tstramer / arcane-diffusion
- Public
- 100.4K runs
-
A100 (80GB)
Prediction
tstramer/arcane-diffusion:1f4cdaee82b13c1f92706a211f55d92d7ee87b13e2c2ba6b998a7817ffc5017fIDko2j466wlrgj3dfjzzixswdv4yStatusSucceededSourceWebHardwareA100 (40GB)Total durationCreatedInput
- width
- 512
- height
- 512
- prompt
- a magical princess with golden hair, arcane style
- scheduler
- K-LMS
- num_outputs
- 1
- guidance_scale
- 7.5
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 512, "height": 512, "prompt": "a magical princess with golden hair, arcane style", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "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"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run tstramer/arcane-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "tstramer/arcane-diffusion:1f4cdaee82b13c1f92706a211f55d92d7ee87b13e2c2ba6b998a7817ffc5017f", { input: { width: 512, height: 512, prompt: "a magical princess with golden hair, arcane style", scheduler: "K-LMS", num_outputs: 1, guidance_scale: 7.5, 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 tstramer/arcane-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "tstramer/arcane-diffusion:1f4cdaee82b13c1f92706a211f55d92d7ee87b13e2c2ba6b998a7817ffc5017f", input={ "width": 512, "height": 512, "prompt": "a magical princess with golden hair, arcane style", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "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 tstramer/arcane-diffusion 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": "tstramer/arcane-diffusion:1f4cdaee82b13c1f92706a211f55d92d7ee87b13e2c2ba6b998a7817ffc5017f", "input": { "width": 512, "height": 512, "prompt": "a magical princess with golden hair, arcane style", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "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": "2022-11-07T22:19:11.495961Z", "created_at": "2022-11-07T22:16:18.070750Z", "data_removed": false, "error": null, "id": "ko2j466wlrgj3dfjzzixswdv4y", "input": { "width": 512, "height": 512, "prompt": "a magical princess with golden hair, arcane style", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 57086\n\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:02<01:48, 2.21s/it]\n 6%|▌ | 3/50 [00:02<00:29, 1.59it/s]\n 10%|█ | 5/50 [00:02<00:15, 2.91it/s]\n 14%|█▍ | 7/50 [00:02<00:09, 4.39it/s]\n 18%|█▊ | 9/50 [00:02<00:06, 5.90it/s]\n 22%|██▏ | 11/50 [00:02<00:05, 7.41it/s]\n 26%|██▌ | 13/50 [00:03<00:04, 8.42it/s]\n 30%|███ | 15/50 [00:03<00:03, 9.62it/s]\n 34%|███▍ | 17/50 [00:03<00:03, 10.70it/s]\n 38%|███▊ | 19/50 [00:03<00:02, 11.57it/s]\n 42%|████▏ | 21/50 [00:03<00:02, 12.20it/s]\n 46%|████▌ | 23/50 [00:03<00:02, 12.68it/s]\n 50%|█████ | 25/50 [00:03<00:01, 13.04it/s]\n 54%|█████▍ | 27/50 [00:04<00:01, 13.10it/s]\n 58%|█████▊ | 29/50 [00:04<00:01, 13.37it/s]\n 62%|██████▏ | 31/50 [00:04<00:01, 13.59it/s]\n 66%|██████▌ | 33/50 [00:04<00:01, 13.77it/s]\n 70%|███████ | 35/50 [00:04<00:01, 13.88it/s]\n 74%|███████▍ | 37/50 [00:04<00:00, 13.98it/s]\n 78%|███████▊ | 39/50 [00:04<00:00, 14.04it/s]\n 82%|████████▏ | 41/50 [00:05<00:00, 14.01it/s]\n 86%|████████▌ | 43/50 [00:05<00:00, 14.04it/s]\n 90%|█████████ | 45/50 [00:05<00:00, 14.07it/s]\n 94%|█████████▍| 47/50 [00:05<00:00, 14.10it/s]\n 98%|█████████▊| 49/50 [00:05<00:00, 14.11it/s]\n100%|██████████| 50/50 [00:05<00:00, 8.70it/s]", "metrics": { "predict_time": 8.074915, "total_time": 173.425211 }, "output": [ "https://replicate.delivery/pbxt/KM8ZdKfzmdQxYCtwjzqAdt3ykR7FdUnomS6EUpFZ7jhvs5ePA/out-0.png" ], "started_at": "2022-11-07T22:19:03.421046Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ko2j466wlrgj3dfjzzixswdv4y", "cancel": "https://api.replicate.com/v1/predictions/ko2j466wlrgj3dfjzzixswdv4y/cancel" }, "version": "1f4cdaee82b13c1f92706a211f55d92d7ee87b13e2c2ba6b998a7817ffc5017f" }Generated inUsing seed: 57086 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:02<01:48, 2.21s/it] 6%|▌ | 3/50 [00:02<00:29, 1.59it/s] 10%|█ | 5/50 [00:02<00:15, 2.91it/s] 14%|█▍ | 7/50 [00:02<00:09, 4.39it/s] 18%|█▊ | 9/50 [00:02<00:06, 5.90it/s] 22%|██▏ | 11/50 [00:02<00:05, 7.41it/s] 26%|██▌ | 13/50 [00:03<00:04, 8.42it/s] 30%|███ | 15/50 [00:03<00:03, 9.62it/s] 34%|███▍ | 17/50 [00:03<00:03, 10.70it/s] 38%|███▊ | 19/50 [00:03<00:02, 11.57it/s] 42%|████▏ | 21/50 [00:03<00:02, 12.20it/s] 46%|████▌ | 23/50 [00:03<00:02, 12.68it/s] 50%|█████ | 25/50 [00:03<00:01, 13.04it/s] 54%|█████▍ | 27/50 [00:04<00:01, 13.10it/s] 58%|█████▊ | 29/50 [00:04<00:01, 13.37it/s] 62%|██████▏ | 31/50 [00:04<00:01, 13.59it/s] 66%|██████▌ | 33/50 [00:04<00:01, 13.77it/s] 70%|███████ | 35/50 [00:04<00:01, 13.88it/s] 74%|███████▍ | 37/50 [00:04<00:00, 13.98it/s] 78%|███████▊ | 39/50 [00:04<00:00, 14.04it/s] 82%|████████▏ | 41/50 [00:05<00:00, 14.01it/s] 86%|████████▌ | 43/50 [00:05<00:00, 14.04it/s] 90%|█████████ | 45/50 [00:05<00:00, 14.07it/s] 94%|█████████▍| 47/50 [00:05<00:00, 14.10it/s] 98%|█████████▊| 49/50 [00:05<00:00, 14.11it/s] 100%|██████████| 50/50 [00:05<00:00, 8.70it/s]Prediction
tstramer/arcane-diffusion:1f4cdaee82b13c1f92706a211f55d92d7ee87b13e2c2ba6b998a7817ffc5017fID2ggapwprc5cmrpxijznihpzrw4StatusSucceededSourceWebHardwareA100 (40GB)Total durationCreatedInput
- width
- 512
- height
- 512
- prompt
- harry potter, arcane style, intricate highly detailed digital painting artstation concept art smooth sharp focus illustration Unreal Engine 5 8K
- scheduler
- K-LMS
- num_outputs
- 1
- guidance_scale
- 7.5
- prompt_strength
- 0.8
- num_inference_steps
- "150"
{ "width": 512, "height": 512, "prompt": "harry potter, arcane style, intricate highly detailed digital painting artstation concept art smooth sharp focus illustration Unreal Engine 5 8K", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": "150" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run tstramer/arcane-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "tstramer/arcane-diffusion:1f4cdaee82b13c1f92706a211f55d92d7ee87b13e2c2ba6b998a7817ffc5017f", { input: { width: 512, height: 512, prompt: "harry potter, arcane style, intricate highly detailed digital painting artstation concept art smooth sharp focus illustration Unreal Engine 5 8K", scheduler: "K-LMS", num_outputs: 1, guidance_scale: 7.5, prompt_strength: 0.8, num_inference_steps: "150" } } ); // 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 tstramer/arcane-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "tstramer/arcane-diffusion:1f4cdaee82b13c1f92706a211f55d92d7ee87b13e2c2ba6b998a7817ffc5017f", input={ "width": 512, "height": 512, "prompt": "harry potter, arcane style, intricate highly detailed digital painting artstation concept art smooth sharp focus illustration Unreal Engine 5 8K", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": "150" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run tstramer/arcane-diffusion 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": "tstramer/arcane-diffusion:1f4cdaee82b13c1f92706a211f55d92d7ee87b13e2c2ba6b998a7817ffc5017f", "input": { "width": 512, "height": 512, "prompt": "harry potter, arcane style, intricate highly detailed digital painting artstation concept art smooth sharp focus illustration Unreal Engine 5 8K", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": "150" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-11-08T01:58:18.276112Z", "created_at": "2022-11-08T01:55:35.056826Z", "data_removed": false, "error": null, "id": "2ggapwprc5cmrpxijznihpzrw4", "input": { "width": 512, "height": 512, "prompt": "harry potter, arcane style, intricate highly detailed digital painting artstation concept art smooth sharp focus illustration Unreal Engine 5 8K", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": "150" }, "logs": "Using seed: 60774\n\n 0%| | 0/150 [00:00<?, ?it/s]\n 1%| | 1/150 [00:02<05:26, 2.19s/it]\n 2%|▏ | 3/150 [00:02<01:31, 1.60it/s]\n 3%|▎ | 5/150 [00:02<00:49, 2.93it/s]\n 5%|▍ | 7/150 [00:02<00:32, 4.38it/s]\n 6%|▌ | 9/150 [00:02<00:24, 5.86it/s]\n 7%|▋ | 11/150 [00:02<00:19, 7.28it/s]\n 9%|▊ | 13/150 [00:03<00:15, 8.57it/s]\n 10%|█ | 15/150 [00:03<00:13, 9.69it/s]\n 11%|█▏ | 17/150 [00:03<00:12, 10.63it/s]\n 13%|█▎ | 19/150 [00:03<00:11, 11.39it/s]\n 14%|█▍ | 21/150 [00:03<00:10, 11.92it/s]\n 15%|█▌ | 23/150 [00:03<00:10, 12.35it/s]\n 17%|█▋ | 25/150 [00:03<00:09, 12.73it/s]\n 18%|█▊ | 27/150 [00:04<00:09, 12.88it/s]\n 19%|█▉ | 29/150 [00:04<00:09, 12.68it/s]\n 21%|██ | 31/150 [00:04<00:09, 12.99it/s]\n 22%|██▏ | 33/150 [00:04<00:08, 13.24it/s]\n 23%|██▎ | 35/150 [00:04<00:08, 13.29it/s]\n 25%|██▍ | 37/150 [00:04<00:08, 13.46it/s]\n 26%|██▌ | 39/150 [00:05<00:08, 13.51it/s]\n 27%|██▋ | 41/150 [00:05<00:08, 13.52it/s]\n 29%|██▊ | 43/150 [00:05<00:07, 13.57it/s]\n 30%|███ | 45/150 [00:05<00:07, 13.65it/s]\n 31%|███▏ | 47/150 [00:05<00:07, 13.64it/s]\n 33%|███▎ | 49/150 [00:05<00:07, 13.54it/s]\n 34%|███▍ | 51/150 [00:05<00:07, 13.58it/s]\n 35%|███▌ | 53/150 [00:06<00:07, 13.66it/s]\n 37%|███▋ | 55/150 [00:06<00:06, 13.72it/s]\n 38%|███▊ | 57/150 [00:06<00:07, 12.92it/s]\n 39%|███▉ | 59/150 [00:06<00:07, 12.22it/s]\n 41%|████ | 61/150 [00:06<00:07, 12.41it/s]\n 42%|████▏ | 63/150 [00:06<00:06, 12.61it/s]\n 43%|████▎ | 65/150 [00:07<00:06, 12.90it/s]\n 45%|████▍ | 67/150 [00:07<00:06, 13.17it/s]\n 46%|████▌ | 69/150 [00:07<00:06, 13.32it/s]\n 47%|████▋ | 71/150 [00:07<00:05, 13.50it/s]\n 49%|████▊ | 73/150 [00:07<00:05, 13.61it/s]\n 50%|█████ | 75/150 [00:07<00:05, 13.47it/s]\n 51%|█████▏ | 77/150 [00:07<00:05, 13.46it/s]\n 53%|█████▎ | 79/150 [00:08<00:05, 13.55it/s]\n 54%|█████▍ | 81/150 [00:08<00:05, 13.61it/s]\n 55%|█████▌ | 83/150 [00:08<00:04, 13.61it/s]\n 57%|█████▋ | 85/150 [00:08<00:04, 13.62it/s]\n 58%|█████▊ | 87/150 [00:08<00:04, 13.67it/s]\n 59%|█████▉ | 89/150 [00:08<00:04, 13.54it/s]\n 61%|██████ | 91/150 [00:08<00:04, 13.47it/s]\n 62%|██████▏ | 93/150 [00:09<00:04, 13.56it/s]\n 63%|██████▎ | 95/150 [00:09<00:04, 13.48it/s]\n 65%|██████▍ | 97/150 [00:09<00:03, 13.48it/s]\n 66%|██████▌ | 99/150 [00:09<00:03, 13.60it/s]\n 67%|██████▋ | 101/150 [00:09<00:03, 13.63it/s]\n 69%|██████▊ | 103/150 [00:09<00:03, 13.64it/s]\n 70%|███████ | 105/150 [00:09<00:03, 13.56it/s]\n 71%|███████▏ | 107/150 [00:10<00:03, 13.57it/s]\n 73%|███████▎ | 109/150 [00:10<00:03, 13.55it/s]\n 74%|███████▍ | 111/150 [00:10<00:02, 13.64it/s]\n 75%|███████▌ | 113/150 [00:10<00:02, 13.70it/s]\n 77%|███████▋ | 115/150 [00:10<00:02, 13.57it/s]\n 78%|███████▊ | 117/150 [00:10<00:02, 13.72it/s]\n 79%|███████▉ | 119/150 [00:10<00:02, 13.82it/s]\n 81%|████████ | 121/150 [00:11<00:02, 13.86it/s]\n 82%|████████▏ | 123/150 [00:11<00:01, 13.88it/s]\n 83%|████████▎ | 125/150 [00:11<00:01, 13.90it/s]\n 85%|████████▍ | 127/150 [00:11<00:01, 13.93it/s]\n 86%|████████▌ | 129/150 [00:11<00:01, 13.80it/s]\n 87%|████████▋ | 131/150 [00:11<00:01, 13.78it/s]\n 89%|████████▊ | 133/150 [00:11<00:01, 13.80it/s]\n 90%|█████████ | 135/150 [00:12<00:01, 13.86it/s]\n 91%|█████████▏| 137/150 [00:12<00:00, 13.88it/s]\n 93%|█████████▎| 139/150 [00:12<00:00, 13.87it/s]\n 94%|█████████▍| 141/150 [00:12<00:00, 13.87it/s]\n 95%|█████████▌| 143/150 [00:12<00:00, 13.61it/s]\n 97%|█████████▋| 145/150 [00:12<00:00, 13.68it/s]\n 98%|█████████▊| 147/150 [00:13<00:00, 13.50it/s]\n 99%|█████████▉| 149/150 [00:13<00:00, 13.62it/s]\n100%|██████████| 150/150 [00:13<00:00, 11.34it/s]", "metrics": { "predict_time": 16.478773, "total_time": 163.219286 }, "output": [ "https://replicate.delivery/pbxt/x1CKfwEm3OUBda2BRorIZRwaQNMrRNkIFO1uqhX5FXUdT7ePA/out-0.png" ], "started_at": "2022-11-08T01:58:01.797339Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/2ggapwprc5cmrpxijznihpzrw4", "cancel": "https://api.replicate.com/v1/predictions/2ggapwprc5cmrpxijznihpzrw4/cancel" }, "version": "1f4cdaee82b13c1f92706a211f55d92d7ee87b13e2c2ba6b998a7817ffc5017f" }Generated inUsing seed: 60774 0%| | 0/150 [00:00<?, ?it/s] 1%| | 1/150 [00:02<05:26, 2.19s/it] 2%|▏ | 3/150 [00:02<01:31, 1.60it/s] 3%|▎ | 5/150 [00:02<00:49, 2.93it/s] 5%|▍ | 7/150 [00:02<00:32, 4.38it/s] 6%|▌ | 9/150 [00:02<00:24, 5.86it/s] 7%|▋ | 11/150 [00:02<00:19, 7.28it/s] 9%|▊ | 13/150 [00:03<00:15, 8.57it/s] 10%|█ | 15/150 [00:03<00:13, 9.69it/s] 11%|█▏ | 17/150 [00:03<00:12, 10.63it/s] 13%|█▎ | 19/150 [00:03<00:11, 11.39it/s] 14%|█▍ | 21/150 [00:03<00:10, 11.92it/s] 15%|█▌ | 23/150 [00:03<00:10, 12.35it/s] 17%|█▋ | 25/150 [00:03<00:09, 12.73it/s] 18%|█▊ | 27/150 [00:04<00:09, 12.88it/s] 19%|█▉ | 29/150 [00:04<00:09, 12.68it/s] 21%|██ | 31/150 [00:04<00:09, 12.99it/s] 22%|██▏ | 33/150 [00:04<00:08, 13.24it/s] 23%|██▎ | 35/150 [00:04<00:08, 13.29it/s] 25%|██▍ | 37/150 [00:04<00:08, 13.46it/s] 26%|██▌ | 39/150 [00:05<00:08, 13.51it/s] 27%|██▋ | 41/150 [00:05<00:08, 13.52it/s] 29%|██▊ | 43/150 [00:05<00:07, 13.57it/s] 30%|███ | 45/150 [00:05<00:07, 13.65it/s] 31%|███▏ | 47/150 [00:05<00:07, 13.64it/s] 33%|███▎ | 49/150 [00:05<00:07, 13.54it/s] 34%|███▍ | 51/150 [00:05<00:07, 13.58it/s] 35%|███▌ | 53/150 [00:06<00:07, 13.66it/s] 37%|███▋ | 55/150 [00:06<00:06, 13.72it/s] 38%|███▊ | 57/150 [00:06<00:07, 12.92it/s] 39%|███▉ | 59/150 [00:06<00:07, 12.22it/s] 41%|████ | 61/150 [00:06<00:07, 12.41it/s] 42%|████▏ | 63/150 [00:06<00:06, 12.61it/s] 43%|████▎ | 65/150 [00:07<00:06, 12.90it/s] 45%|████▍ | 67/150 [00:07<00:06, 13.17it/s] 46%|████▌ | 69/150 [00:07<00:06, 13.32it/s] 47%|████▋ | 71/150 [00:07<00:05, 13.50it/s] 49%|████▊ | 73/150 [00:07<00:05, 13.61it/s] 50%|█████ | 75/150 [00:07<00:05, 13.47it/s] 51%|█████▏ | 77/150 [00:07<00:05, 13.46it/s] 53%|█████▎ | 79/150 [00:08<00:05, 13.55it/s] 54%|█████▍ | 81/150 [00:08<00:05, 13.61it/s] 55%|█████▌ | 83/150 [00:08<00:04, 13.61it/s] 57%|█████▋ | 85/150 [00:08<00:04, 13.62it/s] 58%|█████▊ | 87/150 [00:08<00:04, 13.67it/s] 59%|█████▉ | 89/150 [00:08<00:04, 13.54it/s] 61%|██████ | 91/150 [00:08<00:04, 13.47it/s] 62%|██████▏ | 93/150 [00:09<00:04, 13.56it/s] 63%|██████▎ | 95/150 [00:09<00:04, 13.48it/s] 65%|██████▍ | 97/150 [00:09<00:03, 13.48it/s] 66%|██████▌ | 99/150 [00:09<00:03, 13.60it/s] 67%|██████▋ | 101/150 [00:09<00:03, 13.63it/s] 69%|██████▊ | 103/150 [00:09<00:03, 13.64it/s] 70%|███████ | 105/150 [00:09<00:03, 13.56it/s] 71%|███████▏ | 107/150 [00:10<00:03, 13.57it/s] 73%|███████▎ | 109/150 [00:10<00:03, 13.55it/s] 74%|███████▍ | 111/150 [00:10<00:02, 13.64it/s] 75%|███████▌ | 113/150 [00:10<00:02, 13.70it/s] 77%|███████▋ | 115/150 [00:10<00:02, 13.57it/s] 78%|███████▊ | 117/150 [00:10<00:02, 13.72it/s] 79%|███████▉ | 119/150 [00:10<00:02, 13.82it/s] 81%|████████ | 121/150 [00:11<00:02, 13.86it/s] 82%|████████▏ | 123/150 [00:11<00:01, 13.88it/s] 83%|████████▎ | 125/150 [00:11<00:01, 13.90it/s] 85%|████████▍ | 127/150 [00:11<00:01, 13.93it/s] 86%|████████▌ | 129/150 [00:11<00:01, 13.80it/s] 87%|████████▋ | 131/150 [00:11<00:01, 13.78it/s] 89%|████████▊ | 133/150 [00:11<00:01, 13.80it/s] 90%|█████████ | 135/150 [00:12<00:01, 13.86it/s] 91%|█████████▏| 137/150 [00:12<00:00, 13.88it/s] 93%|█████████▎| 139/150 [00:12<00:00, 13.87it/s] 94%|█████████▍| 141/150 [00:12<00:00, 13.87it/s] 95%|█████████▌| 143/150 [00:12<00:00, 13.61it/s] 97%|█████████▋| 145/150 [00:12<00:00, 13.68it/s] 98%|█████████▊| 147/150 [00:13<00:00, 13.50it/s] 99%|█████████▉| 149/150 [00:13<00:00, 13.62it/s] 100%|██████████| 150/150 [00:13<00:00, 11.34it/s]
Want to make some of these yourself?
Run this model
