warf23 / agrat_me
- Public
- 85 runs
-
H100
Prediction
warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44ID9qtwygxj3nrm40chns7rc5jg8rStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- AGRAT as Thor, wielding Mjolnir, crackling with lightning, windswept hair, determined expression, heroic upward angle shot, stormy Asgardian sky, rainbow bridge in background
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- extra_lora_scale
- 0.8
- num_inference_steps
- 28
{ "model": "dev", "prompt": "AGRAT as Thor, wielding Mjolnir, crackling with lightning, windswept hair, determined expression, heroic upward angle shot, stormy Asgardian sky, rainbow bridge in background", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "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 warf23/agrat_me using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", { input: { model: "dev", prompt: "AGRAT as Thor, wielding Mjolnir, crackling with lightning, windswept hair, determined expression, heroic upward angle shot, stormy Asgardian sky, rainbow bridge in background", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, extra_lora_scale: 0.8, 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 warf23/agrat_me using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", input={ "model": "dev", "prompt": "AGRAT as Thor, wielding Mjolnir, crackling with lightning, windswept hair, determined expression, heroic upward angle shot, stormy Asgardian sky, rainbow bridge in background", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run warf23/agrat_me 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": "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", "input": { "model": "dev", "prompt": "AGRAT as Thor, wielding Mjolnir, crackling with lightning, windswept hair, determined expression, heroic upward angle shot, stormy Asgardian sky, rainbow bridge in background", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "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": "2024-09-01T15:59:31.265302Z", "created_at": "2024-09-01T15:59:04.221000Z", "data_removed": false, "error": null, "id": "9qtwygxj3nrm40chns7rc5jg8r", "input": { "model": "dev", "prompt": "AGRAT as Thor, wielding Mjolnir, crackling with lightning, windswept hair, determined expression, heroic upward angle shot, stormy Asgardian sky, rainbow bridge in background", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 }, "logs": "Using seed: 4416\nPrompt: AGRAT as Thor, wielding Mjolnir, crackling with lightning, windswept hair, determined expression, heroic upward angle shot, stormy Asgardian sky, rainbow bridge in background\ntxt2img mode\nUsing dev model\nLoaded LoRAs in 8.51s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.67it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.23it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.97it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.85it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.79it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.75it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.73it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.72it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.71it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.70it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.70it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.69it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.69it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.69it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.69it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.69it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.69it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.69it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.69it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.69it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.68it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.69it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.69it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.69it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.68it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 3.68it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.69it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.69it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.71it/s]", "metrics": { "predict_time": 16.627863111, "total_time": 27.044302 }, "output": [ "https://replicate.delivery/yhqm/oOeWSeeeiK0C4Q8fscERgXiF4ulHiRKe6RouQVaAAcv0YBJ2E/out-0.webp" ], "started_at": "2024-09-01T15:59:14.637439Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/9qtwygxj3nrm40chns7rc5jg8r", "cancel": "https://api.replicate.com/v1/predictions/9qtwygxj3nrm40chns7rc5jg8r/cancel" }, "version": "6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44" }Generated inUsing seed: 4416 Prompt: AGRAT as Thor, wielding Mjolnir, crackling with lightning, windswept hair, determined expression, heroic upward angle shot, stormy Asgardian sky, rainbow bridge in background txt2img mode Using dev model Loaded LoRAs in 8.51s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.67it/s] 7%|▋ | 2/28 [00:00<00:06, 4.23it/s] 11%|█ | 3/28 [00:00<00:06, 3.97it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.85it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.79it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.75it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.73it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.72it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.71it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.70it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.70it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.69it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.69it/s] 50%|█████ | 14/28 [00:03<00:03, 3.69it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.69it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.69it/s] 61%|██████ | 17/28 [00:04<00:02, 3.69it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.69it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.69it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.69it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.68it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.69it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.69it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.69it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.68it/s] 93%|█████████▎| 26/28 [00:06<00:00, 3.68it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.69it/s] 100%|██████████| 28/28 [00:07<00:00, 3.69it/s] 100%|██████████| 28/28 [00:07<00:00, 3.71it/s]Prediction
warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44ID8jcwfmwrksrm60chn8cr3bhsh8StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- AGRAT as a 1920s gangster, three-quarter profile, smoky speakeasy setting, film noir style
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- extra_lora_scale
- 0.8
- num_inference_steps
- 28
{ "model": "dev", "prompt": "AGRAT as a 1920s gangster, three-quarter profile, smoky speakeasy setting, film noir style", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "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 warf23/agrat_me using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", { input: { model: "dev", prompt: "AGRAT as a 1920s gangster, three-quarter profile, smoky speakeasy setting, film noir style", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, extra_lora_scale: 0.8, 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 warf23/agrat_me using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", input={ "model": "dev", "prompt": "AGRAT as a 1920s gangster, three-quarter profile, smoky speakeasy setting, film noir style", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run warf23/agrat_me 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": "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", "input": { "model": "dev", "prompt": "AGRAT as a 1920s gangster, three-quarter profile, smoky speakeasy setting, film noir style", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "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": "2024-08-31T20:21:45.967559Z", "created_at": "2024-08-31T20:21:29.886000Z", "data_removed": false, "error": null, "id": "8jcwfmwrksrm60chn8cr3bhsh8", "input": { "model": "dev", "prompt": "AGRAT as a 1920s gangster, three-quarter profile, smoky speakeasy setting, film noir style", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 }, "logs": "Using seed: 64601\nPrompt: AGRAT as a 1920s gangster, three-quarter profile, smoky speakeasy setting, film noir style\ntxt2img mode\nUsing dev model\nLoaded LoRAs in 8.02s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.70it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.25it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.97it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.86it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.79it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.75it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.73it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.72it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.71it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.70it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.69it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.69it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.69it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.69it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.68it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.68it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.68it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.69it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.68it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.69it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.69it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.69it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.68it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.68it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.68it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 3.68it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.68it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.68it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.71it/s]", "metrics": { "predict_time": 16.072758064, "total_time": 16.081559 }, "output": [ "https://replicate.delivery/yhqm/HzkQo3C8PdaRKNYIudta5XvMU4s8CRVeg9WWWtNGHqfZ1SYTA/out-0.webp" ], "started_at": "2024-08-31T20:21:29.894801Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/8jcwfmwrksrm60chn8cr3bhsh8", "cancel": "https://api.replicate.com/v1/predictions/8jcwfmwrksrm60chn8cr3bhsh8/cancel" }, "version": "6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44" }Generated inUsing seed: 64601 Prompt: AGRAT as a 1920s gangster, three-quarter profile, smoky speakeasy setting, film noir style txt2img mode Using dev model Loaded LoRAs in 8.02s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.70it/s] 7%|▋ | 2/28 [00:00<00:06, 4.25it/s] 11%|█ | 3/28 [00:00<00:06, 3.97it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.86it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.79it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.75it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.73it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.72it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.71it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.70it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.69it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.69it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.69it/s] 50%|█████ | 14/28 [00:03<00:03, 3.69it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.68it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.68it/s] 61%|██████ | 17/28 [00:04<00:02, 3.68it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.69it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.68it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.69it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.69it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.69it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.68it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.68it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.68it/s] 93%|█████████▎| 26/28 [00:06<00:00, 3.68it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.68it/s] 100%|██████████| 28/28 [00:07<00:00, 3.68it/s] 100%|██████████| 28/28 [00:07<00:00, 3.71it/s]Prediction
warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44IDv1d1e4s909rm20chnscvsv5fg4StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- AGRAT as Captain America, medium shot, determined expression, holding iconic shield, star-spangled costume with battle wear, golden hour lighting, cinematic color grading
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 16:9
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 96
- extra_lora_scale
- 0.8
- num_inference_steps
- 28
{ "model": "dev", "prompt": "AGRAT as Captain America, medium shot, determined expression, holding iconic shield, star-spangled costume with battle wear, golden hour lighting, cinematic color grading", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 96, "extra_lora_scale": 0.8, "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 warf23/agrat_me using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", { input: { model: "dev", prompt: "AGRAT as Captain America, medium shot, determined expression, holding iconic shield, star-spangled costume with battle wear, golden hour lighting, cinematic color grading", lora_scale: 1, num_outputs: 1, aspect_ratio: "16:9", output_format: "webp", guidance_scale: 3.5, output_quality: 96, extra_lora_scale: 0.8, 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 warf23/agrat_me using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", input={ "model": "dev", "prompt": "AGRAT as Captain America, medium shot, determined expression, holding iconic shield, star-spangled costume with battle wear, golden hour lighting, cinematic color grading", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 96, "extra_lora_scale": 0.8, "num_inference_steps": 28 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run warf23/agrat_me 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": "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", "input": { "model": "dev", "prompt": "AGRAT as Captain America, medium shot, determined expression, holding iconic shield, star-spangled costume with battle wear, golden hour lighting, cinematic color grading", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 96, "extra_lora_scale": 0.8, "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": "2024-09-01T16:09:43.955999Z", "created_at": "2024-09-01T16:09:24.482000Z", "data_removed": false, "error": null, "id": "v1d1e4s909rm20chnscvsv5fg4", "input": { "model": "dev", "prompt": "AGRAT as Captain America, medium shot, determined expression, holding iconic shield, star-spangled costume with battle wear, golden hour lighting, cinematic color grading", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 96, "extra_lora_scale": 0.8, "num_inference_steps": 28 }, "logs": "Using seed: 5151\nPrompt: AGRAT as Captain America, medium shot, determined expression, holding iconic shield, star-spangled costume with battle wear, golden hour lighting, cinematic color grading\ntxt2img mode\nUsing dev model\nfree=9543646593024\nDownloading weights\n2024-09-01T16:09:24Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpxm9y5a60/weights url=https://replicate.delivery/yhqm/wDyCVve1QgUfSkUkfqN4wdTZlalRFUSNe9eUCyCZPkMUc8BbC/trained_model.tar\n2024-09-01T16:09:26Z | INFO | [ Complete ] dest=/tmp/tmpxm9y5a60/weights size=\"172 MB\" total_elapsed=2.159s url=https://replicate.delivery/yhqm/wDyCVve1QgUfSkUkfqN4wdTZlalRFUSNe9eUCyCZPkMUc8BbC/trained_model.tar\nDownloaded weights in 2.19s\nLoaded LoRAs in 11.28s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.66it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.22it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.95it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.83it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.77it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.74it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.72it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.70it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.69it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.68it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.68it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.67it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.66it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.67it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.67it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.66it/s]\n 61%|██████ | 17/28 [00:04<00:03, 3.66it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.66it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.67it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.66it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.66it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.66it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.66it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.66it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.65it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.66it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.66it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.66it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.69it/s]", "metrics": { "predict_time": 19.462761478, "total_time": 19.473999 }, "output": [ "https://replicate.delivery/yhqm/ZmLQ0m5HojKWK9A2Shbl18mk2SzzfWNBuoB3ViL9Mi5jHSsJA/out-0.webp" ], "started_at": "2024-09-01T16:09:24.493238Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/v1d1e4s909rm20chnscvsv5fg4", "cancel": "https://api.replicate.com/v1/predictions/v1d1e4s909rm20chnscvsv5fg4/cancel" }, "version": "6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44" }Generated inUsing seed: 5151 Prompt: AGRAT as Captain America, medium shot, determined expression, holding iconic shield, star-spangled costume with battle wear, golden hour lighting, cinematic color grading txt2img mode Using dev model free=9543646593024 Downloading weights 2024-09-01T16:09:24Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpxm9y5a60/weights url=https://replicate.delivery/yhqm/wDyCVve1QgUfSkUkfqN4wdTZlalRFUSNe9eUCyCZPkMUc8BbC/trained_model.tar 2024-09-01T16:09:26Z | INFO | [ Complete ] dest=/tmp/tmpxm9y5a60/weights size="172 MB" total_elapsed=2.159s url=https://replicate.delivery/yhqm/wDyCVve1QgUfSkUkfqN4wdTZlalRFUSNe9eUCyCZPkMUc8BbC/trained_model.tar Downloaded weights in 2.19s Loaded LoRAs in 11.28s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.66it/s] 7%|▋ | 2/28 [00:00<00:06, 4.22it/s] 11%|█ | 3/28 [00:00<00:06, 3.95it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.83it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.77it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.74it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.72it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.70it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.69it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.68it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.68it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.67it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.66it/s] 50%|█████ | 14/28 [00:03<00:03, 3.67it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.67it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.66it/s] 61%|██████ | 17/28 [00:04<00:03, 3.66it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.66it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.67it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.66it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.66it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.66it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.66it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.66it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.65it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.66it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.66it/s] 100%|██████████| 28/28 [00:07<00:00, 3.66it/s] 100%|██████████| 28/28 [00:07<00:00, 3.69it/s]Prediction
warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44IDyb0r1s7an9rm40chnsd9dh0pxcStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- AGRAT as Hulk, extreme close-up on face, intense green eyes, textured skin, veins bulging, torn clothing visible at edges, moody atmospheric lighting
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 16:9
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 96
- extra_lora_scale
- 0.8
- num_inference_steps
- 28
{ "model": "dev", "prompt": "AGRAT as Hulk, extreme close-up on face, intense green eyes, textured skin, veins bulging, torn clothing visible at edges, moody atmospheric lighting", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 96, "extra_lora_scale": 0.8, "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 warf23/agrat_me using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", { input: { model: "dev", prompt: "AGRAT as Hulk, extreme close-up on face, intense green eyes, textured skin, veins bulging, torn clothing visible at edges, moody atmospheric lighting", lora_scale: 1, num_outputs: 1, aspect_ratio: "16:9", output_format: "webp", guidance_scale: 3.5, output_quality: 96, extra_lora_scale: 0.8, 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 warf23/agrat_me using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", input={ "model": "dev", "prompt": "AGRAT as Hulk, extreme close-up on face, intense green eyes, textured skin, veins bulging, torn clothing visible at edges, moody atmospheric lighting", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 96, "extra_lora_scale": 0.8, "num_inference_steps": 28 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run warf23/agrat_me 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": "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", "input": { "model": "dev", "prompt": "AGRAT as Hulk, extreme close-up on face, intense green eyes, textured skin, veins bulging, torn clothing visible at edges, moody atmospheric lighting", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 96, "extra_lora_scale": 0.8, "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": "2024-09-01T16:11:59.348482Z", "created_at": "2024-09-01T16:11:19.594000Z", "data_removed": false, "error": null, "id": "yb0r1s7an9rm40chnsd9dh0pxc", "input": { "model": "dev", "prompt": "AGRAT as Hulk, extreme close-up on face, intense green eyes, textured skin, veins bulging, torn clothing visible at edges, moody atmospheric lighting", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 96, "extra_lora_scale": 0.8, "num_inference_steps": 28 }, "logs": "Using seed: 48410\nPrompt: AGRAT as Hulk, extreme close-up on face, intense green eyes, textured skin, veins bulging, torn clothing visible at edges, moody atmospheric lighting\ntxt2img mode\nUsing dev model\nfree=9321329057792\nDownloading weights\n2024-09-01T16:11:29Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmp79dr12fv/weights url=https://replicate.delivery/yhqm/wDyCVve1QgUfSkUkfqN4wdTZlalRFUSNe9eUCyCZPkMUc8BbC/trained_model.tar\n2024-09-01T16:11:32Z | INFO | [ Complete ] dest=/tmp/tmp79dr12fv/weights size=\"172 MB\" total_elapsed=2.889s url=https://replicate.delivery/yhqm/wDyCVve1QgUfSkUkfqN4wdTZlalRFUSNe9eUCyCZPkMUc8BbC/trained_model.tar\nDownloaded weights in 2.92s\nLoaded LoRAs in 22.12s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.68it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.24it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.97it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.86it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.79it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.75it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.73it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.72it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.71it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.70it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.70it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.69it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.69it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.69it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.69it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.69it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.69it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.69it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.69it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.69it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.69it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.69it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.69it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.69it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.69it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 3.69it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.69it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.69it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.72it/s]", "metrics": { "predict_time": 30.240363361, "total_time": 39.754482 }, "output": [ "https://replicate.delivery/yhqm/BfJyQRaco9SDIqyfsldifKBN5Z2bl1fodgVXyWa7C6W9ERiNB/out-0.webp" ], "started_at": "2024-09-01T16:11:29.108119Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/yb0r1s7an9rm40chnsd9dh0pxc", "cancel": "https://api.replicate.com/v1/predictions/yb0r1s7an9rm40chnsd9dh0pxc/cancel" }, "version": "6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44" }Generated inUsing seed: 48410 Prompt: AGRAT as Hulk, extreme close-up on face, intense green eyes, textured skin, veins bulging, torn clothing visible at edges, moody atmospheric lighting txt2img mode Using dev model free=9321329057792 Downloading weights 2024-09-01T16:11:29Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmp79dr12fv/weights url=https://replicate.delivery/yhqm/wDyCVve1QgUfSkUkfqN4wdTZlalRFUSNe9eUCyCZPkMUc8BbC/trained_model.tar 2024-09-01T16:11:32Z | INFO | [ Complete ] dest=/tmp/tmp79dr12fv/weights size="172 MB" total_elapsed=2.889s url=https://replicate.delivery/yhqm/wDyCVve1QgUfSkUkfqN4wdTZlalRFUSNe9eUCyCZPkMUc8BbC/trained_model.tar Downloaded weights in 2.92s Loaded LoRAs in 22.12s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.68it/s] 7%|▋ | 2/28 [00:00<00:06, 4.24it/s] 11%|█ | 3/28 [00:00<00:06, 3.97it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.86it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.79it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.75it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.73it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.72it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.71it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.70it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.70it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.69it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.69it/s] 50%|█████ | 14/28 [00:03<00:03, 3.69it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.69it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.69it/s] 61%|██████ | 17/28 [00:04<00:02, 3.69it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.69it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.69it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.69it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.69it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.69it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.69it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.69it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.69it/s] 93%|█████████▎| 26/28 [00:06<00:00, 3.69it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.69it/s] 100%|██████████| 28/28 [00:07<00:00, 3.69it/s] 100%|██████████| 28/28 [00:07<00:00, 3.72it/s]Prediction
warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44ID1nmzq5ypz1rm00chq6a8ygz620StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- image
- model
- dev
- prompt
- AGRAT
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- prompt_strength
- 0.8
- extra_lora_scale
- 0.8
- num_inference_steps
- 28
{ "image": "https://in-media.apjonlinecdn.com/magefan_blog/Your_Guide_to_Building_Your_Perfect_Gaming_Setup_at_Home.png", "model": "dev", "prompt": "AGRAT ", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 0.8, "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 warf23/agrat_me using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", { input: { image: "https://in-media.apjonlinecdn.com/magefan_blog/Your_Guide_to_Building_Your_Perfect_Gaming_Setup_at_Home.png", model: "dev", prompt: "AGRAT ", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, prompt_strength: 0.8, extra_lora_scale: 0.8, 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 warf23/agrat_me using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", input={ "image": "https://in-media.apjonlinecdn.com/magefan_blog/Your_Guide_to_Building_Your_Perfect_Gaming_Setup_at_Home.png", "model": "dev", "prompt": "AGRAT ", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 0.8, "num_inference_steps": 28 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run warf23/agrat_me 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": "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", "input": { "image": "https://in-media.apjonlinecdn.com/magefan_blog/Your_Guide_to_Building_Your_Perfect_Gaming_Setup_at_Home.png", "model": "dev", "prompt": "AGRAT ", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 0.8, "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": "2024-09-03T20:32:55.235083Z", "created_at": "2024-09-03T20:30:25.016000Z", "data_removed": false, "error": null, "id": "1nmzq5ypz1rm00chq6a8ygz620", "input": { "image": "https://in-media.apjonlinecdn.com/magefan_blog/Your_Guide_to_Building_Your_Perfect_Gaming_Setup_at_Home.png", "model": "dev", "prompt": "AGRAT ", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 0.8, "num_inference_steps": 28 }, "logs": "Using seed: 31211\nPrompt: AGRAT\ntxt2img mode\nUsing dev model\nfree=9487719624704\nDownloading weights\n2024-09-03T20:32:29Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpat5x7z8v/weights url=https://replicate.delivery/yhqm/wDyCVve1QgUfSkUkfqN4wdTZlalRFUSNe9eUCyCZPkMUc8BbC/trained_model.tar\n2024-09-03T20:32:31Z | INFO | [ Complete ] dest=/tmp/tmpat5x7z8v/weights size=\"172 MB\" total_elapsed=1.918s url=https://replicate.delivery/yhqm/wDyCVve1QgUfSkUkfqN4wdTZlalRFUSNe9eUCyCZPkMUc8BbC/trained_model.tar\nDownloaded weights in 1.95s\nLoaded LoRAs in 17.25s\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, 3.96it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.73it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.64it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.59it/s]\n 21%|██▏ | 6/28 [00:01<00:06, 3.56it/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, 3.52it/s]\n 39%|███▉ | 11/28 [00:03<00:04, 3.51it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.51it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.51it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.51it/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, 3.51it/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, 3.51it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.50it/s]\n 79%|███████▊ | 22/28 [00:06<00:01, 3.50it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.51it/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, 3.51it/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, 3.53it/s]", "metrics": { "predict_time": 26.483278417, "total_time": 150.219083 }, "output": [ "https://replicate.delivery/yhqm/KZf3a4Hug6ywPaCLUAHZfXwSLLfP701Aj0V3ZS4ZMzIvjkymA/out-0.webp" ], "started_at": "2024-09-03T20:32:28.751805Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/1nmzq5ypz1rm00chq6a8ygz620", "cancel": "https://api.replicate.com/v1/predictions/1nmzq5ypz1rm00chq6a8ygz620/cancel" }, "version": "6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44" }Generated inUsing seed: 31211 Prompt: AGRAT txt2img mode Using dev model free=9487719624704 Downloading weights 2024-09-03T20:32:29Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpat5x7z8v/weights url=https://replicate.delivery/yhqm/wDyCVve1QgUfSkUkfqN4wdTZlalRFUSNe9eUCyCZPkMUc8BbC/trained_model.tar 2024-09-03T20:32:31Z | INFO | [ Complete ] dest=/tmp/tmpat5x7z8v/weights size="172 MB" total_elapsed=1.918s url=https://replicate.delivery/yhqm/wDyCVve1QgUfSkUkfqN4wdTZlalRFUSNe9eUCyCZPkMUc8BbC/trained_model.tar Downloaded weights in 1.95s Loaded LoRAs in 17.25s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.48it/s] 7%|▋ | 2/28 [00:00<00:06, 3.96it/s] 11%|█ | 3/28 [00:00<00:06, 3.73it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.64it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.59it/s] 21%|██▏ | 6/28 [00:01<00:06, 3.56it/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, 3.52it/s] 39%|███▉ | 11/28 [00:03<00:04, 3.51it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.51it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.51it/s] 50%|█████ | 14/28 [00:03<00:03, 3.51it/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, 3.51it/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, 3.51it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.50it/s] 79%|███████▊ | 22/28 [00:06<00:01, 3.50it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.51it/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, 3.51it/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, 3.53it/s]Prediction
warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44IDyj2drysk3srm00chnykvkpt2cmStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- AGRAT in business attire, three-quarter view, dramatic side lighting, crisp details, captured with a wide-angle lens
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- extra_lora_scale
- 0.8
- num_inference_steps
- 28
{ "model": "dev", "prompt": "AGRAT in business attire, three-quarter view, dramatic side lighting, crisp details, captured with a wide-angle lens", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "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 warf23/agrat_me using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", { input: { model: "dev", prompt: "AGRAT in business attire, three-quarter view, dramatic side lighting, crisp details, captured with a wide-angle lens", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, extra_lora_scale: 0.8, 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 warf23/agrat_me using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", input={ "model": "dev", "prompt": "AGRAT in business attire, three-quarter view, dramatic side lighting, crisp details, captured with a wide-angle lens", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run warf23/agrat_me 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": "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", "input": { "model": "dev", "prompt": "AGRAT in business attire, three-quarter view, dramatic side lighting, crisp details, captured with a wide-angle lens", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "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": "2024-09-01T22:14:55.442678Z", "created_at": "2024-09-01T22:14:16.094000Z", "data_removed": false, "error": null, "id": "yj2drysk3srm00chnykvkpt2cm", "input": { "model": "dev", "prompt": "AGRAT in business attire, three-quarter view, dramatic side lighting, crisp details, captured with a wide-angle lens", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 }, "logs": "Using seed: 51125\nPrompt: AGRAT in business attire, three-quarter view, dramatic side lighting, crisp details, captured with a wide-angle lens\ntxt2img mode\nUsing dev model\nLoaded LoRAs in 7.18s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.65it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.19it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.92it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.80it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.74it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.70it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.68it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.67it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.66it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.65it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.64it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.64it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.64it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.63it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.63it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.63it/s]\n 61%|██████ | 17/28 [00:04<00:03, 3.63it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.63it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.62it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.62it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.62it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.62it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.62it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.62it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.62it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.62it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.62it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.63it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.66it/s]", "metrics": { "predict_time": 15.346416419, "total_time": 39.348678 }, "output": [ "https://replicate.delivery/yhqm/SG5ARkwJeCWTCKlUiVmpRA8gEPANHlAJ9nF4Mut3V9jvyUsJA/out-0.webp" ], "started_at": "2024-09-01T22:14:40.096261Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/yj2drysk3srm00chnykvkpt2cm", "cancel": "https://api.replicate.com/v1/predictions/yj2drysk3srm00chnykvkpt2cm/cancel" }, "version": "6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44" }Generated inUsing seed: 51125 Prompt: AGRAT in business attire, three-quarter view, dramatic side lighting, crisp details, captured with a wide-angle lens txt2img mode Using dev model Loaded LoRAs in 7.18s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.65it/s] 7%|▋ | 2/28 [00:00<00:06, 4.19it/s] 11%|█ | 3/28 [00:00<00:06, 3.92it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.80it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.74it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.70it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.68it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.67it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.66it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.65it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.64it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.64it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.64it/s] 50%|█████ | 14/28 [00:03<00:03, 3.63it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.63it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.63it/s] 61%|██████ | 17/28 [00:04<00:03, 3.63it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.63it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.62it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.62it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.62it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.62it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.62it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.62it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.62it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.62it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.62it/s] 100%|██████████| 28/28 [00:07<00:00, 3.63it/s] 100%|██████████| 28/28 [00:07<00:00, 3.66it/s]Prediction
warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44ID9jb3m2t711rm60chnsebg02864StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- AGRAT as Wolverine, feral expression, adamantium claws extended, ripped costume showing muscular physique, forest setting, mist and moonlight, cinematic blue color grade
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 16:9
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 96
- extra_lora_scale
- 0.8
- num_inference_steps
- 28
{ "model": "dev", "prompt": "AGRAT as Wolverine, feral expression, adamantium claws extended, ripped costume showing muscular physique, forest setting, mist and moonlight, cinematic blue color grade", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 96, "extra_lora_scale": 0.8, "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 warf23/agrat_me using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", { input: { model: "dev", prompt: "AGRAT as Wolverine, feral expression, adamantium claws extended, ripped costume showing muscular physique, forest setting, mist and moonlight, cinematic blue color grade", lora_scale: 1, num_outputs: 1, aspect_ratio: "16:9", output_format: "webp", guidance_scale: 3.5, output_quality: 96, extra_lora_scale: 0.8, 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 warf23/agrat_me using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", input={ "model": "dev", "prompt": "AGRAT as Wolverine, feral expression, adamantium claws extended, ripped costume showing muscular physique, forest setting, mist and moonlight, cinematic blue color grade", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 96, "extra_lora_scale": 0.8, "num_inference_steps": 28 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run warf23/agrat_me 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": "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", "input": { "model": "dev", "prompt": "AGRAT as Wolverine, feral expression, adamantium claws extended, ripped costume showing muscular physique, forest setting, mist and moonlight, cinematic blue color grade", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 96, "extra_lora_scale": 0.8, "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": "2024-09-01T16:13:24.247291Z", "created_at": "2024-09-01T16:12:48.776000Z", "data_removed": false, "error": null, "id": "9jb3m2t711rm60chnsebg02864", "input": { "model": "dev", "prompt": "AGRAT as Wolverine, feral expression, adamantium claws extended, ripped costume showing muscular physique, forest setting, mist and moonlight, cinematic blue color grade", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 96, "extra_lora_scale": 0.8, "num_inference_steps": 28 }, "logs": "Using seed: 38923\nPrompt: AGRAT as Wolverine, feral expression, adamantium claws extended, ripped costume showing muscular physique, forest setting, mist and moonlight, cinematic blue color grade\ntxt2img mode\nUsing dev model\nLoaded LoRAs in 8.64s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.69it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.26it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.99it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.87it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.80it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.77it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.75it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.73it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.71it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.71it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.71it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.70it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.70it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.70it/s]\n 54%|█████▎ | 15/28 [00:03<00:03, 3.70it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.70it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.70it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.70it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.70it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.70it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.69it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.69it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.69it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.69it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.68it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 3.69it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.69it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.69it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.72it/s]", "metrics": { "predict_time": 16.726689482, "total_time": 35.471291 }, "output": [ "https://replicate.delivery/yhqm/fqPr04CuGDyYHKpa5FhfoohM5B9GW6nYsTB4UpTdY19kSkYTA/out-0.webp" ], "started_at": "2024-09-01T16:13:07.520602Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/9jb3m2t711rm60chnsebg02864", "cancel": "https://api.replicate.com/v1/predictions/9jb3m2t711rm60chnsebg02864/cancel" }, "version": "6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44" }Generated inUsing seed: 38923 Prompt: AGRAT as Wolverine, feral expression, adamantium claws extended, ripped costume showing muscular physique, forest setting, mist and moonlight, cinematic blue color grade txt2img mode Using dev model Loaded LoRAs in 8.64s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.69it/s] 7%|▋ | 2/28 [00:00<00:06, 4.26it/s] 11%|█ | 3/28 [00:00<00:06, 3.99it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.87it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.80it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.77it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.75it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.73it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.71it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.71it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.71it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.70it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.70it/s] 50%|█████ | 14/28 [00:03<00:03, 3.70it/s] 54%|█████▎ | 15/28 [00:03<00:03, 3.70it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.70it/s] 61%|██████ | 17/28 [00:04<00:02, 3.70it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.70it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.70it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.70it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.69it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.69it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.69it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.69it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.68it/s] 93%|█████████▎| 26/28 [00:06<00:00, 3.69it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.69it/s] 100%|██████████| 28/28 [00:07<00:00, 3.69it/s] 100%|██████████| 28/28 [00:07<00:00, 3.72it/s]Prediction
warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44IDgkywxr3qxdrm40chnymbkthv60StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- Head and shoulders shot of AGRAT in a sleek office setting, rich color palette, sharp focus on facial features, photographed from a slightly elevated angle
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- extra_lora_scale
- 0.8
- num_inference_steps
- 28
{ "model": "dev", "prompt": "Head and shoulders shot of AGRAT in a sleek office setting, rich color palette, sharp focus on facial features, photographed from a slightly elevated angle", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "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 warf23/agrat_me using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", { input: { model: "dev", prompt: "Head and shoulders shot of AGRAT in a sleek office setting, rich color palette, sharp focus on facial features, photographed from a slightly elevated angle", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, extra_lora_scale: 0.8, 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 warf23/agrat_me using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", input={ "model": "dev", "prompt": "Head and shoulders shot of AGRAT in a sleek office setting, rich color palette, sharp focus on facial features, photographed from a slightly elevated angle", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run warf23/agrat_me 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": "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", "input": { "model": "dev", "prompt": "Head and shoulders shot of AGRAT in a sleek office setting, rich color palette, sharp focus on facial features, photographed from a slightly elevated angle", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "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": "2024-09-01T22:15:58.491583Z", "created_at": "2024-09-01T22:15:39.243000Z", "data_removed": false, "error": null, "id": "gkywxr3qxdrm40chnymbkthv60", "input": { "model": "dev", "prompt": "Head and shoulders shot of AGRAT in a sleek office setting, rich color palette, sharp focus on facial features, photographed from a slightly elevated angle", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 }, "logs": "Using seed: 15663\nPrompt: Head and shoulders shot of AGRAT in a sleek office setting, rich color palette, sharp focus on facial features, photographed from a slightly elevated angle\ntxt2img mode\nUsing dev model\nfree=9858978766848\nDownloading weights\n2024-09-01T22:15:39Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpg86a0il3/weights url=https://replicate.delivery/yhqm/wDyCVve1QgUfSkUkfqN4wdTZlalRFUSNe9eUCyCZPkMUc8BbC/trained_model.tar\n2024-09-01T22:15:42Z | INFO | [ Complete ] dest=/tmp/tmpg86a0il3/weights size=\"172 MB\" total_elapsed=3.342s url=https://replicate.delivery/yhqm/wDyCVve1QgUfSkUkfqN4wdTZlalRFUSNe9eUCyCZPkMUc8BbC/trained_model.tar\nDownloaded weights in 3.44s\nLoaded LoRAs in 11.17s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.68it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.23it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.96it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.84it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.78it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.75it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.72it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.71it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.70it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.69it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.69it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.68it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.68it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.68it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.68it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.68it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.67it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.68it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.68it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.67it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.68it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.68it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.68it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.68it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.68it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.68it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.68it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.68it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.71it/s]", "metrics": { "predict_time": 19.241211049, "total_time": 19.248583 }, "output": [ "https://replicate.delivery/yhqm/OFlE1DKSGAIJH5t66y7qeMPHidFzQOneqimg6TN3FK8eMTxmA/out-0.webp" ], "started_at": "2024-09-01T22:15:39.250372Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/gkywxr3qxdrm40chnymbkthv60", "cancel": "https://api.replicate.com/v1/predictions/gkywxr3qxdrm40chnymbkthv60/cancel" }, "version": "6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44" }Generated inUsing seed: 15663 Prompt: Head and shoulders shot of AGRAT in a sleek office setting, rich color palette, sharp focus on facial features, photographed from a slightly elevated angle txt2img mode Using dev model free=9858978766848 Downloading weights 2024-09-01T22:15:39Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpg86a0il3/weights url=https://replicate.delivery/yhqm/wDyCVve1QgUfSkUkfqN4wdTZlalRFUSNe9eUCyCZPkMUc8BbC/trained_model.tar 2024-09-01T22:15:42Z | INFO | [ Complete ] dest=/tmp/tmpg86a0il3/weights size="172 MB" total_elapsed=3.342s url=https://replicate.delivery/yhqm/wDyCVve1QgUfSkUkfqN4wdTZlalRFUSNe9eUCyCZPkMUc8BbC/trained_model.tar Downloaded weights in 3.44s Loaded LoRAs in 11.17s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.68it/s] 7%|▋ | 2/28 [00:00<00:06, 4.23it/s] 11%|█ | 3/28 [00:00<00:06, 3.96it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.84it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.78it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.75it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.72it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.71it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.70it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.69it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.69it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.68it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.68it/s] 50%|█████ | 14/28 [00:03<00:03, 3.68it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.68it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.68it/s] 61%|██████ | 17/28 [00:04<00:02, 3.67it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.68it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.68it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.67it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.68it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.68it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.68it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.68it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.68it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.68it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.68it/s] 100%|██████████| 28/28 [00:07<00:00, 3.68it/s] 100%|██████████| 28/28 [00:07<00:00, 3.71it/s]Prediction
warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44IDdyrejzcs1srm00chnyna84612cStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- Striking portrait of AGRAT in a tailored black suit, shot with a 50mm lens, dramatic chiaroscuro lighting, ultra-high contrast, vibrant skin tones
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- extra_lora_scale
- 0.8
- num_inference_steps
- 28
{ "model": "dev", "prompt": "Striking portrait of AGRAT in a tailored black suit, shot with a 50mm lens, dramatic chiaroscuro lighting, ultra-high contrast, vibrant skin tones", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "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 warf23/agrat_me using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", { input: { model: "dev", prompt: "Striking portrait of AGRAT in a tailored black suit, shot with a 50mm lens, dramatic chiaroscuro lighting, ultra-high contrast, vibrant skin tones", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, extra_lora_scale: 0.8, 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 warf23/agrat_me using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", input={ "model": "dev", "prompt": "Striking portrait of AGRAT in a tailored black suit, shot with a 50mm lens, dramatic chiaroscuro lighting, ultra-high contrast, vibrant skin tones", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run warf23/agrat_me 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": "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", "input": { "model": "dev", "prompt": "Striking portrait of AGRAT in a tailored black suit, shot with a 50mm lens, dramatic chiaroscuro lighting, ultra-high contrast, vibrant skin tones", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "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": "2024-09-01T22:18:15.698036Z", "created_at": "2024-09-01T22:17:58.798000Z", "data_removed": false, "error": null, "id": "dyrejzcs1srm00chnyna84612c", "input": { "model": "dev", "prompt": "Striking portrait of AGRAT in a tailored black suit, shot with a 50mm lens, dramatic chiaroscuro lighting, ultra-high contrast, vibrant skin tones", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 }, "logs": "Using seed: 53027\nPrompt: Striking portrait of AGRAT in a tailored black suit, shot with a 50mm lens, dramatic chiaroscuro lighting, ultra-high contrast, vibrant skin tones\ntxt2img mode\nUsing dev model\nLoaded LoRAs in 8.85s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.68it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.24it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.96it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.84it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.77it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.74it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.72it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.70it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.69it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.69it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.69it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.68it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.68it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.68it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.68it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.68it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.68it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.68it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.68it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.68it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.68it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.68it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.68it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.68it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.67it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.68it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.68it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.68it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.71it/s]", "metrics": { "predict_time": 16.890821372, "total_time": 16.900036 }, "output": [ "https://replicate.delivery/yhqm/HixsfIBfK8rGqkYlWaFYcLUQVtxge5Z3EtM6fiPEpez4ENFbC/out-0.webp" ], "started_at": "2024-09-01T22:17:58.807215Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/dyrejzcs1srm00chnyna84612c", "cancel": "https://api.replicate.com/v1/predictions/dyrejzcs1srm00chnyna84612c/cancel" }, "version": "6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44" }Generated inUsing seed: 53027 Prompt: Striking portrait of AGRAT in a tailored black suit, shot with a 50mm lens, dramatic chiaroscuro lighting, ultra-high contrast, vibrant skin tones txt2img mode Using dev model Loaded LoRAs in 8.85s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.68it/s] 7%|▋ | 2/28 [00:00<00:06, 4.24it/s] 11%|█ | 3/28 [00:00<00:06, 3.96it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.84it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.77it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.74it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.72it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.70it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.69it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.69it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.69it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.68it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.68it/s] 50%|█████ | 14/28 [00:03<00:03, 3.68it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.68it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.68it/s] 61%|██████ | 17/28 [00:04<00:02, 3.68it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.68it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.68it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.68it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.68it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.68it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.68it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.68it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.67it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.68it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.68it/s] 100%|██████████| 28/28 [00:07<00:00, 3.68it/s] 100%|██████████| 28/28 [00:07<00:00, 3.71it/s]Prediction
warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44IDtkhyqx57pdrm60chnyp9y2depmStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- Tight frame on AGRAT's face, black suit visible, shot with a 70mm lens, dramatic side lighting, high contrast, professional backdrop, LinkedIn-optimized crop
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- extra_lora_scale
- 0.8
- num_inference_steps
- 28
{ "model": "dev", "prompt": "Tight frame on AGRAT's face, black suit visible, shot with a 70mm lens, dramatic side lighting, high contrast, professional backdrop, LinkedIn-optimized crop", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "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 warf23/agrat_me using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", { input: { model: "dev", prompt: "Tight frame on AGRAT's face, black suit visible, shot with a 70mm lens, dramatic side lighting, high contrast, professional backdrop, LinkedIn-optimized crop", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, extra_lora_scale: 0.8, 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 warf23/agrat_me using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", input={ "model": "dev", "prompt": "Tight frame on AGRAT's face, black suit visible, shot with a 70mm lens, dramatic side lighting, high contrast, professional backdrop, LinkedIn-optimized crop", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run warf23/agrat_me 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": "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", "input": { "model": "dev", "prompt": "Tight frame on AGRAT\'s face, black suit visible, shot with a 70mm lens, dramatic side lighting, high contrast, professional backdrop, LinkedIn-optimized crop", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "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": "2024-09-01T22:20:31.417912Z", "created_at": "2024-09-01T22:20:13.619000Z", "data_removed": false, "error": null, "id": "tkhyqx57pdrm60chnyp9y2depm", "input": { "model": "dev", "prompt": "Tight frame on AGRAT's face, black suit visible, shot with a 70mm lens, dramatic side lighting, high contrast, professional backdrop, LinkedIn-optimized crop", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 }, "logs": "Using seed: 26183\nPrompt: Tight frame on AGRAT's face, black suit visible, shot with a 70mm lens, dramatic side lighting, high contrast, professional backdrop, LinkedIn-optimized crop\ntxt2img mode\nUsing dev model\nLoaded LoRAs in 9.69s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.70it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.26it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.97it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.85it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.79it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.76it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.73it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.71it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.70it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.70it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.69it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.68it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.69it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.69it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.68it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.68it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.68it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.68it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.68it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.68it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.68it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.68it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.68it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.67it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.68it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.68it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.68it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.68it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.71it/s]", "metrics": { "predict_time": 17.779524404, "total_time": 17.798912 }, "output": [ "https://replicate.delivery/yhqm/d4uqbQQNV5ryJNR9mIarFmgdDSwHj8VA6SKCClBkol4raK2E/out-0.webp" ], "started_at": "2024-09-01T22:20:13.638388Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/tkhyqx57pdrm60chnyp9y2depm", "cancel": "https://api.replicate.com/v1/predictions/tkhyqx57pdrm60chnyp9y2depm/cancel" }, "version": "6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44" }Generated inUsing seed: 26183 Prompt: Tight frame on AGRAT's face, black suit visible, shot with a 70mm lens, dramatic side lighting, high contrast, professional backdrop, LinkedIn-optimized crop txt2img mode Using dev model Loaded LoRAs in 9.69s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.70it/s] 7%|▋ | 2/28 [00:00<00:06, 4.26it/s] 11%|█ | 3/28 [00:00<00:06, 3.97it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.85it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.79it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.76it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.73it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.71it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.70it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.70it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.69it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.68it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.69it/s] 50%|█████ | 14/28 [00:03<00:03, 3.69it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.68it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.68it/s] 61%|██████ | 17/28 [00:04<00:02, 3.68it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.68it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.68it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.68it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.68it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.68it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.68it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.67it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.68it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.68it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.68it/s] 100%|██████████| 28/28 [00:07<00:00, 3.68it/s] 100%|██████████| 28/28 [00:07<00:00, 3.71it/s]Prediction
warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44IDe0jgw8snjdrj20cj8g2bbxd4v8StatusSucceededSourceWebHardwareA100 (80GB)Total durationCreatedInput
- model
- dev
- prompt
- AGRAT walking along the Seine River, iconic Parisian architecture behind him, golden hour glow, saturated colors, medium shot from eye level
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- prompt_strength
- 0.8
- extra_lora_scale
- 0.8
- num_inference_steps
- 28
{ "model": "dev", "prompt": "AGRAT walking along the Seine River, iconic Parisian architecture behind him, golden hour glow, saturated colors, medium shot from eye level", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 0.8, "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 warf23/agrat_me using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", { input: { model: "dev", prompt: "AGRAT walking along the Seine River, iconic Parisian architecture behind him, golden hour glow, saturated colors, medium shot from eye level", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, prompt_strength: 0.8, extra_lora_scale: 0.8, 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 warf23/agrat_me using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", input={ "model": "dev", "prompt": "AGRAT walking along the Seine River, iconic Parisian architecture behind him, golden hour glow, saturated colors, medium shot from eye level", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 0.8, "num_inference_steps": 28 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run warf23/agrat_me 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": "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", "input": { "model": "dev", "prompt": "AGRAT walking along the Seine River, iconic Parisian architecture behind him, golden hour glow, saturated colors, medium shot from eye level", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 0.8, "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": "2024-09-30T17:40:06.329012Z", "created_at": "2024-09-30T17:39:39.539000Z", "data_removed": false, "error": null, "id": "e0jgw8snjdrj20cj8g2bbxd4v8", "input": { "model": "dev", "prompt": "AGRAT walking along the Seine River, iconic Parisian architecture behind him, golden hour glow, saturated colors, medium shot from eye level", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 0.8, "num_inference_steps": 28 }, "logs": "Using seed: 33946\nPrompt: AGRAT walking along the Seine River, iconic Parisian architecture behind him, golden hour glow, saturated colors, medium shot from eye level\n[!] txt2img mode\nUsing dev model\nfree=3030137593856\nDownloading weights\n2024-09-30T17:39:41Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpxhfn7e06/weights url=https://replicate.delivery/yhqm/wDyCVve1QgUfSkUkfqN4wdTZlalRFUSNe9eUCyCZPkMUc8BbC/trained_model.tar\n2024-09-30T17:39:46Z | INFO | [ Complete ] dest=/tmp/tmpxhfn7e06/weights size=\"172 MB\" total_elapsed=5.456s url=https://replicate.delivery/yhqm/wDyCVve1QgUfSkUkfqN4wdTZlalRFUSNe9eUCyCZPkMUc8BbC/trained_model.tar\nDownloaded weights in 5.49s\nLoaded LoRAs in 6.33s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:17, 1.55it/s]\n 7%|▋ | 2/28 [00:01<00:14, 1.74it/s]\n 11%|█ | 3/28 [00:01<00:15, 1.65it/s]\n 14%|█▍ | 4/28 [00:02<00:14, 1.60it/s]\n 18%|█▊ | 5/28 [00:03<00:14, 1.58it/s]\n 21%|██▏ | 6/28 [00:03<00:14, 1.57it/s]\n 25%|██▌ | 7/28 [00:04<00:13, 1.56it/s]\n 29%|██▊ | 8/28 [00:05<00:12, 1.55it/s]\n 32%|███▏ | 9/28 [00:05<00:12, 1.55it/s]\n 36%|███▌ | 10/28 [00:06<00:11, 1.55it/s]\n 39%|███▉ | 11/28 [00:07<00:11, 1.55it/s]\n 43%|████▎ | 12/28 [00:07<00:10, 1.54it/s]\n 46%|████▋ | 13/28 [00:08<00:09, 1.54it/s]\n 50%|█████ | 14/28 [00:08<00:09, 1.54it/s]\n 54%|█████▎ | 15/28 [00:09<00:08, 1.54it/s]\n 57%|█████▋ | 16/28 [00:10<00:07, 1.54it/s]\n 61%|██████ | 17/28 [00:10<00:07, 1.54it/s]\n 64%|██████▍ | 18/28 [00:11<00:06, 1.54it/s]\n 68%|██████▊ | 19/28 [00:12<00:05, 1.54it/s]\n 71%|███████▏ | 20/28 [00:12<00:05, 1.54it/s]\n 75%|███████▌ | 21/28 [00:13<00:04, 1.54it/s]\n 79%|███████▊ | 22/28 [00:14<00:03, 1.54it/s]\n 82%|████████▏ | 23/28 [00:14<00:03, 1.54it/s]\n 86%|████████▌ | 24/28 [00:15<00:02, 1.54it/s]\n 89%|████████▉ | 25/28 [00:16<00:01, 1.54it/s]\n 93%|█████████▎| 26/28 [00:16<00:01, 1.54it/s]\n 96%|█████████▋| 27/28 [00:17<00:00, 1.54it/s]\n100%|██████████| 28/28 [00:18<00:00, 1.54it/s]\n100%|██████████| 28/28 [00:18<00:00, 1.55it/s]", "metrics": { "predict_time": 25.06268512, "total_time": 26.790012 }, "output": [ "https://replicate.delivery/yhqm/fFeVNKKh0TiepodDB8ZQynkZjq7i39eCcigzLu9SzSbZHlIOB/out-0.webp" ], "started_at": "2024-09-30T17:39:41.266327Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/e0jgw8snjdrj20cj8g2bbxd4v8", "cancel": "https://api.replicate.com/v1/predictions/e0jgw8snjdrj20cj8g2bbxd4v8/cancel" }, "version": "6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44" }Generated inUsing seed: 33946 Prompt: AGRAT walking along the Seine River, iconic Parisian architecture behind him, golden hour glow, saturated colors, medium shot from eye level [!] txt2img mode Using dev model free=3030137593856 Downloading weights 2024-09-30T17:39:41Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpxhfn7e06/weights url=https://replicate.delivery/yhqm/wDyCVve1QgUfSkUkfqN4wdTZlalRFUSNe9eUCyCZPkMUc8BbC/trained_model.tar 2024-09-30T17:39:46Z | INFO | [ Complete ] dest=/tmp/tmpxhfn7e06/weights size="172 MB" total_elapsed=5.456s url=https://replicate.delivery/yhqm/wDyCVve1QgUfSkUkfqN4wdTZlalRFUSNe9eUCyCZPkMUc8BbC/trained_model.tar Downloaded weights in 5.49s Loaded LoRAs in 6.33s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:17, 1.55it/s] 7%|▋ | 2/28 [00:01<00:14, 1.74it/s] 11%|█ | 3/28 [00:01<00:15, 1.65it/s] 14%|█▍ | 4/28 [00:02<00:14, 1.60it/s] 18%|█▊ | 5/28 [00:03<00:14, 1.58it/s] 21%|██▏ | 6/28 [00:03<00:14, 1.57it/s] 25%|██▌ | 7/28 [00:04<00:13, 1.56it/s] 29%|██▊ | 8/28 [00:05<00:12, 1.55it/s] 32%|███▏ | 9/28 [00:05<00:12, 1.55it/s] 36%|███▌ | 10/28 [00:06<00:11, 1.55it/s] 39%|███▉ | 11/28 [00:07<00:11, 1.55it/s] 43%|████▎ | 12/28 [00:07<00:10, 1.54it/s] 46%|████▋ | 13/28 [00:08<00:09, 1.54it/s] 50%|█████ | 14/28 [00:08<00:09, 1.54it/s] 54%|█████▎ | 15/28 [00:09<00:08, 1.54it/s] 57%|█████▋ | 16/28 [00:10<00:07, 1.54it/s] 61%|██████ | 17/28 [00:10<00:07, 1.54it/s] 64%|██████▍ | 18/28 [00:11<00:06, 1.54it/s] 68%|██████▊ | 19/28 [00:12<00:05, 1.54it/s] 71%|███████▏ | 20/28 [00:12<00:05, 1.54it/s] 75%|███████▌ | 21/28 [00:13<00:04, 1.54it/s] 79%|███████▊ | 22/28 [00:14<00:03, 1.54it/s] 82%|████████▏ | 23/28 [00:14<00:03, 1.54it/s] 86%|████████▌ | 24/28 [00:15<00:02, 1.54it/s] 89%|████████▉ | 25/28 [00:16<00:01, 1.54it/s] 93%|█████████▎| 26/28 [00:16<00:01, 1.54it/s] 96%|█████████▋| 27/28 [00:17<00:00, 1.54it/s] 100%|██████████| 28/28 [00:18<00:00, 1.54it/s] 100%|██████████| 28/28 [00:18<00:00, 1.55it/s]Prediction
warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44IDabp751b6txrm20cjb09vbf56ycStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- AGRAT in a dimly lit room, seated in an ornate leather chair. He's wearing a dark pinstriped business suit with a striped tie, his hair neatly styled back. The lighting is dramatic and atmospheric, with shadows playing across his contemplative face as he appears deep in thought. The vintage styling and film grain quality suggests this is from a classic movie scene. The composition includes some blurred decorative elements in the background, including what appears to be a circular shape resembling a moon or clock. The overall tone is dark and brooding, creating a sense of power, tension and mystery.
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 4.43
- output_quality
- 82
- prompt_strength
- 0.74
- extra_lora_scale
- 0.8
- num_inference_steps
- 28
{ "model": "dev", "prompt": "AGRAT in a dimly lit room, seated in an ornate leather chair. He's wearing a dark pinstriped business suit with a striped tie, his hair neatly styled back. The lighting is dramatic and atmospheric, with shadows playing across his contemplative face as he appears deep in thought. The vintage styling and film grain quality suggests this is from a classic movie scene. The composition includes some blurred decorative elements in the background, including what appears to be a circular shape resembling a moon or clock. The overall tone is dark and brooding, creating a sense of power, tension and mystery.", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 4.43, "output_quality": 82, "prompt_strength": 0.74, "extra_lora_scale": 0.8, "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 warf23/agrat_me using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", { input: { model: "dev", prompt: "AGRAT in a dimly lit room, seated in an ornate leather chair. He's wearing a dark pinstriped business suit with a striped tie, his hair neatly styled back. The lighting is dramatic and atmospheric, with shadows playing across his contemplative face as he appears deep in thought. The vintage styling and film grain quality suggests this is from a classic movie scene. The composition includes some blurred decorative elements in the background, including what appears to be a circular shape resembling a moon or clock. The overall tone is dark and brooding, creating a sense of power, tension and mystery.", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 4.43, output_quality: 82, prompt_strength: 0.74, extra_lora_scale: 0.8, 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 warf23/agrat_me using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", input={ "model": "dev", "prompt": "AGRAT in a dimly lit room, seated in an ornate leather chair. He's wearing a dark pinstriped business suit with a striped tie, his hair neatly styled back. The lighting is dramatic and atmospheric, with shadows playing across his contemplative face as he appears deep in thought. The vintage styling and film grain quality suggests this is from a classic movie scene. The composition includes some blurred decorative elements in the background, including what appears to be a circular shape resembling a moon or clock. The overall tone is dark and brooding, creating a sense of power, tension and mystery.", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 4.43, "output_quality": 82, "prompt_strength": 0.74, "extra_lora_scale": 0.8, "num_inference_steps": 28 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run warf23/agrat_me 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": "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", "input": { "model": "dev", "prompt": "AGRAT in a dimly lit room, seated in an ornate leather chair. He\'s wearing a dark pinstriped business suit with a striped tie, his hair neatly styled back. The lighting is dramatic and atmospheric, with shadows playing across his contemplative face as he appears deep in thought. The vintage styling and film grain quality suggests this is from a classic movie scene. The composition includes some blurred decorative elements in the background, including what appears to be a circular shape resembling a moon or clock. The overall tone is dark and brooding, creating a sense of power, tension and mystery.", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 4.43, "output_quality": 82, "prompt_strength": 0.74, "extra_lora_scale": 0.8, "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": "2024-10-04T15:09:36.534113Z", "created_at": "2024-10-04T15:08:39.511000Z", "data_removed": false, "error": null, "id": "abp751b6txrm20cjb09vbf56yc", "input": { "model": "dev", "prompt": "AGRAT in a dimly lit room, seated in an ornate leather chair. He's wearing a dark pinstriped business suit with a striped tie, his hair neatly styled back. The lighting is dramatic and atmospheric, with shadows playing across his contemplative face as he appears deep in thought. The vintage styling and film grain quality suggests this is from a classic movie scene. The composition includes some blurred decorative elements in the background, including what appears to be a circular shape resembling a moon or clock. The overall tone is dark and brooding, creating a sense of power, tension and mystery.", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 4.43, "output_quality": 82, "prompt_strength": 0.74, "extra_lora_scale": 0.8, "num_inference_steps": 28 }, "logs": "Using seed: 57052\nPrompt: AGRAT in a dimly lit room, seated in an ornate leather chair. He's wearing a dark pinstriped business suit with a striped tie, his hair neatly styled back. The lighting is dramatic and atmospheric, with shadows playing across his contemplative face as he appears deep in thought. The vintage styling and film grain quality suggests this is from a classic movie scene. The composition includes some blurred decorative elements in the background, including what appears to be a circular shape resembling a moon or clock. The overall tone is dark and brooding, creating a sense of power, tension and mystery.\n[!] txt2img mode\nUsing dev model\nfree=6867258372096\nDownloading weights\n2024-10-04T15:09:24Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpptx7kahe/weights url=https://replicate.delivery/yhqm/wDyCVve1QgUfSkUkfqN4wdTZlalRFUSNe9eUCyCZPkMUc8BbC/trained_model.tar\n2024-10-04T15:09:25Z | INFO | [ Complete ] dest=/tmp/tmpptx7kahe/weights size=\"172 MB\" total_elapsed=1.485s url=https://replicate.delivery/yhqm/wDyCVve1QgUfSkUkfqN4wdTZlalRFUSNe9eUCyCZPkMUc8BbC/trained_model.tar\nDownloaded weights in 1.52s\nLoaded LoRAs in 2.25s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:09, 2.87it/s]\n 7%|▋ | 2/28 [00:00<00:08, 3.20it/s]\n 11%|█ | 3/28 [00:00<00:08, 3.04it/s]\n 14%|█▍ | 4/28 [00:01<00:08, 2.97it/s]\n 18%|█▊ | 5/28 [00:01<00:07, 2.93it/s]\n 21%|██▏ | 6/28 [00:02<00:07, 2.91it/s]\n 25%|██▌ | 7/28 [00:02<00:07, 2.90it/s]\n 29%|██▊ | 8/28 [00:02<00:06, 2.89it/s]\n 32%|███▏ | 9/28 [00:03<00:06, 2.88it/s]\n 36%|███▌ | 10/28 [00:03<00:06, 2.88it/s]\n 39%|███▉ | 11/28 [00:03<00:05, 2.87it/s]\n 43%|████▎ | 12/28 [00:04<00:05, 2.87it/s]\n 46%|████▋ | 13/28 [00:04<00:05, 2.87it/s]\n 50%|█████ | 14/28 [00:04<00:04, 2.87it/s]\n 54%|█████▎ | 15/28 [00:05<00:04, 2.87it/s]\n 57%|█████▋ | 16/28 [00:05<00:04, 2.87it/s]\n 61%|██████ | 17/28 [00:05<00:03, 2.87it/s]\n 64%|██████▍ | 18/28 [00:06<00:03, 2.87it/s]\n 68%|██████▊ | 19/28 [00:06<00:03, 2.87it/s]\n 71%|███████▏ | 20/28 [00:06<00:02, 2.87it/s]\n 75%|███████▌ | 21/28 [00:07<00:02, 2.87it/s]\n 79%|███████▊ | 22/28 [00:07<00:02, 2.87it/s]\n 82%|████████▏ | 23/28 [00:07<00:01, 2.87it/s]\n 86%|████████▌ | 24/28 [00:08<00:01, 2.87it/s]\n 89%|████████▉ | 25/28 [00:08<00:01, 2.87it/s]\n 93%|█████████▎| 26/28 [00:09<00:00, 2.87it/s]\n 96%|█████████▋| 27/28 [00:09<00:00, 2.87it/s]\n100%|██████████| 28/28 [00:09<00:00, 2.87it/s]\n100%|██████████| 28/28 [00:09<00:00, 2.89it/s]", "metrics": { "predict_time": 12.260873195, "total_time": 57.023113 }, "output": [ "https://replicate.delivery/yhqm/2OSCBBbnBU4BAFjQf4A8NZhUqj62VUc7ydwuCTw5JIZYutxJA/out-0.webp" ], "started_at": "2024-10-04T15:09:24.273239Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/abp751b6txrm20cjb09vbf56yc", "cancel": "https://api.replicate.com/v1/predictions/abp751b6txrm20cjb09vbf56yc/cancel" }, "version": "6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44" }Generated inUsing seed: 57052 Prompt: AGRAT in a dimly lit room, seated in an ornate leather chair. He's wearing a dark pinstriped business suit with a striped tie, his hair neatly styled back. The lighting is dramatic and atmospheric, with shadows playing across his contemplative face as he appears deep in thought. The vintage styling and film grain quality suggests this is from a classic movie scene. The composition includes some blurred decorative elements in the background, including what appears to be a circular shape resembling a moon or clock. The overall tone is dark and brooding, creating a sense of power, tension and mystery. [!] txt2img mode Using dev model free=6867258372096 Downloading weights 2024-10-04T15:09:24Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpptx7kahe/weights url=https://replicate.delivery/yhqm/wDyCVve1QgUfSkUkfqN4wdTZlalRFUSNe9eUCyCZPkMUc8BbC/trained_model.tar 2024-10-04T15:09:25Z | INFO | [ Complete ] dest=/tmp/tmpptx7kahe/weights size="172 MB" total_elapsed=1.485s url=https://replicate.delivery/yhqm/wDyCVve1QgUfSkUkfqN4wdTZlalRFUSNe9eUCyCZPkMUc8BbC/trained_model.tar Downloaded weights in 1.52s Loaded LoRAs in 2.25s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:09, 2.87it/s] 7%|▋ | 2/28 [00:00<00:08, 3.20it/s] 11%|█ | 3/28 [00:00<00:08, 3.04it/s] 14%|█▍ | 4/28 [00:01<00:08, 2.97it/s] 18%|█▊ | 5/28 [00:01<00:07, 2.93it/s] 21%|██▏ | 6/28 [00:02<00:07, 2.91it/s] 25%|██▌ | 7/28 [00:02<00:07, 2.90it/s] 29%|██▊ | 8/28 [00:02<00:06, 2.89it/s] 32%|███▏ | 9/28 [00:03<00:06, 2.88it/s] 36%|███▌ | 10/28 [00:03<00:06, 2.88it/s] 39%|███▉ | 11/28 [00:03<00:05, 2.87it/s] 43%|████▎ | 12/28 [00:04<00:05, 2.87it/s] 46%|████▋ | 13/28 [00:04<00:05, 2.87it/s] 50%|█████ | 14/28 [00:04<00:04, 2.87it/s] 54%|█████▎ | 15/28 [00:05<00:04, 2.87it/s] 57%|█████▋ | 16/28 [00:05<00:04, 2.87it/s] 61%|██████ | 17/28 [00:05<00:03, 2.87it/s] 64%|██████▍ | 18/28 [00:06<00:03, 2.87it/s] 68%|██████▊ | 19/28 [00:06<00:03, 2.87it/s] 71%|███████▏ | 20/28 [00:06<00:02, 2.87it/s] 75%|███████▌ | 21/28 [00:07<00:02, 2.87it/s] 79%|███████▊ | 22/28 [00:07<00:02, 2.87it/s] 82%|████████▏ | 23/28 [00:07<00:01, 2.87it/s] 86%|████████▌ | 24/28 [00:08<00:01, 2.87it/s] 89%|████████▉ | 25/28 [00:08<00:01, 2.87it/s] 93%|█████████▎| 26/28 [00:09<00:00, 2.87it/s] 96%|█████████▋| 27/28 [00:09<00:00, 2.87it/s] 100%|██████████| 28/28 [00:09<00:00, 2.87it/s] 100%|██████████| 28/28 [00:09<00:00, 2.89it/s]Prediction
warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44ID3kzkt6apdhrm80cmrxw9j8dpcwStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- Low-angle shot of AGRAT gazing confidently at the camera, neon-lit cityscape sprawling below his glass perch, holographic interface casting dynamic blue highlights across his realistic stubble and pores, 8k facial details contrasting with blurred anti-gravity cars.
- go_fast
- lora_scale
- 1
- megapixels
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- prompt_strength
- 0.8
- extra_lora_scale
- 0.8
- num_inference_steps
- 28
{ "model": "dev", "prompt": "Low-angle shot of AGRAT gazing confidently at the camera, neon-lit cityscape sprawling below his glass perch, holographic interface casting dynamic blue highlights across his realistic stubble and pores, 8k facial details contrasting with blurred anti-gravity cars.", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 0.8, "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 warf23/agrat_me using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", { input: { model: "dev", prompt: "Low-angle shot of AGRAT gazing confidently at the camera, neon-lit cityscape sprawling below his glass perch, holographic interface casting dynamic blue highlights across his realistic stubble and pores, 8k facial details contrasting with blurred anti-gravity cars.", go_fast: false, lora_scale: 1, megapixels: "1", num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, prompt_strength: 0.8, extra_lora_scale: 0.8, 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 warf23/agrat_me using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", input={ "model": "dev", "prompt": "Low-angle shot of AGRAT gazing confidently at the camera, neon-lit cityscape sprawling below his glass perch, holographic interface casting dynamic blue highlights across his realistic stubble and pores, 8k facial details contrasting with blurred anti-gravity cars.", "go_fast": False, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 0.8, "num_inference_steps": 28 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run warf23/agrat_me 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": "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", "input": { "model": "dev", "prompt": "Low-angle shot of AGRAT gazing confidently at the camera, neon-lit cityscape sprawling below his glass perch, holographic interface casting dynamic blue highlights across his realistic stubble and pores, 8k facial details contrasting with blurred anti-gravity cars.", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 0.8, "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": "2025-02-02T16:22:32.339929Z", "created_at": "2025-02-02T16:22:20.012000Z", "data_removed": false, "error": null, "id": "3kzkt6apdhrm80cmrxw9j8dpcw", "input": { "model": "dev", "prompt": "Low-angle shot of AGRAT gazing confidently at the camera, neon-lit cityscape sprawling below his glass perch, holographic interface casting dynamic blue highlights across his realistic stubble and pores, 8k facial details contrasting with blurred anti-gravity cars.", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 0.8, "num_inference_steps": 28 }, "logs": "2025-02-02 16:22:20.064 | DEBUG | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys\n2025-02-02 16:22:20.065 | DEBUG | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted\nApplying LoRA: 0%| | 0/304 [00:00<?, ?it/s]\nApplying LoRA: 90%|█████████ | 275/304 [00:00<00:00, 2728.92it/s]\nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2687.26it/s]\n2025-02-02 16:22:20.178 | SUCCESS | fp8.lora_loading:unload_loras:564 - LoRAs unloaded in 0.11s\nfree=29179406348288\nDownloading weights\n2025-02-02T16:22:20Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmphhxcixbm/weights url=https://replicate.delivery/yhqm/wDyCVve1QgUfSkUkfqN4wdTZlalRFUSNe9eUCyCZPkMUc8BbC/trained_model.tar\n2025-02-02T16:22:26Z | INFO | [ Complete ] dest=/tmp/tmphhxcixbm/weights size=\"172 MB\" total_elapsed=5.830s url=https://replicate.delivery/yhqm/wDyCVve1QgUfSkUkfqN4wdTZlalRFUSNe9eUCyCZPkMUc8BbC/trained_model.tar\nDownloaded weights in 5.86s\n2025-02-02 16:22:26.035 | INFO | fp8.lora_loading:convert_lora_weights:498 - Loading LoRA weights for /src/weights-cache/fb057d9a6c8e0cbf\n2025-02-02 16:22:26.108 | INFO | fp8.lora_loading:convert_lora_weights:519 - LoRA weights loaded\n2025-02-02 16:22:26.108 | DEBUG | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys\n2025-02-02 16:22:26.108 | DEBUG | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted\nApplying LoRA: 0%| | 0/304 [00:00<?, ?it/s]\nApplying LoRA: 90%|█████████ | 275/304 [00:00<00:00, 2734.48it/s]\nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2692.80it/s]\n2025-02-02 16:22:26.221 | SUCCESS | fp8.lora_loading:load_lora:539 - LoRA applied in 0.19s\nUsing seed: 12546\n0it [00:00, ?it/s]\n1it [00:00, 8.32it/s]\n2it [00:00, 5.83it/s]\n3it [00:00, 5.32it/s]\n4it [00:00, 5.11it/s]\n5it [00:00, 4.98it/s]\n6it [00:01, 4.92it/s]\n7it [00:01, 4.87it/s]\n8it [00:01, 4.85it/s]\n9it [00:01, 4.84it/s]\n10it [00:01, 4.83it/s]\n11it [00:02, 4.83it/s]\n12it [00:02, 4.82it/s]\n13it [00:02, 4.81it/s]\n14it [00:02, 4.81it/s]\n15it [00:03, 4.81it/s]\n16it [00:03, 4.80it/s]\n17it [00:03, 4.80it/s]\n18it [00:03, 4.80it/s]\n19it [00:03, 4.81it/s]\n20it [00:04, 4.81it/s]\n21it [00:04, 4.81it/s]\n22it [00:04, 4.81it/s]\n23it [00:04, 4.81it/s]\n24it [00:04, 4.80it/s]\n25it [00:05, 4.81it/s]\n26it [00:05, 4.81it/s]\n27it [00:05, 4.81it/s]\n28it [00:05, 4.80it/s]\n28it [00:05, 4.88it/s]\nTotal safe images: 1 out of 1", "metrics": { "predict_time": 12.27432176, "total_time": 12.327929 }, "output": [ "https://replicate.delivery/xezq/qLiWeJAhx8QZC6D3seBiytHluf51wBgCNdfEvYpYxgbhcTtQB/out-0.webp" ], "started_at": "2025-02-02T16:22:20.065607Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bsvm-ffefpwjejy5n4qdxkpb7beg53j42jyyoubcaebib7vktivl6s7cq", "get": "https://api.replicate.com/v1/predictions/3kzkt6apdhrm80cmrxw9j8dpcw", "cancel": "https://api.replicate.com/v1/predictions/3kzkt6apdhrm80cmrxw9j8dpcw/cancel" }, "version": "6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44" }Generated in2025-02-02 16:22:20.064 | DEBUG | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys 2025-02-02 16:22:20.065 | DEBUG | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted Applying LoRA: 0%| | 0/304 [00:00<?, ?it/s] Applying LoRA: 90%|█████████ | 275/304 [00:00<00:00, 2728.92it/s] Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2687.26it/s] 2025-02-02 16:22:20.178 | SUCCESS | fp8.lora_loading:unload_loras:564 - LoRAs unloaded in 0.11s free=29179406348288 Downloading weights 2025-02-02T16:22:20Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmphhxcixbm/weights url=https://replicate.delivery/yhqm/wDyCVve1QgUfSkUkfqN4wdTZlalRFUSNe9eUCyCZPkMUc8BbC/trained_model.tar 2025-02-02T16:22:26Z | INFO | [ Complete ] dest=/tmp/tmphhxcixbm/weights size="172 MB" total_elapsed=5.830s url=https://replicate.delivery/yhqm/wDyCVve1QgUfSkUkfqN4wdTZlalRFUSNe9eUCyCZPkMUc8BbC/trained_model.tar Downloaded weights in 5.86s 2025-02-02 16:22:26.035 | INFO | fp8.lora_loading:convert_lora_weights:498 - Loading LoRA weights for /src/weights-cache/fb057d9a6c8e0cbf 2025-02-02 16:22:26.108 | INFO | fp8.lora_loading:convert_lora_weights:519 - LoRA weights loaded 2025-02-02 16:22:26.108 | DEBUG | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys 2025-02-02 16:22:26.108 | DEBUG | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted Applying LoRA: 0%| | 0/304 [00:00<?, ?it/s] Applying LoRA: 90%|█████████ | 275/304 [00:00<00:00, 2734.48it/s] Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2692.80it/s] 2025-02-02 16:22:26.221 | SUCCESS | fp8.lora_loading:load_lora:539 - LoRA applied in 0.19s Using seed: 12546 0it [00:00, ?it/s] 1it [00:00, 8.32it/s] 2it [00:00, 5.83it/s] 3it [00:00, 5.32it/s] 4it [00:00, 5.11it/s] 5it [00:00, 4.98it/s] 6it [00:01, 4.92it/s] 7it [00:01, 4.87it/s] 8it [00:01, 4.85it/s] 9it [00:01, 4.84it/s] 10it [00:01, 4.83it/s] 11it [00:02, 4.83it/s] 12it [00:02, 4.82it/s] 13it [00:02, 4.81it/s] 14it [00:02, 4.81it/s] 15it [00:03, 4.81it/s] 16it [00:03, 4.80it/s] 17it [00:03, 4.80it/s] 18it [00:03, 4.80it/s] 19it [00:03, 4.81it/s] 20it [00:04, 4.81it/s] 21it [00:04, 4.81it/s] 22it [00:04, 4.81it/s] 23it [00:04, 4.81it/s] 24it [00:04, 4.80it/s] 25it [00:05, 4.81it/s] 26it [00:05, 4.81it/s] 27it [00:05, 4.81it/s] 28it [00:05, 4.80it/s] 28it [00:05, 4.88it/s] Total safe images: 1 out of 1Prediction
warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44IDwenbbg5b19rme0cmrxwvmpd5awStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- Full-body wide-angle shot of AGRAT standing confidently on a glass platform in a futuristic city, sleek high-tech suit glowing with electric-blue lines, towering skyscrapers and flying cars framing his figure, ultra-realistic skin texture and facial details illuminated by holographic interface light.
- go_fast
- lora_scale
- 1
- megapixels
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- prompt_strength
- 0.8
- extra_lora_scale
- 0.8
- num_inference_steps
- 28
{ "model": "dev", "prompt": "Full-body wide-angle shot of AGRAT standing confidently on a glass platform in a futuristic city, sleek high-tech suit glowing with electric-blue lines, towering skyscrapers and flying cars framing his figure, ultra-realistic skin texture and facial details illuminated by holographic interface light.", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 0.8, "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 warf23/agrat_me using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", { input: { model: "dev", prompt: "Full-body wide-angle shot of AGRAT standing confidently on a glass platform in a futuristic city, sleek high-tech suit glowing with electric-blue lines, towering skyscrapers and flying cars framing his figure, ultra-realistic skin texture and facial details illuminated by holographic interface light.", go_fast: false, lora_scale: 1, megapixels: "1", num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, prompt_strength: 0.8, extra_lora_scale: 0.8, 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 warf23/agrat_me using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", input={ "model": "dev", "prompt": "Full-body wide-angle shot of AGRAT standing confidently on a glass platform in a futuristic city, sleek high-tech suit glowing with electric-blue lines, towering skyscrapers and flying cars framing his figure, ultra-realistic skin texture and facial details illuminated by holographic interface light.", "go_fast": False, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 0.8, "num_inference_steps": 28 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run warf23/agrat_me 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": "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", "input": { "model": "dev", "prompt": "Full-body wide-angle shot of AGRAT standing confidently on a glass platform in a futuristic city, sleek high-tech suit glowing with electric-blue lines, towering skyscrapers and flying cars framing his figure, ultra-realistic skin texture and facial details illuminated by holographic interface light.", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 0.8, "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": "2025-02-02T16:23:58.678218Z", "created_at": "2025-02-02T16:23:47.210000Z", "data_removed": false, "error": null, "id": "wenbbg5b19rme0cmrxwvmpd5aw", "input": { "model": "dev", "prompt": "Full-body wide-angle shot of AGRAT standing confidently on a glass platform in a futuristic city, sleek high-tech suit glowing with electric-blue lines, towering skyscrapers and flying cars framing his figure, ultra-realistic skin texture and facial details illuminated by holographic interface light.", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 0.8, "num_inference_steps": 28 }, "logs": "2025-02-02 16:23:47.237 | DEBUG | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys\n2025-02-02 16:23:47.238 | DEBUG | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted\nApplying LoRA: 0%| | 0/304 [00:00<?, ?it/s]\nApplying LoRA: 90%|█████████ | 275/304 [00:00<00:00, 2749.71it/s]\nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2648.28it/s]\n2025-02-02 16:23:47.353 | SUCCESS | fp8.lora_loading:unload_loras:564 - LoRAs unloaded in 0.12s\nfree=28902375145472\nDownloading weights\n2025-02-02T16:23:47Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpeo8eaht5/weights url=https://replicate.delivery/yhqm/wDyCVve1QgUfSkUkfqN4wdTZlalRFUSNe9eUCyCZPkMUc8BbC/trained_model.tar\n2025-02-02T16:23:52Z | INFO | [ Complete ] dest=/tmp/tmpeo8eaht5/weights size=\"172 MB\" total_elapsed=5.027s url=https://replicate.delivery/yhqm/wDyCVve1QgUfSkUkfqN4wdTZlalRFUSNe9eUCyCZPkMUc8BbC/trained_model.tar\nDownloaded weights in 5.05s\n2025-02-02 16:23:52.406 | INFO | fp8.lora_loading:convert_lora_weights:498 - Loading LoRA weights for /src/weights-cache/fb057d9a6c8e0cbf\n2025-02-02 16:23:52.476 | INFO | fp8.lora_loading:convert_lora_weights:519 - LoRA weights loaded\n2025-02-02 16:23:52.476 | DEBUG | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys\n2025-02-02 16:23:52.476 | DEBUG | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted\nApplying LoRA: 0%| | 0/304 [00:00<?, ?it/s]\nApplying LoRA: 90%|█████████ | 275/304 [00:00<00:00, 2748.63it/s]\nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2647.43it/s]\n2025-02-02 16:23:52.592 | SUCCESS | fp8.lora_loading:load_lora:539 - LoRA applied in 0.19s\nUsing seed: 12191\n0it [00:00, ?it/s]\n1it [00:00, 8.35it/s]\n2it [00:00, 5.85it/s]\n3it [00:00, 5.34it/s]\n4it [00:00, 5.12it/s]\n5it [00:00, 5.00it/s]\n6it [00:01, 4.93it/s]\n7it [00:01, 4.90it/s]\n8it [00:01, 4.88it/s]\n9it [00:01, 4.86it/s]\n10it [00:01, 4.85it/s]\n11it [00:02, 4.84it/s]\n12it [00:02, 4.84it/s]\n13it [00:02, 4.83it/s]\n14it [00:02, 4.81it/s]\n15it [00:03, 4.81it/s]\n16it [00:03, 4.82it/s]\n17it [00:03, 4.82it/s]\n18it [00:03, 4.83it/s]\n19it [00:03, 4.82it/s]\n20it [00:04, 4.82it/s]\n21it [00:04, 4.81it/s]\n22it [00:04, 4.82it/s]\n23it [00:04, 4.83it/s]\n24it [00:04, 4.82it/s]\n25it [00:05, 4.81it/s]\n26it [00:05, 4.81it/s]\n27it [00:05, 4.80it/s]\n28it [00:05, 4.81it/s]\n28it [00:05, 4.89it/s]\nTotal safe images: 1 out of 1", "metrics": { "predict_time": 11.439397224, "total_time": 11.468218 }, "output": [ "https://replicate.delivery/xezq/8voKcM9mLvKfNiwKisGx7JOLfjSl5feeelNlwq9JFsFjHO1CF/out-0.webp" ], "started_at": "2025-02-02T16:23:47.238821Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bsvm-we3dsmj3miqgzgqauiixuxulatxpl74ny5bo4fwl64jkrsisxm6q", "get": "https://api.replicate.com/v1/predictions/wenbbg5b19rme0cmrxwvmpd5aw", "cancel": "https://api.replicate.com/v1/predictions/wenbbg5b19rme0cmrxwvmpd5aw/cancel" }, "version": "6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44" }Generated in2025-02-02 16:23:47.237 | DEBUG | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys 2025-02-02 16:23:47.238 | DEBUG | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted Applying LoRA: 0%| | 0/304 [00:00<?, ?it/s] Applying LoRA: 90%|█████████ | 275/304 [00:00<00:00, 2749.71it/s] Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2648.28it/s] 2025-02-02 16:23:47.353 | SUCCESS | fp8.lora_loading:unload_loras:564 - LoRAs unloaded in 0.12s free=28902375145472 Downloading weights 2025-02-02T16:23:47Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpeo8eaht5/weights url=https://replicate.delivery/yhqm/wDyCVve1QgUfSkUkfqN4wdTZlalRFUSNe9eUCyCZPkMUc8BbC/trained_model.tar 2025-02-02T16:23:52Z | INFO | [ Complete ] dest=/tmp/tmpeo8eaht5/weights size="172 MB" total_elapsed=5.027s url=https://replicate.delivery/yhqm/wDyCVve1QgUfSkUkfqN4wdTZlalRFUSNe9eUCyCZPkMUc8BbC/trained_model.tar Downloaded weights in 5.05s 2025-02-02 16:23:52.406 | INFO | fp8.lora_loading:convert_lora_weights:498 - Loading LoRA weights for /src/weights-cache/fb057d9a6c8e0cbf 2025-02-02 16:23:52.476 | INFO | fp8.lora_loading:convert_lora_weights:519 - LoRA weights loaded 2025-02-02 16:23:52.476 | DEBUG | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys 2025-02-02 16:23:52.476 | DEBUG | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted Applying LoRA: 0%| | 0/304 [00:00<?, ?it/s] Applying LoRA: 90%|█████████ | 275/304 [00:00<00:00, 2748.63it/s] Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2647.43it/s] 2025-02-02 16:23:52.592 | SUCCESS | fp8.lora_loading:load_lora:539 - LoRA applied in 0.19s Using seed: 12191 0it [00:00, ?it/s] 1it [00:00, 8.35it/s] 2it [00:00, 5.85it/s] 3it [00:00, 5.34it/s] 4it [00:00, 5.12it/s] 5it [00:00, 5.00it/s] 6it [00:01, 4.93it/s] 7it [00:01, 4.90it/s] 8it [00:01, 4.88it/s] 9it [00:01, 4.86it/s] 10it [00:01, 4.85it/s] 11it [00:02, 4.84it/s] 12it [00:02, 4.84it/s] 13it [00:02, 4.83it/s] 14it [00:02, 4.81it/s] 15it [00:03, 4.81it/s] 16it [00:03, 4.82it/s] 17it [00:03, 4.82it/s] 18it [00:03, 4.83it/s] 19it [00:03, 4.82it/s] 20it [00:04, 4.82it/s] 21it [00:04, 4.81it/s] 22it [00:04, 4.82it/s] 23it [00:04, 4.83it/s] 24it [00:04, 4.82it/s] 25it [00:05, 4.81it/s] 26it [00:05, 4.81it/s] 27it [00:05, 4.80it/s] 28it [00:05, 4.81it/s] 28it [00:05, 4.89it/s] Total safe images: 1 out of 1Prediction
warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44ID4gdgnc9qf5rma0cmry5tzz8hm8StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- Casual full-body shot of AGRAT leaning against a holographic whiteboard, sleeves rolled up to reveal glowing wrist tech, warm ambient light emphasizing approachability, flying cars faintly blurred in distance.
- go_fast
- lora_scale
- 1
- megapixels
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- prompt_strength
- 0.8
- extra_lora_scale
- 0.8
- num_inference_steps
- 28
{ "model": "dev", "prompt": "Casual full-body shot of AGRAT leaning against a holographic whiteboard, sleeves rolled up to reveal glowing wrist tech, warm ambient light emphasizing approachability, flying cars faintly blurred in distance.", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 0.8, "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 warf23/agrat_me using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", { input: { model: "dev", prompt: "Casual full-body shot of AGRAT leaning against a holographic whiteboard, sleeves rolled up to reveal glowing wrist tech, warm ambient light emphasizing approachability, flying cars faintly blurred in distance.", go_fast: false, lora_scale: 1, megapixels: "1", num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, prompt_strength: 0.8, extra_lora_scale: 0.8, 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 warf23/agrat_me using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", input={ "model": "dev", "prompt": "Casual full-body shot of AGRAT leaning against a holographic whiteboard, sleeves rolled up to reveal glowing wrist tech, warm ambient light emphasizing approachability, flying cars faintly blurred in distance.", "go_fast": False, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 0.8, "num_inference_steps": 28 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run warf23/agrat_me 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": "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", "input": { "model": "dev", "prompt": "Casual full-body shot of AGRAT leaning against a holographic whiteboard, sleeves rolled up to reveal glowing wrist tech, warm ambient light emphasizing approachability, flying cars faintly blurred in distance.", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 0.8, "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": "2025-02-02T16:43:03.733964Z", "created_at": "2025-02-02T16:42:57.273000Z", "data_removed": false, "error": null, "id": "4gdgnc9qf5rma0cmry5tzz8hm8", "input": { "model": "dev", "prompt": "Casual full-body shot of AGRAT leaning against a holographic whiteboard, sleeves rolled up to reveal glowing wrist tech, warm ambient light emphasizing approachability, flying cars faintly blurred in distance.", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 0.8, "num_inference_steps": 28 }, "logs": "2025-02-02 16:42:57.292 | DEBUG | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys\n2025-02-02 16:42:57.292 | DEBUG | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted\nApplying LoRA: 0%| | 0/304 [00:00<?, ?it/s]\nApplying LoRA: 93%|█████████▎| 282/304 [00:00<00:00, 2813.85it/s]\nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2757.36it/s]\n2025-02-02 16:42:57.403 | SUCCESS | fp8.lora_loading:unload_loras:564 - LoRAs unloaded in 0.11s\n2025-02-02 16:42:57.404 | INFO | fp8.lora_loading:convert_lora_weights:498 - Loading LoRA weights for /src/weights-cache/fb057d9a6c8e0cbf\n2025-02-02 16:42:57.523 | INFO | fp8.lora_loading:convert_lora_weights:519 - LoRA weights loaded\n2025-02-02 16:42:57.523 | DEBUG | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys\n2025-02-02 16:42:57.523 | DEBUG | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted\nApplying LoRA: 0%| | 0/304 [00:00<?, ?it/s]\nApplying LoRA: 93%|█████████▎| 282/304 [00:00<00:00, 2814.82it/s]\nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2758.46it/s]\n2025-02-02 16:42:57.634 | SUCCESS | fp8.lora_loading:load_lora:539 - LoRA applied in 0.23s\nUsing seed: 59231\n0it [00:00, ?it/s]\n1it [00:00, 8.36it/s]\n2it [00:00, 5.86it/s]\n3it [00:00, 5.35it/s]\n4it [00:00, 5.14it/s]\n5it [00:00, 5.03it/s]\n6it [00:01, 4.94it/s]\n7it [00:01, 4.90it/s]\n8it [00:01, 4.87it/s]\n9it [00:01, 4.85it/s]\n10it [00:01, 4.84it/s]\n11it [00:02, 4.82it/s]\n12it [00:02, 4.82it/s]\n13it [00:02, 4.81it/s]\n14it [00:02, 4.81it/s]\n15it [00:03, 4.81it/s]\n16it [00:03, 4.80it/s]\n17it [00:03, 4.80it/s]\n18it [00:03, 4.81it/s]\n19it [00:03, 4.81it/s]\n20it [00:04, 4.80it/s]\n21it [00:04, 4.80it/s]\n22it [00:04, 4.80it/s]\n23it [00:04, 4.80it/s]\n24it [00:04, 4.81it/s]\n25it [00:05, 4.81it/s]\n26it [00:05, 4.81it/s]\n27it [00:05, 4.80it/s]\n28it [00:05, 4.80it/s]\n28it [00:05, 4.88it/s]\nTotal safe images: 1 out of 1", "metrics": { "predict_time": 6.440673378, "total_time": 6.460964 }, "output": [ "https://replicate.delivery/xezq/2JJCdlUOqkogKdezMzdXBOgwOdIB47y1eLz1gPopTYZXKVLUA/out-0.webp" ], "started_at": "2025-02-02T16:42:57.293291Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bsvm-ah4y54b6gngptcnj2twctd6czk6jksmn4si3fq6kxiuq634mdb5q", "get": "https://api.replicate.com/v1/predictions/4gdgnc9qf5rma0cmry5tzz8hm8", "cancel": "https://api.replicate.com/v1/predictions/4gdgnc9qf5rma0cmry5tzz8hm8/cancel" }, "version": "6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44" }Generated in2025-02-02 16:42:57.292 | DEBUG | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys 2025-02-02 16:42:57.292 | DEBUG | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted Applying LoRA: 0%| | 0/304 [00:00<?, ?it/s] Applying LoRA: 93%|█████████▎| 282/304 [00:00<00:00, 2813.85it/s] Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2757.36it/s] 2025-02-02 16:42:57.403 | SUCCESS | fp8.lora_loading:unload_loras:564 - LoRAs unloaded in 0.11s 2025-02-02 16:42:57.404 | INFO | fp8.lora_loading:convert_lora_weights:498 - Loading LoRA weights for /src/weights-cache/fb057d9a6c8e0cbf 2025-02-02 16:42:57.523 | INFO | fp8.lora_loading:convert_lora_weights:519 - LoRA weights loaded 2025-02-02 16:42:57.523 | DEBUG | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys 2025-02-02 16:42:57.523 | DEBUG | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted Applying LoRA: 0%| | 0/304 [00:00<?, ?it/s] Applying LoRA: 93%|█████████▎| 282/304 [00:00<00:00, 2814.82it/s] Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2758.46it/s] 2025-02-02 16:42:57.634 | SUCCESS | fp8.lora_loading:load_lora:539 - LoRA applied in 0.23s Using seed: 59231 0it [00:00, ?it/s] 1it [00:00, 8.36it/s] 2it [00:00, 5.86it/s] 3it [00:00, 5.35it/s] 4it [00:00, 5.14it/s] 5it [00:00, 5.03it/s] 6it [00:01, 4.94it/s] 7it [00:01, 4.90it/s] 8it [00:01, 4.87it/s] 9it [00:01, 4.85it/s] 10it [00:01, 4.84it/s] 11it [00:02, 4.82it/s] 12it [00:02, 4.82it/s] 13it [00:02, 4.81it/s] 14it [00:02, 4.81it/s] 15it [00:03, 4.81it/s] 16it [00:03, 4.80it/s] 17it [00:03, 4.80it/s] 18it [00:03, 4.81it/s] 19it [00:03, 4.81it/s] 20it [00:04, 4.80it/s] 21it [00:04, 4.80it/s] 22it [00:04, 4.80it/s] 23it [00:04, 4.80it/s] 24it [00:04, 4.81it/s] 25it [00:05, 4.81it/s] 26it [00:05, 4.81it/s] 27it [00:05, 4.80it/s] 28it [00:05, 4.80it/s] 28it [00:05, 4.88it/s] Total safe images: 1 out of 1Prediction
warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44IDadfvcve2fxrma0cqhrgvf1dp0wStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- image
- model
- dev
- prompt
- A young woman standing next to a AGRAT in a sneaker store, both facing the mirror, taking a casual mirror selfie. The girl has a modern, stylish look, wearing a streetwear outfit (cropped top and high-waisted jeans), with natural makeup, curly or slightly wavy hair, and a confident pose. The lighting is warm and indoor-mall-like, reflecting off the glass shelves and sneakers. She is holding her phone with a relaxed hand, slightly leaning in. Her expression is casual and friendly, matching the man's pose and style. The shoes around them are colorful and Nike-themed. The whole scene should look realistic and seamless, as if both were genuinely in the same store, same moment, same angle. Vertical frame, natural shadows, realistic reflections in the mirror.
- go_fast
- lora_scale
- 1
- megapixels
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- prompt_strength
- 0.8
- extra_lora_scale
- 0.8
- num_inference_steps
- 28
{ "image": "https://replicate.delivery/pbxt/NDh20oFyAIlrYwL6OkpY0mmi3Bcenvdzt4seUxdXzjUAWnh5/WhatsApp%20Image%202025-06-20%20at%2016.48.20.jpeg", "model": "dev", "prompt": "A young woman standing next to a AGRAT in a sneaker store, both facing the mirror, taking a casual mirror selfie. The girl has a modern, stylish look, wearing a streetwear outfit (cropped top and high-waisted jeans), with natural makeup, curly or slightly wavy hair, and a confident pose. The lighting is warm and indoor-mall-like, reflecting off the glass shelves and sneakers. She is holding her phone with a relaxed hand, slightly leaning in. Her expression is casual and friendly, matching the man's pose and style. The shoes around them are colorful and Nike-themed. The whole scene should look realistic and seamless, as if both were genuinely in the same store, same moment, same angle. Vertical frame, natural shadows, realistic reflections in the mirror.", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 0.8, "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 warf23/agrat_me using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", { input: { image: "https://replicate.delivery/pbxt/NDh20oFyAIlrYwL6OkpY0mmi3Bcenvdzt4seUxdXzjUAWnh5/WhatsApp%20Image%202025-06-20%20at%2016.48.20.jpeg", model: "dev", prompt: "A young woman standing next to a AGRAT in a sneaker store, both facing the mirror, taking a casual mirror selfie. The girl has a modern, stylish look, wearing a streetwear outfit (cropped top and high-waisted jeans), with natural makeup, curly or slightly wavy hair, and a confident pose. The lighting is warm and indoor-mall-like, reflecting off the glass shelves and sneakers. She is holding her phone with a relaxed hand, slightly leaning in. Her expression is casual and friendly, matching the man's pose and style. The shoes around them are colorful and Nike-themed. The whole scene should look realistic and seamless, as if both were genuinely in the same store, same moment, same angle. Vertical frame, natural shadows, realistic reflections in the mirror.", go_fast: false, lora_scale: 1, megapixels: "1", num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, prompt_strength: 0.8, extra_lora_scale: 0.8, 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 warf23/agrat_me using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", input={ "image": "https://replicate.delivery/pbxt/NDh20oFyAIlrYwL6OkpY0mmi3Bcenvdzt4seUxdXzjUAWnh5/WhatsApp%20Image%202025-06-20%20at%2016.48.20.jpeg", "model": "dev", "prompt": "A young woman standing next to a AGRAT in a sneaker store, both facing the mirror, taking a casual mirror selfie. The girl has a modern, stylish look, wearing a streetwear outfit (cropped top and high-waisted jeans), with natural makeup, curly or slightly wavy hair, and a confident pose. The lighting is warm and indoor-mall-like, reflecting off the glass shelves and sneakers. She is holding her phone with a relaxed hand, slightly leaning in. Her expression is casual and friendly, matching the man's pose and style. The shoes around them are colorful and Nike-themed. The whole scene should look realistic and seamless, as if both were genuinely in the same store, same moment, same angle. Vertical frame, natural shadows, realistic reflections in the mirror.", "go_fast": False, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 0.8, "num_inference_steps": 28 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run warf23/agrat_me 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": "warf23/agrat_me:6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44", "input": { "image": "https://replicate.delivery/pbxt/NDh20oFyAIlrYwL6OkpY0mmi3Bcenvdzt4seUxdXzjUAWnh5/WhatsApp%20Image%202025-06-20%20at%2016.48.20.jpeg", "model": "dev", "prompt": "A young woman standing next to a AGRAT in a sneaker store, both facing the mirror, taking a casual mirror selfie. The girl has a modern, stylish look, wearing a streetwear outfit (cropped top and high-waisted jeans), with natural makeup, curly or slightly wavy hair, and a confident pose. The lighting is warm and indoor-mall-like, reflecting off the glass shelves and sneakers. She is holding her phone with a relaxed hand, slightly leaning in. Her expression is casual and friendly, matching the man\'s pose and style. The shoes around them are colorful and Nike-themed. The whole scene should look realistic and seamless, as if both were genuinely in the same store, same moment, same angle. Vertical frame, natural shadows, realistic reflections in the mirror.", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 0.8, "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": "2025-06-20T16:17:57.324737Z", "created_at": "2025-06-20T16:17:46.623000Z", "data_removed": false, "error": null, "id": "adfvcve2fxrma0cqhrgvf1dp0w", "input": { "image": "https://replicate.delivery/pbxt/NDh20oFyAIlrYwL6OkpY0mmi3Bcenvdzt4seUxdXzjUAWnh5/WhatsApp%20Image%202025-06-20%20at%2016.48.20.jpeg", "model": "dev", "prompt": "A young woman standing next to a AGRAT in a sneaker store, both facing the mirror, taking a casual mirror selfie. The girl has a modern, stylish look, wearing a streetwear outfit (cropped top and high-waisted jeans), with natural makeup, curly or slightly wavy hair, and a confident pose. The lighting is warm and indoor-mall-like, reflecting off the glass shelves and sneakers. She is holding her phone with a relaxed hand, slightly leaning in. Her expression is casual and friendly, matching the man's pose and style. The shoes around them are colorful and Nike-themed. The whole scene should look realistic and seamless, as if both were genuinely in the same store, same moment, same angle. Vertical frame, natural shadows, realistic reflections in the mirror.", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 0.8, "num_inference_steps": 28 }, "logs": "free=26567937536000\nDownloading weights\n2025-06-20T16:17:46Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpjgh022a0/weights url=https://replicate.delivery/yhqm/wDyCVve1QgUfSkUkfqN4wdTZlalRFUSNe9eUCyCZPkMUc8BbC/trained_model.tar\n2025-06-20T16:17:46Z | INFO | [ Cache Service ] enabled=true scheme=http target=hermes.services.svc.cluster.local\n2025-06-20T16:17:46Z | INFO | [ Cache URL Rewrite ] enabled=true target_url=http://hermes.services.svc.cluster.local/replicate.delivery/yhqm/wDyCVve1QgUfSkUkfqN4wdTZlalRFUSNe9eUCyCZPkMUc8BbC/trained_model.tar url=https://replicate.delivery/yhqm/wDyCVve1QgUfSkUkfqN4wdTZlalRFUSNe9eUCyCZPkMUc8BbC/trained_model.tar\n2025-06-20T16:17:46Z | INFO | [ Redirect ] redirect_url=http://r8-west1-loras-ric1.cwlota.com/2f82e0f5e1e7b95569b452c9fb19a0177f7ec77926eccc6294f27955016ce58b?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Checksum-Mode=ENABLED&X-Amz-Credential=CWNZUVKLDHXVHEZN%2F20250620%2FUS-EAST-04A%2Fs3%2Faws4_request&X-Amz-Date=20250620T161746Z&X-Amz-Expires=600&X-Amz-SignedHeaders=host&x-id=GetObject&X-Amz-Signature=eb4d6dd339c6d3c37639b800ac999b5aef6d9960261b57685d244c1058199dbe url=http://hermes.services.svc.cluster.local/replicate.delivery/yhqm/wDyCVve1QgUfSkUkfqN4wdTZlalRFUSNe9eUCyCZPkMUc8BbC/trained_model.tar\n2025-06-20T16:17:47Z | INFO | [ Complete ] dest=/tmp/tmpjgh022a0/weights size=\"172 MB\" total_elapsed=0.411s url=https://replicate.delivery/yhqm/wDyCVve1QgUfSkUkfqN4wdTZlalRFUSNe9eUCyCZPkMUc8BbC/trained_model.tar\nDownloaded weights in 0.47s\nLoaded LoRAs in 1.04s\nUsing seed: 60077\nPrompt: A young woman standing next to a AGRAT in a sneaker store, both facing the mirror, taking a casual mirror selfie. The girl has a modern, stylish look, wearing a streetwear outfit (cropped top and high-waisted jeans), with natural makeup, curly or slightly wavy hair, and a confident pose. The lighting is warm and indoor-mall-like, reflecting off the glass shelves and sneakers. She is holding her phone with a relaxed hand, slightly leaning in. Her expression is casual and friendly, matching the man's pose and style. The shoes around them are colorful and Nike-themed. The whole scene should look realistic and seamless, as if both were genuinely in the same store, same moment, same angle. Vertical frame, natural shadows, realistic reflections in the mirror.\nInput image size: 2004x2672\n[!] Resizing input image from 2004x2672 to 1088x1440\n[!] img2img mode\n 0%| | 0/23 [00:00<?, ?it/s]\n 4%|▍ | 1/23 [00:00<00:06, 3.23it/s]\n 9%|▊ | 2/23 [00:00<00:07, 2.82it/s]\n 13%|█▎ | 3/23 [00:01<00:07, 2.70it/s]\n 17%|█▋ | 4/23 [00:01<00:07, 2.65it/s]\n 22%|██▏ | 5/23 [00:01<00:06, 2.61it/s]\n 26%|██▌ | 6/23 [00:02<00:06, 2.60it/s]\n 30%|███ | 7/23 [00:02<00:06, 2.59it/s]\n 35%|███▍ | 8/23 [00:03<00:05, 2.59it/s]\n 39%|███▉ | 9/23 [00:03<00:05, 2.59it/s]\n 43%|████▎ | 10/23 [00:03<00:05, 2.59it/s]\n 48%|████▊ | 11/23 [00:04<00:04, 2.59it/s]\n 52%|█████▏ | 12/23 [00:04<00:04, 2.59it/s]\n 57%|█████▋ | 13/23 [00:04<00:03, 2.58it/s]\n 61%|██████ | 14/23 [00:05<00:03, 2.58it/s]\n 65%|██████▌ | 15/23 [00:05<00:03, 2.58it/s]\n 70%|██████▉ | 16/23 [00:06<00:02, 2.59it/s]\n 74%|███████▍ | 17/23 [00:06<00:02, 2.59it/s]\n 78%|███████▊ | 18/23 [00:06<00:01, 2.58it/s]\n 83%|████████▎ | 19/23 [00:07<00:01, 2.59it/s]\n 87%|████████▋ | 20/23 [00:07<00:01, 2.59it/s]\n 91%|█████████▏| 21/23 [00:08<00:00, 2.58it/s]\n 96%|█████████▌| 22/23 [00:08<00:00, 2.58it/s]\n100%|██████████| 23/23 [00:08<00:00, 2.58it/s]\n100%|██████████| 23/23 [00:08<00:00, 2.60it/s]\nTotal safe images: 1 out of 1", "metrics": { "predict_time": 10.620809365, "total_time": 10.701737 }, "output": [ "https://replicate.delivery/xezq/mTCoWj5JBvrmGlNmvMjpSmEzIrVg5HRel8wTtqKXfog1uz4UA/out-0.webp" ], "started_at": "2025-06-20T16:17:46.703928Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-ps2oe4rihazktn5ujz4kzoau3vgsydvqjf3xthbzibrue3tc4amq", "get": "https://api.replicate.com/v1/predictions/adfvcve2fxrma0cqhrgvf1dp0w", "cancel": "https://api.replicate.com/v1/predictions/adfvcve2fxrma0cqhrgvf1dp0w/cancel" }, "version": "6b56c83db0ea376475c5995c4c0f120253dee3c0270276207ae2f0b3471f4b44" }Generated infree=26567937536000 Downloading weights 2025-06-20T16:17:46Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpjgh022a0/weights url=https://replicate.delivery/yhqm/wDyCVve1QgUfSkUkfqN4wdTZlalRFUSNe9eUCyCZPkMUc8BbC/trained_model.tar 2025-06-20T16:17:46Z | INFO | [ Cache Service ] enabled=true scheme=http target=hermes.services.svc.cluster.local 2025-06-20T16:17:46Z | INFO | [ Cache URL Rewrite ] enabled=true target_url=http://hermes.services.svc.cluster.local/replicate.delivery/yhqm/wDyCVve1QgUfSkUkfqN4wdTZlalRFUSNe9eUCyCZPkMUc8BbC/trained_model.tar url=https://replicate.delivery/yhqm/wDyCVve1QgUfSkUkfqN4wdTZlalRFUSNe9eUCyCZPkMUc8BbC/trained_model.tar 2025-06-20T16:17:46Z | INFO | [ Redirect ] redirect_url=http://r8-west1-loras-ric1.cwlota.com/2f82e0f5e1e7b95569b452c9fb19a0177f7ec77926eccc6294f27955016ce58b?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Checksum-Mode=ENABLED&X-Amz-Credential=CWNZUVKLDHXVHEZN%2F20250620%2FUS-EAST-04A%2Fs3%2Faws4_request&X-Amz-Date=20250620T161746Z&X-Amz-Expires=600&X-Amz-SignedHeaders=host&x-id=GetObject&X-Amz-Signature=eb4d6dd339c6d3c37639b800ac999b5aef6d9960261b57685d244c1058199dbe url=http://hermes.services.svc.cluster.local/replicate.delivery/yhqm/wDyCVve1QgUfSkUkfqN4wdTZlalRFUSNe9eUCyCZPkMUc8BbC/trained_model.tar 2025-06-20T16:17:47Z | INFO | [ Complete ] dest=/tmp/tmpjgh022a0/weights size="172 MB" total_elapsed=0.411s url=https://replicate.delivery/yhqm/wDyCVve1QgUfSkUkfqN4wdTZlalRFUSNe9eUCyCZPkMUc8BbC/trained_model.tar Downloaded weights in 0.47s Loaded LoRAs in 1.04s Using seed: 60077 Prompt: A young woman standing next to a AGRAT in a sneaker store, both facing the mirror, taking a casual mirror selfie. The girl has a modern, stylish look, wearing a streetwear outfit (cropped top and high-waisted jeans), with natural makeup, curly or slightly wavy hair, and a confident pose. The lighting is warm and indoor-mall-like, reflecting off the glass shelves and sneakers. She is holding her phone with a relaxed hand, slightly leaning in. Her expression is casual and friendly, matching the man's pose and style. The shoes around them are colorful and Nike-themed. The whole scene should look realistic and seamless, as if both were genuinely in the same store, same moment, same angle. Vertical frame, natural shadows, realistic reflections in the mirror. Input image size: 2004x2672 [!] Resizing input image from 2004x2672 to 1088x1440 [!] img2img mode 0%| | 0/23 [00:00<?, ?it/s] 4%|▍ | 1/23 [00:00<00:06, 3.23it/s] 9%|▊ | 2/23 [00:00<00:07, 2.82it/s] 13%|█▎ | 3/23 [00:01<00:07, 2.70it/s] 17%|█▋ | 4/23 [00:01<00:07, 2.65it/s] 22%|██▏ | 5/23 [00:01<00:06, 2.61it/s] 26%|██▌ | 6/23 [00:02<00:06, 2.60it/s] 30%|███ | 7/23 [00:02<00:06, 2.59it/s] 35%|███▍ | 8/23 [00:03<00:05, 2.59it/s] 39%|███▉ | 9/23 [00:03<00:05, 2.59it/s] 43%|████▎ | 10/23 [00:03<00:05, 2.59it/s] 48%|████▊ | 11/23 [00:04<00:04, 2.59it/s] 52%|█████▏ | 12/23 [00:04<00:04, 2.59it/s] 57%|█████▋ | 13/23 [00:04<00:03, 2.58it/s] 61%|██████ | 14/23 [00:05<00:03, 2.58it/s] 65%|██████▌ | 15/23 [00:05<00:03, 2.58it/s] 70%|██████▉ | 16/23 [00:06<00:02, 2.59it/s] 74%|███████▍ | 17/23 [00:06<00:02, 2.59it/s] 78%|███████▊ | 18/23 [00:06<00:01, 2.58it/s] 83%|████████▎ | 19/23 [00:07<00:01, 2.59it/s] 87%|████████▋ | 20/23 [00:07<00:01, 2.59it/s] 91%|█████████▏| 21/23 [00:08<00:00, 2.58it/s] 96%|█████████▌| 22/23 [00:08<00:00, 2.58it/s] 100%|██████████| 23/23 [00:08<00:00, 2.58it/s] 100%|██████████| 23/23 [00:08<00:00, 2.60it/s] Total safe images: 1 out of 1
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