joanafeitoza
/
juliabeatriz
- Public
- 13 runs
-
H100
Prediction
joanafeitoza/juliabeatriz:4ce769e3069543c4ad0dec90cddb0df1001cb112fc9062bede921f151001fe42IDxw9zggy75xrma0cp0np97hb2hmStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- juliabeatriz
- go_fast
- lora_scale
- 1
- megapixels
- 1
- num_outputs
- 3
- aspect_ratio
- 4:5
- output_format
- png
- guidance_scale
- 2
- output_quality
- 100
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "image": "https://replicate.delivery/pbxt/MmcqARgXpfKq4N0jC9brJI8IQW6OEmlatZiY1VXCmL8keDzz/image%2057.png", "model": "dev", "prompt": "juliabeatriz ", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 3, "aspect_ratio": "4:5", "output_format": "png", "guidance_scale": 2, "output_quality": 100, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run joanafeitoza/juliabeatriz using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "joanafeitoza/juliabeatriz:4ce769e3069543c4ad0dec90cddb0df1001cb112fc9062bede921f151001fe42", { input: { image: "https://replicate.delivery/pbxt/MmcqARgXpfKq4N0jC9brJI8IQW6OEmlatZiY1VXCmL8keDzz/image%2057.png", model: "dev", prompt: "juliabeatriz ", go_fast: false, lora_scale: 1, megapixels: "1", num_outputs: 3, aspect_ratio: "4:5", output_format: "png", guidance_scale: 2, output_quality: 100, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 28 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run joanafeitoza/juliabeatriz using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "joanafeitoza/juliabeatriz:4ce769e3069543c4ad0dec90cddb0df1001cb112fc9062bede921f151001fe42", input={ "image": "https://replicate.delivery/pbxt/MmcqARgXpfKq4N0jC9brJI8IQW6OEmlatZiY1VXCmL8keDzz/image%2057.png", "model": "dev", "prompt": "juliabeatriz ", "go_fast": False, "lora_scale": 1, "megapixels": "1", "num_outputs": 3, "aspect_ratio": "4:5", "output_format": "png", "guidance_scale": 2, "output_quality": 100, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run joanafeitoza/juliabeatriz 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": "4ce769e3069543c4ad0dec90cddb0df1001cb112fc9062bede921f151001fe42", "input": { "image": "https://replicate.delivery/pbxt/MmcqARgXpfKq4N0jC9brJI8IQW6OEmlatZiY1VXCmL8keDzz/image%2057.png", "model": "dev", "prompt": "juliabeatriz ", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 3, "aspect_ratio": "4:5", "output_format": "png", "guidance_scale": 2, "output_quality": 100, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2025-04-05T10:09:01.777255Z", "created_at": "2025-04-05T10:08:57.135000Z", "data_removed": false, "error": null, "id": "xw9zggy75xrma0cp0np97hb2hm", "input": { "image": "https://replicate.delivery/pbxt/MmcqARgXpfKq4N0jC9brJI8IQW6OEmlatZiY1VXCmL8keDzz/image%2057.png", "model": "dev", "prompt": "juliabeatriz ", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 3, "aspect_ratio": "4:5", "output_format": "png", "guidance_scale": 2, "output_quality": 100, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "Loaded LoRAs in 0.55s\nUsing seed: 3061\nPrompt: juliabeatriz\nInput image size: 256x384\n[!] Resizing input image from 256x384 to 256x384\n[!] img2img mode\n 0%| | 0/23 [00:00<?, ?it/s]\n 4%|▍ | 1/23 [00:00<00:02, 8.44it/s]\n 9%|▊ | 2/23 [00:00<00:02, 7.48it/s]\n 13%|█▎ | 3/23 [00:00<00:02, 7.22it/s]\n 17%|█▋ | 4/23 [00:00<00:02, 7.10it/s]\n 22%|██▏ | 5/23 [00:00<00:02, 7.04it/s]\n 26%|██▌ | 6/23 [00:00<00:02, 7.01it/s]\n 30%|███ | 7/23 [00:00<00:02, 6.98it/s]\n 35%|███▍ | 8/23 [00:01<00:02, 6.96it/s]\n 39%|███▉ | 9/23 [00:01<00:02, 6.95it/s]\n 43%|████▎ | 10/23 [00:01<00:01, 6.95it/s]\n 48%|████▊ | 11/23 [00:01<00:01, 6.94it/s]\n 52%|█████▏ | 12/23 [00:01<00:01, 6.94it/s]\n 57%|█████▋ | 13/23 [00:01<00:01, 6.93it/s]\n 61%|██████ | 14/23 [00:01<00:01, 6.93it/s]\n 65%|██████▌ | 15/23 [00:02<00:01, 6.93it/s]\n 70%|██████▉ | 16/23 [00:02<00:01, 6.93it/s]\n 74%|███████▍ | 17/23 [00:02<00:00, 6.93it/s]\n 78%|███████▊ | 18/23 [00:02<00:00, 6.93it/s]\n 83%|████████▎ | 19/23 [00:02<00:00, 6.93it/s]\n 87%|████████▋ | 20/23 [00:02<00:00, 6.93it/s]\n 91%|█████████▏| 21/23 [00:03<00:00, 6.93it/s]\n 96%|█████████▌| 22/23 [00:03<00:00, 6.93it/s]\n100%|██████████| 23/23 [00:03<00:00, 6.93it/s]\n100%|██████████| 23/23 [00:03<00:00, 6.98it/s]\nTotal safe images: 3 out of 3", "metrics": { "predict_time": 4.597165633, "total_time": 4.642255 }, "output": [ "https://replicate.delivery/xezq/qSuafiIBTene0odY3TX3hf89O1Oid58EZSdKXBxuZjT3zsejC/out-0.png", "https://replicate.delivery/xezq/zxsGUwZYqgbyGVW1pUjXmcMtO3h5oJemZWvcjyFMMx3eMrfoA/out-1.png", "https://replicate.delivery/xezq/jeVaBrV6JeuyAULi65xY5rqQ9u6PB4fndX635HVZg1t7ZWfRB/out-2.png" ], "started_at": "2025-04-05T10:08:57.180089Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-l3ifycrmzjfvy6dpfbcqynspwh4njt6avwsmx7xlwow7bxaimn3a", "get": "https://api.replicate.com/v1/predictions/xw9zggy75xrma0cp0np97hb2hm", "cancel": "https://api.replicate.com/v1/predictions/xw9zggy75xrma0cp0np97hb2hm/cancel" }, "version": "4ce769e3069543c4ad0dec90cddb0df1001cb112fc9062bede921f151001fe42" }
Generated inLoaded LoRAs in 0.55s Using seed: 3061 Prompt: juliabeatriz Input image size: 256x384 [!] Resizing input image from 256x384 to 256x384 [!] img2img mode 0%| | 0/23 [00:00<?, ?it/s] 4%|▍ | 1/23 [00:00<00:02, 8.44it/s] 9%|▊ | 2/23 [00:00<00:02, 7.48it/s] 13%|█▎ | 3/23 [00:00<00:02, 7.22it/s] 17%|█▋ | 4/23 [00:00<00:02, 7.10it/s] 22%|██▏ | 5/23 [00:00<00:02, 7.04it/s] 26%|██▌ | 6/23 [00:00<00:02, 7.01it/s] 30%|███ | 7/23 [00:00<00:02, 6.98it/s] 35%|███▍ | 8/23 [00:01<00:02, 6.96it/s] 39%|███▉ | 9/23 [00:01<00:02, 6.95it/s] 43%|████▎ | 10/23 [00:01<00:01, 6.95it/s] 48%|████▊ | 11/23 [00:01<00:01, 6.94it/s] 52%|█████▏ | 12/23 [00:01<00:01, 6.94it/s] 57%|█████▋ | 13/23 [00:01<00:01, 6.93it/s] 61%|██████ | 14/23 [00:01<00:01, 6.93it/s] 65%|██████▌ | 15/23 [00:02<00:01, 6.93it/s] 70%|██████▉ | 16/23 [00:02<00:01, 6.93it/s] 74%|███████▍ | 17/23 [00:02<00:00, 6.93it/s] 78%|███████▊ | 18/23 [00:02<00:00, 6.93it/s] 83%|████████▎ | 19/23 [00:02<00:00, 6.93it/s] 87%|████████▋ | 20/23 [00:02<00:00, 6.93it/s] 91%|█████████▏| 21/23 [00:03<00:00, 6.93it/s] 96%|█████████▌| 22/23 [00:03<00:00, 6.93it/s] 100%|██████████| 23/23 [00:03<00:00, 6.93it/s] 100%|██████████| 23/23 [00:03<00:00, 6.98it/s] Total safe images: 3 out of 3
Prediction
joanafeitoza/juliabeatriz:4ce769e3069543c4ad0dec90cddb0df1001cb112fc9062bede921f151001fe42IDxw9zggy75xrma0cp0np97hb2hmStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- juliabeatriz
- go_fast
- lora_scale
- 1
- megapixels
- 1
- num_outputs
- 3
- aspect_ratio
- 4:5
- output_format
- png
- guidance_scale
- 2
- output_quality
- 100
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "image": "https://replicate.delivery/pbxt/MmcqARgXpfKq4N0jC9brJI8IQW6OEmlatZiY1VXCmL8keDzz/image%2057.png", "model": "dev", "prompt": "juliabeatriz ", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 3, "aspect_ratio": "4:5", "output_format": "png", "guidance_scale": 2, "output_quality": 100, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run joanafeitoza/juliabeatriz using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "joanafeitoza/juliabeatriz:4ce769e3069543c4ad0dec90cddb0df1001cb112fc9062bede921f151001fe42", { input: { image: "https://replicate.delivery/pbxt/MmcqARgXpfKq4N0jC9brJI8IQW6OEmlatZiY1VXCmL8keDzz/image%2057.png", model: "dev", prompt: "juliabeatriz ", go_fast: false, lora_scale: 1, megapixels: "1", num_outputs: 3, aspect_ratio: "4:5", output_format: "png", guidance_scale: 2, output_quality: 100, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 28 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run joanafeitoza/juliabeatriz using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "joanafeitoza/juliabeatriz:4ce769e3069543c4ad0dec90cddb0df1001cb112fc9062bede921f151001fe42", input={ "image": "https://replicate.delivery/pbxt/MmcqARgXpfKq4N0jC9brJI8IQW6OEmlatZiY1VXCmL8keDzz/image%2057.png", "model": "dev", "prompt": "juliabeatriz ", "go_fast": False, "lora_scale": 1, "megapixels": "1", "num_outputs": 3, "aspect_ratio": "4:5", "output_format": "png", "guidance_scale": 2, "output_quality": 100, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run joanafeitoza/juliabeatriz 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": "4ce769e3069543c4ad0dec90cddb0df1001cb112fc9062bede921f151001fe42", "input": { "image": "https://replicate.delivery/pbxt/MmcqARgXpfKq4N0jC9brJI8IQW6OEmlatZiY1VXCmL8keDzz/image%2057.png", "model": "dev", "prompt": "juliabeatriz ", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 3, "aspect_ratio": "4:5", "output_format": "png", "guidance_scale": 2, "output_quality": 100, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2025-04-05T10:09:01.777255Z", "created_at": "2025-04-05T10:08:57.135000Z", "data_removed": false, "error": null, "id": "xw9zggy75xrma0cp0np97hb2hm", "input": { "image": "https://replicate.delivery/pbxt/MmcqARgXpfKq4N0jC9brJI8IQW6OEmlatZiY1VXCmL8keDzz/image%2057.png", "model": "dev", "prompt": "juliabeatriz ", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 3, "aspect_ratio": "4:5", "output_format": "png", "guidance_scale": 2, "output_quality": 100, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "Loaded LoRAs in 0.55s\nUsing seed: 3061\nPrompt: juliabeatriz\nInput image size: 256x384\n[!] Resizing input image from 256x384 to 256x384\n[!] img2img mode\n 0%| | 0/23 [00:00<?, ?it/s]\n 4%|▍ | 1/23 [00:00<00:02, 8.44it/s]\n 9%|▊ | 2/23 [00:00<00:02, 7.48it/s]\n 13%|█▎ | 3/23 [00:00<00:02, 7.22it/s]\n 17%|█▋ | 4/23 [00:00<00:02, 7.10it/s]\n 22%|██▏ | 5/23 [00:00<00:02, 7.04it/s]\n 26%|██▌ | 6/23 [00:00<00:02, 7.01it/s]\n 30%|███ | 7/23 [00:00<00:02, 6.98it/s]\n 35%|███▍ | 8/23 [00:01<00:02, 6.96it/s]\n 39%|███▉ | 9/23 [00:01<00:02, 6.95it/s]\n 43%|████▎ | 10/23 [00:01<00:01, 6.95it/s]\n 48%|████▊ | 11/23 [00:01<00:01, 6.94it/s]\n 52%|█████▏ | 12/23 [00:01<00:01, 6.94it/s]\n 57%|█████▋ | 13/23 [00:01<00:01, 6.93it/s]\n 61%|██████ | 14/23 [00:01<00:01, 6.93it/s]\n 65%|██████▌ | 15/23 [00:02<00:01, 6.93it/s]\n 70%|██████▉ | 16/23 [00:02<00:01, 6.93it/s]\n 74%|███████▍ | 17/23 [00:02<00:00, 6.93it/s]\n 78%|███████▊ | 18/23 [00:02<00:00, 6.93it/s]\n 83%|████████▎ | 19/23 [00:02<00:00, 6.93it/s]\n 87%|████████▋ | 20/23 [00:02<00:00, 6.93it/s]\n 91%|█████████▏| 21/23 [00:03<00:00, 6.93it/s]\n 96%|█████████▌| 22/23 [00:03<00:00, 6.93it/s]\n100%|██████████| 23/23 [00:03<00:00, 6.93it/s]\n100%|██████████| 23/23 [00:03<00:00, 6.98it/s]\nTotal safe images: 3 out of 3", "metrics": { "predict_time": 4.597165633, "total_time": 4.642255 }, "output": [ "https://replicate.delivery/xezq/qSuafiIBTene0odY3TX3hf89O1Oid58EZSdKXBxuZjT3zsejC/out-0.png", "https://replicate.delivery/xezq/zxsGUwZYqgbyGVW1pUjXmcMtO3h5oJemZWvcjyFMMx3eMrfoA/out-1.png", "https://replicate.delivery/xezq/jeVaBrV6JeuyAULi65xY5rqQ9u6PB4fndX635HVZg1t7ZWfRB/out-2.png" ], "started_at": "2025-04-05T10:08:57.180089Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-l3ifycrmzjfvy6dpfbcqynspwh4njt6avwsmx7xlwow7bxaimn3a", "get": "https://api.replicate.com/v1/predictions/xw9zggy75xrma0cp0np97hb2hm", "cancel": "https://api.replicate.com/v1/predictions/xw9zggy75xrma0cp0np97hb2hm/cancel" }, "version": "4ce769e3069543c4ad0dec90cddb0df1001cb112fc9062bede921f151001fe42" }
Generated inLoaded LoRAs in 0.55s Using seed: 3061 Prompt: juliabeatriz Input image size: 256x384 [!] Resizing input image from 256x384 to 256x384 [!] img2img mode 0%| | 0/23 [00:00<?, ?it/s] 4%|▍ | 1/23 [00:00<00:02, 8.44it/s] 9%|▊ | 2/23 [00:00<00:02, 7.48it/s] 13%|█▎ | 3/23 [00:00<00:02, 7.22it/s] 17%|█▋ | 4/23 [00:00<00:02, 7.10it/s] 22%|██▏ | 5/23 [00:00<00:02, 7.04it/s] 26%|██▌ | 6/23 [00:00<00:02, 7.01it/s] 30%|███ | 7/23 [00:00<00:02, 6.98it/s] 35%|███▍ | 8/23 [00:01<00:02, 6.96it/s] 39%|███▉ | 9/23 [00:01<00:02, 6.95it/s] 43%|████▎ | 10/23 [00:01<00:01, 6.95it/s] 48%|████▊ | 11/23 [00:01<00:01, 6.94it/s] 52%|█████▏ | 12/23 [00:01<00:01, 6.94it/s] 57%|█████▋ | 13/23 [00:01<00:01, 6.93it/s] 61%|██████ | 14/23 [00:01<00:01, 6.93it/s] 65%|██████▌ | 15/23 [00:02<00:01, 6.93it/s] 70%|██████▉ | 16/23 [00:02<00:01, 6.93it/s] 74%|███████▍ | 17/23 [00:02<00:00, 6.93it/s] 78%|███████▊ | 18/23 [00:02<00:00, 6.93it/s] 83%|████████▎ | 19/23 [00:02<00:00, 6.93it/s] 87%|████████▋ | 20/23 [00:02<00:00, 6.93it/s] 91%|█████████▏| 21/23 [00:03<00:00, 6.93it/s] 96%|█████████▌| 22/23 [00:03<00:00, 6.93it/s] 100%|██████████| 23/23 [00:03<00:00, 6.93it/s] 100%|██████████| 23/23 [00:03<00:00, 6.98it/s] Total safe images: 3 out of 3
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