nasserfq / andalusian-style
A simple SDXL model trained on artistic patterns in the Andalusian style. (Updated 1 year, 6 months ago)
- Public
- 46 runs
- SDXL fine-tune
Prediction
nasserfq/andalusian-style:2abe58a14fd028b05da062c50dda299752d941cee99a5dc8b21deda9b6d0b1f6IDtmotk4lbmbzqpiljd7ytrsywsaStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- In the style of Andalusia, a house in the desert.
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "In the style of Andalusia, a house in the desert.", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "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 nasserfq/andalusian-style using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "nasserfq/andalusian-style:2abe58a14fd028b05da062c50dda299752d941cee99a5dc8b21deda9b6d0b1f6", { input: { width: 1024, height: 1024, prompt: "In the style of Andalusia, a house in the desert.", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, 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 nasserfq/andalusian-style using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "nasserfq/andalusian-style:2abe58a14fd028b05da062c50dda299752d941cee99a5dc8b21deda9b6d0b1f6", input={ "width": 1024, "height": 1024, "prompt": "In the style of Andalusia, a house in the desert.", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "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 nasserfq/andalusian-style 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": "nasserfq/andalusian-style:2abe58a14fd028b05da062c50dda299752d941cee99a5dc8b21deda9b6d0b1f6", "input": { "width": 1024, "height": 1024, "prompt": "In the style of Andalusia, a house in the desert.", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "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": "2023-12-03T06:36:36.563942Z", "created_at": "2023-12-03T06:36:17.307462Z", "data_removed": false, "error": null, "id": "tmotk4lbmbzqpiljd7ytrsywsa", "input": { "width": 1024, "height": 1024, "prompt": "In the style of Andalusia, a house in the desert.", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 58672\nEnsuring enough disk space...\nFree disk space: 1602148089856\nDownloading weights: https://replicate.delivery/pbxt/fltUDd9QKUQURi9wWqyNxvZM81FLFQpUMS5rxX6ru6wfBdejA/trained_model.tar\nb'Downloaded 186 MB bytes in 1.562s (119 MB/s)\\nExtracted 186 MB in 0.057s (3.3 GB/s)\\n'\nDownloaded weights in 1.830418586730957 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: In the style of Andalusia, a house in the desert.\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.66it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.66it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.66it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.65it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.65it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.65it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.65it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.65it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.64it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.65it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.66it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.66it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.66it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.66it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.66it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.66it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.66it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.66it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.66it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.66it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.65it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.65it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.65it/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, 3.65it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.65it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.65it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.65it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.65it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.65it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.65it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.65it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.65it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.65it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.65it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.65it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.65it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.65it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.65it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.65it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.65it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.65it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.65it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.65it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.65it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.65it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.65it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.65it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.65it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.65it/s]", "metrics": { "predict_time": 17.278309, "total_time": 19.25648 }, "output": [ "https://replicate.delivery/pbxt/eHExhhWfZFhbhk9Tu0HsGJbeft5P5vFfCvFR2hjTxI4heTnfIA/out-0.png" ], "started_at": "2023-12-03T06:36:19.285633Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/tmotk4lbmbzqpiljd7ytrsywsa", "cancel": "https://api.replicate.com/v1/predictions/tmotk4lbmbzqpiljd7ytrsywsa/cancel" }, "version": "2abe58a14fd028b05da062c50dda299752d941cee99a5dc8b21deda9b6d0b1f6" }Generated inUsing seed: 58672 Ensuring enough disk space... Free disk space: 1602148089856 Downloading weights: https://replicate.delivery/pbxt/fltUDd9QKUQURi9wWqyNxvZM81FLFQpUMS5rxX6ru6wfBdejA/trained_model.tar b'Downloaded 186 MB bytes in 1.562s (119 MB/s)\nExtracted 186 MB in 0.057s (3.3 GB/s)\n' Downloaded weights in 1.830418586730957 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: In the style of Andalusia, a house in the desert. txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.66it/s] 4%|▍ | 2/50 [00:00<00:13, 3.66it/s] 6%|▌ | 3/50 [00:00<00:12, 3.66it/s] 8%|▊ | 4/50 [00:01<00:12, 3.65it/s] 10%|█ | 5/50 [00:01<00:12, 3.65it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.65it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.65it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.65it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.64it/s] 20%|██ | 10/50 [00:02<00:10, 3.65it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.66it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.66it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.66it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.66it/s] 30%|███ | 15/50 [00:04<00:09, 3.66it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.66it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.66it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.66it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.66it/s] 40%|████ | 20/50 [00:05<00:08, 3.66it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.65it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.65it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.65it/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, 3.65it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.65it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.65it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.65it/s] 60%|██████ | 30/50 [00:08<00:05, 3.65it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.65it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.65it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.65it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.65it/s] 70%|███████ | 35/50 [00:09<00:04, 3.65it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.65it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.65it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.65it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.65it/s] 80%|████████ | 40/50 [00:10<00:02, 3.65it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.65it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.65it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.65it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.65it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.65it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.65it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.65it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.65it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.65it/s] 100%|██████████| 50/50 [00:13<00:00, 3.65it/s] 100%|██████████| 50/50 [00:13<00:00, 3.65it/s]Prediction
nasserfq/andalusian-style:2abe58a14fd028b05da062c50dda299752d941cee99a5dc8b21deda9b6d0b1f6IDct3mdxtbihp5vomoi663tujhjmStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- In the style of Andalusia, a green and white villa.
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "In the style of Andalusia, a green and white villa.", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "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 nasserfq/andalusian-style using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "nasserfq/andalusian-style:2abe58a14fd028b05da062c50dda299752d941cee99a5dc8b21deda9b6d0b1f6", { input: { width: 1024, height: 1024, prompt: "In the style of Andalusia, a green and white villa.", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, 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 nasserfq/andalusian-style using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "nasserfq/andalusian-style:2abe58a14fd028b05da062c50dda299752d941cee99a5dc8b21deda9b6d0b1f6", input={ "width": 1024, "height": 1024, "prompt": "In the style of Andalusia, a green and white villa.", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "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 nasserfq/andalusian-style 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": "nasserfq/andalusian-style:2abe58a14fd028b05da062c50dda299752d941cee99a5dc8b21deda9b6d0b1f6", "input": { "width": 1024, "height": 1024, "prompt": "In the style of Andalusia, a green and white villa.", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "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": "2023-12-03T06:38:43.660656Z", "created_at": "2023-12-03T06:38:28.294153Z", "data_removed": false, "error": null, "id": "ct3mdxtbihp5vomoi663tujhjm", "input": { "width": 1024, "height": 1024, "prompt": "In the style of Andalusia, a green and white villa.", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 11808\nskipping loading .. weights already loaded\nPrompt: In the style of Andalusia, a green and white villa.\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.69it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.68it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.67it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.66it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.66it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.66it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.66it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.67it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.67it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.67it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.67it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.67it/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, 3.67it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.67it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.67it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.67it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.67it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.67it/s]\n 58%|█████▊ | 29/50 [00:07<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 [00:08<00:04, 3.67it/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, 3.67it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.67it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.67it/s]", "metrics": { "predict_time": 15.359886, "total_time": 15.366503 }, "output": [ "https://replicate.delivery/pbxt/QwDYybwc9AaIDtxzylW2pYeXpecf1ccFnV7876R44pOnj68jA/out-0.png" ], "started_at": "2023-12-03T06:38:28.300770Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ct3mdxtbihp5vomoi663tujhjm", "cancel": "https://api.replicate.com/v1/predictions/ct3mdxtbihp5vomoi663tujhjm/cancel" }, "version": "2abe58a14fd028b05da062c50dda299752d941cee99a5dc8b21deda9b6d0b1f6" }Generated inUsing seed: 11808 skipping loading .. weights already loaded Prompt: In the style of Andalusia, a green and white villa. txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.69it/s] 4%|▍ | 2/50 [00:00<00:13, 3.68it/s] 6%|▌ | 3/50 [00:00<00:12, 3.67it/s] 8%|▊ | 4/50 [00:01<00:12, 3.66it/s] 10%|█ | 5/50 [00:01<00:12, 3.66it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.66it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.66it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.67it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.67it/s] 20%|██ | 10/50 [00:02<00:10, 3.67it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.67it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.67it/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, 3.67it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.67it/s] 50%|█████ | 25/50 [00:06<00:06, 3.67it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.67it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.67it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.67it/s] 58%|█████▊ | 29/50 [00:07<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 [00:08<00:04, 3.67it/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, 3.67it/s] 100%|██████████| 50/50 [00:13<00:00, 3.67it/s] 100%|██████████| 50/50 [00:13<00:00, 3.67it/s]Prediction
nasserfq/andalusian-style:2abe58a14fd028b05da062c50dda299752d941cee99a5dc8b21deda9b6d0b1f6IDlmzz62db6e2vfdtvvxx3hbkmx4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- In the style of Andalusia, picture a house that mixes old and new styles. It's painted in a special purple color and has traditional white walls. The purple adds a bright and unusual touch to the house, making it stand out in the surrounding area.
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- 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": "In the style of Andalusia, picture a house that mixes old and new styles. It's painted in a special purple color and has traditional white walls. The purple adds a bright and unusual touch to the house, making it stand out in the surrounding area.", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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 nasserfq/andalusian-style using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "nasserfq/andalusian-style:2abe58a14fd028b05da062c50dda299752d941cee99a5dc8b21deda9b6d0b1f6", { input: { width: 1024, height: 1024, prompt: "In the style of Andalusia, picture a house that mixes old and new styles. It's painted in a special purple color and has traditional white walls. The purple adds a bright and unusual touch to the house, making it stand out in the surrounding area.", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, 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 nasserfq/andalusian-style using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "nasserfq/andalusian-style:2abe58a14fd028b05da062c50dda299752d941cee99a5dc8b21deda9b6d0b1f6", input={ "width": 1024, "height": 1024, "prompt": "In the style of Andalusia, picture a house that mixes old and new styles. It's painted in a special purple color and has traditional white walls. The purple adds a bright and unusual touch to the house, making it stand out in the surrounding area.", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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 nasserfq/andalusian-style 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": "nasserfq/andalusian-style:2abe58a14fd028b05da062c50dda299752d941cee99a5dc8b21deda9b6d0b1f6", "input": { "width": 1024, "height": 1024, "prompt": "In the style of Andalusia, picture a house that mixes old and new styles. It\'s painted in a special purple color and has traditional white walls. The purple adds a bright and unusual touch to the house, making it stand out in the surrounding area.", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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": "2023-12-03T06:46:57.582712Z", "created_at": "2023-12-03T06:46:14.145932Z", "data_removed": false, "error": null, "id": "lmzz62db6e2vfdtvvxx3hbkmx4", "input": { "width": 1024, "height": 1024, "prompt": "In the style of Andalusia, picture a house that mixes old and new styles. It's painted in a special purple color and has traditional white walls. The purple adds a bright and unusual touch to the house, making it stand out in the surrounding area.", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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: 16478\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: In the style of Andalusia, picture a house that mixes old and new styles. It's painted in a special purple color and has traditional white walls. The purple adds a bright and unusual touch to the house, making it stand out in the surrounding area.\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.69it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.69it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.69it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.69it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.70it/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, 3.69it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.69it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.69it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.69it/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, 3.68it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.69it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.68it/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, 3.67it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.67it/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 [00:07<00:05, 3.67it/s]\n 58%|█████▊ | 29/50 [00:07<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 [00:08<00:04, 3.67it/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, 3.67it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.67it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.68it/s]", "metrics": { "predict_time": 15.763455, "total_time": 43.43678 }, "output": [ "https://replicate.delivery/pbxt/229GGYkY2y4fB6RUhsOoAuiN2ixdkhjm2kTDfG7yvdOhZdejA/out-0.png" ], "started_at": "2023-12-03T06:46:41.819257Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/lmzz62db6e2vfdtvvxx3hbkmx4", "cancel": "https://api.replicate.com/v1/predictions/lmzz62db6e2vfdtvvxx3hbkmx4/cancel" }, "version": "2abe58a14fd028b05da062c50dda299752d941cee99a5dc8b21deda9b6d0b1f6" }Generated inUsing seed: 16478 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: In the style of Andalusia, picture a house that mixes old and new styles. It's painted in a special purple color and has traditional white walls. The purple adds a bright and unusual touch to the house, making it stand out in the surrounding area. txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.69it/s] 4%|▍ | 2/50 [00:00<00:13, 3.69it/s] 6%|▌ | 3/50 [00:00<00:12, 3.69it/s] 8%|▊ | 4/50 [00:01<00:12, 3.69it/s] 10%|█ | 5/50 [00:01<00:12, 3.70it/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, 3.69it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.69it/s] 20%|██ | 10/50 [00:02<00:10, 3.69it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.69it/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, 3.68it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.69it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.68it/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, 3.67it/s] 50%|█████ | 25/50 [00:06<00:06, 3.67it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.68it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.67it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.67it/s] 58%|█████▊ | 29/50 [00:07<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 [00:08<00:04, 3.67it/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, 3.67it/s] 100%|██████████| 50/50 [00:13<00:00, 3.67it/s] 100%|██████████| 50/50 [00:13<00:00, 3.68it/s]Prediction
nasserfq/andalusian-style:2abe58a14fd028b05da062c50dda299752d941cee99a5dc8b21deda9b6d0b1f6IDyadqd4lbhacjye3xz6emsaf5gyStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- Generate an image featuring a villa designed in the Andalusian style.
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- 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": "Generate an image featuring a villa designed in the Andalusian style.", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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 nasserfq/andalusian-style using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "nasserfq/andalusian-style:2abe58a14fd028b05da062c50dda299752d941cee99a5dc8b21deda9b6d0b1f6", { input: { width: 1024, height: 1024, prompt: "Generate an image featuring a villa designed in the Andalusian style.", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, 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 nasserfq/andalusian-style using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "nasserfq/andalusian-style:2abe58a14fd028b05da062c50dda299752d941cee99a5dc8b21deda9b6d0b1f6", input={ "width": 1024, "height": 1024, "prompt": "Generate an image featuring a villa designed in the Andalusian style.", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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 nasserfq/andalusian-style 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": "nasserfq/andalusian-style:2abe58a14fd028b05da062c50dda299752d941cee99a5dc8b21deda9b6d0b1f6", "input": { "width": 1024, "height": 1024, "prompt": "Generate an image featuring a villa designed in the Andalusian style.", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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": "2023-12-03T06:53:52.929624Z", "created_at": "2023-12-03T06:53:35.454946Z", "data_removed": false, "error": null, "id": "yadqd4lbhacjye3xz6emsaf5gy", "input": { "width": 1024, "height": 1024, "prompt": "Generate an image featuring a villa designed in the Andalusian style.", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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: 62564\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: Generate an image featuring a villa designed in the Andalusian style.\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.69it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.67it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.67it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.66it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.66it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.66it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.65it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.65it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.66it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.66it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.67it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.67it/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, 3.66it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.66it/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 [00:06<00:07, 3.66it/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, 3.66it/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 [00:09<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, 3.65it/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, 3.66it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.66it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.66it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.66it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.66it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.66it/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, 3.66it/s]", "metrics": { "predict_time": 15.726054, "total_time": 17.474678 }, "output": [ "https://replicate.delivery/pbxt/PwVgzAEmmhpLCVEoTp3xx2w9SwLuujFfxS7kG5IxSyaAwOfRA/out-0.png" ], "started_at": "2023-12-03T06:53:37.203570Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/yadqd4lbhacjye3xz6emsaf5gy", "cancel": "https://api.replicate.com/v1/predictions/yadqd4lbhacjye3xz6emsaf5gy/cancel" }, "version": "2abe58a14fd028b05da062c50dda299752d941cee99a5dc8b21deda9b6d0b1f6" }Generated inUsing seed: 62564 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: Generate an image featuring a villa designed in the Andalusian style. txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.69it/s] 4%|▍ | 2/50 [00:00<00:13, 3.67it/s] 6%|▌ | 3/50 [00:00<00:12, 3.67it/s] 8%|▊ | 4/50 [00:01<00:12, 3.66it/s] 10%|█ | 5/50 [00:01<00:12, 3.66it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.66it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.65it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.65it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.66it/s] 20%|██ | 10/50 [00:02<00:10, 3.66it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.67it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.67it/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, 3.66it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.66it/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 [00:06<00:07, 3.66it/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, 3.66it/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 [00:09<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, 3.65it/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, 3.66it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.66it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.66it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.66it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.66it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.66it/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, 3.66it/s]Prediction
nasserfq/andalusian-style:2abe58a14fd028b05da062c50dda299752d941cee99a5dc8b21deda9b6d0b1f6IDtpyvx5dbvve4f7wp6izshoyxhyStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- In the style of Andalusia, a white horse.
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- 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": "In the style of Andalusia, a white horse.", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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 nasserfq/andalusian-style using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "nasserfq/andalusian-style:2abe58a14fd028b05da062c50dda299752d941cee99a5dc8b21deda9b6d0b1f6", { input: { width: 1024, height: 1024, prompt: "In the style of Andalusia, a white horse.", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, 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 nasserfq/andalusian-style using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "nasserfq/andalusian-style:2abe58a14fd028b05da062c50dda299752d941cee99a5dc8b21deda9b6d0b1f6", input={ "width": 1024, "height": 1024, "prompt": "In the style of Andalusia, a white horse.", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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 nasserfq/andalusian-style 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": "nasserfq/andalusian-style:2abe58a14fd028b05da062c50dda299752d941cee99a5dc8b21deda9b6d0b1f6", "input": { "width": 1024, "height": 1024, "prompt": "In the style of Andalusia, a white horse.", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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": "2023-12-03T06:54:47.966672Z", "created_at": "2023-12-03T06:54:19.975577Z", "data_removed": false, "error": null, "id": "tpyvx5dbvve4f7wp6izshoyxhy", "input": { "width": 1024, "height": 1024, "prompt": "In the style of Andalusia, a white horse.", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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: 41184\nEnsuring enough disk space...\nFree disk space: 2440216399872\nDownloading weights: https://replicate.delivery/pbxt/fltUDd9QKUQURi9wWqyNxvZM81FLFQpUMS5rxX6ru6wfBdejA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.070s (2.7 GB/s)\\nExtracted 186 MB in 0.064s (2.9 GB/s)\\n'\nDownloaded weights in 0.34466028213500977 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: In the style of Andalusia, a white horse.\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.71it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.69it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.69it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.69it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.68it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.68it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.69it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.69it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.69it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.69it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.69it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.69it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.70it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.70it/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, 3.69it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.69it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.69it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.69it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.69it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.69it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.69it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.69it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.69it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.69it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.69it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.69it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.69it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.68it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.68it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.68it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.68it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.68it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.69it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.68it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.69it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.69it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.68it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.68it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.68it/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, 3.67it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.69it/s]", "metrics": { "predict_time": 15.766665, "total_time": 27.991095 }, "output": [ "https://replicate.delivery/pbxt/YflhPkfDX3pNSURa8pPabMgx3gbrcgk6ISNDblWkUxu3gdejA/out-0.png" ], "started_at": "2023-12-03T06:54:32.200007Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/tpyvx5dbvve4f7wp6izshoyxhy", "cancel": "https://api.replicate.com/v1/predictions/tpyvx5dbvve4f7wp6izshoyxhy/cancel" }, "version": "2abe58a14fd028b05da062c50dda299752d941cee99a5dc8b21deda9b6d0b1f6" }Generated inUsing seed: 41184 Ensuring enough disk space... Free disk space: 2440216399872 Downloading weights: https://replicate.delivery/pbxt/fltUDd9QKUQURi9wWqyNxvZM81FLFQpUMS5rxX6ru6wfBdejA/trained_model.tar b'Downloaded 186 MB bytes in 0.070s (2.7 GB/s)\nExtracted 186 MB in 0.064s (2.9 GB/s)\n' Downloaded weights in 0.34466028213500977 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: In the style of Andalusia, a white horse. txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.71it/s] 4%|▍ | 2/50 [00:00<00:13, 3.69it/s] 6%|▌ | 3/50 [00:00<00:12, 3.69it/s] 8%|▊ | 4/50 [00:01<00:12, 3.69it/s] 10%|█ | 5/50 [00:01<00:12, 3.68it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.68it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.69it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.69it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.69it/s] 20%|██ | 10/50 [00:02<00:10, 3.69it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.69it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.69it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.70it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.70it/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, 3.69it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.69it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.69it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.69it/s] 50%|█████ | 25/50 [00:06<00:06, 3.69it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.69it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.69it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.69it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.69it/s] 60%|██████ | 30/50 [00:08<00:05, 3.69it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.69it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.69it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.69it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.68it/s] 70%|███████ | 35/50 [00:09<00:04, 3.68it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.68it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.68it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.68it/s] 78%|███████▊ | 39/50 [00:10<00:02, 3.69it/s] 80%|████████ | 40/50 [00:10<00:02, 3.68it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.69it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.69it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.68it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.68it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.68it/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, 3.67it/s] 100%|██████████| 50/50 [00:13<00:00, 3.69it/s]Prediction
nasserfq/andalusian-style:2abe58a14fd028b05da062c50dda299752d941cee99a5dc8b21deda9b6d0b1f6IDmsqos63bsckvmwgji7ho4qoxcaStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- Andalusian,, traditional door.
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- 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": "Andalusian,, traditional door.", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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 nasserfq/andalusian-style using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "nasserfq/andalusian-style:2abe58a14fd028b05da062c50dda299752d941cee99a5dc8b21deda9b6d0b1f6", { input: { width: 1024, height: 1024, prompt: "Andalusian,, traditional door.", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, 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 nasserfq/andalusian-style using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "nasserfq/andalusian-style:2abe58a14fd028b05da062c50dda299752d941cee99a5dc8b21deda9b6d0b1f6", input={ "width": 1024, "height": 1024, "prompt": "Andalusian,, traditional door.", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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 nasserfq/andalusian-style 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": "nasserfq/andalusian-style:2abe58a14fd028b05da062c50dda299752d941cee99a5dc8b21deda9b6d0b1f6", "input": { "width": 1024, "height": 1024, "prompt": "Andalusian,, traditional door.", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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": "2023-12-03T07:08:08.688661Z", "created_at": "2023-12-03T07:07:49.414385Z", "data_removed": false, "error": null, "id": "msqos63bsckvmwgji7ho4qoxca", "input": { "width": 1024, "height": 1024, "prompt": "Andalusian,, traditional door.", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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: 63597\nEnsuring enough disk space...\nFree disk space: 2221039599616\nDownloading weights: https://replicate.delivery/pbxt/fltUDd9QKUQURi9wWqyNxvZM81FLFQpUMS5rxX6ru6wfBdejA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.344s (540 MB/s)\\nExtracted 186 MB in 0.053s (3.5 GB/s)\\n'\nDownloaded weights in 0.5610873699188232 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: Andalusian,, traditional door.\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.63it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.62it/s]\n 6%|▌ | 3/50 [00:00<00:13, 3.56it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.59it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.60it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.61it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.62it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.62it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.63it/s]\n 20%|██ | 10/50 [00:02<00:11, 3.63it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.63it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.63it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.62it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.63it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.63it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.62it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.62it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.62it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.62it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.62it/s]\n 42%|████▏ | 21/50 [00:05<00:08, 3.61it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.61it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.61it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.61it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.61it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.61it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.61it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.61it/s]\n 58%|█████▊ | 29/50 [00:08<00:05, 3.61it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.61it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.61it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.61it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.61it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.61it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.61it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.61it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.61it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.61it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.61it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.61it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.61it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.61it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.61it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.60it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.60it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.61it/s]\n 94%|█████████▍| 47/50 [00:13<00:00, 3.61it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.61it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.61it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.61it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.61it/s]", "metrics": { "predict_time": 17.239847, "total_time": 19.274276 }, "output": [ "https://replicate.delivery/pbxt/agos3fr1yj1BbSswuXrXC76TTpj6Ok5gCqcXuBB6Wn1r2OfRA/out-0.png" ], "started_at": "2023-12-03T07:07:51.448814Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/msqos63bsckvmwgji7ho4qoxca", "cancel": "https://api.replicate.com/v1/predictions/msqos63bsckvmwgji7ho4qoxca/cancel" }, "version": "2abe58a14fd028b05da062c50dda299752d941cee99a5dc8b21deda9b6d0b1f6" }Generated inUsing seed: 63597 Ensuring enough disk space... Free disk space: 2221039599616 Downloading weights: https://replicate.delivery/pbxt/fltUDd9QKUQURi9wWqyNxvZM81FLFQpUMS5rxX6ru6wfBdejA/trained_model.tar b'Downloaded 186 MB bytes in 0.344s (540 MB/s)\nExtracted 186 MB in 0.053s (3.5 GB/s)\n' Downloaded weights in 0.5610873699188232 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: Andalusian,, traditional door. txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.63it/s] 4%|▍ | 2/50 [00:00<00:13, 3.62it/s] 6%|▌ | 3/50 [00:00<00:13, 3.56it/s] 8%|▊ | 4/50 [00:01<00:12, 3.59it/s] 10%|█ | 5/50 [00:01<00:12, 3.60it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.61it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.62it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.62it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.63it/s] 20%|██ | 10/50 [00:02<00:11, 3.63it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.63it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.63it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.62it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.63it/s] 30%|███ | 15/50 [00:04<00:09, 3.63it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.62it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.62it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.62it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.62it/s] 40%|████ | 20/50 [00:05<00:08, 3.62it/s] 42%|████▏ | 21/50 [00:05<00:08, 3.61it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.61it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.61it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.61it/s] 50%|█████ | 25/50 [00:06<00:06, 3.61it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.61it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.61it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.61it/s] 58%|█████▊ | 29/50 [00:08<00:05, 3.61it/s] 60%|██████ | 30/50 [00:08<00:05, 3.61it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.61it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.61it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.61it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.61it/s] 70%|███████ | 35/50 [00:09<00:04, 3.61it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.61it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.61it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.61it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.61it/s] 80%|████████ | 40/50 [00:11<00:02, 3.61it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.61it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.61it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.61it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.60it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.60it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.61it/s] 94%|█████████▍| 47/50 [00:13<00:00, 3.61it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.61it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.61it/s] 100%|██████████| 50/50 [00:13<00:00, 3.61it/s] 100%|██████████| 50/50 [00:13<00:00, 3.61it/s]Prediction
nasserfq/andalusian-style:2abe58a14fd028b05da062c50dda299752d941cee99a5dc8b21deda9b6d0b1f6IDjiqv4ktb7pmdx4243oz6x4hh3uStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- Classic Traditional and Andalusian house
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- 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": "Classic Traditional and Andalusian house", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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 nasserfq/andalusian-style using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "nasserfq/andalusian-style:2abe58a14fd028b05da062c50dda299752d941cee99a5dc8b21deda9b6d0b1f6", { input: { width: 1024, height: 1024, prompt: "Classic Traditional and Andalusian house", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, 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 nasserfq/andalusian-style using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "nasserfq/andalusian-style:2abe58a14fd028b05da062c50dda299752d941cee99a5dc8b21deda9b6d0b1f6", input={ "width": 1024, "height": 1024, "prompt": "Classic Traditional and Andalusian house", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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 nasserfq/andalusian-style 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": "nasserfq/andalusian-style:2abe58a14fd028b05da062c50dda299752d941cee99a5dc8b21deda9b6d0b1f6", "input": { "width": 1024, "height": 1024, "prompt": "Classic Traditional and Andalusian house", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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": "2023-12-03T07:11:39.210959Z", "created_at": "2023-12-03T07:11:17.779569Z", "data_removed": false, "error": null, "id": "jiqv4ktb7pmdx4243oz6x4hh3u", "input": { "width": 1024, "height": 1024, "prompt": "Classic Traditional and Andalusian house", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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: 4205\nEnsuring enough disk space...\nFree disk space: 2135425490944\nDownloading weights: https://replicate.delivery/pbxt/fltUDd9QKUQURi9wWqyNxvZM81FLFQpUMS5rxX6ru6wfBdejA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.234s (796 MB/s)\\nExtracted 186 MB in 0.067s (2.8 GB/s)\\n'\nDownloaded weights in 0.4078834056854248 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: Classic Traditional and Andalusian house\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]/usr/local/lib/python3.9/site-packages/torch/nn/modules/conv.py:459: UserWarning: Applied workaround for CuDNN issue, install nvrtc.so (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:80.)\nreturn F.conv2d(input, weight, bias, self.stride,\n 2%|▏ | 1/50 [00:00<00:43, 1.13it/s]\n 4%|▍ | 2/50 [00:01<00:25, 1.91it/s]\n 6%|▌ | 3/50 [00:01<00:19, 2.45it/s]\n 8%|▊ | 4/50 [00:01<00:16, 2.82it/s]\n 10%|█ | 5/50 [00:01<00:14, 3.08it/s]\n 12%|█▏ | 6/50 [00:02<00:13, 3.26it/s]\n 14%|█▍ | 7/50 [00:02<00:12, 3.39it/s]\n 16%|█▌ | 8/50 [00:02<00:12, 3.47it/s]\n 18%|█▊ | 9/50 [00:03<00:11, 3.53it/s]\n 20%|██ | 10/50 [00:03<00:11, 3.57it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.61it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.62it/s]\n 26%|██▌ | 13/50 [00:04<00:10, 3.64it/s]\n 28%|██▊ | 14/50 [00:04<00:09, 3.65it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.65it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.66it/s]\n 34%|███▍ | 17/50 [00:05<00:09, 3.66it/s]\n 36%|███▌ | 18/50 [00:05<00:08, 3.66it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.67it/s]\n 40%|████ | 20/50 [00:06<00:08, 3.66it/s]\n 42%|████▏ | 21/50 [00:06<00:07, 3.66it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.66it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.66it/s]\n 48%|████▊ | 24/50 [00:07<00:07, 3.66it/s]\n 50%|█████ | 25/50 [00:07<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 [00:08<00:06, 3.66it/s]\n 58%|█████▊ | 29/50 [00:08<00:05, 3.66it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.66it/s]\n 62%|██████▏ | 31/50 [00:09<00:05, 3.66it/s]\n 64%|██████▍ | 32/50 [00:09<00:04, 3.66it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.66it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.66it/s]\n 70%|███████ | 35/50 [00:10<00:04, 3.66it/s]\n 72%|███████▏ | 36/50 [00:10<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 [00:11<00:03, 3.66it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.66it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.66it/s]\n 84%|████████▍ | 42/50 [00:12<00:02, 3.66it/s]\n 86%|████████▌ | 43/50 [00:12<00:01, 3.66it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.66it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.65it/s]\n 92%|█████████▏| 46/50 [00:13<00:01, 3.66it/s]\n 94%|█████████▍| 47/50 [00:13<00:00, 3.66it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.66it/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, 3.51it/s]", "metrics": { "predict_time": 17.769448, "total_time": 21.43139 }, "output": [ "https://replicate.delivery/pbxt/pOY4toYVCcrgJts52Lh0F0CefxBaf9HCqjpPyvvf2XarC35HB/out-0.png" ], "started_at": "2023-12-03T07:11:21.441511Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/jiqv4ktb7pmdx4243oz6x4hh3u", "cancel": "https://api.replicate.com/v1/predictions/jiqv4ktb7pmdx4243oz6x4hh3u/cancel" }, "version": "2abe58a14fd028b05da062c50dda299752d941cee99a5dc8b21deda9b6d0b1f6" }Generated inUsing seed: 4205 Ensuring enough disk space... Free disk space: 2135425490944 Downloading weights: https://replicate.delivery/pbxt/fltUDd9QKUQURi9wWqyNxvZM81FLFQpUMS5rxX6ru6wfBdejA/trained_model.tar b'Downloaded 186 MB bytes in 0.234s (796 MB/s)\nExtracted 186 MB in 0.067s (2.8 GB/s)\n' Downloaded weights in 0.4078834056854248 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: Classic Traditional and Andalusian house txt2img mode 0%| | 0/50 [00:00<?, ?it/s]/usr/local/lib/python3.9/site-packages/torch/nn/modules/conv.py:459: UserWarning: Applied workaround for CuDNN issue, install nvrtc.so (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:80.) return F.conv2d(input, weight, bias, self.stride, 2%|▏ | 1/50 [00:00<00:43, 1.13it/s] 4%|▍ | 2/50 [00:01<00:25, 1.91it/s] 6%|▌ | 3/50 [00:01<00:19, 2.45it/s] 8%|▊ | 4/50 [00:01<00:16, 2.82it/s] 10%|█ | 5/50 [00:01<00:14, 3.08it/s] 12%|█▏ | 6/50 [00:02<00:13, 3.26it/s] 14%|█▍ | 7/50 [00:02<00:12, 3.39it/s] 16%|█▌ | 8/50 [00:02<00:12, 3.47it/s] 18%|█▊ | 9/50 [00:03<00:11, 3.53it/s] 20%|██ | 10/50 [00:03<00:11, 3.57it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.61it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.62it/s] 26%|██▌ | 13/50 [00:04<00:10, 3.64it/s] 28%|██▊ | 14/50 [00:04<00:09, 3.65it/s] 30%|███ | 15/50 [00:04<00:09, 3.65it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.66it/s] 34%|███▍ | 17/50 [00:05<00:09, 3.66it/s] 36%|███▌ | 18/50 [00:05<00:08, 3.66it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.67it/s] 40%|████ | 20/50 [00:06<00:08, 3.66it/s] 42%|████▏ | 21/50 [00:06<00:07, 3.66it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.66it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.66it/s] 48%|████▊ | 24/50 [00:07<00:07, 3.66it/s] 50%|█████ | 25/50 [00:07<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 [00:08<00:06, 3.66it/s] 58%|█████▊ | 29/50 [00:08<00:05, 3.66it/s] 60%|██████ | 30/50 [00:08<00:05, 3.66it/s] 62%|██████▏ | 31/50 [00:09<00:05, 3.66it/s] 64%|██████▍ | 32/50 [00:09<00:04, 3.66it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.66it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.66it/s] 70%|███████ | 35/50 [00:10<00:04, 3.66it/s] 72%|███████▏ | 36/50 [00:10<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 [00:11<00:03, 3.66it/s] 80%|████████ | 40/50 [00:11<00:02, 3.66it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.66it/s] 84%|████████▍ | 42/50 [00:12<00:02, 3.66it/s] 86%|████████▌ | 43/50 [00:12<00:01, 3.66it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.66it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.65it/s] 92%|█████████▏| 46/50 [00:13<00:01, 3.66it/s] 94%|█████████▍| 47/50 [00:13<00:00, 3.66it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.66it/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, 3.51it/s]Prediction
nasserfq/andalusian-style:2abe58a14fd028b05da062c50dda299752d941cee99a5dc8b21deda9b6d0b1f6IDxtfroldbszfhompx3athgf6nxaStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- In the style of Andalusia, Alhambra palace in granada.
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- 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": "In the style of Andalusia, Alhambra palace in granada.", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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 nasserfq/andalusian-style using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "nasserfq/andalusian-style:2abe58a14fd028b05da062c50dda299752d941cee99a5dc8b21deda9b6d0b1f6", { input: { width: 1024, height: 1024, prompt: "In the style of Andalusia, Alhambra palace in granada.", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, 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 nasserfq/andalusian-style using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "nasserfq/andalusian-style:2abe58a14fd028b05da062c50dda299752d941cee99a5dc8b21deda9b6d0b1f6", input={ "width": 1024, "height": 1024, "prompt": "In the style of Andalusia, Alhambra palace in granada.", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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 nasserfq/andalusian-style 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": "nasserfq/andalusian-style:2abe58a14fd028b05da062c50dda299752d941cee99a5dc8b21deda9b6d0b1f6", "input": { "width": 1024, "height": 1024, "prompt": "In the style of Andalusia, Alhambra palace in granada.", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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": "2023-12-03T20:36:39.962189Z", "created_at": "2023-12-03T20:36:22.821440Z", "data_removed": false, "error": null, "id": "xtfroldbszfhompx3athgf6nxa", "input": { "width": 1024, "height": 1024, "prompt": "In the style of Andalusia, Alhambra palace in granada.", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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: 15748\nEnsuring enough disk space...\nFree disk space: 1471507922944\nDownloading weights: https://replicate.delivery/pbxt/fltUDd9QKUQURi9wWqyNxvZM81FLFQpUMS5rxX6ru6wfBdejA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.647s (288 MB/s)\\nExtracted 186 MB in 0.066s (2.8 GB/s)\\n'\nDownloaded weights in 0.8313889503479004 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: In the style of Andalusia, Alhambra palace in granada.\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.75it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.73it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.73it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.72it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.71it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.71it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.71it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.70it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.72it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.72it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.72it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.73it/s]\n 26%|██▌ | 13/50 [00:03<00:09, 3.73it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.73it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.73it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.73it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.73it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.73it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.73it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.73it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.73it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.73it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.73it/s]\n 48%|████▊ | 24/50 [00:06<00:06, 3.73it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.73it/s]\n 52%|█████▏ | 26/50 [00:06<00:06, 3.73it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.73it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.72it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.72it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.73it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.72it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.72it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.72it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.72it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.72it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.72it/s]\n 74%|███████▍ | 37/50 [00:09<00:03, 3.72it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.72it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.71it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.71it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.72it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.72it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.72it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.72it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.72it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.72it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.71it/s]\n 96%|█████████▌| 48/50 [00:12<00:00, 3.72it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.71it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.72it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.72it/s]", "metrics": { "predict_time": 16.257557, "total_time": 17.140749 }, "output": [ "https://replicate.delivery/pbxt/Ls4uDOnWOJa4AZwY8OVHXk28gO103tAgbv8Ow1moJyw1YqfIA/out-0.png" ], "started_at": "2023-12-03T20:36:23.704632Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/xtfroldbszfhompx3athgf6nxa", "cancel": "https://api.replicate.com/v1/predictions/xtfroldbszfhompx3athgf6nxa/cancel" }, "version": "2abe58a14fd028b05da062c50dda299752d941cee99a5dc8b21deda9b6d0b1f6" }Generated inUsing seed: 15748 Ensuring enough disk space... Free disk space: 1471507922944 Downloading weights: https://replicate.delivery/pbxt/fltUDd9QKUQURi9wWqyNxvZM81FLFQpUMS5rxX6ru6wfBdejA/trained_model.tar b'Downloaded 186 MB bytes in 0.647s (288 MB/s)\nExtracted 186 MB in 0.066s (2.8 GB/s)\n' Downloaded weights in 0.8313889503479004 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: In the style of Andalusia, Alhambra palace in granada. txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.75it/s] 4%|▍ | 2/50 [00:00<00:12, 3.73it/s] 6%|▌ | 3/50 [00:00<00:12, 3.73it/s] 8%|▊ | 4/50 [00:01<00:12, 3.72it/s] 10%|█ | 5/50 [00:01<00:12, 3.71it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.71it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.71it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.70it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.72it/s] 20%|██ | 10/50 [00:02<00:10, 3.72it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.72it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.73it/s] 26%|██▌ | 13/50 [00:03<00:09, 3.73it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.73it/s] 30%|███ | 15/50 [00:04<00:09, 3.73it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.73it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.73it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.73it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.73it/s] 40%|████ | 20/50 [00:05<00:08, 3.73it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.73it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.73it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.73it/s] 48%|████▊ | 24/50 [00:06<00:06, 3.73it/s] 50%|█████ | 25/50 [00:06<00:06, 3.73it/s] 52%|█████▏ | 26/50 [00:06<00:06, 3.73it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.73it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.72it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.72it/s] 60%|██████ | 30/50 [00:08<00:05, 3.73it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.72it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.72it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.72it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.72it/s] 70%|███████ | 35/50 [00:09<00:04, 3.72it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.72it/s] 74%|███████▍ | 37/50 [00:09<00:03, 3.72it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.72it/s] 78%|███████▊ | 39/50 [00:10<00:02, 3.71it/s] 80%|████████ | 40/50 [00:10<00:02, 3.71it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.72it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.72it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.72it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.72it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.72it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.72it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.71it/s] 96%|█████████▌| 48/50 [00:12<00:00, 3.72it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.71it/s] 100%|██████████| 50/50 [00:13<00:00, 3.72it/s] 100%|██████████| 50/50 [00:13<00:00, 3.72it/s]
Want to make some of these yourself?
Run this model