Media Gen SolutionCustomizations

Checkpoints

Custom checkpoints are fine-tuned versions of the original model and allow users to refine customizations while creating images or videos.

Custom Stable Diffusion checkpoints are fine-tuned versions of the original model, trained to capture particular styles, subjects, or objects. They are designed to provide users with more control and customization options when generating images. These checkpoints can be tailored to produce images in various styles, such as realistic photography, artwork, or even specific themes like landscapes or portraits. While checkpoints represent a significant investment in terms of storage and computational resources, they excel in maintaining the desired customizations consistently. OctoAI’s Asset Library boasts a rich collection of pre-loaded custom checkpoints, offering a diverse array of styles to enhance your images. Additionally, users have the flexibility to import bespoke checkpoints from external sources, integrating them seamlessly into OctoAI’s Asset Library as personalized assets.

The image results with different checkpoints, even using the same prompt, can be significantly different. Using the simple prompt A photo of an Australian cattle dog running through a park, you can see see the results from the SDXL base model (left) and samaritan model (right). The samaritan model represents a 3D-cartoon image style.

Using SDXL base model

Using samaritan checkpoint

Example Code:

$curl -X POST "https://image.octoai.run/generate/sdxl" \
>    -H "Content-Type: application/json" \
>    -H "Authorization: Bearer $OCTOAI_TOKEN" \
>    --data-raw '{
>        "prompt": "A photo of an Australian cattle dog running through a park",
>        "negative_prompt": "Blurry photo, distortion, low-res, poor quality",
>        "checkpoint": "octoai:samaritan",
>        "width": 1024,
>        "height": 1024,
>        "num_images": 1,
>        "sampler": "DDIM",
>        "steps": 30,
>        "cfg_scale": 12,
>        "use_refiner": true,
>        "high_noise_frac": 0.8,
>        "style_preset": "base"
>    }' | jq -r ".images[0].image_b64" | base64 -d >result.jpg