hudsongraeme
/
robotaxi
Renders a Tesla CyberCab in any setting. Use "a photo of TOK" to refer to the vehicle.
- Public
- 34 runs
-
H100
Prediction
hudsongraeme/robotaxi:6e4c9f977b2982eb397bfbc8ef25a8907da1d313d594c4417289c62a15403b43IDdg4gc8dctsrm60cjf7gr3p7e14StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- A photo of TOK, futuristic vehicle, bronze color, inside a tube shaped white underground tunnel. Black asphalt pavement road surface with LED light strips illuminating the white tubular tunnel
- 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, bronze color, inside a tube shaped white underground tunnel. Black asphalt pavement road surface with LED light strips illuminating the white tubular tunnel", "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"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run hudsongraeme/robotaxi using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "hudsongraeme/robotaxi:6e4c9f977b2982eb397bfbc8ef25a8907da1d313d594c4417289c62a15403b43", { input: { model: "dev", prompt: "A photo of TOK, futuristic vehicle, bronze color, inside a tube shaped white underground tunnel. Black asphalt pavement road surface with LED light strips illuminating the white tubular tunnel", 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/robotaxi using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "hudsongraeme/robotaxi:6e4c9f977b2982eb397bfbc8ef25a8907da1d313d594c4417289c62a15403b43", input={ "model": "dev", "prompt": "A photo of TOK, futuristic vehicle, bronze color, inside a tube shaped white underground tunnel. Black asphalt pavement road surface with LED light strips illuminating the white tubular tunnel", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run hudsongraeme/robotaxi 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": "6e4c9f977b2982eb397bfbc8ef25a8907da1d313d594c4417289c62a15403b43", "input": { "model": "dev", "prompt": "A photo of TOK, futuristic vehicle, bronze color, inside a tube shaped white underground tunnel. Black asphalt pavement road surface with LED light strips illuminating the white tubular tunnel", "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": "2024-10-11T04:41:36.745195Z", "created_at": "2024-10-11T04:41:25.974000Z", "data_removed": false, "error": null, "id": "dg4gc8dctsrm60cjf7gr3p7e14", "input": { "model": "dev", "prompt": "A photo of TOK, futuristic vehicle, bronze color, inside a tube shaped white underground tunnel. Black asphalt pavement road surface with LED light strips illuminating the white tubular tunnel", "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: 7117\nPrompt: A photo of TOK, futuristic vehicle, bronze color, inside a tube shaped white underground tunnel. Black asphalt pavement road surface with LED light strips illuminating the white tubular tunnel\n[!] txt2img mode\nUsing dev model\nLoaded LoRAs in 0.71s\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, 2.87it/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, 2.86it/s]\n 61%|██████ | 17/28 [00:05<00:03, 2.86it/s]\n 64%|██████▍ | 18/28 [00:06<00:03, 2.86it/s]\n 68%|██████▊ | 19/28 [00:06<00:03, 2.86it/s]\n 71%|███████▏ | 20/28 [00:06<00:02, 2.86it/s]\n 75%|███████▌ | 21/28 [00:07<00:02, 2.86it/s]\n 79%|███████▊ | 22/28 [00:07<00:02, 2.86it/s]\n 82%|████████▏ | 23/28 [00:07<00:01, 2.86it/s]\n 86%|████████▌ | 24/28 [00:08<00:01, 2.86it/s]\n 89%|████████▉ | 25/28 [00:08<00:01, 2.86it/s]\n 93%|█████████▎| 26/28 [00:09<00:00, 2.86it/s]\n 96%|█████████▋| 27/28 [00:09<00:00, 2.86it/s]\n100%|██████████| 28/28 [00:09<00:00, 2.86it/s]\n100%|██████████| 28/28 [00:09<00:00, 2.88it/s]", "metrics": { "predict_time": 10.758792361, "total_time": 10.771195 }, "output": [ "https://replicate.delivery/yhqm/PWrvf7LeQGlntUupmTNdpWnTUYDo8FdzMWIRdrNHm4sA6llTA/out-0.webp" ], "started_at": "2024-10-11T04:41:25.986403Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/dg4gc8dctsrm60cjf7gr3p7e14", "cancel": "https://api.replicate.com/v1/predictions/dg4gc8dctsrm60cjf7gr3p7e14/cancel" }, "version": "6e4c9f977b2982eb397bfbc8ef25a8907da1d313d594c4417289c62a15403b43" }Generated inUsing seed: 7117 Prompt: A photo of TOK, futuristic vehicle, bronze color, inside a tube shaped white underground tunnel. Black asphalt pavement road surface with LED light strips illuminating the white tubular tunnel [!] txt2img mode Using dev model Loaded LoRAs in 0.71s 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, 2.87it/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, 2.86it/s] 61%|██████ | 17/28 [00:05<00:03, 2.86it/s] 64%|██████▍ | 18/28 [00:06<00:03, 2.86it/s] 68%|██████▊ | 19/28 [00:06<00:03, 2.86it/s] 71%|███████▏ | 20/28 [00:06<00:02, 2.86it/s] 75%|███████▌ | 21/28 [00:07<00:02, 2.86it/s] 79%|███████▊ | 22/28 [00:07<00:02, 2.86it/s] 82%|████████▏ | 23/28 [00:07<00:01, 2.86it/s] 86%|████████▌ | 24/28 [00:08<00:01, 2.86it/s] 89%|████████▉ | 25/28 [00:08<00:01, 2.86it/s] 93%|█████████▎| 26/28 [00:09<00:00, 2.86it/s] 96%|█████████▋| 27/28 [00:09<00:00, 2.86it/s] 100%|██████████| 28/28 [00:09<00:00, 2.86it/s] 100%|██████████| 28/28 [00:09<00:00, 2.88it/s]Prediction
hudsongraeme/robotaxi:6e4c9f977b2982eb397bfbc8ef25a8907da1d313d594c4417289c62a15403b43IDj18pz418nsrm60cjf7kbgw7htrStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- A photo of TOK, futuristic vehicle,inside a tube shaped white underground tunnel. Black asphalt pavement road surface with LED light strips illuminating the white tubular tunnel. Low light, front
- 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,inside a tube shaped white underground tunnel. Black asphalt pavement road surface with LED light strips illuminating the white tubular tunnel. Low light, front", "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"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run hudsongraeme/robotaxi using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "hudsongraeme/robotaxi:6e4c9f977b2982eb397bfbc8ef25a8907da1d313d594c4417289c62a15403b43", { input: { model: "dev", prompt: "A photo of TOK, futuristic vehicle,inside a tube shaped white underground tunnel. Black asphalt pavement road surface with LED light strips illuminating the white tubular tunnel. Low light, front", 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/robotaxi using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "hudsongraeme/robotaxi:6e4c9f977b2982eb397bfbc8ef25a8907da1d313d594c4417289c62a15403b43", input={ "model": "dev", "prompt": "A photo of TOK, futuristic vehicle,inside a tube shaped white underground tunnel. Black asphalt pavement road surface with LED light strips illuminating the white tubular tunnel. Low light, front", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run hudsongraeme/robotaxi 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": "6e4c9f977b2982eb397bfbc8ef25a8907da1d313d594c4417289c62a15403b43", "input": { "model": "dev", "prompt": "A photo of TOK, futuristic vehicle,inside a tube shaped white underground tunnel. Black asphalt pavement road surface with LED light strips illuminating the white tubular tunnel. Low light, front", "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": "2024-10-11T04:46:58.463982Z", "created_at": "2024-10-11T04:46:19.822000Z", "data_removed": false, "error": null, "id": "j18pz418nsrm60cjf7kbgw7htr", "input": { "model": "dev", "prompt": "A photo of TOK, futuristic vehicle,inside a tube shaped white underground tunnel. Black asphalt pavement road surface with LED light strips illuminating the white tubular tunnel. Low light, front", "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: 13248\nPrompt: A photo of TOK, futuristic vehicle,inside a tube shaped white underground tunnel. Black asphalt pavement road surface with LED light strips illuminating the white tubular tunnel. Low light, front\n[!] txt2img mode\nUsing dev model\nLoaded LoRAs in 0.58s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:09, 2.88it/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, 2.95it/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, 2.88it/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, 2.88it/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": 10.578552686, "total_time": 38.641982 }, "output": [ "https://replicate.delivery/yhqm/6tGi41jsjuYYDJUR481h00MT6falXHueSnyh4FglXZtCfLLnA/out-0.webp" ], "started_at": "2024-10-11T04:46:47.885430Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/j18pz418nsrm60cjf7kbgw7htr", "cancel": "https://api.replicate.com/v1/predictions/j18pz418nsrm60cjf7kbgw7htr/cancel" }, "version": "6e4c9f977b2982eb397bfbc8ef25a8907da1d313d594c4417289c62a15403b43" }Generated inUsing seed: 13248 Prompt: A photo of TOK, futuristic vehicle,inside a tube shaped white underground tunnel. Black asphalt pavement road surface with LED light strips illuminating the white tubular tunnel. Low light, front [!] txt2img mode Using dev model Loaded LoRAs in 0.58s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:09, 2.88it/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, 2.95it/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, 2.88it/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, 2.88it/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/robotaxi:6e4c9f977b2982eb397bfbc8ef25a8907da1d313d594c4417289c62a15403b43IDpdt37x6jdnrm40cjf70t838k4wStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- A photo of TOK, futuristic vehicle, rainy street, san francisco
- 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, rainy street, san francisco", "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"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run hudsongraeme/robotaxi using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "hudsongraeme/robotaxi:6e4c9f977b2982eb397bfbc8ef25a8907da1d313d594c4417289c62a15403b43", { input: { model: "dev", prompt: "A photo of TOK, futuristic vehicle, rainy street, san francisco", 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/robotaxi using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "hudsongraeme/robotaxi:6e4c9f977b2982eb397bfbc8ef25a8907da1d313d594c4417289c62a15403b43", input={ "model": "dev", "prompt": "A photo of TOK, futuristic vehicle, rainy street, san francisco", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run hudsongraeme/robotaxi 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": "6e4c9f977b2982eb397bfbc8ef25a8907da1d313d594c4417289c62a15403b43", "input": { "model": "dev", "prompt": "A photo of TOK, futuristic vehicle, rainy street, san francisco", "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": "2024-10-11T04:07:10.355015Z", "created_at": "2024-10-11T04:06:38.445000Z", "data_removed": false, "error": null, "id": "pdt37x6jdnrm40cjf70t838k4w", "input": { "model": "dev", "prompt": "A photo of TOK, futuristic vehicle, rainy street, san francisco", "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: 56472\nPrompt: A photo of TOK, futuristic vehicle, rainy street, san francisco\n[!] txt2img mode\nUsing dev model\nfree=6838057349120\nDownloading weights\n2024-10-11T04:06:56Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmp7cm1igj3/weights url=https://replicate.delivery/yhqm/wi9weYoweHgCYUz26jjgmntOUtR6zb4CIBc3dLnjU9t6VllTA/trained_model.tar\n2024-10-11T04:06:59Z | INFO | [ Complete ] dest=/tmp/tmp7cm1igj3/weights size=\"172 MB\" total_elapsed=3.127s url=https://replicate.delivery/yhqm/wi9weYoweHgCYUz26jjgmntOUtR6zb4CIBc3dLnjU9t6VllTA/trained_model.tar\nDownloaded weights in 3.16s\nLoaded LoRAs in 3.86s\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": 13.913490349, "total_time": 31.910015 }, "output": [ "https://replicate.delivery/yhqm/c84MRVpuxdrhPpwdcW1ZPvJ7gN2oeRmv5dPzemNnqhpuZllTA/out-0.webp" ], "started_at": "2024-10-11T04:06:56.441524Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/pdt37x6jdnrm40cjf70t838k4w", "cancel": "https://api.replicate.com/v1/predictions/pdt37x6jdnrm40cjf70t838k4w/cancel" }, "version": "6e4c9f977b2982eb397bfbc8ef25a8907da1d313d594c4417289c62a15403b43" }Generated inUsing seed: 56472 Prompt: A photo of TOK, futuristic vehicle, rainy street, san francisco [!] txt2img mode Using dev model free=6838057349120 Downloading weights 2024-10-11T04:06:56Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmp7cm1igj3/weights url=https://replicate.delivery/yhqm/wi9weYoweHgCYUz26jjgmntOUtR6zb4CIBc3dLnjU9t6VllTA/trained_model.tar 2024-10-11T04:06:59Z | INFO | [ Complete ] dest=/tmp/tmp7cm1igj3/weights size="172 MB" total_elapsed=3.127s url=https://replicate.delivery/yhqm/wi9weYoweHgCYUz26jjgmntOUtR6zb4CIBc3dLnjU9t6VllTA/trained_model.tar Downloaded weights in 3.16s Loaded LoRAs in 3.86s 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
hudsongraeme/robotaxi:6e4c9f977b2982eb397bfbc8ef25a8907da1d313d594c4417289c62a15403b43ID4ens5myb8hrm00cjf71bwkx2ewStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- A photo of TOK, futuristic vehicle, sunny drive, italian alps
- 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, sunny drive, italian alps", "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"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run hudsongraeme/robotaxi using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "hudsongraeme/robotaxi:6e4c9f977b2982eb397bfbc8ef25a8907da1d313d594c4417289c62a15403b43", { input: { model: "dev", prompt: "A photo of TOK, futuristic vehicle, sunny drive, italian alps", 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/robotaxi using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "hudsongraeme/robotaxi:6e4c9f977b2982eb397bfbc8ef25a8907da1d313d594c4417289c62a15403b43", input={ "model": "dev", "prompt": "A photo of TOK, futuristic vehicle, sunny drive, italian alps", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run hudsongraeme/robotaxi 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": "6e4c9f977b2982eb397bfbc8ef25a8907da1d313d594c4417289c62a15403b43", "input": { "model": "dev", "prompt": "A photo of TOK, futuristic vehicle, sunny drive, italian alps", "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": "2024-10-11T04:07:54.327108Z", "created_at": "2024-10-11T04:07:42.148000Z", "data_removed": false, "error": null, "id": "4ens5myb8hrm00cjf71bwkx2ew", "input": { "model": "dev", "prompt": "A photo of TOK, futuristic vehicle, sunny drive, italian alps", "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: 19251\nPrompt: A photo of TOK, futuristic vehicle, sunny drive, italian alps\n[!] txt2img mode\nUsing dev model\nfree=7356490969088\nDownloading weights\n2024-10-11T04:07:42Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpxf8mnnyp/weights url=https://replicate.delivery/yhqm/wi9weYoweHgCYUz26jjgmntOUtR6zb4CIBc3dLnjU9t6VllTA/trained_model.tar\n2024-10-11T04:07:43Z | INFO | [ Complete ] dest=/tmp/tmpxf8mnnyp/weights size=\"172 MB\" total_elapsed=1.503s url=https://replicate.delivery/yhqm/wi9weYoweHgCYUz26jjgmntOUtR6zb4CIBc3dLnjU9t6VllTA/trained_model.tar\nDownloaded weights in 1.53s\nLoaded LoRAs in 2.13s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:09, 2.88it/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, 2.88it/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, 2.89it/s]", "metrics": { "predict_time": 12.172132131, "total_time": 12.179108 }, "output": [ "https://replicate.delivery/yhqm/9JdVstVGnL57FNdAbvJTKqYWHUTA8YL6fo7LaKNslJMNtyyJA/out-0.webp" ], "started_at": "2024-10-11T04:07:42.154976Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/4ens5myb8hrm00cjf71bwkx2ew", "cancel": "https://api.replicate.com/v1/predictions/4ens5myb8hrm00cjf71bwkx2ew/cancel" }, "version": "6e4c9f977b2982eb397bfbc8ef25a8907da1d313d594c4417289c62a15403b43" }Generated inUsing seed: 19251 Prompt: A photo of TOK, futuristic vehicle, sunny drive, italian alps [!] txt2img mode Using dev model free=7356490969088 Downloading weights 2024-10-11T04:07:42Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpxf8mnnyp/weights url=https://replicate.delivery/yhqm/wi9weYoweHgCYUz26jjgmntOUtR6zb4CIBc3dLnjU9t6VllTA/trained_model.tar 2024-10-11T04:07:43Z | INFO | [ Complete ] dest=/tmp/tmpxf8mnnyp/weights size="172 MB" total_elapsed=1.503s url=https://replicate.delivery/yhqm/wi9weYoweHgCYUz26jjgmntOUtR6zb4CIBc3dLnjU9t6VllTA/trained_model.tar Downloaded weights in 1.53s Loaded LoRAs in 2.13s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:09, 2.88it/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, 2.88it/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, 2.89it/s]Prediction
hudsongraeme/robotaxi:6e4c9f977b2982eb397bfbc8ef25a8907da1d313d594c4417289c62a15403b43IDey1g1ha4rnrm60cjf71r39s018StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- A photo of TOK, futuristic vehicle, racing around an F1 track
- 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, racing around an F1 track", "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"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run hudsongraeme/robotaxi using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "hudsongraeme/robotaxi:6e4c9f977b2982eb397bfbc8ef25a8907da1d313d594c4417289c62a15403b43", { input: { model: "dev", prompt: "A photo of TOK, futuristic vehicle, racing around an F1 track", 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/robotaxi using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "hudsongraeme/robotaxi:6e4c9f977b2982eb397bfbc8ef25a8907da1d313d594c4417289c62a15403b43", input={ "model": "dev", "prompt": "A photo of TOK, futuristic vehicle, racing around an F1 track", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run hudsongraeme/robotaxi 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": "6e4c9f977b2982eb397bfbc8ef25a8907da1d313d594c4417289c62a15403b43", "input": { "model": "dev", "prompt": "A photo of TOK, futuristic vehicle, racing around an F1 track", "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": "2024-10-11T04:08:25.662064Z", "created_at": "2024-10-11T04:08:13.253000Z", "data_removed": false, "error": null, "id": "ey1g1ha4rnrm60cjf71r39s018", "input": { "model": "dev", "prompt": "A photo of TOK, futuristic vehicle, racing around an F1 track", "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: 12667\nPrompt: A photo of TOK, futuristic vehicle, racing around an F1 track\n[!] txt2img mode\nUsing dev model\nfree=7655982915584\nDownloading weights\n2024-10-11T04:08:13Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmp32i8qqqp/weights url=https://replicate.delivery/yhqm/wi9weYoweHgCYUz26jjgmntOUtR6zb4CIBc3dLnjU9t6VllTA/trained_model.tar\n2024-10-11T04:08:14Z | INFO | [ Complete ] dest=/tmp/tmp32i8qqqp/weights size=\"172 MB\" total_elapsed=1.490s url=https://replicate.delivery/yhqm/wi9weYoweHgCYUz26jjgmntOUtR6zb4CIBc3dLnjU9t6VllTA/trained_model.tar\nDownloaded weights in 1.52s\nLoaded LoRAs in 2.40s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:09, 2.88it/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, 2.90it/s]\n 32%|███▏ | 9/28 [00:03<00:06, 2.89it/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, 2.88it/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, 2.88it/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": 12.401207723, "total_time": 12.409064 }, "output": [ "https://replicate.delivery/yhqm/UNpPueJdayxjOizceSHFDbePs8BPROsdKfLpo9AcCaKmrVWOB/out-0.webp" ], "started_at": "2024-10-11T04:08:13.260856Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ey1g1ha4rnrm60cjf71r39s018", "cancel": "https://api.replicate.com/v1/predictions/ey1g1ha4rnrm60cjf71r39s018/cancel" }, "version": "6e4c9f977b2982eb397bfbc8ef25a8907da1d313d594c4417289c62a15403b43" }Generated inUsing seed: 12667 Prompt: A photo of TOK, futuristic vehicle, racing around an F1 track [!] txt2img mode Using dev model free=7655982915584 Downloading weights 2024-10-11T04:08:13Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmp32i8qqqp/weights url=https://replicate.delivery/yhqm/wi9weYoweHgCYUz26jjgmntOUtR6zb4CIBc3dLnjU9t6VllTA/trained_model.tar 2024-10-11T04:08:14Z | INFO | [ Complete ] dest=/tmp/tmp32i8qqqp/weights size="172 MB" total_elapsed=1.490s url=https://replicate.delivery/yhqm/wi9weYoweHgCYUz26jjgmntOUtR6zb4CIBc3dLnjU9t6VllTA/trained_model.tar Downloaded weights in 1.52s Loaded LoRAs in 2.40s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:09, 2.88it/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, 2.90it/s] 32%|███▏ | 9/28 [00:03<00:06, 2.89it/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, 2.88it/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, 2.88it/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/robotaxi:6e4c9f977b2982eb397bfbc8ef25a8907da1d313d594c4417289c62a15403b43IDkjk63xqg2xrm60cjf71thgpay8StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- A photo of TOK, futuristic vehicle, parked in 4k HDR, near a mall
- 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, parked in 4k HDR, near a mall", "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"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run hudsongraeme/robotaxi using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "hudsongraeme/robotaxi:6e4c9f977b2982eb397bfbc8ef25a8907da1d313d594c4417289c62a15403b43", { input: { model: "dev", prompt: "A photo of TOK, futuristic vehicle, parked in 4k HDR, near a mall", 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/robotaxi using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "hudsongraeme/robotaxi:6e4c9f977b2982eb397bfbc8ef25a8907da1d313d594c4417289c62a15403b43", input={ "model": "dev", "prompt": "A photo of TOK, futuristic vehicle, parked in 4k HDR, near a mall", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run hudsongraeme/robotaxi 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": "6e4c9f977b2982eb397bfbc8ef25a8907da1d313d594c4417289c62a15403b43", "input": { "model": "dev", "prompt": "A photo of TOK, futuristic vehicle, parked in 4k HDR, near a mall", "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": "2024-10-11T04:09:08.239455Z", "created_at": "2024-10-11T04:08:57.111000Z", "data_removed": false, "error": null, "id": "kjk63xqg2xrm60cjf71thgpay8", "input": { "model": "dev", "prompt": "A photo of TOK, futuristic vehicle, parked in 4k HDR, near a mall", "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: 35963\nPrompt: A photo of TOK, futuristic vehicle, parked in 4k HDR, near a mall\n[!] txt2img mode\nUsing dev model\nLoaded LoRAs in 0.58s\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": 10.651739444, "total_time": 11.128455 }, "output": [ "https://replicate.delivery/yhqm/1uf8o2OzIUx5ciXKzqisM46XSehsdgqHuMJWPwQvVhGkbllTA/out-0.webp" ], "started_at": "2024-10-11T04:08:57.587716Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/kjk63xqg2xrm60cjf71thgpay8", "cancel": "https://api.replicate.com/v1/predictions/kjk63xqg2xrm60cjf71thgpay8/cancel" }, "version": "6e4c9f977b2982eb397bfbc8ef25a8907da1d313d594c4417289c62a15403b43" }Generated inUsing seed: 35963 Prompt: A photo of TOK, futuristic vehicle, parked in 4k HDR, near a mall [!] txt2img mode Using dev model Loaded LoRAs in 0.58s 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
hudsongraeme/robotaxi:6e4c9f977b2982eb397bfbc8ef25a8907da1d313d594c4417289c62a15403b43ID56csvv54rdrm00cjf72brpndycStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- A photo of TOK, futuristic vehicle, metallic blue, parked in 4k HDR, near a mall
- 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, metallic blue, parked in 4k HDR, near a mall", "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"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run hudsongraeme/robotaxi using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "hudsongraeme/robotaxi:6e4c9f977b2982eb397bfbc8ef25a8907da1d313d594c4417289c62a15403b43", { input: { model: "dev", prompt: "A photo of TOK, futuristic vehicle, metallic blue, parked in 4k HDR, near a mall", 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/robotaxi using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "hudsongraeme/robotaxi:6e4c9f977b2982eb397bfbc8ef25a8907da1d313d594c4417289c62a15403b43", input={ "model": "dev", "prompt": "A photo of TOK, futuristic vehicle, metallic blue, parked in 4k HDR, near a mall", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run hudsongraeme/robotaxi 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": "6e4c9f977b2982eb397bfbc8ef25a8907da1d313d594c4417289c62a15403b43", "input": { "model": "dev", "prompt": "A photo of TOK, futuristic vehicle, metallic blue, parked in 4k HDR, near a mall", "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": "2024-10-11T04:09:57.363993Z", "created_at": "2024-10-11T04:09:43.363000Z", "data_removed": false, "error": null, "id": "56csvv54rdrm00cjf72brpndyc", "input": { "model": "dev", "prompt": "A photo of TOK, futuristic vehicle, metallic blue, parked in 4k HDR, near a mall", "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: 55148\nPrompt: A photo of TOK, futuristic vehicle, metallic blue, parked in 4k HDR, near a mall\n[!] txt2img mode\nUsing dev model\nfree=7323597721600\nDownloading weights\n2024-10-11T04:09:43Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpw5r09sxk/weights url=https://replicate.delivery/yhqm/wi9weYoweHgCYUz26jjgmntOUtR6zb4CIBc3dLnjU9t6VllTA/trained_model.tar\n2024-10-11T04:09:46Z | INFO | [ Complete ] dest=/tmp/tmpw5r09sxk/weights size=\"172 MB\" total_elapsed=3.296s url=https://replicate.delivery/yhqm/wi9weYoweHgCYUz26jjgmntOUtR6zb4CIBc3dLnjU9t6VllTA/trained_model.tar\nDownloaded weights in 3.33s\nLoaded LoRAs in 3.93s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:09, 2.88it/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, 2.91it/s]\n 29%|██▊ | 8/28 [00:02<00:06, 2.90it/s]\n 32%|███▏ | 9/28 [00:03<00:06, 2.89it/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, 2.88it/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, 2.88it/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": 13.992132759, "total_time": 14.000993 }, "output": [ "https://replicate.delivery/yhqm/DRSuZgNS467QAx7n4QI5E1uevG7v9gHKeeB3YPa6FNpr4KLnA/out-0.webp" ], "started_at": "2024-10-11T04:09:43.371860Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/56csvv54rdrm00cjf72brpndyc", "cancel": "https://api.replicate.com/v1/predictions/56csvv54rdrm00cjf72brpndyc/cancel" }, "version": "6e4c9f977b2982eb397bfbc8ef25a8907da1d313d594c4417289c62a15403b43" }Generated inUsing seed: 55148 Prompt: A photo of TOK, futuristic vehicle, metallic blue, parked in 4k HDR, near a mall [!] txt2img mode Using dev model free=7323597721600 Downloading weights 2024-10-11T04:09:43Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpw5r09sxk/weights url=https://replicate.delivery/yhqm/wi9weYoweHgCYUz26jjgmntOUtR6zb4CIBc3dLnjU9t6VllTA/trained_model.tar 2024-10-11T04:09:46Z | INFO | [ Complete ] dest=/tmp/tmpw5r09sxk/weights size="172 MB" total_elapsed=3.296s url=https://replicate.delivery/yhqm/wi9weYoweHgCYUz26jjgmntOUtR6zb4CIBc3dLnjU9t6VllTA/trained_model.tar Downloaded weights in 3.33s Loaded LoRAs in 3.93s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:09, 2.88it/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, 2.91it/s] 29%|██▊ | 8/28 [00:02<00:06, 2.90it/s] 32%|███▏ | 9/28 [00:03<00:06, 2.89it/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, 2.88it/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, 2.88it/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]
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