zeke / this-is-fine
Create your own variants of "this is fine" 🔥☕️🐕
- Public
- 27.6K runs
-
L40S
Prediction
zeke/this-is-fine:11edb7172944ea9372d17aca78e8f016946bebab73d66765e20a9315609ff750IDvetqx4dbi3lklutdnqhm3g7fqmStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- an american truckstop diner in the style of THIS_IS_FINE
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "an american truckstop diner in the style of THIS_IS_FINE", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run zeke/this-is-fine using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "zeke/this-is-fine:11edb7172944ea9372d17aca78e8f016946bebab73d66765e20a9315609ff750", { input: { width: 1024, height: 1024, prompt: "an american truckstop diner in the style of THIS_IS_FINE", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, prompt_strength: 0.8, 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 zeke/this-is-fine using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "zeke/this-is-fine:11edb7172944ea9372d17aca78e8f016946bebab73d66765e20a9315609ff750", input={ "width": 1024, "height": 1024, "prompt": "an american truckstop diner in the style of THIS_IS_FINE", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run zeke/this-is-fine 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": "zeke/this-is-fine:11edb7172944ea9372d17aca78e8f016946bebab73d66765e20a9315609ff750", "input": { "width": 1024, "height": 1024, "prompt": "an american truckstop diner in the style of THIS_IS_FINE", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "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": "2023-08-17T18:38:01.452606Z", "created_at": "2023-08-17T18:35:07.796594Z", "data_removed": false, "error": null, "id": "vetqx4dbi3lklutdnqhm3g7fqm", "input": { "width": 1024, "height": 1024, "prompt": "an american truckstop diner in the style of THIS_IS_FINE", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 43008\nPrompt: an american truckstop diner in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:38, 1.29it/s]\n 4%|▍ | 2/50 [00:01<00:22, 2.09it/s]\n 6%|▌ | 3/50 [00:01<00:17, 2.61it/s]\n 8%|▊ | 4/50 [00:01<00:15, 2.96it/s]\n 10%|█ | 5/50 [00:01<00:14, 3.20it/s]\n 12%|█▏ | 6/50 [00:02<00:13, 3.36it/s]\n 14%|█▍ | 7/50 [00:02<00:12, 3.47it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.54it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.60it/s]\n 20%|██ | 10/50 [00:03<00:11, 3.64it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.66it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.68it/s]\n 26%|██▌ | 13/50 [00:04<00:10, 3.69it/s]\n 28%|██▊ | 14/50 [00:04<00:09, 3.70it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.70it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.71it/s]\n 34%|███▍ | 17/50 [00:05<00:08, 3.71it/s]\n 36%|███▌ | 18/50 [00:05<00:08, 3.71it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.71it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.71it/s]\n 42%|████▏ | 21/50 [00:06<00:07, 3.71it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.71it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.71it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.71it/s]\n 50%|█████ | 25/50 [00:07<00:06, 3.71it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.71it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.71it/s]\n 56%|█████▌ | 28/50 [00:08<00:05, 3.71it/s]\n 58%|█████▊ | 29/50 [00:08<00:05, 3.71it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.71it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.71it/s]\n 64%|██████▍ | 32/50 [00:09<00:04, 3.71it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.70it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.70it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.70it/s]\n 72%|███████▏ | 36/50 [00:10<00:03, 3.70it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.70it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.70it/s]\n 78%|███████▊ | 39/50 [00:11<00:02, 3.70it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.70it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.70it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.70it/s]\n 86%|████████▌ | 43/50 [00:12<00:01, 3.70it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.70it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.70it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.70it/s]\n 94%|█████████▍| 47/50 [00:13<00:00, 3.70it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.70it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.70it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.70it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.57it/s]", "metrics": { "predict_time": 16.571181, "total_time": 173.656012 }, "output": [ "https://replicate.delivery/pbxt/8mDaSZRSxi4zE9DekmZ3g70NnSIUxu3KyxTVrUwJOnVE2gtIA/out-0.png" ], "started_at": "2023-08-17T18:37:44.881425Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/vetqx4dbi3lklutdnqhm3g7fqm", "cancel": "https://api.replicate.com/v1/predictions/vetqx4dbi3lklutdnqhm3g7fqm/cancel" }, "version": "11edb7172944ea9372d17aca78e8f016946bebab73d66765e20a9315609ff750" }
Generated inUsing seed: 43008 Prompt: an american truckstop diner in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:38, 1.29it/s] 4%|▍ | 2/50 [00:01<00:22, 2.09it/s] 6%|▌ | 3/50 [00:01<00:17, 2.61it/s] 8%|▊ | 4/50 [00:01<00:15, 2.96it/s] 10%|█ | 5/50 [00:01<00:14, 3.20it/s] 12%|█▏ | 6/50 [00:02<00:13, 3.36it/s] 14%|█▍ | 7/50 [00:02<00:12, 3.47it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.54it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.60it/s] 20%|██ | 10/50 [00:03<00:11, 3.64it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.66it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.68it/s] 26%|██▌ | 13/50 [00:04<00:10, 3.69it/s] 28%|██▊ | 14/50 [00:04<00:09, 3.70it/s] 30%|███ | 15/50 [00:04<00:09, 3.70it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.71it/s] 34%|███▍ | 17/50 [00:05<00:08, 3.71it/s] 36%|███▌ | 18/50 [00:05<00:08, 3.71it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.71it/s] 40%|████ | 20/50 [00:05<00:08, 3.71it/s] 42%|████▏ | 21/50 [00:06<00:07, 3.71it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.71it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.71it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.71it/s] 50%|█████ | 25/50 [00:07<00:06, 3.71it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.71it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.71it/s] 56%|█████▌ | 28/50 [00:08<00:05, 3.71it/s] 58%|█████▊ | 29/50 [00:08<00:05, 3.71it/s] 60%|██████ | 30/50 [00:08<00:05, 3.71it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.71it/s] 64%|██████▍ | 32/50 [00:09<00:04, 3.71it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.70it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.70it/s] 70%|███████ | 35/50 [00:09<00:04, 3.70it/s] 72%|███████▏ | 36/50 [00:10<00:03, 3.70it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.70it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.70it/s] 78%|███████▊ | 39/50 [00:11<00:02, 3.70it/s] 80%|████████ | 40/50 [00:11<00:02, 3.70it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.70it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.70it/s] 86%|████████▌ | 43/50 [00:12<00:01, 3.70it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.70it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.70it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.70it/s] 94%|█████████▍| 47/50 [00:13<00:00, 3.70it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.70it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.70it/s] 100%|██████████| 50/50 [00:13<00:00, 3.70it/s] 100%|██████████| 50/50 [00:13<00:00, 3.57it/s]
Prediction
zeke/this-is-fine:11edb7172944ea9372d17aca78e8f016946bebab73d66765e20a9315609ff750IDlqbar3tbyvleh62l4jqspihrriStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- a zoom call with many people in the style of THIS_IS_FINE with fire all around
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "a zoom call with many people in the style of THIS_IS_FINE with fire all around", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run zeke/this-is-fine using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "zeke/this-is-fine:11edb7172944ea9372d17aca78e8f016946bebab73d66765e20a9315609ff750", { input: { width: 1024, height: 1024, prompt: "a zoom call with many people in the style of THIS_IS_FINE with fire all around", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, prompt_strength: 0.8, 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 zeke/this-is-fine using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "zeke/this-is-fine:11edb7172944ea9372d17aca78e8f016946bebab73d66765e20a9315609ff750", input={ "width": 1024, "height": 1024, "prompt": "a zoom call with many people in the style of THIS_IS_FINE with fire all around", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run zeke/this-is-fine 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": "zeke/this-is-fine:11edb7172944ea9372d17aca78e8f016946bebab73d66765e20a9315609ff750", "input": { "width": 1024, "height": 1024, "prompt": "a zoom call with many people in the style of THIS_IS_FINE with fire all around", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "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": "2023-08-17T18:42:18.638116Z", "created_at": "2023-08-17T18:42:03.148403Z", "data_removed": false, "error": null, "id": "lqbar3tbyvleh62l4jqspihrri", "input": { "width": 1024, "height": 1024, "prompt": "a zoom call with many people in the style of THIS_IS_FINE with fire all around", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 33835\nPrompt: a zoom call with many people in the style of <s0><s1> with fire all around\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.72it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.72it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.72it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.72it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.72it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.72it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.72it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.72it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.71it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.72it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.71it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.71it/s]\n 26%|██▌ | 13/50 [00:03<00:09, 3.71it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.71it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.71it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.71it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.71it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.70it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.70it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.70it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.70it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.70it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.70it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.70it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.70it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.70it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.70it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.70it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.70it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.70it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.70it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.70it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.70it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.70it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.70it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.70it/s]\n 74%|███████▍ | 37/50 [00:09<00:03, 3.70it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.70it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.70it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.70it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.69it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.69it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.69it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.69it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.69it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.69it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.69it/s]\n 96%|█████████▌| 48/50 [00:12<00:00, 3.69it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.69it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.69it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.70it/s]", "metrics": { "predict_time": 15.485946, "total_time": 15.489713 }, "output": [ "https://replicate.delivery/pbxt/2YSzcZs2Vv5JO1AzgYXzi09gQOW9aSB87KdWl91WPVYCcwWE/out-0.png" ], "started_at": "2023-08-17T18:42:03.152170Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/lqbar3tbyvleh62l4jqspihrri", "cancel": "https://api.replicate.com/v1/predictions/lqbar3tbyvleh62l4jqspihrri/cancel" }, "version": "11edb7172944ea9372d17aca78e8f016946bebab73d66765e20a9315609ff750" }
Generated inUsing seed: 33835 Prompt: a zoom call with many people in the style of <s0><s1> with fire all around txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.72it/s] 4%|▍ | 2/50 [00:00<00:12, 3.72it/s] 6%|▌ | 3/50 [00:00<00:12, 3.72it/s] 8%|▊ | 4/50 [00:01<00:12, 3.72it/s] 10%|█ | 5/50 [00:01<00:12, 3.72it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.72it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.72it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.72it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.71it/s] 20%|██ | 10/50 [00:02<00:10, 3.72it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.71it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.71it/s] 26%|██▌ | 13/50 [00:03<00:09, 3.71it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.71it/s] 30%|███ | 15/50 [00:04<00:09, 3.71it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.71it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.71it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.70it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.70it/s] 40%|████ | 20/50 [00:05<00:08, 3.70it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.70it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.70it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.70it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.70it/s] 50%|█████ | 25/50 [00:06<00:06, 3.70it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.70it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.70it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.70it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.70it/s] 60%|██████ | 30/50 [00:08<00:05, 3.70it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.70it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.70it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.70it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.70it/s] 70%|███████ | 35/50 [00:09<00:04, 3.70it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.70it/s] 74%|███████▍ | 37/50 [00:09<00:03, 3.70it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.70it/s] 78%|███████▊ | 39/50 [00:10<00:02, 3.70it/s] 80%|████████ | 40/50 [00:10<00:02, 3.70it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.69it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.69it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.69it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.69it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.69it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.69it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.69it/s] 96%|█████████▌| 48/50 [00:12<00:00, 3.69it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.69it/s] 100%|██████████| 50/50 [00:13<00:00, 3.69it/s] 100%|██████████| 50/50 [00:13<00:00, 3.70it/s]
Prediction
zeke/this-is-fine:11edb7172944ea9372d17aca78e8f016946bebab73d66765e20a9315609ff750IDmgs2cgdbnynuw2eqyixc56uztyStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- a zoom call with many people in the style of THIS_IS_FINE with fire all around
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "a zoom call with many people in the style of THIS_IS_FINE with fire all around", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run zeke/this-is-fine using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "zeke/this-is-fine:11edb7172944ea9372d17aca78e8f016946bebab73d66765e20a9315609ff750", { input: { width: 1024, height: 1024, prompt: "a zoom call with many people in the style of THIS_IS_FINE with fire all around", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, prompt_strength: 0.8, 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 zeke/this-is-fine using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "zeke/this-is-fine:11edb7172944ea9372d17aca78e8f016946bebab73d66765e20a9315609ff750", input={ "width": 1024, "height": 1024, "prompt": "a zoom call with many people in the style of THIS_IS_FINE with fire all around", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run zeke/this-is-fine 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": "zeke/this-is-fine:11edb7172944ea9372d17aca78e8f016946bebab73d66765e20a9315609ff750", "input": { "width": 1024, "height": 1024, "prompt": "a zoom call with many people in the style of THIS_IS_FINE with fire all around", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "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": "2023-08-17T18:45:55.476567Z", "created_at": "2023-08-17T18:45:40.041215Z", "data_removed": false, "error": null, "id": "mgs2cgdbnynuw2eqyixc56uzty", "input": { "width": 1024, "height": 1024, "prompt": "a zoom call with many people in the style of THIS_IS_FINE with fire all around", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 62742\nPrompt: a zoom call with many people in the style of <s0><s1> with fire all around\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.72it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.71it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.72it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.72it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.72it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.72it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.72it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.72it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.71it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.71it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.71it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.71it/s]\n 26%|██▌ | 13/50 [00:03<00:09, 3.71it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.71it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.71it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.71it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.71it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.71it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.71it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.71it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.70it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.70it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.70it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.70it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.70it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.70it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.69it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.70it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.70it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.70it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.70it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.70it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.70it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.69it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.69it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.69it/s]\n 74%|███████▍ | 37/50 [00:09<00:03, 3.69it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.69it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.69it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.70it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.69it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.69it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.70it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.70it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.69it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.69it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.69it/s]\n 96%|█████████▌| 48/50 [00:12<00:00, 3.69it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.69it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.69it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.70it/s]", "metrics": { "predict_time": 15.471006, "total_time": 15.435352 }, "output": [ "https://replicate.delivery/pbxt/faS9G2SSA327ByBwg05vnKfbCV5OevcF0iKZpot2h7zEnD2iA/out-0.png" ], "started_at": "2023-08-17T18:45:40.005561Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/mgs2cgdbnynuw2eqyixc56uzty", "cancel": "https://api.replicate.com/v1/predictions/mgs2cgdbnynuw2eqyixc56uzty/cancel" }, "version": "11edb7172944ea9372d17aca78e8f016946bebab73d66765e20a9315609ff750" }
Generated inUsing seed: 62742 Prompt: a zoom call with many people in the style of <s0><s1> with fire all around txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.72it/s] 4%|▍ | 2/50 [00:00<00:12, 3.71it/s] 6%|▌ | 3/50 [00:00<00:12, 3.72it/s] 8%|▊ | 4/50 [00:01<00:12, 3.72it/s] 10%|█ | 5/50 [00:01<00:12, 3.72it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.72it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.72it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.72it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.71it/s] 20%|██ | 10/50 [00:02<00:10, 3.71it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.71it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.71it/s] 26%|██▌ | 13/50 [00:03<00:09, 3.71it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.71it/s] 30%|███ | 15/50 [00:04<00:09, 3.71it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.71it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.71it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.71it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.71it/s] 40%|████ | 20/50 [00:05<00:08, 3.71it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.70it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.70it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.70it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.70it/s] 50%|█████ | 25/50 [00:06<00:06, 3.70it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.70it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.69it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.70it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.70it/s] 60%|██████ | 30/50 [00:08<00:05, 3.70it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.70it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.70it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.70it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.69it/s] 70%|███████ | 35/50 [00:09<00:04, 3.69it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.69it/s] 74%|███████▍ | 37/50 [00:09<00:03, 3.69it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.69it/s] 78%|███████▊ | 39/50 [00:10<00:02, 3.69it/s] 80%|████████ | 40/50 [00:10<00:02, 3.70it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.69it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.69it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.70it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.70it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.69it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.69it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.69it/s] 96%|█████████▌| 48/50 [00:12<00:00, 3.69it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.69it/s] 100%|██████████| 50/50 [00:13<00:00, 3.69it/s] 100%|██████████| 50/50 [00:13<00:00, 3.70it/s]
Prediction
zeke/this-is-fine:11edb7172944ea9372d17aca78e8f016946bebab73d66765e20a9315609ff750IDnoqyrptbztm7cpvjggc2qdazsiStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- a cat sitting at a table in the style of THIS_IS_FINE with fire all around
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "a cat sitting at a table in the style of THIS_IS_FINE with fire all around", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run zeke/this-is-fine using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "zeke/this-is-fine:11edb7172944ea9372d17aca78e8f016946bebab73d66765e20a9315609ff750", { input: { width: 1024, height: 1024, prompt: "a cat sitting at a table in the style of THIS_IS_FINE with fire all around", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, prompt_strength: 0.8, 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 zeke/this-is-fine using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "zeke/this-is-fine:11edb7172944ea9372d17aca78e8f016946bebab73d66765e20a9315609ff750", input={ "width": 1024, "height": 1024, "prompt": "a cat sitting at a table in the style of THIS_IS_FINE with fire all around", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run zeke/this-is-fine 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": "zeke/this-is-fine:11edb7172944ea9372d17aca78e8f016946bebab73d66765e20a9315609ff750", "input": { "width": 1024, "height": 1024, "prompt": "a cat sitting at a table in the style of THIS_IS_FINE with fire all around", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "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": "2023-08-17T18:49:52.179150Z", "created_at": "2023-08-17T18:49:36.654178Z", "data_removed": false, "error": null, "id": "noqyrptbztm7cpvjggc2qdazsi", "input": { "width": 1024, "height": 1024, "prompt": "a cat sitting at a table in the style of THIS_IS_FINE with fire all around", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 13090\nPrompt: a cat sitting at a table in the style of <s0><s1> with fire all around\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.73it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.72it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.72it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.71it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.71it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.71it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.72it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.72it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.72it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.72it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.72it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.73it/s]\n 26%|██▌ | 13/50 [00:03<00:09, 3.72it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.72it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.72it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.72it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.72it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.71it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.72it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.72it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.71it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.71it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.71it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.71it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.71it/s]\n 52%|█████▏ | 26/50 [00:06<00:06, 3.71it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.71it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.71it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.71it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.71it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.71it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.71it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.71it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.71it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.71it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.70it/s]\n 74%|███████▍ | 37/50 [00:09<00:03, 3.70it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.70it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.71it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.70it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.70it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.70it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.70it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.70it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.70it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.70it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.70it/s]\n 96%|█████████▌| 48/50 [00:12<00:00, 3.69it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.69it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.70it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.71it/s]", "metrics": { "predict_time": 15.518085, "total_time": 15.524972 }, "output": [ "https://replicate.delivery/pbxt/UihRKyivYoblPpPKaUHfaZX8QeqAV4f3olJffQjqQQ4fzdwWE/out-0.png" ], "started_at": "2023-08-17T18:49:36.661065Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/noqyrptbztm7cpvjggc2qdazsi", "cancel": "https://api.replicate.com/v1/predictions/noqyrptbztm7cpvjggc2qdazsi/cancel" }, "version": "11edb7172944ea9372d17aca78e8f016946bebab73d66765e20a9315609ff750" }
Generated inUsing seed: 13090 Prompt: a cat sitting at a table in the style of <s0><s1> with fire all around txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.73it/s] 4%|▍ | 2/50 [00:00<00:12, 3.72it/s] 6%|▌ | 3/50 [00:00<00:12, 3.72it/s] 8%|▊ | 4/50 [00:01<00:12, 3.71it/s] 10%|█ | 5/50 [00:01<00:12, 3.71it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.71it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.72it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.72it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.72it/s] 20%|██ | 10/50 [00:02<00:10, 3.72it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.72it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.73it/s] 26%|██▌ | 13/50 [00:03<00:09, 3.72it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.72it/s] 30%|███ | 15/50 [00:04<00:09, 3.72it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.72it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.72it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.71it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.72it/s] 40%|████ | 20/50 [00:05<00:08, 3.72it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.71it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.71it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.71it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.71it/s] 50%|█████ | 25/50 [00:06<00:06, 3.71it/s] 52%|█████▏ | 26/50 [00:06<00:06, 3.71it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.71it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.71it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.71it/s] 60%|██████ | 30/50 [00:08<00:05, 3.71it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.71it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.71it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.71it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.71it/s] 70%|███████ | 35/50 [00:09<00:04, 3.71it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.70it/s] 74%|███████▍ | 37/50 [00:09<00:03, 3.70it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.70it/s] 78%|███████▊ | 39/50 [00:10<00:02, 3.71it/s] 80%|████████ | 40/50 [00:10<00:02, 3.70it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.70it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.70it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.70it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.70it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.70it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.70it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.70it/s] 96%|█████████▌| 48/50 [00:12<00:00, 3.69it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.69it/s] 100%|██████████| 50/50 [00:13<00:00, 3.70it/s] 100%|██████████| 50/50 [00:13<00:00, 3.71it/s]
Prediction
zeke/this-is-fine:11edb7172944ea9372d17aca78e8f016946bebab73d66765e20a9315609ff750ID6pr62udbpsmlo5p77hlasaeu6eStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedby @zekeInput
- width
- 1024
- height
- 1024
- prompt
- a llama sitting at a table in the style of THIS_IS_FINE with fire all around
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "a llama sitting at a table in the style of THIS_IS_FINE with fire all around", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run zeke/this-is-fine using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "zeke/this-is-fine:11edb7172944ea9372d17aca78e8f016946bebab73d66765e20a9315609ff750", { input: { width: 1024, height: 1024, prompt: "a llama sitting at a table in the style of THIS_IS_FINE with fire all around", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, prompt_strength: 0.8, 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 zeke/this-is-fine using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "zeke/this-is-fine:11edb7172944ea9372d17aca78e8f016946bebab73d66765e20a9315609ff750", input={ "width": 1024, "height": 1024, "prompt": "a llama sitting at a table in the style of THIS_IS_FINE with fire all around", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run zeke/this-is-fine 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": "zeke/this-is-fine:11edb7172944ea9372d17aca78e8f016946bebab73d66765e20a9315609ff750", "input": { "width": 1024, "height": 1024, "prompt": "a llama sitting at a table in the style of THIS_IS_FINE with fire all around", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "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": "2023-08-17T18:50:23.377411Z", "created_at": "2023-08-17T18:50:07.818823Z", "data_removed": false, "error": null, "id": "6pr62udbpsmlo5p77hlasaeu6e", "input": { "width": 1024, "height": 1024, "prompt": "a llama sitting at a table in the style of THIS_IS_FINE with fire all around", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 54488\nPrompt: a llama sitting at a table in the style of <s0><s1> with fire all around\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.72it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.72it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.72it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.72it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.72it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.71it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.67it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.68it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.69it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.70it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.70it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.70it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.70it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.70it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.70it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.70it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.70it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.70it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.70it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.70it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.70it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.70it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.69it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.70it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.69it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.69it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.69it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.69it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.69it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.70it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.69it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.70it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.70it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.69it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.69it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.69it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.69it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.69it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.69it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.69it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.69it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.69it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.69it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.69it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.69it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.69it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.69it/s]\n 96%|█████████▌| 48/50 [00:12<00:00, 3.69it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.69it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.69it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.69it/s]", "metrics": { "predict_time": 15.573238, "total_time": 15.558588 }, "output": [ "https://replicate.delivery/pbxt/jPVBJemj8O2VOKYv9qZeeJLN0JO4TyQFDEnWiT3cPpUdvD2iA/out-0.png" ], "started_at": "2023-08-17T18:50:07.804173Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/6pr62udbpsmlo5p77hlasaeu6e", "cancel": "https://api.replicate.com/v1/predictions/6pr62udbpsmlo5p77hlasaeu6e/cancel" }, "version": "11edb7172944ea9372d17aca78e8f016946bebab73d66765e20a9315609ff750" }
Generated inUsing seed: 54488 Prompt: a llama sitting at a table in the style of <s0><s1> with fire all around txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.72it/s] 4%|▍ | 2/50 [00:00<00:12, 3.72it/s] 6%|▌ | 3/50 [00:00<00:12, 3.72it/s] 8%|▊ | 4/50 [00:01<00:12, 3.72it/s] 10%|█ | 5/50 [00:01<00:12, 3.72it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.71it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.67it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.68it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.69it/s] 20%|██ | 10/50 [00:02<00:10, 3.70it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.70it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.70it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.70it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.70it/s] 30%|███ | 15/50 [00:04<00:09, 3.70it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.70it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.70it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.70it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.70it/s] 40%|████ | 20/50 [00:05<00:08, 3.70it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.70it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.70it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.69it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.70it/s] 50%|█████ | 25/50 [00:06<00:06, 3.69it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.69it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.69it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.69it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.69it/s] 60%|██████ | 30/50 [00:08<00:05, 3.70it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.69it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.70it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.70it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.69it/s] 70%|███████ | 35/50 [00:09<00:04, 3.69it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.69it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.69it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.69it/s] 78%|███████▊ | 39/50 [00:10<00:02, 3.69it/s] 80%|████████ | 40/50 [00:10<00:02, 3.69it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.69it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.69it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.69it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.69it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.69it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.69it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.69it/s] 96%|█████████▌| 48/50 [00:12<00:00, 3.69it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.69it/s] 100%|██████████| 50/50 [00:13<00:00, 3.69it/s] 100%|██████████| 50/50 [00:13<00:00, 3.69it/s]
Prediction
zeke/this-is-fine:11edb7172944ea9372d17aca78e8f016946bebab73d66765e20a9315609ff750IDvrklhllb55owlueskp3fi3p4keStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedby @fofrInput
- width
- 1024
- height
- 1024
- prompt
- planet earth sitting at a table in the style of THIS_IS_FINE with fire all around
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 4
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "planet earth sitting at a table in the style of THIS_IS_FINE with fire all around", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run zeke/this-is-fine using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "zeke/this-is-fine:11edb7172944ea9372d17aca78e8f016946bebab73d66765e20a9315609ff750", { input: { width: 1024, height: 1024, prompt: "planet earth sitting at a table in the style of THIS_IS_FINE with fire all around", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 4, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.8, prompt_strength: 0.8, 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 zeke/this-is-fine using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "zeke/this-is-fine:11edb7172944ea9372d17aca78e8f016946bebab73d66765e20a9315609ff750", input={ "width": 1024, "height": 1024, "prompt": "planet earth sitting at a table in the style of THIS_IS_FINE with fire all around", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run zeke/this-is-fine 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": "zeke/this-is-fine:11edb7172944ea9372d17aca78e8f016946bebab73d66765e20a9315609ff750", "input": { "width": 1024, "height": 1024, "prompt": "planet earth sitting at a table in the style of THIS_IS_FINE with fire all around", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "prompt_strength": 0.8, "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": "2023-08-17T19:38:27.170639Z", "created_at": "2023-08-17T19:36:16.296016Z", "data_removed": false, "error": null, "id": "vrklhllb55owlueskp3fi3p4ke", "input": { "width": 1024, "height": 1024, "prompt": "planet earth sitting at a table in the style of THIS_IS_FINE with fire all around", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 58438\nPrompt: planet earth sitting at a table in the style of <s0><s1> with fire all around\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:01<01:15, 1.53s/it]\n 4%|▍ | 2/50 [00:02<00:58, 1.22s/it]\n 6%|▌ | 3/50 [00:03<00:52, 1.12s/it]\n 8%|▊ | 4/50 [00:04<00:49, 1.08s/it]\n 10%|█ | 5/50 [00:05<00:47, 1.05s/it]\n 12%|█▏ | 6/50 [00:06<00:45, 1.03s/it]\n 14%|█▍ | 7/50 [00:07<00:43, 1.02s/it]\n 16%|█▌ | 8/50 [00:08<00:42, 1.02s/it]\n 18%|█▊ | 9/50 [00:09<00:41, 1.01s/it]\n 20%|██ | 10/50 [00:10<00:40, 1.01s/it]\n 22%|██▏ | 11/50 [00:11<00:39, 1.01s/it]\n 24%|██▍ | 12/50 [00:12<00:38, 1.01s/it]\n 26%|██▌ | 13/50 [00:13<00:37, 1.00s/it]\n 28%|██▊ | 14/50 [00:14<00:36, 1.00s/it]\n 30%|███ | 15/50 [00:15<00:35, 1.00s/it]\n 32%|███▏ | 16/50 [00:16<00:34, 1.00s/it]\n 34%|███▍ | 17/50 [00:17<00:33, 1.00s/it]\n 36%|███▌ | 18/50 [00:18<00:32, 1.00s/it]\n 38%|███▊ | 19/50 [00:19<00:31, 1.01s/it]\n 40%|████ | 20/50 [00:20<00:30, 1.01s/it]\n 42%|████▏ | 21/50 [00:21<00:29, 1.01s/it]\n 44%|████▍ | 22/50 [00:22<00:28, 1.01s/it]\n 46%|████▌ | 23/50 [00:23<00:27, 1.01s/it]\n 48%|████▊ | 24/50 [00:24<00:26, 1.01s/it]\n 50%|█████ | 25/50 [00:25<00:25, 1.01s/it]\n 52%|█████▏ | 26/50 [00:26<00:24, 1.01s/it]\n 54%|█████▍ | 27/50 [00:27<00:23, 1.01s/it]\n 56%|█████▌ | 28/50 [00:28<00:22, 1.01s/it]\n 58%|█████▊ | 29/50 [00:29<00:21, 1.01s/it]\n 60%|██████ | 30/50 [00:30<00:20, 1.01s/it]\n 62%|██████▏ | 31/50 [00:31<00:19, 1.01s/it]\n 64%|██████▍ | 32/50 [00:32<00:18, 1.01s/it]\n 66%|██████▌ | 33/50 [00:33<00:17, 1.01s/it]\n 68%|██████▊ | 34/50 [00:34<00:16, 1.01s/it]\n 70%|███████ | 35/50 [00:35<00:15, 1.01s/it]\n 72%|███████▏ | 36/50 [00:36<00:14, 1.01s/it]\n 74%|███████▍ | 37/50 [00:37<00:13, 1.01s/it]\n 76%|███████▌ | 38/50 [00:38<00:12, 1.01s/it]\n 78%|███████▊ | 39/50 [00:39<00:11, 1.01s/it]\n 80%|████████ | 40/50 [00:40<00:10, 1.01s/it]\n 82%|████████▏ | 41/50 [00:41<00:09, 1.01s/it]\n 84%|████████▍ | 42/50 [00:42<00:08, 1.01s/it]\n 86%|████████▌ | 43/50 [00:43<00:07, 1.01s/it]\n 88%|████████▊ | 44/50 [00:44<00:06, 1.01s/it]\n 90%|█████████ | 45/50 [00:45<00:05, 1.01s/it]\n 92%|█████████▏| 46/50 [00:46<00:04, 1.01s/it]\n 94%|█████████▍| 47/50 [00:47<00:03, 1.01s/it]\n 96%|█████████▌| 48/50 [00:48<00:02, 1.01s/it]\n 98%|█████████▊| 49/50 [00:49<00:01, 1.01s/it]\n100%|██████████| 50/50 [00:50<00:00, 1.01s/it]\n100%|██████████| 50/50 [00:50<00:00, 1.02s/it]\nPotential NSFW content was detected in one or more images. A black image will be returned instead. Try again with a different prompt and/or seed.\nNSFW content detected in image 1", "metrics": { "predict_time": 56.253718, "total_time": 130.874623 }, "output": [ "https://replicate.delivery/pbxt/oBkFTMYfyE2rMqfH9VjVN6LqwEUUNd3ptlQfykbgrudjJF2iA/out-0.png", "https://replicate.delivery/pbxt/vjZqaIi2HA7PDJryZfdW4hyBW2oGy1GGumYjhDoKB5UZShtIA/out-2.png", "https://replicate.delivery/pbxt/fa9TbNiN6mX4SqDLoj3qncvM6XGMjqun9dPEl1M2r6OZShtIA/out-3.png" ], "started_at": "2023-08-17T19:37:30.916921Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/vrklhllb55owlueskp3fi3p4ke", "cancel": "https://api.replicate.com/v1/predictions/vrklhllb55owlueskp3fi3p4ke/cancel" }, "version": "11edb7172944ea9372d17aca78e8f016946bebab73d66765e20a9315609ff750" }
Generated inUsing seed: 58438 Prompt: planet earth sitting at a table in the style of <s0><s1> with fire all around txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:01<01:15, 1.53s/it] 4%|▍ | 2/50 [00:02<00:58, 1.22s/it] 6%|▌ | 3/50 [00:03<00:52, 1.12s/it] 8%|▊ | 4/50 [00:04<00:49, 1.08s/it] 10%|█ | 5/50 [00:05<00:47, 1.05s/it] 12%|█▏ | 6/50 [00:06<00:45, 1.03s/it] 14%|█▍ | 7/50 [00:07<00:43, 1.02s/it] 16%|█▌ | 8/50 [00:08<00:42, 1.02s/it] 18%|█▊ | 9/50 [00:09<00:41, 1.01s/it] 20%|██ | 10/50 [00:10<00:40, 1.01s/it] 22%|██▏ | 11/50 [00:11<00:39, 1.01s/it] 24%|██▍ | 12/50 [00:12<00:38, 1.01s/it] 26%|██▌ | 13/50 [00:13<00:37, 1.00s/it] 28%|██▊ | 14/50 [00:14<00:36, 1.00s/it] 30%|███ | 15/50 [00:15<00:35, 1.00s/it] 32%|███▏ | 16/50 [00:16<00:34, 1.00s/it] 34%|███▍ | 17/50 [00:17<00:33, 1.00s/it] 36%|███▌ | 18/50 [00:18<00:32, 1.00s/it] 38%|███▊ | 19/50 [00:19<00:31, 1.01s/it] 40%|████ | 20/50 [00:20<00:30, 1.01s/it] 42%|████▏ | 21/50 [00:21<00:29, 1.01s/it] 44%|████▍ | 22/50 [00:22<00:28, 1.01s/it] 46%|████▌ | 23/50 [00:23<00:27, 1.01s/it] 48%|████▊ | 24/50 [00:24<00:26, 1.01s/it] 50%|█████ | 25/50 [00:25<00:25, 1.01s/it] 52%|█████▏ | 26/50 [00:26<00:24, 1.01s/it] 54%|█████▍ | 27/50 [00:27<00:23, 1.01s/it] 56%|█████▌ | 28/50 [00:28<00:22, 1.01s/it] 58%|█████▊ | 29/50 [00:29<00:21, 1.01s/it] 60%|██████ | 30/50 [00:30<00:20, 1.01s/it] 62%|██████▏ | 31/50 [00:31<00:19, 1.01s/it] 64%|██████▍ | 32/50 [00:32<00:18, 1.01s/it] 66%|██████▌ | 33/50 [00:33<00:17, 1.01s/it] 68%|██████▊ | 34/50 [00:34<00:16, 1.01s/it] 70%|███████ | 35/50 [00:35<00:15, 1.01s/it] 72%|███████▏ | 36/50 [00:36<00:14, 1.01s/it] 74%|███████▍ | 37/50 [00:37<00:13, 1.01s/it] 76%|███████▌ | 38/50 [00:38<00:12, 1.01s/it] 78%|███████▊ | 39/50 [00:39<00:11, 1.01s/it] 80%|████████ | 40/50 [00:40<00:10, 1.01s/it] 82%|████████▏ | 41/50 [00:41<00:09, 1.01s/it] 84%|████████▍ | 42/50 [00:42<00:08, 1.01s/it] 86%|████████▌ | 43/50 [00:43<00:07, 1.01s/it] 88%|████████▊ | 44/50 [00:44<00:06, 1.01s/it] 90%|█████████ | 45/50 [00:45<00:05, 1.01s/it] 92%|█████████▏| 46/50 [00:46<00:04, 1.01s/it] 94%|█████████▍| 47/50 [00:47<00:03, 1.01s/it] 96%|█████████▌| 48/50 [00:48<00:02, 1.01s/it] 98%|█████████▊| 49/50 [00:49<00:01, 1.01s/it] 100%|██████████| 50/50 [00:50<00:00, 1.01s/it] 100%|██████████| 50/50 [00:50<00:00, 1.02s/it] Potential NSFW content was detected in one or more images. A black image will be returned instead. Try again with a different prompt and/or seed. NSFW content detected in image 1
Want to make some of these yourself?
Run this model