georgedavila
/
sdxl-vermeer
SDXL LoRA finetuned on Vermeer paintings
- Public
- 89 runs
-
L40S
- GitHub
Prediction
georgedavila/sdxl-vermeer:18b95d64ID4437iudbcg3ns3c3brctr23qhiStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- myprompt
- A painting of an alien in the style of TOK
- outWidth
- 1024
- outHeight
- 1024
- lora_scale
- 0.6
- num_outputs
- 1
- guidanceScale
- 7.5
- promptAddendum
- high_noise_frac
- 0.8
- negative_prompt
- num_inference_steps
- 50
{ "myprompt": "A painting of an alien in the style of TOK", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "", "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 georgedavila/sdxl-vermeer using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "georgedavila/sdxl-vermeer:18b95d644b4e388463d772943810d34121e410140f6ee6f0078bb3cae64a73ff", { input: { myprompt: "A painting of an alien in the style of TOK", outWidth: 1024, outHeight: 1024, lora_scale: 0.6, num_outputs: 1, guidanceScale: 7.5, promptAddendum: "", high_noise_frac: 0.8, negative_prompt: "", 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 georgedavila/sdxl-vermeer using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "georgedavila/sdxl-vermeer:18b95d644b4e388463d772943810d34121e410140f6ee6f0078bb3cae64a73ff", input={ "myprompt": "A painting of an alien in the style of TOK", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "", "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 georgedavila/sdxl-vermeer 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": "18b95d644b4e388463d772943810d34121e410140f6ee6f0078bb3cae64a73ff", "input": { "myprompt": "A painting of an alien in the style of TOK", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "", "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-01-07T22:06:52.711271Z", "created_at": "2024-01-07T22:04:56.429592Z", "data_removed": false, "error": null, "id": "4437iudbcg3ns3c3brctr23qhi", "input": { "myprompt": "A painting of an alien in the style of TOK", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "", "num_inference_steps": 50 }, "logs": "Using seed: 26970\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:16, 2.92it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.61it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.90it/s]\n 8%|▊ | 4/50 [00:01<00:11, 4.05it/s]\n 10%|█ | 5/50 [00:01<00:10, 4.14it/s]\n 12%|█▏ | 6/50 [00:01<00:10, 4.19it/s]\n 14%|█▍ | 7/50 [00:01<00:10, 4.22it/s]\n 16%|█▌ | 8/50 [00:01<00:09, 4.24it/s]\n 18%|█▊ | 9/50 [00:02<00:09, 4.26it/s]\n 20%|██ | 10/50 [00:02<00:09, 4.26it/s]\n 22%|██▏ | 11/50 [00:02<00:09, 4.27it/s]\n 24%|██▍ | 12/50 [00:02<00:08, 4.28it/s]\n 26%|██▌ | 13/50 [00:03<00:08, 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36/50 [00:08<00:03, 4.30it/s]\n 74%|███████▍ | 37/50 [00:08<00:03, 4.30it/s]\n 76%|███████▌ | 38/50 [00:08<00:02, 4.30it/s]\n 78%|███████▊ | 39/50 [00:09<00:02, 4.30it/s]\n 80%|████████ | 40/50 [00:09<00:02, 4.29it/s]\n 82%|████████▏ | 41/50 [00:09<00:02, 4.24it/s]\n 84%|████████▍ | 42/50 [00:09<00:01, 4.26it/s]\n 86%|████████▌ | 43/50 [00:10<00:01, 4.27it/s]\n 88%|████████▊ | 44/50 [00:10<00:01, 4.27it/s]\n 90%|█████████ | 45/50 [00:10<00:01, 4.28it/s]\n 92%|█████████▏| 46/50 [00:10<00:00, 4.28it/s]\n 94%|█████████▍| 47/50 [00:11<00:00, 4.29it/s]\n 96%|█████████▌| 48/50 [00:11<00:00, 4.29it/s]\n 98%|█████████▊| 49/50 [00:11<00:00, 4.29it/s]\n100%|██████████| 50/50 [00:11<00:00, 4.29it/s]\n100%|██████████| 50/50 [00:11<00:00, 4.25it/s]", "metrics": { "predict_time": 13.897944, "total_time": 116.281679 }, "output": [ "https://replicate.delivery/pbxt/ZMzjydFceERPOqbT4TDH1S2fvkWRoa7edx507gGOzefaPpRRC/out-0.png" ], "started_at": "2024-01-07T22:06:38.813327Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/4437iudbcg3ns3c3brctr23qhi", "cancel": "https://api.replicate.com/v1/predictions/4437iudbcg3ns3c3brctr23qhi/cancel" }, "version": "18b95d644b4e388463d772943810d34121e410140f6ee6f0078bb3cae64a73ff" }Generated inUsing seed: 26970 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:16, 2.92it/s] 4%|▍ | 2/50 [00:00<00:13, 3.61it/s] 6%|▌ | 3/50 [00:00<00:12, 3.90it/s] 8%|▊ | 4/50 [00:01<00:11, 4.05it/s] 10%|█ | 5/50 [00:01<00:10, 4.14it/s] 12%|█▏ | 6/50 [00:01<00:10, 4.19it/s] 14%|█▍ | 7/50 [00:01<00:10, 4.22it/s] 16%|█▌ | 8/50 [00:01<00:09, 4.24it/s] 18%|█▊ | 9/50 [00:02<00:09, 4.26it/s] 20%|██ | 10/50 [00:02<00:09, 4.26it/s] 22%|██▏ | 11/50 [00:02<00:09, 4.27it/s] 24%|██▍ | 12/50 [00:02<00:08, 4.28it/s] 26%|██▌ | 13/50 [00:03<00:08, 4.28it/s] 28%|██▊ | 14/50 [00:03<00:08, 4.29it/s] 30%|███ | 15/50 [00:03<00:08, 4.28it/s] 32%|███▏ | 16/50 [00:03<00:07, 4.29it/s] 34%|███▍ | 17/50 [00:04<00:07, 4.29it/s] 36%|███▌ | 18/50 [00:04<00:07, 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4.24it/s] 84%|████████▍ | 42/50 [00:09<00:01, 4.26it/s] 86%|████████▌ | 43/50 [00:10<00:01, 4.27it/s] 88%|████████▊ | 44/50 [00:10<00:01, 4.27it/s] 90%|█████████ | 45/50 [00:10<00:01, 4.28it/s] 92%|█████████▏| 46/50 [00:10<00:00, 4.28it/s] 94%|█████████▍| 47/50 [00:11<00:00, 4.29it/s] 96%|█████████▌| 48/50 [00:11<00:00, 4.29it/s] 98%|█████████▊| 49/50 [00:11<00:00, 4.29it/s] 100%|██████████| 50/50 [00:11<00:00, 4.29it/s] 100%|██████████| 50/50 [00:11<00:00, 4.25it/s]Prediction
georgedavila/sdxl-vermeer:18b95d64ID7mpahhdb4f4scor4wuiamqjf7yStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- myprompt
- a painting of a capybara in the style of TOK
- outWidth
- 1024
- outHeight
- 1024
- lora_scale
- 0.6
- num_outputs
- 1
- guidanceScale
- 7.5
- promptAddendum
- high_noise_frac
- 0.8
- negative_prompt
- num_inference_steps
- 50
{ "myprompt": "a painting of a capybara in the style of TOK", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "", "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 georgedavila/sdxl-vermeer using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "georgedavila/sdxl-vermeer:18b95d644b4e388463d772943810d34121e410140f6ee6f0078bb3cae64a73ff", { input: { myprompt: "a painting of a capybara in the style of TOK", outWidth: 1024, outHeight: 1024, lora_scale: 0.6, num_outputs: 1, guidanceScale: 7.5, promptAddendum: "", high_noise_frac: 0.8, negative_prompt: "", 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 georgedavila/sdxl-vermeer using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "georgedavila/sdxl-vermeer:18b95d644b4e388463d772943810d34121e410140f6ee6f0078bb3cae64a73ff", input={ "myprompt": "a painting of a capybara in the style of TOK", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "", "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 georgedavila/sdxl-vermeer 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": "18b95d644b4e388463d772943810d34121e410140f6ee6f0078bb3cae64a73ff", "input": { "myprompt": "a painting of a capybara in the style of TOK", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "", "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-01-07T22:08:34.509817Z", "created_at": "2024-01-07T22:08:20.933633Z", "data_removed": false, "error": null, "id": "7mpahhdb4f4scor4wuiamqjf7y", "input": { "myprompt": "a painting of a capybara in the style of TOK", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "", "num_inference_steps": 50 }, "logs": "Using seed: 47680\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:11, 4.31it/s]\n 4%|▍ | 2/50 [00:00<00:11, 4.30it/s]\n 6%|▌ | 3/50 [00:00<00:10, 4.30it/s]\n 8%|▊ | 4/50 [00:00<00:10, 4.30it/s]\n 10%|█ | 5/50 [00:01<00:10, 4.30it/s]\n 12%|█▏ | 6/50 [00:01<00:10, 4.30it/s]\n 14%|█▍ | 7/50 [00:01<00:09, 4.30it/s]\n 16%|█▌ | 8/50 [00:01<00:09, 4.30it/s]\n 18%|█▊ | 9/50 [00:02<00:09, 4.30it/s]\n 20%|██ | 10/50 [00:02<00:09, 4.30it/s]\n 22%|██▏ | 11/50 [00:02<00:09, 4.30it/s]\n 24%|██▍ | 12/50 [00:02<00:08, 4.29it/s]\n 26%|██▌ | 13/50 [00:03<00:08, 4.29it/s]\n 28%|██▊ | 14/50 [00:03<00:08, 4.30it/s]\n 30%|███ | 15/50 [00:03<00:08, 4.29it/s]\n 32%|███▏ | 16/50 [00:03<00:07, 4.30it/s]\n 34%|███▍ | 17/50 [00:03<00:07, 4.29it/s]\n 36%|███▌ | 18/50 [00:04<00:07, 4.29it/s]\n 38%|███▊ | 19/50 [00:04<00:07, 4.29it/s]\n 40%|████ | 20/50 [00:04<00:06, 4.29it/s]\n 42%|████▏ | 21/50 [00:04<00:06, 4.29it/s]\n 44%|████▍ | 22/50 [00:05<00:06, 4.29it/s]\n 46%|████▌ | 23/50 [00:05<00:06, 4.29it/s]\n 48%|████▊ | 24/50 [00:05<00:06, 4.28it/s]\n 50%|█████ | 25/50 [00:05<00:05, 4.28it/s]\n 52%|█████▏ | 26/50 [00:06<00:05, 4.28it/s]\n 54%|█████▍ | 27/50 [00:06<00:05, 4.28it/s]\n 56%|█████▌ | 28/50 [00:06<00:05, 4.28it/s]\n 58%|█████▊ | 29/50 [00:06<00:04, 4.28it/s]\n 60%|██████ | 30/50 [00:06<00:04, 4.28it/s]\n 62%|██████▏ | 31/50 [00:07<00:04, 4.27it/s]\n 64%|██████▍ | 32/50 [00:07<00:04, 4.23it/s]\n 66%|██████▌ | 33/50 [00:07<00:04, 4.22it/s]\n 68%|██████▊ | 34/50 [00:07<00:03, 4.24it/s]\n 70%|███████ | 35/50 [00:08<00:03, 4.25it/s]\n 72%|███████▏ | 36/50 [00:08<00:03, 4.26it/s]\n 74%|███████▍ | 37/50 [00:08<00:03, 4.26it/s]\n 76%|███████▌ | 38/50 [00:08<00:02, 4.27it/s]\n 78%|███████▊ | 39/50 [00:09<00:02, 4.27it/s]\n 80%|████████ | 40/50 [00:09<00:02, 4.27it/s]\n 82%|████████▏ | 41/50 [00:09<00:02, 4.27it/s]\n 84%|████████▍ | 42/50 [00:09<00:01, 4.27it/s]\n 86%|████████▌ | 43/50 [00:10<00:01, 4.27it/s]\n 88%|████████▊ | 44/50 [00:10<00:01, 4.27it/s]\n 90%|█████████ | 45/50 [00:10<00:01, 4.27it/s]\n 92%|█████████▏| 46/50 [00:10<00:00, 4.27it/s]\n 94%|█████████▍| 47/50 [00:10<00:00, 4.27it/s]\n 96%|█████████▌| 48/50 [00:11<00:00, 4.27it/s]\n 98%|█████████▊| 49/50 [00:11<00:00, 4.27it/s]\n100%|██████████| 50/50 [00:11<00:00, 4.27it/s]\n100%|██████████| 50/50 [00:11<00:00, 4.28it/s]", "metrics": { "predict_time": 13.54043, "total_time": 13.576184 }, "output": [ "https://replicate.delivery/pbxt/tiKX4vPAFqZMO990YfvrxPfxHPZAj5OFH79zesvTG6FDXaUkA/out-0.png" ], "started_at": "2024-01-07T22:08:20.969387Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/7mpahhdb4f4scor4wuiamqjf7y", "cancel": "https://api.replicate.com/v1/predictions/7mpahhdb4f4scor4wuiamqjf7y/cancel" }, "version": "18b95d644b4e388463d772943810d34121e410140f6ee6f0078bb3cae64a73ff" }Generated inUsing seed: 47680 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:11, 4.31it/s] 4%|▍ | 2/50 [00:00<00:11, 4.30it/s] 6%|▌ | 3/50 [00:00<00:10, 4.30it/s] 8%|▊ | 4/50 [00:00<00:10, 4.30it/s] 10%|█ | 5/50 [00:01<00:10, 4.30it/s] 12%|█▏ | 6/50 [00:01<00:10, 4.30it/s] 14%|█▍ | 7/50 [00:01<00:09, 4.30it/s] 16%|█▌ | 8/50 [00:01<00:09, 4.30it/s] 18%|█▊ | 9/50 [00:02<00:09, 4.30it/s] 20%|██ | 10/50 [00:02<00:09, 4.30it/s] 22%|██▏ | 11/50 [00:02<00:09, 4.30it/s] 24%|██▍ | 12/50 [00:02<00:08, 4.29it/s] 26%|██▌ | 13/50 [00:03<00:08, 4.29it/s] 28%|██▊ | 14/50 [00:03<00:08, 4.30it/s] 30%|███ | 15/50 [00:03<00:08, 4.29it/s] 32%|███▏ | 16/50 [00:03<00:07, 4.30it/s] 34%|███▍ | 17/50 [00:03<00:07, 4.29it/s] 36%|███▌ | 18/50 [00:04<00:07, 4.29it/s] 38%|███▊ | 19/50 [00:04<00:07, 4.29it/s] 40%|████ | 20/50 [00:04<00:06, 4.29it/s] 42%|████▏ | 21/50 [00:04<00:06, 4.29it/s] 44%|████▍ | 22/50 [00:05<00:06, 4.29it/s] 46%|████▌ | 23/50 [00:05<00:06, 4.29it/s] 48%|████▊ | 24/50 [00:05<00:06, 4.28it/s] 50%|█████ | 25/50 [00:05<00:05, 4.28it/s] 52%|█████▏ | 26/50 [00:06<00:05, 4.28it/s] 54%|█████▍ | 27/50 [00:06<00:05, 4.28it/s] 56%|█████▌ | 28/50 [00:06<00:05, 4.28it/s] 58%|█████▊ | 29/50 [00:06<00:04, 4.28it/s] 60%|██████ | 30/50 [00:06<00:04, 4.28it/s] 62%|██████▏ | 31/50 [00:07<00:04, 4.27it/s] 64%|██████▍ | 32/50 [00:07<00:04, 4.23it/s] 66%|██████▌ | 33/50 [00:07<00:04, 4.22it/s] 68%|██████▊ | 34/50 [00:07<00:03, 4.24it/s] 70%|███████ | 35/50 [00:08<00:03, 4.25it/s] 72%|███████▏ | 36/50 [00:08<00:03, 4.26it/s] 74%|███████▍ | 37/50 [00:08<00:03, 4.26it/s] 76%|███████▌ | 38/50 [00:08<00:02, 4.27it/s] 78%|███████▊ | 39/50 [00:09<00:02, 4.27it/s] 80%|████████ | 40/50 [00:09<00:02, 4.27it/s] 82%|████████▏ | 41/50 [00:09<00:02, 4.27it/s] 84%|████████▍ | 42/50 [00:09<00:01, 4.27it/s] 86%|████████▌ | 43/50 [00:10<00:01, 4.27it/s] 88%|████████▊ | 44/50 [00:10<00:01, 4.27it/s] 90%|█████████ | 45/50 [00:10<00:01, 4.27it/s] 92%|█████████▏| 46/50 [00:10<00:00, 4.27it/s] 94%|█████████▍| 47/50 [00:10<00:00, 4.27it/s] 96%|█████████▌| 48/50 [00:11<00:00, 4.27it/s] 98%|█████████▊| 49/50 [00:11<00:00, 4.27it/s] 100%|██████████| 50/50 [00:11<00:00, 4.27it/s] 100%|██████████| 50/50 [00:11<00:00, 4.28it/s]Prediction
georgedavila/sdxl-vermeer:18b95d64ID2pzmvddbdnff7ug2ovteuqy45eStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- myprompt
- a painting of a man wearing a VR headset, in the style of TOK
- outWidth
- 1024
- outHeight
- 1024
- lora_scale
- 0.6
- num_outputs
- 1
- guidanceScale
- 7.5
- promptAddendum
- high_noise_frac
- 0.8
- negative_prompt
- monochrome
- num_inference_steps
- 50
{ "myprompt": "a painting of a man wearing a VR headset, in the style of TOK", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "monochrome", "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 georgedavila/sdxl-vermeer using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "georgedavila/sdxl-vermeer:18b95d644b4e388463d772943810d34121e410140f6ee6f0078bb3cae64a73ff", { input: { myprompt: "a painting of a man wearing a VR headset, in the style of TOK", outWidth: 1024, outHeight: 1024, lora_scale: 0.6, num_outputs: 1, guidanceScale: 7.5, promptAddendum: "", high_noise_frac: 0.8, negative_prompt: "monochrome", 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 georgedavila/sdxl-vermeer using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "georgedavila/sdxl-vermeer:18b95d644b4e388463d772943810d34121e410140f6ee6f0078bb3cae64a73ff", input={ "myprompt": "a painting of a man wearing a VR headset, in the style of TOK", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "monochrome", "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 georgedavila/sdxl-vermeer 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": "18b95d644b4e388463d772943810d34121e410140f6ee6f0078bb3cae64a73ff", "input": { "myprompt": "a painting of a man wearing a VR headset, in the style of TOK", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "monochrome", "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-01-07T22:10:03.446051Z", "created_at": "2024-01-07T22:09:49.813408Z", "data_removed": false, "error": null, "id": "2pzmvddbdnff7ug2ovteuqy45e", "input": { "myprompt": "a painting of a man wearing a VR headset, in the style of TOK", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "monochrome", "num_inference_steps": 50 }, "logs": "Using seed: 40706\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:11, 4.33it/s]\n 4%|▍ | 2/50 [00:00<00:11, 4.30it/s]\n 6%|▌ | 3/50 [00:00<00:10, 4.30it/s]\n 8%|▊ | 4/50 [00:00<00:10, 4.29it/s]\n 10%|█ | 5/50 [00:01<00:10, 4.29it/s]\n 12%|█▏ | 6/50 [00:01<00:10, 4.28it/s]\n 14%|█▍ | 7/50 [00:01<00:10, 4.28it/s]\n 16%|█▌ | 8/50 [00:01<00:09, 4.28it/s]\n 18%|█▊ | 9/50 [00:02<00:09, 4.28it/s]\n 20%|██ | 10/50 [00:02<00:09, 4.29it/s]\n 22%|██▏ | 11/50 [00:02<00:09, 4.29it/s]\n 24%|██▍ | 12/50 [00:02<00:08, 4.29it/s]\n 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[00:08<00:03, 4.29it/s]\n 72%|███████▏ | 36/50 [00:08<00:03, 4.29it/s]\n 74%|███████▍ | 37/50 [00:08<00:03, 4.29it/s]\n 76%|███████▌ | 38/50 [00:08<00:02, 4.29it/s]\n 78%|███████▊ | 39/50 [00:09<00:02, 4.28it/s]\n 80%|████████ | 40/50 [00:09<00:02, 4.29it/s]\n 82%|████████▏ | 41/50 [00:09<00:02, 4.29it/s]\n 84%|████████▍ | 42/50 [00:09<00:01, 4.29it/s]\n 86%|████████▌ | 43/50 [00:10<00:01, 4.29it/s]\n 88%|████████▊ | 44/50 [00:10<00:01, 4.29it/s]\n 90%|█████████ | 45/50 [00:10<00:01, 4.29it/s]\n 92%|█████████▏| 46/50 [00:10<00:00, 4.28it/s]\n 94%|█████████▍| 47/50 [00:10<00:00, 4.28it/s]\n 96%|█████████▌| 48/50 [00:11<00:00, 4.28it/s]\n 98%|█████████▊| 49/50 [00:11<00:00, 4.28it/s]\n100%|██████████| 50/50 [00:11<00:00, 4.28it/s]\n100%|██████████| 50/50 [00:11<00:00, 4.29it/s]", "metrics": { "predict_time": 13.596866, "total_time": 13.632643 }, "output": [ "https://replicate.delivery/pbxt/zMxGOEb5Ts5PBd9MYMII2gL1YiAD1vvIRr5XBE0OOvvOTjiE/out-0.png" ], "started_at": "2024-01-07T22:09:49.849185Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/2pzmvddbdnff7ug2ovteuqy45e", "cancel": "https://api.replicate.com/v1/predictions/2pzmvddbdnff7ug2ovteuqy45e/cancel" }, "version": "18b95d644b4e388463d772943810d34121e410140f6ee6f0078bb3cae64a73ff" }Generated inUsing seed: 40706 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:11, 4.33it/s] 4%|▍ | 2/50 [00:00<00:11, 4.30it/s] 6%|▌ | 3/50 [00:00<00:10, 4.30it/s] 8%|▊ | 4/50 [00:00<00:10, 4.29it/s] 10%|█ | 5/50 [00:01<00:10, 4.29it/s] 12%|█▏ | 6/50 [00:01<00:10, 4.28it/s] 14%|█▍ | 7/50 [00:01<00:10, 4.28it/s] 16%|█▌ | 8/50 [00:01<00:09, 4.28it/s] 18%|█▊ | 9/50 [00:02<00:09, 4.28it/s] 20%|██ | 10/50 [00:02<00:09, 4.29it/s] 22%|██▏ | 11/50 [00:02<00:09, 4.29it/s] 24%|██▍ | 12/50 [00:02<00:08, 4.29it/s] 26%|██▌ | 13/50 [00:03<00:08, 4.30it/s] 28%|██▊ | 14/50 [00:03<00:08, 4.30it/s] 30%|███ | 15/50 [00:03<00:08, 4.30it/s] 32%|███▏ | 16/50 [00:03<00:07, 4.30it/s] 34%|███▍ | 17/50 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[00:09<00:02, 4.29it/s] 82%|████████▏ | 41/50 [00:09<00:02, 4.29it/s] 84%|████████▍ | 42/50 [00:09<00:01, 4.29it/s] 86%|████████▌ | 43/50 [00:10<00:01, 4.29it/s] 88%|████████▊ | 44/50 [00:10<00:01, 4.29it/s] 90%|█████████ | 45/50 [00:10<00:01, 4.29it/s] 92%|█████████▏| 46/50 [00:10<00:00, 4.28it/s] 94%|█████████▍| 47/50 [00:10<00:00, 4.28it/s] 96%|█████████▌| 48/50 [00:11<00:00, 4.28it/s] 98%|█████████▊| 49/50 [00:11<00:00, 4.28it/s] 100%|██████████| 50/50 [00:11<00:00, 4.28it/s] 100%|██████████| 50/50 [00:11<00:00, 4.29it/s]Prediction
georgedavila/sdxl-vermeer:18b95d64IDetlfqilbydnabxyocoavscqz34StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- myprompt
- a painting of an android assassin, in the style of TOK
- outWidth
- 1024
- outHeight
- 1024
- lora_scale
- 0.6
- num_outputs
- 1
- guidanceScale
- 7.5
- promptAddendum
- high_noise_frac
- 0.8
- negative_prompt
- monochrome
- num_inference_steps
- 50
{ "myprompt": "a painting of an android assassin, in the style of TOK", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "monochrome", "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 georgedavila/sdxl-vermeer using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "georgedavila/sdxl-vermeer:18b95d644b4e388463d772943810d34121e410140f6ee6f0078bb3cae64a73ff", { input: { myprompt: "a painting of an android assassin, in the style of TOK", outWidth: 1024, outHeight: 1024, lora_scale: 0.6, num_outputs: 1, guidanceScale: 7.5, promptAddendum: "", high_noise_frac: 0.8, negative_prompt: "monochrome", 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 georgedavila/sdxl-vermeer using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "georgedavila/sdxl-vermeer:18b95d644b4e388463d772943810d34121e410140f6ee6f0078bb3cae64a73ff", input={ "myprompt": "a painting of an android assassin, in the style of TOK", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "monochrome", "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 georgedavila/sdxl-vermeer 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": "18b95d644b4e388463d772943810d34121e410140f6ee6f0078bb3cae64a73ff", "input": { "myprompt": "a painting of an android assassin, in the style of TOK", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "monochrome", "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-01-07T22:12:35.200697Z", "created_at": "2024-01-07T22:12:21.432493Z", "data_removed": false, "error": null, "id": "etlfqilbydnabxyocoavscqz34", "input": { "myprompt": "a painting of an android assassin, in the style of TOK", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "monochrome", "num_inference_steps": 50 }, "logs": "Using seed: 60421\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:11, 4.32it/s]\n 4%|▍ | 2/50 [00:00<00:11, 4.30it/s]\n 6%|▌ | 3/50 [00:00<00:10, 4.29it/s]\n 8%|▊ | 4/50 [00:00<00:10, 4.28it/s]\n 10%|█ | 5/50 [00:01<00:10, 4.27it/s]\n 12%|█▏ | 6/50 [00:01<00:10, 4.27it/s]\n 14%|█▍ | 7/50 [00:01<00:10, 4.27it/s]\n 16%|█▌ | 8/50 [00:01<00:09, 4.26it/s]\n 18%|█▊ | 9/50 [00:02<00:09, 4.27it/s]\n 20%|██ | 10/50 [00:02<00:09, 4.27it/s]\n 22%|██▏ | 11/50 [00:02<00:09, 4.27it/s]\n 24%|██▍ | 12/50 [00:02<00:08, 4.28it/s]\n 26%|██▌ | 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4.28it/s]\n 72%|███████▏ | 36/50 [00:08<00:03, 4.28it/s]\n 74%|███████▍ | 37/50 [00:08<00:03, 4.28it/s]\n 76%|███████▌ | 38/50 [00:08<00:02, 4.27it/s]\n 78%|███████▊ | 39/50 [00:09<00:02, 4.27it/s]\n 80%|████████ | 40/50 [00:09<00:02, 4.27it/s]\n 82%|████████▏ | 41/50 [00:09<00:02, 4.27it/s]\n 84%|████████▍ | 42/50 [00:09<00:01, 4.27it/s]\n 86%|████████▌ | 43/50 [00:10<00:01, 4.27it/s]\n 88%|████████▊ | 44/50 [00:10<00:01, 4.27it/s]\n 90%|█████████ | 45/50 [00:10<00:01, 4.28it/s]\n 92%|█████████▏| 46/50 [00:10<00:00, 4.28it/s]\n 94%|█████████▍| 47/50 [00:10<00:00, 4.28it/s]\n 96%|█████████▌| 48/50 [00:11<00:00, 4.28it/s]\n 98%|█████████▊| 49/50 [00:11<00:00, 4.28it/s]\n100%|██████████| 50/50 [00:11<00:00, 4.28it/s]\n100%|██████████| 50/50 [00:11<00:00, 4.28it/s]", "metrics": { "predict_time": 13.701292, "total_time": 13.768204 }, "output": [ "https://replicate.delivery/pbxt/CatFO7zkC6K5CBYWIwA1XSPywP28LMlstN5ACim90Bn0TjiE/out-0.png" ], "started_at": "2024-01-07T22:12:21.499405Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/etlfqilbydnabxyocoavscqz34", "cancel": "https://api.replicate.com/v1/predictions/etlfqilbydnabxyocoavscqz34/cancel" }, "version": "18b95d644b4e388463d772943810d34121e410140f6ee6f0078bb3cae64a73ff" }Generated inUsing seed: 60421 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:11, 4.32it/s] 4%|▍ | 2/50 [00:00<00:11, 4.30it/s] 6%|▌ | 3/50 [00:00<00:10, 4.29it/s] 8%|▊ | 4/50 [00:00<00:10, 4.28it/s] 10%|█ | 5/50 [00:01<00:10, 4.27it/s] 12%|█▏ | 6/50 [00:01<00:10, 4.27it/s] 14%|█▍ | 7/50 [00:01<00:10, 4.27it/s] 16%|█▌ | 8/50 [00:01<00:09, 4.26it/s] 18%|█▊ | 9/50 [00:02<00:09, 4.27it/s] 20%|██ | 10/50 [00:02<00:09, 4.27it/s] 22%|██▏ | 11/50 [00:02<00:09, 4.27it/s] 24%|██▍ | 12/50 [00:02<00:08, 4.28it/s] 26%|██▌ | 13/50 [00:03<00:08, 4.28it/s] 28%|██▊ | 14/50 [00:03<00:08, 4.28it/s] 30%|███ | 15/50 [00:03<00:08, 4.29it/s] 32%|███▏ | 16/50 [00:03<00:07, 4.29it/s] 34%|███▍ | 17/50 [00:03<00:07, 4.29it/s] 36%|███▌ | 18/50 [00:04<00:07, 4.29it/s] 38%|███▊ | 19/50 [00:04<00:07, 4.29it/s] 40%|████ | 20/50 [00:04<00:06, 4.29it/s] 42%|████▏ | 21/50 [00:04<00:06, 4.29it/s] 44%|████▍ | 22/50 [00:05<00:06, 4.29it/s] 46%|████▌ | 23/50 [00:05<00:06, 4.29it/s] 48%|████▊ | 24/50 [00:05<00:06, 4.29it/s] 50%|█████ | 25/50 [00:05<00:05, 4.28it/s] 52%|█████▏ | 26/50 [00:06<00:05, 4.28it/s] 54%|█████▍ | 27/50 [00:06<00:05, 4.28it/s] 56%|█████▌ | 28/50 [00:06<00:05, 4.28it/s] 58%|█████▊ | 29/50 [00:06<00:04, 4.28it/s] 60%|██████ | 30/50 [00:07<00:04, 4.28it/s] 62%|██████▏ | 31/50 [00:07<00:04, 4.28it/s] 64%|██████▍ | 32/50 [00:07<00:04, 4.28it/s] 66%|██████▌ | 33/50 [00:07<00:03, 4.28it/s] 68%|██████▊ | 34/50 [00:07<00:03, 4.28it/s] 70%|███████ | 35/50 [00:08<00:03, 4.28it/s] 72%|███████▏ | 36/50 [00:08<00:03, 4.28it/s] 74%|███████▍ | 37/50 [00:08<00:03, 4.28it/s] 76%|███████▌ | 38/50 [00:08<00:02, 4.27it/s] 78%|███████▊ | 39/50 [00:09<00:02, 4.27it/s] 80%|████████ | 40/50 [00:09<00:02, 4.27it/s] 82%|████████▏ | 41/50 [00:09<00:02, 4.27it/s] 84%|████████▍ | 42/50 [00:09<00:01, 4.27it/s] 86%|████████▌ | 43/50 [00:10<00:01, 4.27it/s] 88%|████████▊ | 44/50 [00:10<00:01, 4.27it/s] 90%|█████████ | 45/50 [00:10<00:01, 4.28it/s] 92%|█████████▏| 46/50 [00:10<00:00, 4.28it/s] 94%|█████████▍| 47/50 [00:10<00:00, 4.28it/s] 96%|█████████▌| 48/50 [00:11<00:00, 4.28it/s] 98%|█████████▊| 49/50 [00:11<00:00, 4.28it/s] 100%|██████████| 50/50 [00:11<00:00, 4.28it/s] 100%|██████████| 50/50 [00:11<00:00, 4.28it/s]Prediction
georgedavila/sdxl-vermeer:18b95d64IDwfhwzv3bgwqwatn5hkynhg5mhqStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- myprompt
- a painting of a girl wearing a pearl earring, in the style of TOK
- outWidth
- 1024
- outHeight
- 1024
- lora_scale
- 0.6
- num_outputs
- 1
- guidanceScale
- 7.5
- promptAddendum
- high_noise_frac
- 0.8
- negative_prompt
- monochrome
- num_inference_steps
- 50
{ "myprompt": "a painting of a girl wearing a pearl earring, in the style of TOK", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "monochrome", "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 georgedavila/sdxl-vermeer using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "georgedavila/sdxl-vermeer:18b95d644b4e388463d772943810d34121e410140f6ee6f0078bb3cae64a73ff", { input: { myprompt: "a painting of a girl wearing a pearl earring, in the style of TOK", outWidth: 1024, outHeight: 1024, lora_scale: 0.6, num_outputs: 1, guidanceScale: 7.5, promptAddendum: "", high_noise_frac: 0.8, negative_prompt: "monochrome", 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 georgedavila/sdxl-vermeer using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "georgedavila/sdxl-vermeer:18b95d644b4e388463d772943810d34121e410140f6ee6f0078bb3cae64a73ff", input={ "myprompt": "a painting of a girl wearing a pearl earring, in the style of TOK", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "monochrome", "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 georgedavila/sdxl-vermeer 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": "18b95d644b4e388463d772943810d34121e410140f6ee6f0078bb3cae64a73ff", "input": { "myprompt": "a painting of a girl wearing a pearl earring, in the style of TOK", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "monochrome", "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-01-07T23:33:21.302273Z", "created_at": "2024-01-07T23:30:27.200126Z", "data_removed": false, "error": null, "id": "wfhwzv3bgwqwatn5hkynhg5mhq", "input": { "myprompt": "a painting of a girl wearing a pearl earring, in the style of TOK", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "monochrome", "num_inference_steps": 50 }, "logs": "Using seed: 31245\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:33, 1.48it/s]\n 4%|▍ | 2/50 [00:00<00:19, 2.41it/s]\n 6%|▌ | 3/50 [00:01<00:15, 3.01it/s]\n 8%|▊ | 4/50 [00:01<00:13, 3.41it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.68it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.85it/s]\n 14%|█▍ | 7/50 [00:02<00:10, 3.97it/s]\n 16%|█▌ | 8/50 [00:02<00:10, 4.06it/s]\n 18%|█▊ | 9/50 [00:02<00:09, 4.12it/s]\n 20%|██ | 10/50 [00:02<00:09, 4.16it/s]\n 22%|██▏ | 11/50 [00:03<00:09, 4.19it/s]\n 24%|██▍ | 12/50 [00:03<00:09, 4.21it/s]\n 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[00:08<00:03, 4.27it/s]\n 72%|███████▏ | 36/50 [00:08<00:03, 4.27it/s]\n 74%|███████▍ | 37/50 [00:09<00:03, 4.27it/s]\n 76%|███████▌ | 38/50 [00:09<00:02, 4.27it/s]\n 78%|███████▊ | 39/50 [00:09<00:02, 4.27it/s]\n 80%|████████ | 40/50 [00:09<00:02, 4.27it/s]\n 82%|████████▏ | 41/50 [00:10<00:02, 4.27it/s]\n 84%|████████▍ | 42/50 [00:10<00:01, 4.27it/s]\n 86%|████████▌ | 43/50 [00:10<00:01, 4.27it/s]\n 88%|████████▊ | 44/50 [00:10<00:01, 4.27it/s]\n 90%|█████████ | 45/50 [00:10<00:01, 4.27it/s]\n 92%|█████████▏| 46/50 [00:11<00:00, 4.27it/s]\n 94%|█████████▍| 47/50 [00:11<00:00, 4.27it/s]\n 96%|█████████▌| 48/50 [00:11<00:00, 4.27it/s]\n 98%|█████████▊| 49/50 [00:11<00:00, 4.27it/s]\n100%|██████████| 50/50 [00:12<00:00, 4.27it/s]\n100%|██████████| 50/50 [00:12<00:00, 4.11it/s]", "metrics": { "predict_time": 14.304477, "total_time": 174.102147 }, "output": [ "https://replicate.delivery/pbxt/w2vxrq6ZMCZDOBQZRijtAzbXfqNwD0mnfHmYbYnPGTNAbOKSA/out-0.png" ], "started_at": "2024-01-07T23:33:06.997796Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/wfhwzv3bgwqwatn5hkynhg5mhq", "cancel": "https://api.replicate.com/v1/predictions/wfhwzv3bgwqwatn5hkynhg5mhq/cancel" }, "version": "18b95d644b4e388463d772943810d34121e410140f6ee6f0078bb3cae64a73ff" }Generated inUsing seed: 31245 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:33, 1.48it/s] 4%|▍ | 2/50 [00:00<00:19, 2.41it/s] 6%|▌ | 3/50 [00:01<00:15, 3.01it/s] 8%|▊ | 4/50 [00:01<00:13, 3.41it/s] 10%|█ | 5/50 [00:01<00:12, 3.68it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.85it/s] 14%|█▍ | 7/50 [00:02<00:10, 3.97it/s] 16%|█▌ | 8/50 [00:02<00:10, 4.06it/s] 18%|█▊ | 9/50 [00:02<00:09, 4.12it/s] 20%|██ | 10/50 [00:02<00:09, 4.16it/s] 22%|██▏ | 11/50 [00:03<00:09, 4.19it/s] 24%|██▍ | 12/50 [00:03<00:09, 4.21it/s] 26%|██▌ | 13/50 [00:03<00:08, 4.23it/s] 28%|██▊ | 14/50 [00:03<00:08, 4.23it/s] 30%|███ | 15/50 [00:03<00:08, 4.24it/s] 32%|███▏ | 16/50 [00:04<00:08, 4.24it/s] 34%|███▍ | 17/50 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georgedavila/sdxl-vermeer:18b95d64IDjc7v3u3bl7v75kaiwyvqjkllvqStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- myprompt
- a painting of a modern girl wearing a pearl earring rainbow background, in the style of TOK
- outWidth
- 1024
- outHeight
- 1024
- lora_scale
- 0.6
- num_outputs
- 1
- guidanceScale
- 7.5
- promptAddendum
- high_noise_frac
- 0.8
- negative_prompt
- monochrome
- num_inference_steps
- 50
{ "myprompt": "a painting of a modern girl wearing a pearl earring rainbow background, in the style of TOK", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "monochrome", "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 georgedavila/sdxl-vermeer using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "georgedavila/sdxl-vermeer:18b95d644b4e388463d772943810d34121e410140f6ee6f0078bb3cae64a73ff", { input: { myprompt: "a painting of a modern girl wearing a pearl earring rainbow background, in the style of TOK", outWidth: 1024, outHeight: 1024, lora_scale: 0.6, num_outputs: 1, guidanceScale: 7.5, promptAddendum: "", high_noise_frac: 0.8, negative_prompt: "monochrome", 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 georgedavila/sdxl-vermeer using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "georgedavila/sdxl-vermeer:18b95d644b4e388463d772943810d34121e410140f6ee6f0078bb3cae64a73ff", input={ "myprompt": "a painting of a modern girl wearing a pearl earring rainbow background, in the style of TOK", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "monochrome", "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 georgedavila/sdxl-vermeer 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": "18b95d644b4e388463d772943810d34121e410140f6ee6f0078bb3cae64a73ff", "input": { "myprompt": "a painting of a modern girl wearing a pearl earring rainbow background, in the style of TOK", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "monochrome", "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-01-07T23:34:28.273958Z", "created_at": "2024-01-07T23:34:14.449718Z", "data_removed": false, "error": null, "id": "jc7v3u3bl7v75kaiwyvqjkllvq", "input": { "myprompt": "a painting of a modern girl wearing a pearl earring rainbow background, in the style of TOK", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "monochrome", "num_inference_steps": 50 }, "logs": "Using seed: 50264\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:11, 4.29it/s]\n 4%|▍ | 2/50 [00:00<00:11, 4.27it/s]\n 6%|▌ | 3/50 [00:00<00:11, 4.27it/s]\n 8%|▊ | 4/50 [00:00<00:10, 4.27it/s]\n 10%|█ | 5/50 [00:01<00:10, 4.27it/s]\n 12%|█▏ | 6/50 [00:01<00:10, 4.27it/s]\n 14%|█▍ | 7/50 [00:01<00:10, 4.27it/s]\n 16%|█▌ | 8/50 [00:01<00:09, 4.27it/s]\n 18%|█▊ | 9/50 [00:02<00:09, 4.27it/s]\n 20%|██ | 10/50 [00:02<00:09, 4.27it/s]\n 22%|██▏ | 11/50 [00:02<00:09, 4.27it/s]\n 24%|██▍ | 12/50 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70%|███████ | 35/50 [00:08<00:03, 4.25it/s]\n 72%|███████▏ | 36/50 [00:08<00:03, 4.25it/s]\n 74%|███████▍ | 37/50 [00:08<00:03, 4.25it/s]\n 76%|███████▌ | 38/50 [00:08<00:02, 4.24it/s]\n 78%|███████▊ | 39/50 [00:09<00:02, 4.24it/s]\n 80%|████████ | 40/50 [00:09<00:02, 4.24it/s]\n 82%|████████▏ | 41/50 [00:09<00:02, 4.24it/s]\n 84%|████████▍ | 42/50 [00:09<00:01, 4.24it/s]\n 86%|████████▌ | 43/50 [00:10<00:01, 4.24it/s]\n 88%|████████▊ | 44/50 [00:10<00:01, 4.24it/s]\n 90%|█████████ | 45/50 [00:10<00:01, 4.24it/s]\n 92%|█████████▏| 46/50 [00:10<00:00, 4.24it/s]\n 94%|█████████▍| 47/50 [00:11<00:00, 4.24it/s]\n 96%|█████████▌| 48/50 [00:11<00:00, 4.24it/s]\n 98%|█████████▊| 49/50 [00:11<00:00, 4.24it/s]\n100%|██████████| 50/50 [00:11<00:00, 4.18it/s]\n100%|██████████| 50/50 [00:11<00:00, 4.25it/s]", "metrics": { "predict_time": 13.788706, "total_time": 13.82424 }, "output": [ "https://replicate.delivery/pbxt/6zQKgCKeo600OS7ncvAOjsdPcjXBySWpT8D5IH1IxM4BOHFJA/out-0.png" ], "started_at": "2024-01-07T23:34:14.485252Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/jc7v3u3bl7v75kaiwyvqjkllvq", "cancel": "https://api.replicate.com/v1/predictions/jc7v3u3bl7v75kaiwyvqjkllvq/cancel" }, "version": "18b95d644b4e388463d772943810d34121e410140f6ee6f0078bb3cae64a73ff" }Generated inUsing seed: 50264 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:11, 4.29it/s] 4%|▍ | 2/50 [00:00<00:11, 4.27it/s] 6%|▌ | 3/50 [00:00<00:11, 4.27it/s] 8%|▊ | 4/50 [00:00<00:10, 4.27it/s] 10%|█ | 5/50 [00:01<00:10, 4.27it/s] 12%|█▏ | 6/50 [00:01<00:10, 4.27it/s] 14%|█▍ | 7/50 [00:01<00:10, 4.27it/s] 16%|█▌ | 8/50 [00:01<00:09, 4.27it/s] 18%|█▊ | 9/50 [00:02<00:09, 4.27it/s] 20%|██ | 10/50 [00:02<00:09, 4.27it/s] 22%|██▏ | 11/50 [00:02<00:09, 4.27it/s] 24%|██▍ | 12/50 [00:02<00:08, 4.27it/s] 26%|██▌ | 13/50 [00:03<00:08, 4.27it/s] 28%|██▊ | 14/50 [00:03<00:08, 4.26it/s] 30%|███ | 15/50 [00:03<00:08, 4.26it/s] 32%|███▏ | 16/50 [00:03<00:07, 4.27it/s] 34%|███▍ | 17/50 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