tstramer / ghibli-diffusion
- Public
- 44.7K runs
-
A100 (80GB)
Prediction
tstramer/ghibli-diffusion:b190224e487827d5fdeb8bf8bcdaa71c41b09a152f175de9f170aa0e088f66e9IDvuj42xsehbgcbld4gdy7o5754mStatusSucceededSourceWebHardware–Total durationCreatedInput
- width
- 512
- height
- 512
- prompt
- ghibli style elf
- scheduler
- K-LMS
- num_outputs
- 1
- guidance_scale
- 7.5
- prompt_strength
- 0.8
- num_inference_steps
- "150"
{ "width": 512, "height": 512, "prompt": "ghibli style elf", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": "150" }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 tstramer/ghibli-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "tstramer/ghibli-diffusion:b190224e487827d5fdeb8bf8bcdaa71c41b09a152f175de9f170aa0e088f66e9", { input: { width: 512, height: 512, prompt: "ghibli style elf", scheduler: "K-LMS", num_outputs: 1, guidance_scale: 7.5, prompt_strength: 0.8, num_inference_steps: "150" } } ); // 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 tstramer/ghibli-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "tstramer/ghibli-diffusion:b190224e487827d5fdeb8bf8bcdaa71c41b09a152f175de9f170aa0e088f66e9", input={ "width": 512, "height": 512, "prompt": "ghibli style elf", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": "150" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run tstramer/ghibli-diffusion 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": "tstramer/ghibli-diffusion:b190224e487827d5fdeb8bf8bcdaa71c41b09a152f175de9f170aa0e088f66e9", "input": { "width": 512, "height": 512, "prompt": "ghibli style elf", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": "150" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-11-22T02:40:27.132264Z", "created_at": "2022-11-22T02:40:15.884998Z", "data_removed": false, "error": null, "id": "vuj42xsehbgcbld4gdy7o5754m", "input": { "width": 512, "height": 512, "prompt": "ghibli style elf", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": "150" }, "logs": "Using seed: 14453\n 0%| | 0/150 [00:00<?, ?it/s]\n 1%|▏ | 2/150 [00:00<00:11, 13.36it/s]\n 3%|▎ | 4/150 [00:00<00:10, 13.77it/s]\n 4%|▍ | 6/150 [00:00<00:10, 13.96it/s]\n 5%|▌ | 8/150 [00:00<00:10, 13.47it/s]\n 7%|▋ | 10/150 [00:00<00:10, 13.69it/s]\n 8%|▊ | 12/150 [00:00<00:09, 13.91it/s]\n 9%|▉ | 14/150 [00:01<00:09, 13.96it/s]\n 11%|█ | 16/150 [00:01<00:09, 13.98it/s]\n 12%|█▏ | 18/150 [00:01<00:09, 14.10it/s]\n 13%|█▎ | 20/150 [00:01<00:09, 14.09it/s]\n 15%|█▍ | 22/150 [00:01<00:09, 14.07it/s]\n 16%|█▌ | 24/150 [00:01<00:09, 13.97it/s]\n 17%|█▋ | 26/150 [00:01<00:08, 14.00it/s]\n 19%|█▊ | 28/150 [00:02<00:08, 14.02it/s]\n 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11.211884, "total_time": 11.247266 }, "output": [ "https://replicate.delivery/pbxt/EBBNVwxeuQwQZKbuecO6mMgSQ11l1D715fvMrEra3A50E9EgA/out-0.png" ], "started_at": "2022-11-22T02:40:15.920380Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/vuj42xsehbgcbld4gdy7o5754m", "cancel": "https://api.replicate.com/v1/predictions/vuj42xsehbgcbld4gdy7o5754m/cancel" }, "version": "b190224e487827d5fdeb8bf8bcdaa71c41b09a152f175de9f170aa0e088f66e9" }Generated inUsing seed: 14453 0%| | 0/150 [00:00<?, ?it/s] 1%|▏ | 2/150 [00:00<00:11, 13.36it/s] 3%|▎ | 4/150 [00:00<00:10, 13.77it/s] 4%|▍ | 6/150 [00:00<00:10, 13.96it/s] 5%|▌ | 8/150 [00:00<00:10, 13.47it/s] 7%|▋ | 10/150 [00:00<00:10, 13.69it/s] 8%|▊ | 12/150 [00:00<00:09, 13.91it/s] 9%|▉ | 14/150 [00:01<00:09, 13.96it/s] 11%|█ | 16/150 [00:01<00:09, 13.98it/s] 12%|█▏ | 18/150 [00:01<00:09, 14.10it/s] 13%|█▎ | 20/150 [00:01<00:09, 14.09it/s] 15%|█▍ | 22/150 [00:01<00:09, 14.07it/s] 16%|█▌ | 24/150 [00:01<00:09, 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