Collage Diffusion

We seek to give users precise control over diffusion-based image generation by modeling complex scenes as sequences of layers, which define the desired spatial arrangement and visual attributes of objects in the scene. Collage Diffusion harmonizes the input layers to make objects fit together -- the key challenge involves minimizing changes in the positions and key visual attributes of the input layers while allowing other attributes to change in the harmonization process. We ensure that objects are generated in the correct locations by modifying text-image cross-attention with the layers' alpha masks. We preserve key visual attributes of input layers by learning specialized text representations per layer and by extending ControlNet to operate on layers. Layer input allows users to control the extent of image harmonization on a per-object basis, and users can even iteratively edit individual objects in generated images while keeping other objects fixed. By leveraging the rich information present in layer input, Collage Diffusion generates globally harmonized images that maintain desired object characteristics better than prior approaches

SIMULATING OZONE EFFECTS ON FOREST PRODUCTIVITY: INTERACTIONS AMONG LEAF‐, CANOPY‐, AND STAND‐LEVEL PROCESSES

Ozone pollution in the lower atmosphere is known to have adverse effects on forest vegetation, but the degree to which mature forests are impacted has been very difficult to assess directly. In this study, we combined leaf‐level ozone response data from independent ozone fumigation studies with a forest ecosystem model in order simulate the effects of ambient ozone on mature hardwood forests. Reductions in leaf carbon gain were determined as a linear function of ozone flux to the leaf interior, calculated as the product of ozone concentration and leaf stomatal conductance. This relationship was applied to individual canopy layers within the model in order to allow interaction with stand‐ and canopy‐level factors such as light attenuation, leaf morphology, soil water limitations, and vertical ozone gradients. The resulting model was applied to 64 locations across the northeastern United States using ambient ozone data from 1987 to 1992. Predicted declines in annual net primary production ranged from 3 to 16% with greatest reductions in southern portions of the region where ozone levels were highest, and on soils with high water‐holding capacity where drought stress was absent. Reductions in predicted wood growth were slightly greater (3–22%) because wood is a lower carbon allocation priority in the model than leaf and root growth. Interannual variation in predicted ozone effects was small due to concurrent fluctuations in ozone and climate. Periods of high ozone often coincided with hot, dry weather conditions, causing reduced stomatal conductance and ozone uptake. Within‐canopy ozone concentration gradients had little effect on predicted growth reductions because concentrations remained high through upper canopy layers where net carbon assimilation and ozone uptake were greatest. Sensitivity analyses indicate a trade‐off between model sensitivity to available soil water and foliar nitrogen and demonstrate uncertainties regarding several assumptions used in the model. Uncertainties surrounding ozone effects on stomatal function and plant water use efficiency were found to have important implications on current predictions. Field measurements of ozone effects on mature forests will be needed before the accuracy of model predictions can be fully assessed