hudsongraeme / robovan
- Public
- 22 runs
-
H100
Prediction
hudsongraeme/robovan:ff9808afd09e423c61d3d627e96342b96703861e7e7d031c46abd6a567268e93IDk77qf2gbhdrm60cjf7ka84k8h8StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- A photo of TOK, futuristic vehicle, no text, driving down sunset boulevard
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 90
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "dev", "prompt": "A photo of TOK, futuristic vehicle, no text, driving down sunset boulevard", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run hudsongraeme/robovan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "hudsongraeme/robovan:ff9808afd09e423c61d3d627e96342b96703861e7e7d031c46abd6a567268e93", { input: { model: "dev", prompt: "A photo of TOK, futuristic vehicle, no text, driving down sunset boulevard", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 90, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 28 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun hudsongraeme/robovan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "hudsongraeme/robovan:ff9808afd09e423c61d3d627e96342b96703861e7e7d031c46abd6a567268e93", input={ "model": "dev", "prompt": "A photo of TOK, futuristic vehicle, no text, driving down sunset boulevard", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) # To access the file URL: 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 hudsongraeme/robovan 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": "hudsongraeme/robovan:ff9808afd09e423c61d3d627e96342b96703861e7e7d031c46abd6a567268e93", "input": { "model": "dev", "prompt": "A photo of TOK, futuristic vehicle, no text, driving down sunset boulevard", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-10-11T04:46:54.950913Z", "created_at": "2024-10-11T04:46:12.363000Z", "data_removed": false, "error": null, "id": "k77qf2gbhdrm60cjf7ka84k8h8", "input": { "model": "dev", "prompt": "A photo of TOK, futuristic vehicle, no text, driving down sunset boulevard", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "Using seed: 33396\nPrompt: A photo of TOK, futuristic vehicle, no text, driving down sunset boulevard\n[!] txt2img mode\nUsing dev model\nfree=6704647262208\nDownloading weights\n2024-10-11T04:46:41Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmphg_3q7v9/weights url=https://replicate.delivery/yhqm/ugL38JpxSf09AaZqVo1dtQ2Zqxk2JGrFsQXn8LpWj1DX9yyJA/trained_model.tar\n2024-10-11T04:46:44Z | INFO | [ Complete ] dest=/tmp/tmphg_3q7v9/weights size=\"172 MB\" total_elapsed=2.750s url=https://replicate.delivery/yhqm/ugL38JpxSf09AaZqVo1dtQ2Zqxk2JGrFsQXn8LpWj1DX9yyJA/trained_model.tar\nDownloaded weights in 2.78s\nLoaded LoRAs in 3.53s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:09, 2.89it/s]\n 7%|▋ | 2/28 [00:00<00:08, 3.22it/s]\n 11%|█ | 3/28 [00:00<00:08, 3.06it/s]\n 14%|█▍ | 4/28 [00:01<00:08, 2.99it/s]\n 18%|█▊ | 5/28 [00:01<00:07, 2.95it/s]\n 21%|██▏ | 6/28 [00:02<00:07, 2.93it/s]\n 25%|██▌ | 7/28 [00:02<00:07, 2.91it/s]\n 29%|██▊ | 8/28 [00:02<00:06, 2.90it/s]\n 32%|███▏ | 9/28 [00:03<00:06, 2.90it/s]\n 36%|███▌ | 10/28 [00:03<00:06, 2.89it/s]\n 39%|███▉ | 11/28 [00:03<00:05, 2.89it/s]\n 43%|████▎ | 12/28 [00:04<00:05, 2.89it/s]\n 46%|████▋ | 13/28 [00:04<00:05, 2.89it/s]\n 50%|█████ | 14/28 [00:04<00:04, 2.89it/s]\n 54%|█████▎ | 15/28 [00:05<00:04, 2.89it/s]\n 57%|█████▋ | 16/28 [00:05<00:04, 2.89it/s]\n 61%|██████ | 17/28 [00:05<00:03, 2.89it/s]\n 64%|██████▍ | 18/28 [00:06<00:03, 2.89it/s]\n 68%|██████▊ | 19/28 [00:06<00:03, 2.88it/s]\n 71%|███████▏ | 20/28 [00:06<00:02, 2.88it/s]\n 75%|███████▌ | 21/28 [00:07<00:02, 2.88it/s]\n 79%|███████▊ | 22/28 [00:07<00:02, 2.88it/s]\n 82%|████████▏ | 23/28 [00:07<00:01, 2.88it/s]\n 86%|████████▌ | 24/28 [00:08<00:01, 2.88it/s]\n 89%|████████▉ | 25/28 [00:08<00:01, 2.88it/s]\n 93%|█████████▎| 26/28 [00:08<00:00, 2.88it/s]\n 96%|█████████▋| 27/28 [00:09<00:00, 2.88it/s]\n100%|██████████| 28/28 [00:09<00:00, 2.88it/s]\n100%|██████████| 28/28 [00:09<00:00, 2.90it/s]", "metrics": { "predict_time": 13.507678446, "total_time": 42.587913 }, "output": [ "https://replicate.delivery/yhqm/Inrg5wTmnPqPEV8g9W8KZyEwTseReAf4tiza0kanU0n99LLnA/out-0.webp" ], "started_at": "2024-10-11T04:46:41.443235Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/k77qf2gbhdrm60cjf7ka84k8h8", "cancel": "https://api.replicate.com/v1/predictions/k77qf2gbhdrm60cjf7ka84k8h8/cancel" }, "version": "ff9808afd09e423c61d3d627e96342b96703861e7e7d031c46abd6a567268e93" }Generated inUsing seed: 33396 Prompt: A photo of TOK, futuristic vehicle, no text, driving down sunset boulevard [!] txt2img mode Using dev model free=6704647262208 Downloading weights 2024-10-11T04:46:41Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmphg_3q7v9/weights url=https://replicate.delivery/yhqm/ugL38JpxSf09AaZqVo1dtQ2Zqxk2JGrFsQXn8LpWj1DX9yyJA/trained_model.tar 2024-10-11T04:46:44Z | INFO | [ Complete ] dest=/tmp/tmphg_3q7v9/weights size="172 MB" total_elapsed=2.750s url=https://replicate.delivery/yhqm/ugL38JpxSf09AaZqVo1dtQ2Zqxk2JGrFsQXn8LpWj1DX9yyJA/trained_model.tar Downloaded weights in 2.78s Loaded LoRAs in 3.53s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:09, 2.89it/s] 7%|▋ | 2/28 [00:00<00:08, 3.22it/s] 11%|█ | 3/28 [00:00<00:08, 3.06it/s] 14%|█▍ | 4/28 [00:01<00:08, 2.99it/s] 18%|█▊ | 5/28 [00:01<00:07, 2.95it/s] 21%|██▏ | 6/28 [00:02<00:07, 2.93it/s] 25%|██▌ | 7/28 [00:02<00:07, 2.91it/s] 29%|██▊ | 8/28 [00:02<00:06, 2.90it/s] 32%|███▏ | 9/28 [00:03<00:06, 2.90it/s] 36%|███▌ | 10/28 [00:03<00:06, 2.89it/s] 39%|███▉ | 11/28 [00:03<00:05, 2.89it/s] 43%|████▎ | 12/28 [00:04<00:05, 2.89it/s] 46%|████▋ | 13/28 [00:04<00:05, 2.89it/s] 50%|█████ | 14/28 [00:04<00:04, 2.89it/s] 54%|█████▎ | 15/28 [00:05<00:04, 2.89it/s] 57%|█████▋ | 16/28 [00:05<00:04, 2.89it/s] 61%|██████ | 17/28 [00:05<00:03, 2.89it/s] 64%|██████▍ | 18/28 [00:06<00:03, 2.89it/s] 68%|██████▊ | 19/28 [00:06<00:03, 2.88it/s] 71%|███████▏ | 20/28 [00:06<00:02, 2.88it/s] 75%|███████▌ | 21/28 [00:07<00:02, 2.88it/s] 79%|███████▊ | 22/28 [00:07<00:02, 2.88it/s] 82%|████████▏ | 23/28 [00:07<00:01, 2.88it/s] 86%|████████▌ | 24/28 [00:08<00:01, 2.88it/s] 89%|████████▉ | 25/28 [00:08<00:01, 2.88it/s] 93%|█████████▎| 26/28 [00:08<00:00, 2.88it/s] 96%|█████████▋| 27/28 [00:09<00:00, 2.88it/s] 100%|██████████| 28/28 [00:09<00:00, 2.88it/s] 100%|██████████| 28/28 [00:09<00:00, 2.90it/s]Prediction
hudsongraeme/robovan:ff9808afd09e423c61d3d627e96342b96703861e7e7d031c46abd6a567268e93ID97ws9147csrm40cjf7mv0hwp5cStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- A photo of TOK, driving, san francisco, rainy, iPhone camera
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 90
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "dev", "prompt": "A photo of TOK, driving, san francisco, rainy, iPhone camera", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run hudsongraeme/robovan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "hudsongraeme/robovan:ff9808afd09e423c61d3d627e96342b96703861e7e7d031c46abd6a567268e93", { input: { model: "dev", prompt: "A photo of TOK, driving, san francisco, rainy, iPhone camera", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 90, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 28 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun hudsongraeme/robovan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "hudsongraeme/robovan:ff9808afd09e423c61d3d627e96342b96703861e7e7d031c46abd6a567268e93", input={ "model": "dev", "prompt": "A photo of TOK, driving, san francisco, rainy, iPhone camera", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) # To access the file URL: 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 hudsongraeme/robovan 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": "hudsongraeme/robovan:ff9808afd09e423c61d3d627e96342b96703861e7e7d031c46abd6a567268e93", "input": { "model": "dev", "prompt": "A photo of TOK, driving, san francisco, rainy, iPhone camera", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-10-11T04:50:12.734879Z", "created_at": "2024-10-11T04:50:00.678000Z", "data_removed": false, "error": null, "id": "97ws9147csrm40cjf7mv0hwp5c", "input": { "model": "dev", "prompt": "A photo of TOK, driving, san francisco, rainy, iPhone camera", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "Using seed: 17198\nPrompt: A photo of TOK, driving, san francisco, rainy, iPhone camera\n[!] txt2img mode\nUsing dev model\nfree=6689195331584\nDownloading weights\n2024-10-11T04:50:00Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpk9yqba9t/weights url=https://replicate.delivery/yhqm/ugL38JpxSf09AaZqVo1dtQ2Zqxk2JGrFsQXn8LpWj1DX9yyJA/trained_model.tar\n2024-10-11T04:50:02Z | INFO | [ Complete ] dest=/tmp/tmpk9yqba9t/weights size=\"172 MB\" total_elapsed=1.291s url=https://replicate.delivery/yhqm/ugL38JpxSf09AaZqVo1dtQ2Zqxk2JGrFsQXn8LpWj1DX9yyJA/trained_model.tar\nDownloaded weights in 1.32s\nLoaded LoRAs in 2.03s\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, 3.21it/s]\n 11%|█ | 3/28 [00:00<00:08, 3.05it/s]\n 14%|█▍ | 4/28 [00:01<00:08, 2.98it/s]\n 18%|█▊ | 5/28 [00:01<00:07, 2.94it/s]\n 21%|██▏ | 6/28 [00:02<00:07, 2.92it/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, 2.89it/s]\n 36%|███▌ | 10/28 [00:03<00:06, 2.88it/s]\n 39%|███▉ | 11/28 [00:03<00:05, 2.88it/s]\n 43%|████▎ | 12/28 [00:04<00:05, 2.88it/s]\n 46%|████▋ | 13/28 [00:04<00:05, 2.88it/s]\n 50%|█████ | 14/28 [00:04<00:04, 2.88it/s]\n 54%|█████▎ | 15/28 [00:05<00:04, 2.88it/s]\n 57%|█████▋ | 16/28 [00:05<00:04, 2.88it/s]\n 61%|██████ | 17/28 [00:05<00:03, 2.88it/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 [00:08<00:00, 2.87it/s]\n 96%|█████████▋| 27/28 [00:09<00:00, 2.88it/s]\n100%|██████████| 28/28 [00:09<00:00, 2.88it/s]\n100%|██████████| 28/28 [00:09<00:00, 2.89it/s]", "metrics": { "predict_time": 12.048672053, "total_time": 12.056879 }, "output": [ "https://replicate.delivery/yhqm/WfevxE25pFhKZ0oGg3ZjreVm1XMYkU7c9aRyGzTUQ1NIEMLnA/out-0.webp" ], "started_at": "2024-10-11T04:50:00.686207Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/97ws9147csrm40cjf7mv0hwp5c", "cancel": "https://api.replicate.com/v1/predictions/97ws9147csrm40cjf7mv0hwp5c/cancel" }, "version": "ff9808afd09e423c61d3d627e96342b96703861e7e7d031c46abd6a567268e93" }Generated inUsing seed: 17198 Prompt: A photo of TOK, driving, san francisco, rainy, iPhone camera [!] txt2img mode Using dev model free=6689195331584 Downloading weights 2024-10-11T04:50:00Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpk9yqba9t/weights url=https://replicate.delivery/yhqm/ugL38JpxSf09AaZqVo1dtQ2Zqxk2JGrFsQXn8LpWj1DX9yyJA/trained_model.tar 2024-10-11T04:50:02Z | INFO | [ Complete ] dest=/tmp/tmpk9yqba9t/weights size="172 MB" total_elapsed=1.291s url=https://replicate.delivery/yhqm/ugL38JpxSf09AaZqVo1dtQ2Zqxk2JGrFsQXn8LpWj1DX9yyJA/trained_model.tar Downloaded weights in 1.32s Loaded LoRAs in 2.03s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:09, 2.87it/s] 7%|▋ | 2/28 [00:00<00:08, 3.21it/s] 11%|█ | 3/28 [00:00<00:08, 3.05it/s] 14%|█▍ | 4/28 [00:01<00:08, 2.98it/s] 18%|█▊ | 5/28 [00:01<00:07, 2.94it/s] 21%|██▏ | 6/28 [00:02<00:07, 2.92it/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, 2.89it/s] 36%|███▌ | 10/28 [00:03<00:06, 2.88it/s] 39%|███▉ | 11/28 [00:03<00:05, 2.88it/s] 43%|████▎ | 12/28 [00:04<00:05, 2.88it/s] 46%|████▋ | 13/28 [00:04<00:05, 2.88it/s] 50%|█████ | 14/28 [00:04<00:04, 2.88it/s] 54%|█████▎ | 15/28 [00:05<00:04, 2.88it/s] 57%|█████▋ | 16/28 [00:05<00:04, 2.88it/s] 61%|██████ | 17/28 [00:05<00:03, 2.88it/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 [00:08<00:00, 2.87it/s] 96%|█████████▋| 27/28 [00:09<00:00, 2.88it/s] 100%|██████████| 28/28 [00:09<00:00, 2.88it/s] 100%|██████████| 28/28 [00:09<00:00, 2.89it/s]
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