charlesmccarthy / terminus-xl-otaku-v1
Terminus XL Otaku is a latent diffusion model that uses zero-terminal SNR noise schedule and velocity prediction objective at training and inference time.
- Public
- 40 runs
-
L40S
Prediction
charlesmccarthy/terminus-xl-otaku-v1:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bccaIDv4yarmdbozamrlwcgytylzuaseStatusSucceededSourceWebHardwareA40Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- joe biden eating a cheeeseburger at mcdonalds
- scheduler
- K_EULER
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- negative_prompt
- num_inference_steps
- 25
{ "width": 1024, "height": 1024, "prompt": "joe biden eating a cheeeseburger at mcdonalds", "scheduler": "K_EULER", "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "negative_prompt": "", "num_inference_steps": 25 }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 charlesmccarthy/terminus-xl-otaku-v1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "charlesmccarthy/terminus-xl-otaku-v1:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca", { input: { width: 1024, height: 1024, prompt: "joe biden eating a cheeeseburger at mcdonalds", scheduler: "K_EULER", num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, negative_prompt: "", 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 replicateImport the client:import replicateRun charlesmccarthy/terminus-xl-otaku-v1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "charlesmccarthy/terminus-xl-otaku-v1:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca", input={ "width": 1024, "height": 1024, "prompt": "joe biden eating a cheeeseburger at mcdonalds", "scheduler": "K_EULER", "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "negative_prompt": "", "num_inference_steps": 25 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run charlesmccarthy/terminus-xl-otaku-v1 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": "charlesmccarthy/terminus-xl-otaku-v1:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca", "input": { "width": 1024, "height": 1024, "prompt": "joe biden eating a cheeeseburger at mcdonalds", "scheduler": "K_EULER", "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "negative_prompt": "", "num_inference_steps": 25 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-26T05:17:33.431718Z", "created_at": "2023-12-26T05:14:30.102887Z", "data_removed": false, "error": null, "id": "v4yarmdbozamrlwcgytylzuase", "input": { "width": 1024, "height": 1024, "prompt": "joe biden eating a cheeeseburger at mcdonalds", "scheduler": "K_EULER", "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "negative_prompt": "", "num_inference_steps": 25 }, "logs": "Using seed: 4177073\n 0%| | 0/25 [00:00<?, ?it/s]\n 4%|▍ | 1/25 [00:00<00:08, 2.98it/s]\n 8%|▊ | 2/25 [00:00<00:05, 3.93it/s]\n 12%|█▏ | 3/25 [00:00<00:05, 4.37it/s]\n 16%|█▌ | 4/25 [00:00<00:04, 4.61it/s]\n 20%|██ | 5/25 [00:01<00:04, 4.76it/s]\n 24%|██▍ | 6/25 [00:01<00:03, 4.84it/s]\n 28%|██▊ | 7/25 [00:01<00:03, 4.89it/s]\n 32%|███▏ | 8/25 [00:01<00:03, 4.93it/s]\n 36%|███▌ | 9/25 [00:01<00:03, 4.96it/s]\n 40%|████ | 10/25 [00:02<00:03, 4.97it/s]\n 44%|████▍ | 11/25 [00:02<00:02, 4.98it/s]\n 48%|████▊ | 12/25 [00:02<00:02, 4.99it/s]\n 52%|█████▏ | 13/25 [00:02<00:02, 4.98it/s]\n 56%|█████▌ | 14/25 [00:02<00:02, 4.99it/s]\n 60%|██████ | 15/25 [00:03<00:02, 4.99it/s]\n 64%|██████▍ | 16/25 [00:03<00:01, 4.99it/s]\n 68%|██████▊ | 17/25 [00:03<00:01, 5.00it/s]\n 72%|███████▏ | 18/25 [00:03<00:01, 4.99it/s]\n 76%|███████▌ | 19/25 [00:03<00:01, 5.00it/s]\n 80%|████████ | 20/25 [00:04<00:01, 5.00it/s]\n 84%|████████▍ | 21/25 [00:04<00:00, 5.00it/s]\n 88%|████████▊ | 22/25 [00:04<00:00, 5.00it/s]\n 92%|█████████▏| 23/25 [00:04<00:00, 5.00it/s]\n 96%|█████████▌| 24/25 [00:04<00:00, 5.00it/s]\n100%|██████████| 25/25 [00:05<00:00, 5.00it/s]\n100%|██████████| 25/25 [00:05<00:00, 4.87it/s]", "metrics": { "predict_time": 7.122212, "total_time": 183.328831 }, "output": [ "https://replicate.delivery/pbxt/IEYnPTueeWs9HUb2xtL2mR1CLasKcnFX5Dv6vDX0ERvsPBGSA/out-0.png" ], "started_at": "2023-12-26T05:17:26.309506Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/v4yarmdbozamrlwcgytylzuase", "cancel": "https://api.replicate.com/v1/predictions/v4yarmdbozamrlwcgytylzuase/cancel" }, "version": "7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca" }Generated inUsing seed: 4177073 0%| | 0/25 [00:00<?, ?it/s] 4%|▍ | 1/25 [00:00<00:08, 2.98it/s] 8%|▊ | 2/25 [00:00<00:05, 3.93it/s] 12%|█▏ | 3/25 [00:00<00:05, 4.37it/s] 16%|█▌ | 4/25 [00:00<00:04, 4.61it/s] 20%|██ | 5/25 [00:01<00:04, 4.76it/s] 24%|██▍ | 6/25 [00:01<00:03, 4.84it/s] 28%|██▊ | 7/25 [00:01<00:03, 4.89it/s] 32%|███▏ | 8/25 [00:01<00:03, 4.93it/s] 36%|███▌ | 9/25 [00:01<00:03, 4.96it/s] 40%|████ | 10/25 [00:02<00:03, 4.97it/s] 44%|████▍ | 11/25 [00:02<00:02, 4.98it/s] 48%|████▊ | 12/25 [00:02<00:02, 4.99it/s] 52%|█████▏ | 13/25 [00:02<00:02, 4.98it/s] 56%|█████▌ | 14/25 [00:02<00:02, 4.99it/s] 60%|██████ | 15/25 [00:03<00:02, 4.99it/s] 64%|██████▍ | 16/25 [00:03<00:01, 4.99it/s] 68%|██████▊ | 17/25 [00:03<00:01, 5.00it/s] 72%|███████▏ | 18/25 [00:03<00:01, 4.99it/s] 76%|███████▌ | 19/25 [00:03<00:01, 5.00it/s] 80%|████████ | 20/25 [00:04<00:01, 5.00it/s] 84%|████████▍ | 21/25 [00:04<00:00, 5.00it/s] 88%|████████▊ | 22/25 [00:04<00:00, 5.00it/s] 92%|█████████▏| 23/25 [00:04<00:00, 5.00it/s] 96%|█████████▌| 24/25 [00:04<00:00, 5.00it/s] 100%|██████████| 25/25 [00:05<00:00, 5.00it/s] 100%|██████████| 25/25 [00:05<00:00, 4.87it/s]Prediction
charlesmccarthy/terminus-xl-otaku-v1:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bccaIDdovjsu3bbeus5elbpcdvm7fecyStatusSucceededSourceWebHardwareA40Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- jlo as artwork
- scheduler
- DPMSolverMultistep
- num_outputs
- 1
- guidance_scale
- 6.5
- apply_watermark
- negative_prompt
- num_inference_steps
- 33
{ "width": 1024, "height": 1024, "prompt": "jlo as artwork", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": 6.5, "apply_watermark": true, "negative_prompt": "", "num_inference_steps": 33 }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 charlesmccarthy/terminus-xl-otaku-v1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "charlesmccarthy/terminus-xl-otaku-v1:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca", { input: { width: 1024, height: 1024, prompt: "jlo as artwork", scheduler: "DPMSolverMultistep", num_outputs: 1, guidance_scale: 6.5, apply_watermark: true, negative_prompt: "", num_inference_steps: 33 } } ); // 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 charlesmccarthy/terminus-xl-otaku-v1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "charlesmccarthy/terminus-xl-otaku-v1:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca", input={ "width": 1024, "height": 1024, "prompt": "jlo as artwork", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": 6.5, "apply_watermark": True, "negative_prompt": "", "num_inference_steps": 33 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run charlesmccarthy/terminus-xl-otaku-v1 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": "charlesmccarthy/terminus-xl-otaku-v1:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca", "input": { "width": 1024, "height": 1024, "prompt": "jlo as artwork", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": 6.5, "apply_watermark": true, "negative_prompt": "", "num_inference_steps": 33 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-26T05:23:27.786839Z", "created_at": "2023-12-26T05:23:19.193719Z", "data_removed": false, "error": null, "id": "dovjsu3bbeus5elbpcdvm7fecy", "input": { "width": 1024, "height": 1024, "prompt": "jlo as artwork", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": 6.5, "apply_watermark": true, "negative_prompt": "", "num_inference_steps": 33 }, "logs": "Using seed: 11974137\n 0%| | 0/33 [00:00<?, ?it/s]\n 3%|▎ | 1/33 [00:00<00:06, 5.06it/s]\n 9%|▉ | 3/33 [00:00<00:04, 6.24it/s]\n 12%|█▏ | 4/33 [00:00<00:05, 5.76it/s]\n 15%|█▌ | 5/33 [00:00<00:05, 5.51it/s]\n 18%|█▊ | 6/33 [00:01<00:05, 5.34it/s]\n 21%|██ | 7/33 [00:01<00:04, 5.24it/s]\n 24%|██▍ | 8/33 [00:01<00:04, 5.18it/s]\n 27%|██▋ | 9/33 [00:01<00:04, 5.14it/s]\n 30%|███ | 10/33 [00:01<00:04, 5.11it/s]\n 33%|███▎ | 11/33 [00:02<00:04, 5.08it/s]\n 36%|███▋ | 12/33 [00:02<00:04, 5.07it/s]\n 39%|███▉ | 13/33 [00:02<00:03, 5.06it/s]\n 42%|████▏ | 14/33 [00:02<00:03, 5.03it/s]\n 45%|████▌ | 15/33 [00:02<00:03, 4.96it/s]\n 48%|████▊ | 16/33 [00:03<00:03, 4.98it/s]\n 52%|█████▏ | 17/33 [00:03<00:03, 4.99it/s]\n 55%|█████▍ | 18/33 [00:03<00:02, 5.00it/s]\n 58%|█████▊ | 19/33 [00:03<00:02, 5.01it/s]\n 61%|██████ | 20/33 [00:03<00:02, 5.01it/s]\n 64%|██████▎ | 21/33 [00:04<00:02, 5.02it/s]\n 67%|██████▋ | 22/33 [00:04<00:02, 5.02it/s]\n 70%|██████▉ | 23/33 [00:04<00:01, 5.02it/s]\n 73%|███████▎ | 24/33 [00:04<00:01, 5.02it/s]\n 76%|███████▌ | 25/33 [00:04<00:01, 5.02it/s]\n 79%|███████▉ | 26/33 [00:05<00:01, 5.02it/s]\n 82%|████████▏ | 27/33 [00:05<00:01, 5.02it/s]\n 85%|████████▍ | 28/33 [00:05<00:00, 5.01it/s]\n 88%|████████▊ | 29/33 [00:05<00:00, 5.01it/s]\n 91%|█████████ | 30/33 [00:05<00:00, 5.00it/s]\n 94%|█████████▍| 31/33 [00:06<00:00, 5.00it/s]\n 97%|█████████▋| 32/33 [00:06<00:00, 5.01it/s]\n100%|██████████| 33/33 [00:06<00:00, 5.00it/s]\n100%|██████████| 33/33 [00:06<00:00, 5.10it/s]", "metrics": { "predict_time": 8.55192, "total_time": 8.59312 }, "output": [ "https://replicate.delivery/pbxt/5LfQDeOcydhR3UIce0JP70V27VY62vZaGBEM95XSyRudqCMkA/out-0.png" ], "started_at": "2023-12-26T05:23:19.234919Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/dovjsu3bbeus5elbpcdvm7fecy", "cancel": "https://api.replicate.com/v1/predictions/dovjsu3bbeus5elbpcdvm7fecy/cancel" }, "version": "7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca" }Generated inUsing seed: 11974137 0%| | 0/33 [00:00<?, ?it/s] 3%|▎ | 1/33 [00:00<00:06, 5.06it/s] 9%|▉ | 3/33 [00:00<00:04, 6.24it/s] 12%|█▏ | 4/33 [00:00<00:05, 5.76it/s] 15%|█▌ | 5/33 [00:00<00:05, 5.51it/s] 18%|█▊ | 6/33 [00:01<00:05, 5.34it/s] 21%|██ | 7/33 [00:01<00:04, 5.24it/s] 24%|██▍ | 8/33 [00:01<00:04, 5.18it/s] 27%|██▋ | 9/33 [00:01<00:04, 5.14it/s] 30%|███ | 10/33 [00:01<00:04, 5.11it/s] 33%|███▎ | 11/33 [00:02<00:04, 5.08it/s] 36%|███▋ | 12/33 [00:02<00:04, 5.07it/s] 39%|███▉ | 13/33 [00:02<00:03, 5.06it/s] 42%|████▏ | 14/33 [00:02<00:03, 5.03it/s] 45%|████▌ | 15/33 [00:02<00:03, 4.96it/s] 48%|████▊ | 16/33 [00:03<00:03, 4.98it/s] 52%|█████▏ | 17/33 [00:03<00:03, 4.99it/s] 55%|█████▍ | 18/33 [00:03<00:02, 5.00it/s] 58%|█████▊ | 19/33 [00:03<00:02, 5.01it/s] 61%|██████ | 20/33 [00:03<00:02, 5.01it/s] 64%|██████▎ | 21/33 [00:04<00:02, 5.02it/s] 67%|██████▋ | 22/33 [00:04<00:02, 5.02it/s] 70%|██████▉ | 23/33 [00:04<00:01, 5.02it/s] 73%|███████▎ | 24/33 [00:04<00:01, 5.02it/s] 76%|███████▌ | 25/33 [00:04<00:01, 5.02it/s] 79%|███████▉ | 26/33 [00:05<00:01, 5.02it/s] 82%|████████▏ | 27/33 [00:05<00:01, 5.02it/s] 85%|████████▍ | 28/33 [00:05<00:00, 5.01it/s] 88%|████████▊ | 29/33 [00:05<00:00, 5.01it/s] 91%|█████████ | 30/33 [00:05<00:00, 5.00it/s] 94%|█████████▍| 31/33 [00:06<00:00, 5.00it/s] 97%|█████████▋| 32/33 [00:06<00:00, 5.01it/s] 100%|██████████| 33/33 [00:06<00:00, 5.00it/s] 100%|██████████| 33/33 [00:06<00:00, 5.10it/s]Prediction
charlesmccarthy/terminus-xl-otaku-v1:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bccaIDnwndyklbyng3rnilchojoyg3biStatusSucceededSourceWebHardwareA40Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- The Joker, Cinematic still, vibrant, bokeh
- scheduler
- K_EULER_ANCESTRAL
- num_outputs
- 1
- guidance_scale
- 6.5
- apply_watermark
- negative_prompt
- num_inference_steps
- 33
{ "width": 1024, "height": 1024, "prompt": "The Joker, Cinematic still, vibrant, bokeh", "scheduler": "K_EULER_ANCESTRAL", "num_outputs": 1, "guidance_scale": 6.5, "apply_watermark": true, "negative_prompt": "", "num_inference_steps": 33 }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 charlesmccarthy/terminus-xl-otaku-v1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "charlesmccarthy/terminus-xl-otaku-v1:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca", { input: { width: 1024, height: 1024, prompt: "The Joker, Cinematic still, vibrant, bokeh", scheduler: "K_EULER_ANCESTRAL", num_outputs: 1, guidance_scale: 6.5, apply_watermark: true, negative_prompt: "", num_inference_steps: 33 } } ); // 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 charlesmccarthy/terminus-xl-otaku-v1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "charlesmccarthy/terminus-xl-otaku-v1:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca", input={ "width": 1024, "height": 1024, "prompt": "The Joker, Cinematic still, vibrant, bokeh", "scheduler": "K_EULER_ANCESTRAL", "num_outputs": 1, "guidance_scale": 6.5, "apply_watermark": True, "negative_prompt": "", "num_inference_steps": 33 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run charlesmccarthy/terminus-xl-otaku-v1 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": "charlesmccarthy/terminus-xl-otaku-v1:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca", "input": { "width": 1024, "height": 1024, "prompt": "The Joker, Cinematic still, vibrant, bokeh", "scheduler": "K_EULER_ANCESTRAL", "num_outputs": 1, "guidance_scale": 6.5, "apply_watermark": true, "negative_prompt": "", "num_inference_steps": 33 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-26T05:28:00.493628Z", "created_at": "2023-12-26T05:27:51.924837Z", "data_removed": false, "error": null, "id": "nwndyklbyng3rnilchojoyg3bi", "input": { "width": 1024, "height": 1024, "prompt": "The Joker, Cinematic still, vibrant, bokeh", "scheduler": "K_EULER_ANCESTRAL", "num_outputs": 1, "guidance_scale": 6.5, "apply_watermark": true, "negative_prompt": "", "num_inference_steps": 33 }, "logs": "Using seed: 1899355\n 0%| | 0/33 [00:00<?, ?it/s]\n 6%|▌ | 2/33 [00:00<00:04, 6.81it/s]\n 9%|▉ | 3/33 [00:00<00:05, 5.94it/s]\n 12%|█▏ | 4/33 [00:00<00:05, 5.57it/s]\n 15%|█▌ | 5/33 [00:00<00:05, 5.37it/s]\n 18%|█▊ | 6/33 [00:01<00:05, 5.24it/s]\n 21%|██ | 7/33 [00:01<00:05, 5.17it/s]\n 24%|██▍ | 8/33 [00:01<00:04, 5.12it/s]\n 27%|██▋ | 9/33 [00:01<00:04, 5.09it/s]\n 30%|███ | 10/33 [00:01<00:04, 5.06it/s]\n 33%|███▎ | 11/33 [00:02<00:04, 5.04it/s]\n 36%|███▋ | 12/33 [00:02<00:04, 5.03it/s]\n 39%|███▉ | 13/33 [00:02<00:03, 5.02it/s]\n 42%|████▏ | 14/33 [00:02<00:03, 5.02it/s]\n 45%|████▌ | 15/33 [00:02<00:03, 5.01it/s]\n 48%|████▊ | 16/33 [00:03<00:03, 5.01it/s]\n 52%|█████▏ | 17/33 [00:03<00:03, 5.01it/s]\n 55%|█████▍ | 18/33 [00:03<00:02, 5.00it/s]\n 58%|█████▊ | 19/33 [00:03<00:02, 5.00it/s]\n 61%|██████ | 20/33 [00:03<00:02, 5.00it/s]\n 64%|██████▎ | 21/33 [00:04<00:02, 5.00it/s]\n 67%|██████▋ | 22/33 [00:04<00:02, 5.00it/s]\n 70%|██████▉ | 23/33 [00:04<00:02, 5.00it/s]\n 73%|███████▎ | 24/33 [00:04<00:01, 4.99it/s]\n 76%|███████▌ | 25/33 [00:04<00:01, 4.99it/s]\n 79%|███████▉ | 26/33 [00:05<00:01, 4.99it/s]\n 82%|████████▏ | 27/33 [00:05<00:01, 4.99it/s]\n 85%|████████▍ | 28/33 [00:05<00:01, 4.99it/s]\n 88%|████████▊ | 29/33 [00:05<00:00, 4.99it/s]\n 91%|█████████ | 30/33 [00:05<00:00, 4.98it/s]\n 94%|█████████▍| 31/33 [00:06<00:00, 4.98it/s]\n 97%|█████████▋| 32/33 [00:06<00:00, 4.98it/s]\n100%|██████████| 33/33 [00:06<00:00, 4.98it/s]\n100%|██████████| 33/33 [00:06<00:00, 5.08it/s]", "metrics": { "predict_time": 8.529504, "total_time": 8.568791 }, "output": [ "https://replicate.delivery/pbxt/WL0lEwlzmR4fOyQoNDtI3kpY2OmDH9ypRf5KfxBkkdMflFYIB/out-0.png" ], "started_at": "2023-12-26T05:27:51.964124Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/nwndyklbyng3rnilchojoyg3bi", "cancel": "https://api.replicate.com/v1/predictions/nwndyklbyng3rnilchojoyg3bi/cancel" }, "version": "7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca" }Generated inUsing seed: 1899355 0%| | 0/33 [00:00<?, ?it/s] 6%|▌ | 2/33 [00:00<00:04, 6.81it/s] 9%|▉ | 3/33 [00:00<00:05, 5.94it/s] 12%|█▏ | 4/33 [00:00<00:05, 5.57it/s] 15%|█▌ | 5/33 [00:00<00:05, 5.37it/s] 18%|█▊ | 6/33 [00:01<00:05, 5.24it/s] 21%|██ | 7/33 [00:01<00:05, 5.17it/s] 24%|██▍ | 8/33 [00:01<00:04, 5.12it/s] 27%|██▋ | 9/33 [00:01<00:04, 5.09it/s] 30%|███ | 10/33 [00:01<00:04, 5.06it/s] 33%|███▎ | 11/33 [00:02<00:04, 5.04it/s] 36%|███▋ | 12/33 [00:02<00:04, 5.03it/s] 39%|███▉ | 13/33 [00:02<00:03, 5.02it/s] 42%|████▏ | 14/33 [00:02<00:03, 5.02it/s] 45%|████▌ | 15/33 [00:02<00:03, 5.01it/s] 48%|████▊ | 16/33 [00:03<00:03, 5.01it/s] 52%|█████▏ | 17/33 [00:03<00:03, 5.01it/s] 55%|█████▍ | 18/33 [00:03<00:02, 5.00it/s] 58%|█████▊ | 19/33 [00:03<00:02, 5.00it/s] 61%|██████ | 20/33 [00:03<00:02, 5.00it/s] 64%|██████▎ | 21/33 [00:04<00:02, 5.00it/s] 67%|██████▋ | 22/33 [00:04<00:02, 5.00it/s] 70%|██████▉ | 23/33 [00:04<00:02, 5.00it/s] 73%|███████▎ | 24/33 [00:04<00:01, 4.99it/s] 76%|███████▌ | 25/33 [00:04<00:01, 4.99it/s] 79%|███████▉ | 26/33 [00:05<00:01, 4.99it/s] 82%|████████▏ | 27/33 [00:05<00:01, 4.99it/s] 85%|████████▍ | 28/33 [00:05<00:01, 4.99it/s] 88%|████████▊ | 29/33 [00:05<00:00, 4.99it/s] 91%|█████████ | 30/33 [00:05<00:00, 4.98it/s] 94%|█████████▍| 31/33 [00:06<00:00, 4.98it/s] 97%|█████████▋| 32/33 [00:06<00:00, 4.98it/s] 100%|██████████| 33/33 [00:06<00:00, 4.98it/s] 100%|██████████| 33/33 [00:06<00:00, 5.08it/s]Prediction
charlesmccarthy/terminus-xl-otaku-v1:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bccaID35au3olbdn7ejhs7ipblddninuStatusSucceededSourceWebHardwareA40Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- jlo as artwork
- scheduler
- DPMSolverMultistep
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- negative_prompt
- num_inference_steps
- 33
{ "width": 1024, "height": 1024, "prompt": "jlo as artwork", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "negative_prompt": "", "num_inference_steps": 33 }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 charlesmccarthy/terminus-xl-otaku-v1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "charlesmccarthy/terminus-xl-otaku-v1:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca", { input: { width: 1024, height: 1024, prompt: "jlo as artwork", scheduler: "DPMSolverMultistep", num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, negative_prompt: "", num_inference_steps: 33 } } ); // 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 charlesmccarthy/terminus-xl-otaku-v1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "charlesmccarthy/terminus-xl-otaku-v1:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca", input={ "width": 1024, "height": 1024, "prompt": "jlo as artwork", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "negative_prompt": "", "num_inference_steps": 33 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run charlesmccarthy/terminus-xl-otaku-v1 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": "charlesmccarthy/terminus-xl-otaku-v1:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca", "input": { "width": 1024, "height": 1024, "prompt": "jlo as artwork", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "negative_prompt": "", "num_inference_steps": 33 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-26T05:23:03.651623Z", "created_at": "2023-12-26T05:22:55.083057Z", "data_removed": false, "error": null, "id": "35au3olbdn7ejhs7ipblddninu", "input": { "width": 1024, "height": 1024, "prompt": "jlo as artwork", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "negative_prompt": "", "num_inference_steps": 33 }, "logs": "Using seed: 3751455\n 0%| | 0/33 [00:00<?, ?it/s]\n 3%|▎ | 1/33 [00:00<00:06, 5.07it/s]\n 9%|▉ | 3/33 [00:00<00:04, 6.26it/s]\n 12%|█▏ | 4/33 [00:00<00:05, 5.78it/s]\n 15%|█▌ | 5/33 [00:00<00:05, 5.51it/s]\n 18%|█▊ | 6/33 [00:01<00:05, 5.34it/s]\n 21%|██ | 7/33 [00:01<00:04, 5.23it/s]\n 24%|██▍ | 8/33 [00:01<00:04, 5.16it/s]\n 27%|██▋ | 9/33 [00:01<00:04, 5.13it/s]\n 30%|███ | 10/33 [00:01<00:04, 5.11it/s]\n 33%|███▎ | 11/33 [00:02<00:04, 5.09it/s]\n 36%|███▋ | 12/33 [00:02<00:04, 5.08it/s]\n 39%|███▉ | 13/33 [00:02<00:03, 5.07it/s]\n 42%|████▏ | 14/33 [00:02<00:03, 5.06it/s]\n 45%|████▌ | 15/33 [00:02<00:03, 5.05it/s]\n 48%|████▊ | 16/33 [00:03<00:03, 5.05it/s]\n 52%|█████▏ | 17/33 [00:03<00:03, 5.05it/s]\n 55%|█████▍ | 18/33 [00:03<00:02, 5.04it/s]\n 58%|█████▊ | 19/33 [00:03<00:02, 5.04it/s]\n 61%|██████ | 20/33 [00:03<00:02, 5.04it/s]\n 64%|██████▎ | 21/33 [00:04<00:02, 5.04it/s]\n 67%|██████▋ | 22/33 [00:04<00:02, 5.04it/s]\n 70%|██████▉ | 23/33 [00:04<00:01, 5.04it/s]\n 73%|███████▎ | 24/33 [00:04<00:01, 5.04it/s]\n 76%|███████▌ | 25/33 [00:04<00:01, 5.03it/s]\n 79%|███████▉ | 26/33 [00:05<00:01, 5.03it/s]\n 82%|████████▏ | 27/33 [00:05<00:01, 5.04it/s]\n 85%|████████▍ | 28/33 [00:05<00:00, 5.04it/s]\n 88%|████████▊ | 29/33 [00:05<00:00, 5.03it/s]\n 91%|█████████ | 30/33 [00:05<00:00, 5.03it/s]\n 94%|█████████▍| 31/33 [00:06<00:00, 5.03it/s]\n 97%|█████████▋| 32/33 [00:06<00:00, 5.03it/s]\n100%|██████████| 33/33 [00:06<00:00, 5.03it/s]\n100%|██████████| 33/33 [00:06<00:00, 5.12it/s]", "metrics": { "predict_time": 8.531742, "total_time": 8.568566 }, "output": [ "https://replicate.delivery/pbxt/s3QJiy5o4j6CGFQF0e2JvJLtmJGfnoaJhWEbvfYWb30tpCMkA/out-0.png" ], "started_at": "2023-12-26T05:22:55.119881Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/35au3olbdn7ejhs7ipblddninu", "cancel": "https://api.replicate.com/v1/predictions/35au3olbdn7ejhs7ipblddninu/cancel" }, "version": "7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca" }Generated inUsing seed: 3751455 0%| | 0/33 [00:00<?, ?it/s] 3%|▎ | 1/33 [00:00<00:06, 5.07it/s] 9%|▉ | 3/33 [00:00<00:04, 6.26it/s] 12%|█▏ | 4/33 [00:00<00:05, 5.78it/s] 15%|█▌ | 5/33 [00:00<00:05, 5.51it/s] 18%|█▊ | 6/33 [00:01<00:05, 5.34it/s] 21%|██ | 7/33 [00:01<00:04, 5.23it/s] 24%|██▍ | 8/33 [00:01<00:04, 5.16it/s] 27%|██▋ | 9/33 [00:01<00:04, 5.13it/s] 30%|███ | 10/33 [00:01<00:04, 5.11it/s] 33%|███▎ | 11/33 [00:02<00:04, 5.09it/s] 36%|███▋ | 12/33 [00:02<00:04, 5.08it/s] 39%|███▉ | 13/33 [00:02<00:03, 5.07it/s] 42%|████▏ | 14/33 [00:02<00:03, 5.06it/s] 45%|████▌ | 15/33 [00:02<00:03, 5.05it/s] 48%|████▊ | 16/33 [00:03<00:03, 5.05it/s] 52%|█████▏ | 17/33 [00:03<00:03, 5.05it/s] 55%|█████▍ | 18/33 [00:03<00:02, 5.04it/s] 58%|█████▊ | 19/33 [00:03<00:02, 5.04it/s] 61%|██████ | 20/33 [00:03<00:02, 5.04it/s] 64%|██████▎ | 21/33 [00:04<00:02, 5.04it/s] 67%|██████▋ | 22/33 [00:04<00:02, 5.04it/s] 70%|██████▉ | 23/33 [00:04<00:01, 5.04it/s] 73%|███████▎ | 24/33 [00:04<00:01, 5.04it/s] 76%|███████▌ | 25/33 [00:04<00:01, 5.03it/s] 79%|███████▉ | 26/33 [00:05<00:01, 5.03it/s] 82%|████████▏ | 27/33 [00:05<00:01, 5.04it/s] 85%|████████▍ | 28/33 [00:05<00:00, 5.04it/s] 88%|████████▊ | 29/33 [00:05<00:00, 5.03it/s] 91%|█████████ | 30/33 [00:05<00:00, 5.03it/s] 94%|█████████▍| 31/33 [00:06<00:00, 5.03it/s] 97%|█████████▋| 32/33 [00:06<00:00, 5.03it/s] 100%|██████████| 33/33 [00:06<00:00, 5.03it/s] 100%|██████████| 33/33 [00:06<00:00, 5.12it/s]Prediction
charlesmccarthy/terminus-xl-otaku-v1:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bccaIDz4f5ro3beaphl7etukvf7pzkimStatusSucceededSourceWebHardwareA40Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- jlo as artwork
- scheduler
- K_EULER_ANCESTRAL
- num_outputs
- 1
- guidance_scale
- 6.5
- apply_watermark
- negative_prompt
- num_inference_steps
- 33
{ "width": 1024, "height": 1024, "prompt": "jlo as artwork", "scheduler": "K_EULER_ANCESTRAL", "num_outputs": 1, "guidance_scale": 6.5, "apply_watermark": true, "negative_prompt": "", "num_inference_steps": 33 }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 charlesmccarthy/terminus-xl-otaku-v1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "charlesmccarthy/terminus-xl-otaku-v1:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca", { input: { width: 1024, height: 1024, prompt: "jlo as artwork", scheduler: "K_EULER_ANCESTRAL", num_outputs: 1, guidance_scale: 6.5, apply_watermark: true, negative_prompt: "", num_inference_steps: 33 } } ); // 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 charlesmccarthy/terminus-xl-otaku-v1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "charlesmccarthy/terminus-xl-otaku-v1:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca", input={ "width": 1024, "height": 1024, "prompt": "jlo as artwork", "scheduler": "K_EULER_ANCESTRAL", "num_outputs": 1, "guidance_scale": 6.5, "apply_watermark": True, "negative_prompt": "", "num_inference_steps": 33 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run charlesmccarthy/terminus-xl-otaku-v1 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": "charlesmccarthy/terminus-xl-otaku-v1:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca", "input": { "width": 1024, "height": 1024, "prompt": "jlo as artwork", "scheduler": "K_EULER_ANCESTRAL", "num_outputs": 1, "guidance_scale": 6.5, "apply_watermark": true, "negative_prompt": "", "num_inference_steps": 33 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-26T05:24:52.672850Z", "created_at": "2023-12-26T05:24:44.108746Z", "data_removed": false, "error": null, "id": "z4f5ro3beaphl7etukvf7pzkim", "input": { "width": 1024, "height": 1024, "prompt": "jlo as artwork", "scheduler": "K_EULER_ANCESTRAL", "num_outputs": 1, "guidance_scale": 6.5, "apply_watermark": true, "negative_prompt": "", "num_inference_steps": 33 }, "logs": "Using seed: 6031873\n 0%| | 0/33 [00:00<?, ?it/s]\n 6%|▌ | 2/33 [00:00<00:04, 6.82it/s]\n 9%|▉ | 3/33 [00:00<00:05, 5.94it/s]\n 12%|█▏ | 4/33 [00:00<00:05, 5.57it/s]\n 15%|█▌ | 5/33 [00:00<00:05, 5.37it/s]\n 18%|█▊ | 6/33 [00:01<00:05, 5.23it/s]\n 21%|██ | 7/33 [00:01<00:05, 5.16it/s]\n 24%|██▍ | 8/33 [00:01<00:04, 5.12it/s]\n 27%|██▋ | 9/33 [00:01<00:04, 5.08it/s]\n 30%|███ | 10/33 [00:01<00:04, 5.06it/s]\n 33%|███▎ | 11/33 [00:02<00:04, 5.04it/s]\n 36%|███▋ | 12/33 [00:02<00:04, 5.03it/s]\n 39%|███▉ | 13/33 [00:02<00:03, 5.02it/s]\n 42%|████▏ | 14/33 [00:02<00:03, 5.02it/s]\n 45%|████▌ | 15/33 [00:02<00:03, 5.01it/s]\n 48%|████▊ | 16/33 [00:03<00:03, 5.01it/s]\n 52%|█████▏ | 17/33 [00:03<00:03, 5.00it/s]\n 55%|█████▍ | 18/33 [00:03<00:03, 5.00it/s]\n 58%|█████▊ | 19/33 [00:03<00:02, 4.99it/s]\n 61%|██████ | 20/33 [00:03<00:02, 4.99it/s]\n 64%|██████▎ | 21/33 [00:04<00:02, 4.99it/s]\n 67%|██████▋ | 22/33 [00:04<00:02, 4.99it/s]\n 70%|██████▉ | 23/33 [00:04<00:02, 4.98it/s]\n 73%|███████▎ | 24/33 [00:04<00:01, 4.98it/s]\n 76%|███████▌ | 25/33 [00:04<00:01, 4.98it/s]\n 79%|███████▉ | 26/33 [00:05<00:01, 4.98it/s]\n 82%|████████▏ | 27/33 [00:05<00:01, 4.98it/s]\n 85%|████████▍ | 28/33 [00:05<00:01, 4.98it/s]\n 88%|████████▊ | 29/33 [00:05<00:00, 4.97it/s]\n 91%|█████████ | 30/33 [00:05<00:00, 4.98it/s]\n 94%|█████████▍| 31/33 [00:06<00:00, 4.98it/s]\n 97%|█████████▋| 32/33 [00:06<00:00, 4.98it/s]\n100%|██████████| 33/33 [00:06<00:00, 4.98it/s]\n100%|██████████| 33/33 [00:06<00:00, 5.08it/s]", "metrics": { "predict_time": 8.527941, "total_time": 8.564104 }, "output": [ "https://replicate.delivery/pbxt/4yHLkENPZlLeISTt96eXjfMbDu2TUAelsai1G5h6r3fY0KwQC/out-0.png" ], "started_at": "2023-12-26T05:24:44.144909Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/z4f5ro3beaphl7etukvf7pzkim", "cancel": "https://api.replicate.com/v1/predictions/z4f5ro3beaphl7etukvf7pzkim/cancel" }, "version": "7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca" }Generated inUsing seed: 6031873 0%| | 0/33 [00:00<?, ?it/s] 6%|▌ | 2/33 [00:00<00:04, 6.82it/s] 9%|▉ | 3/33 [00:00<00:05, 5.94it/s] 12%|█▏ | 4/33 [00:00<00:05, 5.57it/s] 15%|█▌ | 5/33 [00:00<00:05, 5.37it/s] 18%|█▊ | 6/33 [00:01<00:05, 5.23it/s] 21%|██ | 7/33 [00:01<00:05, 5.16it/s] 24%|██▍ | 8/33 [00:01<00:04, 5.12it/s] 27%|██▋ | 9/33 [00:01<00:04, 5.08it/s] 30%|███ | 10/33 [00:01<00:04, 5.06it/s] 33%|███▎ | 11/33 [00:02<00:04, 5.04it/s] 36%|███▋ | 12/33 [00:02<00:04, 5.03it/s] 39%|███▉ | 13/33 [00:02<00:03, 5.02it/s] 42%|████▏ | 14/33 [00:02<00:03, 5.02it/s] 45%|████▌ | 15/33 [00:02<00:03, 5.01it/s] 48%|████▊ | 16/33 [00:03<00:03, 5.01it/s] 52%|█████▏ | 17/33 [00:03<00:03, 5.00it/s] 55%|█████▍ | 18/33 [00:03<00:03, 5.00it/s] 58%|█████▊ | 19/33 [00:03<00:02, 4.99it/s] 61%|██████ | 20/33 [00:03<00:02, 4.99it/s] 64%|██████▎ | 21/33 [00:04<00:02, 4.99it/s] 67%|██████▋ | 22/33 [00:04<00:02, 4.99it/s] 70%|██████▉ | 23/33 [00:04<00:02, 4.98it/s] 73%|███████▎ | 24/33 [00:04<00:01, 4.98it/s] 76%|███████▌ | 25/33 [00:04<00:01, 4.98it/s] 79%|███████▉ | 26/33 [00:05<00:01, 4.98it/s] 82%|████████▏ | 27/33 [00:05<00:01, 4.98it/s] 85%|████████▍ | 28/33 [00:05<00:01, 4.98it/s] 88%|████████▊ | 29/33 [00:05<00:00, 4.97it/s] 91%|█████████ | 30/33 [00:05<00:00, 4.98it/s] 94%|█████████▍| 31/33 [00:06<00:00, 4.98it/s] 97%|█████████▋| 32/33 [00:06<00:00, 4.98it/s] 100%|██████████| 33/33 [00:06<00:00, 4.98it/s] 100%|██████████| 33/33 [00:06<00:00, 5.08it/s]
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