jakedahn
/
sdxl-70s-scifi
- Public
- 5.9K runs
-
L40S
Prediction
jakedahn/sdxl-70s-scifi:426affa4IDpmkhsx3b6wh7rkklooalfzywe4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedby @jakedahnInput
- width
- 1024
- height
- 1024
- prompt
- solarpunk future cityscape, in the style of TOK, Retrofuturistic NASA Space Art
- 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": "solarpunk future cityscape, in the style of TOK, Retrofuturistic NASA Space Art", "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 replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run jakedahn/sdxl-70s-scifi using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jakedahn/sdxl-70s-scifi:426affa4cca9beb69b34c92c54133196902a4bf72dba90718f0de3124418eedb", { input: { width: 1024, height: 1024, prompt: "solarpunk future cityscape, in the style of TOK, Retrofuturistic NASA Space Art", 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 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun jakedahn/sdxl-70s-scifi using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jakedahn/sdxl-70s-scifi:426affa4cca9beb69b34c92c54133196902a4bf72dba90718f0de3124418eedb", input={ "width": 1024, "height": 1024, "prompt": "solarpunk future cityscape, in the style of TOK, Retrofuturistic NASA Space Art", "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.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run jakedahn/sdxl-70s-scifi 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": "426affa4cca9beb69b34c92c54133196902a4bf72dba90718f0de3124418eedb", "input": { "width": 1024, "height": 1024, "prompt": "solarpunk future cityscape, in the style of TOK, Retrofuturistic NASA Space Art", "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/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/jakedahn/sdxl-70s-scifi@sha256:426affa4cca9beb69b34c92c54133196902a4bf72dba90718f0de3124418eedb \ -i 'width=1024' \ -i 'height=1024' \ -i 'prompt="solarpunk future cityscape, in the style of TOK, Retrofuturistic NASA Space Art"' \ -i 'refine="no_refiner"' \ -i 'scheduler="K_EULER"' \ -i 'lora_scale=0.6' \ -i 'num_outputs=4' \ -i 'guidance_scale=7.5' \ -i 'apply_watermark=false' \ -i 'high_noise_frac=0.8' \ -i 'prompt_strength=0.8' \ -i 'num_inference_steps=50'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/jakedahn/sdxl-70s-scifi@sha256:426affa4cca9beb69b34c92c54133196902a4bf72dba90718f0de3124418eedbcurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 1024, "height": 1024, "prompt": "solarpunk future cityscape, in the style of TOK, Retrofuturistic NASA Space Art", "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 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-08-09T05:41:26.970833Z", "created_at": "2023-08-09T05:40:32.234926Z", "data_removed": false, "error": null, "id": "pmkhsx3b6wh7rkklooalfzywe4", "input": { "width": 1024, "height": 1024, "prompt": "solarpunk future cityscape, in the style of TOK, Retrofuturistic NASA Space Art", "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: 20051\nPrompt: solarpunk future cityscape, in the style of <s0><s1>, Retrofuturistic NASA Space Art\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:48, 1.00it/s]\n 4%|▍ | 2/50 [00:02<00:48, 1.00s/it]\n 6%|▌ | 3/50 [00:03<00:47, 1.00s/it]\n 8%|▊ | 4/50 [00:04<00:46, 1.00s/it]\n 10%|█ | 5/50 [00:05<00:45, 1.00s/it]\n 12%|█▏ | 6/50 [00:06<00:44, 1.00s/it]\n 14%|█▍ | 7/50 [00:07<00:43, 1.00s/it]\n 16%|█▌ | 8/50 [00:08<00:42, 1.00s/it]\n 18%|█▊ | 9/50 [00:09<00:41, 1.00s/it]\n 20%|██ | 10/50 [00:10<00:40, 1.00s/it]\n 22%|██▏ | 11/50 [00:11<00:39, 1.00s/it]\n 24%|██▍ | 12/50 [00:12<00:38, 1.00s/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, 1.00s/it]\n 40%|████ | 20/50 [00:20<00:30, 1.00s/it]\n 42%|████▏ | 21/50 [00:21<00:29, 1.00s/it]\n 44%|████▍ | 22/50 [00:22<00:28, 1.00s/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, 1.01s/it]", "metrics": { "predict_time": 54.7868, "total_time": 54.735907 }, "output": [ "https://pbxt.replicate.delivery/oHaMV841zoLyOZcXfNYGbOBexqPf1fcXzcpD0QIIb0VWQ2gFB/out-0.png", "https://pbxt.replicate.delivery/Qplm8y5eTC1yD6DG3syLPf5soOlVS3KxxIYCkIqeCsSKIbwiA/out-1.png", "https://pbxt.replicate.delivery/ne3fisfYMyU3XptIipSS9HcfT5wtYzdndywhGuBfQtO3gsBLC/out-2.png", "https://pbxt.replicate.delivery/kfkcJxG7FBy5PKTzyfe2fT5OUuOePKeee4PTq17B602eNIbwiA/out-3.png" ], "started_at": "2023-08-09T05:40:32.184033Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/pmkhsx3b6wh7rkklooalfzywe4", "cancel": "https://api.replicate.com/v1/predictions/pmkhsx3b6wh7rkklooalfzywe4/cancel" }, "version": "426affa4cca9beb69b34c92c54133196902a4bf72dba90718f0de3124418eedb" }Generated inUsing seed: 20051 Prompt: solarpunk future cityscape, in the style of <s0><s1>, Retrofuturistic NASA Space Art txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:48, 1.00it/s] 4%|▍ | 2/50 [00:02<00:48, 1.00s/it] 6%|▌ | 3/50 [00:03<00:47, 1.00s/it] 8%|▊ | 4/50 [00:04<00:46, 1.00s/it] 10%|█ | 5/50 [00:05<00:45, 1.00s/it] 12%|█▏ | 6/50 [00:06<00:44, 1.00s/it] 14%|█▍ | 7/50 [00:07<00:43, 1.00s/it] 16%|█▌ | 8/50 [00:08<00:42, 1.00s/it] 18%|█▊ | 9/50 [00:09<00:41, 1.00s/it] 20%|██ | 10/50 [00:10<00:40, 1.00s/it] 22%|██▏ | 11/50 [00:11<00:39, 1.00s/it] 24%|██▍ | 12/50 [00:12<00:38, 1.00s/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, 1.00s/it] 40%|████ | 20/50 [00:20<00:30, 1.00s/it] 42%|████▏ | 21/50 [00:21<00:29, 1.00s/it] 44%|████▍ | 22/50 [00:22<00:28, 1.00s/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, 1.01s/it]Prediction
jakedahn/sdxl-70s-scifi:426affa4IDcatzujlbikt7ejulil7iu4tqvuStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- old filling station on the moon, in the style of TOK
- refine
- expert_ensemble_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": "old filling station on the moon, in the style of TOK", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "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 replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run jakedahn/sdxl-70s-scifi using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jakedahn/sdxl-70s-scifi:426affa4cca9beb69b34c92c54133196902a4bf72dba90718f0de3124418eedb", { input: { width: 1024, height: 1024, prompt: "old filling station on the moon, in the style of TOK", refine: "expert_ensemble_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 4, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, prompt_strength: 0.8, num_inference_steps: 50 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun jakedahn/sdxl-70s-scifi using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jakedahn/sdxl-70s-scifi:426affa4cca9beb69b34c92c54133196902a4bf72dba90718f0de3124418eedb", input={ "width": 1024, "height": 1024, "prompt": "old filling station on the moon, in the style of TOK", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "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.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run jakedahn/sdxl-70s-scifi 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": "426affa4cca9beb69b34c92c54133196902a4bf72dba90718f0de3124418eedb", "input": { "width": 1024, "height": 1024, "prompt": "old filling station on the moon, in the style of TOK", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "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/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/jakedahn/sdxl-70s-scifi@sha256:426affa4cca9beb69b34c92c54133196902a4bf72dba90718f0de3124418eedb \ -i 'width=1024' \ -i 'height=1024' \ -i 'prompt="old filling station on the moon, in the style of TOK"' \ -i 'refine="expert_ensemble_refiner"' \ -i 'scheduler="K_EULER"' \ -i 'lora_scale=0.6' \ -i 'num_outputs=4' \ -i 'guidance_scale=7.5' \ -i 'apply_watermark=true' \ -i 'high_noise_frac=0.8' \ -i 'prompt_strength=0.8' \ -i 'num_inference_steps=50'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/jakedahn/sdxl-70s-scifi@sha256:426affa4cca9beb69b34c92c54133196902a4bf72dba90718f0de3124418eedbcurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 1024, "height": 1024, "prompt": "old filling station on the moon, in the style of TOK", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-08-09T16:16:20.186243Z", "created_at": "2023-08-09T16:15:25.200358Z", "data_removed": false, "error": null, "id": "catzujlbikt7ejulil7iu4tqvu", "input": { "width": 1024, "height": 1024, "prompt": "old filling station on the moon, in the style of TOK", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 59377\nPrompt: old filling station on the moon, in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:00<00:38, 1.00it/s]\n 5%|▌ | 2/40 [00:01<00:37, 1.00it/s]\n 8%|▊ | 3/40 [00:03<00:37, 1.00s/it]\n 10%|█ | 4/40 [00:04<00:36, 1.00s/it]\n 12%|█▎ | 5/40 [00:05<00:35, 1.00s/it]\n 15%|█▌ | 6/40 [00:06<00:34, 1.00s/it]\n 18%|█▊ | 7/40 [00:07<00:33, 1.00s/it]\n 20%|██ | 8/40 [00:08<00:32, 1.00s/it]\n 22%|██▎ | 9/40 [00:09<00:31, 1.00s/it]\n 25%|██▌ | 10/40 [00:10<00:30, 1.00s/it]\n 28%|██▊ | 11/40 [00:11<00:28, 1.00it/s]\n 30%|███ | 12/40 [00:12<00:27, 1.00it/s]\n 32%|███▎ | 13/40 [00:13<00:26, 1.00it/s]\n 35%|███▌ | 14/40 [00:14<00:25, 1.00it/s]\n 38%|███▊ | 15/40 [00:15<00:24, 1.00it/s]\n 40%|████ | 16/40 [00:16<00:24, 1.00s/it]\n 42%|████▎ | 17/40 [00:17<00:23, 1.00s/it]\n 45%|████▌ | 18/40 [00:18<00:22, 1.00s/it]\n 48%|████▊ | 19/40 [00:19<00:21, 1.00s/it]\n 50%|█████ | 20/40 [00:20<00:20, 1.00s/it]\n 52%|█████▎ | 21/40 [00:21<00:19, 1.00s/it]\n 55%|█████▌ | 22/40 [00:22<00:18, 1.00s/it]\n 57%|█████▊ | 23/40 [00:23<00:17, 1.00s/it]\n 60%|██████ | 24/40 [00:24<00:16, 1.00s/it]\n 62%|██████▎ | 25/40 [00:25<00:15, 1.00s/it]\n 65%|██████▌ | 26/40 [00:26<00:14, 1.00s/it]\n 68%|██████▊ | 27/40 [00:27<00:13, 1.00s/it]\n 70%|███████ | 28/40 [00:28<00:12, 1.01s/it]\n 72%|███████▎ | 29/40 [00:29<00:11, 1.01s/it]\n 75%|███████▌ | 30/40 [00:30<00:10, 1.01s/it]\n 78%|███████▊ | 31/40 [00:31<00:09, 1.01s/it]\n 80%|████████ | 32/40 [00:32<00:08, 1.01s/it]\n 82%|████████▎ | 33/40 [00:33<00:07, 1.01s/it]\n 85%|████████▌ | 34/40 [00:34<00:06, 1.01s/it]\n 88%|████████▊ | 35/40 [00:35<00:05, 1.01s/it]\n 90%|█████████ | 36/40 [00:36<00:04, 1.01s/it]\n 92%|█████████▎| 37/40 [00:37<00:03, 1.01s/it]\n 95%|█████████▌| 38/40 [00:38<00:02, 1.01s/it]\n 98%|█████████▊| 39/40 [00:39<00:01, 1.01s/it]\n100%|██████████| 40/40 [00:40<00:00, 1.01s/it]\n100%|██████████| 40/40 [00:40<00:00, 1.00s/it]\n 0%| | 0/10 [00:00<?, ?it/s]\n 10%|█ | 1/10 [00:00<00:07, 1.22it/s]\n 20%|██ | 2/10 [00:01<00:06, 1.22it/s]\n 30%|███ | 3/10 [00:02<00:05, 1.22it/s]\n 40%|████ | 4/10 [00:03<00:04, 1.22it/s]\n 50%|█████ | 5/10 [00:04<00:04, 1.22it/s]\n 60%|██████ | 6/10 [00:04<00:03, 1.22it/s]\n 70%|███████ | 7/10 [00:05<00:02, 1.22it/s]\n 80%|████████ | 8/10 [00:06<00:01, 1.22it/s]\n 90%|█████████ | 9/10 [00:07<00:00, 1.22it/s]\n100%|██████████| 10/10 [00:08<00:00, 1.22it/s]\n100%|██████████| 10/10 [00:08<00:00, 1.22it/s]", "metrics": { "predict_time": 54.98183, "total_time": 54.985885 }, "output": [ "https://pbxt.replicate.delivery/OJLvgdBn06IRF1brEoQWtxcf16acljXzw6GQlxLzvH0obLsIA/out-0.png", "https://pbxt.replicate.delivery/H5IkH12ytTp8CRCCNZePA3oY8i3lKW3EUpGM1KrvzfCS3WYRA/out-1.png", "https://pbxt.replicate.delivery/S0l6YN8XbQ54PNbZefOp9uyhsCmx4ZkljgaNKpMTVt0T3WYRA/out-2.png", "https://pbxt.replicate.delivery/c85rLvOnnaL8NhB2vrLR5LZSwZ4KKf71lLk89SdRoIrpbLsIA/out-3.png" ], "started_at": "2023-08-09T16:15:25.204413Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/catzujlbikt7ejulil7iu4tqvu", "cancel": "https://api.replicate.com/v1/predictions/catzujlbikt7ejulil7iu4tqvu/cancel" }, "version": "426affa4cca9beb69b34c92c54133196902a4bf72dba90718f0de3124418eedb" }Generated inUsing seed: 59377 Prompt: old filling station on the moon, in the style of <s0><s1> txt2img mode 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:00<00:38, 1.00it/s] 5%|▌ | 2/40 [00:01<00:37, 1.00it/s] 8%|▊ | 3/40 [00:03<00:37, 1.00s/it] 10%|█ | 4/40 [00:04<00:36, 1.00s/it] 12%|█▎ | 5/40 [00:05<00:35, 1.00s/it] 15%|█▌ | 6/40 [00:06<00:34, 1.00s/it] 18%|█▊ | 7/40 [00:07<00:33, 1.00s/it] 20%|██ | 8/40 [00:08<00:32, 1.00s/it] 22%|██▎ | 9/40 [00:09<00:31, 1.00s/it] 25%|██▌ | 10/40 [00:10<00:30, 1.00s/it] 28%|██▊ | 11/40 [00:11<00:28, 1.00it/s] 30%|███ | 12/40 [00:12<00:27, 1.00it/s] 32%|███▎ | 13/40 [00:13<00:26, 1.00it/s] 35%|███▌ | 14/40 [00:14<00:25, 1.00it/s] 38%|███▊ | 15/40 [00:15<00:24, 1.00it/s] 40%|████ | 16/40 [00:16<00:24, 1.00s/it] 42%|████▎ | 17/40 [00:17<00:23, 1.00s/it] 45%|████▌ | 18/40 [00:18<00:22, 1.00s/it] 48%|████▊ | 19/40 [00:19<00:21, 1.00s/it] 50%|█████ | 20/40 [00:20<00:20, 1.00s/it] 52%|█████▎ | 21/40 [00:21<00:19, 1.00s/it] 55%|█████▌ | 22/40 [00:22<00:18, 1.00s/it] 57%|█████▊ | 23/40 [00:23<00:17, 1.00s/it] 60%|██████ | 24/40 [00:24<00:16, 1.00s/it] 62%|██████▎ | 25/40 [00:25<00:15, 1.00s/it] 65%|██████▌ | 26/40 [00:26<00:14, 1.00s/it] 68%|██████▊ | 27/40 [00:27<00:13, 1.00s/it] 70%|███████ | 28/40 [00:28<00:12, 1.01s/it] 72%|███████▎ | 29/40 [00:29<00:11, 1.01s/it] 75%|███████▌ | 30/40 [00:30<00:10, 1.01s/it] 78%|███████▊ | 31/40 [00:31<00:09, 1.01s/it] 80%|████████ | 32/40 [00:32<00:08, 1.01s/it] 82%|████████▎ | 33/40 [00:33<00:07, 1.01s/it] 85%|████████▌ | 34/40 [00:34<00:06, 1.01s/it] 88%|████████▊ | 35/40 [00:35<00:05, 1.01s/it] 90%|█████████ | 36/40 [00:36<00:04, 1.01s/it] 92%|█████████▎| 37/40 [00:37<00:03, 1.01s/it] 95%|█████████▌| 38/40 [00:38<00:02, 1.01s/it] 98%|█████████▊| 39/40 [00:39<00:01, 1.01s/it] 100%|██████████| 40/40 [00:40<00:00, 1.01s/it] 100%|██████████| 40/40 [00:40<00:00, 1.00s/it] 0%| | 0/10 [00:00<?, ?it/s] 10%|█ | 1/10 [00:00<00:07, 1.22it/s] 20%|██ | 2/10 [00:01<00:06, 1.22it/s] 30%|███ | 3/10 [00:02<00:05, 1.22it/s] 40%|████ | 4/10 [00:03<00:04, 1.22it/s] 50%|█████ | 5/10 [00:04<00:04, 1.22it/s] 60%|██████ | 6/10 [00:04<00:03, 1.22it/s] 70%|███████ | 7/10 [00:05<00:02, 1.22it/s] 80%|████████ | 8/10 [00:06<00:01, 1.22it/s] 90%|█████████ | 9/10 [00:07<00:00, 1.22it/s] 100%|██████████| 10/10 [00:08<00:00, 1.22it/s] 100%|██████████| 10/10 [00:08<00:00, 1.22it/s]
Want to make some of these yourself?
Run this model