Solution: Fine-grained control through nodal workflows
The randomness generated by ordinary AI tools often does not meet professional requirements, and FLORA solves the problem through hierarchical control:
- Multi-stage regulation nodes: Connect optimization nodes such as Image Variation/Text Refinement after the base generation node, and handle details in layers like traditional design software (e.g., separate adjustment of material and lighting parameters for architectural renderings).
- Model Switching Comparison: Multiple image generation nodes can be concatenated for the same cue word, and different models such as Stable Diffusion, DALL-E 3, etc., are invoked respectively to select the most suitable scheme through visual effect comparison.
- Preset Style Library: Search for specialized domain keywords (e.g., product packaging, architectural visualization) in the community template and directly reuse industry-validated parameter combinations.
- Physical parameter injection: Enter dimensional data exported from CAD software through text nodes to ensure that the generated content conforms to engineering specifications.
Typical case flow: initial concept → 3D rendering → material refinement → environment fusion → final rendering, each link can add control nodes fine-tuning.
This answer comes from the articleFLORA: A canvas-based AI image, video creative workflow platformThe
May not be reproduced without permission:AI productivity tools " How to improve the match between AI-generated content and professional design needs?
