cjwbw / compositional-vsual-generation-with-composable-diffusion-models-pytorch
Composable Diffusion
Prediction
cjwbw/compositional-vsual-generation-with-composable-diffusion-models-pytorch:9093eef2cad6a0445f108067a38e1a17e22f8c8073d9f7d60d95cf354def4d29Input
- prompt
- A red car parked in a desert | Hills behind the car | Aurora in the sky
{ "prompt": "A red car parked in a desert | Hills behind the car | Aurora in the sky" }
Install Replicate’s Node.js client library:npm install replicate
Import 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 cjwbw/compositional-vsual-generation-with-composable-diffusion-models-pytorch using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cjwbw/compositional-vsual-generation-with-composable-diffusion-models-pytorch:9093eef2cad6a0445f108067a38e1a17e22f8c8073d9f7d60d95cf354def4d29", { input: { prompt: "A red car parked in a desert | Hills behind the car | Aurora in the sky" } } ); // 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 cjwbw/compositional-vsual-generation-with-composable-diffusion-models-pytorch using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cjwbw/compositional-vsual-generation-with-composable-diffusion-models-pytorch:9093eef2cad6a0445f108067a38e1a17e22f8c8073d9f7d60d95cf354def4d29", input={ "prompt": "A red car parked in a desert | Hills behind the car | Aurora in the sky" } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run cjwbw/compositional-vsual-generation-with-composable-diffusion-models-pytorch 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": "cjwbw/compositional-vsual-generation-with-composable-diffusion-models-pytorch:9093eef2cad6a0445f108067a38e1a17e22f8c8073d9f7d60d95cf354def4d29", "input": { "prompt": "A red car parked in a desert | Hills behind the car | Aurora in the sky" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-08-04T18:05:56.206371Z", "created_at": "2022-08-04T18:05:36.641735Z", "data_removed": false, "error": null, "id": "sbtd2l4mnbh63hwdrz4ygfkfve", "input": { "prompt": "A red car parked in a desert | Hills behind the car | Aurora in the sky" }, "logs": "\n 0%| | 0/100 [00:00<?, ?it/s]\n 1%| | 1/100 [00:00<00:20, 4.85it/s]\n 2%|▏ | 2/100 [00:00<00:18, 5.36it/s]\n 3%|▎ | 3/100 [00:00<00:16, 5.79it/s]\n 4%|▍ | 4/100 [00:00<00:15, 6.14it/s]\n 5%|▌ | 5/100 [00:00<00:14, 6.41it/s]\n 6%|▌ | 6/100 [00:00<00:14, 6.63it/s]\n 7%|▋ | 7/100 [00:01<00:13, 6.78it/s]\n 8%|▊ | 8/100 [00:01<00:13, 6.88it/s]\n 9%|▉ | 9/100 [00:01<00:13, 6.95it/s]\n 10%|█ | 10/100 [00:01<00:12, 7.01it/s]\n 11%|█ | 11/100 [00:01<00:12, 7.04it/s]\n 12%|█▏ | 12/100 [00:01<00:12, 7.07it/s]\n 13%|█▎ | 13/100 [00:01<00:12, 7.11it/s]\n 14%|█▍ | 14/100 [00:02<00:12, 7.10it/s]\n 15%|█▌ | 15/100 [00:02<00:11, 7.09it/s]\n 16%|█▌ | 16/100 [00:02<00:11, 7.10it/s]\n 17%|█▋ | 17/100 [00:02<00:11, 7.12it/s]\n 18%|█▊ 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5.85it/s]\n 96%|█████████▋| 26/27 [00:04<00:00, 5.86it/s]\n100%|██████████| 27/27 [00:04<00:00, 5.83it/s]\n100%|██████████| 27/27 [00:04<00:00, 5.84it/s]", "metrics": { "predict_time": 19.391179, "total_time": 19.564636 }, "output": "https://replicate.delivery/mgxm/9de5cffc-da99-43d0-9391-7ef4e9d4be1e/output.png", "started_at": "2022-08-04T18:05:36.815192Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/sbtd2l4mnbh63hwdrz4ygfkfve", "cancel": "https://api.replicate.com/v1/predictions/sbtd2l4mnbh63hwdrz4ygfkfve/cancel" }, "version": "9093eef2cad6a0445f108067a38e1a17e22f8c8073d9f7d60d95cf354def4d29" }
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Prediction
cjwbw/compositional-vsual-generation-with-composable-diffusion-models-pytorch:9093eef2cad6a0445f108067a38e1a17e22f8c8073d9f7d60d95cf354def4d29IDt3sbrmwfu5hujkk74povqzvfqqStatusSucceededSourceWebHardware–Total durationCreatedInput
- prompt
- a road leading into mountains | red trees on side of the road
{ "prompt": "a road leading into mountains | red trees on side of the road" }
Install Replicate’s Node.js client library:npm install replicate
Import 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 cjwbw/compositional-vsual-generation-with-composable-diffusion-models-pytorch using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cjwbw/compositional-vsual-generation-with-composable-diffusion-models-pytorch:9093eef2cad6a0445f108067a38e1a17e22f8c8073d9f7d60d95cf354def4d29", { input: { prompt: "a road leading into mountains | red trees on side of the road" } } ); // 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 cjwbw/compositional-vsual-generation-with-composable-diffusion-models-pytorch using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cjwbw/compositional-vsual-generation-with-composable-diffusion-models-pytorch:9093eef2cad6a0445f108067a38e1a17e22f8c8073d9f7d60d95cf354def4d29", input={ "prompt": "a road leading into mountains | red trees on side of the road" } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run cjwbw/compositional-vsual-generation-with-composable-diffusion-models-pytorch 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": "cjwbw/compositional-vsual-generation-with-composable-diffusion-models-pytorch:9093eef2cad6a0445f108067a38e1a17e22f8c8073d9f7d60d95cf354def4d29", "input": { "prompt": "a road leading into mountains | red trees on side of the road" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-08-04T18:06:48.906321Z", "created_at": "2022-08-04T18:06:31.917293Z", "data_removed": false, "error": null, "id": "t3sbrmwfu5hujkk74povqzvfqq", "input": { "prompt": "a road leading into mountains | red trees on side of the road" }, "logs": "\n 0%| | 0/100 [00:00<?, ?it/s]\n 1%| | 1/100 [00:00<00:18, 5.45it/s]\n 2%|▏ | 2/100 [00:00<00:16, 5.82it/s]\n 3%|▎ | 3/100 [00:00<00:14, 6.47it/s]\n 4%|▍ | 4/100 [00:00<00:13, 7.04it/s]\n 5%|▌ | 5/100 [00:00<00:12, 7.46it/s]\n 6%|▌ | 6/100 [00:00<00:12, 7.80it/s]\n 7%|▋ | 7/100 [00:00<00:11, 8.09it/s]\n 8%|▊ | 8/100 [00:01<00:11, 8.26it/s]\n 9%|▉ | 9/100 [00:01<00:10, 8.43it/s]\n 10%|█ | 10/100 [00:01<00:10, 8.53it/s]\n 11%|█ | 11/100 [00:01<00:10, 8.62it/s]\n 12%|█▏ | 12/100 [00:01<00:10, 8.69it/s]\n 13%|█▎ | 13/100 [00:01<00:09, 8.71it/s]\n 14%|█▍ | 14/100 [00:01<00:09, 8.77it/s]\n 15%|█▌ | 15/100 [00:01<00:09, 8.77it/s]\n 16%|█▌ | 16/100 [00:01<00:09, 8.78it/s]\n 17%|█▋ | 17/100 [00:02<00:09, 8.78it/s]\n 18%|█▊ | 18/100 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5.80it/s]\n 96%|█████████▋| 26/27 [00:04<00:00, 5.80it/s]\n100%|██████████| 27/27 [00:04<00:00, 5.79it/s]\n100%|██████████| 27/27 [00:04<00:00, 5.81it/s]", "metrics": { "predict_time": 16.826057, "total_time": 16.989028 }, "output": "https://replicate.delivery/mgxm/c1e8102d-1b7a-4d47-add8-6df91795a98c/output.png", "started_at": "2022-08-04T18:06:32.080264Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/t3sbrmwfu5hujkk74povqzvfqq", "cancel": "https://api.replicate.com/v1/predictions/t3sbrmwfu5hujkk74povqzvfqq/cancel" }, "version": "9093eef2cad6a0445f108067a38e1a17e22f8c8073d9f7d60d95cf354def4d29" }
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Prediction
cjwbw/compositional-vsual-generation-with-composable-diffusion-models-pytorch:9093eef2cad6a0445f108067a38e1a17e22f8c8073d9f7d60d95cf354def4d29IDr3uvrnxvyzfexiy723dbrjadhiStatusSucceededSourceWebHardware–Total durationCreatedInput
- prompt
- A Ferris wheel | A lake next to the Ferris wheel | Buildings next to the lake
{ "prompt": "A Ferris wheel | A lake next to the Ferris wheel | Buildings next to the lake" }
Install Replicate’s Node.js client library:npm install replicate
Import 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 cjwbw/compositional-vsual-generation-with-composable-diffusion-models-pytorch using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cjwbw/compositional-vsual-generation-with-composable-diffusion-models-pytorch:9093eef2cad6a0445f108067a38e1a17e22f8c8073d9f7d60d95cf354def4d29", { input: { prompt: "A Ferris wheel | A lake next to the Ferris wheel | Buildings next to the lake" } } ); // 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 cjwbw/compositional-vsual-generation-with-composable-diffusion-models-pytorch using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cjwbw/compositional-vsual-generation-with-composable-diffusion-models-pytorch:9093eef2cad6a0445f108067a38e1a17e22f8c8073d9f7d60d95cf354def4d29", input={ "prompt": "A Ferris wheel | A lake next to the Ferris wheel | Buildings next to the lake" } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run cjwbw/compositional-vsual-generation-with-composable-diffusion-models-pytorch 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": "cjwbw/compositional-vsual-generation-with-composable-diffusion-models-pytorch:9093eef2cad6a0445f108067a38e1a17e22f8c8073d9f7d60d95cf354def4d29", "input": { "prompt": "A Ferris wheel | A lake next to the Ferris wheel | Buildings next to the lake" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-08-04T18:07:17.867926Z", "created_at": "2022-08-04T18:06:57.924036Z", "data_removed": false, "error": null, "id": "r3uvrnxvyzfexiy723dbrjadhi", "input": { "prompt": "A Ferris wheel | A lake next to the Ferris wheel | Buildings next to the lake" }, "logs": "\n 0%| | 0/100 [00:00<?, ?it/s]\n 1%| | 1/100 [00:00<00:19, 5.08it/s]\n 2%|▏ | 2/100 [00:00<00:18, 5.38it/s]\n 3%|▎ | 3/100 [00:00<00:16, 5.79it/s]\n 4%|▍ | 4/100 [00:00<00:15, 6.12it/s]\n 5%|▌ | 5/100 [00:00<00:14, 6.36it/s]\n 6%|▌ | 6/100 [00:00<00:14, 6.54it/s]\n 7%|▋ | 7/100 [00:01<00:13, 6.66it/s]\n 8%|▊ | 8/100 [00:01<00:13, 6.77it/s]\n 9%|▉ | 9/100 [00:01<00:13, 6.84it/s]\n 10%|█ | 10/100 [00:01<00:13, 6.90it/s]\n 11%|█ | 11/100 [00:01<00:12, 6.94it/s]\n 12%|█▏ | 12/100 [00:01<00:12, 6.95it/s]\n 13%|█▎ | 13/100 [00:01<00:12, 6.94it/s]\n 14%|█▍ | 14/100 [00:02<00:12, 6.97it/s]\n 15%|█▌ | 15/100 [00:02<00:12, 6.96it/s]\n 16%|█▌ | 16/100 [00:02<00:12, 6.96it/s]\n 17%|█▋ | 17/100 [00:02<00:11, 6.95it/s]\n 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5.69it/s]\n 96%|█████████▋| 26/27 [00:04<00:00, 5.69it/s]\n100%|██████████| 27/27 [00:04<00:00, 5.66it/s]\n100%|██████████| 27/27 [00:04<00:00, 5.70it/s]", "metrics": { "predict_time": 19.756739, "total_time": 19.94389 }, "output": "https://replicate.delivery/mgxm/27e99b02-fd91-4e07-978b-ff98a93258fe/output.png", "started_at": "2022-08-04T18:06:58.111187Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/r3uvrnxvyzfexiy723dbrjadhi", "cancel": "https://api.replicate.com/v1/predictions/r3uvrnxvyzfexiy723dbrjadhi/cancel" }, "version": "9093eef2cad6a0445f108067a38e1a17e22f8c8073d9f7d60d95cf354def4d29" }
Generated in0%| | 0/100 [00:00<?, ?it/s] 1%| | 1/100 [00:00<00:19, 5.08it/s] 2%|▏ | 2/100 [00:00<00:18, 5.38it/s] 3%|▎ | 3/100 [00:00<00:16, 5.79it/s] 4%|▍ | 4/100 [00:00<00:15, 6.12it/s] 5%|▌ | 5/100 [00:00<00:14, 6.36it/s] 6%|▌ | 6/100 [00:00<00:14, 6.54it/s] 7%|▋ | 7/100 [00:01<00:13, 6.66it/s] 8%|▊ | 8/100 [00:01<00:13, 6.77it/s] 9%|▉ | 9/100 [00:01<00:13, 6.84it/s] 10%|█ | 10/100 [00:01<00:13, 6.90it/s] 11%|█ | 11/100 [00:01<00:12, 6.94it/s] 12%|█▏ | 12/100 [00:01<00:12, 6.95it/s] 13%|█▎ | 13/100 [00:01<00:12, 6.94it/s] 14%|█▍ | 14/100 [00:02<00:12, 6.97it/s] 15%|█▌ | 15/100 [00:02<00:12, 6.96it/s] 16%|█▌ | 16/100 [00:02<00:12, 6.96it/s] 17%|█▋ | 17/100 [00:02<00:11, 6.95it/s] 18%|█▊ | 18/100 [00:02<00:11, 6.95it/s] 19%|█▉ | 19/100 [00:02<00:11, 6.95it/s] 20%|██ | 20/100 [00:02<00:11, 6.93it/s] 21%|██ | 21/100 [00:03<00:11, 6.95it/s] 22%|██▏ | 22/100 [00:03<00:11, 6.93it/s] 23%|██▎ | 23/100 [00:03<00:11, 6.93it/s] 24%|██▍ | 24/100 [00:03<00:10, 6.94it/s] 25%|██▌ | 25/100 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5.71it/s] 56%|█████▌ | 15/27 [00:02<00:02, 5.69it/s] 59%|█████▉ | 16/27 [00:02<00:01, 5.68it/s] 63%|██████▎ | 17/27 [00:02<00:01, 5.69it/s] 67%|██████▋ | 18/27 [00:03<00:01, 5.70it/s] 70%|███████ | 19/27 [00:03<00:01, 5.70it/s] 74%|███████▍ | 20/27 [00:03<00:01, 5.70it/s] 78%|███████▊ | 21/27 [00:03<00:01, 5.69it/s] 81%|████████▏ | 22/27 [00:03<00:00, 5.69it/s] 85%|████████▌ | 23/27 [00:04<00:00, 5.70it/s] 89%|████████▉ | 24/27 [00:04<00:00, 5.69it/s] 93%|█████████▎| 25/27 [00:04<00:00, 5.69it/s] 96%|█████████▋| 26/27 [00:04<00:00, 5.69it/s] 100%|██████████| 27/27 [00:04<00:00, 5.66it/s] 100%|██████████| 27/27 [00:04<00:00, 5.70it/s]
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