This thesis is a theoretically guided empirical discussion of fashion and its role within the production of national identity in Germany. In recent years, a new patriotism in contemporary German fashion could be observed, starting with the fashion designer Eva Gronbach in 2001. I will approach the term patriotism with the aid of one of Michel Foucault's key terms, the notion of the episteme. In my case study, singular fashion images from three consecutive collections by Gronbach are examined with regard to their role in the discourse of German patriotism. But I am not only interested in the "how" of this discourse. Building up upon Antonio Gramsci's notion of "cultural hegemony", I also explain the recent rise of this fashion patriotism. Thus, my discourse analysis of Gronbach's fashion becomes embedded in social struggles and transformations in Germany. Argueing that fashion is a discursive practice that can show up as well as promote changes in discursive formations, I assume a dialectical structure-agency conception: On the one hand the case of Gronbach hints at the deeper structural problematic of patriotism and social cohesion which allowed Gronbach to become popular. On the other hand, this structure is also produced via discursive practices such as Gronbach´s. The what I term "inclusionary patriotism" comprises cultural normalisation. Thus, the case of Gronbach demonstrates a "constrained heterogeneity" with regard to the discourse of patriotism in Germany, in which diversity is only acceptable within certain discursively constructed limits.

Language-based fashion image editing has promising applications in design, sustainability, and art. However, it is considered a challenging problem in computer vision and graphics. The diversity of human poses and the complexity of clothing shapes and textures make the editing problem difficult. Inspired by recent progress in editing face images through manipulating latent representations, such as StyleCLIP and HairCLIP, we apply those methods in editing the images of full-body humans in fashion datasets and evaluate their effectiveness. First, we assess different methodologies to find a latent representation of an image via Generative Adversarial Network (GAN) inversion; then, we apply three image manipulation schemes. Thus, a pre-trained e4e encoder is initially utilized for the inversion process, while the results are compared to a more accurate method, Pivotal Tuning Inversion (PTI). Next, we employ an optimization scheme that uses the Contrastive Language Image Pre-training (CLIP) model to guide the latent representation of an image in the direction of attributes described in the input text. We address the problem of the accuracy and speed of the process by incorporating a mapper network. Finally, we propose an optimized mapper called Text-Driven Garment Editing Mapper (TD-GEM) to achieve high-quality image editing in a disentangled way. Our empirical results show that the proposed method can edit fashion items for changing color and sleeve length. / Språkbaserad bildredigering inom mode har lovande tillämpningar inom design, hållbarhet och konst. Det betraktas dock som ett utmanande problem inom datorseende och grafik. Mångfalden och variationen av mänskliga poser och komplexiteten i klädform och texturer gör redigeringsproblemet svårt. Inspirerade av den senaste utvecklingen inom redigering av ansiktsbilder genom manipulation av latenta representationer, såsom StyleCLIP och HairCLIP, tillämpar vi dessa metoder för att redigera bilderna av fullständiga mänskliga kroppar i mode-dataset och utvärderar deras effektivitet. Först jämför vi olika metoder för att hitta en latent representation av en bild via så kallade Generative Adversarial Network (GAN) inversion; sedan tillämpar vi tre bildmanipulationsscheman. En förtränad (eng: pre-trained) e4e-encoder model används först för inversionsprocessen, medan resultaten jämförs med en mer exakt metod, Pivotal Tuning Inversion (PTI). Därefter använder vi en optimeringmetod som använder Contrastive Language Image Pre-training (CLIP) -modell för att vägleda den latenta representationen av en bild i riktning mot attribut som beskrivs i inmatningstexten. Vi tar upp problemet med noggrannhet och hastigheten i processen genom att integrera en mapper-nätverk. Slutligen föreslår vi en optimerad mapper som kallas TD-GEM för att uppnå högkvalitativ bildredigering på ett lösgjort sätt. Våra empiriska resultat visar att den föreslagna metoden kan redigera modeobjekt för att ändra färg och ärmens längd.