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conditional balance improving multi conditioning trade offs in image generation conditional balance improving multi conditioning trade offs in image generation nadav z cohen 1 oron nir 1 2 ariel shamir 1 1 reichman university 2 microsoft arxiv repository code sdxl code sd3 5 code analysis evaluation set abstract balancing content fidelity and artistic style is a pivotal challenge in image generation while traditional style transfer methods and modern denoising diffusion probabilistic models ddpms strive to achieve this balance they often struggle to do so without sacrificing either style content or sometimes both this work addresses this challenge by analyzing the ability of ddpms to maintain content and style equilibrium we introduce a novel method to identify sensitivities within the ddpm attention layers identifying specific layers that correspond to different stylistic aspects by directing conditional inputs only to these sensitive layers our approach enables fine grained control over style and content significantly reducing issues arising from over constrained inputs our findings demonstrate that this method enhances recent stylization techniques by better aligning style and content ultimately improving the quality of generated visual content conditional balance an easy to follow video explaining the idea behind our method how does it work our experiments with current style generation methods reveal that various methods apply style to the output image with varying levels of intensity some methods apply excessive style leading to content loss as the content conditionals are overshadowed while others apply insufficient style resulting in weak stylization as the content conditionals dominate the style we hypothesize that achieving the optimal balance of conditioning for content and style and directing it to the appropriate parts of the model can produce a visually satisfying harmony between content and style conditionals below we briefly explain how we detect these sensitivities image collections we first generate conditional inputs which vary the style aspect we wish to find its sensitive parts in the generation process to make sure our analysis is clean we solidify all other aspects in the following examples we solidify the content and structure of the image while varying style collection mapping using the pre generated conditionals we generate the collection of images which vary only in the spesific analyzed aspect while generating the images we extract the information from the attention layers of the denoising unet and store them for further analysis layer ranking after storing the attention features for each layer at each timestep we rank the layers by sensitivity at each timestep the ranking is based on a clustering score that evaluates each layer by the closeness of its features for images sharing the same style inner distance and the separation of features for images with different styles outer distance balanced inference by analyzing the features for both style and structure aspects we set λ s blue and λ t orange to determine the ratio of the most sensitive layers to use for conditioning by directing the conditionals to layers with higher sensitivity to style and content we enable all conditionals to be utilized effectively without overshadowing one another balancing style λ s using our balanced style injection strategy we can generate images conditioned by complex combinations of style and content inputs without compromising conditional information or image quality to achieve this balance we introduce an balancing parameter λ s which determines the number of style sensitive layers utilized for conditioning at each timestep below we illustrate the impact of λ s on the balancing effect when λ s 0 relying solely on text stylization the generated image appears highly realistic at our recommended value of λ s 0 4 the output achieves a harmonious balance between content and style however as λ s exceeds 0 5 the style begins to dominate over the content as we use inject style to content sensitive layers leading to visible artifacts and a noticeable misalignment with the content notice that the styilization potential reaches its maximum at λ s 0 4 when using higher value the style stays approximatly the same realistic output no text stylization loading a woman riding on the back of a large camel in a sandy desert she is wearing a wide straw hat and holding a stick the surrounding is covered with cactus in the style of s style reference s results geometric style interpolation λ t our experiments showed that using structure conditioning images such as canny and depth maps significantly overshadows the effect of the style conditioning input more specifically as the structure conditioning image imposes geometric information on the generated image we found that the geometric aspects of a style are affected the most to address this we used our analysis method to identify the parts of the diffusion process that are most sensitive to the geometric aspects of an artistic style subsequently we leveraged these layers as indicators of where we should not apply structure conditioning enabling the model to generate a structure conditioned image while preserving its geometric style freedom to achieve this effect we introduced an interpolation parameter λ t which controls the geometric freedom of the model when conditioned on both a style image and a structure conditioning image using λ t 0 applies the default conditioning which enforces a strong structure constraint on the generated image while increasing λ t up to λ t 1 gradually enhances the model s geometric style freedom content input loading a portrait of a woman in the style of s style reference s results additional applications in addition to balanced style generation our method can be utilized for other style oriented applications we present a few ideas in the following sections copying the works of old masters is a time honored tradition in the art world dating back to the origins of painting itself this practice serves as a tool for artists to refine their techniques and develop their unique personal styles throughout history many renowned painters have engaged in this approach examples include vincent van gogh who copied works by jean françois millet and pablo picasso who reinterpreted works by diego velázquez such as las meninas this tradition has even given rise to several iconic artworks such as edgar degas studies of old masters like nicolas poussin and rembrandt or francis bacon s reimaginings of diego velázquez s portrait of pope innocent x inspired by this classical method of artistic learning we utilize our stylization approach which enables the application of distinctive styles to the works of old masters a process we call restyle additionally our method extends the model s geometric flexibility allowing for the reimagining of an artwork s content in innovative ways a feature we refer to as recontent style transfer is a well known technique that applies the artistic style of a given style image to a separate content image while diffusion models have significantly advanced computer based artistic generation they still face challenges in achieving traditional style transfer this is because diffusion models generate images starting from noise rather than directly modifying a content image requiring innovative methods for representing style within conditioning techniques rather than relying on direct optimization in our experiments we found that integrating our layer selection strategy into existing methods substantially enhances the balance between style and content in style transfer tasks here we present results using b lora frenkel et al with parameters set to λ s 0 57 and λ t 0 85 bibtex misc cohen2024conditionalbalanceimprovingmulticonditioning title conditional balance improving multi conditioning trade offs in image generation author nadav z cohen and oron nir and ariel shamir year 2024 eprint 2412 19853 archiveprefix arxiv primaryclass cs cv url https arxiv org abs 2412 19853 this website is based on the nerfies webpage and is licensed under a creative commons attribution sharealike 4 0 international license this means you are free to borrow the source code of this website we just ask that you link back to this page in the footer