Realisitic Vision V3 Inpainting
realisitic-vision-v3-inpainting
Realistic Vision V3.0 Inpainting
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": "realisitic-vision-v3-inpainting","version": "0.0.1","input": {"mask": "your mask here","image": "your image here","steps": "20","prompt": "RAW photo, a portrait photo of Katie Read in casual clothes, natural skin, 8k uhd, high quality, film grain, Fujifilm XT3","strength": "0.8","num_outputs": "1","guaidance_scale": "7.5","negative_prompt": "(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime:1.4), text, close up, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck"}})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: ~75 seconds
- Rate limit: 60 requests/minute
- Concurrent requests: 10 maximum
- Use long-polling to check prediction status until completion
Overview
Realistic Vision V3 Inpainting is an AI model , designed to perform inpainting tasks—reconstructing missing or corrupted parts of an image. This model is part of the Realistic Vision series, which focuses on generating high-quality, photorealistic images for various applications
Technical Specifications
- Architecture: The model is based on a diffusion framework, optimized for high-quality inpainting tasks. It uses advanced neural network layers to reconstruct and blend missing regions seamlessly.
- Training Dataset: Trained on a diverse dataset of high-resolution images, ensuring robust performance across various image types and styles.
- Resolution: Supports inpainting tasks for images up to 1024x1024 pixels, with performance optimized for standard high-resolution image sizes.
Key Considerations
Model Accuracy: While the model is proficient at generating realistic inpainted images, the quality of the output heavily depends on the accuracy of the input mask and the clarity of the prompt provided.
Processing Time: The time required for inpainting may vary based on input complexity and parameter settings. Be prepared for longer processing times with higher-resolution images or more intricate prompts.
Tips & Tricks
Iterative Refinement: Start with broader prompts and progressively refine them to achieve the desired output. This iterative approach can help in honing in on the exact image characteristics you seek.
Mask Precision: Use precise masks to define the inpainting area accurately. This precision aids the model in generating seamless integrations with the existing image content.
Negative Prompts: Leverage negative prompts to exclude unwanted elements from the output, enhancing the relevance and quality of the inpainted regions.
Input Requirements: Provide a high-resolution image along with a corresponding mask that indicates the areas to be inpainted. Ensure the mask accurately covers the regions requiring reconstruction for optimal results.
Prompt Guidance: Utilize descriptive text prompts to guide the inpainting process, especially when reconstructing complex or detailed areas. This helps the model generate content that aligns with your expectations.
Parameter Adjustment: Experiment with parameters such as strength, steps, and guidance scale to fine-tune the output. Adjusting these settings can influence the balance between adherence to the prompt and the diversity of the generated images.
Capabilities
High-Quality Inpainting: Generates realistic and seamless inpainted regions that integrate well with the original image content.
Prompt-Driven Generation: Utilizes textual prompts to guide the inpainting process, allowing for creative control over the reconstructed areas.
Versatile Application: Applicable to a wide range of image editing tasks, including object removal, restoration of damaged photos, and creative image manipulation.
What can I use for?
Image Restoration: Repair and reconstruct damaged or missing parts of photographs, enhancing their visual appeal and completeness.
Object Removal: Eliminate unwanted elements from images, such as background distractions or undesired objects, to improve composition.
Creative Editing: Modify images by replacing certain areas with new content guided by prompts, enabling artistic transformations and enhancements.
Things to be aware of
Style Variation: Experiment with different prompts to inpaint regions in various artistic styles, adding a unique touch to your images.
Scenario Alteration: Use the model to change specific aspects of an image, such as altering the background or modifying objects within the scene, to create diverse visual narratives.
Limitations
Complex Scene Reconstruction: The model may encounter challenges when reconstructing highly complex or abstract scenes, potentially leading to less accurate inpainting results.
Dependency on Input Quality: The effectiveness of the inpainting is directly influenced by the quality of the input image and mask; low-quality inputs can result in suboptimal outputs.
Output Format: JPG
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