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You're looking at a specific version of this model. Jump to the model overview.
Input
Run this model in Node.js with one line of code:
npx create-replicate --model=laion-ai/ongo
or set up a project from scratch
Install Replicate’s Node.js client library:
npm install replicateSet the
REPLICATE_API_TOKEN environment 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 laion-ai/ongo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"laion-ai/ongo:41ad32228705648d785fc33be19d98dfa058cfd59d1f9a7c2248757e3652cdf6",
{
input: {
seed: -1,
steps: 100,
width: 256,
height: 256,
prompt: "bunny rabbit in style of van gogh",
negative: "",
batch_size: 3,
guidance_scale: 5,
aesthetic_rating: 8,
aesthetic_weight: 0.1,
init_skip_fraction: 0
}
}
);
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 the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:
import replicateRun laion-ai/ongo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"laion-ai/ongo:41ad32228705648d785fc33be19d98dfa058cfd59d1f9a7c2248757e3652cdf6",
input={
"seed": -1,
"steps": 100,
"width": 256,
"height": 256,
"prompt": "bunny rabbit in style of van gogh",
"negative": "",
"batch_size": 3,
"guidance_scale": 5,
"aesthetic_rating": 8,
"aesthetic_weight": 0.1,
"init_skip_fraction": 0
}
)
# The laion-ai/ongo model can stream output as it's running.
# The predict method returns an iterator, and you can iterate over that output.
for item in output:
# https://replicate.com/laion-ai/ongo/api#output-schema
print(item)To learn more, take a look at the guide on getting started with Python.
Set the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run laion-ai/ongo 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": "41ad32228705648d785fc33be19d98dfa058cfd59d1f9a7c2248757e3652cdf6",
"input": {
"seed": -1,
"steps": 100,
"width": 256,
"height": 256,
"prompt": "bunny rabbit in style of van gogh",
"negative": "",
"batch_size": 3,
"guidance_scale": 5,
"aesthetic_rating": 8,
"aesthetic_weight": 0.1,
"init_skip_fraction": 0
}
}' \
https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:
brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/laion-ai/ongo@sha256:41ad32228705648d785fc33be19d98dfa058cfd59d1f9a7c2248757e3652cdf6 \
-i 'seed=-1' \
-i 'steps=100' \
-i 'width=256' \
-i 'height=256' \
-i 'prompt="bunny rabbit in style of van gogh"' \
-i 'negative=""' \
-i 'batch_size=3' \
-i 'guidance_scale=5' \
-i 'aesthetic_rating=8' \
-i 'aesthetic_weight=0.1' \
-i 'init_skip_fraction=0'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/laion-ai/ongo@sha256:41ad32228705648d785fc33be19d98dfa058cfd59d1f9a7c2248757e3652cdf6curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "seed": -1, "steps": 100, "width": 256, "height": 256, "prompt": "bunny rabbit in style of van gogh", "negative": "", "batch_size": 3, "guidance_scale": 5, "aesthetic_rating": 8, "aesthetic_weight": 0.1, "init_skip_fraction": 0 } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Add a payment method to run this model.
Each run costs approximately $0.014. Alternatively, try out our featured models for free.
Sign in with GitHub
By signing in, you agree to our
terms of service and privacy policy
Output
{
"completed_at": "2022-06-24T23:10:08.972024Z",
"created_at": "2022-06-24T23:09:08.495722Z",
"data_removed": false,
"error": null,
"id": "lvc2nt4xu5emlkxffo7hp6f2py",
"input": {
"seed": -1,
"steps": "100",
"width": 256,
"height": 256,
"prompt": "bunny rabbit in style of van gogh",
"batch_size": "3",
"guidance_scale": 5,
"aesthetic_rating": 8,
"aesthetic_weight": 0.1
},
"logs": "Using seed 3823169816\nRunning simulation for bunny rabbit in style of van gogh\nEncoding text embeddings with bunny rabbit in style of van gogh dimensions\nUsing aesthetic embedding 8 with weight 0.1\nUsing inpaint model but no image is provided. Initializing with zeros.\nRunning diffusion...\n\n0it [00:00, ?it/s]\n\nTimestep 0 - saving sample\n 0%| | 0/100 [00:00<?, ?it/s]\u001b[A\n1it [00:02, 2.33s/it]\n\n 1%| | 1/100 [00:02<03:51, 2.33s/it]\u001b[A\n2it [00:04, 2.09s/it]\n\n 2%|▏ | 2/100 [00:04<03:24, 2.09s/it]\u001b[A\n3it [00:06, 2.02s/it]\n\n 3%|▎ | 3/100 [00:06<03:15, 2.02s/it]\u001b[A\n4it [00:06, 1.42s/it]\n\n 4%|▍ | 4/100 [00:06<02:15, 1.42s/it]\u001b[A\n5it [00:07, 1.08s/it]\n\n 5%|▌ | 5/100 [00:07<01:42, 1.08s/it]\u001b[A\n6it [00:07, 1.14it/s]\n\n 6%|▌ | 6/100 [00:07<01:22, 1.14it/s]\u001b[A\n7it [00:08, 1.33it/s]\n\n 7%|▋ | 7/100 [00:08<01:09, 1.33it/s]\u001b[A\n8it [00:08, 1.49it/s]\n\n 8%|▊ | 8/100 [00:08<01:01, 1.49it/s]\u001b[A\n9it [00:09, 1.63it/s]\n\n 9%|▉ | 9/100 [00:09<00:55, 1.63it/s]\u001b[A\n10it [00:09, 1.73it/s]\n\nTimestep 10 - saving sample\n 10%|█ | 10/100 [00:09<00:51, 1.73it/s]\u001b[A\n11it [00:10, 1.48it/s]\n\n 11%|█ | 11/100 [00:10<01:00, 1.48it/s]\u001b[A\n12it [00:11, 1.61it/s]\n\n 12%|█▏ | 12/100 [00:11<00:54, 1.61it/s]\u001b[A\n13it [00:11, 1.71it/s]\n\n 13%|█▎ | 13/100 [00:11<00:50, 1.71it/s]\u001b[A\n14it [00:12, 1.80it/s]\n\n 14%|█▍ | 14/100 [00:12<00:47, 1.80it/s]\u001b[A\n15it [00:12, 1.85it/s]\n\n 15%|█▌ | 15/100 [00:12<00:45, 1.85it/s]\u001b[A\n16it [00:13, 1.89it/s]\n\n 16%|█▌ | 16/100 [00:13<00:44, 1.89it/s]\u001b[A\n17it [00:13, 1.92it/s]\n\n 17%|█▋ | 17/100 [00:13<00:43, 1.92it/s]\u001b[A\n18it [00:14, 1.93it/s]\n\n 18%|█▊ | 18/100 [00:14<00:42, 1.93it/s]\u001b[A\n19it [00:14, 1.95it/s]\n\n 19%|█▉ | 19/100 [00:14<00:41, 1.95it/s]\u001b[A\n20it [00:15, 1.96it/s]\n\nTimestep 20 - saving sample\n 20%|██ | 20/100 [00:15<00:40, 1.96it/s]\u001b[A\n21it [00:15, 1.58it/s]\n\n 21%|██ | 21/100 [00:15<00:50, 1.58it/s]\u001b[A\n22it [00:16, 1.69it/s]\n\n 22%|██▏ | 22/100 [00:16<00:46, 1.69it/s]\u001b[A\n23it [00:16, 1.77it/s]\n\n 23%|██▎ | 23/100 [00:16<00:43, 1.77it/s]\u001b[A\n24it [00:17, 1.82it/s]\n\n 24%|██▍ | 24/100 [00:17<00:41, 1.82it/s]\u001b[A\n25it [00:17, 1.87it/s]\n\n 25%|██▌ | 25/100 [00:17<00:40, 1.87it/s]\u001b[A\n26it [00:18, 1.90it/s]\n\n 26%|██▌ | 26/100 [00:18<00:39, 1.90it/s]\u001b[A\n27it [00:18, 1.92it/s]\n\n 27%|██▋ | 27/100 [00:18<00:38, 1.92it/s]\u001b[A\n28it [00:19, 1.93it/s]\n\n 28%|██▊ | 28/100 [00:19<00:37, 1.93it/s]\u001b[A\n29it [00:20, 1.93it/s]\n\n 29%|██▉ | 29/100 [00:20<00:36, 1.93it/s]\u001b[A\n30it [00:20, 1.95it/s]\n\nTimestep 30 - saving sample\n 30%|███ | 30/100 [00:20<00:35, 1.95it/s]\u001b[A\n31it [00:21, 1.57it/s]\n\n 31%|███ | 31/100 [00:21<00:43, 1.57it/s]\u001b[A\n32it [00:21, 1.67it/s]\n\n 32%|███▏ | 32/100 [00:21<00:40, 1.67it/s]\u001b[A\n33it [00:22, 1.75it/s]\n\n 33%|███▎ | 33/100 [00:22<00:38, 1.75it/s]\u001b[A\n34it [00:22, 1.81it/s]\n\n 34%|███▍ | 34/100 [00:22<00:36, 1.81it/s]\u001b[A\n35it [00:23, 1.86it/s]\n\n 35%|███▌ | 35/100 [00:23<00:35, 1.86it/s]\u001b[A\n36it [00:23, 1.89it/s]\n\n 36%|███▌ | 36/100 [00:23<00:33, 1.89it/s]\u001b[A\n37it [00:24, 1.92it/s]\n\n 37%|███▋ | 37/100 [00:24<00:32, 1.92it/s]\u001b[A\n38it [00:24, 1.93it/s]\n\n 38%|███▊ | 38/100 [00:24<00:32, 1.93it/s]\u001b[A\n39it [00:25, 1.95it/s]\n\n 39%|███▉ | 39/100 [00:25<00:31, 1.95it/s]\u001b[A\n40it [00:25, 1.96it/s]\n\nTimestep 40 - saving sample\n 40%|████ | 40/100 [00:25<00:30, 1.96it/s]\u001b[A\n41it [00:26, 1.58it/s]\n\n 41%|████ | 41/100 [00:26<00:37, 1.58it/s]\u001b[A\n42it [00:27, 1.70it/s]\n\n 42%|████▏ | 42/100 [00:27<00:34, 1.70it/s]\u001b[A\n43it [00:27, 1.78it/s]\n\n 43%|████▎ | 43/100 [00:27<00:32, 1.78it/s]\u001b[A\n44it [00:28, 1.84it/s]\n\n 44%|████▍ | 44/100 [00:28<00:30, 1.84it/s]\u001b[A\n45it [00:28, 1.89it/s]\n\n 45%|████▌ | 45/100 [00:28<00:29, 1.89it/s]\u001b[A\n46it [00:29, 1.93it/s]\n\n 46%|████▌ | 46/100 [00:29<00:28, 1.93it/s]\u001b[A\n47it [00:29, 1.95it/s]\n\n 47%|████▋ | 47/100 [00:29<00:27, 1.95it/s]\u001b[A\n48it [00:30, 1.97it/s]\n\n 48%|████▊ | 48/100 [00:30<00:26, 1.97it/s]\u001b[A\n49it [00:30, 1.98it/s]\n\n 49%|████▉ | 49/100 [00:30<00:25, 1.98it/s]\u001b[A\n50it [00:31, 2.00it/s]\n\nTimestep 50 - saving sample\n 50%|█████ | 50/100 [00:31<00:25, 2.00it/s]\u001b[A\n51it [00:32, 1.61it/s]\n\n 51%|█████ | 51/100 [00:32<00:30, 1.61it/s]\u001b[A\n52it [00:32, 1.72it/s]\n\n 52%|█████▏ | 52/100 [00:32<00:27, 1.72it/s]\u001b[A\n53it [00:33, 1.81it/s]\n\n 53%|█████▎ | 53/100 [00:33<00:25, 1.81it/s]\u001b[A\n54it [00:33, 1.87it/s]\n\n 54%|█████▍ | 54/100 [00:33<00:24, 1.87it/s]\u001b[A\n55it [00:34, 1.92it/s]\n\n 55%|█████▌ | 55/100 [00:34<00:23, 1.92it/s]\u001b[A\n56it [00:34, 1.96it/s]\n\n 56%|█████▌ | 56/100 [00:34<00:22, 1.96it/s]\u001b[A\n57it [00:35, 1.99it/s]\n\n 57%|█████▋ | 57/100 [00:35<00:21, 1.99it/s]\u001b[A\n58it [00:35, 2.00it/s]\n\n 58%|█████▊ | 58/100 [00:35<00:20, 2.00it/s]\u001b[A\n59it [00:36, 2.02it/s]\n\n 59%|█████▉ | 59/100 [00:36<00:20, 2.02it/s]\u001b[A\n60it [00:36, 2.03it/s]\n\nTimestep 60 - saving sample\n 60%|██████ | 60/100 [00:36<00:19, 2.03it/s]\u001b[A\n61it [00:37, 1.64it/s]\n\n 61%|██████ | 61/100 [00:37<00:23, 1.64it/s]\u001b[A\n62it [00:38, 1.76it/s]\n\n 62%|██████▏ | 62/100 [00:38<00:21, 1.76it/s]\u001b[A\n63it [00:38, 1.84it/s]\n\n 63%|██████▎ | 63/100 [00:38<00:20, 1.84it/s]\u001b[A\n64it [00:38, 1.91it/s]\n\n 64%|██████▍ | 64/100 [00:38<00:18, 1.91it/s]\u001b[A\n65it [00:39, 1.95it/s]\n\n 65%|██████▌ | 65/100 [00:39<00:17, 1.95it/s]\u001b[A\n66it [00:39, 1.99it/s]\n\n 66%|██████▌ | 66/100 [00:39<00:17, 1.99it/s]\u001b[A\n67it [00:40, 2.01it/s]\n\n 67%|██████▋ | 67/100 [00:40<00:16, 2.01it/s]\u001b[A\n68it [00:40, 2.04it/s]\n\n 68%|██████▊ | 68/100 [00:40<00:15, 2.04it/s]\u001b[A\n69it [00:41, 2.05it/s]\n\n 69%|██████▉ | 69/100 [00:41<00:15, 2.05it/s]\u001b[A\n70it [00:41, 2.07it/s]\n\nTimestep 70 - saving sample\n 70%|███████ | 70/100 [00:41<00:14, 2.07it/s]\u001b[A\n71it [00:42, 1.67it/s]\n\n 71%|███████ | 71/100 [00:42<00:17, 1.67it/s]\u001b[A\n72it [00:43, 1.78it/s]\n\n 72%|███████▏ | 72/100 [00:43<00:15, 1.78it/s]\u001b[A\n73it [00:43, 1.87it/s]\n\n 73%|███████▎ | 73/100 [00:43<00:14, 1.87it/s]\u001b[A\n74it [00:44, 1.93it/s]\n\n 74%|███████▍ | 74/100 [00:44<00:13, 1.93it/s]\u001b[A\n75it [00:44, 1.98it/s]\n\n 75%|███████▌ | 75/100 [00:44<00:12, 1.98it/s]\u001b[A\n76it [00:45, 2.01it/s]\n\n 76%|███████▌ | 76/100 [00:45<00:11, 2.01it/s]\u001b[A\n77it [00:45, 2.04it/s]\n\n 77%|███████▋ | 77/100 [00:45<00:11, 2.04it/s]\u001b[A\n78it [00:46, 2.06it/s]\n\n 78%|███████▊ | 78/100 [00:46<00:10, 2.06it/s]\u001b[A\n79it [00:46, 2.08it/s]\n\n 79%|███████▉ | 79/100 [00:46<00:10, 2.08it/s]\u001b[A\n80it [00:46, 2.09it/s]\n\nTimestep 80 - saving sample\n 80%|████████ | 80/100 [00:46<00:09, 2.09it/s]\u001b[A\n81it [00:47, 1.68it/s]\n\n 81%|████████ | 81/100 [00:47<00:11, 1.68it/s]\u001b[A\n82it [00:48, 1.80it/s]\n\n 82%|████████▏ | 82/100 [00:48<00:10, 1.80it/s]\u001b[A\n83it [00:48, 1.88it/s]\n\n 83%|████████▎ | 83/100 [00:48<00:09, 1.88it/s]\u001b[A\n84it [00:49, 1.95it/s]\n\n 84%|████████▍ | 84/100 [00:49<00:08, 1.95it/s]\u001b[A\n85it [00:49, 2.00it/s]\n\n 85%|████████▌ | 85/100 [00:49<00:07, 2.00it/s]\u001b[A\n86it [00:50, 2.03it/s]\n\n 86%|████████▌ | 86/100 [00:50<00:06, 2.03it/s]\u001b[A\n87it [00:50, 2.06it/s]\n\n 87%|████████▋ | 87/100 [00:50<00:06, 2.06it/s]\u001b[A\n88it [00:51, 2.08it/s]\n\n 88%|████████▊ | 88/100 [00:51<00:05, 2.08it/s]\u001b[A\n89it [00:51, 2.10it/s]\n\n 89%|████████▉ | 89/100 [00:51<00:05, 2.10it/s]\u001b[A\n90it [00:52, 2.10it/s]\n\nTimestep 90 - saving sample\n 90%|█████████ | 90/100 [00:52<00:04, 2.10it/s]\u001b[A\n91it [00:52, 1.69it/s]\n\n 91%|█████████ | 91/100 [00:52<00:05, 1.69it/s]\u001b[A\n92it [00:53, 1.81it/s]\n\n 92%|█████████▏| 92/100 [00:53<00:04, 1.81it/s]\u001b[A\n93it [00:53, 1.90it/s]\n\n 93%|█████████▎| 93/100 [00:53<00:03, 1.90it/s]\u001b[A\n94it [00:54, 1.96it/s]\n\n 94%|█████████▍| 94/100 [00:54<00:03, 1.96it/s]\u001b[A\n95it [00:54, 2.01it/s]\n\n 95%|█████████▌| 95/100 [00:54<00:02, 2.01it/s]\u001b[A\n96it [00:55, 2.05it/s]\n\n 96%|█████████▌| 96/100 [00:55<00:01, 2.05it/s]\u001b[A\n97it [00:55, 2.08it/s]\n\n 97%|█████████▋| 97/100 [00:55<00:01, 2.08it/s]\u001b[A\n98it [00:56, 2.10it/s]\n\n 98%|█████████▊| 98/100 [00:56<00:00, 2.10it/s]\u001b[A\n99it [00:56, 2.11it/s]\n\nTimestep 99 - saving final sample\n 99%|█████████▉| 99/100 [00:56<00:00, 2.11it/s]\u001b[A\n100it [00:57, 1.70it/s]\n\n100%|██████████| 100/100 [00:57<00:00, 1.70it/s]\u001b[A\n100%|██████████| 100/100 [00:57<00:00, 1.74it/s]\n\n100it [00:57, 1.74it/s]",
"metrics": {
"predict_time": 60.32768,
"total_time": 60.476302
},
"output": [
[
"https://replicate.delivery/mgxm/9353d053-81a7-45cf-8a74-bb90f4079115/current_0.jpg",
"https://replicate.delivery/mgxm/8ed00b10-709e-4e49-88e3-d668997a6981/current_1.jpg",
"https://replicate.delivery/mgxm/9b80506b-c375-4e0f-bee8-8443789e4018/current_2.jpg"
],
[
"https://replicate.delivery/mgxm/a9b76357-22cc-4c70-b824-517ac86bf469/current_0.jpg",
"https://replicate.delivery/mgxm/a37b8810-3420-40f9-8e7f-37d386794cfd/current_1.jpg",
"https://replicate.delivery/mgxm/9f7b1277-e14a-402b-bac3-e18ac1d86a00/current_2.jpg"
],
[
"https://replicate.delivery/mgxm/fa4c7166-ed00-4d6f-82f3-e7d8387ea59d/current_0.jpg",
"https://replicate.delivery/mgxm/94cff6ce-fd73-4b5d-a127-2c1edb6217d7/current_1.jpg",
"https://replicate.delivery/mgxm/8783659d-8242-40bf-8317-6cf6fa65c394/current_2.jpg"
],
[
"https://replicate.delivery/mgxm/da74c322-b33b-4f76-b837-8a34b2fb0ade/current_0.jpg",
"https://replicate.delivery/mgxm/4dd71239-2fe0-4ebe-ba53-6a7d1e6b35a6/current_1.jpg",
"https://replicate.delivery/mgxm/a02c25b0-ce30-469a-b822-ce04e6236a93/current_2.jpg"
],
[
"https://replicate.delivery/mgxm/025e8f3d-bacb-4d54-88dd-69b5ddb5edbe/current_0.jpg",
"https://replicate.delivery/mgxm/b2e8a2a0-ff27-4a47-8a4e-3438f89b131d/current_1.jpg",
"https://replicate.delivery/mgxm/31874e33-57cf-43fb-8966-8cf9dee52175/current_2.jpg"
],
[
"https://replicate.delivery/mgxm/e0b9fdd6-824d-4eaf-b810-ca8acfba2a29/current_0.jpg",
"https://replicate.delivery/mgxm/978aa7b0-b21c-4b1f-9b67-e0e6a57bac60/current_1.jpg",
"https://replicate.delivery/mgxm/20eebe0c-bef3-4448-8d9b-1624da68892e/current_2.jpg"
],
[
"https://replicate.delivery/mgxm/350e6127-192d-4fdf-92d5-2929c65a3875/current_0.jpg",
"https://replicate.delivery/mgxm/a1d3db1f-57e8-4999-b9a6-a42982f2bbad/current_1.jpg",
"https://replicate.delivery/mgxm/443f96cb-752e-45c8-b384-5693468ff4de/current_2.jpg"
],
[
"https://replicate.delivery/mgxm/f250dc45-3546-4baa-a3cc-7ac453cd7424/current_0.jpg",
"https://replicate.delivery/mgxm/44d6c554-93e6-4bfe-b729-38177ef0503f/current_1.jpg",
"https://replicate.delivery/mgxm/312561c5-5b6e-4d6c-a11a-8111285cae96/current_2.jpg"
],
[
"https://replicate.delivery/mgxm/bcf82116-bab9-45f2-bc20-821c04c74029/current_0.jpg",
"https://replicate.delivery/mgxm/13efbc35-99b6-4700-872e-17b16cb4fa9b/current_1.jpg",
"https://replicate.delivery/mgxm/b30623f5-1cc3-483d-b358-c0f59bb3a978/current_2.jpg"
],
[
"https://replicate.delivery/mgxm/9612dde2-2e28-4e25-95ec-f8bbf98b6e93/current_0.jpg",
"https://replicate.delivery/mgxm/880166d2-44db-4e31-aa1a-f3e36f626d3c/current_1.jpg",
"https://replicate.delivery/mgxm/d2d3d4c3-a3b7-41f0-be0c-cb03ef9212d2/current_2.jpg"
],
[
"https://replicate.delivery/mgxm/641c2320-4d9f-4a7b-bc3f-bcd225f70eee/current_0.jpg",
"https://replicate.delivery/mgxm/b46093ec-2abd-49b0-aea5-1e2dc7421ee5/current_1.jpg",
"https://replicate.delivery/mgxm/21dfe26e-7782-4242-a33c-142721f40b37/current_2.jpg"
]
],
"started_at": "2022-06-24T23:09:08.644344Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/lvc2nt4xu5emlkxffo7hp6f2py",
"cancel": "https://api.replicate.com/v1/predictions/lvc2nt4xu5emlkxffo7hp6f2py/cancel"
},
"version": "41ad32228705648d785fc33be19d98dfa058cfd59d1f9a7c2248757e3652cdf6"
}Generated in
Using seed 3823169816
Running simulation for bunny rabbit in style of van gogh
Encoding text embeddings with bunny rabbit in style of van gogh dimensions
Using aesthetic embedding 8 with weight 0.1
Using inpaint model but no image is provided. Initializing with zeros.
Running diffusion...
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Timestep 90 - saving sample
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Timestep 99 - saving final sample
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