ell-hol / clip-interrogator
- Public
- 628 runs
-
A100 (80GB)
Prediction
ell-hol/clip-interrogator:e1a695055be38e02995e96d6bcec13c2bd5c52ef756ed339b88aef83f46380a2IDflvaujrbbkqergxofrqountwieStatusSucceededSourceWebHardwareA100 (40GB)Total durationCreatedInput
{ "mode": "best", "image": "https://replicate.delivery/pbxt/JY4L52BedTqyekyFRaL9MeFYUJtVXK0MYqXxyAHoCAuplANW/interiorLiv.jpeg", "clip_model_name": "ViT-bigG-14/laion2b_s39b_b160k" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run ell-hol/clip-interrogator using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "ell-hol/clip-interrogator:e1a695055be38e02995e96d6bcec13c2bd5c52ef756ed339b88aef83f46380a2", { input: { mode: "best", image: "https://replicate.delivery/pbxt/JY4L52BedTqyekyFRaL9MeFYUJtVXK0MYqXxyAHoCAuplANW/interiorLiv.jpeg", clip_model_name: "ViT-bigG-14/laion2b_s39b_b160k" } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run ell-hol/clip-interrogator using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "ell-hol/clip-interrogator:e1a695055be38e02995e96d6bcec13c2bd5c52ef756ed339b88aef83f46380a2", input={ "mode": "best", "image": "https://replicate.delivery/pbxt/JY4L52BedTqyekyFRaL9MeFYUJtVXK0MYqXxyAHoCAuplANW/interiorLiv.jpeg", "clip_model_name": "ViT-bigG-14/laion2b_s39b_b160k" } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run ell-hol/clip-interrogator using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "ell-hol/clip-interrogator:e1a695055be38e02995e96d6bcec13c2bd5c52ef756ed339b88aef83f46380a2", "input": { "mode": "best", "image": "https://replicate.delivery/pbxt/JY4L52BedTqyekyFRaL9MeFYUJtVXK0MYqXxyAHoCAuplANW/interiorLiv.jpeg", "clip_model_name": "ViT-bigG-14/laion2b_s39b_b160k" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
there is a white kitchen with a table and chairs in it, an ambient occlusion render, golden ration composition, blueshift render, house interior, light dispersion, 1 0 2 4 farben, multi - level, faint light, infini - d - render, object design, 3 meters, with depth of field, ambient oclusion, multiple{ "completed_at": "2023-09-18T09:47:41.945068Z", "created_at": "2023-09-18T09:43:57.947901Z", "data_removed": false, "error": null, "id": "flvaujrbbkqergxofrqountwie", "input": { "mode": "best", "image": "https://replicate.delivery/pbxt/JY4L52BedTqyekyFRaL9MeFYUJtVXK0MYqXxyAHoCAuplANW/interiorLiv.jpeg", "clip_model_name": "ViT-bigG-14/laion2b_s39b_b160k" }, "logs": "Loading CLIP model ViT-bigG-14/laion2b_s39b_b160k...\nDownloading (…)ip_pytorch_model.bin: 0%| | 0.00/10.2G [00:00<?, ?B/s]\nDownloading (…)ip_pytorch_model.bin: 0%| | 31.5M/10.2G [00:00<00:42, 240MB/s]\nDownloading (…)ip_pytorch_model.bin: 1%| | 62.9M/10.2G [00:00<00:44, 229MB/s]\nDownloading (…)ip_pytorch_model.bin: 1%| | 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2.32s/it]\nFlavor chain: 38%|███▊ | 12/32 [00:27<00:47, 2.39s/it]\nFlavor chain: 41%|████ | 13/32 [00:30<00:48, 2.57s/it]\nFlavor chain: 44%|████▍ | 14/32 [00:32<00:45, 2.55s/it]\nFlavor chain: 44%|████▍ | 14/32 [00:35<00:45, 2.50s/it]\n 0%| | 0/55 [00:00<?, ?it/s]\n 27%|██▋ | 15/55 [00:00<00:00, 147.85it/s]\n 56%|█████▋ | 31/55 [00:00<00:00, 152.85it/s]\n 85%|████████▌ | 47/55 [00:00<00:00, 154.58it/s]\n100%|██████████| 55/55 [00:00<00:00, 155.47it/s]\n 0%| | 0/6 [00:00<?, ?it/s]\n100%|██████████| 6/6 [00:00<00:00, 125.38it/s]\n 0%| | 0/50 [00:00<?, ?it/s]\n 34%|███▍ | 17/50 [00:00<00:00, 160.93it/s]\n 68%|██████▊ | 34/50 [00:00<00:00, 163.53it/s]\n100%|██████████| 50/50 [00:00<00:00, 165.68it/s]", "metrics": { "predict_time": 125.740243, "total_time": 223.997167 }, "output": "there is a white kitchen with a table and chairs in it, an ambient occlusion render, golden ration composition, blueshift render, house interior, light dispersion, 1 0 2 4 farben, multi - level, faint light, infini - d - render, object design, 3 meters, with depth of field, ambient oclusion, multiple", "started_at": "2023-09-18T09:45:36.204825Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/flvaujrbbkqergxofrqountwie", "cancel": "https://api.replicate.com/v1/predictions/flvaujrbbkqergxofrqountwie/cancel" }, "version": "e1a695055be38e02995e96d6bcec13c2bd5c52ef756ed339b88aef83f46380a2" }
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and data in 86.98 seconds. 0%| | 0/55 [00:00<?, ?it/s] 27%|██▋ | 15/55 [00:00<00:00, 146.95it/s] 55%|█████▍ | 30/55 [00:00<00:00, 92.72it/s] 85%|████████▌ | 47/55 [00:00<00:00, 117.43it/s] 100%|██████████| 55/55 [00:00<00:00, 120.71it/s] Flavor chain: 0%| | 0/32 [00:00<?, ?it/s] Flavor chain: 3%|▎ | 1/32 [00:02<01:06, 2.14s/it] Flavor chain: 6%|▋ | 2/32 [00:04<01:03, 2.12s/it] Flavor chain: 9%|▉ | 3/32 [00:06<01:01, 2.13s/it] Flavor chain: 12%|█▎ | 4/32 [00:08<01:00, 2.15s/it] Flavor chain: 16%|█▌ | 5/32 [00:10<00:58, 2.15s/it] Flavor chain: 19%|█▉ | 6/32 [00:12<00:56, 2.17s/it] Flavor chain: 22%|██▏ | 7/32 [00:15<00:54, 2.20s/it] Flavor chain: 25%|██▌ | 8/32 [00:17<00:53, 2.22s/it] Flavor chain: 28%|██▊ | 9/32 [00:19<00:51, 2.24s/it] Flavor chain: 31%|███▏ | 10/32 [00:22<00:50, 2.28s/it] Flavor chain: 34%|███▍ | 11/32 [00:24<00:48, 2.32s/it] Flavor chain: 38%|███▊ | 12/32 [00:27<00:47, 2.39s/it] Flavor chain: 41%|████ | 13/32 [00:30<00:48, 2.57s/it] Flavor chain: 44%|████▍ | 14/32 [00:32<00:45, 2.55s/it] Flavor chain: 44%|████▍ | 14/32 [00:35<00:45, 2.50s/it] 0%| | 0/55 [00:00<?, ?it/s] 27%|██▋ | 15/55 [00:00<00:00, 147.85it/s] 56%|█████▋ | 31/55 [00:00<00:00, 152.85it/s] 85%|████████▌ | 47/55 [00:00<00:00, 154.58it/s] 100%|██████████| 55/55 [00:00<00:00, 155.47it/s] 0%| | 0/6 [00:00<?, ?it/s] 100%|██████████| 6/6 [00:00<00:00, 125.38it/s] 0%| | 0/50 [00:00<?, ?it/s] 34%|███▍ | 17/50 [00:00<00:00, 160.93it/s] 68%|██████▊ | 34/50 [00:00<00:00, 163.53it/s] 100%|██████████| 50/50 [00:00<00:00, 165.68it/s]
Prediction
ell-hol/clip-interrogator:e1a695055be38e02995e96d6bcec13c2bd5c52ef756ed339b88aef83f46380a2IDvgrndirbohnhwf4lzrvsqq3ecaStatusSucceededSourceWebHardwareA100 (40GB)Total durationCreatedInput
{ "mode": "best", "image": "https://replicate.delivery/pbxt/JY4R7tIVA78ESNdslvoixd99Wcv3oxARhcZRUuos0zMnuwz4/amphitheatre.jpeg", "clip_model_name": "ViT-bigG-14/laion2b_s39b_b160k" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run ell-hol/clip-interrogator using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "ell-hol/clip-interrogator:e1a695055be38e02995e96d6bcec13c2bd5c52ef756ed339b88aef83f46380a2", { input: { mode: "best", image: "https://replicate.delivery/pbxt/JY4R7tIVA78ESNdslvoixd99Wcv3oxARhcZRUuos0zMnuwz4/amphitheatre.jpeg", clip_model_name: "ViT-bigG-14/laion2b_s39b_b160k" } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run ell-hol/clip-interrogator using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "ell-hol/clip-interrogator:e1a695055be38e02995e96d6bcec13c2bd5c52ef756ed339b88aef83f46380a2", input={ "mode": "best", "image": "https://replicate.delivery/pbxt/JY4R7tIVA78ESNdslvoixd99Wcv3oxARhcZRUuos0zMnuwz4/amphitheatre.jpeg", "clip_model_name": "ViT-bigG-14/laion2b_s39b_b160k" } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run ell-hol/clip-interrogator using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "ell-hol/clip-interrogator:e1a695055be38e02995e96d6bcec13c2bd5c52ef756ed339b88aef83f46380a2", "input": { "mode": "best", "image": "https://replicate.delivery/pbxt/JY4R7tIVA78ESNdslvoixd99Wcv3oxARhcZRUuos0zMnuwz4/amphitheatre.jpeg", "clip_model_name": "ViT-bigG-14/laion2b_s39b_b160k" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
a sketch of a lecture hall with rows of chairs, line art!!, ƒ / 3. 5, offices, angle view, stage, contemporary, university, celebrities, white outline, autocad, white studio, diagramm, ticket, at college, grand{ "completed_at": "2023-09-18T09:51:01.861981Z", "created_at": "2023-09-18T09:50:21.340568Z", "data_removed": false, "error": null, "id": "vgrndirbohnhwf4lzrvsqq3eca", "input": { "mode": "best", "image": "https://replicate.delivery/pbxt/JY4R7tIVA78ESNdslvoixd99Wcv3oxARhcZRUuos0zMnuwz4/amphitheatre.jpeg", "clip_model_name": "ViT-bigG-14/laion2b_s39b_b160k" }, "logs": "0%| | 0/55 [00:00<?, ?it/s]\n 29%|██▉ | 16/55 [00:00<00:00, 155.11it/s]\n 60%|██████ | 33/55 [00:00<00:00, 159.14it/s]\n 91%|█████████ | 50/55 [00:00<00:00, 160.91it/s]\n100%|██████████| 55/55 [00:00<00:00, 160.74it/s]\nFlavor chain: 0%| | 0/32 [00:00<?, ?it/s]\nFlavor chain: 3%|▎ | 1/32 [00:02<01:06, 2.15s/it]\nFlavor chain: 6%|▋ | 2/32 [00:04<01:03, 2.10s/it]\nFlavor chain: 9%|▉ | 3/32 [00:06<01:02, 2.17s/it]\nFlavor chain: 12%|█▎ | 4/32 [00:08<01:01, 2.21s/it]\nFlavor chain: 16%|█▌ | 5/32 [00:11<01:00, 2.25s/it]\nFlavor chain: 19%|█▉ | 6/32 [00:13<00:58, 2.26s/it]\nFlavor chain: 22%|██▏ | 7/32 [00:15<00:57, 2.29s/it]\nFlavor chain: 25%|██▌ | 8/32 [00:18<00:55, 2.31s/it]\nFlavor chain: 28%|██▊ | 9/32 [00:20<00:53, 2.34s/it]\nFlavor chain: 31%|███▏ | 10/32 [00:22<00:52, 2.38s/it]\nFlavor chain: 34%|███▍ | 11/32 [00:25<00:50, 2.42s/it]\nFlavor chain: 38%|███▊ | 12/32 [00:28<00:49, 2.47s/it]\nFlavor chain: 41%|████ | 13/32 [00:30<00:47, 2.51s/it]\nFlavor chain: 44%|████▍ | 14/32 [00:33<00:45, 2.54s/it]\nFlavor chain: 47%|████▋ | 15/32 [00:35<00:43, 2.58s/it]\nFlavor chain: 47%|████▋ | 15/32 [00:38<00:43, 2.57s/it]\n 0%| | 0/55 [00:00<?, ?it/s]\n 29%|██▉ | 16/55 [00:00<00:00, 151.79it/s]\n 60%|██████ | 33/55 [00:00<00:00, 160.07it/s]\n 91%|█████████ | 50/55 [00:00<00:00, 161.93it/s]\n100%|██████████| 55/55 [00:00<00:00, 161.58it/s]\n 0%| | 0/6 [00:00<?, ?it/s]\n100%|██████████| 6/6 [00:00<00:00, 124.97it/s]\n 0%| | 0/50 [00:00<?, ?it/s]\n 34%|███▍ | 17/50 [00:00<00:00, 166.15it/s]\n 68%|██████▊ | 34/50 [00:00<00:00, 166.77it/s]\n100%|██████████| 50/50 [00:00<00:00, 168.83it/s]", "metrics": { "predict_time": 40.550053, "total_time": 40.521413 }, "output": "a sketch of a lecture hall with rows of chairs, line art!!, ƒ / 3. 5, offices, angle view, stage, contemporary, university, celebrities, white outline, autocad, white studio, diagramm, ticket, at college, grand", "started_at": "2023-09-18T09:50:21.311928Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/vgrndirbohnhwf4lzrvsqq3eca", "cancel": "https://api.replicate.com/v1/predictions/vgrndirbohnhwf4lzrvsqq3eca/cancel" }, "version": "e1a695055be38e02995e96d6bcec13c2bd5c52ef756ed339b88aef83f46380a2" }
Generated in0%| | 0/55 [00:00<?, ?it/s] 29%|██▉ | 16/55 [00:00<00:00, 155.11it/s] 60%|██████ | 33/55 [00:00<00:00, 159.14it/s] 91%|█████████ | 50/55 [00:00<00:00, 160.91it/s] 100%|██████████| 55/55 [00:00<00:00, 160.74it/s] Flavor chain: 0%| | 0/32 [00:00<?, ?it/s] Flavor chain: 3%|▎ | 1/32 [00:02<01:06, 2.15s/it] Flavor chain: 6%|▋ | 2/32 [00:04<01:03, 2.10s/it] Flavor chain: 9%|▉ | 3/32 [00:06<01:02, 2.17s/it] Flavor chain: 12%|█▎ | 4/32 [00:08<01:01, 2.21s/it] Flavor chain: 16%|█▌ | 5/32 [00:11<01:00, 2.25s/it] Flavor chain: 19%|█▉ | 6/32 [00:13<00:58, 2.26s/it] Flavor chain: 22%|██▏ | 7/32 [00:15<00:57, 2.29s/it] Flavor chain: 25%|██▌ | 8/32 [00:18<00:55, 2.31s/it] Flavor chain: 28%|██▊ | 9/32 [00:20<00:53, 2.34s/it] Flavor chain: 31%|███▏ | 10/32 [00:22<00:52, 2.38s/it] Flavor chain: 34%|███▍ | 11/32 [00:25<00:50, 2.42s/it] Flavor chain: 38%|███▊ | 12/32 [00:28<00:49, 2.47s/it] Flavor chain: 41%|████ | 13/32 [00:30<00:47, 2.51s/it] Flavor chain: 44%|████▍ | 14/32 [00:33<00:45, 2.54s/it] Flavor chain: 47%|████▋ | 15/32 [00:35<00:43, 2.58s/it] Flavor chain: 47%|████▋ | 15/32 [00:38<00:43, 2.57s/it] 0%| | 0/55 [00:00<?, ?it/s] 29%|██▉ | 16/55 [00:00<00:00, 151.79it/s] 60%|██████ | 33/55 [00:00<00:00, 160.07it/s] 91%|█████████ | 50/55 [00:00<00:00, 161.93it/s] 100%|██████████| 55/55 [00:00<00:00, 161.58it/s] 0%| | 0/6 [00:00<?, ?it/s] 100%|██████████| 6/6 [00:00<00:00, 124.97it/s] 0%| | 0/50 [00:00<?, ?it/s] 34%|███▍ | 17/50 [00:00<00:00, 166.15it/s] 68%|██████▊ | 34/50 [00:00<00:00, 166.77it/s] 100%|██████████| 50/50 [00:00<00:00, 168.83it/s]
Prediction
ell-hol/clip-interrogator:e1a695055be38e02995e96d6bcec13c2bd5c52ef756ed339b88aef83f46380a2IDzfetcqrb2hj222zb3buf6xcke4StatusSucceededSourceWebHardwareA100 (40GB)Total durationCreatedInput
{ "mode": "best", "image": "https://replicate.delivery/pbxt/JZ8i45qDcsouImNAmyzAzmoR6AAxLfGntsaelsawvNTcoKQT/vignette_PARIS_carte_secteur_vegetal.png", "clip_model_name": "ViT-bigG-14/laion2b_s39b_b160k" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run ell-hol/clip-interrogator using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "ell-hol/clip-interrogator:e1a695055be38e02995e96d6bcec13c2bd5c52ef756ed339b88aef83f46380a2", { input: { mode: "best", image: "https://replicate.delivery/pbxt/JZ8i45qDcsouImNAmyzAzmoR6AAxLfGntsaelsawvNTcoKQT/vignette_PARIS_carte_secteur_vegetal.png", clip_model_name: "ViT-bigG-14/laion2b_s39b_b160k" } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run ell-hol/clip-interrogator using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "ell-hol/clip-interrogator:e1a695055be38e02995e96d6bcec13c2bd5c52ef756ed339b88aef83f46380a2", input={ "mode": "best", "image": "https://replicate.delivery/pbxt/JZ8i45qDcsouImNAmyzAzmoR6AAxLfGntsaelsawvNTcoKQT/vignette_PARIS_carte_secteur_vegetal.png", "clip_model_name": "ViT-bigG-14/laion2b_s39b_b160k" } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run ell-hol/clip-interrogator using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "ell-hol/clip-interrogator:e1a695055be38e02995e96d6bcec13c2bd5c52ef756ed339b88aef83f46380a2", "input": { "mode": "best", "image": "https://replicate.delivery/pbxt/JZ8i45qDcsouImNAmyzAzmoR6AAxLfGntsaelsawvNTcoKQT/vignette_PARIS_carte_secteur_vegetal.png", "clip_model_name": "ViT-bigG-14/laion2b_s39b_b160k" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
map of the area of the city of paris with a green arrow, vegetated roofs, vlop, n - 4, beef, 8/8, vat, detail content, vtmb, scores, pastoral environment, brussels, pic, elegrant, edd, high, portrait 4 / 3, victor, deforestation, high-quality photo, labrador, color{ "completed_at": "2023-09-21T10:11:48.673858Z", "created_at": "2023-09-21T10:06:55.670734Z", "data_removed": false, "error": null, "id": "zfetcqrb2hj222zb3buf6xcke4", "input": { "mode": "best", "image": "https://replicate.delivery/pbxt/JZ8i45qDcsouImNAmyzAzmoR6AAxLfGntsaelsawvNTcoKQT/vignette_PARIS_carte_secteur_vegetal.png", "clip_model_name": "ViT-bigG-14/laion2b_s39b_b160k" }, "logs": "Loading CLIP model ViT-bigG-14/laion2b_s39b_b160k...\nDownloading (…)ip_pytorch_model.bin: 0%| | 0.00/10.2G [00:00<?, ?B/s]\nDownloading (…)ip_pytorch_model.bin: 0%| | 10.5M/10.2G [00:00<04:06, 41.3MB/s]\nDownloading (…)ip_pytorch_model.bin: 0%| | 41.9M/10.2G [00:00<01:26, 118MB/s] \nDownloading (…)ip_pytorch_model.bin: 1%| | 73.4M/10.2G 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?it/s]\n 27%|██▋ | 15/55 [00:00<00:00, 142.94it/s]\n 56%|█████▋ | 31/55 [00:00<00:00, 149.29it/s]\n 87%|████████▋ | 48/55 [00:00<00:00, 155.58it/s]\n100%|██████████| 55/55 [00:00<00:00, 154.16it/s]\n 0%| | 0/6 [00:00<?, ?it/s]\n100%|██████████| 6/6 [00:00<00:00, 96.07it/s]\n 0%| | 0/50 [00:00<?, ?it/s]\n 28%|██▊ | 14/50 [00:00<00:00, 135.65it/s]\n 56%|█████▌ | 28/50 [00:00<00:00, 135.91it/s]\n 86%|████████▌ | 43/50 [00:00<00:00, 140.01it/s]\n100%|██████████| 50/50 [00:00<00:00, 141.17it/s]", "metrics": { "predict_time": 152.017284, "total_time": 293.003124 }, "output": "map of the area of the city of paris with a green arrow, vegetated roofs, vlop, n - 4, beef, 8/8, vat, detail content, vtmb, scores, pastoral environment, brussels, pic, elegrant, edd, high, portrait 4 / 3, victor, deforestation, high-quality photo, labrador, color", "started_at": "2023-09-21T10:09:16.656574Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/zfetcqrb2hj222zb3buf6xcke4", "cancel": "https://api.replicate.com/v1/predictions/zfetcqrb2hj222zb3buf6xcke4/cancel" }, "version": "e1a695055be38e02995e96d6bcec13c2bd5c52ef756ed339b88aef83f46380a2" }
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