anhappdev / test
Image Inpainting
- Public
- 4K runs
-
A100 (80GB)
Prediction
anhappdev/test:1e4a05970ece5b18cf3670cf2b5fe4a97bd595853ec19abced65d8fe9c98def9IDoyj2r4rnfbhlrnecq2vlo22s3qStatusSucceededSourceWebHardware–Total durationCreatedInput
- image
- prompt
- a black++ leather++ couch in a living room-- in front of wall--
- mask_image
- num_outputs
- 3
- preprocessing
- pad
- guidance_scale
- 7.5
- num_inference_steps
- 30
{ "image": "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg", "prompt": "a black++ leather++ couch in a living room-- in front of wall--", "mask_image": "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg", "num_outputs": 3, "preprocessing": "pad", "guidance_scale": 7.5, "num_inference_steps": 30 }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 anhappdev/test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "anhappdev/test:1e4a05970ece5b18cf3670cf2b5fe4a97bd595853ec19abced65d8fe9c98def9", { input: { image: "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg", prompt: "a black++ leather++ couch in a living room-- in front of wall--", mask_image: "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg", num_outputs: 3, preprocessing: "pad", guidance_scale: 7.5, num_inference_steps: 30 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);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 anhappdev/test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "anhappdev/test:1e4a05970ece5b18cf3670cf2b5fe4a97bd595853ec19abced65d8fe9c98def9", input={ "image": "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg", "prompt": "a black++ leather++ couch in a living room-- in front of wall--", "mask_image": "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg", "num_outputs": 3, "preprocessing": "pad", "guidance_scale": 7.5, "num_inference_steps": 30 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run anhappdev/test 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": "anhappdev/test:1e4a05970ece5b18cf3670cf2b5fe4a97bd595853ec19abced65d8fe9c98def9", "input": { "image": "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg", "prompt": "a black++ leather++ couch in a living room-- in front of wall--", "mask_image": "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg", "num_outputs": 3, "preprocessing": "pad", "guidance_scale": 7.5, "num_inference_steps": 30 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-04-02T07:54:29.142719Z", "created_at": "2023-04-02T07:54:02.912224Z", "data_removed": false, "error": null, "id": "oyj2r4rnfbhlrnecq2vlo22s3q", "input": { "image": "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg", "prompt": "a black++ leather++ couch in a living room-- in front of wall--", "mask_image": "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg", "num_outputs": 3, "preprocessing": "pad", "guidance_scale": 7.5, "num_inference_steps": 30 }, "logs": "2023-04-02 07:54:03,638 [DEBUG] main.py.predict():162 :: Running pipeline using stabilityai/stable-diffusion-2-inpainting\n2023-04-02 07:54:03,639 [INFO] main.py.predict():163 :: Using seed: 5245\n2023-04-02 07:54:03,639 [DEBUG] main.py.predict():164 :: Using preprocessing_choice: pad\n2023-04-02 07:54:03,908 [DEBUG] TiffImagePlugin.py.load():856 :: tag: Orientation (274) - type: short (3) - value: b'\\x00\\x01'\n2023-04-02 07:54:03,908 [DEBUG] TiffImagePlugin.py.load():856 :: tag: ExifIFD (34665) - type: long (4) - value: b'\\x00\\x00\\x00&'\n2023-04-02 07:54:05,398 [DEBUG] main.py.predict():178 :: Using prompt: RAW photo, a black++ leather++ couch in a living room-- in front of wall--, 8k uhd, dslr, high quality, high resolution\n2023-04-02 07:54:05,398 [DEBUG] main.py.predict():179 :: Using negative_prompt: , sketch, cartoon, drawing, anime, deformed, distorted, disfigured\n 0%| | 0/30 [00:00<?, ?it/s]\n 3%|▎ | 1/30 [00:01<00:40, 1.39s/it]\n 7%|▋ | 2/30 [00:01<00:25, 1.08it/s]\n 10%|█ | 3/30 [00:02<00:21, 1.27it/s]\n 13%|█▎ | 4/30 [00:03<00:18, 1.38it/s]\n 17%|█▋ | 5/30 [00:03<00:17, 1.46it/s]\n 20%|██ | 6/30 [00:04<00:15, 1.50it/s]\n 23%|██▎ | 7/30 [00:05<00:15, 1.53it/s]\n 27%|██▋ | 8/30 [00:05<00:14, 1.55it/s]\n 30%|███ | 9/30 [00:06<00:13, 1.55it/s]\n 33%|███▎ | 10/30 [00:07<00:12, 1.57it/s]\n 37%|███▋ | 11/30 [00:07<00:12, 1.57it/s]\n 40%|████ | 12/30 [00:08<00:11, 1.57it/s]\n 43%|████▎ | 13/30 [00:08<00:10, 1.57it/s]\n 47%|████▋ | 14/30 [00:09<00:10, 1.56it/s]\n 50%|█████ | 15/30 [00:10<00:09, 1.56it/s]\n 53%|█████▎ | 16/30 [00:10<00:08, 1.56it/s]\n 57%|█████▋ | 17/30 [00:11<00:08, 1.55it/s]\n 60%|██████ | 18/30 [00:12<00:07, 1.55it/s]\n 63%|██████▎ | 19/30 [00:12<00:07, 1.54it/s]\n 67%|██████▋ | 20/30 [00:13<00:06, 1.54it/s]\n 70%|███████ | 21/30 [00:14<00:05, 1.54it/s]\n 73%|███████▎ | 22/30 [00:14<00:05, 1.54it/s]\n 77%|███████▋ | 23/30 [00:15<00:04, 1.53it/s]\n 80%|████████ | 24/30 [00:16<00:03, 1.53it/s]\n 83%|████████▎ | 25/30 [00:16<00:03, 1.53it/s]\n 87%|████████▋ | 26/30 [00:17<00:02, 1.53it/s]\n 90%|█████████ | 27/30 [00:18<00:01, 1.52it/s]\n 93%|█████████▎| 28/30 [00:18<00:01, 1.51it/s]\n 97%|█████████▋| 29/30 [00:19<00:00, 1.51it/s]\n100%|██████████| 30/30 [00:20<00:00, 1.51it/s]\n100%|██████████| 30/30 [00:20<00:00, 1.50it/s]\n2023-04-02 07:54:26,723 [DEBUG] main.py.predict():197 :: Saved to /content/drive/MyDrive/tmp/images/out/out-0.png\n2023-04-02 07:54:26,846 [DEBUG] main.py.predict():197 :: Saved to /content/drive/MyDrive/tmp/images/out/out-1.png\n2023-04-02 07:54:26,961 [DEBUG] main.py.predict():197 :: Saved to /content/drive/MyDrive/tmp/images/out/out-2.png", "metrics": { "predict_time": 26.070355, "total_time": 26.230495 }, "output": [ "https://replicate.delivery/pbxt/pUH6mzB5ARYFMdKDWjd7vxXu2SeRtc36GkenL8fQLC1l1cbhA/out-0.png", "https://replicate.delivery/pbxt/6uOXSpdgDVLGJhhVVtV6lsitWTuXlHAvKrzPUeFPeoDzautQA/out-1.png", "https://replicate.delivery/pbxt/aQelQG63kSTcUqy9wBQkEDJT520ovmEv2gcwK6w6lDLaN3WIA/out-2.png" ], "started_at": "2023-04-02T07:54:03.072364Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/oyj2r4rnfbhlrnecq2vlo22s3q", "cancel": "https://api.replicate.com/v1/predictions/oyj2r4rnfbhlrnecq2vlo22s3q/cancel" }, "version": "1e4a05970ece5b18cf3670cf2b5fe4a97bd595853ec19abced65d8fe9c98def9" }Generated in2023-04-02 07:54:03,638 [DEBUG] main.py.predict():162 :: Running pipeline using stabilityai/stable-diffusion-2-inpainting 2023-04-02 07:54:03,639 [INFO] main.py.predict():163 :: Using seed: 5245 2023-04-02 07:54:03,639 [DEBUG] main.py.predict():164 :: Using preprocessing_choice: pad 2023-04-02 07:54:03,908 [DEBUG] TiffImagePlugin.py.load():856 :: tag: Orientation (274) - type: short (3) - value: b'\x00\x01' 2023-04-02 07:54:03,908 [DEBUG] TiffImagePlugin.py.load():856 :: tag: ExifIFD (34665) - type: long (4) - value: b'\x00\x00\x00&' 2023-04-02 07:54:05,398 [DEBUG] main.py.predict():178 :: Using prompt: RAW photo, a black++ leather++ couch in a living room-- in front of wall--, 8k uhd, dslr, high quality, high resolution 2023-04-02 07:54:05,398 [DEBUG] main.py.predict():179 :: Using negative_prompt: , sketch, cartoon, drawing, anime, deformed, distorted, disfigured 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:01<00:40, 1.39s/it] 7%|▋ | 2/30 [00:01<00:25, 1.08it/s] 10%|█ | 3/30 [00:02<00:21, 1.27it/s] 13%|█▎ | 4/30 [00:03<00:18, 1.38it/s] 17%|█▋ | 5/30 [00:03<00:17, 1.46it/s] 20%|██ | 6/30 [00:04<00:15, 1.50it/s] 23%|██▎ | 7/30 [00:05<00:15, 1.53it/s] 27%|██▋ | 8/30 [00:05<00:14, 1.55it/s] 30%|███ | 9/30 [00:06<00:13, 1.55it/s] 33%|███▎ | 10/30 [00:07<00:12, 1.57it/s] 37%|███▋ | 11/30 [00:07<00:12, 1.57it/s] 40%|████ | 12/30 [00:08<00:11, 1.57it/s] 43%|████▎ | 13/30 [00:08<00:10, 1.57it/s] 47%|████▋ | 14/30 [00:09<00:10, 1.56it/s] 50%|█████ | 15/30 [00:10<00:09, 1.56it/s] 53%|█████▎ | 16/30 [00:10<00:08, 1.56it/s] 57%|█████▋ | 17/30 [00:11<00:08, 1.55it/s] 60%|██████ | 18/30 [00:12<00:07, 1.55it/s] 63%|██████▎ | 19/30 [00:12<00:07, 1.54it/s] 67%|██████▋ | 20/30 [00:13<00:06, 1.54it/s] 70%|███████ | 21/30 [00:14<00:05, 1.54it/s] 73%|███████▎ | 22/30 [00:14<00:05, 1.54it/s] 77%|███████▋ | 23/30 [00:15<00:04, 1.53it/s] 80%|████████ | 24/30 [00:16<00:03, 1.53it/s] 83%|████████▎ | 25/30 [00:16<00:03, 1.53it/s] 87%|████████▋ | 26/30 [00:17<00:02, 1.53it/s] 90%|█████████ | 27/30 [00:18<00:01, 1.52it/s] 93%|█████████▎| 28/30 [00:18<00:01, 1.51it/s] 97%|█████████▋| 29/30 [00:19<00:00, 1.51it/s] 100%|██████████| 30/30 [00:20<00:00, 1.51it/s] 100%|██████████| 30/30 [00:20<00:00, 1.50it/s] 2023-04-02 07:54:26,723 [DEBUG] main.py.predict():197 :: Saved to /content/drive/MyDrive/tmp/images/out/out-0.png 2023-04-02 07:54:26,846 [DEBUG] main.py.predict():197 :: Saved to /content/drive/MyDrive/tmp/images/out/out-1.png 2023-04-02 07:54:26,961 [DEBUG] main.py.predict():197 :: Saved to /content/drive/MyDrive/tmp/images/out/out-2.pngPrediction
anhappdev/test:1e4a05970ece5b18cf3670cf2b5fe4a97bd595853ec19abced65d8fe9c98def9IDlrdwjvvfu5athkvuez7mrtigcyStatusSucceededSourceWebHardware–Total durationCreatedInput
- image
- prompt
- A big beautiful++ Christmas++ tree++++ in a living room--
- mask_image
- num_outputs
- 3
- preprocessing
- pad
- guidance_scale
- 7.5
- num_inference_steps
- 30
{ "image": "https://replicate.delivery/pbxt/Ia2sH4mZsg448cYoYBspSFbg5MtdDXCSBmtbQJ1sUscw0rVl/2.jpg", "prompt": "A big beautiful++ Christmas++ tree++++ in a living room--", "mask_image": "https://replicate.delivery/pbxt/Ia2sGuBRcn7Va7fcHH1IapXZgguPWEOSoqjkM6B6NxziW2YV/2_mask.jpg", "num_outputs": 3, "preprocessing": "pad", "guidance_scale": 7.5, "num_inference_steps": 30 }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 anhappdev/test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "anhappdev/test:1e4a05970ece5b18cf3670cf2b5fe4a97bd595853ec19abced65d8fe9c98def9", { input: { image: "https://replicate.delivery/pbxt/Ia2sH4mZsg448cYoYBspSFbg5MtdDXCSBmtbQJ1sUscw0rVl/2.jpg", prompt: "A big beautiful++ Christmas++ tree++++ in a living room--", mask_image: "https://replicate.delivery/pbxt/Ia2sGuBRcn7Va7fcHH1IapXZgguPWEOSoqjkM6B6NxziW2YV/2_mask.jpg", num_outputs: 3, preprocessing: "pad", guidance_scale: 7.5, num_inference_steps: 30 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);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 anhappdev/test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "anhappdev/test:1e4a05970ece5b18cf3670cf2b5fe4a97bd595853ec19abced65d8fe9c98def9", input={ "image": "https://replicate.delivery/pbxt/Ia2sH4mZsg448cYoYBspSFbg5MtdDXCSBmtbQJ1sUscw0rVl/2.jpg", "prompt": "A big beautiful++ Christmas++ tree++++ in a living room--", "mask_image": "https://replicate.delivery/pbxt/Ia2sGuBRcn7Va7fcHH1IapXZgguPWEOSoqjkM6B6NxziW2YV/2_mask.jpg", "num_outputs": 3, "preprocessing": "pad", "guidance_scale": 7.5, "num_inference_steps": 30 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run anhappdev/test 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": "anhappdev/test:1e4a05970ece5b18cf3670cf2b5fe4a97bd595853ec19abced65d8fe9c98def9", "input": { "image": "https://replicate.delivery/pbxt/Ia2sH4mZsg448cYoYBspSFbg5MtdDXCSBmtbQJ1sUscw0rVl/2.jpg", "prompt": "A big beautiful++ Christmas++ tree++++ in a living room--", "mask_image": "https://replicate.delivery/pbxt/Ia2sGuBRcn7Va7fcHH1IapXZgguPWEOSoqjkM6B6NxziW2YV/2_mask.jpg", "num_outputs": 3, "preprocessing": "pad", "guidance_scale": 7.5, "num_inference_steps": 30 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-04-02T07:59:22.638048Z", "created_at": "2023-04-02T07:58:58.106414Z", "data_removed": false, "error": null, "id": "lrdwjvvfu5athkvuez7mrtigcy", "input": { "image": "https://replicate.delivery/pbxt/Ia2sH4mZsg448cYoYBspSFbg5MtdDXCSBmtbQJ1sUscw0rVl/2.jpg", "prompt": "A big beautiful++ Christmas++ tree++++ in a living room--", "mask_image": "https://replicate.delivery/pbxt/Ia2sGuBRcn7Va7fcHH1IapXZgguPWEOSoqjkM6B6NxziW2YV/2_mask.jpg", "num_outputs": 3, "preprocessing": "pad", "guidance_scale": 7.5, "num_inference_steps": 30 }, "logs": "2023-04-02 07:58:58,625 [DEBUG] main.py.predict():162 :: Running pipeline using stabilityai/stable-diffusion-2-inpainting\n2023-04-02 07:58:58,625 [INFO] main.py.predict():163 :: Using seed: 64692\n2023-04-02 07:58:58,625 [DEBUG] main.py.predict():164 :: Using preprocessing_choice: pad\n2023-04-02 07:58:58,820 [DEBUG] TiffImagePlugin.py.load():856 :: tag: Orientation (274) - type: short (3) - value: b'\\x00\\x01'\n2023-04-02 07:58:58,820 [DEBUG] TiffImagePlugin.py.load():856 :: tag: ExifIFD (34665) - type: long (4) - value: b'\\x00\\x00\\x00&'\n2023-04-02 07:59:00,105 [DEBUG] main.py.predict():178 :: Using prompt: RAW photo, A big beautiful++ Christmas++ tree++++ in a living room--, 8k uhd, dslr, high quality, high resolution\n2023-04-02 07:59:00,105 [DEBUG] main.py.predict():179 :: Using negative_prompt: , sketch, cartoon, drawing, anime, deformed, distorted, disfigured\n 0%| | 0/30 [00:00<?, ?it/s]\n 3%|▎ | 1/30 [00:01<00:40, 1.41s/it]\n 7%|▋ | 2/30 [00:02<00:26, 1.07it/s]\n 10%|█ | 3/30 [00:02<00:21, 1.26it/s]\n 13%|█▎ | 4/30 [00:03<00:18, 1.38it/s]\n 17%|█▋ | 5/30 [00:03<00:17, 1.46it/s]\n 20%|██ | 6/30 [00:04<00:15, 1.51it/s]\n 23%|██▎ | 7/30 [00:05<00:14, 1.54it/s]\n 27%|██▋ | 8/30 [00:05<00:14, 1.56it/s]\n 30%|███ | 9/30 [00:06<00:13, 1.57it/s]\n 33%|███▎ | 10/30 [00:06<00:12, 1.58it/s]\n 37%|███▋ | 11/30 [00:07<00:12, 1.57it/s]\n 40%|████ | 12/30 [00:08<00:11, 1.58it/s]\n 43%|████▎ | 13/30 [00:08<00:10, 1.58it/s]\n 47%|████▋ | 14/30 [00:09<00:10, 1.58it/s]\n 50%|█████ | 15/30 [00:10<00:09, 1.58it/s]\n 53%|█████▎ | 16/30 [00:10<00:08, 1.58it/s]\n 57%|█████▋ | 17/30 [00:11<00:08, 1.57it/s]\n 60%|██████ | 18/30 [00:12<00:07, 1.57it/s]\n 63%|██████▎ | 19/30 [00:12<00:07, 1.56it/s]\n 67%|██████▋ | 20/30 [00:13<00:06, 1.56it/s]\n 70%|███████ | 21/30 [00:14<00:05, 1.55it/s]\n 73%|███████▎ | 22/30 [00:14<00:05, 1.55it/s]\n 77%|███████▋ | 23/30 [00:15<00:04, 1.55it/s]\n 80%|████████ | 24/30 [00:15<00:03, 1.54it/s]\n 83%|████████▎ | 25/30 [00:16<00:03, 1.54it/s]\n 87%|████████▋ | 26/30 [00:17<00:02, 1.53it/s]\n 90%|█████████ | 27/30 [00:17<00:01, 1.52it/s]\n 93%|█████████▎| 28/30 [00:18<00:01, 1.52it/s]\n 97%|█████████▋| 29/30 [00:19<00:00, 1.52it/s]\n100%|██████████| 30/30 [00:19<00:00, 1.52it/s]\n100%|██████████| 30/30 [00:19<00:00, 1.51it/s]\n2023-04-02 07:59:21,259 [DEBUG] main.py.predict():197 :: Saved to /content/drive/MyDrive/tmp/images/out/out-0.png\n2023-04-02 07:59:21,378 [DEBUG] main.py.predict():197 :: Saved to /content/drive/MyDrive/tmp/images/out/out-1.png\n2023-04-02 07:59:21,503 [DEBUG] main.py.predict():197 :: Saved to /content/drive/MyDrive/tmp/images/out/out-2.png", "metrics": { "predict_time": 24.436446, "total_time": 24.531634 }, "output": [ "https://replicate.delivery/pbxt/CsXPTW2i43rfDyfm6MBKpc9VtefL3Nkrps0ayRzWOQZm952CB/out-0.png", "https://replicate.delivery/pbxt/3ZCqsRjsGI4PDBXFoIpG31lfD44Ko7rYt3KlvqbNQrhsP3WIA/out-1.png", "https://replicate.delivery/pbxt/0heV3vCi7txwUqqyARJ03cHmCBhEJODaqDLQcavEAhZtP3WIA/out-2.png" ], "started_at": "2023-04-02T07:58:58.201602Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/lrdwjvvfu5athkvuez7mrtigcy", "cancel": "https://api.replicate.com/v1/predictions/lrdwjvvfu5athkvuez7mrtigcy/cancel" }, "version": "1e4a05970ece5b18cf3670cf2b5fe4a97bd595853ec19abced65d8fe9c98def9" }Generated in2023-04-02 07:58:58,625 [DEBUG] main.py.predict():162 :: Running pipeline using stabilityai/stable-diffusion-2-inpainting 2023-04-02 07:58:58,625 [INFO] main.py.predict():163 :: Using seed: 64692 2023-04-02 07:58:58,625 [DEBUG] main.py.predict():164 :: Using preprocessing_choice: pad 2023-04-02 07:58:58,820 [DEBUG] TiffImagePlugin.py.load():856 :: tag: Orientation (274) - type: short (3) - value: b'\x00\x01' 2023-04-02 07:58:58,820 [DEBUG] TiffImagePlugin.py.load():856 :: tag: ExifIFD (34665) - type: long (4) - value: b'\x00\x00\x00&' 2023-04-02 07:59:00,105 [DEBUG] main.py.predict():178 :: Using prompt: RAW photo, A big beautiful++ Christmas++ tree++++ in a living room--, 8k uhd, dslr, high quality, high resolution 2023-04-02 07:59:00,105 [DEBUG] main.py.predict():179 :: Using negative_prompt: , sketch, cartoon, drawing, anime, deformed, distorted, disfigured 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:01<00:40, 1.41s/it] 7%|▋ | 2/30 [00:02<00:26, 1.07it/s] 10%|█ | 3/30 [00:02<00:21, 1.26it/s] 13%|█▎ | 4/30 [00:03<00:18, 1.38it/s] 17%|█▋ | 5/30 [00:03<00:17, 1.46it/s] 20%|██ | 6/30 [00:04<00:15, 1.51it/s] 23%|██▎ | 7/30 [00:05<00:14, 1.54it/s] 27%|██▋ | 8/30 [00:05<00:14, 1.56it/s] 30%|███ | 9/30 [00:06<00:13, 1.57it/s] 33%|███▎ | 10/30 [00:06<00:12, 1.58it/s] 37%|███▋ | 11/30 [00:07<00:12, 1.57it/s] 40%|████ | 12/30 [00:08<00:11, 1.58it/s] 43%|████▎ | 13/30 [00:08<00:10, 1.58it/s] 47%|████▋ | 14/30 [00:09<00:10, 1.58it/s] 50%|█████ | 15/30 [00:10<00:09, 1.58it/s] 53%|█████▎ | 16/30 [00:10<00:08, 1.58it/s] 57%|█████▋ | 17/30 [00:11<00:08, 1.57it/s] 60%|██████ | 18/30 [00:12<00:07, 1.57it/s] 63%|██████▎ | 19/30 [00:12<00:07, 1.56it/s] 67%|██████▋ | 20/30 [00:13<00:06, 1.56it/s] 70%|███████ | 21/30 [00:14<00:05, 1.55it/s] 73%|███████▎ | 22/30 [00:14<00:05, 1.55it/s] 77%|███████▋ | 23/30 [00:15<00:04, 1.55it/s] 80%|████████ | 24/30 [00:15<00:03, 1.54it/s] 83%|████████▎ | 25/30 [00:16<00:03, 1.54it/s] 87%|████████▋ | 26/30 [00:17<00:02, 1.53it/s] 90%|█████████ | 27/30 [00:17<00:01, 1.52it/s] 93%|█████████▎| 28/30 [00:18<00:01, 1.52it/s] 97%|█████████▋| 29/30 [00:19<00:00, 1.52it/s] 100%|██████████| 30/30 [00:19<00:00, 1.52it/s] 100%|██████████| 30/30 [00:19<00:00, 1.51it/s] 2023-04-02 07:59:21,259 [DEBUG] main.py.predict():197 :: Saved to /content/drive/MyDrive/tmp/images/out/out-0.png 2023-04-02 07:59:21,378 [DEBUG] main.py.predict():197 :: Saved to /content/drive/MyDrive/tmp/images/out/out-1.png 2023-04-02 07:59:21,503 [DEBUG] main.py.predict():197 :: Saved to /content/drive/MyDrive/tmp/images/out/out-2.pngPrediction
anhappdev/test:129b460d1504e8bd6264229d822badefbc4e0f9ccf5e3adaeee9b7d93da100e3IDamelwbyjpnfonijvq4dc57sle4StatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 2
- image
- prompt
- a lila++ leather++ couch
- upscale
- mask_image
- num_outputs
- 3
- guidance_scale
- 7.5
- num_inference_steps
- 30
{ "seed": 2, "image": "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg", "prompt": "a lila++ leather++ couch", "upscale": true, "mask_image": "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg", "num_outputs": 3, "guidance_scale": 7.5, "num_inference_steps": 30 }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 anhappdev/test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "anhappdev/test:129b460d1504e8bd6264229d822badefbc4e0f9ccf5e3adaeee9b7d93da100e3", { input: { seed: 2, image: "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg", prompt: "a lila++ leather++ couch", upscale: true, mask_image: "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg", num_outputs: 3, guidance_scale: 7.5, num_inference_steps: 30 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);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 anhappdev/test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "anhappdev/test:129b460d1504e8bd6264229d822badefbc4e0f9ccf5e3adaeee9b7d93da100e3", input={ "seed": 2, "image": "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg", "prompt": "a lila++ leather++ couch", "upscale": True, "mask_image": "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg", "num_outputs": 3, "guidance_scale": 7.5, "num_inference_steps": 30 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run anhappdev/test 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": "anhappdev/test:129b460d1504e8bd6264229d822badefbc4e0f9ccf5e3adaeee9b7d93da100e3", "input": { "seed": 2, "image": "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg", "prompt": "a lila++ leather++ couch", "upscale": true, "mask_image": "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg", "num_outputs": 3, "guidance_scale": 7.5, "num_inference_steps": 30 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-04-13T14:41:11.432517Z", "created_at": "2023-04-13T14:35:23.695450Z", "data_removed": false, "error": null, "id": "amelwbyjpnfonijvq4dc57sle4", "input": { "seed": 2, "image": "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg", "prompt": "a lila++ leather++ couch", "upscale": true, "mask_image": "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg", "num_outputs": 3, "guidance_scale": 7.5, "num_inference_steps": 30 }, "logs": "2023-04-13 14:41:03,123 [INFO] :: Running pipeline using stabilityai/stable-diffusion-2-inpainting\n2023-04-13 14:41:03,123 [INFO] :: Using seed: 2\n 0%| | 0/30 [00:00<?, ?it/s]\n 3%|▎ | 1/30 [00:00<00:06, 4.29it/s]\n 10%|█ | 3/30 [00:00<00:03, 7.53it/s]\n 17%|█▋ | 5/30 [00:00<00:02, 8.73it/s]\n 23%|██▎ | 7/30 [00:00<00:02, 9.33it/s]\n 30%|███ | 9/30 [00:01<00:02, 9.66it/s]\n 37%|███▋ | 11/30 [00:01<00:01, 9.87it/s]\n 43%|████▎ | 13/30 [00:01<00:01, 9.99it/s]\n 50%|█████ | 15/30 [00:01<00:01, 10.08it/s]\n 57%|█████▋ | 17/30 [00:01<00:01, 10.14it/s]\n 63%|██████▎ | 19/30 [00:01<00:01, 10.17it/s]\n 70%|███████ | 21/30 [00:02<00:00, 10.20it/s]\n 77%|███████▋ | 23/30 [00:02<00:00, 10.21it/s]\n 83%|████████▎ | 25/30 [00:02<00:00, 10.22it/s]\n 90%|█████████ | 27/30 [00:02<00:00, 10.22it/s]\n 97%|█████████▋| 29/30 [00:02<00:00, 10.23it/s]\n100%|██████████| 30/30 [00:03<00:00, 9.80it/s]", "metrics": { "predict_time": 9.448477, "total_time": 347.737067 }, "output": [ "https://replicate.delivery/pbxt/2pvvYlQ51Ir2CpEmigwF483idYNm6gXSKefjaCC1fzLK04ihA/out-0.jpeg", "https://replicate.delivery/pbxt/b4dZZ7f8N92ESqsWhMih0twFAaYB6tHYHntyetdeYRaN04ihA/out-1.jpeg", "https://replicate.delivery/pbxt/9i8E2MUoM1rtNVDnzl8cM0684P8UGjKnSMIs3NHeaKEDNuYIA/out-2.jpeg" ], "started_at": "2023-04-13T14:41:01.984040Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/amelwbyjpnfonijvq4dc57sle4", "cancel": "https://api.replicate.com/v1/predictions/amelwbyjpnfonijvq4dc57sle4/cancel" }, "version": "129b460d1504e8bd6264229d822badefbc4e0f9ccf5e3adaeee9b7d93da100e3" }Generated in2023-04-13 14:41:03,123 [INFO] :: Running pipeline using stabilityai/stable-diffusion-2-inpainting 2023-04-13 14:41:03,123 [INFO] :: Using seed: 2 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:00<00:06, 4.29it/s] 10%|█ | 3/30 [00:00<00:03, 7.53it/s] 17%|█▋ | 5/30 [00:00<00:02, 8.73it/s] 23%|██▎ | 7/30 [00:00<00:02, 9.33it/s] 30%|███ | 9/30 [00:01<00:02, 9.66it/s] 37%|███▋ | 11/30 [00:01<00:01, 9.87it/s] 43%|████▎ | 13/30 [00:01<00:01, 9.99it/s] 50%|█████ | 15/30 [00:01<00:01, 10.08it/s] 57%|█████▋ | 17/30 [00:01<00:01, 10.14it/s] 63%|██████▎ | 19/30 [00:01<00:01, 10.17it/s] 70%|███████ | 21/30 [00:02<00:00, 10.20it/s] 77%|███████▋ | 23/30 [00:02<00:00, 10.21it/s] 83%|████████▎ | 25/30 [00:02<00:00, 10.22it/s] 90%|█████████ | 27/30 [00:02<00:00, 10.22it/s] 97%|█████████▋| 29/30 [00:02<00:00, 10.23it/s] 100%|██████████| 30/30 [00:03<00:00, 9.80it/s]Prediction
anhappdev/test:1ae4f947fc9876534b8f680074b92274e32017bd85e1a3c1f96ae172c3c98f0eIDqkgs7fkzgbb67cedbzondvalxyStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 123
- image
- prompt
- a yellow++ leather++ couch
- mask_image
- num_outputs
- 3
- guidance_scale
- 7.5
- num_inference_steps
- 50
{ "seed": 123, "image": "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg", "prompt": "a yellow++ leather++ couch", "mask_image": "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg", "num_outputs": 3, "guidance_scale": 7.5, "num_inference_steps": 50 }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 anhappdev/test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "anhappdev/test:1ae4f947fc9876534b8f680074b92274e32017bd85e1a3c1f96ae172c3c98f0e", { input: { seed: 123, image: "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg", prompt: "a yellow++ leather++ couch", mask_image: "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg", num_outputs: 3, guidance_scale: 7.5, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);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 anhappdev/test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "anhappdev/test:1ae4f947fc9876534b8f680074b92274e32017bd85e1a3c1f96ae172c3c98f0e", input={ "seed": 123, "image": "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg", "prompt": "a yellow++ leather++ couch", "mask_image": "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg", "num_outputs": 3, "guidance_scale": 7.5, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run anhappdev/test 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": "anhappdev/test:1ae4f947fc9876534b8f680074b92274e32017bd85e1a3c1f96ae172c3c98f0e", "input": { "seed": 123, "image": "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg", "prompt": "a yellow++ leather++ couch", "mask_image": "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg", "num_outputs": 3, "guidance_scale": 7.5, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-04-14T12:04:30.700705Z", "created_at": "2023-04-14T12:04:20.533101Z", "data_removed": false, "error": null, "id": "qkgs7fkzgbb67cedbzondvalxy", "input": { "seed": 123, "image": "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg", "prompt": "a yellow++ leather++ couch", "mask_image": "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg", "num_outputs": 3, "guidance_scale": 7.5, "num_inference_steps": 50 }, "logs": "2023-04-14 12:04:21,757 [INFO] :: Running pipeline using stabilityai/stable-diffusion-2-inpainting\n2023-04-14 12:04:21,757 [INFO] :: Using seed: 123\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:11, 4.38it/s]\n 6%|▌ | 3/50 [00:00<00:06, 7.59it/s]\n 10%|█ | 5/50 [00:00<00:05, 8.76it/s]\n 14%|█▍ | 7/50 [00:00<00:04, 9.34it/s]\n 18%|█▊ | 9/50 [00:01<00:04, 9.67it/s]\n 22%|██▏ | 11/50 [00:01<00:03, 9.87it/s]\n 26%|██▌ | 13/50 [00:01<00:03, 10.00it/s]\n 30%|███ | 15/50 [00:01<00:03, 10.09it/s]\n 34%|███▍ | 17/50 [00:01<00:03, 10.14it/s]\n 38%|███▊ | 19/50 [00:01<00:03, 10.16it/s]\n 42%|████▏ | 21/50 [00:02<00:02, 10.18it/s]\n 46%|████▌ | 23/50 [00:02<00:02, 10.20it/s]\n 50%|█████ | 25/50 [00:02<00:02, 10.22it/s]\n 54%|█████▍ | 27/50 [00:02<00:02, 10.22it/s]\n 58%|█████▊ | 29/50 [00:02<00:02, 10.23it/s]\n 62%|██████▏ | 31/50 [00:03<00:01, 10.24it/s]\n 66%|██████▌ | 33/50 [00:03<00:01, 10.24it/s]\n 70%|███████ | 35/50 [00:03<00:01, 10.25it/s]\n 74%|███████▍ | 37/50 [00:03<00:01, 10.26it/s]\n 78%|███████▊ | 39/50 [00:03<00:01, 10.24it/s]\n 82%|████████▏ | 41/50 [00:04<00:00, 10.25it/s]\n 86%|████████▌ | 43/50 [00:04<00:00, 10.25it/s]\n 90%|█████████ | 45/50 [00:04<00:00, 10.25it/s]\n 94%|█████████▍| 47/50 [00:04<00:00, 10.25it/s]\n 98%|█████████▊| 49/50 [00:04<00:00, 10.25it/s]\n100%|██████████| 50/50 [00:05<00:00, 9.98it/s]", "metrics": { "predict_time": 10.060166, "total_time": 10.167604 }, "output": [ "https://replicate.delivery/pbxt/p9Lt6PXyWYIRMhyoeZkIEYfeLnWEVNsg2ClIgwrvCvKdaeGDB/out-0.jpeg", "https://replicate.delivery/pbxt/vJguW4mhU7rXPxc1xtFiUE3OI1Q0f4Wb3VoIz2sWPgQnm3YIA/out-1.jpeg", "https://replicate.delivery/pbxt/waG1aBbQ5AJyOR0GpuXpQoqhfeuTAcAGfPhl2mKIKTAdaeGDB/out-2.jpeg" ], "started_at": "2023-04-14T12:04:20.640539Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/qkgs7fkzgbb67cedbzondvalxy", "cancel": "https://api.replicate.com/v1/predictions/qkgs7fkzgbb67cedbzondvalxy/cancel" }, "version": "1ae4f947fc9876534b8f680074b92274e32017bd85e1a3c1f96ae172c3c98f0e" }Generated in2023-04-14 12:04:21,757 [INFO] :: Running pipeline using stabilityai/stable-diffusion-2-inpainting 2023-04-14 12:04:21,757 [INFO] :: Using seed: 123 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:11, 4.38it/s] 6%|▌ | 3/50 [00:00<00:06, 7.59it/s] 10%|█ | 5/50 [00:00<00:05, 8.76it/s] 14%|█▍ | 7/50 [00:00<00:04, 9.34it/s] 18%|█▊ | 9/50 [00:01<00:04, 9.67it/s] 22%|██▏ | 11/50 [00:01<00:03, 9.87it/s] 26%|██▌ | 13/50 [00:01<00:03, 10.00it/s] 30%|███ | 15/50 [00:01<00:03, 10.09it/s] 34%|███▍ | 17/50 [00:01<00:03, 10.14it/s] 38%|███▊ | 19/50 [00:01<00:03, 10.16it/s] 42%|████▏ | 21/50 [00:02<00:02, 10.18it/s] 46%|████▌ | 23/50 [00:02<00:02, 10.20it/s] 50%|█████ | 25/50 [00:02<00:02, 10.22it/s] 54%|█████▍ | 27/50 [00:02<00:02, 10.22it/s] 58%|█████▊ | 29/50 [00:02<00:02, 10.23it/s] 62%|██████▏ | 31/50 [00:03<00:01, 10.24it/s] 66%|██████▌ | 33/50 [00:03<00:01, 10.24it/s] 70%|███████ | 35/50 [00:03<00:01, 10.25it/s] 74%|███████▍ | 37/50 [00:03<00:01, 10.26it/s] 78%|███████▊ | 39/50 [00:03<00:01, 10.24it/s] 82%|████████▏ | 41/50 [00:04<00:00, 10.25it/s] 86%|████████▌ | 43/50 [00:04<00:00, 10.25it/s] 90%|█████████ | 45/50 [00:04<00:00, 10.25it/s] 94%|█████████▍| 47/50 [00:04<00:00, 10.25it/s] 98%|█████████▊| 49/50 [00:04<00:00, 10.25it/s] 100%|██████████| 50/50 [00:05<00:00, 9.98it/s]Prediction
anhappdev/test:1ae4f947fc9876534b8f680074b92274e32017bd85e1a3c1f96ae172c3c98f0eInput
- seed
- 858673248675678
- image
- prompt
- a small beautiful modern vase++
- mask_image
- num_outputs
- 3
- guidance_scale
- 9
- negative_prompt
- num_inference_steps
- 30
{ "seed": 858673248675678, "image": "https://replicate.delivery/pbxt/IeftcfFRDsujdcQJf4HLtRptc4kG8RJQr1NoWhxG9txGxFhl/4.jpg", "prompt": "a small beautiful modern vase++", "mask_image": "https://replicate.delivery/pbxt/IeftdUxniQRmfNvasPOcSQihEj7H1z4mlTcZzSbVcWjcCXYx/4_mask.jpg", "num_outputs": 3, "guidance_scale": 9, "negative_prompt": "", "num_inference_steps": 30 }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 anhappdev/test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "anhappdev/test:1ae4f947fc9876534b8f680074b92274e32017bd85e1a3c1f96ae172c3c98f0e", { input: { seed: 858673248675678, image: "https://replicate.delivery/pbxt/IeftcfFRDsujdcQJf4HLtRptc4kG8RJQr1NoWhxG9txGxFhl/4.jpg", prompt: "a small beautiful modern vase++", mask_image: "https://replicate.delivery/pbxt/IeftdUxniQRmfNvasPOcSQihEj7H1z4mlTcZzSbVcWjcCXYx/4_mask.jpg", num_outputs: 3, guidance_scale: 9, negative_prompt: "", num_inference_steps: 30 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);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 anhappdev/test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "anhappdev/test:1ae4f947fc9876534b8f680074b92274e32017bd85e1a3c1f96ae172c3c98f0e", input={ "seed": 858673248675678, "image": "https://replicate.delivery/pbxt/IeftcfFRDsujdcQJf4HLtRptc4kG8RJQr1NoWhxG9txGxFhl/4.jpg", "prompt": "a small beautiful modern vase++", "mask_image": "https://replicate.delivery/pbxt/IeftdUxniQRmfNvasPOcSQihEj7H1z4mlTcZzSbVcWjcCXYx/4_mask.jpg", "num_outputs": 3, "guidance_scale": 9, "negative_prompt": "", "num_inference_steps": 30 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run anhappdev/test 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": "anhappdev/test:1ae4f947fc9876534b8f680074b92274e32017bd85e1a3c1f96ae172c3c98f0e", "input": { "seed": 858673248675678, "image": "https://replicate.delivery/pbxt/IeftcfFRDsujdcQJf4HLtRptc4kG8RJQr1NoWhxG9txGxFhl/4.jpg", "prompt": "a small beautiful modern vase++", "mask_image": "https://replicate.delivery/pbxt/IeftdUxniQRmfNvasPOcSQihEj7H1z4mlTcZzSbVcWjcCXYx/4_mask.jpg", "num_outputs": 3, "guidance_scale": 9, "negative_prompt": "", "num_inference_steps": 30 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-04-15T08:18:43.980315Z", "created_at": "2023-04-15T08:18:34.587424Z", "data_removed": false, "error": null, "id": "x7iw6hylrvhude7wrcih4upfqq", "input": { "seed": 858673248675678, "image": "https://replicate.delivery/pbxt/IeftcfFRDsujdcQJf4HLtRptc4kG8RJQr1NoWhxG9txGxFhl/4.jpg", "prompt": "a small beautiful modern vase++", "mask_image": "https://replicate.delivery/pbxt/IeftdUxniQRmfNvasPOcSQihEj7H1z4mlTcZzSbVcWjcCXYx/4_mask.jpg", "num_outputs": 3, "guidance_scale": 9, "negative_prompt": "", "num_inference_steps": 30 }, "logs": "2023-04-15 08:18:35,690 [INFO] :: Running pipeline using stabilityai/stable-diffusion-2-inpainting\n2023-04-15 08:18:35,690 [INFO] :: Using seed: 858673248675678\n 0%| | 0/30 [00:00<?, ?it/s]\n 3%|▎ | 1/30 [00:00<00:06, 4.18it/s]\n 10%|█ | 3/30 [00:00<00:03, 7.47it/s]\n 17%|█▋ | 5/30 [00:00<00:02, 8.71it/s]\n 23%|██▎ | 7/30 [00:00<00:02, 9.33it/s]\n 30%|███ | 9/30 [00:01<00:02, 9.69it/s]\n 37%|███▋ | 11/30 [00:01<00:01, 9.92it/s]\n 43%|████▎ | 13/30 [00:01<00:01, 10.05it/s]\n 50%|█████ | 15/30 [00:01<00:01, 10.14it/s]\n 57%|█████▋ | 17/30 [00:01<00:01, 10.21it/s]\n 63%|██████▎ | 19/30 [00:01<00:01, 10.25it/s]\n 70%|███████ | 21/30 [00:02<00:00, 10.27it/s]\n 77%|███████▋ | 23/30 [00:02<00:00, 10.28it/s]\n 83%|████████▎ | 25/30 [00:02<00:00, 10.30it/s]\n 90%|█████████ | 27/30 [00:02<00:00, 10.31it/s]\n 97%|█████████▋| 29/30 [00:02<00:00, 10.32it/s]\n100%|██████████| 30/30 [00:03<00:00, 9.85it/s]", "metrics": { "predict_time": 9.281921, "total_time": 9.392891 }, "output": [ "https://replicate.delivery/pbxt/GYMyI7oN6qYYCJmgwWguYIaPsLnq4euoXauQ0XWwtGywfAyQA/out-0.jpeg", "https://replicate.delivery/pbxt/d2wtfP4Coeq9hkUXsR6ftI3xJqRudUl6oGsKPeYAMlfX8HQGC/out-1.jpeg", "https://replicate.delivery/pbxt/flmQG2VCiYw8QKIRsITBOf5iM9aC0fzldewLZvBdIMGMeHQGC/out-2.jpeg" ], "started_at": "2023-04-15T08:18:34.698394Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/x7iw6hylrvhude7wrcih4upfqq", "cancel": "https://api.replicate.com/v1/predictions/x7iw6hylrvhude7wrcih4upfqq/cancel" }, "version": "1ae4f947fc9876534b8f680074b92274e32017bd85e1a3c1f96ae172c3c98f0e" }Generated in2023-04-15 08:18:35,690 [INFO] :: Running pipeline using stabilityai/stable-diffusion-2-inpainting 2023-04-15 08:18:35,690 [INFO] :: Using seed: 858673248675678 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:00<00:06, 4.18it/s] 10%|█ | 3/30 [00:00<00:03, 7.47it/s] 17%|█▋ | 5/30 [00:00<00:02, 8.71it/s] 23%|██▎ | 7/30 [00:00<00:02, 9.33it/s] 30%|███ | 9/30 [00:01<00:02, 9.69it/s] 37%|███▋ | 11/30 [00:01<00:01, 9.92it/s] 43%|████▎ | 13/30 [00:01<00:01, 10.05it/s] 50%|█████ | 15/30 [00:01<00:01, 10.14it/s] 57%|█████▋ | 17/30 [00:01<00:01, 10.21it/s] 63%|██████▎ | 19/30 [00:01<00:01, 10.25it/s] 70%|███████ | 21/30 [00:02<00:00, 10.27it/s] 77%|███████▋ | 23/30 [00:02<00:00, 10.28it/s] 83%|████████▎ | 25/30 [00:02<00:00, 10.30it/s] 90%|█████████ | 27/30 [00:02<00:00, 10.31it/s] 97%|█████████▋| 29/30 [00:02<00:00, 10.32it/s] 100%|██████████| 30/30 [00:03<00:00, 9.85it/s]Prediction
anhappdev/test:1ae4f947fc9876534b8f680074b92274e32017bd85e1a3c1f96ae172c3c98f0eIDdyyppqg3r5fchd7jsikevyjcryStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 7345
- image
- prompt
- a small beautiful minimalist round blue++ vase++
- mask_image
- num_outputs
- 3
- guidance_scale
- 8
- negative_prompt
- book
- num_inference_steps
- 40
{ "seed": 7345, "image": "https://replicate.delivery/pbxt/IefvTi7kgOPFHoMjbnDjQbaGH5eVaWCPr5281rIPzsE91xtB/4.jpg", "prompt": "a small beautiful minimalist round blue++ vase++", "mask_image": "https://replicate.delivery/pbxt/IefvTBYf3sCeqRvESXVtmOc6VJtDFfwOXCoTMDnK4AYrnWEu/4_mask.jpg", "num_outputs": 3, "guidance_scale": 8, "negative_prompt": "book", "num_inference_steps": 40 }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 anhappdev/test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "anhappdev/test:1ae4f947fc9876534b8f680074b92274e32017bd85e1a3c1f96ae172c3c98f0e", { input: { seed: 7345, image: "https://replicate.delivery/pbxt/IefvTi7kgOPFHoMjbnDjQbaGH5eVaWCPr5281rIPzsE91xtB/4.jpg", prompt: "a small beautiful minimalist round blue++ vase++", mask_image: "https://replicate.delivery/pbxt/IefvTBYf3sCeqRvESXVtmOc6VJtDFfwOXCoTMDnK4AYrnWEu/4_mask.jpg", num_outputs: 3, guidance_scale: 8, negative_prompt: "book", num_inference_steps: 40 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);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 anhappdev/test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "anhappdev/test:1ae4f947fc9876534b8f680074b92274e32017bd85e1a3c1f96ae172c3c98f0e", input={ "seed": 7345, "image": "https://replicate.delivery/pbxt/IefvTi7kgOPFHoMjbnDjQbaGH5eVaWCPr5281rIPzsE91xtB/4.jpg", "prompt": "a small beautiful minimalist round blue++ vase++", "mask_image": "https://replicate.delivery/pbxt/IefvTBYf3sCeqRvESXVtmOc6VJtDFfwOXCoTMDnK4AYrnWEu/4_mask.jpg", "num_outputs": 3, "guidance_scale": 8, "negative_prompt": "book", "num_inference_steps": 40 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run anhappdev/test 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": "anhappdev/test:1ae4f947fc9876534b8f680074b92274e32017bd85e1a3c1f96ae172c3c98f0e", "input": { "seed": 7345, "image": "https://replicate.delivery/pbxt/IefvTi7kgOPFHoMjbnDjQbaGH5eVaWCPr5281rIPzsE91xtB/4.jpg", "prompt": "a small beautiful minimalist round blue++ vase++", "mask_image": "https://replicate.delivery/pbxt/IefvTBYf3sCeqRvESXVtmOc6VJtDFfwOXCoTMDnK4AYrnWEu/4_mask.jpg", "num_outputs": 3, "guidance_scale": 8, "negative_prompt": "book", "num_inference_steps": 40 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-04-15T08:31:44.602341Z", "created_at": "2023-04-15T08:31:34.791097Z", "data_removed": false, "error": null, "id": "dyyppqg3r5fchd7jsikevyjcry", "input": { "seed": 7345, "image": "https://replicate.delivery/pbxt/IefvTi7kgOPFHoMjbnDjQbaGH5eVaWCPr5281rIPzsE91xtB/4.jpg", "prompt": "a small beautiful minimalist round blue++ vase++", "mask_image": "https://replicate.delivery/pbxt/IefvTBYf3sCeqRvESXVtmOc6VJtDFfwOXCoTMDnK4AYrnWEu/4_mask.jpg", "num_outputs": 3, "guidance_scale": 8, "negative_prompt": "book", "num_inference_steps": 40 }, "logs": "2023-04-15 08:31:35,577 [INFO] :: Running pipeline using stabilityai/stable-diffusion-2-inpainting\n2023-04-15 08:31:35,578 [INFO] :: Using seed: 7345\n 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:00<00:09, 4.19it/s]\n 8%|▊ | 3/40 [00:00<00:04, 7.48it/s]\n 12%|█▎ | 5/40 [00:00<00:04, 8.71it/s]\n 18%|█▊ | 7/40 [00:00<00:03, 9.32it/s]\n 22%|██▎ | 9/40 [00:01<00:03, 9.67it/s]\n 28%|██▊ | 11/40 [00:01<00:02, 9.89it/s]\n 32%|███▎ | 13/40 [00:01<00:02, 10.04it/s]\n 38%|███▊ | 15/40 [00:01<00:02, 10.13it/s]\n 42%|████▎ | 17/40 [00:01<00:02, 10.16it/s]\n 48%|████▊ | 19/40 [00:01<00:02, 10.21it/s]\n 52%|█████▎ | 21/40 [00:02<00:01, 10.24it/s]\n 57%|█████▊ | 23/40 [00:02<00:01, 10.27it/s]\n 62%|██████▎ | 25/40 [00:02<00:01, 10.28it/s]\n 68%|██████▊ | 27/40 [00:02<00:01, 10.29it/s]\n 72%|███████▎ | 29/40 [00:02<00:01, 10.31it/s]\n 78%|███████▊ | 31/40 [00:03<00:00, 10.32it/s]\n 82%|████████▎ | 33/40 [00:03<00:00, 10.33it/s]\n 88%|████████▊ | 35/40 [00:03<00:00, 10.33it/s]\n 92%|█████████▎| 37/40 [00:03<00:00, 10.33it/s]\n 98%|█████████▊| 39/40 [00:03<00:00, 10.33it/s]\n100%|██████████| 40/40 [00:04<00:00, 9.96it/s]", "metrics": { "predict_time": 9.69272, "total_time": 9.811244 }, "output": [ "https://replicate.delivery/pbxt/jns0RrtIfNTxNSej8F278v2amnqByzxxwqQUPZmWcNhuLByQA/out-0.jpeg", "https://replicate.delivery/pbxt/8XyCPH3gaLosKF6pSZ95Twe1RThuZZl1f4VgHOVuFdQvLByQA/out-1.jpeg", "https://replicate.delivery/pbxt/JexGDBXLWBVwGimCZtLhGI7eCJl2wqEk2dJew7m6TMMeuEIDB/out-2.jpeg" ], "started_at": "2023-04-15T08:31:34.909621Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/dyyppqg3r5fchd7jsikevyjcry", "cancel": "https://api.replicate.com/v1/predictions/dyyppqg3r5fchd7jsikevyjcry/cancel" }, "version": "1ae4f947fc9876534b8f680074b92274e32017bd85e1a3c1f96ae172c3c98f0e" }Generated in2023-04-15 08:31:35,577 [INFO] :: Running pipeline using stabilityai/stable-diffusion-2-inpainting 2023-04-15 08:31:35,578 [INFO] :: Using seed: 7345 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:00<00:09, 4.19it/s] 8%|▊ | 3/40 [00:00<00:04, 7.48it/s] 12%|█▎ | 5/40 [00:00<00:04, 8.71it/s] 18%|█▊ | 7/40 [00:00<00:03, 9.32it/s] 22%|██▎ | 9/40 [00:01<00:03, 9.67it/s] 28%|██▊ | 11/40 [00:01<00:02, 9.89it/s] 32%|███▎ | 13/40 [00:01<00:02, 10.04it/s] 38%|███▊ | 15/40 [00:01<00:02, 10.13it/s] 42%|████▎ | 17/40 [00:01<00:02, 10.16it/s] 48%|████▊ | 19/40 [00:01<00:02, 10.21it/s] 52%|█████▎ | 21/40 [00:02<00:01, 10.24it/s] 57%|█████▊ | 23/40 [00:02<00:01, 10.27it/s] 62%|██████▎ | 25/40 [00:02<00:01, 10.28it/s] 68%|██████▊ | 27/40 [00:02<00:01, 10.29it/s] 72%|███████▎ | 29/40 [00:02<00:01, 10.31it/s] 78%|███████▊ | 31/40 [00:03<00:00, 10.32it/s] 82%|████████▎ | 33/40 [00:03<00:00, 10.33it/s] 88%|████████▊ | 35/40 [00:03<00:00, 10.33it/s] 92%|█████████▎| 37/40 [00:03<00:00, 10.33it/s] 98%|█████████▊| 39/40 [00:03<00:00, 10.33it/s] 100%|██████████| 40/40 [00:04<00:00, 9.96it/s]Prediction
anhappdev/test:1ba8d8120f8f784b48c3b4a3a97747237dea0d19d655e8d90fd23f0c1f58db30IDjeois36xj5fu5apniqeflsyhtiStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 0
- image
- prompt
- a black++ leather++ couch
- mask_image
- num_outputs
- 3
- output_size
- "768"
- guidance_scale
- 7.5
- num_inference_steps
- 30
{ "seed": 0, "image": "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg", "prompt": "a black++ leather++ couch", "mask_image": "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg", "num_outputs": 3, "output_size": "768", "guidance_scale": 7.5, "num_inference_steps": 30 }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 anhappdev/test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "anhappdev/test:1ba8d8120f8f784b48c3b4a3a97747237dea0d19d655e8d90fd23f0c1f58db30", { input: { seed: 0, image: "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg", prompt: "a black++ leather++ couch", mask_image: "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg", num_outputs: 3, output_size: "768", guidance_scale: 7.5, num_inference_steps: 30 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);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 anhappdev/test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "anhappdev/test:1ba8d8120f8f784b48c3b4a3a97747237dea0d19d655e8d90fd23f0c1f58db30", input={ "seed": 0, "image": "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg", "prompt": "a black++ leather++ couch", "mask_image": "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg", "num_outputs": 3, "output_size": "768", "guidance_scale": 7.5, "num_inference_steps": 30 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run anhappdev/test 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": "anhappdev/test:1ba8d8120f8f784b48c3b4a3a97747237dea0d19d655e8d90fd23f0c1f58db30", "input": { "seed": 0, "image": "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg", "prompt": "a black++ leather++ couch", "mask_image": "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg", "num_outputs": 3, "output_size": "768", "guidance_scale": 7.5, "num_inference_steps": 30 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-04-07T15:41:49.175122Z", "created_at": "2023-04-07T15:41:34.752945Z", "data_removed": false, "error": null, "id": "jeois36xj5fu5apniqeflsyhti", "input": { "seed": 0, "image": "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg", "prompt": "a black++ leather++ couch", "mask_image": "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg", "num_outputs": 3, "output_size": "768", "guidance_scale": 7.5, "num_inference_steps": 30 }, "logs": "2023-04-07 15:41:35,287 [INFO] :: Running pipeline using stabilityai/stable-diffusion-2-inpainting\n2023-04-07 15:41:35,288 [INFO] :: Using seed: 0\n 0%| | 0/30 [00:00<?, ?it/s]\n 3%|▎ | 1/30 [00:00<00:20, 1.42it/s]\n 7%|▋ | 2/30 [00:01<00:13, 2.12it/s]\n 10%|█ | 3/30 [00:01<00:10, 2.52it/s]\n 13%|█▎ | 4/30 [00:01<00:09, 2.77it/s]\n 17%|█▋ | 5/30 [00:01<00:08, 2.93it/s]\n 20%|██ | 6/30 [00:02<00:07, 3.03it/s]\n 23%|██▎ | 7/30 [00:02<00:07, 3.10it/s]\n 27%|██▋ | 8/30 [00:02<00:06, 3.15it/s]\n 30%|███ | 9/30 [00:03<00:06, 3.18it/s]\n 33%|███▎ | 10/30 [00:03<00:06, 3.20it/s]\n 37%|███▋ | 11/30 [00:03<00:05, 3.22it/s]\n 40%|████ | 12/30 [00:04<00:05, 3.23it/s]\n 43%|████▎ | 13/30 [00:04<00:05, 3.23it/s]\n 47%|████▋ | 14/30 [00:04<00:04, 3.24it/s]\n 50%|█████ | 15/30 [00:05<00:04, 3.24it/s]\n 53%|█████▎ | 16/30 [00:05<00:04, 3.25it/s]\n 57%|█████▋ | 17/30 [00:05<00:04, 3.25it/s]\n 60%|██████ | 18/30 [00:05<00:03, 3.25it/s]\n 63%|██████▎ | 19/30 [00:06<00:03, 3.25it/s]\n 67%|██████▋ | 20/30 [00:06<00:03, 3.25it/s]\n 70%|███████ | 21/30 [00:06<00:02, 3.25it/s]\n 73%|███████▎ | 22/30 [00:07<00:02, 3.25it/s]\n 77%|███████▋ | 23/30 [00:07<00:02, 3.25it/s]\n 80%|████████ | 24/30 [00:07<00:01, 3.25it/s]\n 83%|████████▎ | 25/30 [00:08<00:01, 3.25it/s]\n 87%|████████▋ | 26/30 [00:08<00:01, 3.25it/s]\n 90%|█████████ | 27/30 [00:08<00:00, 3.25it/s]\n 93%|█████████▎| 28/30 [00:09<00:00, 3.25it/s]\n 97%|█████████▋| 29/30 [00:09<00:00, 3.25it/s]\n100%|██████████| 30/30 [00:09<00:00, 3.25it/s]\n100%|██████████| 30/30 [00:09<00:00, 3.12it/s]", "metrics": { "predict_time": 14.296766, "total_time": 14.422177 }, "output": [ "https://replicate.delivery/pbxt/jTSqDXU99dZpAF0CaoLpF3CKM4wNmEgqvuBpwhi67KedXvXIA/out-0.png", "https://replicate.delivery/pbxt/aeyC6D59SzyTJqbS1PwUle6Hxf9hlbZrA7mxM6VL0FQ3d9eCB/out-1.png", "https://replicate.delivery/pbxt/leLvm68MPL33c63dly9e5Jyz4JbW3C95tJHvLfscGesz769CB/out-2.png" ], "started_at": "2023-04-07T15:41:34.878356Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/jeois36xj5fu5apniqeflsyhti", "cancel": "https://api.replicate.com/v1/predictions/jeois36xj5fu5apniqeflsyhti/cancel" }, "version": "1ba8d8120f8f784b48c3b4a3a97747237dea0d19d655e8d90fd23f0c1f58db30" }Generated in2023-04-07 15:41:35,287 [INFO] :: Running pipeline using stabilityai/stable-diffusion-2-inpainting 2023-04-07 15:41:35,288 [INFO] :: Using seed: 0 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:00<00:20, 1.42it/s] 7%|▋ | 2/30 [00:01<00:13, 2.12it/s] 10%|█ | 3/30 [00:01<00:10, 2.52it/s] 13%|█▎ | 4/30 [00:01<00:09, 2.77it/s] 17%|█▋ | 5/30 [00:01<00:08, 2.93it/s] 20%|██ | 6/30 [00:02<00:07, 3.03it/s] 23%|██▎ | 7/30 [00:02<00:07, 3.10it/s] 27%|██▋ | 8/30 [00:02<00:06, 3.15it/s] 30%|███ | 9/30 [00:03<00:06, 3.18it/s] 33%|███▎ | 10/30 [00:03<00:06, 3.20it/s] 37%|███▋ | 11/30 [00:03<00:05, 3.22it/s] 40%|████ | 12/30 [00:04<00:05, 3.23it/s] 43%|████▎ | 13/30 [00:04<00:05, 3.23it/s] 47%|████▋ | 14/30 [00:04<00:04, 3.24it/s] 50%|█████ | 15/30 [00:05<00:04, 3.24it/s] 53%|█████▎ | 16/30 [00:05<00:04, 3.25it/s] 57%|█████▋ | 17/30 [00:05<00:04, 3.25it/s] 60%|██████ | 18/30 [00:05<00:03, 3.25it/s] 63%|██████▎ | 19/30 [00:06<00:03, 3.25it/s] 67%|██████▋ | 20/30 [00:06<00:03, 3.25it/s] 70%|███████ | 21/30 [00:06<00:02, 3.25it/s] 73%|███████▎ | 22/30 [00:07<00:02, 3.25it/s] 77%|███████▋ | 23/30 [00:07<00:02, 3.25it/s] 80%|████████ | 24/30 [00:07<00:01, 3.25it/s] 83%|████████▎ | 25/30 [00:08<00:01, 3.25it/s] 87%|████████▋ | 26/30 [00:08<00:01, 3.25it/s] 90%|█████████ | 27/30 [00:08<00:00, 3.25it/s] 93%|█████████▎| 28/30 [00:09<00:00, 3.25it/s] 97%|█████████▋| 29/30 [00:09<00:00, 3.25it/s] 100%|██████████| 30/30 [00:09<00:00, 3.25it/s] 100%|██████████| 30/30 [00:09<00:00, 3.12it/s]Prediction
anhappdev/test:0fa0486be93271b56f9fa3f5449a79585f4a362ba024ea76be15eeb569e0e198IDkirnqrqmwzb2dgvolbepb3piqmStatusSucceededSourceWebHardware–Total durationCreatedInput
- image
- prompt
- A big beautiful++ Christmas++ tree++++ in a living room--
- mask_image
- num_outputs
- 3
- output_size
- 768
- guidance_scale
- 7.5
- num_inference_steps
- 30
{ "image": "https://replicate.delivery/pbxt/Ia2sH4mZsg448cYoYBspSFbg5MtdDXCSBmtbQJ1sUscw0rVl/2.jpg", "prompt": "A big beautiful++ Christmas++ tree++++ in a living room--", "mask_image": "https://replicate.delivery/pbxt/Ia2sGuBRcn7Va7fcHH1IapXZgguPWEOSoqjkM6B6NxziW2YV/2_mask.jpg", "num_outputs": 3, "output_size": 768, "guidance_scale": 7.5, "num_inference_steps": 30 }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 anhappdev/test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "anhappdev/test:0fa0486be93271b56f9fa3f5449a79585f4a362ba024ea76be15eeb569e0e198", { input: { image: "https://replicate.delivery/pbxt/Ia2sH4mZsg448cYoYBspSFbg5MtdDXCSBmtbQJ1sUscw0rVl/2.jpg", prompt: "A big beautiful++ Christmas++ tree++++ in a living room--", mask_image: "https://replicate.delivery/pbxt/Ia2sGuBRcn7Va7fcHH1IapXZgguPWEOSoqjkM6B6NxziW2YV/2_mask.jpg", num_outputs: 3, output_size: 768, guidance_scale: 7.5, num_inference_steps: 30 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);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 anhappdev/test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "anhappdev/test:0fa0486be93271b56f9fa3f5449a79585f4a362ba024ea76be15eeb569e0e198", input={ "image": "https://replicate.delivery/pbxt/Ia2sH4mZsg448cYoYBspSFbg5MtdDXCSBmtbQJ1sUscw0rVl/2.jpg", "prompt": "A big beautiful++ Christmas++ tree++++ in a living room--", "mask_image": "https://replicate.delivery/pbxt/Ia2sGuBRcn7Va7fcHH1IapXZgguPWEOSoqjkM6B6NxziW2YV/2_mask.jpg", "num_outputs": 3, "output_size": 768, "guidance_scale": 7.5, "num_inference_steps": 30 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run anhappdev/test 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": "anhappdev/test:0fa0486be93271b56f9fa3f5449a79585f4a362ba024ea76be15eeb569e0e198", "input": { "image": "https://replicate.delivery/pbxt/Ia2sH4mZsg448cYoYBspSFbg5MtdDXCSBmtbQJ1sUscw0rVl/2.jpg", "prompt": "A big beautiful++ Christmas++ tree++++ in a living room--", "mask_image": "https://replicate.delivery/pbxt/Ia2sGuBRcn7Va7fcHH1IapXZgguPWEOSoqjkM6B6NxziW2YV/2_mask.jpg", "num_outputs": 3, "output_size": 768, "guidance_scale": 7.5, "num_inference_steps": 30 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-04-07T15:49:45.901149Z", "created_at": "2023-04-07T15:47:02.578771Z", "data_removed": false, "error": null, "id": "kirnqrqmwzb2dgvolbepb3piqm", "input": { "image": "https://replicate.delivery/pbxt/Ia2sH4mZsg448cYoYBspSFbg5MtdDXCSBmtbQJ1sUscw0rVl/2.jpg", "prompt": "A big beautiful++ Christmas++ tree++++ in a living room--", "mask_image": "https://replicate.delivery/pbxt/Ia2sGuBRcn7Va7fcHH1IapXZgguPWEOSoqjkM6B6NxziW2YV/2_mask.jpg", "num_outputs": 3, "output_size": 768, "guidance_scale": 7.5, "num_inference_steps": 30 }, "logs": "2023-04-07 15:49:30,274 [INFO] :: Running pipeline using stabilityai/stable-diffusion-2-inpainting\n2023-04-07 15:49:30,275 [INFO] :: Using seed: 55881\n 0%| | 0/30 [00:00<?, ?it/s]\n 3%|▎ | 1/30 [00:00<00:18, 1.54it/s]\n 7%|▋ | 2/30 [00:00<00:12, 2.23it/s]\n 10%|█ | 3/30 [00:01<00:10, 2.60it/s]\n 13%|█▎ | 4/30 [00:01<00:09, 2.82it/s]\n 17%|█▋ | 5/30 [00:01<00:08, 2.97it/s]\n 20%|██ | 6/30 [00:02<00:07, 3.06it/s]\n 23%|██▎ | 7/30 [00:02<00:07, 3.12it/s]\n 27%|██▋ | 8/30 [00:02<00:06, 3.16it/s]\n 30%|███ | 9/30 [00:03<00:06, 3.19it/s]\n 33%|███▎ | 10/30 [00:03<00:06, 3.21it/s]\n 37%|███▋ | 11/30 [00:03<00:05, 3.22it/s]\n 40%|████ | 12/30 [00:04<00:05, 3.23it/s]\n 43%|████▎ | 13/30 [00:04<00:05, 3.24it/s]\n 47%|████▋ | 14/30 [00:04<00:04, 3.24it/s]\n 50%|█████ | 15/30 [00:04<00:04, 3.24it/s]\n 53%|█████▎ | 16/30 [00:05<00:04, 3.25it/s]\n 57%|█████▋ | 17/30 [00:05<00:04, 3.25it/s]\n 60%|██████ | 18/30 [00:05<00:03, 3.25it/s]\n 63%|██████▎ | 19/30 [00:06<00:03, 3.25it/s]\n 67%|██████▋ | 20/30 [00:06<00:03, 3.25it/s]\n 70%|███████ | 21/30 [00:06<00:02, 3.25it/s]\n 73%|███████▎ | 22/30 [00:07<00:02, 3.25it/s]\n 77%|███████▋ | 23/30 [00:07<00:02, 3.25it/s]\n 80%|████████ | 24/30 [00:07<00:01, 3.25it/s]\n 83%|████████▎ | 25/30 [00:08<00:01, 3.25it/s]\n 87%|████████▋ | 26/30 [00:08<00:01, 3.25it/s]\n 90%|█████████ | 27/30 [00:08<00:00, 3.25it/s]\n 93%|█████████▎| 28/30 [00:08<00:00, 3.25it/s]\n 97%|█████████▋| 29/30 [00:09<00:00, 3.25it/s]\n100%|██████████| 30/30 [00:09<00:00, 3.25it/s]\n100%|██████████| 30/30 [00:09<00:00, 3.13it/s]", "metrics": { "predict_time": 16.059918, "total_time": 163.322378 }, "output": [ "https://replicate.delivery/pbxt/OUoepGFNfyrlQ0gaYP4ifBzFF6YKDhHqhyANhiquTEdus9eCB/out-0.png", "https://replicate.delivery/pbxt/JenTCP0F1vRGdKq5LXRlf8zahRRjzEeZrnfFNecOpLEFz27FC/out-1.png", "https://replicate.delivery/pbxt/TobJEZKM2U5ULpofB7UEiYYPbwSzv7zvUGw8ekoGdKjZ2eeCB/out-2.png" ], "started_at": "2023-04-07T15:49:29.841231Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/kirnqrqmwzb2dgvolbepb3piqm", "cancel": "https://api.replicate.com/v1/predictions/kirnqrqmwzb2dgvolbepb3piqm/cancel" }, "version": "0fa0486be93271b56f9fa3f5449a79585f4a362ba024ea76be15eeb569e0e198" }Generated in2023-04-07 15:49:30,274 [INFO] :: Running pipeline using stabilityai/stable-diffusion-2-inpainting 2023-04-07 15:49:30,275 [INFO] :: Using seed: 55881 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:00<00:18, 1.54it/s] 7%|▋ | 2/30 [00:00<00:12, 2.23it/s] 10%|█ | 3/30 [00:01<00:10, 2.60it/s] 13%|█▎ | 4/30 [00:01<00:09, 2.82it/s] 17%|█▋ | 5/30 [00:01<00:08, 2.97it/s] 20%|██ | 6/30 [00:02<00:07, 3.06it/s] 23%|██▎ | 7/30 [00:02<00:07, 3.12it/s] 27%|██▋ | 8/30 [00:02<00:06, 3.16it/s] 30%|███ | 9/30 [00:03<00:06, 3.19it/s] 33%|███▎ | 10/30 [00:03<00:06, 3.21it/s] 37%|███▋ | 11/30 [00:03<00:05, 3.22it/s] 40%|████ | 12/30 [00:04<00:05, 3.23it/s] 43%|████▎ | 13/30 [00:04<00:05, 3.24it/s] 47%|████▋ | 14/30 [00:04<00:04, 3.24it/s] 50%|█████ | 15/30 [00:04<00:04, 3.24it/s] 53%|█████▎ | 16/30 [00:05<00:04, 3.25it/s] 57%|█████▋ | 17/30 [00:05<00:04, 3.25it/s] 60%|██████ | 18/30 [00:05<00:03, 3.25it/s] 63%|██████▎ | 19/30 [00:06<00:03, 3.25it/s] 67%|██████▋ | 20/30 [00:06<00:03, 3.25it/s] 70%|███████ | 21/30 [00:06<00:02, 3.25it/s] 73%|███████▎ | 22/30 [00:07<00:02, 3.25it/s] 77%|███████▋ | 23/30 [00:07<00:02, 3.25it/s] 80%|████████ | 24/30 [00:07<00:01, 3.25it/s] 83%|████████▎ | 25/30 [00:08<00:01, 3.25it/s] 87%|████████▋ | 26/30 [00:08<00:01, 3.25it/s] 90%|█████████ | 27/30 [00:08<00:00, 3.25it/s] 93%|█████████▎| 28/30 [00:08<00:00, 3.25it/s] 97%|█████████▋| 29/30 [00:09<00:00, 3.25it/s] 100%|██████████| 30/30 [00:09<00:00, 3.25it/s] 100%|██████████| 30/30 [00:09<00:00, 3.13it/s]Prediction
anhappdev/test:1ae4f947fc9876534b8f680074b92274e32017bd85e1a3c1f96ae172c3c98f0eIDiqkruiv7vrccpbi5hnzaq7qnwyStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 123
- image
- prompt
- A big beautiful++ Christmas++ tree++++ in a living room--
- mask_image
- num_outputs
- 3
- guidance_scale
- 7.5
- num_inference_steps
- 40
{ "seed": 123, "image": "https://replicate.delivery/pbxt/Ia2sH4mZsg448cYoYBspSFbg5MtdDXCSBmtbQJ1sUscw0rVl/2.jpg", "prompt": "A big beautiful++ Christmas++ tree++++ in a living room--", "mask_image": "https://replicate.delivery/pbxt/Ia2sGuBRcn7Va7fcHH1IapXZgguPWEOSoqjkM6B6NxziW2YV/2_mask.jpg", "num_outputs": 3, "guidance_scale": 7.5, "num_inference_steps": 40 }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 anhappdev/test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "anhappdev/test:1ae4f947fc9876534b8f680074b92274e32017bd85e1a3c1f96ae172c3c98f0e", { input: { seed: 123, image: "https://replicate.delivery/pbxt/Ia2sH4mZsg448cYoYBspSFbg5MtdDXCSBmtbQJ1sUscw0rVl/2.jpg", prompt: "A big beautiful++ Christmas++ tree++++ in a living room--", mask_image: "https://replicate.delivery/pbxt/Ia2sGuBRcn7Va7fcHH1IapXZgguPWEOSoqjkM6B6NxziW2YV/2_mask.jpg", num_outputs: 3, guidance_scale: 7.5, num_inference_steps: 40 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);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 anhappdev/test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "anhappdev/test:1ae4f947fc9876534b8f680074b92274e32017bd85e1a3c1f96ae172c3c98f0e", input={ "seed": 123, "image": "https://replicate.delivery/pbxt/Ia2sH4mZsg448cYoYBspSFbg5MtdDXCSBmtbQJ1sUscw0rVl/2.jpg", "prompt": "A big beautiful++ Christmas++ tree++++ in a living room--", "mask_image": "https://replicate.delivery/pbxt/Ia2sGuBRcn7Va7fcHH1IapXZgguPWEOSoqjkM6B6NxziW2YV/2_mask.jpg", "num_outputs": 3, "guidance_scale": 7.5, "num_inference_steps": 40 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run anhappdev/test 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": "anhappdev/test:1ae4f947fc9876534b8f680074b92274e32017bd85e1a3c1f96ae172c3c98f0e", "input": { "seed": 123, "image": "https://replicate.delivery/pbxt/Ia2sH4mZsg448cYoYBspSFbg5MtdDXCSBmtbQJ1sUscw0rVl/2.jpg", "prompt": "A big beautiful++ Christmas++ tree++++ in a living room--", "mask_image": "https://replicate.delivery/pbxt/Ia2sGuBRcn7Va7fcHH1IapXZgguPWEOSoqjkM6B6NxziW2YV/2_mask.jpg", "num_outputs": 3, "guidance_scale": 7.5, "num_inference_steps": 40 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-04-14T12:05:54.408925Z", "created_at": "2023-04-14T12:05:45.000517Z", "data_removed": false, "error": null, "id": "iqkruiv7vrccpbi5hnzaq7qnwy", "input": { "seed": 123, "image": "https://replicate.delivery/pbxt/Ia2sH4mZsg448cYoYBspSFbg5MtdDXCSBmtbQJ1sUscw0rVl/2.jpg", "prompt": "A big beautiful++ Christmas++ tree++++ in a living room--", "mask_image": "https://replicate.delivery/pbxt/Ia2sGuBRcn7Va7fcHH1IapXZgguPWEOSoqjkM6B6NxziW2YV/2_mask.jpg", "num_outputs": 3, "guidance_scale": 7.5, "num_inference_steps": 40 }, "logs": "2023-04-14 12:05:46,224 [INFO] :: Running pipeline using stabilityai/stable-diffusion-2-inpainting\n2023-04-14 12:05:46,224 [INFO] :: Using seed: 123\n 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:00<00:09, 4.09it/s]\n 8%|▊ | 3/40 [00:00<00:05, 7.35it/s]\n 12%|█▎ | 5/40 [00:00<00:04, 8.61it/s]\n 18%|█▊ | 7/40 [00:00<00:03, 9.24it/s]\n 22%|██▎ | 9/40 [00:01<00:03, 9.60it/s]\n 28%|██▊ | 11/40 [00:01<00:02, 9.82it/s]\n 32%|███▎ | 13/40 [00:01<00:02, 9.96it/s]\n 38%|███▊ | 15/40 [00:01<00:02, 10.06it/s]\n 42%|████▎ | 17/40 [00:01<00:02, 10.13it/s]\n 48%|████▊ | 19/40 [00:01<00:02, 10.17it/s]\n 52%|█████▎ | 21/40 [00:02<00:01, 10.19it/s]\n 57%|█████▊ | 23/40 [00:02<00:01, 10.20it/s]\n 62%|██████▎ | 25/40 [00:02<00:01, 10.22it/s]\n 68%|██████▊ | 27/40 [00:02<00:01, 10.23it/s]\n 72%|███████▎ | 29/40 [00:02<00:01, 10.24it/s]\n 78%|███████▊ | 31/40 [00:03<00:00, 10.25it/s]\n 82%|████████▎ | 33/40 [00:03<00:00, 10.20it/s]\n 88%|████████▊ | 35/40 [00:03<00:00, 10.22it/s]\n 92%|█████████▎| 37/40 [00:03<00:00, 10.23it/s]\n 98%|█████████▊| 39/40 [00:03<00:00, 10.23it/s]\n100%|██████████| 40/40 [00:04<00:00, 9.87it/s]", "metrics": { "predict_time": 9.299582, "total_time": 9.408408 }, "output": [ "https://replicate.delivery/pbxt/CGLr7LVy6WayPdxuHgJoCaMG8Ew9t2sMonu2nSA0IKTozbME/out-0.jpeg", "https://replicate.delivery/pbxt/NCMbRaT383bwBden1l9S98iTlfvIyC3cluwKWmPjXyGiOvxQA/out-1.jpeg", "https://replicate.delivery/pbxt/2liMFvmZegSRB6eeEruHYKKexllY2V2oPFeQzXHIBBcQ05NGC/out-2.jpeg" ], "started_at": "2023-04-14T12:05:45.109343Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/iqkruiv7vrccpbi5hnzaq7qnwy", "cancel": "https://api.replicate.com/v1/predictions/iqkruiv7vrccpbi5hnzaq7qnwy/cancel" }, "version": "1ae4f947fc9876534b8f680074b92274e32017bd85e1a3c1f96ae172c3c98f0e" }Generated in2023-04-14 12:05:46,224 [INFO] :: Running pipeline using stabilityai/stable-diffusion-2-inpainting 2023-04-14 12:05:46,224 [INFO] :: Using seed: 123 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:00<00:09, 4.09it/s] 8%|▊ | 3/40 [00:00<00:05, 7.35it/s] 12%|█▎ | 5/40 [00:00<00:04, 8.61it/s] 18%|█▊ | 7/40 [00:00<00:03, 9.24it/s] 22%|██▎ | 9/40 [00:01<00:03, 9.60it/s] 28%|██▊ | 11/40 [00:01<00:02, 9.82it/s] 32%|███▎ | 13/40 [00:01<00:02, 9.96it/s] 38%|███▊ | 15/40 [00:01<00:02, 10.06it/s] 42%|████▎ | 17/40 [00:01<00:02, 10.13it/s] 48%|████▊ | 19/40 [00:01<00:02, 10.17it/s] 52%|█████▎ | 21/40 [00:02<00:01, 10.19it/s] 57%|█████▊ | 23/40 [00:02<00:01, 10.20it/s] 62%|██████▎ | 25/40 [00:02<00:01, 10.22it/s] 68%|██████▊ | 27/40 [00:02<00:01, 10.23it/s] 72%|███████▎ | 29/40 [00:02<00:01, 10.24it/s] 78%|███████▊ | 31/40 [00:03<00:00, 10.25it/s] 82%|████████▎ | 33/40 [00:03<00:00, 10.20it/s] 88%|████████▊ | 35/40 [00:03<00:00, 10.22it/s] 92%|█████████▎| 37/40 [00:03<00:00, 10.23it/s] 98%|█████████▊| 39/40 [00:03<00:00, 10.23it/s] 100%|██████████| 40/40 [00:04<00:00, 9.87it/s]Prediction
anhappdev/test:1ae4f947fc9876534b8f680074b92274e32017bd85e1a3c1f96ae172c3c98f0eIDozybbydsc5bnzpgu3azzwf6xf4StatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 87435768
- image
- prompt
- a small beautiful minimalist back and white vase++
- mask_image
- num_outputs
- 3
- guidance_scale
- 8
- negative_prompt
- num_inference_steps
- 30
{ "seed": 87435768, "image": "https://replicate.delivery/pbxt/IefvTi7kgOPFHoMjbnDjQbaGH5eVaWCPr5281rIPzsE91xtB/4.jpg", "prompt": "a small beautiful minimalist back and white vase++", "mask_image": "https://replicate.delivery/pbxt/IefvTBYf3sCeqRvESXVtmOc6VJtDFfwOXCoTMDnK4AYrnWEu/4_mask.jpg", "num_outputs": 3, "guidance_scale": 8, "negative_prompt": "", "num_inference_steps": 30 }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 anhappdev/test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "anhappdev/test:1ae4f947fc9876534b8f680074b92274e32017bd85e1a3c1f96ae172c3c98f0e", { input: { seed: 87435768, image: "https://replicate.delivery/pbxt/IefvTi7kgOPFHoMjbnDjQbaGH5eVaWCPr5281rIPzsE91xtB/4.jpg", prompt: "a small beautiful minimalist back and white vase++", mask_image: "https://replicate.delivery/pbxt/IefvTBYf3sCeqRvESXVtmOc6VJtDFfwOXCoTMDnK4AYrnWEu/4_mask.jpg", num_outputs: 3, guidance_scale: 8, negative_prompt: "", num_inference_steps: 30 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);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 anhappdev/test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "anhappdev/test:1ae4f947fc9876534b8f680074b92274e32017bd85e1a3c1f96ae172c3c98f0e", input={ "seed": 87435768, "image": "https://replicate.delivery/pbxt/IefvTi7kgOPFHoMjbnDjQbaGH5eVaWCPr5281rIPzsE91xtB/4.jpg", "prompt": "a small beautiful minimalist back and white vase++", "mask_image": "https://replicate.delivery/pbxt/IefvTBYf3sCeqRvESXVtmOc6VJtDFfwOXCoTMDnK4AYrnWEu/4_mask.jpg", "num_outputs": 3, "guidance_scale": 8, "negative_prompt": "", "num_inference_steps": 30 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run anhappdev/test 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": "anhappdev/test:1ae4f947fc9876534b8f680074b92274e32017bd85e1a3c1f96ae172c3c98f0e", "input": { "seed": 87435768, "image": "https://replicate.delivery/pbxt/IefvTi7kgOPFHoMjbnDjQbaGH5eVaWCPr5281rIPzsE91xtB/4.jpg", "prompt": "a small beautiful minimalist back and white vase++", "mask_image": "https://replicate.delivery/pbxt/IefvTBYf3sCeqRvESXVtmOc6VJtDFfwOXCoTMDnK4AYrnWEu/4_mask.jpg", "num_outputs": 3, "guidance_scale": 8, "negative_prompt": "", "num_inference_steps": 30 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-04-15T08:20:41.134630Z", "created_at": "2023-04-15T08:20:31.988902Z", "data_removed": false, "error": null, "id": "ozybbydsc5bnzpgu3azzwf6xf4", "input": { "seed": 87435768, "image": "https://replicate.delivery/pbxt/IefvTi7kgOPFHoMjbnDjQbaGH5eVaWCPr5281rIPzsE91xtB/4.jpg", "prompt": "a small beautiful minimalist back and white vase++", "mask_image": "https://replicate.delivery/pbxt/IefvTBYf3sCeqRvESXVtmOc6VJtDFfwOXCoTMDnK4AYrnWEu/4_mask.jpg", "num_outputs": 3, "guidance_scale": 8, "negative_prompt": "", "num_inference_steps": 30 }, "logs": "2023-04-15 08:20:33,040 [INFO] :: Running pipeline using stabilityai/stable-diffusion-2-inpainting\n2023-04-15 08:20:33,040 [INFO] :: Using seed: 87435768\n 0%| | 0/30 [00:00<?, ?it/s]\n 3%|▎ | 1/30 [00:00<00:07, 4.10it/s]\n 10%|█ | 3/30 [00:00<00:03, 7.41it/s]\n 17%|█▋ | 5/30 [00:00<00:02, 8.67it/s]\n 23%|██▎ | 7/30 [00:00<00:02, 9.31it/s]\n 30%|███ | 9/30 [00:01<00:02, 9.68it/s]\n 37%|███▋ | 11/30 [00:01<00:01, 9.90it/s]\n 43%|████▎ | 13/30 [00:01<00:01, 10.04it/s]\n 50%|█████ | 15/30 [00:01<00:01, 10.13it/s]\n 57%|█████▋ | 17/30 [00:01<00:01, 10.19it/s]\n 63%|██████▎ | 19/30 [00:01<00:01, 10.23it/s]\n 70%|███████ | 21/30 [00:02<00:00, 10.26it/s]\n 77%|███████▋ | 23/30 [00:02<00:00, 10.28it/s]\n 83%|████████▎ | 25/30 [00:02<00:00, 10.29it/s]\n 90%|█████████ | 27/30 [00:02<00:00, 10.30it/s]\n 97%|█████████▋| 29/30 [00:02<00:00, 10.31it/s]\n100%|██████████| 30/30 [00:03<00:00, 9.83it/s]", "metrics": { "predict_time": 9.026319, "total_time": 9.145728 }, "output": [ "https://replicate.delivery/pbxt/I9cucts8UGIDF5L1IEM8z8TVU5jCmDjpeIQobKGurqqrgAZIA/out-0.jpeg", "https://replicate.delivery/pbxt/N4KHzMDNO7olORlqY920bOwnsFzXfUbqeKGW2k8Q48vXBByQA/out-1.jpeg", "https://replicate.delivery/pbxt/rS2j6NVk6NYtPxsfT4ls95hj9v3HiAKulmMmD5PuMMDsgAZIA/out-2.jpeg" ], "started_at": "2023-04-15T08:20:32.108311Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ozybbydsc5bnzpgu3azzwf6xf4", "cancel": "https://api.replicate.com/v1/predictions/ozybbydsc5bnzpgu3azzwf6xf4/cancel" }, "version": "1ae4f947fc9876534b8f680074b92274e32017bd85e1a3c1f96ae172c3c98f0e" }Generated in2023-04-15 08:20:33,040 [INFO] :: Running pipeline using stabilityai/stable-diffusion-2-inpainting 2023-04-15 08:20:33,040 [INFO] :: Using seed: 87435768 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:00<00:07, 4.10it/s] 10%|█ | 3/30 [00:00<00:03, 7.41it/s] 17%|█▋ | 5/30 [00:00<00:02, 8.67it/s] 23%|██▎ | 7/30 [00:00<00:02, 9.31it/s] 30%|███ | 9/30 [00:01<00:02, 9.68it/s] 37%|███▋ | 11/30 [00:01<00:01, 9.90it/s] 43%|████▎ | 13/30 [00:01<00:01, 10.04it/s] 50%|█████ | 15/30 [00:01<00:01, 10.13it/s] 57%|█████▋ | 17/30 [00:01<00:01, 10.19it/s] 63%|██████▎ | 19/30 [00:01<00:01, 10.23it/s] 70%|███████ | 21/30 [00:02<00:00, 10.26it/s] 77%|███████▋ | 23/30 [00:02<00:00, 10.28it/s] 83%|████████▎ | 25/30 [00:02<00:00, 10.29it/s] 90%|█████████ | 27/30 [00:02<00:00, 10.30it/s] 97%|█████████▋| 29/30 [00:02<00:00, 10.31it/s] 100%|██████████| 30/30 [00:03<00:00, 9.83it/s]Prediction
anhappdev/test:10ba78e98238c724dd80edabd0636066540a22df40e0e12fecdf180bdb487235IDb5a4pesonfgw5klij6lnxcpu5mStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 123
- image
- method
- replace
- prompt
- a yellow++ leather++ couch
- mask_image
- num_outputs
- 3
- guidance_scale
- 7.5
- num_inference_steps
- 50
{ "seed": 123, "image": "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg", "method": "replace", "prompt": "a yellow++ leather++ couch", "mask_image": "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg", "num_outputs": 3, "guidance_scale": 7.5, "num_inference_steps": 50 }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 anhappdev/test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "anhappdev/test:10ba78e98238c724dd80edabd0636066540a22df40e0e12fecdf180bdb487235", { input: { seed: 123, image: "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg", method: "replace", prompt: "a yellow++ leather++ couch", mask_image: "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg", num_outputs: 3, guidance_scale: 7.5, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);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 anhappdev/test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "anhappdev/test:10ba78e98238c724dd80edabd0636066540a22df40e0e12fecdf180bdb487235", input={ "seed": 123, "image": "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg", "method": "replace", "prompt": "a yellow++ leather++ couch", "mask_image": "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg", "num_outputs": 3, "guidance_scale": 7.5, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run anhappdev/test 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": "anhappdev/test:10ba78e98238c724dd80edabd0636066540a22df40e0e12fecdf180bdb487235", "input": { "seed": 123, "image": "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg", "method": "replace", "prompt": "a yellow++ leather++ couch", "mask_image": "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg", "num_outputs": 3, "guidance_scale": 7.5, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-04-24T12:58:53.449064Z", "created_at": "2023-04-24T12:56:49.779116Z", "data_removed": false, "error": null, "id": "b5a4pesonfgw5klij6lnxcpu5m", "input": { "seed": 123, "image": "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg", "method": "replace", "prompt": "a yellow++ leather++ couch", "mask_image": "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg", "num_outputs": 3, "guidance_scale": 7.5, "num_inference_steps": 50 }, "logs": "2023-04-24 12:58:44,617 [INFO] :: Using seed: 123\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:07, 6.98it/s]\n 6%|▌ | 3/50 [00:00<00:03, 11.91it/s]\n 10%|█ | 5/50 [00:00<00:03, 13.93it/s]\n 14%|█▍ | 7/50 [00:00<00:02, 14.82it/s]\n 18%|█▊ | 9/50 [00:00<00:02, 15.45it/s]\n 22%|██▏ | 11/50 [00:00<00:02, 15.70it/s]\n 26%|██▌ | 13/50 [00:00<00:02, 15.88it/s]\n 30%|███ | 15/50 [00:01<00:02, 15.95it/s]\n 34%|███▍ | 17/50 [00:01<00:02, 16.10it/s]\n 38%|███▊ | 19/50 [00:01<00:01, 16.32it/s]\n 42%|████▏ | 21/50 [00:01<00:01, 16.49it/s]\n 46%|████▌ | 23/50 [00:01<00:01, 16.49it/s]\n 50%|█████ | 25/50 [00:01<00:01, 16.56it/s]\n 54%|█████▍ | 27/50 [00:01<00:01, 16.52it/s]\n 58%|█████▊ | 29/50 [00:01<00:01, 16.33it/s]\n 62%|██████▏ | 31/50 [00:01<00:01, 16.27it/s]\n 66%|██████▌ | 33/50 [00:02<00:01, 16.25it/s]\n 70%|███████ | 35/50 [00:02<00:00, 16.34it/s]\n 74%|███████▍ | 37/50 [00:02<00:00, 16.37it/s]\n 78%|███████▊ | 39/50 [00:02<00:00, 16.47it/s]\n 82%|████████▏ | 41/50 [00:02<00:00, 16.54it/s]\n 86%|████████▌ | 43/50 [00:02<00:00, 16.60it/s]\n 90%|█████████ | 45/50 [00:02<00:00, 16.61it/s]\n 94%|█████████▍| 47/50 [00:02<00:00, 16.67it/s]\n 98%|█████████▊| 49/50 [00:03<00:00, 16.69it/s]\n100%|██████████| 50/50 [00:03<00:00, 16.02it/s]", "metrics": { "predict_time": 10.088599, "total_time": 123.669948 }, "output": [ "https://replicate.delivery/pbxt/ejfwZwb8cOqSH0kJQLzDaoKfXJqJHyeqw0GimaXssggvwLUDB/out-0.jpg", "https://replicate.delivery/pbxt/jE8uTi45GMp2OFqZ8JGWIWw8aJKE0PeeTqqQcnPmECCM8C1QA/out-1.jpg", "https://replicate.delivery/pbxt/5NEneufID7gLa0WWTeq1URlMSYgvC9RcQHgNGOvajULZ4FqhA/out-2.jpg" ], "started_at": "2023-04-24T12:58:43.360465Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/b5a4pesonfgw5klij6lnxcpu5m", "cancel": "https://api.replicate.com/v1/predictions/b5a4pesonfgw5klij6lnxcpu5m/cancel" }, "version": "10ba78e98238c724dd80edabd0636066540a22df40e0e12fecdf180bdb487235" }Generated in2023-04-24 12:58:44,617 [INFO] :: Using seed: 123 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:07, 6.98it/s] 6%|▌ | 3/50 [00:00<00:03, 11.91it/s] 10%|█ | 5/50 [00:00<00:03, 13.93it/s] 14%|█▍ | 7/50 [00:00<00:02, 14.82it/s] 18%|█▊ | 9/50 [00:00<00:02, 15.45it/s] 22%|██▏ | 11/50 [00:00<00:02, 15.70it/s] 26%|██▌ | 13/50 [00:00<00:02, 15.88it/s] 30%|███ | 15/50 [00:01<00:02, 15.95it/s] 34%|███▍ | 17/50 [00:01<00:02, 16.10it/s] 38%|███▊ | 19/50 [00:01<00:01, 16.32it/s] 42%|████▏ | 21/50 [00:01<00:01, 16.49it/s] 46%|████▌ | 23/50 [00:01<00:01, 16.49it/s] 50%|█████ | 25/50 [00:01<00:01, 16.56it/s] 54%|█████▍ | 27/50 [00:01<00:01, 16.52it/s] 58%|█████▊ | 29/50 [00:01<00:01, 16.33it/s] 62%|██████▏ | 31/50 [00:01<00:01, 16.27it/s] 66%|██████▌ | 33/50 [00:02<00:01, 16.25it/s] 70%|███████ | 35/50 [00:02<00:00, 16.34it/s] 74%|███████▍ | 37/50 [00:02<00:00, 16.37it/s] 78%|███████▊ | 39/50 [00:02<00:00, 16.47it/s] 82%|████████▏ | 41/50 [00:02<00:00, 16.54it/s] 86%|████████▌ | 43/50 [00:02<00:00, 16.60it/s] 90%|█████████ | 45/50 [00:02<00:00, 16.61it/s] 94%|█████████▍| 47/50 [00:02<00:00, 16.67it/s] 98%|█████████▊| 49/50 [00:03<00:00, 16.69it/s] 100%|██████████| 50/50 [00:03<00:00, 16.02it/s]Prediction
anhappdev/test:10ba78e98238c724dd80edabd0636066540a22df40e0e12fecdf180bdb487235IDw3nwzyelgve4xbbgqote4mfrfmStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 123
- image
- method
- remove
- prompt
- a yellow++ leather++ couch
- mask_image
- num_outputs
- 3
- guidance_scale
- 7.5
- num_inference_steps
- 50
{ "seed": 123, "image": "https://replicate.delivery/pbxt/IhwJRcwoEoNKQgPxTKreBbjsQHlh3ChNdIPLBUzQceiG24hf/3.jpg", "method": "remove", "prompt": "a yellow++ leather++ couch", "mask_image": "https://replicate.delivery/pbxt/IhwJSA23gEEPMFGsRmYKTtS8UjuxaLj9IgHYVb3LAG7Q1p86/3_mask.jpg", "num_outputs": 3, "guidance_scale": 7.5, "num_inference_steps": 50 }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 anhappdev/test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "anhappdev/test:10ba78e98238c724dd80edabd0636066540a22df40e0e12fecdf180bdb487235", { input: { seed: 123, image: "https://replicate.delivery/pbxt/IhwJRcwoEoNKQgPxTKreBbjsQHlh3ChNdIPLBUzQceiG24hf/3.jpg", method: "remove", prompt: "a yellow++ leather++ couch", mask_image: "https://replicate.delivery/pbxt/IhwJSA23gEEPMFGsRmYKTtS8UjuxaLj9IgHYVb3LAG7Q1p86/3_mask.jpg", num_outputs: 3, guidance_scale: 7.5, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);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 anhappdev/test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "anhappdev/test:10ba78e98238c724dd80edabd0636066540a22df40e0e12fecdf180bdb487235", input={ "seed": 123, "image": "https://replicate.delivery/pbxt/IhwJRcwoEoNKQgPxTKreBbjsQHlh3ChNdIPLBUzQceiG24hf/3.jpg", "method": "remove", "prompt": "a yellow++ leather++ couch", "mask_image": "https://replicate.delivery/pbxt/IhwJSA23gEEPMFGsRmYKTtS8UjuxaLj9IgHYVb3LAG7Q1p86/3_mask.jpg", "num_outputs": 3, "guidance_scale": 7.5, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run anhappdev/test 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": "anhappdev/test:10ba78e98238c724dd80edabd0636066540a22df40e0e12fecdf180bdb487235", "input": { "seed": 123, "image": "https://replicate.delivery/pbxt/IhwJRcwoEoNKQgPxTKreBbjsQHlh3ChNdIPLBUzQceiG24hf/3.jpg", "method": "remove", "prompt": "a yellow++ leather++ couch", "mask_image": "https://replicate.delivery/pbxt/IhwJSA23gEEPMFGsRmYKTtS8UjuxaLj9IgHYVb3LAG7Q1p86/3_mask.jpg", "num_outputs": 3, "guidance_scale": 7.5, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-04-24T13:03:24.526234Z", "created_at": "2023-04-24T13:03:16.112774Z", "data_removed": false, "error": null, "id": "w3nwzyelgve4xbbgqote4mfrfm", "input": { "seed": 123, "image": "https://replicate.delivery/pbxt/IhwJRcwoEoNKQgPxTKreBbjsQHlh3ChNdIPLBUzQceiG24hf/3.jpg", "method": "remove", "prompt": "a yellow++ leather++ couch", "mask_image": "https://replicate.delivery/pbxt/IhwJSA23gEEPMFGsRmYKTtS8UjuxaLj9IgHYVb3LAG7Q1p86/3_mask.jpg", "num_outputs": 3, "guidance_scale": 7.5, "num_inference_steps": 50 }, "logs": null, "metrics": { "predict_time": 8.288868, "total_time": 8.41346 }, "output": [ "https://replicate.delivery/pbxt/HZlv8KttjVrJOlMACYZFh7XjAUQUJCFdxe9lWKcEbe2bAD1QA/out-0.jpg" ], "started_at": "2023-04-24T13:03:16.237366Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/w3nwzyelgve4xbbgqote4mfrfm", "cancel": "https://api.replicate.com/v1/predictions/w3nwzyelgve4xbbgqote4mfrfm/cancel" }, "version": "10ba78e98238c724dd80edabd0636066540a22df40e0e12fecdf180bdb487235" }Generated inPrediction
anhappdev/test:10ba78e98238c724dd80edabd0636066540a22df40e0e12fecdf180bdb487235IDcy3zp5pjgjh5xnejmoj577ar4eStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 7345
- image
- method
- remove
- prompt
- mask_image
- num_outputs
- 3
- guidance_scale
- 8
- negative_prompt
- num_inference_steps
- 40
{ "seed": 7345, "image": "https://replicate.delivery/pbxt/IefvTi7kgOPFHoMjbnDjQbaGH5eVaWCPr5281rIPzsE91xtB/4.jpg", "method": "remove", "prompt": "", "mask_image": "https://replicate.delivery/pbxt/IhwihI8dlI0dxfctM8Eols2XysWTJCsaP398tQseYY3N291V/4_mask2.jpg", "num_outputs": 3, "guidance_scale": 8, "negative_prompt": "", "num_inference_steps": 40 }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 anhappdev/test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "anhappdev/test:10ba78e98238c724dd80edabd0636066540a22df40e0e12fecdf180bdb487235", { input: { seed: 7345, image: "https://replicate.delivery/pbxt/IefvTi7kgOPFHoMjbnDjQbaGH5eVaWCPr5281rIPzsE91xtB/4.jpg", method: "remove", prompt: "", mask_image: "https://replicate.delivery/pbxt/IhwihI8dlI0dxfctM8Eols2XysWTJCsaP398tQseYY3N291V/4_mask2.jpg", num_outputs: 3, guidance_scale: 8, negative_prompt: "", num_inference_steps: 40 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);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 anhappdev/test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "anhappdev/test:10ba78e98238c724dd80edabd0636066540a22df40e0e12fecdf180bdb487235", input={ "seed": 7345, "image": "https://replicate.delivery/pbxt/IefvTi7kgOPFHoMjbnDjQbaGH5eVaWCPr5281rIPzsE91xtB/4.jpg", "method": "remove", "prompt": "", "mask_image": "https://replicate.delivery/pbxt/IhwihI8dlI0dxfctM8Eols2XysWTJCsaP398tQseYY3N291V/4_mask2.jpg", "num_outputs": 3, "guidance_scale": 8, "negative_prompt": "", "num_inference_steps": 40 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run anhappdev/test 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": "anhappdev/test:10ba78e98238c724dd80edabd0636066540a22df40e0e12fecdf180bdb487235", "input": { "seed": 7345, "image": "https://replicate.delivery/pbxt/IefvTi7kgOPFHoMjbnDjQbaGH5eVaWCPr5281rIPzsE91xtB/4.jpg", "method": "remove", "prompt": "", "mask_image": "https://replicate.delivery/pbxt/IhwihI8dlI0dxfctM8Eols2XysWTJCsaP398tQseYY3N291V/4_mask2.jpg", "num_outputs": 3, "guidance_scale": 8, "negative_prompt": "", "num_inference_steps": 40 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-04-24T13:29:57.483170Z", "created_at": "2023-04-24T13:29:55.346509Z", "data_removed": false, "error": null, "id": "cy3zp5pjgjh5xnejmoj577ar4e", "input": { "seed": 7345, "image": "https://replicate.delivery/pbxt/IefvTi7kgOPFHoMjbnDjQbaGH5eVaWCPr5281rIPzsE91xtB/4.jpg", "method": "remove", "prompt": "", "mask_image": "https://replicate.delivery/pbxt/IhwihI8dlI0dxfctM8Eols2XysWTJCsaP398tQseYY3N291V/4_mask2.jpg", "num_outputs": 3, "guidance_scale": 8, "negative_prompt": "", "num_inference_steps": 40 }, "logs": null, "metrics": { "predict_time": 2.006812, "total_time": 2.136661 }, "output": [ "https://replicate.delivery/pbxt/U4ULHf4CM9wXDyLFFj96yhT1uNG1PqSRWfR1EPLjT7EVZD1QA/out-0.jpg" ], "started_at": "2023-04-24T13:29:55.476358Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/cy3zp5pjgjh5xnejmoj577ar4e", "cancel": "https://api.replicate.com/v1/predictions/cy3zp5pjgjh5xnejmoj577ar4e/cancel" }, "version": "10ba78e98238c724dd80edabd0636066540a22df40e0e12fecdf180bdb487235" }Generated inPrediction
anhappdev/test:7f7d040c40f663a4dce135c3cb524f9b69ef394e72ee72b8e8e6a9a898206641IDpavvq3aqxfdcvpxnwus4vq746iStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 12345
- image
- method
- adjust
- prompt
- a green leather couch
- mask_image
- num_outputs
- 4
- control_scale
- 0.5
- guidance_scale
- 8
- num_inference_steps
- 30
{ "seed": 12345, "image": "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg", "method": "adjust", "prompt": "a green leather couch", "mask_image": "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg", "num_outputs": 4, "control_scale": 0.5, "guidance_scale": 8, "num_inference_steps": 30 }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 anhappdev/test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "anhappdev/test:7f7d040c40f663a4dce135c3cb524f9b69ef394e72ee72b8e8e6a9a898206641", { input: { seed: 12345, image: "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg", method: "adjust", prompt: "a green leather couch", mask_image: "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg", num_outputs: 4, control_scale: 0.5, guidance_scale: 8, num_inference_steps: 30 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);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 anhappdev/test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "anhappdev/test:7f7d040c40f663a4dce135c3cb524f9b69ef394e72ee72b8e8e6a9a898206641", input={ "seed": 12345, "image": "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg", "method": "adjust", "prompt": "a green leather couch", "mask_image": "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg", "num_outputs": 4, "control_scale": 0.5, "guidance_scale": 8, "num_inference_steps": 30 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run anhappdev/test 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": "anhappdev/test:7f7d040c40f663a4dce135c3cb524f9b69ef394e72ee72b8e8e6a9a898206641", "input": { "seed": 12345, "image": "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg", "method": "adjust", "prompt": "a green leather couch", "mask_image": "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg", "num_outputs": 4, "control_scale": 0.5, "guidance_scale": 8, "num_inference_steps": 30 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-04-30T09:08:18.740721Z", "created_at": "2023-04-30T09:06:48.057578Z", "data_removed": false, "error": null, "id": "pavvq3aqxfdcvpxnwus4vq746i", "input": { "seed": 12345, "image": "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg", "method": "adjust", "prompt": "a green leather couch", "mask_image": "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg", "num_outputs": 4, "control_scale": 0.5, "guidance_scale": 8, "num_inference_steps": 30 }, "logs": "2023-04-30 09:07:12,255 [INFO] :: Using seed: 12345\n 0%| | 0/30 [00:00<?, ?it/s][2023-04-30 09:07:19,285] torch._dynamo.convert_frame: [WARNING] torch._dynamo hit config.cache_size_limit (64)\nfunction: 'forward' (/root/.pyenv/versions/3.9.16/lib/python3.9/site-packages/diffusers/models/attention_processor.py:263)\nreasons: tensor 'hidden_states' size mismatch at index 0. expected 6, actual 8\nto diagnose recompilation issues, see https://pytorch.org/docs/master/dynamo/troubleshooting.html.\n[2023-04-30 09:07:19,287] torch._dynamo.convert_frame: [WARNING] torch._dynamo hit config.cache_size_limit (64)\nfunction: '__call__' (/root/.pyenv/versions/3.9.16/lib/python3.9/site-packages/diffusers/models/attention_processor.py:714)\nreasons: tensor 'hidden_states' size mismatch at index 0. expected 6, actual 8\nto diagnose recompilation issues, see https://pytorch.org/docs/master/dynamo/troubleshooting.html.\n 3%|▎ | 1/30 [00:55<26:44, 55.31s/it]\n 7%|▋ | 2/30 [00:55<10:39, 22.85s/it]\n 10%|█ | 3/30 [00:55<05:36, 12.46s/it]\n 17%|█▋ | 5/30 [00:55<02:16, 5.46s/it]\n 20%|██ | 6/30 [00:55<01:34, 3.92s/it]\n 27%|██▋ | 8/30 [00:56<00:48, 2.20s/it]\n 33%|███▎ | 10/30 [00:56<00:27, 1.37s/it]\n 40%|████ | 12/30 [00:56<00:16, 1.09it/s]\n 47%|████▋ | 14/30 [00:56<00:10, 1.56it/s]\n 53%|█████▎ | 16/30 [00:56<00:06, 2.16it/s]\n 60%|██████ | 18/30 [00:57<00:04, 2.88it/s]\n 67%|██████▋ | 20/30 [00:57<00:02, 3.71it/s]\n 73%|███████▎ | 22/30 [00:57<00:01, 4.60it/s]\n 80%|████████ | 24/30 [00:57<00:01, 5.52it/s]\n 87%|████████▋ | 26/30 [00:57<00:00, 6.40it/s]\n 93%|█████████▎| 28/30 [00:58<00:00, 7.19it/s]\n100%|██████████| 30/30 [00:58<00:00, 7.88it/s]\n100%|██████████| 30/30 [00:58<00:00, 1.94s/it]", "metrics": { "predict_time": 67.429766, "total_time": 90.683143 }, "output": [ "https://replicate.delivery/pbxt/CWZ1F8mEmUaPMFYTVKQNieLjKwCNL9ix5MCxqyYbTU3fHethA/adjust-out-0.jpg", "https://replicate.delivery/pbxt/flNJ1u5Gbej7b0DfgXtMBKg8FJiizVyNQuZifPZ0v12Bg4bDB/adjust-out-1.jpg", "https://replicate.delivery/pbxt/JM4NtBUneux9RqnGaW6N2MEHaQAuDcN3DuC7JDnopFrAEf2QA/adjust-out-2.jpg", "https://replicate.delivery/pbxt/1fLY4ZJfguj0c0lTtQuW81dd0gql8Ee8S76G4uWu3EKEQ8thA/adjust-out-3.jpg" ], "started_at": "2023-04-30T09:07:11.310955Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/pavvq3aqxfdcvpxnwus4vq746i", "cancel": "https://api.replicate.com/v1/predictions/pavvq3aqxfdcvpxnwus4vq746i/cancel" }, "version": "7f7d040c40f663a4dce135c3cb524f9b69ef394e72ee72b8e8e6a9a898206641" }Generated in2023-04-30 09:07:12,255 [INFO] :: Using seed: 12345 0%| | 0/30 [00:00<?, ?it/s][2023-04-30 09:07:19,285] torch._dynamo.convert_frame: [WARNING] torch._dynamo hit config.cache_size_limit (64) function: 'forward' (/root/.pyenv/versions/3.9.16/lib/python3.9/site-packages/diffusers/models/attention_processor.py:263) reasons: tensor 'hidden_states' size mismatch at index 0. expected 6, actual 8 to diagnose recompilation issues, see https://pytorch.org/docs/master/dynamo/troubleshooting.html. [2023-04-30 09:07:19,287] torch._dynamo.convert_frame: [WARNING] torch._dynamo hit config.cache_size_limit (64) function: '__call__' (/root/.pyenv/versions/3.9.16/lib/python3.9/site-packages/diffusers/models/attention_processor.py:714) reasons: tensor 'hidden_states' size mismatch at index 0. expected 6, actual 8 to diagnose recompilation issues, see https://pytorch.org/docs/master/dynamo/troubleshooting.html. 3%|▎ | 1/30 [00:55<26:44, 55.31s/it] 7%|▋ | 2/30 [00:55<10:39, 22.85s/it] 10%|█ | 3/30 [00:55<05:36, 12.46s/it] 17%|█▋ | 5/30 [00:55<02:16, 5.46s/it] 20%|██ | 6/30 [00:55<01:34, 3.92s/it] 27%|██▋ | 8/30 [00:56<00:48, 2.20s/it] 33%|███▎ | 10/30 [00:56<00:27, 1.37s/it] 40%|████ | 12/30 [00:56<00:16, 1.09it/s] 47%|████▋ | 14/30 [00:56<00:10, 1.56it/s] 53%|█████▎ | 16/30 [00:56<00:06, 2.16it/s] 60%|██████ | 18/30 [00:57<00:04, 2.88it/s] 67%|██████▋ | 20/30 [00:57<00:02, 3.71it/s] 73%|███████▎ | 22/30 [00:57<00:01, 4.60it/s] 80%|████████ | 24/30 [00:57<00:01, 5.52it/s] 87%|████████▋ | 26/30 [00:57<00:00, 6.40it/s] 93%|█████████▎| 28/30 [00:58<00:00, 7.19it/s] 100%|██████████| 30/30 [00:58<00:00, 7.88it/s] 100%|██████████| 30/30 [00:58<00:00, 1.94s/it]Prediction
anhappdev/test:75d9faf1ca08a01f8c8fedc0d1a537b3fc974c619b02b49beff1ad0b9bff9038IDq4xezfnjzzdvjgjxg2gp3zu5uuStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 1234
- image
- method
- replace
- prompt
- a purple leather couch
- mask_image
- num_outputs
- 3
- control_scale
- 0.5
- guidance_scale
- 5
- num_inference_steps
- 30
{ "seed": 1234, "image": "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg", "method": "replace", "prompt": "a purple leather couch", "mask_image": "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg", "num_outputs": 3, "control_scale": 0.5, "guidance_scale": 5, "num_inference_steps": 30 }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 anhappdev/test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "anhappdev/test:75d9faf1ca08a01f8c8fedc0d1a537b3fc974c619b02b49beff1ad0b9bff9038", { input: { seed: 1234, image: "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg", method: "replace", prompt: "a purple leather couch", mask_image: "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg", num_outputs: 3, control_scale: 0.5, guidance_scale: 5, num_inference_steps: 30 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);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 anhappdev/test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "anhappdev/test:75d9faf1ca08a01f8c8fedc0d1a537b3fc974c619b02b49beff1ad0b9bff9038", input={ "seed": 1234, "image": "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg", "method": "replace", "prompt": "a purple leather couch", "mask_image": "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg", "num_outputs": 3, "control_scale": 0.5, "guidance_scale": 5, "num_inference_steps": 30 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run anhappdev/test 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": "anhappdev/test:75d9faf1ca08a01f8c8fedc0d1a537b3fc974c619b02b49beff1ad0b9bff9038", "input": { "seed": 1234, "image": "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg", "method": "replace", "prompt": "a purple leather couch", "mask_image": "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg", "num_outputs": 3, "control_scale": 0.5, "guidance_scale": 5, "num_inference_steps": 30 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-05-01T15:30:17.925717Z", "created_at": "2023-05-01T15:30:08.529925Z", "data_removed": false, "error": null, "id": "q4xezfnjzzdvjgjxg2gp3zu5uu", "input": { "seed": 1234, "image": "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg", "method": "replace", "prompt": "a purple leather couch", "mask_image": "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg", "num_outputs": 3, "control_scale": 0.5, "guidance_scale": 5, "num_inference_steps": 30 }, "logs": "2023-05-01 15:30:09,511 [INFO] :: Using seed: 1234\n 0%| | 0/30 [00:00<?, ?it/s]\n 3%|▎ | 1/30 [00:00<00:04, 6.29it/s]\n 10%|█ | 3/30 [00:00<00:02, 10.90it/s]\n 17%|█▋ | 5/30 [00:00<00:01, 12.69it/s]\n 23%|██▎ | 7/30 [00:00<00:01, 13.57it/s]\n 30%|███ | 9/30 [00:00<00:01, 14.08it/s]\n 37%|███▋ | 11/30 [00:00<00:01, 14.35it/s]\n 43%|████▎ | 13/30 [00:00<00:01, 14.60it/s]\n 50%|█████ | 15/30 [00:01<00:01, 14.66it/s]\n 57%|█████▋ | 17/30 [00:01<00:00, 14.76it/s]\n 63%|██████▎ | 19/30 [00:01<00:00, 14.67it/s]\n 70%|███████ | 21/30 [00:01<00:00, 14.77it/s]\n 77%|███████▋ | 23/30 [00:01<00:00, 14.90it/s]\n 83%|████████▎ | 25/30 [00:01<00:00, 14.90it/s]\n 90%|█████████ | 27/30 [00:01<00:00, 15.01it/s]\n 97%|█████████▋| 29/30 [00:02<00:00, 15.07it/s]\n100%|██████████| 30/30 [00:02<00:00, 14.30it/s]", "metrics": { "predict_time": 9.277669, "total_time": 9.395792 }, "output": [ "https://replicate.delivery/pbxt/Q6B9oT3BynaTCBhCiqWuGBjM8hmeg5DthFrb4UJaaIzDasbIA/replace-out-0.jpg", "https://replicate.delivery/pbxt/j4QOo0jFGNbfL6dnqYVfLS48McxNL6z5JdjIQ9ryK63I0Y3QA/replace-out-1.jpg", "https://replicate.delivery/pbxt/kRFbmKJBC855HJdlruYN5q8ZbHBKMewmKTJPfJk04CZJ0Y3QA/replace-out-2.jpg" ], "started_at": "2023-05-01T15:30:08.648048Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/q4xezfnjzzdvjgjxg2gp3zu5uu", "cancel": "https://api.replicate.com/v1/predictions/q4xezfnjzzdvjgjxg2gp3zu5uu/cancel" }, "version": "75d9faf1ca08a01f8c8fedc0d1a537b3fc974c619b02b49beff1ad0b9bff9038" }Generated in2023-05-01 15:30:09,511 [INFO] :: Using seed: 1234 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:00<00:04, 6.29it/s] 10%|█ | 3/30 [00:00<00:02, 10.90it/s] 17%|█▋ | 5/30 [00:00<00:01, 12.69it/s] 23%|██▎ | 7/30 [00:00<00:01, 13.57it/s] 30%|███ | 9/30 [00:00<00:01, 14.08it/s] 37%|███▋ | 11/30 [00:00<00:01, 14.35it/s] 43%|████▎ | 13/30 [00:00<00:01, 14.60it/s] 50%|█████ | 15/30 [00:01<00:01, 14.66it/s] 57%|█████▋ | 17/30 [00:01<00:00, 14.76it/s] 63%|██████▎ | 19/30 [00:01<00:00, 14.67it/s] 70%|███████ | 21/30 [00:01<00:00, 14.77it/s] 77%|███████▋ | 23/30 [00:01<00:00, 14.90it/s] 83%|████████▎ | 25/30 [00:01<00:00, 14.90it/s] 90%|█████████ | 27/30 [00:01<00:00, 15.01it/s] 97%|█████████▋| 29/30 [00:02<00:00, 15.07it/s] 100%|██████████| 30/30 [00:02<00:00, 14.30it/s]Prediction
anhappdev/test:75d9faf1ca08a01f8c8fedc0d1a537b3fc974c619b02b49beff1ad0b9bff9038IDsnj6asmam5aw7pzwxrw6ettgbmStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 12345678
- image
- method
- replace
- prompt
- a Christmas tree
- mask_image
- num_outputs
- 3
- control_scale
- 0.5
- guidance_scale
- 5
- num_inference_steps
- 30
{ "seed": 12345678, "image": "https://replicate.delivery/pbxt/Ia2sH4mZsg448cYoYBspSFbg5MtdDXCSBmtbQJ1sUscw0rVl/2.jpg", "method": "replace", "prompt": "a Christmas tree", "mask_image": "https://replicate.delivery/pbxt/Ia2sGuBRcn7Va7fcHH1IapXZgguPWEOSoqjkM6B6NxziW2YV/2_mask.jpg", "num_outputs": 3, "control_scale": 0.5, "guidance_scale": 5, "num_inference_steps": 30 }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 anhappdev/test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "anhappdev/test:75d9faf1ca08a01f8c8fedc0d1a537b3fc974c619b02b49beff1ad0b9bff9038", { input: { seed: 12345678, image: "https://replicate.delivery/pbxt/Ia2sH4mZsg448cYoYBspSFbg5MtdDXCSBmtbQJ1sUscw0rVl/2.jpg", method: "replace", prompt: "a Christmas tree", mask_image: "https://replicate.delivery/pbxt/Ia2sGuBRcn7Va7fcHH1IapXZgguPWEOSoqjkM6B6NxziW2YV/2_mask.jpg", num_outputs: 3, control_scale: 0.5, guidance_scale: 5, num_inference_steps: 30 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);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 anhappdev/test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "anhappdev/test:75d9faf1ca08a01f8c8fedc0d1a537b3fc974c619b02b49beff1ad0b9bff9038", input={ "seed": 12345678, "image": "https://replicate.delivery/pbxt/Ia2sH4mZsg448cYoYBspSFbg5MtdDXCSBmtbQJ1sUscw0rVl/2.jpg", "method": "replace", "prompt": "a Christmas tree", "mask_image": "https://replicate.delivery/pbxt/Ia2sGuBRcn7Va7fcHH1IapXZgguPWEOSoqjkM6B6NxziW2YV/2_mask.jpg", "num_outputs": 3, "control_scale": 0.5, "guidance_scale": 5, "num_inference_steps": 30 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run anhappdev/test 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": "anhappdev/test:75d9faf1ca08a01f8c8fedc0d1a537b3fc974c619b02b49beff1ad0b9bff9038", "input": { "seed": 12345678, "image": "https://replicate.delivery/pbxt/Ia2sH4mZsg448cYoYBspSFbg5MtdDXCSBmtbQJ1sUscw0rVl/2.jpg", "method": "replace", "prompt": "a Christmas tree", "mask_image": "https://replicate.delivery/pbxt/Ia2sGuBRcn7Va7fcHH1IapXZgguPWEOSoqjkM6B6NxziW2YV/2_mask.jpg", "num_outputs": 3, "control_scale": 0.5, "guidance_scale": 5, "num_inference_steps": 30 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-05-01T15:33:04.721513Z", "created_at": "2023-05-01T15:32:55.212814Z", "data_removed": false, "error": null, "id": "snj6asmam5aw7pzwxrw6ettgbm", "input": { "seed": 12345678, "image": "https://replicate.delivery/pbxt/Ia2sH4mZsg448cYoYBspSFbg5MtdDXCSBmtbQJ1sUscw0rVl/2.jpg", "method": "replace", "prompt": "a Christmas tree", "mask_image": "https://replicate.delivery/pbxt/Ia2sGuBRcn7Va7fcHH1IapXZgguPWEOSoqjkM6B6NxziW2YV/2_mask.jpg", "num_outputs": 3, "control_scale": 0.5, "guidance_scale": 5, "num_inference_steps": 30 }, "logs": "2023-05-01 15:32:56,232 [INFO] :: Using seed: 12345678\n 0%| | 0/30 [00:00<?, ?it/s]\n 3%|▎ | 1/30 [00:00<00:04, 6.32it/s]\n 10%|█ | 3/30 [00:00<00:02, 11.09it/s]\n 17%|█▋ | 5/30 [00:00<00:01, 12.76it/s]\n 23%|██▎ | 7/30 [00:00<00:01, 13.56it/s]\n 30%|███ | 9/30 [00:00<00:01, 14.09it/s]\n 37%|███▋ | 11/30 [00:00<00:01, 14.30it/s]\n 43%|████▎ | 13/30 [00:00<00:01, 14.56it/s]\n 50%|█████ | 15/30 [00:01<00:01, 14.83it/s]\n 57%|█████▋ | 17/30 [00:01<00:00, 15.00it/s]\n 63%|██████▎ | 19/30 [00:01<00:00, 15.08it/s]\n 70%|███████ | 21/30 [00:01<00:00, 15.19it/s]\n 77%|███████▋ | 23/30 [00:01<00:00, 15.23it/s]\n 83%|████████▎ | 25/30 [00:01<00:00, 15.08it/s]\n 90%|█████████ | 27/30 [00:01<00:00, 15.04it/s]\n 97%|█████████▋| 29/30 [00:02<00:00, 15.05it/s]\n100%|██████████| 30/30 [00:02<00:00, 14.41it/s]", "metrics": { "predict_time": 9.384267, "total_time": 9.508699 }, "output": [ "https://replicate.delivery/pbxt/TWMMhYnweHTRVaoAjzoMGHePiZFFVPiWb5lgiMsHeJQctxuhA/replace-out-0.jpg", "https://replicate.delivery/pbxt/rTiZxbOO2obfLiml7efoA3l0GXYggmtxruZkr5zlX3BfajdDB/replace-out-1.jpg", "https://replicate.delivery/pbxt/4zP4sezo0gyDJ6IERf7i1aHx0bg8nUbN4SxdYfuvoyEgtxuhA/replace-out-2.jpg" ], "started_at": "2023-05-01T15:32:55.337246Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/snj6asmam5aw7pzwxrw6ettgbm", "cancel": "https://api.replicate.com/v1/predictions/snj6asmam5aw7pzwxrw6ettgbm/cancel" }, "version": "75d9faf1ca08a01f8c8fedc0d1a537b3fc974c619b02b49beff1ad0b9bff9038" }Generated in2023-05-01 15:32:56,232 [INFO] :: Using seed: 12345678 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:00<00:04, 6.32it/s] 10%|█ | 3/30 [00:00<00:02, 11.09it/s] 17%|█▋ | 5/30 [00:00<00:01, 12.76it/s] 23%|██▎ | 7/30 [00:00<00:01, 13.56it/s] 30%|███ | 9/30 [00:00<00:01, 14.09it/s] 37%|███▋ | 11/30 [00:00<00:01, 14.30it/s] 43%|████▎ | 13/30 [00:00<00:01, 14.56it/s] 50%|█████ | 15/30 [00:01<00:01, 14.83it/s] 57%|█████▋ | 17/30 [00:01<00:00, 15.00it/s] 63%|██████▎ | 19/30 [00:01<00:00, 15.08it/s] 70%|███████ | 21/30 [00:01<00:00, 15.19it/s] 77%|███████▋ | 23/30 [00:01<00:00, 15.23it/s] 83%|████████▎ | 25/30 [00:01<00:00, 15.08it/s] 90%|█████████ | 27/30 [00:01<00:00, 15.04it/s] 97%|█████████▋| 29/30 [00:02<00:00, 15.05it/s] 100%|██████████| 30/30 [00:02<00:00, 14.41it/s]Prediction
anhappdev/test:ef4e1e6402b90b4164caf1f8141e4994a7ae752e47cab0a452dc94039cec550aIDrkywf4latvc53pdu5lo6bn4kvqStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 1234
- image
- method
- replace
- prompt
- a Christmas tree
- mask_image
- num_outputs
- 3
- guidance_scale
- 4
- num_inference_steps
- 40
- preview_input_image
{ "seed": 1234, "image": "https://replicate.delivery/pbxt/Ia2sH4mZsg448cYoYBspSFbg5MtdDXCSBmtbQJ1sUscw0rVl/2.jpg", "method": "replace", "prompt": "a Christmas tree", "mask_image": "https://replicate.delivery/pbxt/Ia2sGuBRcn7Va7fcHH1IapXZgguPWEOSoqjkM6B6NxziW2YV/2_mask.jpg", "num_outputs": 3, "guidance_scale": 4, "num_inference_steps": 40, "preview_input_image": true }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 anhappdev/test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "anhappdev/test:ef4e1e6402b90b4164caf1f8141e4994a7ae752e47cab0a452dc94039cec550a", { input: { seed: 1234, image: "https://replicate.delivery/pbxt/Ia2sH4mZsg448cYoYBspSFbg5MtdDXCSBmtbQJ1sUscw0rVl/2.jpg", method: "replace", prompt: "a Christmas tree", mask_image: "https://replicate.delivery/pbxt/Ia2sGuBRcn7Va7fcHH1IapXZgguPWEOSoqjkM6B6NxziW2YV/2_mask.jpg", num_outputs: 3, guidance_scale: 4, num_inference_steps: 40, preview_input_image: true } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);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 anhappdev/test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "anhappdev/test:ef4e1e6402b90b4164caf1f8141e4994a7ae752e47cab0a452dc94039cec550a", input={ "seed": 1234, "image": "https://replicate.delivery/pbxt/Ia2sH4mZsg448cYoYBspSFbg5MtdDXCSBmtbQJ1sUscw0rVl/2.jpg", "method": "replace", "prompt": "a Christmas tree", "mask_image": "https://replicate.delivery/pbxt/Ia2sGuBRcn7Va7fcHH1IapXZgguPWEOSoqjkM6B6NxziW2YV/2_mask.jpg", "num_outputs": 3, "guidance_scale": 4, "num_inference_steps": 40, "preview_input_image": True } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run anhappdev/test 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": "anhappdev/test:ef4e1e6402b90b4164caf1f8141e4994a7ae752e47cab0a452dc94039cec550a", "input": { "seed": 1234, "image": "https://replicate.delivery/pbxt/Ia2sH4mZsg448cYoYBspSFbg5MtdDXCSBmtbQJ1sUscw0rVl/2.jpg", "method": "replace", "prompt": "a Christmas tree", "mask_image": "https://replicate.delivery/pbxt/Ia2sGuBRcn7Va7fcHH1IapXZgguPWEOSoqjkM6B6NxziW2YV/2_mask.jpg", "num_outputs": 3, "guidance_scale": 4, "num_inference_steps": 40, "preview_input_image": true } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-05-11T16:09:28.274747Z", "created_at": "2023-05-11T16:09:15.192448Z", "data_removed": false, "error": null, "id": "rkywf4latvc53pdu5lo6bn4kvq", "input": { "seed": 1234, "image": "https://replicate.delivery/pbxt/Ia2sH4mZsg448cYoYBspSFbg5MtdDXCSBmtbQJ1sUscw0rVl/2.jpg", "method": "replace", "prompt": "a Christmas tree", "mask_image": "https://replicate.delivery/pbxt/Ia2sGuBRcn7Va7fcHH1IapXZgguPWEOSoqjkM6B6NxziW2YV/2_mask.jpg", "num_outputs": 3, "guidance_scale": 4, "num_inference_steps": 40, "preview_input_image": true }, "logs": "2023-05-11 16:09:16,079 [INFO] :: Using seed: 1234\n 0%| | 0/40 [00:00<?, ?it/s]\n 5%|▌ | 2/40 [00:00<00:03, 11.82it/s]\n 10%|█ | 4/40 [00:00<00:03, 11.96it/s]\n 15%|█▌ | 6/40 [00:00<00:02, 12.74it/s]\n 20%|██ | 8/40 [00:00<00:02, 12.75it/s]\n 25%|██▌ | 10/40 [00:00<00:02, 12.87it/s]\n 30%|███ | 12/40 [00:00<00:02, 12.87it/s]\n 35%|███▌ | 14/40 [00:01<00:02, 12.89it/s]\n 40%|████ | 16/40 [00:01<00:01, 12.71it/s]\n 45%|████▌ | 18/40 [00:01<00:01, 12.93it/s]\n 50%|█████ | 20/40 [00:01<00:01, 12.79it/s]\n 55%|█████▌ | 22/40 [00:01<00:01, 12.76it/s]\n 60%|██████ | 24/40 [00:01<00:01, 13.21it/s]\n 65%|██████▌ | 26/40 [00:02<00:01, 13.55it/s]\n 70%|███████ | 28/40 [00:02<00:00, 13.40it/s]\n 75%|███████▌ | 30/40 [00:02<00:00, 13.20it/s]\n 80%|████████ | 32/40 [00:02<00:00, 13.07it/s]\n 85%|████████▌ | 34/40 [00:02<00:00, 12.82it/s]\n 90%|█████████ | 36/40 [00:02<00:00, 13.40it/s]\n 95%|█████████▌| 38/40 [00:02<00:00, 13.85it/s]\n100%|██████████| 40/40 [00:03<00:00, 14.02it/s]\n100%|██████████| 40/40 [00:03<00:00, 13.15it/s]", "metrics": { "predict_time": 13.038941, "total_time": 13.082299 }, "output": [ "https://replicate.delivery/pbxt/dwimchKBLOb8ORkAnRRAKDf59r8f7iflcX51NOKcB51rpY1hA/replace-masked-image.jpg", "https://replicate.delivery/pbxt/0N3feGIm3efSORZXfGsAMlJ5b12WKSXs9RBNA859yGrxmiVHC/replace-out-0.jpg", "https://replicate.delivery/pbxt/KeXIe0yxh1uwlkCHqQl4PsFX6YaqVBfw9dcYwLOUJO4spY1hA/replace-out-1.jpg", "https://replicate.delivery/pbxt/LstIB3uC7QJyDNQfUnnMCpL0o0cYZkLGfbecCsJUe6cdTxqDB/replace-out-2.jpg" ], "started_at": "2023-05-11T16:09:15.235806Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/rkywf4latvc53pdu5lo6bn4kvq", "cancel": "https://api.replicate.com/v1/predictions/rkywf4latvc53pdu5lo6bn4kvq/cancel" }, "version": "ef4e1e6402b90b4164caf1f8141e4994a7ae752e47cab0a452dc94039cec550a" }Generated in2023-05-11 16:09:16,079 [INFO] :: Using seed: 1234 0%| | 0/40 [00:00<?, ?it/s] 5%|▌ | 2/40 [00:00<00:03, 11.82it/s] 10%|█ | 4/40 [00:00<00:03, 11.96it/s] 15%|█▌ | 6/40 [00:00<00:02, 12.74it/s] 20%|██ | 8/40 [00:00<00:02, 12.75it/s] 25%|██▌ | 10/40 [00:00<00:02, 12.87it/s] 30%|███ | 12/40 [00:00<00:02, 12.87it/s] 35%|███▌ | 14/40 [00:01<00:02, 12.89it/s] 40%|████ | 16/40 [00:01<00:01, 12.71it/s] 45%|████▌ | 18/40 [00:01<00:01, 12.93it/s] 50%|█████ | 20/40 [00:01<00:01, 12.79it/s] 55%|█████▌ | 22/40 [00:01<00:01, 12.76it/s] 60%|██████ | 24/40 [00:01<00:01, 13.21it/s] 65%|██████▌ | 26/40 [00:02<00:01, 13.55it/s] 70%|███████ | 28/40 [00:02<00:00, 13.40it/s] 75%|███████▌ | 30/40 [00:02<00:00, 13.20it/s] 80%|████████ | 32/40 [00:02<00:00, 13.07it/s] 85%|████████▌ | 34/40 [00:02<00:00, 12.82it/s] 90%|█████████ | 36/40 [00:02<00:00, 13.40it/s] 95%|█████████▌| 38/40 [00:02<00:00, 13.85it/s] 100%|██████████| 40/40 [00:03<00:00, 14.02it/s] 100%|██████████| 40/40 [00:03<00:00, 13.15it/s]Prediction
anhappdev/test:c72d8651fe7f8e534dca29c636156b681b976253673a1a7d6c397224b3c6cb8eID46u5gqzuynd4novpfvyvwllk34StatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 12345
- image
- method
- replace
- prompt
- a black leather couch
- mask_image
- num_outputs
- 3
- guidance_scale
- 5
- num_inference_steps
- 20
- preview_input_image
{ "seed": 12345, "image": "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg", "method": "replace", "prompt": "a black leather couch", "mask_image": "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg", "num_outputs": 3, "guidance_scale": 5, "num_inference_steps": 20, "preview_input_image": true }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 anhappdev/test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "anhappdev/test:c72d8651fe7f8e534dca29c636156b681b976253673a1a7d6c397224b3c6cb8e", { input: { seed: 12345, image: "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg", method: "replace", prompt: "a black leather couch", mask_image: "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg", num_outputs: 3, guidance_scale: 5, num_inference_steps: 20, preview_input_image: true } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);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 anhappdev/test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "anhappdev/test:c72d8651fe7f8e534dca29c636156b681b976253673a1a7d6c397224b3c6cb8e", input={ "seed": 12345, "image": "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg", "method": "replace", "prompt": "a black leather couch", "mask_image": "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg", "num_outputs": 3, "guidance_scale": 5, "num_inference_steps": 20, "preview_input_image": True } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run anhappdev/test 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": "anhappdev/test:c72d8651fe7f8e534dca29c636156b681b976253673a1a7d6c397224b3c6cb8e", "input": { "seed": 12345, "image": "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg", "method": "replace", "prompt": "a black leather couch", "mask_image": "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg", "num_outputs": 3, "guidance_scale": 5, "num_inference_steps": 20, "preview_input_image": true } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-05-14T03:54:56.567211Z", "created_at": "2023-05-14T03:54:41.023753Z", "data_removed": false, "error": null, "id": "46u5gqzuynd4novpfvyvwllk34", "input": { "seed": 12345, "image": "https://replicate.delivery/pbxt/Ia2E3MUrVDVXITRVSKCHcCYi9WMYGoTpqxtqZ0PuqRoaY3FW/1.jpg", "method": "replace", "prompt": "a black leather couch", "mask_image": "https://replicate.delivery/pbxt/Ia2E2SDOE51oEfLeToezFAIYKw84oX0wHJYNVO5GUr6YMFMi/1_mask.jpg", "num_outputs": 3, "guidance_scale": 5, "num_inference_steps": 20, "preview_input_image": true }, "logs": "2023-05-14 03:54:42,165 [INFO] :: Using seed: 12345\n 0%| | 0/20 [00:00<?, ?it/s]\n 5%|▌ | 1/20 [00:00<00:04, 4.11it/s]\n 10%|█ | 2/20 [00:00<00:03, 5.28it/s]\n 15%|█▌ | 3/20 [00:00<00:02, 5.81it/s]\n 20%|██ | 4/20 [00:00<00:02, 6.11it/s]\n 25%|██▌ | 5/20 [00:00<00:02, 6.27it/s]\n 30%|███ | 6/20 [00:01<00:02, 6.38it/s]\n 35%|███▌ | 7/20 [00:01<00:02, 6.45it/s]\n 40%|████ | 8/20 [00:01<00:01, 6.52it/s]\n 45%|████▌ | 9/20 [00:01<00:01, 6.53it/s]\n 50%|█████ | 10/20 [00:01<00:01, 6.56it/s]\n 55%|█████▌ | 11/20 [00:01<00:01, 6.58it/s]\n 60%|██████ | 12/20 [00:01<00:01, 6.59it/s]\n 65%|██████▌ | 13/20 [00:02<00:01, 6.59it/s]\n 70%|███████ | 14/20 [00:02<00:00, 6.59it/s]\n 75%|███████▌ | 15/20 [00:02<00:00, 6.59it/s]\n 80%|████████ | 16/20 [00:02<00:00, 6.58it/s]\n 85%|████████▌ | 17/20 [00:02<00:00, 6.59it/s]\n 90%|█████████ | 18/20 [00:02<00:00, 6.59it/s]\n 95%|█████████▌| 19/20 [00:02<00:00, 6.58it/s]\n100%|██████████| 20/20 [00:03<00:00, 6.60it/s]\n100%|██████████| 20/20 [00:03<00:00, 6.41it/s]", "metrics": { "predict_time": 15.460722, "total_time": 15.543458 }, "output": [ "https://replicate.delivery/pbxt/fwlYIMKSiIzfoEcWeRsL6hbGMQJDnYvm43a7zXMpxkvbsB3hA/replace-masked-image.jpg", "https://replicate.delivery/pbxt/MPfJSJobh00vIaNRMnp27FgLSTr1BokKfJ5zpPZDFEEO2g7QA/replace-out-0.jpg", "https://replicate.delivery/pbxt/IYeL9GKP7Yy0eUp5jQS6RSiJmPR5OkJbM7Xvcb71hs9P2g7QA/replace-out-1.jpg", "https://replicate.delivery/pbxt/SjAZOny5wL57DtUQ64aL636sEcoK8LSedZm7jjU1DlpHbwdIA/replace-out-2.jpg" ], "started_at": "2023-05-14T03:54:41.106489Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/46u5gqzuynd4novpfvyvwllk34", "cancel": "https://api.replicate.com/v1/predictions/46u5gqzuynd4novpfvyvwllk34/cancel" }, "version": "c72d8651fe7f8e534dca29c636156b681b976253673a1a7d6c397224b3c6cb8e" }Generated in2023-05-14 03:54:42,165 [INFO] :: Using seed: 12345 0%| | 0/20 [00:00<?, ?it/s] 5%|▌ | 1/20 [00:00<00:04, 4.11it/s] 10%|█ | 2/20 [00:00<00:03, 5.28it/s] 15%|█▌ | 3/20 [00:00<00:02, 5.81it/s] 20%|██ | 4/20 [00:00<00:02, 6.11it/s] 25%|██▌ | 5/20 [00:00<00:02, 6.27it/s] 30%|███ | 6/20 [00:01<00:02, 6.38it/s] 35%|███▌ | 7/20 [00:01<00:02, 6.45it/s] 40%|████ | 8/20 [00:01<00:01, 6.52it/s] 45%|████▌ | 9/20 [00:01<00:01, 6.53it/s] 50%|█████ | 10/20 [00:01<00:01, 6.56it/s] 55%|█████▌ | 11/20 [00:01<00:01, 6.58it/s] 60%|██████ | 12/20 [00:01<00:01, 6.59it/s] 65%|██████▌ | 13/20 [00:02<00:01, 6.59it/s] 70%|███████ | 14/20 [00:02<00:00, 6.59it/s] 75%|███████▌ | 15/20 [00:02<00:00, 6.59it/s] 80%|████████ | 16/20 [00:02<00:00, 6.58it/s] 85%|████████▌ | 17/20 [00:02<00:00, 6.59it/s] 90%|█████████ | 18/20 [00:02<00:00, 6.59it/s] 95%|█████████▌| 19/20 [00:02<00:00, 6.58it/s] 100%|██████████| 20/20 [00:03<00:00, 6.60it/s] 100%|██████████| 20/20 [00:03<00:00, 6.41it/s]
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