 | "[{""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/cab7bd5be8a912541996/berries_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/360706859b4f497804cdd1bc742077f6a912e9fa/berries_output/berries_depth_16bit.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/ace9fc12e651074b9ffe/berries_depth_colored.png"", ""url"": ""/file=/tmp/gradio/360706859b4f497804cdd1bc742077f6a912e9fa/berries_output/berries_depth_colored.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]","[{""path"": ""gradio_cached_examples/18/Depth outputs/b264a159bd653f1548e3/berries_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/360706859b4f497804cdd1bc742077f6a912e9fa/berries_output/berries_depth_16bit.png"", ""size"": 2150087, ""orig_name"": ""berries_depth_16bit.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/1112b1ca9f107132c5ff/berries_depth_fp32.npy"", ""url"": ""/file=/tmp/gradio/360706859b4f497804cdd1bc742077f6a912e9fa/berries_output/berries_depth_fp32.npy"", ""size"": 6291584, ""orig_name"": ""berries_depth_fp32.npy"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/7b1d5a7cecd449b13a5e/berries_depth_colored.png"", ""url"": ""/file=/tmp/gradio/360706859b4f497804cdd1bc742077f6a912e9fa/berries_output/berries_depth_colored.png"", ""size"": 880971, ""orig_name"": ""berries_depth_colored.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]",,,2024-03-22 11:00:45.426994
 4 | "[{""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/6d576b810915984be5d9/butterfly_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/e8fb619b1a79383f61006553ba3a44c0796ac97b/butterfly_output/butterfly_depth_16bit.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/0968bcc549dace8031f8/butterfly_depth_colored.png"", ""url"": ""/file=/tmp/gradio/e8fb619b1a79383f61006553ba3a44c0796ac97b/butterfly_output/butterfly_depth_colored.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]","[{""path"": ""gradio_cached_examples/18/Depth outputs/a7486302ab044e374c04/butterfly_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/e8fb619b1a79383f61006553ba3a44c0796ac97b/butterfly_output/butterfly_depth_16bit.png"", ""size"": 2182986, ""orig_name"": ""butterfly_depth_16bit.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/743f28023ee74ec7ba8a/butterfly_depth_fp32.npy"", ""url"": ""/file=/tmp/gradio/e8fb619b1a79383f61006553ba3a44c0796ac97b/butterfly_output/butterfly_depth_fp32.npy"", ""size"": 6701184, ""orig_name"": ""butterfly_depth_fp32.npy"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/4758b54d8928c9860970/butterfly_depth_colored.png"", ""url"": ""/file=/tmp/gradio/e8fb619b1a79383f61006553ba3a44c0796ac97b/butterfly_output/butterfly_depth_colored.png"", ""size"": 1089045, ""orig_name"": ""butterfly_depth_colored.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]",,,2024-03-22 11:00:46.715558
 5 | "[{""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/295edcd8c90a1d58bff5/cat_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/0eb46afdf6eda566f3240443388cfa5360203ddc/cat_output/cat_depth_16bit.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/ccbf8e60d9ee20e4e70b/cat_depth_colored.png"", ""url"": ""/file=/tmp/gradio/0eb46afdf6eda566f3240443388cfa5360203ddc/cat_output/cat_depth_colored.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]","[{""path"": ""gradio_cached_examples/18/Depth outputs/ca3ffeaccc005c6fd5ee/cat_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/0eb46afdf6eda566f3240443388cfa5360203ddc/cat_output/cat_depth_16bit.png"", ""size"": 1736687, ""orig_name"": ""cat_depth_16bit.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/299ab3ef0144ff335248/cat_depth_fp32.npy"", ""url"": ""/file=/tmp/gradio/0eb46afdf6eda566f3240443388cfa5360203ddc/cat_output/cat_depth_fp32.npy"", ""size"": 5237888, ""orig_name"": ""cat_depth_fp32.npy"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/e29c660ea20526461142/cat_depth_colored.png"", ""url"": ""/file=/tmp/gradio/0eb46afdf6eda566f3240443388cfa5360203ddc/cat_output/cat_depth_colored.png"", ""size"": 540541, ""orig_name"": ""cat_depth_colored.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]",,,2024-03-22 11:00:48.087808
 6 | "[{""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/9c492b4a05b0c27418e7/concert_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/9edb0689265a2e2ec95890233d3511566980abc4/concert_output/concert_depth_16bit.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/88870588e2c8198c46db/concert_depth_colored.png"", ""url"": ""/file=/tmp/gradio/9edb0689265a2e2ec95890233d3511566980abc4/concert_output/concert_depth_colored.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]","[{""path"": ""gradio_cached_examples/18/Depth outputs/ee9d905b3a5337c2db21/concert_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/9edb0689265a2e2ec95890233d3511566980abc4/concert_output/concert_depth_16bit.png"", ""size"": 1725191, ""orig_name"": ""concert_depth_16bit.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/58f4bc0302114565854f/concert_depth_fp32.npy"", ""url"": ""/file=/tmp/gradio/9edb0689265a2e2ec95890233d3511566980abc4/concert_output/concert_depth_fp32.npy"", ""size"": 6291584, ""orig_name"": ""concert_depth_fp32.npy"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/c9d2b99fd4995c01c932/concert_depth_colored.png"", ""url"": ""/file=/tmp/gradio/9edb0689265a2e2ec95890233d3511566980abc4/concert_output/concert_depth_colored.png"", ""size"": 544306, ""orig_name"": ""concert_depth_colored.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]",,,2024-03-22 11:00:49.352419
 7 | "[{""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/879cfd69d9c554f83d96/dog_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/462945ded9e98ca6abb38493b252d119a5069c53/dog_output/dog_depth_16bit.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/f10d9f52ef10b6ce2819/dog_depth_colored.png"", ""url"": ""/file=/tmp/gradio/462945ded9e98ca6abb38493b252d119a5069c53/dog_output/dog_depth_colored.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]","[{""path"": ""gradio_cached_examples/18/Depth outputs/a4dcdec4a501e723b211/dog_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/462945ded9e98ca6abb38493b252d119a5069c53/dog_output/dog_depth_16bit.png"", ""size"": 2190077, ""orig_name"": ""dog_depth_16bit.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/c99982e80e8d9c0b50a9/dog_depth_fp32.npy"", ""url"": ""/file=/tmp/gradio/462945ded9e98ca6abb38493b252d119a5069c53/dog_output/dog_depth_fp32.npy"", ""size"": 6291584, ""orig_name"": ""dog_depth_fp32.npy"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/2847b2df4b1c235f7651/dog_depth_colored.png"", ""url"": ""/file=/tmp/gradio/462945ded9e98ca6abb38493b252d119a5069c53/dog_output/dog_depth_colored.png"", ""size"": 842692, ""orig_name"": ""dog_depth_colored.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]",,,2024-03-22 11:00:50.686283
 8 | "[{""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/25947e3f3a07b767d2e3/doughnuts_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/1ea9f3a9aff1df4f2f9df1b2148c2b9e75fc174e/doughnuts_output/doughnuts_depth_16bit.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/9f9ad00dda4a8b76a9d3/doughnuts_depth_colored.png"", ""url"": ""/file=/tmp/gradio/1ea9f3a9aff1df4f2f9df1b2148c2b9e75fc174e/doughnuts_output/doughnuts_depth_colored.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]","[{""path"": ""gradio_cached_examples/18/Depth outputs/c28a0efd69ada6c1de17/doughnuts_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/1ea9f3a9aff1df4f2f9df1b2148c2b9e75fc174e/doughnuts_output/doughnuts_depth_16bit.png"", ""size"": 1768092, ""orig_name"": ""doughnuts_depth_16bit.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/fd79bd61b90eb3c8750e/doughnuts_depth_fp32.npy"", ""url"": ""/file=/tmp/gradio/1ea9f3a9aff1df4f2f9df1b2148c2b9e75fc174e/doughnuts_output/doughnuts_depth_fp32.npy"", ""size"": 6291584, ""orig_name"": ""doughnuts_depth_fp32.npy"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/3fea61e86ad657759143/doughnuts_depth_colored.png"", ""url"": ""/file=/tmp/gradio/1ea9f3a9aff1df4f2f9df1b2148c2b9e75fc174e/doughnuts_output/doughnuts_depth_colored.png"", ""size"": 347441, ""orig_name"": ""doughnuts_depth_colored.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]",,,2024-03-22 11:00:51.980768
 9 | "[{""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/f74d13a60bda9b080432/einstein_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/ea36dd96d126ddf35c2b3652ba451b133ce44d8c/einstein_output/einstein_depth_16bit.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/e9c962c7d4300842fbd2/einstein_depth_colored.png"", ""url"": ""/file=/tmp/gradio/ea36dd96d126ddf35c2b3652ba451b133ce44d8c/einstein_output/einstein_depth_colored.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]","[{""path"": ""gradio_cached_examples/18/Depth outputs/a5db29c9ded284668775/einstein_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/ea36dd96d126ddf35c2b3652ba451b133ce44d8c/einstein_output/einstein_depth_16bit.png"", ""size"": 1492165, ""orig_name"": ""einstein_depth_16bit.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/08a115340ea524934177/einstein_depth_fp32.npy"", ""url"": ""/file=/tmp/gradio/ea36dd96d126ddf35c2b3652ba451b133ce44d8c/einstein_output/einstein_depth_fp32.npy"", ""size"": 4194432, ""orig_name"": ""einstein_depth_fp32.npy"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/70a6ecd79155c8fb213b/einstein_depth_colored.png"", ""url"": ""/file=/tmp/gradio/ea36dd96d126ddf35c2b3652ba451b133ce44d8c/einstein_output/einstein_depth_colored.png"", ""size"": 693251, ""orig_name"": ""einstein_depth_colored.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]",,,2024-03-22 11:00:53.546064
10 | "[{""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/a1e0b4145b283feacb69/food_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/a69b5239bc9b0d955739849b83ae54ed33bb7b4d/food_output/food_depth_16bit.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/b56ad8cd93517abd7a57/food_depth_colored.png"", ""url"": ""/file=/tmp/gradio/a69b5239bc9b0d955739849b83ae54ed33bb7b4d/food_output/food_depth_colored.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]","[{""path"": ""gradio_cached_examples/18/Depth outputs/e0911d2e7a4a5debfbab/food_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/a69b5239bc9b0d955739849b83ae54ed33bb7b4d/food_output/food_depth_16bit.png"", ""size"": 2009101, ""orig_name"": ""food_depth_16bit.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/2de7e7ec84f177bb092d/food_depth_fp32.npy"", ""url"": ""/file=/tmp/gradio/a69b5239bc9b0d955739849b83ae54ed33bb7b4d/food_output/food_depth_fp32.npy"", ""size"": 6291584, ""orig_name"": ""food_depth_fp32.npy"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/a639dbc49c03860c86a7/food_depth_colored.png"", ""url"": ""/file=/tmp/gradio/a69b5239bc9b0d955739849b83ae54ed33bb7b4d/food_output/food_depth_colored.png"", ""size"": 617833, ""orig_name"": ""food_depth_colored.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]",,,2024-03-22 11:00:54.884463
11 | "[{""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/432db43f24ab254cdc77/glasses_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/d3dfd6042cf590254b63e08246c48f6e4080a351/glasses_output/glasses_depth_16bit.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/bb1b89e55f54f6ad11a4/glasses_depth_colored.png"", ""url"": ""/file=/tmp/gradio/d3dfd6042cf590254b63e08246c48f6e4080a351/glasses_output/glasses_depth_colored.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]","[{""path"": ""gradio_cached_examples/18/Depth outputs/2a35fc317380d4737290/glasses_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/d3dfd6042cf590254b63e08246c48f6e4080a351/glasses_output/glasses_depth_16bit.png"", ""size"": 1703574, ""orig_name"": ""glasses_depth_16bit.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/13cfd7cd0acfd90c3608/glasses_depth_fp32.npy"", ""url"": ""/file=/tmp/gradio/d3dfd6042cf590254b63e08246c48f6e4080a351/glasses_output/glasses_depth_fp32.npy"", ""size"": 6283392, ""orig_name"": ""glasses_depth_fp32.npy"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/6b7fa86ff307ce280458/glasses_depth_colored.png"", ""url"": ""/file=/tmp/gradio/d3dfd6042cf590254b63e08246c48f6e4080a351/glasses_output/glasses_depth_colored.png"", ""size"": 332572, ""orig_name"": ""glasses_depth_colored.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]",,,2024-03-22 11:00:56.147658
12 | "[{""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/c80da87c39169644afa1/house_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/31fe791c82956ded6a72d25a0bc7c398545442cd/house_output/house_depth_16bit.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/71ea41371688664fd898/house_depth_colored.png"", ""url"": ""/file=/tmp/gradio/31fe791c82956ded6a72d25a0bc7c398545442cd/house_output/house_depth_colored.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]","[{""path"": ""gradio_cached_examples/18/Depth outputs/ed486427beaead8e6d4d/house_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/31fe791c82956ded6a72d25a0bc7c398545442cd/house_output/house_depth_16bit.png"", ""size"": 16604424, ""orig_name"": ""house_depth_16bit.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/de994c47587fcbb3e990/house_depth_fp32.npy"", ""url"": ""/file=/tmp/gradio/31fe791c82956ded6a72d25a0bc7c398545442cd/house_output/house_depth_fp32.npy"", ""size"": 57509536, ""orig_name"": ""house_depth_fp32.npy"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/f53efcc50ebfeaac3b8f/house_depth_colored.png"", ""url"": ""/file=/tmp/gradio/31fe791c82956ded6a72d25a0bc7c398545442cd/house_output/house_depth_colored.png"", ""size"": 3921236, ""orig_name"": ""house_depth_colored.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]",,,2024-03-22 11:01:02.007949
13 | "[{""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/f8bc9d03bcb924e64306/lake_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/6b204bfbf1d0e8e51b8b4887df9b97e1acbaef25/lake_output/lake_depth_16bit.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/d28b5d4dcd878e6c5daf/lake_depth_colored.png"", ""url"": ""/file=/tmp/gradio/6b204bfbf1d0e8e51b8b4887df9b97e1acbaef25/lake_output/lake_depth_colored.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]","[{""path"": ""gradio_cached_examples/18/Depth outputs/5ef3eed2059133180232/lake_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/6b204bfbf1d0e8e51b8b4887df9b97e1acbaef25/lake_output/lake_depth_16bit.png"", ""size"": 1896565, ""orig_name"": ""lake_depth_16bit.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/3c6ba7efdd300e7b7764/lake_depth_fp32.npy"", ""url"": ""/file=/tmp/gradio/6b204bfbf1d0e8e51b8b4887df9b97e1acbaef25/lake_output/lake_depth_fp32.npy"", ""size"": 6291584, ""orig_name"": ""lake_depth_fp32.npy"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/357b353412dd9759ad6a/lake_depth_colored.png"", ""url"": ""/file=/tmp/gradio/6b204bfbf1d0e8e51b8b4887df9b97e1acbaef25/lake_output/lake_depth_colored.png"", ""size"": 550204, ""orig_name"": ""lake_depth_colored.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]",,,2024-03-22 11:01:03.320847
14 | "[{""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/24ec8180e34432fcb5d3/marigold_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/2241b3fd1273451981e6b1ed8a6d510ecc98d190/marigold_output/marigold_depth_16bit.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/18e6abaa75c6b4266122/marigold_depth_colored.png"", ""url"": ""/file=/tmp/gradio/2241b3fd1273451981e6b1ed8a6d510ecc98d190/marigold_output/marigold_depth_colored.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]","[{""path"": ""gradio_cached_examples/18/Depth outputs/8d6952f2bb813c899fb0/marigold_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/2241b3fd1273451981e6b1ed8a6d510ecc98d190/marigold_output/marigold_depth_16bit.png"", ""size"": 1413951, ""orig_name"": ""marigold_depth_16bit.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/9faf8f227f90f70bbdaa/marigold_depth_fp32.npy"", ""url"": ""/file=/tmp/gradio/2241b3fd1273451981e6b1ed8a6d510ecc98d190/marigold_output/marigold_depth_fp32.npy"", ""size"": 4616740, ""orig_name"": ""marigold_depth_fp32.npy"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/ed2db910fdec870cb513/marigold_depth_colored.png"", ""url"": ""/file=/tmp/gradio/2241b3fd1273451981e6b1ed8a6d510ecc98d190/marigold_output/marigold_depth_colored.png"", ""size"": 335290, ""orig_name"": ""marigold_depth_colored.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]",,,2024-03-22 11:01:04.763173
15 | "[{""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/e14a6d59396caef161db/portrait_1_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/49f19b622ea5564b11e67e743094c750dca25f12/portrait_1_output/portrait_1_depth_16bit.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/3b7e23ac2f10ff0acd40/portrait_1_depth_colored.png"", ""url"": ""/file=/tmp/gradio/49f19b622ea5564b11e67e743094c750dca25f12/portrait_1_output/portrait_1_depth_colored.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]","[{""path"": ""gradio_cached_examples/18/Depth outputs/bf0a2b7b9d7d20d3fba4/portrait_1_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/49f19b622ea5564b11e67e743094c750dca25f12/portrait_1_output/portrait_1_depth_16bit.png"", ""size"": 1368255, ""orig_name"": ""portrait_1_depth_16bit.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/534e159c4967aec56bcd/portrait_1_depth_fp32.npy"", ""url"": ""/file=/tmp/gradio/49f19b622ea5564b11e67e743094c750dca25f12/portrait_1_output/portrait_1_depth_fp32.npy"", ""size"": 4194432, ""orig_name"": ""portrait_1_depth_fp32.npy"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/068c389756186ef0ec21/portrait_1_depth_colored.png"", ""url"": ""/file=/tmp/gradio/49f19b622ea5564b11e67e743094c750dca25f12/portrait_1_output/portrait_1_depth_colored.png"", ""size"": 499699, ""orig_name"": ""portrait_1_depth_colored.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]",,,2024-03-22 11:01:06.328852
16 | "[{""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/2e503f2592bb798b7d75/portrait_2_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/f8582d9f6e1c9a70b9bdd7ddcc142abfdb808cdd/portrait_2_output/portrait_2_depth_16bit.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/e58db2a94f058c3cbda5/portrait_2_depth_colored.png"", ""url"": ""/file=/tmp/gradio/f8582d9f6e1c9a70b9bdd7ddcc142abfdb808cdd/portrait_2_output/portrait_2_depth_colored.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]","[{""path"": ""gradio_cached_examples/18/Depth outputs/6eeb531b1c22dd97fae4/portrait_2_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/f8582d9f6e1c9a70b9bdd7ddcc142abfdb808cdd/portrait_2_output/portrait_2_depth_16bit.png"", ""size"": 1248383, ""orig_name"": ""portrait_2_depth_16bit.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/7fce16f173010012f6b6/portrait_2_depth_fp32.npy"", ""url"": ""/file=/tmp/gradio/f8582d9f6e1c9a70b9bdd7ddcc142abfdb808cdd/portrait_2_output/portrait_2_depth_fp32.npy"", ""size"": 4194432, ""orig_name"": ""portrait_2_depth_fp32.npy"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/0fb09ff06177b86b75cc/portrait_2_depth_colored.png"", ""url"": ""/file=/tmp/gradio/f8582d9f6e1c9a70b9bdd7ddcc142abfdb808cdd/portrait_2_output/portrait_2_depth_colored.png"", ""size"": 304975, ""orig_name"": ""portrait_2_depth_colored.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]",,,2024-03-22 11:01:07.888284
17 | "[{""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/66dedd2f9b9a56204b8a/pumpkins_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/26fc68ecac60d48462f24dee8fea4a2be9bb6b0d/pumpkins_output/pumpkins_depth_16bit.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/37a744be12464174a9fe/pumpkins_depth_colored.png"", ""url"": ""/file=/tmp/gradio/26fc68ecac60d48462f24dee8fea4a2be9bb6b0d/pumpkins_output/pumpkins_depth_colored.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]","[{""path"": ""gradio_cached_examples/18/Depth outputs/dbc8049901349d03b961/pumpkins_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/26fc68ecac60d48462f24dee8fea4a2be9bb6b0d/pumpkins_output/pumpkins_depth_16bit.png"", ""size"": 16880885, ""orig_name"": ""pumpkins_depth_16bit.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/f024df95b9f9d82cd10b/pumpkins_depth_fp32.npy"", ""url"": ""/file=/tmp/gradio/26fc68ecac60d48462f24dee8fea4a2be9bb6b0d/pumpkins_output/pumpkins_depth_fp32.npy"", ""size"": 63701120, ""orig_name"": ""pumpkins_depth_fp32.npy"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/a20cbbb5b772a7b1763e/pumpkins_depth_colored.png"", ""url"": ""/file=/tmp/gradio/26fc68ecac60d48462f24dee8fea4a2be9bb6b0d/pumpkins_output/pumpkins_depth_colored.png"", ""size"": 5383025, ""orig_name"": ""pumpkins_depth_colored.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]",,,2024-03-22 11:01:13.567507
18 | "[{""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/d22944415f5ee905e9e0/puzzle_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/e16c8cc18655d240a547b949aa54d12ec4bcd563/puzzle_output/puzzle_depth_16bit.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/db15ec0fec292ce702f3/puzzle_depth_colored.png"", ""url"": ""/file=/tmp/gradio/e16c8cc18655d240a547b949aa54d12ec4bcd563/puzzle_output/puzzle_depth_colored.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]","[{""path"": ""gradio_cached_examples/18/Depth outputs/e747489c970c86200120/puzzle_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/e16c8cc18655d240a547b949aa54d12ec4bcd563/puzzle_output/puzzle_depth_16bit.png"", ""size"": 1746321, ""orig_name"": ""puzzle_depth_16bit.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/b58afb3c3ab61448a7dd/puzzle_depth_fp32.npy"", ""url"": ""/file=/tmp/gradio/e16c8cc18655d240a547b949aa54d12ec4bcd563/puzzle_output/puzzle_depth_fp32.npy"", ""size"": 6291584, ""orig_name"": ""puzzle_depth_fp32.npy"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/3b9f1c0a73b1eb66b3dc/puzzle_depth_colored.png"", ""url"": ""/file=/tmp/gradio/e16c8cc18655d240a547b949aa54d12ec4bcd563/puzzle_output/puzzle_depth_colored.png"", ""size"": 405008, ""orig_name"": ""puzzle_depth_colored.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]",,,2024-03-22 11:01:14.850407
19 | "[{""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/bf130ca9410ab692b150/road_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/a83a653227be3d8a2069b7f154d816f97376c887/road_output/road_depth_16bit.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/19eb29bd0e44aa61bd6f/road_depth_colored.png"", ""url"": ""/file=/tmp/gradio/a83a653227be3d8a2069b7f154d816f97376c887/road_output/road_depth_colored.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]","[{""path"": ""gradio_cached_examples/18/Depth outputs/d0a4e40b24fa717ffabb/road_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/a83a653227be3d8a2069b7f154d816f97376c887/road_output/road_depth_16bit.png"", ""size"": 11871381, ""orig_name"": ""road_depth_16bit.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/565217320fe1057733c9/road_depth_fp32.npy"", ""url"": ""/file=/tmp/gradio/a83a653227be3d8a2069b7f154d816f97376c887/road_output/road_depth_fp32.npy"", ""size"": 41809728, ""orig_name"": ""road_depth_fp32.npy"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/2456fcca77a17345e260/road_depth_colored.png"", ""url"": ""/file=/tmp/gradio/a83a653227be3d8a2069b7f154d816f97376c887/road_output/road_depth_colored.png"", ""size"": 4683563, ""orig_name"": ""road_depth_colored.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]",,,2024-03-22 11:01:19.006979
20 | "[{""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/8d5b5a9cc5bde83ad215/scientists_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/98fa537b95bea2aa76bef351e5cdf1801d25af1a/scientists_output/scientists_depth_16bit.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/f9fb6b3768c97d11bf63/scientists_depth_colored.png"", ""url"": ""/file=/tmp/gradio/98fa537b95bea2aa76bef351e5cdf1801d25af1a/scientists_output/scientists_depth_colored.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]","[{""path"": ""gradio_cached_examples/18/Depth outputs/67a09b9fd46c50fb1a30/scientists_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/98fa537b95bea2aa76bef351e5cdf1801d25af1a/scientists_output/scientists_depth_16bit.png"", ""size"": 3187383, ""orig_name"": ""scientists_depth_16bit.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/8c1f452606cac27a66c0/scientists_depth_fp32.npy"", ""url"": ""/file=/tmp/gradio/98fa537b95bea2aa76bef351e5cdf1801d25af1a/scientists_output/scientists_depth_fp32.npy"", ""size"": 12441480, ""orig_name"": ""scientists_depth_fp32.npy"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/099bb138bf851d8de112/scientists_depth_colored.png"", ""url"": ""/file=/tmp/gradio/98fa537b95bea2aa76bef351e5cdf1801d25af1a/scientists_output/scientists_depth_colored.png"", ""size"": 631879, ""orig_name"": ""scientists_depth_colored.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]",,,2024-03-22 11:01:20.793540
21 | "[{""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/adf7d8a23971611c0c2c/surfboards_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/9baa0dbd53217426fb6a431cdb38f8c43520a602/surfboards_output/surfboards_depth_16bit.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/1cb720d1f47690df2bf2/surfboards_depth_colored.png"", ""url"": ""/file=/tmp/gradio/9baa0dbd53217426fb6a431cdb38f8c43520a602/surfboards_output/surfboards_depth_colored.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]","[{""path"": ""gradio_cached_examples/18/Depth outputs/f6b17b1b9adbe5433e03/surfboards_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/9baa0dbd53217426fb6a431cdb38f8c43520a602/surfboards_output/surfboards_depth_16bit.png"", ""size"": 2196782, ""orig_name"": ""surfboards_depth_16bit.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/eb7676ae3737abdd8b33/surfboards_depth_fp32.npy"", ""url"": ""/file=/tmp/gradio/9baa0dbd53217426fb6a431cdb38f8c43520a602/surfboards_output/surfboards_depth_fp32.npy"", ""size"": 6291584, ""orig_name"": ""surfboards_depth_fp32.npy"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/4256ccd86bcb885f93a6/surfboards_depth_colored.png"", ""url"": ""/file=/tmp/gradio/9baa0dbd53217426fb6a431cdb38f8c43520a602/surfboards_output/surfboards_depth_colored.png"", ""size"": 1098012, ""orig_name"": ""surfboards_depth_colored.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]",,,2024-03-22 11:01:22.105430
22 | "[{""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/b82a8e2c9968943eaf37/surfer_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/2750467aff3db899fd1651196d2632ad30d86156/surfer_output/surfer_depth_16bit.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/edb434ab1e157595210a/surfer_depth_colored.png"", ""url"": ""/file=/tmp/gradio/2750467aff3db899fd1651196d2632ad30d86156/surfer_output/surfer_depth_colored.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]","[{""path"": ""gradio_cached_examples/18/Depth outputs/6d5d27b6ae03d7a40b7c/surfer_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/2750467aff3db899fd1651196d2632ad30d86156/surfer_output/surfer_depth_16bit.png"", ""size"": 2080093, ""orig_name"": ""surfer_depth_16bit.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/b42b9d9b1dd973751204/surfer_depth_fp32.npy"", ""url"": ""/file=/tmp/gradio/2750467aff3db899fd1651196d2632ad30d86156/surfer_output/surfer_depth_fp32.npy"", ""size"": 6291584, ""orig_name"": ""surfer_depth_fp32.npy"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/26b0eacc0203a3b721be/surfer_depth_colored.png"", ""url"": ""/file=/tmp/gradio/2750467aff3db899fd1651196d2632ad30d86156/surfer_output/surfer_depth_colored.png"", ""size"": 622703, ""orig_name"": ""surfer_depth_colored.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]",,,2024-03-22 11:01:23.430815
23 | "[{""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/313712cc25c7afeec5e2/swings_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/6fbbe1e4b4d648584e32090b1ca47847997f8ffe/swings_output/swings_depth_16bit.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/983c2e26ef549272c897/swings_depth_colored.png"", ""url"": ""/file=/tmp/gradio/6fbbe1e4b4d648584e32090b1ca47847997f8ffe/swings_output/swings_depth_colored.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]","[{""path"": ""gradio_cached_examples/18/Depth outputs/4cb13fd8b9ec9608e19f/swings_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/6fbbe1e4b4d648584e32090b1ca47847997f8ffe/swings_output/swings_depth_16bit.png"", ""size"": 520701, ""orig_name"": ""swings_depth_16bit.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/ef56552b1f18d14a2c19/swings_depth_fp32.npy"", ""url"": ""/file=/tmp/gradio/6fbbe1e4b4d648584e32090b1ca47847997f8ffe/swings_output/swings_depth_fp32.npy"", ""size"": 1398912, ""orig_name"": ""swings_depth_fp32.npy"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/2f5832324077dab185ef/swings_depth_colored.png"", ""url"": ""/file=/tmp/gradio/6fbbe1e4b4d648584e32090b1ca47847997f8ffe/swings_output/swings_depth_colored.png"", ""size"": 291481, ""orig_name"": ""swings_depth_colored.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]",,,2024-03-22 11:01:24.401264
24 | "[{""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/b019af76a770991b9f05/switzerland_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/698e53b1ecf5d7a4b25ec7119890921dd1d16dbd/switzerland_output/switzerland_depth_16bit.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/dfced8fb6c3b5b3cdd85/switzerland_depth_colored.png"", ""url"": ""/file=/tmp/gradio/698e53b1ecf5d7a4b25ec7119890921dd1d16dbd/switzerland_output/switzerland_depth_colored.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]","[{""path"": ""gradio_cached_examples/18/Depth outputs/d8b07ff54e7a206832a0/switzerland_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/698e53b1ecf5d7a4b25ec7119890921dd1d16dbd/switzerland_output/switzerland_depth_16bit.png"", ""size"": 1344029, ""orig_name"": ""switzerland_depth_16bit.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/2b8e100af28995f679f3/switzerland_depth_fp32.npy"", ""url"": ""/file=/tmp/gradio/698e53b1ecf5d7a4b25ec7119890921dd1d16dbd/switzerland_output/switzerland_depth_fp32.npy"", ""size"": 4593728, ""orig_name"": ""switzerland_depth_fp32.npy"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/115d14d955de41ae1e33/switzerland_depth_colored.png"", ""url"": ""/file=/tmp/gradio/698e53b1ecf5d7a4b25ec7119890921dd1d16dbd/switzerland_output/switzerland_depth_colored.png"", ""size"": 296596, ""orig_name"": ""switzerland_depth_colored.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]",,,2024-03-22 11:01:25.565142
25 | "[{""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/2345a66ea597f0724ac6/teamwork_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/2ea4d118ae672ebcc547ee0e8fbb335d8e8c0410/teamwork_output/teamwork_depth_16bit.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/d3d1aea16f58fa4e9c34/teamwork_depth_colored.png"", ""url"": ""/file=/tmp/gradio/2ea4d118ae672ebcc547ee0e8fbb335d8e8c0410/teamwork_output/teamwork_depth_colored.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]","[{""path"": ""gradio_cached_examples/18/Depth outputs/1ab135c405036a016085/teamwork_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/2ea4d118ae672ebcc547ee0e8fbb335d8e8c0410/teamwork_output/teamwork_depth_16bit.png"", ""size"": 2243230, ""orig_name"": ""teamwork_depth_16bit.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/8c492010216806b7c84c/teamwork_depth_fp32.npy"", ""url"": ""/file=/tmp/gradio/2ea4d118ae672ebcc547ee0e8fbb335d8e8c0410/teamwork_output/teamwork_depth_fp32.npy"", ""size"": 8388736, ""orig_name"": ""teamwork_depth_fp32.npy"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/af5de30ca8186879928b/teamwork_depth_colored.png"", ""url"": ""/file=/tmp/gradio/2ea4d118ae672ebcc547ee0e8fbb335d8e8c0410/teamwork_output/teamwork_depth_colored.png"", ""size"": 659822, ""orig_name"": ""teamwork_depth_colored.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]",,,2024-03-22 11:01:26.825845
26 | "[{""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/478e837f6ca2f7737dc5/wave_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/5e596f88b8edb682aedad4a6f83067a9dee2fd0e/wave_output/wave_depth_16bit.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Predicted depth red-near blue-far/7b90e60f0490de180bc9/wave_depth_colored.png"", ""url"": ""/file=/tmp/gradio/5e596f88b8edb682aedad4a6f83067a9dee2fd0e/wave_output/wave_depth_colored.png"", ""size"": null, ""orig_name"": null, ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]","[{""path"": ""gradio_cached_examples/18/Depth outputs/550e81d9c69c40fa22b0/wave_depth_16bit.png"", ""url"": ""/file=/tmp/gradio/5e596f88b8edb682aedad4a6f83067a9dee2fd0e/wave_output/wave_depth_16bit.png"", ""size"": 1790462, ""orig_name"": ""wave_depth_16bit.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/6a0860f054b0406feef5/wave_depth_fp32.npy"", ""url"": ""/file=/tmp/gradio/5e596f88b8edb682aedad4a6f83067a9dee2fd0e/wave_output/wave_depth_fp32.npy"", ""size"": 6291584, ""orig_name"": ""wave_depth_fp32.npy"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}, {""path"": ""gradio_cached_examples/18/Depth outputs/ef827c6d4ffc7d48c85a/wave_depth_colored.png"", ""url"": ""/file=/tmp/gradio/5e596f88b8edb682aedad4a6f83067a9dee2fd0e/wave_output/wave_depth_colored.png"", ""size"": 353870, ""orig_name"": ""wave_depth_colored.png"", ""mime_type"": null, ""is_stream"": false, ""meta"": {""_type"": ""gradio.FileData""}}]",,,2024-03-22 11:01:28.105115
27 | 


--------------------------------------------------------------------------------