m1guelpf / emoji-diffusion

Emojis on Stable Diffusion via Dreambooth

Cold

Public
2.8K runs
A100 (80GB)
GitHub
License

Run with an API

Playground API Examples README Versions

Input

prompt

string

Shift + Return to add a new line

Input prompt. Use the word "emoji" somewhere in the prompt.

Default: ""

negative_prompt

string

Shift + Return to add a new line

The prompt NOT to guide the image generation. Ignored when not using guidance

width

integer

Width of output image. Maximum size is 1024x768 or 768x1024 because of memory limits

Default: 512

height

integer

Height of output image. Maximum size is 1024x768 or 768x1024 because of memory limits

Default: 512

init_image

file

Initial image to generate variations of. Will be resized to the specified width and height

mask

file

Black and white image to use as mask for inpainting over init_image. Black pixels are inpainted and white pixels are preserved. Tends to work better with prompt strength of 0.5-0.7. Consider using https://replicate.com/andreasjansson/stable-diffusion-inpainting instead.

prompt_strength

number

Prompt strength when using init image. 1.0 corresponds to full destruction of information in init image

Default: 0.8

num_outputs

integer

(minimum: 1, maximum: 10)

Number of images to output. If the NSFW filter is triggered, you may get fewer outputs than this.

Default: 1

num_inference_steps

integer

(minimum: 1, maximum: 500)

Number of denoising steps

Default: 50

guidance_scale

number

(minimum: 1, maximum: 20)

Scale for classifier-free guidance

Default: 7.5

scheduler

string

Choose a scheduler. If you use an init image, PNDM will be used

Default: "K-LMS"

seed

integer

Random seed. Leave blank to randomize the seed

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

npx create-replicate --model=m1guelpf/emoji-diffusion

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 m1guelpf/emoji-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "m1guelpf/emoji-diffusion:ba30355ce02ca4755d60f7f083e3c0e2593fd3ee687a0dc1fca236deb6046e4d",
  {
    input: {
      width: 512,
      height: 512,
      prompt: "snowy montain emoji",
      scheduler: "DDIM",
      num_outputs: 1,
      guidance_scale: 7.5,
      prompt_strength: 0.8,
      num_inference_steps: 50
    }
  }
);
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 m1guelpf/emoji-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "m1guelpf/emoji-diffusion:ba30355ce02ca4755d60f7f083e3c0e2593fd3ee687a0dc1fca236deb6046e4d",
    input={
        "width": 512,
        "height": 512,
        "prompt": "snowy montain emoji",
        "scheduler": "DDIM",
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "prompt_strength": 0.8,
        "num_inference_steps": 50
    }
)
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 m1guelpf/emoji-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": "ba30355ce02ca4755d60f7f083e3c0e2593fd3ee687a0dc1fca236deb6046e4d",
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "snowy montain emoji",
      "scheduler": "DDIM",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "prompt_strength": 0.8,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2022-11-22T12:35:19.906080Z",
  "created_at": "2022-11-22T12:35:16.003645Z",
  "data_removed": false,
  "error": null,
  "id": "a7rgcuwruvbudg6d5gcq4nnt4y",
  "input": {
    "width": 512,
    "height": 512,
    "prompt": "snowy montain emoji",
    "scheduler": "DDIM",
    "num_outputs": 1,
    "guidance_scale": 7.5,
    "prompt_strength": 0.8,
    "num_inference_steps": 50
  },
  "logs": "Using seed: 17811\n  0%|          | 0/50 [00:00<?, ?it/s]\n  4%|▍         | 2/50 [00:00<00:03, 13.60it/s]\n  8%|▊         | 4/50 [00:00<00:03, 14.33it/s]\n 12%|█▏        | 6/50 [00:00<00:03, 14.58it/s]\n 16%|█▌        | 8/50 [00:00<00:02, 14.43it/s]\n 20%|██        | 10/50 [00:00<00:02, 14.44it/s]\n 24%|██▍       | 12/50 [00:00<00:02, 14.63it/s]\n 28%|██▊       | 14/50 [00:00<00:02, 14.76it/s]\n 32%|███▏      | 16/50 [00:01<00:02, 14.89it/s]\n 36%|███▌      | 18/50 [00:01<00:02, 14.96it/s]\n 40%|████      | 20/50 [00:01<00:02, 14.96it/s]\n 44%|████▍     | 22/50 [00:01<00:01, 14.88it/s]\n 48%|████▊     | 24/50 [00:01<00:01, 14.80it/s]\n 52%|█████▏    | 26/50 [00:01<00:01, 14.80it/s]\n 56%|█████▌    | 28/50 [00:01<00:01, 14.80it/s]\n 60%|██████    | 30/50 [00:02<00:01, 14.76it/s]\n 64%|██████▍   | 32/50 [00:02<00:01, 14.80it/s]\n 68%|██████▊   | 34/50 [00:02<00:01, 14.78it/s]\n 72%|███████▏  | 36/50 [00:02<00:00, 14.74it/s]\n 76%|███████▌  | 38/50 [00:02<00:00, 14.58it/s]\n 80%|████████  | 40/50 [00:02<00:00, 14.61it/s]\n 84%|████████▍ | 42/50 [00:02<00:00, 14.66it/s]\n 88%|████████▊ | 44/50 [00:02<00:00, 14.64it/s]\n 92%|█████████▏| 46/50 [00:03<00:00, 14.71it/s]\n 96%|█████████▌| 48/50 [00:03<00:00, 14.74it/s]\n100%|██████████| 50/50 [00:03<00:00, 14.40it/s]\n100%|██████████| 50/50 [00:03<00:00, 14.66it/s]",
  "metrics": {
    "predict_time": 3.868399,
    "total_time": 3.902435
  },
  "output": [
    "https://replicate.delivery/pbxt/KFyqEQeeEEutbUaUxeCf2tDdyfUXQNeZz1zOT3QNvx1wB0pAE/out-0.png"
  ],
  "started_at": "2022-11-22T12:35:16.037681Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/a7rgcuwruvbudg6d5gcq4nnt4y",
    "cancel": "https://api.replicate.com/v1/predictions/a7rgcuwruvbudg6d5gcq4nnt4y/cancel"
  },
  "version": "ba30355ce02ca4755d60f7f083e3c0e2593fd3ee687a0dc1fca236deb6046e4d"
}

Generated in

3.9 seconds

Tweak itReport

Examples

View more examples

Run time and cost

This model costs approximately $0.0060 to run on Replicate, or 166 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 A100 (80GB) GPU hardware. Predictions typically complete within 5 seconds. The predict time for this model varies significantly based on the inputs.

Readme

stable diffusion finetuned on emoji dataset

emoji-diffusion is a stable diffusion model fine-tuned on the russian-emoji dataset to generate emoji images. Below are some samples generated using the model.

License

This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies:

You can’t use the model to deliberately produce nor share illegal or harmful outputs or content
The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license here

Downstream Uses

This model can be used for entertainment purposes and as a generative art assistant.