jack000 / glid-3-xl

A 1.4B parameter text2im model from CompVis, finetuned on CLIP text embeds and curated data.

Cold

Public
45.5K runs
T4
GitHub
Paper
License

Run with an API

Playground API Examples README Versions

Input

prompt

string

Shift + Return to add a new line

a cyberpunk girl with a scifi neuralink device on her heada cyberpunk girl with a scifi neuralink device on her head

Your text prompt.

Default: ""

negative

string

Shift + Return to add a new line

(optional) Negate the model's prediction for this text from the model's prediction for the target text.

Default: ""

init_image

file

(optional) Initial image to use for the model's prediction.

init_skip_fraction

number

(minimum: 0, maximum: 1)

Fraction of sampling steps to skip when using an init image.

Default: 0

batch_size

integer

Batch size.

Default: 4

width

integer

Target width

Default: 256

height

integer

Target height

Default: 256

seed

integer

(minimum: -1, maximum: 4294967295)

Seed for random number generator.

Default: -1

guidance_scale

number

(minimum: -20, maximum: 100)

Classifier-free guidance scale. Higher values will result in more guidance toward caption, with diminishing returns. Try values between 1.0 and 40.0.

Default: 5

steps

integer

(minimum: 15, maximum: 250)

Number of diffusion steps to run.

Default: 50

Run this model in Node.js with one line of code:

npx create-replicate --model=jack000/glid-3-xl

or set up a project from scratch

Install Replicate’s Node.js client library

npm install replicate

Set 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 jack000/glid-3-xl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "jack000/glid-3-xl:3ab3bf32ffee63490913279e39948bd72b0eb5da1317288c3de6d4546344c167",
  {
    input: {
      seed: 24,
      steps: 250,
      width: 256,
      height: 256,
      prompt: "a cyberpunk girl with a scifi neuralink device on her head",
      negative: "",
      batch_size: 1,
      guidance_scale: 5,
      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 replicate

Set 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 replicate

Run jack000/glid-3-xl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "jack000/glid-3-xl:3ab3bf32ffee63490913279e39948bd72b0eb5da1317288c3de6d4546344c167",
    input={
        "seed": 24,
        "steps": 250,
        "width": 256,
        "height": 256,
        "prompt": "a cyberpunk girl with a scifi neuralink device on her head",
        "negative": "",
        "batch_size": 1,
        "guidance_scale": 5,
        "init_skip_fraction": 0
    }
)

# The jack000/glid-3-xl 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/jack000/glid-3-xl/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 jack000/glid-3-xl 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": "3ab3bf32ffee63490913279e39948bd72b0eb5da1317288c3de6d4546344c167",
    "input": {
      "seed": 24,
      "steps": 250,
      "width": 256,
      "height": 256,
      "prompt": "a cyberpunk girl with a scifi neuralink device on her head",
      "negative": "",
      "batch_size": 1,
      "guidance_scale": 5,
      "init_skip_fraction": 0
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Install Cog

brew install cog

If you don’t have Homebrew, there are other installation options available.

Pull and run jack000/glid-3-xl using Cog (this will download the full model and run it in your local environment):

cog predict r8.im/jack000/glid-3-xl@sha256:3ab3bf32ffee63490913279e39948bd72b0eb5da1317288c3de6d4546344c167 \
  -i 'seed=24' \
  -i 'steps=250' \
  -i 'width=256' \
  -i 'height=256' \
  -i 'prompt="a cyberpunk girl with a scifi neuralink device on her head"' \
  -i 'negative=""' \
  -i 'batch_size=1' \
  -i 'guidance_scale=5' \
  -i 'init_skip_fraction=0'

To learn more, take a look at the Cog documentation.

Pull and run jack000/glid-3-xl using Docker (this will download the full model and run it in your local environment):

docker run -d -p 5000:5000 --gpus=all r8.im/jack000/glid-3-xl@sha256:3ab3bf32ffee63490913279e39948bd72b0eb5da1317288c3de6d4546344c167
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "seed": 24,
      "steps": 250,
      "width": 256,
      "height": 256,
      "prompt": "a cyberpunk girl with a scifi neuralink device on her head",
      "negative": "",
      "batch_size": 1,
      "guidance_scale": 5,
      "init_skip_fraction": 0
    }
  }' \
  http://localhost:5000/predictions

Output

{
  "completed_at": "2022-05-06T10:03:03.903071Z",
  "created_at": "2022-05-06T10:01:12.655834Z",
  "data_removed": false,
  "error": null,
  "id": "7w6fxaqgw5aofddcxkrwzqu2lm",
  "input": {
    "seed": 24,
    "steps": "250",
    "width": "256",
    "height": "256",
    "prompt": "a cyberpunk girl with a scifi neuralink device on her head",
    "batch_size": "1",
    "guidance_scale": "5"
  },
  "logs": "Encoding text with BERT\nEncoding text with CLIP\nPacking CLIP and BERT embeddings into kwargs\nRunning diffusion...\n\n0it [00:00, ?it/s]\n\n  0%|          | 0/250 [00:00<?, ?it/s]\u001b[A\n1it [00:00,  1.16it/s]\n\n  0%|          | 1/250 [00:00<03:34,  1.16it/s]\u001b[A\n2it [00:01,  1.20it/s]\n\n  1%|          | 2/250 [00:01<03:26,  1.20it/s]\u001b[A\n3it [00:02,  1.22it/s]\n\n  1%|          | 3/250 [00:02<03:22,  1.22it/s]\u001b[A\n4it [00:02,  1.61it/s]\n\n  2%|▏         | 4/250 [00:02<02:32,  1.61it/s]\u001b[A\n5it [00:03,  1.96it/s]\n\n  2%|▏         | 5/250 [00:03<02:04,  1.97it/s]\u001b[A\n6it [00:03,  2.27it/s]\n\n  2%|▏         | 6/250 [00:03<01:47,  2.27it/s]\u001b[A\n7it [00:03,  2.51it/s]\n\n  3%|▎         | 7/250 [00:03<01:36,  2.51it/s]\u001b[A\n8it [00:04,  2.69it/s]\n\n  3%|▎         | 8/250 [00:04<01:29,  2.69it/s]\u001b[A\n9it [00:04,  2.82it/s]\n\n  4%|▎         | 9/250 [00:04<01:25,  2.82it/s]\u001b[A\n10it [00:04,  2.92it/s]\n\n  4%|▍         | 10/250 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[01:14<00:06,  3.24it/s]\u001b[A\n230it [01:15,  3.25it/s]\n\n 92%|█████████▏| 230/250 [01:15<00:06,  3.25it/s]\u001b[A\n231it [01:15,  3.25it/s]\n\n 92%|█████████▏| 231/250 [01:15<00:05,  3.25it/s]\u001b[A\n232it [01:15,  3.24it/s]\n\n 93%|█████████▎| 232/250 [01:15<00:05,  3.24it/s]\u001b[A\n233it [01:16,  3.24it/s]\n\n 93%|█████████▎| 233/250 [01:16<00:05,  3.24it/s]\u001b[A\n234it [01:16,  3.24it/s]\n\n 94%|█████████▎| 234/250 [01:16<00:04,  3.24it/s]\u001b[A\n235it [01:16,  3.25it/s]\n\n 94%|█████████▍| 235/250 [01:16<00:04,  3.25it/s]\u001b[A\n236it [01:16,  3.25it/s]\n\n 94%|█████████▍| 236/250 [01:16<00:04,  3.25it/s]\u001b[A\n237it [01:17,  3.25it/s]\n\n 95%|█████████▍| 237/250 [01:17<00:04,  3.25it/s]\u001b[A\n238it [01:17,  3.24it/s]\n\n 95%|█████████▌| 238/250 [01:17<00:03,  3.24it/s]\u001b[A\n239it [01:17,  3.24it/s]\n\n 96%|█████████▌| 239/250 [01:17<00:03,  3.24it/s]\u001b[A\n240it [01:18,  3.24it/s]\n\n 96%|█████████▌| 240/250 [01:18<00:03,  3.24it/s]\u001b[A\n241it [01:18,  3.24it/s]\n\n 96%|█████████▋| 241/250 [01:18<00:02,  3.24it/s]\u001b[A\n242it [01:18,  3.24it/s]\n\n 97%|█████████▋| 242/250 [01:18<00:02,  3.24it/s]\u001b[A\n243it [01:19,  3.24it/s]\n\n 97%|█████████▋| 243/250 [01:19<00:02,  3.24it/s]\u001b[A\n244it [01:19,  3.25it/s]\n\n 98%|█████████▊| 244/250 [01:19<00:01,  3.25it/s]\u001b[A\n245it [01:19,  3.25it/s]\n\n 98%|█████████▊| 245/250 [01:19<00:01,  3.25it/s]\u001b[A\n246it [01:20,  3.25it/s]\n\n 98%|█████████▊| 246/250 [01:20<00:01,  3.25it/s]\u001b[A\n247it [01:20,  3.25it/s]\n\n 99%|█████████▉| 247/250 [01:20<00:00,  3.25it/s]\u001b[A\n248it [01:20,  3.78it/s]\n\n 99%|█████████▉| 248/250 [01:20<00:00,  3.77it/s]\u001b[A\n249it [01:20,  3.58it/s]\n\n100%|█████████▉| 249/250 [01:20<00:00,  3.58it/s]\u001b[A\n250it [01:21,  3.47it/s]\n\n100%|██████████| 250/250 [01:21<00:00,  3.47it/s]\u001b[A\n100%|██████████| 250/250 [01:21<00:00,  3.08it/s]\n\n250it [01:21,  3.08it/s]\nDone",
  "metrics": {
    "predict_time": 111.048486,
    "total_time": 111.247237
  },
  "output": [
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    "https://replicate.delivery/mgxm/efe77a5c-8ef9-49ed-8637-0b9eef1227eb/current.png",
    "https://replicate.delivery/mgxm/6a51993b-1a1b-4751-937b-889c873a7a3e/current.png",
    "https://replicate.delivery/mgxm/574749ad-d04e-4910-92b5-729d6f426173/current.png",
    "https://replicate.delivery/mgxm/83c84fb7-b8a8-4ace-86e2-bca16a36066e/current.png",
    "https://replicate.delivery/mgxm/2a788c7e-a92d-46b6-bb53-e1552cbe2b18/current.png",
    "https://replicate.delivery/mgxm/86a2cdb6-73fe-4093-97d1-91357e05173e/current.png",
    "https://replicate.delivery/mgxm/e5fc7db9-71f1-449e-ae4a-f9b71ec38459/current.png",
    "https://replicate.delivery/mgxm/a37af709-e881-4bd6-947c-8f1666833d31/current.png",
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Generated in

1 minute 51 seconds

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Done

This example was created by a different version, jack000/glid-3-xl:d17813f3.

Examples

View more examples

Run time and cost

This model costs approximately $0.067 to run on Replicate, or 14 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 5 minutes. The predict time for this model varies significantly based on the inputs.

Readme

GLID-3-XL

GLID-3-xl is the 1.4B latent diffusion model from CompVis back-ported to the guided diffusion codebase

The model has been split into three checkpoints. This lets us fine tune the diffusion model on new datasets and for additional tasks like inpainting and super-resolution

Download model files

# text encoder (required)
wget https://dall-3.com/models/glid-3-xl/bert.pt

# ldm first stage (required)
wget https://dall-3.com/models/glid-3-xl/kl-f8.pt

# there are several diffusion models to choose from:

# original diffusion model from CompVis
wget https://dall-3.com/models/glid-3-xl/diffusion.pt

# new model fine tuned on a cleaner dataset (will not generate watermarks, split images or blurry images)
wget https://dall-3.com/models/glid-3-xl/finetune.pt

# inpaint
wget https://dall-3.com/models/glid-3-xl/inpaint.pt