wren93 / consisti2v
A diffusion-based method to enhance visual consistency for I2V generation
Prediction
wren93/consisti2v:ce1f2912887d0e1926988004122f9049ff69f4b749b6221ed1dfcfc9487dc71bIDkxy2kzdb7iwjt4khbd76bmhusaStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- image
- prompt
- timelapse at the snow land with aurora in the sky
- num_inference_steps
- 50
- text_guidance_scale
- 7.5
- image_guidance_scale
- 1
{ "image": "https://replicate.delivery/pbxt/KVX68CBiatzs0asnIyqWZJORA4g5K5TWjts1WSmonroU9kAv/example_01.png", "prompt": "timelapse at the snow land with aurora in the sky", "num_inference_steps": 50, "text_guidance_scale": 7.5, "image_guidance_scale": 1 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run wren93/consisti2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "wren93/consisti2v:ce1f2912887d0e1926988004122f9049ff69f4b749b6221ed1dfcfc9487dc71b", { input: { image: "https://replicate.delivery/pbxt/KVX68CBiatzs0asnIyqWZJORA4g5K5TWjts1WSmonroU9kAv/example_01.png", prompt: "timelapse at the snow land with aurora in the sky", num_inference_steps: 50, text_guidance_scale: 7.5, image_guidance_scale: 1 } } ); // 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 replicateImport the client:import replicateRun wren93/consisti2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "wren93/consisti2v:ce1f2912887d0e1926988004122f9049ff69f4b749b6221ed1dfcfc9487dc71b", input={ "image": "https://replicate.delivery/pbxt/KVX68CBiatzs0asnIyqWZJORA4g5K5TWjts1WSmonroU9kAv/example_01.png", "prompt": "timelapse at the snow land with aurora in the sky", "num_inference_steps": 50, "text_guidance_scale": 7.5, "image_guidance_scale": 1 } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())To learn more, take a look at the guide on getting started with Python.
Run wren93/consisti2v 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": "wren93/consisti2v:ce1f2912887d0e1926988004122f9049ff69f4b749b6221ed1dfcfc9487dc71b", "input": { "image": "https://replicate.delivery/pbxt/KVX68CBiatzs0asnIyqWZJORA4g5K5TWjts1WSmonroU9kAv/example_01.png", "prompt": "timelapse at the snow land with aurora in the sky", "num_inference_steps": 50, "text_guidance_scale": 7.5, "image_guidance_scale": 1 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-03-03T22:20:30.784723Z", "created_at": "2024-03-03T22:19:09.356391Z", "data_removed": false, "error": null, "id": "kxy2kzdb7iwjt4khbd76bmhusa", "input": { "image": "https://replicate.delivery/pbxt/KVX68CBiatzs0asnIyqWZJORA4g5K5TWjts1WSmonroU9kAv/example_01.png", "prompt": "timelapse at the snow land with aurora in the sky", "num_inference_steps": 50, "text_guidance_scale": 7.5, "image_guidance_scale": 1 }, "logs": "Using seed: 57509\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:33, 1.45it/s]\n 4%|▍ | 2/50 [00:01<00:32, 1.46it/s]\n 6%|▌ | 3/50 [00:02<00:32, 1.46it/s]\n 8%|▊ | 4/50 [00:02<00:31, 1.47it/s]\n 10%|█ | 5/50 [00:03<00:30, 1.47it/s]\n 12%|█▏ | 6/50 [00:04<00:29, 1.47it/s]\n 14%|█▍ | 7/50 [00:04<00:29, 1.47it/s]\n 16%|█▌ | 8/50 [00:05<00:28, 1.46it/s]\n 18%|█▊ | 9/50 [00:06<00:28, 1.46it/s]\n 20%|██ | 10/50 [00:06<00:27, 1.47it/s]\n 22%|██▏ | 11/50 [00:07<00:26, 1.47it/s]\n 24%|██▍ | 12/50 [00:08<00:25, 1.47it/s]\n 26%|██▌ | 13/50 [00:08<00:25, 1.47it/s]\n 28%|██▊ | 14/50 [00:09<00:24, 1.47it/s]\n 30%|███ | 15/50 [00:10<00:23, 1.47it/s]\n 32%|███▏ | 16/50 [00:10<00:23, 1.47it/s]\n 34%|███▍ | 17/50 [00:11<00:22, 1.47it/s]\n 36%|███▌ | 18/50 [00:12<00:21, 1.47it/s]\n 38%|███▊ | 19/50 [00:12<00:21, 1.47it/s]\n 40%|████ | 20/50 [00:13<00:20, 1.47it/s]\n 42%|████▏ | 21/50 [00:14<00:19, 1.47it/s]\n 44%|████▍ | 22/50 [00:14<00:19, 1.47it/s]\n 46%|████▌ | 23/50 [00:15<00:18, 1.47it/s]\n 48%|████▊ | 24/50 [00:16<00:17, 1.47it/s]\n 50%|█████ | 25/50 [00:17<00:17, 1.47it/s]\n 52%|█████▏ | 26/50 [00:17<00:16, 1.47it/s]\n 54%|█████▍ | 27/50 [00:18<00:15, 1.47it/s]\n 56%|█████▌ | 28/50 [00:19<00:15, 1.47it/s]\n 58%|█████▊ | 29/50 [00:19<00:14, 1.46it/s]\n 60%|██████ | 30/50 [00:20<00:13, 1.46it/s]\n 62%|██████▏ | 31/50 [00:21<00:12, 1.46it/s]\n 64%|██████▍ | 32/50 [00:21<00:12, 1.46it/s]\n 66%|██████▌ | 33/50 [00:22<00:11, 1.46it/s]\n 68%|██████▊ | 34/50 [00:23<00:10, 1.46it/s]\n 70%|███████ | 35/50 [00:23<00:10, 1.46it/s]\n 72%|███████▏ | 36/50 [00:24<00:09, 1.46it/s]\n 74%|███████▍ | 37/50 [00:25<00:08, 1.46it/s]\n 76%|███████▌ | 38/50 [00:25<00:08, 1.46it/s]\n 78%|███████▊ | 39/50 [00:26<00:07, 1.46it/s]\n 80%|████████ | 40/50 [00:27<00:06, 1.46it/s]\n 82%|████████▏ | 41/50 [00:27<00:06, 1.46it/s]\n 84%|████████▍ | 42/50 [00:28<00:05, 1.46it/s]\n 86%|████████▌ | 43/50 [00:29<00:04, 1.46it/s]\n 88%|████████▊ | 44/50 [00:30<00:04, 1.45it/s]\n 90%|█████████ | 45/50 [00:30<00:03, 1.45it/s]\n 92%|█████████▏| 46/50 [00:31<00:02, 1.45it/s]\n 94%|█████████▍| 47/50 [00:32<00:02, 1.45it/s]\n 96%|█████████▌| 48/50 [00:32<00:01, 1.45it/s]\n 98%|█████████▊| 49/50 [00:33<00:00, 1.46it/s]\n100%|██████████| 50/50 [00:34<00:00, 1.46it/s]\n100%|██████████| 50/50 [00:34<00:00, 1.46it/s]\n 0%| | 0/16 [00:00<?, ?it/s]\n 50%|█████ | 8/16 [00:00<00:00, 67.44it/s]\n 94%|█████████▍| 15/16 [00:00<00:00, 51.03it/s]\n100%|██████████| 16/16 [00:00<00:00, 52.30it/s]", "metrics": { "predict_time": 37.080701, "total_time": 81.428332 }, "output": "https://replicate.delivery/pbxt/yAa0lgdYecXmVqG1D04eNkhLjU7eBCc3x790PF0WvG1bNV5kA/out.mp4", "started_at": "2024-03-03T22:19:53.704022Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/kxy2kzdb7iwjt4khbd76bmhusa", "cancel": "https://api.replicate.com/v1/predictions/kxy2kzdb7iwjt4khbd76bmhusa/cancel" }, "version": "ce1f2912887d0e1926988004122f9049ff69f4b749b6221ed1dfcfc9487dc71b" }Generated inUsing seed: 57509 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:33, 1.45it/s] 4%|▍ | 2/50 [00:01<00:32, 1.46it/s] 6%|▌ | 3/50 [00:02<00:32, 1.46it/s] 8%|▊ | 4/50 [00:02<00:31, 1.47it/s] 10%|█ | 5/50 [00:03<00:30, 1.47it/s] 12%|█▏ | 6/50 [00:04<00:29, 1.47it/s] 14%|█▍ | 7/50 [00:04<00:29, 1.47it/s] 16%|█▌ | 8/50 [00:05<00:28, 1.46it/s] 18%|█▊ | 9/50 [00:06<00:28, 1.46it/s] 20%|██ | 10/50 [00:06<00:27, 1.47it/s] 22%|██▏ | 11/50 [00:07<00:26, 1.47it/s] 24%|██▍ | 12/50 [00:08<00:25, 1.47it/s] 26%|██▌ | 13/50 [00:08<00:25, 1.47it/s] 28%|██▊ | 14/50 [00:09<00:24, 1.47it/s] 30%|███ | 15/50 [00:10<00:23, 1.47it/s] 32%|███▏ | 16/50 [00:10<00:23, 1.47it/s] 34%|███▍ | 17/50 [00:11<00:22, 1.47it/s] 36%|███▌ | 18/50 [00:12<00:21, 1.47it/s] 38%|███▊ | 19/50 [00:12<00:21, 1.47it/s] 40%|████ | 20/50 [00:13<00:20, 1.47it/s] 42%|████▏ | 21/50 [00:14<00:19, 1.47it/s] 44%|████▍ | 22/50 [00:14<00:19, 1.47it/s] 46%|████▌ | 23/50 [00:15<00:18, 1.47it/s] 48%|████▊ | 24/50 [00:16<00:17, 1.47it/s] 50%|█████ | 25/50 [00:17<00:17, 1.47it/s] 52%|█████▏ | 26/50 [00:17<00:16, 1.47it/s] 54%|█████▍ | 27/50 [00:18<00:15, 1.47it/s] 56%|█████▌ | 28/50 [00:19<00:15, 1.47it/s] 58%|█████▊ | 29/50 [00:19<00:14, 1.46it/s] 60%|██████ | 30/50 [00:20<00:13, 1.46it/s] 62%|██████▏ | 31/50 [00:21<00:12, 1.46it/s] 64%|██████▍ | 32/50 [00:21<00:12, 1.46it/s] 66%|██████▌ | 33/50 [00:22<00:11, 1.46it/s] 68%|██████▊ | 34/50 [00:23<00:10, 1.46it/s] 70%|███████ | 35/50 [00:23<00:10, 1.46it/s] 72%|███████▏ | 36/50 [00:24<00:09, 1.46it/s] 74%|███████▍ | 37/50 [00:25<00:08, 1.46it/s] 76%|███████▌ | 38/50 [00:25<00:08, 1.46it/s] 78%|███████▊ | 39/50 [00:26<00:07, 1.46it/s] 80%|████████ | 40/50 [00:27<00:06, 1.46it/s] 82%|████████▏ | 41/50 [00:27<00:06, 1.46it/s] 84%|████████▍ | 42/50 [00:28<00:05, 1.46it/s] 86%|████████▌ | 43/50 [00:29<00:04, 1.46it/s] 88%|████████▊ | 44/50 [00:30<00:04, 1.45it/s] 90%|█████████ | 45/50 [00:30<00:03, 1.45it/s] 92%|█████████▏| 46/50 [00:31<00:02, 1.45it/s] 94%|█████████▍| 47/50 [00:32<00:02, 1.45it/s] 96%|█████████▌| 48/50 [00:32<00:01, 1.45it/s] 98%|█████████▊| 49/50 [00:33<00:00, 1.46it/s] 100%|██████████| 50/50 [00:34<00:00, 1.46it/s] 100%|██████████| 50/50 [00:34<00:00, 1.46it/s] 0%| | 0/16 [00:00<?, ?it/s] 50%|█████ | 8/16 [00:00<00:00, 67.44it/s] 94%|█████████▍| 15/16 [00:00<00:00, 51.03it/s] 100%|██████████| 16/16 [00:00<00:00, 52.30it/s]Prediction
wren93/consisti2v:ce1f2912887d0e1926988004122f9049ff69f4b749b6221ed1dfcfc9487dc71bIDzbgbwddb3ctorsfgzkm54vp6ruStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- image
- prompt
- fireworks
- negative_prompt
- num_inference_steps
- 50
- text_guidance_scale
- 7.5
- image_guidance_scale
- 1
{ "image": "https://replicate.delivery/pbxt/KVXDOYT7bDaOmEVUDtNtqYaou0d5cyxkYORXFsrOIxaPRZGp/example_02.png", "prompt": "fireworks", "negative_prompt": "", "num_inference_steps": 50, "text_guidance_scale": 7.5, "image_guidance_scale": 1 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run wren93/consisti2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "wren93/consisti2v:ce1f2912887d0e1926988004122f9049ff69f4b749b6221ed1dfcfc9487dc71b", { input: { image: "https://replicate.delivery/pbxt/KVXDOYT7bDaOmEVUDtNtqYaou0d5cyxkYORXFsrOIxaPRZGp/example_02.png", prompt: "fireworks", negative_prompt: "", num_inference_steps: 50, text_guidance_scale: 7.5, image_guidance_scale: 1 } } ); // 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 replicateImport the client:import replicateRun wren93/consisti2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "wren93/consisti2v:ce1f2912887d0e1926988004122f9049ff69f4b749b6221ed1dfcfc9487dc71b", input={ "image": "https://replicate.delivery/pbxt/KVXDOYT7bDaOmEVUDtNtqYaou0d5cyxkYORXFsrOIxaPRZGp/example_02.png", "prompt": "fireworks", "negative_prompt": "", "num_inference_steps": 50, "text_guidance_scale": 7.5, "image_guidance_scale": 1 } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())To learn more, take a look at the guide on getting started with Python.
Run wren93/consisti2v 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": "wren93/consisti2v:ce1f2912887d0e1926988004122f9049ff69f4b749b6221ed1dfcfc9487dc71b", "input": { "image": "https://replicate.delivery/pbxt/KVXDOYT7bDaOmEVUDtNtqYaou0d5cyxkYORXFsrOIxaPRZGp/example_02.png", "prompt": "fireworks", "negative_prompt": "", "num_inference_steps": 50, "text_guidance_scale": 7.5, "image_guidance_scale": 1 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-03-03T22:28:51.977435Z", "created_at": "2024-03-03T22:26:48.707643Z", "data_removed": false, "error": null, "id": "zbgbwddb3ctorsfgzkm54vp6ru", "input": { "image": "https://replicate.delivery/pbxt/KVXDOYT7bDaOmEVUDtNtqYaou0d5cyxkYORXFsrOIxaPRZGp/example_02.png", "prompt": "fireworks", "negative_prompt": "", "num_inference_steps": 50, "text_guidance_scale": 7.5, "image_guidance_scale": 1 }, "logs": "Using seed: 53487\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:34, 1.44it/s]\n 4%|▍ | 2/50 [00:01<00:32, 1.46it/s]\n 6%|▌ | 3/50 [00:02<00:32, 1.46it/s]\n 8%|▊ | 4/50 [00:02<00:31, 1.46it/s]\n 10%|█ | 5/50 [00:03<00:30, 1.46it/s]\n 12%|█▏ | 6/50 [00:04<00:30, 1.46it/s]\n 14%|█▍ | 7/50 [00:04<00:29, 1.46it/s]\n 16%|█▌ | 8/50 [00:05<00:28, 1.46it/s]\n 18%|█▊ | 9/50 [00:06<00:28, 1.46it/s]\n 20%|██ | 10/50 [00:06<00:27, 1.47it/s]\n 22%|██▏ | 11/50 [00:07<00:26, 1.47it/s]\n 24%|██▍ | 12/50 [00:08<00:25, 1.47it/s]\n 26%|██▌ | 13/50 [00:08<00:25, 1.47it/s]\n 28%|██▊ | 14/50 [00:09<00:24, 1.47it/s]\n 30%|███ | 15/50 [00:10<00:23, 1.47it/s]\n 32%|███▏ | 16/50 [00:10<00:23, 1.47it/s]\n 34%|███▍ | 17/50 [00:11<00:22, 1.47it/s]\n 36%|███▌ | 18/50 [00:12<00:21, 1.47it/s]\n 38%|███▊ | 19/50 [00:12<00:21, 1.47it/s]\n 40%|████ | 20/50 [00:13<00:20, 1.47it/s]\n 42%|████▏ | 21/50 [00:14<00:19, 1.47it/s]\n 44%|████▍ | 22/50 [00:15<00:19, 1.47it/s]\n 46%|████▌ | 23/50 [00:15<00:18, 1.47it/s]\n 48%|████▊ | 24/50 [00:16<00:17, 1.47it/s]\n 50%|█████ | 25/50 [00:17<00:17, 1.47it/s]\n 52%|█████▏ | 26/50 [00:17<00:16, 1.47it/s]\n 54%|█████▍ | 27/50 [00:18<00:15, 1.47it/s]\n 56%|█████▌ | 28/50 [00:19<00:15, 1.46it/s]\n 58%|█████▊ | 29/50 [00:19<00:14, 1.46it/s]\n 60%|██████ | 30/50 [00:20<00:13, 1.46it/s]\n 62%|██████▏ | 31/50 [00:21<00:12, 1.46it/s]\n 64%|██████▍ | 32/50 [00:21<00:12, 1.46it/s]\n 66%|██████▌ | 33/50 [00:22<00:11, 1.46it/s]\n 68%|██████▊ | 34/50 [00:23<00:10, 1.46it/s]\n 70%|███████ | 35/50 [00:23<00:10, 1.46it/s]\n 72%|███████▏ | 36/50 [00:24<00:09, 1.46it/s]\n 74%|███████▍ | 37/50 [00:25<00:08, 1.46it/s]\n 76%|███████▌ | 38/50 [00:25<00:08, 1.46it/s]\n 78%|███████▊ | 39/50 [00:26<00:07, 1.46it/s]\n 80%|████████ | 40/50 [00:27<00:06, 1.46it/s]\n 82%|████████▏ | 41/50 [00:27<00:06, 1.46it/s]\n 84%|████████▍ | 42/50 [00:28<00:05, 1.46it/s]\n 86%|████████▌ | 43/50 [00:29<00:04, 1.46it/s]\n 88%|████████▊ | 44/50 [00:30<00:04, 1.46it/s]\n 90%|█████████ | 45/50 [00:30<00:03, 1.46it/s]\n 92%|█████████▏| 46/50 [00:31<00:02, 1.46it/s]\n 94%|█████████▍| 47/50 [00:32<00:02, 1.46it/s]\n 96%|█████████▌| 48/50 [00:32<00:01, 1.46it/s]\n 98%|█████████▊| 49/50 [00:33<00:00, 1.46it/s]\n100%|██████████| 50/50 [00:34<00:00, 1.46it/s]\n100%|██████████| 50/50 [00:34<00:00, 1.46it/s]\n 0%| | 0/16 [00:00<?, ?it/s]\n 50%|█████ | 8/16 [00:00<00:00, 67.48it/s]\n 94%|█████████▍| 15/16 [00:00<00:00, 50.95it/s]\n100%|██████████| 16/16 [00:00<00:00, 52.22it/s]", "metrics": { "predict_time": 37.141583, "total_time": 123.269792 }, "output": "https://replicate.delivery/pbxt/83PafehNjls1gkVrvw9R7uD0EMug8tckM1ODEBWEWesFdV5kA/out.mp4", "started_at": "2024-03-03T22:28:14.835852Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/zbgbwddb3ctorsfgzkm54vp6ru", "cancel": "https://api.replicate.com/v1/predictions/zbgbwddb3ctorsfgzkm54vp6ru/cancel" }, "version": "ce1f2912887d0e1926988004122f9049ff69f4b749b6221ed1dfcfc9487dc71b" }Generated inUsing seed: 53487 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:34, 1.44it/s] 4%|▍ | 2/50 [00:01<00:32, 1.46it/s] 6%|▌ | 3/50 [00:02<00:32, 1.46it/s] 8%|▊ | 4/50 [00:02<00:31, 1.46it/s] 10%|█ | 5/50 [00:03<00:30, 1.46it/s] 12%|█▏ | 6/50 [00:04<00:30, 1.46it/s] 14%|█▍ | 7/50 [00:04<00:29, 1.46it/s] 16%|█▌ | 8/50 [00:05<00:28, 1.46it/s] 18%|█▊ | 9/50 [00:06<00:28, 1.46it/s] 20%|██ | 10/50 [00:06<00:27, 1.47it/s] 22%|██▏ | 11/50 [00:07<00:26, 1.47it/s] 24%|██▍ | 12/50 [00:08<00:25, 1.47it/s] 26%|██▌ | 13/50 [00:08<00:25, 1.47it/s] 28%|██▊ | 14/50 [00:09<00:24, 1.47it/s] 30%|███ | 15/50 [00:10<00:23, 1.47it/s] 32%|███▏ | 16/50 [00:10<00:23, 1.47it/s] 34%|███▍ | 17/50 [00:11<00:22, 1.47it/s] 36%|███▌ | 18/50 [00:12<00:21, 1.47it/s] 38%|███▊ | 19/50 [00:12<00:21, 1.47it/s] 40%|████ | 20/50 [00:13<00:20, 1.47it/s] 42%|████▏ | 21/50 [00:14<00:19, 1.47it/s] 44%|████▍ | 22/50 [00:15<00:19, 1.47it/s] 46%|████▌ | 23/50 [00:15<00:18, 1.47it/s] 48%|████▊ | 24/50 [00:16<00:17, 1.47it/s] 50%|█████ | 25/50 [00:17<00:17, 1.47it/s] 52%|█████▏ | 26/50 [00:17<00:16, 1.47it/s] 54%|█████▍ | 27/50 [00:18<00:15, 1.47it/s] 56%|█████▌ | 28/50 [00:19<00:15, 1.46it/s] 58%|█████▊ | 29/50 [00:19<00:14, 1.46it/s] 60%|██████ | 30/50 [00:20<00:13, 1.46it/s] 62%|██████▏ | 31/50 [00:21<00:12, 1.46it/s] 64%|██████▍ | 32/50 [00:21<00:12, 1.46it/s] 66%|██████▌ | 33/50 [00:22<00:11, 1.46it/s] 68%|██████▊ | 34/50 [00:23<00:10, 1.46it/s] 70%|███████ | 35/50 [00:23<00:10, 1.46it/s] 72%|███████▏ | 36/50 [00:24<00:09, 1.46it/s] 74%|███████▍ | 37/50 [00:25<00:08, 1.46it/s] 76%|███████▌ | 38/50 [00:25<00:08, 1.46it/s] 78%|███████▊ | 39/50 [00:26<00:07, 1.46it/s] 80%|████████ | 40/50 [00:27<00:06, 1.46it/s] 82%|████████▏ | 41/50 [00:27<00:06, 1.46it/s] 84%|████████▍ | 42/50 [00:28<00:05, 1.46it/s] 86%|████████▌ | 43/50 [00:29<00:04, 1.46it/s] 88%|████████▊ | 44/50 [00:30<00:04, 1.46it/s] 90%|█████████ | 45/50 [00:30<00:03, 1.46it/s] 92%|█████████▏| 46/50 [00:31<00:02, 1.46it/s] 94%|█████████▍| 47/50 [00:32<00:02, 1.46it/s] 96%|█████████▌| 48/50 [00:32<00:01, 1.46it/s] 98%|█████████▊| 49/50 [00:33<00:00, 1.46it/s] 100%|██████████| 50/50 [00:34<00:00, 1.46it/s] 100%|██████████| 50/50 [00:34<00:00, 1.46it/s] 0%| | 0/16 [00:00<?, ?it/s] 50%|█████ | 8/16 [00:00<00:00, 67.48it/s] 94%|█████████▍| 15/16 [00:00<00:00, 50.95it/s] 100%|██████████| 16/16 [00:00<00:00, 52.22it/s]Prediction
wren93/consisti2v:ce1f2912887d0e1926988004122f9049ff69f4b749b6221ed1dfcfc9487dc71bIDrtk45rdbcn4rvgiid5hndcy4niStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- image
- prompt
- clown fish swimming through the coral reef.
- num_inference_steps
- 50
- text_guidance_scale
- 7.5
- image_guidance_scale
- 1
{ "image": "https://replicate.delivery/pbxt/KVXDulJ9iFtyYfwJgRslzBEddFpX0aV1sCcWAZ6ODWM0zDV0/example_03.png", "prompt": "clown fish swimming through the coral reef.", "num_inference_steps": 50, "text_guidance_scale": 7.5, "image_guidance_scale": 1 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run wren93/consisti2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "wren93/consisti2v:ce1f2912887d0e1926988004122f9049ff69f4b749b6221ed1dfcfc9487dc71b", { input: { image: "https://replicate.delivery/pbxt/KVXDulJ9iFtyYfwJgRslzBEddFpX0aV1sCcWAZ6ODWM0zDV0/example_03.png", prompt: "clown fish swimming through the coral reef.", num_inference_steps: 50, text_guidance_scale: 7.5, image_guidance_scale: 1 } } ); // 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 replicateImport the client:import replicateRun wren93/consisti2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "wren93/consisti2v:ce1f2912887d0e1926988004122f9049ff69f4b749b6221ed1dfcfc9487dc71b", input={ "image": "https://replicate.delivery/pbxt/KVXDulJ9iFtyYfwJgRslzBEddFpX0aV1sCcWAZ6ODWM0zDV0/example_03.png", "prompt": "clown fish swimming through the coral reef.", "num_inference_steps": 50, "text_guidance_scale": 7.5, "image_guidance_scale": 1 } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())To learn more, take a look at the guide on getting started with Python.
Run wren93/consisti2v 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": "wren93/consisti2v:ce1f2912887d0e1926988004122f9049ff69f4b749b6221ed1dfcfc9487dc71b", "input": { "image": "https://replicate.delivery/pbxt/KVXDulJ9iFtyYfwJgRslzBEddFpX0aV1sCcWAZ6ODWM0zDV0/example_03.png", "prompt": "clown fish swimming through the coral reef.", "num_inference_steps": 50, "text_guidance_scale": 7.5, "image_guidance_scale": 1 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-03-03T22:29:29.466697Z", "created_at": "2024-03-03T22:27:21.986706Z", "data_removed": false, "error": null, "id": "rtk45rdbcn4rvgiid5hndcy4ni", "input": { "image": "https://replicate.delivery/pbxt/KVXDulJ9iFtyYfwJgRslzBEddFpX0aV1sCcWAZ6ODWM0zDV0/example_03.png", "prompt": "clown fish swimming through the coral reef.", "num_inference_steps": 50, "text_guidance_scale": 7.5, "image_guidance_scale": 1 }, "logs": "Using seed: 717\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:33, 1.47it/s]\n 4%|▍ | 2/50 [00:01<00:32, 1.46it/s]\n 6%|▌ | 3/50 [00:02<00:32, 1.46it/s]\n 8%|▊ | 4/50 [00:02<00:31, 1.46it/s]\n 10%|█ | 5/50 [00:03<00:30, 1.46it/s]\n 12%|█▏ | 6/50 [00:04<00:30, 1.46it/s]\n 14%|█▍ | 7/50 [00:04<00:29, 1.46it/s]\n 16%|█▌ | 8/50 [00:05<00:28, 1.46it/s]\n 18%|█▊ | 9/50 [00:06<00:28, 1.46it/s]\n 20%|██ | 10/50 [00:06<00:27, 1.46it/s]\n 22%|██▏ | 11/50 [00:07<00:26, 1.46it/s]\n 24%|██▍ | 12/50 [00:08<00:26, 1.45it/s]\n 26%|██▌ | 13/50 [00:08<00:25, 1.46it/s]\n 28%|██▊ | 14/50 [00:09<00:24, 1.46it/s]\n 30%|███ | 15/50 [00:10<00:24, 1.45it/s]\n 32%|███▏ | 16/50 [00:10<00:23, 1.46it/s]\n 34%|███▍ | 17/50 [00:11<00:22, 1.45it/s]\n 36%|███▌ | 18/50 [00:12<00:21, 1.45it/s]\n 38%|███▊ | 19/50 [00:13<00:21, 1.45it/s]\n 40%|████ | 20/50 [00:13<00:20, 1.45it/s]\n 42%|████▏ | 21/50 [00:14<00:19, 1.45it/s]\n 44%|████▍ | 22/50 [00:15<00:19, 1.45it/s]\n 46%|████▌ | 23/50 [00:15<00:18, 1.45it/s]\n 48%|████▊ | 24/50 [00:16<00:17, 1.45it/s]\n 50%|█████ | 25/50 [00:17<00:17, 1.45it/s]\n 52%|█████▏ | 26/50 [00:17<00:16, 1.45it/s]\n 54%|█████▍ | 27/50 [00:18<00:15, 1.45it/s]\n 56%|█████▌ | 28/50 [00:19<00:15, 1.45it/s]\n 58%|█████▊ | 29/50 [00:19<00:14, 1.45it/s]\n 60%|██████ | 30/50 [00:20<00:13, 1.45it/s]\n 62%|██████▏ | 31/50 [00:21<00:13, 1.45it/s]\n 64%|██████▍ | 32/50 [00:22<00:12, 1.45it/s]\n 66%|██████▌ | 33/50 [00:22<00:11, 1.44it/s]\n 68%|██████▊ | 34/50 [00:23<00:11, 1.45it/s]\n 70%|███████ | 35/50 [00:24<00:10, 1.44it/s]\n 72%|███████▏ | 36/50 [00:24<00:09, 1.44it/s]\n 74%|███████▍ | 37/50 [00:25<00:09, 1.44it/s]\n 76%|███████▌ | 38/50 [00:26<00:08, 1.44it/s]\n 78%|███████▊ | 39/50 [00:26<00:07, 1.44it/s]\n 80%|████████ | 40/50 [00:27<00:06, 1.44it/s]\n 82%|████████▏ | 41/50 [00:28<00:06, 1.44it/s]\n 84%|████████▍ | 42/50 [00:28<00:05, 1.44it/s]\n 86%|████████▌ | 43/50 [00:29<00:04, 1.44it/s]\n 88%|████████▊ | 44/50 [00:30<00:04, 1.44it/s]\n 90%|█████████ | 45/50 [00:31<00:03, 1.44it/s]\n 92%|█████████▏| 46/50 [00:31<00:02, 1.44it/s]\n 94%|█████████▍| 47/50 [00:32<00:02, 1.44it/s]\n 96%|█████████▌| 48/50 [00:33<00:01, 1.44it/s]\n 98%|█████████▊| 49/50 [00:33<00:00, 1.44it/s]\n100%|██████████| 50/50 [00:34<00:00, 1.44it/s]\n100%|██████████| 50/50 [00:34<00:00, 1.45it/s]\n 0%| | 0/16 [00:00<?, ?it/s]\n 50%|█████ | 8/16 [00:00<00:00, 69.61it/s]\n 94%|█████████▍| 15/16 [00:00<00:00, 51.15it/s]\n100%|██████████| 16/16 [00:00<00:00, 52.54it/s]", "metrics": { "predict_time": 37.040284, "total_time": 127.479991 }, "output": "https://replicate.delivery/pbxt/cCzkzLpZa6LCFR7MVUh6fcLn0MK9KS4NSLQLbBI87rVkXVOJA/out.mp4", "started_at": "2024-03-03T22:28:52.426413Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/rtk45rdbcn4rvgiid5hndcy4ni", "cancel": "https://api.replicate.com/v1/predictions/rtk45rdbcn4rvgiid5hndcy4ni/cancel" }, "version": "ce1f2912887d0e1926988004122f9049ff69f4b749b6221ed1dfcfc9487dc71b" }Generated inUsing seed: 717 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:33, 1.47it/s] 4%|▍ | 2/50 [00:01<00:32, 1.46it/s] 6%|▌ | 3/50 [00:02<00:32, 1.46it/s] 8%|▊ | 4/50 [00:02<00:31, 1.46it/s] 10%|█ | 5/50 [00:03<00:30, 1.46it/s] 12%|█▏ | 6/50 [00:04<00:30, 1.46it/s] 14%|█▍ | 7/50 [00:04<00:29, 1.46it/s] 16%|█▌ | 8/50 [00:05<00:28, 1.46it/s] 18%|█▊ | 9/50 [00:06<00:28, 1.46it/s] 20%|██ | 10/50 [00:06<00:27, 1.46it/s] 22%|██▏ | 11/50 [00:07<00:26, 1.46it/s] 24%|██▍ | 12/50 [00:08<00:26, 1.45it/s] 26%|██▌ | 13/50 [00:08<00:25, 1.46it/s] 28%|██▊ | 14/50 [00:09<00:24, 1.46it/s] 30%|███ | 15/50 [00:10<00:24, 1.45it/s] 32%|███▏ | 16/50 [00:10<00:23, 1.46it/s] 34%|███▍ | 17/50 [00:11<00:22, 1.45it/s] 36%|███▌ | 18/50 [00:12<00:21, 1.45it/s] 38%|███▊ | 19/50 [00:13<00:21, 1.45it/s] 40%|████ | 20/50 [00:13<00:20, 1.45it/s] 42%|████▏ | 21/50 [00:14<00:19, 1.45it/s] 44%|████▍ | 22/50 [00:15<00:19, 1.45it/s] 46%|████▌ | 23/50 [00:15<00:18, 1.45it/s] 48%|████▊ | 24/50 [00:16<00:17, 1.45it/s] 50%|█████ | 25/50 [00:17<00:17, 1.45it/s] 52%|█████▏ | 26/50 [00:17<00:16, 1.45it/s] 54%|█████▍ | 27/50 [00:18<00:15, 1.45it/s] 56%|█████▌ | 28/50 [00:19<00:15, 1.45it/s] 58%|█████▊ | 29/50 [00:19<00:14, 1.45it/s] 60%|██████ | 30/50 [00:20<00:13, 1.45it/s] 62%|██████▏ | 31/50 [00:21<00:13, 1.45it/s] 64%|██████▍ | 32/50 [00:22<00:12, 1.45it/s] 66%|██████▌ | 33/50 [00:22<00:11, 1.44it/s] 68%|██████▊ | 34/50 [00:23<00:11, 1.45it/s] 70%|███████ | 35/50 [00:24<00:10, 1.44it/s] 72%|███████▏ | 36/50 [00:24<00:09, 1.44it/s] 74%|███████▍ | 37/50 [00:25<00:09, 1.44it/s] 76%|███████▌ | 38/50 [00:26<00:08, 1.44it/s] 78%|███████▊ | 39/50 [00:26<00:07, 1.44it/s] 80%|████████ | 40/50 [00:27<00:06, 1.44it/s] 82%|████████▏ | 41/50 [00:28<00:06, 1.44it/s] 84%|████████▍ | 42/50 [00:28<00:05, 1.44it/s] 86%|████████▌ | 43/50 [00:29<00:04, 1.44it/s] 88%|████████▊ | 44/50 [00:30<00:04, 1.44it/s] 90%|█████████ | 45/50 [00:31<00:03, 1.44it/s] 92%|█████████▏| 46/50 [00:31<00:02, 1.44it/s] 94%|█████████▍| 47/50 [00:32<00:02, 1.44it/s] 96%|█████████▌| 48/50 [00:33<00:01, 1.44it/s] 98%|█████████▊| 49/50 [00:33<00:00, 1.44it/s] 100%|██████████| 50/50 [00:34<00:00, 1.44it/s] 100%|██████████| 50/50 [00:34<00:00, 1.45it/s] 0%| | 0/16 [00:00<?, ?it/s] 50%|█████ | 8/16 [00:00<00:00, 69.61it/s] 94%|█████████▍| 15/16 [00:00<00:00, 51.15it/s] 100%|██████████| 16/16 [00:00<00:00, 52.54it/s]Prediction
wren93/consisti2v:ce1f2912887d0e1926988004122f9049ff69f4b749b6221ed1dfcfc9487dc71bIDgwmold3bmdbtyldjo5waf5eshiStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- image
- prompt
- melting ice cream dripping down the cone.
- negative_prompt
- num_inference_steps
- 50
- text_guidance_scale
- 7.5
- image_guidance_scale
- 1
{ "image": "https://replicate.delivery/pbxt/KVX9dWFAhJKfnS0la5lWtO9U8c574qoXOEpwuJsbqrrsNK8t/example_04.png", "prompt": "melting ice cream dripping down the cone.", "negative_prompt": "", "num_inference_steps": 50, "text_guidance_scale": 7.5, "image_guidance_scale": 1 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run wren93/consisti2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "wren93/consisti2v:ce1f2912887d0e1926988004122f9049ff69f4b749b6221ed1dfcfc9487dc71b", { input: { image: "https://replicate.delivery/pbxt/KVX9dWFAhJKfnS0la5lWtO9U8c574qoXOEpwuJsbqrrsNK8t/example_04.png", prompt: "melting ice cream dripping down the cone.", negative_prompt: "", num_inference_steps: 50, text_guidance_scale: 7.5, image_guidance_scale: 1 } } ); // 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 replicateImport the client:import replicateRun wren93/consisti2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "wren93/consisti2v:ce1f2912887d0e1926988004122f9049ff69f4b749b6221ed1dfcfc9487dc71b", input={ "image": "https://replicate.delivery/pbxt/KVX9dWFAhJKfnS0la5lWtO9U8c574qoXOEpwuJsbqrrsNK8t/example_04.png", "prompt": "melting ice cream dripping down the cone.", "negative_prompt": "", "num_inference_steps": 50, "text_guidance_scale": 7.5, "image_guidance_scale": 1 } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())To learn more, take a look at the guide on getting started with Python.
Run wren93/consisti2v 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": "wren93/consisti2v:ce1f2912887d0e1926988004122f9049ff69f4b749b6221ed1dfcfc9487dc71b", "input": { "image": "https://replicate.delivery/pbxt/KVX9dWFAhJKfnS0la5lWtO9U8c574qoXOEpwuJsbqrrsNK8t/example_04.png", "prompt": "melting ice cream dripping down the cone.", "negative_prompt": "", "num_inference_steps": 50, "text_guidance_scale": 7.5, "image_guidance_scale": 1 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-03-03T22:30:12.569135Z", "created_at": "2024-03-03T22:27:41.682924Z", "data_removed": false, "error": null, "id": "gwmold3bmdbtyldjo5waf5eshi", "input": { "image": "https://replicate.delivery/pbxt/KVX9dWFAhJKfnS0la5lWtO9U8c574qoXOEpwuJsbqrrsNK8t/example_04.png", "prompt": "melting ice cream dripping down the cone.", "negative_prompt": "", "num_inference_steps": 50, "text_guidance_scale": 7.5, "image_guidance_scale": 1 }, "logs": "Using seed: 27189\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:33, 1.46it/s]\n 4%|▍ | 2/50 [00:01<00:33, 1.45it/s]\n 6%|▌ | 3/50 [00:02<00:32, 1.45it/s]\n 8%|▊ | 4/50 [00:02<00:31, 1.45it/s]\n 10%|█ | 5/50 [00:03<00:31, 1.45it/s]\n 12%|█▏ | 6/50 [00:04<00:30, 1.44it/s]\n 14%|█▍ | 7/50 [00:04<00:29, 1.44it/s]\n 16%|█▌ | 8/50 [00:05<00:29, 1.44it/s]\n 18%|█▊ | 9/50 [00:06<00:28, 1.44it/s]\n 20%|██ | 10/50 [00:06<00:27, 1.44it/s]\n 22%|██▏ | 11/50 [00:07<00:27, 1.44it/s]\n 24%|██▍ | 12/50 [00:08<00:26, 1.44it/s]\n 26%|██▌ | 13/50 [00:09<00:25, 1.44it/s]\n 28%|██▊ | 14/50 [00:09<00:24, 1.44it/s]\n 30%|███ | 15/50 [00:10<00:24, 1.44it/s]\n 32%|███▏ | 16/50 [00:11<00:23, 1.44it/s]\n 34%|███▍ | 17/50 [00:11<00:22, 1.44it/s]\n 36%|███▌ | 18/50 [00:12<00:22, 1.44it/s]\n 38%|███▊ | 19/50 [00:13<00:21, 1.44it/s]\n 40%|████ | 20/50 [00:13<00:20, 1.44it/s]\n 42%|████▏ | 21/50 [00:14<00:20, 1.44it/s]\n 44%|████▍ | 22/50 [00:15<00:19, 1.44it/s]\n 46%|████▌ | 23/50 [00:15<00:18, 1.44it/s]\n 48%|████▊ | 24/50 [00:16<00:18, 1.44it/s]\n 50%|█████ | 25/50 [00:17<00:17, 1.44it/s]\n 52%|█████▏ | 26/50 [00:18<00:16, 1.44it/s]\n 54%|█████▍ | 27/50 [00:18<00:16, 1.44it/s]\n 56%|█████▌ | 28/50 [00:19<00:15, 1.44it/s]\n 58%|█████▊ | 29/50 [00:20<00:14, 1.44it/s]\n 60%|██████ | 30/50 [00:20<00:13, 1.44it/s]\n 62%|██████▏ | 31/50 [00:21<00:13, 1.44it/s]\n 64%|██████▍ | 32/50 [00:22<00:12, 1.44it/s]\n 66%|██████▌ | 33/50 [00:22<00:11, 1.44it/s]\n 68%|██████▊ | 34/50 [00:23<00:11, 1.44it/s]\n 70%|███████ | 35/50 [00:24<00:10, 1.44it/s]\n 72%|███████▏ | 36/50 [00:24<00:09, 1.44it/s]\n 74%|███████▍ | 37/50 [00:25<00:09, 1.44it/s]\n 76%|███████▌ | 38/50 [00:26<00:08, 1.44it/s]\n 78%|███████▊ | 39/50 [00:27<00:07, 1.44it/s]\n 80%|████████ | 40/50 [00:27<00:06, 1.44it/s]\n 82%|████████▏ | 41/50 [00:28<00:06, 1.44it/s]\n 84%|████████▍ | 42/50 [00:29<00:05, 1.44it/s]\n 86%|████████▌ | 43/50 [00:29<00:04, 1.44it/s]\n 88%|████████▊ | 44/50 [00:30<00:04, 1.44it/s]\n 90%|█████████ | 45/50 [00:31<00:03, 1.44it/s]\n 92%|█████████▏| 46/50 [00:31<00:02, 1.44it/s]\n 94%|█████████▍| 47/50 [00:32<00:02, 1.44it/s]\n 96%|█████████▌| 48/50 [00:33<00:01, 1.44it/s]\n 98%|█████████▊| 49/50 [00:34<00:00, 1.44it/s]\n100%|██████████| 50/50 [00:34<00:00, 1.44it/s]\n100%|██████████| 50/50 [00:34<00:00, 1.44it/s]\n 0%| | 0/16 [00:00<?, ?it/s]\n 50%|█████ | 8/16 [00:00<00:00, 69.40it/s]\n 94%|█████████▍| 15/16 [00:00<00:00, 50.97it/s]\n100%|██████████| 16/16 [00:00<00:00, 52.36it/s]", "metrics": { "predict_time": 42.821964, "total_time": 150.886211 }, "output": "https://replicate.delivery/pbxt/B58eig6x2evHwkflMogVKFByLF9ZRySYa5XkogeGeG0z9VlTC/out.mp4", "started_at": "2024-03-03T22:29:29.747171Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/gwmold3bmdbtyldjo5waf5eshi", "cancel": "https://api.replicate.com/v1/predictions/gwmold3bmdbtyldjo5waf5eshi/cancel" }, "version": "ce1f2912887d0e1926988004122f9049ff69f4b749b6221ed1dfcfc9487dc71b" }Generated inUsing seed: 27189 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:33, 1.46it/s] 4%|▍ | 2/50 [00:01<00:33, 1.45it/s] 6%|▌ | 3/50 [00:02<00:32, 1.45it/s] 8%|▊ | 4/50 [00:02<00:31, 1.45it/s] 10%|█ | 5/50 [00:03<00:31, 1.45it/s] 12%|█▏ | 6/50 [00:04<00:30, 1.44it/s] 14%|█▍ | 7/50 [00:04<00:29, 1.44it/s] 16%|█▌ | 8/50 [00:05<00:29, 1.44it/s] 18%|█▊ | 9/50 [00:06<00:28, 1.44it/s] 20%|██ | 10/50 [00:06<00:27, 1.44it/s] 22%|██▏ | 11/50 [00:07<00:27, 1.44it/s] 24%|██▍ | 12/50 [00:08<00:26, 1.44it/s] 26%|██▌ | 13/50 [00:09<00:25, 1.44it/s] 28%|██▊ | 14/50 [00:09<00:24, 1.44it/s] 30%|███ | 15/50 [00:10<00:24, 1.44it/s] 32%|███▏ | 16/50 [00:11<00:23, 1.44it/s] 34%|███▍ | 17/50 [00:11<00:22, 1.44it/s] 36%|███▌ | 18/50 [00:12<00:22, 1.44it/s] 38%|███▊ | 19/50 [00:13<00:21, 1.44it/s] 40%|████ | 20/50 [00:13<00:20, 1.44it/s] 42%|████▏ | 21/50 [00:14<00:20, 1.44it/s] 44%|████▍ | 22/50 [00:15<00:19, 1.44it/s] 46%|████▌ | 23/50 [00:15<00:18, 1.44it/s] 48%|████▊ | 24/50 [00:16<00:18, 1.44it/s] 50%|█████ | 25/50 [00:17<00:17, 1.44it/s] 52%|█████▏ | 26/50 [00:18<00:16, 1.44it/s] 54%|█████▍ | 27/50 [00:18<00:16, 1.44it/s] 56%|█████▌ | 28/50 [00:19<00:15, 1.44it/s] 58%|█████▊ | 29/50 [00:20<00:14, 1.44it/s] 60%|██████ | 30/50 [00:20<00:13, 1.44it/s] 62%|██████▏ | 31/50 [00:21<00:13, 1.44it/s] 64%|██████▍ | 32/50 [00:22<00:12, 1.44it/s] 66%|██████▌ | 33/50 [00:22<00:11, 1.44it/s] 68%|██████▊ | 34/50 [00:23<00:11, 1.44it/s] 70%|███████ | 35/50 [00:24<00:10, 1.44it/s] 72%|███████▏ | 36/50 [00:24<00:09, 1.44it/s] 74%|███████▍ | 37/50 [00:25<00:09, 1.44it/s] 76%|███████▌ | 38/50 [00:26<00:08, 1.44it/s] 78%|███████▊ | 39/50 [00:27<00:07, 1.44it/s] 80%|████████ | 40/50 [00:27<00:06, 1.44it/s] 82%|████████▏ | 41/50 [00:28<00:06, 1.44it/s] 84%|████████▍ | 42/50 [00:29<00:05, 1.44it/s] 86%|████████▌ | 43/50 [00:29<00:04, 1.44it/s] 88%|████████▊ | 44/50 [00:30<00:04, 1.44it/s] 90%|█████████ | 45/50 [00:31<00:03, 1.44it/s] 92%|█████████▏| 46/50 [00:31<00:02, 1.44it/s] 94%|█████████▍| 47/50 [00:32<00:02, 1.44it/s] 96%|█████████▌| 48/50 [00:33<00:01, 1.44it/s] 98%|█████████▊| 49/50 [00:34<00:00, 1.44it/s] 100%|██████████| 50/50 [00:34<00:00, 1.44it/s] 100%|██████████| 50/50 [00:34<00:00, 1.44it/s] 0%| | 0/16 [00:00<?, ?it/s] 50%|█████ | 8/16 [00:00<00:00, 69.40it/s] 94%|█████████▍| 15/16 [00:00<00:00, 50.97it/s] 100%|██████████| 16/16 [00:00<00:00, 52.36it/s]
Want to make some of these yourself?
Run this model