Readme
A port of the original latent diffusion model that was spun off into GLID-3-XL.
Original Github Here: https://github.com/CompVis/latent-diffusion
nicholascelestin / latent-diffusion
High-Resolution Image Synthesis with Latent Diffusion Models
Input
Run this model in Node.js with one line of code:
npx create-replicate --model=nicholascelestin/latent-diffusion
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";
import fs from "node:fs";
const replicate = new Replicate({
auth: process.env.REPLICATE_API_TOKEN,
});Run nicholascelestin/latent-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"nicholascelestin/latent-diffusion:25cf1f58250f98328ffb225e7eccadcedcb72bb1ab26e463e9fa0fd8a05533df",
{
input: {
eta: 0,
plms: false,
scale: 5,
steps: 100,
width: 256,
height: 256,
prompt: "A beautiful view of a fantasy kingdom",
batches: 1,
batch_size: 4
}
}
);
// 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 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 nicholascelestin/latent-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"nicholascelestin/latent-diffusion:25cf1f58250f98328ffb225e7eccadcedcb72bb1ab26e463e9fa0fd8a05533df",
input={
"eta": 0,
"plms": False,
"scale": 5,
"steps": 100,
"width": 256,
"height": 256,
"prompt": "A beautiful view of a fantasy kingdom",
"batches": 1,
"batch_size": 4
}
)
print(output)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 nicholascelestin/latent-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": "nicholascelestin/latent-diffusion:25cf1f58250f98328ffb225e7eccadcedcb72bb1ab26e463e9fa0fd8a05533df",
"input": {
"eta": 0,
"plms": false,
"scale": 5,
"steps": 100,
"width": 256,
"height": 256,
"prompt": "A beautiful view of a fantasy kingdom",
"batches": 1,
"batch_size": 4
}
}' \
https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Add a payment method to run this model.
Sign in with GitHub
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terms of service and privacy policy
Output
{
"completed_at": "2022-06-24T20:13:24.099900Z",
"created_at": "2022-06-24T20:12:25.078186Z",
"data_removed": false,
"error": null,
"id": "spi4ffj46baktckvxrgs2nucbq",
"input": {
"plms": false,
"scale": 5,
"steps": 100,
"width": 256,
"height": 256,
"prompt": "A beautiful view of a fantasy kingdom",
"batches": 1,
"batch_size": 4
},
"logs": "Prediction started!\nSampler loaded\n\nData shape for DDIM sampling is (4, 4, 32, 32), eta 0\nRunning DDIM Sampling with 100 timesteps\nSampling: 0%| | 0/1 [00:00<?, ?it/s]\n\nDDIM Sampler: 0%| | 0/100 [00:00<?, ?it/s]\u001b[A\n\nDDIM Sampler: 1%| | 1/100 [00:00<00:52, 1.87it/s]\u001b[A\n\nDDIM Sampler: 2%|▏ | 2/100 [00:01<00:52, 1.85it/s]\u001b[A\n\nDDIM Sampler: 3%|▎ | 3/100 [00:01<00:52, 1.85it/s]\u001b[A\n\nDDIM Sampler: 4%|▍ | 4/100 [00:02<00:51, 1.85it/s]\u001b[A\n\nDDIM Sampler: 5%|▌ | 5/100 [00:02<00:51, 1.85it/s]\u001b[A\n\nDDIM Sampler: 6%|▌ | 6/100 [00:03<00:51, 1.84it/s]\u001b[A\n\nDDIM Sampler: 7%|▋ | 7/100 [00:03<00:50, 1.84it/s]\u001b[A\n\nDDIM Sampler: 8%|▊ | 8/100 [00:04<00:50, 1.84it/s]\u001b[A\n\nDDIM Sampler: 9%|▉ | 9/100 [00:04<00:49, 1.83it/s]\u001b[A\n\nDDIM Sampler: 10%|█ | 10/100 [00:05<00:49, 1.83it/s]\u001b[A\n\nDDIM Sampler: 11%|█ | 11/100 [00:05<00:48, 1.83it/s]\u001b[A\n\nDDIM Sampler: 12%|█▏ | 12/100 [00:06<00:48, 1.83it/s]\u001b[A\n\nDDIM Sampler: 13%|█▎ | 13/100 [00:07<00:47, 1.83it/s]\u001b[A\n\nDDIM Sampler: 14%|█▍ | 14/100 [00:07<00:47, 1.83it/s]\u001b[A\n\nDDIM Sampler: 15%|█▌ | 15/100 [00:08<00:46, 1.83it/s]\u001b[A\n\nDDIM Sampler: 16%|█▌ | 16/100 [00:08<00:45, 1.83it/s]\u001b[A\n\nDDIM Sampler: 17%|█▋ | 17/100 [00:09<00:45, 1.82it/s]\u001b[A\n\nDDIM Sampler: 18%|█▊ | 18/100 [00:09<00:45, 1.81it/s]\u001b[A\n\nDDIM Sampler: 19%|█▉ | 19/100 [00:10<00:44, 1.81it/s]\u001b[A\n\nDDIM Sampler: 20%|██ | 20/100 [00:10<00:44, 1.81it/s]\u001b[A\n\nDDIM Sampler: 21%|██ | 21/100 [00:11<00:43, 1.81it/s]\u001b[A\n\nDDIM Sampler: 22%|██▏ | 22/100 [00:12<00:43, 1.80it/s]\u001b[A\n\nDDIM Sampler: 23%|██▎ | 23/100 [00:12<00:42, 1.81it/s]\u001b[A\n\nDDIM Sampler: 24%|██▍ | 24/100 [00:13<00:42, 1.80it/s]\u001b[A\n\nDDIM Sampler: 25%|██▌ | 25/100 [00:13<00:41, 1.80it/s]\u001b[A\n\nDDIM Sampler: 26%|██▌ | 26/100 [00:14<00:41, 1.79it/s]\u001b[A\n\nDDIM Sampler: 27%|██▋ | 27/100 [00:14<00:40, 1.79it/s]\u001b[A\n\nDDIM Sampler: 28%|██▊ | 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1.77it/s]\u001b[A\n\nDDIM Sampler: 86%|████████▌ | 86/100 [00:48<00:07, 1.77it/s]\u001b[A\n\nDDIM Sampler: 87%|████████▋ | 87/100 [00:49<00:07, 1.78it/s]\u001b[A\n\nDDIM Sampler: 88%|████████▊ | 88/100 [00:49<00:06, 1.78it/s]\u001b[A\n\nDDIM Sampler: 89%|████████▉ | 89/100 [00:50<00:06, 1.78it/s]\u001b[A\n\nDDIM Sampler: 90%|█████████ | 90/100 [00:50<00:05, 1.79it/s]\u001b[A\n\nDDIM Sampler: 91%|█████████ | 91/100 [00:51<00:05, 1.78it/s]\u001b[A\n\nDDIM Sampler: 92%|█████████▏| 92/100 [00:51<00:04, 1.79it/s]\u001b[A\n\nDDIM Sampler: 93%|█████████▎| 93/100 [00:52<00:03, 1.79it/s]\u001b[A\n\nDDIM Sampler: 94%|█████████▍| 94/100 [00:53<00:03, 1.80it/s]\u001b[A\n\nDDIM Sampler: 95%|█████████▌| 95/100 [00:53<00:02, 1.80it/s]\u001b[A\n\nDDIM Sampler: 96%|█████████▌| 96/100 [00:54<00:02, 1.80it/s]\u001b[A\n\nDDIM Sampler: 97%|█████████▋| 97/100 [00:54<00:01, 1.80it/s]\u001b[A\n\nDDIM Sampler: 98%|█████████▊| 98/100 [00:55<00:01, 1.80it/s]\u001b[A\n\nDDIM Sampler: 99%|█████████▉| 99/100 [00:55<00:00, 1.81it/s]\u001b[A\n\nDDIM Sampler: 100%|██████████| 100/100 [00:56<00:00, 1.81it/s]\u001b[A\nDDIM Sampler: 100%|██████████| 100/100 [00:56<00:00, 1.77it/s]\n\nSampling: 100%|██████████| 1/1 [00:57<00:00, 57.29s/it]\nSampling: 100%|██████████| 1/1 [00:57<00:00, 57.29s/it]",
"metrics": {
"predict_time": 58.863214,
"total_time": 59.021714
},
"output": [
"https://replicate.delivery/mgxm/ff92ae5e-806b-4e39-bd7c-3a6279aec266/8de0f698-6812-4f52-bc43-b8e70262ea5a.png",
"https://replicate.delivery/mgxm/2d814af3-b2b6-41c3-9007-df9c5d130316/e58ac926-d0c9-4aa1-89f4-31460f0fd0c6.png",
"https://replicate.delivery/mgxm/574c5b57-1f95-4842-a630-b7a724124fb2/66482d1a-a3cb-42f6-8be0-f9b03e19fedd.png",
"https://replicate.delivery/mgxm/c2a27b3f-d0c5-49d0-a996-538db17073b4/a29589fb-5b36-4f0c-8d30-2ae0f3042f4a.png"
],
"started_at": "2022-06-24T20:12:25.236686Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/spi4ffj46baktckvxrgs2nucbq",
"cancel": "https://api.replicate.com/v1/predictions/spi4ffj46baktckvxrgs2nucbq/cancel"
},
"version": "25cf1f58250f98328ffb225e7eccadcedcb72bb1ab26e463e9fa0fd8a05533df"
}Generated in
Prediction started!
Sampler loaded
Data shape for DDIM sampling is (4, 4, 32, 32), eta 0
Running DDIM Sampling with 100 timesteps
Sampling: 0%| | 0/1 [00:00<?, ?it/s]
DDIM Sampler: 0%| | 0/100 [00:00<?, ?it/s]
DDIM Sampler: 1%| | 1/100 [00:00<00:52, 1.87it/s]
DDIM Sampler: 2%|▏ | 2/100 [00:01<00:52, 1.85it/s]
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DDIM Sampler: 4%|▍ | 4/100 [00:02<00:51, 1.85it/s]
DDIM Sampler: 5%|▌ | 5/100 [00:02<00:51, 1.85it/s]
DDIM Sampler: 6%|▌ | 6/100 [00:03<00:51, 1.84it/s]
DDIM Sampler: 7%|▋ | 7/100 [00:03<00:50, 1.84it/s]
DDIM Sampler: 8%|▊ | 8/100 [00:04<00:50, 1.84it/s]
DDIM Sampler: 9%|▉ | 9/100 [00:04<00:49, 1.83it/s]
DDIM Sampler: 10%|█ | 10/100 [00:05<00:49, 1.83it/s]
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DDIM Sampler: 20%|██ | 20/100 [00:10<00:44, 1.81it/s]
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DDIM Sampler: 58%|█████▊ | 58/100 [00:32<00:24, 1.71it/s]
DDIM Sampler: 59%|█████▉ | 59/100 [00:33<00:23, 1.72it/s]
DDIM Sampler: 60%|██████ | 60/100 [00:33<00:23, 1.72it/s]
DDIM Sampler: 61%|██████ | 61/100 [00:34<00:22, 1.72it/s]
DDIM Sampler: 62%|██████▏ | 62/100 [00:34<00:22, 1.72it/s]
DDIM Sampler: 63%|██████▎ | 63/100 [00:35<00:21, 1.73it/s]
DDIM Sampler: 64%|██████▍ | 64/100 [00:36<00:20, 1.73it/s]
DDIM Sampler: 65%|██████▌ | 65/100 [00:36<00:20, 1.73it/s]
DDIM Sampler: 66%|██████▌ | 66/100 [00:37<00:19, 1.73it/s]
DDIM Sampler: 67%|██████▋ | 67/100 [00:37<00:19, 1.74it/s]
DDIM Sampler: 68%|██████▊ | 68/100 [00:38<00:18, 1.74it/s]
DDIM Sampler: 69%|██████▉ | 69/100 [00:39<00:17, 1.75it/s]
DDIM Sampler: 70%|███████ | 70/100 [00:39<00:17, 1.74it/s]
DDIM Sampler: 71%|███████ | 71/100 [00:40<00:16, 1.74it/s]
DDIM Sampler: 72%|███████▏ | 72/100 [00:40<00:16, 1.75it/s]
DDIM Sampler: 73%|███████▎ | 73/100 [00:41<00:15, 1.75it/s]
DDIM Sampler: 74%|███████▍ | 74/100 [00:41<00:14, 1.76it/s]
DDIM Sampler: 75%|███████▌ | 75/100 [00:42<00:14, 1.76it/s]
DDIM Sampler: 76%|███████▌ | 76/100 [00:42<00:13, 1.76it/s]
DDIM Sampler: 77%|███████▋ | 77/100 [00:43<00:13, 1.76it/s]
DDIM Sampler: 78%|███████▊ | 78/100 [00:44<00:12, 1.76it/s]
DDIM Sampler: 79%|███████▉ | 79/100 [00:44<00:11, 1.77it/s]
DDIM Sampler: 80%|████████ | 80/100 [00:45<00:11, 1.77it/s]
DDIM Sampler: 81%|████████ | 81/100 [00:45<00:10, 1.77it/s]
DDIM Sampler: 82%|████████▏ | 82/100 [00:46<00:10, 1.77it/s]
DDIM Sampler: 83%|████████▎ | 83/100 [00:46<00:09, 1.77it/s]
DDIM Sampler: 84%|████████▍ | 84/100 [00:47<00:09, 1.77it/s]
DDIM Sampler: 85%|████████▌ | 85/100 [00:48<00:08, 1.77it/s]
DDIM Sampler: 86%|████████▌ | 86/100 [00:48<00:07, 1.77it/s]
DDIM Sampler: 87%|████████▋ | 87/100 [00:49<00:07, 1.78it/s]
DDIM Sampler: 88%|████████▊ | 88/100 [00:49<00:06, 1.78it/s]
DDIM Sampler: 89%|████████▉ | 89/100 [00:50<00:06, 1.78it/s]
DDIM Sampler: 90%|█████████ | 90/100 [00:50<00:05, 1.79it/s]
DDIM Sampler: 91%|█████████ | 91/100 [00:51<00:05, 1.78it/s]
DDIM Sampler: 92%|█████████▏| 92/100 [00:51<00:04, 1.79it/s]
DDIM Sampler: 93%|█████████▎| 93/100 [00:52<00:03, 1.79it/s]
DDIM Sampler: 94%|█████████▍| 94/100 [00:53<00:03, 1.80it/s]
DDIM Sampler: 95%|█████████▌| 95/100 [00:53<00:02, 1.80it/s]
DDIM Sampler: 96%|█████████▌| 96/100 [00:54<00:02, 1.80it/s]
DDIM Sampler: 97%|█████████▋| 97/100 [00:54<00:01, 1.80it/s]
DDIM Sampler: 98%|█████████▊| 98/100 [00:55<00:01, 1.80it/s]
DDIM Sampler: 99%|█████████▉| 99/100 [00:55<00:00, 1.81it/s]
DDIM Sampler: 100%|██████████| 100/100 [00:56<00:00, 1.81it/s]
DDIM Sampler: 100%|██████████| 100/100 [00:56<00:00, 1.77it/s]
Sampling: 100%|██████████| 1/1 [00:57<00:00, 57.29s/it]
Sampling: 100%|██████████| 1/1 [00:57<00:00, 57.29s/it]Examples
Run time and cost
This model costs approximately $0.014 to run on Replicate, or 71 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.
This model runs on Nvidia T4 GPU hardware. Predictions typically complete within 62 seconds. The predict time for this model varies significantly based on the inputs.
