jbilcke
/
sdxl-botw
A SDXL LoRA inspired by Breath of the Wild
- Public
- 578 runs
-
L40S
Prediction
jbilcke/sdxl-botw:bf412da3ID4zrx3m3b2yo5x5m2i2euvjley4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- Link riding a llama, in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.83
- num_outputs
- 1
- guidance_scale
- 18.41
- 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 style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.83, "num_outputs": 1, "guidance_scale": 18.41, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed", "prompt_strength": 0.8, "num_inference_steps": 50 }npm install replicateSet theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run jbilcke/sdxl-botw using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jbilcke/sdxl-botw:bf412da351d41547f117391eff2824ab0301b6ba1c6c010c4b5f766a492d62fc", { input: { width: 1024, height: 1024, prompt: "Link riding a llama, in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.83, num_outputs: 1, guidance_scale: 18.41, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "overexposed", prompt_strength: 0.8, num_inference_steps: 50 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
pip install replicateSet theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the clientimport replicateRun jbilcke/sdxl-botw using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jbilcke/sdxl-botw:bf412da351d41547f117391eff2824ab0301b6ba1c6c010c4b5f766a492d62fc", input={ "width": 1024, "height": 1024, "prompt": "Link riding a llama, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.83, "num_outputs": 1, "guidance_scale": 18.41, "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.
Set theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run jbilcke/sdxl-botw 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": "bf412da351d41547f117391eff2824ab0301b6ba1c6c010c4b5f766a492d62fc", "input": { "width": 1024, "height": 1024, "prompt": "Link riding a llama, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.83, "num_outputs": 1, "guidance_scale": 18.41, "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": "2023-08-30T15:59:19.223401Z", "created_at": "2023-08-30T15:59:04.273206Z", "data_removed": false, "error": null, "id": "4zrx3m3b2yo5x5m2i2euvjley4", "input": { "width": 1024, "height": 1024, "prompt": "Link riding a llama, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.83, "num_outputs": 1, "guidance_scale": 18.41, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 3176\nPrompt: Link riding a llama, 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, 3.69it/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, 3.67it/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, 3.67it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.66it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.66it/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, 3.68it/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, 3.69it/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, 3.68it/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, 3.68it/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": 14.971958, "total_time": 14.950195 }, "output": [ "https://pbxt.replicate.delivery/L5P6dSEzH276JR7QawvxTdN5AQW1GVg5AJbvksFrsckVZ0XE/out-0.png" ], "started_at": "2023-08-30T15:59:04.251443Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/4zrx3m3b2yo5x5m2i2euvjley4", "cancel": "https://api.replicate.com/v1/predictions/4zrx3m3b2yo5x5m2i2euvjley4/cancel" }, "version": "bf412da351d41547f117391eff2824ab0301b6ba1c6c010c4b5f766a492d62fc" }Generated inUsing seed: 3176 Prompt: Link riding a llama, 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, 3.69it/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, 3.67it/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, 3.67it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.66it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.66it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.67it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.68it/s] 30%|███ | 15/50 [00:04<00:09, 3.68it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.68it/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, 3.69it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.68it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.69it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.69it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.69it/s] 50%|█████ | 25/50 [00:06<00:06, 3.69it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.68it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.69it/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, 3.68it/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-botw:bf412da3IDs6pqtm3b4ixczlr3r63bxg2jpuStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- Link playing soccer with trolls, in a beautiful landscape and soccer ground, in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.83
- num_outputs
- 1
- guidance_scale
- 18.41
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- overexposed
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "Link playing soccer with trolls, in a beautiful landscape and soccer ground, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.83, "num_outputs": 1, "guidance_scale": 18.41, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed", "prompt_strength": 0.8, "num_inference_steps": 50 }npm install replicateSet theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run jbilcke/sdxl-botw using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jbilcke/sdxl-botw:bf412da351d41547f117391eff2824ab0301b6ba1c6c010c4b5f766a492d62fc", { input: { width: 1024, height: 1024, prompt: "Link playing soccer with trolls, in a beautiful landscape and soccer ground, in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.83, num_outputs: 1, guidance_scale: 18.41, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "overexposed", prompt_strength: 0.8, num_inference_steps: 50 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
pip install replicateSet theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the clientimport replicateRun jbilcke/sdxl-botw using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jbilcke/sdxl-botw:bf412da351d41547f117391eff2824ab0301b6ba1c6c010c4b5f766a492d62fc", input={ "width": 1024, "height": 1024, "prompt": "Link playing soccer with trolls, in a beautiful landscape and soccer ground, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.83, "num_outputs": 1, "guidance_scale": 18.41, "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.
Set theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run jbilcke/sdxl-botw 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": "bf412da351d41547f117391eff2824ab0301b6ba1c6c010c4b5f766a492d62fc", "input": { "width": 1024, "height": 1024, "prompt": "Link playing soccer with trolls, in a beautiful landscape and soccer ground, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.83, "num_outputs": 1, "guidance_scale": 18.41, "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": "2023-08-30T17:41:31.122143Z", "created_at": "2023-08-30T17:41:16.199102Z", "data_removed": false, "error": null, "id": "s6pqtm3b4ixczlr3r63bxg2jpu", "input": { "width": 1024, "height": 1024, "prompt": "Link playing soccer with trolls, in a beautiful landscape and soccer ground, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.83, "num_outputs": 1, "guidance_scale": 18.41, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 14227\nPrompt: Link playing soccer with trolls, in a beautiful landscape and soccer ground, 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, 3.67it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.67it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.68it/s]\n 14%|█▍ | 7/50 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[00:13<00:00, 3.68it/s]", "metrics": { "predict_time": 14.954718, "total_time": 14.923041 }, "output": [ "https://pbxt.replicate.delivery/v6qulveePMi4AU9VW6YNzPvbhwjqguTNu6XGoBqKebqUKmeFB/out-0.png" ], "started_at": "2023-08-30T17:41:16.167425Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/s6pqtm3b4ixczlr3r63bxg2jpu", "cancel": "https://api.replicate.com/v1/predictions/s6pqtm3b4ixczlr3r63bxg2jpu/cancel" }, "version": "bf412da351d41547f117391eff2824ab0301b6ba1c6c010c4b5f766a492d62fc" }Generated inUsing seed: 14227 Prompt: Link playing soccer with trolls, in a beautiful landscape and soccer ground, 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, 3.67it/s] 10%|█ | 5/50 [00:01<00:12, 3.67it/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, 3.69it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.69it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.69it/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, 3.69it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.69it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.69it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.69it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.68it/s] 50%|█████ | 25/50 [00:06<00:06, 3.69it/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, 3.68it/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, 3.67it/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, 3.67it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.67it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.67it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.67it/s] 100%|██████████| 50/50 [00:13<00:00, 3.67it/s] 100%|██████████| 50/50 [00:13<00:00, 3.68it/s]Prediction
jbilcke/sdxl-botw:bf412da3IDbamrex3bdwv5l6um6vii7itqceStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- image
- width
- 1024
- height
- 1024
- prompt
- Zelda looking at a computer, drinking a coke, amused, in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.83
- num_outputs
- 1
- guidance_scale
- 18.41
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- overexposed
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "image": "https://replicate.delivery/pbxt/JRSKSF1IGIsA2IGVib7fdeF2IfavA36myNxf0nqvAw8YHhvA/Capture%20d%E2%80%99e%CC%81cran%202023-08-30%20a%CC%80%2020.33.54.png", "width": 1024, "height": 1024, "prompt": "Zelda looking at a computer, drinking a coke, amused, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.83, "num_outputs": 1, "guidance_scale": 18.41, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed", "prompt_strength": 0.8, "num_inference_steps": 50 }npm install replicateSet theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run jbilcke/sdxl-botw using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jbilcke/sdxl-botw:bf412da351d41547f117391eff2824ab0301b6ba1c6c010c4b5f766a492d62fc", { input: { image: "https://replicate.delivery/pbxt/JRSKSF1IGIsA2IGVib7fdeF2IfavA36myNxf0nqvAw8YHhvA/Capture%20d%E2%80%99e%CC%81cran%202023-08-30%20a%CC%80%2020.33.54.png", width: 1024, height: 1024, prompt: "Zelda looking at a computer, drinking a coke, amused, in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.83, num_outputs: 1, guidance_scale: 18.41, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "overexposed", prompt_strength: 0.8, num_inference_steps: 50 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
pip install replicateSet theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the clientimport replicateRun jbilcke/sdxl-botw using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jbilcke/sdxl-botw:bf412da351d41547f117391eff2824ab0301b6ba1c6c010c4b5f766a492d62fc", input={ "image": "https://replicate.delivery/pbxt/JRSKSF1IGIsA2IGVib7fdeF2IfavA36myNxf0nqvAw8YHhvA/Capture%20d%E2%80%99e%CC%81cran%202023-08-30%20a%CC%80%2020.33.54.png", "width": 1024, "height": 1024, "prompt": "Zelda looking at a computer, drinking a coke, amused, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.83, "num_outputs": 1, "guidance_scale": 18.41, "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.
Set theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run jbilcke/sdxl-botw 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": "bf412da351d41547f117391eff2824ab0301b6ba1c6c010c4b5f766a492d62fc", "input": { "image": "https://replicate.delivery/pbxt/JRSKSF1IGIsA2IGVib7fdeF2IfavA36myNxf0nqvAw8YHhvA/Capture%20d%E2%80%99e%CC%81cran%202023-08-30%20a%CC%80%2020.33.54.png", "width": 1024, "height": 1024, "prompt": "Zelda looking at a computer, drinking a coke, amused, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.83, "num_outputs": 1, "guidance_scale": 18.41, "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": "2023-08-30T18:36:00.620314Z", "created_at": "2023-08-30T18:35:50.196133Z", "data_removed": false, "error": null, "id": "bamrex3bdwv5l6um6vii7itqce", "input": { "image": "https://replicate.delivery/pbxt/JRSKSF1IGIsA2IGVib7fdeF2IfavA36myNxf0nqvAw8YHhvA/Capture%20d%E2%80%99e%CC%81cran%202023-08-30%20a%CC%80%2020.33.54.png", "width": 1024, "height": 1024, "prompt": "Zelda looking at a computer, drinking a coke, amused, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.83, "num_outputs": 1, "guidance_scale": 18.41, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 2862\nPrompt: Zelda looking at a computer, drinking a coke, amused, in the style of <s0><s1>\nimg2img mode\n 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:00<00:07, 5.01it/s]\n 5%|▌ | 2/40 [00:00<00:07, 5.00it/s]\n 8%|▊ | 3/40 [00:00<00:07, 5.00it/s]\n 10%|█ | 4/40 [00:00<00:07, 4.99it/s]\n 12%|█▎ | 5/40 [00:01<00:07, 4.98it/s]\n 15%|█▌ | 6/40 [00:01<00:06, 4.97it/s]\n 18%|█▊ | 7/40 [00:01<00:06, 4.97it/s]\n 20%|██ | 8/40 [00:01<00:06, 4.96it/s]\n 22%|██▎ | 9/40 [00:01<00:06, 4.96it/s]\n 25%|██▌ | 10/40 [00:02<00:06, 4.96it/s]\n 28%|██▊ | 11/40 [00:02<00:05, 4.96it/s]\n 30%|███ | 12/40 [00:02<00:05, 4.96it/s]\n 32%|███▎ | 13/40 [00:02<00:05, 4.96it/s]\n 35%|███▌ | 14/40 [00:02<00:05, 4.96it/s]\n 38%|███▊ | 15/40 [00:03<00:05, 4.96it/s]\n 40%|████ | 16/40 [00:03<00:04, 4.95it/s]\n 42%|████▎ | 17/40 [00:03<00:04, 4.95it/s]\n 45%|████▌ | 18/40 [00:03<00:04, 4.95it/s]\n 48%|████▊ | 19/40 [00:03<00:04, 4.95it/s]\n 50%|█████ | 20/40 [00:04<00:04, 4.95it/s]\n 52%|█████▎ | 21/40 [00:04<00:03, 4.95it/s]\n 55%|█████▌ | 22/40 [00:04<00:03, 4.95it/s]\n 57%|█████▊ | 23/40 [00:04<00:03, 4.94it/s]\n 60%|██████ | 24/40 [00:04<00:03, 4.94it/s]\n 62%|██████▎ | 25/40 [00:05<00:03, 4.94it/s]\n 65%|██████▌ | 26/40 [00:05<00:02, 4.94it/s]\n 68%|██████▊ | 27/40 [00:05<00:02, 4.94it/s]\n 70%|███████ | 28/40 [00:05<00:02, 4.94it/s]\n 72%|███████▎ | 29/40 [00:05<00:02, 4.94it/s]\n 75%|███████▌ | 30/40 [00:06<00:02, 4.94it/s]\n 78%|███████▊ | 31/40 [00:06<00:01, 4.94it/s]\n 80%|████████ | 32/40 [00:06<00:01, 4.93it/s]\n 82%|████████▎ | 33/40 [00:06<00:01, 4.93it/s]\n 85%|████████▌ | 34/40 [00:06<00:01, 4.92it/s]\n 88%|████████▊ | 35/40 [00:07<00:01, 4.92it/s]\n 90%|█████████ | 36/40 [00:07<00:00, 4.92it/s]\n 92%|█████████▎| 37/40 [00:07<00:00, 4.92it/s]\n 95%|█████████▌| 38/40 [00:07<00:00, 4.92it/s]\n 98%|█████████▊| 39/40 [00:07<00:00, 4.91it/s]\n100%|██████████| 40/40 [00:08<00:00, 4.91it/s]\n100%|██████████| 40/40 [00:08<00:00, 4.95it/s]", "metrics": { "predict_time": 10.522509, "total_time": 10.424181 }, "output": [ "https://pbxt.replicate.delivery/mFI8tV0HcJLhElY1t7p2XwPbvWgaUkBDRsr04q4s34xDepvIA/out-0.png" ], "started_at": "2023-08-30T18:35:50.097805Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/bamrex3bdwv5l6um6vii7itqce", "cancel": "https://api.replicate.com/v1/predictions/bamrex3bdwv5l6um6vii7itqce/cancel" }, "version": "bf412da351d41547f117391eff2824ab0301b6ba1c6c010c4b5f766a492d62fc" }Generated inUsing seed: 2862 Prompt: Zelda looking at a computer, drinking a coke, amused, in the style of <s0><s1> img2img mode 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:00<00:07, 5.01it/s] 5%|▌ | 2/40 [00:00<00:07, 5.00it/s] 8%|▊ | 3/40 [00:00<00:07, 5.00it/s] 10%|█ | 4/40 [00:00<00:07, 4.99it/s] 12%|█▎ | 5/40 [00:01<00:07, 4.98it/s] 15%|█▌ | 6/40 [00:01<00:06, 4.97it/s] 18%|█▊ | 7/40 [00:01<00:06, 4.97it/s] 20%|██ | 8/40 [00:01<00:06, 4.96it/s] 22%|██▎ | 9/40 [00:01<00:06, 4.96it/s] 25%|██▌ | 10/40 [00:02<00:06, 4.96it/s] 28%|██▊ | 11/40 [00:02<00:05, 4.96it/s] 30%|███ | 12/40 [00:02<00:05, 4.96it/s] 32%|███▎ | 13/40 [00:02<00:05, 4.96it/s] 35%|███▌ | 14/40 [00:02<00:05, 4.96it/s] 38%|███▊ | 15/40 [00:03<00:05, 4.96it/s] 40%|████ | 16/40 [00:03<00:04, 4.95it/s] 42%|████▎ | 17/40 [00:03<00:04, 4.95it/s] 45%|████▌ | 18/40 [00:03<00:04, 4.95it/s] 48%|████▊ | 19/40 [00:03<00:04, 4.95it/s] 50%|█████ | 20/40 [00:04<00:04, 4.95it/s] 52%|█████▎ | 21/40 [00:04<00:03, 4.95it/s] 55%|█████▌ | 22/40 [00:04<00:03, 4.95it/s] 57%|█████▊ | 23/40 [00:04<00:03, 4.94it/s] 60%|██████ | 24/40 [00:04<00:03, 4.94it/s] 62%|██████▎ | 25/40 [00:05<00:03, 4.94it/s] 65%|██████▌ | 26/40 [00:05<00:02, 4.94it/s] 68%|██████▊ | 27/40 [00:05<00:02, 4.94it/s] 70%|███████ | 28/40 [00:05<00:02, 4.94it/s] 72%|███████▎ | 29/40 [00:05<00:02, 4.94it/s] 75%|███████▌ | 30/40 [00:06<00:02, 4.94it/s] 78%|███████▊ | 31/40 [00:06<00:01, 4.94it/s] 80%|████████ | 32/40 [00:06<00:01, 4.93it/s] 82%|████████▎ | 33/40 [00:06<00:01, 4.93it/s] 85%|████████▌ | 34/40 [00:06<00:01, 4.92it/s] 88%|████████▊ | 35/40 [00:07<00:01, 4.92it/s] 90%|█████████ | 36/40 [00:07<00:00, 4.92it/s] 92%|█████████▎| 37/40 [00:07<00:00, 4.92it/s] 95%|█████████▌| 38/40 [00:07<00:00, 4.92it/s] 98%|█████████▊| 39/40 [00:07<00:00, 4.91it/s] 100%|██████████| 40/40 [00:08<00:00, 4.91it/s] 100%|██████████| 40/40 [00:08<00:00, 4.95it/s]Prediction
jbilcke/sdxl-botw:bf412da3IDxi6igj3byu43q3dbdj4lctseqyStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- image
- width
- 1024
- height
- 1024
- prompt
- Zelda looking at a computer, drinking a coke, amused, in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.83
- num_outputs
- 1
- guidance_scale
- 18.41
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- overexposed
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "image": "https://replicate.delivery/pbxt/JRSLRPP85HVTB5t2YfWqcoQ6hjr1nHvPrkAKuQtMVgtVtBqC/Capture%20d%E2%80%99e%CC%81cran%202023-08-30%20a%CC%80%2020.33.54.png", "width": 1024, "height": 1024, "prompt": "Zelda looking at a computer, drinking a coke, amused, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.83, "num_outputs": 1, "guidance_scale": 18.41, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed", "prompt_strength": 0.8, "num_inference_steps": 50 }npm install replicateSet theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run jbilcke/sdxl-botw using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jbilcke/sdxl-botw:bf412da351d41547f117391eff2824ab0301b6ba1c6c010c4b5f766a492d62fc", { input: { image: "https://replicate.delivery/pbxt/JRSLRPP85HVTB5t2YfWqcoQ6hjr1nHvPrkAKuQtMVgtVtBqC/Capture%20d%E2%80%99e%CC%81cran%202023-08-30%20a%CC%80%2020.33.54.png", width: 1024, height: 1024, prompt: "Zelda looking at a computer, drinking a coke, amused, in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.83, num_outputs: 1, guidance_scale: 18.41, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "overexposed", prompt_strength: 0.8, num_inference_steps: 50 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
pip install replicateSet theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the clientimport replicateRun jbilcke/sdxl-botw using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jbilcke/sdxl-botw:bf412da351d41547f117391eff2824ab0301b6ba1c6c010c4b5f766a492d62fc", input={ "image": "https://replicate.delivery/pbxt/JRSLRPP85HVTB5t2YfWqcoQ6hjr1nHvPrkAKuQtMVgtVtBqC/Capture%20d%E2%80%99e%CC%81cran%202023-08-30%20a%CC%80%2020.33.54.png", "width": 1024, "height": 1024, "prompt": "Zelda looking at a computer, drinking a coke, amused, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.83, "num_outputs": 1, "guidance_scale": 18.41, "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.
Set theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run jbilcke/sdxl-botw 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": "bf412da351d41547f117391eff2824ab0301b6ba1c6c010c4b5f766a492d62fc", "input": { "image": "https://replicate.delivery/pbxt/JRSLRPP85HVTB5t2YfWqcoQ6hjr1nHvPrkAKuQtMVgtVtBqC/Capture%20d%E2%80%99e%CC%81cran%202023-08-30%20a%CC%80%2020.33.54.png", "width": 1024, "height": 1024, "prompt": "Zelda looking at a computer, drinking a coke, amused, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.83, "num_outputs": 1, "guidance_scale": 18.41, "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": "2023-08-30T18:37:03.913613Z", "created_at": "2023-08-30T18:36:53.381548Z", "data_removed": false, "error": null, "id": "xi6igj3byu43q3dbdj4lctseqy", "input": { "image": "https://replicate.delivery/pbxt/JRSLRPP85HVTB5t2YfWqcoQ6hjr1nHvPrkAKuQtMVgtVtBqC/Capture%20d%E2%80%99e%CC%81cran%202023-08-30%20a%CC%80%2020.33.54.png", "width": 1024, "height": 1024, "prompt": "Zelda looking at a computer, drinking a coke, amused, in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.83, "num_outputs": 1, "guidance_scale": 18.41, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "overexposed", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 58833\nPrompt: Zelda looking at a computer, drinking a coke, amused, in the style of <s0><s1>\nimg2img 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, 4.99it/s]\n 8%|▊ | 3/40 [00:00<00:07, 4.98it/s]\n 10%|█ | 4/40 [00:00<00:07, 4.97it/s]\n 12%|█▎ | 5/40 [00:01<00:07, 4.96it/s]\n 15%|█▌ | 6/40 [00:01<00:06, 4.96it/s]\n 18%|█▊ | 7/40 [00:01<00:06, 4.96it/s]\n 20%|██ | 8/40 [00:01<00:06, 4.96it/s]\n 22%|██▎ | 9/40 [00:01<00:06, 4.96it/s]\n 25%|██▌ | 10/40 [00:02<00:06, 4.95it/s]\n 28%|██▊ | 11/40 [00:02<00:05, 4.95it/s]\n 30%|███ | 12/40 [00:02<00:05, 4.95it/s]\n 32%|███▎ | 13/40 [00:02<00:05, 4.95it/s]\n 35%|███▌ | 14/40 [00:02<00:05, 4.94it/s]\n 38%|███▊ | 15/40 [00:03<00:05, 4.94it/s]\n 40%|████ | 16/40 [00:03<00:04, 4.94it/s]\n 42%|████▎ | 17/40 [00:03<00:04, 4.93it/s]\n 45%|████▌ | 18/40 [00:03<00:04, 4.93it/s]\n 48%|████▊ | 19/40 [00:03<00:04, 4.93it/s]\n 50%|█████ | 20/40 [00:04<00:04, 4.93it/s]\n 52%|█████▎ | 21/40 [00:04<00:03, 4.93it/s]\n 55%|█████▌ | 22/40 [00:04<00:03, 4.93it/s]\n 57%|█████▊ | 23/40 [00:04<00:03, 4.92it/s]\n 60%|██████ | 24/40 [00:04<00:03, 4.92it/s]\n 62%|██████▎ | 25/40 [00:05<00:03, 4.92it/s]\n 65%|██████▌ | 26/40 [00:05<00:02, 4.92it/s]\n 68%|██████▊ | 27/40 [00:05<00:02, 4.92it/s]\n 70%|███████ | 28/40 [00:05<00:02, 4.92it/s]\n 72%|███████▎ | 29/40 [00:05<00:02, 4.92it/s]\n 75%|███████▌ | 30/40 [00:06<00:02, 4.92it/s]\n 78%|███████▊ | 31/40 [00:06<00:01, 4.92it/s]\n 80%|████████ | 32/40 [00:06<00:01, 4.92it/s]\n 82%|████████▎ | 33/40 [00:06<00:01, 4.92it/s]\n 85%|████████▌ | 34/40 [00:06<00:01, 4.91it/s]\n 88%|████████▊ | 35/40 [00:07<00:01, 4.91it/s]\n 90%|█████████ | 36/40 [00:07<00:00, 4.91it/s]\n 92%|█████████▎| 37/40 [00:07<00:00, 4.91it/s]\n 95%|█████████▌| 38/40 [00:07<00:00, 4.91it/s]\n 98%|█████████▊| 39/40 [00:07<00:00, 4.91it/s]\n100%|██████████| 40/40 [00:08<00:00, 4.91it/s]\n100%|██████████| 40/40 [00:08<00:00, 4.93it/s]", "metrics": { "predict_time": 10.538237, "total_time": 10.532065 }, "output": [ "https://pbxt.replicate.delivery/fSikzXB4LHVmMqNvkpTIYLtqXTo6TfSlUXdX0t4x14tP5TfiA/out-0.png" ], "started_at": "2023-08-30T18:36:53.375376Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/xi6igj3byu43q3dbdj4lctseqy", "cancel": "https://api.replicate.com/v1/predictions/xi6igj3byu43q3dbdj4lctseqy/cancel" }, "version": "bf412da351d41547f117391eff2824ab0301b6ba1c6c010c4b5f766a492d62fc" }Generated inUsing seed: 58833 Prompt: Zelda looking at a computer, drinking a coke, amused, in the style of <s0><s1> img2img 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, 4.99it/s] 8%|▊ | 3/40 [00:00<00:07, 4.98it/s] 10%|█ | 4/40 [00:00<00:07, 4.97it/s] 12%|█▎ | 5/40 [00:01<00:07, 4.96it/s] 15%|█▌ | 6/40 [00:01<00:06, 4.96it/s] 18%|█▊ | 7/40 [00:01<00:06, 4.96it/s] 20%|██ | 8/40 [00:01<00:06, 4.96it/s] 22%|██▎ | 9/40 [00:01<00:06, 4.96it/s] 25%|██▌ | 10/40 [00:02<00:06, 4.95it/s] 28%|██▊ | 11/40 [00:02<00:05, 4.95it/s] 30%|███ | 12/40 [00:02<00:05, 4.95it/s] 32%|███▎ | 13/40 [00:02<00:05, 4.95it/s] 35%|███▌ | 14/40 [00:02<00:05, 4.94it/s] 38%|███▊ | 15/40 [00:03<00:05, 4.94it/s] 40%|████ | 16/40 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