mlu108 / visualmetaphor
- Public
- 122 runs
-
L40S
- SDXL fine-tune
Prediction
mlu108/visualmetaphor:9ddd7fb741206cec630c6525da5d84ed7ad8935157c5e7697b7c6b2414797708IDmnkah9z0c9rgg0ceva1reyq504StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- image
- width
- 1024
- height
- 1024
- prompt
- Create a visual metaphor using this image and Coffee Latte combined
- 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
{ "image": "https://replicate.delivery/pbxt/Kk9YG2CrOYbHZoq51tUust9ow20eQ6f0i7F5MS7HAAozXwlW/Screenshot%202024-04-13%20at%2010.48.41%20PM.png", "width": 1024, "height": 1024, "prompt": "Create a visual metaphor using this image and Coffee Latte combined", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run mlu108/visualmetaphor using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "mlu108/visualmetaphor:9ddd7fb741206cec630c6525da5d84ed7ad8935157c5e7697b7c6b2414797708", { input: { image: "https://replicate.delivery/pbxt/Kk9YG2CrOYbHZoq51tUust9ow20eQ6f0i7F5MS7HAAozXwlW/Screenshot%202024-04-13%20at%2010.48.41%20PM.png", width: 1024, height: 1024, prompt: "Create a visual metaphor using this image and Coffee Latte combined", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun mlu108/visualmetaphor using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "mlu108/visualmetaphor:9ddd7fb741206cec630c6525da5d84ed7ad8935157c5e7697b7c6b2414797708", input={ "image": "https://replicate.delivery/pbxt/Kk9YG2CrOYbHZoq51tUust9ow20eQ6f0i7F5MS7HAAozXwlW/Screenshot%202024-04-13%20at%2010.48.41%20PM.png", "width": 1024, "height": 1024, "prompt": "Create a visual metaphor using this image and Coffee Latte combined", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run mlu108/visualmetaphor 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": "mlu108/visualmetaphor:9ddd7fb741206cec630c6525da5d84ed7ad8935157c5e7697b7c6b2414797708", "input": { "image": "https://replicate.delivery/pbxt/Kk9YG2CrOYbHZoq51tUust9ow20eQ6f0i7F5MS7HAAozXwlW/Screenshot%202024-04-13%20at%2010.48.41%20PM.png", "width": 1024, "height": 1024, "prompt": "Create a visual metaphor using this image and Coffee Latte combined", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-04-14T02:51:23.099842Z", "created_at": "2024-04-14T02:50:59.554000Z", "data_removed": false, "error": null, "id": "mnkah9z0c9rgg0ceva1reyq504", "input": { "image": "https://replicate.delivery/pbxt/Kk9YG2CrOYbHZoq51tUust9ow20eQ6f0i7F5MS7HAAozXwlW/Screenshot%202024-04-13%20at%2010.48.41%20PM.png", "width": 1024, "height": 1024, "prompt": "Create a visual metaphor using this image and Coffee Latte combined", "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: 58452\nskipping loading .. weights already loaded\nPrompt: Create a visual metaphor using this image and Coffee Latte combined\nimg2img mode\n 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:00<00:20, 1.92it/s]\n 5%|▌ | 2/40 [00:01<00:19, 1.91it/s]\n 8%|▊ | 3/40 [00:01<00:19, 1.91it/s]\n 10%|█ | 4/40 [00:02<00:18, 1.91it/s]\n 12%|█▎ | 5/40 [00:02<00:18, 1.91it/s]\n 15%|█▌ | 6/40 [00:03<00:17, 1.91it/s]\n 18%|█▊ | 7/40 [00:03<00:17, 1.90it/s]\n 20%|██ | 8/40 [00:04<00:16, 1.90it/s]\n 22%|██▎ | 9/40 [00:04<00:16, 1.90it/s]\n 25%|██▌ | 10/40 [00:05<00:15, 1.90it/s]\n 28%|██▊ | 11/40 [00:05<00:15, 1.90it/s]\n 30%|███ | 12/40 [00:06<00:14, 1.90it/s]\n 32%|███▎ | 13/40 [00:06<00:14, 1.90it/s]\n 35%|███▌ | 14/40 [00:07<00:13, 1.90it/s]\n 38%|███▊ | 15/40 [00:07<00:13, 1.90it/s]\n 40%|████ | 16/40 [00:08<00:12, 1.90it/s]\n 42%|████▎ | 17/40 [00:08<00:12, 1.90it/s]\n 45%|████▌ | 18/40 [00:09<00:11, 1.90it/s]\n 48%|████▊ | 19/40 [00:09<00:11, 1.90it/s]\n 50%|█████ | 20/40 [00:10<00:10, 1.90it/s]\n 52%|█████▎ | 21/40 [00:11<00:09, 1.90it/s]\n 55%|█████▌ | 22/40 [00:11<00:09, 1.90it/s]\n 57%|█████▊ | 23/40 [00:12<00:08, 1.90it/s]\n 60%|██████ | 24/40 [00:12<00:08, 1.90it/s]\n 62%|██████▎ | 25/40 [00:13<00:07, 1.90it/s]\n 65%|██████▌ | 26/40 [00:13<00:07, 1.90it/s]\n 68%|██████▊ | 27/40 [00:14<00:06, 1.90it/s]\n 70%|███████ | 28/40 [00:14<00:06, 1.90it/s]\n 72%|███████▎ | 29/40 [00:15<00:05, 1.90it/s]\n 75%|███████▌ | 30/40 [00:15<00:05, 1.90it/s]\n 78%|███████▊ | 31/40 [00:16<00:04, 1.90it/s]\n 80%|████████ | 32/40 [00:16<00:04, 1.90it/s]\n 82%|████████▎ | 33/40 [00:17<00:03, 1.90it/s]\n 85%|████████▌ | 34/40 [00:17<00:03, 1.90it/s]\n 88%|████████▊ | 35/40 [00:18<00:02, 1.90it/s]\n 90%|█████████ | 36/40 [00:18<00:02, 1.90it/s]\n 92%|█████████▎| 37/40 [00:19<00:01, 1.90it/s]\n 95%|█████████▌| 38/40 [00:19<00:01, 1.90it/s]\n 98%|█████████▊| 39/40 [00:20<00:00, 1.90it/s]\n100%|██████████| 40/40 [00:21<00:00, 1.90it/s]\n100%|██████████| 40/40 [00:21<00:00, 1.90it/s]", "metrics": { "predict_time": 23.536878, "total_time": 23.545842 }, "output": [ "https://replicate.delivery/pbxt/8iFwkIVfO33kU6Ca3noN69PvwA9heB6q7pyZGDoxq0LqaPqSA/out-0.png" ], "started_at": "2024-04-14T02:50:59.562964Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/mnkah9z0c9rgg0ceva1reyq504", "cancel": "https://api.replicate.com/v1/predictions/mnkah9z0c9rgg0ceva1reyq504/cancel" }, "version": "9ddd7fb741206cec630c6525da5d84ed7ad8935157c5e7697b7c6b2414797708" }Generated inUsing seed: 58452 skipping loading .. weights already loaded Prompt: Create a visual metaphor using this image and Coffee Latte combined img2img mode 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:00<00:20, 1.92it/s] 5%|▌ | 2/40 [00:01<00:19, 1.91it/s] 8%|▊ | 3/40 [00:01<00:19, 1.91it/s] 10%|█ | 4/40 [00:02<00:18, 1.91it/s] 12%|█▎ | 5/40 [00:02<00:18, 1.91it/s] 15%|█▌ | 6/40 [00:03<00:17, 1.91it/s] 18%|█▊ | 7/40 [00:03<00:17, 1.90it/s] 20%|██ | 8/40 [00:04<00:16, 1.90it/s] 22%|██▎ | 9/40 [00:04<00:16, 1.90it/s] 25%|██▌ | 10/40 [00:05<00:15, 1.90it/s] 28%|██▊ | 11/40 [00:05<00:15, 1.90it/s] 30%|███ | 12/40 [00:06<00:14, 1.90it/s] 32%|███▎ | 13/40 [00:06<00:14, 1.90it/s] 35%|███▌ | 14/40 [00:07<00:13, 1.90it/s] 38%|███▊ | 15/40 [00:07<00:13, 1.90it/s] 40%|████ | 16/40 [00:08<00:12, 1.90it/s] 42%|████▎ | 17/40 [00:08<00:12, 1.90it/s] 45%|████▌ | 18/40 [00:09<00:11, 1.90it/s] 48%|████▊ | 19/40 [00:09<00:11, 1.90it/s] 50%|█████ | 20/40 [00:10<00:10, 1.90it/s] 52%|█████▎ | 21/40 [00:11<00:09, 1.90it/s] 55%|█████▌ | 22/40 [00:11<00:09, 1.90it/s] 57%|█████▊ | 23/40 [00:12<00:08, 1.90it/s] 60%|██████ | 24/40 [00:12<00:08, 1.90it/s] 62%|██████▎ | 25/40 [00:13<00:07, 1.90it/s] 65%|██████▌ | 26/40 [00:13<00:07, 1.90it/s] 68%|██████▊ | 27/40 [00:14<00:06, 1.90it/s] 70%|███████ | 28/40 [00:14<00:06, 1.90it/s] 72%|███████▎ | 29/40 [00:15<00:05, 1.90it/s] 75%|███████▌ | 30/40 [00:15<00:05, 1.90it/s] 78%|███████▊ | 31/40 [00:16<00:04, 1.90it/s] 80%|████████ | 32/40 [00:16<00:04, 1.90it/s] 82%|████████▎ | 33/40 [00:17<00:03, 1.90it/s] 85%|████████▌ | 34/40 [00:17<00:03, 1.90it/s] 88%|████████▊ | 35/40 [00:18<00:02, 1.90it/s] 90%|█████████ | 36/40 [00:18<00:02, 1.90it/s] 92%|█████████▎| 37/40 [00:19<00:01, 1.90it/s] 95%|█████████▌| 38/40 [00:19<00:01, 1.90it/s] 98%|█████████▊| 39/40 [00:20<00:00, 1.90it/s] 100%|██████████| 40/40 [00:21<00:00, 1.90it/s] 100%|██████████| 40/40 [00:21<00:00, 1.90it/s]Prediction
mlu108/visualmetaphor:9ddd7fb741206cec630c6525da5d84ed7ad8935157c5e7697b7c6b2414797708IDt2db6g098srgp0cf9vqs5z3nwrStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- mask
- image
- width
- 1024
- height
- 1024
- prompt
- Coffee Foam
- 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
{ "mask": "https://replicate.delivery/pbxt/KsB3OE51t3NnhO4Q1mr8u9LkZMojdwc1lvzAYOJfYXUGqadn/mask_1.png", "image": "https://replicate.delivery/pbxt/KsB3OCR5yqcPpux6MPz6AGoCZtu7gljSV5oqFfVIPT2Y4rhW/79ab6a6da7ba3ad4df5375af5e794819-alarm-clock-illustration.png", "width": 1024, "height": 1024, "prompt": "Coffee Foam", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run mlu108/visualmetaphor using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "mlu108/visualmetaphor:9ddd7fb741206cec630c6525da5d84ed7ad8935157c5e7697b7c6b2414797708", { input: { mask: "https://replicate.delivery/pbxt/KsB3OE51t3NnhO4Q1mr8u9LkZMojdwc1lvzAYOJfYXUGqadn/mask_1.png", image: "https://replicate.delivery/pbxt/KsB3OCR5yqcPpux6MPz6AGoCZtu7gljSV5oqFfVIPT2Y4rhW/79ab6a6da7ba3ad4df5375af5e794819-alarm-clock-illustration.png", width: 1024, height: 1024, prompt: "Coffee Foam", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun mlu108/visualmetaphor using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "mlu108/visualmetaphor:9ddd7fb741206cec630c6525da5d84ed7ad8935157c5e7697b7c6b2414797708", input={ "mask": "https://replicate.delivery/pbxt/KsB3OE51t3NnhO4Q1mr8u9LkZMojdwc1lvzAYOJfYXUGqadn/mask_1.png", "image": "https://replicate.delivery/pbxt/KsB3OCR5yqcPpux6MPz6AGoCZtu7gljSV5oqFfVIPT2Y4rhW/79ab6a6da7ba3ad4df5375af5e794819-alarm-clock-illustration.png", "width": 1024, "height": 1024, "prompt": "Coffee Foam", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run mlu108/visualmetaphor 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": "mlu108/visualmetaphor:9ddd7fb741206cec630c6525da5d84ed7ad8935157c5e7697b7c6b2414797708", "input": { "mask": "https://replicate.delivery/pbxt/KsB3OE51t3NnhO4Q1mr8u9LkZMojdwc1lvzAYOJfYXUGqadn/mask_1.png", "image": "https://replicate.delivery/pbxt/KsB3OCR5yqcPpux6MPz6AGoCZtu7gljSV5oqFfVIPT2Y4rhW/79ab6a6da7ba3ad4df5375af5e794819-alarm-clock-illustration.png", "width": 1024, "height": 1024, "prompt": "Coffee Foam", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-05-06T17:24:16.032658Z", "created_at": "2024-05-06T17:23:59.430000Z", "data_removed": false, "error": null, "id": "t2db6g098srgp0cf9vqs5z3nwr", "input": { "mask": "https://replicate.delivery/pbxt/KsB3OE51t3NnhO4Q1mr8u9LkZMojdwc1lvzAYOJfYXUGqadn/mask_1.png", "image": "https://replicate.delivery/pbxt/KsB3OCR5yqcPpux6MPz6AGoCZtu7gljSV5oqFfVIPT2Y4rhW/79ab6a6da7ba3ad4df5375af5e794819-alarm-clock-illustration.png", "width": 1024, "height": 1024, "prompt": "Coffee Foam", "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: 5355\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: Coffee Foam\ninpainting mode\n 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:00<00:10, 3.66it/s]\n 5%|▌ | 2/40 [00:00<00:10, 3.68it/s]\n 8%|▊ | 3/40 [00:00<00:10, 3.67it/s]\n 10%|█ | 4/40 [00:01<00:09, 3.67it/s]\n 12%|█▎ | 5/40 [00:01<00:09, 3.67it/s]\n 15%|█▌ | 6/40 [00:01<00:09, 3.67it/s]\n 18%|█▊ | 7/40 [00:01<00:08, 3.67it/s]\n 20%|██ | 8/40 [00:02<00:08, 3.67it/s]\n 22%|██▎ | 9/40 [00:02<00:08, 3.67it/s]\n 25%|██▌ | 10/40 [00:02<00:08, 3.67it/s]\n 28%|██▊ | 11/40 [00:02<00:07, 3.66it/s]\n 30%|███ | 12/40 [00:03<00:07, 3.66it/s]\n 32%|███▎ | 13/40 [00:03<00:07, 3.66it/s]\n 35%|███▌ | 14/40 [00:03<00:07, 3.66it/s]\n 38%|███▊ | 15/40 [00:04<00:06, 3.66it/s]\n 40%|████ | 16/40 [00:04<00:06, 3.66it/s]\n 42%|████▎ | 17/40 [00:04<00:06, 3.66it/s]\n 45%|████▌ | 18/40 [00:04<00:06, 3.66it/s]\n 48%|████▊ | 19/40 [00:05<00:05, 3.66it/s]\n 50%|█████ | 20/40 [00:05<00:05, 3.66it/s]\n 52%|█████▎ | 21/40 [00:05<00:05, 3.66it/s]\n 55%|█████▌ | 22/40 [00:06<00:04, 3.66it/s]\n 57%|█████▊ | 23/40 [00:06<00:04, 3.66it/s]\n 60%|██████ | 24/40 [00:06<00:04, 3.66it/s]\n 62%|██████▎ | 25/40 [00:06<00:04, 3.66it/s]\n 65%|██████▌ | 26/40 [00:07<00:03, 3.66it/s]\n 68%|██████▊ | 27/40 [00:07<00:03, 3.66it/s]\n 70%|███████ | 28/40 [00:07<00:03, 3.66it/s]\n 72%|███████▎ | 29/40 [00:07<00:03, 3.66it/s]\n 75%|███████▌ | 30/40 [00:08<00:02, 3.66it/s]\n 78%|███████▊ | 31/40 [00:08<00:02, 3.65it/s]\n 80%|████████ | 32/40 [00:08<00:02, 3.65it/s]\n 82%|████████▎ | 33/40 [00:09<00:01, 3.66it/s]\n 85%|████████▌ | 34/40 [00:09<00:01, 3.66it/s]\n 88%|████████▊ | 35/40 [00:09<00:01, 3.65it/s]\n 90%|█████████ | 36/40 [00:09<00:01, 3.65it/s]\n 92%|█████████▎| 37/40 [00:10<00:00, 3.66it/s]\n 95%|█████████▌| 38/40 [00:10<00:00, 3.66it/s]\n 98%|█████████▊| 39/40 [00:10<00:00, 3.66it/s]\n100%|██████████| 40/40 [00:10<00:00, 3.66it/s]\n100%|██████████| 40/40 [00:10<00:00, 3.66it/s]", "metrics": { "predict_time": 15.605692, "total_time": 16.602658 }, "output": [ "https://replicate.delivery/pbxt/iseSkBWskp2dDa3KvLcMFMeV8KUCenfoOnLayJebSfBoPEbsE/out-0.png" ], "started_at": "2024-05-06T17:24:00.426966Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/t2db6g098srgp0cf9vqs5z3nwr", "cancel": "https://api.replicate.com/v1/predictions/t2db6g098srgp0cf9vqs5z3nwr/cancel" }, "version": "9ddd7fb741206cec630c6525da5d84ed7ad8935157c5e7697b7c6b2414797708" }Generated inUsing seed: 5355 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: Coffee Foam inpainting mode 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:00<00:10, 3.66it/s] 5%|▌ | 2/40 [00:00<00:10, 3.68it/s] 8%|▊ | 3/40 [00:00<00:10, 3.67it/s] 10%|█ | 4/40 [00:01<00:09, 3.67it/s] 12%|█▎ | 5/40 [00:01<00:09, 3.67it/s] 15%|█▌ | 6/40 [00:01<00:09, 3.67it/s] 18%|█▊ | 7/40 [00:01<00:08, 3.67it/s] 20%|██ | 8/40 [00:02<00:08, 3.67it/s] 22%|██▎ | 9/40 [00:02<00:08, 3.67it/s] 25%|██▌ | 10/40 [00:02<00:08, 3.67it/s] 28%|██▊ | 11/40 [00:02<00:07, 3.66it/s] 30%|███ | 12/40 [00:03<00:07, 3.66it/s] 32%|███▎ | 13/40 [00:03<00:07, 3.66it/s] 35%|███▌ | 14/40 [00:03<00:07, 3.66it/s] 38%|███▊ | 15/40 [00:04<00:06, 3.66it/s] 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