laion-ai / deep-image-diffusion-prior

Generate an image using text by visualizing CLIP features.

Cold

Public
1.1K runs
T4
GitHub
License

Run with an API

Playground API Examples README Versions

Input

prompt

string

Shift + Return to add a new line

An oil painting of mountains, in the style of monetAn oil painting of mountains, in the style of monet

Prompt to generate

Default: ""

offset_type

string

Offset type

Default: "none"

num_scales

integer

(minimum: 1, maximum: 10)

Number of scales

Default: 6

input_noise_strength

number

(minimum: 0, maximum: 1)

Strength of input noise

Default: 0

number

(minimum: 0, maximum: 10)

Learning rate

Default: 0.001

offset_lr_fac

number

(minimum: 0, maximum: 10)

Learning rate factor for offset

Default: 1

lr_decay

number

(minimum: 0, maximum: 1)

Learning rate decay

Default: 0.995

param_noise_strength

number

(minimum: 0, maximum: 1)

Strength of parameter noise

Default: 0

display_freq

integer

(minimum: 0, maximum: 100)

Display frequency

Default: 25

iterations

integer

(minimum: 0, maximum: 1000)

Number of iterations

Default: 250

num_samples_per_batch

integer

(minimum: 1, maximum: 10)

Number of samples per batch

Default: 2

num_cutouts

integer

(minimum: 4, maximum: 32)

Number of cutouts

Default: 8

guidance_scale

number

(minimum: 0, maximum: 10)

Scale of conditioning

Default: 1

seed

integer

(minimum: -1, maximum: 100000)

Random seed

Default: -1

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

npx create-replicate --model=laion-ai/deep-image-diffusion-prior

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 laion-ai/deep-image-diffusion-prior using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "laion-ai/deep-image-diffusion-prior:4ed232e4ebe82b3d70ee5e0526257aeeb07187f6727ee617795a7f94d2133e2d",
  {
    input: {
      lr: 0.001,
      seed: -1,
      prompt: "An oil painting of mountains, in the style of monet",
      lr_decay: 0.995,
      iterations: 250,
      num_scales: 6,
      num_cutouts: 8,
      offset_type: "none",
      display_freq: 25,
      offset_lr_fac: 1,
      guidance_scale: 1,
      input_noise_strength: 0,
      param_noise_strength: 0,
      num_samples_per_batch: 1
    }
  }
);

// 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 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 laion-ai/deep-image-diffusion-prior using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "laion-ai/deep-image-diffusion-prior:4ed232e4ebe82b3d70ee5e0526257aeeb07187f6727ee617795a7f94d2133e2d",
    input={
        "lr": 0.001,
        "seed": -1,
        "prompt": "An oil painting of mountains, in the style of monet",
        "lr_decay": 0.995,
        "iterations": 250,
        "num_scales": 6,
        "num_cutouts": 8,
        "offset_type": "none",
        "display_freq": 25,
        "offset_lr_fac": 1,
        "guidance_scale": 1,
        "input_noise_strength": 0,
        "param_noise_strength": 0,
        "num_samples_per_batch": 1
    }
)

# The laion-ai/deep-image-diffusion-prior 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/deep-image-diffusion-prior/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/deep-image-diffusion-prior 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": "4ed232e4ebe82b3d70ee5e0526257aeeb07187f6727ee617795a7f94d2133e2d",
    "input": {
      "lr": 0.001,
      "seed": -1,
      "prompt": "An oil painting of mountains, in the style of monet",
      "lr_decay": 0.995,
      "iterations": 250,
      "num_scales": 6,
      "num_cutouts": 8,
      "offset_type": "none",
      "display_freq": 25,
      "offset_lr_fac": 1,
      "guidance_scale": 1,
      "input_noise_strength": 0,
      "param_noise_strength": 0,
      "num_samples_per_batch": 1
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2022-07-03T01:46:33.270423Z",
  "created_at": "2022-07-03T01:42:59.952944Z",
  "data_removed": false,
  "error": null,
  "id": "iz4anzmjzfgubakxieksaceysm",
  "input": {
    "lr": 0.001,
    "seed": -1,
    "prompt": "An oil painting of mountains, in the style of monet",
    "lr_decay": 0.995,
    "iterations": 250,
    "num_scales": 6,
    "num_cutouts": "8",
    "offset_type": "none",
    "display_freq": 25,
    "offset_lr_fac": 1,
    "guidance_scale": 1,
    "num_samples_per_batch": "1"
  },
  "logs": "Initializing deep image prior net...\n\nsampling loop time step:   0%|          | 0/1000 [00:00<?, ?it/s]\nsampling loop time step:   0%|          | 2/1000 [00:00<01:02, 16.07it/s]\nsampling loop time step:   0%|          | 4/1000 [00:00<00:58, 16.98it/s]\nsampling loop time step:   1%|          | 6/1000 [00:00<00:57, 17.43it/s]\nsampling loop time step:   1%|          | 8/1000 [00:00<00:55, 17.79it/s]\nsampling loop time step:   1%|          | 10/1000 [00:00<00:55, 17.88it/s]\nsampling loop time step:   1%|          | 12/1000 [00:00<00:55, 17.70it/s]\nsampling loop time step:   1%|▏         | 14/1000 [00:00<00:55, 17.65it/s]\nsampling loop time step:   2%|▏         | 16/1000 [00:00<00:55, 17.61it/s]\nsampling loop time step:   2%|▏         | 18/1000 [00:01<00:54, 17.92it/s]\nsampling loop time step:   2%|▏         | 20/1000 [00:01<00:53, 18.26it/s]\nsampling loop time step:   2%|▏         | 22/1000 [00:01<00:53, 18.37it/s]\nsampling loop time step:   2%|▏         | 24/1000 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It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n  right_pads = (((out_sz - fw_arange(num_convs, fw, device) - 1)  # (1)\n/root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2157.)\n  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n\n  0%|          | 1/250 [00:00<02:41,  1.54it/s]\n  1%|          | 2/250 [00:01<02:28,  1.67it/s]\n  1%|          | 3/250 [00:01<02:18,  1.78it/s]\n  2%|▏         | 4/250 [00:02<02:15,  1.81it/s]\n  2%|▏         | 5/250 [00:02<02:12,  1.84it/s]\n  2%|▏         | 6/250 [00:03<02:10,  1.87it/s]\n  3%|▎         | 7/250 [00:03<02:08,  1.89it/s]\n  3%|▎         | 8/250 [00:04<02:08,  1.89it/s]\n  4%|▎         | 9/250 [00:04<02:08,  1.87it/s]\n  4%|▍         | 10/250 [00:05<02:09,  1.85it/s]\n  4%|▍         | 11/250 [00:05<02:08,  1.86it/s]\n  5%|▍         | 12/250 [00:06<02:07,  1.86it/s]\n  5%|▌         | 13/250 [00:07<02:08,  1.85it/s]\n  6%|▌         | 14/250 [00:07<02:08,  1.84it/s]\n  6%|▌         | 15/250 [00:08<02:06,  1.86it/s]\n  6%|▋         | 16/250 [00:08<02:04,  1.87it/s]\n  7%|▋         | 17/250 [00:09<02:04,  1.87it/s]\n  7%|▋      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loss: 0.105186, losses: 0.105186\n3it [00:54, 13.56s/it]\n 40%|███▉      | 99/250 [00:54<01:22,  1.82it/s]\n\n\n\n\nSaved image to output/2022-07-03_01-45-09/out_00100.png\n3it [00:54, 13.56s/it]\n 40%|███▉      | 99/250 [00:54<01:22,  1.82it/s]\n4it [00:54, 13.62s/it]\n 40%|████      | 100/250 [00:54<01:24,  1.78it/s]\n 40%|████      | 101/250 [00:54<01:22,  1.80it/s]\n 41%|████      | 102/250 [00:55<01:21,  1.82it/s]\n 41%|████      | 103/250 [00:55<01:20,  1.83it/s]\n 42%|████▏     | 104/250 [00:56<01:20,  1.82it/s]\n 42%|████▏     | 105/250 [00:56<01:19,  1.83it/s]\n 42%|████▏     | 106/250 [00:57<01:19,  1.81it/s]\n 43%|████▎     | 107/250 [00:58<01:19,  1.80it/s]\n 43%|████▎     | 108/250 [00:58<01:18,  1.80it/s]\n 44%|████▎     | 109/250 [00:59<01:17,  1.81it/s]\n 44%|████▍     | 110/250 [00:59<01:17,  1.80it/s]\n 44%|████▍     | 111/250 [01:00<01:17,  1.79it/s]\n 45%|████▍     | 112/250 [01:00<01:17,  1.77it/s]\n 45%|████▌     | 113/250 [01:01<01:18,  1.75it/s]\n 46%|████▌     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[01:28<00:49,  1.78it/s]\n 65%|██████▍   | 162/250 [01:28<00:49,  1.79it/s]\n 65%|██████▌   | 163/250 [01:29<00:48,  1.80it/s]\n 66%|██████▌   | 164/250 [01:30<00:47,  1.81it/s]\n 66%|██████▌   | 165/250 [01:30<00:46,  1.81it/s]\n 66%|██████▋   | 166/250 [01:31<00:46,  1.81it/s]\n 67%|██████▋   | 167/250 [01:31<00:46,  1.79it/s]\n 67%|██████▋   | 168/250 [01:32<00:45,  1.79it/s]\n 68%|██████▊   | 169/250 [01:32<00:45,  1.78it/s]\n 68%|██████▊   | 170/250 [01:33<00:44,  1.78it/s]\n 68%|██████▊   | 171/250 [01:33<00:44,  1.80it/s]\n 69%|██████▉   | 172/250 [01:34<00:43,  1.80it/s]\n 69%|██████▉   | 173/250 [01:35<00:42,  1.81it/s]\n 70%|██████▉   | 174/250 [01:35<00:42,  1.80it/s]\n\n\n\n\ni: 175, loss: 0.0815946, losses: 0.0815946\n6it [01:36, 13.83s/it]\n 70%|██████▉   | 174/250 [01:36<00:42,  1.80it/s]\n\n\nSaved image to output/2022-07-03_01-45-51/out_00175.png\n\n\n6it [01:36, 13.83s/it]\n 70%|██████▉   | 174/250 [01:36<00:42,  1.80it/s]\n7it [01:36, 13.87s/it]\n 70%|███████   | 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output/2022-07-03_01-46-18/out_00225.png\n\n\n\n\n8it [02:03, 13.86s/it]\n 90%|████████▉ | 224/250 [02:03<00:14,  1.82it/s]\n9it [02:03, 13.84s/it]\n 90%|█████████ | 225/250 [02:03<00:13,  1.79it/s]\n 90%|█████████ | 226/250 [02:04<00:13,  1.79it/s]\n 91%|█████████ | 227/250 [02:04<00:12,  1.79it/s]\n 91%|█████████ | 228/250 [02:05<00:12,  1.79it/s]\n 92%|█████████▏| 229/250 [02:06<00:11,  1.78it/s]\n 92%|█████████▏| 230/250 [02:06<00:11,  1.80it/s]\n 92%|█████████▏| 231/250 [02:07<00:10,  1.79it/s]\n 93%|█████████▎| 232/250 [02:07<00:10,  1.80it/s]\n 93%|█████████▎| 233/250 [02:08<00:09,  1.81it/s]\n 94%|█████████▎| 234/250 [02:08<00:08,  1.80it/s]\n 94%|█████████▍| 235/250 [02:09<00:08,  1.82it/s]\n 94%|█████████▍| 236/250 [02:09<00:07,  1.80it/s]\n 95%|█████████▍| 237/250 [02:10<00:07,  1.79it/s]\n 95%|█████████▌| 238/250 [02:11<00:06,  1.81it/s]\n 96%|█████████▌| 239/250 [02:11<00:06,  1.80it/s]\n 96%|█████████▌| 240/250 [02:12<00:05,  1.81it/s]\n 96%|█████████▋| 241/250 [02:12<00:04,  1.82it/s]\n 97%|█████████▋| 242/250 [02:13<00:04,  1.84it/s]\n 97%|█████████▋| 243/250 [02:13<00:03,  1.84it/s]\n 98%|█████████▊| 244/250 [02:14<00:03,  1.83it/s]\n 98%|█████████▊| 245/250 [02:14<00:02,  1.84it/s]\n 98%|█████████▊| 246/250 [02:15<00:02,  1.83it/s]\n 99%|█████████▉| 247/250 [02:15<00:01,  1.82it/s]\n 99%|█████████▉| 248/250 [02:16<00:01,  1.82it/s]\n100%|█████████▉| 249/250 [02:17<00:00,  1.82it/s]\n\n\n\n\ni: 250, loss: 0.0951135, losses: 0.0951135\n9it [02:17, 13.84s/it]\n100%|█████████▉| 249/250 [02:17<00:00,  1.82it/s]\nSaved image to output/2022-07-03_01-46-32/out_00250.png\n\n\n\n\n9it [02:17, 13.84s/it]\n100%|█████████▉| 249/250 [02:17<00:00,  1.82it/s]\n10it [02:17, 13.84s/it]\n100%|██████████| 250/250 [02:17<00:00,  1.77it/s]\n100%|██████████| 250/250 [02:17<00:00,  1.82it/s]\n\n10it [02:17, 13.77s/it]",
  "metrics": {
    "predict_time": 189.116149,
    "total_time": 213.317479
  },
  "output": [
    "https://replicate.delivery/mgxm/8ccfd17c-8ed6-478f-89ee-8642a562b09a/out_00025.png",
    "https://replicate.delivery/mgxm/860df9a9-1cec-4437-8c56-78ce77dae821/out_00050.png",
    "https://replicate.delivery/mgxm/af52dbde-8b2d-481c-bb4d-d3ac7cef93e5/out_00075.png",
    "https://replicate.delivery/mgxm/91c116d5-4013-4329-bbda-487a2cc0795f/out_00100.png",
    "https://replicate.delivery/mgxm/b4c24c6b-9a37-436a-9611-5f917c52d59f/out_00125.png",
    "https://replicate.delivery/mgxm/fa1d545a-12a8-43ff-8634-d10c998c88f6/out_00150.png",
    "https://replicate.delivery/mgxm/95577f4b-fec0-4e29-8776-15dbff32d64d/out_00175.png",
    "https://replicate.delivery/mgxm/2f40bd34-fa12-4551-84fe-208aa3a9c34a/out_00200.png",
    "https://replicate.delivery/mgxm/1fed3af3-1247-447c-a662-d58361e54bbf/out_00225.png",
    "https://replicate.delivery/mgxm/427d7301-e4d9-4893-a210-8cc91a48d188/out_00250.png"
  ],
  "started_at": "2022-07-03T01:43:24.154274Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/iz4anzmjzfgubakxieksaceysm",
    "cancel": "https://api.replicate.com/v1/predictions/iz4anzmjzfgubakxieksaceysm/cancel"
  },
  "version": "4ed232e4ebe82b3d70ee5e0526257aeeb07187f6727ee617795a7f94d2133e2d"
}

Generated in

3 minutes 9 seconds

Tweak itReport

Initializing deep image prior net...

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Using device: cuda
Seed: 46177840
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  right_pads = (((out_sz - fw_arange(num_convs, fw, device) - 1)  # (1)
/root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2157.)
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i: 50, loss: 0.147673, losses: 0.147673

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i: 100, loss: 0.105186, losses: 0.105186
3it [00:54, 13.56s/it]
 40%|███▉      | 99/250 [00:54<01:22,  1.82it/s]




Saved image to output/2022-07-03_01-45-09/out_00100.png
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i: 125, loss: 0.111876, losses: 0.111876



4it [01:08, 13.62s/it]
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Saved image to output/2022-07-03_01-45-23/out_00125.png


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i: 150, loss: 0.102834, losses: 0.102834

5it [01:22, 13.76s/it]
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Saved image to output/2022-07-03_01-45-37/out_00150.png
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i: 175, loss: 0.0815946, losses: 0.0815946
6it [01:36, 13.83s/it]
 70%|██████▉   | 174/250 [01:36<00:42,  1.80it/s]


Saved image to output/2022-07-03_01-45-51/out_00175.png


6it [01:36, 13.83s/it]
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i: 200, loss: 0.0897339, losses: 0.0897339
7it [01:50, 13.87s/it]
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Saved image to output/2022-07-03_01-46-05/out_00200.png


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i: 225, loss: 0.0799063, losses: 0.0799063



8it [02:03, 13.86s/it]
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Saved image to output/2022-07-03_01-46-18/out_00225.png




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i: 250, loss: 0.0951135, losses: 0.0951135
9it [02:17, 13.84s/it]
100%|█████████▉| 249/250 [02:17<00:00,  1.82it/s]
Saved image to output/2022-07-03_01-46-32/out_00250.png




9it [02:17, 13.84s/it]
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10it [02:17, 13.84s/it]
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10it [02:17, 13.77s/it]

Examples

View more examples

Run time and cost

This model costs approximately $0.080 to run on Replicate, or 12 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 6 minutes. The predict time for this model varies significantly based on the inputs.

Readme

Deep Image Diffusion Prior

by @nousr

Model description

Inverts CLIP text embeds to image embeds and visualizes with deep-image-prior.

Acknowledgements

Code and weights by @nousr, with help from:

LAION for support, resources, and community
@RiversHaveWings for making me aware of this technique
Stability AI for compute which makes these models possible
lucidrains for spearheading the open-source replication of DALLE 2

Just to avoid any confusion, this research is a recreation of (one part of) OpenAI’s DALLE2 paper. It is not, “DALLE2”, the product/service from OpenAI you may have seen on the web.

Intended use

See the world “through CLIP’s eyes” by taking advantage of the diffusion prior as replicated by Laion to invert CLIP “ViT-L/14” text embeds to image embeds (as in unCLIP/DALLE2). After, a process known as deep-image-prior developed by Katherine Crowson is run to visualize the features in CLIP’s weights corresponding to activations from your prompt.

Caveats and recommendations

These visualizations can be quite abstract compared to other text-2-image models. However, you can often find a sort of dream like quality due to this. Many outputs are artistically fantastic because of this, but whether or not the visual matches your prompt as often is another matter.