jfals82 / mason_zoom
(Updated 9 months ago)
- Public
- 30 runs
- SDXL fine-tune
Prediction
jfals82/mason_zoom:8131e0bdda244be92efcefa91d45450d950a2cafa0234f35ee753bf086f9db72IDfncbc2729drgp0chzjttxkgzarStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- TOK, wearing a birthday hat and holding a bunny
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 10
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- hair
- prompt_strength
- 0.75
- num_inference_steps
- 200
{ "width": 1024, "height": 1024, "prompt": "TOK, wearing a birthday hat and holding a bunny", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 10, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "hair", "prompt_strength": 0.75, "num_inference_steps": 200 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run jfals82/mason_zoom using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jfals82/mason_zoom:8131e0bdda244be92efcefa91d45450d950a2cafa0234f35ee753bf086f9db72", { input: { width: 1024, height: 1024, prompt: "TOK, wearing a birthday hat and holding a bunny", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 10, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "hair", prompt_strength: 0.75, num_inference_steps: 200 } } ); // 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 jfals82/mason_zoom using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jfals82/mason_zoom:8131e0bdda244be92efcefa91d45450d950a2cafa0234f35ee753bf086f9db72", input={ "width": 1024, "height": 1024, "prompt": "TOK, wearing a birthday hat and holding a bunny", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 10, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "hair", "prompt_strength": 0.75, "num_inference_steps": 200 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run jfals82/mason_zoom 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": "jfals82/mason_zoom:8131e0bdda244be92efcefa91d45450d950a2cafa0234f35ee753bf086f9db72", "input": { "width": 1024, "height": 1024, "prompt": "TOK, wearing a birthday hat and holding a bunny", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 10, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "hair", "prompt_strength": 0.75, "num_inference_steps": 200 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-09-16T21:21:58.356971Z", "created_at": "2024-09-16T21:21:04.075000Z", "data_removed": false, "error": null, "id": "fncbc2729drgp0chzjttxkgzar", "input": { "width": 1024, "height": 1024, "prompt": "TOK, wearing a birthday hat and holding a bunny", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 10, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "hair", "prompt_strength": 0.75, "num_inference_steps": 200 }, "logs": "Using seed: 46420\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: <s0><s1>, wearing a birthday hat and holding a bunny\ntxt2img mode\n 0%| | 0/200 [00:00<?, ?it/s]\n 0%| | 1/200 [00:00<00:46, 4.31it/s]\n 1%| | 2/200 [00:00<00:46, 4.28it/s]\n 2%|▏ | 3/200 [00:00<00:46, 4.27it/s]\n 2%|▏ | 4/200 [00:00<00:45, 4.27it/s]\n 2%|▎ | 5/200 [00:01<00:45, 4.27it/s]\n 3%|▎ | 6/200 [00:01<00:45, 4.27it/s]\n 4%|▎ | 7/200 [00:01<00:45, 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"total_time": 54.281971 }, "output": [ "https://replicate.delivery/pbxt/wkTDAP0tIy50C9x2fpesF6PFQCfm40heIhx023FF5T6S3U2NB/out-0.png" ], "started_at": "2024-09-16T21:21:06.121862Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/fncbc2729drgp0chzjttxkgzar", "cancel": "https://api.replicate.com/v1/predictions/fncbc2729drgp0chzjttxkgzar/cancel" }, "version": "8131e0bdda244be92efcefa91d45450d950a2cafa0234f35ee753bf086f9db72" }Generated inUsing seed: 46420 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: <s0><s1>, wearing a birthday hat and holding a bunny txt2img mode 0%| | 0/200 [00:00<?, ?it/s] 0%| | 1/200 [00:00<00:46, 4.31it/s] 1%| | 2/200 [00:00<00:46, 4.28it/s] 2%|▏ | 3/200 [00:00<00:46, 4.27it/s] 2%|▏ | 4/200 [00:00<00:45, 4.27it/s] 2%|▎ | 5/200 [00:01<00:45, 4.27it/s] 3%|▎ | 6/200 [00:01<00:45, 4.27it/s] 4%|▎ | 7/200 [00:01<00:45, 4.27it/s] 4%|▍ | 8/200 [00:01<00:45, 4.26it/s] 4%|▍ | 9/200 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