nsfw-api/oldcore | Run with an API on Replicate

Public

1 run

A100 (80GB)

Run with an API

Pricing

Playground API Examples README Versions

Input

prompt

string

Shift + Return to add a new line

The text prompt describing your video scene.

Default: ""

lora_url

string

Shift + Return to add a new line

A URL pointing to your LoRA .safetensors file or a Hugging Face repo (e.g. 'user/repo' - uses the first .safetensors file).

Default: ""

lora_strength

number

(minimum: -10, maximum: 10)

Scale/strength for your LoRA.

Default: 1

lora2_url

string

Shift + Return to add a new line

URL or Hugging Face repo for your second LoRA .safetensors file.

Default: ""

lora2_strength

number

(minimum: -10, maximum: 10)

Scale/strength for your second LoRA.

Default: 1

scheduler

string

Algorithm used to generate the video frames.

Default: "DPMSolverMultistepScheduler"

steps

integer

(minimum: 1, maximum: 150)

Number of diffusion steps.

Default: 50

guidance_scale

number

(minimum: 0, maximum: 30)

Overall influence of text vs. model.

Default: 6

flow_shift

integer

(minimum: 0, maximum: 20)

Video continuity factor (flow).

Default: 9

num_frames

integer

(minimum: 1, maximum: 1440)

How many frames (duration) in the resulting video.

Default: 33

width

integer

(minimum: 64, maximum: 1536)

Width for the generated video.

Default: 640

height

integer

(minimum: 64, maximum: 1024)

Height for the generated video.

Default: 360

denoise_strength

number

(minimum: 0, maximum: 2)

Controls how strongly noise is applied each step.

Default: 1

force_offload

boolean

Whether to force model layers offloaded to CPU.

Default: true

frame_rate

integer

(minimum: 1, maximum: 60)

Video frame rate.

Default: 16

crf

integer

(minimum: 0, maximum: 51)

CRF (quality) for H264 encoding. Lower values = higher quality.

Default: 19

enhance_weight

number

(minimum: 0, maximum: 2)

Strength of the video enhancement effect.

Default: 0.3

enhance_single

boolean

Apply enhancement to individual frames.

Default: true

enhance_double

boolean

Apply enhancement across frame pairs.

Default: true

enhance_start

number

(minimum: 0, maximum: 1)

When to start enhancement in the video. Must be less than enhance_end.

Default: 0

enhance_end

number

(minimum: 0, maximum: 1)

When to end enhancement in the video. Must be greater than enhance_start.

Default: 1

seed

integer

Set a seed for reproducibility. Random by default.

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

npx create-replicate --model=nsfw-api/oldcore

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";
import fs from "node:fs";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run nsfw-api/oldcore using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "nsfw-api/oldcore:d1627a74784d1ca91e6a73ceba46aa7a58119e5126870927aaf462b1faaf59a3",
  {
    input: {
      crf: 19,
      steps: 50,
      width: 640,
      height: 360,
      prompt: "",
      lora_url: "",
      lora2_url: "",
      scheduler: "DPMSolverMultistepScheduler",
      flow_shift: 9,
      frame_rate: 16,
      num_frames: 33,
      enhance_end: 1,
      enhance_start: 0,
      force_offload: true,
      lora_strength: 1,
      enhance_double: true,
      enhance_single: true,
      enhance_weight: 0.3,
      guidance_scale: 6,
      lora2_strength: 1,
      denoise_strength: 1
    }
  }
);

// To access the file URL:
console.log(output.url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", 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 nsfw-api/oldcore using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "nsfw-api/oldcore:d1627a74784d1ca91e6a73ceba46aa7a58119e5126870927aaf462b1faaf59a3",
    input={
        "crf": 19,
        "steps": 50,
        "width": 640,
        "height": 360,
        "prompt": "",
        "lora_url": "",
        "lora2_url": "",
        "scheduler": "DPMSolverMultistepScheduler",
        "flow_shift": 9,
        "frame_rate": 16,
        "num_frames": 33,
        "enhance_end": 1,
        "enhance_start": 0,
        "force_offload": True,
        "lora_strength": 1,
        "enhance_double": True,
        "enhance_single": True,
        "enhance_weight": 0.3,
        "guidance_scale": 6,
        "lora2_strength": 1,
        "denoise_strength": 1
    }
)

# To access the file URL:
print(output.url())
#=> "http://example.com"

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output.read())

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 nsfw-api/oldcore 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": "nsfw-api/oldcore:d1627a74784d1ca91e6a73ceba46aa7a58119e5126870927aaf462b1faaf59a3",
    "input": {
      "crf": 19,
      "steps": 50,
      "width": 640,
      "height": 360,
      "prompt": "",
      "lora_url": "",
      "lora2_url": "",
      "scheduler": "DPMSolverMultistepScheduler",
      "flow_shift": 9,
      "frame_rate": 16,
      "num_frames": 33,
      "enhance_end": 1,
      "enhance_start": 0,
      "force_offload": true,
      "lora_strength": 1,
      "enhance_double": true,
      "enhance_single": true,
      "enhance_weight": 0.3,
      "guidance_scale": 6,
      "lora2_strength": 1,
      "denoise_strength": 1
    }
  }' \
  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/nsfw-api/oldcore@sha256:d1627a74784d1ca91e6a73ceba46aa7a58119e5126870927aaf462b1faaf59a3 \
  -i 'crf=19' \
  -i 'steps=50' \
  -i 'width=640' \
  -i 'height=360' \
  -i 'prompt=""' \
  -i 'lora_url=""' \
  -i 'lora2_url=""' \
  -i 'scheduler="DPMSolverMultistepScheduler"' \
  -i 'flow_shift=9' \
  -i 'frame_rate=16' \
  -i 'num_frames=33' \
  -i 'enhance_end=1' \
  -i 'enhance_start=0' \
  -i 'force_offload=true' \
  -i 'lora_strength=1' \
  -i 'enhance_double=true' \
  -i 'enhance_single=true' \
  -i 'enhance_weight=0.3' \
  -i 'guidance_scale=6' \
  -i 'lora2_strength=1' \
  -i 'denoise_strength=1'

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/nsfw-api/oldcore@sha256:d1627a74784d1ca91e6a73ceba46aa7a58119e5126870927aaf462b1faaf59a3
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "crf": 19,
      "steps": 50,
      "width": 640,
      "height": 360,
      "prompt": "",
      "lora_url": "",
      "lora2_url": "",
      "scheduler": "DPMSolverMultistepScheduler",
      "flow_shift": 9,
      "frame_rate": 16,
      "num_frames": 33,
      "enhance_end": 1,
      "enhance_start": 0,
      "force_offload": true,
      "lora_strength": 1,
      "enhance_double": true,
      "enhance_single": true,
      "enhance_weight": 0.3,
      "guidance_scale": 6,
      "lora2_strength": 1,
      "denoise_strength": 1
    }
  }' \
  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 A100 (80GB) GPU hardware. We don't yet have enough runs of this model to provide performance information.

Readme

This model doesn't have a readme.