stasdeep / superside-demo
(Updated 1 year, 4 months ago)
- Public
- 90 runs
-
L40S
- SDXL fine-tune
Prediction
stasdeep/superside-demo:df77eb89157536c333325466f91d4c0c4684385022ccad433ee8a9232afc1997ID337rx7lb3ux4ftjmw7fjyg47b4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- In the style of TOK, vector illustration of a man in a space suit pushing a shopping cart
- refine
- expert_ensemble_refiner
- scheduler
- K_EULER
- lora_scale
- 0.85
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- ugly, broken, disfigured, people
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "In the style of TOK, vector illustration of a man in a space suit pushing a shopping cart", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.85, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "ugly, broken, disfigured, people", "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 stasdeep/superside-demo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "stasdeep/superside-demo:df77eb89157536c333325466f91d4c0c4684385022ccad433ee8a9232afc1997", { input: { width: 1024, height: 1024, prompt: "In the style of TOK, vector illustration of a man in a space suit pushing a shopping cart", refine: "expert_ensemble_refiner", scheduler: "K_EULER", lora_scale: 0.85, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "ugly, broken, disfigured, people", 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 stasdeep/superside-demo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "stasdeep/superside-demo:df77eb89157536c333325466f91d4c0c4684385022ccad433ee8a9232afc1997", input={ "width": 1024, "height": 1024, "prompt": "In the style of TOK, vector illustration of a man in a space suit pushing a shopping cart", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.85, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "ugly, broken, disfigured, people", "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 stasdeep/superside-demo 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": "stasdeep/superside-demo:df77eb89157536c333325466f91d4c0c4684385022ccad433ee8a9232afc1997", "input": { "width": 1024, "height": 1024, "prompt": "In the style of TOK, vector illustration of a man in a space suit pushing a shopping cart", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.85, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "ugly, broken, disfigured, people", "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": "2024-02-13T09:29:14.446510Z", "created_at": "2024-02-13T09:28:17.795647Z", "data_removed": false, "error": null, "id": "337rx7lb3ux4ftjmw7fjyg47b4", "input": { "width": 1024, "height": 1024, "prompt": "In the style of TOK, vector illustration of a man in a space suit pushing a shopping cart", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.85, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "ugly, broken, disfigured, people", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 59368\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: In the style of <s0><s1>, vector illustration of a man in a space suit pushing a shopping cart\ntxt2img mode\n 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:00<00:10, 3.68it/s]\n 5%|▌ | 2/40 [00:00<00:10, 3.69it/s]\n 8%|▊ | 3/40 [00:00<00:10, 3.69it/s]\n 10%|█ | 4/40 [00:01<00:09, 3.67it/s]\n 12%|█▎ | 5/40 [00:01<00:09, 3.68it/s]\n 15%|█▌ | 6/40 [00:01<00:09, 3.68it/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, 3.67it/s]\n 30%|███ | 12/40 [00:03<00:07, 3.67it/s]\n 32%|███▎ | 13/40 [00:03<00:07, 3.67it/s]\n 35%|███▌ | 14/40 [00:03<00:07, 3.67it/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, 3.65it/s]\n 50%|█████ | 20/40 [00:05<00:05, 3.65it/s]\n 52%|█████▎ | 21/40 [00:05<00:05, 3.65it/s]\n 55%|█████▌ | 22/40 [00:06<00:04, 3.65it/s]\n 57%|█████▊ | 23/40 [00:06<00:04, 3.65it/s]\n 60%|██████ | 24/40 [00:06<00:04, 3.65it/s]\n 62%|██████▎ | 25/40 [00:06<00:04, 3.65it/s]\n 65%|██████▌ | 26/40 [00:07<00:03, 3.65it/s]\n 68%|██████▊ | 27/40 [00:07<00:03, 3.65it/s]\n 70%|███████ | 28/40 [00:07<00:03, 3.65it/s]\n 72%|███████▎ | 29/40 [00:07<00:03, 3.65it/s]\n 75%|███████▌ | 30/40 [00:08<00:02, 3.65it/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, 3.65it/s]\n 85%|████████▌ | 34/40 [00:09<00:01, 3.65it/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, 3.65it/s]\n 95%|█████████▌| 38/40 [00:10<00:00, 3.65it/s]\n 98%|█████████▊| 39/40 [00:10<00:00, 3.65it/s]\n100%|██████████| 40/40 [00:10<00:00, 3.65it/s]\n100%|██████████| 40/40 [00:10<00:00, 3.66it/s]\n 0%| | 0/10 [00:00<?, ?it/s]\n 10%|█ | 1/10 [00:00<00:02, 4.23it/s]\n 20%|██ | 2/10 [00:00<00:01, 4.20it/s]\n 30%|███ | 3/10 [00:00<00:01, 4.20it/s]\n 40%|████ | 4/10 [00:00<00:01, 4.18it/s]\n 50%|█████ | 5/10 [00:01<00:01, 4.19it/s]\n 60%|██████ | 6/10 [00:01<00:00, 4.19it/s]\n 70%|███████ | 7/10 [00:01<00:00, 4.20it/s]\n 80%|████████ | 8/10 [00:01<00:00, 4.20it/s]\n 90%|█████████ | 9/10 [00:02<00:00, 4.20it/s]\n100%|██████████| 10/10 [00:02<00:00, 4.20it/s]\n100%|██████████| 10/10 [00:02<00:00, 4.20it/s]", "metrics": { "predict_time": 16.779065, "total_time": 56.650863 }, "output": [ "https://replicate.delivery/pbxt/ANSS4OUsDtI6INBziWNaLf0rce647oo0dfoCnlM5FUzQDdskA/out-0.png" ], "started_at": "2024-02-13T09:28:57.667445Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/337rx7lb3ux4ftjmw7fjyg47b4", "cancel": "https://api.replicate.com/v1/predictions/337rx7lb3ux4ftjmw7fjyg47b4/cancel" }, "version": "df77eb89157536c333325466f91d4c0c4684385022ccad433ee8a9232afc1997" }Generated inUsing seed: 59368 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: In the style of <s0><s1>, vector illustration of a man in a space suit pushing a shopping cart txt2img mode 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:00<00:10, 3.68it/s] 5%|▌ | 2/40 [00:00<00:10, 3.69it/s] 8%|▊ | 3/40 [00:00<00:10, 3.69it/s] 10%|█ | 4/40 [00:01<00:09, 3.67it/s] 12%|█▎ | 5/40 [00:01<00:09, 3.68it/s] 15%|█▌ | 6/40 [00:01<00:09, 3.68it/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, 3.67it/s] 30%|███ | 12/40 [00:03<00:07, 3.67it/s] 32%|███▎ | 13/40 [00:03<00:07, 3.67it/s] 35%|███▌ | 14/40 [00:03<00:07, 3.67it/s] 38%|███▊ | 15/40 [00:04<00:06, 3.66it/s] 40%|████ | 16/40 [00:04<00:06, 3.66it/s] 42%|████▎ | 17/40 [00:04<00:06, 3.66it/s] 45%|████▌ | 18/40 [00:04<00:06, 3.66it/s] 48%|████▊ | 19/40 [00:05<00:05, 3.65it/s] 50%|█████ | 20/40 [00:05<00:05, 3.65it/s] 52%|█████▎ | 21/40 [00:05<00:05, 3.65it/s] 55%|█████▌ | 22/40 [00:06<00:04, 3.65it/s] 57%|█████▊ | 23/40 [00:06<00:04, 3.65it/s] 60%|██████ | 24/40 [00:06<00:04, 3.65it/s] 62%|██████▎ | 25/40 [00:06<00:04, 3.65it/s] 65%|██████▌ | 26/40 [00:07<00:03, 3.65it/s] 68%|██████▊ | 27/40 [00:07<00:03, 3.65it/s] 70%|███████ | 28/40 [00:07<00:03, 3.65it/s] 72%|███████▎ | 29/40 [00:07<00:03, 3.65it/s] 75%|███████▌ | 30/40 [00:08<00:02, 3.65it/s] 78%|███████▊ | 31/40 [00:08<00:02, 3.65it/s] 80%|████████ | 32/40 [00:08<00:02, 3.65it/s] 82%|████████▎ | 33/40 [00:09<00:01, 3.65it/s] 85%|████████▌ | 34/40 [00:09<00:01, 3.65it/s] 88%|████████▊ | 35/40 [00:09<00:01, 3.65it/s] 90%|█████████ | 36/40 [00:09<00:01, 3.65it/s] 92%|█████████▎| 37/40 [00:10<00:00, 3.65it/s] 95%|█████████▌| 38/40 [00:10<00:00, 3.65it/s] 98%|█████████▊| 39/40 [00:10<00:00, 3.65it/s] 100%|██████████| 40/40 [00:10<00:00, 3.65it/s] 100%|██████████| 40/40 [00:10<00:00, 3.66it/s] 0%| | 0/10 [00:00<?, ?it/s] 10%|█ | 1/10 [00:00<00:02, 4.23it/s] 20%|██ | 2/10 [00:00<00:01, 4.20it/s] 30%|███ | 3/10 [00:00<00:01, 4.20it/s] 40%|████ | 4/10 [00:00<00:01, 4.18it/s] 50%|█████ | 5/10 [00:01<00:01, 4.19it/s] 60%|██████ | 6/10 [00:01<00:00, 4.19it/s] 70%|███████ | 7/10 [00:01<00:00, 4.20it/s] 80%|████████ | 8/10 [00:01<00:00, 4.20it/s] 90%|█████████ | 9/10 [00:02<00:00, 4.20it/s] 100%|██████████| 10/10 [00:02<00:00, 4.20it/s] 100%|██████████| 10/10 [00:02<00:00, 4.20it/s]Prediction
stasdeep/superside-demo:df77eb89157536c333325466f91d4c0c4684385022ccad433ee8a9232afc1997ID4lgbszdbnx2xtqzf2y44nm7pluStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- In the style of TOK, vector illustration of a man in a space suit pushing a shopping cart
- refine
- expert_ensemble_refiner
- scheduler
- K_EULER
- lora_scale
- 0.85
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- ugly, broken, disfigured, people logos, text
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "In the style of TOK, vector illustration of a man in a space suit pushing a shopping cart", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.85, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "ugly, broken, disfigured, people\nlogos, text", "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 stasdeep/superside-demo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "stasdeep/superside-demo:df77eb89157536c333325466f91d4c0c4684385022ccad433ee8a9232afc1997", { input: { width: 1024, height: 1024, prompt: "In the style of TOK, vector illustration of a man in a space suit pushing a shopping cart", refine: "expert_ensemble_refiner", scheduler: "K_EULER", lora_scale: 0.85, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "ugly, broken, disfigured, people\nlogos, text", 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 stasdeep/superside-demo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "stasdeep/superside-demo:df77eb89157536c333325466f91d4c0c4684385022ccad433ee8a9232afc1997", input={ "width": 1024, "height": 1024, "prompt": "In the style of TOK, vector illustration of a man in a space suit pushing a shopping cart", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.85, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "ugly, broken, disfigured, people\nlogos, text", "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 stasdeep/superside-demo 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": "stasdeep/superside-demo:df77eb89157536c333325466f91d4c0c4684385022ccad433ee8a9232afc1997", "input": { "width": 1024, "height": 1024, "prompt": "In the style of TOK, vector illustration of a man in a space suit pushing a shopping cart", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.85, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "ugly, broken, disfigured, people\\nlogos, text", "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": "2024-02-13T09:35:36.156400Z", "created_at": "2024-02-13T09:35:14.991799Z", "data_removed": false, "error": null, "id": "4lgbszdbnx2xtqzf2y44nm7plu", "input": { "width": 1024, "height": 1024, "prompt": "In the style of TOK, vector illustration of a man in a space suit pushing a shopping cart", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.85, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "ugly, broken, disfigured, people\nlogos, text", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 13582\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: In the style of <s0><s1>, vector illustration of a man in a space suit pushing a shopping cart\ntxt2img mode\n 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:00<00:10, 3.63it/s]\n 5%|▌ | 2/40 [00:00<00:10, 3.62it/s]\n 8%|▊ | 3/40 [00:00<00:10, 3.62it/s]\n 10%|█ | 4/40 [00:01<00:09, 3.60it/s]\n 12%|█▎ | 5/40 [00:01<00:09, 3.60it/s]\n 15%|█▌ | 6/40 [00:01<00:09, 3.60it/s]\n 18%|█▊ | 7/40 [00:01<00:09, 3.60it/s]\n 20%|██ | 8/40 [00:02<00:08, 3.60it/s]\n 22%|██▎ | 9/40 [00:02<00:08, 3.60it/s]\n 25%|██▌ | 10/40 [00:02<00:08, 3.60it/s]\n 28%|██▊ | 11/40 [00:03<00:08, 3.60it/s]\n 30%|███ | 12/40 [00:03<00:07, 3.59it/s]\n 32%|███▎ | 13/40 [00:03<00:07, 3.59it/s]\n 35%|███▌ | 14/40 [00:03<00:07, 3.59it/s]\n 38%|███▊ | 15/40 [00:04<00:06, 3.59it/s]\n 40%|████ | 16/40 [00:04<00:06, 3.59it/s]\n 42%|████▎ | 17/40 [00:04<00:06, 3.59it/s]\n 45%|████▌ | 18/40 [00:05<00:06, 3.59it/s]\n 48%|████▊ | 19/40 [00:05<00:05, 3.59it/s]\n 50%|█████ | 20/40 [00:05<00:05, 3.59it/s]\n 52%|█████▎ | 21/40 [00:05<00:05, 3.59it/s]\n 55%|█████▌ | 22/40 [00:06<00:05, 3.59it/s]\n 57%|█████▊ | 23/40 [00:06<00:04, 3.58it/s]\n 60%|██████ | 24/40 [00:06<00:04, 3.58it/s]\n 62%|██████▎ | 25/40 [00:06<00:04, 3.58it/s]\n 65%|██████▌ | 26/40 [00:07<00:03, 3.57it/s]\n 68%|██████▊ | 27/40 [00:07<00:03, 3.57it/s]\n 70%|███████ | 28/40 [00:07<00:03, 3.57it/s]\n 72%|███████▎ | 29/40 [00:08<00:03, 3.57it/s]\n 75%|███████▌ | 30/40 [00:08<00:02, 3.58it/s]\n 78%|███████▊ | 31/40 [00:08<00:02, 3.57it/s]\n 80%|████████ | 32/40 [00:08<00:02, 3.57it/s]\n 82%|████████▎ | 33/40 [00:09<00:01, 3.57it/s]\n 85%|████████▌ | 34/40 [00:09<00:01, 3.57it/s]\n 88%|████████▊ | 35/40 [00:09<00:01, 3.57it/s]\n 90%|█████████ | 36/40 [00:10<00:01, 3.57it/s]\n 92%|█████████▎| 37/40 [00:10<00:00, 3.57it/s]\n 95%|█████████▌| 38/40 [00:10<00:00, 3.57it/s]\n 98%|█████████▊| 39/40 [00:10<00:00, 3.57it/s]\n100%|██████████| 40/40 [00:11<00:00, 3.57it/s]\n100%|██████████| 40/40 [00:11<00:00, 3.58it/s]\n 0%| | 0/10 [00:00<?, ?it/s]\n 10%|█ | 1/10 [00:00<00:02, 4.16it/s]\n 20%|██ | 2/10 [00:00<00:01, 4.13it/s]\n 30%|███ | 3/10 [00:00<00:01, 4.13it/s]\n 40%|████ | 4/10 [00:00<00:01, 4.11it/s]\n 50%|█████ | 5/10 [00:01<00:01, 4.11it/s]\n 60%|██████ | 6/10 [00:01<00:00, 4.12it/s]\n 70%|███████ | 7/10 [00:01<00:00, 4.11it/s]\n 80%|████████ | 8/10 [00:01<00:00, 4.11it/s]\n 90%|█████████ | 9/10 [00:02<00:00, 4.10it/s]\n100%|██████████| 10/10 [00:02<00:00, 4.11it/s]\n100%|██████████| 10/10 [00:02<00:00, 4.11it/s]", "metrics": { "predict_time": 16.760314, "total_time": 21.164601 }, "output": [ "https://replicate.delivery/pbxt/Q1ioJMa5qbJtDRuyRdMlTTPFimZea3otE3h9cfe8ECtNPdskA/out-0.png" ], "started_at": "2024-02-13T09:35:19.396086Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/4lgbszdbnx2xtqzf2y44nm7plu", "cancel": "https://api.replicate.com/v1/predictions/4lgbszdbnx2xtqzf2y44nm7plu/cancel" }, "version": "df77eb89157536c333325466f91d4c0c4684385022ccad433ee8a9232afc1997" }Generated inUsing seed: 13582 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: In the style of <s0><s1>, vector illustration of a man in a space suit pushing a shopping cart txt2img mode 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:00<00:10, 3.63it/s] 5%|▌ | 2/40 [00:00<00:10, 3.62it/s] 8%|▊ | 3/40 [00:00<00:10, 3.62it/s] 10%|█ | 4/40 [00:01<00:09, 3.60it/s] 12%|█▎ | 5/40 [00:01<00:09, 3.60it/s] 15%|█▌ | 6/40 [00:01<00:09, 3.60it/s] 18%|█▊ | 7/40 [00:01<00:09, 3.60it/s] 20%|██ | 8/40 [00:02<00:08, 3.60it/s] 22%|██▎ | 9/40 [00:02<00:08, 3.60it/s] 25%|██▌ | 10/40 [00:02<00:08, 3.60it/s] 28%|██▊ | 11/40 [00:03<00:08, 3.60it/s] 30%|███ | 12/40 [00:03<00:07, 3.59it/s] 32%|███▎ | 13/40 [00:03<00:07, 3.59it/s] 35%|███▌ | 14/40 [00:03<00:07, 3.59it/s] 38%|███▊ | 15/40 [00:04<00:06, 3.59it/s] 40%|████ | 16/40 [00:04<00:06, 3.59it/s] 42%|████▎ | 17/40 [00:04<00:06, 3.59it/s] 45%|████▌ | 18/40 [00:05<00:06, 3.59it/s] 48%|████▊ | 19/40 [00:05<00:05, 3.59it/s] 50%|█████ | 20/40 [00:05<00:05, 3.59it/s] 52%|█████▎ | 21/40 [00:05<00:05, 3.59it/s] 55%|█████▌ | 22/40 [00:06<00:05, 3.59it/s] 57%|█████▊ | 23/40 [00:06<00:04, 3.58it/s] 60%|██████ | 24/40 [00:06<00:04, 3.58it/s] 62%|██████▎ | 25/40 [00:06<00:04, 3.58it/s] 65%|██████▌ | 26/40 [00:07<00:03, 3.57it/s] 68%|██████▊ | 27/40 [00:07<00:03, 3.57it/s] 70%|███████ | 28/40 [00:07<00:03, 3.57it/s] 72%|███████▎ | 29/40 [00:08<00:03, 3.57it/s] 75%|███████▌ | 30/40 [00:08<00:02, 3.58it/s] 78%|███████▊ | 31/40 [00:08<00:02, 3.57it/s] 80%|████████ | 32/40 [00:08<00:02, 3.57it/s] 82%|████████▎ | 33/40 [00:09<00:01, 3.57it/s] 85%|████████▌ | 34/40 [00:09<00:01, 3.57it/s] 88%|████████▊ | 35/40 [00:09<00:01, 3.57it/s] 90%|█████████ | 36/40 [00:10<00:01, 3.57it/s] 92%|█████████▎| 37/40 [00:10<00:00, 3.57it/s] 95%|█████████▌| 38/40 [00:10<00:00, 3.57it/s] 98%|█████████▊| 39/40 [00:10<00:00, 3.57it/s] 100%|██████████| 40/40 [00:11<00:00, 3.57it/s] 100%|██████████| 40/40 [00:11<00:00, 3.58it/s] 0%| | 0/10 [00:00<?, ?it/s] 10%|█ | 1/10 [00:00<00:02, 4.16it/s] 20%|██ | 2/10 [00:00<00:01, 4.13it/s] 30%|███ | 3/10 [00:00<00:01, 4.13it/s] 40%|████ | 4/10 [00:00<00:01, 4.11it/s] 50%|█████ | 5/10 [00:01<00:01, 4.11it/s] 60%|██████ | 6/10 [00:01<00:00, 4.12it/s] 70%|███████ | 7/10 [00:01<00:00, 4.11it/s] 80%|████████ | 8/10 [00:01<00:00, 4.11it/s] 90%|█████████ | 9/10 [00:02<00:00, 4.10it/s] 100%|██████████| 10/10 [00:02<00:00, 4.11it/s] 100%|██████████| 10/10 [00:02<00:00, 4.11it/s]
Want to make some of these yourself?
Run this model