prompthero
/
openjourney
Stable Diffusion fine tuned on Midjourney v4 images.
- Public
- 12M runs
-
A100 (80GB)
- Paper
Prediction
prompthero/openjourney:9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05ebIDebfk5pzbf5hqhndiinbmqhdrpiStatusSucceededSourceWebHardware–Total durationCreatedInput
- width
- 512
- height
- 512
- prompt
- mdjrny-v4 style a highly detailed matte painting of a man on a hill watching a rocket launch in the distance by studio ghibli, makoto shinkai, by artgerm, by wlop, by greg rutkowski, volumetric lighting, octane render, 4 k resolution, trending on artstation, masterpiece
- num_outputs
- 1
- guidance_scale
- "14"
- num_inference_steps
- 50
{ "width": 512, "height": 512, "prompt": "mdjrny-v4 style a highly detailed matte painting of a man on a hill watching a rocket launch in the distance by studio ghibli, makoto shinkai, by artgerm, by wlop, by greg rutkowski, volumetric lighting, octane render, 4 k resolution, trending on artstation, masterpiece", "num_outputs": 1, "guidance_scale": "14", "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 prompthero/openjourney using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "prompthero/openjourney:9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb", { input: { width: 512, height: 512, prompt: "mdjrny-v4 style a highly detailed matte painting of a man on a hill watching a rocket launch in the distance by studio ghibli, makoto shinkai, by artgerm, by wlop, by greg rutkowski, volumetric lighting, octane render, 4 k resolution, trending on artstation, masterpiece", num_outputs: 1, guidance_scale: "14", 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 prompthero/openjourney using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "prompthero/openjourney:9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb", input={ "width": 512, "height": 512, "prompt": "mdjrny-v4 style a highly detailed matte painting of a man on a hill watching a rocket launch in the distance by studio ghibli, makoto shinkai, by artgerm, by wlop, by greg rutkowski, volumetric lighting, octane render, 4 k resolution, trending on artstation, masterpiece", "num_outputs": 1, "guidance_scale": "14", "num_inference_steps": 50 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run prompthero/openjourney 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": "9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb", "input": { "width": 512, "height": 512, "prompt": "mdjrny-v4 style a highly detailed matte painting of a man on a hill watching a rocket launch in the distance by studio ghibli, makoto shinkai, by artgerm, by wlop, by greg rutkowski, volumetric lighting, octane render, 4 k resolution, trending on artstation, masterpiece", "num_outputs": 1, "guidance_scale": "14", "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictions
To 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 cog
If 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/prompthero/openjourney@sha256:9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb \ -i 'width=512' \ -i 'height=512' \ -i 'prompt="mdjrny-v4 style a highly detailed matte painting of a man on a hill watching a rocket launch in the distance by studio ghibli, makoto shinkai, by artgerm, by wlop, by greg rutkowski, volumetric lighting, octane render, 4 k resolution, trending on artstation, masterpiece"' \ -i 'num_outputs=1' \ -i 'guidance_scale="14"' \ -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/prompthero/openjourney@sha256:9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 512, "height": 512, "prompt": "mdjrny-v4 style a highly detailed matte painting of a man on a hill watching a rocket launch in the distance by studio ghibli, makoto shinkai, by artgerm, by wlop, by greg rutkowski, volumetric lighting, octane render, 4 k resolution, trending on artstation, masterpiece", "num_outputs": 1, "guidance_scale": "14", "num_inference_steps": 50 } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-11-15T02:15:48.269631Z", "created_at": "2022-11-15T02:15:44.197622Z", "data_removed": false, "error": null, "id": "ebfk5pzbf5hqhndiinbmqhdrpi", "input": { "width": 512, "height": 512, "prompt": "mdjrny-v4 style a highly detailed matte painting of a man on a hill watching a rocket launch in the distance by studio ghibli, makoto shinkai, by artgerm, by wlop, by greg rutkowski, volumetric lighting, octane render, 4 k resolution, trending on artstation, masterpiece", "num_outputs": 1, "guidance_scale": "14", "num_inference_steps": 50 }, "logs": "Using seed: 6924\nGlobal seed set to 6924\n 0%| | 0/50 [00:00<?, ?it/s]\n 4%|▍ | 2/50 [00:00<00:03, 12.79it/s]\n 8%|▊ | 4/50 [00:00<00:03, 13.67it/s]\n 12%|█▏ | 6/50 [00:00<00:03, 13.96it/s]\n 16%|█▌ | 8/50 [00:00<00:02, 14.13it/s]\n 20%|██ | 10/50 [00:00<00:02, 14.14it/s]\n 24%|██▍ | 12/50 [00:00<00:02, 14.20it/s]\n 28%|██▊ | 14/50 [00:00<00:02, 14.24it/s]\n 32%|███▏ | 16/50 [00:01<00:02, 14.29it/s]\n 36%|███▌ | 18/50 [00:01<00:02, 14.35it/s]\n 40%|████ | 20/50 [00:01<00:02, 14.19it/s]\n 44%|████▍ | 22/50 [00:01<00:01, 14.30it/s]\n 48%|████▊ | 24/50 [00:01<00:01, 14.36it/s]\n 52%|█████▏ | 26/50 [00:01<00:01, 14.40it/s]\n 56%|█████▌ | 28/50 [00:01<00:01, 14.38it/s]\n 60%|██████ | 30/50 [00:02<00:01, 14.41it/s]\n 64%|██████▍ | 32/50 [00:02<00:01, 14.37it/s]\n 68%|██████▊ | 34/50 [00:02<00:01, 14.34it/s]\n 72%|███████▏ | 36/50 [00:02<00:00, 14.34it/s]\n 76%|███████▌ | 38/50 [00:02<00:00, 14.35it/s]\n 80%|████████ | 40/50 [00:02<00:00, 14.36it/s]\n 84%|████████▍ | 42/50 [00:02<00:00, 14.17it/s]\n 88%|████████▊ | 44/50 [00:03<00:00, 14.23it/s]\n 92%|█████████▏| 46/50 [00:03<00:00, 14.32it/s]\n 96%|█████████▌| 48/50 [00:03<00:00, 14.36it/s]\n100%|██████████| 50/50 [00:03<00:00, 13.86it/s]\n100%|██████████| 50/50 [00:03<00:00, 14.20it/s]", "metrics": { "predict_time": 4.036521, "total_time": 4.072009 }, "output": [ "https://replicate.delivery/pbxt/VueyYHELjzV9WiveDN9TfybM57OXpuC66hQUms0uJHioCVAgA/out-0.png" ], "started_at": "2022-11-15T02:15:44.233110Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ebfk5pzbf5hqhndiinbmqhdrpi", "cancel": "https://api.replicate.com/v1/predictions/ebfk5pzbf5hqhndiinbmqhdrpi/cancel" }, "version": "9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb" }
Generated inUsing seed: 6924 Global seed set to 6924 0%| | 0/50 [00:00<?, ?it/s] 4%|▍ | 2/50 [00:00<00:03, 12.79it/s] 8%|▊ | 4/50 [00:00<00:03, 13.67it/s] 12%|█▏ | 6/50 [00:00<00:03, 13.96it/s] 16%|█▌ | 8/50 [00:00<00:02, 14.13it/s] 20%|██ | 10/50 [00:00<00:02, 14.14it/s] 24%|██▍ | 12/50 [00:00<00:02, 14.20it/s] 28%|██▊ | 14/50 [00:00<00:02, 14.24it/s] 32%|███▏ | 16/50 [00:01<00:02, 14.29it/s] 36%|███▌ | 18/50 [00:01<00:02, 14.35it/s] 40%|████ | 20/50 [00:01<00:02, 14.19it/s] 44%|████▍ | 22/50 [00:01<00:01, 14.30it/s] 48%|████▊ | 24/50 [00:01<00:01, 14.36it/s] 52%|█████▏ | 26/50 [00:01<00:01, 14.40it/s] 56%|█████▌ | 28/50 [00:01<00:01, 14.38it/s] 60%|██████ | 30/50 [00:02<00:01, 14.41it/s] 64%|██████▍ | 32/50 [00:02<00:01, 14.37it/s] 68%|██████▊ | 34/50 [00:02<00:01, 14.34it/s] 72%|███████▏ | 36/50 [00:02<00:00, 14.34it/s] 76%|███████▌ | 38/50 [00:02<00:00, 14.35it/s] 80%|████████ | 40/50 [00:02<00:00, 14.36it/s] 84%|████████▍ | 42/50 [00:02<00:00, 14.17it/s] 88%|████████▊ | 44/50 [00:03<00:00, 14.23it/s] 92%|█████████▏| 46/50 [00:03<00:00, 14.32it/s] 96%|█████████▌| 48/50 [00:03<00:00, 14.36it/s] 100%|██████████| 50/50 [00:03<00:00, 13.86it/s] 100%|██████████| 50/50 [00:03<00:00, 14.20it/s]
Prediction
prompthero/openjourney:9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05ebIDhnv34qbn5nc2fkhfgxa2nhe5kaStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- null
- width
- 512
- height
- 512
- prompt
- mdjrny-v4 style portrait of female elf, intricate, elegant, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha, 8k
- num_outputs
- 1
- guidance_scale
- "7"
- num_inference_steps
- 50
{ "seed": null, "width": 512, "height": 512, "prompt": "mdjrny-v4 style portrait of female elf, intricate, elegant, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha, 8k", "num_outputs": 1, "guidance_scale": "7", "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 prompthero/openjourney using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "prompthero/openjourney:9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb", { input: { width: 512, height: 512, prompt: "mdjrny-v4 style portrait of female elf, intricate, elegant, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha, 8k", num_outputs: 1, guidance_scale: "7", 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 prompthero/openjourney using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "prompthero/openjourney:9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb", input={ "width": 512, "height": 512, "prompt": "mdjrny-v4 style portrait of female elf, intricate, elegant, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha, 8k", "num_outputs": 1, "guidance_scale": "7", "num_inference_steps": 50 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run prompthero/openjourney 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": "9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb", "input": { "width": 512, "height": 512, "prompt": "mdjrny-v4 style portrait of female elf, intricate, elegant, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha, 8k", "num_outputs": 1, "guidance_scale": "7", "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictions
To 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 cog
If 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/prompthero/openjourney@sha256:9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb \ -i 'width=512' \ -i 'height=512' \ -i 'prompt="mdjrny-v4 style portrait of female elf, intricate, elegant, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha, 8k"' \ -i 'num_outputs=1' \ -i 'guidance_scale="7"' \ -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/prompthero/openjourney@sha256:9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 512, "height": 512, "prompt": "mdjrny-v4 style portrait of female elf, intricate, elegant, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha, 8k", "num_outputs": 1, "guidance_scale": "7", "num_inference_steps": 50 } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-11-15T02:17:31Z", "created_at": "2022-11-15T02:17:27.218186Z", "data_removed": false, "error": "", "id": "hnv34qbn5nc2fkhfgxa2nhe5ka", "input": { "seed": null, "width": 512, "height": 512, "prompt": "mdjrny-v4 style portrait of female elf, intricate, elegant, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha, 8k", "num_outputs": 1, "guidance_scale": "7", "num_inference_steps": 50 }, "logs": "Using seed: 46041\r\nGlobal seed set to 46041\r\n 0%| | 0/50 [00:00<?, ?it/s]\r\n 4%|▍ | 2/50 [00:00<00:03, 13.23it/s]\r\n 8%|▊ | 4/50 [00:00<00:03, 13.55it/s]\r\n 12%|█▏ | 6/50 [00:00<00:03, 13.93it/s]\r\n 16%|█▌ | 8/50 [00:00<00:02, 14.09it/s]\r\n 20%|██ | 10/50 [00:00<00:02, 14.08it/s]\r\n 24%|██▍ | 12/50 [00:00<00:02, 13.94it/s]\r\n 28%|██▊ | 14/50 [00:01<00:02, 14.03it/s]\r\n 32%|███▏ | 16/50 [00:01<00:02, 14.05it/s]\r\n 36%|███▌ | 18/50 [00:01<00:02, 14.11it/s]\r\n 40%|████ | 20/50 [00:01<00:02, 14.17it/s]\r\n 44%|████▍ | 22/50 [00:01<00:01, 14.22it/s]\r\n 48%|████▊ | 24/50 [00:01<00:01, 14.27it/s]\r\n 52%|█████▏ | 26/50 [00:01<00:01, 14.30it/s]\r\n 56%|█████▌ | 28/50 [00:01<00:01, 14.32it/s]\r\n 60%|██████ | 30/50 [00:02<00:01, 14.31it/s]\r\n 64%|██████▍ | 32/50 [00:02<00:01, 14.30it/s]\r\n 68%|██████▊ | 34/50 [00:02<00:01, 14.29it/s]\r\n 72%|███████▏ | 36/50 [00:02<00:00, 14.29it/s]\r\n 76%|███████▌ | 38/50 [00:02<00:00, 14.33it/s]\r\n 80%|████████ | 40/50 [00:02<00:00, 14.04it/s]\r\n 84%|████████▍ | 42/50 [00:02<00:00, 14.09it/s]\r\n 88%|████████▊ | 44/50 [00:03<00:00, 14.15it/s]\r\n 92%|█████████▏| 46/50 [00:03<00:00, 14.20it/s]\r\n 96%|█████████▌| 48/50 [00:03<00:00, 14.18it/s]\r\n100%|██████████| 50/50 [00:03<00:00, 14.18it/s]\r\n100%|██████████| 50/50 [00:03<00:00, 14.15it/s]", "metrics": { "predict_time": 4, "total_time": 3.781814 }, "output": [ "https://replicate.delivery/pbxt/LHh12rAtngYkItdmraLbWntEODUjeCI4g9wn9pXfiMO7iKAQA/out-0.png" ], "started_at": "2022-11-15T02:17:27Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/hnv34qbn5nc2fkhfgxa2nhe5ka", "cancel": "https://api.replicate.com/v1/predictions/hnv34qbn5nc2fkhfgxa2nhe5ka/cancel" }, "version": "9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb" }
Generated inUsing seed: 46041 Global seed set to 46041 0%| | 0/50 [00:00<?, ?it/s] 4%|▍ | 2/50 [00:00<00:03, 13.23it/s] 8%|▊ | 4/50 [00:00<00:03, 13.55it/s] 12%|█▏ | 6/50 [00:00<00:03, 13.93it/s] 16%|█▌ | 8/50 [00:00<00:02, 14.09it/s] 20%|██ | 10/50 [00:00<00:02, 14.08it/s] 24%|██▍ | 12/50 [00:00<00:02, 13.94it/s] 28%|██▊ | 14/50 [00:01<00:02, 14.03it/s] 32%|███▏ | 16/50 [00:01<00:02, 14.05it/s] 36%|███▌ | 18/50 [00:01<00:02, 14.11it/s] 40%|████ | 20/50 [00:01<00:02, 14.17it/s] 44%|████▍ | 22/50 [00:01<00:01, 14.22it/s] 48%|████▊ | 24/50 [00:01<00:01, 14.27it/s] 52%|█████▏ | 26/50 [00:01<00:01, 14.30it/s] 56%|█████▌ | 28/50 [00:01<00:01, 14.32it/s] 60%|██████ | 30/50 [00:02<00:01, 14.31it/s] 64%|██████▍ | 32/50 [00:02<00:01, 14.30it/s] 68%|██████▊ | 34/50 [00:02<00:01, 14.29it/s] 72%|███████▏ | 36/50 [00:02<00:00, 14.29it/s] 76%|███████▌ | 38/50 [00:02<00:00, 14.33it/s] 80%|████████ | 40/50 [00:02<00:00, 14.04it/s] 84%|████████▍ | 42/50 [00:02<00:00, 14.09it/s] 88%|████████▊ | 44/50 [00:03<00:00, 14.15it/s] 92%|█████████▏| 46/50 [00:03<00:00, 14.20it/s] 96%|█████████▌| 48/50 [00:03<00:00, 14.18it/s] 100%|██████████| 50/50 [00:03<00:00, 14.18it/s] 100%|██████████| 50/50 [00:03<00:00, 14.15it/s]
Prediction
prompthero/openjourney:9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05ebIDtxy3tnvrmzdgdot3pjjov2l6k4StatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 23191
- width
- 512
- height
- 512
- prompt
- whimsical fantasy elegant rose floral botany maximalism with a wave of flowers garden flowing flowers floating in misty soft pink, aqua, soft apricot, smoke fractal, moody and big scale realistic flowers, octane render, by josephine wall art, isabelle menin, Jean, amy brown
- num_outputs
- 1
- guidance_scale
- "7"
- num_inference_steps
- 50
{ "seed": 23191, "width": 512, "height": 512, "prompt": "whimsical fantasy elegant rose floral botany maximalism with a wave of flowers garden flowing flowers floating in misty soft pink, aqua, soft apricot, smoke fractal, moody and big scale realistic flowers, octane render, by josephine wall art, isabelle menin, Jean, amy brown", "num_outputs": 1, "guidance_scale": "7", "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 prompthero/openjourney using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "prompthero/openjourney:9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb", { input: { seed: 23191, width: 512, height: 512, prompt: "whimsical fantasy elegant rose floral botany maximalism with a wave of flowers garden flowing flowers floating in misty soft pink, aqua, soft apricot, smoke fractal, moody and big scale realistic flowers, octane render, by josephine wall art, isabelle menin, Jean, amy brown", num_outputs: 1, guidance_scale: "7", 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 prompthero/openjourney using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "prompthero/openjourney:9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb", input={ "seed": 23191, "width": 512, "height": 512, "prompt": "whimsical fantasy elegant rose floral botany maximalism with a wave of flowers garden flowing flowers floating in misty soft pink, aqua, soft apricot, smoke fractal, moody and big scale realistic flowers, octane render, by josephine wall art, isabelle menin, Jean, amy brown", "num_outputs": 1, "guidance_scale": "7", "num_inference_steps": 50 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run prompthero/openjourney 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": "9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb", "input": { "seed": 23191, "width": 512, "height": 512, "prompt": "whimsical fantasy elegant rose floral botany maximalism with a wave of flowers garden flowing flowers floating in misty soft pink, aqua, soft apricot, smoke fractal, moody and big scale realistic flowers, octane render, by josephine wall art, isabelle menin, Jean, amy brown", "num_outputs": 1, "guidance_scale": "7", "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictions
To 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 cog
If 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/prompthero/openjourney@sha256:9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb \ -i 'seed=23191' \ -i 'width=512' \ -i 'height=512' \ -i 'prompt="whimsical fantasy elegant rose floral botany maximalism with a wave of flowers garden flowing flowers floating in misty soft pink, aqua, soft apricot, smoke fractal, moody and big scale realistic flowers, octane render, by josephine wall art, isabelle menin, Jean, amy brown"' \ -i 'num_outputs=1' \ -i 'guidance_scale="7"' \ -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/prompthero/openjourney@sha256:9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "seed": 23191, "width": 512, "height": 512, "prompt": "whimsical fantasy elegant rose floral botany maximalism with a wave of flowers garden flowing flowers floating in misty soft pink, aqua, soft apricot, smoke fractal, moody and big scale realistic flowers, octane render, by josephine wall art, isabelle menin, Jean, amy brown", "num_outputs": 1, "guidance_scale": "7", "num_inference_steps": 50 } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-11-15T02:20:27.507686Z", "created_at": "2022-11-15T02:20:23.435425Z", "data_removed": false, "error": null, "id": "txy3tnvrmzdgdot3pjjov2l6k4", "input": { "seed": 23191, "width": 512, "height": 512, "prompt": "whimsical fantasy elegant rose floral botany maximalism with a wave of flowers garden flowing flowers floating in misty soft pink, aqua, soft apricot, smoke fractal, moody and big scale realistic flowers, octane render, by josephine wall art, isabelle menin, Jean, amy brown", "num_outputs": 1, "guidance_scale": "7", "num_inference_steps": 50 }, "logs": "Using seed: 23191\nGlobal seed set to 23191\n 0%| | 0/50 [00:00<?, ?it/s]\n 4%|▍ | 2/50 [00:00<00:03, 12.80it/s]\n 8%|▊ | 4/50 [00:00<00:03, 13.68it/s]\n 12%|█▏ | 6/50 [00:00<00:03, 13.97it/s]\n 16%|█▌ | 8/50 [00:00<00:03, 13.96it/s]\n 20%|██ | 10/50 [00:00<00:02, 14.01it/s]\n 24%|██▍ | 12/50 [00:00<00:02, 14.10it/s]\n 28%|██▊ | 14/50 [00:01<00:02, 14.16it/s]\n 32%|███▏ | 16/50 [00:01<00:02, 14.23it/s]\n 36%|███▌ | 18/50 [00:01<00:02, 14.27it/s]\n 40%|████ | 20/50 [00:01<00:02, 14.25it/s]\n 44%|████▍ | 22/50 [00:01<00:01, 14.26it/s]\n 48%|████▊ | 24/50 [00:01<00:01, 14.22it/s]\n 52%|█████▏ | 26/50 [00:01<00:01, 14.20it/s]\n 56%|█████▌ | 28/50 [00:01<00:01, 14.25it/s]\n 60%|██████ | 30/50 [00:02<00:01, 14.18it/s]\n 64%|██████▍ | 32/50 [00:02<00:01, 14.26it/s]\n 68%|██████▊ | 34/50 [00:02<00:01, 14.28it/s]\n 72%|███████▏ | 36/50 [00:02<00:00, 14.33it/s]\n 76%|███████▌ | 38/50 [00:02<00:00, 14.24it/s]\n 80%|████████ | 40/50 [00:02<00:00, 14.27it/s]\n 84%|████████▍ | 42/50 [00:02<00:00, 14.25it/s]\n 88%|████████▊ | 44/50 [00:03<00:00, 14.25it/s]\n 92%|█████████▏| 46/50 [00:03<00:00, 14.29it/s]\n 96%|█████████▌| 48/50 [00:03<00:00, 14.30it/s]\n100%|██████████| 50/50 [00:03<00:00, 14.33it/s]\n100%|██████████| 50/50 [00:03<00:00, 14.20it/s]", "metrics": { "predict_time": 4.036595, "total_time": 4.072261 }, "output": [ "https://replicate.delivery/pbxt/eRFcnOLtIAyZBigLGEQO6HWiWRec6kJndEeMmpP9xFPXLVAgA/out-0.png" ], "started_at": "2022-11-15T02:20:23.471091Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/txy3tnvrmzdgdot3pjjov2l6k4", "cancel": "https://api.replicate.com/v1/predictions/txy3tnvrmzdgdot3pjjov2l6k4/cancel" }, "version": "9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb" }
Generated inUsing seed: 23191 Global seed set to 23191 0%| | 0/50 [00:00<?, ?it/s] 4%|▍ | 2/50 [00:00<00:03, 12.80it/s] 8%|▊ | 4/50 [00:00<00:03, 13.68it/s] 12%|█▏ | 6/50 [00:00<00:03, 13.97it/s] 16%|█▌ | 8/50 [00:00<00:03, 13.96it/s] 20%|██ | 10/50 [00:00<00:02, 14.01it/s] 24%|██▍ | 12/50 [00:00<00:02, 14.10it/s] 28%|██▊ | 14/50 [00:01<00:02, 14.16it/s] 32%|███▏ | 16/50 [00:01<00:02, 14.23it/s] 36%|███▌ | 18/50 [00:01<00:02, 14.27it/s] 40%|████ | 20/50 [00:01<00:02, 14.25it/s] 44%|████▍ | 22/50 [00:01<00:01, 14.26it/s] 48%|████▊ | 24/50 [00:01<00:01, 14.22it/s] 52%|█████▏ | 26/50 [00:01<00:01, 14.20it/s] 56%|█████▌ | 28/50 [00:01<00:01, 14.25it/s] 60%|██████ | 30/50 [00:02<00:01, 14.18it/s] 64%|██████▍ | 32/50 [00:02<00:01, 14.26it/s] 68%|██████▊ | 34/50 [00:02<00:01, 14.28it/s] 72%|███████▏ | 36/50 [00:02<00:00, 14.33it/s] 76%|███████▌ | 38/50 [00:02<00:00, 14.24it/s] 80%|████████ | 40/50 [00:02<00:00, 14.27it/s] 84%|████████▍ | 42/50 [00:02<00:00, 14.25it/s] 88%|████████▊ | 44/50 [00:03<00:00, 14.25it/s] 92%|█████████▏| 46/50 [00:03<00:00, 14.29it/s] 96%|█████████▌| 48/50 [00:03<00:00, 14.30it/s] 100%|██████████| 50/50 [00:03<00:00, 14.33it/s] 100%|██████████| 50/50 [00:03<00:00, 14.20it/s]
Prediction
prompthero/openjourney:9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05ebIDbrcu77klunelpnulx5uebhce64StatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 50490
- width
- 512
- height
- 512
- prompt
- anatomical correct detailed skull, cyborg head, Dramatic, Dark, Super-Resolution, Evil, Neon Lamp, Cinematic Lighting, Chromatic Aberration, insanely detailed and intricate, hypermaximalist, elegant, ornate, hyper realistic, super detailed, Unreal Engine
- num_outputs
- 1
- guidance_scale
- "7"
- num_inference_steps
- 50
{ "seed": 50490, "width": 512, "height": 512, "prompt": "anatomical correct detailed skull, cyborg head, Dramatic, Dark, Super-Resolution, Evil, Neon Lamp, Cinematic Lighting, Chromatic Aberration, insanely detailed and intricate, hypermaximalist, elegant, ornate, hyper realistic, super detailed, Unreal Engine", "num_outputs": 1, "guidance_scale": "7", "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 prompthero/openjourney using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "prompthero/openjourney:9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb", { input: { seed: 50490, width: 512, height: 512, prompt: "anatomical correct detailed skull, cyborg head, Dramatic, Dark, Super-Resolution, Evil, Neon Lamp, Cinematic Lighting, Chromatic Aberration, insanely detailed and intricate, hypermaximalist, elegant, ornate, hyper realistic, super detailed, Unreal Engine", num_outputs: 1, guidance_scale: "7", 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 prompthero/openjourney using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "prompthero/openjourney:9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb", input={ "seed": 50490, "width": 512, "height": 512, "prompt": "anatomical correct detailed skull, cyborg head, Dramatic, Dark, Super-Resolution, Evil, Neon Lamp, Cinematic Lighting, Chromatic Aberration, insanely detailed and intricate, hypermaximalist, elegant, ornate, hyper realistic, super detailed, Unreal Engine", "num_outputs": 1, "guidance_scale": "7", "num_inference_steps": 50 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run prompthero/openjourney 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": "9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb", "input": { "seed": 50490, "width": 512, "height": 512, "prompt": "anatomical correct detailed skull, cyborg head, Dramatic, Dark, Super-Resolution, Evil, Neon Lamp, Cinematic Lighting, Chromatic Aberration, insanely detailed and intricate, hypermaximalist, elegant, ornate, hyper realistic, super detailed, Unreal Engine", "num_outputs": 1, "guidance_scale": "7", "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictions
To 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 cog
If 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/prompthero/openjourney@sha256:9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb \ -i 'seed=50490' \ -i 'width=512' \ -i 'height=512' \ -i 'prompt="anatomical correct detailed skull, cyborg head, Dramatic, Dark, Super-Resolution, Evil, Neon Lamp, Cinematic Lighting, Chromatic Aberration, insanely detailed and intricate, hypermaximalist, elegant, ornate, hyper realistic, super detailed, Unreal Engine"' \ -i 'num_outputs=1' \ -i 'guidance_scale="7"' \ -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/prompthero/openjourney@sha256:9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "seed": 50490, "width": 512, "height": 512, "prompt": "anatomical correct detailed skull, cyborg head, Dramatic, Dark, Super-Resolution, Evil, Neon Lamp, Cinematic Lighting, Chromatic Aberration, insanely detailed and intricate, hypermaximalist, elegant, ornate, hyper realistic, super detailed, Unreal Engine", "num_outputs": 1, "guidance_scale": "7", "num_inference_steps": 50 } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-11-15T02:21:39.113572Z", "created_at": "2022-11-15T02:21:35.073521Z", "data_removed": false, "error": null, "id": "brcu77klunelpnulx5uebhce64", "input": { "seed": 50490, "width": 512, "height": 512, "prompt": "anatomical correct detailed skull, cyborg head, Dramatic, Dark, Super-Resolution, Evil, Neon Lamp, Cinematic Lighting, Chromatic Aberration, insanely detailed and intricate, hypermaximalist, elegant, ornate, hyper realistic, super detailed, Unreal Engine", "num_outputs": 1, "guidance_scale": "7", "num_inference_steps": 50 }, "logs": "Using seed: 50490\nGlobal seed set to 50490\n 0%| | 0/50 [00:00<?, ?it/s]\n 4%|▍ | 2/50 [00:00<00:03, 13.59it/s]\n 8%|▊ | 4/50 [00:00<00:03, 13.91it/s]\n 12%|█▏ | 6/50 [00:00<00:03, 14.02it/s]\n 16%|█▌ | 8/50 [00:00<00:02, 14.07it/s]\n 20%|██ | 10/50 [00:00<00:02, 14.09it/s]\n 24%|██▍ | 12/50 [00:00<00:02, 14.05it/s]\n 28%|██▊ | 14/50 [00:00<00:02, 14.00it/s]\n 32%|███▏ | 16/50 [00:01<00:02, 14.06it/s]\n 36%|███▌ | 18/50 [00:01<00:02, 14.11it/s]\n 40%|████ | 20/50 [00:01<00:02, 14.13it/s]\n 44%|████▍ | 22/50 [00:01<00:01, 14.16it/s]\n 48%|████▊ | 24/50 [00:01<00:01, 14.15it/s]\n 52%|█████▏ | 26/50 [00:01<00:01, 14.17it/s]\n 56%|█████▌ | 28/50 [00:01<00:01, 14.19it/s]\n 60%|██████ | 30/50 [00:02<00:01, 14.16it/s]\n 64%|██████▍ | 32/50 [00:02<00:01, 14.17it/s]\n 68%|██████▊ | 34/50 [00:02<00:01, 14.18it/s]\n 72%|███████▏ | 36/50 [00:02<00:01, 13.96it/s]\n 76%|███████▌ | 38/50 [00:02<00:00, 13.88it/s]\n 80%|████████ | 40/50 [00:02<00:00, 13.98it/s]\n 84%|████████▍ | 42/50 [00:02<00:00, 14.07it/s]\n 88%|████████▊ | 44/50 [00:03<00:00, 14.16it/s]\n 92%|█████████▏| 46/50 [00:03<00:00, 14.17it/s]\n 96%|█████████▌| 48/50 [00:03<00:00, 14.20it/s]\n100%|██████████| 50/50 [00:03<00:00, 14.25it/s]\n100%|██████████| 50/50 [00:03<00:00, 14.11it/s]", "metrics": { "predict_time": 4.004495, "total_time": 4.040051 }, "output": [ "https://replicate.delivery/pbxt/54XaDvH8JGY2NNXPAFfWPs1TPmDael4AvGQIjE9K3O0ymKAQA/out-0.png" ], "started_at": "2022-11-15T02:21:35.109077Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/brcu77klunelpnulx5uebhce64", "cancel": "https://api.replicate.com/v1/predictions/brcu77klunelpnulx5uebhce64/cancel" }, "version": "9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb" }
Generated inUsing seed: 50490 Global seed set to 50490 0%| | 0/50 [00:00<?, ?it/s] 4%|▍ | 2/50 [00:00<00:03, 13.59it/s] 8%|▊ | 4/50 [00:00<00:03, 13.91it/s] 12%|█▏ | 6/50 [00:00<00:03, 14.02it/s] 16%|█▌ | 8/50 [00:00<00:02, 14.07it/s] 20%|██ | 10/50 [00:00<00:02, 14.09it/s] 24%|██▍ | 12/50 [00:00<00:02, 14.05it/s] 28%|██▊ | 14/50 [00:00<00:02, 14.00it/s] 32%|███▏ | 16/50 [00:01<00:02, 14.06it/s] 36%|███▌ | 18/50 [00:01<00:02, 14.11it/s] 40%|████ | 20/50 [00:01<00:02, 14.13it/s] 44%|████▍ | 22/50 [00:01<00:01, 14.16it/s] 48%|████▊ | 24/50 [00:01<00:01, 14.15it/s] 52%|█████▏ | 26/50 [00:01<00:01, 14.17it/s] 56%|█████▌ | 28/50 [00:01<00:01, 14.19it/s] 60%|██████ | 30/50 [00:02<00:01, 14.16it/s] 64%|██████▍ | 32/50 [00:02<00:01, 14.17it/s] 68%|██████▊ | 34/50 [00:02<00:01, 14.18it/s] 72%|███████▏ | 36/50 [00:02<00:01, 13.96it/s] 76%|███████▌ | 38/50 [00:02<00:00, 13.88it/s] 80%|████████ | 40/50 [00:02<00:00, 13.98it/s] 84%|████████▍ | 42/50 [00:02<00:00, 14.07it/s] 88%|████████▊ | 44/50 [00:03<00:00, 14.16it/s] 92%|█████████▏| 46/50 [00:03<00:00, 14.17it/s] 96%|█████████▌| 48/50 [00:03<00:00, 14.20it/s] 100%|██████████| 50/50 [00:03<00:00, 14.25it/s] 100%|██████████| 50/50 [00:03<00:00, 14.11it/s]
Prediction
prompthero/openjourney:9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05ebIDjtkzkxaqe5dljhq2fpdql4zeqqStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 1386599936
- width
- 512
- height
- 512
- prompt
- masterpiece wallpaper of modern car, shinny metal, city, low angle, intrincate, elegant, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha, 8k
- num_outputs
- 1
- guidance_scale
- "8"
- num_inference_steps
- "50"
{ "seed": 1386599936, "width": 512, "height": 512, "prompt": "masterpiece wallpaper of modern car, shinny metal, city, low angle, intrincate, elegant, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha, 8k", "num_outputs": 1, "guidance_scale": "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 prompthero/openjourney using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "prompthero/openjourney:9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb", { input: { seed: 1386599936, width: 512, height: 512, prompt: "masterpiece wallpaper of modern car, shinny metal, city, low angle, intrincate, elegant, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha, 8k", num_outputs: 1, guidance_scale: "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 prompthero/openjourney using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "prompthero/openjourney:9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb", input={ "seed": 1386599936, "width": 512, "height": 512, "prompt": "masterpiece wallpaper of modern car, shinny metal, city, low angle, intrincate, elegant, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha, 8k", "num_outputs": 1, "guidance_scale": "8", "num_inference_steps": "50" } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run prompthero/openjourney 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": "9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb", "input": { "seed": 1386599936, "width": 512, "height": 512, "prompt": "masterpiece wallpaper of modern car, shinny metal, city, low angle, intrincate, elegant, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha, 8k", "num_outputs": 1, "guidance_scale": "8", "num_inference_steps": "50" } }' \ https://api.replicate.com/v1/predictions
To 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 cog
If 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/prompthero/openjourney@sha256:9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb \ -i 'seed=1386599936' \ -i 'width=512' \ -i 'height=512' \ -i 'prompt="masterpiece wallpaper of modern car, shinny metal, city, low angle, intrincate, elegant, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha, 8k"' \ -i 'num_outputs=1' \ -i 'guidance_scale="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/prompthero/openjourney@sha256:9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "seed": 1386599936, "width": 512, "height": 512, "prompt": "masterpiece wallpaper of modern car, shinny metal, city, low angle, intrincate, elegant, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha, 8k", "num_outputs": 1, "guidance_scale": "8", "num_inference_steps": "50" } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-11-15T02:27:51.455888Z", "created_at": "2022-11-15T02:27:47.363186Z", "data_removed": false, "error": null, "id": "jtkzkxaqe5dljhq2fpdql4zeqq", "input": { "seed": 1386599936, "width": 512, "height": 512, "prompt": "masterpiece wallpaper of modern car, shinny metal, city, low angle, intrincate, elegant, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha, 8k", "num_outputs": 1, "guidance_scale": "8", "num_inference_steps": "50" }, "logs": "Using seed: 1386599936\nGlobal seed set to 1386599936\n 0%| | 0/50 [00:00<?, ?it/s]\n 4%|▍ | 2/50 [00:00<00:03, 12.62it/s]\n 8%|▊ | 4/50 [00:00<00:03, 13.08it/s]\n 12%|█▏ | 6/50 [00:00<00:03, 12.76it/s]\n 16%|█▌ | 8/50 [00:00<00:03, 13.26it/s]\n 20%|██ | 10/50 [00:00<00:02, 13.42it/s]\n 24%|██▍ | 12/50 [00:00<00:02, 13.68it/s]\n 28%|██▊ | 14/50 [00:01<00:02, 13.78it/s]\n 32%|███▏ | 16/50 [00:01<00:02, 13.85it/s]\n 36%|███▌ | 18/50 [00:01<00:02, 13.89it/s]\n 40%|████ | 20/50 [00:01<00:02, 14.02it/s]\n 44%|████▍ | 22/50 [00:01<00:01, 14.09it/s]\n 48%|████▊ | 24/50 [00:01<00:01, 14.04it/s]\n 52%|█████▏ | 26/50 [00:01<00:01, 14.10it/s]\n 56%|█████▌ | 28/50 [00:02<00:01, 13.99it/s]\n 60%|██████ | 30/50 [00:02<00:01, 13.84it/s]\n 64%|██████▍ | 32/50 [00:02<00:01, 13.94it/s]\n 68%|██████▊ | 34/50 [00:02<00:01, 14.03it/s]\n 72%|███████▏ | 36/50 [00:02<00:00, 14.11it/s]\n 76%|███████▌ | 38/50 [00:02<00:00, 14.06it/s]\n 80%|████████ | 40/50 [00:02<00:00, 14.14it/s]\n 84%|████████▍ | 42/50 [00:03<00:00, 14.17it/s]\n 88%|████████▊ | 44/50 [00:03<00:00, 14.19it/s]\n 92%|█████████▏| 46/50 [00:03<00:00, 14.11it/s]\n 96%|█████████▌| 48/50 [00:03<00:00, 14.15it/s]\n100%|██████████| 50/50 [00:03<00:00, 14.20it/s]\n100%|██████████| 50/50 [00:03<00:00, 13.92it/s]", "metrics": { "predict_time": 4.054146, "total_time": 4.092702 }, "output": [ "https://replicate.delivery/pbxt/sTZ4JrWfVm2rK66LWsCXmr7gPxfxpBFHV87h1Ymfpfe7kVBAC/out-0.png" ], "started_at": "2022-11-15T02:27:47.401742Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/jtkzkxaqe5dljhq2fpdql4zeqq", "cancel": "https://api.replicate.com/v1/predictions/jtkzkxaqe5dljhq2fpdql4zeqq/cancel" }, "version": "9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb" }
Generated inUsing seed: 1386599936 Global seed set to 1386599936 0%| | 0/50 [00:00<?, ?it/s] 4%|▍ | 2/50 [00:00<00:03, 12.62it/s] 8%|▊ | 4/50 [00:00<00:03, 13.08it/s] 12%|█▏ | 6/50 [00:00<00:03, 12.76it/s] 16%|█▌ | 8/50 [00:00<00:03, 13.26it/s] 20%|██ | 10/50 [00:00<00:02, 13.42it/s] 24%|██▍ | 12/50 [00:00<00:02, 13.68it/s] 28%|██▊ | 14/50 [00:01<00:02, 13.78it/s] 32%|███▏ | 16/50 [00:01<00:02, 13.85it/s] 36%|███▌ | 18/50 [00:01<00:02, 13.89it/s] 40%|████ | 20/50 [00:01<00:02, 14.02it/s] 44%|████▍ | 22/50 [00:01<00:01, 14.09it/s] 48%|████▊ | 24/50 [00:01<00:01, 14.04it/s] 52%|█████▏ | 26/50 [00:01<00:01, 14.10it/s] 56%|█████▌ | 28/50 [00:02<00:01, 13.99it/s] 60%|██████ | 30/50 [00:02<00:01, 13.84it/s] 64%|██████▍ | 32/50 [00:02<00:01, 13.94it/s] 68%|██████▊ | 34/50 [00:02<00:01, 14.03it/s] 72%|███████▏ | 36/50 [00:02<00:00, 14.11it/s] 76%|███████▌ | 38/50 [00:02<00:00, 14.06it/s] 80%|████████ | 40/50 [00:02<00:00, 14.14it/s] 84%|████████▍ | 42/50 [00:03<00:00, 14.17it/s] 88%|████████▊ | 44/50 [00:03<00:00, 14.19it/s] 92%|█████████▏| 46/50 [00:03<00:00, 14.11it/s] 96%|█████████▌| 48/50 [00:03<00:00, 14.15it/s] 100%|██████████| 50/50 [00:03<00:00, 14.20it/s] 100%|██████████| 50/50 [00:03<00:00, 13.92it/s]
Prediction
prompthero/openjourney:9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05ebIDjefzpom6xfb4jnrahybd6qtziaStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 2913619048
- width
- 512
- height
- 512
- prompt
- mdjrny-v4 style portrait of a gorgeous blond female in the style of stefan kostic, realistic, half body shot, sharp focus, 8 k high definition, insanely detailed, intricate, elegant, art by stanley lau and artgerm, extreme blur flames background
- num_outputs
- 1
- guidance_scale
- "17"
- num_inference_steps
- "80"
{ "seed": 2913619048, "width": 512, "height": 512, "prompt": "mdjrny-v4 style portrait of a gorgeous blond female in the style of stefan kostic, realistic, half body shot, sharp focus, 8 k high definition, insanely detailed, intricate, elegant, art by stanley lau and artgerm, extreme blur flames background", "num_outputs": 1, "guidance_scale": "17", "num_inference_steps": "80" }
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 prompthero/openjourney using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "prompthero/openjourney:9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb", { input: { seed: 2913619048, width: 512, height: 512, prompt: "mdjrny-v4 style portrait of a gorgeous blond female in the style of stefan kostic, realistic, half body shot, sharp focus, 8 k high definition, insanely detailed, intricate, elegant, art by stanley lau and artgerm, extreme blur flames background", num_outputs: 1, guidance_scale: "17", num_inference_steps: "80" } } ); // 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 prompthero/openjourney using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "prompthero/openjourney:9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb", input={ "seed": 2913619048, "width": 512, "height": 512, "prompt": "mdjrny-v4 style portrait of a gorgeous blond female in the style of stefan kostic, realistic, half body shot, sharp focus, 8 k high definition, insanely detailed, intricate, elegant, art by stanley lau and artgerm, extreme blur flames background", "num_outputs": 1, "guidance_scale": "17", "num_inference_steps": "80" } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run prompthero/openjourney 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": "9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb", "input": { "seed": 2913619048, "width": 512, "height": 512, "prompt": "mdjrny-v4 style portrait of a gorgeous blond female in the style of stefan kostic, realistic, half body shot, sharp focus, 8 k high definition, insanely detailed, intricate, elegant, art by stanley lau and artgerm, extreme blur flames background", "num_outputs": 1, "guidance_scale": "17", "num_inference_steps": "80" } }' \ https://api.replicate.com/v1/predictions
To 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 cog
If 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/prompthero/openjourney@sha256:9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb \ -i 'seed=2913619048' \ -i 'width=512' \ -i 'height=512' \ -i 'prompt="mdjrny-v4 style portrait of a gorgeous blond female in the style of stefan kostic, realistic, half body shot, sharp focus, 8 k high definition, insanely detailed, intricate, elegant, art by stanley lau and artgerm, extreme blur flames background"' \ -i 'num_outputs=1' \ -i 'guidance_scale="17"' \ -i 'num_inference_steps="80"'
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/prompthero/openjourney@sha256:9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "seed": 2913619048, "width": 512, "height": 512, "prompt": "mdjrny-v4 style portrait of a gorgeous blond female in the style of stefan kostic, realistic, half body shot, sharp focus, 8 k high definition, insanely detailed, intricate, elegant, art by stanley lau and artgerm, extreme blur flames background", "num_outputs": 1, "guidance_scale": "17", "num_inference_steps": "80" } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-11-15T02:31:03.248051Z", "created_at": "2022-11-15T02:30:56.510079Z", "data_removed": false, "error": null, "id": "jefzpom6xfb4jnrahybd6qtzia", "input": { "seed": 2913619048, "width": 512, "height": 512, "prompt": "mdjrny-v4 style portrait of a gorgeous blond female in the style of stefan kostic, realistic, half body shot, sharp focus, 8 k high definition, insanely detailed, intricate, elegant, art by stanley lau and artgerm, extreme blur flames background", "num_outputs": 1, "guidance_scale": "17", "num_inference_steps": "80" }, "logs": "Using seed: 2913619048\nGlobal seed set to 2913619048\n 0%| | 0/80 [00:00<?, ?it/s]\n 2%|▎ | 2/80 [00:00<00:06, 12.79it/s]\n 5%|▌ | 4/80 [00:00<00:05, 13.59it/s]\n 8%|▊ | 6/80 [00:00<00:05, 13.92it/s]\n 10%|█ | 8/80 [00:00<00:05, 14.06it/s]\n 12%|█▎ | 10/80 [00:00<00:04, 14.13it/s]\n 15%|█▌ | 12/80 [00:00<00:04, 14.18it/s]\n 18%|█▊ | 14/80 [00:00<00:04, 14.20it/s]\n 20%|██ | 16/80 [00:01<00:04, 13.89it/s]\n 22%|██▎ | 18/80 [00:01<00:04, 14.01it/s]\n 25%|██▌ | 20/80 [00:01<00:04, 14.09it/s]\n 28%|██▊ | 22/80 [00:01<00:04, 14.21it/s]\n 30%|███ | 24/80 [00:01<00:03, 14.26it/s]\n 32%|███▎ | 26/80 [00:01<00:03, 14.25it/s]\n 35%|███▌ | 28/80 [00:01<00:03, 14.28it/s]\n 38%|███▊ | 30/80 [00:02<00:03, 14.32it/s]\n 40%|████ | 32/80 [00:02<00:03, 14.33it/s]\n 42%|████▎ | 34/80 [00:02<00:03, 14.18it/s]\n 45%|████▌ | 36/80 [00:02<00:03, 14.14it/s]\n 48%|████▊ | 38/80 [00:02<00:02, 14.17it/s]\n 50%|█████ | 40/80 [00:02<00:02, 14.12it/s]\n 52%|█████▎ | 42/80 [00:02<00:02, 14.17it/s]\n 55%|█████▌ | 44/80 [00:03<00:02, 14.20it/s]\n 57%|█████▊ | 46/80 [00:03<00:02, 14.20it/s]\n 60%|██████ | 48/80 [00:03<00:02, 14.21it/s]\n 62%|██████▎ | 50/80 [00:03<00:02, 14.21it/s]\n 65%|██████▌ | 52/80 [00:03<00:01, 14.24it/s]\n 68%|██████▊ | 54/80 [00:03<00:01, 14.24it/s]\n 70%|███████ | 56/80 [00:03<00:01, 14.26it/s]\n 72%|███████▎ | 58/80 [00:04<00:01, 14.28it/s]\n 75%|███████▌ | 60/80 [00:04<00:01, 14.29it/s]\n 78%|███████▊ | 62/80 [00:04<00:01, 14.29it/s]\n 80%|████████ | 64/80 [00:04<00:01, 14.25it/s]\n 82%|████████▎ | 66/80 [00:04<00:00, 14.26it/s]\n 85%|████████▌ | 68/80 [00:04<00:00, 14.18it/s]\n 88%|████████▊ | 70/80 [00:04<00:00, 14.22it/s]\n 90%|█████████ | 72/80 [00:05<00:00, 14.17it/s]\n 92%|█████████▎| 74/80 [00:05<00:00, 14.10it/s]\n 95%|█████████▌| 76/80 [00:05<00:00, 13.99it/s]\n 98%|█████████▊| 78/80 [00:05<00:00, 13.94it/s]\n100%|██████████| 80/80 [00:05<00:00, 13.87it/s]\n100%|██████████| 80/80 [00:05<00:00, 14.13it/s]", "metrics": { "predict_time": 6.698847, "total_time": 6.737972 }, "output": [ "https://replicate.delivery/pbxt/5XQKeuiuWOy4Gy8SIIAp0t4cGJcB5CdEYptGTNxY2WQzXFAIA/out-0.png" ], "started_at": "2022-11-15T02:30:56.549204Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/jefzpom6xfb4jnrahybd6qtzia", "cancel": "https://api.replicate.com/v1/predictions/jefzpom6xfb4jnrahybd6qtzia/cancel" }, "version": "9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb" }
Generated inUsing seed: 2913619048 Global seed set to 2913619048 0%| | 0/80 [00:00<?, ?it/s] 2%|▎ | 2/80 [00:00<00:06, 12.79it/s] 5%|▌ | 4/80 [00:00<00:05, 13.59it/s] 8%|▊ | 6/80 [00:00<00:05, 13.92it/s] 10%|█ | 8/80 [00:00<00:05, 14.06it/s] 12%|█▎ | 10/80 [00:00<00:04, 14.13it/s] 15%|█▌ | 12/80 [00:00<00:04, 14.18it/s] 18%|█▊ | 14/80 [00:00<00:04, 14.20it/s] 20%|██ | 16/80 [00:01<00:04, 13.89it/s] 22%|██▎ | 18/80 [00:01<00:04, 14.01it/s] 25%|██▌ | 20/80 [00:01<00:04, 14.09it/s] 28%|██▊ | 22/80 [00:01<00:04, 14.21it/s] 30%|███ | 24/80 [00:01<00:03, 14.26it/s] 32%|███▎ | 26/80 [00:01<00:03, 14.25it/s] 35%|███▌ | 28/80 [00:01<00:03, 14.28it/s] 38%|███▊ | 30/80 [00:02<00:03, 14.32it/s] 40%|████ | 32/80 [00:02<00:03, 14.33it/s] 42%|████▎ | 34/80 [00:02<00:03, 14.18it/s] 45%|████▌ | 36/80 [00:02<00:03, 14.14it/s] 48%|████▊ | 38/80 [00:02<00:02, 14.17it/s] 50%|█████ | 40/80 [00:02<00:02, 14.12it/s] 52%|█████▎ | 42/80 [00:02<00:02, 14.17it/s] 55%|█████▌ | 44/80 [00:03<00:02, 14.20it/s] 57%|█████▊ | 46/80 [00:03<00:02, 14.20it/s] 60%|██████ | 48/80 [00:03<00:02, 14.21it/s] 62%|██████▎ | 50/80 [00:03<00:02, 14.21it/s] 65%|██████▌ | 52/80 [00:03<00:01, 14.24it/s] 68%|██████▊ | 54/80 [00:03<00:01, 14.24it/s] 70%|███████ | 56/80 [00:03<00:01, 14.26it/s] 72%|███████▎ | 58/80 [00:04<00:01, 14.28it/s] 75%|███████▌ | 60/80 [00:04<00:01, 14.29it/s] 78%|███████▊ | 62/80 [00:04<00:01, 14.29it/s] 80%|████████ | 64/80 [00:04<00:01, 14.25it/s] 82%|████████▎ | 66/80 [00:04<00:00, 14.26it/s] 85%|████████▌ | 68/80 [00:04<00:00, 14.18it/s] 88%|████████▊ | 70/80 [00:04<00:00, 14.22it/s] 90%|█████████ | 72/80 [00:05<00:00, 14.17it/s] 92%|█████████▎| 74/80 [00:05<00:00, 14.10it/s] 95%|█████████▌| 76/80 [00:05<00:00, 13.99it/s] 98%|█████████▊| 78/80 [00:05<00:00, 13.94it/s] 100%|██████████| 80/80 [00:05<00:00, 13.87it/s] 100%|██████████| 80/80 [00:05<00:00, 14.13it/s]
Prediction
prompthero/openjourney:9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05ebIDiv4sclcm6jgydotukqkxtjkl6eStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 55440
- width
- 512
- height
- 512
- prompt
- devil of the old world, awake under the sea, emanating dark energy, terrible presence, arcane magic, intricate artwork . octane render, trending on artstation. cinematic, hyper realism, high detail, octane render, 8k
- num_outputs
- 1
- guidance_scale
- "7.5"
- num_inference_steps
- "70"
{ "seed": 55440, "width": 512, "height": 512, "prompt": "devil of the old world, awake under the sea, emanating dark energy, terrible presence, arcane magic, intricate artwork . octane render, trending on artstation. cinematic, hyper realism, high detail, octane render, 8k", "num_outputs": 1, "guidance_scale": "7.5", "num_inference_steps": "70" }
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 prompthero/openjourney using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "prompthero/openjourney:9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb", { input: { seed: 55440, width: 512, height: 512, prompt: "devil of the old world, awake under the sea, emanating dark energy, terrible presence, arcane magic, intricate artwork . octane render, trending on artstation. cinematic, hyper realism, high detail, octane render, 8k", num_outputs: 1, guidance_scale: "7.5", num_inference_steps: "70" } } ); // 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 prompthero/openjourney using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "prompthero/openjourney:9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb", input={ "seed": 55440, "width": 512, "height": 512, "prompt": "devil of the old world, awake under the sea, emanating dark energy, terrible presence, arcane magic, intricate artwork . octane render, trending on artstation. cinematic, hyper realism, high detail, octane render, 8k", "num_outputs": 1, "guidance_scale": "7.5", "num_inference_steps": "70" } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run prompthero/openjourney 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": "9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb", "input": { "seed": 55440, "width": 512, "height": 512, "prompt": "devil of the old world, awake under the sea, emanating dark energy, terrible presence, arcane magic, intricate artwork . octane render, trending on artstation. cinematic, hyper realism, high detail, octane render, 8k", "num_outputs": 1, "guidance_scale": "7.5", "num_inference_steps": "70" } }' \ https://api.replicate.com/v1/predictions
To 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 cog
If 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/prompthero/openjourney@sha256:9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb \ -i 'seed=55440' \ -i 'width=512' \ -i 'height=512' \ -i 'prompt="devil of the old world, awake under the sea, emanating dark energy, terrible presence, arcane magic, intricate artwork . octane render, trending on artstation. cinematic, hyper realism, high detail, octane render, 8k"' \ -i 'num_outputs=1' \ -i 'guidance_scale="7.5"' \ -i 'num_inference_steps="70"'
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/prompthero/openjourney@sha256:9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "seed": 55440, "width": 512, "height": 512, "prompt": "devil of the old world, awake under the sea, emanating dark energy, terrible presence, arcane magic, intricate artwork . octane render, trending on artstation. cinematic, hyper realism, high detail, octane render, 8k", "num_outputs": 1, "guidance_scale": "7.5", "num_inference_steps": "70" } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-11-15T10:52:42.876486Z", "created_at": "2022-11-15T10:51:55.188253Z", "data_removed": false, "error": null, "id": "iv4sclcm6jgydotukqkxtjkl6e", "input": { "seed": 55440, "width": 512, "height": 512, "prompt": "devil of the old world, awake under the sea, emanating dark energy, terrible presence, arcane magic, intricate artwork . octane render, trending on artstation. cinematic, hyper realism, high detail, octane render, 8k", "num_outputs": 1, "guidance_scale": "7.5", "num_inference_steps": "70" }, "logs": "Using seed: 55440\nGlobal seed set to 55440\n 0%| | 0/70 [00:00<?, ?it/s]\n 1%|▏ | 1/70 [00:01<01:19, 1.15s/it]\n 4%|▍ | 3/70 [00:01<00:23, 2.83it/s]\n 7%|▋ | 5/70 [00:01<00:13, 4.79it/s]\n 10%|█ | 7/70 [00:01<00:09, 6.58it/s]\n 13%|█▎ | 9/70 [00:01<00:07, 8.23it/s]\n 16%|█▌ | 11/70 [00:01<00:06, 9.63it/s]\n 19%|█▊ | 13/70 [00:02<00:05, 10.71it/s]\n 21%|██▏ | 15/70 [00:02<00:04, 11.40it/s]\n 24%|██▍ | 17/70 [00:02<00:04, 12.02it/s]\n 27%|██▋ | 19/70 [00:02<00:04, 12.52it/s]\n 30%|███ | 21/70 [00:02<00:03, 12.69it/s]\n 33%|███▎ | 23/70 [00:02<00:03, 12.82it/s]\n 36%|███▌ | 25/70 [00:02<00:03, 13.13it/s]\n 39%|███▊ | 27/70 [00:03<00:03, 13.22it/s]\n 41%|████▏ | 29/70 [00:03<00:03, 13.43it/s]\n 44%|████▍ | 31/70 [00:03<00:02, 13.56it/s]\n 47%|████▋ | 33/70 [00:03<00:02, 13.71it/s]\n 50%|█████ | 35/70 [00:03<00:02, 13.36it/s]\n 53%|█████▎ | 37/70 [00:03<00:02, 13.56it/s]\n 56%|█████▌ | 39/70 [00:03<00:02, 13.57it/s]\n 59%|█████▊ | 41/70 [00:04<00:02, 13.68it/s]\n 61%|██████▏ | 43/70 [00:04<00:01, 13.77it/s]\n 64%|██████▍ | 45/70 [00:04<00:01, 13.87it/s]\n 67%|██████▋ | 47/70 [00:04<00:01, 13.83it/s]\n 70%|███████ | 49/70 [00:04<00:01, 13.74it/s]\n 73%|███████▎ | 51/70 [00:04<00:01, 13.82it/s]\n 76%|███████▌ | 53/70 [00:04<00:01, 13.87it/s]\n 79%|███████▊ | 55/70 [00:05<00:01, 13.97it/s]\n 81%|████████▏ | 57/70 [00:05<00:00, 14.02it/s]\n 84%|████████▍ | 59/70 [00:05<00:00, 14.11it/s]\n 87%|████████▋ | 61/70 [00:05<00:00, 13.45it/s]\n 90%|█████████ | 63/70 [00:05<00:00, 13.08it/s]\n 93%|█████████▎| 65/70 [00:05<00:00, 13.28it/s]\n 96%|█████████▌| 67/70 [00:05<00:00, 13.53it/s]\n 99%|█████████▊| 69/70 [00:06<00:00, 13.74it/s]\n100%|██████████| 70/70 [00:06<00:00, 11.30it/s]", "metrics": { "predict_time": 9.065325, "total_time": 47.688233 }, "output": [ "https://replicate.delivery/pbxt/YtDDfu90uLQgGKrxKWtjfjshflYfB3IOHxZXACT9898pXIBAB/out-0.png" ], "started_at": "2022-11-15T10:52:33.811161Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/iv4sclcm6jgydotukqkxtjkl6e", "cancel": "https://api.replicate.com/v1/predictions/iv4sclcm6jgydotukqkxtjkl6e/cancel" }, "version": "9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb" }
Generated inUsing seed: 55440 Global seed set to 55440 0%| | 0/70 [00:00<?, ?it/s] 1%|▏ | 1/70 [00:01<01:19, 1.15s/it] 4%|▍ | 3/70 [00:01<00:23, 2.83it/s] 7%|▋ | 5/70 [00:01<00:13, 4.79it/s] 10%|█ | 7/70 [00:01<00:09, 6.58it/s] 13%|█▎ | 9/70 [00:01<00:07, 8.23it/s] 16%|█▌ | 11/70 [00:01<00:06, 9.63it/s] 19%|█▊ | 13/70 [00:02<00:05, 10.71it/s] 21%|██▏ | 15/70 [00:02<00:04, 11.40it/s] 24%|██▍ | 17/70 [00:02<00:04, 12.02it/s] 27%|██▋ | 19/70 [00:02<00:04, 12.52it/s] 30%|███ | 21/70 [00:02<00:03, 12.69it/s] 33%|███▎ | 23/70 [00:02<00:03, 12.82it/s] 36%|███▌ | 25/70 [00:02<00:03, 13.13it/s] 39%|███▊ | 27/70 [00:03<00:03, 13.22it/s] 41%|████▏ | 29/70 [00:03<00:03, 13.43it/s] 44%|████▍ | 31/70 [00:03<00:02, 13.56it/s] 47%|████▋ | 33/70 [00:03<00:02, 13.71it/s] 50%|█████ | 35/70 [00:03<00:02, 13.36it/s] 53%|█████▎ | 37/70 [00:03<00:02, 13.56it/s] 56%|█████▌ | 39/70 [00:03<00:02, 13.57it/s] 59%|█████▊ | 41/70 [00:04<00:02, 13.68it/s] 61%|██████▏ | 43/70 [00:04<00:01, 13.77it/s] 64%|██████▍ | 45/70 [00:04<00:01, 13.87it/s] 67%|██████▋ | 47/70 [00:04<00:01, 13.83it/s] 70%|███████ | 49/70 [00:04<00:01, 13.74it/s] 73%|███████▎ | 51/70 [00:04<00:01, 13.82it/s] 76%|███████▌ | 53/70 [00:04<00:01, 13.87it/s] 79%|███████▊ | 55/70 [00:05<00:01, 13.97it/s] 81%|████████▏ | 57/70 [00:05<00:00, 14.02it/s] 84%|████████▍ | 59/70 [00:05<00:00, 14.11it/s] 87%|████████▋ | 61/70 [00:05<00:00, 13.45it/s] 90%|█████████ | 63/70 [00:05<00:00, 13.08it/s] 93%|█████████▎| 65/70 [00:05<00:00, 13.28it/s] 96%|█████████▌| 67/70 [00:05<00:00, 13.53it/s] 99%|█████████▊| 69/70 [00:06<00:00, 13.74it/s] 100%|██████████| 70/70 [00:06<00:00, 11.30it/s]
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