fermatresearch / magic-style-transfer
Restyle an image with the style of another one. I strongly suggest to upscale the results with Clarity AI
- Public
- 40.4K runs
-
L40S
- GitHub
Prediction
fermatresearch/magic-style-transfer:3b5fa5d360c361090f11164292e45cc5d14cea8d089591d47c580cac9ec1c7caIDto3qoctblqu5ahbqpga6rtlpb4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- prompt
- shot in the style of sksfer, words "MARC AGUILAR" by Van Gogh, colorful pattern
- ip_scale
- 0.83
- strength
- 1
- scheduler
- K_EULER
- lora_scale
- 0.3
- num_outputs
- 1
- guidance_scale
- 7.5
- resizing_scale
- 1
- apply_watermark
- negative_prompt
- background_color
- #A2A2A2
- num_inference_steps
- 30
- condition_canny_scale
- 0.9
- condition_depth_scale
- 0.09
{ "image": "https://replicate.delivery/pbxt/KbVPZ9LQyS45RXuKvIZBjM227DBlJDfUrSXF4xbrirvqPa2B/Screenshot%202024-03-20%20at%2018.55.55.png", "prompt": "shot in the style of sksfer, words \"MARC AGUILAR\" by Van Gogh, colorful pattern", "ip_image": "https://replicate.delivery/pbxt/KbVPYv5r3u4huNV7Os1NwMvhCQVaSdPrhTrbUBkbOozkMRxQ/339009%402x.jpg", "ip_scale": 0.83, "strength": 1, "scheduler": "K_EULER", "lora_scale": 0.3, "num_outputs": 1, "guidance_scale": 7.5, "resizing_scale": 1, "apply_watermark": true, "negative_prompt": "", "background_color": "#A2A2A2", "num_inference_steps": 30, "condition_canny_scale": 0.9, "condition_depth_scale": 0.09 }
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 fermatresearch/magic-style-transfer using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fermatresearch/magic-style-transfer:3b5fa5d360c361090f11164292e45cc5d14cea8d089591d47c580cac9ec1c7ca", { input: { image: "https://replicate.delivery/pbxt/KbVPZ9LQyS45RXuKvIZBjM227DBlJDfUrSXF4xbrirvqPa2B/Screenshot%202024-03-20%20at%2018.55.55.png", prompt: "shot in the style of sksfer, words \"MARC AGUILAR\" by Van Gogh, colorful pattern", ip_image: "https://replicate.delivery/pbxt/KbVPYv5r3u4huNV7Os1NwMvhCQVaSdPrhTrbUBkbOozkMRxQ/339009%402x.jpg", ip_scale: 0.83, strength: 1, scheduler: "K_EULER", lora_scale: 0.3, num_outputs: 1, guidance_scale: 7.5, resizing_scale: 1, apply_watermark: true, negative_prompt: "", background_color: "#A2A2A2", num_inference_steps: 30, condition_canny_scale: 0.9, condition_depth_scale: 0.09 } } ); // 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 fermatresearch/magic-style-transfer using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fermatresearch/magic-style-transfer:3b5fa5d360c361090f11164292e45cc5d14cea8d089591d47c580cac9ec1c7ca", input={ "image": "https://replicate.delivery/pbxt/KbVPZ9LQyS45RXuKvIZBjM227DBlJDfUrSXF4xbrirvqPa2B/Screenshot%202024-03-20%20at%2018.55.55.png", "prompt": "shot in the style of sksfer, words \"MARC AGUILAR\" by Van Gogh, colorful pattern", "ip_image": "https://replicate.delivery/pbxt/KbVPYv5r3u4huNV7Os1NwMvhCQVaSdPrhTrbUBkbOozkMRxQ/339009%402x.jpg", "ip_scale": 0.83, "strength": 1, "scheduler": "K_EULER", "lora_scale": 0.3, "num_outputs": 1, "guidance_scale": 7.5, "resizing_scale": 1, "apply_watermark": True, "negative_prompt": "", "background_color": "#A2A2A2", "num_inference_steps": 30, "condition_canny_scale": 0.9, "condition_depth_scale": 0.09 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run fermatresearch/magic-style-transfer 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": "fermatresearch/magic-style-transfer:3b5fa5d360c361090f11164292e45cc5d14cea8d089591d47c580cac9ec1c7ca", "input": { "image": "https://replicate.delivery/pbxt/KbVPZ9LQyS45RXuKvIZBjM227DBlJDfUrSXF4xbrirvqPa2B/Screenshot%202024-03-20%20at%2018.55.55.png", "prompt": "shot in the style of sksfer, words \\"MARC AGUILAR\\" by Van Gogh, colorful pattern", "ip_image": "https://replicate.delivery/pbxt/KbVPYv5r3u4huNV7Os1NwMvhCQVaSdPrhTrbUBkbOozkMRxQ/339009%402x.jpg", "ip_scale": 0.83, "strength": 1, "scheduler": "K_EULER", "lora_scale": 0.3, "num_outputs": 1, "guidance_scale": 7.5, "resizing_scale": 1, "apply_watermark": true, "negative_prompt": "", "background_color": "#A2A2A2", "num_inference_steps": 30, "condition_canny_scale": 0.9, "condition_depth_scale": 0.09 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-03-20T18:10:02.478677Z", "created_at": "2024-03-20T18:09:48.998771Z", "data_removed": false, "error": null, "id": "to3qoctblqu5ahbqpga6rtlpb4", "input": { "image": "https://replicate.delivery/pbxt/KbVPZ9LQyS45RXuKvIZBjM227DBlJDfUrSXF4xbrirvqPa2B/Screenshot%202024-03-20%20at%2018.55.55.png", "prompt": "shot in the style of sksfer, words \"MARC AGUILAR\" by Van Gogh, colorful pattern", "ip_image": "https://replicate.delivery/pbxt/KbVPYv5r3u4huNV7Os1NwMvhCQVaSdPrhTrbUBkbOozkMRxQ/339009%402x.jpg", "ip_scale": 0.83, "strength": 1, "scheduler": "K_EULER", "lora_scale": 0.3, "num_outputs": 1, "guidance_scale": 7.5, "resizing_scale": 1, "apply_watermark": true, "negative_prompt": "", "background_color": "#A2A2A2", "num_inference_steps": 30, "condition_canny_scale": 0.9, "condition_depth_scale": 0.09 }, "logs": "Using seed: 1649\nPrompt: shot in the style of sksfer, words \"MARC AGUILAR\" by Van Gogh, colorful pattern\nOriginal width:908, height:580\nAspect Ratio: 1.57\nnew_width:1280, new_height:768\nYou have 2 ControlNets and you have passed 1 prompts. The conditionings will be fixed across the prompts.\n 0%| | 0/30 [00:00<?, ?it/s]\n 3%|▎ | 1/30 [00:00<00:08, 3.41it/s]\n 7%|▋ | 2/30 [00:00<00:09, 2.91it/s]\n 10%|█ | 3/30 [00:01<00:09, 2.77it/s]\n 13%|█▎ | 4/30 [00:01<00:09, 2.72it/s]\n 17%|█▋ | 5/30 [00:01<00:09, 2.69it/s]\n 20%|██ | 6/30 [00:02<00:08, 2.67it/s]\n 23%|██▎ | 7/30 [00:02<00:08, 2.66it/s]\n 27%|██▋ | 8/30 [00:02<00:08, 2.65it/s]\n 30%|███ | 9/30 [00:03<00:07, 2.64it/s]\n 33%|███▎ | 10/30 [00:03<00:07, 2.58it/s]\n 37%|███▋ | 11/30 [00:04<00:07, 2.59it/s]\n 40%|████ | 12/30 [00:04<00:06, 2.60it/s]\n 43%|████▎ | 13/30 [00:04<00:06, 2.61it/s]\n 47%|████▋ | 14/30 [00:05<00:06, 2.61it/s]\n 50%|█████ | 15/30 [00:05<00:05, 2.62it/s]\n 53%|█████▎ | 16/30 [00:06<00:05, 2.62it/s]\n 57%|█████▋ | 17/30 [00:06<00:04, 2.62it/s]\n 60%|██████ | 18/30 [00:06<00:04, 2.62it/s]\n 63%|██████▎ | 19/30 [00:07<00:04, 2.62it/s]\n 67%|██████▋ | 20/30 [00:07<00:03, 2.62it/s]\n 70%|███████ | 21/30 [00:07<00:03, 2.62it/s]\n 73%|███████▎ | 22/30 [00:08<00:03, 2.62it/s]\n 77%|███████▋ | 23/30 [00:08<00:02, 2.62it/s]\n 80%|████████ | 24/30 [00:09<00:02, 2.62it/s]\n 83%|████████▎ | 25/30 [00:09<00:01, 2.62it/s]\n 87%|████████▋ | 26/30 [00:09<00:01, 2.62it/s]\n 90%|█████████ | 27/30 [00:10<00:01, 2.61it/s]\n 93%|█████████▎| 28/30 [00:10<00:00, 2.62it/s]\n 97%|█████████▋| 29/30 [00:10<00:00, 2.62it/s]\n100%|██████████| 30/30 [00:11<00:00, 2.62it/s]\n100%|██████████| 30/30 [00:11<00:00, 2.64it/s]", "metrics": { "predict_time": 13.458817, "total_time": 13.479906 }, "output": [ "https://replicate.delivery/pbxt/HfFqRl7bhRznaqae9ud1eLfp4dBEuWPsqoGnjkFzmwFlH2IKB/out-0.png" ], "started_at": "2024-03-20T18:09:49.019860Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/to3qoctblqu5ahbqpga6rtlpb4", "cancel": "https://api.replicate.com/v1/predictions/to3qoctblqu5ahbqpga6rtlpb4/cancel" }, "version": "3b5fa5d360c361090f11164292e45cc5d14cea8d089591d47c580cac9ec1c7ca" }
Generated inUsing seed: 1649 Prompt: shot in the style of sksfer, words "MARC AGUILAR" by Van Gogh, colorful pattern Original width:908, height:580 Aspect Ratio: 1.57 new_width:1280, new_height:768 You have 2 ControlNets and you have passed 1 prompts. The conditionings will be fixed across the prompts. 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:00<00:08, 3.41it/s] 7%|▋ | 2/30 [00:00<00:09, 2.91it/s] 10%|█ | 3/30 [00:01<00:09, 2.77it/s] 13%|█▎ | 4/30 [00:01<00:09, 2.72it/s] 17%|█▋ | 5/30 [00:01<00:09, 2.69it/s] 20%|██ | 6/30 [00:02<00:08, 2.67it/s] 23%|██▎ | 7/30 [00:02<00:08, 2.66it/s] 27%|██▋ | 8/30 [00:02<00:08, 2.65it/s] 30%|███ | 9/30 [00:03<00:07, 2.64it/s] 33%|███▎ | 10/30 [00:03<00:07, 2.58it/s] 37%|███▋ | 11/30 [00:04<00:07, 2.59it/s] 40%|████ | 12/30 [00:04<00:06, 2.60it/s] 43%|████▎ | 13/30 [00:04<00:06, 2.61it/s] 47%|████▋ | 14/30 [00:05<00:06, 2.61it/s] 50%|█████ | 15/30 [00:05<00:05, 2.62it/s] 53%|█████▎ | 16/30 [00:06<00:05, 2.62it/s] 57%|█████▋ | 17/30 [00:06<00:04, 2.62it/s] 60%|██████ | 18/30 [00:06<00:04, 2.62it/s] 63%|██████▎ | 19/30 [00:07<00:04, 2.62it/s] 67%|██████▋ | 20/30 [00:07<00:03, 2.62it/s] 70%|███████ | 21/30 [00:07<00:03, 2.62it/s] 73%|███████▎ | 22/30 [00:08<00:03, 2.62it/s] 77%|███████▋ | 23/30 [00:08<00:02, 2.62it/s] 80%|████████ | 24/30 [00:09<00:02, 2.62it/s] 83%|████████▎ | 25/30 [00:09<00:01, 2.62it/s] 87%|████████▋ | 26/30 [00:09<00:01, 2.62it/s] 90%|█████████ | 27/30 [00:10<00:01, 2.61it/s] 93%|█████████▎| 28/30 [00:10<00:00, 2.62it/s] 97%|█████████▋| 29/30 [00:10<00:00, 2.62it/s] 100%|██████████| 30/30 [00:11<00:00, 2.62it/s] 100%|██████████| 30/30 [00:11<00:00, 2.64it/s]
Prediction
fermatresearch/magic-style-transfer:3b5fa5d360c361090f11164292e45cc5d14cea8d089591d47c580cac9ec1c7caIDl362oidbq3lho4epawqurablleStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- prompt
- shot in the style of sksfer, words "MARC AGUILAR" by Van Gogh, colorful pattern
- ip_scale
- 0.83
- strength
- 1
- scheduler
- K_EULER
- lora_scale
- 0.3
- num_outputs
- 1
- guidance_scale
- 7.5
- resizing_scale
- 1
- apply_watermark
- negative_prompt
- background_color
- #A2A2A2
- num_inference_steps
- 30
- condition_canny_scale
- 0.9
- condition_depth_scale
- 0.09
{ "image": "https://replicate.delivery/pbxt/KbVQ4ItrGWdUk9jN7KdNo5nr7h9CxSvJncYIhKtDlpA61nyl/Screenshot%202024-03-20%20at%2019.09.12.png", "prompt": "shot in the style of sksfer, words \"MARC AGUILAR\" by Van Gogh, colorful pattern", "ip_image": "https://replicate.delivery/pbxt/KbVQ45hsJ2OSfsMuV7FUWjCodQCIosVO3LofwKHfO7rghXGs/339009%402x.jpg", "ip_scale": 0.83, "strength": 1, "scheduler": "K_EULER", "lora_scale": 0.3, "num_outputs": 1, "guidance_scale": 7.5, "resizing_scale": 1, "apply_watermark": true, "negative_prompt": "", "background_color": "#A2A2A2", "num_inference_steps": 30, "condition_canny_scale": 0.9, "condition_depth_scale": 0.09 }
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 fermatresearch/magic-style-transfer using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fermatresearch/magic-style-transfer:3b5fa5d360c361090f11164292e45cc5d14cea8d089591d47c580cac9ec1c7ca", { input: { image: "https://replicate.delivery/pbxt/KbVQ4ItrGWdUk9jN7KdNo5nr7h9CxSvJncYIhKtDlpA61nyl/Screenshot%202024-03-20%20at%2019.09.12.png", prompt: "shot in the style of sksfer, words \"MARC AGUILAR\" by Van Gogh, colorful pattern", ip_image: "https://replicate.delivery/pbxt/KbVQ45hsJ2OSfsMuV7FUWjCodQCIosVO3LofwKHfO7rghXGs/339009%402x.jpg", ip_scale: 0.83, strength: 1, scheduler: "K_EULER", lora_scale: 0.3, num_outputs: 1, guidance_scale: 7.5, resizing_scale: 1, apply_watermark: true, negative_prompt: "", background_color: "#A2A2A2", num_inference_steps: 30, condition_canny_scale: 0.9, condition_depth_scale: 0.09 } } ); // 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 fermatresearch/magic-style-transfer using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fermatresearch/magic-style-transfer:3b5fa5d360c361090f11164292e45cc5d14cea8d089591d47c580cac9ec1c7ca", input={ "image": "https://replicate.delivery/pbxt/KbVQ4ItrGWdUk9jN7KdNo5nr7h9CxSvJncYIhKtDlpA61nyl/Screenshot%202024-03-20%20at%2019.09.12.png", "prompt": "shot in the style of sksfer, words \"MARC AGUILAR\" by Van Gogh, colorful pattern", "ip_image": "https://replicate.delivery/pbxt/KbVQ45hsJ2OSfsMuV7FUWjCodQCIosVO3LofwKHfO7rghXGs/339009%402x.jpg", "ip_scale": 0.83, "strength": 1, "scheduler": "K_EULER", "lora_scale": 0.3, "num_outputs": 1, "guidance_scale": 7.5, "resizing_scale": 1, "apply_watermark": True, "negative_prompt": "", "background_color": "#A2A2A2", "num_inference_steps": 30, "condition_canny_scale": 0.9, "condition_depth_scale": 0.09 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run fermatresearch/magic-style-transfer 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": "fermatresearch/magic-style-transfer:3b5fa5d360c361090f11164292e45cc5d14cea8d089591d47c580cac9ec1c7ca", "input": { "image": "https://replicate.delivery/pbxt/KbVQ4ItrGWdUk9jN7KdNo5nr7h9CxSvJncYIhKtDlpA61nyl/Screenshot%202024-03-20%20at%2019.09.12.png", "prompt": "shot in the style of sksfer, words \\"MARC AGUILAR\\" by Van Gogh, colorful pattern", "ip_image": "https://replicate.delivery/pbxt/KbVQ45hsJ2OSfsMuV7FUWjCodQCIosVO3LofwKHfO7rghXGs/339009%402x.jpg", "ip_scale": 0.83, "strength": 1, "scheduler": "K_EULER", "lora_scale": 0.3, "num_outputs": 1, "guidance_scale": 7.5, "resizing_scale": 1, "apply_watermark": true, "negative_prompt": "", "background_color": "#A2A2A2", "num_inference_steps": 30, "condition_canny_scale": 0.9, "condition_depth_scale": 0.09 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-03-20T18:10:35.114254Z", "created_at": "2024-03-20T18:10:21.157751Z", "data_removed": false, "error": null, "id": "l362oidbq3lho4epawqurablle", "input": { "image": "https://replicate.delivery/pbxt/KbVQ4ItrGWdUk9jN7KdNo5nr7h9CxSvJncYIhKtDlpA61nyl/Screenshot%202024-03-20%20at%2019.09.12.png", "prompt": "shot in the style of sksfer, words \"MARC AGUILAR\" by Van Gogh, colorful pattern", "ip_image": "https://replicate.delivery/pbxt/KbVQ45hsJ2OSfsMuV7FUWjCodQCIosVO3LofwKHfO7rghXGs/339009%402x.jpg", "ip_scale": 0.83, "strength": 1, "scheduler": "K_EULER", "lora_scale": 0.3, "num_outputs": 1, "guidance_scale": 7.5, "resizing_scale": 1, "apply_watermark": true, "negative_prompt": "", "background_color": "#A2A2A2", "num_inference_steps": 30, "condition_canny_scale": 0.9, "condition_depth_scale": 0.09 }, "logs": "Using seed: 55864\nPrompt: shot in the style of sksfer, words \"MARC AGUILAR\" by Van Gogh, colorful pattern\nOriginal width:1420, height:774\nAspect Ratio: 1.83\nnew_width:1344, new_height:768\nYou have 2 ControlNets and you have passed 1 prompts. The conditionings will be fixed across the prompts.\n 0%| | 0/30 [00:00<?, ?it/s]\n 3%|▎ | 1/30 [00:00<00:09, 2.97it/s]\n 7%|▋ | 2/30 [00:00<00:10, 2.77it/s]\n 10%|█ | 3/30 [00:01<00:10, 2.65it/s]\n 13%|█▎ | 4/30 [00:01<00:09, 2.61it/s]\n 17%|█▋ | 5/30 [00:01<00:09, 2.59it/s]\n 20%|██ | 6/30 [00:02<00:09, 2.57it/s]\n 23%|██▎ | 7/30 [00:02<00:08, 2.56it/s]\n 27%|██▋ | 8/30 [00:03<00:08, 2.55it/s]\n 30%|███ | 9/30 [00:03<00:08, 2.55it/s]\n 33%|███▎ | 10/30 [00:03<00:07, 2.54it/s]\n 37%|███▋ | 11/30 [00:04<00:07, 2.54it/s]\n 40%|████ | 12/30 [00:04<00:07, 2.54it/s]\n 43%|████▎ | 13/30 [00:05<00:06, 2.54it/s]\n 47%|████▋ | 14/30 [00:05<00:06, 2.54it/s]\n 50%|█████ | 15/30 [00:05<00:05, 2.54it/s]\n 53%|█████▎ | 16/30 [00:06<00:05, 2.54it/s]\n 57%|█████▋ | 17/30 [00:06<00:05, 2.54it/s]\n 60%|██████ | 18/30 [00:07<00:04, 2.53it/s]\n 63%|██████▎ | 19/30 [00:07<00:04, 2.53it/s]\n 67%|██████▋ | 20/30 [00:07<00:03, 2.53it/s]\n 70%|███████ | 21/30 [00:08<00:03, 2.53it/s]\n 73%|███████▎ | 22/30 [00:08<00:03, 2.53it/s]\n 77%|███████▋ | 23/30 [00:08<00:02, 2.53it/s]\n 80%|████████ | 24/30 [00:09<00:02, 2.53it/s]\n 83%|████████▎ | 25/30 [00:09<00:01, 2.53it/s]\n 87%|████████▋ | 26/30 [00:10<00:01, 2.53it/s]\n 90%|█████████ | 27/30 [00:10<00:01, 2.53it/s]\n 93%|█████████▎| 28/30 [00:10<00:00, 2.53it/s]\n 97%|█████████▋| 29/30 [00:11<00:00, 2.53it/s]\n100%|██████████| 30/30 [00:11<00:00, 2.52it/s]\n100%|██████████| 30/30 [00:11<00:00, 2.55it/s]", "metrics": { "predict_time": 13.938835, "total_time": 13.956503 }, "output": [ "https://replicate.delivery/pbxt/U8K8kORxUV76CJeWFZSPIu8AX2fPLZreCEzY9VD7Z7J1EbElA/out-0.png" ], "started_at": "2024-03-20T18:10:21.175419Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/l362oidbq3lho4epawqurablle", "cancel": "https://api.replicate.com/v1/predictions/l362oidbq3lho4epawqurablle/cancel" }, "version": "3b5fa5d360c361090f11164292e45cc5d14cea8d089591d47c580cac9ec1c7ca" }
Generated inUsing seed: 55864 Prompt: shot in the style of sksfer, words "MARC AGUILAR" by Van Gogh, colorful pattern Original width:1420, height:774 Aspect Ratio: 1.83 new_width:1344, new_height:768 You have 2 ControlNets and you have passed 1 prompts. The conditionings will be fixed across the prompts. 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:00<00:09, 2.97it/s] 7%|▋ | 2/30 [00:00<00:10, 2.77it/s] 10%|█ | 3/30 [00:01<00:10, 2.65it/s] 13%|█▎ | 4/30 [00:01<00:09, 2.61it/s] 17%|█▋ | 5/30 [00:01<00:09, 2.59it/s] 20%|██ | 6/30 [00:02<00:09, 2.57it/s] 23%|██▎ | 7/30 [00:02<00:08, 2.56it/s] 27%|██▋ | 8/30 [00:03<00:08, 2.55it/s] 30%|███ | 9/30 [00:03<00:08, 2.55it/s] 33%|███▎ | 10/30 [00:03<00:07, 2.54it/s] 37%|███▋ | 11/30 [00:04<00:07, 2.54it/s] 40%|████ | 12/30 [00:04<00:07, 2.54it/s] 43%|████▎ | 13/30 [00:05<00:06, 2.54it/s] 47%|████▋ | 14/30 [00:05<00:06, 2.54it/s] 50%|█████ | 15/30 [00:05<00:05, 2.54it/s] 53%|█████▎ | 16/30 [00:06<00:05, 2.54it/s] 57%|█████▋ | 17/30 [00:06<00:05, 2.54it/s] 60%|██████ | 18/30 [00:07<00:04, 2.53it/s] 63%|██████▎ | 19/30 [00:07<00:04, 2.53it/s] 67%|██████▋ | 20/30 [00:07<00:03, 2.53it/s] 70%|███████ | 21/30 [00:08<00:03, 2.53it/s] 73%|███████▎ | 22/30 [00:08<00:03, 2.53it/s] 77%|███████▋ | 23/30 [00:08<00:02, 2.53it/s] 80%|████████ | 24/30 [00:09<00:02, 2.53it/s] 83%|████████▎ | 25/30 [00:09<00:01, 2.53it/s] 87%|████████▋ | 26/30 [00:10<00:01, 2.53it/s] 90%|█████████ | 27/30 [00:10<00:01, 2.53it/s] 93%|█████████▎| 28/30 [00:10<00:00, 2.53it/s] 97%|█████████▋| 29/30 [00:11<00:00, 2.53it/s] 100%|██████████| 30/30 [00:11<00:00, 2.52it/s] 100%|██████████| 30/30 [00:11<00:00, 2.55it/s]
Prediction
fermatresearch/magic-style-transfer:3b5fa5d360c361090f11164292e45cc5d14cea8d089591d47c580cac9ec1c7caIDimodeq3bqxpre5bebcdqoi75l4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- prompt
- modern interior living room
- ip_scale
- 1
- strength
- 1
- scheduler
- K_EULER
- lora_scale
- 0.9
- num_outputs
- 1
- guidance_scale
- 4
- resizing_scale
- 1
- apply_watermark
- negative_prompt
- background_color
- #A2A2A2
- num_inference_steps
- 30
- condition_canny_scale
- 0.5
- condition_depth_scale
- 0.5
{ "image": "https://replicate.delivery/pbxt/KbUKCMMihGkSc5MXYYYkjEI6hlf1GNmGgwugu6JfSQATqiCN/3d.jpeg", "prompt": "modern interior living room", "ip_image": "https://replicate.delivery/pbxt/KbUKD3uoQjyTzdWbeAqYrypl0RWVJc0gI5UHHa3yYWNaqIoc/DALL%C2%B7E%202024-03-20%2017.57.33%20-%20Create%20a%20professional%20photograph%20of%20a%20modern%20interior%20design.%20This%20image%20should%20capture%20a%20living%20room%20space%20that%20combines%20contemporary%20elegance%20with%20c.webp", "ip_scale": 1, "strength": 1, "scheduler": "K_EULER", "lora_scale": 0.9, "num_outputs": 1, "guidance_scale": 4, "resizing_scale": 1, "apply_watermark": true, "negative_prompt": "", "background_color": "#A2A2A2", "num_inference_steps": 30, "condition_canny_scale": 0.5, "condition_depth_scale": 0.5 }
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 fermatresearch/magic-style-transfer using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fermatresearch/magic-style-transfer:3b5fa5d360c361090f11164292e45cc5d14cea8d089591d47c580cac9ec1c7ca", { input: { image: "https://replicate.delivery/pbxt/KbUKCMMihGkSc5MXYYYkjEI6hlf1GNmGgwugu6JfSQATqiCN/3d.jpeg", prompt: "modern interior living room", ip_image: "https://replicate.delivery/pbxt/KbUKD3uoQjyTzdWbeAqYrypl0RWVJc0gI5UHHa3yYWNaqIoc/DALL%C2%B7E%202024-03-20%2017.57.33%20-%20Create%20a%20professional%20photograph%20of%20a%20modern%20interior%20design.%20This%20image%20should%20capture%20a%20living%20room%20space%20that%20combines%20contemporary%20elegance%20with%20c.webp", ip_scale: 1, strength: 1, scheduler: "K_EULER", lora_scale: 0.9, num_outputs: 1, guidance_scale: 4, resizing_scale: 1, apply_watermark: true, negative_prompt: "", background_color: "#A2A2A2", num_inference_steps: 30, condition_canny_scale: 0.5, condition_depth_scale: 0.5 } } ); // 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 fermatresearch/magic-style-transfer using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fermatresearch/magic-style-transfer:3b5fa5d360c361090f11164292e45cc5d14cea8d089591d47c580cac9ec1c7ca", input={ "image": "https://replicate.delivery/pbxt/KbUKCMMihGkSc5MXYYYkjEI6hlf1GNmGgwugu6JfSQATqiCN/3d.jpeg", "prompt": "modern interior living room", "ip_image": "https://replicate.delivery/pbxt/KbUKD3uoQjyTzdWbeAqYrypl0RWVJc0gI5UHHa3yYWNaqIoc/DALL%C2%B7E%202024-03-20%2017.57.33%20-%20Create%20a%20professional%20photograph%20of%20a%20modern%20interior%20design.%20This%20image%20should%20capture%20a%20living%20room%20space%20that%20combines%20contemporary%20elegance%20with%20c.webp", "ip_scale": 1, "strength": 1, "scheduler": "K_EULER", "lora_scale": 0.9, "num_outputs": 1, "guidance_scale": 4, "resizing_scale": 1, "apply_watermark": True, "negative_prompt": "", "background_color": "#A2A2A2", "num_inference_steps": 30, "condition_canny_scale": 0.5, "condition_depth_scale": 0.5 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run fermatresearch/magic-style-transfer 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": "fermatresearch/magic-style-transfer:3b5fa5d360c361090f11164292e45cc5d14cea8d089591d47c580cac9ec1c7ca", "input": { "image": "https://replicate.delivery/pbxt/KbUKCMMihGkSc5MXYYYkjEI6hlf1GNmGgwugu6JfSQATqiCN/3d.jpeg", "prompt": "modern interior living room", "ip_image": "https://replicate.delivery/pbxt/KbUKD3uoQjyTzdWbeAqYrypl0RWVJc0gI5UHHa3yYWNaqIoc/DALL%C2%B7E%202024-03-20%2017.57.33%20-%20Create%20a%20professional%20photograph%20of%20a%20modern%20interior%20design.%20This%20image%20should%20capture%20a%20living%20room%20space%20that%20combines%20contemporary%20elegance%20with%20c.webp", "ip_scale": 1, "strength": 1, "scheduler": "K_EULER", "lora_scale": 0.9, "num_outputs": 1, "guidance_scale": 4, "resizing_scale": 1, "apply_watermark": true, "negative_prompt": "", "background_color": "#A2A2A2", "num_inference_steps": 30, "condition_canny_scale": 0.5, "condition_depth_scale": 0.5 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-03-20T17:01:32.618449Z", "created_at": "2024-03-20T16:58:43.897037Z", "data_removed": false, "error": null, "id": "imodeq3bqxpre5bebcdqoi75l4", "input": { "image": "https://replicate.delivery/pbxt/KbUKCMMihGkSc5MXYYYkjEI6hlf1GNmGgwugu6JfSQATqiCN/3d.jpeg", "prompt": "modern interior living room", "ip_image": "https://replicate.delivery/pbxt/KbUKD3uoQjyTzdWbeAqYrypl0RWVJc0gI5UHHa3yYWNaqIoc/DALL%C2%B7E%202024-03-20%2017.57.33%20-%20Create%20a%20professional%20photograph%20of%20a%20modern%20interior%20design.%20This%20image%20should%20capture%20a%20living%20room%20space%20that%20combines%20contemporary%20elegance%20with%20c.webp", "ip_scale": 1, "strength": 1, "scheduler": "K_EULER", "lora_scale": 0.9, "num_outputs": 1, "guidance_scale": 4, "resizing_scale": 1, "apply_watermark": true, "negative_prompt": "", "background_color": "#A2A2A2", "num_inference_steps": 30, "condition_canny_scale": 0.5, "condition_depth_scale": 0.5 }, "logs": "Using seed: 28214\nPrompt: modern interior living room\nOriginal width:1024, height:1024\nAspect Ratio: 1.00\nnew_width:1024, new_height:1024\nYou have 2 ControlNets and you have passed 1 prompts. The conditionings will be fixed across the prompts.\n 0%| | 0/30 [00:00<?, ?it/s]\n 3%|▎ | 1/30 [00:00<00:13, 2.17it/s]\n 7%|▋ | 2/30 [00:00<00:10, 2.69it/s]\n 10%|█ | 3/30 [00:01<00:10, 2.59it/s]\n 13%|█▎ | 4/30 [00:01<00:10, 2.55it/s]\n 17%|█▋ | 5/30 [00:01<00:09, 2.52it/s]\n 20%|██ | 6/30 [00:02<00:09, 2.51it/s]\n 23%|██▎ | 7/30 [00:02<00:09, 2.50it/s]\n 27%|██▋ | 8/30 [00:03<00:08, 2.49it/s]\n 30%|███ | 9/30 [00:03<00:08, 2.49it/s]\n 33%|███▎ | 10/30 [00:03<00:08, 2.49it/s]\n 37%|███▋ | 11/30 [00:04<00:07, 2.49it/s]\n 40%|████ | 12/30 [00:04<00:07, 2.49it/s]\n 43%|████▎ | 13/30 [00:05<00:06, 2.49it/s]\n 47%|████▋ | 14/30 [00:05<00:06, 2.49it/s]\n 50%|█████ | 15/30 [00:05<00:06, 2.49it/s]\n 53%|█████▎ | 16/30 [00:06<00:05, 2.49it/s]\n 57%|█████▋ | 17/30 [00:06<00:05, 2.48it/s]\n 60%|██████ | 18/30 [00:07<00:04, 2.48it/s]\n 63%|██████▎ | 19/30 [00:07<00:04, 2.48it/s]\n 67%|██████▋ | 20/30 [00:08<00:04, 2.49it/s]\n 70%|███████ | 21/30 [00:08<00:03, 2.48it/s]\n 73%|███████▎ | 22/30 [00:08<00:03, 2.49it/s]\n 77%|███████▋ | 23/30 [00:09<00:02, 2.49it/s]\n 80%|████████ | 24/30 [00:09<00:02, 2.49it/s]\n 83%|████████▎ | 25/30 [00:10<00:02, 2.49it/s]\n 87%|████████▋ | 26/30 [00:10<00:01, 2.49it/s]\n 90%|█████████ | 27/30 [00:10<00:01, 2.49it/s]\n 93%|█████████▎| 28/30 [00:11<00:00, 2.49it/s]\n 97%|█████████▋| 29/30 [00:11<00:00, 2.49it/s]\n100%|██████████| 30/30 [00:12<00:00, 2.49it/s]\n100%|██████████| 30/30 [00:12<00:00, 2.49it/s]", "metrics": { "predict_time": 14.568553, "total_time": 168.721412 }, "output": [ "https://replicate.delivery/pbxt/CgdTGuA9wdoWGhVUMgpPIv9mh4rpLnYYViUmeLKV8wF2QGRJA/out-0.png" ], "started_at": "2024-03-20T17:01:18.049896Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/imodeq3bqxpre5bebcdqoi75l4", "cancel": "https://api.replicate.com/v1/predictions/imodeq3bqxpre5bebcdqoi75l4/cancel" }, "version": "3b5fa5d360c361090f11164292e45cc5d14cea8d089591d47c580cac9ec1c7ca" }
Generated inUsing seed: 28214 Prompt: modern interior living room Original width:1024, height:1024 Aspect Ratio: 1.00 new_width:1024, new_height:1024 You have 2 ControlNets and you have passed 1 prompts. The conditionings will be fixed across the prompts. 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:00<00:13, 2.17it/s] 7%|▋ | 2/30 [00:00<00:10, 2.69it/s] 10%|█ | 3/30 [00:01<00:10, 2.59it/s] 13%|█▎ | 4/30 [00:01<00:10, 2.55it/s] 17%|█▋ | 5/30 [00:01<00:09, 2.52it/s] 20%|██ | 6/30 [00:02<00:09, 2.51it/s] 23%|██▎ | 7/30 [00:02<00:09, 2.50it/s] 27%|██▋ | 8/30 [00:03<00:08, 2.49it/s] 30%|███ | 9/30 [00:03<00:08, 2.49it/s] 33%|███▎ | 10/30 [00:03<00:08, 2.49it/s] 37%|███▋ | 11/30 [00:04<00:07, 2.49it/s] 40%|████ | 12/30 [00:04<00:07, 2.49it/s] 43%|████▎ | 13/30 [00:05<00:06, 2.49it/s] 47%|████▋ | 14/30 [00:05<00:06, 2.49it/s] 50%|█████ | 15/30 [00:05<00:06, 2.49it/s] 53%|█████▎ | 16/30 [00:06<00:05, 2.49it/s] 57%|█████▋ | 17/30 [00:06<00:05, 2.48it/s] 60%|██████ | 18/30 [00:07<00:04, 2.48it/s] 63%|██████▎ | 19/30 [00:07<00:04, 2.48it/s] 67%|██████▋ | 20/30 [00:08<00:04, 2.49it/s] 70%|███████ | 21/30 [00:08<00:03, 2.48it/s] 73%|███████▎ | 22/30 [00:08<00:03, 2.49it/s] 77%|███████▋ | 23/30 [00:09<00:02, 2.49it/s] 80%|████████ | 24/30 [00:09<00:02, 2.49it/s] 83%|████████▎ | 25/30 [00:10<00:02, 2.49it/s] 87%|████████▋ | 26/30 [00:10<00:01, 2.49it/s] 90%|█████████ | 27/30 [00:10<00:01, 2.49it/s] 93%|█████████▎| 28/30 [00:11<00:00, 2.49it/s] 97%|█████████▋| 29/30 [00:11<00:00, 2.49it/s] 100%|██████████| 30/30 [00:12<00:00, 2.49it/s] 100%|██████████| 30/30 [00:12<00:00, 2.49it/s]
Want to make some of these yourself?
Run this model