adventurepizza / minimalist-design
SDXL fine tuned on minimalist design by imageapp.xyz
- Public
- 2.8K runs
-
L40S
- SDXL fine-tune
Prediction
adventurepizza/minimalist-design:0928373f4d7748a075b7ed4439c8ac818c58d9117a8cb2874220e349cf7ff9d9ID2fr5iqtb5u6zohfo7st5ofty7qStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- In the style of TOK, dark themed kitchen
- 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 TOK, dark themed kitchen", "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 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 adventurepizza/minimalist-design using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "adventurepizza/minimalist-design:0928373f4d7748a075b7ed4439c8ac818c58d9117a8cb2874220e349cf7ff9d9", { input: { width: 1024, height: 1024, prompt: "In the style of TOK, dark themed kitchen", 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 replicate
Import the client:import replicate
Run adventurepizza/minimalist-design using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "adventurepizza/minimalist-design:0928373f4d7748a075b7ed4439c8ac818c58d9117a8cb2874220e349cf7ff9d9", input={ "width": 1024, "height": 1024, "prompt": "In the style of TOK, dark themed kitchen", "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: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())
To learn more, take a look at the guide on getting started with Python.
Run adventurepizza/minimalist-design 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": "adventurepizza/minimalist-design:0928373f4d7748a075b7ed4439c8ac818c58d9117a8cb2874220e349cf7ff9d9", "input": { "width": 1024, "height": 1024, "prompt": "In the style of TOK, dark themed kitchen", "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/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-09-29T04:35:32.862922Z", "created_at": "2023-09-29T04:35:17.379564Z", "data_removed": false, "error": null, "id": "2fr5iqtb5u6zohfo7st5ofty7q", "input": { "width": 1024, "height": 1024, "prompt": "In the style of TOK, dark themed kitchen", "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: 2469\nPrompt: In the style of <s0><s1>, dark themed kitchen\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, 3.66it/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, 3.65it/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, 3.65it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.65it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.65it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.65it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.65it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.65it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.65it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.65it/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, 3.66it/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 [00:06<00:07, 3.67it/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, 3.67it/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, 3.66it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.66it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.66it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.66it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.66it/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, 3.65it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.65it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.66it/s]", "metrics": { "predict_time": 15.619033, "total_time": 15.483358 }, "output": [ "https://pbxt.replicate.delivery/o89Kr3kpAWZYNl88AgjC1c7YrAuqsUBHedoaL1Y36yjJMg0IA/out-0.png" ], "started_at": "2023-09-29T04:35:17.243889Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/2fr5iqtb5u6zohfo7st5ofty7q", "cancel": "https://api.replicate.com/v1/predictions/2fr5iqtb5u6zohfo7st5ofty7q/cancel" }, "version": "0928373f4d7748a075b7ed4439c8ac818c58d9117a8cb2874220e349cf7ff9d9" }
Generated inUsing seed: 2469 Prompt: In the style of <s0><s1>, dark themed kitchen 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, 3.66it/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, 3.65it/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, 3.65it/s] 20%|██ | 10/50 [00:02<00:10, 3.65it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.65it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.65it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.65it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.65it/s] 30%|███ | 15/50 [00:04<00:09, 3.65it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.65it/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, 3.66it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.66it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.66it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.67it/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, 3.67it/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, 3.66it/s] 80%|████████ | 40/50 [00:10<00:02, 3.66it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.66it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.66it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.66it/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, 3.65it/s] 100%|██████████| 50/50 [00:13<00:00, 3.65it/s] 100%|██████████| 50/50 [00:13<00:00, 3.66it/s]
Prediction
adventurepizza/minimalist-design:0928373f4d7748a075b7ed4439c8ac818c58d9117a8cb2874220e349cf7ff9d9IDzyuzkntb5qmqtawkrt2hgn5j7qStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- In the style of TOK, a kitchen
- 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 TOK, a kitchen", "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 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 adventurepizza/minimalist-design using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "adventurepizza/minimalist-design:0928373f4d7748a075b7ed4439c8ac818c58d9117a8cb2874220e349cf7ff9d9", { input: { width: 1024, height: 1024, prompt: "In the style of TOK, a kitchen", 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 replicate
Import the client:import replicate
Run adventurepizza/minimalist-design using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "adventurepizza/minimalist-design:0928373f4d7748a075b7ed4439c8ac818c58d9117a8cb2874220e349cf7ff9d9", input={ "width": 1024, "height": 1024, "prompt": "In the style of TOK, a kitchen", "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: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())
To learn more, take a look at the guide on getting started with Python.
Run adventurepizza/minimalist-design 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": "adventurepizza/minimalist-design:0928373f4d7748a075b7ed4439c8ac818c58d9117a8cb2874220e349cf7ff9d9", "input": { "width": 1024, "height": 1024, "prompt": "In the style of TOK, a kitchen", "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/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-09-29T04:04:26.098677Z", "created_at": "2023-09-29T04:04:08.078932Z", "data_removed": false, "error": null, "id": "zyuzkntb5qmqtawkrt2hgn5j7q", "input": { "width": 1024, "height": 1024, "prompt": "In the style of TOK, a kitchen", "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: 5941\nPrompt: In the style of <s0><s1>, a kitchen\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:38, 1.28it/s]\n 4%|▍ | 2/50 [00:01<00:22, 2.09it/s]\n 6%|▌ | 3/50 [00:01<00:18, 2.61it/s]\n 8%|▊ | 4/50 [00:01<00:15, 2.95it/s]\n 10%|█ | 5/50 [00:01<00:14, 3.19it/s]\n 12%|█▏ | 6/50 [00:02<00:13, 3.34it/s]\n 14%|█▍ | 7/50 [00:02<00:12, 3.45it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.53it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.58it/s]\n 20%|██ | 10/50 [00:03<00:11, 3.62it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.64it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.66it/s]\n 26%|██▌ | 13/50 [00:04<00:10, 3.67it/s]\n 28%|██▊ | 14/50 [00:04<00:09, 3.68it/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 [00:05<00:08, 3.69it/s]\n 36%|███▌ | 18/50 [00:05<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 [00:06<00:07, 3.69it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.69it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.69it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.70it/s]\n 50%|█████ | 25/50 [00:07<00:06, 3.70it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.70it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.71it/s]\n 56%|█████▌ | 28/50 [00:08<00:05, 3.71it/s]\n 58%|█████▊ | 29/50 [00:08<00:05, 3.71it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.71it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.71it/s]\n 64%|██████▍ | 32/50 [00:09<00:04, 3.71it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.71it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.71it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.71it/s]\n 72%|███████▏ | 36/50 [00:10<00:03, 3.71it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.71it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.71it/s]\n 78%|███████▊ | 39/50 [00:11<00:02, 3.71it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.71it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.71it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.71it/s]\n 86%|████████▌ | 43/50 [00:12<00:01, 3.71it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.71it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.67it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.68it/s]\n 94%|█████████▍| 47/50 [00:13<00:00, 3.68it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.69it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.69it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.70it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.57it/s]", "metrics": { "predict_time": 16.843449, "total_time": 18.019745 }, "output": [ "https://pbxt.replicate.delivery/87kRusXhx9LyONpdAeHkxp6bfueKsxC12JIKu4behDilsfHNC/out-0.png" ], "started_at": "2023-09-29T04:04:09.255228Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/zyuzkntb5qmqtawkrt2hgn5j7q", "cancel": "https://api.replicate.com/v1/predictions/zyuzkntb5qmqtawkrt2hgn5j7q/cancel" }, "version": "0928373f4d7748a075b7ed4439c8ac818c58d9117a8cb2874220e349cf7ff9d9" }
Generated inUsing seed: 5941 Prompt: In the style of <s0><s1>, a kitchen txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:38, 1.28it/s] 4%|▍ | 2/50 [00:01<00:22, 2.09it/s] 6%|▌ | 3/50 [00:01<00:18, 2.61it/s] 8%|▊ | 4/50 [00:01<00:15, 2.95it/s] 10%|█ | 5/50 [00:01<00:14, 3.19it/s] 12%|█▏ | 6/50 [00:02<00:13, 3.34it/s] 14%|█▍ | 7/50 [00:02<00:12, 3.45it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.53it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.58it/s] 20%|██ | 10/50 [00:03<00:11, 3.62it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.64it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.66it/s] 26%|██▌ | 13/50 [00:04<00:10, 3.67it/s] 28%|██▊ | 14/50 [00:04<00:09, 3.68it/s] 30%|███ | 15/50 [00:04<00:09, 3.69it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.69it/s] 34%|███▍ | 17/50 [00:05<00:08, 3.69it/s] 36%|███▌ | 18/50 [00:05<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 [00:06<00:07, 3.69it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.69it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.69it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.70it/s] 50%|█████ | 25/50 [00:07<00:06, 3.70it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.70it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.71it/s] 56%|█████▌ | 28/50 [00:08<00:05, 3.71it/s] 58%|█████▊ | 29/50 [00:08<00:05, 3.71it/s] 60%|██████ | 30/50 [00:08<00:05, 3.71it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.71it/s] 64%|██████▍ | 32/50 [00:09<00:04, 3.71it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.71it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.71it/s] 70%|███████ | 35/50 [00:09<00:04, 3.71it/s] 72%|███████▏ | 36/50 [00:10<00:03, 3.71it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.71it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.71it/s] 78%|███████▊ | 39/50 [00:11<00:02, 3.71it/s] 80%|████████ | 40/50 [00:11<00:02, 3.71it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.71it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.71it/s] 86%|████████▌ | 43/50 [00:12<00:01, 3.71it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.71it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.67it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.68it/s] 94%|█████████▍| 47/50 [00:13<00:00, 3.68it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.69it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.69it/s] 100%|██████████| 50/50 [00:14<00:00, 3.70it/s] 100%|██████████| 50/50 [00:14<00:00, 3.57it/s]
Prediction
adventurepizza/minimalist-design:0928373f4d7748a075b7ed4439c8ac818c58d9117a8cb2874220e349cf7ff9d9IDr72aeidbqk7hfljc2altxamcoaStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- In the style of TOK, a beach 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": "In the style of TOK, a beach 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 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 adventurepizza/minimalist-design using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "adventurepizza/minimalist-design:0928373f4d7748a075b7ed4439c8ac818c58d9117a8cb2874220e349cf7ff9d9", { input: { width: 1024, height: 1024, prompt: "In the style of TOK, a beach 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 replicate
Import the client:import replicate
Run adventurepizza/minimalist-design using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "adventurepizza/minimalist-design:0928373f4d7748a075b7ed4439c8ac818c58d9117a8cb2874220e349cf7ff9d9", input={ "width": 1024, "height": 1024, "prompt": "In the style of TOK, a beach 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: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())
To learn more, take a look at the guide on getting started with Python.
Run adventurepizza/minimalist-design 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": "adventurepizza/minimalist-design:0928373f4d7748a075b7ed4439c8ac818c58d9117a8cb2874220e349cf7ff9d9", "input": { "width": 1024, "height": 1024, "prompt": "In the style of TOK, a beach 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/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-09-29T04:17:35.335958Z", "created_at": "2023-09-29T04:15:25.546995Z", "data_removed": false, "error": null, "id": "r72aeidbqk7hfljc2altxamcoa", "input": { "width": 1024, "height": 1024, "prompt": "In the style of TOK, a beach 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: 61972\nPrompt: In the style of <s0><s1>, a beach house\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:38, 1.27it/s]\n 4%|▍ | 2/50 [00:01<00:23, 2.07it/s]\n 6%|▌ | 3/50 [00:01<00:18, 2.60it/s]\n 8%|▊ | 4/50 [00:01<00:15, 2.94it/s]\n 10%|█ | 5/50 [00:01<00:14, 3.18it/s]\n 12%|█▏ | 6/50 [00:02<00:13, 3.33it/s]\n 14%|█▍ | 7/50 [00:02<00:12, 3.45it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.53it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.58it/s]\n 20%|██ | 10/50 [00:03<00:11, 3.62it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.65it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.67it/s]\n 26%|██▌ | 13/50 [00:04<00:10, 3.69it/s]\n 28%|██▊ | 14/50 [00:04<00:09, 3.70it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.71it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.71it/s]\n 34%|███▍ | 17/50 [00:05<00:08, 3.72it/s]\n 36%|███▌ | 18/50 [00:05<00:08, 3.72it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.72it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.72it/s]\n 42%|████▏ | 21/50 [00:06<00:07, 3.72it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.72it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.72it/s]\n 48%|████▊ | 24/50 [00:06<00:06, 3.72it/s]\n 50%|█████ | 25/50 [00:07<00:06, 3.72it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.72it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.72it/s]\n 56%|█████▌ | 28/50 [00:08<00:05, 3.71it/s]\n 58%|█████▊ | 29/50 [00:08<00:05, 3.71it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.71it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.71it/s]\n 64%|██████▍ | 32/50 [00:09<00:04, 3.71it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.71it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.71it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.72it/s]\n 72%|███████▏ | 36/50 [00:10<00:03, 3.72it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.72it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.71it/s]\n 78%|███████▊ | 39/50 [00:11<00:02, 3.72it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.72it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.71it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.72it/s]\n 86%|████████▌ | 43/50 [00:12<00:01, 3.72it/s]\n 88%|████████▊ | 44/50 [00:12<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 [00:13<00:00, 3.72it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.71it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.71it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.71it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.58it/s]", "metrics": { "predict_time": 16.313427, "total_time": 129.788963 }, "output": [ "https://pbxt.replicate.delivery/eJ97aa369pwPBqo6z0UPdTZdZ145yscGd434vwRUAsTvDg0IA/out-0.png" ], "started_at": "2023-09-29T04:17:19.022531Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/r72aeidbqk7hfljc2altxamcoa", "cancel": "https://api.replicate.com/v1/predictions/r72aeidbqk7hfljc2altxamcoa/cancel" }, "version": "0928373f4d7748a075b7ed4439c8ac818c58d9117a8cb2874220e349cf7ff9d9" }
Generated inUsing seed: 61972 Prompt: In the style of <s0><s1>, a beach house txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:38, 1.27it/s] 4%|▍ | 2/50 [00:01<00:23, 2.07it/s] 6%|▌ | 3/50 [00:01<00:18, 2.60it/s] 8%|▊ | 4/50 [00:01<00:15, 2.94it/s] 10%|█ | 5/50 [00:01<00:14, 3.18it/s] 12%|█▏ | 6/50 [00:02<00:13, 3.33it/s] 14%|█▍ | 7/50 [00:02<00:12, 3.45it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.53it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.58it/s] 20%|██ | 10/50 [00:03<00:11, 3.62it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.65it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.67it/s] 26%|██▌ | 13/50 [00:04<00:10, 3.69it/s] 28%|██▊ | 14/50 [00:04<00:09, 3.70it/s] 30%|███ | 15/50 [00:04<00:09, 3.71it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.71it/s] 34%|███▍ | 17/50 [00:05<00:08, 3.72it/s] 36%|███▌ | 18/50 [00:05<00:08, 3.72it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.72it/s] 40%|████ | 20/50 [00:05<00:08, 3.72it/s] 42%|████▏ | 21/50 [00:06<00:07, 3.72it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.72it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.72it/s] 48%|████▊ | 24/50 [00:06<00:06, 3.72it/s] 50%|█████ | 25/50 [00:07<00:06, 3.72it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.72it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.72it/s] 56%|█████▌ | 28/50 [00:08<00:05, 3.71it/s] 58%|█████▊ | 29/50 [00:08<00:05, 3.71it/s] 60%|██████ | 30/50 [00:08<00:05, 3.71it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.71it/s] 64%|██████▍ | 32/50 [00:09<00:04, 3.71it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.71it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.71it/s] 70%|███████ | 35/50 [00:09<00:04, 3.72it/s] 72%|███████▏ | 36/50 [00:10<00:03, 3.72it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.72it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.71it/s] 78%|███████▊ | 39/50 [00:11<00:02, 3.72it/s] 80%|████████ | 40/50 [00:11<00:02, 3.72it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.71it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.72it/s] 86%|████████▌ | 43/50 [00:12<00:01, 3.72it/s] 88%|████████▊ | 44/50 [00:12<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 [00:13<00:00, 3.72it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.71it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.71it/s] 100%|██████████| 50/50 [00:13<00:00, 3.71it/s] 100%|██████████| 50/50 [00:13<00:00, 3.58it/s]
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