Ultimate SD Upscale
ultimate-sd-upscale
Ultimate SD Upscale is a Face Enhancer AI model that improves facial details and enhances image quality.
Model Information
Input
Configure model parameters
Output
View generated results
Result
Preview, share or download your results with a single click.
Prerequisites
- Create an API Key from the Eachlabs Console
- Install the required dependencies for your chosen language (e.g., requests for Python)
API Integration Steps
1. Create a Prediction
Send a POST request to create a new prediction. This will return a prediction ID that you'll use to check the result. The request should include your model inputs and API key.
import requestsimport timeAPI_KEY = "YOUR_API_KEY" # Replace with your API keyHEADERS = {"X-API-Key": API_KEY,"Content-Type": "application/json"}def create_prediction():response = requests.post("https://api.eachlabs.ai/v1/prediction/",headers=HEADERS,json={"model": "ultimate-sd-upscale","version": "0.0.1","input": {"cfg": 8,"seed": 0,"image": "your_file.image/jpeg","steps": 20,"denoise": 0.2,"upscaler": "4x-UltraSharp","mask_blur": 8,"mode_type": "Linear","scheduler": "normal","tile_width": 512,"upscale_by": 2,"tile_height": 512,"sampler_name": "euler","tile_padding": 32,"seam_fix_mode": "None","seam_fix_width": 64,"negative_prompt": "your negative prompt here","positive_prompt": "your positive prompt here","seam_fix_denoise": 1,"seam_fix_padding": 16,"seam_fix_mask_blur": 8,"controlnet_strength": 1,"force_uniform_tiles": false,"use_controlnet_tile": false}})prediction = response.json()if prediction["status"] != "success":raise Exception(f"Prediction failed: {prediction}")return prediction["predictionID"]
2. Get Prediction Result
Poll the prediction endpoint with the prediction ID until the result is ready. The API uses long-polling, so you'll need to repeatedly check until you receive a success status.
def get_prediction(prediction_id):while True:result = requests.get(f"https://api.eachlabs.ai/v1/prediction/{prediction_id}",headers=HEADERS).json()if result["status"] == "success":return resultelif result["status"] == "error":raise Exception(f"Prediction failed: {result}")time.sleep(1) # Wait before polling again
3. Complete Example
Here's a complete example that puts it all together, including error handling and result processing. This shows how to create a prediction and wait for the result in a production environment.
try:# Create predictionprediction_id = create_prediction()print(f"Prediction created: {prediction_id}")# Get resultresult = get_prediction(prediction_id)print(f"Output URL: {result['output']}")print(f"Processing time: {result['metrics']['predict_time']}s")except Exception as e:print(f"Error: {e}")
Additional Information
- The API uses a two-step process: create prediction and poll for results
- Response time: ~40 seconds
- Rate limit: 60 requests/minute
- Concurrent requests: 10 maximum
- Use long-polling to check prediction status until completion
Overview
Ultimate SD Upscale is designed for high-quality image upscaling while maintaining fine details and improving overall image fidelity. With advanced options for tile-based upscaling, ControlNet integration, and seamless blending, it allows users to upscale images with precision, making it ideal for creative projects requiring enhanced visuals.
Technical Specifications
Upscaling Methods: Includes multiple upscaling algorithms like 4x_NMKD-Siax_200k and RealESRGAN_x4plus, each optimized for different use cases (e.g., anime, sharp details).
Tile Handling: Supports customizable tile sizes and padding to ensure memory efficiency and seamless image blending.
ControlNet Integration: Adds structure-preserving capabilities, adjustable through the controlnet_strength parameter.
Flexible Sampling and Scheduling: Offers advanced sampling techniques (e.g., dpmpp_2m, euler_ancestral) and scheduling options (karras, exponential) for fine control over the upscaling process.
Key Considerations
Resource Usage: Larger images or higher tile overlap settings may increase processing time and resource consumption.
Tile Seams: Ensure appropriate seam-fix settings to avoid visible tile boundaries in the final output.
Prompt Optimization: Misaligned prompts can lead to unintended artifacts. Carefully curate positive and negative prompts.
ControlNet Activation: ControlNet can introduce noise if improperly calibrated. Adjust the strength based on the input image and desired output.
Tips & Tricks
Input-Specific Adjustments
- Upscaler:
- Use 4x_NMKD-Siax_200k for highly detailed imagery.
- Choose RealESRGAN_x4plus_anime_6B for anime-style images.
- Select 4x-UltraSharp for photos requiring enhanced sharpness.
- Sampler Name:
- dpmpp_2m and dpmpp_2m_sde are recommended for smooth and balanced outputs.
- euler_ancestral provides a softer, artistic finish.
- Scheduler:
- Use karras for natural-looking results with gradual transitions.
- exponential is suited for sharper, high-contrast images.
- Mode Type:
- Select Linear for consistent upscaling across the image.
- Use Chess for complex, tiled designs to minimize memory usage.
- Tile Dimensions and Padding:
- Start with a tile width and height of 512x512 for most cases.
- Increase padding to 32 for better blending at tile edges.
General Settings
- Positive and Negative Prompts:
- Be explicit in positive prompts to emphasize desired features (e.g., "highly detailed, sharp textures").
- Use negative prompts to avoid unwanted artifacts (e.g., "no blurriness, no distorted edges").
- ControlNet:
- For intricate details, set controlnet_strength between 0.5 and 0.7.
- Activate Use ControlNet Tile for highly structured or grid-like images.
- Denoise and CFG Scale:
- Keep denoise between 0.4 and 0.6 to preserve image structure while reducing unwanted noise.
- Set cfg to 7.0 for balanced creativity and prompt adherence.
- Seam Fix Options:
- Use Half Tile + Intersections for seamless transitions in tiled outputs.
- Adjust seam_fix_denoise to 0.3 and seam_fix_mask_blur to 8 for better blending.
Capabilities
Upscales images up to 4x their original resolution.
Supports tile-based processing for high-resolution images.
Integrates seamlessly with ControlNet for structure-aware upscaling.
Flexible parameter options for fine-tuning outputs.
What can I use for?
Enhance image quality for digital art, photography, or animations.
Upscale anime and other stylized visuals while preserving intricate details.
Generate high-resolution assets for professional and creative projects.
Things to be aware of
Use Linear mode and 4x-UltraSharp for realistic photographs requiring sharpness.
Test tiled upscaling with a tile size of 256x256 and seam fix enabled for large images.
Activate ControlNet for architectural or grid-like images, adjusting controlnet_strength to preserve details.
Limitations
Processing Time: Larger images or high tile overlap may lead to slower processing.
Tile Artifacts: Improper settings may leave visible seams or artifacts in tiled outputs.
Prompt Dependency: Results are highly dependent on prompt accuracy and clarity.
ControlNet Overuse: Excessive ControlNet strength may distort image structure.
Output Format:PNG
