jschoormans / lora-laagaam
(Updated 1 year, 9 months ago)
- Public
- 139 runs
Prediction
jschoormans/lora-laagaam:d3b9eea061eb73aa65c27accbedf44f20932e1ecdc6e1f430b6ec2695a414057IDro7zqkdbkp2zig2lej3xrnoi64StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- a woman wearing a TOK jacket
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "a woman wearing a TOK jacket", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run jschoormans/lora-laagaam using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jschoormans/lora-laagaam:d3b9eea061eb73aa65c27accbedf44f20932e1ecdc6e1f430b6ec2695a414057", { input: { width: 1024, height: 1024, prompt: "a woman wearing a TOK jacket", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun jschoormans/lora-laagaam using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jschoormans/lora-laagaam:d3b9eea061eb73aa65c27accbedf44f20932e1ecdc6e1f430b6ec2695a414057", input={ "width": 1024, "height": 1024, "prompt": "a woman wearing a TOK jacket", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run jschoormans/lora-laagaam 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": "jschoormans/lora-laagaam:d3b9eea061eb73aa65c27accbedf44f20932e1ecdc6e1f430b6ec2695a414057", "input": { "width": 1024, "height": 1024, "prompt": "a woman wearing a TOK jacket", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-09-11T18:45:51.241559Z", "created_at": "2023-09-11T18:44:52.094139Z", "data_removed": false, "error": null, "id": "ro7zqkdbkp2zig2lej3xrnoi64", "input": { "width": 1024, "height": 1024, "prompt": "a woman wearing a TOK jacket", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 47062\nPrompt: a woman wearing a <s0><s1> jacket\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:38, 1.28it/s]\n 4%|▍ | 2/50 [00:01<00:23, 2.08it/s]\n 6%|▌ | 3/50 [00:01<00:18, 2.60it/s]\n 8%|▊ | 4/50 [00:01<00:15, 2.93it/s]\n 10%|█ | 5/50 [00:01<00:14, 3.17it/s]\n 12%|█▏ | 6/50 [00:02<00:13, 3.32it/s]\n 14%|█▍ | 7/50 [00:02<00:12, 3.43it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.50it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.55it/s]\n 20%|██ | 10/50 [00:03<00:11, 3.59it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.62it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.63it/s]\n 26%|██▌ | 13/50 [00:04<00:10, 3.65it/s]\n 28%|██▊ | 14/50 [00:04<00:09, 3.65it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.66it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.66it/s]\n 34%|███▍ | 17/50 [00:05<00:09, 3.66it/s]\n 36%|███▌ | 18/50 [00:05<00:08, 3.66it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.67it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.67it/s]\n 42%|████▏ | 21/50 [00:06<00:07, 3.67it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.67it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.67it/s]\n 48%|████▊ | 24/50 [00:07<00:07, 3.67it/s]\n 50%|█████ | 25/50 [00:07<00:06, 3.67it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.66it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.66it/s]\n 56%|█████▌ | 28/50 [00:08<00:06, 3.67it/s]\n 58%|█████▊ | 29/50 [00:08<00:05, 3.67it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.67it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.67it/s]\n 64%|██████▍ | 32/50 [00:09<00:04, 3.68it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.68it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.68it/s]\n 70%|███████ | 35/50 [00:10<00:04, 3.68it/s]\n 72%|███████▏ | 36/50 [00:10<00:03, 3.68it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.68it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.68it/s]\n 78%|███████▊ | 39/50 [00:11<00:02, 3.68it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.68it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.68it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.68it/s]\n 86%|████████▌ | 43/50 [00:12<00:01, 3.68it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.68it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.68it/s]\n 92%|█████████▏| 46/50 [00:13<00:01, 3.68it/s]\n 94%|█████████▍| 47/50 [00:13<00:00, 3.68it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.68it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.68it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.68it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.54it/s]", "metrics": { "predict_time": 16.619695, "total_time": 59.14742 }, "output": [ "https://replicate.delivery/pbxt/Ogz7HjWATmK0K1P1XmEedCu8VDEiDC128NvsCsOxoSeeSiGjA/out-0.png" ], "started_at": "2023-09-11T18:45:34.621864Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ro7zqkdbkp2zig2lej3xrnoi64", "cancel": "https://api.replicate.com/v1/predictions/ro7zqkdbkp2zig2lej3xrnoi64/cancel" }, "version": "d3b9eea061eb73aa65c27accbedf44f20932e1ecdc6e1f430b6ec2695a414057" }Generated inUsing seed: 47062 Prompt: a woman wearing a <s0><s1> jacket txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:38, 1.28it/s] 4%|▍ | 2/50 [00:01<00:23, 2.08it/s] 6%|▌ | 3/50 [00:01<00:18, 2.60it/s] 8%|▊ | 4/50 [00:01<00:15, 2.93it/s] 10%|█ | 5/50 [00:01<00:14, 3.17it/s] 12%|█▏ | 6/50 [00:02<00:13, 3.32it/s] 14%|█▍ | 7/50 [00:02<00:12, 3.43it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.50it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.55it/s] 20%|██ | 10/50 [00:03<00:11, 3.59it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.62it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.63it/s] 26%|██▌ | 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3.68it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.68it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.68it/s] 78%|███████▊ | 39/50 [00:11<00:02, 3.68it/s] 80%|████████ | 40/50 [00:11<00:02, 3.68it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.68it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.68it/s] 86%|████████▌ | 43/50 [00:12<00:01, 3.68it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.68it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.68it/s] 92%|█████████▏| 46/50 [00:13<00:01, 3.68it/s] 94%|█████████▍| 47/50 [00:13<00:00, 3.68it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.68it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.68it/s] 100%|██████████| 50/50 [00:14<00:00, 3.68it/s] 100%|██████████| 50/50 [00:14<00:00, 3.54it/s]Prediction
jschoormans/lora-laagaam:d3b9eea061eb73aa65c27accbedf44f20932e1ecdc6e1f430b6ec2695a414057ID4m7wu5db5fau6odrcqr7abzqxeStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- many people wearing TOK jackets in the zoo
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 20.47
- apply_watermark
- high_noise_frac
- 0.9
- prompt_strength
- 0.79
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "many people wearing TOK jackets in the zoo", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 20.47, "apply_watermark": true, "high_noise_frac": 0.9, "prompt_strength": 0.79, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run jschoormans/lora-laagaam using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jschoormans/lora-laagaam:d3b9eea061eb73aa65c27accbedf44f20932e1ecdc6e1f430b6ec2695a414057", { input: { width: 1024, height: 1024, prompt: "many people wearing TOK jackets in the zoo", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 20.47, apply_watermark: true, high_noise_frac: 0.9, prompt_strength: 0.79, 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 replicateImport the client:import replicateRun jschoormans/lora-laagaam using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jschoormans/lora-laagaam:d3b9eea061eb73aa65c27accbedf44f20932e1ecdc6e1f430b6ec2695a414057", input={ "width": 1024, "height": 1024, "prompt": "many people wearing TOK jackets in the zoo", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 20.47, "apply_watermark": True, "high_noise_frac": 0.9, "prompt_strength": 0.79, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run jschoormans/lora-laagaam 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": "jschoormans/lora-laagaam:d3b9eea061eb73aa65c27accbedf44f20932e1ecdc6e1f430b6ec2695a414057", "input": { "width": 1024, "height": 1024, "prompt": "many people wearing TOK jackets in the zoo", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 20.47, "apply_watermark": true, "high_noise_frac": 0.9, "prompt_strength": 0.79, "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": "2023-09-11T18:52:59.981989Z", "created_at": "2023-09-11T18:52:44.181070Z", "data_removed": false, "error": null, "id": "4m7wu5db5fau6odrcqr7abzqxe", "input": { "width": 1024, "height": 1024, "prompt": "many people wearing TOK jackets in the zoo", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 20.47, "apply_watermark": true, "high_noise_frac": 0.9, "prompt_strength": 0.79, "num_inference_steps": 50 }, "logs": "Using seed: 10958\nPrompt: many people wearing <s0><s1> jackets in the zoo\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.70it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.69it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.68it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.68it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.68it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.68it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.67it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.67it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.67it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.67it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.66it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.67it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.67it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.66it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.66it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.66it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.66it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.65it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.65it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.65it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.65it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.65it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.65it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.65it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.65it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.65it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.65it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.65it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.65it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.65it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.65it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.65it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.65it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.65it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.65it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.65it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.65it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.65it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.65it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.65it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.65it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.65it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.65it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.65it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.65it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.65it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.65it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.65it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.65it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.65it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.66it/s]", "metrics": { "predict_time": 15.795945, "total_time": 15.800919 }, "output": [ "https://replicate.delivery/pbxt/LAivBpxPcPIkH1e416vFs4QucZ6SJZh3fGT7z1faiqbUgiGjA/out-0.png" ], "started_at": "2023-09-11T18:52:44.186044Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/4m7wu5db5fau6odrcqr7abzqxe", "cancel": "https://api.replicate.com/v1/predictions/4m7wu5db5fau6odrcqr7abzqxe/cancel" }, "version": "d3b9eea061eb73aa65c27accbedf44f20932e1ecdc6e1f430b6ec2695a414057" }Generated inUsing seed: 10958 Prompt: many people wearing <s0><s1> jackets in the zoo txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.70it/s] 4%|▍ | 2/50 [00:00<00:13, 3.69it/s] 6%|▌ | 3/50 [00:00<00:12, 3.68it/s] 8%|▊ | 4/50 [00:01<00:12, 3.68it/s] 10%|█ | 5/50 [00:01<00:12, 3.68it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.68it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.67it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.67it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.67it/s] 20%|██ | 10/50 [00:02<00:10, 3.67it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.66it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.67it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.67it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.66it/s] 30%|███ | 15/50 [00:04<00:09, 3.66it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.66it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.66it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.65it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.65it/s] 40%|████ | 20/50 [00:05<00:08, 3.65it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.65it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.65it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.65it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.65it/s] 50%|█████ | 25/50 [00:06<00:06, 3.65it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.65it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.65it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.65it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.65it/s] 60%|██████ | 30/50 [00:08<00:05, 3.65it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.65it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.65it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.65it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.65it/s] 70%|███████ | 35/50 [00:09<00:04, 3.65it/s] 72%|███████▏ | 36/50 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jschoormans/lora-laagaam:d3b9eea061eb73aa65c27accbedf44f20932e1ecdc6e1f430b6ec2695a414057ID6q3cfblbd3g5zkfq22zju6oyjqStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- many people wearing TOK jackets in the zoo
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.8
- num_outputs
- 1
- guidance_scale
- 20.47
- apply_watermark
- high_noise_frac
- 0.9
- prompt_strength
- 0.79
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "many people wearing TOK jackets in the zoo", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 20.47, "apply_watermark": true, "high_noise_frac": 0.9, "prompt_strength": 0.79, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run jschoormans/lora-laagaam using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jschoormans/lora-laagaam:d3b9eea061eb73aa65c27accbedf44f20932e1ecdc6e1f430b6ec2695a414057", { input: { width: 1024, height: 1024, prompt: "many people wearing TOK jackets in the zoo", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.8, num_outputs: 1, guidance_scale: 20.47, apply_watermark: true, high_noise_frac: 0.9, prompt_strength: 0.79, 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 replicateImport the client:import replicateRun jschoormans/lora-laagaam using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jschoormans/lora-laagaam:d3b9eea061eb73aa65c27accbedf44f20932e1ecdc6e1f430b6ec2695a414057", input={ "width": 1024, "height": 1024, "prompt": "many people wearing TOK jackets in the zoo", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 20.47, "apply_watermark": True, "high_noise_frac": 0.9, "prompt_strength": 0.79, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run jschoormans/lora-laagaam 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": "jschoormans/lora-laagaam:d3b9eea061eb73aa65c27accbedf44f20932e1ecdc6e1f430b6ec2695a414057", "input": { "width": 1024, "height": 1024, "prompt": "many people wearing TOK jackets in the zoo", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 20.47, "apply_watermark": true, "high_noise_frac": 0.9, "prompt_strength": 0.79, "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": "2023-09-11T18:54:11.070261Z", "created_at": "2023-09-11T18:53:55.512879Z", "data_removed": false, "error": null, "id": "6q3cfblbd3g5zkfq22zju6oyjq", "input": { "width": 1024, "height": 1024, "prompt": "many people wearing TOK jackets in the zoo", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 20.47, "apply_watermark": true, "high_noise_frac": 0.9, "prompt_strength": 0.79, "num_inference_steps": 50 }, "logs": "Using seed: 22942\nPrompt: many people wearing <s0><s1> jackets in the zoo\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.69it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.67it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.67it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.67it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.66it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.67it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.67it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.68it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.68it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.68it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.68it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.68it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.68it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.68it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.68it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.68it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.68it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.67it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.67it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.67it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.67it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.67it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.67it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.67it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.67it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.67it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.68it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.68it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.67it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.67it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.67it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.67it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.67it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.67it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.67it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.67it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.67it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.67it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.67it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.67it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.67it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.67it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.67it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.67it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.67it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.67it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.67it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.67it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.67it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.67it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.67it/s]", "metrics": { "predict_time": 15.545697, "total_time": 15.557382 }, "output": [ "https://replicate.delivery/pbxt/Lum46Nijy8qIBlQof6Nc7EaUZdXSeVYPob5bXwdsJFekiiGjA/out-0.png" ], "started_at": "2023-09-11T18:53:55.524564Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/6q3cfblbd3g5zkfq22zju6oyjq", "cancel": "https://api.replicate.com/v1/predictions/6q3cfblbd3g5zkfq22zju6oyjq/cancel" }, "version": "d3b9eea061eb73aa65c27accbedf44f20932e1ecdc6e1f430b6ec2695a414057" }Generated inUsing seed: 22942 Prompt: many people wearing <s0><s1> jackets in the zoo txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.69it/s] 4%|▍ | 2/50 [00:00<00:13, 3.67it/s] 6%|▌ | 3/50 [00:00<00:12, 3.67it/s] 8%|▊ | 4/50 [00:01<00:12, 3.67it/s] 10%|█ | 5/50 [00:01<00:12, 3.66it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.67it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.67it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.68it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.68it/s] 20%|██ | 10/50 [00:02<00:10, 3.68it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.68it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.68it/s] 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