tstramer
/
ghibli-diffusion
- Public
- 44.6K runs
-
A100 (80GB)
Prediction
tstramer/ghibli-diffusion:b76aa203IDvuj42xsehbgcbld4gdy7o5754mStatusSucceededSourceWebHardware–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 replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import 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:b76aa203ed8b55a2bd69bacdab46b48b981984181476570b7ba75699ae286025", { 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" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import 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:b76aa203ed8b55a2bd69bacdab46b48b981984181476570b7ba75699ae286025", 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.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
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": "b76aa203ed8b55a2bd69bacdab46b48b981984181476570b7ba75699ae286025", "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 20%|██ | 30/150 [00:02<00:08, 13.99it/s]\n 21%|██▏ | 32/150 [00:02<00:08, 14.00it/s]\n 23%|██▎ | 34/150 [00:02<00:08, 14.05it/s]\n 24%|██▍ | 36/150 [00:02<00:08, 14.07it/s]\n 25%|██▌ | 38/150 [00:02<00:08, 13.94it/s]\n 27%|██▋ | 40/150 [00:02<00:07, 14.00it/s]\n 28%|██▊ | 42/150 [00:03<00:07, 14.06it/s]\n 29%|██▉ | 44/150 [00:03<00:07, 14.12it/s]\n 31%|███ | 46/150 [00:03<00:07, 14.21it/s]\n 32%|███▏ | 48/150 [00:03<00:07, 14.30it/s]\n 33%|███▎ | 50/150 [00:03<00:06, 14.35it/s]\n 35%|███▍ | 52/150 [00:03<00:06, 14.24it/s]\n 36%|███▌ | 54/150 [00:03<00:06, 14.22it/s]\n 37%|███▋ | 56/150 [00:03<00:06, 14.20it/s]\n 39%|███▊ | 58/150 [00:04<00:06, 14.22it/s]\n 40%|████ | 60/150 [00:04<00:06, 14.25it/s]\n 41%|████▏ | 62/150 [00:04<00:06, 14.29it/s]\n 43%|████▎ | 64/150 [00:04<00:06, 14.26it/s]\n 44%|████▍ | 66/150 [00:04<00:06, 13.95it/s]\n 45%|████▌ | 68/150 [00:04<00:05, 13.86it/s]\n 47%|████▋ | 70/150 [00:04<00:05, 13.90it/s]\n 48%|████▊ | 72/150 [00:05<00:05, 14.06it/s]\n 49%|████▉ | 74/150 [00:05<00:05, 14.16it/s]\n 51%|█████ | 76/150 [00:05<00:05, 14.25it/s]\n 52%|█████▏ | 78/150 [00:05<00:05, 14.29it/s]\n 53%|█████▎ | 80/150 [00:05<00:04, 14.05it/s]\n 55%|█████▍ | 82/150 [00:05<00:04, 14.08it/s]\n 56%|█████▌ | 84/150 [00:05<00:04, 14.07it/s]\n 57%|█████▋ | 86/150 [00:06<00:04, 14.10it/s]\n 59%|█████▊ | 88/150 [00:06<00:04, 14.13it/s]\n 60%|██████ | 90/150 [00:06<00:04, 14.14it/s]\n 61%|██████▏ | 92/150 [00:06<00:04, 14.11it/s]\n 63%|██████▎ | 94/150 [00:06<00:04, 13.90it/s]\n 64%|██████▍ | 96/150 [00:06<00:03, 13.96it/s]\n 65%|██████▌ | 98/150 [00:06<00:03, 13.99it/s]\n 67%|██████▋ | 100/150 [00:07<00:03, 13.99it/s]\n 68%|██████▊ | 102/150 [00:07<00:03, 14.00it/s]\n 69%|██████▉ | 104/150 [00:07<00:03, 14.01it/s]\n 71%|███████ | 106/150 [00:07<00:03, 13.96it/s]\n 72%|███████▏ | 108/150 [00:07<00:03, 13.82it/s]\n 73%|███████▎ | 110/150 [00:07<00:02, 13.90it/s]\n 75%|███████▍ | 112/150 [00:07<00:02, 13.99it/s]\n 76%|███████▌ | 114/150 [00:08<00:02, 14.04it/s]\n 77%|███████▋ | 116/150 [00:08<00:02, 14.01it/s]\n 79%|███████▊ | 118/150 [00:08<00:02, 14.05it/s]\n 80%|████████ | 120/150 [00:08<00:02, 13.95it/s]\n 81%|████████▏ | 122/150 [00:08<00:02, 13.89it/s]\n 83%|████████▎ | 124/150 [00:08<00:01, 13.93it/s]\n 84%|████████▍ | 126/150 [00:08<00:01, 13.96it/s]\n 85%|████████▌ | 128/150 [00:09<00:01, 13.95it/s]\n 87%|████████▋ | 130/150 [00:09<00:01, 14.02it/s]\n 88%|████████▊ | 132/150 [00:09<00:01, 14.00it/s]\n 89%|████████▉ | 134/150 [00:09<00:01, 14.01it/s]\n 91%|█████████ | 136/150 [00:09<00:01, 13.88it/s]\n 92%|█████████▏| 138/150 [00:09<00:00, 13.77it/s]\n 93%|█████████▎| 140/150 [00:09<00:00, 13.72it/s]\n 95%|█████████▍| 142/150 [00:10<00:00, 13.74it/s]\n 96%|█████████▌| 144/150 [00:10<00:00, 13.76it/s]\n 97%|█████████▋| 146/150 [00:10<00:00, 13.79it/s]\n 99%|█████████▊| 148/150 [00:10<00:00, 13.81it/s]\n100%|██████████| 150/150 [00:10<00:00, 13.62it/s]\n100%|██████████| 150/150 [00:10<00:00, 13.99it/s]", "metrics": { "predict_time": 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, 13.97it/s] 17%|█▋ | 26/150 [00:01<00:08, 14.00it/s] 19%|█▊ | 28/150 [00:02<00:08, 14.02it/s] 20%|██ | 30/150 [00:02<00:08, 13.99it/s] 21%|██▏ | 32/150 [00:02<00:08, 14.00it/s] 23%|██▎ | 34/150 [00:02<00:08, 14.05it/s] 24%|██▍ | 36/150 [00:02<00:08, 14.07it/s] 25%|██▌ | 38/150 [00:02<00:08, 13.94it/s] 27%|██▋ | 40/150 [00:02<00:07, 14.00it/s] 28%|██▊ | 42/150 [00:03<00:07, 14.06it/s] 29%|██▉ | 44/150 [00:03<00:07, 14.12it/s] 31%|███ | 46/150 [00:03<00:07, 14.21it/s] 32%|███▏ | 48/150 [00:03<00:07, 14.30it/s] 33%|███▎ | 50/150 [00:03<00:06, 14.35it/s] 35%|███▍ | 52/150 [00:03<00:06, 14.24it/s] 36%|███▌ | 54/150 [00:03<00:06, 14.22it/s] 37%|███▋ | 56/150 [00:03<00:06, 14.20it/s] 39%|███▊ | 58/150 [00:04<00:06, 14.22it/s] 40%|████ | 60/150 [00:04<00:06, 14.25it/s] 41%|████▏ | 62/150 [00:04<00:06, 14.29it/s] 43%|████▎ | 64/150 [00:04<00:06, 14.26it/s] 44%|████▍ | 66/150 [00:04<00:06, 13.95it/s] 45%|████▌ | 68/150 [00:04<00:05, 13.86it/s] 47%|████▋ | 70/150 [00:04<00:05, 13.90it/s] 48%|████▊ | 72/150 [00:05<00:05, 14.06it/s] 49%|████▉ | 74/150 [00:05<00:05, 14.16it/s] 51%|█████ | 76/150 [00:05<00:05, 14.25it/s] 52%|█████▏ | 78/150 [00:05<00:05, 14.29it/s] 53%|█████▎ | 80/150 [00:05<00:04, 14.05it/s] 55%|█████▍ | 82/150 [00:05<00:04, 14.08it/s] 56%|█████▌ | 84/150 [00:05<00:04, 14.07it/s] 57%|█████▋ | 86/150 [00:06<00:04, 14.10it/s] 59%|█████▊ | 88/150 [00:06<00:04, 14.13it/s] 60%|██████ | 90/150 [00:06<00:04, 14.14it/s] 61%|██████▏ | 92/150 [00:06<00:04, 14.11it/s] 63%|██████▎ | 94/150 [00:06<00:04, 13.90it/s] 64%|██████▍ | 96/150 [00:06<00:03, 13.96it/s] 65%|██████▌ | 98/150 [00:06<00:03, 13.99it/s] 67%|██████▋ | 100/150 [00:07<00:03, 13.99it/s] 68%|██████▊ | 102/150 [00:07<00:03, 14.00it/s] 69%|██████▉ | 104/150 [00:07<00:03, 14.01it/s] 71%|███████ | 106/150 [00:07<00:03, 13.96it/s] 72%|███████▏ | 108/150 [00:07<00:03, 13.82it/s] 73%|███████▎ | 110/150 [00:07<00:02, 13.90it/s] 75%|███████▍ | 112/150 [00:07<00:02, 13.99it/s] 76%|███████▌ | 114/150 [00:08<00:02, 14.04it/s] 77%|███████▋ | 116/150 [00:08<00:02, 14.01it/s] 79%|███████▊ | 118/150 [00:08<00:02, 14.05it/s] 80%|████████ | 120/150 [00:08<00:02, 13.95it/s] 81%|████████▏ | 122/150 [00:08<00:02, 13.89it/s] 83%|████████▎ | 124/150 [00:08<00:01, 13.93it/s] 84%|████████▍ | 126/150 [00:08<00:01, 13.96it/s] 85%|████████▌ | 128/150 [00:09<00:01, 13.95it/s] 87%|████████▋ | 130/150 [00:09<00:01, 14.02it/s] 88%|████████▊ | 132/150 [00:09<00:01, 14.00it/s] 89%|████████▉ | 134/150 [00:09<00:01, 14.01it/s] 91%|█████████ | 136/150 [00:09<00:01, 13.88it/s] 92%|█████████▏| 138/150 [00:09<00:00, 13.77it/s] 93%|█████████▎| 140/150 [00:09<00:00, 13.72it/s] 95%|█████████▍| 142/150 [00:10<00:00, 13.74it/s] 96%|█████████▌| 144/150 [00:10<00:00, 13.76it/s] 97%|█████████▋| 146/150 [00:10<00:00, 13.79it/s] 99%|█████████▊| 148/150 [00:10<00:00, 13.81it/s] 100%|██████████| 150/150 [00:10<00:00, 13.62it/s] 100%|██████████| 150/150 [00:10<00:00, 13.99it/s]
Want to make some of these yourself?
Run this model