Below are the most common reasons and how to improve accuracy.
Common reasons and how to improve
Input image does not clearly represent the product
The AI relies entirely on your input image to understand the product.
Common issues:
Low-resolution or blurry images
Poor lighting or unclear details
Product partially hidden or cropped
What you can do:
Use high-quality, well-lit images
Ensure the product is fully visible
Keep the product centered and clear
Input contains multiple or unclear elements
If the input image includes too many elements, the output may not focus correctly on your product.
Common issues:
Multiple products in one image
Background elements competing with the product
Accessories that confuse product identity
What you can do
Use a single, clear product per image
Minimize background distractions
Avoid mixing unrelated elements
Product details are difficult to interpret
Certain product characteristics maybe harder for AI to reproduce accurately
Common issues:
Complex patterns or textures
Small logos or fine details
Reflective or transparent materials
What you can do:
Use clearer images that highlight important details
Provide detail images (close-up views) if necessary
Review outputs carefully and refine using Image Editing
Natural variation in AI-generated outputs
AI-generated content may not always reproduce products with perfect consistency.
Possible outcomes:
Slight differences in shape, color, or proportion
Inconsistent small details
Variations across different outputs
What you can do
Generate multiple variants and compare results
Select the closest match
Use Image Editing to refine details
📌 From Dreem team: We are aware that output consistency and stability are important for production workflows, and we are working on product refinements that help improve accuracy, reliability, and review efficiency across generated results. However, because AI-generated content can still vary from the original input or intended styling, final QA remains an important step before publishing.
Key takeaway
Accurate outputs depend on clear inputs, correct setup, and iterative refinement. In most cases, improving how the product is represented will significantly improve the result.