anotherjesse
/
zeroscope-v2-xl
Zeroscope V2 XL & 576w
Prediction
anotherjesse/zeroscope-v2-xl:27c876dd1456b35480db697a3cf6ec2bcea40a9ec2b874937e852b59b9fbe7f9IDfqlkuhbb3wearhwlantgwtc6xuStatusSucceededSourceWebHardwareA100 (40GB)Total durationCreatedby @replicateInput
- fps
- 8
- width
- 576
- height
- 320
- prompt
- A macro video of a bee pollinating a flower
- num_frames
- 27
- guidance_scale
- 7.5
- num_inference_steps
- 50
{ "fps": 8, "width": 576, "height": 320, "prompt": "A macro video of a bee pollinating a flower", "num_frames": 27, "guidance_scale": 7.5, "num_inference_steps": 50 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run anotherjesse/zeroscope-v2-xl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "anotherjesse/zeroscope-v2-xl:27c876dd1456b35480db697a3cf6ec2bcea40a9ec2b874937e852b59b9fbe7f9", { input: { fps: 8, width: 576, height: 320, prompt: "A macro video of a bee pollinating a flower", num_frames: 27, guidance_scale: 7.5, num_inference_steps: 50 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run anotherjesse/zeroscope-v2-xl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "anotherjesse/zeroscope-v2-xl:27c876dd1456b35480db697a3cf6ec2bcea40a9ec2b874937e852b59b9fbe7f9", input={ "fps": 8, "width": 576, "height": 320, "prompt": "A macro video of a bee pollinating a flower", "num_frames": 27, "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 anotherjesse/zeroscope-v2-xl 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": "27c876dd1456b35480db697a3cf6ec2bcea40a9ec2b874937e852b59b9fbe7f9", "input": { "fps": 8, "width": 576, "height": 320, "prompt": "A macro video of a bee pollinating a flower", "num_frames": 27, "guidance_scale": 7.5, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-06-24T20:47:27.112636Z", "created_at": "2023-06-24T20:46:55.551773Z", "data_removed": false, "error": null, "id": "fqlkuhbb3wearhwlantgwtc6xu", "input": { "fps": 8, "width": 576, "height": 320, "prompt": "A macro video of a bee pollinating a flower", "num_frames": 27, "guidance_scale": 7.5, "num_inference_steps": 50 }, "logs": "Using seed: 16125\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:01<01:24, 1.72s/it]\n 4%|▍ | 2/50 [00:02<00:48, 1.02s/it]\n 6%|▌ | 3/50 [00:02<00:37, 1.25it/s]\n 8%|▊ | 4/50 [00:03<00:31, 1.44it/s]\n 10%|█ | 5/50 [00:03<00:28, 1.57it/s]\n 12%|█▏ | 6/50 [00:04<00:26, 1.66it/s]\n 14%|█▍ | 7/50 [00:04<00:24, 1.72it/s]\n 16%|█▌ | 8/50 [00:05<00:23, 1.77it/s]\n 18%|█▊ | 9/50 [00:05<00:22, 1.80it/s]\n 20%|██ | 10/50 [00:06<00:21, 1.82it/s]\n 22%|██▏ | 11/50 [00:07<00:21, 1.84it/s]\n 24%|██▍ | 12/50 [00:07<00:20, 1.85it/s]\n 26%|██▌ | 13/50 [00:08<00:19, 1.86it/s]\n 28%|██▊ | 14/50 [00:08<00:19, 1.86it/s]\n 30%|███ | 15/50 [00:09<00:18, 1.86it/s]\n 32%|███▏ | 16/50 [00:09<00:18, 1.87it/s]\n 34%|███▍ | 17/50 [00:10<00:17, 1.87it/s]\n 36%|███▌ | 18/50 [00:10<00:17, 1.87it/s]\n 38%|███▊ | 19/50 [00:11<00:16, 1.88it/s]\n 40%|████ | 20/50 [00:11<00:15, 1.88it/s]\n 42%|████▏ | 21/50 [00:12<00:15, 1.87it/s]\n 44%|████▍ | 22/50 [00:12<00:15, 1.87it/s]\n 46%|████▌ | 23/50 [00:13<00:14, 1.86it/s]\n 48%|████▊ | 24/50 [00:14<00:13, 1.87it/s]\n 50%|█████ | 25/50 [00:14<00:13, 1.87it/s]\n 52%|█████▏ | 26/50 [00:15<00:12, 1.88it/s]\n 54%|█████▍ | 27/50 [00:15<00:12, 1.87it/s]\n 56%|█████▌ | 28/50 [00:16<00:11, 1.87it/s]\n 58%|█████▊ | 29/50 [00:16<00:11, 1.87it/s]\n 60%|██████ | 30/50 [00:17<00:10, 1.86it/s]\n 62%|██████▏ | 31/50 [00:17<00:10, 1.86it/s]\n 64%|██████▍ | 32/50 [00:18<00:09, 1.87it/s]\n 66%|██████▌ | 33/50 [00:18<00:09, 1.86it/s]\n 68%|██████▊ | 34/50 [00:19<00:08, 1.86it/s]\n 70%|███████ | 35/50 [00:19<00:08, 1.87it/s]\n 72%|███████▏ | 36/50 [00:20<00:07, 1.87it/s]\n 74%|███████▍ | 37/50 [00:20<00:06, 1.87it/s]\n 76%|███████▌ | 38/50 [00:21<00:06, 1.87it/s]\n 78%|███████▊ | 39/50 [00:22<00:05, 1.87it/s]\n 80%|████████ | 40/50 [00:22<00:05, 1.85it/s]\n 82%|████████▏ | 41/50 [00:23<00:04, 1.86it/s]\n 84%|████████▍ | 42/50 [00:23<00:04, 1.87it/s]\n 86%|████████▌ | 43/50 [00:24<00:03, 1.87it/s]\n 88%|████████▊ | 44/50 [00:24<00:03, 1.87it/s]\n 90%|█████████ | 45/50 [00:25<00:02, 1.87it/s]\n 92%|█████████▏| 46/50 [00:25<00:02, 1.87it/s]\n 94%|█████████▍| 47/50 [00:26<00:01, 1.87it/s]\n 96%|█████████▌| 48/50 [00:26<00:01, 1.87it/s]\n 98%|█████████▊| 49/50 [00:27<00:00, 1.87it/s]\n100%|██████████| 50/50 [00:27<00:00, 1.87it/s]\n100%|██████████| 50/50 [00:27<00:00, 1.79it/s]", "metrics": { "predict_time": 31.590164, "total_time": 31.560863 }, "output": "https://replicate.delivery/pbxt/nhbRFwFPfLwQGaM0iXIDXLxTy7iqEQXJ0vEfaidx3MteChSiA/out.mp4", "started_at": "2023-06-24T20:46:55.522472Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/fqlkuhbb3wearhwlantgwtc6xu", "cancel": "https://api.replicate.com/v1/predictions/fqlkuhbb3wearhwlantgwtc6xu/cancel" }, "version": "27c876dd1456b35480db697a3cf6ec2bcea40a9ec2b874937e852b59b9fbe7f9" }
Generated inUsing seed: 16125 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:01<01:24, 1.72s/it] 4%|▍ | 2/50 [00:02<00:48, 1.02s/it] 6%|▌ | 3/50 [00:02<00:37, 1.25it/s] 8%|▊ | 4/50 [00:03<00:31, 1.44it/s] 10%|█ | 5/50 [00:03<00:28, 1.57it/s] 12%|█▏ | 6/50 [00:04<00:26, 1.66it/s] 14%|█▍ | 7/50 [00:04<00:24, 1.72it/s] 16%|█▌ | 8/50 [00:05<00:23, 1.77it/s] 18%|█▊ | 9/50 [00:05<00:22, 1.80it/s] 20%|██ | 10/50 [00:06<00:21, 1.82it/s] 22%|██▏ | 11/50 [00:07<00:21, 1.84it/s] 24%|██▍ | 12/50 [00:07<00:20, 1.85it/s] 26%|██▌ | 13/50 [00:08<00:19, 1.86it/s] 28%|██▊ | 14/50 [00:08<00:19, 1.86it/s] 30%|███ | 15/50 [00:09<00:18, 1.86it/s] 32%|███▏ | 16/50 [00:09<00:18, 1.87it/s] 34%|███▍ | 17/50 [00:10<00:17, 1.87it/s] 36%|███▌ | 18/50 [00:10<00:17, 1.87it/s] 38%|███▊ | 19/50 [00:11<00:16, 1.88it/s] 40%|████ | 20/50 [00:11<00:15, 1.88it/s] 42%|████▏ | 21/50 [00:12<00:15, 1.87it/s] 44%|████▍ | 22/50 [00:12<00:15, 1.87it/s] 46%|████▌ | 23/50 [00:13<00:14, 1.86it/s] 48%|████▊ | 24/50 [00:14<00:13, 1.87it/s] 50%|█████ | 25/50 [00:14<00:13, 1.87it/s] 52%|█████▏ | 26/50 [00:15<00:12, 1.88it/s] 54%|█████▍ | 27/50 [00:15<00:12, 1.87it/s] 56%|█████▌ | 28/50 [00:16<00:11, 1.87it/s] 58%|█████▊ | 29/50 [00:16<00:11, 1.87it/s] 60%|██████ | 30/50 [00:17<00:10, 1.86it/s] 62%|██████▏ | 31/50 [00:17<00:10, 1.86it/s] 64%|██████▍ | 32/50 [00:18<00:09, 1.87it/s] 66%|██████▌ | 33/50 [00:18<00:09, 1.86it/s] 68%|██████▊ | 34/50 [00:19<00:08, 1.86it/s] 70%|███████ | 35/50 [00:19<00:08, 1.87it/s] 72%|███████▏ | 36/50 [00:20<00:07, 1.87it/s] 74%|███████▍ | 37/50 [00:20<00:06, 1.87it/s] 76%|███████▌ | 38/50 [00:21<00:06, 1.87it/s] 78%|███████▊ | 39/50 [00:22<00:05, 1.87it/s] 80%|████████ | 40/50 [00:22<00:05, 1.85it/s] 82%|████████▏ | 41/50 [00:23<00:04, 1.86it/s] 84%|████████▍ | 42/50 [00:23<00:04, 1.87it/s] 86%|████████▌ | 43/50 [00:24<00:03, 1.87it/s] 88%|████████▊ | 44/50 [00:24<00:03, 1.87it/s] 90%|█████████ | 45/50 [00:25<00:02, 1.87it/s] 92%|█████████▏| 46/50 [00:25<00:02, 1.87it/s] 94%|█████████▍| 47/50 [00:26<00:01, 1.87it/s] 96%|█████████▌| 48/50 [00:26<00:01, 1.87it/s] 98%|█████████▊| 49/50 [00:27<00:00, 1.87it/s] 100%|██████████| 50/50 [00:27<00:00, 1.87it/s] 100%|██████████| 50/50 [00:27<00:00, 1.79it/s]
Prediction
anotherjesse/zeroscope-v2-xl:1f0dd155aeff719af56f4a2e516c7f7d4c91a38c7b8e9e81808e7c71bde9b868IDubgvgbdbtgzes4gt3nloha2c3uStatusSucceededSourceWebHardwareA100 (40GB)Total durationCreatedby @replicateInput
- fps
- 24
- fast
- width
- 1024
- height
- 576
- prompt
- A deep sea video of a bioluminescent siphonophore, 8k, beautiful, award winning, close up
- num_frames
- 24
- guidance_scale
- 17.5
- negative_prompt
- noisy, washed out, ugly, distorted, broken
- num_inference_steps
- 50
{ "fps": 24, "fast": false, "width": 1024, "height": 576, "prompt": "A deep sea video of a bioluminescent siphonophore, 8k, beautiful, award winning, close up", "num_frames": 24, "guidance_scale": 17.5, "negative_prompt": "noisy, washed out, ugly, distorted, broken", "num_inference_steps": 50 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run anotherjesse/zeroscope-v2-xl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "anotherjesse/zeroscope-v2-xl:1f0dd155aeff719af56f4a2e516c7f7d4c91a38c7b8e9e81808e7c71bde9b868", { input: { fps: 24, fast: false, width: 1024, height: 576, prompt: "A deep sea video of a bioluminescent siphonophore, 8k, beautiful, award winning, close up", num_frames: 24, guidance_scale: 17.5, negative_prompt: "noisy, washed out, ugly, distorted, broken", num_inference_steps: 50 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run anotherjesse/zeroscope-v2-xl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "anotherjesse/zeroscope-v2-xl:1f0dd155aeff719af56f4a2e516c7f7d4c91a38c7b8e9e81808e7c71bde9b868", input={ "fps": 24, "fast": False, "width": 1024, "height": 576, "prompt": "A deep sea video of a bioluminescent siphonophore, 8k, beautiful, award winning, close up", "num_frames": 24, "guidance_scale": 17.5, "negative_prompt": "noisy, washed out, ugly, distorted, broken", "num_inference_steps": 50 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run anotherjesse/zeroscope-v2-xl 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": "1f0dd155aeff719af56f4a2e516c7f7d4c91a38c7b8e9e81808e7c71bde9b868", "input": { "fps": 24, "fast": false, "width": 1024, "height": 576, "prompt": "A deep sea video of a bioluminescent siphonophore, 8k, beautiful, award winning, close up", "num_frames": 24, "guidance_scale": 17.5, "negative_prompt": "noisy, washed out, ugly, distorted, broken", "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-06-24T23:38:17.597087Z", "created_at": "2023-06-24T23:36:51.936486Z", "data_removed": false, "error": null, "id": "ubgvgbdbtgzes4gt3nloha2c3u", "input": { "fps": 24, "fast": false, "width": 1024, "height": 576, "prompt": "A deep sea video of a bioluminescent siphonophore, 8k, beautiful, award winning, close up", "num_frames": 24, "guidance_scale": 17.5, "negative_prompt": "noisy, washed out, ugly, distorted, broken", "num_inference_steps": 50 }, "logs": "Using seed: 36780\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:02<01:45, 2.15s/it]\n 4%|▍ | 2/50 [00:03<01:27, 1.82s/it]\n 6%|▌ | 3/50 [00:05<01:20, 1.71s/it]\n 8%|▊ | 4/50 [00:06<01:16, 1.66s/it]\n 10%|█ | 5/50 [00:08<01:13, 1.63s/it]\n 12%|█▏ | 6/50 [00:10<01:11, 1.62s/it]\n 14%|█▍ | 7/50 [00:11<01:09, 1.61s/it]\n 16%|█▌ | 8/50 [00:13<01:07, 1.60s/it]\n 18%|█▊ | 9/50 [00:14<01:05, 1.60s/it]\n 20%|██ | 10/50 [00:16<01:03, 1.59s/it]\n 22%|██▏ | 11/50 [00:17<01:02, 1.59s/it]\n 24%|██▍ | 12/50 [00:19<01:00, 1.59s/it]\n 26%|██▌ | 13/50 [00:21<00:58, 1.59s/it]\n 28%|██▊ | 14/50 [00:22<00:57, 1.59s/it]\n 30%|███ | 15/50 [00:24<00:55, 1.59s/it]\n 32%|███▏ | 16/50 [00:25<00:54, 1.59s/it]\n 34%|███▍ | 17/50 [00:27<00:52, 1.59s/it]\n 36%|███▌ | 18/50 [00:29<00:50, 1.59s/it]\n 38%|███▊ | 19/50 [00:30<00:49, 1.59s/it]\n 40%|████ | 20/50 [00:32<00:47, 1.59s/it]\n 42%|████▏ | 21/50 [00:33<00:46, 1.59s/it]\n 44%|████▍ | 22/50 [00:35<00:44, 1.59s/it]\n 46%|████▌ | 23/50 [00:37<00:42, 1.59s/it]\n 48%|████▊ | 24/50 [00:38<00:41, 1.59s/it]\n 50%|█████ | 25/50 [00:40<00:39, 1.59s/it]\n 52%|█████▏ | 26/50 [00:41<00:38, 1.59s/it]\n 54%|█████▍ | 27/50 [00:43<00:36, 1.59s/it]\n 56%|█████▌ | 28/50 [00:45<00:35, 1.59s/it]\n 58%|█████▊ | 29/50 [00:46<00:33, 1.60s/it]\n 60%|██████ | 30/50 [00:48<00:31, 1.60s/it]\n 62%|██████▏ | 31/50 [00:49<00:30, 1.60s/it]\n 64%|██████▍ | 32/50 [00:51<00:28, 1.60s/it]\n 66%|██████▌ | 33/50 [00:53<00:27, 1.60s/it]\n 68%|██████▊ | 34/50 [00:54<00:25, 1.60s/it]\n 70%|███████ | 35/50 [00:56<00:23, 1.59s/it]\n 72%|███████▏ | 36/50 [00:57<00:22, 1.59s/it]\n 74%|███████▍ | 37/50 [00:59<00:20, 1.59s/it]\n 76%|███████▌ | 38/50 [01:01<00:19, 1.60s/it]\n 78%|███████▊ | 39/50 [01:02<00:17, 1.59s/it]\n 80%|████████ | 40/50 [01:04<00:15, 1.59s/it]\n 82%|████████▏ | 41/50 [01:05<00:14, 1.59s/it]\n 84%|████████▍ | 42/50 [01:07<00:12, 1.60s/it]\n 86%|████████▌ | 43/50 [01:08<00:11, 1.60s/it]\n 88%|████████▊ | 44/50 [01:10<00:09, 1.60s/it]\n 90%|█████████ | 45/50 [01:12<00:07, 1.60s/it]\n 92%|█████████▏| 46/50 [01:13<00:06, 1.60s/it]\n 94%|█████████▍| 47/50 [01:15<00:04, 1.60s/it]\n 96%|█████████▌| 48/50 [01:16<00:03, 1.60s/it]\n 98%|█████████▊| 49/50 [01:18<00:01, 1.60s/it]\n100%|██████████| 50/50 [01:20<00:00, 1.60s/it]\n100%|██████████| 50/50 [01:20<00:00, 1.60s/it]", "metrics": { "predict_time": 85.647114, "total_time": 85.660601 }, "output": "https://replicate.delivery/pbxt/oROx2wlhdpq7Gd4TKivqomqGpTdClmDb52tvealhanS0gpkIA/out.mp4", "started_at": "2023-06-24T23:36:51.949973Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ubgvgbdbtgzes4gt3nloha2c3u", "cancel": "https://api.replicate.com/v1/predictions/ubgvgbdbtgzes4gt3nloha2c3u/cancel" }, "version": "1f0dd155aeff719af56f4a2e516c7f7d4c91a38c7b8e9e81808e7c71bde9b868" }
Generated inUsing seed: 36780 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:02<01:45, 2.15s/it] 4%|▍ | 2/50 [00:03<01:27, 1.82s/it] 6%|▌ | 3/50 [00:05<01:20, 1.71s/it] 8%|▊ | 4/50 [00:06<01:16, 1.66s/it] 10%|█ | 5/50 [00:08<01:13, 1.63s/it] 12%|█▏ | 6/50 [00:10<01:11, 1.62s/it] 14%|█▍ | 7/50 [00:11<01:09, 1.61s/it] 16%|█▌ | 8/50 [00:13<01:07, 1.60s/it] 18%|█▊ | 9/50 [00:14<01:05, 1.60s/it] 20%|██ | 10/50 [00:16<01:03, 1.59s/it] 22%|██▏ | 11/50 [00:17<01:02, 1.59s/it] 24%|██▍ | 12/50 [00:19<01:00, 1.59s/it] 26%|██▌ | 13/50 [00:21<00:58, 1.59s/it] 28%|██▊ | 14/50 [00:22<00:57, 1.59s/it] 30%|███ | 15/50 [00:24<00:55, 1.59s/it] 32%|███▏ | 16/50 [00:25<00:54, 1.59s/it] 34%|███▍ | 17/50 [00:27<00:52, 1.59s/it] 36%|███▌ | 18/50 [00:29<00:50, 1.59s/it] 38%|███▊ | 19/50 [00:30<00:49, 1.59s/it] 40%|████ | 20/50 [00:32<00:47, 1.59s/it] 42%|████▏ | 21/50 [00:33<00:46, 1.59s/it] 44%|████▍ | 22/50 [00:35<00:44, 1.59s/it] 46%|████▌ | 23/50 [00:37<00:42, 1.59s/it] 48%|████▊ | 24/50 [00:38<00:41, 1.59s/it] 50%|█████ | 25/50 [00:40<00:39, 1.59s/it] 52%|█████▏ | 26/50 [00:41<00:38, 1.59s/it] 54%|█████▍ | 27/50 [00:43<00:36, 1.59s/it] 56%|█████▌ | 28/50 [00:45<00:35, 1.59s/it] 58%|█████▊ | 29/50 [00:46<00:33, 1.60s/it] 60%|██████ | 30/50 [00:48<00:31, 1.60s/it] 62%|██████▏ | 31/50 [00:49<00:30, 1.60s/it] 64%|██████▍ | 32/50 [00:51<00:28, 1.60s/it] 66%|██████▌ | 33/50 [00:53<00:27, 1.60s/it] 68%|██████▊ | 34/50 [00:54<00:25, 1.60s/it] 70%|███████ | 35/50 [00:56<00:23, 1.59s/it] 72%|███████▏ | 36/50 [00:57<00:22, 1.59s/it] 74%|███████▍ | 37/50 [00:59<00:20, 1.59s/it] 76%|███████▌ | 38/50 [01:01<00:19, 1.60s/it] 78%|███████▊ | 39/50 [01:02<00:17, 1.59s/it] 80%|████████ | 40/50 [01:04<00:15, 1.59s/it] 82%|████████▏ | 41/50 [01:05<00:14, 1.59s/it] 84%|████████▍ | 42/50 [01:07<00:12, 1.60s/it] 86%|████████▌ | 43/50 [01:08<00:11, 1.60s/it] 88%|████████▊ | 44/50 [01:10<00:09, 1.60s/it] 90%|█████████ | 45/50 [01:12<00:07, 1.60s/it] 92%|█████████▏| 46/50 [01:13<00:06, 1.60s/it] 94%|█████████▍| 47/50 [01:15<00:04, 1.60s/it] 96%|█████████▌| 48/50 [01:16<00:03, 1.60s/it] 98%|█████████▊| 49/50 [01:18<00:01, 1.60s/it] 100%|██████████| 50/50 [01:20<00:00, 1.60s/it] 100%|██████████| 50/50 [01:20<00:00, 1.60s/it]
Prediction
anotherjesse/zeroscope-v2-xl:1f0dd155aeff719af56f4a2e516c7f7d4c91a38c7b8e9e81808e7c71bde9b868ID7pan64dbqju3dxr6qsnxnekk5qStatusSucceededSourceWebHardwareA100 (40GB)Total durationCreatedby @replicateInput
- fps
- 24
- fast
- width
- 1024
- height
- 576
- prompt
- Clown fish swimming in a coral reef, beautiful, 8k, perfect, award winning, national geographic
- num_frames
- 24
- guidance_scale
- 17.5
- negative_prompt
- very blue, dust, noisy, washed out, ugly, distorted, broken
- num_inference_steps
- 50
{ "fps": 24, "fast": false, "width": 1024, "height": 576, "prompt": "Clown fish swimming in a coral reef, beautiful, 8k, perfect, award winning, national geographic", "num_frames": 24, "guidance_scale": 17.5, "negative_prompt": "very blue, dust, noisy, washed out, ugly, distorted, broken", "num_inference_steps": 50 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run anotherjesse/zeroscope-v2-xl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "anotherjesse/zeroscope-v2-xl:1f0dd155aeff719af56f4a2e516c7f7d4c91a38c7b8e9e81808e7c71bde9b868", { input: { fps: 24, fast: false, width: 1024, height: 576, prompt: "Clown fish swimming in a coral reef, beautiful, 8k, perfect, award winning, national geographic", num_frames: 24, guidance_scale: 17.5, negative_prompt: "very blue, dust, noisy, washed out, ugly, distorted, broken", num_inference_steps: 50 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run anotherjesse/zeroscope-v2-xl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "anotherjesse/zeroscope-v2-xl:1f0dd155aeff719af56f4a2e516c7f7d4c91a38c7b8e9e81808e7c71bde9b868", input={ "fps": 24, "fast": False, "width": 1024, "height": 576, "prompt": "Clown fish swimming in a coral reef, beautiful, 8k, perfect, award winning, national geographic", "num_frames": 24, "guidance_scale": 17.5, "negative_prompt": "very blue, dust, noisy, washed out, ugly, distorted, broken", "num_inference_steps": 50 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run anotherjesse/zeroscope-v2-xl 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": "1f0dd155aeff719af56f4a2e516c7f7d4c91a38c7b8e9e81808e7c71bde9b868", "input": { "fps": 24, "fast": false, "width": 1024, "height": 576, "prompt": "Clown fish swimming in a coral reef, beautiful, 8k, perfect, award winning, national geographic", "num_frames": 24, "guidance_scale": 17.5, "negative_prompt": "very blue, dust, noisy, washed out, ugly, distorted, broken", "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-06-25T00:06:59.201740Z", "created_at": "2023-06-25T00:03:12.598367Z", "data_removed": false, "error": null, "id": "7pan64dbqju3dxr6qsnxnekk5q", "input": { "fps": 24, "fast": false, "width": 1024, "height": 576, "prompt": "Clown fish swimming in a coral reef, beautiful, 8k, perfect, award winning, national geographic", "num_frames": 24, "guidance_scale": 17.5, "negative_prompt": "very blue, dust, noisy, washed out, ugly, distorted, broken", "num_inference_steps": 50 }, "logs": "Using seed: 21459\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:02<02:18, 2.83s/it]\n 4%|▍ | 2/50 [00:04<01:41, 2.11s/it]\n 6%|▌ | 3/50 [00:06<01:28, 1.88s/it]\n 8%|▊ | 4/50 [00:07<01:21, 1.77s/it]\n 10%|█ | 5/50 [00:09<01:16, 1.71s/it]\n 12%|█▏ | 6/50 [00:10<01:13, 1.67s/it]\n 14%|█▍ | 7/50 [00:12<01:10, 1.65s/it]\n 16%|█▌ | 8/50 [00:14<01:08, 1.63s/it]\n 18%|█▊ | 9/50 [00:15<01:06, 1.63s/it]\n 20%|██ | 10/50 [00:17<01:04, 1.62s/it]\n 22%|██▏ | 11/50 [00:18<01:02, 1.61s/it]\n 24%|██▍ | 12/50 [00:20<01:01, 1.61s/it]\n 26%|██▌ | 13/50 [00:22<00:59, 1.61s/it]\n 28%|██▊ | 14/50 [00:23<00:57, 1.61s/it]\n 30%|███ | 15/50 [00:25<00:56, 1.61s/it]\n 32%|███▏ | 16/50 [00:26<00:54, 1.61s/it]\n 34%|███▍ | 17/50 [00:28<00:53, 1.61s/it]\n 36%|███▌ | 18/50 [00:30<00:51, 1.61s/it]\n 38%|███▊ | 19/50 [00:31<00:49, 1.61s/it]\n 40%|████ | 20/50 [00:33<00:48, 1.61s/it]\n 42%|████▏ | 21/50 [00:34<00:46, 1.61s/it]\n 44%|████▍ | 22/50 [00:36<00:45, 1.61s/it]\n 46%|████▌ | 23/50 [00:38<00:43, 1.61s/it]\n 48%|████▊ | 24/50 [00:39<00:41, 1.61s/it]\n 50%|█████ | 25/50 [00:41<00:40, 1.61s/it]\n 52%|█████▏ | 26/50 [00:42<00:38, 1.61s/it]\n 54%|█████▍ | 27/50 [00:44<00:37, 1.61s/it]\n 56%|█████▌ | 28/50 [00:46<00:35, 1.61s/it]\n 58%|█████▊ | 29/50 [00:47<00:33, 1.61s/it]\n 60%|██████ | 30/50 [00:49<00:32, 1.61s/it]\n 62%|██████▏ | 31/50 [00:51<00:30, 1.61s/it]\n 64%|██████▍ | 32/50 [00:52<00:28, 1.61s/it]\n 66%|██████▌ | 33/50 [00:54<00:27, 1.61s/it]\n 68%|██████▊ | 34/50 [00:55<00:25, 1.61s/it]\n 70%|███████ | 35/50 [00:57<00:24, 1.61s/it]\n 72%|███████▏ | 36/50 [00:59<00:22, 1.61s/it]\n 74%|███████▍ | 37/50 [01:00<00:20, 1.61s/it]\n 76%|███████▌ | 38/50 [01:02<00:19, 1.61s/it]\n 78%|███████▊ | 39/50 [01:03<00:17, 1.61s/it]\n 80%|████████ | 40/50 [01:05<00:16, 1.61s/it]\n 82%|████████▏ | 41/50 [01:07<00:14, 1.61s/it]\n 84%|████████▍ | 42/50 [01:08<00:12, 1.61s/it]\n 86%|████████▌ | 43/50 [01:10<00:11, 1.61s/it]\n 88%|████████▊ | 44/50 [01:12<00:09, 1.61s/it]\n 90%|█████████ | 45/50 [01:13<00:08, 1.61s/it]\n 92%|█████████▏| 46/50 [01:15<00:06, 1.61s/it]\n 94%|█████████▍| 47/50 [01:16<00:04, 1.61s/it]\n 96%|█████████▌| 48/50 [01:18<00:03, 1.61s/it]\n 98%|█████████▊| 49/50 [01:20<00:01, 1.61s/it]\n100%|██████████| 50/50 [01:21<00:00, 1.61s/it]\n100%|██████████| 50/50 [01:21<00:00, 1.63s/it]", "metrics": { "predict_time": 88.371825, "total_time": 226.603373 }, "output": "https://replicate.delivery/pbxt/BxOCqncnxzI9NZZdsRd3N7i1IO2uNH053x6pGrOWtQnI3USE/out.mp4", "started_at": "2023-06-25T00:05:30.829915Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/7pan64dbqju3dxr6qsnxnekk5q", "cancel": "https://api.replicate.com/v1/predictions/7pan64dbqju3dxr6qsnxnekk5q/cancel" }, "version": "1f0dd155aeff719af56f4a2e516c7f7d4c91a38c7b8e9e81808e7c71bde9b868" }
Generated inUsing seed: 21459 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:02<02:18, 2.83s/it] 4%|▍ | 2/50 [00:04<01:41, 2.11s/it] 6%|▌ | 3/50 [00:06<01:28, 1.88s/it] 8%|▊ | 4/50 [00:07<01:21, 1.77s/it] 10%|█ | 5/50 [00:09<01:16, 1.71s/it] 12%|█▏ | 6/50 [00:10<01:13, 1.67s/it] 14%|█▍ | 7/50 [00:12<01:10, 1.65s/it] 16%|█▌ | 8/50 [00:14<01:08, 1.63s/it] 18%|█▊ | 9/50 [00:15<01:06, 1.63s/it] 20%|██ | 10/50 [00:17<01:04, 1.62s/it] 22%|██▏ | 11/50 [00:18<01:02, 1.61s/it] 24%|██▍ | 12/50 [00:20<01:01, 1.61s/it] 26%|██▌ | 13/50 [00:22<00:59, 1.61s/it] 28%|██▊ | 14/50 [00:23<00:57, 1.61s/it] 30%|███ | 15/50 [00:25<00:56, 1.61s/it] 32%|███▏ | 16/50 [00:26<00:54, 1.61s/it] 34%|███▍ | 17/50 [00:28<00:53, 1.61s/it] 36%|███▌ | 18/50 [00:30<00:51, 1.61s/it] 38%|███▊ | 19/50 [00:31<00:49, 1.61s/it] 40%|████ | 20/50 [00:33<00:48, 1.61s/it] 42%|████▏ | 21/50 [00:34<00:46, 1.61s/it] 44%|████▍ | 22/50 [00:36<00:45, 1.61s/it] 46%|████▌ | 23/50 [00:38<00:43, 1.61s/it] 48%|████▊ | 24/50 [00:39<00:41, 1.61s/it] 50%|█████ | 25/50 [00:41<00:40, 1.61s/it] 52%|█████▏ | 26/50 [00:42<00:38, 1.61s/it] 54%|█████▍ | 27/50 [00:44<00:37, 1.61s/it] 56%|█████▌ | 28/50 [00:46<00:35, 1.61s/it] 58%|█████▊ | 29/50 [00:47<00:33, 1.61s/it] 60%|██████ | 30/50 [00:49<00:32, 1.61s/it] 62%|██████▏ | 31/50 [00:51<00:30, 1.61s/it] 64%|██████▍ | 32/50 [00:52<00:28, 1.61s/it] 66%|██████▌ | 33/50 [00:54<00:27, 1.61s/it] 68%|██████▊ | 34/50 [00:55<00:25, 1.61s/it] 70%|███████ | 35/50 [00:57<00:24, 1.61s/it] 72%|███████▏ | 36/50 [00:59<00:22, 1.61s/it] 74%|███████▍ | 37/50 [01:00<00:20, 1.61s/it] 76%|███████▌ | 38/50 [01:02<00:19, 1.61s/it] 78%|███████▊ | 39/50 [01:03<00:17, 1.61s/it] 80%|████████ | 40/50 [01:05<00:16, 1.61s/it] 82%|████████▏ | 41/50 [01:07<00:14, 1.61s/it] 84%|████████▍ | 42/50 [01:08<00:12, 1.61s/it] 86%|████████▌ | 43/50 [01:10<00:11, 1.61s/it] 88%|████████▊ | 44/50 [01:12<00:09, 1.61s/it] 90%|█████████ | 45/50 [01:13<00:08, 1.61s/it] 92%|█████████▏| 46/50 [01:15<00:06, 1.61s/it] 94%|█████████▍| 47/50 [01:16<00:04, 1.61s/it] 96%|█████████▌| 48/50 [01:18<00:03, 1.61s/it] 98%|█████████▊| 49/50 [01:20<00:01, 1.61s/it] 100%|██████████| 50/50 [01:21<00:00, 1.61s/it] 100%|██████████| 50/50 [01:21<00:00, 1.63s/it]
Prediction
anotherjesse/zeroscope-v2-xl:1f0dd155aeff719af56f4a2e516c7f7d4c91a38c7b8e9e81808e7c71bde9b868IDkkf73olbstlfhenieub7pcug5eStatusSucceededSourceWebHardwareA100 (40GB)Total durationCreatedby @replicateInput
- fps
- 24
- fast
- width
- 1024
- height
- 576
- prompt
- Clown fish swimming in a coral reef, beautiful, 8k, perfect, award winning, national geographic
- num_frames
- 24
- guidance_scale
- 17.5
- negative_prompt
- dust, noisy, washed out, ugly, distorted, broken
- num_inference_steps
- 50
{ "fps": 24, "fast": false, "width": 1024, "height": 576, "prompt": "Clown fish swimming in a coral reef, beautiful, 8k, perfect, award winning, national geographic", "num_frames": 24, "guidance_scale": 17.5, "negative_prompt": "dust, noisy, washed out, ugly, distorted, broken", "num_inference_steps": 50 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run anotherjesse/zeroscope-v2-xl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "anotherjesse/zeroscope-v2-xl:1f0dd155aeff719af56f4a2e516c7f7d4c91a38c7b8e9e81808e7c71bde9b868", { input: { fps: 24, fast: false, width: 1024, height: 576, prompt: "Clown fish swimming in a coral reef, beautiful, 8k, perfect, award winning, national geographic", num_frames: 24, guidance_scale: 17.5, negative_prompt: "dust, noisy, washed out, ugly, distorted, broken", num_inference_steps: 50 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run anotherjesse/zeroscope-v2-xl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "anotherjesse/zeroscope-v2-xl:1f0dd155aeff719af56f4a2e516c7f7d4c91a38c7b8e9e81808e7c71bde9b868", input={ "fps": 24, "fast": False, "width": 1024, "height": 576, "prompt": "Clown fish swimming in a coral reef, beautiful, 8k, perfect, award winning, national geographic", "num_frames": 24, "guidance_scale": 17.5, "negative_prompt": "dust, noisy, washed out, ugly, distorted, broken", "num_inference_steps": 50 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run anotherjesse/zeroscope-v2-xl 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": "1f0dd155aeff719af56f4a2e516c7f7d4c91a38c7b8e9e81808e7c71bde9b868", "input": { "fps": 24, "fast": false, "width": 1024, "height": 576, "prompt": "Clown fish swimming in a coral reef, beautiful, 8k, perfect, award winning, national geographic", "num_frames": 24, "guidance_scale": 17.5, "negative_prompt": "dust, noisy, washed out, ugly, distorted, broken", "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-06-24T23:54:45.827420Z", "created_at": "2023-06-24T23:51:42.041149Z", "data_removed": false, "error": null, "id": "kkf73olbstlfhenieub7pcug5e", "input": { "fps": 24, "fast": false, "width": 1024, "height": 576, "prompt": "Clown fish swimming in a coral reef, beautiful, 8k, perfect, award winning, national geographic", "num_frames": 24, "guidance_scale": 17.5, "negative_prompt": "dust, noisy, washed out, ugly, distorted, broken", "num_inference_steps": 50 }, "logs": "Using seed: 26258\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:02<01:57, 2.40s/it]\n 4%|▍ | 2/50 [00:04<01:33, 1.94s/it]\n 6%|▌ | 3/50 [00:05<01:24, 1.80s/it]\n 8%|▊ | 4/50 [00:07<01:19, 1.73s/it]\n 10%|█ | 5/50 [00:08<01:16, 1.69s/it]\n 12%|█▏ | 6/50 [00:10<01:13, 1.67s/it]\n 14%|█▍ | 7/50 [00:12<01:11, 1.65s/it]\n 16%|█▌ | 8/50 [00:13<01:08, 1.64s/it]\n 18%|█▊ | 9/50 [00:15<01:07, 1.63s/it]\n 20%|██ | 10/50 [00:16<01:05, 1.63s/it]\n 22%|██▏ | 11/50 [00:18<01:03, 1.63s/it]\n 24%|██▍ | 12/50 [00:20<01:01, 1.63s/it]\n 26%|██▌ | 13/50 [00:21<01:00, 1.62s/it]\n 28%|██▊ | 14/50 [00:23<00:58, 1.62s/it]\n 30%|███ | 15/50 [00:25<00:56, 1.62s/it]\n 32%|███▏ | 16/50 [00:26<00:55, 1.62s/it]\n 34%|███▍ | 17/50 [00:28<00:53, 1.62s/it]\n 36%|███▌ | 18/50 [00:29<00:51, 1.62s/it]\n 38%|███▊ | 19/50 [00:31<00:50, 1.62s/it]\n 40%|████ | 20/50 [00:33<00:48, 1.62s/it]\n 42%|████▏ | 21/50 [00:34<00:47, 1.62s/it]\n 44%|████▍ | 22/50 [00:36<00:45, 1.62s/it]\n 46%|████▌ | 23/50 [00:38<00:43, 1.62s/it]\n 48%|████▊ | 24/50 [00:39<00:42, 1.62s/it]\n 50%|█████ | 25/50 [00:41<00:40, 1.62s/it]\n 52%|█████▏ | 26/50 [00:42<00:38, 1.62s/it]\n 54%|█████▍ | 27/50 [00:44<00:37, 1.62s/it]\n 56%|█████▌ | 28/50 [00:46<00:35, 1.62s/it]\n 58%|█████▊ | 29/50 [00:47<00:34, 1.62s/it]\n 60%|██████ | 30/50 [00:49<00:32, 1.62s/it]\n 62%|██████▏ | 31/50 [00:51<00:30, 1.62s/it]\n 64%|██████▍ | 32/50 [00:52<00:29, 1.62s/it]\n 66%|██████▌ | 33/50 [00:54<00:27, 1.62s/it]\n 68%|██████▊ | 34/50 [00:55<00:25, 1.62s/it]\n 70%|███████ | 35/50 [00:57<00:24, 1.62s/it]\n 72%|███████▏ | 36/50 [00:59<00:22, 1.62s/it]\n 74%|███████▍ | 37/50 [01:00<00:21, 1.62s/it]\n 76%|███████▌ | 38/50 [01:02<00:19, 1.62s/it]\n 78%|███████▊ | 39/50 [01:04<00:17, 1.62s/it]\n 80%|████████ | 40/50 [01:05<00:16, 1.62s/it]\n 82%|████████▏ | 41/50 [01:07<00:14, 1.62s/it]\n 84%|████████▍ | 42/50 [01:08<00:12, 1.62s/it]\n 86%|████████▌ | 43/50 [01:10<00:11, 1.62s/it]\n 88%|████████▊ | 44/50 [01:12<00:09, 1.62s/it]\n 90%|█████████ | 45/50 [01:13<00:08, 1.62s/it]\n 92%|█████████▏| 46/50 [01:15<00:06, 1.62s/it]\n 94%|█████████▍| 47/50 [01:17<00:04, 1.62s/it]\n 96%|█████████▌| 48/50 [01:18<00:03, 1.62s/it]\n 98%|█████████▊| 49/50 [01:20<00:01, 1.62s/it]\n100%|██████████| 50/50 [01:21<00:00, 1.62s/it]\n100%|██████████| 50/50 [01:21<00:00, 1.64s/it]", "metrics": { "predict_time": 87.182815, "total_time": 183.786271 }, "output": "https://replicate.delivery/pbxt/GVd95Fn4q3L9MtxfbYoLoZfyxtyRGecLTrNdNsCH6FqLimSiA/out.mp4", "started_at": "2023-06-24T23:53:18.644605Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/kkf73olbstlfhenieub7pcug5e", "cancel": "https://api.replicate.com/v1/predictions/kkf73olbstlfhenieub7pcug5e/cancel" }, "version": "1f0dd155aeff719af56f4a2e516c7f7d4c91a38c7b8e9e81808e7c71bde9b868" }
Generated inUsing seed: 26258 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:02<01:57, 2.40s/it] 4%|▍ | 2/50 [00:04<01:33, 1.94s/it] 6%|▌ | 3/50 [00:05<01:24, 1.80s/it] 8%|▊ | 4/50 [00:07<01:19, 1.73s/it] 10%|█ | 5/50 [00:08<01:16, 1.69s/it] 12%|█▏ | 6/50 [00:10<01:13, 1.67s/it] 14%|█▍ | 7/50 [00:12<01:11, 1.65s/it] 16%|█▌ | 8/50 [00:13<01:08, 1.64s/it] 18%|█▊ | 9/50 [00:15<01:07, 1.63s/it] 20%|██ | 10/50 [00:16<01:05, 1.63s/it] 22%|██▏ | 11/50 [00:18<01:03, 1.63s/it] 24%|██▍ | 12/50 [00:20<01:01, 1.63s/it] 26%|██▌ | 13/50 [00:21<01:00, 1.62s/it] 28%|██▊ | 14/50 [00:23<00:58, 1.62s/it] 30%|███ | 15/50 [00:25<00:56, 1.62s/it] 32%|███▏ | 16/50 [00:26<00:55, 1.62s/it] 34%|███▍ | 17/50 [00:28<00:53, 1.62s/it] 36%|███▌ | 18/50 [00:29<00:51, 1.62s/it] 38%|███▊ | 19/50 [00:31<00:50, 1.62s/it] 40%|████ | 20/50 [00:33<00:48, 1.62s/it] 42%|████▏ | 21/50 [00:34<00:47, 1.62s/it] 44%|████▍ | 22/50 [00:36<00:45, 1.62s/it] 46%|████▌ | 23/50 [00:38<00:43, 1.62s/it] 48%|████▊ | 24/50 [00:39<00:42, 1.62s/it] 50%|█████ | 25/50 [00:41<00:40, 1.62s/it] 52%|█████▏ | 26/50 [00:42<00:38, 1.62s/it] 54%|█████▍ | 27/50 [00:44<00:37, 1.62s/it] 56%|█████▌ | 28/50 [00:46<00:35, 1.62s/it] 58%|█████▊ | 29/50 [00:47<00:34, 1.62s/it] 60%|██████ | 30/50 [00:49<00:32, 1.62s/it] 62%|██████▏ | 31/50 [00:51<00:30, 1.62s/it] 64%|██████▍ | 32/50 [00:52<00:29, 1.62s/it] 66%|██████▌ | 33/50 [00:54<00:27, 1.62s/it] 68%|██████▊ | 34/50 [00:55<00:25, 1.62s/it] 70%|███████ | 35/50 [00:57<00:24, 1.62s/it] 72%|███████▏ | 36/50 [00:59<00:22, 1.62s/it] 74%|███████▍ | 37/50 [01:00<00:21, 1.62s/it] 76%|███████▌ | 38/50 [01:02<00:19, 1.62s/it] 78%|███████▊ | 39/50 [01:04<00:17, 1.62s/it] 80%|████████ | 40/50 [01:05<00:16, 1.62s/it] 82%|████████▏ | 41/50 [01:07<00:14, 1.62s/it] 84%|████████▍ | 42/50 [01:08<00:12, 1.62s/it] 86%|████████▌ | 43/50 [01:10<00:11, 1.62s/it] 88%|████████▊ | 44/50 [01:12<00:09, 1.62s/it] 90%|█████████ | 45/50 [01:13<00:08, 1.62s/it] 92%|█████████▏| 46/50 [01:15<00:06, 1.62s/it] 94%|█████████▍| 47/50 [01:17<00:04, 1.62s/it] 96%|█████████▌| 48/50 [01:18<00:03, 1.62s/it] 98%|█████████▊| 49/50 [01:20<00:01, 1.62s/it] 100%|██████████| 50/50 [01:21<00:00, 1.62s/it] 100%|██████████| 50/50 [01:21<00:00, 1.64s/it]
Prediction
anotherjesse/zeroscope-v2-xl:71996d331e8ede8ef7bd76eba9fae076d31792e4ddf4ad057779b443d6aea62fID6mkizd3bmlsj4y7ilhze3z6ifeStatusSucceededSourceWebHardwareA100 (40GB)Total durationCreatedby @replicateInput
- fps
- 8
- model
- 576w
- width
- 1024
- height
- 640
- prompt
- The Neon Rain of Tokyo. A strange phenomenon occurs over Tokyo. The rain falls in bright, neon colors, painting the city in a surreal light.
- batch_size
- 1
- init_video
- num_frames
- 24
- init_weight
- 0.3
- guidance_scale
- 17.5
- negative_prompt
- num_inference_steps
- 50
{ "fps": 8, "model": "576w", "width": 1024, "height": 640, "prompt": "The Neon Rain of Tokyo. A strange phenomenon occurs over Tokyo. The rain falls in bright, neon colors, painting the city in a surreal light.", "batch_size": 1, "init_video": "https://replicate.delivery/pbxt/J5vmbCAVboOYgPBRmvJ7CdJ3R15lVVbutLLU8Wc4eIKIskiY/replicate-prediction-6jpvfblbrgeh7yuh4tce5yuacq.mp4", "num_frames": 24, "init_weight": 0.3, "guidance_scale": 17.5, "negative_prompt": "", "num_inference_steps": 50 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run anotherjesse/zeroscope-v2-xl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "anotherjesse/zeroscope-v2-xl:71996d331e8ede8ef7bd76eba9fae076d31792e4ddf4ad057779b443d6aea62f", { input: { fps: 8, model: "576w", width: 1024, height: 640, prompt: "The Neon Rain of Tokyo. A strange phenomenon occurs over Tokyo. The rain falls in bright, neon colors, painting the city in a surreal light.", batch_size: 1, init_video: "https://replicate.delivery/pbxt/J5vmbCAVboOYgPBRmvJ7CdJ3R15lVVbutLLU8Wc4eIKIskiY/replicate-prediction-6jpvfblbrgeh7yuh4tce5yuacq.mp4", num_frames: 24, init_weight: 0.3, guidance_scale: 17.5, negative_prompt: "", 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 replicate
Import the client:import replicate
Run anotherjesse/zeroscope-v2-xl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "anotherjesse/zeroscope-v2-xl:71996d331e8ede8ef7bd76eba9fae076d31792e4ddf4ad057779b443d6aea62f", input={ "fps": 8, "model": "576w", "width": 1024, "height": 640, "prompt": "The Neon Rain of Tokyo. A strange phenomenon occurs over Tokyo. The rain falls in bright, neon colors, painting the city in a surreal light.", "batch_size": 1, "init_video": "https://replicate.delivery/pbxt/J5vmbCAVboOYgPBRmvJ7CdJ3R15lVVbutLLU8Wc4eIKIskiY/replicate-prediction-6jpvfblbrgeh7yuh4tce5yuacq.mp4", "num_frames": 24, "init_weight": 0.3, "guidance_scale": 17.5, "negative_prompt": "", "num_inference_steps": 50 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run anotherjesse/zeroscope-v2-xl 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": "71996d331e8ede8ef7bd76eba9fae076d31792e4ddf4ad057779b443d6aea62f", "input": { "fps": 8, "model": "576w", "width": 1024, "height": 640, "prompt": "The Neon Rain of Tokyo. A strange phenomenon occurs over Tokyo. The rain falls in bright, neon colors, painting the city in a surreal light.", "batch_size": 1, "init_video": "https://replicate.delivery/pbxt/J5vmbCAVboOYgPBRmvJ7CdJ3R15lVVbutLLU8Wc4eIKIskiY/replicate-prediction-6jpvfblbrgeh7yuh4tce5yuacq.mp4", "num_frames": 24, "init_weight": 0.3, "guidance_scale": 17.5, "negative_prompt": "", "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-07-01T03:14:09.468032Z", "created_at": "2023-07-01T03:12:41.182751Z", "data_removed": false, "error": null, "id": "6mkizd3bmlsj4y7ilhze3z6ife", "input": { "fps": 8, "model": "576w", "width": 1024, "height": 640, "prompt": "The Neon Rain of Tokyo. A strange phenomenon occurs over Tokyo. The rain falls in bright, neon colors, painting the city in a surreal light.", "batch_size": 1, "init_video": "https://replicate.delivery/pbxt/J5vmbCAVboOYgPBRmvJ7CdJ3R15lVVbutLLU8Wc4eIKIskiY/replicate-prediction-6jpvfblbrgeh7yuh4tce5yuacq.mp4", "num_frames": 24, "init_weight": 0.3, "guidance_scale": 17.5, "negative_prompt": "", "num_inference_steps": 50 }, "logs": "Using seed: 53199\ninit_video 421531\n/root/.pyenv/versions/3.10.12/lib/python3.10/site-packages/diffusers/configuration_utils.py:135: FutureWarning: Accessing config attribute `num_train_timesteps` directly via 'DPMSolverMultistepScheduler' object attribute is deprecated. Please access 'num_train_timesteps' over 'DPMSolverMultistepScheduler's config object instead, e.g. 'scheduler.config.num_train_timesteps'.\ndeprecate(\"direct config name access\", \"1.0.0\", deprecation_message, standard_warn=False)\n 0%| | 0/35 [00:00<?, ?it/s]\n 3%|▎ | 1/35 [00:02<01:12, 2.12s/it]\n 6%|▌ | 2/35 [00:04<01:10, 2.12s/it]\n 9%|▊ | 3/35 [00:06<01:08, 2.13s/it]\n 11%|█▏ | 4/35 [00:08<01:05, 2.13s/it]\n 14%|█▍ | 5/35 [00:10<01:03, 2.13s/it]\n 17%|█▋ | 6/35 [00:12<01:01, 2.13s/it]\n 20%|██ | 7/35 [00:14<00:59, 2.13s/it]\n 23%|██▎ | 8/35 [00:17<00:57, 2.13s/it]\n 26%|██▌ | 9/35 [00:19<00:55, 2.13s/it]\n 29%|██▊ | 10/35 [00:21<00:53, 2.13s/it]\n 31%|███▏ | 11/35 [00:23<00:51, 2.13s/it]\n 34%|███▍ | 12/35 [00:25<00:48, 2.13s/it]\n 37%|███▋ | 13/35 [00:27<00:46, 2.13s/it]\n 40%|████ | 14/35 [00:29<00:44, 2.13s/it]\n 43%|████▎ | 15/35 [00:31<00:42, 2.13s/it]\n 46%|████▌ | 16/35 [00:34<00:40, 2.13s/it]\n 49%|████▊ | 17/35 [00:36<00:38, 2.13s/it]\n 51%|█████▏ | 18/35 [00:38<00:36, 2.13s/it]\n 54%|█████▍ | 19/35 [00:40<00:34, 2.13s/it]\n 57%|█████▋ | 20/35 [00:42<00:31, 2.13s/it]\n 60%|██████ | 21/35 [00:44<00:29, 2.13s/it]\n 63%|██████▎ | 22/35 [00:46<00:27, 2.13s/it]\n 66%|██████▌ | 23/35 [00:48<00:25, 2.13s/it]\n 69%|██████▊ | 24/35 [00:51<00:23, 2.13s/it]\n 71%|███████▏ | 25/35 [00:53<00:21, 2.13s/it]\n 74%|███████▍ | 26/35 [00:55<00:19, 2.13s/it]\n 77%|███████▋ | 27/35 [00:57<00:17, 2.13s/it]\n 80%|████████ | 28/35 [00:59<00:14, 2.13s/it]\n 83%|████████▎ | 29/35 [01:01<00:12, 2.13s/it]\n 86%|████████▌ | 30/35 [01:03<00:10, 2.13s/it]\n 89%|████████▊ | 31/35 [01:06<00:08, 2.13s/it]\n 91%|█████████▏| 32/35 [01:08<00:06, 2.13s/it]\n 94%|█████████▍| 33/35 [01:10<00:04, 2.13s/it]\n 97%|█████████▋| 34/35 [01:12<00:02, 2.14s/it]\n100%|██████████| 35/35 [01:14<00:00, 2.13s/it]\n100%|██████████| 35/35 [01:14<00:00, 2.13s/it]", "metrics": { "predict_time": 88.278255, "total_time": 88.285281 }, "output": [ "https://replicate.delivery/pbxt/lekta3hQqH1GYidnkoUS0OtuUbrOcqLBP086P54xs8NAYqlIA/output-0.mp4" ], "started_at": "2023-07-01T03:12:41.189777Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/6mkizd3bmlsj4y7ilhze3z6ife", "cancel": "https://api.replicate.com/v1/predictions/6mkizd3bmlsj4y7ilhze3z6ife/cancel" }, "version": "71996d331e8ede8ef7bd76eba9fae076d31792e4ddf4ad057779b443d6aea62f" }
Generated inUsing seed: 53199 init_video 421531 /root/.pyenv/versions/3.10.12/lib/python3.10/site-packages/diffusers/configuration_utils.py:135: FutureWarning: Accessing config attribute `num_train_timesteps` directly via 'DPMSolverMultistepScheduler' object attribute is deprecated. Please access 'num_train_timesteps' over 'DPMSolverMultistepScheduler's config object instead, e.g. 'scheduler.config.num_train_timesteps'. deprecate("direct config name access", "1.0.0", deprecation_message, standard_warn=False) 0%| | 0/35 [00:00<?, ?it/s] 3%|▎ | 1/35 [00:02<01:12, 2.12s/it] 6%|▌ | 2/35 [00:04<01:10, 2.12s/it] 9%|▊ | 3/35 [00:06<01:08, 2.13s/it] 11%|█▏ | 4/35 [00:08<01:05, 2.13s/it] 14%|█▍ | 5/35 [00:10<01:03, 2.13s/it] 17%|█▋ | 6/35 [00:12<01:01, 2.13s/it] 20%|██ | 7/35 [00:14<00:59, 2.13s/it] 23%|██▎ | 8/35 [00:17<00:57, 2.13s/it] 26%|██▌ | 9/35 [00:19<00:55, 2.13s/it] 29%|██▊ | 10/35 [00:21<00:53, 2.13s/it] 31%|███▏ | 11/35 [00:23<00:51, 2.13s/it] 34%|███▍ | 12/35 [00:25<00:48, 2.13s/it] 37%|███▋ | 13/35 [00:27<00:46, 2.13s/it] 40%|████ | 14/35 [00:29<00:44, 2.13s/it] 43%|████▎ | 15/35 [00:31<00:42, 2.13s/it] 46%|████▌ | 16/35 [00:34<00:40, 2.13s/it] 49%|████▊ | 17/35 [00:36<00:38, 2.13s/it] 51%|█████▏ | 18/35 [00:38<00:36, 2.13s/it] 54%|█████▍ | 19/35 [00:40<00:34, 2.13s/it] 57%|█████▋ | 20/35 [00:42<00:31, 2.13s/it] 60%|██████ | 21/35 [00:44<00:29, 2.13s/it] 63%|██████▎ | 22/35 [00:46<00:27, 2.13s/it] 66%|██████▌ | 23/35 [00:48<00:25, 2.13s/it] 69%|██████▊ | 24/35 [00:51<00:23, 2.13s/it] 71%|███████▏ | 25/35 [00:53<00:21, 2.13s/it] 74%|███████▍ | 26/35 [00:55<00:19, 2.13s/it] 77%|███████▋ | 27/35 [00:57<00:17, 2.13s/it] 80%|████████ | 28/35 [00:59<00:14, 2.13s/it] 83%|████████▎ | 29/35 [01:01<00:12, 2.13s/it] 86%|████████▌ | 30/35 [01:03<00:10, 2.13s/it] 89%|████████▊ | 31/35 [01:06<00:08, 2.13s/it] 91%|█████████▏| 32/35 [01:08<00:06, 2.13s/it] 94%|█████████▍| 33/35 [01:10<00:04, 2.13s/it] 97%|█████████▋| 34/35 [01:12<00:02, 2.14s/it] 100%|██████████| 35/35 [01:14<00:00, 2.13s/it] 100%|██████████| 35/35 [01:14<00:00, 2.13s/it]
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