Practical solutions to AI image generation quality problems
When using Monet Vision for AI image generation, it is possible to encounter situations where the results do not match expectations. The following provides a systematic solution:
- Refinement of input descriptionMake sure you use specific adjectives and details, e.g. "Sunset waves, impressionistic style" is more likely to produce the desired effect than simply writing "Sunset on the beach".
- Multi-model comparison test: Utilize the platform's built-in model comparison function, while using different engines such as DALL-E and Flux to generate images, with different models specializing in different style domains
- Parameter optimization adjustment: The resolution is recommended to start testing from 1080p, and the style intensity can be set to 70%-100% to get more obvious effect.
- Batch Generation Filtering: Generating 3-5 versions at a time significantly improves the probability of obtaining quality results
Remedy: If the initial generation is not satisfactory, you can also use the style conversion function to secondary process the existing images, or use the built-in editing tools to adjust the brightness, contrast and other basic parameters.
This answer comes from the articleMonet Vision: an AI authoring platform that generates professional images and videos with a single clickThe
May not be reproduced without permission:AI productivity tools " How to solve the fundamental problem of AI image generation results not meeting expectations?
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