andreasjansson / monkey-island-rdm
Monkey Island database for Retrieval-augmented Diffusion model
Prediction
andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63ID6zl3evu2u5dotcmiim53yx7qvqStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- -1
- scale
- 5
- steps
- 100
- prompt
- Guybrush sword fighting a giant squid on a pirate ship
- num_database_results
- 10
{ "seed": -1, "scale": 5, "steps": 100, "prompt": "Guybrush sword fighting a giant squid on a pirate ship", "num_database_results": 10 }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 andreasjansson/monkey-island-rdm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", { input: { seed: -1, scale: 5, steps: 100, prompt: "Guybrush sword fighting a giant squid on a pirate ship", num_database_results: 10 } } ); // 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 andreasjansson/monkey-island-rdm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", input={ "seed": -1, "scale": 5, "steps": 100, "prompt": "Guybrush sword fighting a giant squid on a pirate ship", "num_database_results": 10 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run andreasjansson/monkey-island-rdm 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": "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", "input": { "seed": -1, "scale": 5, "steps": 100, "prompt": "Guybrush sword fighting a giant squid on a pirate ship", "num_database_results": 10 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-08-19T16:14:47.906579Z", "created_at": "2022-08-19T16:14:38.294953Z", "data_removed": false, "error": null, "id": "6zl3evu2u5dotcmiim53yx7qvq", "input": { "seed": -1, "scale": 5, "steps": 100, "prompt": "Guybrush sword fighting a giant squid on a pirate ship", "num_database_results": 10 }, "logs": "Using random seed 2713816767\nSeed: -1\nCLIP Text Embed: torch.Size([1, 1, 768])\nData shape for PLMS sampling is (1, 16, 48, 48)\nRunning PLMS Sampling with 100 timesteps\n\nPLMS Sampler: 0%| | 0/100 [00:00<?, ?it/s]\nPLMS Sampler: 1%| | 1/100 [00:00<00:17, 5.65it/s]\nPLMS Sampler: 3%|▎ | 3/100 [00:00<00:09, 9.84it/s]\nPLMS Sampler: 5%|▌ | 5/100 [00:00<00:08, 11.11it/s]\nPLMS Sampler: 7%|▋ | 7/100 [00:00<00:07, 11.67it/s]\nPLMS Sampler: 9%|▉ | 9/100 [00:00<00:07, 11.89it/s]\nPLMS Sampler: 11%|█ | 11/100 [00:00<00:07, 12.42it/s]\nPLMS Sampler: 13%|█▎ | 13/100 [00:01<00:06, 12.69it/s]\nPLMS Sampler: 15%|█▌ | 15/100 [00:01<00:06, 12.34it/s]\nPLMS Sampler: 17%|█▋ | 17/100 [00:01<00:06, 12.11it/s]\nPLMS Sampler: 19%|█▉ | 19/100 [00:01<00:06, 12.10it/s]\nPLMS Sampler: 21%|██ | 21/100 [00:01<00:06, 12.20it/s]\nPLMS Sampler: 23%|██▎ | 23/100 [00:01<00:06, 12.17it/s]\nPLMS Sampler: 25%|██▌ | 25/100 [00:02<00:06, 12.25it/s]\nPLMS Sampler: 27%|██▋ | 27/100 [00:02<00:06, 12.11it/s]\nPLMS Sampler: 29%|██▉ | 29/100 [00:02<00:05, 12.15it/s]\nPLMS Sampler: 31%|███ | 31/100 [00:02<00:05, 12.11it/s]\nPLMS Sampler: 33%|███▎ | 33/100 [00:02<00:05, 12.20it/s]\nPLMS Sampler: 35%|███▌ | 35/100 [00:02<00:05, 12.34it/s]\nPLMS Sampler: 37%|███▋ | 37/100 [00:03<00:05, 12.48it/s]\nPLMS Sampler: 39%|███▉ | 39/100 [00:03<00:04, 12.69it/s]\nPLMS Sampler: 41%|████ | 41/100 [00:03<00:04, 12.75it/s]\nPLMS Sampler: 43%|████▎ | 43/100 [00:03<00:04, 12.73it/s]\nPLMS Sampler: 45%|████▌ | 45/100 [00:03<00:04, 12.03it/s]\nPLMS Sampler: 47%|████▋ | 47/100 [00:03<00:04, 12.38it/s]\nPLMS Sampler: 49%|████▉ | 49/100 [00:04<00:04, 12.48it/s]\nPLMS Sampler: 51%|█████ | 51/100 [00:04<00:04, 12.18it/s]\nPLMS Sampler: 53%|█████▎ | 53/100 [00:04<00:03, 12.32it/s]\nPLMS Sampler: 55%|█████▌ | 55/100 [00:04<00:03, 12.35it/s]\nPLMS Sampler: 57%|█████▋ | 57/100 [00:04<00:03, 12.32it/s]\nPLMS Sampler: 59%|█████▉ | 59/100 [00:04<00:03, 12.45it/s]\nPLMS Sampler: 61%|██████ | 61/100 [00:05<00:03, 12.57it/s]\nPLMS Sampler: 63%|██████▎ | 63/100 [00:05<00:02, 12.88it/s]\nPLMS Sampler: 65%|██████▌ | 65/100 [00:05<00:02, 12.54it/s]\nPLMS Sampler: 67%|██████▋ | 67/100 [00:05<00:02, 12.48it/s]\nPLMS Sampler: 69%|██████▉ | 69/100 [00:05<00:02, 12.68it/s]\nPLMS Sampler: 71%|███████ | 71/100 [00:05<00:02, 12.79it/s]\nPLMS Sampler: 73%|███████▎ | 73/100 [00:05<00:02, 12.91it/s]\nPLMS Sampler: 75%|███████▌ | 75/100 [00:06<00:01, 12.81it/s]\nPLMS Sampler: 77%|███████▋ | 77/100 [00:06<00:01, 12.83it/s]\nPLMS Sampler: 79%|███████▉ | 79/100 [00:06<00:01, 13.01it/s]\nPLMS Sampler: 81%|████████ | 81/100 [00:06<00:01, 12.67it/s]\nPLMS Sampler: 83%|████████▎ | 83/100 [00:06<00:01, 12.58it/s]\nPLMS Sampler: 85%|████████▌ | 85/100 [00:06<00:01, 12.15it/s]\nPLMS Sampler: 87%|████████▋ | 87/100 [00:07<00:01, 11.71it/s]\nPLMS Sampler: 89%|████████▉ | 89/100 [00:07<00:00, 11.91it/s]\nPLMS Sampler: 91%|█████████ | 91/100 [00:07<00:00, 12.07it/s]\nPLMS Sampler: 93%|█████████▎| 93/100 [00:07<00:00, 12.08it/s]\nPLMS Sampler: 95%|█████████▌| 95/100 [00:07<00:00, 12.25it/s]\nPLMS Sampler: 97%|█████████▋| 97/100 [00:07<00:00, 12.18it/s]\nPLMS Sampler: 99%|█████████▉| 99/100 [00:08<00:00, 12.08it/s]\nPLMS Sampler: 100%|██████████| 100/100 [00:08<00:00, 12.26it/s]", "metrics": { "predict_time": 9.47142, "total_time": 9.611626 }, "output": "https://replicate.delivery/mgxm/99b7c910-d94b-4235-9044-f135c003f508/out.png", "started_at": "2022-08-19T16:14:38.435159Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/6zl3evu2u5dotcmiim53yx7qvq", "cancel": "https://api.replicate.com/v1/predictions/6zl3evu2u5dotcmiim53yx7qvq/cancel" }, "version": "2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63" }Generated inUsing random seed 2713816767 Seed: -1 CLIP Text Embed: torch.Size([1, 1, 768]) Data shape for PLMS sampling is (1, 16, 48, 48) Running PLMS Sampling with 100 timesteps PLMS Sampler: 0%| | 0/100 [00:00<?, ?it/s] PLMS Sampler: 1%| | 1/100 [00:00<00:17, 5.65it/s] PLMS Sampler: 3%|▎ | 3/100 [00:00<00:09, 9.84it/s] PLMS Sampler: 5%|▌ | 5/100 [00:00<00:08, 11.11it/s] PLMS Sampler: 7%|▋ | 7/100 [00:00<00:07, 11.67it/s] PLMS Sampler: 9%|▉ | 9/100 [00:00<00:07, 11.89it/s] PLMS Sampler: 11%|█ | 11/100 [00:00<00:07, 12.42it/s] PLMS Sampler: 13%|█▎ | 13/100 [00:01<00:06, 12.69it/s] PLMS Sampler: 15%|█▌ | 15/100 [00:01<00:06, 12.34it/s] PLMS Sampler: 17%|█▋ | 17/100 [00:01<00:06, 12.11it/s] PLMS Sampler: 19%|█▉ | 19/100 [00:01<00:06, 12.10it/s] PLMS Sampler: 21%|██ | 21/100 [00:01<00:06, 12.20it/s] PLMS Sampler: 23%|██▎ | 23/100 [00:01<00:06, 12.17it/s] PLMS Sampler: 25%|██▌ | 25/100 [00:02<00:06, 12.25it/s] PLMS Sampler: 27%|██▋ | 27/100 [00:02<00:06, 12.11it/s] PLMS Sampler: 29%|██▉ | 29/100 [00:02<00:05, 12.15it/s] PLMS Sampler: 31%|███ | 31/100 [00:02<00:05, 12.11it/s] PLMS Sampler: 33%|███▎ | 33/100 [00:02<00:05, 12.20it/s] PLMS Sampler: 35%|███▌ | 35/100 [00:02<00:05, 12.34it/s] PLMS Sampler: 37%|███▋ | 37/100 [00:03<00:05, 12.48it/s] PLMS Sampler: 39%|███▉ | 39/100 [00:03<00:04, 12.69it/s] PLMS Sampler: 41%|████ | 41/100 [00:03<00:04, 12.75it/s] PLMS Sampler: 43%|████▎ | 43/100 [00:03<00:04, 12.73it/s] PLMS Sampler: 45%|████▌ | 45/100 [00:03<00:04, 12.03it/s] PLMS Sampler: 47%|████▋ | 47/100 [00:03<00:04, 12.38it/s] PLMS Sampler: 49%|████▉ | 49/100 [00:04<00:04, 12.48it/s] PLMS Sampler: 51%|█████ | 51/100 [00:04<00:04, 12.18it/s] PLMS Sampler: 53%|█████▎ | 53/100 [00:04<00:03, 12.32it/s] PLMS Sampler: 55%|█████▌ | 55/100 [00:04<00:03, 12.35it/s] PLMS Sampler: 57%|█████▋ | 57/100 [00:04<00:03, 12.32it/s] PLMS Sampler: 59%|█████▉ | 59/100 [00:04<00:03, 12.45it/s] PLMS Sampler: 61%|██████ | 61/100 [00:05<00:03, 12.57it/s] PLMS Sampler: 63%|██████▎ | 63/100 [00:05<00:02, 12.88it/s] PLMS Sampler: 65%|██████▌ | 65/100 [00:05<00:02, 12.54it/s] PLMS Sampler: 67%|██████▋ | 67/100 [00:05<00:02, 12.48it/s] PLMS Sampler: 69%|██████▉ | 69/100 [00:05<00:02, 12.68it/s] PLMS Sampler: 71%|███████ | 71/100 [00:05<00:02, 12.79it/s] PLMS Sampler: 73%|███████▎ | 73/100 [00:05<00:02, 12.91it/s] PLMS Sampler: 75%|███████▌ | 75/100 [00:06<00:01, 12.81it/s] PLMS Sampler: 77%|███████▋ | 77/100 [00:06<00:01, 12.83it/s] PLMS Sampler: 79%|███████▉ | 79/100 [00:06<00:01, 13.01it/s] PLMS Sampler: 81%|████████ | 81/100 [00:06<00:01, 12.67it/s] PLMS Sampler: 83%|████████▎ | 83/100 [00:06<00:01, 12.58it/s] PLMS Sampler: 85%|████████▌ | 85/100 [00:06<00:01, 12.15it/s] PLMS Sampler: 87%|████████▋ | 87/100 [00:07<00:01, 11.71it/s] PLMS Sampler: 89%|████████▉ | 89/100 [00:07<00:00, 11.91it/s] PLMS Sampler: 91%|█████████ | 91/100 [00:07<00:00, 12.07it/s] PLMS Sampler: 93%|█████████▎| 93/100 [00:07<00:00, 12.08it/s] PLMS Sampler: 95%|█████████▌| 95/100 [00:07<00:00, 12.25it/s] PLMS Sampler: 97%|█████████▋| 97/100 [00:07<00:00, 12.18it/s] PLMS Sampler: 99%|█████████▉| 99/100 [00:08<00:00, 12.08it/s] PLMS Sampler: 100%|██████████| 100/100 [00:08<00:00, 12.26it/s]Prediction
andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63ID47hoourhfngi3njtnbpsyb2t4aStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- -1
- scale
- 5
- steps
- 100
- prompt
- A skeleton ghost flies over town
- num_database_results
- 10
{ "seed": -1, "scale": 5, "steps": 100, "prompt": "A skeleton ghost flies over town", "num_database_results": 10 }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 andreasjansson/monkey-island-rdm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", { input: { seed: -1, scale: 5, steps: 100, prompt: "A skeleton ghost flies over town", num_database_results: 10 } } ); // 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 andreasjansson/monkey-island-rdm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", input={ "seed": -1, "scale": 5, "steps": 100, "prompt": "A skeleton ghost flies over town", "num_database_results": 10 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run andreasjansson/monkey-island-rdm 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": "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", "input": { "seed": -1, "scale": 5, "steps": 100, "prompt": "A skeleton ghost flies over town", "num_database_results": 10 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-08-19T16:21:29.144187Z", "created_at": "2022-08-19T16:21:19.495245Z", "data_removed": false, "error": null, "id": "47hoourhfngi3njtnbpsyb2t4a", "input": { "seed": -1, "scale": 5, "steps": 100, "prompt": "A skeleton ghost flies over town", "num_database_results": 10 }, "logs": "Using random seed 2902119577\nSeed: -1\nCLIP Text Embed: torch.Size([1, 1, 768])\nData shape for PLMS sampling is (1, 16, 48, 48)\nRunning PLMS Sampling with 100 timesteps\n\nPLMS Sampler: 0%| | 0/100 [00:00<?, ?it/s]\nPLMS Sampler: 1%| | 1/100 [00:00<00:17, 5.55it/s]\nPLMS Sampler: 3%|▎ | 3/100 [00:00<00:11, 8.69it/s]\nPLMS Sampler: 5%|▌ | 5/100 [00:00<00:09, 9.98it/s]\nPLMS Sampler: 7%|▋ | 7/100 [00:00<00:08, 10.84it/s]\nPLMS Sampler: 9%|▉ | 9/100 [00:00<00:07, 11.55it/s]\nPLMS Sampler: 11%|█ | 11/100 [00:01<00:07, 12.19it/s]\nPLMS Sampler: 13%|█▎ | 13/100 [00:01<00:06, 12.47it/s]\nPLMS Sampler: 15%|█▌ | 15/100 [00:01<00:06, 12.91it/s]\nPLMS Sampler: 17%|█▋ | 17/100 [00:01<00:06, 12.51it/s]\nPLMS Sampler: 19%|█▉ | 19/100 [00:01<00:06, 12.82it/s]\nPLMS Sampler: 21%|██ | 21/100 [00:01<00:06, 12.90it/s]\nPLMS Sampler: 23%|██▎ | 23/100 [00:01<00:06, 12.63it/s]\nPLMS Sampler: 25%|██▌ | 25/100 [00:02<00:05, 12.59it/s]\nPLMS Sampler: 27%|██▋ | 27/100 [00:02<00:05, 12.69it/s]\nPLMS Sampler: 29%|██▉ | 29/100 [00:02<00:05, 12.69it/s]\nPLMS Sampler: 31%|███ | 31/100 [00:02<00:05, 12.50it/s]\nPLMS Sampler: 33%|███▎ | 33/100 [00:02<00:05, 12.38it/s]\nPLMS Sampler: 35%|███▌ | 35/100 [00:02<00:05, 12.82it/s]\nPLMS Sampler: 37%|███▋ | 37/100 [00:03<00:04, 12.88it/s]\nPLMS Sampler: 39%|███▉ | 39/100 [00:03<00:04, 13.17it/s]\nPLMS Sampler: 41%|████ | 41/100 [00:03<00:04, 13.43it/s]\nPLMS Sampler: 43%|████▎ | 43/100 [00:03<00:04, 12.85it/s]\nPLMS Sampler: 45%|████▌ | 45/100 [00:03<00:04, 12.56it/s]\nPLMS Sampler: 47%|████▋ | 47/100 [00:03<00:04, 12.37it/s]\nPLMS Sampler: 49%|████▉ | 49/100 [00:03<00:04, 12.48it/s]\nPLMS Sampler: 51%|█████ | 51/100 [00:04<00:04, 12.21it/s]\nPLMS Sampler: 53%|█████▎ | 53/100 [00:04<00:03, 12.18it/s]\nPLMS Sampler: 55%|█████▌ | 55/100 [00:04<00:03, 12.03it/s]\nPLMS Sampler: 57%|█████▋ | 57/100 [00:04<00:03, 12.14it/s]\nPLMS Sampler: 59%|█████▉ | 59/100 [00:04<00:03, 12.54it/s]\nPLMS Sampler: 61%|██████ | 61/100 [00:04<00:03, 12.65it/s]\nPLMS Sampler: 63%|██████▎ | 63/100 [00:05<00:02, 12.48it/s]\nPLMS Sampler: 65%|██████▌ | 65/100 [00:05<00:02, 12.50it/s]\nPLMS Sampler: 67%|██████▋ | 67/100 [00:05<00:02, 12.30it/s]\nPLMS Sampler: 69%|██████▉ | 69/100 [00:05<00:02, 12.16it/s]\nPLMS Sampler: 71%|███████ | 71/100 [00:05<00:02, 11.85it/s]\nPLMS Sampler: 73%|███████▎ | 73/100 [00:05<00:02, 12.34it/s]\nPLMS Sampler: 75%|███████▌ | 75/100 [00:06<00:02, 12.18it/s]\nPLMS Sampler: 77%|███████▋ | 77/100 [00:06<00:01, 11.92it/s]\nPLMS Sampler: 79%|███████▉ | 79/100 [00:06<00:01, 11.85it/s]\nPLMS Sampler: 81%|████████ | 81/100 [00:06<00:01, 11.99it/s]\nPLMS Sampler: 83%|████████▎ | 83/100 [00:06<00:01, 12.43it/s]\nPLMS Sampler: 85%|████████▌ | 85/100 [00:06<00:01, 12.72it/s]\nPLMS Sampler: 87%|████████▋ | 87/100 [00:07<00:01, 12.68it/s]\nPLMS Sampler: 89%|████████▉ | 89/100 [00:07<00:00, 12.54it/s]\nPLMS Sampler: 91%|█████████ | 91/100 [00:07<00:00, 12.28it/s]\nPLMS Sampler: 93%|█████████▎| 93/100 [00:07<00:00, 12.08it/s]\nPLMS Sampler: 95%|█████████▌| 95/100 [00:07<00:00, 12.10it/s]\nPLMS Sampler: 97%|█████████▋| 97/100 [00:07<00:00, 12.01it/s]\nPLMS Sampler: 99%|█████████▉| 99/100 [00:08<00:00, 11.94it/s]\nPLMS Sampler: 100%|██████████| 100/100 [00:08<00:00, 12.24it/s]", "metrics": { "predict_time": 9.473762, "total_time": 9.648942 }, "output": "https://replicate.delivery/mgxm/eeab097e-b670-473d-b6f9-b237e9e3973d/out.png", "started_at": "2022-08-19T16:21:19.670425Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/47hoourhfngi3njtnbpsyb2t4a", "cancel": "https://api.replicate.com/v1/predictions/47hoourhfngi3njtnbpsyb2t4a/cancel" }, "version": "2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63" }Generated inUsing random seed 2902119577 Seed: -1 CLIP Text Embed: torch.Size([1, 1, 768]) Data shape for PLMS sampling is (1, 16, 48, 48) Running PLMS Sampling with 100 timesteps PLMS Sampler: 0%| | 0/100 [00:00<?, ?it/s] PLMS Sampler: 1%| | 1/100 [00:00<00:17, 5.55it/s] PLMS Sampler: 3%|▎ | 3/100 [00:00<00:11, 8.69it/s] PLMS Sampler: 5%|▌ | 5/100 [00:00<00:09, 9.98it/s] PLMS Sampler: 7%|▋ | 7/100 [00:00<00:08, 10.84it/s] PLMS Sampler: 9%|▉ | 9/100 [00:00<00:07, 11.55it/s] PLMS Sampler: 11%|█ | 11/100 [00:01<00:07, 12.19it/s] PLMS Sampler: 13%|█▎ | 13/100 [00:01<00:06, 12.47it/s] PLMS Sampler: 15%|█▌ | 15/100 [00:01<00:06, 12.91it/s] PLMS Sampler: 17%|█▋ | 17/100 [00:01<00:06, 12.51it/s] PLMS Sampler: 19%|█▉ | 19/100 [00:01<00:06, 12.82it/s] PLMS Sampler: 21%|██ | 21/100 [00:01<00:06, 12.90it/s] PLMS Sampler: 23%|██▎ | 23/100 [00:01<00:06, 12.63it/s] PLMS Sampler: 25%|██▌ | 25/100 [00:02<00:05, 12.59it/s] PLMS 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61/100 [00:04<00:03, 12.65it/s] PLMS Sampler: 63%|██████▎ | 63/100 [00:05<00:02, 12.48it/s] PLMS Sampler: 65%|██████▌ | 65/100 [00:05<00:02, 12.50it/s] PLMS Sampler: 67%|██████▋ | 67/100 [00:05<00:02, 12.30it/s] PLMS Sampler: 69%|██████▉ | 69/100 [00:05<00:02, 12.16it/s] PLMS Sampler: 71%|███████ | 71/100 [00:05<00:02, 11.85it/s] PLMS Sampler: 73%|███████▎ | 73/100 [00:05<00:02, 12.34it/s] PLMS Sampler: 75%|███████▌ | 75/100 [00:06<00:02, 12.18it/s] PLMS Sampler: 77%|███████▋ | 77/100 [00:06<00:01, 11.92it/s] PLMS Sampler: 79%|███████▉ | 79/100 [00:06<00:01, 11.85it/s] PLMS Sampler: 81%|████████ | 81/100 [00:06<00:01, 11.99it/s] PLMS Sampler: 83%|████████▎ | 83/100 [00:06<00:01, 12.43it/s] PLMS Sampler: 85%|████████▌ | 85/100 [00:06<00:01, 12.72it/s] PLMS Sampler: 87%|████████▋ | 87/100 [00:07<00:01, 12.68it/s] PLMS Sampler: 89%|████████▉ | 89/100 [00:07<00:00, 12.54it/s] PLMS Sampler: 91%|█████████ | 91/100 [00:07<00:00, 12.28it/s] PLMS Sampler: 93%|█████████▎| 93/100 [00:07<00:00, 12.08it/s] PLMS Sampler: 95%|█████████▌| 95/100 [00:07<00:00, 12.10it/s] PLMS Sampler: 97%|█████████▋| 97/100 [00:07<00:00, 12.01it/s] PLMS Sampler: 99%|█████████▉| 99/100 [00:08<00:00, 11.94it/s] PLMS Sampler: 100%|██████████| 100/100 [00:08<00:00, 12.24it/s]Prediction
andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63IDbgtnni2gm5axzfmdyn4svqjqouStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- -1
- scale
- 5
- steps
- 100
- prompt
- A sea monster is attacking a pirate ship
- num_database_results
- "3"
{ "seed": -1, "scale": 5, "steps": 100, "prompt": "A sea monster is attacking a pirate ship", "num_database_results": "3" }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 andreasjansson/monkey-island-rdm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", { input: { seed: -1, scale: 5, steps: 100, prompt: "A sea monster is attacking a pirate ship", num_database_results: "3" } } ); // 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 andreasjansson/monkey-island-rdm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", input={ "seed": -1, "scale": 5, "steps": 100, "prompt": "A sea monster is attacking a pirate ship", "num_database_results": "3" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run andreasjansson/monkey-island-rdm 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": "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", "input": { "seed": -1, "scale": 5, "steps": 100, "prompt": "A sea monster is attacking a pirate ship", "num_database_results": "3" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-08-19T16:26:06.634290Z", "created_at": "2022-08-19T16:25:57.299940Z", "data_removed": false, "error": null, "id": "bgtnni2gm5axzfmdyn4svqjqou", "input": { "seed": -1, "scale": 5, "steps": 100, "prompt": "A sea monster is attacking a pirate ship", "num_database_results": "3" }, "logs": "Using random seed 1701279479\nSeed: -1\nCLIP Text Embed: torch.Size([1, 1, 768])\nData shape for PLMS sampling is (1, 16, 48, 48)\nRunning PLMS Sampling with 100 timesteps\n\nPLMS Sampler: 0%| | 0/100 [00:00<?, ?it/s]\nPLMS Sampler: 1%| | 1/100 [00:00<00:17, 5.73it/s]\nPLMS Sampler: 3%|▎ | 3/100 [00:00<00:09, 9.77it/s]\nPLMS Sampler: 5%|▌ | 5/100 [00:00<00:08, 10.84it/s]\nPLMS Sampler: 7%|▋ | 7/100 [00:00<00:08, 11.07it/s]\nPLMS Sampler: 9%|▉ | 9/100 [00:00<00:07, 11.40it/s]\nPLMS Sampler: 11%|█ | 11/100 [00:00<00:07, 11.73it/s]\nPLMS Sampler: 13%|█▎ | 13/100 [00:01<00:07, 11.85it/s]\nPLMS Sampler: 15%|█▌ | 15/100 [00:01<00:07, 11.74it/s]\nPLMS Sampler: 17%|█▋ | 17/100 [00:01<00:06, 11.86it/s]\nPLMS Sampler: 19%|█▉ | 19/100 [00:01<00:06, 11.97it/s]\nPLMS Sampler: 21%|██ | 21/100 [00:01<00:06, 12.36it/s]\nPLMS Sampler: 23%|██▎ | 23/100 [00:01<00:06, 12.75it/s]\nPLMS Sampler: 25%|██▌ | 25/100 [00:02<00:05, 12.78it/s]\nPLMS Sampler: 27%|██▋ | 27/100 [00:02<00:05, 12.83it/s]\nPLMS Sampler: 29%|██▉ | 29/100 [00:02<00:05, 12.99it/s]\nPLMS Sampler: 31%|███ | 31/100 [00:02<00:05, 12.67it/s]\nPLMS Sampler: 33%|███▎ | 33/100 [00:02<00:05, 12.40it/s]\nPLMS Sampler: 35%|███▌ | 35/100 [00:02<00:05, 12.35it/s]\nPLMS Sampler: 37%|███▋ | 37/100 [00:03<00:05, 12.35it/s]\nPLMS Sampler: 39%|███▉ | 39/100 [00:03<00:04, 12.55it/s]\nPLMS Sampler: 41%|████ | 41/100 [00:03<00:04, 12.42it/s]\nPLMS Sampler: 43%|████▎ | 43/100 [00:03<00:04, 12.55it/s]\nPLMS Sampler: 45%|████▌ | 45/100 [00:03<00:04, 12.61it/s]\nPLMS Sampler: 47%|████▋ | 47/100 [00:03<00:04, 12.44it/s]\nPLMS Sampler: 49%|████▉ | 49/100 [00:04<00:04, 12.41it/s]\nPLMS Sampler: 51%|█████ | 51/100 [00:04<00:03, 12.40it/s]\nPLMS Sampler: 53%|█████▎ | 53/100 [00:04<00:03, 12.29it/s]\nPLMS Sampler: 55%|█████▌ | 55/100 [00:04<00:03, 12.19it/s]\nPLMS Sampler: 57%|█████▋ | 57/100 [00:04<00:03, 12.23it/s]\nPLMS Sampler: 59%|█████▉ | 59/100 [00:04<00:03, 12.28it/s]\nPLMS Sampler: 61%|██████ | 61/100 [00:05<00:03, 12.69it/s]\nPLMS Sampler: 63%|██████▎ | 63/100 [00:05<00:02, 12.91it/s]\nPLMS Sampler: 65%|██████▌ | 65/100 [00:05<00:02, 13.11it/s]\nPLMS Sampler: 67%|██████▋ | 67/100 [00:05<00:02, 13.38it/s]\nPLMS Sampler: 69%|██████▉ | 69/100 [00:05<00:02, 13.29it/s]\nPLMS Sampler: 71%|███████ | 71/100 [00:05<00:02, 13.45it/s]\nPLMS Sampler: 73%|███████▎ | 73/100 [00:05<00:01, 13.59it/s]\nPLMS Sampler: 75%|███████▌ | 75/100 [00:06<00:01, 13.63it/s]\nPLMS Sampler: 77%|███████▋ | 77/100 [00:06<00:01, 13.70it/s]\nPLMS Sampler: 79%|███████▉ | 79/100 [00:06<00:01, 13.74it/s]\nPLMS Sampler: 81%|████████ | 81/100 [00:06<00:01, 13.81it/s]\nPLMS Sampler: 83%|████████▎ | 83/100 [00:06<00:01, 13.71it/s]\nPLMS Sampler: 85%|████████▌ | 85/100 [00:06<00:01, 13.36it/s]\nPLMS Sampler: 87%|████████▋ | 87/100 [00:06<00:00, 13.58it/s]\nPLMS Sampler: 89%|████████▉ | 89/100 [00:07<00:00, 13.72it/s]\nPLMS Sampler: 91%|█████████ | 91/100 [00:07<00:00, 13.83it/s]\nPLMS Sampler: 93%|█████████▎| 93/100 [00:07<00:00, 13.88it/s]\nPLMS Sampler: 95%|█████████▌| 95/100 [00:07<00:00, 13.77it/s]\nPLMS Sampler: 97%|█████████▋| 97/100 [00:07<00:00, 13.71it/s]\nPLMS Sampler: 99%|█████████▉| 99/100 [00:07<00:00, 13.39it/s]\nPLMS Sampler: 100%|██████████| 100/100 [00:07<00:00, 12.70it/s]", "metrics": { "predict_time": 9.160247, "total_time": 9.33435 }, "output": "https://replicate.delivery/mgxm/2a3f3240-113e-42c0-896d-ecc59acb30ae/out.png", "started_at": "2022-08-19T16:25:57.474043Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/bgtnni2gm5axzfmdyn4svqjqou", "cancel": "https://api.replicate.com/v1/predictions/bgtnni2gm5axzfmdyn4svqjqou/cancel" }, "version": "2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63" }Generated inUsing random seed 1701279479 Seed: -1 CLIP Text Embed: torch.Size([1, 1, 768]) Data shape for PLMS sampling is (1, 16, 48, 48) Running PLMS Sampling with 100 timesteps PLMS Sampler: 0%| | 0/100 [00:00<?, ?it/s] PLMS Sampler: 1%| | 1/100 [00:00<00:17, 5.73it/s] PLMS Sampler: 3%|▎ | 3/100 [00:00<00:09, 9.77it/s] PLMS Sampler: 5%|▌ | 5/100 [00:00<00:08, 10.84it/s] PLMS Sampler: 7%|▋ | 7/100 [00:00<00:08, 11.07it/s] PLMS Sampler: 9%|▉ | 9/100 [00:00<00:07, 11.40it/s] PLMS Sampler: 11%|█ | 11/100 [00:00<00:07, 11.73it/s] PLMS Sampler: 13%|█▎ | 13/100 [00:01<00:07, 11.85it/s] PLMS Sampler: 15%|█▌ | 15/100 [00:01<00:07, 11.74it/s] PLMS Sampler: 17%|█▋ | 17/100 [00:01<00:06, 11.86it/s] PLMS Sampler: 19%|█▉ | 19/100 [00:01<00:06, 11.97it/s] PLMS Sampler: 21%|██ | 21/100 [00:01<00:06, 12.36it/s] PLMS Sampler: 23%|██▎ | 23/100 [00:01<00:06, 12.75it/s] PLMS Sampler: 25%|██▌ | 25/100 [00:02<00:05, 12.78it/s] PLMS Sampler: 27%|██▋ | 27/100 [00:02<00:05, 12.83it/s] PLMS Sampler: 29%|██▉ | 29/100 [00:02<00:05, 12.99it/s] PLMS Sampler: 31%|███ | 31/100 [00:02<00:05, 12.67it/s] PLMS Sampler: 33%|███▎ | 33/100 [00:02<00:05, 12.40it/s] PLMS Sampler: 35%|███▌ | 35/100 [00:02<00:05, 12.35it/s] PLMS Sampler: 37%|███▋ | 37/100 [00:03<00:05, 12.35it/s] PLMS Sampler: 39%|███▉ | 39/100 [00:03<00:04, 12.55it/s] PLMS Sampler: 41%|████ | 41/100 [00:03<00:04, 12.42it/s] PLMS Sampler: 43%|████▎ | 43/100 [00:03<00:04, 12.55it/s] PLMS Sampler: 45%|████▌ | 45/100 [00:03<00:04, 12.61it/s] PLMS Sampler: 47%|████▋ | 47/100 [00:03<00:04, 12.44it/s] PLMS Sampler: 49%|████▉ | 49/100 [00:04<00:04, 12.41it/s] PLMS Sampler: 51%|█████ | 51/100 [00:04<00:03, 12.40it/s] PLMS Sampler: 53%|█████▎ | 53/100 [00:04<00:03, 12.29it/s] PLMS Sampler: 55%|█████▌ | 55/100 [00:04<00:03, 12.19it/s] PLMS Sampler: 57%|█████▋ | 57/100 [00:04<00:03, 12.23it/s] PLMS Sampler: 59%|█████▉ | 59/100 [00:04<00:03, 12.28it/s] PLMS Sampler: 61%|██████ | 61/100 [00:05<00:03, 12.69it/s] PLMS Sampler: 63%|██████▎ | 63/100 [00:05<00:02, 12.91it/s] PLMS Sampler: 65%|██████▌ | 65/100 [00:05<00:02, 13.11it/s] PLMS Sampler: 67%|██████▋ | 67/100 [00:05<00:02, 13.38it/s] PLMS Sampler: 69%|██████▉ | 69/100 [00:05<00:02, 13.29it/s] PLMS Sampler: 71%|███████ | 71/100 [00:05<00:02, 13.45it/s] PLMS Sampler: 73%|███████▎ | 73/100 [00:05<00:01, 13.59it/s] PLMS Sampler: 75%|███████▌ | 75/100 [00:06<00:01, 13.63it/s] PLMS Sampler: 77%|███████▋ | 77/100 [00:06<00:01, 13.70it/s] PLMS Sampler: 79%|███████▉ | 79/100 [00:06<00:01, 13.74it/s] PLMS Sampler: 81%|████████ | 81/100 [00:06<00:01, 13.81it/s] PLMS Sampler: 83%|████████▎ | 83/100 [00:06<00:01, 13.71it/s] PLMS Sampler: 85%|████████▌ | 85/100 [00:06<00:01, 13.36it/s] PLMS Sampler: 87%|████████▋ | 87/100 [00:06<00:00, 13.58it/s] PLMS Sampler: 89%|████████▉ | 89/100 [00:07<00:00, 13.72it/s] PLMS Sampler: 91%|█████████ | 91/100 [00:07<00:00, 13.83it/s] PLMS Sampler: 93%|█████████▎| 93/100 [00:07<00:00, 13.88it/s] PLMS Sampler: 95%|█████████▌| 95/100 [00:07<00:00, 13.77it/s] PLMS Sampler: 97%|█████████▋| 97/100 [00:07<00:00, 13.71it/s] PLMS Sampler: 99%|█████████▉| 99/100 [00:07<00:00, 13.39it/s] PLMS Sampler: 100%|██████████| 100/100 [00:07<00:00, 12.70it/s]Prediction
andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63IDprfbveti45gopkygswwedtkxp4StatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- -1
- scale
- 5
- steps
- 100
- prompt
- A beautiful day by the sea
- num_database_results
- "5"
{ "seed": -1, "scale": 5, "steps": 100, "prompt": "A beautiful day by the sea", "num_database_results": "5" }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 andreasjansson/monkey-island-rdm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", { input: { seed: -1, scale: 5, steps: 100, prompt: "A beautiful day by the sea", num_database_results: "5" } } ); // 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 andreasjansson/monkey-island-rdm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", input={ "seed": -1, "scale": 5, "steps": 100, "prompt": "A beautiful day by the sea", "num_database_results": "5" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run andreasjansson/monkey-island-rdm 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": "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", "input": { "seed": -1, "scale": 5, "steps": 100, "prompt": "A beautiful day by the sea", "num_database_results": "5" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-08-19T16:26:35.605090Z", "created_at": "2022-08-19T16:26:26.105387Z", "data_removed": false, "error": null, "id": "prfbveti45gopkygswwedtkxp4", "input": { "seed": -1, "scale": 5, "steps": 100, "prompt": "A beautiful day by the sea", "num_database_results": "5" }, "logs": "Using random seed 1081123518\nSeed: -1\nCLIP Text Embed: torch.Size([1, 1, 768])\nData shape for PLMS sampling is (1, 16, 48, 48)\nRunning PLMS Sampling with 100 timesteps\n\nPLMS Sampler: 0%| | 0/100 [00:00<?, ?it/s]\nPLMS Sampler: 1%| | 1/100 [00:00<00:16, 6.09it/s]\nPLMS Sampler: 3%|▎ | 3/100 [00:00<00:09, 10.26it/s]\nPLMS Sampler: 5%|▌ | 5/100 [00:00<00:08, 11.25it/s]\nPLMS Sampler: 7%|▋ | 7/100 [00:00<00:07, 12.04it/s]\nPLMS Sampler: 9%|▉ | 9/100 [00:00<00:07, 12.68it/s]\nPLMS Sampler: 11%|█ | 11/100 [00:00<00:07, 12.49it/s]\nPLMS Sampler: 13%|█▎ | 13/100 [00:01<00:06, 12.64it/s]\nPLMS Sampler: 15%|█▌ | 15/100 [00:01<00:06, 12.81it/s]\nPLMS Sampler: 17%|█▋ | 17/100 [00:01<00:06, 12.56it/s]\nPLMS Sampler: 19%|█▉ | 19/100 [00:01<00:06, 12.33it/s]\nPLMS Sampler: 21%|██ | 21/100 [00:01<00:06, 12.56it/s]\nPLMS Sampler: 23%|██▎ | 23/100 [00:01<00:06, 12.78it/s]\nPLMS Sampler: 25%|██▌ | 25/100 [00:02<00:05, 12.61it/s]\nPLMS Sampler: 27%|██▋ | 27/100 [00:02<00:05, 12.71it/s]\nPLMS Sampler: 29%|██▉ | 29/100 [00:02<00:05, 12.83it/s]\nPLMS Sampler: 31%|███ | 31/100 [00:02<00:05, 12.80it/s]\nPLMS Sampler: 33%|███▎ | 33/100 [00:02<00:05, 12.51it/s]\nPLMS Sampler: 35%|███▌ | 35/100 [00:02<00:05, 12.34it/s]\nPLMS Sampler: 37%|███▋ | 37/100 [00:03<00:05, 12.31it/s]\nPLMS Sampler: 39%|███▉ | 39/100 [00:03<00:04, 12.29it/s]\nPLMS Sampler: 41%|████ | 41/100 [00:03<00:04, 12.44it/s]\nPLMS Sampler: 43%|████▎ | 43/100 [00:03<00:04, 12.36it/s]\nPLMS Sampler: 45%|████▌ | 45/100 [00:03<00:04, 12.79it/s]\nPLMS Sampler: 47%|████▋ | 47/100 [00:03<00:04, 12.68it/s]\nPLMS Sampler: 49%|████▉ | 49/100 [00:03<00:04, 12.27it/s]\nPLMS Sampler: 51%|█████ | 51/100 [00:04<00:04, 12.13it/s]\nPLMS Sampler: 53%|█████▎ | 53/100 [00:04<00:03, 12.64it/s]\nPLMS Sampler: 55%|█████▌ | 55/100 [00:04<00:03, 13.03it/s]\nPLMS Sampler: 57%|█████▋ | 57/100 [00:04<00:03, 13.33it/s]\nPLMS Sampler: 59%|█████▉ | 59/100 [00:04<00:03, 13.56it/s]\nPLMS Sampler: 61%|██████ | 61/100 [00:04<00:02, 13.68it/s]\nPLMS Sampler: 63%|██████▎ | 63/100 [00:04<00:02, 13.58it/s]\nPLMS Sampler: 65%|██████▌ | 65/100 [00:05<00:02, 13.70it/s]\nPLMS Sampler: 67%|██████▋ | 67/100 [00:05<00:02, 13.77it/s]\nPLMS Sampler: 69%|██████▉ | 69/100 [00:05<00:02, 13.82it/s]\nPLMS Sampler: 71%|███████ | 71/100 [00:05<00:02, 13.69it/s]\nPLMS Sampler: 73%|███████▎ | 73/100 [00:05<00:02, 13.37it/s]\nPLMS Sampler: 75%|███████▌ | 75/100 [00:05<00:01, 12.74it/s]\nPLMS Sampler: 77%|███████▋ | 77/100 [00:06<00:01, 12.67it/s]\nPLMS Sampler: 79%|███████▉ | 79/100 [00:06<00:01, 12.79it/s]\nPLMS Sampler: 81%|████████ | 81/100 [00:06<00:01, 12.41it/s]\nPLMS Sampler: 83%|████████▎ | 83/100 [00:06<00:01, 12.45it/s]\nPLMS Sampler: 85%|████████▌ | 85/100 [00:06<00:01, 12.50it/s]\nPLMS Sampler: 87%|████████▋ | 87/100 [00:06<00:01, 12.32it/s]\nPLMS Sampler: 89%|████████▉ | 89/100 [00:07<00:00, 12.35it/s]\nPLMS Sampler: 91%|█████████ | 91/100 [00:07<00:00, 12.28it/s]\nPLMS Sampler: 93%|█████████▎| 93/100 [00:07<00:00, 11.88it/s]\nPLMS Sampler: 95%|█████████▌| 95/100 [00:07<00:00, 12.18it/s]\nPLMS Sampler: 97%|█████████▋| 97/100 [00:07<00:00, 12.63it/s]\nPLMS Sampler: 99%|█████████▉| 99/100 [00:07<00:00, 12.36it/s]\nPLMS Sampler: 100%|██████████| 100/100 [00:07<00:00, 12.59it/s]", "metrics": { "predict_time": 9.309219, "total_time": 9.499703 }, "output": "https://replicate.delivery/mgxm/4ddd717a-e619-438b-a929-dd8268bc25d7/out.png", "started_at": "2022-08-19T16:26:26.295871Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/prfbveti45gopkygswwedtkxp4", "cancel": "https://api.replicate.com/v1/predictions/prfbveti45gopkygswwedtkxp4/cancel" }, "version": "2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63" }Generated inUsing random seed 1081123518 Seed: -1 CLIP Text Embed: torch.Size([1, 1, 768]) Data shape for PLMS sampling is (1, 16, 48, 48) Running PLMS Sampling with 100 timesteps PLMS Sampler: 0%| | 0/100 [00:00<?, ?it/s] PLMS Sampler: 1%| | 1/100 [00:00<00:16, 6.09it/s] PLMS Sampler: 3%|▎ | 3/100 [00:00<00:09, 10.26it/s] PLMS Sampler: 5%|▌ | 5/100 [00:00<00:08, 11.25it/s] PLMS Sampler: 7%|▋ | 7/100 [00:00<00:07, 12.04it/s] PLMS Sampler: 9%|▉ | 9/100 [00:00<00:07, 12.68it/s] PLMS Sampler: 11%|█ | 11/100 [00:00<00:07, 12.49it/s] PLMS Sampler: 13%|█▎ | 13/100 [00:01<00:06, 12.64it/s] PLMS Sampler: 15%|█▌ | 15/100 [00:01<00:06, 12.81it/s] PLMS Sampler: 17%|█▋ | 17/100 [00:01<00:06, 12.56it/s] PLMS Sampler: 19%|█▉ | 19/100 [00:01<00:06, 12.33it/s] PLMS Sampler: 21%|██ | 21/100 [00:01<00:06, 12.56it/s] PLMS Sampler: 23%|██▎ | 23/100 [00:01<00:06, 12.78it/s] PLMS Sampler: 25%|██▌ | 25/100 [00:02<00:05, 12.61it/s] PLMS Sampler: 27%|██▋ | 27/100 [00:02<00:05, 12.71it/s] PLMS Sampler: 29%|██▉ | 29/100 [00:02<00:05, 12.83it/s] PLMS Sampler: 31%|███ | 31/100 [00:02<00:05, 12.80it/s] PLMS Sampler: 33%|███▎ | 33/100 [00:02<00:05, 12.51it/s] PLMS Sampler: 35%|███▌ | 35/100 [00:02<00:05, 12.34it/s] PLMS Sampler: 37%|███▋ | 37/100 [00:03<00:05, 12.31it/s] PLMS Sampler: 39%|███▉ | 39/100 [00:03<00:04, 12.29it/s] PLMS Sampler: 41%|████ | 41/100 [00:03<00:04, 12.44it/s] PLMS Sampler: 43%|████▎ | 43/100 [00:03<00:04, 12.36it/s] PLMS Sampler: 45%|████▌ | 45/100 [00:03<00:04, 12.79it/s] PLMS Sampler: 47%|████▋ | 47/100 [00:03<00:04, 12.68it/s] PLMS Sampler: 49%|████▉ | 49/100 [00:03<00:04, 12.27it/s] PLMS Sampler: 51%|█████ | 51/100 [00:04<00:04, 12.13it/s] PLMS Sampler: 53%|█████▎ | 53/100 [00:04<00:03, 12.64it/s] PLMS Sampler: 55%|█████▌ | 55/100 [00:04<00:03, 13.03it/s] PLMS Sampler: 57%|█████▋ | 57/100 [00:04<00:03, 13.33it/s] PLMS Sampler: 59%|█████▉ | 59/100 [00:04<00:03, 13.56it/s] PLMS Sampler: 61%|██████ | 61/100 [00:04<00:02, 13.68it/s] PLMS Sampler: 63%|██████▎ | 63/100 [00:04<00:02, 13.58it/s] PLMS Sampler: 65%|██████▌ | 65/100 [00:05<00:02, 13.70it/s] PLMS Sampler: 67%|██████▋ | 67/100 [00:05<00:02, 13.77it/s] PLMS Sampler: 69%|██████▉ | 69/100 [00:05<00:02, 13.82it/s] PLMS Sampler: 71%|███████ | 71/100 [00:05<00:02, 13.69it/s] PLMS Sampler: 73%|███████▎ | 73/100 [00:05<00:02, 13.37it/s] PLMS Sampler: 75%|███████▌ | 75/100 [00:05<00:01, 12.74it/s] PLMS Sampler: 77%|███████▋ | 77/100 [00:06<00:01, 12.67it/s] PLMS Sampler: 79%|███████▉ | 79/100 [00:06<00:01, 12.79it/s] PLMS Sampler: 81%|████████ | 81/100 [00:06<00:01, 12.41it/s] PLMS Sampler: 83%|████████▎ | 83/100 [00:06<00:01, 12.45it/s] PLMS Sampler: 85%|████████▌ | 85/100 [00:06<00:01, 12.50it/s] PLMS Sampler: 87%|████████▋ | 87/100 [00:06<00:01, 12.32it/s] PLMS Sampler: 89%|████████▉ | 89/100 [00:07<00:00, 12.35it/s] PLMS Sampler: 91%|█████████ | 91/100 [00:07<00:00, 12.28it/s] PLMS Sampler: 93%|█████████▎| 93/100 [00:07<00:00, 11.88it/s] PLMS Sampler: 95%|█████████▌| 95/100 [00:07<00:00, 12.18it/s] PLMS Sampler: 97%|█████████▋| 97/100 [00:07<00:00, 12.63it/s] PLMS Sampler: 99%|█████████▉| 99/100 [00:07<00:00, 12.36it/s] PLMS Sampler: 100%|██████████| 100/100 [00:07<00:00, 12.59it/s]Prediction
andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63IDm4fcewluj5azxcc7h63iwqqubaStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- -1
- scale
- 5
- steps
- 100
- prompt
- Guybrush Threepwood taking a walk in town
- num_database_results
- "10"
{ "seed": -1, "scale": 5, "steps": 100, "prompt": "Guybrush Threepwood taking a walk in town", "num_database_results": "10" }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 andreasjansson/monkey-island-rdm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", { input: { seed: -1, scale: 5, steps: 100, prompt: "Guybrush Threepwood taking a walk in town", num_database_results: "10" } } ); // 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 andreasjansson/monkey-island-rdm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", input={ "seed": -1, "scale": 5, "steps": 100, "prompt": "Guybrush Threepwood taking a walk in town", "num_database_results": "10" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run andreasjansson/monkey-island-rdm 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": "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", "input": { "seed": -1, "scale": 5, "steps": 100, "prompt": "Guybrush Threepwood taking a walk in town", "num_database_results": "10" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-08-19T16:29:47.288522Z", "created_at": "2022-08-19T16:29:38.419645Z", "data_removed": false, "error": null, "id": "m4fcewluj5azxcc7h63iwqquba", "input": { "seed": -1, "scale": 5, "steps": 100, "prompt": "Guybrush Threepwood taking a walk in town", "num_database_results": "10" }, "logs": "Using random seed 3024559446\nSeed: -1\nCLIP Text Embed: torch.Size([1, 1, 768])\nData shape for PLMS sampling is (1, 16, 48, 48)\nRunning PLMS Sampling with 100 timesteps\n\nPLMS Sampler: 0%| | 0/100 [00:00<?, ?it/s]\nPLMS Sampler: 1%| | 1/100 [00:00<00:16, 6.05it/s]\nPLMS Sampler: 3%|▎ | 3/100 [00:00<00:09, 10.39it/s]\nPLMS Sampler: 5%|▌ | 5/100 [00:00<00:07, 11.92it/s]\nPLMS Sampler: 7%|▋ | 7/100 [00:00<00:07, 12.71it/s]\nPLMS Sampler: 9%|▉ | 9/100 [00:00<00:07, 12.97it/s]\nPLMS Sampler: 11%|█ | 11/100 [00:00<00:06, 13.30it/s]\nPLMS Sampler: 13%|█▎ | 13/100 [00:01<00:06, 13.44it/s]\nPLMS Sampler: 15%|█▌ | 15/100 [00:01<00:06, 13.39it/s]\nPLMS Sampler: 17%|█▋ | 17/100 [00:01<00:06, 13.53it/s]\nPLMS Sampler: 19%|█▉ | 19/100 [00:01<00:05, 13.62it/s]\nPLMS Sampler: 21%|██ | 21/100 [00:01<00:05, 13.70it/s]\nPLMS Sampler: 23%|██▎ | 23/100 [00:01<00:05, 13.71it/s]\nPLMS Sampler: 25%|██▌ | 25/100 [00:01<00:05, 13.76it/s]\nPLMS Sampler: 27%|██▋ | 27/100 [00:02<00:05, 13.82it/s]\nPLMS Sampler: 29%|██▉ | 29/100 [00:02<00:05, 13.86it/s]\nPLMS Sampler: 31%|███ | 31/100 [00:02<00:05, 13.62it/s]\nPLMS Sampler: 33%|███▎ | 33/100 [00:02<00:04, 13.65it/s]\nPLMS Sampler: 35%|███▌ | 35/100 [00:02<00:04, 13.62it/s]\nPLMS Sampler: 37%|███▋ | 37/100 [00:02<00:04, 13.72it/s]\nPLMS Sampler: 39%|███▉ | 39/100 [00:02<00:04, 13.80it/s]\nPLMS Sampler: 41%|████ | 41/100 [00:03<00:04, 13.85it/s]\nPLMS Sampler: 43%|████▎ | 43/100 [00:03<00:04, 13.90it/s]\nPLMS Sampler: 45%|████▌ | 45/100 [00:03<00:03, 13.94it/s]\nPLMS Sampler: 47%|████▋ | 47/100 [00:03<00:03, 13.97it/s]\nPLMS Sampler: 49%|████▉ | 49/100 [00:03<00:03, 13.84it/s]\nPLMS Sampler: 51%|█████ | 51/100 [00:03<00:03, 13.87it/s]\nPLMS Sampler: 53%|█████▎ | 53/100 [00:03<00:03, 13.87it/s]\nPLMS Sampler: 55%|█████▌ | 55/100 [00:04<00:03, 13.73it/s]\nPLMS Sampler: 57%|█████▋ | 57/100 [00:04<00:03, 13.77it/s]\nPLMS Sampler: 59%|█████▉ | 59/100 [00:04<00:02, 13.83it/s]\nPLMS Sampler: 61%|██████ | 61/100 [00:04<00:02, 13.87it/s]\nPLMS Sampler: 63%|██████▎ | 63/100 [00:04<00:02, 13.78it/s]\nPLMS Sampler: 65%|██████▌ | 65/100 [00:04<00:02, 13.63it/s]\nPLMS Sampler: 67%|██████▋ | 67/100 [00:04<00:02, 13.63it/s]\nPLMS Sampler: 69%|██████▉ | 69/100 [00:05<00:02, 13.74it/s]\nPLMS Sampler: 71%|███████ | 71/100 [00:05<00:02, 13.77it/s]\nPLMS Sampler: 73%|███████▎ | 73/100 [00:05<00:01, 13.84it/s]\nPLMS Sampler: 75%|███████▌ | 75/100 [00:05<00:01, 13.63it/s]\nPLMS Sampler: 77%|███████▋ | 77/100 [00:05<00:01, 13.44it/s]\nPLMS Sampler: 79%|███████▉ | 79/100 [00:05<00:01, 13.58it/s]\nPLMS Sampler: 81%|████████ | 81/100 [00:05<00:01, 13.68it/s]\nPLMS Sampler: 83%|████████▎ | 83/100 [00:06<00:01, 13.74it/s]\nPLMS Sampler: 85%|████████▌ | 85/100 [00:06<00:01, 13.81it/s]\nPLMS Sampler: 87%|████████▋ | 87/100 [00:06<00:00, 13.84it/s]\nPLMS Sampler: 89%|████████▉ | 89/100 [00:06<00:00, 13.85it/s]\nPLMS Sampler: 91%|█████████ | 91/100 [00:06<00:00, 13.88it/s]\nPLMS Sampler: 93%|█████████▎| 93/100 [00:06<00:00, 13.86it/s]\nPLMS Sampler: 95%|█████████▌| 95/100 [00:06<00:00, 13.89it/s]\nPLMS Sampler: 97%|█████████▋| 97/100 [00:07<00:00, 13.91it/s]\nPLMS Sampler: 99%|█████████▉| 99/100 [00:07<00:00, 13.90it/s]\nPLMS Sampler: 100%|██████████| 100/100 [00:07<00:00, 13.61it/s]", "metrics": { "predict_time": 8.67741, "total_time": 8.868877 }, "output": "https://replicate.delivery/mgxm/1d901c08-c38d-42ea-941d-fb785700c218/out.png", "started_at": "2022-08-19T16:29:38.611112Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/m4fcewluj5azxcc7h63iwqquba", "cancel": "https://api.replicate.com/v1/predictions/m4fcewluj5azxcc7h63iwqquba/cancel" }, "version": "2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63" }Generated inUsing random seed 3024559446 Seed: -1 CLIP Text Embed: torch.Size([1, 1, 768]) Data shape for PLMS sampling is (1, 16, 48, 48) Running PLMS Sampling with 100 timesteps PLMS Sampler: 0%| | 0/100 [00:00<?, ?it/s] PLMS Sampler: 1%| | 1/100 [00:00<00:16, 6.05it/s] PLMS Sampler: 3%|▎ | 3/100 [00:00<00:09, 10.39it/s] PLMS Sampler: 5%|▌ | 5/100 [00:00<00:07, 11.92it/s] PLMS Sampler: 7%|▋ | 7/100 [00:00<00:07, 12.71it/s] PLMS Sampler: 9%|▉ | 9/100 [00:00<00:07, 12.97it/s] PLMS Sampler: 11%|█ | 11/100 [00:00<00:06, 13.30it/s] PLMS Sampler: 13%|█▎ | 13/100 [00:01<00:06, 13.44it/s] PLMS Sampler: 15%|█▌ | 15/100 [00:01<00:06, 13.39it/s] PLMS Sampler: 17%|█▋ | 17/100 [00:01<00:06, 13.53it/s] PLMS Sampler: 19%|█▉ | 19/100 [00:01<00:05, 13.62it/s] PLMS Sampler: 21%|██ | 21/100 [00:01<00:05, 13.70it/s] PLMS Sampler: 23%|██▎ | 23/100 [00:01<00:05, 13.71it/s] PLMS Sampler: 25%|██▌ | 25/100 [00:01<00:05, 13.76it/s] PLMS Sampler: 27%|██▋ | 27/100 [00:02<00:05, 13.82it/s] PLMS Sampler: 29%|██▉ | 29/100 [00:02<00:05, 13.86it/s] PLMS Sampler: 31%|███ | 31/100 [00:02<00:05, 13.62it/s] PLMS Sampler: 33%|███▎ | 33/100 [00:02<00:04, 13.65it/s] PLMS Sampler: 35%|███▌ | 35/100 [00:02<00:04, 13.62it/s] PLMS Sampler: 37%|███▋ | 37/100 [00:02<00:04, 13.72it/s] PLMS Sampler: 39%|███▉ | 39/100 [00:02<00:04, 13.80it/s] PLMS Sampler: 41%|████ | 41/100 [00:03<00:04, 13.85it/s] PLMS Sampler: 43%|████▎ | 43/100 [00:03<00:04, 13.90it/s] PLMS Sampler: 45%|████▌ | 45/100 [00:03<00:03, 13.94it/s] PLMS Sampler: 47%|████▋ | 47/100 [00:03<00:03, 13.97it/s] PLMS Sampler: 49%|████▉ | 49/100 [00:03<00:03, 13.84it/s] PLMS Sampler: 51%|█████ | 51/100 [00:03<00:03, 13.87it/s] PLMS Sampler: 53%|█████▎ | 53/100 [00:03<00:03, 13.87it/s] PLMS Sampler: 55%|█████▌ | 55/100 [00:04<00:03, 13.73it/s] PLMS Sampler: 57%|█████▋ | 57/100 [00:04<00:03, 13.77it/s] PLMS Sampler: 59%|█████▉ | 59/100 [00:04<00:02, 13.83it/s] PLMS Sampler: 61%|██████ | 61/100 [00:04<00:02, 13.87it/s] PLMS Sampler: 63%|██████▎ | 63/100 [00:04<00:02, 13.78it/s] PLMS Sampler: 65%|██████▌ | 65/100 [00:04<00:02, 13.63it/s] PLMS Sampler: 67%|██████▋ | 67/100 [00:04<00:02, 13.63it/s] PLMS Sampler: 69%|██████▉ | 69/100 [00:05<00:02, 13.74it/s] PLMS Sampler: 71%|███████ | 71/100 [00:05<00:02, 13.77it/s] PLMS Sampler: 73%|███████▎ | 73/100 [00:05<00:01, 13.84it/s] PLMS Sampler: 75%|███████▌ | 75/100 [00:05<00:01, 13.63it/s] PLMS Sampler: 77%|███████▋ | 77/100 [00:05<00:01, 13.44it/s] PLMS Sampler: 79%|███████▉ | 79/100 [00:05<00:01, 13.58it/s] PLMS Sampler: 81%|████████ | 81/100 [00:05<00:01, 13.68it/s] PLMS Sampler: 83%|████████▎ | 83/100 [00:06<00:01, 13.74it/s] PLMS Sampler: 85%|████████▌ | 85/100 [00:06<00:01, 13.81it/s] PLMS Sampler: 87%|████████▋ | 87/100 [00:06<00:00, 13.84it/s] PLMS Sampler: 89%|████████▉ | 89/100 [00:06<00:00, 13.85it/s] PLMS Sampler: 91%|█████████ | 91/100 [00:06<00:00, 13.88it/s] PLMS Sampler: 93%|█████████▎| 93/100 [00:06<00:00, 13.86it/s] PLMS Sampler: 95%|█████████▌| 95/100 [00:06<00:00, 13.89it/s] PLMS Sampler: 97%|█████████▋| 97/100 [00:07<00:00, 13.91it/s] PLMS Sampler: 99%|█████████▉| 99/100 [00:07<00:00, 13.90it/s] PLMS Sampler: 100%|██████████| 100/100 [00:07<00:00, 13.61it/s]Prediction
andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63IDtockkav6fzeb5lfonggibwofjqStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- -1
- scale
- 5
- steps
- 100
- prompt
- Pirates are ballroom dancing
- num_database_results
- "5"
{ "seed": -1, "scale": 5, "steps": 100, "prompt": "Pirates are ballroom dancing", "num_database_results": "5" }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 andreasjansson/monkey-island-rdm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", { input: { seed: -1, scale: 5, steps: 100, prompt: "Pirates are ballroom dancing", num_database_results: "5" } } ); // 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 andreasjansson/monkey-island-rdm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", input={ "seed": -1, "scale": 5, "steps": 100, "prompt": "Pirates are ballroom dancing", "num_database_results": "5" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run andreasjansson/monkey-island-rdm 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": "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", "input": { "seed": -1, "scale": 5, "steps": 100, "prompt": "Pirates are ballroom dancing", "num_database_results": "5" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-08-19T16:37:07.950150Z", "created_at": "2022-08-19T16:36:58.257868Z", "data_removed": false, "error": null, "id": "tockkav6fzeb5lfonggibwofjq", "input": { "seed": -1, "scale": 5, "steps": 100, "prompt": "Pirates are ballroom dancing", "num_database_results": "5" }, "logs": "Using random seed 2197080794\nSeed: -1\nCLIP Text Embed: torch.Size([1, 1, 768])\nData shape for PLMS sampling is (1, 16, 48, 48)\nRunning PLMS Sampling with 100 timesteps\n\nPLMS Sampler: 0%| | 0/100 [00:00<?, ?it/s]\nPLMS Sampler: 1%| | 1/100 [00:00<00:15, 6.32it/s]\nPLMS Sampler: 3%|▎ | 3/100 [00:00<00:09, 10.16it/s]\nPLMS Sampler: 5%|▌ | 5/100 [00:00<00:08, 11.32it/s]\nPLMS Sampler: 7%|▋ | 7/100 [00:00<00:07, 11.85it/s]\nPLMS Sampler: 9%|▉ | 9/100 [00:00<00:07, 12.01it/s]\nPLMS Sampler: 11%|█ | 11/100 [00:00<00:07, 12.07it/s]\nPLMS Sampler: 13%|█▎ | 13/100 [00:01<00:07, 12.17it/s]\nPLMS Sampler: 15%|█▌ | 15/100 [00:01<00:06, 12.28it/s]\nPLMS Sampler: 17%|█▋ | 17/100 [00:01<00:06, 12.44it/s]\nPLMS Sampler: 19%|█▉ | 19/100 [00:01<00:06, 12.27it/s]\nPLMS Sampler: 21%|██ | 21/100 [00:01<00:06, 12.21it/s]\nPLMS Sampler: 23%|██▎ | 23/100 [00:01<00:06, 12.03it/s]\nPLMS Sampler: 25%|██▌ | 25/100 [00:02<00:06, 12.43it/s]\nPLMS Sampler: 27%|██▋ | 27/100 [00:02<00:05, 12.24it/s]\nPLMS Sampler: 29%|██▉ | 29/100 [00:02<00:05, 12.32it/s]\nPLMS Sampler: 31%|███ | 31/100 [00:02<00:05, 12.15it/s]\nPLMS Sampler: 33%|███▎ | 33/100 [00:02<00:05, 12.48it/s]\nPLMS Sampler: 35%|███▌ | 35/100 [00:02<00:05, 12.24it/s]\nPLMS Sampler: 37%|███▋ | 37/100 [00:03<00:05, 12.56it/s]\nPLMS Sampler: 39%|███▉ | 39/100 [00:03<00:04, 12.39it/s]\nPLMS Sampler: 41%|████ | 41/100 [00:03<00:04, 12.14it/s]\nPLMS Sampler: 43%|████▎ | 43/100 [00:03<00:04, 12.13it/s]\nPLMS Sampler: 45%|████▌ | 45/100 [00:03<00:04, 11.92it/s]\nPLMS Sampler: 47%|████▋ | 47/100 [00:03<00:04, 12.10it/s]\nPLMS Sampler: 49%|████▉ | 49/100 [00:04<00:04, 12.40it/s]\nPLMS Sampler: 51%|█████ | 51/100 [00:04<00:03, 12.38it/s]\nPLMS Sampler: 53%|█████▎ | 53/100 [00:04<00:03, 12.16it/s]\nPLMS Sampler: 55%|█████▌ | 55/100 [00:04<00:03, 12.14it/s]\nPLMS Sampler: 57%|█████▋ | 57/100 [00:04<00:03, 12.51it/s]\nPLMS Sampler: 59%|█████▉ | 59/100 [00:04<00:03, 12.24it/s]\nPLMS Sampler: 61%|██████ | 61/100 [00:05<00:03, 12.45it/s]\nPLMS Sampler: 63%|██████▎ | 63/100 [00:05<00:02, 12.37it/s]\nPLMS Sampler: 65%|██████▌ | 65/100 [00:05<00:02, 12.49it/s]\nPLMS Sampler: 67%|██████▋ | 67/100 [00:05<00:02, 12.86it/s]\nPLMS Sampler: 69%|██████▉ | 69/100 [00:05<00:02, 13.17it/s]\nPLMS Sampler: 71%|███████ | 71/100 [00:05<00:02, 13.16it/s]\nPLMS Sampler: 73%|███████▎ | 73/100 [00:05<00:02, 13.03it/s]\nPLMS Sampler: 75%|███████▌ | 75/100 [00:06<00:01, 12.56it/s]\nPLMS Sampler: 77%|███████▋ | 77/100 [00:06<00:01, 12.31it/s]\nPLMS Sampler: 79%|███████▉ | 79/100 [00:06<00:01, 12.03it/s]\nPLMS Sampler: 81%|████████ | 81/100 [00:06<00:01, 11.79it/s]\nPLMS Sampler: 83%|████████▎ | 83/100 [00:06<00:01, 11.75it/s]\nPLMS Sampler: 85%|████████▌ | 85/100 [00:06<00:01, 11.84it/s]\nPLMS Sampler: 87%|████████▋ | 87/100 [00:07<00:01, 11.62it/s]\nPLMS Sampler: 89%|████████▉ | 89/100 [00:07<00:00, 11.62it/s]\nPLMS Sampler: 91%|█████████ | 91/100 [00:07<00:00, 11.59it/s]\nPLMS Sampler: 93%|█████████▎| 93/100 [00:07<00:00, 11.96it/s]\nPLMS Sampler: 95%|█████████▌| 95/100 [00:07<00:00, 11.88it/s]\nPLMS Sampler: 97%|█████████▋| 97/100 [00:07<00:00, 11.88it/s]\nPLMS Sampler: 99%|█████████▉| 99/100 [00:08<00:00, 12.07it/s]\nPLMS Sampler: 100%|██████████| 100/100 [00:08<00:00, 12.16it/s]", "metrics": { "predict_time": 9.513308, "total_time": 9.692282 }, "output": "https://replicate.delivery/mgxm/3f8d79c5-4ec0-4ed1-ae48-0ea68a015065/out.png", "started_at": "2022-08-19T16:36:58.436842Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/tockkav6fzeb5lfonggibwofjq", "cancel": "https://api.replicate.com/v1/predictions/tockkav6fzeb5lfonggibwofjq/cancel" }, "version": "2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63" }Generated inUsing random seed 2197080794 Seed: -1 CLIP Text Embed: torch.Size([1, 1, 768]) Data shape for PLMS sampling is (1, 16, 48, 48) Running PLMS Sampling with 100 timesteps PLMS Sampler: 0%| | 0/100 [00:00<?, ?it/s] PLMS Sampler: 1%| | 1/100 [00:00<00:15, 6.32it/s] PLMS Sampler: 3%|▎ | 3/100 [00:00<00:09, 10.16it/s] PLMS Sampler: 5%|▌ | 5/100 [00:00<00:08, 11.32it/s] PLMS Sampler: 7%|▋ | 7/100 [00:00<00:07, 11.85it/s] PLMS Sampler: 9%|▉ | 9/100 [00:00<00:07, 12.01it/s] PLMS Sampler: 11%|█ | 11/100 [00:00<00:07, 12.07it/s] PLMS Sampler: 13%|█▎ | 13/100 [00:01<00:07, 12.17it/s] PLMS Sampler: 15%|█▌ | 15/100 [00:01<00:06, 12.28it/s] PLMS Sampler: 17%|█▋ | 17/100 [00:01<00:06, 12.44it/s] PLMS Sampler: 19%|█▉ | 19/100 [00:01<00:06, 12.27it/s] PLMS Sampler: 21%|██ | 21/100 [00:01<00:06, 12.21it/s] PLMS Sampler: 23%|██▎ | 23/100 [00:01<00:06, 12.03it/s] PLMS Sampler: 25%|██▌ | 25/100 [00:02<00:06, 12.43it/s] PLMS Sampler: 27%|██▋ | 27/100 [00:02<00:05, 12.24it/s] PLMS Sampler: 29%|██▉ | 29/100 [00:02<00:05, 12.32it/s] PLMS Sampler: 31%|███ | 31/100 [00:02<00:05, 12.15it/s] PLMS Sampler: 33%|███▎ | 33/100 [00:02<00:05, 12.48it/s] PLMS Sampler: 35%|███▌ | 35/100 [00:02<00:05, 12.24it/s] PLMS Sampler: 37%|███▋ | 37/100 [00:03<00:05, 12.56it/s] PLMS Sampler: 39%|███▉ | 39/100 [00:03<00:04, 12.39it/s] PLMS Sampler: 41%|████ | 41/100 [00:03<00:04, 12.14it/s] PLMS Sampler: 43%|████▎ | 43/100 [00:03<00:04, 12.13it/s] PLMS Sampler: 45%|████▌ | 45/100 [00:03<00:04, 11.92it/s] PLMS Sampler: 47%|████▋ | 47/100 [00:03<00:04, 12.10it/s] PLMS Sampler: 49%|████▉ | 49/100 [00:04<00:04, 12.40it/s] PLMS Sampler: 51%|█████ | 51/100 [00:04<00:03, 12.38it/s] PLMS Sampler: 53%|█████▎ | 53/100 [00:04<00:03, 12.16it/s] PLMS Sampler: 55%|█████▌ | 55/100 [00:04<00:03, 12.14it/s] PLMS Sampler: 57%|█████▋ | 57/100 [00:04<00:03, 12.51it/s] PLMS Sampler: 59%|█████▉ | 59/100 [00:04<00:03, 12.24it/s] PLMS Sampler: 61%|██████ | 61/100 [00:05<00:03, 12.45it/s] PLMS Sampler: 63%|██████▎ | 63/100 [00:05<00:02, 12.37it/s] PLMS Sampler: 65%|██████▌ | 65/100 [00:05<00:02, 12.49it/s] PLMS Sampler: 67%|██████▋ | 67/100 [00:05<00:02, 12.86it/s] PLMS Sampler: 69%|██████▉ | 69/100 [00:05<00:02, 13.17it/s] PLMS Sampler: 71%|███████ | 71/100 [00:05<00:02, 13.16it/s] PLMS Sampler: 73%|███████▎ | 73/100 [00:05<00:02, 13.03it/s] PLMS Sampler: 75%|███████▌ | 75/100 [00:06<00:01, 12.56it/s] PLMS Sampler: 77%|███████▋ | 77/100 [00:06<00:01, 12.31it/s] PLMS Sampler: 79%|███████▉ | 79/100 [00:06<00:01, 12.03it/s] PLMS Sampler: 81%|████████ | 81/100 [00:06<00:01, 11.79it/s] PLMS Sampler: 83%|████████▎ | 83/100 [00:06<00:01, 11.75it/s] PLMS Sampler: 85%|████████▌ | 85/100 [00:06<00:01, 11.84it/s] PLMS Sampler: 87%|████████▋ | 87/100 [00:07<00:01, 11.62it/s] PLMS Sampler: 89%|████████▉ | 89/100 [00:07<00:00, 11.62it/s] PLMS Sampler: 91%|█████████ | 91/100 [00:07<00:00, 11.59it/s] PLMS Sampler: 93%|█████████▎| 93/100 [00:07<00:00, 11.96it/s] PLMS Sampler: 95%|█████████▌| 95/100 [00:07<00:00, 11.88it/s] PLMS Sampler: 97%|█████████▋| 97/100 [00:07<00:00, 11.88it/s] PLMS Sampler: 99%|█████████▉| 99/100 [00:08<00:00, 12.07it/s] PLMS Sampler: 100%|██████████| 100/100 [00:08<00:00, 12.16it/s]Prediction
andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63IDzlkk5i4s75hgnek44p3eop3xniStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- -1
- scale
- 5
- steps
- 100
- prompt
- A castle on top of the grassy hill, on a sunny day
- num_database_results
- "5"
{ "seed": -1, "scale": 5, "steps": 100, "prompt": "A castle on top of the grassy hill, on a sunny day", "num_database_results": "5" }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 andreasjansson/monkey-island-rdm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", { input: { seed: -1, scale: 5, steps: 100, prompt: "A castle on top of the grassy hill, on a sunny day", num_database_results: "5" } } ); // 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 andreasjansson/monkey-island-rdm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", input={ "seed": -1, "scale": 5, "steps": 100, "prompt": "A castle on top of the grassy hill, on a sunny day", "num_database_results": "5" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run andreasjansson/monkey-island-rdm 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": "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", "input": { "seed": -1, "scale": 5, "steps": 100, "prompt": "A castle on top of the grassy hill, on a sunny day", "num_database_results": "5" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-08-19T16:46:13.353653Z", "created_at": "2022-08-19T16:46:04.827899Z", "data_removed": false, "error": null, "id": "zlkk5i4s75hgnek44p3eop3xni", "input": { "seed": -1, "scale": 5, "steps": 100, "prompt": "A castle on top of the grassy hill, on a sunny day", "num_database_results": "5" }, "logs": "Using random seed 4176908438\nSeed: -1\nCLIP Text Embed: torch.Size([1, 1, 768])\nData shape for PLMS sampling is (1, 16, 48, 48)\nRunning PLMS Sampling with 100 timesteps\n\nPLMS Sampler: 0%| | 0/100 [00:00<?, ?it/s]\nPLMS Sampler: 1%| | 1/100 [00:00<00:16, 6.10it/s]\nPLMS Sampler: 3%|▎ | 3/100 [00:00<00:09, 10.13it/s]\nPLMS Sampler: 5%|▌ | 5/100 [00:00<00:08, 11.72it/s]\nPLMS Sampler: 7%|▋ | 7/100 [00:00<00:07, 12.49it/s]\nPLMS Sampler: 9%|▉ | 9/100 [00:00<00:07, 12.95it/s]\nPLMS Sampler: 11%|█ | 11/100 [00:00<00:06, 13.24it/s]\nPLMS Sampler: 13%|█▎ | 13/100 [00:01<00:06, 13.42it/s]\nPLMS Sampler: 15%|█▌ | 15/100 [00:01<00:06, 13.46it/s]\nPLMS Sampler: 17%|█▋ | 17/100 [00:01<00:06, 13.45it/s]\nPLMS Sampler: 19%|█▉ | 19/100 [00:01<00:05, 13.59it/s]\nPLMS Sampler: 21%|██ | 21/100 [00:01<00:05, 13.69it/s]\nPLMS Sampler: 23%|██▎ | 23/100 [00:01<00:05, 13.73it/s]\nPLMS Sampler: 25%|██▌ | 25/100 [00:01<00:05, 13.77it/s]\nPLMS Sampler: 27%|██▋ | 27/100 [00:02<00:05, 13.81it/s]\nPLMS Sampler: 29%|██▉ | 29/100 [00:02<00:05, 13.84it/s]\nPLMS Sampler: 31%|███ | 31/100 [00:02<00:05, 13.78it/s]\nPLMS Sampler: 33%|███▎ | 33/100 [00:02<00:04, 13.84it/s]\nPLMS Sampler: 35%|███▌ | 35/100 [00:02<00:04, 13.89it/s]\nPLMS Sampler: 37%|███▋ | 37/100 [00:02<00:04, 13.79it/s]\nPLMS Sampler: 39%|███▉ | 39/100 [00:02<00:04, 13.74it/s]\nPLMS Sampler: 41%|████ | 41/100 [00:03<00:04, 13.80it/s]\nPLMS Sampler: 43%|████▎ | 43/100 [00:03<00:04, 13.78it/s]\nPLMS Sampler: 45%|████▌ | 45/100 [00:03<00:03, 13.82it/s]\nPLMS Sampler: 47%|████▋ | 47/100 [00:03<00:03, 13.85it/s]\nPLMS Sampler: 49%|████▉ | 49/100 [00:03<00:03, 13.88it/s]\nPLMS Sampler: 51%|█████ | 51/100 [00:03<00:03, 13.90it/s]\nPLMS Sampler: 53%|█████▎ | 53/100 [00:03<00:03, 13.74it/s]\nPLMS Sampler: 55%|█████▌ | 55/100 [00:04<00:03, 13.80it/s]\nPLMS Sampler: 57%|█████▋ | 57/100 [00:04<00:03, 13.83it/s]\nPLMS Sampler: 59%|█████▉ | 59/100 [00:04<00:02, 13.87it/s]\nPLMS Sampler: 61%|██████ | 61/100 [00:04<00:02, 13.86it/s]\nPLMS Sampler: 63%|██████▎ | 63/100 [00:04<00:02, 13.85it/s]\nPLMS Sampler: 65%|██████▌ | 65/100 [00:04<00:02, 13.89it/s]\nPLMS Sampler: 67%|██████▋ | 67/100 [00:04<00:02, 13.89it/s]\nPLMS Sampler: 69%|██████▉ | 69/100 [00:05<00:02, 13.78it/s]\nPLMS Sampler: 71%|███████ | 71/100 [00:05<00:02, 13.79it/s]\nPLMS Sampler: 73%|███████▎ | 73/100 [00:05<00:01, 13.68it/s]\nPLMS Sampler: 75%|███████▌ | 75/100 [00:05<00:01, 13.56it/s]\nPLMS Sampler: 77%|███████▋ | 77/100 [00:05<00:01, 13.70it/s]\nPLMS Sampler: 79%|███████▉ | 79/100 [00:05<00:01, 13.81it/s]\nPLMS Sampler: 81%|████████ | 81/100 [00:05<00:01, 13.89it/s]\nPLMS Sampler: 83%|████████▎ | 83/100 [00:06<00:01, 13.89it/s]\nPLMS Sampler: 85%|████████▌ | 85/100 [00:06<00:01, 13.91it/s]\nPLMS Sampler: 87%|████████▋ | 87/100 [00:06<00:00, 13.93it/s]\nPLMS Sampler: 89%|████████▉ | 89/100 [00:06<00:00, 13.94it/s]\nPLMS Sampler: 91%|█████████ | 91/100 [00:06<00:00, 13.94it/s]\nPLMS Sampler: 93%|█████████▎| 93/100 [00:06<00:00, 13.94it/s]\nPLMS Sampler: 95%|█████████▌| 95/100 [00:06<00:00, 13.95it/s]\nPLMS Sampler: 97%|█████████▋| 97/100 [00:07<00:00, 13.97it/s]\nPLMS Sampler: 99%|█████████▉| 99/100 [00:07<00:00, 13.89it/s]\nPLMS Sampler: 100%|██████████| 100/100 [00:07<00:00, 13.64it/s]", "metrics": { "predict_time": 8.359365, "total_time": 8.525754 }, "output": "https://replicate.delivery/mgxm/4ecff125-dcf6-4892-93b7-823b9c7c856f/out.png", "started_at": "2022-08-19T16:46:04.994288Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/zlkk5i4s75hgnek44p3eop3xni", "cancel": "https://api.replicate.com/v1/predictions/zlkk5i4s75hgnek44p3eop3xni/cancel" }, "version": "2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63" }Generated inUsing random seed 4176908438 Seed: -1 CLIP Text Embed: torch.Size([1, 1, 768]) Data shape for PLMS sampling is (1, 16, 48, 48) Running PLMS Sampling with 100 timesteps PLMS Sampler: 0%| | 0/100 [00:00<?, ?it/s] PLMS Sampler: 1%| | 1/100 [00:00<00:16, 6.10it/s] PLMS Sampler: 3%|▎ | 3/100 [00:00<00:09, 10.13it/s] PLMS Sampler: 5%|▌ | 5/100 [00:00<00:08, 11.72it/s] PLMS Sampler: 7%|▋ | 7/100 [00:00<00:07, 12.49it/s] PLMS Sampler: 9%|▉ | 9/100 [00:00<00:07, 12.95it/s] PLMS Sampler: 11%|█ | 11/100 [00:00<00:06, 13.24it/s] PLMS Sampler: 13%|█▎ | 13/100 [00:01<00:06, 13.42it/s] PLMS Sampler: 15%|█▌ | 15/100 [00:01<00:06, 13.46it/s] PLMS Sampler: 17%|█▋ | 17/100 [00:01<00:06, 13.45it/s] PLMS Sampler: 19%|█▉ | 19/100 [00:01<00:05, 13.59it/s] PLMS Sampler: 21%|██ | 21/100 [00:01<00:05, 13.69it/s] PLMS Sampler: 23%|██▎ | 23/100 [00:01<00:05, 13.73it/s] PLMS Sampler: 25%|██▌ | 25/100 [00:01<00:05, 13.77it/s] PLMS Sampler: 27%|██▋ | 27/100 [00:02<00:05, 13.81it/s] PLMS Sampler: 29%|██▉ | 29/100 [00:02<00:05, 13.84it/s] PLMS Sampler: 31%|███ | 31/100 [00:02<00:05, 13.78it/s] PLMS Sampler: 33%|███▎ | 33/100 [00:02<00:04, 13.84it/s] PLMS Sampler: 35%|███▌ | 35/100 [00:02<00:04, 13.89it/s] PLMS Sampler: 37%|███▋ | 37/100 [00:02<00:04, 13.79it/s] PLMS Sampler: 39%|███▉ | 39/100 [00:02<00:04, 13.74it/s] PLMS Sampler: 41%|████ | 41/100 [00:03<00:04, 13.80it/s] PLMS Sampler: 43%|████▎ | 43/100 [00:03<00:04, 13.78it/s] PLMS Sampler: 45%|████▌ | 45/100 [00:03<00:03, 13.82it/s] PLMS Sampler: 47%|████▋ | 47/100 [00:03<00:03, 13.85it/s] PLMS Sampler: 49%|████▉ | 49/100 [00:03<00:03, 13.88it/s] PLMS Sampler: 51%|█████ | 51/100 [00:03<00:03, 13.90it/s] PLMS Sampler: 53%|█████▎ | 53/100 [00:03<00:03, 13.74it/s] PLMS Sampler: 55%|█████▌ | 55/100 [00:04<00:03, 13.80it/s] PLMS Sampler: 57%|█████▋ | 57/100 [00:04<00:03, 13.83it/s] PLMS Sampler: 59%|█████▉ | 59/100 [00:04<00:02, 13.87it/s] PLMS Sampler: 61%|██████ | 61/100 [00:04<00:02, 13.86it/s] PLMS Sampler: 63%|██████▎ | 63/100 [00:04<00:02, 13.85it/s] PLMS Sampler: 65%|██████▌ | 65/100 [00:04<00:02, 13.89it/s] PLMS Sampler: 67%|██████▋ | 67/100 [00:04<00:02, 13.89it/s] PLMS Sampler: 69%|██████▉ | 69/100 [00:05<00:02, 13.78it/s] PLMS Sampler: 71%|███████ | 71/100 [00:05<00:02, 13.79it/s] PLMS Sampler: 73%|███████▎ | 73/100 [00:05<00:01, 13.68it/s] PLMS Sampler: 75%|███████▌ | 75/100 [00:05<00:01, 13.56it/s] PLMS Sampler: 77%|███████▋ | 77/100 [00:05<00:01, 13.70it/s] PLMS Sampler: 79%|███████▉ | 79/100 [00:05<00:01, 13.81it/s] PLMS Sampler: 81%|████████ | 81/100 [00:05<00:01, 13.89it/s] PLMS Sampler: 83%|████████▎ | 83/100 [00:06<00:01, 13.89it/s] PLMS Sampler: 85%|████████▌ | 85/100 [00:06<00:01, 13.91it/s] PLMS Sampler: 87%|████████▋ | 87/100 [00:06<00:00, 13.93it/s] PLMS Sampler: 89%|████████▉ | 89/100 [00:06<00:00, 13.94it/s] PLMS Sampler: 91%|█████████ | 91/100 [00:06<00:00, 13.94it/s] PLMS Sampler: 93%|█████████▎| 93/100 [00:06<00:00, 13.94it/s] PLMS Sampler: 95%|█████████▌| 95/100 [00:06<00:00, 13.95it/s] PLMS Sampler: 97%|█████████▋| 97/100 [00:07<00:00, 13.97it/s] PLMS Sampler: 99%|█████████▉| 99/100 [00:07<00:00, 13.89it/s] PLMS Sampler: 100%|██████████| 100/100 [00:07<00:00, 13.64it/s]Prediction
andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63IDnrxprragojawlpdwbfymdw26raStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- -1
- scale
- 5
- steps
- 100
- prompt
- Guybrush taking off into space on a burning rocket
- num_database_results
- "10"
{ "seed": -1, "scale": 5, "steps": 100, "prompt": "Guybrush taking off into space on a burning rocket", "num_database_results": "10" }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 andreasjansson/monkey-island-rdm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", { input: { seed: -1, scale: 5, steps: 100, prompt: "Guybrush taking off into space on a burning rocket", num_database_results: "10" } } ); // 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 andreasjansson/monkey-island-rdm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", input={ "seed": -1, "scale": 5, "steps": 100, "prompt": "Guybrush taking off into space on a burning rocket", "num_database_results": "10" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run andreasjansson/monkey-island-rdm 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": "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", "input": { "seed": -1, "scale": 5, "steps": 100, "prompt": "Guybrush taking off into space on a burning rocket", "num_database_results": "10" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-08-19T16:48:16.623248Z", "created_at": "2022-08-19T16:48:07.925635Z", "data_removed": false, "error": null, "id": "nrxprragojawlpdwbfymdw26ra", "input": { "seed": -1, "scale": 5, "steps": 100, "prompt": "Guybrush taking off into space on a burning rocket", "num_database_results": "10" }, "logs": "Using random seed 1215705649\nSeed: -1\nCLIP Text Embed: torch.Size([1, 1, 768])\nData shape for PLMS sampling is (1, 16, 48, 48)\nRunning PLMS Sampling with 100 timesteps\n\nPLMS Sampler: 0%| | 0/100 [00:00<?, ?it/s]\nPLMS Sampler: 1%| | 1/100 [00:00<00:16, 6.08it/s]\nPLMS Sampler: 3%|▎ | 3/100 [00:00<00:09, 10.50it/s]\nPLMS Sampler: 5%|▌ | 5/100 [00:00<00:07, 12.09it/s]\nPLMS Sampler: 7%|▋ | 7/100 [00:00<00:07, 12.88it/s]\nPLMS Sampler: 9%|▉ | 9/100 [00:00<00:06, 13.11it/s]\nPLMS Sampler: 11%|█ | 11/100 [00:00<00:06, 13.33it/s]\nPLMS Sampler: 13%|█▎ | 13/100 [00:01<00:06, 13.46it/s]\nPLMS Sampler: 15%|█▌ | 15/100 [00:01<00:06, 13.61it/s]\nPLMS Sampler: 17%|█▋ | 17/100 [00:01<00:06, 13.79it/s]\nPLMS Sampler: 19%|█▉ | 19/100 [00:01<00:05, 13.90it/s]\nPLMS Sampler: 21%|██ | 21/100 [00:01<00:05, 14.01it/s]\nPLMS Sampler: 23%|██▎ | 23/100 [00:01<00:05, 14.04it/s]\nPLMS Sampler: 25%|██▌ | 25/100 [00:01<00:05, 14.00it/s]\nPLMS Sampler: 27%|██▋ | 27/100 [00:02<00:05, 14.01it/s]\nPLMS Sampler: 29%|██▉ | 29/100 [00:02<00:05, 13.71it/s]\nPLMS Sampler: 31%|███ | 31/100 [00:02<00:05, 13.55it/s]\nPLMS Sampler: 33%|███▎ | 33/100 [00:02<00:04, 13.58it/s]\nPLMS Sampler: 35%|███▌ | 35/100 [00:02<00:04, 13.66it/s]\nPLMS Sampler: 37%|███▋ | 37/100 [00:02<00:04, 13.61it/s]\nPLMS Sampler: 39%|███▉ | 39/100 [00:02<00:04, 13.75it/s]\nPLMS Sampler: 41%|████ | 41/100 [00:03<00:04, 13.83it/s]\nPLMS Sampler: 43%|████▎ | 43/100 [00:03<00:04, 13.84it/s]\nPLMS Sampler: 45%|████▌ | 45/100 [00:03<00:03, 13.81it/s]\nPLMS Sampler: 47%|████▋ | 47/100 [00:03<00:03, 13.85it/s]\nPLMS Sampler: 49%|████▉ | 49/100 [00:03<00:03, 13.90it/s]\nPLMS Sampler: 51%|█████ | 51/100 [00:03<00:03, 13.94it/s]\nPLMS Sampler: 53%|█████▎ | 53/100 [00:03<00:03, 13.79it/s]\nPLMS Sampler: 55%|█████▌ | 55/100 [00:04<00:03, 13.84it/s]\nPLMS Sampler: 57%|█████▋ | 57/100 [00:04<00:03, 13.74it/s]\nPLMS Sampler: 59%|█████▉ | 59/100 [00:04<00:02, 13.84it/s]\nPLMS Sampler: 61%|██████ | 61/100 [00:04<00:02, 13.94it/s]\nPLMS Sampler: 63%|██████▎ | 63/100 [00:04<00:02, 13.64it/s]\nPLMS Sampler: 65%|██████▌ | 65/100 [00:04<00:02, 13.67it/s]\nPLMS Sampler: 67%|██████▋ | 67/100 [00:04<00:02, 13.64it/s]\nPLMS Sampler: 69%|██████▉ | 69/100 [00:05<00:02, 13.51it/s]\nPLMS Sampler: 71%|███████ | 71/100 [00:05<00:02, 13.50it/s]\nPLMS Sampler: 73%|███████▎ | 73/100 [00:05<00:01, 13.62it/s]\nPLMS Sampler: 75%|███████▌ | 75/100 [00:05<00:01, 13.54it/s]\nPLMS Sampler: 77%|███████▋ | 77/100 [00:05<00:01, 13.65it/s]\nPLMS Sampler: 79%|███████▉ | 79/100 [00:05<00:01, 13.73it/s]\nPLMS Sampler: 81%|████████ | 81/100 [00:05<00:01, 13.79it/s]\nPLMS Sampler: 83%|████████▎ | 83/100 [00:06<00:01, 13.67it/s]\nPLMS Sampler: 85%|████████▌ | 85/100 [00:06<00:01, 13.77it/s]\nPLMS Sampler: 87%|████████▋ | 87/100 [00:06<00:00, 13.84it/s]\nPLMS Sampler: 89%|████████▉ | 89/100 [00:06<00:00, 13.89it/s]\nPLMS Sampler: 91%|█████████ | 91/100 [00:06<00:00, 13.92it/s]\nPLMS Sampler: 93%|█████████▎| 93/100 [00:06<00:00, 13.95it/s]\nPLMS Sampler: 95%|█████████▌| 95/100 [00:06<00:00, 13.95it/s]\nPLMS Sampler: 97%|█████████▋| 97/100 [00:07<00:00, 13.77it/s]\nPLMS Sampler: 99%|█████████▉| 99/100 [00:07<00:00, 13.83it/s]\nPLMS Sampler: 100%|██████████| 100/100 [00:07<00:00, 13.63it/s]", "metrics": { "predict_time": 8.503516, "total_time": 8.697613 }, "output": "https://replicate.delivery/mgxm/631af529-f95f-4677-8e57-8dfe5771e8ec/out.png", "started_at": "2022-08-19T16:48:08.119732Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/nrxprragojawlpdwbfymdw26ra", "cancel": "https://api.replicate.com/v1/predictions/nrxprragojawlpdwbfymdw26ra/cancel" }, "version": "2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63" }Generated inUsing random seed 1215705649 Seed: -1 CLIP Text Embed: torch.Size([1, 1, 768]) Data shape for PLMS sampling is (1, 16, 48, 48) Running PLMS Sampling with 100 timesteps PLMS Sampler: 0%| | 0/100 [00:00<?, ?it/s] PLMS Sampler: 1%| | 1/100 [00:00<00:16, 6.08it/s] PLMS Sampler: 3%|▎ | 3/100 [00:00<00:09, 10.50it/s] PLMS Sampler: 5%|▌ | 5/100 [00:00<00:07, 12.09it/s] PLMS Sampler: 7%|▋ | 7/100 [00:00<00:07, 12.88it/s] PLMS Sampler: 9%|▉ | 9/100 [00:00<00:06, 13.11it/s] PLMS Sampler: 11%|█ | 11/100 [00:00<00:06, 13.33it/s] PLMS Sampler: 13%|█▎ | 13/100 [00:01<00:06, 13.46it/s] PLMS Sampler: 15%|█▌ | 15/100 [00:01<00:06, 13.61it/s] PLMS Sampler: 17%|█▋ | 17/100 [00:01<00:06, 13.79it/s] PLMS Sampler: 19%|█▉ | 19/100 [00:01<00:05, 13.90it/s] PLMS Sampler: 21%|██ | 21/100 [00:01<00:05, 14.01it/s] PLMS Sampler: 23%|██▎ | 23/100 [00:01<00:05, 14.04it/s] PLMS Sampler: 25%|██▌ | 25/100 [00:01<00:05, 14.00it/s] PLMS Sampler: 27%|██▋ | 27/100 [00:02<00:05, 14.01it/s] PLMS Sampler: 29%|██▉ | 29/100 [00:02<00:05, 13.71it/s] PLMS Sampler: 31%|███ | 31/100 [00:02<00:05, 13.55it/s] PLMS Sampler: 33%|███▎ | 33/100 [00:02<00:04, 13.58it/s] PLMS Sampler: 35%|███▌ | 35/100 [00:02<00:04, 13.66it/s] PLMS Sampler: 37%|███▋ | 37/100 [00:02<00:04, 13.61it/s] PLMS Sampler: 39%|███▉ | 39/100 [00:02<00:04, 13.75it/s] PLMS Sampler: 41%|████ | 41/100 [00:03<00:04, 13.83it/s] PLMS Sampler: 43%|████▎ | 43/100 [00:03<00:04, 13.84it/s] PLMS Sampler: 45%|████▌ | 45/100 [00:03<00:03, 13.81it/s] PLMS Sampler: 47%|████▋ | 47/100 [00:03<00:03, 13.85it/s] PLMS Sampler: 49%|████▉ | 49/100 [00:03<00:03, 13.90it/s] PLMS Sampler: 51%|█████ | 51/100 [00:03<00:03, 13.94it/s] PLMS Sampler: 53%|█████▎ | 53/100 [00:03<00:03, 13.79it/s] PLMS Sampler: 55%|█████▌ | 55/100 [00:04<00:03, 13.84it/s] PLMS Sampler: 57%|█████▋ | 57/100 [00:04<00:03, 13.74it/s] PLMS Sampler: 59%|█████▉ | 59/100 [00:04<00:02, 13.84it/s] PLMS Sampler: 61%|██████ | 61/100 [00:04<00:02, 13.94it/s] PLMS Sampler: 63%|██████▎ | 63/100 [00:04<00:02, 13.64it/s] PLMS Sampler: 65%|██████▌ | 65/100 [00:04<00:02, 13.67it/s] PLMS Sampler: 67%|██████▋ | 67/100 [00:04<00:02, 13.64it/s] PLMS Sampler: 69%|██████▉ | 69/100 [00:05<00:02, 13.51it/s] PLMS Sampler: 71%|███████ | 71/100 [00:05<00:02, 13.50it/s] PLMS Sampler: 73%|███████▎ | 73/100 [00:05<00:01, 13.62it/s] PLMS Sampler: 75%|███████▌ | 75/100 [00:05<00:01, 13.54it/s] PLMS Sampler: 77%|███████▋ | 77/100 [00:05<00:01, 13.65it/s] PLMS Sampler: 79%|███████▉ | 79/100 [00:05<00:01, 13.73it/s] PLMS Sampler: 81%|████████ | 81/100 [00:05<00:01, 13.79it/s] PLMS Sampler: 83%|████████▎ | 83/100 [00:06<00:01, 13.67it/s] PLMS Sampler: 85%|████████▌ | 85/100 [00:06<00:01, 13.77it/s] PLMS Sampler: 87%|████████▋ | 87/100 [00:06<00:00, 13.84it/s] PLMS Sampler: 89%|████████▉ | 89/100 [00:06<00:00, 13.89it/s] PLMS Sampler: 91%|█████████ | 91/100 [00:06<00:00, 13.92it/s] PLMS Sampler: 93%|█████████▎| 93/100 [00:06<00:00, 13.95it/s] PLMS Sampler: 95%|█████████▌| 95/100 [00:06<00:00, 13.95it/s] PLMS Sampler: 97%|█████████▋| 97/100 [00:07<00:00, 13.77it/s] PLMS Sampler: 99%|█████████▉| 99/100 [00:07<00:00, 13.83it/s] PLMS Sampler: 100%|██████████| 100/100 [00:07<00:00, 13.63it/s]Prediction
andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63IDbh5dthdlevci5lnyjpu2zckmpmStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- -1
- scale
- 5
- steps
- 100
- prompt
- Guybrush meets a friendly alien on a distant planet
- num_database_results
- "10"
{ "seed": -1, "scale": 5, "steps": 100, "prompt": "Guybrush meets a friendly alien on a distant planet", "num_database_results": "10" }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 andreasjansson/monkey-island-rdm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", { input: { seed: -1, scale: 5, steps: 100, prompt: "Guybrush meets a friendly alien on a distant planet", num_database_results: "10" } } ); // 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 andreasjansson/monkey-island-rdm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", input={ "seed": -1, "scale": 5, "steps": 100, "prompt": "Guybrush meets a friendly alien on a distant planet", "num_database_results": "10" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run andreasjansson/monkey-island-rdm 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": "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", "input": { "seed": -1, "scale": 5, "steps": 100, "prompt": "Guybrush meets a friendly alien on a distant planet", "num_database_results": "10" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-08-19T16:48:47.508284Z", "created_at": "2022-08-19T16:48:38.788632Z", "data_removed": false, "error": null, "id": "bh5dthdlevci5lnyjpu2zckmpm", "input": { "seed": -1, "scale": 5, "steps": 100, "prompt": "Guybrush meets a friendly alien on a distant planet", "num_database_results": "10" }, "logs": "Using random seed 2022426247\nSeed: -1\nCLIP Text Embed: torch.Size([1, 1, 768])\nData shape for PLMS sampling is (1, 16, 48, 48)\nRunning PLMS Sampling with 100 timesteps\n\nPLMS Sampler: 0%| | 0/100 [00:00<?, ?it/s]\nPLMS Sampler: 1%| | 1/100 [00:00<00:16, 6.10it/s]\nPLMS Sampler: 3%|▎ | 3/100 [00:00<00:09, 10.19it/s]\nPLMS Sampler: 5%|▌ | 5/100 [00:00<00:07, 11.90it/s]\nPLMS Sampler: 7%|▋ | 7/100 [00:00<00:07, 12.74it/s]\nPLMS Sampler: 9%|▉ | 9/100 [00:00<00:06, 13.16it/s]\nPLMS Sampler: 11%|█ | 11/100 [00:00<00:06, 13.34it/s]\nPLMS Sampler: 13%|█▎ | 13/100 [00:01<00:06, 13.58it/s]\nPLMS Sampler: 15%|█▌ | 15/100 [00:01<00:06, 13.54it/s]\nPLMS Sampler: 17%|█▋ | 17/100 [00:01<00:06, 13.62it/s]\nPLMS Sampler: 19%|█▉ | 19/100 [00:01<00:05, 13.52it/s]\nPLMS Sampler: 21%|██ | 21/100 [00:01<00:05, 13.65it/s]\nPLMS Sampler: 23%|██▎ | 23/100 [00:01<00:05, 13.75it/s]\nPLMS Sampler: 25%|██▌ | 25/100 [00:01<00:05, 13.64it/s]\nPLMS Sampler: 27%|██▋ | 27/100 [00:02<00:05, 13.72it/s]\nPLMS Sampler: 29%|██▉ | 29/100 [00:02<00:05, 13.84it/s]\nPLMS Sampler: 31%|███ | 31/100 [00:02<00:04, 13.82it/s]\nPLMS Sampler: 33%|███▎ | 33/100 [00:02<00:04, 13.86it/s]\nPLMS Sampler: 35%|███▌ | 35/100 [00:02<00:04, 13.90it/s]\nPLMS Sampler: 37%|███▋ | 37/100 [00:02<00:04, 13.92it/s]\nPLMS Sampler: 39%|███▉ | 39/100 [00:02<00:04, 13.94it/s]\nPLMS Sampler: 41%|████ | 41/100 [00:03<00:04, 13.96it/s]\nPLMS Sampler: 43%|████▎ | 43/100 [00:03<00:04, 13.76it/s]\nPLMS Sampler: 45%|████▌ | 45/100 [00:03<00:03, 13.79it/s]\nPLMS Sampler: 47%|████▋ | 47/100 [00:03<00:03, 13.74it/s]\nPLMS Sampler: 49%|████▉ | 49/100 [00:03<00:03, 13.81it/s]\nPLMS Sampler: 51%|█████ | 51/100 [00:03<00:03, 13.69it/s]\nPLMS Sampler: 53%|█████▎ | 53/100 [00:03<00:03, 13.65it/s]\nPLMS Sampler: 55%|█████▌ | 55/100 [00:04<00:03, 13.75it/s]\nPLMS Sampler: 57%|█████▋ | 57/100 [00:04<00:03, 13.79it/s]\nPLMS Sampler: 59%|█████▉ | 59/100 [00:04<00:02, 13.85it/s]\nPLMS Sampler: 61%|██████ | 61/100 [00:04<00:02, 13.87it/s]\nPLMS Sampler: 63%|██████▎ | 63/100 [00:04<00:02, 13.88it/s]\nPLMS Sampler: 65%|██████▌ | 65/100 [00:04<00:02, 13.88it/s]\nPLMS Sampler: 67%|██████▋ | 67/100 [00:04<00:02, 13.89it/s]\nPLMS Sampler: 69%|██████▉ | 69/100 [00:05<00:02, 13.90it/s]\nPLMS Sampler: 71%|███████ | 71/100 [00:05<00:02, 13.94it/s]\nPLMS Sampler: 73%|███████▎ | 73/100 [00:05<00:02, 13.49it/s]\nPLMS Sampler: 75%|███████▌ | 75/100 [00:05<00:01, 13.51it/s]\nPLMS Sampler: 77%|███████▋ | 77/100 [00:05<00:01, 13.43it/s]\nPLMS Sampler: 79%|███████▉ | 79/100 [00:05<00:01, 13.57it/s]\nPLMS Sampler: 81%|████████ | 81/100 [00:05<00:01, 13.68it/s]\nPLMS Sampler: 83%|████████▎ | 83/100 [00:06<00:01, 13.75it/s]\nPLMS Sampler: 85%|████████▌ | 85/100 [00:06<00:01, 13.83it/s]\nPLMS Sampler: 87%|████████▋ | 87/100 [00:06<00:00, 13.91it/s]\nPLMS Sampler: 89%|████████▉ | 89/100 [00:06<00:00, 13.98it/s]\nPLMS Sampler: 91%|█████████ | 91/100 [00:06<00:00, 14.03it/s]\nPLMS Sampler: 93%|█████████▎| 93/100 [00:06<00:00, 13.75it/s]\nPLMS Sampler: 95%|█████████▌| 95/100 [00:06<00:00, 13.71it/s]\nPLMS Sampler: 97%|█████████▋| 97/100 [00:07<00:00, 13.77it/s]\nPLMS Sampler: 99%|█████████▉| 99/100 [00:07<00:00, 13.81it/s]\nPLMS Sampler: 100%|██████████| 100/100 [00:07<00:00, 13.62it/s]", "metrics": { "predict_time": 8.523578, "total_time": 8.719652 }, "output": "https://replicate.delivery/mgxm/f73c9fa0-227b-4734-91b9-e2f2f24b3bf0/out.png", "started_at": "2022-08-19T16:48:38.984706Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/bh5dthdlevci5lnyjpu2zckmpm", "cancel": "https://api.replicate.com/v1/predictions/bh5dthdlevci5lnyjpu2zckmpm/cancel" }, "version": "2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63" }Generated inUsing random seed 2022426247 Seed: -1 CLIP Text Embed: torch.Size([1, 1, 768]) Data shape for PLMS sampling is (1, 16, 48, 48) Running PLMS Sampling with 100 timesteps PLMS Sampler: 0%| | 0/100 [00:00<?, ?it/s] PLMS Sampler: 1%| | 1/100 [00:00<00:16, 6.10it/s] PLMS Sampler: 3%|▎ | 3/100 [00:00<00:09, 10.19it/s] PLMS Sampler: 5%|▌ | 5/100 [00:00<00:07, 11.90it/s] PLMS Sampler: 7%|▋ | 7/100 [00:00<00:07, 12.74it/s] PLMS Sampler: 9%|▉ | 9/100 [00:00<00:06, 13.16it/s] PLMS Sampler: 11%|█ | 11/100 [00:00<00:06, 13.34it/s] PLMS Sampler: 13%|█▎ | 13/100 [00:01<00:06, 13.58it/s] PLMS Sampler: 15%|█▌ | 15/100 [00:01<00:06, 13.54it/s] PLMS Sampler: 17%|█▋ | 17/100 [00:01<00:06, 13.62it/s] PLMS Sampler: 19%|█▉ | 19/100 [00:01<00:05, 13.52it/s] PLMS Sampler: 21%|██ | 21/100 [00:01<00:05, 13.65it/s] PLMS Sampler: 23%|██▎ | 23/100 [00:01<00:05, 13.75it/s] PLMS Sampler: 25%|██▌ | 25/100 [00:01<00:05, 13.64it/s] PLMS Sampler: 27%|██▋ | 27/100 [00:02<00:05, 13.72it/s] PLMS Sampler: 29%|██▉ | 29/100 [00:02<00:05, 13.84it/s] PLMS Sampler: 31%|███ | 31/100 [00:02<00:04, 13.82it/s] PLMS Sampler: 33%|███▎ | 33/100 [00:02<00:04, 13.86it/s] PLMS Sampler: 35%|███▌ | 35/100 [00:02<00:04, 13.90it/s] PLMS Sampler: 37%|███▋ | 37/100 [00:02<00:04, 13.92it/s] PLMS Sampler: 39%|███▉ | 39/100 [00:02<00:04, 13.94it/s] PLMS Sampler: 41%|████ | 41/100 [00:03<00:04, 13.96it/s] PLMS Sampler: 43%|████▎ | 43/100 [00:03<00:04, 13.76it/s] PLMS Sampler: 45%|████▌ | 45/100 [00:03<00:03, 13.79it/s] PLMS Sampler: 47%|████▋ | 47/100 [00:03<00:03, 13.74it/s] PLMS Sampler: 49%|████▉ | 49/100 [00:03<00:03, 13.81it/s] PLMS Sampler: 51%|█████ | 51/100 [00:03<00:03, 13.69it/s] PLMS Sampler: 53%|█████▎ | 53/100 [00:03<00:03, 13.65it/s] PLMS Sampler: 55%|█████▌ | 55/100 [00:04<00:03, 13.75it/s] PLMS Sampler: 57%|█████▋ | 57/100 [00:04<00:03, 13.79it/s] PLMS Sampler: 59%|█████▉ | 59/100 [00:04<00:02, 13.85it/s] PLMS Sampler: 61%|██████ | 61/100 [00:04<00:02, 13.87it/s] PLMS Sampler: 63%|██████▎ | 63/100 [00:04<00:02, 13.88it/s] PLMS Sampler: 65%|██████▌ | 65/100 [00:04<00:02, 13.88it/s] PLMS Sampler: 67%|██████▋ | 67/100 [00:04<00:02, 13.89it/s] PLMS Sampler: 69%|██████▉ | 69/100 [00:05<00:02, 13.90it/s] PLMS Sampler: 71%|███████ | 71/100 [00:05<00:02, 13.94it/s] PLMS Sampler: 73%|███████▎ | 73/100 [00:05<00:02, 13.49it/s] PLMS Sampler: 75%|███████▌ | 75/100 [00:05<00:01, 13.51it/s] PLMS Sampler: 77%|███████▋ | 77/100 [00:05<00:01, 13.43it/s] PLMS Sampler: 79%|███████▉ | 79/100 [00:05<00:01, 13.57it/s] PLMS Sampler: 81%|████████ | 81/100 [00:05<00:01, 13.68it/s] PLMS Sampler: 83%|████████▎ | 83/100 [00:06<00:01, 13.75it/s] PLMS Sampler: 85%|████████▌ | 85/100 [00:06<00:01, 13.83it/s] PLMS Sampler: 87%|████████▋ | 87/100 [00:06<00:00, 13.91it/s] PLMS Sampler: 89%|████████▉ | 89/100 [00:06<00:00, 13.98it/s] PLMS Sampler: 91%|█████████ | 91/100 [00:06<00:00, 14.03it/s] PLMS Sampler: 93%|█████████▎| 93/100 [00:06<00:00, 13.75it/s] PLMS Sampler: 95%|█████████▌| 95/100 [00:06<00:00, 13.71it/s] PLMS Sampler: 97%|█████████▋| 97/100 [00:07<00:00, 13.77it/s] PLMS Sampler: 99%|█████████▉| 99/100 [00:07<00:00, 13.81it/s] PLMS Sampler: 100%|██████████| 100/100 [00:07<00:00, 13.62it/s]Prediction
andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63IDkvza5yr4nrbkzk6fkdtyhpm3qiStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- -1
- scale
- 5
- steps
- 100
- prompt
- An enormous monster alien on a distant planet
- num_database_results
- "10"
{ "seed": -1, "scale": 5, "steps": 100, "prompt": "An enormous monster alien on a distant planet", "num_database_results": "10" }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 andreasjansson/monkey-island-rdm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", { input: { seed: -1, scale: 5, steps: 100, prompt: "An enormous monster alien on a distant planet", num_database_results: "10" } } ); // 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 andreasjansson/monkey-island-rdm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", input={ "seed": -1, "scale": 5, "steps": 100, "prompt": "An enormous monster alien on a distant planet", "num_database_results": "10" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run andreasjansson/monkey-island-rdm 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": "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", "input": { "seed": -1, "scale": 5, "steps": 100, "prompt": "An enormous monster alien on a distant planet", "num_database_results": "10" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-08-19T16:53:06.069317Z", "created_at": "2022-08-19T16:52:57.152417Z", "data_removed": false, "error": null, "id": "kvza5yr4nrbkzk6fkdtyhpm3qi", "input": { "seed": -1, "scale": 5, "steps": 100, "prompt": "An enormous monster alien on a distant planet", "num_database_results": "10" }, "logs": "Using random seed 3029702370\nSeed: -1\nCLIP Text Embed: torch.Size([1, 1, 768])\nData shape for PLMS sampling is (1, 16, 48, 48)\nRunning PLMS Sampling with 100 timesteps\n\nPLMS Sampler: 0%| | 0/100 [00:00<?, ?it/s]\nPLMS Sampler: 1%| | 1/100 [00:00<00:16, 6.10it/s]\nPLMS Sampler: 3%|▎ | 3/100 [00:00<00:09, 10.21it/s]\nPLMS Sampler: 5%|▌ | 5/100 [00:00<00:08, 11.81it/s]\nPLMS Sampler: 7%|▋ | 7/100 [00:00<00:07, 12.60it/s]\nPLMS Sampler: 9%|▉ | 9/100 [00:00<00:06, 13.04it/s]\nPLMS Sampler: 11%|█ | 11/100 [00:00<00:06, 13.33it/s]\nPLMS Sampler: 13%|█▎ | 13/100 [00:01<00:06, 13.13it/s]\nPLMS Sampler: 15%|█▌ | 15/100 [00:01<00:06, 13.28it/s]\nPLMS Sampler: 17%|█▋ | 17/100 [00:01<00:06, 13.42it/s]\nPLMS Sampler: 19%|█▉ | 19/100 [00:01<00:05, 13.57it/s]\nPLMS Sampler: 21%|██ | 21/100 [00:01<00:05, 13.63it/s]\nPLMS Sampler: 23%|██▎ | 23/100 [00:01<00:05, 13.72it/s]\nPLMS Sampler: 25%|██▌ | 25/100 [00:01<00:05, 13.52it/s]\nPLMS Sampler: 27%|██▋ | 27/100 [00:02<00:05, 13.49it/s]\nPLMS Sampler: 29%|██▉ | 29/100 [00:02<00:05, 13.61it/s]\nPLMS Sampler: 31%|███ | 31/100 [00:02<00:05, 13.67it/s]\nPLMS Sampler: 33%|███▎ | 33/100 [00:02<00:04, 13.73it/s]\nPLMS Sampler: 35%|███▌ | 35/100 [00:02<00:04, 13.57it/s]\nPLMS Sampler: 37%|███▋ | 37/100 [00:02<00:04, 13.58it/s]\nPLMS Sampler: 39%|███▉ | 39/100 [00:02<00:04, 13.31it/s]\nPLMS Sampler: 41%|████ | 41/100 [00:03<00:04, 13.39it/s]\nPLMS Sampler: 43%|████▎ | 43/100 [00:03<00:04, 13.49it/s]\nPLMS Sampler: 45%|████▌ | 45/100 [00:03<00:04, 13.62it/s]\nPLMS Sampler: 47%|████▋ | 47/100 [00:03<00:03, 13.57it/s]\nPLMS Sampler: 49%|████▉ | 49/100 [00:03<00:03, 13.70it/s]\nPLMS Sampler: 51%|█████ | 51/100 [00:03<00:03, 13.79it/s]\nPLMS Sampler: 53%|█████▎ | 53/100 [00:03<00:03, 13.83it/s]\nPLMS Sampler: 55%|█████▌ | 55/100 [00:04<00:03, 13.85it/s]\nPLMS Sampler: 57%|█████▋ | 57/100 [00:04<00:03, 13.84it/s]\nPLMS Sampler: 59%|█████▉ | 59/100 [00:04<00:02, 13.87it/s]\nPLMS Sampler: 61%|██████ | 61/100 [00:04<00:02, 13.91it/s]\nPLMS Sampler: 63%|██████▎ | 63/100 [00:04<00:02, 13.93it/s]\nPLMS Sampler: 65%|██████▌ | 65/100 [00:04<00:02, 13.92it/s]\nPLMS Sampler: 67%|██████▋ | 67/100 [00:04<00:02, 13.89it/s]\nPLMS Sampler: 69%|██████▉ | 69/100 [00:05<00:02, 13.88it/s]\nPLMS Sampler: 71%|███████ | 71/100 [00:05<00:02, 13.87it/s]\nPLMS Sampler: 73%|███████▎ | 73/100 [00:05<00:02, 13.46it/s]\nPLMS Sampler: 75%|███████▌ | 75/100 [00:05<00:01, 13.61it/s]\nPLMS Sampler: 77%|███████▋ | 77/100 [00:05<00:01, 13.26it/s]\nPLMS Sampler: 79%|███████▉ | 79/100 [00:05<00:01, 13.07it/s]\nPLMS Sampler: 81%|████████ | 81/100 [00:06<00:01, 13.29it/s]\nPLMS Sampler: 83%|████████▎ | 83/100 [00:06<00:01, 13.47it/s]\nPLMS Sampler: 85%|████████▌ | 85/100 [00:06<00:01, 13.61it/s]\nPLMS Sampler: 87%|████████▋ | 87/100 [00:06<00:00, 13.69it/s]\nPLMS Sampler: 89%|████████▉ | 89/100 [00:06<00:00, 13.76it/s]\nPLMS Sampler: 91%|█████████ | 91/100 [00:06<00:00, 13.73it/s]\nPLMS Sampler: 93%|█████████▎| 93/100 [00:06<00:00, 13.74it/s]\nPLMS Sampler: 95%|█████████▌| 95/100 [00:07<00:00, 13.75it/s]\nPLMS Sampler: 97%|█████████▋| 97/100 [00:07<00:00, 13.77it/s]\nPLMS Sampler: 99%|█████████▉| 99/100 [00:07<00:00, 13.80it/s]\nPLMS Sampler: 100%|██████████| 100/100 [00:07<00:00, 13.49it/s]", "metrics": { "predict_time": 8.720541, "total_time": 8.9169 }, "output": "https://replicate.delivery/mgxm/cd6f1508-6e00-42d6-b10d-ef8233138559/out.png", "started_at": "2022-08-19T16:52:57.348776Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/kvza5yr4nrbkzk6fkdtyhpm3qi", "cancel": "https://api.replicate.com/v1/predictions/kvza5yr4nrbkzk6fkdtyhpm3qi/cancel" }, "version": "2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63" }Generated inUsing random seed 3029702370 Seed: -1 CLIP Text Embed: torch.Size([1, 1, 768]) Data shape for PLMS sampling is (1, 16, 48, 48) Running PLMS Sampling with 100 timesteps PLMS Sampler: 0%| | 0/100 [00:00<?, ?it/s] PLMS Sampler: 1%| | 1/100 [00:00<00:16, 6.10it/s] PLMS Sampler: 3%|▎ | 3/100 [00:00<00:09, 10.21it/s] PLMS Sampler: 5%|▌ | 5/100 [00:00<00:08, 11.81it/s] PLMS Sampler: 7%|▋ | 7/100 [00:00<00:07, 12.60it/s] PLMS Sampler: 9%|▉ | 9/100 [00:00<00:06, 13.04it/s] PLMS Sampler: 11%|█ | 11/100 [00:00<00:06, 13.33it/s] PLMS Sampler: 13%|█▎ | 13/100 [00:01<00:06, 13.13it/s] PLMS Sampler: 15%|█▌ | 15/100 [00:01<00:06, 13.28it/s] PLMS Sampler: 17%|█▋ | 17/100 [00:01<00:06, 13.42it/s] PLMS Sampler: 19%|█▉ | 19/100 [00:01<00:05, 13.57it/s] PLMS Sampler: 21%|██ | 21/100 [00:01<00:05, 13.63it/s] PLMS Sampler: 23%|██▎ | 23/100 [00:01<00:05, 13.72it/s] PLMS Sampler: 25%|██▌ | 25/100 [00:01<00:05, 13.52it/s] PLMS Sampler: 27%|██▋ | 27/100 [00:02<00:05, 13.49it/s] PLMS Sampler: 29%|██▉ | 29/100 [00:02<00:05, 13.61it/s] PLMS Sampler: 31%|███ | 31/100 [00:02<00:05, 13.67it/s] PLMS Sampler: 33%|███▎ | 33/100 [00:02<00:04, 13.73it/s] PLMS Sampler: 35%|███▌ | 35/100 [00:02<00:04, 13.57it/s] PLMS Sampler: 37%|███▋ | 37/100 [00:02<00:04, 13.58it/s] PLMS Sampler: 39%|███▉ | 39/100 [00:02<00:04, 13.31it/s] PLMS Sampler: 41%|████ | 41/100 [00:03<00:04, 13.39it/s] PLMS Sampler: 43%|████▎ | 43/100 [00:03<00:04, 13.49it/s] PLMS Sampler: 45%|████▌ | 45/100 [00:03<00:04, 13.62it/s] PLMS Sampler: 47%|████▋ | 47/100 [00:03<00:03, 13.57it/s] PLMS Sampler: 49%|████▉ | 49/100 [00:03<00:03, 13.70it/s] PLMS Sampler: 51%|█████ | 51/100 [00:03<00:03, 13.79it/s] PLMS Sampler: 53%|█████▎ | 53/100 [00:03<00:03, 13.83it/s] PLMS Sampler: 55%|█████▌ | 55/100 [00:04<00:03, 13.85it/s] PLMS Sampler: 57%|█████▋ | 57/100 [00:04<00:03, 13.84it/s] PLMS Sampler: 59%|█████▉ | 59/100 [00:04<00:02, 13.87it/s] PLMS Sampler: 61%|██████ | 61/100 [00:04<00:02, 13.91it/s] PLMS Sampler: 63%|██████▎ | 63/100 [00:04<00:02, 13.93it/s] PLMS Sampler: 65%|██████▌ | 65/100 [00:04<00:02, 13.92it/s] PLMS Sampler: 67%|██████▋ | 67/100 [00:04<00:02, 13.89it/s] PLMS Sampler: 69%|██████▉ | 69/100 [00:05<00:02, 13.88it/s] PLMS Sampler: 71%|███████ | 71/100 [00:05<00:02, 13.87it/s] PLMS Sampler: 73%|███████▎ | 73/100 [00:05<00:02, 13.46it/s] PLMS Sampler: 75%|███████▌ | 75/100 [00:05<00:01, 13.61it/s] PLMS Sampler: 77%|███████▋ | 77/100 [00:05<00:01, 13.26it/s] PLMS Sampler: 79%|███████▉ | 79/100 [00:05<00:01, 13.07it/s] PLMS Sampler: 81%|████████ | 81/100 [00:06<00:01, 13.29it/s] PLMS Sampler: 83%|████████▎ | 83/100 [00:06<00:01, 13.47it/s] PLMS Sampler: 85%|████████▌ | 85/100 [00:06<00:01, 13.61it/s] PLMS Sampler: 87%|████████▋ | 87/100 [00:06<00:00, 13.69it/s] PLMS Sampler: 89%|████████▉ | 89/100 [00:06<00:00, 13.76it/s] PLMS Sampler: 91%|█████████ | 91/100 [00:06<00:00, 13.73it/s] PLMS Sampler: 93%|█████████▎| 93/100 [00:06<00:00, 13.74it/s] PLMS Sampler: 95%|█████████▌| 95/100 [00:07<00:00, 13.75it/s] PLMS Sampler: 97%|█████████▋| 97/100 [00:07<00:00, 13.77it/s] PLMS Sampler: 99%|█████████▉| 99/100 [00:07<00:00, 13.80it/s] PLMS Sampler: 100%|██████████| 100/100 [00:07<00:00, 13.49it/s]Prediction
andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63IDg3mgcoi3djgyjppwx3umuq546qStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- -1
- scale
- 5
- steps
- 100
- prompt
- Two suns are rising on this distant planet
- num_database_results
- "10"
{ "seed": -1, "scale": 5, "steps": 100, "prompt": "Two suns are rising on this distant planet", "num_database_results": "10" }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 andreasjansson/monkey-island-rdm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", { input: { seed: -1, scale: 5, steps: 100, prompt: "Two suns are rising on this distant planet", num_database_results: "10" } } ); // 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 andreasjansson/monkey-island-rdm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", input={ "seed": -1, "scale": 5, "steps": 100, "prompt": "Two suns are rising on this distant planet", "num_database_results": "10" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run andreasjansson/monkey-island-rdm 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": "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", "input": { "seed": -1, "scale": 5, "steps": 100, "prompt": "Two suns are rising on this distant planet", "num_database_results": "10" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-08-19T16:53:41.644397Z", "created_at": "2022-08-19T16:53:33.046686Z", "data_removed": false, "error": null, "id": "g3mgcoi3djgyjppwx3umuq546q", "input": { "seed": -1, "scale": 5, "steps": 100, "prompt": "Two suns are rising on this distant planet", "num_database_results": "10" }, "logs": "Using random seed 921409632\nSeed: -1\nCLIP Text Embed: torch.Size([1, 1, 768])\nData shape for PLMS sampling is (1, 16, 48, 48)\nRunning PLMS Sampling with 100 timesteps\n\nPLMS Sampler: 0%| | 0/100 [00:00<?, ?it/s]\nPLMS Sampler: 1%| | 1/100 [00:00<00:16, 6.10it/s]\nPLMS Sampler: 3%|▎ | 3/100 [00:00<00:09, 10.32it/s]\nPLMS Sampler: 5%|▌ | 5/100 [00:00<00:07, 11.88it/s]\nPLMS Sampler: 7%|▋ | 7/100 [00:00<00:07, 12.64it/s]\nPLMS Sampler: 9%|▉ | 9/100 [00:00<00:06, 13.11it/s]\nPLMS Sampler: 11%|█ | 11/100 [00:00<00:06, 13.41it/s]\nPLMS Sampler: 13%|█▎ | 13/100 [00:01<00:06, 13.60it/s]\nPLMS Sampler: 15%|█▌ | 15/100 [00:01<00:06, 13.61it/s]\nPLMS Sampler: 17%|█▋ | 17/100 [00:01<00:06, 13.71it/s]\nPLMS Sampler: 19%|█▉ | 19/100 [00:01<00:05, 13.76it/s]\nPLMS Sampler: 21%|██ | 21/100 [00:01<00:05, 13.81it/s]\nPLMS Sampler: 23%|██▎ | 23/100 [00:01<00:05, 13.84it/s]\nPLMS Sampler: 25%|██▌ | 25/100 [00:01<00:05, 13.89it/s]\nPLMS Sampler: 27%|██▋ | 27/100 [00:02<00:05, 13.84it/s]\nPLMS Sampler: 29%|██▉ | 29/100 [00:02<00:05, 13.85it/s]\nPLMS Sampler: 31%|███ | 31/100 [00:02<00:04, 13.86it/s]\nPLMS Sampler: 33%|███▎ | 33/100 [00:02<00:04, 13.86it/s]\nPLMS Sampler: 35%|███▌ | 35/100 [00:02<00:04, 13.75it/s]\nPLMS Sampler: 37%|███▋ | 37/100 [00:02<00:04, 13.75it/s]\nPLMS Sampler: 39%|███▉ | 39/100 [00:02<00:04, 13.61it/s]\nPLMS Sampler: 41%|████ | 41/100 [00:03<00:04, 13.33it/s]\nPLMS Sampler: 43%|████▎ | 43/100 [00:03<00:04, 13.45it/s]\nPLMS Sampler: 45%|████▌ | 45/100 [00:03<00:04, 13.59it/s]\nPLMS Sampler: 47%|████▋ | 47/100 [00:03<00:03, 13.69it/s]\nPLMS Sampler: 49%|████▉ | 49/100 [00:03<00:03, 13.73it/s]\nPLMS Sampler: 51%|█████ | 51/100 [00:03<00:03, 13.76it/s]\nPLMS Sampler: 53%|█████▎ | 53/100 [00:03<00:03, 13.75it/s]\nPLMS Sampler: 55%|█████▌ | 55/100 [00:04<00:03, 13.67it/s]\nPLMS Sampler: 57%|█████▋ | 57/100 [00:04<00:03, 13.65it/s]\nPLMS Sampler: 59%|█████▉ | 59/100 [00:04<00:03, 13.63it/s]\nPLMS Sampler: 61%|██████ | 61/100 [00:04<00:02, 13.66it/s]\nPLMS Sampler: 63%|██████▎ | 63/100 [00:04<00:02, 13.68it/s]\nPLMS Sampler: 65%|██████▌ | 65/100 [00:04<00:02, 13.56it/s]\nPLMS Sampler: 67%|██████▋ | 67/100 [00:04<00:02, 13.63it/s]\nPLMS Sampler: 69%|██████▉ | 69/100 [00:05<00:02, 13.69it/s]\nPLMS Sampler: 71%|███████ | 71/100 [00:05<00:02, 13.72it/s]\nPLMS Sampler: 73%|███████▎ | 73/100 [00:05<00:01, 13.67it/s]\nPLMS Sampler: 75%|███████▌ | 75/100 [00:05<00:01, 13.72it/s]\nPLMS Sampler: 77%|███████▋ | 77/100 [00:05<00:01, 13.76it/s]\nPLMS Sampler: 79%|███████▉ | 79/100 [00:05<00:01, 13.79it/s]\nPLMS Sampler: 81%|████████ | 81/100 [00:05<00:01, 13.76it/s]\nPLMS Sampler: 83%|████████▎ | 83/100 [00:06<00:01, 13.76it/s]\nPLMS Sampler: 85%|████████▌ | 85/100 [00:06<00:01, 13.76it/s]\nPLMS Sampler: 87%|████████▋ | 87/100 [00:06<00:00, 13.67it/s]\nPLMS Sampler: 89%|████████▉ | 89/100 [00:06<00:00, 13.66it/s]\nPLMS Sampler: 91%|█████████ | 91/100 [00:06<00:00, 13.68it/s]\nPLMS Sampler: 93%|█████████▎| 93/100 [00:06<00:00, 13.67it/s]\nPLMS Sampler: 95%|█████████▌| 95/100 [00:07<00:00, 13.67it/s]\nPLMS Sampler: 97%|█████████▋| 97/100 [00:07<00:00, 13.58it/s]\nPLMS Sampler: 99%|█████████▉| 99/100 [00:07<00:00, 13.28it/s]\nPLMS Sampler: 100%|██████████| 100/100 [00:07<00:00, 13.53it/s]", "metrics": { "predict_time": 8.417311, "total_time": 8.597711 }, "output": "https://replicate.delivery/mgxm/fee4a7aa-42ca-4c89-8e43-7604ea0c5045/out.png", "started_at": "2022-08-19T16:53:33.227086Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/g3mgcoi3djgyjppwx3umuq546q", "cancel": "https://api.replicate.com/v1/predictions/g3mgcoi3djgyjppwx3umuq546q/cancel" }, "version": "2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63" }Generated inUsing random seed 921409632 Seed: -1 CLIP Text Embed: torch.Size([1, 1, 768]) Data shape for PLMS sampling is (1, 16, 48, 48) Running PLMS Sampling with 100 timesteps PLMS Sampler: 0%| | 0/100 [00:00<?, ?it/s] PLMS Sampler: 1%| | 1/100 [00:00<00:16, 6.10it/s] PLMS Sampler: 3%|▎ | 3/100 [00:00<00:09, 10.32it/s] PLMS Sampler: 5%|▌ | 5/100 [00:00<00:07, 11.88it/s] PLMS Sampler: 7%|▋ | 7/100 [00:00<00:07, 12.64it/s] PLMS Sampler: 9%|▉ | 9/100 [00:00<00:06, 13.11it/s] PLMS Sampler: 11%|█ | 11/100 [00:00<00:06, 13.41it/s] PLMS Sampler: 13%|█▎ | 13/100 [00:01<00:06, 13.60it/s] PLMS Sampler: 15%|█▌ | 15/100 [00:01<00:06, 13.61it/s] PLMS Sampler: 17%|█▋ | 17/100 [00:01<00:06, 13.71it/s] PLMS Sampler: 19%|█▉ | 19/100 [00:01<00:05, 13.76it/s] PLMS Sampler: 21%|██ | 21/100 [00:01<00:05, 13.81it/s] PLMS Sampler: 23%|██▎ | 23/100 [00:01<00:05, 13.84it/s] PLMS Sampler: 25%|██▌ | 25/100 [00:01<00:05, 13.89it/s] PLMS Sampler: 27%|██▋ | 27/100 [00:02<00:05, 13.84it/s] PLMS Sampler: 29%|██▉ | 29/100 [00:02<00:05, 13.85it/s] PLMS Sampler: 31%|███ | 31/100 [00:02<00:04, 13.86it/s] PLMS Sampler: 33%|███▎ | 33/100 [00:02<00:04, 13.86it/s] PLMS Sampler: 35%|███▌ | 35/100 [00:02<00:04, 13.75it/s] PLMS Sampler: 37%|███▋ | 37/100 [00:02<00:04, 13.75it/s] PLMS Sampler: 39%|███▉ | 39/100 [00:02<00:04, 13.61it/s] PLMS Sampler: 41%|████ | 41/100 [00:03<00:04, 13.33it/s] PLMS Sampler: 43%|████▎ | 43/100 [00:03<00:04, 13.45it/s] PLMS Sampler: 45%|████▌ | 45/100 [00:03<00:04, 13.59it/s] PLMS Sampler: 47%|████▋ | 47/100 [00:03<00:03, 13.69it/s] PLMS Sampler: 49%|████▉ | 49/100 [00:03<00:03, 13.73it/s] PLMS Sampler: 51%|█████ | 51/100 [00:03<00:03, 13.76it/s] PLMS Sampler: 53%|█████▎ | 53/100 [00:03<00:03, 13.75it/s] PLMS Sampler: 55%|█████▌ | 55/100 [00:04<00:03, 13.67it/s] PLMS Sampler: 57%|█████▋ | 57/100 [00:04<00:03, 13.65it/s] PLMS Sampler: 59%|█████▉ | 59/100 [00:04<00:03, 13.63it/s] PLMS Sampler: 61%|██████ | 61/100 [00:04<00:02, 13.66it/s] PLMS Sampler: 63%|██████▎ | 63/100 [00:04<00:02, 13.68it/s] PLMS Sampler: 65%|██████▌ | 65/100 [00:04<00:02, 13.56it/s] PLMS Sampler: 67%|██████▋ | 67/100 [00:04<00:02, 13.63it/s] PLMS Sampler: 69%|██████▉ | 69/100 [00:05<00:02, 13.69it/s] PLMS Sampler: 71%|███████ | 71/100 [00:05<00:02, 13.72it/s] PLMS Sampler: 73%|███████▎ | 73/100 [00:05<00:01, 13.67it/s] PLMS Sampler: 75%|███████▌ | 75/100 [00:05<00:01, 13.72it/s] PLMS Sampler: 77%|███████▋ | 77/100 [00:05<00:01, 13.76it/s] PLMS Sampler: 79%|███████▉ | 79/100 [00:05<00:01, 13.79it/s] PLMS Sampler: 81%|████████ | 81/100 [00:05<00:01, 13.76it/s] PLMS Sampler: 83%|████████▎ | 83/100 [00:06<00:01, 13.76it/s] PLMS Sampler: 85%|████████▌ | 85/100 [00:06<00:01, 13.76it/s] PLMS Sampler: 87%|████████▋ | 87/100 [00:06<00:00, 13.67it/s] PLMS Sampler: 89%|████████▉ | 89/100 [00:06<00:00, 13.66it/s] PLMS Sampler: 91%|█████████ | 91/100 [00:06<00:00, 13.68it/s] PLMS Sampler: 93%|█████████▎| 93/100 [00:06<00:00, 13.67it/s] PLMS Sampler: 95%|█████████▌| 95/100 [00:07<00:00, 13.67it/s] PLMS Sampler: 97%|█████████▋| 97/100 [00:07<00:00, 13.58it/s] PLMS Sampler: 99%|█████████▉| 99/100 [00:07<00:00, 13.28it/s] PLMS Sampler: 100%|██████████| 100/100 [00:07<00:00, 13.53it/s]Prediction
andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63ID4cd4cdemsrgsjmmwquauzn5m5uStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- -1
- scale
- 5
- steps
- 100
- prompt
- Guybrush flying a spaceship between two distant planets
- num_database_results
- "10"
{ "seed": -1, "scale": 5, "steps": 100, "prompt": "Guybrush flying a spaceship between two distant planets", "num_database_results": "10" }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 andreasjansson/monkey-island-rdm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", { input: { seed: -1, scale: 5, steps: 100, prompt: "Guybrush flying a spaceship between two distant planets", num_database_results: "10" } } ); // 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 andreasjansson/monkey-island-rdm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", input={ "seed": -1, "scale": 5, "steps": 100, "prompt": "Guybrush flying a spaceship between two distant planets", "num_database_results": "10" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run andreasjansson/monkey-island-rdm 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": "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", "input": { "seed": -1, "scale": 5, "steps": 100, "prompt": "Guybrush flying a spaceship between two distant planets", "num_database_results": "10" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-08-19T16:54:27.620050Z", "created_at": "2022-08-19T16:54:19.072408Z", "data_removed": false, "error": null, "id": "4cd4cdemsrgsjmmwquauzn5m5u", "input": { "seed": -1, "scale": 5, "steps": 100, "prompt": "Guybrush flying a spaceship between two distant planets", "num_database_results": "10" }, "logs": "Using random seed 3452364376\nSeed: -1\nCLIP Text Embed: torch.Size([1, 1, 768])\nData shape for PLMS sampling is (1, 16, 48, 48)\nRunning PLMS Sampling with 100 timesteps\n\nPLMS Sampler: 0%| | 0/100 [00:00<?, ?it/s]\nPLMS Sampler: 1%| | 1/100 [00:00<00:16, 6.12it/s]\nPLMS Sampler: 3%|▎ | 3/100 [00:00<00:09, 10.15it/s]\nPLMS Sampler: 5%|▌ | 5/100 [00:00<00:08, 11.84it/s]\nPLMS Sampler: 7%|▋ | 7/100 [00:00<00:07, 12.64it/s]\nPLMS Sampler: 9%|▉ | 9/100 [00:00<00:07, 12.93it/s]\nPLMS Sampler: 11%|█ | 11/100 [00:00<00:06, 13.29it/s]\nPLMS Sampler: 13%|█▎ | 13/100 [00:01<00:06, 13.48it/s]\nPLMS Sampler: 15%|█▌ | 15/100 [00:01<00:06, 13.48it/s]\nPLMS Sampler: 17%|█▋ | 17/100 [00:01<00:06, 13.61it/s]\nPLMS Sampler: 19%|█▉ | 19/100 [00:01<00:05, 13.76it/s]\nPLMS Sampler: 21%|██ | 21/100 [00:01<00:05, 13.86it/s]\nPLMS Sampler: 23%|██▎ | 23/100 [00:01<00:05, 13.92it/s]\nPLMS Sampler: 25%|██▌ | 25/100 [00:01<00:05, 13.72it/s]\nPLMS Sampler: 27%|██▋ | 27/100 [00:02<00:05, 13.52it/s]\nPLMS Sampler: 29%|██▉ | 29/100 [00:02<00:05, 13.70it/s]\nPLMS Sampler: 31%|███ | 31/100 [00:02<00:04, 13.82it/s]\nPLMS Sampler: 33%|███▎ | 33/100 [00:02<00:04, 13.90it/s]\nPLMS Sampler: 35%|███▌ | 35/100 [00:02<00:04, 13.95it/s]\nPLMS Sampler: 37%|███▋ | 37/100 [00:02<00:04, 13.98it/s]\nPLMS Sampler: 39%|███▉ | 39/100 [00:02<00:04, 13.98it/s]\nPLMS Sampler: 41%|████ | 41/100 [00:03<00:04, 13.78it/s]\nPLMS Sampler: 43%|████▎ | 43/100 [00:03<00:04, 13.80it/s]\nPLMS Sampler: 45%|████▌ | 45/100 [00:03<00:03, 13.81it/s]\nPLMS Sampler: 47%|████▋ | 47/100 [00:03<00:03, 13.53it/s]\nPLMS Sampler: 49%|████▉ | 49/100 [00:03<00:03, 13.58it/s]\nPLMS Sampler: 51%|█████ | 51/100 [00:03<00:03, 13.62it/s]\nPLMS Sampler: 53%|█████▎ | 53/100 [00:03<00:03, 13.65it/s]\nPLMS Sampler: 55%|█████▌ | 55/100 [00:04<00:03, 13.78it/s]\nPLMS Sampler: 57%|█████▋ | 57/100 [00:04<00:03, 13.87it/s]\nPLMS Sampler: 59%|█████▉ | 59/100 [00:04<00:02, 13.92it/s]\nPLMS Sampler: 61%|██████ | 61/100 [00:04<00:02, 13.93it/s]\nPLMS Sampler: 63%|██████▎ | 63/100 [00:04<00:02, 13.80it/s]\nPLMS Sampler: 65%|██████▌ | 65/100 [00:04<00:02, 13.82it/s]\nPLMS Sampler: 67%|██████▋ | 67/100 [00:04<00:02, 13.57it/s]\nPLMS Sampler: 69%|██████▉ | 69/100 [00:05<00:02, 13.71it/s]\nPLMS Sampler: 71%|███████ | 71/100 [00:05<00:02, 13.80it/s]\nPLMS Sampler: 73%|███████▎ | 73/100 [00:05<00:01, 13.88it/s]\nPLMS Sampler: 75%|███████▌ | 75/100 [00:05<00:01, 13.74it/s]\nPLMS Sampler: 77%|███████▋ | 77/100 [00:05<00:01, 13.74it/s]\nPLMS Sampler: 79%|███████▉ | 79/100 [00:05<00:01, 13.65it/s]\nPLMS Sampler: 81%|████████ | 81/100 [00:05<00:01, 13.49it/s]\nPLMS Sampler: 83%|████████▎ | 83/100 [00:06<00:01, 13.34it/s]\nPLMS Sampler: 85%|████████▌ | 85/100 [00:06<00:01, 13.47it/s]\nPLMS Sampler: 87%|████████▋ | 87/100 [00:06<00:00, 13.58it/s]\nPLMS Sampler: 89%|████████▉ | 89/100 [00:06<00:00, 13.66it/s]\nPLMS Sampler: 91%|█████████ | 91/100 [00:06<00:00, 13.71it/s]\nPLMS Sampler: 93%|█████████▎| 93/100 [00:06<00:00, 13.75it/s]\nPLMS Sampler: 95%|█████████▌| 95/100 [00:07<00:00, 13.72it/s]\nPLMS Sampler: 97%|█████████▋| 97/100 [00:07<00:00, 13.76it/s]\nPLMS Sampler: 99%|█████████▉| 99/100 [00:07<00:00, 13.72it/s]\nPLMS Sampler: 100%|██████████| 100/100 [00:07<00:00, 13.57it/s]", "metrics": { "predict_time": 8.378423, "total_time": 8.547642 }, "output": "https://replicate.delivery/mgxm/9c52d2c4-ed81-444c-b7b8-a91239000b0a/out.png", "started_at": "2022-08-19T16:54:19.241627Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/4cd4cdemsrgsjmmwquauzn5m5u", "cancel": "https://api.replicate.com/v1/predictions/4cd4cdemsrgsjmmwquauzn5m5u/cancel" }, "version": "2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63" }Generated inUsing random seed 3452364376 Seed: -1 CLIP Text Embed: torch.Size([1, 1, 768]) Data shape for PLMS sampling is (1, 16, 48, 48) Running PLMS Sampling with 100 timesteps PLMS Sampler: 0%| | 0/100 [00:00<?, ?it/s] PLMS Sampler: 1%| | 1/100 [00:00<00:16, 6.12it/s] PLMS Sampler: 3%|▎ | 3/100 [00:00<00:09, 10.15it/s] PLMS Sampler: 5%|▌ | 5/100 [00:00<00:08, 11.84it/s] PLMS Sampler: 7%|▋ | 7/100 [00:00<00:07, 12.64it/s] PLMS Sampler: 9%|▉ | 9/100 [00:00<00:07, 12.93it/s] PLMS Sampler: 11%|█ | 11/100 [00:00<00:06, 13.29it/s] PLMS Sampler: 13%|█▎ | 13/100 [00:01<00:06, 13.48it/s] PLMS Sampler: 15%|█▌ | 15/100 [00:01<00:06, 13.48it/s] PLMS Sampler: 17%|█▋ | 17/100 [00:01<00:06, 13.61it/s] PLMS Sampler: 19%|█▉ | 19/100 [00:01<00:05, 13.76it/s] PLMS Sampler: 21%|██ | 21/100 [00:01<00:05, 13.86it/s] PLMS Sampler: 23%|██▎ | 23/100 [00:01<00:05, 13.92it/s] PLMS Sampler: 25%|██▌ | 25/100 [00:01<00:05, 13.72it/s] PLMS 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61/100 [00:04<00:02, 13.93it/s] PLMS Sampler: 63%|██████▎ | 63/100 [00:04<00:02, 13.80it/s] PLMS Sampler: 65%|██████▌ | 65/100 [00:04<00:02, 13.82it/s] PLMS Sampler: 67%|██████▋ | 67/100 [00:04<00:02, 13.57it/s] PLMS Sampler: 69%|██████▉ | 69/100 [00:05<00:02, 13.71it/s] PLMS Sampler: 71%|███████ | 71/100 [00:05<00:02, 13.80it/s] PLMS Sampler: 73%|███████▎ | 73/100 [00:05<00:01, 13.88it/s] PLMS Sampler: 75%|███████▌ | 75/100 [00:05<00:01, 13.74it/s] PLMS Sampler: 77%|███████▋ | 77/100 [00:05<00:01, 13.74it/s] PLMS Sampler: 79%|███████▉ | 79/100 [00:05<00:01, 13.65it/s] PLMS Sampler: 81%|████████ | 81/100 [00:05<00:01, 13.49it/s] PLMS Sampler: 83%|████████▎ | 83/100 [00:06<00:01, 13.34it/s] PLMS Sampler: 85%|████████▌ | 85/100 [00:06<00:01, 13.47it/s] PLMS Sampler: 87%|████████▋ | 87/100 [00:06<00:00, 13.58it/s] PLMS Sampler: 89%|████████▉ | 89/100 [00:06<00:00, 13.66it/s] PLMS Sampler: 91%|█████████ | 91/100 [00:06<00:00, 13.71it/s] PLMS Sampler: 93%|█████████▎| 93/100 [00:06<00:00, 13.75it/s] PLMS Sampler: 95%|█████████▌| 95/100 [00:07<00:00, 13.72it/s] PLMS Sampler: 97%|█████████▋| 97/100 [00:07<00:00, 13.76it/s] PLMS Sampler: 99%|█████████▉| 99/100 [00:07<00:00, 13.72it/s] PLMS Sampler: 100%|██████████| 100/100 [00:07<00:00, 13.57it/s]Prediction
andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63IDiaz4fooghbb6hpliha3pp7jqwaStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- -1
- scale
- 5
- steps
- 100
- prompt
- A magical cove lit by small fires
- num_database_results
- "10"
{ "seed": -1, "scale": 5, "steps": 100, "prompt": "A magical cove lit by small fires", "num_database_results": "10" }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 andreasjansson/monkey-island-rdm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", { input: { seed: -1, scale: 5, steps: 100, prompt: "A magical cove lit by small fires", num_database_results: "10" } } ); // 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 andreasjansson/monkey-island-rdm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", input={ "seed": -1, "scale": 5, "steps": 100, "prompt": "A magical cove lit by small fires", "num_database_results": "10" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run andreasjansson/monkey-island-rdm 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": "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", "input": { "seed": -1, "scale": 5, "steps": 100, "prompt": "A magical cove lit by small fires", "num_database_results": "10" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-08-19T16:56:11.785744Z", "created_at": "2022-08-19T16:56:03.007768Z", "data_removed": false, "error": null, "id": "iaz4fooghbb6hpliha3pp7jqwa", "input": { "seed": -1, "scale": 5, "steps": 100, "prompt": "A magical cove lit by small fires", "num_database_results": "10" }, "logs": "Using random seed 3773847673\nSeed: -1\nCLIP Text Embed: torch.Size([1, 1, 768])\nData shape for PLMS sampling is (1, 16, 48, 48)\nRunning PLMS Sampling with 100 timesteps\n\nPLMS Sampler: 0%| | 0/100 [00:00<?, ?it/s]\nPLMS Sampler: 1%| | 1/100 [00:00<00:16, 6.03it/s]\nPLMS Sampler: 3%|▎ | 3/100 [00:00<00:09, 10.26it/s]\nPLMS Sampler: 5%|▌ | 5/100 [00:00<00:08, 11.81it/s]\nPLMS Sampler: 7%|▋ | 7/100 [00:00<00:07, 12.53it/s]\nPLMS Sampler: 9%|▉ | 9/100 [00:00<00:06, 13.01it/s]\nPLMS Sampler: 11%|█ | 11/100 [00:00<00:06, 13.29it/s]\nPLMS Sampler: 13%|█▎ | 13/100 [00:01<00:06, 13.42it/s]\nPLMS Sampler: 15%|█▌ | 15/100 [00:01<00:06, 13.58it/s]\nPLMS Sampler: 17%|█▋ | 17/100 [00:01<00:06, 13.60it/s]\nPLMS Sampler: 19%|█▉ | 19/100 [00:01<00:06, 13.35it/s]\nPLMS Sampler: 21%|██ | 21/100 [00:01<00:05, 13.42it/s]\nPLMS Sampler: 23%|██▎ | 23/100 [00:01<00:05, 13.50it/s]\nPLMS Sampler: 25%|██▌ | 25/100 [00:01<00:05, 13.40it/s]\nPLMS Sampler: 27%|██▋ | 27/100 [00:02<00:05, 13.58it/s]\nPLMS Sampler: 29%|██▉ | 29/100 [00:02<00:05, 13.65it/s]\nPLMS Sampler: 31%|███ | 31/100 [00:02<00:05, 13.71it/s]\nPLMS Sampler: 33%|███▎ | 33/100 [00:02<00:04, 13.78it/s]\nPLMS Sampler: 35%|███▌ | 35/100 [00:02<00:04, 13.70it/s]\nPLMS Sampler: 37%|███▋ | 37/100 [00:02<00:04, 13.32it/s]\nPLMS Sampler: 39%|███▉ | 39/100 [00:02<00:04, 13.24it/s]\nPLMS Sampler: 41%|████ | 41/100 [00:03<00:04, 13.33it/s]\nPLMS Sampler: 43%|████▎ | 43/100 [00:03<00:04, 13.48it/s]\nPLMS Sampler: 45%|████▌ | 45/100 [00:03<00:04, 13.57it/s]\nPLMS Sampler: 47%|████▋ | 47/100 [00:03<00:03, 13.65it/s]\nPLMS Sampler: 49%|████▉ | 49/100 [00:03<00:03, 13.77it/s]\nPLMS Sampler: 51%|█████ | 51/100 [00:03<00:03, 13.85it/s]\nPLMS Sampler: 53%|█████▎ | 53/100 [00:03<00:03, 13.90it/s]\nPLMS Sampler: 55%|█████▌ | 55/100 [00:04<00:03, 13.59it/s]\nPLMS Sampler: 57%|█████▋ | 57/100 [00:04<00:03, 13.57it/s]\nPLMS Sampler: 59%|█████▉ | 59/100 [00:04<00:03, 13.66it/s]\nPLMS Sampler: 61%|██████ | 61/100 [00:04<00:02, 13.75it/s]\nPLMS Sampler: 63%|██████▎ | 63/100 [00:04<00:02, 13.76it/s]\nPLMS Sampler: 65%|██████▌ | 65/100 [00:04<00:02, 13.80it/s]\nPLMS Sampler: 67%|██████▋ | 67/100 [00:05<00:02, 13.79it/s]\nPLMS Sampler: 69%|██████▉ | 69/100 [00:05<00:02, 13.42it/s]\nPLMS Sampler: 71%|███████ | 71/100 [00:05<00:02, 13.42it/s]\nPLMS Sampler: 73%|███████▎ | 73/100 [00:05<00:01, 13.53it/s]\nPLMS Sampler: 75%|███████▌ | 75/100 [00:05<00:01, 13.68it/s]\nPLMS Sampler: 77%|███████▋ | 77/100 [00:05<00:01, 13.54it/s]\nPLMS Sampler: 79%|███████▉ | 79/100 [00:05<00:01, 13.56it/s]\nPLMS Sampler: 81%|████████ | 81/100 [00:06<00:01, 13.55it/s]\nPLMS Sampler: 83%|████████▎ | 83/100 [00:06<00:01, 13.38it/s]\nPLMS Sampler: 85%|████████▌ | 85/100 [00:06<00:01, 13.53it/s]\nPLMS Sampler: 87%|████████▋ | 87/100 [00:06<00:00, 13.62it/s]\nPLMS Sampler: 89%|████████▉ | 89/100 [00:06<00:00, 13.56it/s]\nPLMS Sampler: 91%|█████████ | 91/100 [00:06<00:00, 13.65it/s]\nPLMS Sampler: 93%|█████████▎| 93/100 [00:06<00:00, 13.29it/s]\nPLMS Sampler: 95%|█████████▌| 95/100 [00:07<00:00, 13.05it/s]\nPLMS Sampler: 97%|█████████▋| 97/100 [00:07<00:00, 12.77it/s]\nPLMS Sampler: 99%|█████████▉| 99/100 [00:07<00:00, 13.04it/s]\nPLMS Sampler: 100%|██████████| 100/100 [00:07<00:00, 13.37it/s]", "metrics": { "predict_time": 8.610024, "total_time": 8.777976 }, "output": "https://replicate.delivery/mgxm/d45ab961-52d4-40ce-9c8c-7d02a5cab88e/out.png", "started_at": "2022-08-19T16:56:03.175720Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/iaz4fooghbb6hpliha3pp7jqwa", "cancel": "https://api.replicate.com/v1/predictions/iaz4fooghbb6hpliha3pp7jqwa/cancel" }, "version": "2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63" }Generated inUsing random seed 3773847673 Seed: -1 CLIP Text Embed: torch.Size([1, 1, 768]) Data shape for PLMS sampling is (1, 16, 48, 48) Running PLMS Sampling with 100 timesteps PLMS Sampler: 0%| | 0/100 [00:00<?, ?it/s] PLMS Sampler: 1%| | 1/100 [00:00<00:16, 6.03it/s] PLMS Sampler: 3%|▎ | 3/100 [00:00<00:09, 10.26it/s] PLMS Sampler: 5%|▌ | 5/100 [00:00<00:08, 11.81it/s] PLMS Sampler: 7%|▋ | 7/100 [00:00<00:07, 12.53it/s] PLMS Sampler: 9%|▉ | 9/100 [00:00<00:06, 13.01it/s] PLMS Sampler: 11%|█ | 11/100 [00:00<00:06, 13.29it/s] PLMS Sampler: 13%|█▎ | 13/100 [00:01<00:06, 13.42it/s] PLMS Sampler: 15%|█▌ | 15/100 [00:01<00:06, 13.58it/s] PLMS Sampler: 17%|█▋ | 17/100 [00:01<00:06, 13.60it/s] PLMS Sampler: 19%|█▉ | 19/100 [00:01<00:06, 13.35it/s] PLMS Sampler: 21%|██ | 21/100 [00:01<00:05, 13.42it/s] PLMS Sampler: 23%|██▎ | 23/100 [00:01<00:05, 13.50it/s] PLMS Sampler: 25%|██▌ | 25/100 [00:01<00:05, 13.40it/s] PLMS Sampler: 27%|██▋ | 27/100 [00:02<00:05, 13.58it/s] PLMS Sampler: 29%|██▉ | 29/100 [00:02<00:05, 13.65it/s] PLMS Sampler: 31%|███ | 31/100 [00:02<00:05, 13.71it/s] PLMS Sampler: 33%|███▎ | 33/100 [00:02<00:04, 13.78it/s] PLMS Sampler: 35%|███▌ | 35/100 [00:02<00:04, 13.70it/s] PLMS Sampler: 37%|███▋ | 37/100 [00:02<00:04, 13.32it/s] PLMS Sampler: 39%|███▉ | 39/100 [00:02<00:04, 13.24it/s] PLMS Sampler: 41%|████ | 41/100 [00:03<00:04, 13.33it/s] PLMS Sampler: 43%|████▎ | 43/100 [00:03<00:04, 13.48it/s] PLMS Sampler: 45%|████▌ | 45/100 [00:03<00:04, 13.57it/s] PLMS Sampler: 47%|████▋ | 47/100 [00:03<00:03, 13.65it/s] PLMS Sampler: 49%|████▉ | 49/100 [00:03<00:03, 13.77it/s] PLMS Sampler: 51%|█████ | 51/100 [00:03<00:03, 13.85it/s] PLMS Sampler: 53%|█████▎ | 53/100 [00:03<00:03, 13.90it/s] PLMS Sampler: 55%|█████▌ | 55/100 [00:04<00:03, 13.59it/s] PLMS Sampler: 57%|█████▋ | 57/100 [00:04<00:03, 13.57it/s] PLMS Sampler: 59%|█████▉ | 59/100 [00:04<00:03, 13.66it/s] PLMS Sampler: 61%|██████ | 61/100 [00:04<00:02, 13.75it/s] PLMS Sampler: 63%|██████▎ | 63/100 [00:04<00:02, 13.76it/s] PLMS Sampler: 65%|██████▌ | 65/100 [00:04<00:02, 13.80it/s] PLMS Sampler: 67%|██████▋ | 67/100 [00:05<00:02, 13.79it/s] PLMS Sampler: 69%|██████▉ | 69/100 [00:05<00:02, 13.42it/s] PLMS Sampler: 71%|███████ | 71/100 [00:05<00:02, 13.42it/s] PLMS Sampler: 73%|███████▎ | 73/100 [00:05<00:01, 13.53it/s] PLMS Sampler: 75%|███████▌ | 75/100 [00:05<00:01, 13.68it/s] PLMS Sampler: 77%|███████▋ | 77/100 [00:05<00:01, 13.54it/s] PLMS Sampler: 79%|███████▉ | 79/100 [00:05<00:01, 13.56it/s] PLMS Sampler: 81%|████████ | 81/100 [00:06<00:01, 13.55it/s] PLMS Sampler: 83%|████████▎ | 83/100 [00:06<00:01, 13.38it/s] PLMS Sampler: 85%|████████▌ | 85/100 [00:06<00:01, 13.53it/s] PLMS Sampler: 87%|████████▋ | 87/100 [00:06<00:00, 13.62it/s] PLMS Sampler: 89%|████████▉ | 89/100 [00:06<00:00, 13.56it/s] PLMS Sampler: 91%|█████████ | 91/100 [00:06<00:00, 13.65it/s] PLMS Sampler: 93%|█████████▎| 93/100 [00:06<00:00, 13.29it/s] PLMS Sampler: 95%|█████████▌| 95/100 [00:07<00:00, 13.05it/s] PLMS Sampler: 97%|█████████▋| 97/100 [00:07<00:00, 12.77it/s] PLMS Sampler: 99%|█████████▉| 99/100 [00:07<00:00, 13.04it/s] PLMS Sampler: 100%|██████████| 100/100 [00:07<00:00, 13.37it/s]Prediction
andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63IDl3zwiq4ylrcchj3trzrfxpu2xuStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- -1
- scale
- 5
- steps
- 100
- prompt
- Time travelers arrive on Monkey Island from a space time vortex
- num_database_results
- "10"
{ "seed": -1, "scale": 5, "steps": 100, "prompt": "Time travelers arrive on Monkey Island from a space time vortex", "num_database_results": "10" }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 andreasjansson/monkey-island-rdm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", { input: { seed: -1, scale: 5, steps: 100, prompt: "Time travelers arrive on Monkey Island from a space time vortex", num_database_results: "10" } } ); // 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 andreasjansson/monkey-island-rdm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", input={ "seed": -1, "scale": 5, "steps": 100, "prompt": "Time travelers arrive on Monkey Island from a space time vortex", "num_database_results": "10" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run andreasjansson/monkey-island-rdm 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": "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", "input": { "seed": -1, "scale": 5, "steps": 100, "prompt": "Time travelers arrive on Monkey Island from a space time vortex", "num_database_results": "10" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-08-19T17:00:56.403642Z", "created_at": "2022-08-19T17:00:45.699346Z", "data_removed": false, "error": null, "id": "l3zwiq4ylrcchj3trzrfxpu2xu", "input": { "seed": -1, "scale": 5, "steps": 100, "prompt": "Time travelers arrive on Monkey Island from a space time vortex", "num_database_results": "10" }, "logs": "Using random seed 1299970299\nSeed: -1\nCLIP Text Embed: torch.Size([1, 1, 768])\nData shape for PLMS sampling is (1, 16, 48, 48)\nRunning PLMS Sampling with 100 timesteps\n\nPLMS Sampler: 0%| | 0/100 [00:00<?, ?it/s]\nPLMS Sampler: 1%| | 1/100 [00:00<00:17, 5.62it/s]\nPLMS Sampler: 3%|▎ | 3/100 [00:00<00:10, 9.37it/s]\nPLMS Sampler: 5%|▌ | 5/100 [00:00<00:09, 10.48it/s]\nPLMS Sampler: 7%|▋ | 7/100 [00:00<00:08, 11.07it/s]\nPLMS Sampler: 9%|▉ | 9/100 [00:00<00:07, 11.42it/s]\nPLMS Sampler: 11%|█ | 11/100 [00:01<00:07, 11.65it/s]\nPLMS Sampler: 13%|█▎ | 13/100 [00:01<00:07, 11.84it/s]\nPLMS Sampler: 15%|█▌ | 15/100 [00:01<00:07, 11.57it/s]\nPLMS Sampler: 17%|█▋ | 17/100 [00:01<00:07, 11.58it/s]\nPLMS Sampler: 19%|█▉ | 19/100 [00:01<00:07, 11.47it/s]\nPLMS Sampler: 21%|██ | 21/100 [00:01<00:06, 11.35it/s]\nPLMS Sampler: 23%|██▎ | 23/100 [00:02<00:06, 11.42it/s]\nPLMS Sampler: 25%|██▌ | 25/100 [00:02<00:06, 11.68it/s]\nPLMS Sampler: 27%|██▋ | 27/100 [00:02<00:06, 11.61it/s]\nPLMS Sampler: 29%|██▉ | 29/100 [00:02<00:06, 11.71it/s]\nPLMS Sampler: 31%|███ | 31/100 [00:02<00:05, 11.62it/s]\nPLMS Sampler: 33%|███▎ | 33/100 [00:02<00:05, 11.68it/s]\nPLMS Sampler: 35%|███▌ | 35/100 [00:03<00:05, 11.55it/s]\nPLMS Sampler: 37%|███▋ | 37/100 [00:03<00:05, 11.71it/s]\nPLMS Sampler: 39%|███▉ | 39/100 [00:03<00:05, 11.68it/s]\nPLMS Sampler: 41%|████ | 41/100 [00:03<00:05, 11.74it/s]\nPLMS Sampler: 43%|████▎ | 43/100 [00:03<00:04, 11.95it/s]\nPLMS Sampler: 45%|████▌ | 45/100 [00:03<00:04, 11.98it/s]\nPLMS Sampler: 47%|████▋ | 47/100 [00:04<00:04, 11.94it/s]\nPLMS Sampler: 49%|████▉ | 49/100 [00:04<00:04, 12.04it/s]\nPLMS Sampler: 51%|█████ | 51/100 [00:04<00:04, 11.82it/s]\nPLMS Sampler: 53%|█████▎ | 53/100 [00:04<00:03, 11.93it/s]\nPLMS Sampler: 55%|█████▌ | 55/100 [00:04<00:03, 11.81it/s]\nPLMS Sampler: 57%|█████▋ | 57/100 [00:04<00:03, 11.76it/s]\nPLMS Sampler: 59%|█████▉ | 59/100 [00:05<00:03, 11.77it/s]\nPLMS Sampler: 61%|██████ | 61/100 [00:05<00:03, 11.74it/s]\nPLMS Sampler: 63%|██████▎ | 63/100 [00:05<00:03, 11.69it/s]\nPLMS Sampler: 65%|██████▌ | 65/100 [00:05<00:02, 11.82it/s]\nPLMS Sampler: 67%|██████▋ | 67/100 [00:05<00:02, 11.81it/s]\nPLMS Sampler: 69%|██████▉ | 69/100 [00:05<00:02, 11.93it/s]\nPLMS Sampler: 71%|███████ | 71/100 [00:06<00:02, 11.97it/s]\nPLMS Sampler: 73%|███████▎ | 73/100 [00:06<00:02, 11.84it/s]\nPLMS Sampler: 75%|███████▌ | 75/100 [00:06<00:02, 11.85it/s]\nPLMS Sampler: 77%|███████▋ | 77/100 [00:06<00:01, 11.97it/s]\nPLMS Sampler: 79%|███████▉ | 79/100 [00:06<00:01, 11.75it/s]\nPLMS Sampler: 81%|████████ | 81/100 [00:06<00:01, 11.68it/s]\nPLMS Sampler: 83%|████████▎ | 83/100 [00:07<00:01, 11.86it/s]\nPLMS Sampler: 85%|████████▌ | 85/100 [00:07<00:01, 11.90it/s]\nPLMS Sampler: 87%|████████▋ | 87/100 [00:07<00:01, 11.89it/s]\nPLMS Sampler: 89%|████████▉ | 89/100 [00:07<00:00, 12.10it/s]\nPLMS Sampler: 91%|█████████ | 91/100 [00:07<00:00, 11.94it/s]\nPLMS Sampler: 93%|█████████▎| 93/100 [00:07<00:00, 12.03it/s]\nPLMS Sampler: 95%|█████████▌| 95/100 [00:08<00:00, 12.21it/s]\nPLMS Sampler: 97%|█████████▋| 97/100 [00:08<00:00, 11.95it/s]\nPLMS Sampler: 99%|█████████▉| 99/100 [00:08<00:00, 12.02it/s]\nPLMS Sampler: 100%|██████████| 100/100 [00:08<00:00, 11.71it/s]", "metrics": { "predict_time": 10.535024, "total_time": 10.704296 }, "output": "https://replicate.delivery/mgxm/c2f9b43c-f94e-42ff-ae47-c2f9e0672c5c/out.png", "started_at": "2022-08-19T17:00:45.868618Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/l3zwiq4ylrcchj3trzrfxpu2xu", "cancel": "https://api.replicate.com/v1/predictions/l3zwiq4ylrcchj3trzrfxpu2xu/cancel" }, "version": "2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63" }Generated inUsing random seed 1299970299 Seed: -1 CLIP Text Embed: torch.Size([1, 1, 768]) Data shape for PLMS sampling is (1, 16, 48, 48) Running PLMS Sampling with 100 timesteps PLMS Sampler: 0%| | 0/100 [00:00<?, ?it/s] PLMS Sampler: 1%| | 1/100 [00:00<00:17, 5.62it/s] PLMS Sampler: 3%|▎ | 3/100 [00:00<00:10, 9.37it/s] PLMS Sampler: 5%|▌ | 5/100 [00:00<00:09, 10.48it/s] PLMS Sampler: 7%|▋ | 7/100 [00:00<00:08, 11.07it/s] PLMS Sampler: 9%|▉ | 9/100 [00:00<00:07, 11.42it/s] PLMS Sampler: 11%|█ | 11/100 [00:01<00:07, 11.65it/s] PLMS Sampler: 13%|█▎ | 13/100 [00:01<00:07, 11.84it/s] PLMS Sampler: 15%|█▌ | 15/100 [00:01<00:07, 11.57it/s] PLMS Sampler: 17%|█▋ | 17/100 [00:01<00:07, 11.58it/s] PLMS Sampler: 19%|█▉ | 19/100 [00:01<00:07, 11.47it/s] PLMS Sampler: 21%|██ | 21/100 [00:01<00:06, 11.35it/s] PLMS Sampler: 23%|██▎ | 23/100 [00:02<00:06, 11.42it/s] PLMS Sampler: 25%|██▌ | 25/100 [00:02<00:06, 11.68it/s] PLMS Sampler: 27%|██▋ | 27/100 [00:02<00:06, 11.61it/s] PLMS Sampler: 29%|██▉ | 29/100 [00:02<00:06, 11.71it/s] PLMS Sampler: 31%|███ | 31/100 [00:02<00:05, 11.62it/s] PLMS Sampler: 33%|███▎ | 33/100 [00:02<00:05, 11.68it/s] PLMS Sampler: 35%|███▌ | 35/100 [00:03<00:05, 11.55it/s] PLMS Sampler: 37%|███▋ | 37/100 [00:03<00:05, 11.71it/s] PLMS Sampler: 39%|███▉ | 39/100 [00:03<00:05, 11.68it/s] PLMS Sampler: 41%|████ | 41/100 [00:03<00:05, 11.74it/s] PLMS Sampler: 43%|████▎ | 43/100 [00:03<00:04, 11.95it/s] PLMS Sampler: 45%|████▌ | 45/100 [00:03<00:04, 11.98it/s] PLMS Sampler: 47%|████▋ | 47/100 [00:04<00:04, 11.94it/s] PLMS Sampler: 49%|████▉ | 49/100 [00:04<00:04, 12.04it/s] PLMS Sampler: 51%|█████ | 51/100 [00:04<00:04, 11.82it/s] PLMS Sampler: 53%|█████▎ | 53/100 [00:04<00:03, 11.93it/s] PLMS Sampler: 55%|█████▌ | 55/100 [00:04<00:03, 11.81it/s] PLMS Sampler: 57%|█████▋ | 57/100 [00:04<00:03, 11.76it/s] PLMS Sampler: 59%|█████▉ | 59/100 [00:05<00:03, 11.77it/s] PLMS Sampler: 61%|██████ | 61/100 [00:05<00:03, 11.74it/s] PLMS Sampler: 63%|██████▎ | 63/100 [00:05<00:03, 11.69it/s] PLMS Sampler: 65%|██████▌ | 65/100 [00:05<00:02, 11.82it/s] PLMS Sampler: 67%|██████▋ | 67/100 [00:05<00:02, 11.81it/s] PLMS Sampler: 69%|██████▉ | 69/100 [00:05<00:02, 11.93it/s] PLMS Sampler: 71%|███████ | 71/100 [00:06<00:02, 11.97it/s] PLMS Sampler: 73%|███████▎ | 73/100 [00:06<00:02, 11.84it/s] PLMS Sampler: 75%|███████▌ | 75/100 [00:06<00:02, 11.85it/s] PLMS Sampler: 77%|███████▋ | 77/100 [00:06<00:01, 11.97it/s] PLMS Sampler: 79%|███████▉ | 79/100 [00:06<00:01, 11.75it/s] PLMS Sampler: 81%|████████ | 81/100 [00:06<00:01, 11.68it/s] PLMS Sampler: 83%|████████▎ | 83/100 [00:07<00:01, 11.86it/s] PLMS Sampler: 85%|████████▌ | 85/100 [00:07<00:01, 11.90it/s] PLMS Sampler: 87%|████████▋ | 87/100 [00:07<00:01, 11.89it/s] PLMS Sampler: 89%|████████▉ | 89/100 [00:07<00:00, 12.10it/s] PLMS Sampler: 91%|█████████ | 91/100 [00:07<00:00, 11.94it/s] PLMS Sampler: 93%|█████████▎| 93/100 [00:07<00:00, 12.03it/s] PLMS Sampler: 95%|█████████▌| 95/100 [00:08<00:00, 12.21it/s] PLMS Sampler: 97%|█████████▋| 97/100 [00:08<00:00, 11.95it/s] PLMS Sampler: 99%|█████████▉| 99/100 [00:08<00:00, 12.02it/s] PLMS Sampler: 100%|██████████| 100/100 [00:08<00:00, 11.71it/s]Prediction
andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63ID4acxbbpujvf2nfxvmwa7koonqaStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- -1
- scale
- 5
- steps
- 100
- prompt
- An owl deep in thought sitting on a branch
- num_database_results
- "10"
{ "seed": -1, "scale": 5, "steps": 100, "prompt": "An owl deep in thought sitting on a branch", "num_database_results": "10" }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 andreasjansson/monkey-island-rdm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", { input: { seed: -1, scale: 5, steps: 100, prompt: "An owl deep in thought sitting on a branch", num_database_results: "10" } } ); // 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 andreasjansson/monkey-island-rdm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", input={ "seed": -1, "scale": 5, "steps": 100, "prompt": "An owl deep in thought sitting on a branch", "num_database_results": "10" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run andreasjansson/monkey-island-rdm 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": "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", "input": { "seed": -1, "scale": 5, "steps": 100, "prompt": "An owl deep in thought sitting on a branch", "num_database_results": "10" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-08-19T17:05:52.142218Z", "created_at": "2022-08-19T17:05:42.285432Z", "data_removed": false, "error": null, "id": "4acxbbpujvf2nfxvmwa7koonqa", "input": { "seed": -1, "scale": 5, "steps": 100, "prompt": "An owl deep in thought sitting on a branch", "num_database_results": "10" }, "logs": "Using random seed 248180608\nSeed: -1\nCLIP Text Embed: torch.Size([1, 1, 768])\nData shape for PLMS sampling is (1, 16, 48, 48)\nRunning PLMS Sampling with 100 timesteps\n\nPLMS Sampler: 0%| | 0/100 [00:00<?, ?it/s]\nPLMS Sampler: 1%| | 1/100 [00:00<00:18, 5.46it/s]\nPLMS Sampler: 3%|▎ | 3/100 [00:00<00:10, 9.45it/s]\nPLMS Sampler: 5%|▌ | 5/100 [00:00<00:08, 11.00it/s]\nPLMS Sampler: 7%|▋ | 7/100 [00:00<00:07, 11.64it/s]\nPLMS Sampler: 9%|▉ | 9/100 [00:00<00:07, 11.76it/s]\nPLMS Sampler: 11%|█ | 11/100 [00:00<00:07, 12.38it/s]\nPLMS Sampler: 13%|█▎ | 13/100 [00:01<00:06, 12.61it/s]\nPLMS Sampler: 15%|█▌ | 15/100 [00:01<00:06, 12.16it/s]\nPLMS Sampler: 17%|█▋ | 17/100 [00:01<00:06, 12.02it/s]\nPLMS Sampler: 19%|█▉ | 19/100 [00:01<00:06, 12.17it/s]\nPLMS Sampler: 21%|██ | 21/100 [00:01<00:06, 12.21it/s]\nPLMS Sampler: 23%|██▎ | 23/100 [00:01<00:06, 12.13it/s]\nPLMS Sampler: 25%|██▌ | 25/100 [00:02<00:06, 12.43it/s]\nPLMS Sampler: 27%|██▋ | 27/100 [00:02<00:05, 12.40it/s]\nPLMS Sampler: 29%|██▉ | 29/100 [00:02<00:05, 12.43it/s]\nPLMS Sampler: 31%|███ | 31/100 [00:02<00:05, 12.76it/s]\nPLMS Sampler: 33%|███▎ | 33/100 [00:02<00:05, 12.77it/s]\nPLMS Sampler: 35%|███▌ | 35/100 [00:02<00:05, 12.48it/s]\nPLMS Sampler: 37%|███▋ | 37/100 [00:03<00:05, 12.36it/s]\nPLMS Sampler: 39%|███▉ | 39/100 [00:03<00:04, 12.61it/s]\nPLMS Sampler: 41%|████ | 41/100 [00:03<00:04, 12.38it/s]\nPLMS Sampler: 43%|████▎ | 43/100 [00:03<00:04, 12.28it/s]\nPLMS Sampler: 45%|████▌ | 45/100 [00:03<00:04, 12.25it/s]\nPLMS Sampler: 47%|████▋ | 47/100 [00:03<00:04, 12.04it/s]\nPLMS Sampler: 49%|████▉ | 49/100 [00:04<00:04, 12.06it/s]\nPLMS Sampler: 51%|█████ | 51/100 [00:04<00:04, 11.95it/s]\nPLMS Sampler: 53%|█████▎ | 53/100 [00:04<00:03, 12.18it/s]\nPLMS Sampler: 55%|█████▌ | 55/100 [00:04<00:03, 12.56it/s]\nPLMS Sampler: 57%|█████▋ | 57/100 [00:04<00:03, 12.68it/s]\nPLMS Sampler: 59%|█████▉ | 59/100 [00:04<00:03, 12.43it/s]\nPLMS Sampler: 61%|██████ | 61/100 [00:05<00:03, 12.33it/s]\nPLMS Sampler: 63%|██████▎ | 63/100 [00:05<00:02, 12.44it/s]\nPLMS Sampler: 65%|██████▌ | 65/100 [00:05<00:02, 11.99it/s]\nPLMS Sampler: 67%|██████▋ | 67/100 [00:05<00:02, 12.04it/s]\nPLMS Sampler: 69%|██████▉ | 69/100 [00:05<00:02, 11.89it/s]\nPLMS Sampler: 71%|███████ | 71/100 [00:05<00:02, 12.18it/s]\nPLMS Sampler: 73%|███████▎ | 73/100 [00:06<00:02, 12.04it/s]\nPLMS Sampler: 75%|███████▌ | 75/100 [00:06<00:02, 11.94it/s]\nPLMS Sampler: 77%|███████▋ | 77/100 [00:06<00:01, 12.31it/s]\nPLMS Sampler: 79%|███████▉ | 79/100 [00:06<00:01, 12.01it/s]\nPLMS Sampler: 81%|████████ | 81/100 [00:06<00:01, 11.88it/s]\nPLMS Sampler: 83%|████████▎ | 83/100 [00:06<00:01, 12.06it/s]\nPLMS Sampler: 85%|████████▌ | 85/100 [00:07<00:01, 11.90it/s]\nPLMS Sampler: 87%|████████▋ | 87/100 [00:07<00:01, 11.71it/s]\nPLMS Sampler: 89%|████████▉ | 89/100 [00:07<00:00, 11.63it/s]\nPLMS Sampler: 91%|█████████ | 91/100 [00:07<00:00, 11.68it/s]\nPLMS Sampler: 93%|█████████▎| 93/100 [00:07<00:00, 11.41it/s]\nPLMS Sampler: 95%|█████████▌| 95/100 [00:07<00:00, 11.95it/s]\nPLMS Sampler: 97%|█████████▋| 97/100 [00:08<00:00, 11.80it/s]\nPLMS Sampler: 99%|█████████▉| 99/100 [00:08<00:00, 11.80it/s]\nPLMS Sampler: 100%|██████████| 100/100 [00:08<00:00, 12.04it/s]", "metrics": { "predict_time": 9.675961, "total_time": 9.856786 }, "output": "https://replicate.delivery/mgxm/249a0058-f3f8-43b2-83d3-f5b0155bbad1/out.png", "started_at": "2022-08-19T17:05:42.466257Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/4acxbbpujvf2nfxvmwa7koonqa", "cancel": "https://api.replicate.com/v1/predictions/4acxbbpujvf2nfxvmwa7koonqa/cancel" }, "version": "2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63" }Generated inUsing random seed 248180608 Seed: -1 CLIP Text Embed: torch.Size([1, 1, 768]) Data shape for PLMS sampling is (1, 16, 48, 48) Running PLMS Sampling with 100 timesteps PLMS Sampler: 0%| | 0/100 [00:00<?, ?it/s] PLMS Sampler: 1%| | 1/100 [00:00<00:18, 5.46it/s] PLMS Sampler: 3%|▎ | 3/100 [00:00<00:10, 9.45it/s] PLMS Sampler: 5%|▌ | 5/100 [00:00<00:08, 11.00it/s] PLMS Sampler: 7%|▋ | 7/100 [00:00<00:07, 11.64it/s] PLMS Sampler: 9%|▉ | 9/100 [00:00<00:07, 11.76it/s] PLMS Sampler: 11%|█ | 11/100 [00:00<00:07, 12.38it/s] PLMS Sampler: 13%|█▎ | 13/100 [00:01<00:06, 12.61it/s] PLMS Sampler: 15%|█▌ | 15/100 [00:01<00:06, 12.16it/s] PLMS Sampler: 17%|█▋ | 17/100 [00:01<00:06, 12.02it/s] PLMS Sampler: 19%|█▉ | 19/100 [00:01<00:06, 12.17it/s] PLMS Sampler: 21%|██ | 21/100 [00:01<00:06, 12.21it/s] PLMS Sampler: 23%|██▎ | 23/100 [00:01<00:06, 12.13it/s] PLMS Sampler: 25%|██▌ | 25/100 [00:02<00:06, 12.43it/s] PLMS Sampler: 27%|██▋ | 27/100 [00:02<00:05, 12.40it/s] PLMS Sampler: 29%|██▉ | 29/100 [00:02<00:05, 12.43it/s] PLMS Sampler: 31%|███ | 31/100 [00:02<00:05, 12.76it/s] PLMS Sampler: 33%|███▎ | 33/100 [00:02<00:05, 12.77it/s] PLMS Sampler: 35%|███▌ | 35/100 [00:02<00:05, 12.48it/s] PLMS Sampler: 37%|███▋ | 37/100 [00:03<00:05, 12.36it/s] PLMS Sampler: 39%|███▉ | 39/100 [00:03<00:04, 12.61it/s] PLMS Sampler: 41%|████ | 41/100 [00:03<00:04, 12.38it/s] PLMS Sampler: 43%|████▎ | 43/100 [00:03<00:04, 12.28it/s] PLMS Sampler: 45%|████▌ | 45/100 [00:03<00:04, 12.25it/s] PLMS Sampler: 47%|████▋ | 47/100 [00:03<00:04, 12.04it/s] PLMS Sampler: 49%|████▉ | 49/100 [00:04<00:04, 12.06it/s] PLMS Sampler: 51%|█████ | 51/100 [00:04<00:04, 11.95it/s] PLMS Sampler: 53%|█████▎ | 53/100 [00:04<00:03, 12.18it/s] PLMS Sampler: 55%|█████▌ | 55/100 [00:04<00:03, 12.56it/s] PLMS Sampler: 57%|█████▋ | 57/100 [00:04<00:03, 12.68it/s] PLMS Sampler: 59%|█████▉ | 59/100 [00:04<00:03, 12.43it/s] PLMS Sampler: 61%|██████ | 61/100 [00:05<00:03, 12.33it/s] PLMS Sampler: 63%|██████▎ | 63/100 [00:05<00:02, 12.44it/s] PLMS Sampler: 65%|██████▌ | 65/100 [00:05<00:02, 11.99it/s] PLMS Sampler: 67%|██████▋ | 67/100 [00:05<00:02, 12.04it/s] PLMS Sampler: 69%|██████▉ | 69/100 [00:05<00:02, 11.89it/s] PLMS Sampler: 71%|███████ | 71/100 [00:05<00:02, 12.18it/s] PLMS Sampler: 73%|███████▎ | 73/100 [00:06<00:02, 12.04it/s] PLMS Sampler: 75%|███████▌ | 75/100 [00:06<00:02, 11.94it/s] PLMS Sampler: 77%|███████▋ | 77/100 [00:06<00:01, 12.31it/s] PLMS Sampler: 79%|███████▉ | 79/100 [00:06<00:01, 12.01it/s] PLMS Sampler: 81%|████████ | 81/100 [00:06<00:01, 11.88it/s] PLMS Sampler: 83%|████████▎ | 83/100 [00:06<00:01, 12.06it/s] PLMS Sampler: 85%|████████▌ | 85/100 [00:07<00:01, 11.90it/s] PLMS Sampler: 87%|████████▋ | 87/100 [00:07<00:01, 11.71it/s] PLMS Sampler: 89%|████████▉ | 89/100 [00:07<00:00, 11.63it/s] PLMS Sampler: 91%|█████████ | 91/100 [00:07<00:00, 11.68it/s] PLMS Sampler: 93%|█████████▎| 93/100 [00:07<00:00, 11.41it/s] PLMS Sampler: 95%|█████████▌| 95/100 [00:07<00:00, 11.95it/s] PLMS Sampler: 97%|█████████▋| 97/100 [00:08<00:00, 11.80it/s] PLMS Sampler: 99%|█████████▉| 99/100 [00:08<00:00, 11.80it/s] PLMS Sampler: 100%|██████████| 100/100 [00:08<00:00, 12.04it/s]Prediction
andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63IDxmdvmnfjgfgmtnasjjlzzhuvmuStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- -1
- scale
- 5
- steps
- 100
- prompt
- A pirate wedding
- num_database_results
- "10"
{ "seed": -1, "scale": 5, "steps": 100, "prompt": "A pirate wedding", "num_database_results": "10" }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 andreasjansson/monkey-island-rdm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", { input: { seed: -1, scale: 5, steps: 100, prompt: "A pirate wedding", num_database_results: "10" } } ); // 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 andreasjansson/monkey-island-rdm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", input={ "seed": -1, "scale": 5, "steps": 100, "prompt": "A pirate wedding", "num_database_results": "10" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run andreasjansson/monkey-island-rdm 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": "andreasjansson/monkey-island-rdm:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63", "input": { "seed": -1, "scale": 5, "steps": 100, "prompt": "A pirate wedding", "num_database_results": "10" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-08-19T17:18:09.719232Z", "created_at": "2022-08-19T17:18:00.536839Z", "data_removed": false, "error": null, "id": "xmdvmnfjgfgmtnasjjlzzhuvmu", "input": { "seed": -1, "scale": 5, "steps": 100, "prompt": "A pirate wedding", "num_database_results": "10" }, "logs": "Using random seed 3030840391\nSeed: -1\nCLIP Text Embed: torch.Size([1, 1, 768])\nData shape for PLMS sampling is (1, 16, 48, 48)\nRunning PLMS Sampling with 100 timesteps\n\nPLMS Sampler: 0%| | 0/100 [00:00<?, ?it/s]\nPLMS Sampler: 1%| | 1/100 [00:00<00:16, 6.07it/s]\nPLMS Sampler: 3%|▎ | 3/100 [00:00<00:09, 10.21it/s]\nPLMS Sampler: 5%|▌ | 5/100 [00:00<00:08, 11.59it/s]\nPLMS Sampler: 7%|▋ | 7/100 [00:00<00:07, 12.42it/s]\nPLMS Sampler: 9%|▉ | 9/100 [00:00<00:07, 12.90it/s]\nPLMS Sampler: 11%|█ | 11/100 [00:00<00:06, 13.18it/s]\nPLMS Sampler: 13%|█▎ | 13/100 [00:01<00:06, 13.36it/s]\nPLMS Sampler: 15%|█▌ | 15/100 [00:01<00:06, 13.40it/s]\nPLMS Sampler: 17%|█▋ | 17/100 [00:01<00:06, 13.46it/s]\nPLMS Sampler: 19%|█▉ | 19/100 [00:01<00:06, 13.38it/s]\nPLMS Sampler: 21%|██ | 21/100 [00:01<00:05, 13.39it/s]\nPLMS Sampler: 23%|██▎ | 23/100 [00:01<00:05, 13.18it/s]\nPLMS Sampler: 25%|██▌ | 25/100 [00:01<00:05, 12.75it/s]\nPLMS Sampler: 27%|██▋ | 27/100 [00:02<00:05, 12.85it/s]\nPLMS Sampler: 29%|██▉ | 29/100 [00:02<00:05, 12.84it/s]\nPLMS Sampler: 31%|███ | 31/100 [00:02<00:05, 12.87it/s]\nPLMS Sampler: 33%|███▎ | 33/100 [00:02<00:05, 12.76it/s]\nPLMS Sampler: 35%|███▌ | 35/100 [00:02<00:05, 13.00it/s]\nPLMS Sampler: 37%|███▋ | 37/100 [00:02<00:04, 13.12it/s]\nPLMS Sampler: 39%|███▉ | 39/100 [00:03<00:04, 13.10it/s]\nPLMS Sampler: 41%|████ | 41/100 [00:03<00:04, 13.22it/s]\nPLMS Sampler: 43%|████▎ | 43/100 [00:03<00:04, 13.34it/s]\nPLMS Sampler: 45%|████▌ | 45/100 [00:03<00:04, 13.15it/s]\nPLMS Sampler: 47%|████▋ | 47/100 [00:03<00:04, 13.21it/s]\nPLMS Sampler: 49%|████▉ | 49/100 [00:03<00:03, 13.26it/s]\nPLMS Sampler: 51%|█████ | 51/100 [00:03<00:03, 13.32it/s]\nPLMS Sampler: 53%|█████▎ | 53/100 [00:04<00:03, 13.39it/s]\nPLMS Sampler: 55%|█████▌ | 55/100 [00:04<00:03, 13.42it/s]\nPLMS Sampler: 57%|█████▋ | 57/100 [00:04<00:03, 13.36it/s]\nPLMS Sampler: 59%|█████▉ | 59/100 [00:04<00:03, 13.38it/s]\nPLMS Sampler: 61%|██████ | 61/100 [00:04<00:02, 13.42it/s]\nPLMS Sampler: 63%|██████▎ | 63/100 [00:04<00:02, 13.44it/s]\nPLMS Sampler: 65%|██████▌ | 65/100 [00:04<00:02, 13.42it/s]\nPLMS Sampler: 67%|██████▋ | 67/100 [00:05<00:02, 13.40it/s]\nPLMS Sampler: 69%|██████▉ | 69/100 [00:05<00:02, 13.36it/s]\nPLMS Sampler: 71%|███████ | 71/100 [00:05<00:02, 13.25it/s]\nPLMS Sampler: 73%|███████▎ | 73/100 [00:05<00:02, 12.99it/s]\nPLMS Sampler: 75%|███████▌ | 75/100 [00:05<00:01, 13.19it/s]\nPLMS Sampler: 77%|███████▋ | 77/100 [00:05<00:01, 13.30it/s]\nPLMS Sampler: 79%|███████▉ | 79/100 [00:06<00:01, 13.39it/s]\nPLMS Sampler: 81%|████████ | 81/100 [00:06<00:01, 13.48it/s]\nPLMS Sampler: 83%|████████▎ | 83/100 [00:06<00:01, 13.56it/s]\nPLMS Sampler: 85%|████████▌ | 85/100 [00:06<00:01, 13.49it/s]\nPLMS Sampler: 87%|████████▋ | 87/100 [00:06<00:00, 13.48it/s]\nPLMS Sampler: 89%|████████▉ | 89/100 [00:06<00:00, 13.43it/s]\nPLMS Sampler: 91%|█████████ | 91/100 [00:06<00:00, 13.38it/s]\nPLMS Sampler: 93%|█████████▎| 93/100 [00:07<00:00, 13.19it/s]\nPLMS Sampler: 95%|█████████▌| 95/100 [00:07<00:00, 13.07it/s]\nPLMS Sampler: 97%|█████████▋| 97/100 [00:07<00:00, 13.10it/s]\nPLMS Sampler: 99%|█████████▉| 99/100 [00:07<00:00, 13.08it/s]\nPLMS Sampler: 100%|██████████| 100/100 [00:07<00:00, 13.12it/s]", "metrics": { "predict_time": 8.974872, "total_time": 9.182393 }, "output": "https://replicate.delivery/mgxm/a006fb86-eba8-47d4-8c15-297064af68f0/out.png", "started_at": "2022-08-19T17:18:00.744360Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/xmdvmnfjgfgmtnasjjlzzhuvmu", "cancel": "https://api.replicate.com/v1/predictions/xmdvmnfjgfgmtnasjjlzzhuvmu/cancel" }, "version": "2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63" }Generated inUsing random seed 3030840391 Seed: -1 CLIP Text Embed: torch.Size([1, 1, 768]) Data shape for PLMS sampling is (1, 16, 48, 48) Running PLMS Sampling with 100 timesteps PLMS Sampler: 0%| | 0/100 [00:00<?, ?it/s] PLMS Sampler: 1%| | 1/100 [00:00<00:16, 6.07it/s] PLMS Sampler: 3%|▎ | 3/100 [00:00<00:09, 10.21it/s] PLMS Sampler: 5%|▌ | 5/100 [00:00<00:08, 11.59it/s] PLMS Sampler: 7%|▋ | 7/100 [00:00<00:07, 12.42it/s] PLMS Sampler: 9%|▉ | 9/100 [00:00<00:07, 12.90it/s] PLMS Sampler: 11%|█ | 11/100 [00:00<00:06, 13.18it/s] PLMS Sampler: 13%|█▎ | 13/100 [00:01<00:06, 13.36it/s] PLMS Sampler: 15%|█▌ | 15/100 [00:01<00:06, 13.40it/s] PLMS Sampler: 17%|█▋ | 17/100 [00:01<00:06, 13.46it/s] PLMS Sampler: 19%|█▉ | 19/100 [00:01<00:06, 13.38it/s] PLMS Sampler: 21%|██ | 21/100 [00:01<00:05, 13.39it/s] PLMS Sampler: 23%|██▎ | 23/100 [00:01<00:05, 13.18it/s] PLMS Sampler: 25%|██▌ | 25/100 [00:01<00:05, 12.75it/s] PLMS Sampler: 27%|██▋ | 27/100 [00:02<00:05, 12.85it/s] PLMS Sampler: 29%|██▉ | 29/100 [00:02<00:05, 12.84it/s] PLMS Sampler: 31%|███ | 31/100 [00:02<00:05, 12.87it/s] PLMS Sampler: 33%|███▎ | 33/100 [00:02<00:05, 12.76it/s] PLMS Sampler: 35%|███▌ | 35/100 [00:02<00:05, 13.00it/s] PLMS Sampler: 37%|███▋ | 37/100 [00:02<00:04, 13.12it/s] PLMS Sampler: 39%|███▉ | 39/100 [00:03<00:04, 13.10it/s] PLMS Sampler: 41%|████ | 41/100 [00:03<00:04, 13.22it/s] PLMS Sampler: 43%|████▎ | 43/100 [00:03<00:04, 13.34it/s] PLMS Sampler: 45%|████▌ | 45/100 [00:03<00:04, 13.15it/s] PLMS Sampler: 47%|████▋ | 47/100 [00:03<00:04, 13.21it/s] PLMS Sampler: 49%|████▉ | 49/100 [00:03<00:03, 13.26it/s] PLMS Sampler: 51%|█████ | 51/100 [00:03<00:03, 13.32it/s] PLMS Sampler: 53%|█████▎ | 53/100 [00:04<00:03, 13.39it/s] PLMS Sampler: 55%|█████▌ | 55/100 [00:04<00:03, 13.42it/s] PLMS Sampler: 57%|█████▋ | 57/100 [00:04<00:03, 13.36it/s] PLMS Sampler: 59%|█████▉ | 59/100 [00:04<00:03, 13.38it/s] PLMS Sampler: 61%|██████ | 61/100 [00:04<00:02, 13.42it/s] PLMS Sampler: 63%|██████▎ | 63/100 [00:04<00:02, 13.44it/s] PLMS Sampler: 65%|██████▌ | 65/100 [00:04<00:02, 13.42it/s] PLMS Sampler: 67%|██████▋ | 67/100 [00:05<00:02, 13.40it/s] PLMS Sampler: 69%|██████▉ | 69/100 [00:05<00:02, 13.36it/s] PLMS Sampler: 71%|███████ | 71/100 [00:05<00:02, 13.25it/s] PLMS Sampler: 73%|███████▎ | 73/100 [00:05<00:02, 12.99it/s] PLMS Sampler: 75%|███████▌ | 75/100 [00:05<00:01, 13.19it/s] PLMS Sampler: 77%|███████▋ | 77/100 [00:05<00:01, 13.30it/s] PLMS Sampler: 79%|███████▉ | 79/100 [00:06<00:01, 13.39it/s] PLMS Sampler: 81%|████████ | 81/100 [00:06<00:01, 13.48it/s] PLMS Sampler: 83%|████████▎ | 83/100 [00:06<00:01, 13.56it/s] PLMS Sampler: 85%|████████▌ | 85/100 [00:06<00:01, 13.49it/s] PLMS Sampler: 87%|████████▋ | 87/100 [00:06<00:00, 13.48it/s] PLMS Sampler: 89%|████████▉ | 89/100 [00:06<00:00, 13.43it/s] PLMS Sampler: 91%|█████████ | 91/100 [00:06<00:00, 13.38it/s] PLMS Sampler: 93%|█████████▎| 93/100 [00:07<00:00, 13.19it/s] PLMS Sampler: 95%|█████████▌| 95/100 [00:07<00:00, 13.07it/s] PLMS Sampler: 97%|█████████▋| 97/100 [00:07<00:00, 13.10it/s] PLMS Sampler: 99%|█████████▉| 99/100 [00:07<00:00, 13.08it/s] PLMS Sampler: 100%|██████████| 100/100 [00:07<00:00, 13.12it/s]Prediction
andreasjansson/monkey-island-rdm:a92806a9b36a2fe24ad6ec7c3b9e45b62dfde32320629c40062a7c139febafdbIDljdxdknxefdhrlwgu3khj2cdo4StatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- -1
- scale
- 5
- steps
- 100
- prompt
- The sun rises over the village
- num_database_results
- "5"
{ "seed": -1, "scale": 5, "steps": 100, "prompt": "The sun rises over the village", "num_database_results": "5" }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 andreasjansson/monkey-island-rdm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "andreasjansson/monkey-island-rdm:a92806a9b36a2fe24ad6ec7c3b9e45b62dfde32320629c40062a7c139febafdb", { input: { seed: -1, scale: 5, steps: 100, prompt: "The sun rises over the village", num_database_results: "5" } } ); // 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 andreasjansson/monkey-island-rdm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "andreasjansson/monkey-island-rdm:a92806a9b36a2fe24ad6ec7c3b9e45b62dfde32320629c40062a7c139febafdb", input={ "seed": -1, "scale": 5, "steps": 100, "prompt": "The sun rises over the village", "num_database_results": "5" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run andreasjansson/monkey-island-rdm 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": "andreasjansson/monkey-island-rdm:a92806a9b36a2fe24ad6ec7c3b9e45b62dfde32320629c40062a7c139febafdb", "input": { "seed": -1, "scale": 5, "steps": 100, "prompt": "The sun rises over the village", "num_database_results": "5" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-08-19T22:43:02.110521Z", "created_at": "2022-08-19T22:42:51.210314Z", "data_removed": false, "error": null, "id": "ljdxdknxefdhrlwgu3khj2cdo4", "input": { "seed": -1, "scale": 5, "steps": 100, "prompt": "The sun rises over the village", "num_database_results": "5" }, "logs": "Using random seed 729305746\nSeed: -1\nCLIP Text Embed: torch.Size([1, 1, 768])\nData shape for PLMS sampling is (1, 16, 48, 48)\nRunning PLMS Sampling with 100 timesteps\n\nPLMS Sampler: 0%| | 0/100 [00:00<?, ?it/s]\nPLMS Sampler: 1%| | 1/100 [00:00<00:16, 5.93it/s]\nPLMS Sampler: 3%|▎ | 3/100 [00:00<00:10, 9.42it/s]\nPLMS Sampler: 5%|▌ | 5/100 [00:00<00:08, 10.81it/s]\nPLMS Sampler: 7%|▋ | 7/100 [00:00<00:08, 11.14it/s]\nPLMS Sampler: 9%|▉ | 9/100 [00:00<00:07, 11.41it/s]\nPLMS Sampler: 11%|█ | 11/100 [00:00<00:07, 11.85it/s]\nPLMS Sampler: 13%|█▎ | 13/100 [00:01<00:07, 12.06it/s]\nPLMS Sampler: 15%|█▌ | 15/100 [00:01<00:07, 12.05it/s]\nPLMS Sampler: 17%|█▋ | 17/100 [00:01<00:06, 12.05it/s]\nPLMS Sampler: 19%|█▉ | 19/100 [00:01<00:06, 12.14it/s]\nPLMS Sampler: 21%|██ | 21/100 [00:01<00:06, 12.02it/s]\nPLMS Sampler: 23%|██▎ | 23/100 [00:01<00:06, 12.01it/s]\nPLMS Sampler: 25%|██▌ | 25/100 [00:02<00:06, 12.12it/s]\nPLMS Sampler: 27%|██▋ | 27/100 [00:02<00:06, 12.15it/s]\nPLMS Sampler: 29%|██▉ | 29/100 [00:02<00:05, 12.13it/s]\nPLMS Sampler: 31%|███ | 31/100 [00:02<00:05, 12.12it/s]\nPLMS Sampler: 33%|███▎ | 33/100 [00:02<00:05, 12.11it/s]\nPLMS Sampler: 35%|███▌ | 35/100 [00:02<00:05, 12.32it/s]\nPLMS Sampler: 37%|███▋ | 37/100 [00:03<00:05, 12.50it/s]\nPLMS Sampler: 39%|███▉ | 39/100 [00:03<00:04, 12.22it/s]\nPLMS Sampler: 41%|████ | 41/100 [00:03<00:04, 12.18it/s]\nPLMS Sampler: 43%|████▎ | 43/100 [00:03<00:04, 11.92it/s]\nPLMS Sampler: 45%|████▌ | 45/100 [00:03<00:04, 11.96it/s]\nPLMS Sampler: 47%|████▋ | 47/100 [00:03<00:04, 12.07it/s]\nPLMS Sampler: 49%|████▉ | 49/100 [00:04<00:04, 12.23it/s]\nPLMS Sampler: 51%|█████ | 51/100 [00:04<00:04, 12.20it/s]\nPLMS Sampler: 53%|█████▎ | 53/100 [00:04<00:03, 12.44it/s]\nPLMS Sampler: 55%|█████▌ | 55/100 [00:04<00:03, 12.80it/s]\nPLMS Sampler: 57%|█████▋ | 57/100 [00:04<00:03, 12.71it/s]\nPLMS Sampler: 59%|█████▉ | 59/100 [00:04<00:03, 12.77it/s]\nPLMS Sampler: 61%|██████ | 61/100 [00:05<00:03, 12.91it/s]\nPLMS Sampler: 63%|██████▎ | 63/100 [00:05<00:02, 12.93it/s]\nPLMS Sampler: 65%|██████▌ | 65/100 [00:05<00:02, 12.97it/s]\nPLMS Sampler: 67%|██████▋ | 67/100 [00:05<00:02, 13.18it/s]\nPLMS Sampler: 69%|██████▉ | 69/100 [00:05<00:02, 13.27it/s]\nPLMS Sampler: 71%|███████ | 71/100 [00:05<00:02, 13.11it/s]\nPLMS Sampler: 73%|███████▎ | 73/100 [00:05<00:02, 12.83it/s]\nPLMS Sampler: 75%|███████▌ | 75/100 [00:06<00:01, 12.82it/s]\nPLMS Sampler: 77%|███████▋ | 77/100 [00:06<00:01, 12.56it/s]\nPLMS Sampler: 79%|███████▉ | 79/100 [00:06<00:01, 12.73it/s]\nPLMS Sampler: 81%|████████ | 81/100 [00:06<00:01, 12.65it/s]\nPLMS Sampler: 83%|████████▎ | 83/100 [00:06<00:01, 12.65it/s]\nPLMS Sampler: 85%|████████▌ | 85/100 [00:06<00:01, 12.47it/s]\nPLMS Sampler: 87%|████████▋ | 87/100 [00:07<00:01, 12.26it/s]\nPLMS Sampler: 89%|████████▉ | 89/100 [00:07<00:00, 12.40it/s]\nPLMS Sampler: 91%|█████████ | 91/100 [00:07<00:00, 12.69it/s]\nPLMS Sampler: 93%|█████████▎| 93/100 [00:07<00:00, 12.87it/s]\nPLMS Sampler: 95%|█████████▌| 95/100 [00:07<00:00, 12.92it/s]\nPLMS Sampler: 97%|█████████▋| 97/100 [00:07<00:00, 12.99it/s]\nPLMS Sampler: 99%|█████████▉| 99/100 [00:08<00:00, 12.69it/s]\nPLMS Sampler: 100%|██████████| 100/100 [00:08<00:00, 12.34it/s]", "metrics": { "predict_time": 9.504001, "total_time": 10.900207 }, "output": "https://replicate.delivery/mgxm/e91bedee-bb06-4b72-8559-a341ed8ec781/out.png", "started_at": "2022-08-19T22:42:52.606520Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ljdxdknxefdhrlwgu3khj2cdo4", "cancel": "https://api.replicate.com/v1/predictions/ljdxdknxefdhrlwgu3khj2cdo4/cancel" }, "version": "a92806a9b36a2fe24ad6ec7c3b9e45b62dfde32320629c40062a7c139febafdb" }Generated inUsing random seed 729305746 Seed: -1 CLIP Text Embed: torch.Size([1, 1, 768]) Data shape for PLMS sampling is (1, 16, 48, 48) Running PLMS Sampling with 100 timesteps PLMS Sampler: 0%| | 0/100 [00:00<?, ?it/s] PLMS Sampler: 1%| | 1/100 [00:00<00:16, 5.93it/s] PLMS Sampler: 3%|▎ | 3/100 [00:00<00:10, 9.42it/s] PLMS Sampler: 5%|▌ | 5/100 [00:00<00:08, 10.81it/s] PLMS Sampler: 7%|▋ | 7/100 [00:00<00:08, 11.14it/s] PLMS Sampler: 9%|▉ | 9/100 [00:00<00:07, 11.41it/s] PLMS Sampler: 11%|█ | 11/100 [00:00<00:07, 11.85it/s] PLMS Sampler: 13%|█▎ | 13/100 [00:01<00:07, 12.06it/s] PLMS Sampler: 15%|█▌ | 15/100 [00:01<00:07, 12.05it/s] PLMS Sampler: 17%|█▋ | 17/100 [00:01<00:06, 12.05it/s] PLMS Sampler: 19%|█▉ | 19/100 [00:01<00:06, 12.14it/s] PLMS Sampler: 21%|██ | 21/100 [00:01<00:06, 12.02it/s] PLMS Sampler: 23%|██▎ | 23/100 [00:01<00:06, 12.01it/s] PLMS Sampler: 25%|██▌ | 25/100 [00:02<00:06, 12.12it/s] PLMS Sampler: 27%|██▋ | 27/100 [00:02<00:06, 12.15it/s] PLMS Sampler: 29%|██▉ | 29/100 [00:02<00:05, 12.13it/s] PLMS Sampler: 31%|███ | 31/100 [00:02<00:05, 12.12it/s] PLMS Sampler: 33%|███▎ | 33/100 [00:02<00:05, 12.11it/s] PLMS Sampler: 35%|███▌ | 35/100 [00:02<00:05, 12.32it/s] PLMS Sampler: 37%|███▋ | 37/100 [00:03<00:05, 12.50it/s] PLMS Sampler: 39%|███▉ | 39/100 [00:03<00:04, 12.22it/s] PLMS Sampler: 41%|████ | 41/100 [00:03<00:04, 12.18it/s] PLMS Sampler: 43%|████▎ | 43/100 [00:03<00:04, 11.92it/s] PLMS Sampler: 45%|████▌ | 45/100 [00:03<00:04, 11.96it/s] PLMS Sampler: 47%|████▋ | 47/100 [00:03<00:04, 12.07it/s] PLMS Sampler: 49%|████▉ | 49/100 [00:04<00:04, 12.23it/s] PLMS Sampler: 51%|█████ | 51/100 [00:04<00:04, 12.20it/s] PLMS Sampler: 53%|█████▎ | 53/100 [00:04<00:03, 12.44it/s] PLMS Sampler: 55%|█████▌ | 55/100 [00:04<00:03, 12.80it/s] PLMS Sampler: 57%|█████▋ | 57/100 [00:04<00:03, 12.71it/s] PLMS Sampler: 59%|█████▉ | 59/100 [00:04<00:03, 12.77it/s] PLMS Sampler: 61%|██████ | 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12.87it/s] PLMS Sampler: 95%|█████████▌| 95/100 [00:07<00:00, 12.92it/s] PLMS Sampler: 97%|█████████▋| 97/100 [00:07<00:00, 12.99it/s] PLMS Sampler: 99%|█████████▉| 99/100 [00:08<00:00, 12.69it/s] PLMS Sampler: 100%|██████████| 100/100 [00:08<00:00, 12.34it/s]Prediction
andreasjansson/monkey-island-rdm:f907c5ef70ba1efac7c6462f595ce5e68f67159596158928cec7886d91c739baIDbkpa7rtjdfedtjxtglt4hpoeeaStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- -1
- scale
- 5
- steps
- 100
- prompt
- A diver wearing a steampunk diving helmet
- num_database_results
- "10"
{ "seed": -1, "scale": 5, "steps": 100, "prompt": "A diver wearing a steampunk diving helmet", "num_database_results": "10" }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 andreasjansson/monkey-island-rdm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "andreasjansson/monkey-island-rdm:f907c5ef70ba1efac7c6462f595ce5e68f67159596158928cec7886d91c739ba", { input: { seed: -1, scale: 5, steps: 100, prompt: "A diver wearing a steampunk diving helmet", num_database_results: "10" } } ); // 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 andreasjansson/monkey-island-rdm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "andreasjansson/monkey-island-rdm:f907c5ef70ba1efac7c6462f595ce5e68f67159596158928cec7886d91c739ba", input={ "seed": -1, "scale": 5, "steps": 100, "prompt": "A diver wearing a steampunk diving helmet", "num_database_results": "10" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run andreasjansson/monkey-island-rdm 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": "andreasjansson/monkey-island-rdm:f907c5ef70ba1efac7c6462f595ce5e68f67159596158928cec7886d91c739ba", "input": { "seed": -1, "scale": 5, "steps": 100, "prompt": "A diver wearing a steampunk diving helmet", "num_database_results": "10" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-08-19T23:24:28.511109Z", "created_at": "2022-08-19T23:24:18.501606Z", "data_removed": false, "error": null, "id": "bkpa7rtjdfedtjxtglt4hpoeea", "input": { "seed": -1, "scale": 5, "steps": 100, "prompt": "A diver wearing a steampunk diving helmet", "num_database_results": "10" }, "logs": "Using random seed 2338626329\nSeed: -1\nCLIP Text Embed: torch.Size([1, 1, 768])\nData shape for PLMS sampling is (1, 16, 48, 48)\nRunning PLMS Sampling with 100 timesteps\n\nPLMS Sampler: 0%| | 0/100 [00:00<?, ?it/s]\nPLMS Sampler: 1%| | 1/100 [00:00<00:18, 5.31it/s]\nPLMS Sampler: 3%|▎ | 3/100 [00:00<00:11, 8.70it/s]\nPLMS Sampler: 5%|▌ | 5/100 [00:00<00:09, 10.13it/s]\nPLMS Sampler: 7%|▋ | 7/100 [00:00<00:08, 10.77it/s]\nPLMS Sampler: 9%|▉ | 9/100 [00:00<00:08, 11.03it/s]\nPLMS Sampler: 11%|█ | 11/100 [00:01<00:07, 11.35it/s]\nPLMS Sampler: 13%|█▎ | 13/100 [00:01<00:07, 11.39it/s]\nPLMS Sampler: 15%|█▌ | 15/100 [00:01<00:07, 11.09it/s]\nPLMS Sampler: 17%|█▋ | 17/100 [00:01<00:07, 11.11it/s]\nPLMS Sampler: 19%|█▉ | 19/100 [00:01<00:07, 11.08it/s]\nPLMS Sampler: 21%|██ | 21/100 [00:01<00:07, 11.27it/s]\nPLMS Sampler: 23%|██▎ | 23/100 [00:02<00:06, 11.48it/s]\nPLMS Sampler: 25%|██▌ | 25/100 [00:02<00:06, 11.36it/s]\nPLMS Sampler: 27%|██▋ | 27/100 [00:02<00:06, 11.13it/s]\nPLMS Sampler: 29%|██▉ | 29/100 [00:02<00:06, 11.18it/s]\nPLMS Sampler: 31%|███ | 31/100 [00:02<00:06, 11.37it/s]\nPLMS Sampler: 33%|███▎ | 33/100 [00:02<00:05, 11.67it/s]\nPLMS Sampler: 35%|███▌ | 35/100 [00:03<00:05, 11.92it/s]\nPLMS Sampler: 37%|███▋ | 37/100 [00:03<00:05, 11.95it/s]\nPLMS Sampler: 39%|███▉ | 39/100 [00:03<00:05, 12.19it/s]\nPLMS Sampler: 41%|████ | 41/100 [00:03<00:04, 12.13it/s]\nPLMS Sampler: 43%|████▎ | 43/100 [00:03<00:04, 12.03it/s]\nPLMS Sampler: 45%|████▌ | 45/100 [00:03<00:04, 12.17it/s]\nPLMS Sampler: 47%|████▋ | 47/100 [00:04<00:04, 12.35it/s]\nPLMS Sampler: 49%|████▉ | 49/100 [00:04<00:04, 12.01it/s]\nPLMS Sampler: 51%|█████ | 51/100 [00:04<00:04, 12.17it/s]\nPLMS Sampler: 53%|█████▎ | 53/100 [00:04<00:03, 12.30it/s]\nPLMS Sampler: 55%|█████▌ | 55/100 [00:04<00:03, 12.35it/s]\nPLMS Sampler: 57%|█████▋ | 57/100 [00:04<00:03, 12.38it/s]\nPLMS Sampler: 59%|█████▉ | 59/100 [00:05<00:03, 12.41it/s]\nPLMS Sampler: 61%|██████ | 61/100 [00:05<00:03, 12.46it/s]\nPLMS Sampler: 63%|██████▎ | 63/100 [00:05<00:02, 12.37it/s]\nPLMS Sampler: 65%|██████▌ | 65/100 [00:05<00:02, 12.45it/s]\nPLMS Sampler: 67%|██████▋ | 67/100 [00:05<00:02, 12.48it/s]\nPLMS Sampler: 69%|██████▉ | 69/100 [00:05<00:02, 12.50it/s]\nPLMS Sampler: 71%|███████ | 71/100 [00:06<00:02, 12.55it/s]\nPLMS Sampler: 73%|███████▎ | 73/100 [00:06<00:02, 12.62it/s]\nPLMS Sampler: 75%|███████▌ | 75/100 [00:06<00:02, 12.32it/s]\nPLMS Sampler: 77%|███████▋ | 77/100 [00:06<00:01, 12.04it/s]\nPLMS Sampler: 79%|███████▉ | 79/100 [00:06<00:01, 11.70it/s]\nPLMS Sampler: 81%|████████ | 81/100 [00:06<00:01, 11.89it/s]\nPLMS Sampler: 83%|████████▎ | 83/100 [00:07<00:01, 12.16it/s]\nPLMS Sampler: 85%|████████▌ | 85/100 [00:07<00:01, 12.37it/s]\nPLMS Sampler: 87%|████████▋ | 87/100 [00:07<00:01, 12.40it/s]\nPLMS Sampler: 89%|████████▉ | 89/100 [00:07<00:00, 12.53it/s]\nPLMS Sampler: 91%|█████████ | 91/100 [00:07<00:00, 12.52it/s]\nPLMS Sampler: 93%|█████████▎| 93/100 [00:07<00:00, 12.46it/s]\nPLMS Sampler: 95%|█████████▌| 95/100 [00:08<00:00, 12.35it/s]\nPLMS Sampler: 97%|█████████▋| 97/100 [00:08<00:00, 12.33it/s]\nPLMS Sampler: 99%|█████████▉| 99/100 [00:08<00:00, 12.36it/s]\nPLMS Sampler: 100%|██████████| 100/100 [00:08<00:00, 11.87it/s]", "metrics": { "predict_time": 9.822098, "total_time": 10.009503 }, "output": "https://replicate.delivery/mgxm/59dc3466-28bd-4f30-a8eb-5fc557b4b713/out.png", "started_at": "2022-08-19T23:24:18.689011Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/bkpa7rtjdfedtjxtglt4hpoeea", "cancel": "https://api.replicate.com/v1/predictions/bkpa7rtjdfedtjxtglt4hpoeea/cancel" }, "version": "f907c5ef70ba1efac7c6462f595ce5e68f67159596158928cec7886d91c739ba" }Generated inUsing random seed 2338626329 Seed: -1 CLIP Text Embed: torch.Size([1, 1, 768]) Data shape for PLMS sampling is (1, 16, 48, 48) Running PLMS Sampling with 100 timesteps PLMS Sampler: 0%| | 0/100 [00:00<?, ?it/s] PLMS Sampler: 1%| | 1/100 [00:00<00:18, 5.31it/s] PLMS Sampler: 3%|▎ | 3/100 [00:00<00:11, 8.70it/s] PLMS Sampler: 5%|▌ | 5/100 [00:00<00:09, 10.13it/s] PLMS Sampler: 7%|▋ | 7/100 [00:00<00:08, 10.77it/s] PLMS Sampler: 9%|▉ | 9/100 [00:00<00:08, 11.03it/s] PLMS Sampler: 11%|█ | 11/100 [00:01<00:07, 11.35it/s] PLMS Sampler: 13%|█▎ | 13/100 [00:01<00:07, 11.39it/s] PLMS Sampler: 15%|█▌ | 15/100 [00:01<00:07, 11.09it/s] PLMS Sampler: 17%|█▋ | 17/100 [00:01<00:07, 11.11it/s] PLMS Sampler: 19%|█▉ | 19/100 [00:01<00:07, 11.08it/s] PLMS Sampler: 21%|██ | 21/100 [00:01<00:07, 11.27it/s] PLMS Sampler: 23%|██▎ | 23/100 [00:02<00:06, 11.48it/s] PLMS Sampler: 25%|██▌ | 25/100 [00:02<00:06, 11.36it/s] PLMS 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12.46it/s] PLMS Sampler: 95%|█████████▌| 95/100 [00:08<00:00, 12.35it/s] PLMS Sampler: 97%|█████████▋| 97/100 [00:08<00:00, 12.33it/s] PLMS Sampler: 99%|█████████▉| 99/100 [00:08<00:00, 12.36it/s] PLMS Sampler: 100%|██████████| 100/100 [00:08<00:00, 11.87it/s]
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