sevensevenimages / rolxsub
- Public
- 103 runs
Prediction
sevensevenimages/rolxsub:d8b67bc5502f1477d6c06d73722668e864ba4a426e80b351962a4163f1a125c1ID032zn416csrm00chp978bys2egStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- width
- 384
- height
- 601
- prompt
- close up shot of rolxsub watch underwater with ray light
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 16:9
- output_format
- png
- guidance_scale
- 3.5
- output_quality
- 80
- extra_lora_scale
- 0.8
- num_inference_steps
- 28
{ "model": "dev", "width": 384, "height": 601, "prompt": "close up shot of rolxsub watch underwater with ray light", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "png", "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 sevensevenimages/rolxsub using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "sevensevenimages/rolxsub:d8b67bc5502f1477d6c06d73722668e864ba4a426e80b351962a4163f1a125c1", { input: { model: "dev", width: 384, height: 601, prompt: "close up shot of rolxsub watch underwater with ray light", lora_scale: 1, num_outputs: 1, aspect_ratio: "16:9", output_format: "png", 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 sevensevenimages/rolxsub using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "sevensevenimages/rolxsub:d8b67bc5502f1477d6c06d73722668e864ba4a426e80b351962a4163f1a125c1", input={ "model": "dev", "width": 384, "height": 601, "prompt": "close up shot of rolxsub watch underwater with ray light", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "png", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run sevensevenimages/rolxsub 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": "sevensevenimages/rolxsub:d8b67bc5502f1477d6c06d73722668e864ba4a426e80b351962a4163f1a125c1", "input": { "model": "dev", "width": 384, "height": 601, "prompt": "close up shot of rolxsub watch underwater with ray light", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "png", "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": "2024-09-02T10:36:11.327419Z", "created_at": "2024-09-02T10:35:51.782000Z", "data_removed": false, "error": null, "id": "032zn416csrm00chp978bys2eg", "input": { "model": "dev", "width": 384, "height": 601, "prompt": "close up shot of rolxsub watch underwater with ray light", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "png", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 }, "logs": "Using seed: 59355\nPrompt: close up shot of rolxsub watch underwater with ray light\ntxt2img mode\nUsing dev model\nfree=9360858804224\nDownloading weights\n2024-09-02T10:35:51Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpde93tiwf/weights url=https://replicate.delivery/yhqm/zlGFdY0AaeVRZSGE6BFpa4UrERAq2ZexyUJkiFSmJYgi8zYTA/trained_model.tar\n2024-09-02T10:35:53Z | INFO | [ Complete ] dest=/tmp/tmpde93tiwf/weights size=\"215 MB\" total_elapsed=1.694s url=https://replicate.delivery/yhqm/zlGFdY0AaeVRZSGE6BFpa4UrERAq2ZexyUJkiFSmJYgi8zYTA/trained_model.tar\nDownloaded weights in 1.73s\nLoaded LoRAs in 10.98s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.61it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.15it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.89it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.78it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.72it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.68it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.66it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.64it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.63it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.63it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.62it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.62it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.62it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.62it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.62it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.61it/s]\n 61%|██████ | 17/28 [00:04<00:03, 3.61it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.61it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.61it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.61it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.61it/s]\n 79%|███████▊ | 22/28 [00:06<00:01, 3.61it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.61it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.61it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.61it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.61it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.61it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.61it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.64it/s]", "metrics": { "predict_time": 19.536132794, "total_time": 19.545419 }, "output": [ "https://replicate.delivery/yhqm/5Zg4O0iwDjqnApkBhgQBfqNfKiCazXddO7pn7Awg7bYbc0YTA/out-0.png" ], "started_at": "2024-09-02T10:35:51.791286Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/032zn416csrm00chp978bys2eg", "cancel": "https://api.replicate.com/v1/predictions/032zn416csrm00chp978bys2eg/cancel" }, "version": "d8b67bc5502f1477d6c06d73722668e864ba4a426e80b351962a4163f1a125c1" }Generated inUsing seed: 59355 Prompt: close up shot of rolxsub watch underwater with ray light txt2img mode Using dev model free=9360858804224 Downloading weights 2024-09-02T10:35:51Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpde93tiwf/weights url=https://replicate.delivery/yhqm/zlGFdY0AaeVRZSGE6BFpa4UrERAq2ZexyUJkiFSmJYgi8zYTA/trained_model.tar 2024-09-02T10:35:53Z | INFO | [ Complete ] dest=/tmp/tmpde93tiwf/weights size="215 MB" total_elapsed=1.694s url=https://replicate.delivery/yhqm/zlGFdY0AaeVRZSGE6BFpa4UrERAq2ZexyUJkiFSmJYgi8zYTA/trained_model.tar Downloaded weights in 1.73s Loaded LoRAs in 10.98s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.61it/s] 7%|▋ | 2/28 [00:00<00:06, 4.15it/s] 11%|█ | 3/28 [00:00<00:06, 3.89it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.78it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.72it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.68it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.66it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.64it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.63it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.63it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.62it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.62it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.62it/s] 50%|█████ | 14/28 [00:03<00:03, 3.62it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.62it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.61it/s] 61%|██████ | 17/28 [00:04<00:03, 3.61it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.61it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.61it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.61it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.61it/s] 79%|███████▊ | 22/28 [00:06<00:01, 3.61it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.61it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.61it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.61it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.61it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.61it/s] 100%|██████████| 28/28 [00:07<00:00, 3.61it/s] 100%|██████████| 28/28 [00:07<00:00, 3.64it/s]Prediction
sevensevenimages/rolxsub:d8b67bc5502f1477d6c06d73722668e864ba4a426e80b351962a4163f1a125c1IDkx44tvbkrhrm60chp98bb5462gStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- width
- 350
- height
- 450
- prompt
- close up shot of rolxsub watch underwater with ray light
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 16:9
- output_format
- png
- guidance_scale
- 3.5
- output_quality
- 80
- extra_lora_scale
- 0.8
- num_inference_steps
- 28
{ "model": "dev", "width": 350, "height": 450, "prompt": "close up shot of rolxsub watch underwater with ray light", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "png", "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 sevensevenimages/rolxsub using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "sevensevenimages/rolxsub:d8b67bc5502f1477d6c06d73722668e864ba4a426e80b351962a4163f1a125c1", { input: { model: "dev", width: 350, height: 450, prompt: "close up shot of rolxsub watch underwater with ray light", lora_scale: 1, num_outputs: 1, aspect_ratio: "16:9", output_format: "png", 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 sevensevenimages/rolxsub using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "sevensevenimages/rolxsub:d8b67bc5502f1477d6c06d73722668e864ba4a426e80b351962a4163f1a125c1", input={ "model": "dev", "width": 350, "height": 450, "prompt": "close up shot of rolxsub watch underwater with ray light", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "png", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run sevensevenimages/rolxsub 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": "sevensevenimages/rolxsub:d8b67bc5502f1477d6c06d73722668e864ba4a426e80b351962a4163f1a125c1", "input": { "model": "dev", "width": 350, "height": 450, "prompt": "close up shot of rolxsub watch underwater with ray light", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "png", "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": "2024-09-02T10:38:41.685455Z", "created_at": "2024-09-02T10:38:22.660000Z", "data_removed": false, "error": null, "id": "kx44tvbkrhrm60chp98bb5462g", "input": { "model": "dev", "width": 350, "height": 450, "prompt": "close up shot of rolxsub watch underwater with ray light", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "png", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 }, "logs": "Using seed: 23740\nPrompt: close up shot of rolxsub watch underwater with ray light\ntxt2img mode\nUsing dev model\nLoaded LoRAs in 10.41s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.62it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.15it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.89it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.78it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.72it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.68it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.66it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.64it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.63it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.63it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.63it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.62it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.62it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.62it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.62it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.62it/s]\n 61%|██████ | 17/28 [00:04<00:03, 3.62it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.62it/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 [00:06<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, 3.61it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.61it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.64it/s]", "metrics": { "predict_time": 19.01613591, "total_time": 19.025455 }, "output": [ "https://replicate.delivery/yhqm/hTPyYgUINP5EAJ0wZVPO6SugegoA6l9KcTi9d7l0bemxeoxmA/out-0.png" ], "started_at": "2024-09-02T10:38:22.669319Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/kx44tvbkrhrm60chp98bb5462g", "cancel": "https://api.replicate.com/v1/predictions/kx44tvbkrhrm60chp98bb5462g/cancel" }, "version": "d8b67bc5502f1477d6c06d73722668e864ba4a426e80b351962a4163f1a125c1" }Generated inUsing seed: 23740 Prompt: close up shot of rolxsub watch underwater with ray light txt2img mode Using dev model Loaded LoRAs in 10.41s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.62it/s] 7%|▋ | 2/28 [00:00<00:06, 4.15it/s] 11%|█ | 3/28 [00:00<00:06, 3.89it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.78it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.72it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.68it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.66it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.64it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.63it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.63it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.63it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.62it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.62it/s] 50%|█████ | 14/28 [00:03<00:03, 3.62it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.62it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.62it/s] 61%|██████ | 17/28 [00:04<00:03, 3.62it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.62it/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 [00:06<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, 3.61it/s] 100%|██████████| 28/28 [00:07<00:00, 3.61it/s] 100%|██████████| 28/28 [00:07<00:00, 3.64it/s]
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