baldesco/food_sd – Run with an API on Replicate

baldesco / food_sd

Cold

Public
31 runs
T4

Run with an API

Playground API Examples README Versions

Input

prompt

string

Shift + Return to add a new line

A fantasy landscape, trending on artstationA fantasy landscape, trending on artstation

Input prompt

Default: "A fantasy landscape, trending on artstation"

negative_prompt

string

Shift + Return to add a new line

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

image

*file

Inital image to generate variations of.

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

prompt_strength

number

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

Default: 0.8

num_outputs

integer

(minimum: 1, maximum: 8)

Number of images to output. Higher number of outputs may OOM.

Default: 1

num_inference_steps

integer

(minimum: 1, maximum: 500)

Number of denoising steps

Default: 25

guidance_scale

number

(minimum: 1, maximum: 20)

Scale for classifier-free guidance

Default: 7.5

scheduler

string

Choose a scheduler.

Default: "DPMSolverMultistep"

seed

integer

Random seed. Leave blank to randomize the seed

embeddings_txt

file

Plain text file containing one textual inversion embedding.

embeddings_zip

file

Path to a zip file containing textual inversion embedding(s).

hf_token

string

Shift + Return to add a new line

Token to download HuggingFace models

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

npx create-replicate --model=baldesco/food_sd

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 baldesco/food_sd using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "baldesco/food_sd:9b8a40eaafe2287dae0d2e7e910acbb0363099aedca1dad50a508db0dd6b683c",
  {
    input: {
      width: 512,
      height: 512,
      prompt: "A fantasy landscape, trending on artstation",
      scheduler: "DPMSolverMultistep",
      num_outputs: 1,
      guidance_scale: 7.5,
      prompt_strength: 0.8,
      num_inference_steps: 25
    }
  }
);
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 baldesco/food_sd using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "baldesco/food_sd:9b8a40eaafe2287dae0d2e7e910acbb0363099aedca1dad50a508db0dd6b683c",
    input={
        "width": 512,
        "height": 512,
        "prompt": "A fantasy landscape, trending on artstation",
        "scheduler": "DPMSolverMultistep",
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "prompt_strength": 0.8,
        "num_inference_steps": 25
    }
)
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 baldesco/food_sd 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": "9b8a40eaafe2287dae0d2e7e910acbb0363099aedca1dad50a508db0dd6b683c",
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "A fantasy landscape, trending on artstation",
      "scheduler": "DPMSolverMultistep",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "prompt_strength": 0.8,
      "num_inference_steps": 25
    }
  }' \
  https://api.replicate.com/v1/predictions

To 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 cog

If 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/baldesco/food_sd@sha256:9b8a40eaafe2287dae0d2e7e910acbb0363099aedca1dad50a508db0dd6b683c \
  -i 'width=512' \
  -i 'height=512' \
  -i 'prompt="A fantasy landscape, trending on artstation"' \
  -i 'scheduler="DPMSolverMultistep"' \
  -i 'num_outputs=1' \
  -i 'guidance_scale=7.5' \
  -i 'prompt_strength=0.8' \
  -i 'num_inference_steps=25'

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/baldesco/food_sd@sha256:9b8a40eaafe2287dae0d2e7e910acbb0363099aedca1dad50a508db0dd6b683c
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "A fantasy landscape, trending on artstation",
      "scheduler": "DPMSolverMultistep",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "prompt_strength": 0.8,
      "num_inference_steps": 25
    }
  }' \
  http://localhost:5000/predictions

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

Output

No output yet! Press "Submit" to start a prediction.

Run time and cost

This model runs on Nvidia T4 GPU hardware. We don't yet have enough runs of this model to provide performance information.

Readme

This model doesn't have a readme.