arusterholz-edu / bioshock
- Public
- 49 runs
-
L40S
- SDXL fine-tune
Prediction
arusterholz-edu/bioshock:320b5bb7e09d624f15cc149b2cd316cbaa492c3e936a421dc953acd1bf5eeee5ID5ikz74db3trmoaibg4kshcbmyiStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- digital art in the style of JSP
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 4
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "digital art in the style of JSP", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }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 arusterholz-edu/bioshock using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "arusterholz-edu/bioshock:320b5bb7e09d624f15cc149b2cd316cbaa492c3e936a421dc953acd1bf5eeee5", { input: { width: 1024, height: 1024, prompt: "digital art in the style of JSP", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 4, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 50 } } ); // 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 arusterholz-edu/bioshock using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "arusterholz-edu/bioshock:320b5bb7e09d624f15cc149b2cd316cbaa492c3e936a421dc953acd1bf5eeee5", input={ "width": 1024, "height": 1024, "prompt": "digital art in the style of JSP", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run arusterholz-edu/bioshock 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": "arusterholz-edu/bioshock:320b5bb7e09d624f15cc149b2cd316cbaa492c3e936a421dc953acd1bf5eeee5", "input": { "width": 1024, "height": 1024, "prompt": "digital art in the style of JSP", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-01-04T03:29:52.961382Z", "created_at": "2024-01-04T03:28:41.552409Z", "data_removed": false, "error": null, "id": "5ikz74db3trmoaibg4kshcbmyi", "input": { "width": 1024, "height": 1024, "prompt": "digital art in the style of JSP", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 39691\nEnsuring enough disk space...\nFree disk space: 1737327738880\nDownloading weights: https://replicate.delivery/pbxt/4chDAWk9f2zGCanK952TjKgL90axUOE6nQoeFjtFiiFdl5ISA/trained_model.tar\n2024-01-04T03:28:46Z | INFO | [ Initiating ] dest=/src/weights-cache/bdc2bcf9d1517b59 minimum_chunk_size=150M url=https://replicate.delivery/pbxt/4chDAWk9f2zGCanK952TjKgL90axUOE6nQoeFjtFiiFdl5ISA/trained_model.tar\n2024-01-04T03:28:53Z | INFO | [ Complete ] dest=/src/weights-cache/bdc2bcf9d1517b59 size=\"186 MB\" total_elapsed=6.394s url=https://replicate.delivery/pbxt/4chDAWk9f2zGCanK952TjKgL90axUOE6nQoeFjtFiiFdl5ISA/trained_model.tar\nb''\nDownloaded weights in 6.592799186706543 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: digital art in the style of JSP\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:01<00:51, 1.05s/it]\n 4%|▍ | 2/50 [00:02<00:50, 1.05s/it]\n 6%|▌ | 3/50 [00:03<00:49, 1.05s/it]\n 8%|▊ | 4/50 [00:04<00:48, 1.05s/it]\n 10%|█ | 5/50 [00:05<00:47, 1.05s/it]\n 12%|█▏ | 6/50 [00:06<00:46, 1.05s/it]\n 14%|█▍ | 7/50 [00:07<00:45, 1.05s/it]\n 16%|█▌ | 8/50 [00:08<00:44, 1.05s/it]\n 18%|█▊ | 9/50 [00:09<00:43, 1.05s/it]\n 20%|██ | 10/50 [00:10<00:42, 1.05s/it]\n 22%|██▏ | 11/50 [00:11<00:41, 1.05s/it]\n 24%|██▍ | 12/50 [00:12<00:40, 1.05s/it]\n 26%|██▌ | 13/50 [00:13<00:39, 1.05s/it]\n 28%|██▊ | 14/50 [00:14<00:37, 1.05s/it]\n 30%|███ | 15/50 [00:15<00:36, 1.06s/it]\n 32%|███▏ | 16/50 [00:16<00:35, 1.06s/it]\n 34%|███▍ | 17/50 [00:17<00:34, 1.06s/it]\n 36%|███▌ | 18/50 [00:18<00:33, 1.06s/it]\n 38%|███▊ | 19/50 [00:20<00:32, 1.06s/it]\n 40%|████ | 20/50 [00:21<00:31, 1.06s/it]\n 42%|████▏ | 21/50 [00:22<00:30, 1.06s/it]\n 44%|████▍ | 22/50 [00:23<00:29, 1.06s/it]\n 46%|████▌ | 23/50 [00:24<00:28, 1.06s/it]\n 48%|████▊ | 24/50 [00:25<00:27, 1.06s/it]\n 50%|█████ | 25/50 [00:26<00:26, 1.06s/it]\n 52%|█████▏ | 26/50 [00:27<00:25, 1.06s/it]\n 54%|█████▍ | 27/50 [00:28<00:24, 1.06s/it]\n 56%|█████▌ | 28/50 [00:29<00:23, 1.06s/it]\n 58%|█████▊ | 29/50 [00:30<00:22, 1.06s/it]\n 60%|██████ | 30/50 [00:31<00:21, 1.06s/it]\n 62%|██████▏ | 31/50 [00:32<00:20, 1.06s/it]\n 64%|██████▍ | 32/50 [00:33<00:19, 1.06s/it]\n 66%|██████▌ | 33/50 [00:34<00:18, 1.06s/it]\n 68%|██████▊ | 34/50 [00:35<00:16, 1.06s/it]\n 70%|███████ | 35/50 [00:36<00:15, 1.06s/it]\n 72%|███████▏ | 36/50 [00:38<00:14, 1.06s/it]\n 74%|███████▍ | 37/50 [00:39<00:13, 1.06s/it]\n 76%|███████▌ | 38/50 [00:40<00:12, 1.06s/it]\n 78%|███████▊ | 39/50 [00:41<00:11, 1.06s/it]\n 80%|████████ | 40/50 [00:42<00:10, 1.06s/it]\n 82%|████████▏ | 41/50 [00:43<00:09, 1.06s/it]\n 84%|████████▍ | 42/50 [00:44<00:08, 1.06s/it]\n 86%|████████▌ | 43/50 [00:45<00:07, 1.06s/it]\n 88%|████████▊ | 44/50 [00:46<00:06, 1.06s/it]\n 90%|█████████ | 45/50 [00:47<00:05, 1.06s/it]\n 92%|█████████▏| 46/50 [00:48<00:04, 1.06s/it]\n 94%|█████████▍| 47/50 [00:49<00:03, 1.06s/it]\n 96%|█████████▌| 48/50 [00:50<00:02, 1.06s/it]\n 98%|█████████▊| 49/50 [00:51<00:01, 1.06s/it]\n100%|██████████| 50/50 [00:52<00:00, 1.06s/it]\n100%|██████████| 50/50 [00:52<00:00, 1.06s/it]", "metrics": { "predict_time": 66.426866, "total_time": 71.408973 }, "output": [ "https://replicate.delivery/pbxt/JfgJz8xXhK0REqfi6Z8hn4xOKTy9SAp4SPdYacZYeLEeC2jIB/out-0.png", "https://replicate.delivery/pbxt/AFX01IMXyy5vC5tW45fluRZ32BkCfS0PeXwnFCThy3OeC2jIB/out-1.png", "https://replicate.delivery/pbxt/ArXk0Vl72ILsBR9EQioIHydTN5mxJKrUtw7mhF2fXfEwg9ISA/out-2.png", "https://replicate.delivery/pbxt/pF8eM6TVlyX1HquhaUuVq3ah0fLJo70GAQSRtBqFNO2wg9ISA/out-3.png" ], "started_at": "2024-01-04T03:28:46.534516Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/5ikz74db3trmoaibg4kshcbmyi", "cancel": "https://api.replicate.com/v1/predictions/5ikz74db3trmoaibg4kshcbmyi/cancel" }, "version": "320b5bb7e09d624f15cc149b2cd316cbaa492c3e936a421dc953acd1bf5eeee5" }Generated inUsing seed: 39691 Ensuring enough disk space... Free disk space: 1737327738880 Downloading weights: https://replicate.delivery/pbxt/4chDAWk9f2zGCanK952TjKgL90axUOE6nQoeFjtFiiFdl5ISA/trained_model.tar 2024-01-04T03:28:46Z | INFO | [ Initiating ] dest=/src/weights-cache/bdc2bcf9d1517b59 minimum_chunk_size=150M url=https://replicate.delivery/pbxt/4chDAWk9f2zGCanK952TjKgL90axUOE6nQoeFjtFiiFdl5ISA/trained_model.tar 2024-01-04T03:28:53Z | INFO | [ Complete ] dest=/src/weights-cache/bdc2bcf9d1517b59 size="186 MB" total_elapsed=6.394s url=https://replicate.delivery/pbxt/4chDAWk9f2zGCanK952TjKgL90axUOE6nQoeFjtFiiFdl5ISA/trained_model.tar b'' Downloaded weights in 6.592799186706543 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: digital art in the style of JSP txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:01<00:51, 1.05s/it] 4%|▍ | 2/50 [00:02<00:50, 1.05s/it] 6%|▌ | 3/50 [00:03<00:49, 1.05s/it] 8%|▊ | 4/50 [00:04<00:48, 1.05s/it] 10%|█ | 5/50 [00:05<00:47, 1.05s/it] 12%|█▏ | 6/50 [00:06<00:46, 1.05s/it] 14%|█▍ | 7/50 [00:07<00:45, 1.05s/it] 16%|█▌ | 8/50 [00:08<00:44, 1.05s/it] 18%|█▊ | 9/50 [00:09<00:43, 1.05s/it] 20%|██ | 10/50 [00:10<00:42, 1.05s/it] 22%|██▏ | 11/50 [00:11<00:41, 1.05s/it] 24%|██▍ | 12/50 [00:12<00:40, 1.05s/it] 26%|██▌ | 13/50 [00:13<00:39, 1.05s/it] 28%|██▊ | 14/50 [00:14<00:37, 1.05s/it] 30%|███ | 15/50 [00:15<00:36, 1.06s/it] 32%|███▏ | 16/50 [00:16<00:35, 1.06s/it] 34%|███▍ | 17/50 [00:17<00:34, 1.06s/it] 36%|███▌ | 18/50 [00:18<00:33, 1.06s/it] 38%|███▊ | 19/50 [00:20<00:32, 1.06s/it] 40%|████ | 20/50 [00:21<00:31, 1.06s/it] 42%|████▏ | 21/50 [00:22<00:30, 1.06s/it] 44%|████▍ | 22/50 [00:23<00:29, 1.06s/it] 46%|████▌ | 23/50 [00:24<00:28, 1.06s/it] 48%|████▊ | 24/50 [00:25<00:27, 1.06s/it] 50%|█████ | 25/50 [00:26<00:26, 1.06s/it] 52%|█████▏ | 26/50 [00:27<00:25, 1.06s/it] 54%|█████▍ | 27/50 [00:28<00:24, 1.06s/it] 56%|█████▌ | 28/50 [00:29<00:23, 1.06s/it] 58%|█████▊ | 29/50 [00:30<00:22, 1.06s/it] 60%|██████ | 30/50 [00:31<00:21, 1.06s/it] 62%|██████▏ | 31/50 [00:32<00:20, 1.06s/it] 64%|██████▍ | 32/50 [00:33<00:19, 1.06s/it] 66%|██████▌ | 33/50 [00:34<00:18, 1.06s/it] 68%|██████▊ | 34/50 [00:35<00:16, 1.06s/it] 70%|███████ | 35/50 [00:36<00:15, 1.06s/it] 72%|███████▏ | 36/50 [00:38<00:14, 1.06s/it] 74%|███████▍ | 37/50 [00:39<00:13, 1.06s/it] 76%|███████▌ | 38/50 [00:40<00:12, 1.06s/it] 78%|███████▊ | 39/50 [00:41<00:11, 1.06s/it] 80%|████████ | 40/50 [00:42<00:10, 1.06s/it] 82%|████████▏ | 41/50 [00:43<00:09, 1.06s/it] 84%|████████▍ | 42/50 [00:44<00:08, 1.06s/it] 86%|████████▌ | 43/50 [00:45<00:07, 1.06s/it] 88%|████████▊ | 44/50 [00:46<00:06, 1.06s/it] 90%|█████████ | 45/50 [00:47<00:05, 1.06s/it] 92%|█████████▏| 46/50 [00:48<00:04, 1.06s/it] 94%|█████████▍| 47/50 [00:49<00:03, 1.06s/it] 96%|█████████▌| 48/50 [00:50<00:02, 1.06s/it] 98%|█████████▊| 49/50 [00:51<00:01, 1.06s/it] 100%|██████████| 50/50 [00:52<00:00, 1.06s/it] 100%|██████████| 50/50 [00:52<00:00, 1.06s/it]
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