jbilcke
/
sdxl-zelda64
A SDXL LoRA inspired by Zelda games on Nintendo 64
- Public
- 488 runs
-
L40S
Prediction
jbilcke/sdxl-zelda64:435913219645a80ee6743ca500940ab8708889172ca5c4c71bbb701309bb4a60IDkz2xwgdbgzchopxyg2w6vgho5mStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedby @jbilckeInput
- width
- 1024
- height
- 1024
- prompt
- Link working as a pizza delivery driver, on a scooter, in new york, in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.82
- num_outputs
- 1
- guidance_scale
- 18.02
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- overexposed
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "Link working as a pizza delivery driver, on a scooter, in new york, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.82, "num_outputs": 1, "guidance_scale": 18.02, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed", "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-zelda64 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jbilcke/sdxl-zelda64:435913219645a80ee6743ca500940ab8708889172ca5c4c71bbb701309bb4a60", { input: { width: 1024, height: 1024, prompt: "Link working as a pizza delivery driver, on a scooter, in new york, in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.82, num_outputs: 1, guidance_scale: 18.02, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "overexposed", 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-zelda64 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jbilcke/sdxl-zelda64:435913219645a80ee6743ca500940ab8708889172ca5c4c71bbb701309bb4a60", input={ "width": 1024, "height": 1024, "prompt": "Link working as a pizza delivery driver, on a scooter, in new york, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.82, "num_outputs": 1, "guidance_scale": 18.02, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "overexposed", "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-zelda64 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": "435913219645a80ee6743ca500940ab8708889172ca5c4c71bbb701309bb4a60", "input": { "width": 1024, "height": 1024, "prompt": "Link working as a pizza delivery driver, on a scooter, in new york, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.82, "num_outputs": 1, "guidance_scale": 18.02, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed", "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": "2023-08-31T12:14:14.303146Z", "created_at": "2023-08-31T12:13:58.651351Z", "data_removed": false, "error": null, "id": "kz2xwgdbgzchopxyg2w6vgho5m", "input": { "width": 1024, "height": 1024, "prompt": "Link working as a pizza delivery driver, on a scooter, in new york, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.82, "num_outputs": 1, "guidance_scale": 18.02, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 64644\nPrompt: Link working as a pizza delivery driver, on a scooter, in new york, in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.70it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.69it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.68it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.67it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.67it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.66it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.67it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.66it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.66it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.66it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.65it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.65it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.65it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.65it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.65it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.65it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.65it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.65it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.65it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.65it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.65it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.65it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.65it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.66it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.66it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.66it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.66it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.66it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.66it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.66it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.66it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.67it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.67it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.66it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.66it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.67it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.66it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.66it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.66it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.66it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.66it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.66it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.66it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.66it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.66it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.66it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.66it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.66it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.66it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.66it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.66it/s]", "metrics": { "predict_time": 15.638057, "total_time": 15.651795 }, "output": [ "https://replicate.delivery/pbxt/TZOxq0jLiI6rEdKed2QmxTHZaDknGiDweOrAbUffBpSVhNeLC/out-0.png" ], "started_at": "2023-08-31T12:13:58.665089Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/kz2xwgdbgzchopxyg2w6vgho5m", "cancel": "https://api.replicate.com/v1/predictions/kz2xwgdbgzchopxyg2w6vgho5m/cancel" }, "version": "435913219645a80ee6743ca500940ab8708889172ca5c4c71bbb701309bb4a60" }Generated inUsing seed: 64644 Prompt: Link working as a pizza delivery driver, on a scooter, in new york, in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.70it/s] 4%|▍ | 2/50 [00:00<00:13, 3.69it/s] 6%|▌ | 3/50 [00:00<00:12, 3.68it/s] 8%|▊ | 4/50 [00:01<00:12, 3.67it/s] 10%|█ | 5/50 [00:01<00:12, 3.67it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.66it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.67it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.66it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.66it/s] 20%|██ | 10/50 [00:02<00:10, 3.66it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.65it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.65it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.65it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.65it/s] 30%|███ | 15/50 [00:04<00:09, 3.65it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.65it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.65it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.65it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.65it/s] 40%|████ | 20/50 [00:05<00:08, 3.65it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.65it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.65it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.65it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.66it/s] 50%|█████ | 25/50 [00:06<00:06, 3.66it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.66it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.66it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.66it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.66it/s] 60%|██████ | 30/50 [00:08<00:05, 3.66it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.66it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.67it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.67it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.66it/s] 70%|███████ | 35/50 [00:09<00:04, 3.66it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.67it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.66it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.66it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.66it/s] 80%|████████ | 40/50 [00:10<00:02, 3.66it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.66it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.66it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.66it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.66it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.66it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.66it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.66it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.66it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.66it/s] 100%|██████████| 50/50 [00:13<00:00, 3.66it/s] 100%|██████████| 50/50 [00:13<00:00, 3.66it/s]Prediction
jbilcke/sdxl-zelda64:0c5040980eb4801216e0edbb7a338cd23ab5e091dae7cb49d22f23f8ecbf9d35IDpjhtjqdbqu6rds3ovsfx3szofmStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- Link taking a selfie on the beach, in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.82
- num_outputs
- 1
- guidance_scale
- 18.02
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "Link taking a selfie on the beach, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.82, "num_outputs": 1, "guidance_scale": 18.02, "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-zelda64 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jbilcke/sdxl-zelda64:0c5040980eb4801216e0edbb7a338cd23ab5e091dae7cb49d22f23f8ecbf9d35", { input: { width: 1024, height: 1024, prompt: "Link taking a selfie on the beach, in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.82, num_outputs: 1, guidance_scale: 18.02, 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-zelda64 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jbilcke/sdxl-zelda64:0c5040980eb4801216e0edbb7a338cd23ab5e091dae7cb49d22f23f8ecbf9d35", input={ "width": 1024, "height": 1024, "prompt": "Link taking a selfie on the beach, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.82, "num_outputs": 1, "guidance_scale": 18.02, "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-zelda64 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": "0c5040980eb4801216e0edbb7a338cd23ab5e091dae7cb49d22f23f8ecbf9d35", "input": { "width": 1024, "height": 1024, "prompt": "Link taking a selfie on the beach, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.82, "num_outputs": 1, "guidance_scale": 18.02, "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": "2023-08-29T10:08:42.634760Z", "created_at": "2023-08-29T10:08:27.107317Z", "data_removed": false, "error": null, "id": "pjhtjqdbqu6rds3ovsfx3szofm", "input": { "width": 1024, "height": 1024, "prompt": "Link taking a selfie on the beach, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.82, "num_outputs": 1, "guidance_scale": 18.02, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 48587\nPrompt: Link taking a selfie on the beach, in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.70it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.68it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.68it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.68it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.67it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.67it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.67it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.67it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.67it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.68it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.68it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.68it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.69it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.69it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.69it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.69it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.69it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.69it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.69it/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, 3.68it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.68it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.68it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.68it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.68it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.68it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.68it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.68it/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, 3.67it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.67it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.67it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.67it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.67it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.67it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.67it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.67it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.67it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.67it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.67it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.66it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.66it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.66it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.66it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.66it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.68it/s]", "metrics": { "predict_time": 15.55509, "total_time": 15.527443 }, "output": [ "https://pbxt.replicate.delivery/BTfjTHi4GLQ5K6bHfrR4cOyNp5edZi361O5dLDWFeHrnad7FB/out-0.png" ], "started_at": "2023-08-29T10:08:27.079670Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/pjhtjqdbqu6rds3ovsfx3szofm", "cancel": "https://api.replicate.com/v1/predictions/pjhtjqdbqu6rds3ovsfx3szofm/cancel" }, "version": "0c5040980eb4801216e0edbb7a338cd23ab5e091dae7cb49d22f23f8ecbf9d35" }Generated inUsing seed: 48587 Prompt: Link taking a selfie on the beach, in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.70it/s] 4%|▍ | 2/50 [00:00<00:13, 3.68it/s] 6%|▌ | 3/50 [00:00<00:12, 3.68it/s] 8%|▊ | 4/50 [00:01<00:12, 3.68it/s] 10%|█ | 5/50 [00:01<00:12, 3.67it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.67it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.67it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.67it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.67it/s] 20%|██ | 10/50 [00:02<00:10, 3.68it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.68it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.68it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.69it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.69it/s] 30%|███ | 15/50 [00:04<00:09, 3.69it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.69it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.69it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.69it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.69it/s] 40%|████ | 20/50 [00:05<00:08, 3.68it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.68it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.68it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.68it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.68it/s] 50%|█████ | 25/50 [00:06<00:06, 3.68it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.68it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.68it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.68it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.68it/s] 60%|██████ | 30/50 [00:08<00:05, 3.68it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.68it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.68it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.68it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.68it/s] 70%|███████ | 35/50 [00:09<00:04, 3.67it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.67it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.67it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.67it/s] 78%|███████▊ | 39/50 [00:10<00:02, 3.67it/s] 80%|████████ | 40/50 [00:10<00:02, 3.67it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.67it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.67it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.67it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.67it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.67it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.66it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.66it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.66it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.66it/s] 100%|██████████| 50/50 [00:13<00:00, 3.66it/s] 100%|██████████| 50/50 [00:13<00:00, 3.68it/s]Prediction
jbilcke/sdxl-zelda64:0c5040980eb4801216e0edbb7a338cd23ab5e091dae7cb49d22f23f8ecbf9d35IDx4qalilbwg3ulzq42crm7xjqu4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- Link programming on a computer, in a Parisian cafe, in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.82
- num_outputs
- 1
- guidance_scale
- 18.02
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- overexposed
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "Link programming on a computer, in a Parisian cafe, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.82, "num_outputs": 1, "guidance_scale": 18.02, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed", "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-zelda64 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jbilcke/sdxl-zelda64:0c5040980eb4801216e0edbb7a338cd23ab5e091dae7cb49d22f23f8ecbf9d35", { input: { width: 1024, height: 1024, prompt: "Link programming on a computer, in a Parisian cafe, in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.82, num_outputs: 1, guidance_scale: 18.02, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "overexposed", 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-zelda64 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jbilcke/sdxl-zelda64:0c5040980eb4801216e0edbb7a338cd23ab5e091dae7cb49d22f23f8ecbf9d35", input={ "width": 1024, "height": 1024, "prompt": "Link programming on a computer, in a Parisian cafe, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.82, "num_outputs": 1, "guidance_scale": 18.02, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "overexposed", "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-zelda64 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": "0c5040980eb4801216e0edbb7a338cd23ab5e091dae7cb49d22f23f8ecbf9d35", "input": { "width": 1024, "height": 1024, "prompt": "Link programming on a computer, in a Parisian cafe, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.82, "num_outputs": 1, "guidance_scale": 18.02, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed", "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": "2023-08-30T14:55:59.011659Z", "created_at": "2023-08-30T14:55:44.157615Z", "data_removed": false, "error": null, "id": "x4qalilbwg3ulzq42crm7xjqu4", "input": { "width": 1024, "height": 1024, "prompt": "Link programming on a computer, in a Parisian cafe, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.82, "num_outputs": 1, "guidance_scale": 18.02, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 6129\nPrompt: Link programming on a computer, in a Parisian cafe, 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, 3.72it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.71it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.71it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.71it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.71it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.70it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.71it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.71it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.71it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.72it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.71it/s]\n 26%|██▌ | 13/50 [00:03<00:09, 3.71it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.72it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.72it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.72it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.72it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.72it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.71it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.71it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.71it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.71it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.72it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.71it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.71it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.71it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.71it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.71it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.71it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.71it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.71it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.71it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.71it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.71it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.71it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.71it/s]\n 74%|███████▍ | 37/50 [00:09<00:03, 3.70it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.70it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.70it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.70it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.70it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.70it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.70it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.70it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.70it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.70it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.70it/s]\n 96%|█████████▌| 48/50 [00:12<00:00, 3.70it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.70it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.70it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.71it/s]", "metrics": { "predict_time": 14.876337, "total_time": 14.854044 }, "output": [ "https://pbxt.replicate.delivery/ApvR79ebDfjOnEKpZrO1BCmZUdCWyd96iRpOd622YbjeTheFB/out-0.png" ], "started_at": "2023-08-30T14:55:44.135322Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/x4qalilbwg3ulzq42crm7xjqu4", "cancel": "https://api.replicate.com/v1/predictions/x4qalilbwg3ulzq42crm7xjqu4/cancel" }, "version": "0c5040980eb4801216e0edbb7a338cd23ab5e091dae7cb49d22f23f8ecbf9d35" }Generated inUsing seed: 6129 Prompt: Link programming on a computer, in a Parisian cafe, 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, 3.72it/s] 6%|▌ | 3/50 [00:00<00:12, 3.71it/s] 8%|▊ | 4/50 [00:01<00:12, 3.71it/s] 10%|█ | 5/50 [00:01<00:12, 3.71it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.71it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.70it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.71it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.71it/s] 20%|██ | 10/50 [00:02<00:10, 3.71it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.72it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.71it/s] 26%|██▌ | 13/50 [00:03<00:09, 3.71it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.72it/s] 30%|███ | 15/50 [00:04<00:09, 3.72it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.72it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.72it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.72it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.71it/s] 40%|████ | 20/50 [00:05<00:08, 3.71it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.71it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.71it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.72it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.71it/s] 50%|█████ | 25/50 [00:06<00:06, 3.71it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.71it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.71it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.71it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.71it/s] 60%|██████ | 30/50 [00:08<00:05, 3.71it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.71it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.71it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.71it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.71it/s] 70%|███████ | 35/50 [00:09<00:04, 3.71it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.71it/s] 74%|███████▍ | 37/50 [00:09<00:03, 3.70it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.70it/s] 78%|███████▊ | 39/50 [00:10<00:02, 3.70it/s] 80%|████████ | 40/50 [00:10<00:02, 3.70it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.70it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.70it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.70it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.70it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.70it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.70it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.70it/s] 96%|█████████▌| 48/50 [00:12<00:00, 3.70it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.70it/s] 100%|██████████| 50/50 [00:13<00:00, 3.70it/s] 100%|██████████| 50/50 [00:13<00:00, 3.71it/s]Prediction
jbilcke/sdxl-zelda64:435913219645a80ee6743ca500940ab8708889172ca5c4c71bbb701309bb4a60IDbgkuh3db3mm6kdqm7bxabnrzbuStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- Link riding a llama in the mountain, in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.82
- num_outputs
- 1
- guidance_scale
- 18.02
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- overexposed
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "Link riding a llama in the mountain, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.82, "num_outputs": 1, "guidance_scale": 18.02, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed", "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-zelda64 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jbilcke/sdxl-zelda64:435913219645a80ee6743ca500940ab8708889172ca5c4c71bbb701309bb4a60", { input: { width: 1024, height: 1024, prompt: "Link riding a llama in the mountain, in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.82, num_outputs: 1, guidance_scale: 18.02, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "overexposed", 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-zelda64 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jbilcke/sdxl-zelda64:435913219645a80ee6743ca500940ab8708889172ca5c4c71bbb701309bb4a60", input={ "width": 1024, "height": 1024, "prompt": "Link riding a llama in the mountain, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.82, "num_outputs": 1, "guidance_scale": 18.02, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "overexposed", "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-zelda64 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": "435913219645a80ee6743ca500940ab8708889172ca5c4c71bbb701309bb4a60", "input": { "width": 1024, "height": 1024, "prompt": "Link riding a llama in the mountain, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.82, "num_outputs": 1, "guidance_scale": 18.02, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed", "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": "2023-08-30T15:49:03.159852Z", "created_at": "2023-08-30T15:48:47.554535Z", "data_removed": false, "error": null, "id": "bgkuh3db3mm6kdqm7bxabnrzbu", "input": { "width": 1024, "height": 1024, "prompt": "Link riding a llama in the mountain, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.82, "num_outputs": 1, "guidance_scale": 18.02, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 12625\nPrompt: Link riding a llama in the mountain, in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.71it/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, 3.69it/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, 3.67it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.68it/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<00:09, 3.66it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.66it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.66it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.66it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.66it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.66it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.66it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.66it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.66it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.66it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.66it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.66it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.66it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.66it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.66it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.66it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.66it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.66it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.66it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.66it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.65it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.65it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.65it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.65it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.65it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.65it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.65it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.65it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.65it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.65it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.65it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.65it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.65it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.65it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.66it/s]", "metrics": { "predict_time": 15.603238, "total_time": 15.605317 }, "output": [ "https://replicate.delivery/pbxt/38MvIG6iPja7GJs4KKsGckstJrlr4aKnLIkedwC8jcN3tovIA/out-0.png" ], "started_at": "2023-08-30T15:48:47.556614Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/bgkuh3db3mm6kdqm7bxabnrzbu", "cancel": "https://api.replicate.com/v1/predictions/bgkuh3db3mm6kdqm7bxabnrzbu/cancel" }, "version": "435913219645a80ee6743ca500940ab8708889172ca5c4c71bbb701309bb4a60" }Generated inUsing seed: 12625 Prompt: Link riding a llama in the mountain, in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.71it/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, 3.69it/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, 3.67it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.68it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.67it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.67it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.67it/s] 30%|███ | 15/50 [00:04<00:09, 3.67it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.67it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.66it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.66it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.66it/s] 40%|████ | 20/50 [00:05<00:08, 3.66it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.66it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.66it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.66it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.66it/s] 50%|█████ | 25/50 [00:06<00:06, 3.66it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.66it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.66it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.66it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.66it/s] 60%|██████ | 30/50 [00:08<00:05, 3.66it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.66it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.66it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.66it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.66it/s] 70%|███████ | 35/50 [00:09<00:04, 3.66it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.66it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.65it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.65it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.65it/s] 80%|████████ | 40/50 [00:10<00:02, 3.65it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.65it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.65it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.65it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.65it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.65it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.65it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.65it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.65it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.65it/s] 100%|██████████| 50/50 [00:13<00:00, 3.65it/s] 100%|██████████| 50/50 [00:13<00:00, 3.66it/s]Prediction
jbilcke/sdxl-zelda64:435913219645a80ee6743ca500940ab8708889172ca5c4c71bbb701309bb4a60IDmqoo2r3bckikpbgjltp3f5jqp4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- Link piloting a podracer, in star wars, tatooine, in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.82
- num_outputs
- 1
- guidance_scale
- 18.02
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- overexposed
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "Link piloting a podracer, in star wars, tatooine, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.82, "num_outputs": 1, "guidance_scale": 18.02, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed", "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-zelda64 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jbilcke/sdxl-zelda64:435913219645a80ee6743ca500940ab8708889172ca5c4c71bbb701309bb4a60", { input: { width: 1024, height: 1024, prompt: "Link piloting a podracer, in star wars, tatooine, in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.82, num_outputs: 1, guidance_scale: 18.02, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "overexposed", 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-zelda64 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jbilcke/sdxl-zelda64:435913219645a80ee6743ca500940ab8708889172ca5c4c71bbb701309bb4a60", input={ "width": 1024, "height": 1024, "prompt": "Link piloting a podracer, in star wars, tatooine, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.82, "num_outputs": 1, "guidance_scale": 18.02, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "overexposed", "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-zelda64 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": "435913219645a80ee6743ca500940ab8708889172ca5c4c71bbb701309bb4a60", "input": { "width": 1024, "height": 1024, "prompt": "Link piloting a podracer, in star wars, tatooine, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.82, "num_outputs": 1, "guidance_scale": 18.02, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed", "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": "2023-09-08T14:51:37.423218Z", "created_at": "2023-09-08T14:51:21.739764Z", "data_removed": false, "error": null, "id": "mqoo2r3bckikpbgjltp3f5jqp4", "input": { "width": 1024, "height": 1024, "prompt": "Link piloting a podracer, in star wars, tatooine, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.82, "num_outputs": 1, "guidance_scale": 18.02, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 52315\nPrompt: Link piloting a podracer, in star wars, tatooine, in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.71it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.69it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.68it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.67it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.66it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.66it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.66it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.66it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.67it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.67it/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, 3.68it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.68it/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, 3.67it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.67it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.67it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.67it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.67it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.67it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.67it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.67it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.67it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.67it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.67it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.67it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.67it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.67it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.67it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.67it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.67it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.67it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.67it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.67it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.66it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.67it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.67it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.67it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.66it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.67it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.66it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.66it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.66it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.66it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.66it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.66it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.66it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.67it/s]", "metrics": { "predict_time": 15.671223, "total_time": 15.683454 }, "output": [ "https://replicate.delivery/pbxt/KTIHIKbhTJK3Hp3miIrEAvBK6ObD2mtDBh03hc02K5JeNHxIA/out-0.png" ], "started_at": "2023-09-08T14:51:21.751995Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/mqoo2r3bckikpbgjltp3f5jqp4", "cancel": "https://api.replicate.com/v1/predictions/mqoo2r3bckikpbgjltp3f5jqp4/cancel" }, "version": "435913219645a80ee6743ca500940ab8708889172ca5c4c71bbb701309bb4a60" }Generated inUsing seed: 52315 Prompt: Link piloting a podracer, in star wars, tatooine, in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.71it/s] 4%|▍ | 2/50 [00:00<00:13, 3.69it/s] 6%|▌ | 3/50 [00:00<00:12, 3.68it/s] 8%|▊ | 4/50 [00:01<00:12, 3.67it/s] 10%|█ | 5/50 [00:01<00:12, 3.66it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.66it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.66it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.66it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.67it/s] 20%|██ | 10/50 [00:02<00:10, 3.67it/s] 22%|██▏ | 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