hunkim98 / kandinsky-2-1
- Public
- 76 runs
-
A100 (80GB)
Prediction
hunkim98/kandinsky-2-1:9b2c5164e6bf7cb168f3d3228740fcc00b6a766e5e9f71fab35178ff8d786b8fIDsawuhnzbjbcfr2uspvh4pvppe4StatusSucceededSourceWebHardwareA100 (40GB)Total durationCreatedInput
- task
- text2img
- width
- 256
- height
- 256
- prompt
- A alien cheeseburger creature eating itself, claymation, cinematic, moody lighting
- strength
- 0.3
- num_outputs
- 1
- guidance_scale
- 4
- negative_prompt
- low quality, bad quality
- num_steps_prior
- 25
- num_inference_steps
- 100
{ "task": "text2img", "width": 256, "height": 256, "prompt": "A alien cheeseburger creature eating itself, claymation, cinematic, moody lighting", "strength": 0.3, "num_outputs": 1, "guidance_scale": 4, "negative_prompt": "low quality, bad quality", "num_steps_prior": 25, "num_inference_steps": 100 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run hunkim98/kandinsky-2-1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "hunkim98/kandinsky-2-1:9b2c5164e6bf7cb168f3d3228740fcc00b6a766e5e9f71fab35178ff8d786b8f", { input: { task: "text2img", width: 256, height: 256, prompt: "A alien cheeseburger creature eating itself, claymation, cinematic, moody lighting", strength: 0.3, num_outputs: 1, guidance_scale: 4, negative_prompt: "low quality, bad quality", num_steps_prior: 25, num_inference_steps: 100 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun hunkim98/kandinsky-2-1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "hunkim98/kandinsky-2-1:9b2c5164e6bf7cb168f3d3228740fcc00b6a766e5e9f71fab35178ff8d786b8f", input={ "task": "text2img", "width": 256, "height": 256, "prompt": "A alien cheeseburger creature eating itself, claymation, cinematic, moody lighting", "strength": 0.3, "num_outputs": 1, "guidance_scale": 4, "negative_prompt": "low quality, bad quality", "num_steps_prior": 25, "num_inference_steps": 100 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run hunkim98/kandinsky-2-1 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": "hunkim98/kandinsky-2-1:9b2c5164e6bf7cb168f3d3228740fcc00b6a766e5e9f71fab35178ff8d786b8f", "input": { "task": "text2img", "width": 256, "height": 256, "prompt": "A alien cheeseburger creature eating itself, claymation, cinematic, moody lighting", "strength": 0.3, "num_outputs": 1, "guidance_scale": 4, "negative_prompt": "low quality, bad quality", "num_steps_prior": 25, "num_inference_steps": 100 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-10-04T15:54:21.484276Z", "created_at": "2023-10-04T15:49:41.442246Z", "data_removed": false, "error": null, "id": "sawuhnzbjbcfr2uspvh4pvppe4", "input": { "task": "text2img", "width": 256, "height": 256, "prompt": "A alien cheeseburger creature eating itself, claymation, cinematic, moody lighting", "strength": 0.3, "num_outputs": 1, "guidance_scale": 4, "negative_prompt": "low quality, bad quality", "num_steps_prior": 25, "num_inference_steps": 100 }, "logs": "Using seed: 65300\n 0%| | 0/25 [00:00<?, ?it/s]\n 20%|██ | 5/25 [00:00<00:00, 41.78it/s]\n 40%|████ | 10/25 [00:00<00:00, 44.61it/s]\n 60%|██████ | 15/25 [00:00<00:00, 45.93it/s]\n 80%|████████ | 20/25 [00:00<00:00, 46.87it/s]\n100%|██████████| 25/25 [00:00<00:00, 47.88it/s]\n100%|██████████| 25/25 [00:00<00:00, 46.68it/s]\n 0%| | 0/100 [00:00<?, ?it/s]\n 1%| | 1/100 [00:00<01:13, 1.34it/s]\n 3%|▎ | 3/100 [00:00<00:22, 4.30it/s]\n 5%|▌ | 5/100 [00:00<00:13, 7.15it/s]\n 7%|▋ | 7/100 [00:01<00:09, 9.73it/s]\n 9%|▉ | 9/100 [00:01<00:07, 11.75it/s]\n 11%|█ | 11/100 [00:01<00:06, 13.51it/s]\n 13%|█▎ | 13/100 [00:01<00:05, 14.90it/s]\n 15%|█▌ | 15/100 [00:01<00:05, 15.94it/s]\n 17%|█▋ | 17/100 [00:01<00:04, 16.80it/s]\n 19%|█▉ | 19/100 [00:01<00:04, 17.47it/s]\n 21%|██ | 21/100 [00:01<00:04, 17.94it/s]\n 23%|██▎ | 23/100 [00:01<00:04, 18.30it/s]\n 25%|██▌ | 25/100 [00:02<00:04, 18.41it/s]\n 27%|██▋ | 27/100 [00:02<00:03, 18.65it/s]\n 29%|██▉ | 29/100 [00:02<00:03, 18.12it/s]\n 31%|███ | 31/100 [00:02<00:03, 18.44it/s]\n 33%|███▎ | 33/100 [00:02<00:03, 18.66it/s]\n 35%|███▌ | 35/100 [00:02<00:03, 18.84it/s]\n 37%|███▋ | 37/100 [00:02<00:03, 19.01it/s]\n 39%|███▉ | 39/100 [00:02<00:03, 18.93it/s]\n 41%|████ | 41/100 [00:02<00:03, 18.74it/s]\n 43%|████▎ | 43/100 [00:02<00:03, 18.74it/s]\n 45%|████▌ | 45/100 [00:03<00:02, 18.87it/s]\n 47%|████▋ | 47/100 [00:03<00:02, 18.43it/s]\n 49%|████▉ | 49/100 [00:03<00:02, 18.52it/s]\n 51%|█████ | 51/100 [00:03<00:02, 18.67it/s]\n 53%|█████▎ | 53/100 [00:03<00:02, 18.70it/s]\n 55%|█████▌ | 55/100 [00:03<00:02, 18.68it/s]\n 57%|█████▋ | 57/100 [00:03<00:02, 18.76it/s]\n 59%|█████▉ | 59/100 [00:03<00:02, 18.87it/s]\n 61%|██████ | 61/100 [00:03<00:02, 18.92it/s]\n 63%|██████▎ | 63/100 [00:04<00:01, 19.01it/s]\n 65%|██████▌ | 65/100 [00:04<00:01, 18.93it/s]\n 67%|██████▋ | 67/100 [00:04<00:01, 18.24it/s]\n 69%|██████▉ | 69/100 [00:04<00:01, 18.47it/s]\n 71%|███████ | 71/100 [00:04<00:01, 18.67it/s]\n 73%|███████▎ | 73/100 [00:04<00:01, 18.87it/s]\n 75%|███████▌ | 75/100 [00:04<00:01, 18.92it/s]\n 77%|███████▋ | 77/100 [00:04<00:01, 18.95it/s]\n 79%|███████▉ | 79/100 [00:04<00:01, 19.05it/s]\n 81%|████████ | 81/100 [00:04<00:00, 19.04it/s]\n 83%|████████▎ | 83/100 [00:05<00:00, 19.18it/s]\n 85%|████████▌ | 85/100 [00:05<00:00, 18.82it/s]\n 87%|████████▋ | 87/100 [00:05<00:00, 18.96it/s]\n 89%|████████▉ | 89/100 [00:05<00:00, 18.89it/s]\n 91%|█████████ | 91/100 [00:05<00:00, 18.48it/s]\n 93%|█████████▎| 93/100 [00:05<00:00, 18.74it/s]\n 95%|█████████▌| 95/100 [00:05<00:00, 18.77it/s]\n 97%|█████████▋| 97/100 [00:05<00:00, 18.98it/s]\n 99%|█████████▉| 99/100 [00:05<00:00, 19.15it/s]\n100%|██████████| 100/100 [00:06<00:00, 16.66it/s]", "metrics": { "predict_time": 7.731231, "total_time": 280.04203 }, "output": [ "https://replicate.delivery/pbxt/SiPgjTAat9qbHJQIVqcQpJcsYk3WIwSZzrMVyZXukEYr8saE/out-0.png" ], "started_at": "2023-10-04T15:54:13.753045Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/sawuhnzbjbcfr2uspvh4pvppe4", "cancel": "https://api.replicate.com/v1/predictions/sawuhnzbjbcfr2uspvh4pvppe4/cancel" }, "version": "9b2c5164e6bf7cb168f3d3228740fcc00b6a766e5e9f71fab35178ff8d786b8f" }Generated inUsing seed: 65300 0%| | 0/25 [00:00<?, ?it/s] 20%|██ | 5/25 [00:00<00:00, 41.78it/s] 40%|████ | 10/25 [00:00<00:00, 44.61it/s] 60%|██████ | 15/25 [00:00<00:00, 45.93it/s] 80%|████████ | 20/25 [00:00<00:00, 46.87it/s] 100%|██████████| 25/25 [00:00<00:00, 47.88it/s] 100%|██████████| 25/25 [00:00<00:00, 46.68it/s] 0%| | 0/100 [00:00<?, ?it/s] 1%| | 1/100 [00:00<01:13, 1.34it/s] 3%|▎ | 3/100 [00:00<00:22, 4.30it/s] 5%|▌ | 5/100 [00:00<00:13, 7.15it/s] 7%|▋ | 7/100 [00:01<00:09, 9.73it/s] 9%|▉ | 9/100 [00:01<00:07, 11.75it/s] 11%|█ | 11/100 [00:01<00:06, 13.51it/s] 13%|█▎ | 13/100 [00:01<00:05, 14.90it/s] 15%|█▌ | 15/100 [00:01<00:05, 15.94it/s] 17%|█▋ | 17/100 [00:01<00:04, 16.80it/s] 19%|█▉ | 19/100 [00:01<00:04, 17.47it/s] 21%|██ | 21/100 [00:01<00:04, 17.94it/s] 23%|██▎ | 23/100 [00:01<00:04, 18.30it/s] 25%|██▌ | 25/100 [00:02<00:04, 18.41it/s] 27%|██▋ | 27/100 [00:02<00:03, 18.65it/s] 29%|██▉ | 29/100 [00:02<00:03, 18.12it/s] 31%|███ | 31/100 [00:02<00:03, 18.44it/s] 33%|███▎ | 33/100 [00:02<00:03, 18.66it/s] 35%|███▌ | 35/100 [00:02<00:03, 18.84it/s] 37%|███▋ | 37/100 [00:02<00:03, 19.01it/s] 39%|███▉ | 39/100 [00:02<00:03, 18.93it/s] 41%|████ | 41/100 [00:02<00:03, 18.74it/s] 43%|████▎ | 43/100 [00:02<00:03, 18.74it/s] 45%|████▌ | 45/100 [00:03<00:02, 18.87it/s] 47%|████▋ | 47/100 [00:03<00:02, 18.43it/s] 49%|████▉ | 49/100 [00:03<00:02, 18.52it/s] 51%|█████ | 51/100 [00:03<00:02, 18.67it/s] 53%|█████▎ | 53/100 [00:03<00:02, 18.70it/s] 55%|█████▌ | 55/100 [00:03<00:02, 18.68it/s] 57%|█████▋ | 57/100 [00:03<00:02, 18.76it/s] 59%|█████▉ | 59/100 [00:03<00:02, 18.87it/s] 61%|██████ | 61/100 [00:03<00:02, 18.92it/s] 63%|██████▎ | 63/100 [00:04<00:01, 19.01it/s] 65%|██████▌ | 65/100 [00:04<00:01, 18.93it/s] 67%|██████▋ | 67/100 [00:04<00:01, 18.24it/s] 69%|██████▉ | 69/100 [00:04<00:01, 18.47it/s] 71%|███████ | 71/100 [00:04<00:01, 18.67it/s] 73%|███████▎ | 73/100 [00:04<00:01, 18.87it/s] 75%|███████▌ | 75/100 [00:04<00:01, 18.92it/s] 77%|███████▋ | 77/100 [00:04<00:01, 18.95it/s] 79%|███████▉ | 79/100 [00:04<00:01, 19.05it/s] 81%|████████ | 81/100 [00:04<00:00, 19.04it/s] 83%|████████▎ | 83/100 [00:05<00:00, 19.18it/s] 85%|████████▌ | 85/100 [00:05<00:00, 18.82it/s] 87%|████████▋ | 87/100 [00:05<00:00, 18.96it/s] 89%|████████▉ | 89/100 [00:05<00:00, 18.89it/s] 91%|█████████ | 91/100 [00:05<00:00, 18.48it/s] 93%|█████████▎| 93/100 [00:05<00:00, 18.74it/s] 95%|█████████▌| 95/100 [00:05<00:00, 18.77it/s] 97%|█████████▋| 97/100 [00:05<00:00, 18.98it/s] 99%|█████████▉| 99/100 [00:05<00:00, 19.15it/s] 100%|██████████| 100/100 [00:06<00:00, 16.66it/s]
Want to make some of these yourself?
Run this model