lisandroe / loralisandro
- Public
- 258 runs
-
H100
Prediction
lisandroe/loralisandro:edee113e85629e0c068637a9aa19591085cfac22d5f1e508e172d9e80d0f1236IDcsy6xevadnrm20chrfyvs7fh7cStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- A man boarding a military plane looking towards the camera and a sunset in the background
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 90
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "dev", "prompt": "A man boarding a military plane looking towards the camera and a sunset in the background", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }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 lisandroe/loralisandro using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "lisandroe/loralisandro:edee113e85629e0c068637a9aa19591085cfac22d5f1e508e172d9e80d0f1236", { input: { model: "dev", prompt: "A man boarding a military plane looking towards the camera and a sunset in the background", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 90, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 28 } } ); // 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 lisandroe/loralisandro using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "lisandroe/loralisandro:edee113e85629e0c068637a9aa19591085cfac22d5f1e508e172d9e80d0f1236", input={ "model": "dev", "prompt": "A man boarding a military plane looking towards the camera and a sunset in the background", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run lisandroe/loralisandro 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": "lisandroe/loralisandro:edee113e85629e0c068637a9aa19591085cfac22d5f1e508e172d9e80d0f1236", "input": { "model": "dev", "prompt": "A man boarding a military plane looking towards the camera and a sunset in the background", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-09-05T21:01:51.003027Z", "created_at": "2024-09-05T21:00:50.669000Z", "data_removed": false, "error": null, "id": "csy6xevadnrm20chrfyvs7fh7c", "input": { "model": "dev", "prompt": "A man boarding a military plane looking towards the camera and a sunset in the background", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "Using seed: 24098\nPrompt: A man boarding a military plane looking towards the camera and a sunset in the background\n[!] txt2img mode\nUsing dev model\nLoaded LoRAs in 20.64s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.49it/s]\n 7%|▋ | 2/28 [00:00<00:06, 3.94it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.72it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.62it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.57it/s]\n 21%|██▏ | 6/28 [00:01<00:06, 3.54it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.53it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.51it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.51it/s]\n 36%|███▌ | 10/28 [00:02<00:05, 3.50it/s]\n 39%|███▉ | 11/28 [00:03<00:04, 3.50it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.49it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.49it/s]\n 50%|█████ | 14/28 [00:03<00:04, 3.49it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.49it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.49it/s]\n 61%|██████ | 17/28 [00:04<00:03, 3.49it/s]\n 64%|██████▍ | 18/28 [00:05<00:02, 3.49it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.49it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.49it/s]\n 75%|███████▌ | 21/28 [00:05<00:02, 3.48it/s]\n 79%|███████▊ | 22/28 [00:06<00:01, 3.49it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.49it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.49it/s]\n 89%|████████▉ | 25/28 [00:07<00:00, 3.49it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.49it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.49it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.49it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.51it/s]", "metrics": { "predict_time": 29.149335441, "total_time": 60.334027 }, "output": [ "https://replicate.delivery/yhqm/BWXdD0tQDKqXCpkjPAeSgc7ErkJmxxm1Aa3M3rYmeL2ex5zmA/out-0.webp" ], "started_at": "2024-09-05T21:01:21.853692Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/csy6xevadnrm20chrfyvs7fh7c", "cancel": "https://api.replicate.com/v1/predictions/csy6xevadnrm20chrfyvs7fh7c/cancel" }, "version": "edee113e85629e0c068637a9aa19591085cfac22d5f1e508e172d9e80d0f1236" }Generated inUsing seed: 24098 Prompt: A man boarding a military plane looking towards the camera and a sunset in the background [!] txt2img mode Using dev model Loaded LoRAs in 20.64s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.49it/s] 7%|▋ | 2/28 [00:00<00:06, 3.94it/s] 11%|█ | 3/28 [00:00<00:06, 3.72it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.62it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.57it/s] 21%|██▏ | 6/28 [00:01<00:06, 3.54it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.53it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.51it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.51it/s] 36%|███▌ | 10/28 [00:02<00:05, 3.50it/s] 39%|███▉ | 11/28 [00:03<00:04, 3.50it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.49it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.49it/s] 50%|█████ | 14/28 [00:03<00:04, 3.49it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.49it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.49it/s] 61%|██████ | 17/28 [00:04<00:03, 3.49it/s] 64%|██████▍ | 18/28 [00:05<00:02, 3.49it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.49it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.49it/s] 75%|███████▌ | 21/28 [00:05<00:02, 3.48it/s] 79%|███████▊ | 22/28 [00:06<00:01, 3.49it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.49it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.49it/s] 89%|████████▉ | 25/28 [00:07<00:00, 3.49it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.49it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.49it/s] 100%|██████████| 28/28 [00:07<00:00, 3.49it/s] 100%|██████████| 28/28 [00:07<00:00, 3.51it/s]Prediction
lisandroe/loralisandro:edee113e85629e0c068637a9aa19591085cfac22d5f1e508e172d9e80d0f1236IDstgkk097g1rm00chrg3t7dqpn0StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- A man walking in the snow staring into the camera, behind him a polar bear with her baby bears
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 90
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "dev", "prompt": "A man walking in the snow staring into the camera, behind him a polar bear with her baby bears", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }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 lisandroe/loralisandro using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "lisandroe/loralisandro:edee113e85629e0c068637a9aa19591085cfac22d5f1e508e172d9e80d0f1236", { input: { model: "dev", prompt: "A man walking in the snow staring into the camera, behind him a polar bear with her baby bears", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 90, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 28 } } ); // 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 lisandroe/loralisandro using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "lisandroe/loralisandro:edee113e85629e0c068637a9aa19591085cfac22d5f1e508e172d9e80d0f1236", input={ "model": "dev", "prompt": "A man walking in the snow staring into the camera, behind him a polar bear with her baby bears", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run lisandroe/loralisandro 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": "lisandroe/loralisandro:edee113e85629e0c068637a9aa19591085cfac22d5f1e508e172d9e80d0f1236", "input": { "model": "dev", "prompt": "A man walking in the snow staring into the camera, behind him a polar bear with her baby bears", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-09-05T21:11:47.885571Z", "created_at": "2024-09-05T21:11:28.896000Z", "data_removed": false, "error": null, "id": "stgkk097g1rm00chrg3t7dqpn0", "input": { "model": "dev", "prompt": "A man walking in the snow staring into the camera, behind him a polar bear with her baby bears", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "Using seed: 896\nPrompt: A man walking in the snow staring into the camera, behind him a polar bear with her baby bears\n[!] txt2img mode\nUsing dev model\nfree=8663819939840\nDownloading weights\n2024-09-05T21:11:28Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpncxdar5k/weights url=https://replicate.delivery/yhqm/hlaOoS1Zfet56EEhrbFbyIuNmYZWeIlwUlescp5LZWVgYqnNB/trained_model.tar\n2024-09-05T21:11:30Z | INFO | [ Complete ] dest=/tmp/tmpncxdar5k/weights size=\"172 MB\" total_elapsed=1.737s url=https://replicate.delivery/yhqm/hlaOoS1Zfet56EEhrbFbyIuNmYZWeIlwUlescp5LZWVgYqnNB/trained_model.tar\nDownloaded weights in 1.77s\nLoaded LoRAs in 10.55s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.51it/s]\n 7%|▋ | 2/28 [00:00<00:06, 3.98it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.75it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.65it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.60it/s]\n 21%|██▏ | 6/28 [00:01<00:06, 3.58it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.56it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.54it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.53it/s]\n 36%|███▌ | 10/28 [00:02<00:05, 3.53it/s]\n 39%|███▉ | 11/28 [00:03<00:04, 3.53it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.53it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.52it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.52it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.52it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.52it/s]\n 61%|██████ | 17/28 [00:04<00:03, 3.52it/s]\n 64%|██████▍ | 18/28 [00:05<00:02, 3.52it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.52it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.52it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.52it/s]\n 79%|███████▊ | 22/28 [00:06<00:01, 3.52it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.52it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.52it/s]\n 89%|████████▉ | 25/28 [00:07<00:00, 3.52it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.52it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.52it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.52it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.54it/s]", "metrics": { "predict_time": 18.980509471, "total_time": 18.989571 }, "output": [ "https://replicate.delivery/yhqm/dXzg96mU3K4wOhi11Bzc8En7kjYoCPpsFipdD5xDFdykQfsJA/out-0.webp" ], "started_at": "2024-09-05T21:11:28.905062Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/stgkk097g1rm00chrg3t7dqpn0", "cancel": "https://api.replicate.com/v1/predictions/stgkk097g1rm00chrg3t7dqpn0/cancel" }, "version": "edee113e85629e0c068637a9aa19591085cfac22d5f1e508e172d9e80d0f1236" }Generated inUsing seed: 896 Prompt: A man walking in the snow staring into the camera, behind him a polar bear with her baby bears [!] txt2img mode Using dev model free=8663819939840 Downloading weights 2024-09-05T21:11:28Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpncxdar5k/weights url=https://replicate.delivery/yhqm/hlaOoS1Zfet56EEhrbFbyIuNmYZWeIlwUlescp5LZWVgYqnNB/trained_model.tar 2024-09-05T21:11:30Z | INFO | [ Complete ] dest=/tmp/tmpncxdar5k/weights size="172 MB" total_elapsed=1.737s url=https://replicate.delivery/yhqm/hlaOoS1Zfet56EEhrbFbyIuNmYZWeIlwUlescp5LZWVgYqnNB/trained_model.tar Downloaded weights in 1.77s Loaded LoRAs in 10.55s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.51it/s] 7%|▋ | 2/28 [00:00<00:06, 3.98it/s] 11%|█ | 3/28 [00:00<00:06, 3.75it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.65it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.60it/s] 21%|██▏ | 6/28 [00:01<00:06, 3.58it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.56it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.54it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.53it/s] 36%|███▌ | 10/28 [00:02<00:05, 3.53it/s] 39%|███▉ | 11/28 [00:03<00:04, 3.53it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.53it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.52it/s] 50%|█████ | 14/28 [00:03<00:03, 3.52it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.52it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.52it/s] 61%|██████ | 17/28 [00:04<00:03, 3.52it/s] 64%|██████▍ | 18/28 [00:05<00:02, 3.52it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.52it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.52it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.52it/s] 79%|███████▊ | 22/28 [00:06<00:01, 3.52it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.52it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.52it/s] 89%|████████▉ | 25/28 [00:07<00:00, 3.52it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.52it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.52it/s] 100%|██████████| 28/28 [00:07<00:00, 3.52it/s] 100%|██████████| 28/28 [00:07<00:00, 3.54it/s]Prediction
lisandroe/loralisandro:edee113e85629e0c068637a9aa19591085cfac22d5f1e508e172d9e80d0f1236IDe6y9ztj7bnrm40chrg4vx4fw4cStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- A man walking in the snow staring into the camera, the camera in the foreground
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 90
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "dev", "prompt": "A man walking in the snow staring into the camera, the camera in the foreground", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }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 lisandroe/loralisandro using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "lisandroe/loralisandro:edee113e85629e0c068637a9aa19591085cfac22d5f1e508e172d9e80d0f1236", { input: { model: "dev", prompt: "A man walking in the snow staring into the camera, the camera in the foreground", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 90, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 28 } } ); // 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 lisandroe/loralisandro using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "lisandroe/loralisandro:edee113e85629e0c068637a9aa19591085cfac22d5f1e508e172d9e80d0f1236", input={ "model": "dev", "prompt": "A man walking in the snow staring into the camera, the camera in the foreground", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run lisandroe/loralisandro 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": "lisandroe/loralisandro:edee113e85629e0c068637a9aa19591085cfac22d5f1e508e172d9e80d0f1236", "input": { "model": "dev", "prompt": "A man walking in the snow staring into the camera, the camera in the foreground", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-09-05T21:14:07.802187Z", "created_at": "2024-09-05T21:13:48.125000Z", "data_removed": false, "error": null, "id": "e6y9ztj7bnrm40chrg4vx4fw4c", "input": { "model": "dev", "prompt": "A man walking in the snow staring into the camera, the camera in the foreground", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "Using seed: 35159\nPrompt: A man walking in the snow staring into the camera, the camera in the foreground\n[!] txt2img mode\nUsing dev model\nfree=8570515976192\nDownloading weights\n2024-09-05T21:13:50Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpw944i8dt/weights url=https://replicate.delivery/yhqm/hlaOoS1Zfet56EEhrbFbyIuNmYZWeIlwUlescp5LZWVgYqnNB/trained_model.tar\n2024-09-05T21:13:51Z | INFO | [ Complete ] dest=/tmp/tmpw944i8dt/weights size=\"172 MB\" total_elapsed=1.285s url=https://replicate.delivery/yhqm/hlaOoS1Zfet56EEhrbFbyIuNmYZWeIlwUlescp5LZWVgYqnNB/trained_model.tar\nDownloaded weights in 1.31s\nLoaded LoRAs in 9.19s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.48it/s]\n 7%|▋ | 2/28 [00:00<00:06, 3.93it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.71it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.61it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.56it/s]\n 21%|██▏ | 6/28 [00:01<00:06, 3.53it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.51it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.49it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.49it/s]\n 36%|███▌ | 10/28 [00:02<00:05, 3.48it/s]\n 39%|███▉ | 11/28 [00:03<00:04, 3.48it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.47it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.47it/s]\n 50%|█████ | 14/28 [00:03<00:04, 3.47it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.47it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.47it/s]\n 61%|██████ | 17/28 [00:04<00:03, 3.47it/s]\n 64%|██████▍ | 18/28 [00:05<00:02, 3.47it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.47it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.47it/s]\n 75%|███████▌ | 21/28 [00:05<00:02, 3.47it/s]\n 79%|███████▊ | 22/28 [00:06<00:01, 3.47it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.47it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.47it/s]\n 89%|████████▉ | 25/28 [00:07<00:00, 3.47it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.47it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.47it/s]\n100%|██████████| 28/28 [00:08<00:00, 3.47it/s]\n100%|██████████| 28/28 [00:08<00:00, 3.49it/s]", "metrics": { "predict_time": 17.761344618, "total_time": 19.677187 }, "output": [ "https://replicate.delivery/yhqm/fyhpJrAPwuxXOCeTeUu0gaSjrT4iNISzPwTHyFfgVHH8R0nNB/out-0.webp" ], "started_at": "2024-09-05T21:13:50.040842Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/e6y9ztj7bnrm40chrg4vx4fw4c", "cancel": "https://api.replicate.com/v1/predictions/e6y9ztj7bnrm40chrg4vx4fw4c/cancel" }, "version": "edee113e85629e0c068637a9aa19591085cfac22d5f1e508e172d9e80d0f1236" }Generated inUsing seed: 35159 Prompt: A man walking in the snow staring into the camera, the camera in the foreground [!] txt2img mode Using dev model free=8570515976192 Downloading weights 2024-09-05T21:13:50Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpw944i8dt/weights url=https://replicate.delivery/yhqm/hlaOoS1Zfet56EEhrbFbyIuNmYZWeIlwUlescp5LZWVgYqnNB/trained_model.tar 2024-09-05T21:13:51Z | INFO | [ Complete ] dest=/tmp/tmpw944i8dt/weights size="172 MB" total_elapsed=1.285s url=https://replicate.delivery/yhqm/hlaOoS1Zfet56EEhrbFbyIuNmYZWeIlwUlescp5LZWVgYqnNB/trained_model.tar Downloaded weights in 1.31s Loaded LoRAs in 9.19s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.48it/s] 7%|▋ | 2/28 [00:00<00:06, 3.93it/s] 11%|█ | 3/28 [00:00<00:06, 3.71it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.61it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.56it/s] 21%|██▏ | 6/28 [00:01<00:06, 3.53it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.51it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.49it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.49it/s] 36%|███▌ | 10/28 [00:02<00:05, 3.48it/s] 39%|███▉ | 11/28 [00:03<00:04, 3.48it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.47it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.47it/s] 50%|█████ | 14/28 [00:03<00:04, 3.47it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.47it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.47it/s] 61%|██████ | 17/28 [00:04<00:03, 3.47it/s] 64%|██████▍ | 18/28 [00:05<00:02, 3.47it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.47it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.47it/s] 75%|███████▌ | 21/28 [00:05<00:02, 3.47it/s] 79%|███████▊ | 22/28 [00:06<00:01, 3.47it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.47it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.47it/s] 89%|████████▉ | 25/28 [00:07<00:00, 3.47it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.47it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.47it/s] 100%|██████████| 28/28 [00:08<00:00, 3.47it/s] 100%|██████████| 28/28 [00:08<00:00, 3.49it/s]Prediction
lisandroe/loralisandro:edee113e85629e0c068637a9aa19591085cfac22d5f1e508e172d9e80d0f1236IDwtpqapcymnrm40chrg5bw3ev1wStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- A man walking in the snow staring into the camera, the camera in the foreground
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 90
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "dev", "prompt": "A man walking in the snow staring into the camera, the camera in the foreground", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }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 lisandroe/loralisandro using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "lisandroe/loralisandro:edee113e85629e0c068637a9aa19591085cfac22d5f1e508e172d9e80d0f1236", { input: { model: "dev", prompt: "A man walking in the snow staring into the camera, the camera in the foreground", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 90, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 28 } } ); // 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 lisandroe/loralisandro using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "lisandroe/loralisandro:edee113e85629e0c068637a9aa19591085cfac22d5f1e508e172d9e80d0f1236", input={ "model": "dev", "prompt": "A man walking in the snow staring into the camera, the camera in the foreground", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run lisandroe/loralisandro 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": "lisandroe/loralisandro:edee113e85629e0c068637a9aa19591085cfac22d5f1e508e172d9e80d0f1236", "input": { "model": "dev", "prompt": "A man walking in the snow staring into the camera, the camera in the foreground", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-09-05T21:15:54.261499Z", "created_at": "2024-09-05T21:15:16.005000Z", "data_removed": false, "error": null, "id": "wtpqapcymnrm40chrg5bw3ev1w", "input": { "model": "dev", "prompt": "A man walking in the snow staring into the camera, the camera in the foreground", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "Using seed: 37768\nPrompt: A man walking in the snow staring into the camera, the camera in the foreground\n[!] txt2img mode\nUsing dev model\nLoaded LoRAs in 16.44s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.47it/s]\n 7%|▋ | 2/28 [00:00<00:06, 3.93it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.71it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.61it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.57it/s]\n 21%|██▏ | 6/28 [00:01<00:06, 3.53it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.51it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.50it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.49it/s]\n 36%|███▌ | 10/28 [00:02<00:05, 3.49it/s]\n 39%|███▉ | 11/28 [00:03<00:04, 3.49it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.48it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.48it/s]\n 50%|█████ | 14/28 [00:03<00:04, 3.48it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.48it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.48it/s]\n 61%|██████ | 17/28 [00:04<00:03, 3.48it/s]\n 64%|██████▍ | 18/28 [00:05<00:02, 3.48it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.48it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.47it/s]\n 75%|███████▌ | 21/28 [00:05<00:02, 3.48it/s]\n 79%|███████▊ | 22/28 [00:06<00:01, 3.47it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.48it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.47it/s]\n 89%|████████▉ | 25/28 [00:07<00:00, 3.47it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.48it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.48it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.48it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.50it/s]", "metrics": { "predict_time": 22.386695512, "total_time": 38.256499 }, "output": [ "https://replicate.delivery/yhqm/oqwSeGrgseug6kHmtbjMlTOJ2AsLzvl0RjsZ4iGqVLKKG9ZTA/out-0.webp" ], "started_at": "2024-09-05T21:15:31.874803Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/wtpqapcymnrm40chrg5bw3ev1w", "cancel": "https://api.replicate.com/v1/predictions/wtpqapcymnrm40chrg5bw3ev1w/cancel" }, "version": "edee113e85629e0c068637a9aa19591085cfac22d5f1e508e172d9e80d0f1236" }Generated inUsing seed: 37768 Prompt: A man walking in the snow staring into the camera, the camera in the foreground [!] txt2img mode Using dev model Loaded LoRAs in 16.44s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.47it/s] 7%|▋ | 2/28 [00:00<00:06, 3.93it/s] 11%|█ | 3/28 [00:00<00:06, 3.71it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.61it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.57it/s] 21%|██▏ | 6/28 [00:01<00:06, 3.53it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.51it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.50it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.49it/s] 36%|███▌ | 10/28 [00:02<00:05, 3.49it/s] 39%|███▉ | 11/28 [00:03<00:04, 3.49it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.48it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.48it/s] 50%|█████ | 14/28 [00:03<00:04, 3.48it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.48it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.48it/s] 61%|██████ | 17/28 [00:04<00:03, 3.48it/s] 64%|██████▍ | 18/28 [00:05<00:02, 3.48it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.48it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.47it/s] 75%|███████▌ | 21/28 [00:05<00:02, 3.48it/s] 79%|███████▊ | 22/28 [00:06<00:01, 3.47it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.48it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.47it/s] 89%|████████▉ | 25/28 [00:07<00:00, 3.47it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.48it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.48it/s] 100%|██████████| 28/28 [00:07<00:00, 3.48it/s] 100%|██████████| 28/28 [00:07<00:00, 3.50it/s]Prediction
lisandroe/loralisandro:edee113e85629e0c068637a9aa19591085cfac22d5f1e508e172d9e80d0f1236IDfgj772y5yhrm20chrg5s82azhmStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- A man walking in the snow staring into the camera, behind him a polar bear with her cubs, the camera in the foreground, behind him a sign that says Canada
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 90
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "dev", "prompt": "A man walking in the snow staring into the camera, behind him a polar bear with her cubs, the camera in the foreground, behind him a sign that says Canada", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }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 lisandroe/loralisandro using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "lisandroe/loralisandro:edee113e85629e0c068637a9aa19591085cfac22d5f1e508e172d9e80d0f1236", { input: { model: "dev", prompt: "A man walking in the snow staring into the camera, behind him a polar bear with her cubs, the camera in the foreground, behind him a sign that says Canada", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 90, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 28 } } ); // 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 lisandroe/loralisandro using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "lisandroe/loralisandro:edee113e85629e0c068637a9aa19591085cfac22d5f1e508e172d9e80d0f1236", input={ "model": "dev", "prompt": "A man walking in the snow staring into the camera, behind him a polar bear with her cubs, the camera in the foreground, behind him a sign that says Canada", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run lisandroe/loralisandro 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": "lisandroe/loralisandro:edee113e85629e0c068637a9aa19591085cfac22d5f1e508e172d9e80d0f1236", "input": { "model": "dev", "prompt": "A man walking in the snow staring into the camera, behind him a polar bear with her cubs, the camera in the foreground, behind him a sign that says Canada", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-09-05T21:16:56.208790Z", "created_at": "2024-09-05T21:16:31.604000Z", "data_removed": false, "error": null, "id": "fgj772y5yhrm20chrg5s82azhm", "input": { "model": "dev", "prompt": "A man walking in the snow staring into the camera, behind him a polar bear with her cubs, the camera in the foreground, behind him a sign that says Canada", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "Using seed: 49428\nPrompt: A man walking in the snow staring into the camera, behind him a polar bear with her cubs, the camera in the foreground, behind him a sign that says Canada\n[!] txt2img mode\nUsing dev model\nLoaded LoRAs in 7.54s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.47it/s]\n 7%|▋ | 2/28 [00:00<00:06, 3.94it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.72it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.63it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.59it/s]\n 21%|██▏ | 6/28 [00:01<00:06, 3.55it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.54it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.53it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.52it/s]\n 36%|███▌ | 10/28 [00:02<00:05, 3.51it/s]\n 39%|███▉ | 11/28 [00:03<00:04, 3.51it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.50it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.50it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.50it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.50it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.50it/s]\n 61%|██████ | 17/28 [00:04<00:03, 3.50it/s]\n 64%|██████▍ | 18/28 [00:05<00:02, 3.50it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.50it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.50it/s]\n 75%|███████▌ | 21/28 [00:05<00:02, 3.50it/s]\n 79%|███████▊ | 22/28 [00:06<00:01, 3.50it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.50it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.50it/s]\n 89%|████████▉ | 25/28 [00:07<00:00, 3.50it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.50it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.50it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.50it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.52it/s]", "metrics": { "predict_time": 16.034132383, "total_time": 24.60479 }, "output": [ "https://replicate.delivery/yhqm/sJXGlfrzA9WNHCrRemb788REdWweSAvx8DFzqoXrPL4RO6zmA/out-0.webp" ], "started_at": "2024-09-05T21:16:40.174657Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/fgj772y5yhrm20chrg5s82azhm", "cancel": "https://api.replicate.com/v1/predictions/fgj772y5yhrm20chrg5s82azhm/cancel" }, "version": "edee113e85629e0c068637a9aa19591085cfac22d5f1e508e172d9e80d0f1236" }Generated inUsing seed: 49428 Prompt: A man walking in the snow staring into the camera, behind him a polar bear with her cubs, the camera in the foreground, behind him a sign that says Canada [!] txt2img mode Using dev model Loaded LoRAs in 7.54s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.47it/s] 7%|▋ | 2/28 [00:00<00:06, 3.94it/s] 11%|█ | 3/28 [00:00<00:06, 3.72it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.63it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.59it/s] 21%|██▏ | 6/28 [00:01<00:06, 3.55it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.54it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.53it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.52it/s] 36%|███▌ | 10/28 [00:02<00:05, 3.51it/s] 39%|███▉ | 11/28 [00:03<00:04, 3.51it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.50it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.50it/s] 50%|█████ | 14/28 [00:03<00:03, 3.50it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.50it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.50it/s] 61%|██████ | 17/28 [00:04<00:03, 3.50it/s] 64%|██████▍ | 18/28 [00:05<00:02, 3.50it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.50it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.50it/s] 75%|███████▌ | 21/28 [00:05<00:02, 3.50it/s] 79%|███████▊ | 22/28 [00:06<00:01, 3.50it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.50it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.50it/s] 89%|████████▉ | 25/28 [00:07<00:00, 3.50it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.50it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.50it/s] 100%|██████████| 28/28 [00:07<00:00, 3.50it/s] 100%|██████████| 28/28 [00:07<00:00, 3.52it/s]Prediction
lisandroe/loralisandro:edee113e85629e0c068637a9aa19591085cfac22d5f1e508e172d9e80d0f1236IDz2mc7pshkxrm00chrjbv5nkde0StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- Create a realistic photo of a man dressed in a white lab coat observing a microprocessor which is being analyzed with a microscope
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 90
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "dev", "prompt": "Create a realistic photo of a man dressed in a white lab coat observing a microprocessor which is being analyzed with a microscope", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }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 lisandroe/loralisandro using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "lisandroe/loralisandro:edee113e85629e0c068637a9aa19591085cfac22d5f1e508e172d9e80d0f1236", { input: { model: "dev", prompt: "Create a realistic photo of a man dressed in a white lab coat observing a microprocessor which is being analyzed with a microscope", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 90, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 28 } } ); // 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 lisandroe/loralisandro using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "lisandroe/loralisandro:edee113e85629e0c068637a9aa19591085cfac22d5f1e508e172d9e80d0f1236", input={ "model": "dev", "prompt": "Create a realistic photo of a man dressed in a white lab coat observing a microprocessor which is being analyzed with a microscope", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run lisandroe/loralisandro 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": "lisandroe/loralisandro:edee113e85629e0c068637a9aa19591085cfac22d5f1e508e172d9e80d0f1236", "input": { "model": "dev", "prompt": "Create a realistic photo of a man dressed in a white lab coat observing a microprocessor which is being analyzed with a microscope", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-09-05T23:49:33.340155Z", "created_at": "2024-09-05T23:48:48.671000Z", "data_removed": false, "error": null, "id": "z2mc7pshkxrm00chrjbv5nkde0", "input": { "model": "dev", "prompt": "Create a realistic photo of a man dressed in a white lab coat observing a microprocessor which is being analyzed with a microscope", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "Using seed: 7946\nPrompt: Create a realistic photo of a man dressed in a white lab coat observing a microprocessor which is being analyzed with a microscope\n[!] txt2img mode\nUsing dev model\nLoaded LoRAs in 18.95s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.48it/s]\n 7%|▋ | 2/28 [00:00<00:06, 3.93it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.70it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.60it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.55it/s]\n 21%|██▏ | 6/28 [00:01<00:06, 3.52it/s]\n 25%|██▌ | 7/28 [00:01<00:06, 3.50it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.48it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.48it/s]\n 36%|███▌ | 10/28 [00:02<00:05, 3.48it/s]\n 39%|███▉ | 11/28 [00:03<00:04, 3.47it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.47it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.47it/s]\n 50%|█████ | 14/28 [00:03<00:04, 3.47it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.47it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.47it/s]\n 61%|██████ | 17/28 [00:04<00:03, 3.47it/s]\n 64%|██████▍ | 18/28 [00:05<00:02, 3.47it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.47it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.48it/s]\n 75%|███████▌ | 21/28 [00:05<00:02, 3.48it/s]\n 79%|███████▊ | 22/28 [00:06<00:01, 3.47it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.47it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.47it/s]\n 89%|████████▉ | 25/28 [00:07<00:00, 3.47it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.47it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.47it/s]\n100%|██████████| 28/28 [00:08<00:00, 3.47it/s]\n100%|██████████| 28/28 [00:08<00:00, 3.49it/s]", "metrics": { "predict_time": 27.56749184, "total_time": 44.669155 }, "output": [ "https://replicate.delivery/yhqm/OYESZD5eZNRHVSAFYjBoWvYBHRFnFxvnB0P7YsoAlQzGrfZTA/out-0.webp" ], "started_at": "2024-09-05T23:49:05.772663Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/z2mc7pshkxrm00chrjbv5nkde0", "cancel": "https://api.replicate.com/v1/predictions/z2mc7pshkxrm00chrjbv5nkde0/cancel" }, "version": "edee113e85629e0c068637a9aa19591085cfac22d5f1e508e172d9e80d0f1236" }Generated inUsing seed: 7946 Prompt: Create a realistic photo of a man dressed in a white lab coat observing a microprocessor which is being analyzed with a microscope [!] txt2img mode Using dev model Loaded LoRAs in 18.95s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.48it/s] 7%|▋ | 2/28 [00:00<00:06, 3.93it/s] 11%|█ | 3/28 [00:00<00:06, 3.70it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.60it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.55it/s] 21%|██▏ | 6/28 [00:01<00:06, 3.52it/s] 25%|██▌ | 7/28 [00:01<00:06, 3.50it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.48it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.48it/s] 36%|███▌ | 10/28 [00:02<00:05, 3.48it/s] 39%|███▉ | 11/28 [00:03<00:04, 3.47it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.47it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.47it/s] 50%|█████ | 14/28 [00:03<00:04, 3.47it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.47it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.47it/s] 61%|██████ | 17/28 [00:04<00:03, 3.47it/s] 64%|██████▍ | 18/28 [00:05<00:02, 3.47it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.47it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.48it/s] 75%|███████▌ | 21/28 [00:05<00:02, 3.48it/s] 79%|███████▊ | 22/28 [00:06<00:01, 3.47it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.47it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.47it/s] 89%|████████▉ | 25/28 [00:07<00:00, 3.47it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.47it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.47it/s] 100%|██████████| 28/28 [00:08<00:00, 3.47it/s] 100%|██████████| 28/28 [00:08<00:00, 3.49it/s]Prediction
lisandroe/loralisandro:edee113e85629e0c068637a9aa19591085cfac22d5f1e508e172d9e80d0f1236ID4c7gtfkb9srm40chrjj8b4aqtrStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- Create a realistic photo of a man showing tickets to a basketball game in front of the Chicago Bulls stadium
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 90
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "dev", "prompt": "Create a realistic photo of a man showing tickets to a basketball game in front of the Chicago Bulls stadium", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }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 lisandroe/loralisandro using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "lisandroe/loralisandro:edee113e85629e0c068637a9aa19591085cfac22d5f1e508e172d9e80d0f1236", { input: { model: "dev", prompt: "Create a realistic photo of a man showing tickets to a basketball game in front of the Chicago Bulls stadium", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 90, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 28 } } ); // 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 lisandroe/loralisandro using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "lisandroe/loralisandro:edee113e85629e0c068637a9aa19591085cfac22d5f1e508e172d9e80d0f1236", input={ "model": "dev", "prompt": "Create a realistic photo of a man showing tickets to a basketball game in front of the Chicago Bulls stadium", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run lisandroe/loralisandro 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": "lisandroe/loralisandro:edee113e85629e0c068637a9aa19591085cfac22d5f1e508e172d9e80d0f1236", "input": { "model": "dev", "prompt": "Create a realistic photo of a man showing tickets to a basketball game in front of the Chicago Bulls stadium", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-09-06T00:03:32.225263Z", "created_at": "2024-09-06T00:03:15.406000Z", "data_removed": false, "error": null, "id": "4c7gtfkb9srm40chrjj8b4aqtr", "input": { "model": "dev", "prompt": "Create a realistic photo of a man showing tickets to a basketball game in front of the Chicago Bulls stadium", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "Using seed: 37204\nPrompt: Create a realistic photo of a man showing tickets to a basketball game in front of the Chicago Bulls stadium\n[!] txt2img mode\nUsing dev model\nLoaded LoRAs in 8.23s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.46it/s]\n 7%|▋ | 2/28 [00:00<00:06, 3.94it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.71it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.62it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.57it/s]\n 21%|██▏ | 6/28 [00:01<00:06, 3.53it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.52it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.51it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.50it/s]\n 36%|███▌ | 10/28 [00:02<00:05, 3.49it/s]\n 39%|███▉ | 11/28 [00:03<00:04, 3.49it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.48it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.48it/s]\n 50%|█████ | 14/28 [00:03<00:04, 3.48it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.48it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.48it/s]\n 61%|██████ | 17/28 [00:04<00:03, 3.48it/s]\n 64%|██████▍ | 18/28 [00:05<00:02, 3.48it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.48it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.48it/s]\n 75%|███████▌ | 21/28 [00:05<00:02, 3.48it/s]\n 79%|███████▊ | 22/28 [00:06<00:01, 3.48it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.48it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.48it/s]\n 89%|████████▉ | 25/28 [00:07<00:00, 3.48it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.48it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.48it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.48it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.50it/s]", "metrics": { "predict_time": 16.809170434, "total_time": 16.819263 }, "output": [ "https://replicate.delivery/yhqm/f9I9b3rfyfVf4RLUDDvW8ChFK4iXUjmQdgvQw8zTUALTNePbC/out-0.webp" ], "started_at": "2024-09-06T00:03:15.416092Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/4c7gtfkb9srm40chrjj8b4aqtr", "cancel": "https://api.replicate.com/v1/predictions/4c7gtfkb9srm40chrjj8b4aqtr/cancel" }, "version": "edee113e85629e0c068637a9aa19591085cfac22d5f1e508e172d9e80d0f1236" }Generated inUsing seed: 37204 Prompt: Create a realistic photo of a man showing tickets to a basketball game in front of the Chicago Bulls stadium [!] txt2img mode Using dev model Loaded LoRAs in 8.23s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.46it/s] 7%|▋ | 2/28 [00:00<00:06, 3.94it/s] 11%|█ | 3/28 [00:00<00:06, 3.71it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.62it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.57it/s] 21%|██▏ | 6/28 [00:01<00:06, 3.53it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.52it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.51it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.50it/s] 36%|███▌ | 10/28 [00:02<00:05, 3.49it/s] 39%|███▉ | 11/28 [00:03<00:04, 3.49it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.48it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.48it/s] 50%|█████ | 14/28 [00:03<00:04, 3.48it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.48it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.48it/s] 61%|██████ | 17/28 [00:04<00:03, 3.48it/s] 64%|██████▍ | 18/28 [00:05<00:02, 3.48it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.48it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.48it/s] 75%|███████▌ | 21/28 [00:05<00:02, 3.48it/s] 79%|███████▊ | 22/28 [00:06<00:01, 3.48it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.48it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.48it/s] 89%|████████▉ | 25/28 [00:07<00:00, 3.48it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.48it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.48it/s] 100%|██████████| 28/28 [00:07<00:00, 3.48it/s] 100%|██████████| 28/28 [00:07<00:00, 3.50it/s]
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