jbilcke / sdxl-tombraider
A SDXL LoRA inspired by Tomb Raider (1996)
- Public
- 242 runs
-
L40S
- SDXL fine-tune
Prediction
jbilcke/sdxl-tombraider:5bf6edfbf5741e9b5d021f75781294418a79fe3a54c63a7cce1dd07b97572c67IDlqvrt6dbfrw5jq4cazc6ojd4xmStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- Lara driving a car in Paris, in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.85
- num_outputs
- 1
- guidance_scale
- 18.25
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- overexposed
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "Lara driving a car in Paris, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.85, "num_outputs": 1, "guidance_scale": 18.25, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed", "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"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run jbilcke/sdxl-tombraider using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jbilcke/sdxl-tombraider:5bf6edfbf5741e9b5d021f75781294418a79fe3a54c63a7cce1dd07b97572c67", { input: { width: 1024, height: 1024, prompt: "Lara driving a car in Paris, in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.85, num_outputs: 1, guidance_scale: 18.25, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "overexposed", 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 jbilcke/sdxl-tombraider using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jbilcke/sdxl-tombraider:5bf6edfbf5741e9b5d021f75781294418a79fe3a54c63a7cce1dd07b97572c67", input={ "width": 1024, "height": 1024, "prompt": "Lara driving a car in Paris, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.85, "num_outputs": 1, "guidance_scale": 18.25, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "overexposed", "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 jbilcke/sdxl-tombraider 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": "jbilcke/sdxl-tombraider:5bf6edfbf5741e9b5d021f75781294418a79fe3a54c63a7cce1dd07b97572c67", "input": { "width": 1024, "height": 1024, "prompt": "Lara driving a car in Paris, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.85, "num_outputs": 1, "guidance_scale": 18.25, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed", "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": "2023-08-31T12:10:39.802590Z", "created_at": "2023-08-31T12:09:16.700285Z", "data_removed": false, "error": null, "id": "lqvrt6dbfrw5jq4cazc6ojd4xm", "input": { "width": 1024, "height": 1024, "prompt": "Lara driving a car in Paris, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.85, "num_outputs": 1, "guidance_scale": 18.25, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 7689\nPrompt: Lara driving a car in Paris, in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:41, 1.19it/s]\n 4%|▍ | 2/50 [00:01<00:24, 1.98it/s]\n 6%|▌ | 3/50 [00:01<00:18, 2.52it/s]\n 8%|▊ | 4/50 [00:01<00:15, 2.88it/s]\n 10%|█ | 5/50 [00:01<00:14, 3.13it/s]\n 12%|█▏ | 6/50 [00:02<00:13, 3.30it/s]\n 14%|█▍ | 7/50 [00:02<00:12, 3.42it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.50it/s]\n 18%|█▊ | 9/50 [00:03<00:11, 3.56it/s]\n 20%|██ | 10/50 [00:03<00:11, 3.60it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.63it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.65it/s]\n 26%|██▌ | 13/50 [00:04<00:10, 3.66it/s]\n 28%|██▊ | 14/50 [00:04<00:09, 3.67it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.67it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.68it/s]\n 34%|███▍ | 17/50 [00:05<00:08, 3.68it/s]\n 36%|███▌ | 18/50 [00:05<00:08, 3.68it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.68it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.68it/s]\n 42%|████▏ | 21/50 [00:06<00:07, 3.68it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.68it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.68it/s]\n 48%|████▊ | 24/50 [00:07<00:07, 3.68it/s]\n 50%|█████ | 25/50 [00:07<00:06, 3.68it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.68it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.67it/s]\n 56%|█████▌ | 28/50 [00:08<00:05, 3.67it/s]\n 58%|█████▊ | 29/50 [00:08<00:05, 3.67it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.67it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.67it/s]\n 64%|██████▍ | 32/50 [00:09<00:04, 3.67it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.68it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.67it/s]\n 70%|███████ | 35/50 [00:10<00:04, 3.67it/s]\n 72%|███████▏ | 36/50 [00:10<00:03, 3.67it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.67it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.67it/s]\n 78%|███████▊ | 39/50 [00:11<00:02, 3.67it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.67it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.67it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.67it/s]\n 86%|████████▌ | 43/50 [00:12<00:01, 3.67it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.67it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.67it/s]\n 92%|█████████▏| 46/50 [00:13<00:01, 3.67it/s]\n 94%|█████████▍| 47/50 [00:13<00:00, 3.67it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.67it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.67it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.67it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.53it/s]", "metrics": { "predict_time": 16.775731, "total_time": 83.102305 }, "output": [ "https://replicate.delivery/pbxt/JbOE0IDcYx5eDaBJTIHWn4soaEbqtDxdeEatnD45dCmepGfFB/out-0.png" ], "started_at": "2023-08-31T12:10:23.026859Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/lqvrt6dbfrw5jq4cazc6ojd4xm", "cancel": "https://api.replicate.com/v1/predictions/lqvrt6dbfrw5jq4cazc6ojd4xm/cancel" }, "version": "5bf6edfbf5741e9b5d021f75781294418a79fe3a54c63a7cce1dd07b97572c67" }Generated inUsing seed: 7689 Prompt: Lara driving a car in Paris, in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:41, 1.19it/s] 4%|▍ | 2/50 [00:01<00:24, 1.98it/s] 6%|▌ | 3/50 [00:01<00:18, 2.52it/s] 8%|▊ | 4/50 [00:01<00:15, 2.88it/s] 10%|█ | 5/50 [00:01<00:14, 3.13it/s] 12%|█▏ | 6/50 [00:02<00:13, 3.30it/s] 14%|█▍ | 7/50 [00:02<00:12, 3.42it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.50it/s] 18%|█▊ | 9/50 [00:03<00:11, 3.56it/s] 20%|██ | 10/50 [00:03<00:11, 3.60it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.63it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.65it/s] 26%|██▌ | 13/50 [00:04<00:10, 3.66it/s] 28%|██▊ | 14/50 [00:04<00:09, 3.67it/s] 30%|███ | 15/50 [00:04<00:09, 3.67it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.68it/s] 34%|███▍ | 17/50 [00:05<00:08, 3.68it/s] 36%|███▌ | 18/50 [00:05<00:08, 3.68it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.68it/s] 40%|████ | 20/50 [00:05<00:08, 3.68it/s] 42%|████▏ | 21/50 [00:06<00:07, 3.68it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.68it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.68it/s] 48%|████▊ | 24/50 [00:07<00:07, 3.68it/s] 50%|█████ | 25/50 [00:07<00:06, 3.68it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.68it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.67it/s] 56%|█████▌ | 28/50 [00:08<00:05, 3.67it/s] 58%|█████▊ | 29/50 [00:08<00:05, 3.67it/s] 60%|██████ | 30/50 [00:08<00:05, 3.67it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.67it/s] 64%|██████▍ | 32/50 [00:09<00:04, 3.67it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.68it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.67it/s] 70%|███████ | 35/50 [00:10<00:04, 3.67it/s] 72%|███████▏ | 36/50 [00:10<00:03, 3.67it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.67it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.67it/s] 78%|███████▊ | 39/50 [00:11<00:02, 3.67it/s] 80%|████████ | 40/50 [00:11<00:02, 3.67it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.67it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.67it/s] 86%|████████▌ | 43/50 [00:12<00:01, 3.67it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.67it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.67it/s] 92%|█████████▏| 46/50 [00:13<00:01, 3.67it/s] 94%|█████████▍| 47/50 [00:13<00:00, 3.67it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.67it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.67it/s] 100%|██████████| 50/50 [00:14<00:00, 3.67it/s] 100%|██████████| 50/50 [00:14<00:00, 3.53it/s]Prediction
jbilcke/sdxl-tombraider:5bf6edfbf5741e9b5d021f75781294418a79fe3a54c63a7cce1dd07b97572c67IDoc7yfmdbn6n2cuedxrt5p5mtjmStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- Lara working on a computer, in an office, drinking from a mug, in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.85
- num_outputs
- 1
- guidance_scale
- 18.25
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- overexposed
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "Lara working on a computer, in an office, drinking from a mug, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.85, "num_outputs": 1, "guidance_scale": 18.25, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed", "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"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run jbilcke/sdxl-tombraider using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jbilcke/sdxl-tombraider:5bf6edfbf5741e9b5d021f75781294418a79fe3a54c63a7cce1dd07b97572c67", { input: { width: 1024, height: 1024, prompt: "Lara working on a computer, in an office, drinking from a mug, in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.85, num_outputs: 1, guidance_scale: 18.25, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "overexposed", 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 jbilcke/sdxl-tombraider using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jbilcke/sdxl-tombraider:5bf6edfbf5741e9b5d021f75781294418a79fe3a54c63a7cce1dd07b97572c67", input={ "width": 1024, "height": 1024, "prompt": "Lara working on a computer, in an office, drinking from a mug, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.85, "num_outputs": 1, "guidance_scale": 18.25, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "overexposed", "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 jbilcke/sdxl-tombraider 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": "jbilcke/sdxl-tombraider:5bf6edfbf5741e9b5d021f75781294418a79fe3a54c63a7cce1dd07b97572c67", "input": { "width": 1024, "height": 1024, "prompt": "Lara working on a computer, in an office, drinking from a mug, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.85, "num_outputs": 1, "guidance_scale": 18.25, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed", "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": "2023-08-31T12:14:50.850988Z", "created_at": "2023-08-31T12:14:35.334629Z", "data_removed": false, "error": null, "id": "oc7yfmdbn6n2cuedxrt5p5mtjm", "input": { "width": 1024, "height": 1024, "prompt": "Lara working on a computer, in an office, drinking from a mug, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.85, "num_outputs": 1, "guidance_scale": 18.25, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 5136\nPrompt: Lara working on a computer, in an office, drinking from a mug, in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.72it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.70it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.70it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.69it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.69it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.69it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.69it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.68it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.68it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.68it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.67it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.68it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.67it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.67it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.67it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.67it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.67it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.67it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.67it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.67it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.67it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.67it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.66it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.66it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.66it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.66it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.66it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.66it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.67it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.66it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.66it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.66it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.66it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.66it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.66it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.66it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.66it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.66it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.66it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.66it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.66it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.66it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.67it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.67it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.67it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.68it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.68it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.68it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.67it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.68it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.67it/s]", "metrics": { "predict_time": 15.520195, "total_time": 15.516359 }, "output": [ "https://replicate.delivery/pbxt/A1whnmDFs0a6FhtrrWDrs2TUuNg6K2JkFgHYpPmWcNRO24XE/out-0.png" ], "started_at": "2023-08-31T12:14:35.330793Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/oc7yfmdbn6n2cuedxrt5p5mtjm", "cancel": "https://api.replicate.com/v1/predictions/oc7yfmdbn6n2cuedxrt5p5mtjm/cancel" }, "version": "5bf6edfbf5741e9b5d021f75781294418a79fe3a54c63a7cce1dd07b97572c67" }Generated inUsing seed: 5136 Prompt: Lara working on a computer, in an office, drinking from a mug, in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.72it/s] 4%|▍ | 2/50 [00:00<00:12, 3.70it/s] 6%|▌ | 3/50 [00:00<00:12, 3.70it/s] 8%|▊ | 4/50 [00:01<00:12, 3.69it/s] 10%|█ | 5/50 [00:01<00:12, 3.69it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.69it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.69it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.68it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.68it/s] 20%|██ | 10/50 [00:02<00:10, 3.68it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.67it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.68it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.67it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.67it/s] 30%|███ | 15/50 [00:04<00:09, 3.67it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.67it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.67it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.67it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.67it/s] 40%|████ | 20/50 [00:05<00:08, 3.67it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.67it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.67it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.66it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.66it/s] 50%|█████ | 25/50 [00:06<00:06, 3.66it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.66it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.66it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.66it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.67it/s] 60%|██████ | 30/50 [00:08<00:05, 3.66it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.66it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.66it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.66it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.66it/s] 70%|███████ | 35/50 [00:09<00:04, 3.66it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.66it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.66it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.66it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.66it/s] 80%|████████ | 40/50 [00:10<00:02, 3.66it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.66it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.66it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.67it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.67it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.67it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.68it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.68it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.68it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.67it/s] 100%|██████████| 50/50 [00:13<00:00, 3.68it/s] 100%|██████████| 50/50 [00:13<00:00, 3.67it/s]Prediction
jbilcke/sdxl-tombraider:5bf6edfbf5741e9b5d021f75781294418a79fe3a54c63a7cce1dd07b97572c67IDyh5qlutbaueevnzt4f4vb7z5uqStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- Lara riding a velociraptor, in a canyon, in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.85
- num_outputs
- 1
- guidance_scale
- 18.25
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- overexposed
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "Lara riding a velociraptor, in a canyon, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.85, "num_outputs": 1, "guidance_scale": 18.25, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed", "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"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run jbilcke/sdxl-tombraider using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jbilcke/sdxl-tombraider:5bf6edfbf5741e9b5d021f75781294418a79fe3a54c63a7cce1dd07b97572c67", { input: { width: 1024, height: 1024, prompt: "Lara riding a velociraptor, in a canyon, in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.85, num_outputs: 1, guidance_scale: 18.25, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "overexposed", 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 jbilcke/sdxl-tombraider using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jbilcke/sdxl-tombraider:5bf6edfbf5741e9b5d021f75781294418a79fe3a54c63a7cce1dd07b97572c67", input={ "width": 1024, "height": 1024, "prompt": "Lara riding a velociraptor, in a canyon, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.85, "num_outputs": 1, "guidance_scale": 18.25, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "overexposed", "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 jbilcke/sdxl-tombraider 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": "jbilcke/sdxl-tombraider:5bf6edfbf5741e9b5d021f75781294418a79fe3a54c63a7cce1dd07b97572c67", "input": { "width": 1024, "height": 1024, "prompt": "Lara riding a velociraptor, in a canyon, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.85, "num_outputs": 1, "guidance_scale": 18.25, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed", "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": "2023-08-31T12:58:00.830322Z", "created_at": "2023-08-31T12:57:45.831652Z", "data_removed": false, "error": null, "id": "yh5qlutbaueevnzt4f4vb7z5uq", "input": { "width": 1024, "height": 1024, "prompt": "Lara riding a velociraptor, in a canyon, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.85, "num_outputs": 1, "guidance_scale": 18.25, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 27814\nPrompt: Lara riding a velociraptor, in a canyon, in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.70it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.70it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.70it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.70it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.70it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.70it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.70it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.70it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.70it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.70it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.70it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.69it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.69it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.69it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.69it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.69it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.69it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.69it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.69it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.69it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.68it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.68it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.68it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.68it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.68it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.68it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.68it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.68it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.68it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.68it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.67it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.68it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.67it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.67it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.67it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.67it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.67it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.67it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.67it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.67it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.67it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.67it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.67it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.67it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.67it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.67it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.67it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.67it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.66it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.66it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.68it/s]", "metrics": { "predict_time": 14.995377, "total_time": 14.99867 }, "output": [ "https://replicate.delivery/pbxt/1lDueZXLToVJIyiyc4wGUe4fGXTLyGyY0P2KztNodtFxCIfFB/out-0.png" ], "started_at": "2023-08-31T12:57:45.834945Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/yh5qlutbaueevnzt4f4vb7z5uq", "cancel": "https://api.replicate.com/v1/predictions/yh5qlutbaueevnzt4f4vb7z5uq/cancel" }, "version": "5bf6edfbf5741e9b5d021f75781294418a79fe3a54c63a7cce1dd07b97572c67" }Generated inUsing seed: 27814 Prompt: Lara riding a velociraptor, in a canyon, in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.70it/s] 4%|▍ | 2/50 [00:00<00:12, 3.70it/s] 6%|▌ | 3/50 [00:00<00:12, 3.70it/s] 8%|▊ | 4/50 [00:01<00:12, 3.70it/s] 10%|█ | 5/50 [00:01<00:12, 3.70it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.70it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.70it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.70it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.70it/s] 20%|██ | 10/50 [00:02<00:10, 3.70it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.70it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.69it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.69it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.69it/s] 30%|███ | 15/50 [00:04<00:09, 3.69it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.69it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.69it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.69it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.69it/s] 40%|████ | 20/50 [00:05<00:08, 3.69it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.68it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.68it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.68it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.68it/s] 50%|█████ | 25/50 [00:06<00:06, 3.68it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.68it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.68it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.68it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.68it/s] 60%|██████ | 30/50 [00:08<00:05, 3.68it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.67it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.68it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.67it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.67it/s] 70%|███████ | 35/50 [00:09<00:04, 3.67it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.67it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.67it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.67it/s] 78%|███████▊ | 39/50 [00:10<00:02, 3.67it/s] 80%|████████ | 40/50 [00:10<00:02, 3.67it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.67it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.67it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.67it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.67it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.67it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.67it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.67it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.67it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.66it/s] 100%|██████████| 50/50 [00:13<00:00, 3.66it/s] 100%|██████████| 50/50 [00:13<00:00, 3.68it/s]Prediction
jbilcke/sdxl-tombraider:5bf6edfbf5741e9b5d021f75781294418a79fe3a54c63a7cce1dd07b97572c67IDychxfqtb2fxw6ydxeryb52vdmmStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 768
- prompt
- Lara walking in the streets of San Francisco, pastel houses, in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.85
- num_outputs
- 1
- guidance_scale
- 18.25
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- overexposed
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 768, "prompt": "Lara walking in the streets of San Francisco, pastel houses, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.85, "num_outputs": 1, "guidance_scale": 18.25, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed", "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"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run jbilcke/sdxl-tombraider using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jbilcke/sdxl-tombraider:5bf6edfbf5741e9b5d021f75781294418a79fe3a54c63a7cce1dd07b97572c67", { input: { width: 1024, height: 768, prompt: "Lara walking in the streets of San Francisco, pastel houses, in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.85, num_outputs: 1, guidance_scale: 18.25, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "overexposed", 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 jbilcke/sdxl-tombraider using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jbilcke/sdxl-tombraider:5bf6edfbf5741e9b5d021f75781294418a79fe3a54c63a7cce1dd07b97572c67", input={ "width": 1024, "height": 768, "prompt": "Lara walking in the streets of San Francisco, pastel houses, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.85, "num_outputs": 1, "guidance_scale": 18.25, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "overexposed", "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 jbilcke/sdxl-tombraider 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": "jbilcke/sdxl-tombraider:5bf6edfbf5741e9b5d021f75781294418a79fe3a54c63a7cce1dd07b97572c67", "input": { "width": 1024, "height": 768, "prompt": "Lara walking in the streets of San Francisco, pastel houses, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.85, "num_outputs": 1, "guidance_scale": 18.25, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed", "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": "2023-09-07T23:02:39.852558Z", "created_at": "2023-09-07T23:01:29.083465Z", "data_removed": false, "error": null, "id": "ychxfqtb2fxw6ydxeryb52vdmm", "input": { "width": 1024, "height": 768, "prompt": "Lara walking in the streets of San Francisco, pastel houses, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.85, "num_outputs": 1, "guidance_scale": 18.25, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 49299\nPrompt: Lara walking in the streets of San Francisco, pastel houses, in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:35, 1.38it/s]\n 4%|▍ | 2/50 [00:00<00:20, 2.39it/s]\n 6%|▌ | 3/50 [00:01<00:15, 3.12it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.64it/s]\n 10%|█ | 5/50 [00:01<00:11, 4.01it/s]\n 12%|█▏ | 6/50 [00:01<00:10, 4.28it/s]\n 14%|█▍ | 7/50 [00:01<00:09, 4.46it/s]\n 16%|█▌ | 8/50 [00:02<00:09, 4.59it/s]\n 18%|█▊ | 9/50 [00:02<00:08, 4.68it/s]\n 20%|██ | 10/50 [00:02<00:08, 4.75it/s]\n 22%|██▏ | 11/50 [00:02<00:08, 4.79it/s]\n 24%|██▍ | 12/50 [00:02<00:07, 4.83it/s]\n 26%|██▌ | 13/50 [00:03<00:07, 4.85it/s]\n 28%|██▊ | 14/50 [00:03<00:07, 4.86it/s]\n 30%|███ | 15/50 [00:03<00:07, 4.86it/s]\n 32%|███▏ | 16/50 [00:03<00:06, 4.88it/s]\n 34%|███▍ | 17/50 [00:03<00:06, 4.88it/s]\n 36%|███▌ | 18/50 [00:04<00:06, 4.88it/s]\n 38%|███▊ | 19/50 [00:04<00:06, 4.89it/s]\n 40%|████ | 20/50 [00:04<00:06, 4.89it/s]\n 42%|████▏ | 21/50 [00:04<00:05, 4.88it/s]\n 44%|████▍ | 22/50 [00:05<00:05, 4.89it/s]\n 46%|████▌ | 23/50 [00:05<00:05, 4.90it/s]\n 48%|████▊ | 24/50 [00:05<00:05, 4.90it/s]\n 50%|█████ | 25/50 [00:05<00:05, 4.91it/s]\n 52%|█████▏ | 26/50 [00:05<00:04, 4.91it/s]\n 54%|█████▍ | 27/50 [00:06<00:04, 4.92it/s]\n 56%|█████▌ | 28/50 [00:06<00:04, 4.92it/s]\n 58%|█████▊ | 29/50 [00:06<00:04, 4.92it/s]\n 60%|██████ | 30/50 [00:06<00:04, 4.92it/s]\n 62%|██████▏ | 31/50 [00:06<00:03, 4.91it/s]\n 64%|██████▍ | 32/50 [00:07<00:03, 4.91it/s]\n 66%|██████▌ | 33/50 [00:07<00:03, 4.91it/s]\n 68%|██████▊ | 34/50 [00:07<00:03, 4.91it/s]\n 70%|███████ | 35/50 [00:07<00:03, 4.91it/s]\n 72%|███████▏ | 36/50 [00:07<00:02, 4.91it/s]\n 74%|███████▍ | 37/50 [00:08<00:02, 4.91it/s]\n 76%|███████▌ | 38/50 [00:08<00:02, 4.91it/s]\n 78%|███████▊ | 39/50 [00:08<00:02, 4.85it/s]\n 80%|████████ | 40/50 [00:08<00:02, 4.85it/s]\n 82%|████████▏ | 41/50 [00:08<00:01, 4.86it/s]\n 84%|████████▍ | 42/50 [00:09<00:01, 4.88it/s]\n 86%|████████▌ | 43/50 [00:09<00:01, 4.89it/s]\n 88%|████████▊ | 44/50 [00:09<00:01, 4.90it/s]\n 90%|█████████ | 45/50 [00:09<00:01, 4.90it/s]\n 92%|█████████▏| 46/50 [00:09<00:00, 4.90it/s]\n 94%|█████████▍| 47/50 [00:10<00:00, 4.90it/s]\n 96%|█████████▌| 48/50 [00:10<00:00, 4.91it/s]\n 98%|█████████▊| 49/50 [00:10<00:00, 4.91it/s]\n100%|██████████| 50/50 [00:10<00:00, 4.91it/s]\n100%|██████████| 50/50 [00:10<00:00, 4.66it/s]", "metrics": { "predict_time": 13.125583, "total_time": 70.769093 }, "output": [ "https://replicate.delivery/pbxt/jSPrbAf8uWU5dyUThFNYbaxyc4thJbFKfpSplDyeS8XdEBEjA/out-0.png" ], "started_at": "2023-09-07T23:02:26.726975Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ychxfqtb2fxw6ydxeryb52vdmm", "cancel": "https://api.replicate.com/v1/predictions/ychxfqtb2fxw6ydxeryb52vdmm/cancel" }, "version": "5bf6edfbf5741e9b5d021f75781294418a79fe3a54c63a7cce1dd07b97572c67" }Generated inUsing seed: 49299 Prompt: Lara walking in the streets of San Francisco, pastel houses, in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:35, 1.38it/s] 4%|▍ | 2/50 [00:00<00:20, 2.39it/s] 6%|▌ | 3/50 [00:01<00:15, 3.12it/s] 8%|▊ | 4/50 [00:01<00:12, 3.64it/s] 10%|█ | 5/50 [00:01<00:11, 4.01it/s] 12%|█▏ | 6/50 [00:01<00:10, 4.28it/s] 14%|█▍ | 7/50 [00:01<00:09, 4.46it/s] 16%|█▌ | 8/50 [00:02<00:09, 4.59it/s] 18%|█▊ | 9/50 [00:02<00:08, 4.68it/s] 20%|██ | 10/50 [00:02<00:08, 4.75it/s] 22%|██▏ | 11/50 [00:02<00:08, 4.79it/s] 24%|██▍ | 12/50 [00:02<00:07, 4.83it/s] 26%|██▌ | 13/50 [00:03<00:07, 4.85it/s] 28%|██▊ | 14/50 [00:03<00:07, 4.86it/s] 30%|███ | 15/50 [00:03<00:07, 4.86it/s] 32%|███▏ | 16/50 [00:03<00:06, 4.88it/s] 34%|███▍ | 17/50 [00:03<00:06, 4.88it/s] 36%|███▌ | 18/50 [00:04<00:06, 4.88it/s] 38%|███▊ | 19/50 [00:04<00:06, 4.89it/s] 40%|████ | 20/50 [00:04<00:06, 4.89it/s] 42%|████▏ | 21/50 [00:04<00:05, 4.88it/s] 44%|████▍ | 22/50 [00:05<00:05, 4.89it/s] 46%|████▌ | 23/50 [00:05<00:05, 4.90it/s] 48%|████▊ | 24/50 [00:05<00:05, 4.90it/s] 50%|█████ | 25/50 [00:05<00:05, 4.91it/s] 52%|█████▏ | 26/50 [00:05<00:04, 4.91it/s] 54%|█████▍ | 27/50 [00:06<00:04, 4.92it/s] 56%|█████▌ | 28/50 [00:06<00:04, 4.92it/s] 58%|█████▊ | 29/50 [00:06<00:04, 4.92it/s] 60%|██████ | 30/50 [00:06<00:04, 4.92it/s] 62%|██████▏ | 31/50 [00:06<00:03, 4.91it/s] 64%|██████▍ | 32/50 [00:07<00:03, 4.91it/s] 66%|██████▌ | 33/50 [00:07<00:03, 4.91it/s] 68%|██████▊ | 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