sullyo / pixi-2000-200class
- Public
- 174 runs
-
A100 (80GB)
Prediction
sullyo/pixi-2000-200class:446ceac8359aa48d48adefeca508d8f723b761621456018dfd3bd378de13d567Input
- seed
- 3033232192
- width
- 512
- height
- 512
- prompt
- sks dog with big mustache dressed as a antropormophhic sks dog veteran colonel of the first world war german army, a sks dog as a human, highly detailed painting by gaston bussiere, craig mullins, j. c. leyendecker
- num_outputs
- "8"
- guidance_scale
- "7.93"
- num_inference_steps
- 50
{ "seed": 3033232192, "width": 512, "height": 512, "prompt": "sks dog with big mustache dressed as a antropormophhic sks dog veteran colonel of the first world war german army, a sks dog as a human, highly detailed painting by gaston bussiere, craig mullins, j. c. leyendecker ", "num_outputs": "8", "guidance_scale": "7.93", "num_inference_steps": 50 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run sullyo/pixi-2000-200class using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "sullyo/pixi-2000-200class:446ceac8359aa48d48adefeca508d8f723b761621456018dfd3bd378de13d567", { input: { seed: 3033232192, width: 512, height: 512, prompt: "sks dog with big mustache dressed as a antropormophhic sks dog veteran colonel of the first world war german army, a sks dog as a human, highly detailed painting by gaston bussiere, craig mullins, j. c. leyendecker ", num_outputs: "8", guidance_scale: "7.93", num_inference_steps: 50 } } ); // 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 replicate
Import the client:import replicate
Run sullyo/pixi-2000-200class using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "sullyo/pixi-2000-200class:446ceac8359aa48d48adefeca508d8f723b761621456018dfd3bd378de13d567", input={ "seed": 3033232192, "width": 512, "height": 512, "prompt": "sks dog with big mustache dressed as a antropormophhic sks dog veteran colonel of the first world war german army, a sks dog as a human, highly detailed painting by gaston bussiere, craig mullins, j. c. leyendecker ", "num_outputs": "8", "guidance_scale": "7.93", "num_inference_steps": 50 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run sullyo/pixi-2000-200class 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": "sullyo/pixi-2000-200class:446ceac8359aa48d48adefeca508d8f723b761621456018dfd3bd378de13d567", "input": { "seed": 3033232192, "width": 512, "height": 512, "prompt": "sks dog with big mustache dressed as a antropormophhic sks dog veteran colonel of the first world war german army, a sks dog as a human, highly detailed painting by gaston bussiere, craig mullins, j. c. leyendecker ", "num_outputs": "8", "guidance_scale": "7.93", "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-11-16T03:03:42.667960Z", "created_at": "2022-11-16T03:03:22.623886Z", "data_removed": false, "error": null, "id": "araf7zumffeclfh4squbuqlivy", "input": { "seed": 3033232192, "width": 512, "height": 512, "prompt": "sks dog with big mustache dressed as a antropormophhic sks dog veteran colonel of the first world war german army, a sks dog as a human, highly detailed painting by gaston bussiere, craig mullins, j. c. leyendecker ", "num_outputs": "8", "guidance_scale": "7.93", "num_inference_steps": 50 }, "logs": "Loading pipeline...\nUsing seed: 3033232192\nGlobal seed set to 3033232192\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:17, 2.83it/s]\n 4%|▍ | 2/50 [00:00<00:16, 2.94it/s]\n 6%|▌ | 3/50 [00:01<00:15, 2.98it/s]\n 8%|▊ | 4/50 [00:01<00:15, 3.00it/s]\n 10%|█ | 5/50 [00:01<00:14, 3.01it/s]\n 12%|█▏ | 6/50 [00:02<00:14, 3.01it/s]\n 14%|█▍ | 7/50 [00:02<00:14, 3.02it/s]\n 16%|█▌ | 8/50 [00:02<00:13, 3.02it/s]\n 18%|█▊ | 9/50 [00:02<00:13, 3.02it/s]\n 20%|██ | 10/50 [00:03<00:13, 3.02it/s]\n 22%|██▏ | 11/50 [00:03<00:12, 3.02it/s]\n 24%|██▍ | 12/50 [00:03<00:12, 3.03it/s]\n 26%|██▌ | 13/50 [00:04<00:12, 3.03it/s]\n 28%|██▊ | 14/50 [00:04<00:11, 3.03it/s]\n 30%|███ | 15/50 [00:04<00:11, 3.03it/s]\n 32%|███▏ | 16/50 [00:05<00:11, 3.02it/s]\n 34%|███▍ | 17/50 [00:05<00:10, 3.02it/s]\n 36%|███▌ | 18/50 [00:05<00:10, 3.02it/s]\n 38%|███▊ | 19/50 [00:06<00:10, 3.03it/s]\n 40%|████ | 20/50 [00:06<00:09, 3.03it/s]\n 42%|████▏ | 21/50 [00:06<00:09, 3.03it/s]\n 44%|████▍ | 22/50 [00:07<00:09, 3.03it/s]\n 46%|████▌ | 23/50 [00:07<00:08, 3.03it/s]\n 48%|████▊ | 24/50 [00:07<00:08, 3.03it/s]\n 50%|█████ | 25/50 [00:08<00:08, 3.03it/s]\n 52%|█████▏ | 26/50 [00:08<00:07, 3.03it/s]\n 54%|█████▍ | 27/50 [00:08<00:07, 3.03it/s]\n 56%|█████▌ | 28/50 [00:09<00:07, 3.03it/s]\n 58%|█████▊ | 29/50 [00:09<00:06, 3.03it/s]\n 60%|██████ | 30/50 [00:09<00:06, 3.03it/s]\n 62%|██████▏ | 31/50 [00:10<00:06, 3.03it/s]\n 64%|██████▍ | 32/50 [00:10<00:05, 3.03it/s]\n 66%|██████▌ | 33/50 [00:10<00:05, 3.03it/s]\n 68%|██████▊ | 34/50 [00:11<00:05, 3.03it/s]\n 70%|███████ | 35/50 [00:11<00:04, 3.03it/s]\n 72%|███████▏ | 36/50 [00:11<00:04, 3.03it/s]\n 74%|███████▍ | 37/50 [00:12<00:04, 3.03it/s]\n 76%|███████▌ | 38/50 [00:12<00:03, 3.03it/s]\n 78%|███████▊ | 39/50 [00:12<00:03, 3.03it/s]\n 80%|████████ | 40/50 [00:13<00:03, 3.03it/s]\n 82%|████████▏ | 41/50 [00:13<00:02, 3.03it/s]\n 84%|████████▍ | 42/50 [00:13<00:02, 3.03it/s]\n 86%|████████▌ | 43/50 [00:14<00:02, 3.03it/s]\n 88%|████████▊ | 44/50 [00:14<00:01, 3.03it/s]\n 90%|█████████ | 45/50 [00:14<00:01, 3.03it/s]\n 92%|█████████▏| 46/50 [00:15<00:01, 3.03it/s]\n 94%|█████████▍| 47/50 [00:15<00:00, 3.03it/s]\n 96%|█████████▌| 48/50 [00:15<00:00, 3.03it/s]\n 98%|█████████▊| 49/50 [00:16<00:00, 3.03it/s]\n100%|██████████| 50/50 [00:16<00:00, 3.03it/s]\n100%|██████████| 50/50 [00:16<00:00, 3.02it/s]", "metrics": { "predict_time": 20.008216, "total_time": 20.044074 }, "output": [ "https://replicate.delivery/pbxt/yxfgxTTbrE1WPCL37eqfJIVAfcj5aAEnzGJhGLcYkeFihCEAC/out-0.png", "https://replicate.delivery/pbxt/fm8DOEH5qGzICibBBqaeQg7iLe8nYX5QffDPHllfiGwCDFIAE/out-1.png", "https://replicate.delivery/pbxt/9jkWze6J9hSWTSfI2xuJMgOvNyJfI4OzGCedA5xZdWG2QBCAB/out-2.png", "https://replicate.delivery/pbxt/LuHyijNUeC0IXqDQuBwG8uBVtjc0lnxV58oENRHrmwzGKQAIA/out-3.png", "https://replicate.delivery/pbxt/2KN8gphmWcJEH16zzi4D7qZUg9FsbD3SBJoCtp1grFdDFIAE/out-4.png", "https://replicate.delivery/pbxt/Pq0kui6wDcrLFpXWAB7ls3QpsqJ6mN6ttdKwDDPFvckDFIAE/out-5.png", "https://replicate.delivery/pbxt/ehgGZHPdBjVxJqKYTorJh3WGkBC86IVGR8Tsfxl3nItOUgAQA/out-6.png", "https://replicate.delivery/pbxt/9HxowCqdlhr4BZ2YngZ9bxbyAuT4fjibHHTl6kMdhTPHKQAIA/out-7.png" ], "started_at": "2022-11-16T03:03:22.659744Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/araf7zumffeclfh4squbuqlivy", "cancel": "https://api.replicate.com/v1/predictions/araf7zumffeclfh4squbuqlivy/cancel" }, "version": "446ceac8359aa48d48adefeca508d8f723b761621456018dfd3bd378de13d567" }
Generated inLoading pipeline... Using seed: 3033232192 Global seed set to 3033232192 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:17, 2.83it/s] 4%|▍ | 2/50 [00:00<00:16, 2.94it/s] 6%|▌ | 3/50 [00:01<00:15, 2.98it/s] 8%|▊ | 4/50 [00:01<00:15, 3.00it/s] 10%|█ | 5/50 [00:01<00:14, 3.01it/s] 12%|█▏ | 6/50 [00:02<00:14, 3.01it/s] 14%|█▍ | 7/50 [00:02<00:14, 3.02it/s] 16%|█▌ | 8/50 [00:02<00:13, 3.02it/s] 18%|█▊ | 9/50 [00:02<00:13, 3.02it/s] 20%|██ | 10/50 [00:03<00:13, 3.02it/s] 22%|██▏ | 11/50 [00:03<00:12, 3.02it/s] 24%|██▍ | 12/50 [00:03<00:12, 3.03it/s] 26%|██▌ | 13/50 [00:04<00:12, 3.03it/s] 28%|██▊ | 14/50 [00:04<00:11, 3.03it/s] 30%|███ | 15/50 [00:04<00:11, 3.03it/s] 32%|███▏ | 16/50 [00:05<00:11, 3.02it/s] 34%|███▍ | 17/50 [00:05<00:10, 3.02it/s] 36%|███▌ | 18/50 [00:05<00:10, 3.02it/s] 38%|███▊ | 19/50 [00:06<00:10, 3.03it/s] 40%|████ | 20/50 [00:06<00:09, 3.03it/s] 42%|████▏ | 21/50 [00:06<00:09, 3.03it/s] 44%|████▍ | 22/50 [00:07<00:09, 3.03it/s] 46%|████▌ | 23/50 [00:07<00:08, 3.03it/s] 48%|████▊ | 24/50 [00:07<00:08, 3.03it/s] 50%|█████ | 25/50 [00:08<00:08, 3.03it/s] 52%|█████▏ | 26/50 [00:08<00:07, 3.03it/s] 54%|█████▍ | 27/50 [00:08<00:07, 3.03it/s] 56%|█████▌ | 28/50 [00:09<00:07, 3.03it/s] 58%|█████▊ | 29/50 [00:09<00:06, 3.03it/s] 60%|██████ | 30/50 [00:09<00:06, 3.03it/s] 62%|██████▏ | 31/50 [00:10<00:06, 3.03it/s] 64%|██████▍ | 32/50 [00:10<00:05, 3.03it/s] 66%|██████▌ | 33/50 [00:10<00:05, 3.03it/s] 68%|██████▊ | 34/50 [00:11<00:05, 3.03it/s] 70%|███████ | 35/50 [00:11<00:04, 3.03it/s] 72%|███████▏ | 36/50 [00:11<00:04, 3.03it/s] 74%|███████▍ | 37/50 [00:12<00:04, 3.03it/s] 76%|███████▌ | 38/50 [00:12<00:03, 3.03it/s] 78%|███████▊ | 39/50 [00:12<00:03, 3.03it/s] 80%|████████ | 40/50 [00:13<00:03, 3.03it/s] 82%|████████▏ | 41/50 [00:13<00:02, 3.03it/s] 84%|████████▍ | 42/50 [00:13<00:02, 3.03it/s] 86%|████████▌ | 43/50 [00:14<00:02, 3.03it/s] 88%|████████▊ | 44/50 [00:14<00:01, 3.03it/s] 90%|█████████ | 45/50 [00:14<00:01, 3.03it/s] 92%|█████████▏| 46/50 [00:15<00:01, 3.03it/s] 94%|█████████▍| 47/50 [00:15<00:00, 3.03it/s] 96%|█████████▌| 48/50 [00:15<00:00, 3.03it/s] 98%|█████████▊| 49/50 [00:16<00:00, 3.03it/s] 100%|██████████| 50/50 [00:16<00:00, 3.03it/s] 100%|██████████| 50/50 [00:16<00:00, 3.02it/s]
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