anotherjesse/streaming-sdxl:15320413 – Run with an API on Replicate

Version

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anotherjesse /streaming-sdxl:15320413

Playground API

Input

prompt

string

Shift + Return to add a new line

Input prompt

Default: "An astronaut riding a rainbow unicorn"

negative_prompt

string

Shift + Return to add a new line

Input Negative Prompt

Default: ""

image

file

Input image for img2img or inpaint mode

mask

file

Input mask for inpaint mode. Black areas will be preserved, white areas will be inpainted.

width

integer

Width of output image

Default: 1024

height

integer

Height of output image

Default: 1024

scheduler

string

scheduler

Default: "K_EULER"

num_inference_steps

integer

(minimum: 1, maximum: 500)

Number of denoising steps

Default: 50

guidance_scale

number

(minimum: 1, maximum: 50)

Scale for classifier-free guidance

Default: 7.5

prompt_strength

number

(minimum: 0, maximum: 1)

Prompt strength when using img2img / inpaint. 1.0 corresponds to full destruction of information in image

Default: 0.8

seed

integer

Random seed. Leave blank to randomize the seed

refine

string

Which refine style to use

Default: "no_refiner"

high_noise_frac

number

(minimum: 0, maximum: 1)

For expert_ensemble_refiner, the fraction of noise to use

Default: 0.8

refine_steps

integer

For base_image_refiner, the number of steps to refine, defaults to num_inference_steps

apply_watermark

boolean

Applies a watermark to enable determining if an image is generated in downstream applications. If you have other provisions for generating or deploying images safely, you can use this to disable watermarking.

Default: true

lora_scale

number

(minimum: 0, maximum: 1)

LoRA additive scale. Only applicable on trained models.

Default: 0.6

preview_steps

integer

Number of steps to preview

preview_size

integer

Size of preview image in pixels

Default: 256

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

npx create-replicate --model=anotherjesse/streaming-sdxl

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 anotherjesse/streaming-sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "anotherjesse/streaming-sdxl:153204130f521e631a916ef067a0c5e09dcb8782bcfd90926b8e73763b161959",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "An astronaut riding a rainbow unicorn",
      refine: "no_refiner",
      scheduler: "K_EULER",
      lora_scale: 0.6,
      preview_size: 256,
      preview_steps: 5,
      guidance_scale: 7.5,
      apply_watermark: true,
      high_noise_frac: 0.8,
      negative_prompt: "",
      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 anotherjesse/streaming-sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "anotherjesse/streaming-sdxl:153204130f521e631a916ef067a0c5e09dcb8782bcfd90926b8e73763b161959",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "An astronaut riding a rainbow unicorn",
        "refine": "no_refiner",
        "scheduler": "K_EULER",
        "lora_scale": 0.6,
        "preview_size": 256,
        "preview_steps": 5,
        "guidance_scale": 7.5,
        "apply_watermark": True,
        "high_noise_frac": 0.8,
        "negative_prompt": "",
        "prompt_strength": 0.8,
        "num_inference_steps": 50
    }
)

# The anotherjesse/streaming-sdxl 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/anotherjesse/streaming-sdxl/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 anotherjesse/streaming-sdxl 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": "153204130f521e631a916ef067a0c5e09dcb8782bcfd90926b8e73763b161959",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "An astronaut riding a rainbow unicorn",
      "refine": "no_refiner",
      "scheduler": "K_EULER",
      "lora_scale": 0.6,
      "preview_size": 256,
      "preview_steps": 5,
      "guidance_scale": 7.5,
      "apply_watermark": true,
      "high_noise_frac": 0.8,
      "negative_prompt": "",
      "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.

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/anotherjesse/streaming-sdxl@sha256:153204130f521e631a916ef067a0c5e09dcb8782bcfd90926b8e73763b161959 \
  -i 'width=1024' \
  -i 'height=1024' \
  -i 'prompt="An astronaut riding a rainbow unicorn"' \
  -i 'refine="no_refiner"' \
  -i 'scheduler="K_EULER"' \
  -i 'lora_scale=0.6' \
  -i 'preview_size=256' \
  -i 'preview_steps=5' \
  -i 'guidance_scale=7.5' \
  -i 'apply_watermark=true' \
  -i 'high_noise_frac=0.8' \
  -i 'negative_prompt=""' \
  -i 'prompt_strength=0.8' \
  -i 'num_inference_steps=50'

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/anotherjesse/streaming-sdxl@sha256:153204130f521e631a916ef067a0c5e09dcb8782bcfd90926b8e73763b161959
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "An astronaut riding a rainbow unicorn",
      "refine": "no_refiner",
      "scheduler": "K_EULER",
      "lora_scale": 0.6,
      "preview_size": 256,
      "preview_steps": 5,
      "guidance_scale": 7.5,
      "apply_watermark": true,
      "high_noise_frac": 0.8,
      "negative_prompt": "",
      "prompt_strength": 0.8,
      "num_inference_steps": 50
    }
  }' \
  http://localhost:5000/predictions

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

Output

{
  "completed_at": "2023-09-13T22:51:39.972594Z",
  "created_at": "2023-09-13T22:51:26.023554Z",
  "data_removed": false,
  "error": null,
  "id": "ezpvhitbro5zgiugi7wzx5kv2y",
  "input": {
    "width": 1024,
    "height": 1024,
    "prompt": "An astronaut riding a rainbow unicorn",
    "refine": "no_refiner",
    "scheduler": "K_EULER",
    "lora_scale": 0.6,
    "preview_steps": 5,
    "guidance_scale": 7.5,
    "apply_watermark": true,
    "high_noise_frac": 0.8,
    "negative_prompt": "",
    "prompt_strength": 0.8,
    "num_inference_steps": 50
  },
  "logs": "Using seed: 36830\nPrompt: An astronaut riding a rainbow unicorn\ntxt2img mode\n  0%|          | 0/50 [00:00<?, ?it/s]\nyielding latents\n  2%|▏         | 1/50 [00:00<00:09,  4.97it/s]\n  4%|▍         | 2/50 [00:00<00:16,  2.85it/s]\n  6%|▌         | 3/50 [00:00<00:13,  3.54it/s]\n  8%|▊         | 4/50 [00:01<00:11,  3.99it/s]\n 10%|█         | 5/50 [00:01<00:10,  4.28it/s]\nyielding latents\n 12%|█▏        | 6/50 [00:01<00:09,  4.48it/s]\n 14%|█▍        | 7/50 [00:01<00:12,  3.38it/s]\n 16%|█▌        | 8/50 [00:02<00:11,  3.76it/s]\n 18%|█▊        | 9/50 [00:02<00:10,  4.07it/s]\n 20%|██        | 10/50 [00:02<00:09,  4.29it/s]\nyielding latents\n 22%|██▏       | 11/50 [00:02<00:08,  4.47it/s]\n 24%|██▍       | 12/50 [00:03<00:10,  3.46it/s]\n 26%|██▌       | 13/50 [00:03<00:09,  3.80it/s]\n 28%|██▊       | 14/50 [00:03<00:08,  4.09it/s]\n 30%|███       | 15/50 [00:03<00:08,  4.32it/s]\nyielding latents\n 32%|███▏      | 16/50 [00:03<00:07,  4.45it/s]\n 34%|███▍      | 17/50 [00:04<00:10,  3.29it/s]\n 36%|███▌      | 18/50 [00:04<00:08,  3.65it/s]\n 38%|███▊      | 19/50 [00:04<00:07,  3.96it/s]\n 40%|████      | 20/50 [00:05<00:07,  4.20it/s]\nyielding latents\n 42%|████▏     | 21/50 [00:05<00:06,  4.38it/s]\n 44%|████▍     | 22/50 [00:05<00:08,  3.26it/s]\n 46%|████▌     | 23/50 [00:05<00:07,  3.63it/s]\n 48%|████▊     | 24/50 [00:06<00:06,  3.94it/s]\n 50%|█████     | 25/50 [00:06<00:05,  4.19it/s]\nyielding latents\n 52%|█████▏    | 26/50 [00:06<00:05,  4.38it/s]\n 54%|█████▍    | 27/50 [00:07<00:06,  3.40it/s]\n 56%|█████▌    | 28/50 [00:07<00:05,  3.74it/s]\n 58%|█████▊    | 29/50 [00:07<00:05,  4.03it/s]\n 60%|██████    | 30/50 [00:07<00:04,  4.26it/s]\nyielding latents\n 62%|██████▏   | 31/50 [00:07<00:04,  4.43it/s]\n 64%|██████▍   | 32/50 [00:08<00:05,  3.45it/s]\n 66%|██████▌   | 33/50 [00:08<00:04,  3.79it/s]\n 68%|██████▊   | 34/50 [00:08<00:03,  4.06it/s]\n 70%|███████   | 35/50 [00:08<00:03,  4.28it/s]\nyielding latents\n 72%|███████▏  | 36/50 [00:09<00:03,  4.45it/s]\n 74%|███████▍  | 37/50 [00:09<00:03,  3.43it/s]\n 76%|███████▌  | 38/50 [00:09<00:03,  3.77it/s]\n 78%|███████▊  | 39/50 [00:09<00:02,  4.05it/s]\n 80%|████████  | 40/50 [00:10<00:02,  4.27it/s]\nyielding latents\n 82%|████████▏ | 41/50 [00:10<00:02,  4.44it/s]\n 84%|████████▍ | 42/50 [00:10<00:02,  3.44it/s]\n 86%|████████▌ | 43/50 [00:11<00:01,  3.78it/s]\n 88%|████████▊ | 44/50 [00:11<00:01,  4.06it/s]\n 90%|█████████ | 45/50 [00:11<00:01,  4.28it/s]\nyielding latents\n 92%|█████████▏| 46/50 [00:11<00:00,  4.45it/s]\n 94%|█████████▍| 47/50 [00:12<00:00,  3.45it/s]\n 96%|█████████▌| 48/50 [00:12<00:00,  3.79it/s]\n 98%|█████████▊| 49/50 [00:12<00:00,  4.07it/s]\n100%|██████████| 50/50 [00:12<00:00,  4.29it/s]\n100%|██████████| 50/50 [00:12<00:00,  3.94it/s]",
  "metrics": {
    "predict_time": 13.983038,
    "total_time": 13.94904
  },
  "output": [
    "https://pbxt.replicate.delivery/kW8fAcvc6KyKSqXAx2GXY72W7yVRFczm5uPJGHIL8wZ3dfjRA/0000.jpg",
    "https://pbxt.replicate.delivery/JZxWZi0P5SZFN5gnFu4Vop20XFGlG4DztdNDCJO8pfw3dfjRA/0005.jpg",
    "https://pbxt.replicate.delivery/XSojnlcTcJ5iBNaQ4ftDuC1OQEmeMp4NYFa01eYAFosi39HjA/0010.jpg",
    "https://pbxt.replicate.delivery/5JUydfceTosdAkePUmIMcbIVBZYBcigTrWrB2sjsLNDl39HjA/0015.jpg",
    "https://pbxt.replicate.delivery/5WIMaXF4XQIFO1uTSvc7tTjsDEQuOeVFXYJqRIrrXPn5dfjRA/0020.jpg",
    "https://pbxt.replicate.delivery/g3O1iks3we34MSkOgmBWjAGJsekOzTUeCrTw9bjbaeKXv7PGB/0025.jpg",
    "https://pbxt.replicate.delivery/tWRQcYcHIApAERLcG2gn0S5znCfcqtdoOevVbaUJ8ct27eHjA/0030.jpg",
    "https://pbxt.replicate.delivery/bzSZnPxdkS7nHZaDT60QXN9GxvNO77z0fXoFi6oH7Bx7dfjRA/0035.jpg",
    "https://pbxt.replicate.delivery/ajx1LpNLcH5dPNbe8jFscGijwN0t8afuKgOakBiDCxX47eHjA/0040.jpg",
    "https://pbxt.replicate.delivery/a8BBeeGfbZCbnJk8EV2gdNEYPeYE94dWBiJ2KeYC2VZVfufxIA/0045.jpg",
    "https://pbxt.replicate.delivery/kht9VnpIDnaBAJa4i2bHAHob7ruWXWDvJTderc7Mzb59dfjRA/results.png"
  ],
  "started_at": "2023-09-13T22:51:25.989556Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/ezpvhitbro5zgiugi7wzx5kv2y",
    "cancel": "https://api.replicate.com/v1/predictions/ezpvhitbro5zgiugi7wzx5kv2y/cancel"
  },
  "version": "342f66e96ce05221d91654ab7479593a3e0554004e502d5c3e28cc84fa0b0245"
}

Generated in

14.0 seconds

Tweak itReport

Using seed: 36830
Prompt: An astronaut riding a rainbow unicorn
txt2img mode
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This example was created by a different version, anotherjesse/streaming-sdxl:342f66e9.