thudm
/
cogvideox-t2v
Text-to-Video Diffusion Models with An Expert Transformer
Prediction
thudm/cogvideox-t2v:e047b1d734c550671fb4de7f7df7f9341ed498b4aa7cd88b82533b60dfec33e3IDrh6vtn9ntdrgj0cj2kks7nb1nwStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- prompt
- A garden comes to life as a kaleidoscope of butterflies flutters amidst the blossoms, their delicate wings casting shadows on the petals below. In the background, a grand fountain cascades water with a gentle splendor, its rhythmic sound providing a soothing backdrop. Beneath the cool shade of a mature tree, a solitary wooden chair invites solitude and reflection, its smooth surface worn by the touch of countless visitors seeking a moment of tranquility in nature's embrace.
- num_frames
- 49
- guidance_scale
- 6
- num_inference_steps
- 40
{ "prompt": "A garden comes to life as a kaleidoscope of butterflies flutters amidst the blossoms, their delicate wings casting shadows on the petals below. In the background, a grand fountain cascades water with a gentle splendor, its rhythmic sound providing a soothing backdrop. Beneath the cool shade of a mature tree, a solitary wooden chair invites solitude and reflection, its smooth surface worn by the touch of countless visitors seeking a moment of tranquility in nature's embrace.", "num_frames": 49, "guidance_scale": 6, "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 thudm/cogvideox-t2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "thudm/cogvideox-t2v:e047b1d734c550671fb4de7f7df7f9341ed498b4aa7cd88b82533b60dfec33e3", { input: { prompt: "A garden comes to life as a kaleidoscope of butterflies flutters amidst the blossoms, their delicate wings casting shadows on the petals below. In the background, a grand fountain cascades water with a gentle splendor, its rhythmic sound providing a soothing backdrop. Beneath the cool shade of a mature tree, a solitary wooden chair invites solitude and reflection, its smooth surface worn by the touch of countless visitors seeking a moment of tranquility in nature's embrace.", num_frames: 49, guidance_scale: 6, num_inference_steps: 40 } } ); // 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 thudm/cogvideox-t2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "thudm/cogvideox-t2v:e047b1d734c550671fb4de7f7df7f9341ed498b4aa7cd88b82533b60dfec33e3", input={ "prompt": "A garden comes to life as a kaleidoscope of butterflies flutters amidst the blossoms, their delicate wings casting shadows on the petals below. In the background, a grand fountain cascades water with a gentle splendor, its rhythmic sound providing a soothing backdrop. Beneath the cool shade of a mature tree, a solitary wooden chair invites solitude and reflection, its smooth surface worn by the touch of countless visitors seeking a moment of tranquility in nature's embrace.", "num_frames": 49, "guidance_scale": 6, "num_inference_steps": 40 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run thudm/cogvideox-t2v 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": "thudm/cogvideox-t2v:e047b1d734c550671fb4de7f7df7f9341ed498b4aa7cd88b82533b60dfec33e3", "input": { "prompt": "A garden comes to life as a kaleidoscope of butterflies flutters amidst the blossoms, their delicate wings casting shadows on the petals below. In the background, a grand fountain cascades water with a gentle splendor, its rhythmic sound providing a soothing backdrop. Beneath the cool shade of a mature tree, a solitary wooden chair invites solitude and reflection, its smooth surface worn by the touch of countless visitors seeking a moment of tranquility in nature\'s embrace.", "num_frames": 49, "guidance_scale": 6, "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": "2024-09-21T14:14:34.819222Z", "created_at": "2024-09-21T14:05:49.907000Z", "data_removed": false, "error": null, "id": "rh6vtn9ntdrgj0cj2kks7nb1nw", "input": { "prompt": "A garden comes to life as a kaleidoscope of butterflies flutters amidst the blossoms, their delicate wings casting shadows on the petals below. In the background, a grand fountain cascades water with a gentle splendor, its rhythmic sound providing a soothing backdrop. Beneath the cool shade of a mature tree, a solitary wooden chair invites solitude and reflection, its smooth surface worn by the touch of countless visitors seeking a moment of tranquility in nature's embrace.", "num_frames": 49, "guidance_scale": 6, "num_inference_steps": 40 }, "logs": "Using seed: 43127\n 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:13<08:39, 13.31s/it]\n 5%|▌ | 2/40 [00:20<06:16, 9.91s/it]\n 8%|▊ | 3/40 [00:28<05:26, 8.82s/it]\n 10%|█ | 4/40 [00:35<04:59, 8.32s/it]\n 12%|█▎ | 5/40 [00:43<04:41, 8.06s/it]\n 15%|█▌ | 6/40 [00:51<04:28, 7.90s/it]\n 18%|█▊ | 7/40 [00:58<04:17, 7.81s/it]\n 20%|██ | 8/40 [01:06<04:07, 7.75s/it]\n 22%|██▎ | 9/40 [01:13<03:58, 7.71s/it]\n 25%|██▌ | 10/40 [01:21<03:50, 7.68s/it]\n 28%|██▊ | 11/40 [01:29<03:42, 7.66s/it]\n 30%|███ | 12/40 [01:36<03:34, 7.65s/it]\n 32%|███▎ | 13/40 [01:44<03:26, 7.65s/it]\n 35%|███▌ | 14/40 [01:52<03:18, 7.65s/it]\n 38%|███▊ | 15/40 [01:59<03:11, 7.65s/it]\n 40%|████ | 16/40 [02:07<03:03, 7.65s/it]\n 42%|████▎ | 17/40 [02:15<02:55, 7.65s/it]\n 45%|████▌ | 18/40 [02:22<02:48, 7.65s/it]\n 48%|████▊ | 19/40 [02:30<02:40, 7.66s/it]\n 50%|█████ | 20/40 [02:38<02:33, 7.66s/it]\n 52%|█████▎ | 21/40 [02:45<02:25, 7.66s/it]\n 55%|█████▌ | 22/40 [02:53<02:17, 7.66s/it]\n 57%|█████▊ | 23/40 [03:01<02:10, 7.66s/it]\n 60%|██████ | 24/40 [03:08<02:02, 7.66s/it]\n 62%|██████▎ | 25/40 [03:16<01:54, 7.66s/it]\n 65%|██████▌ | 26/40 [03:23<01:47, 7.66s/it]\n 68%|██████▊ | 27/40 [03:31<01:39, 7.66s/it]\n 70%|███████ | 28/40 [03:39<01:31, 7.66s/it]\n 72%|███████▎ | 29/40 [03:46<01:24, 7.66s/it]\n 75%|███████▌ | 30/40 [03:54<01:16, 7.67s/it]\n 78%|███████▊ | 31/40 [04:02<01:09, 7.67s/it]\n 80%|████████ | 32/40 [04:10<01:01, 7.67s/it]\n 82%|████████▎ | 33/40 [04:17<00:53, 7.67s/it]\n 85%|████████▌ | 34/40 [04:25<00:46, 7.67s/it]\n 88%|████████▊ | 35/40 [04:33<00:38, 7.67s/it]\n 90%|█████████ | 36/40 [04:40<00:30, 7.67s/it]\n 92%|█████████▎| 37/40 [04:48<00:23, 7.67s/it]\n 95%|█████████▌| 38/40 [04:56<00:15, 7.67s/it]\n 98%|█████████▊| 39/40 [05:03<00:07, 7.67s/it]\n100%|██████████| 40/40 [05:11<00:00, 7.67s/it]\n100%|██████████| 40/40 [05:11<00:00, 7.78s/it]", "metrics": { "predict_time": 330.069584572, "total_time": 524.912222 }, "output": "https://replicate.delivery/pbxt/FLJjz7WfbfhPaEToJHET3wbfTXP64OqtsYqIYk3Ib81S2QeNB/out.mp4", "started_at": "2024-09-21T14:09:04.749637Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/rh6vtn9ntdrgj0cj2kks7nb1nw", "cancel": "https://api.replicate.com/v1/predictions/rh6vtn9ntdrgj0cj2kks7nb1nw/cancel" }, "version": "e047b1d734c550671fb4de7f7df7f9341ed498b4aa7cd88b82533b60dfec33e3" }Generated inUsing seed: 43127 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:13<08:39, 13.31s/it] 5%|▌ | 2/40 [00:20<06:16, 9.91s/it] 8%|▊ | 3/40 [00:28<05:26, 8.82s/it] 10%|█ | 4/40 [00:35<04:59, 8.32s/it] 12%|█▎ | 5/40 [00:43<04:41, 8.06s/it] 15%|█▌ | 6/40 [00:51<04:28, 7.90s/it] 18%|█▊ | 7/40 [00:58<04:17, 7.81s/it] 20%|██ | 8/40 [01:06<04:07, 7.75s/it] 22%|██▎ | 9/40 [01:13<03:58, 7.71s/it] 25%|██▌ | 10/40 [01:21<03:50, 7.68s/it] 28%|██▊ | 11/40 [01:29<03:42, 7.66s/it] 30%|███ | 12/40 [01:36<03:34, 7.65s/it] 32%|███▎ | 13/40 [01:44<03:26, 7.65s/it] 35%|███▌ | 14/40 [01:52<03:18, 7.65s/it] 38%|███▊ | 15/40 [01:59<03:11, 7.65s/it] 40%|████ | 16/40 [02:07<03:03, 7.65s/it] 42%|████▎ | 17/40 [02:15<02:55, 7.65s/it] 45%|████▌ | 18/40 [02:22<02:48, 7.65s/it] 48%|████▊ | 19/40 [02:30<02:40, 7.66s/it] 50%|█████ | 20/40 [02:38<02:33, 7.66s/it] 52%|█████▎ | 21/40 [02:45<02:25, 7.66s/it] 55%|█████▌ | 22/40 [02:53<02:17, 7.66s/it] 57%|█████▊ | 23/40 [03:01<02:10, 7.66s/it] 60%|██████ | 24/40 [03:08<02:02, 7.66s/it] 62%|██████▎ | 25/40 [03:16<01:54, 7.66s/it] 65%|██████▌ | 26/40 [03:23<01:47, 7.66s/it] 68%|██████▊ | 27/40 [03:31<01:39, 7.66s/it] 70%|███████ | 28/40 [03:39<01:31, 7.66s/it] 72%|███████▎ | 29/40 [03:46<01:24, 7.66s/it] 75%|███████▌ | 30/40 [03:54<01:16, 7.67s/it] 78%|███████▊ | 31/40 [04:02<01:09, 7.67s/it] 80%|████████ | 32/40 [04:10<01:01, 7.67s/it] 82%|████████▎ | 33/40 [04:17<00:53, 7.67s/it] 85%|████████▌ | 34/40 [04:25<00:46, 7.67s/it] 88%|████████▊ | 35/40 [04:33<00:38, 7.67s/it] 90%|█████████ | 36/40 [04:40<00:30, 7.67s/it] 92%|█████████▎| 37/40 [04:48<00:23, 7.67s/it] 95%|█████████▌| 38/40 [04:56<00:15, 7.67s/it] 98%|█████████▊| 39/40 [05:03<00:07, 7.67s/it] 100%|██████████| 40/40 [05:11<00:00, 7.67s/it] 100%|██████████| 40/40 [05:11<00:00, 7.78s/it]Prediction
thudm/cogvideox-t2v:e047b1d734c550671fb4de7f7df7f9341ed498b4aa7cd88b82533b60dfec33e3IDmb1d59r2n5rgj0cj2kma2kywqgStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- prompt
- A suited astronaut, with the red dust of Mars clinging to their boots, reaches out to shake hands with an alien being, their skin a shimmering blue, under the pink-tinged sky of the fourth planet. In the background, a sleek silver rocket, a beacon of human ingenuity, stands tall, its engines powered down, as the two representatives of different worlds exchange a historic greeting amidst the desolate beauty of the Martian landscape.
- num_frames
- 49
- guidance_scale
- 6
- num_inference_steps
- 50
{ "prompt": "A suited astronaut, with the red dust of Mars clinging to their boots, reaches out to shake hands with an alien being, their skin a shimmering blue, under the pink-tinged sky of the fourth planet. In the background, a sleek silver rocket, a beacon of human ingenuity, stands tall, its engines powered down, as the two representatives of different worlds exchange a historic greeting amidst the desolate beauty of the Martian landscape.", "num_frames": 49, "guidance_scale": 6, "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 thudm/cogvideox-t2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "thudm/cogvideox-t2v:e047b1d734c550671fb4de7f7df7f9341ed498b4aa7cd88b82533b60dfec33e3", { input: { prompt: "A suited astronaut, with the red dust of Mars clinging to their boots, reaches out to shake hands with an alien being, their skin a shimmering blue, under the pink-tinged sky of the fourth planet. In the background, a sleek silver rocket, a beacon of human ingenuity, stands tall, its engines powered down, as the two representatives of different worlds exchange a historic greeting amidst the desolate beauty of the Martian landscape.", num_frames: 49, guidance_scale: 6, 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 replicateImport the client:import replicateRun thudm/cogvideox-t2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "thudm/cogvideox-t2v:e047b1d734c550671fb4de7f7df7f9341ed498b4aa7cd88b82533b60dfec33e3", input={ "prompt": "A suited astronaut, with the red dust of Mars clinging to their boots, reaches out to shake hands with an alien being, their skin a shimmering blue, under the pink-tinged sky of the fourth planet. In the background, a sleek silver rocket, a beacon of human ingenuity, stands tall, its engines powered down, as the two representatives of different worlds exchange a historic greeting amidst the desolate beauty of the Martian landscape.", "num_frames": 49, "guidance_scale": 6, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run thudm/cogvideox-t2v 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": "thudm/cogvideox-t2v:e047b1d734c550671fb4de7f7df7f9341ed498b4aa7cd88b82533b60dfec33e3", "input": { "prompt": "A suited astronaut, with the red dust of Mars clinging to their boots, reaches out to shake hands with an alien being, their skin a shimmering blue, under the pink-tinged sky of the fourth planet. In the background, a sleek silver rocket, a beacon of human ingenuity, stands tall, its engines powered down, as the two representatives of different worlds exchange a historic greeting amidst the desolate beauty of the Martian landscape.", "num_frames": 49, "guidance_scale": 6, "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": "2024-09-21T14:16:28.807419Z", "created_at": "2024-09-21T14:06:42.345000Z", "data_removed": false, "error": null, "id": "mb1d59r2n5rgj0cj2kma2kywqg", "input": { "prompt": "A suited astronaut, with the red dust of Mars clinging to their boots, reaches out to shake hands with an alien being, their skin a shimmering blue, under the pink-tinged sky of the fourth planet. In the background, a sleek silver rocket, a beacon of human ingenuity, stands tall, its engines powered down, as the two representatives of different worlds exchange a historic greeting amidst the desolate beauty of the Martian landscape.", "num_frames": 49, "guidance_scale": 6, "num_inference_steps": 50 }, "logs": "Using seed: 29427\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:13<11:16, 13.82s/it]\n 4%|▍ | 2/50 [00:21<08:04, 10.10s/it]\n 6%|▌ | 3/50 [00:28<06:59, 8.94s/it]\n 8%|▊ | 4/50 [00:36<06:26, 8.40s/it]\n 10%|█ | 5/50 [00:44<06:04, 8.10s/it]\n 12%|█▏ | 6/50 [00:51<05:48, 7.92s/it]\n 14%|█▍ | 7/50 [00:59<05:36, 7.82s/it]\n 16%|█▌ | 8/50 [01:06<05:25, 7.75s/it]\n 18%|█▊ | 9/50 [01:14<05:16, 7.71s/it]\n 20%|██ | 10/50 [01:22<05:07, 7.68s/it]\n 22%|██▏ | 11/50 [01:29<04:59, 7.67s/it]\n 24%|██▍ | 12/50 [01:37<04:51, 7.66s/it]\n 26%|██▌ | 13/50 [01:44<04:43, 7.66s/it]\n 28%|██▊ | 14/50 [01:52<04:35, 7.66s/it]\n 30%|███ | 15/50 [02:00<04:28, 7.66s/it]\n 32%|███▏ | 16/50 [02:07<04:20, 7.66s/it]\n 34%|███▍ | 17/50 [02:15<04:12, 7.66s/it]\n 36%|███▌ | 18/50 [02:23<04:05, 7.67s/it]\n 38%|███▊ | 19/50 [02:30<03:57, 7.67s/it]\n 40%|████ | 20/50 [02:38<03:50, 7.67s/it]\n 42%|████▏ | 21/50 [02:46<03:42, 7.67s/it]\n 44%|████▍ | 22/50 [02:53<03:34, 7.67s/it]\n 46%|████▌ | 23/50 [03:01<03:27, 7.67s/it]\n 48%|████▊ | 24/50 [03:09<03:19, 7.67s/it]\n 50%|█████ | 25/50 [03:16<03:11, 7.67s/it]\n 52%|█████▏ | 26/50 [03:24<03:04, 7.67s/it]\n 54%|█████▍ | 27/50 [03:32<02:56, 7.67s/it]\n 56%|█████▌ | 28/50 [03:40<02:50, 7.73s/it]\n 58%|█████▊ | 29/50 [03:47<02:41, 7.71s/it]\n 60%|██████ | 30/50 [03:55<02:33, 7.70s/it]\n 62%|██████▏ | 31/50 [04:03<02:26, 7.69s/it]\n 64%|██████▍ | 32/50 [04:10<02:18, 7.68s/it]\n 66%|██████▌ | 33/50 [04:18<02:10, 7.68s/it]\n 68%|██████▊ | 34/50 [04:26<02:02, 7.67s/it]\n 70%|███████ | 35/50 [04:33<01:55, 7.67s/it]\n 72%|███████▏ | 36/50 [04:41<01:47, 7.67s/it]\n 74%|███████▍ | 37/50 [04:49<01:39, 7.67s/it]\n 76%|███████▌ | 38/50 [04:56<01:32, 7.67s/it]\n 78%|███████▊ | 39/50 [05:04<01:24, 7.67s/it]\n 80%|████████ | 40/50 [05:12<01:16, 7.67s/it]\n 82%|████████▏ | 41/50 [05:19<01:09, 7.69s/it]\n 84%|████████▍ | 42/50 [05:27<01:01, 7.68s/it]\n 86%|████████▌ | 43/50 [05:35<00:53, 7.68s/it]\n 88%|████████▊ | 44/50 [05:42<00:46, 7.68s/it]\n 90%|█████████ | 45/50 [05:50<00:38, 7.67s/it]\n 92%|█████████▏| 46/50 [05:58<00:30, 7.67s/it]\n 94%|█████████▍| 47/50 [06:05<00:23, 7.67s/it]\n 96%|█████████▌| 48/50 [06:13<00:15, 7.67s/it]\n 98%|█████████▊| 49/50 [06:21<00:07, 7.67s/it]\n100%|██████████| 50/50 [06:28<00:00, 7.66s/it]\n100%|██████████| 50/50 [06:28<00:00, 7.78s/it]", "metrics": { "predict_time": 409.729716065, "total_time": 586.462419 }, "output": "https://replicate.delivery/pbxt/Sd5gUOfKlfrDm0urWCXceD9fAFEko9nD3L6BgxTfxa8knD5bC/out.mp4", "started_at": "2024-09-21T14:09:39.077703Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/mb1d59r2n5rgj0cj2kma2kywqg", "cancel": "https://api.replicate.com/v1/predictions/mb1d59r2n5rgj0cj2kma2kywqg/cancel" }, "version": "e047b1d734c550671fb4de7f7df7f9341ed498b4aa7cd88b82533b60dfec33e3" }Generated inUsing seed: 29427 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:13<11:16, 13.82s/it] 4%|▍ | 2/50 [00:21<08:04, 10.10s/it] 6%|▌ | 3/50 [00:28<06:59, 8.94s/it] 8%|▊ | 4/50 [00:36<06:26, 8.40s/it] 10%|█ | 5/50 [00:44<06:04, 8.10s/it] 12%|█▏ | 6/50 [00:51<05:48, 7.92s/it] 14%|█▍ | 7/50 [00:59<05:36, 7.82s/it] 16%|█▌ | 8/50 [01:06<05:25, 7.75s/it] 18%|█▊ | 9/50 [01:14<05:16, 7.71s/it] 20%|██ | 10/50 [01:22<05:07, 7.68s/it] 22%|██▏ | 11/50 [01:29<04:59, 7.67s/it] 24%|██▍ | 12/50 [01:37<04:51, 7.66s/it] 26%|██▌ | 13/50 [01:44<04:43, 7.66s/it] 28%|██▊ | 14/50 [01:52<04:35, 7.66s/it] 30%|███ | 15/50 [02:00<04:28, 7.66s/it] 32%|███▏ | 16/50 [02:07<04:20, 7.66s/it] 34%|███▍ | 17/50 [02:15<04:12, 7.66s/it] 36%|███▌ | 18/50 [02:23<04:05, 7.67s/it] 38%|███▊ | 19/50 [02:30<03:57, 7.67s/it] 40%|████ | 20/50 [02:38<03:50, 7.67s/it] 42%|████▏ | 21/50 [02:46<03:42, 7.67s/it] 44%|████▍ | 22/50 [02:53<03:34, 7.67s/it] 46%|████▌ | 23/50 [03:01<03:27, 7.67s/it] 48%|████▊ | 24/50 [03:09<03:19, 7.67s/it] 50%|█████ | 25/50 [03:16<03:11, 7.67s/it] 52%|█████▏ | 26/50 [03:24<03:04, 7.67s/it] 54%|█████▍ | 27/50 [03:32<02:56, 7.67s/it] 56%|█████▌ | 28/50 [03:40<02:50, 7.73s/it] 58%|█████▊ | 29/50 [03:47<02:41, 7.71s/it] 60%|██████ | 30/50 [03:55<02:33, 7.70s/it] 62%|██████▏ | 31/50 [04:03<02:26, 7.69s/it] 64%|██████▍ | 32/50 [04:10<02:18, 7.68s/it] 66%|██████▌ | 33/50 [04:18<02:10, 7.68s/it] 68%|██████▊ | 34/50 [04:26<02:02, 7.67s/it] 70%|███████ | 35/50 [04:33<01:55, 7.67s/it] 72%|███████▏ | 36/50 [04:41<01:47, 7.67s/it] 74%|███████▍ | 37/50 [04:49<01:39, 7.67s/it] 76%|███████▌ | 38/50 [04:56<01:32, 7.67s/it] 78%|███████▊ | 39/50 [05:04<01:24, 7.67s/it] 80%|████████ | 40/50 [05:12<01:16, 7.67s/it] 82%|████████▏ | 41/50 [05:19<01:09, 7.69s/it] 84%|████████▍ | 42/50 [05:27<01:01, 7.68s/it] 86%|████████▌ | 43/50 [05:35<00:53, 7.68s/it] 88%|████████▊ | 44/50 [05:42<00:46, 7.68s/it] 90%|█████████ | 45/50 [05:50<00:38, 7.67s/it] 92%|█████████▏| 46/50 [05:58<00:30, 7.67s/it] 94%|█████████▍| 47/50 [06:05<00:23, 7.67s/it] 96%|█████████▌| 48/50 [06:13<00:15, 7.67s/it] 98%|█████████▊| 49/50 [06:21<00:07, 7.67s/it] 100%|██████████| 50/50 [06:28<00:00, 7.66s/it] 100%|██████████| 50/50 [06:28<00:00, 7.78s/it]Prediction
thudm/cogvideox-t2v:e047b1d734c550671fb4de7f7df7f9341ed498b4aa7cd88b82533b60dfec33e3IDq1cg0ctgqxrgg0cj2mea8g8b8wStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedby @chenxwhInput
- prompt
- A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical atmosphere of this unique musical performance.
- num_frames
- 49
- guidance_scale
- 6
- num_inference_steps
- 50
{ "prompt": "A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical atmosphere of this unique musical performance.", "num_frames": 49, "guidance_scale": 6, "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 thudm/cogvideox-t2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "thudm/cogvideox-t2v:e047b1d734c550671fb4de7f7df7f9341ed498b4aa7cd88b82533b60dfec33e3", { input: { prompt: "A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical atmosphere of this unique musical performance.", num_frames: 49, guidance_scale: 6, 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 replicateImport the client:import replicateRun thudm/cogvideox-t2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "thudm/cogvideox-t2v:e047b1d734c550671fb4de7f7df7f9341ed498b4aa7cd88b82533b60dfec33e3", input={ "prompt": "A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical atmosphere of this unique musical performance.", "num_frames": 49, "guidance_scale": 6, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run thudm/cogvideox-t2v 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": "thudm/cogvideox-t2v:e047b1d734c550671fb4de7f7df7f9341ed498b4aa7cd88b82533b60dfec33e3", "input": { "prompt": "A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. The panda\'s fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, casting a gentle glow on the scene. The panda\'s face is expressive, showing concentration and joy as it plays. The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical atmosphere of this unique musical performance.", "num_frames": 49, "guidance_scale": 6, "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": "2024-09-21T15:12:07.197022Z", "created_at": "2024-09-21T15:03:50.207000Z", "data_removed": false, "error": null, "id": "q1cg0ctgqxrgg0cj2mea8g8b8w", "input": { "prompt": "A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical atmosphere of this unique musical performance.", "num_frames": 49, "guidance_scale": 6, "num_inference_steps": 50 }, "logs": "Using seed: 61175\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:13<10:44, 13.16s/it]\n 4%|▍ | 2/50 [00:20<07:52, 9.85s/it]\n 6%|▌ | 3/50 [00:28<06:53, 8.79s/it]\n 8%|▊ | 4/50 [00:35<06:25, 8.38s/it]\n 10%|█ | 5/50 [00:44<06:25, 8.58s/it]\n 12%|█▏ | 6/50 [00:52<06:02, 8.24s/it]\n 14%|█▍ | 7/50 [01:00<05:45, 8.03s/it]\n 16%|█▌ | 8/50 [01:07<05:31, 7.90s/it]\n 18%|█▊ | 9/50 [01:15<05:19, 7.80s/it]\n 20%|██ | 10/50 [01:22<05:09, 7.74s/it]\n 22%|██▏ | 11/50 [01:30<05:00, 7.71s/it]\n 24%|██▍ | 12/50 [01:38<04:53, 7.71s/it]\n 26%|██▌ | 13/50 [01:45<04:44, 7.69s/it]\n 28%|██▊ | 14/50 [01:53<04:35, 7.67s/it]\n 30%|███ | 15/50 [02:01<04:27, 7.65s/it]\n 32%|███▏ | 16/50 [02:08<04:20, 7.65s/it]\n 34%|███▍ | 17/50 [02:16<04:12, 7.64s/it]\n 36%|███▌ | 18/50 [02:24<04:04, 7.64s/it]\n 38%|███▊ | 19/50 [02:31<03:56, 7.64s/it]\n 40%|████ | 20/50 [02:39<03:49, 7.64s/it]\n 42%|████▏ | 21/50 [02:46<03:41, 7.64s/it]\n 44%|████▍ | 22/50 [02:54<03:34, 7.64s/it]\n 46%|████▌ | 23/50 [03:02<03:26, 7.64s/it]\n 48%|████▊ | 24/50 [03:09<03:18, 7.65s/it]\n 50%|█████ | 25/50 [03:17<03:11, 7.66s/it]\n 52%|█████▏ | 26/50 [03:25<03:03, 7.66s/it]\n 54%|█████▍ | 27/50 [03:32<02:56, 7.66s/it]\n 56%|█████▌ | 28/50 [03:40<02:48, 7.65s/it]\n 58%|█████▊ | 29/50 [03:48<02:40, 7.65s/it]\n 60%|██████ | 30/50 [03:55<02:33, 7.65s/it]\n 62%|██████▏ | 31/50 [04:03<02:25, 7.65s/it]\n 64%|██████▍ | 32/50 [04:11<02:17, 7.65s/it]\n 66%|██████▌ | 33/50 [04:18<02:10, 7.65s/it]\n 68%|██████▊ | 34/50 [04:26<02:02, 7.65s/it]\n 70%|███████ | 35/50 [04:34<01:54, 7.65s/it]\n 72%|███████▏ | 36/50 [04:41<01:47, 7.65s/it]\n 74%|███████▍ | 37/50 [04:49<01:39, 7.66s/it]\n 76%|███████▌ | 38/50 [04:57<01:32, 7.75s/it]\n 78%|███████▊ | 39/50 [05:05<01:24, 7.72s/it]\n 80%|████████ | 40/50 [05:12<01:16, 7.70s/it]\n 82%|████████▏ | 41/50 [05:20<01:09, 7.68s/it]\n 84%|████████▍ | 42/50 [05:27<01:01, 7.67s/it]\n 86%|████████▌ | 43/50 [05:35<00:53, 7.67s/it]\n 88%|████████▊ | 44/50 [05:43<00:45, 7.66s/it]\n 90%|█████████ | 45/50 [05:50<00:38, 7.66s/it]\n 92%|█████████▏| 46/50 [05:59<00:31, 7.79s/it]\n 94%|█████████▍| 47/50 [06:07<00:23, 7.96s/it]\n 96%|█████████▌| 48/50 [06:15<00:15, 7.86s/it]\n 98%|█████████▊| 49/50 [06:22<00:07, 7.88s/it]\n100%|██████████| 50/50 [06:30<00:00, 7.81s/it]\n100%|██████████| 50/50 [06:30<00:00, 7.81s/it]", "metrics": { "predict_time": 414.891614188, "total_time": 496.990022 }, "output": "https://replicate.delivery/pbxt/6ed1CxHaTtSIaqBxf4hVmihfIeNPEU8yrjX0pmV8K8cBEl8NB/out.mp4", "started_at": "2024-09-21T15:05:12.305408Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/q1cg0ctgqxrgg0cj2mea8g8b8w", "cancel": "https://api.replicate.com/v1/predictions/q1cg0ctgqxrgg0cj2mea8g8b8w/cancel" }, "version": "e047b1d734c550671fb4de7f7df7f9341ed498b4aa7cd88b82533b60dfec33e3" }Generated inUsing seed: 61175 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:13<10:44, 13.16s/it] 4%|▍ | 2/50 [00:20<07:52, 9.85s/it] 6%|▌ | 3/50 [00:28<06:53, 8.79s/it] 8%|▊ | 4/50 [00:35<06:25, 8.38s/it] 10%|█ | 5/50 [00:44<06:25, 8.58s/it] 12%|█▏ | 6/50 [00:52<06:02, 8.24s/it] 14%|█▍ | 7/50 [01:00<05:45, 8.03s/it] 16%|█▌ | 8/50 [01:07<05:31, 7.90s/it] 18%|█▊ | 9/50 [01:15<05:19, 7.80s/it] 20%|██ | 10/50 [01:22<05:09, 7.74s/it] 22%|██▏ | 11/50 [01:30<05:00, 7.71s/it] 24%|██▍ | 12/50 [01:38<04:53, 7.71s/it] 26%|██▌ | 13/50 [01:45<04:44, 7.69s/it] 28%|██▊ | 14/50 [01:53<04:35, 7.67s/it] 30%|███ | 15/50 [02:01<04:27, 7.65s/it] 32%|███▏ | 16/50 [02:08<04:20, 7.65s/it] 34%|███▍ | 17/50 [02:16<04:12, 7.64s/it] 36%|███▌ | 18/50 [02:24<04:04, 7.64s/it] 38%|███▊ | 19/50 [02:31<03:56, 7.64s/it] 40%|████ | 20/50 [02:39<03:49, 7.64s/it] 42%|████▏ | 21/50 [02:46<03:41, 7.64s/it] 44%|████▍ | 22/50 [02:54<03:34, 7.64s/it] 46%|████▌ | 23/50 [03:02<03:26, 7.64s/it] 48%|████▊ | 24/50 [03:09<03:18, 7.65s/it] 50%|█████ | 25/50 [03:17<03:11, 7.66s/it] 52%|█████▏ | 26/50 [03:25<03:03, 7.66s/it] 54%|█████▍ | 27/50 [03:32<02:56, 7.66s/it] 56%|█████▌ | 28/50 [03:40<02:48, 7.65s/it] 58%|█████▊ | 29/50 [03:48<02:40, 7.65s/it] 60%|██████ | 30/50 [03:55<02:33, 7.65s/it] 62%|██████▏ | 31/50 [04:03<02:25, 7.65s/it] 64%|██████▍ | 32/50 [04:11<02:17, 7.65s/it] 66%|██████▌ | 33/50 [04:18<02:10, 7.65s/it] 68%|██████▊ | 34/50 [04:26<02:02, 7.65s/it] 70%|███████ | 35/50 [04:34<01:54, 7.65s/it] 72%|███████▏ | 36/50 [04:41<01:47, 7.65s/it] 74%|███████▍ | 37/50 [04:49<01:39, 7.66s/it] 76%|███████▌ | 38/50 [04:57<01:32, 7.75s/it] 78%|███████▊ | 39/50 [05:05<01:24, 7.72s/it] 80%|████████ | 40/50 [05:12<01:16, 7.70s/it] 82%|████████▏ | 41/50 [05:20<01:09, 7.68s/it] 84%|████████▍ | 42/50 [05:27<01:01, 7.67s/it] 86%|████████▌ | 43/50 [05:35<00:53, 7.67s/it] 88%|████████▊ | 44/50 [05:43<00:45, 7.66s/it] 90%|█████████ | 45/50 [05:50<00:38, 7.66s/it] 92%|█████████▏| 46/50 [05:59<00:31, 7.79s/it] 94%|█████████▍| 47/50 [06:07<00:23, 7.96s/it] 96%|█████████▌| 48/50 [06:15<00:15, 7.86s/it] 98%|█████████▊| 49/50 [06:22<00:07, 7.88s/it] 100%|██████████| 50/50 [06:30<00:00, 7.81s/it] 100%|██████████| 50/50 [06:30<00:00, 7.81s/it]
Want to make some of these yourself?
Run this model