justmalhar / sdxl-product-mockups
Generate product mockups using fine-tuned SDXL. Use Prompt Prefix: "a product mockup photo of TOK"
- Public
- 1.6K runs
-
L40S
- SDXL fine-tune
Prediction
justmalhar/sdxl-product-mockups:0efdae8698b59b5ed4f7d043410c20242f5f17905454b3d95099f7bf3ca9aa89IDiv37pp3bqjdlhi3ntuznsnauyuStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- a product mockup photo of TOK, laptop with placeholder in greenscreen color
- refine
- expert_ensemble_refiner
- scheduler
- DPMSolverMultistep
- lora_scale
- 0.6
- num_outputs
- 4
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 25
{ "width": 1024, "height": 1024, "prompt": "a product mockup photo of TOK, laptop with placeholder in greenscreen color", "refine": "expert_ensemble_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 25 }
Install Replicate’s Node.js client library:npm install replicate
Import 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 justmalhar/sdxl-product-mockups using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "justmalhar/sdxl-product-mockups:0efdae8698b59b5ed4f7d043410c20242f5f17905454b3d95099f7bf3ca9aa89", { input: { width: 1024, height: 1024, prompt: "a product mockup photo of TOK, laptop with placeholder in greenscreen color", refine: "expert_ensemble_refiner", scheduler: "DPMSolverMultistep", lora_scale: 0.6, num_outputs: 4, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 25 } } ); // 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 justmalhar/sdxl-product-mockups using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "justmalhar/sdxl-product-mockups:0efdae8698b59b5ed4f7d043410c20242f5f17905454b3d95099f7bf3ca9aa89", input={ "width": 1024, "height": 1024, "prompt": "a product mockup photo of TOK, laptop with placeholder in greenscreen color", "refine": "expert_ensemble_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 25 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run justmalhar/sdxl-product-mockups 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": "justmalhar/sdxl-product-mockups:0efdae8698b59b5ed4f7d043410c20242f5f17905454b3d95099f7bf3ca9aa89", "input": { "width": 1024, "height": 1024, "prompt": "a product mockup photo of TOK, laptop with placeholder in greenscreen color", "refine": "expert_ensemble_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 25 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-10-28T21:54:26.256955Z", "created_at": "2023-10-28T21:53:56.532190Z", "data_removed": false, "error": null, "id": "iv37pp3bqjdlhi3ntuznsnauyu", "input": { "width": 1024, "height": 1024, "prompt": "a product mockup photo of TOK, laptop with placeholder in greenscreen color", "refine": "expert_ensemble_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 25 }, "logs": "Using seed: 21964\nPrompt: a product mockup photo of <s0><s1>, laptop with placeholder in greenscreen color\ntxt2img mode\n 0%| | 0/20 [00:00<?, ?it/s]\n 5%|▌ | 1/20 [00:00<00:18, 1.00it/s]\n 10%|█ | 2/20 [00:01<00:17, 1.00it/s]\n 15%|█▌ | 3/20 [00:02<00:16, 1.00it/s]\n 20%|██ | 4/20 [00:03<00:15, 1.00it/s]\n 25%|██▌ | 5/20 [00:04<00:14, 1.00it/s]\n 30%|███ | 6/20 [00:05<00:13, 1.00it/s]\n 35%|███▌ | 7/20 [00:06<00:12, 1.00it/s]\n 40%|████ | 8/20 [00:07<00:11, 1.00it/s]\n 45%|████▌ | 9/20 [00:08<00:11, 1.00s/it]\n 50%|█████ | 10/20 [00:09<00:10, 1.00s/it]\n 55%|█████▌ | 11/20 [00:10<00:08, 1.00it/s]\n 60%|██████ | 12/20 [00:11<00:07, 1.00it/s]\n 65%|██████▌ | 13/20 [00:12<00:06, 1.00it/s]\n 70%|███████ | 14/20 [00:13<00:05, 1.00it/s]\n 75%|███████▌ | 15/20 [00:14<00:04, 1.00it/s]\n 80%|████████ | 16/20 [00:15<00:04, 1.00s/it]\n 85%|████████▌ | 17/20 [00:16<00:03, 1.00s/it]\n 90%|█████████ | 18/20 [00:17<00:02, 1.00s/it]\n 95%|█████████▌| 19/20 [00:18<00:01, 1.00s/it]\n100%|██████████| 20/20 [00:19<00:00, 1.00s/it]\n100%|██████████| 20/20 [00:19<00:00, 1.00it/s]\n 0%| | 0/5 [00:00<?, ?it/s]\n 20%|██ | 1/5 [00:00<00:03, 1.24it/s]\n 40%|████ | 2/5 [00:01<00:02, 1.24it/s]\n 60%|██████ | 3/5 [00:02<00:01, 1.23it/s]\n 80%|████████ | 4/5 [00:03<00:00, 1.23it/s]\n100%|██████████| 5/5 [00:04<00:00, 1.23it/s]\n100%|██████████| 5/5 [00:04<00:00, 1.23it/s]", "metrics": { "predict_time": 29.725604, "total_time": 29.724765 }, "output": [ "https://pbxt.replicate.delivery/k5QO97djfY1e1ElBoxFKlEiYb7x1ePpVPv6TjYcmoe9fhaWOC/out-0.png", "https://pbxt.replicate.delivery/510eE961x1ynOiA02Sq6OdHK7YarjWcFPK8UyTe8U9pQUzyRA/out-1.png", "https://pbxt.replicate.delivery/0UpWlkVj1CoSBRM80pZfsEdKJGvdVi8IfsNTzX52V5kRUzyRA/out-2.png", "https://pbxt.replicate.delivery/PFlesAmjHI3QbKI1sMifk6J8TGRWXehCFjPEcfsDqWsGRNLHB/out-3.png" ], "started_at": "2023-10-28T21:53:56.531351Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/iv37pp3bqjdlhi3ntuznsnauyu", "cancel": "https://api.replicate.com/v1/predictions/iv37pp3bqjdlhi3ntuznsnauyu/cancel" }, "version": "0efdae8698b59b5ed4f7d043410c20242f5f17905454b3d95099f7bf3ca9aa89" }
Generated inUsing seed: 21964 Prompt: a product mockup photo of <s0><s1>, laptop with placeholder in greenscreen color txt2img mode 0%| | 0/20 [00:00<?, ?it/s] 5%|▌ | 1/20 [00:00<00:18, 1.00it/s] 10%|█ | 2/20 [00:01<00:17, 1.00it/s] 15%|█▌ | 3/20 [00:02<00:16, 1.00it/s] 20%|██ | 4/20 [00:03<00:15, 1.00it/s] 25%|██▌ | 5/20 [00:04<00:14, 1.00it/s] 30%|███ | 6/20 [00:05<00:13, 1.00it/s] 35%|███▌ | 7/20 [00:06<00:12, 1.00it/s] 40%|████ | 8/20 [00:07<00:11, 1.00it/s] 45%|████▌ | 9/20 [00:08<00:11, 1.00s/it] 50%|█████ | 10/20 [00:09<00:10, 1.00s/it] 55%|█████▌ | 11/20 [00:10<00:08, 1.00it/s] 60%|██████ | 12/20 [00:11<00:07, 1.00it/s] 65%|██████▌ | 13/20 [00:12<00:06, 1.00it/s] 70%|███████ | 14/20 [00:13<00:05, 1.00it/s] 75%|███████▌ | 15/20 [00:14<00:04, 1.00it/s] 80%|████████ | 16/20 [00:15<00:04, 1.00s/it] 85%|████████▌ | 17/20 [00:16<00:03, 1.00s/it] 90%|█████████ | 18/20 [00:17<00:02, 1.00s/it] 95%|█████████▌| 19/20 [00:18<00:01, 1.00s/it] 100%|██████████| 20/20 [00:19<00:00, 1.00s/it] 100%|██████████| 20/20 [00:19<00:00, 1.00it/s] 0%| | 0/5 [00:00<?, ?it/s] 20%|██ | 1/5 [00:00<00:03, 1.24it/s] 40%|████ | 2/5 [00:01<00:02, 1.24it/s] 60%|██████ | 3/5 [00:02<00:01, 1.23it/s] 80%|████████ | 4/5 [00:03<00:00, 1.23it/s] 100%|██████████| 5/5 [00:04<00:00, 1.23it/s] 100%|██████████| 5/5 [00:04<00:00, 1.23it/s]
Prediction
justmalhar/sdxl-product-mockups:0efdae8698b59b5ed4f7d043410c20242f5f17905454b3d95099f7bf3ca9aa89IDiv37pp3bqjdlhi3ntuznsnauyuStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- a product mockup photo of TOK, laptop with placeholder in greenscreen color
- refine
- expert_ensemble_refiner
- scheduler
- DPMSolverMultistep
- lora_scale
- 0.6
- num_outputs
- 4
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 25
{ "width": 1024, "height": 1024, "prompt": "a product mockup photo of TOK, laptop with placeholder in greenscreen color", "refine": "expert_ensemble_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 25 }
Install Replicate’s Node.js client library:npm install replicate
Import 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 justmalhar/sdxl-product-mockups using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "justmalhar/sdxl-product-mockups:0efdae8698b59b5ed4f7d043410c20242f5f17905454b3d95099f7bf3ca9aa89", { input: { width: 1024, height: 1024, prompt: "a product mockup photo of TOK, laptop with placeholder in greenscreen color", refine: "expert_ensemble_refiner", scheduler: "DPMSolverMultistep", lora_scale: 0.6, num_outputs: 4, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 25 } } ); // 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 justmalhar/sdxl-product-mockups using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "justmalhar/sdxl-product-mockups:0efdae8698b59b5ed4f7d043410c20242f5f17905454b3d95099f7bf3ca9aa89", input={ "width": 1024, "height": 1024, "prompt": "a product mockup photo of TOK, laptop with placeholder in greenscreen color", "refine": "expert_ensemble_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 25 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run justmalhar/sdxl-product-mockups 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": "justmalhar/sdxl-product-mockups:0efdae8698b59b5ed4f7d043410c20242f5f17905454b3d95099f7bf3ca9aa89", "input": { "width": 1024, "height": 1024, "prompt": "a product mockup photo of TOK, laptop with placeholder in greenscreen color", "refine": "expert_ensemble_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 25 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-10-28T21:54:26.256955Z", "created_at": "2023-10-28T21:53:56.532190Z", "data_removed": false, "error": null, "id": "iv37pp3bqjdlhi3ntuznsnauyu", "input": { "width": 1024, "height": 1024, "prompt": "a product mockup photo of TOK, laptop with placeholder in greenscreen color", "refine": "expert_ensemble_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 25 }, "logs": "Using seed: 21964\nPrompt: a product mockup photo of <s0><s1>, laptop with placeholder in greenscreen color\ntxt2img mode\n 0%| | 0/20 [00:00<?, ?it/s]\n 5%|▌ | 1/20 [00:00<00:18, 1.00it/s]\n 10%|█ | 2/20 [00:01<00:17, 1.00it/s]\n 15%|█▌ | 3/20 [00:02<00:16, 1.00it/s]\n 20%|██ | 4/20 [00:03<00:15, 1.00it/s]\n 25%|██▌ | 5/20 [00:04<00:14, 1.00it/s]\n 30%|███ | 6/20 [00:05<00:13, 1.00it/s]\n 35%|███▌ | 7/20 [00:06<00:12, 1.00it/s]\n 40%|████ | 8/20 [00:07<00:11, 1.00it/s]\n 45%|████▌ | 9/20 [00:08<00:11, 1.00s/it]\n 50%|█████ | 10/20 [00:09<00:10, 1.00s/it]\n 55%|█████▌ | 11/20 [00:10<00:08, 1.00it/s]\n 60%|██████ | 12/20 [00:11<00:07, 1.00it/s]\n 65%|██████▌ | 13/20 [00:12<00:06, 1.00it/s]\n 70%|███████ | 14/20 [00:13<00:05, 1.00it/s]\n 75%|███████▌ | 15/20 [00:14<00:04, 1.00it/s]\n 80%|████████ | 16/20 [00:15<00:04, 1.00s/it]\n 85%|████████▌ | 17/20 [00:16<00:03, 1.00s/it]\n 90%|█████████ | 18/20 [00:17<00:02, 1.00s/it]\n 95%|█████████▌| 19/20 [00:18<00:01, 1.00s/it]\n100%|██████████| 20/20 [00:19<00:00, 1.00s/it]\n100%|██████████| 20/20 [00:19<00:00, 1.00it/s]\n 0%| | 0/5 [00:00<?, ?it/s]\n 20%|██ | 1/5 [00:00<00:03, 1.24it/s]\n 40%|████ | 2/5 [00:01<00:02, 1.24it/s]\n 60%|██████ | 3/5 [00:02<00:01, 1.23it/s]\n 80%|████████ | 4/5 [00:03<00:00, 1.23it/s]\n100%|██████████| 5/5 [00:04<00:00, 1.23it/s]\n100%|██████████| 5/5 [00:04<00:00, 1.23it/s]", "metrics": { "predict_time": 29.725604, "total_time": 29.724765 }, "output": [ "https://pbxt.replicate.delivery/k5QO97djfY1e1ElBoxFKlEiYb7x1ePpVPv6TjYcmoe9fhaWOC/out-0.png", "https://pbxt.replicate.delivery/510eE961x1ynOiA02Sq6OdHK7YarjWcFPK8UyTe8U9pQUzyRA/out-1.png", "https://pbxt.replicate.delivery/0UpWlkVj1CoSBRM80pZfsEdKJGvdVi8IfsNTzX52V5kRUzyRA/out-2.png", "https://pbxt.replicate.delivery/PFlesAmjHI3QbKI1sMifk6J8TGRWXehCFjPEcfsDqWsGRNLHB/out-3.png" ], "started_at": "2023-10-28T21:53:56.531351Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/iv37pp3bqjdlhi3ntuznsnauyu", "cancel": "https://api.replicate.com/v1/predictions/iv37pp3bqjdlhi3ntuznsnauyu/cancel" }, "version": "0efdae8698b59b5ed4f7d043410c20242f5f17905454b3d95099f7bf3ca9aa89" }
Generated inUsing seed: 21964 Prompt: a product mockup photo of <s0><s1>, laptop with placeholder in greenscreen color txt2img mode 0%| | 0/20 [00:00<?, ?it/s] 5%|▌ | 1/20 [00:00<00:18, 1.00it/s] 10%|█ | 2/20 [00:01<00:17, 1.00it/s] 15%|█▌ | 3/20 [00:02<00:16, 1.00it/s] 20%|██ | 4/20 [00:03<00:15, 1.00it/s] 25%|██▌ | 5/20 [00:04<00:14, 1.00it/s] 30%|███ | 6/20 [00:05<00:13, 1.00it/s] 35%|███▌ | 7/20 [00:06<00:12, 1.00it/s] 40%|████ | 8/20 [00:07<00:11, 1.00it/s] 45%|████▌ | 9/20 [00:08<00:11, 1.00s/it] 50%|█████ | 10/20 [00:09<00:10, 1.00s/it] 55%|█████▌ | 11/20 [00:10<00:08, 1.00it/s] 60%|██████ | 12/20 [00:11<00:07, 1.00it/s] 65%|██████▌ | 13/20 [00:12<00:06, 1.00it/s] 70%|███████ | 14/20 [00:13<00:05, 1.00it/s] 75%|███████▌ | 15/20 [00:14<00:04, 1.00it/s] 80%|████████ | 16/20 [00:15<00:04, 1.00s/it] 85%|████████▌ | 17/20 [00:16<00:03, 1.00s/it] 90%|█████████ | 18/20 [00:17<00:02, 1.00s/it] 95%|█████████▌| 19/20 [00:18<00:01, 1.00s/it] 100%|██████████| 20/20 [00:19<00:00, 1.00s/it] 100%|██████████| 20/20 [00:19<00:00, 1.00it/s] 0%| | 0/5 [00:00<?, ?it/s] 20%|██ | 1/5 [00:00<00:03, 1.24it/s] 40%|████ | 2/5 [00:01<00:02, 1.24it/s] 60%|██████ | 3/5 [00:02<00:01, 1.23it/s] 80%|████████ | 4/5 [00:03<00:00, 1.23it/s] 100%|██████████| 5/5 [00:04<00:00, 1.23it/s] 100%|██████████| 5/5 [00:04<00:00, 1.23it/s]
Prediction
justmalhar/sdxl-product-mockups:0efdae8698b59b5ed4f7d043410c20242f5f17905454b3d95099f7bf3ca9aa89IDzfdarwtbnbofakdfkbnmmc3cbuStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- a product mockup photo of TOK, apple iPhone in desk background, stationary, with a placeholder greenscreen
- refine
- expert_ensemble_refiner
- scheduler
- DPMSolverMultistep
- lora_scale
- 0.6
- num_outputs
- 4
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 25
{ "width": 1024, "height": 1024, "prompt": "a product mockup photo of TOK, apple iPhone in desk background, stationary, with a placeholder greenscreen ", "refine": "expert_ensemble_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 25 }
Install Replicate’s Node.js client library:npm install replicate
Import 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 justmalhar/sdxl-product-mockups using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "justmalhar/sdxl-product-mockups:0efdae8698b59b5ed4f7d043410c20242f5f17905454b3d95099f7bf3ca9aa89", { input: { width: 1024, height: 1024, prompt: "a product mockup photo of TOK, apple iPhone in desk background, stationary, with a placeholder greenscreen ", refine: "expert_ensemble_refiner", scheduler: "DPMSolverMultistep", lora_scale: 0.6, num_outputs: 4, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 25 } } ); // 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 justmalhar/sdxl-product-mockups using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "justmalhar/sdxl-product-mockups:0efdae8698b59b5ed4f7d043410c20242f5f17905454b3d95099f7bf3ca9aa89", input={ "width": 1024, "height": 1024, "prompt": "a product mockup photo of TOK, apple iPhone in desk background, stationary, with a placeholder greenscreen ", "refine": "expert_ensemble_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 25 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run justmalhar/sdxl-product-mockups 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": "justmalhar/sdxl-product-mockups:0efdae8698b59b5ed4f7d043410c20242f5f17905454b3d95099f7bf3ca9aa89", "input": { "width": 1024, "height": 1024, "prompt": "a product mockup photo of TOK, apple iPhone in desk background, stationary, with a placeholder greenscreen ", "refine": "expert_ensemble_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 25 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-10-28T21:55:33.643387Z", "created_at": "2023-10-28T21:55:03.715287Z", "data_removed": false, "error": null, "id": "zfdarwtbnbofakdfkbnmmc3cbu", "input": { "width": 1024, "height": 1024, "prompt": "a product mockup photo of TOK, apple iPhone in desk background, stationary, with a placeholder greenscreen ", "refine": "expert_ensemble_refiner", "scheduler": "DPMSolverMultistep", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 25 }, "logs": "Using seed: 39968\nPrompt: a product mockup photo of <s0><s1>, apple iPhone in desk background, stationary, with a placeholder greenscreen\ntxt2img mode\n 0%| | 0/20 [00:00<?, ?it/s]\n 5%|▌ | 1/20 [00:00<00:18, 1.00it/s]\n 10%|█ | 2/20 [00:01<00:17, 1.00it/s]\n 15%|█▌ | 3/20 [00:02<00:16, 1.00it/s]\n 20%|██ | 4/20 [00:03<00:15, 1.00it/s]\n 25%|██▌ | 5/20 [00:04<00:14, 1.00it/s]\n 30%|███ | 6/20 [00:05<00:13, 1.00it/s]\n 35%|███▌ | 7/20 [00:06<00:12, 1.00it/s]\n 40%|████ | 8/20 [00:07<00:11, 1.00it/s]\n 45%|████▌ | 9/20 [00:08<00:10, 1.00it/s]\n 50%|█████ | 10/20 [00:09<00:09, 1.00it/s]\n 55%|█████▌ | 11/20 [00:10<00:08, 1.00it/s]\n 60%|██████ | 12/20 [00:11<00:07, 1.00it/s]\n 65%|██████▌ | 13/20 [00:12<00:06, 1.00it/s]\n 70%|███████ | 14/20 [00:13<00:05, 1.00it/s]\n 75%|███████▌ | 15/20 [00:14<00:04, 1.00it/s]\n 80%|████████ | 16/20 [00:15<00:03, 1.00it/s]\n 85%|████████▌ | 17/20 [00:16<00:02, 1.00it/s]\n 90%|█████████ | 18/20 [00:17<00:01, 1.00it/s]\n 95%|█████████▌| 19/20 [00:18<00:00, 1.00it/s]\n100%|██████████| 20/20 [00:19<00:00, 1.00s/it]\n100%|██████████| 20/20 [00:19<00:00, 1.00it/s]\n 0%| | 0/5 [00:00<?, ?it/s]\n 20%|██ | 1/5 [00:00<00:03, 1.24it/s]\n 40%|████ | 2/5 [00:01<00:02, 1.23it/s]\n 60%|██████ | 3/5 [00:02<00:01, 1.23it/s]\n 80%|████████ | 4/5 [00:03<00:00, 1.23it/s]\n100%|██████████| 5/5 [00:04<00:00, 1.23it/s]\n100%|██████████| 5/5 [00:04<00:00, 1.23it/s]", "metrics": { "predict_time": 29.955817, "total_time": 29.9281 }, "output": [ "https://pbxt.replicate.delivery/0UjTFFrGI1otHZtXmvCSbBN5fpIu6Fotv8w49hfygnlTVzyRA/out-0.png", "https://pbxt.replicate.delivery/ntdxiNWRfNUqHCJ1lylnUK19hGLMWCTWnVXFt7m20FBqqZ5IA/out-1.png", "https://pbxt.replicate.delivery/Jo6ybTP5DCKQNlJIY9ecaE7HfkxHvTzR7jbCP74AVawUVzyRA/out-2.png", "https://pbxt.replicate.delivery/bYa8tnYnfbzWaaFGKCbY7KwgLNZqY6WgOceOdGAxoRBVVzyRA/out-3.png" ], "started_at": "2023-10-28T21:55:03.687570Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/zfdarwtbnbofakdfkbnmmc3cbu", "cancel": "https://api.replicate.com/v1/predictions/zfdarwtbnbofakdfkbnmmc3cbu/cancel" }, "version": "0efdae8698b59b5ed4f7d043410c20242f5f17905454b3d95099f7bf3ca9aa89" }
Generated inUsing seed: 39968 Prompt: a product mockup photo of <s0><s1>, apple iPhone in desk background, stationary, with a placeholder greenscreen txt2img mode 0%| | 0/20 [00:00<?, ?it/s] 5%|▌ | 1/20 [00:00<00:18, 1.00it/s] 10%|█ | 2/20 [00:01<00:17, 1.00it/s] 15%|█▌ | 3/20 [00:02<00:16, 1.00it/s] 20%|██ | 4/20 [00:03<00:15, 1.00it/s] 25%|██▌ | 5/20 [00:04<00:14, 1.00it/s] 30%|███ | 6/20 [00:05<00:13, 1.00it/s] 35%|███▌ | 7/20 [00:06<00:12, 1.00it/s] 40%|████ | 8/20 [00:07<00:11, 1.00it/s] 45%|████▌ | 9/20 [00:08<00:10, 1.00it/s] 50%|█████ | 10/20 [00:09<00:09, 1.00it/s] 55%|█████▌ | 11/20 [00:10<00:08, 1.00it/s] 60%|██████ | 12/20 [00:11<00:07, 1.00it/s] 65%|██████▌ | 13/20 [00:12<00:06, 1.00it/s] 70%|███████ | 14/20 [00:13<00:05, 1.00it/s] 75%|███████▌ | 15/20 [00:14<00:04, 1.00it/s] 80%|████████ | 16/20 [00:15<00:03, 1.00it/s] 85%|████████▌ | 17/20 [00:16<00:02, 1.00it/s] 90%|█████████ | 18/20 [00:17<00:01, 1.00it/s] 95%|█████████▌| 19/20 [00:18<00:00, 1.00it/s] 100%|██████████| 20/20 [00:19<00:00, 1.00s/it] 100%|██████████| 20/20 [00:19<00:00, 1.00it/s] 0%| | 0/5 [00:00<?, ?it/s] 20%|██ | 1/5 [00:00<00:03, 1.24it/s] 40%|████ | 2/5 [00:01<00:02, 1.23it/s] 60%|██████ | 3/5 [00:02<00:01, 1.23it/s] 80%|████████ | 4/5 [00:03<00:00, 1.23it/s] 100%|██████████| 5/5 [00:04<00:00, 1.23it/s] 100%|██████████| 5/5 [00:04<00:00, 1.23it/s]
Want to make some of these yourself?
Run this model