cjwbw
/
pix2pix-zero
Zero-shot Image-to-Image Translation
Prediction
cjwbw/pix2pix-zero:f0f9f82d154e7e1ee2098e38762e3528a4de44183915d9967343ea4bbb3003d9IDf2tbzsklufflnnvujh37qk34qeStatusSucceededSourceWebHardware–Total durationCreatedInput
- task
- cat2dog
- image
- xa_guidance
- 0.1
- use_float_16
- num_inference_steps
- 50
- negative_guidance_scale
- 5
{ "task": "cat2dog", "image": "https://replicate.delivery/pbxt/IIQfu3Ctc2HLOKYLRXSph2tTEZcKz0Bxyx7Fe2pZxDJps6q2/cat_1.png", "xa_guidance": 0.1, "use_float_16": true, "num_inference_steps": 50, "negative_guidance_scale": 5 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cjwbw/pix2pix-zero using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cjwbw/pix2pix-zero:f0f9f82d154e7e1ee2098e38762e3528a4de44183915d9967343ea4bbb3003d9", { input: { task: "cat2dog", image: "https://replicate.delivery/pbxt/IIQfu3Ctc2HLOKYLRXSph2tTEZcKz0Bxyx7Fe2pZxDJps6q2/cat_1.png", xa_guidance: 0.1, use_float_16: true, num_inference_steps: 50, negative_guidance_scale: 5 } } ); 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 replicateImport the client:import replicateRun cjwbw/pix2pix-zero using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cjwbw/pix2pix-zero:f0f9f82d154e7e1ee2098e38762e3528a4de44183915d9967343ea4bbb3003d9", input={ "task": "cat2dog", "image": "https://replicate.delivery/pbxt/IIQfu3Ctc2HLOKYLRXSph2tTEZcKz0Bxyx7Fe2pZxDJps6q2/cat_1.png", "xa_guidance": 0.1, "use_float_16": True, "num_inference_steps": 50, "negative_guidance_scale": 5 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cjwbw/pix2pix-zero 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": "f0f9f82d154e7e1ee2098e38762e3528a4de44183915d9967343ea4bbb3003d9", "input": { "task": "cat2dog", "image": "https://replicate.delivery/pbxt/IIQfu3Ctc2HLOKYLRXSph2tTEZcKz0Bxyx7Fe2pZxDJps6q2/cat_1.png", "xa_guidance": 0.1, "use_float_16": true, "num_inference_steps": 50, "negative_guidance_scale": 5 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
edited_image
caption_input_image
a painting of a cat sitting on top of a ballreconstructed_image
{ "completed_at": "2023-02-11T16:14:47.281666Z", "created_at": "2023-02-11T16:12:10.849367Z", "data_removed": false, "error": null, "id": "f2tbzsklufflnnvujh37qk34qe", "input": { "task": "cat2dog", "image": "https://replicate.delivery/pbxt/IIQfu3Ctc2HLOKYLRXSph2tTEZcKz0Bxyx7Fe2pZxDJps6q2/cat_1.png", "xa_guidance": 0.1, "use_float_16": true, "num_inference_steps": 50, "negative_guidance_scale": 5 }, "logs": "/root/.pyenv/versions/3.10.10/lib/python3.10/site-packages/transformers/models/clip/feature_extraction_clip.py:28: FutureWarning: The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use CLIPImageProcessor instead.\nwarnings.warn(\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:15, 3.16it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.73it/s]\n 6%|▌ | 3/50 [00:00<00:11, 3.98it/s]\n 8%|▊ | 4/50 [00:01<00:11, 4.12it/s]\n 10%|█ | 5/50 [00:01<00:10, 4.12it/s]\n 12%|█▏ | 6/50 [00:01<00:10, 4.15it/s]\n 14%|█▍ | 7/50 [00:01<00:10, 4.21it/s]\n 16%|█▌ | 8/50 [00:01<00:09, 4.25it/s]\n 18%|█▊ | 9/50 [00:02<00:09, 4.24it/s]\n 20%|██ | 10/50 [00:02<00:09, 4.08it/s]\n 22%|██▏ | 11/50 [00:02<00:09, 4.16it/s]\n 24%|██▍ | 12/50 [00:02<00:09, 4.21it/s]\n 26%|██▌ | 13/50 [00:03<00:08, 4.24it/s]\n 28%|██▊ | 14/50 [00:03<00:08, 4.24it/s]\n 30%|███ | 15/50 [00:03<00:08, 4.25it/s]\n 32%|███▏ | 16/50 [00:03<00:08, 4.24it/s]\n 34%|███▍ | 17/50 [00:04<00:07, 4.23it/s]\n 36%|███▌ | 18/50 [00:04<00:07, 4.22it/s]\n 38%|███▊ | 19/50 [00:04<00:07, 4.25it/s]\n 40%|████ | 20/50 [00:04<00:07, 4.25it/s]\n 42%|████▏ | 21/50 [00:05<00:06, 4.25it/s]\n 44%|████▍ | 22/50 [00:05<00:06, 4.22it/s]\n 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48/50 [00:09<00:00, 4.96it/s]\n 98%|█████████▊| 49/50 [00:10<00:00, 4.84it/s]\n100%|██████████| 50/50 [00:10<00:00, 4.87it/s]\n100%|██████████| 50/50 [00:10<00:00, 4.86it/s]", "metrics": { "predict_time": 43.167193, "total_time": 156.432299 }, "output": { "edited_image": "https://replicate.delivery/pbxt/XUk0eEBqW2wfQExu4OXP4eEPffc5Gy0Yurbte1TUWQHu9wVHE/edit.png", "caption_input_image": "a painting of a cat sitting on top of a ball", "reconstructed_image": "https://replicate.delivery/pbxt/fV8lF7wjqS0Faqdqwg6P6jO7Wk0SD2NRFRaDLQPCV1o6hrOIA/reconstruction.png" }, "started_at": "2023-02-11T16:14:04.114473Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/f2tbzsklufflnnvujh37qk34qe", "cancel": "https://api.replicate.com/v1/predictions/f2tbzsklufflnnvujh37qk34qe/cancel" }, "version": "f0f9f82d154e7e1ee2098e38762e3528a4de44183915d9967343ea4bbb3003d9" }Generated in/root/.pyenv/versions/3.10.10/lib/python3.10/site-packages/transformers/models/clip/feature_extraction_clip.py:28: FutureWarning: The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use CLIPImageProcessor instead. warnings.warn( 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:15, 3.16it/s] 4%|▍ | 2/50 [00:00<00:12, 3.73it/s] 6%|▌ | 3/50 [00:00<00:11, 3.98it/s] 8%|▊ | 4/50 [00:01<00:11, 4.12it/s] 10%|█ | 5/50 [00:01<00:10, 4.12it/s] 12%|█▏ | 6/50 [00:01<00:10, 4.15it/s] 14%|█▍ | 7/50 [00:01<00:10, 4.21it/s] 16%|█▌ | 8/50 [00:01<00:09, 4.25it/s] 18%|█▊ | 9/50 [00:02<00:09, 4.24it/s] 20%|██ | 10/50 [00:02<00:09, 4.08it/s] 22%|██▏ | 11/50 [00:02<00:09, 4.16it/s] 24%|██▍ | 12/50 [00:02<00:09, 4.21it/s] 26%|██▌ | 13/50 [00:03<00:08, 4.24it/s] 28%|██▊ | 14/50 [00:03<00:08, 4.24it/s] 30%|███ | 15/50 [00:03<00:08, 4.25it/s] 32%|███▏ | 16/50 [00:03<00:08, 4.24it/s] 34%|███▍ | 17/50 [00:04<00:07, 4.23it/s] 36%|███▌ | 18/50 [00:04<00:07, 4.22it/s] 38%|███▊ | 19/50 [00:04<00:07, 4.25it/s] 40%|████ | 20/50 [00:04<00:07, 4.25it/s] 42%|████▏ | 21/50 [00:05<00:06, 4.25it/s] 44%|████▍ | 22/50 [00:05<00:06, 4.22it/s] 46%|████▌ | 23/50 [00:05<00:06, 4.23it/s] 48%|████▊ | 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cjwbw/pix2pix-zero:f0f9f82d154e7e1ee2098e38762e3528a4de44183915d9967343ea4bbb3003d9Input
- task
- dog2cat
- image
- xa_guidance
- 0.1
- use_float_16
- num_inference_steps
- 50
- negative_guidance_scale
- 5
{ "task": "dog2cat", "image": "https://replicate.delivery/pbxt/IIQjbZ1ezT1tFszXDRgoztpJnyHohGBzyvLQq1IEmvqtBwrz/dog_9.png", "xa_guidance": 0.1, "use_float_16": true, "num_inference_steps": 50, "negative_guidance_scale": 5 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cjwbw/pix2pix-zero using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cjwbw/pix2pix-zero:f0f9f82d154e7e1ee2098e38762e3528a4de44183915d9967343ea4bbb3003d9", { input: { task: "dog2cat", image: "https://replicate.delivery/pbxt/IIQjbZ1ezT1tFszXDRgoztpJnyHohGBzyvLQq1IEmvqtBwrz/dog_9.png", xa_guidance: 0.1, use_float_16: true, num_inference_steps: 50, negative_guidance_scale: 5 } } ); 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 replicateImport the client:import replicateRun cjwbw/pix2pix-zero using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cjwbw/pix2pix-zero:f0f9f82d154e7e1ee2098e38762e3528a4de44183915d9967343ea4bbb3003d9", input={ "task": "dog2cat", "image": "https://replicate.delivery/pbxt/IIQjbZ1ezT1tFszXDRgoztpJnyHohGBzyvLQq1IEmvqtBwrz/dog_9.png", "xa_guidance": 0.1, "use_float_16": True, "num_inference_steps": 50, "negative_guidance_scale": 5 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cjwbw/pix2pix-zero 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": "f0f9f82d154e7e1ee2098e38762e3528a4de44183915d9967343ea4bbb3003d9", "input": { "task": "dog2cat", "image": "https://replicate.delivery/pbxt/IIQjbZ1ezT1tFszXDRgoztpJnyHohGBzyvLQq1IEmvqtBwrz/dog_9.png", "xa_guidance": 0.1, "use_float_16": true, "num_inference_steps": 50, "negative_guidance_scale": 5 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
edited_image
caption_input_image
a painting of a dog with a bow on its headreconstructed_image
{ "completed_at": "2023-02-11T16:16:42.301405Z", "created_at": "2023-02-11T16:16:05.966458Z", "data_removed": false, "error": null, "id": "xboctmhfi5cpbfyiwdemcev3hi", "input": { "task": "dog2cat", "image": "https://replicate.delivery/pbxt/IIQjbZ1ezT1tFszXDRgoztpJnyHohGBzyvLQq1IEmvqtBwrz/dog_9.png", "xa_guidance": 0.1, "use_float_16": true, "num_inference_steps": 50, "negative_guidance_scale": 5 }, "logs": "0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:11, 4.21it/s]\n 4%|▍ | 2/50 [00:00<00:11, 4.13it/s]\n 6%|▌ | 3/50 [00:00<00:11, 4.11it/s]\n 8%|▊ | 4/50 [00:00<00:10, 4.19it/s]\n 10%|█ | 5/50 [00:01<00:10, 4.15it/s]\n 12%|█▏ | 6/50 [00:01<00:10, 4.16it/s]\n 14%|█▍ | 7/50 [00:01<00:10, 4.18it/s]\n 16%|█▌ | 8/50 [00:01<00:09, 4.21it/s]\n 18%|█▊ | 9/50 [00:02<00:09, 4.22it/s]\n 20%|██ | 10/50 [00:02<00:09, 4.18it/s]\n 22%|██▏ | 11/50 [00:02<00:09, 4.19it/s]\n 24%|██▍ | 12/50 [00:02<00:09, 4.21it/s]\n 26%|██▌ | 13/50 [00:03<00:08, 4.23it/s]\n 28%|██▊ | 14/50 [00:03<00:08, 4.18it/s]\n 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96%|█████████▌| 48/50 [00:09<00:00, 4.87it/s]\n 98%|█████████▊| 49/50 [00:09<00:00, 4.91it/s]\n100%|██████████| 50/50 [00:10<00:00, 4.95it/s]\n100%|██████████| 50/50 [00:10<00:00, 4.92it/s]", "metrics": { "predict_time": 36.267691, "total_time": 36.334947 }, "output": { "edited_image": "https://replicate.delivery/pbxt/smBWUZbfhlWJKyZLRaV5TvLjagHaIpGjdOuZo7WPcUz0irOIA/edit.png", "caption_input_image": "a painting of a dog with a bow on its head", "reconstructed_image": "https://replicate.delivery/pbxt/NZMUartw9GapO9wfD83IDxbaQN1hyA4NspaGcBOj40O0irOIA/reconstruction.png" }, "started_at": "2023-02-11T16:16:06.033714Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/xboctmhfi5cpbfyiwdemcev3hi", "cancel": "https://api.replicate.com/v1/predictions/xboctmhfi5cpbfyiwdemcev3hi/cancel" }, "version": "f0f9f82d154e7e1ee2098e38762e3528a4de44183915d9967343ea4bbb3003d9" }Generated in0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:11, 4.21it/s] 4%|▍ | 2/50 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[00:11<00:00, 4.15it/s] Image caption generated with BLIP model: a painting of a dog with a bow on its head 0%| | 0/50 [00:00<?, ?it/s] 4%|▍ | 2/50 [00:00<00:03, 12.38it/s] 8%|▊ | 4/50 [00:00<00:03, 12.68it/s] 12%|█▏ | 6/50 [00:00<00:03, 12.79it/s] 16%|█▌ | 8/50 [00:00<00:03, 12.86it/s] 20%|██ | 10/50 [00:00<00:03, 12.72it/s] 24%|██▍ | 12/50 [00:00<00:02, 12.70it/s] 28%|██▊ | 14/50 [00:01<00:02, 12.81it/s] 32%|███▏ | 16/50 [00:01<00:02, 12.89it/s] 36%|███▌ | 18/50 [00:01<00:02, 12.83it/s] 40%|████ | 20/50 [00:01<00:02, 12.89it/s] 44%|████▍ | 22/50 [00:01<00:02, 12.52it/s] 48%|████▊ | 24/50 [00:01<00:02, 12.68it/s] 52%|█████▏ | 26/50 [00:02<00:01, 12.84it/s] 56%|█████▌ | 28/50 [00:02<00:01, 12.97it/s] 60%|██████ | 30/50 [00:02<00:01, 12.92it/s] 64%|██████▍ | 32/50 [00:02<00:01, 12.97it/s] 68%|██████▊ | 34/50 [00:02<00:01, 12.99it/s] 72%|███████▏ | 36/50 [00:02<00:01, 12.94it/s] 76%|███████▌ | 38/50 [00:02<00:00, 12.96it/s] 80%|████████ | 40/50 [00:03<00:00, 12.98it/s] 84%|████████▍ | 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84%|████████▍ | 42/50 [00:08<00:01, 4.86it/s] 86%|████████▌ | 43/50 [00:08<00:01, 4.85it/s] 88%|████████▊ | 44/50 [00:08<00:01, 4.89it/s] 90%|█████████ | 45/50 [00:09<00:01, 4.93it/s] 92%|█████████▏| 46/50 [00:09<00:00, 4.94it/s] 94%|█████████▍| 47/50 [00:09<00:00, 4.89it/s] 96%|█████████▌| 48/50 [00:09<00:00, 4.87it/s] 98%|█████████▊| 49/50 [00:09<00:00, 4.91it/s] 100%|██████████| 50/50 [00:10<00:00, 4.95it/s] 100%|██████████| 50/50 [00:10<00:00, 4.92it/s]Prediction
cjwbw/pix2pix-zero:f0f9f82d154e7e1ee2098e38762e3528a4de44183915d9967343ea4bbb3003d9IDfwwarziqpbas7h2suplyitbxnqStatusSucceededSourceWebHardware–Total durationCreatedInput
- task
- dog2cat
- image
- xa_guidance
- 0.1
- use_float_16
- num_inference_steps
- 50
- negative_guidance_scale
- 5
{ "task": "dog2cat", "image": "https://replicate.delivery/pbxt/IIQkfOneCCZJ1wb3ikQC4qrYdShA9YCV6n9e8ydRPoSndayb/dog_4.png", "xa_guidance": 0.1, "use_float_16": true, "num_inference_steps": 50, "negative_guidance_scale": 5 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cjwbw/pix2pix-zero using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cjwbw/pix2pix-zero:f0f9f82d154e7e1ee2098e38762e3528a4de44183915d9967343ea4bbb3003d9", { input: { task: "dog2cat", image: "https://replicate.delivery/pbxt/IIQkfOneCCZJ1wb3ikQC4qrYdShA9YCV6n9e8ydRPoSndayb/dog_4.png", xa_guidance: 0.1, use_float_16: true, num_inference_steps: 50, negative_guidance_scale: 5 } } ); 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 replicateImport the client:import replicateRun cjwbw/pix2pix-zero using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cjwbw/pix2pix-zero:f0f9f82d154e7e1ee2098e38762e3528a4de44183915d9967343ea4bbb3003d9", input={ "task": "dog2cat", "image": "https://replicate.delivery/pbxt/IIQkfOneCCZJ1wb3ikQC4qrYdShA9YCV6n9e8ydRPoSndayb/dog_4.png", "xa_guidance": 0.1, "use_float_16": True, "num_inference_steps": 50, "negative_guidance_scale": 5 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cjwbw/pix2pix-zero 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": "f0f9f82d154e7e1ee2098e38762e3528a4de44183915d9967343ea4bbb3003d9", "input": { "task": "dog2cat", "image": "https://replicate.delivery/pbxt/IIQkfOneCCZJ1wb3ikQC4qrYdShA9YCV6n9e8ydRPoSndayb/dog_4.png", "xa_guidance": 0.1, "use_float_16": true, "num_inference_steps": 50, "negative_guidance_scale": 5 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
edited_image
caption_input_image
a painting of a golden retriever dogreconstructed_image
{ "completed_at": "2023-02-11T16:17:47.506125Z", "created_at": "2023-02-11T16:17:12.810392Z", "data_removed": false, "error": null, "id": "fwwarziqpbas7h2suplyitbxnq", "input": { "task": "dog2cat", "image": "https://replicate.delivery/pbxt/IIQkfOneCCZJ1wb3ikQC4qrYdShA9YCV6n9e8ydRPoSndayb/dog_4.png", "xa_guidance": 0.1, "use_float_16": true, "num_inference_steps": 50, "negative_guidance_scale": 5 }, "logs": "0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:11, 4.23it/s]\n 4%|▍ | 2/50 [00:00<00:11, 4.23it/s]\n 6%|▌ | 3/50 [00:00<00:11, 4.08it/s]\n 8%|▊ | 4/50 [00:00<00:11, 4.11it/s]\n 10%|█ | 5/50 [00:01<00:10, 4.15it/s]\n 12%|█▏ | 6/50 [00:01<00:10, 4.16it/s]\n 14%|█▍ | 7/50 [00:01<00:10, 4.03it/s]\n 16%|█▌ | 8/50 [00:01<00:10, 4.08it/s]\n 18%|█▊ | 9/50 [00:02<00:09, 4.14it/s]\n 20%|██ | 10/50 [00:02<00:09, 4.11it/s]\n 22%|██▏ | 11/50 [00:02<00:09, 4.11it/s]\n 24%|██▍ | 12/50 [00:02<00:09, 4.10it/s]\n 26%|██▌ | 13/50 [00:03<00:08, 4.16it/s]\n 28%|██▊ | 14/50 [00:03<00:08, 4.14it/s]\n 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[00:09<00:03, 4.16it/s]\n 76%|███████▌ | 38/50 [00:09<00:02, 4.13it/s]\n 78%|███████▊ | 39/50 [00:09<00:02, 4.15it/s]\n 80%|████████ | 40/50 [00:09<00:02, 4.10it/s]\n 82%|████████▏ | 41/50 [00:09<00:02, 4.14it/s]\n 84%|████████▍ | 42/50 [00:10<00:01, 4.18it/s]\n 86%|████████▌ | 43/50 [00:10<00:01, 4.21it/s]\n 88%|████████▊ | 44/50 [00:10<00:01, 4.09it/s]\n 90%|█████████ | 45/50 [00:10<00:01, 4.13it/s]\n 92%|█████████▏| 46/50 [00:11<00:00, 4.17it/s]\n 94%|█████████▍| 47/50 [00:11<00:00, 4.16it/s]\n 96%|█████████▌| 48/50 [00:11<00:00, 4.10it/s]\n96%|█████████▌| 48/50 [00:11<00:00, 4.11it/s]\nImage caption generated with BLIP model: a painting of a golden retriever dog\n 0%| | 0/50 [00:00<?, ?it/s]\n 4%|▍ | 2/50 [00:00<00:03, 12.84it/s]\n 8%|▊ | 4/50 [00:00<00:03, 13.01it/s]\n 12%|█▏ | 6/50 [00:00<00:03, 13.12it/s]\n 16%|█▌ | 8/50 [00:00<00:03, 12.76it/s]\n 20%|██ | 10/50 [00:00<00:03, 12.59it/s]\n 24%|██▍ | 12/50 [00:00<00:02, 12.70it/s]\n 28%|██▊ | 14/50 [00:01<00:02, 12.77it/s]\n 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6%|▌ | 3/50 [00:00<00:09, 5.03it/s]\n 8%|▊ | 4/50 [00:00<00:09, 4.81it/s]\n 10%|█ | 5/50 [00:01<00:09, 4.88it/s]\n 12%|█▏ | 6/50 [00:01<00:08, 4.89it/s]\n 14%|█▍ | 7/50 [00:01<00:08, 4.92it/s]\n 16%|█▌ | 8/50 [00:01<00:08, 4.96it/s]\n 18%|█▊ | 9/50 [00:01<00:08, 4.82it/s]\n 20%|██ | 10/50 [00:02<00:08, 4.85it/s]\n 22%|██▏ | 11/50 [00:02<00:08, 4.87it/s]\n 24%|██▍ | 12/50 [00:02<00:07, 4.90it/s]\n 26%|██▌ | 13/50 [00:02<00:07, 4.91it/s]\n 28%|██▊ | 14/50 [00:02<00:07, 4.83it/s]\n 30%|███ | 15/50 [00:03<00:07, 4.87it/s]\n 32%|███▏ | 16/50 [00:03<00:06, 4.90it/s]\n 34%|███▍ | 17/50 [00:03<00:06, 4.92it/s]\n 36%|███▌ | 18/50 [00:03<00:06, 4.73it/s]\n 38%|███▊ | 19/50 [00:03<00:06, 4.78it/s]\n 40%|████ | 20/50 [00:04<00:06, 4.87it/s]\n 42%|████▏ | 21/50 [00:04<00:05, 4.93it/s]\n 44%|████▍ | 22/50 [00:04<00:05, 4.96it/s]\n 46%|████▌ | 23/50 [00:04<00:05, 5.01it/s]\n 48%|████▊ | 24/50 [00:04<00:05, 4.97it/s]\n 50%|█████ | 25/50 [00:05<00:05, 4.86it/s]\n 52%|█████▏ | 26/50 [00:05<00:04, 4.82it/s]\n 54%|█████▍ | 27/50 [00:05<00:04, 4.84it/s]\n 56%|█████▌ | 28/50 [00:05<00:04, 4.84it/s]\n 58%|█████▊ | 29/50 [00:05<00:04, 4.74it/s]\n 60%|██████ | 30/50 [00:06<00:04, 4.79it/s]\n 62%|██████▏ | 31/50 [00:06<00:03, 4.84it/s]\n 64%|██████▍ | 32/50 [00:06<00:03, 4.82it/s]\n 66%|██████▌ | 33/50 [00:06<00:03, 4.79it/s]\n 68%|██████▊ | 34/50 [00:06<00:03, 4.77it/s]\n 70%|███████ | 35/50 [00:07<00:03, 4.84it/s]\n 72%|███████▏ | 36/50 [00:07<00:02, 4.90it/s]\n 74%|███████▍ | 37/50 [00:07<00:02, 4.87it/s]\n 76%|███████▌ | 38/50 [00:07<00:02, 4.77it/s]\n 78%|███████▊ | 39/50 [00:08<00:02, 4.81it/s]\n 80%|████████ | 40/50 [00:08<00:02, 4.87it/s]\n 82%|████████▏ | 41/50 [00:08<00:01, 4.94it/s]\n 84%|████████▍ | 42/50 [00:08<00:01, 4.97it/s]\n 86%|████████▌ | 43/50 [00:08<00:01, 4.84it/s]\n 88%|████████▊ | 44/50 [00:09<00:01, 4.90it/s]\n 90%|█████████ | 45/50 [00:09<00:01, 4.94it/s]\n 92%|█████████▏| 46/50 [00:09<00:00, 4.92it/s]\n 94%|█████████▍| 47/50 [00:09<00:00, 4.86it/s]\n 96%|█████████▌| 48/50 [00:09<00:00, 4.83it/s]\n 98%|█████████▊| 49/50 [00:10<00:00, 4.87it/s]\n100%|██████████| 50/50 [00:10<00:00, 4.80it/s]\n100%|██████████| 50/50 [00:10<00:00, 4.87it/s]", "metrics": { "predict_time": 34.638651, "total_time": 34.695733 }, "output": { "edited_image": "https://replicate.delivery/pbxt/LNGBXusaao6PDVlaw5r4U5BKj644C0VhCI6mwwNpVM6qxVHE/edit.png", "caption_input_image": "a painting of a golden retriever dog", "reconstructed_image": "https://replicate.delivery/pbxt/ujt0aGbP7A7zH9kg4aCgnebVfXNEgofAT06dCV4Kjp5WNu6gA/reconstruction.png" }, "started_at": "2023-02-11T16:17:12.867474Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/fwwarziqpbas7h2suplyitbxnq", "cancel": "https://api.replicate.com/v1/predictions/fwwarziqpbas7h2suplyitbxnq/cancel" }, "version": "f0f9f82d154e7e1ee2098e38762e3528a4de44183915d9967343ea4bbb3003d9" }Generated in0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:11, 4.23it/s] 4%|▍ | 2/50 [00:00<00:11, 4.23it/s] 6%|▌ | 3/50 [00:00<00:11, 4.08it/s] 8%|▊ | 4/50 [00:00<00:11, 4.11it/s] 10%|█ | 5/50 [00:01<00:10, 4.15it/s] 12%|█▏ | 6/50 [00:01<00:10, 4.16it/s] 14%|█▍ | 7/50 [00:01<00:10, 4.03it/s] 16%|█▌ | 8/50 [00:01<00:10, 4.08it/s] 18%|█▊ | 9/50 [00:02<00:09, 4.14it/s] 20%|██ | 10/50 [00:02<00:09, 4.11it/s] 22%|██▏ | 11/50 [00:02<00:09, 4.11it/s] 24%|██▍ | 12/50 [00:02<00:09, 4.10it/s] 26%|██▌ | 13/50 [00:03<00:08, 4.16it/s] 28%|██▊ | 14/50 [00:03<00:08, 4.14it/s] 30%|███ | 15/50 [00:03<00:08, 4.13it/s] 32%|███▏ | 16/50 [00:03<00:08, 4.11it/s] 34%|███▍ | 17/50 [00:04<00:07, 4.15it/s] 36%|███▌ | 18/50 [00:04<00:07, 4.12it/s] 38%|███▊ | 19/50 [00:04<00:07, 4.13it/s] 40%|████ | 20/50 [00:04<00:07, 4.14it/s] 42%|████▏ | 21/50 [00:05<00:07, 4.14it/s] 44%|████▍ | 22/50 [00:05<00:06, 4.07it/s] 46%|████▌ | 23/50 [00:05<00:06, 4.01it/s] 48%|████▊ | 24/50 [00:05<00:06, 4.04it/s] 50%|█████ | 25/50 [00:06<00:06, 4.03it/s] 52%|█████▏ | 26/50 [00:06<00:05, 4.08it/s] 54%|█████▍ | 27/50 [00:06<00:05, 4.00it/s] 56%|█████▌ | 28/50 [00:06<00:05, 4.00it/s] 58%|█████▊ | 29/50 [00:07<00:05, 4.06it/s] 60%|██████ | 30/50 [00:07<00:04, 4.08it/s] 62%|██████▏ | 31/50 [00:07<00:04, 4.07it/s] 64%|██████▍ | 32/50 [00:07<00:04, 4.08it/s] 66%|██████▌ | 33/50 [00:08<00:04, 4.11it/s] 68%|██████▊ | 34/50 [00:08<00:03, 4.11it/s] 70%|███████ | 35/50 [00:08<00:03, 4.11it/s] 72%|███████▏ | 36/50 [00:08<00:03, 4.11it/s] 74%|███████▍ | 37/50 [00:09<00:03, 4.16it/s] 76%|███████▌ | 38/50 [00:09<00:02, 4.13it/s] 78%|███████▊ | 39/50 [00:09<00:02, 4.15it/s] 80%|████████ | 40/50 [00:09<00:02, 4.10it/s] 82%|████████▏ | 41/50 [00:09<00:02, 4.14it/s] 84%|████████▍ | 42/50 [00:10<00:01, 4.18it/s] 86%|████████▌ | 43/50 [00:10<00:01, 4.21it/s] 88%|████████▊ | 44/50 [00:10<00:01, 4.09it/s] 90%|█████████ | 45/50 [00:10<00:01, 4.13it/s] 92%|█████████▏| 46/50 [00:11<00:00, 4.17it/s] 94%|█████████▍| 47/50 [00:11<00:00, 4.16it/s] 96%|█████████▌| 48/50 [00:11<00:00, 4.10it/s] 96%|█████████▌| 48/50 [00:11<00:00, 4.11it/s] Image caption generated with BLIP model: a painting of a golden retriever dog 0%| | 0/50 [00:00<?, ?it/s] 4%|▍ | 2/50 [00:00<00:03, 12.84it/s] 8%|▊ | 4/50 [00:00<00:03, 13.01it/s] 12%|█▏ | 6/50 [00:00<00:03, 13.12it/s] 16%|█▌ | 8/50 [00:00<00:03, 12.76it/s] 20%|██ | 10/50 [00:00<00:03, 12.59it/s] 24%|██▍ | 12/50 [00:00<00:02, 12.70it/s] 28%|██▊ | 14/50 [00:01<00:02, 12.77it/s] 32%|███▏ | 16/50 [00:01<00:02, 12.83it/s] 36%|███▌ | 18/50 [00:01<00:02, 12.89it/s] 40%|████ | 20/50 [00:01<00:02, 12.63it/s] 44%|████▍ | 22/50 [00:01<00:02, 12.57it/s] 48%|████▊ | 24/50 [00:01<00:02, 12.29it/s] 52%|█████▏ | 26/50 [00:02<00:01, 12.43it/s] 56%|█████▌ | 28/50 [00:02<00:01, 12.56it/s] 60%|██████ | 30/50 [00:02<00:01, 12.68it/s] 64%|██████▍ | 32/50 [00:02<00:01, 12.45it/s] 68%|██████▊ | 34/50 [00:02<00:01, 12.60it/s] 72%|███████▏ | 36/50 [00:02<00:01, 12.62it/s] 76%|███████▌ | 38/50 [00:03<00:00, 12.65it/s] 80%|████████ | 40/50 [00:03<00:00, 12.71it/s] 84%|████████▍ | 42/50 [00:03<00:00, 12.73it/s] 88%|████████▊ | 44/50 [00:03<00:00, 12.62it/s] 92%|█████████▏| 46/50 [00:03<00:00, 12.46it/s] 96%|█████████▌| 48/50 [00:03<00:00, 12.31it/s] 100%|██████████| 50/50 [00:03<00:00, 12.34it/s] 100%|██████████| 50/50 [00:03<00:00, 12.59it/s] 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:09, 5.02it/s] 4%|▍ | 2/50 [00:00<00:09, 5.06it/s] 6%|▌ | 3/50 [00:00<00:09, 5.03it/s] 8%|▊ | 4/50 [00:00<00:09, 4.81it/s] 10%|█ | 5/50 [00:01<00:09, 4.88it/s] 12%|█▏ | 6/50 [00:01<00:08, 4.89it/s] 14%|█▍ | 7/50 [00:01<00:08, 4.92it/s] 16%|█▌ | 8/50 [00:01<00:08, 4.96it/s] 18%|█▊ | 9/50 [00:01<00:08, 4.82it/s] 20%|██ | 10/50 [00:02<00:08, 4.85it/s] 22%|██▏ | 11/50 [00:02<00:08, 4.87it/s] 24%|██▍ | 12/50 [00:02<00:07, 4.90it/s] 26%|██▌ | 13/50 [00:02<00:07, 4.91it/s] 28%|██▊ | 14/50 [00:02<00:07, 4.83it/s] 30%|███ | 15/50 [00:03<00:07, 4.87it/s] 32%|███▏ | 16/50 [00:03<00:06, 4.90it/s] 34%|███▍ | 17/50 [00:03<00:06, 4.92it/s] 36%|███▌ | 18/50 [00:03<00:06, 4.73it/s] 38%|███▊ | 19/50 [00:03<00:06, 4.78it/s] 40%|████ | 20/50 [00:04<00:06, 4.87it/s] 42%|████▏ | 21/50 [00:04<00:05, 4.93it/s] 44%|████▍ | 22/50 [00:04<00:05, 4.96it/s] 46%|████▌ | 23/50 [00:04<00:05, 5.01it/s] 48%|████▊ | 24/50 [00:04<00:05, 4.97it/s] 50%|█████ | 25/50 [00:05<00:05, 4.86it/s] 52%|█████▏ | 26/50 [00:05<00:04, 4.82it/s] 54%|█████▍ | 27/50 [00:05<00:04, 4.84it/s] 56%|█████▌ | 28/50 [00:05<00:04, 4.84it/s] 58%|█████▊ | 29/50 [00:05<00:04, 4.74it/s] 60%|██████ | 30/50 [00:06<00:04, 4.79it/s] 62%|██████▏ | 31/50 [00:06<00:03, 4.84it/s] 64%|██████▍ | 32/50 [00:06<00:03, 4.82it/s] 66%|██████▌ | 33/50 [00:06<00:03, 4.79it/s] 68%|██████▊ | 34/50 [00:06<00:03, 4.77it/s] 70%|███████ | 35/50 [00:07<00:03, 4.84it/s] 72%|███████▏ | 36/50 [00:07<00:02, 4.90it/s] 74%|███████▍ | 37/50 [00:07<00:02, 4.87it/s] 76%|███████▌ | 38/50 [00:07<00:02, 4.77it/s] 78%|███████▊ | 39/50 [00:08<00:02, 4.81it/s] 80%|████████ | 40/50 [00:08<00:02, 4.87it/s] 82%|████████▏ | 41/50 [00:08<00:01, 4.94it/s] 84%|████████▍ | 42/50 [00:08<00:01, 4.97it/s] 86%|████████▌ | 43/50 [00:08<00:01, 4.84it/s] 88%|████████▊ | 44/50 [00:09<00:01, 4.90it/s] 90%|█████████ | 45/50 [00:09<00:01, 4.94it/s] 92%|█████████▏| 46/50 [00:09<00:00, 4.92it/s] 94%|█████████▍| 47/50 [00:09<00:00, 4.86it/s] 96%|█████████▌| 48/50 [00:09<00:00, 4.83it/s] 98%|█████████▊| 49/50 [00:10<00:00, 4.87it/s] 100%|██████████| 50/50 [00:10<00:00, 4.80it/s] 100%|██████████| 50/50 [00:10<00:00, 4.87it/s]
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