jbilcke
/
sdxl-lean-moebius
- Public
- 55 runs
-
L40S
Prediction
jbilcke/sdxl-lean-moebius:6c040a8369369a51d268c10e5a78c9202d079c612898de9447ef1c45bf4fae8aIDy7fq3jlb7htyvwbupxifhzmblyStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- intricate details, beautiful, spaceship flying over canyon, in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.83
- num_outputs
- 1
- guidance_scale
- 18.14
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "intricate details, beautiful, spaceship flying over canyon, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.83, "num_outputs": 1, "guidance_scale": 18.14, "apply_watermark": true, "high_noise_frac": 0.8, "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 jbilcke/sdxl-lean-moebius using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jbilcke/sdxl-lean-moebius:6c040a8369369a51d268c10e5a78c9202d079c612898de9447ef1c45bf4fae8a", { input: { width: 1024, height: 1024, prompt: "intricate details, beautiful, spaceship flying over canyon, in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.83, num_outputs: 1, guidance_scale: 18.14, apply_watermark: true, high_noise_frac: 0.8, 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 jbilcke/sdxl-lean-moebius using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jbilcke/sdxl-lean-moebius:6c040a8369369a51d268c10e5a78c9202d079c612898de9447ef1c45bf4fae8a", input={ "width": 1024, "height": 1024, "prompt": "intricate details, beautiful, spaceship flying over canyon, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.83, "num_outputs": 1, "guidance_scale": 18.14, "apply_watermark": True, "high_noise_frac": 0.8, "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 jbilcke/sdxl-lean-moebius 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": "6c040a8369369a51d268c10e5a78c9202d079c612898de9447ef1c45bf4fae8a", "input": { "width": 1024, "height": 1024, "prompt": "intricate details, beautiful, spaceship flying over canyon, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.83, "num_outputs": 1, "guidance_scale": 18.14, "apply_watermark": true, "high_noise_frac": 0.8, "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": "2023-08-29T10:03:04.332259Z", "created_at": "2023-08-29T10:02:48.833759Z", "data_removed": false, "error": null, "id": "y7fq3jlb7htyvwbupxifhzmbly", "input": { "width": 1024, "height": 1024, "prompt": "intricate details, beautiful, spaceship flying over canyon, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.83, "num_outputs": 1, "guidance_scale": 18.14, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 8050\nPrompt: intricate details, beautiful, spaceship flying over canyon, in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.72it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.70it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.69it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.68it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.68it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.68it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.68it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.68it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.68it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.68it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.67it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.67it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.67it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.67it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.67it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.67it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.67it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.68it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.68it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.68it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.68it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.69it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.69it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.69it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.69it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.68it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.69it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.69it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.69it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.68it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.68it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.68it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.68it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.68it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.68it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.68it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.68it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.68it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.68it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.68it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.68it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.68it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.68it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.68it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.68it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.68it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.68it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.68it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.68it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.68it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.68it/s]", "metrics": { "predict_time": 15.513204, "total_time": 15.4985 }, "output": [ "https://replicate.delivery/pbxt/TKXoCfKZffBvKJChnISsQf1gjVF76yLzv4iKfKudTvn8K62LC/out-0.png" ], "started_at": "2023-08-29T10:02:48.819055Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/y7fq3jlb7htyvwbupxifhzmbly", "cancel": "https://api.replicate.com/v1/predictions/y7fq3jlb7htyvwbupxifhzmbly/cancel" }, "version": "6c040a8369369a51d268c10e5a78c9202d079c612898de9447ef1c45bf4fae8a" }Generated inUsing seed: 8050 Prompt: intricate details, beautiful, spaceship flying over canyon, in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.72it/s] 4%|▍ | 2/50 [00:00<00:12, 3.70it/s] 6%|▌ | 3/50 [00:00<00:12, 3.69it/s] 8%|▊ | 4/50 [00:01<00:12, 3.68it/s] 10%|█ | 5/50 [00:01<00:12, 3.68it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.68it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.68it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.68it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.68it/s] 20%|██ | 10/50 [00:02<00:10, 3.68it/s] 22%|██▏ | 11/50 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70%|███████ | 35/50 [00:09<00:04, 3.68it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.68it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.68it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.68it/s] 78%|███████▊ | 39/50 [00:10<00:02, 3.68it/s] 80%|████████ | 40/50 [00:10<00:02, 3.68it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.68it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.68it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.68it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.68it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.68it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.68it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.68it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.68it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.68it/s] 100%|██████████| 50/50 [00:13<00:00, 3.68it/s] 100%|██████████| 50/50 [00:13<00:00, 3.68it/s]Prediction
jbilcke/sdxl-lean-moebius:6c040a8369369a51d268c10e5a78c9202d079c612898de9447ef1c45bf4fae8aIDet6ilx3bhbaw4ykru2z2b5yizeStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 768
- prompt
- intricate details, small details, beautiful modern house in San Francisco, vintage sportscar, in the style of TOK
- refine
- expert_ensemble_refiner
- scheduler
- K_EULER
- lora_scale
- 0.83
- num_outputs
- 1
- guidance_scale
- 18.14
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- 3D, CGI, photo, cropped, blurry, blurred, blur
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 768, "prompt": "intricate details, small details, beautiful modern house in San Francisco, vintage sportscar, in the style of TOK", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.83, "num_outputs": 1, "guidance_scale": 18.14, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "3D, CGI, photo, cropped, blurry, blurred, blur", "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 jbilcke/sdxl-lean-moebius using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jbilcke/sdxl-lean-moebius:6c040a8369369a51d268c10e5a78c9202d079c612898de9447ef1c45bf4fae8a", { input: { width: 1024, height: 768, prompt: "intricate details, small details, beautiful modern house in San Francisco, vintage sportscar, in the style of TOK", refine: "expert_ensemble_refiner", scheduler: "K_EULER", lora_scale: 0.83, num_outputs: 1, guidance_scale: 18.14, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "3D, CGI, photo, cropped, blurry, blurred, blur", 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 jbilcke/sdxl-lean-moebius using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jbilcke/sdxl-lean-moebius:6c040a8369369a51d268c10e5a78c9202d079c612898de9447ef1c45bf4fae8a", input={ "width": 1024, "height": 768, "prompt": "intricate details, small details, beautiful modern house in San Francisco, vintage sportscar, in the style of TOK", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.83, "num_outputs": 1, "guidance_scale": 18.14, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "3D, CGI, photo, cropped, blurry, blurred, blur", "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 jbilcke/sdxl-lean-moebius 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": "6c040a8369369a51d268c10e5a78c9202d079c612898de9447ef1c45bf4fae8a", "input": { "width": 1024, "height": 768, "prompt": "intricate details, small details, beautiful modern house in San Francisco, vintage sportscar, in the style of TOK", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.83, "num_outputs": 1, "guidance_scale": 18.14, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "3D, CGI, photo, cropped, blurry, blurred, blur", "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": "2023-09-08T14:41:29.553098Z", "created_at": "2023-09-08T14:41:18.162823Z", "data_removed": false, "error": null, "id": "et6ilx3bhbaw4ykru2z2b5yize", "input": { "width": 1024, "height": 768, "prompt": "intricate details, small details, beautiful modern house in San Francisco, vintage sportscar, in the style of TOK", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.83, "num_outputs": 1, "guidance_scale": 18.14, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "3D, CGI, photo, cropped, blurry, blurred, blur", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 30786\nPrompt: intricate details, small details, beautiful modern house in San Francisco, vintage sportscar, in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:00<00:07, 5.00it/s]\n 5%|▌ | 2/40 [00:00<00:07, 4.98it/s]\n 8%|▊ | 3/40 [00:00<00:07, 4.97it/s]\n 10%|█ | 4/40 [00:00<00:07, 4.96it/s]\n 12%|█▎ | 5/40 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[00:01<00:00, 6.28it/s]\n 90%|█████████ | 9/10 [00:01<00:00, 6.28it/s]\n100%|██████████| 10/10 [00:01<00:00, 6.28it/s]\n100%|██████████| 10/10 [00:01<00:00, 6.29it/s]", "metrics": { "predict_time": 11.38767, "total_time": 11.390275 }, "output": [ "https://replicate.delivery/pbxt/fgT8eDBJnvmgZENUv6ERm21Zdhbl2OKmogBUkAkfpazxkcEjA/out-0.png" ], "started_at": "2023-09-08T14:41:18.165428Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/et6ilx3bhbaw4ykru2z2b5yize", "cancel": "https://api.replicate.com/v1/predictions/et6ilx3bhbaw4ykru2z2b5yize/cancel" }, "version": "6c040a8369369a51d268c10e5a78c9202d079c612898de9447ef1c45bf4fae8a" }Generated inUsing seed: 30786 Prompt: intricate details, small details, beautiful modern house in San Francisco, vintage sportscar, in the style of <s0><s1> txt2img mode 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:00<00:07, 5.00it/s] 5%|▌ | 2/40 [00:00<00:07, 4.98it/s] 8%|▊ | 3/40 [00:00<00:07, 4.97it/s] 10%|█ | 4/40 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