{"id":15683,"date":"2023-01-24T14:47:46","date_gmt":"2023-01-24T13:47:46","guid":{"rendered":"https:\/\/www.kickmaker.fr\/blog\/?p=15683"},"modified":"2024-03-22T12:12:12","modified_gmt":"2024-03-22T11:12:12","slug":"ia-modeles-de-diffusion-revolution-artistique-scientifique-et-demain-industrielle","status":"publish","type":"post","link":"https:\/\/www.kickmaker.fr\/blog\/fr\/ia-modeles-de-diffusion-revolution-artistique-scientifique-et-demain-industrielle\/","title":{"rendered":"IA &#038; mod\u00e8les de diffusion : r\u00e9volution artistique, scientifique, et demain industrielle?"},"content":{"rendered":"[vc_row type=&#8221;in_container&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; scene_position=&#8221;center&#8221; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; shape_divider_position=&#8221;bottom&#8221; bg_image_animation=&#8221;none&#8221;][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_spacing=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][image_with_animation image_url=&#8221;15690&#8243; animation=&#8221;Fade In&#8221; hover_animation=&#8221;none&#8221; alignment=&#8221;&#8221; border_radius=&#8221;none&#8221; box_shadow=&#8221;none&#8221; image_loading=&#8221;default&#8221; max_width=&#8221;100%&#8221; max_width_mobile=&#8221;default&#8221;][\/vc_column][\/vc_row][vc_row type=&#8221;in_container&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; scene_position=&#8221;center&#8221; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; shape_divider_position=&#8221;bottom&#8221; bg_image_animation=&#8221;none&#8221;][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_spacing=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][divider line_type=&#8221;No Line&#8221; custom_height=&#8221;30&#8243;][vc_column_text]\n<p style=\"text-align: center;\"><strong><span id=\"p0\">L\u2019intelligence artificielle s\u2019est toujours distingu\u00e9e par cette capacit\u00e9 \u00e0 faire tomber soudainement\u00a0<span id=\"9\">des<span style=\"color: #d55a43;\">\u00a0<\/span><\/span><\/span><span id=\"p1\"><span id=\"10\">murs<\/span>\u00a0techniques que l\u2019on pensait inamovibles.<\/span><span id=\"p2\"> Point sur les nouvelles \u00e9volutions permises par l&#8217;IA.<\/span><\/strong><\/p>\n<p>&nbsp;<\/p>\n<p style=\"text-align: center;\"><strong>Article \u00e9crit par <a href=\"https:\/\/www.linkedin.com\/in\/eric-debeir-a99bb994\/?originalSubdomain=fr\">Eric Debeir<\/a>, lead data scientist<\/strong><\/p>\n[\/vc_column_text][divider line_type=&#8221;No Line&#8221; custom_height=&#8221;30&#8243;][\/vc_column][\/vc_row][vc_row type=&#8221;in_container&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; scene_position=&#8221;center&#8221; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; shape_divider_position=&#8221;bottom&#8221; bg_image_animation=&#8221;none&#8221; shape_type=&#8221;&#8221;][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_spacing=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][vc_column_text]\n<h3>Sommaire<\/h3>\n<ol>\n<li><a href=\"#modeles-diffusion\">Mod\u00e8les de diffusion ?<\/a><\/li>\n<li><a href=\"#plus-que-des-images\">Au-del\u00e0 des images<\/a><\/li>\n<li><a href=\"#modeles-generatifs\">Mod\u00e8les g\u00e9n\u00e9ratifs : pour quels usages ?<\/a><\/li>\n<\/ol>\n[\/vc_column_text][divider line_type=&#8221;No Line&#8221; custom_height=&#8221;40&#8243;][\/vc_column][\/vc_row][vc_row type=&#8221;in_container&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; scene_position=&#8221;center&#8221; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; id=&#8221;modeles-diffusion&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; shape_divider_position=&#8221;bottom&#8221; bg_image_animation=&#8221;none&#8221; shape_type=&#8221;&#8221;][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_spacing=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][vc_column_text]\n<h2>Mod\u00e8les de diffusion ?<\/h2>\n[\/vc_column_text][vc_row_inner column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; text_align=&#8221;left&#8221;][vc_column_inner column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_spacing=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/4&#8243; tablet_width_inherit=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][divider line_type=&#8221;Small Line&#8221; line_alignment=&#8221;default&#8221; line_thickness=&#8221;5&#8243; divider_color=&#8221;accent-color&#8221; custom_height=&#8221;30&#8243;][\/vc_column_inner][vc_column_inner column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_spacing=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/4&#8243; tablet_width_inherit=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][\/vc_column_inner][vc_column_inner column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_spacing=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/4&#8243; tablet_width_inherit=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][\/vc_column_inner][vc_column_inner column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_spacing=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/4&#8243; tablet_width_inherit=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][\/vc_column_inner][\/vc_row_inner][vc_column_text]<span id=\"p2\">Vous avez probablement entendu parler d\u2019une de <span id=\"11\">ces<span style=\"color: #d55a43;\">\u00a0<\/span><\/span><\/span><span id=\"p3\"><span id=\"12\">r\u00e9centes<\/span>\u00a0r\u00e9volutions, avec la g\u00e9n\u00e9ration d\u2019images bluffantes \u00e0 partir de phrases descriptives.<\/span><span id=\"p4\">\u00a0<span id=\"13\">Si ce<span style=\"color: #d55a43;\">\u00a0<\/span><\/span><\/span><span id=\"p5\"><span id=\"14\">n\u2019est pas<\/span>\u00a0le cas, petit rattrapage :<\/span><span id=\"p6\">\u00a0depuis quelques mois, il est possible via de nombreux outils\u00a0<span id=\"15\">de<span style=\"color: #d55a43;\">\u00a0<\/span><\/span><\/span><span id=\"p7\"><span id=\"16\">cr\u00e9er<\/span>\u00a0de nouvelles images \u00e0 base de r\u00e9seaux de neurones\u2026<\/span><span id=\"p8\">\u00a0Quelques exemples :<\/span>[\/vc_column_text][vc_row_inner equal_height=&#8221;yes&#8221; content_placement=&#8221;top&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; text_align=&#8221;left&#8221;][vc_column_inner column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_spacing=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][vc_gallery type=&#8221;flickity_static_height_style&#8221; images=&#8221;15698,15697,15699&#8243; flickity_spacing=&#8221;default&#8221; flickity_controls=&#8221;pagination&#8221; flickity_overflow=&#8221;hidden&#8221; flickity_wrap_around=&#8221;wrap&#8221; flickity_image_scale_on_drag=&#8221;true&#8221; flickity_box_shadow=&#8221;none&#8221; image_loading=&#8221;default&#8221; onclick=&#8221;link_no&#8221;][vc_column_text]\n<ol>\n<li><span style=\"color: #808080;\"><em><strong>Prompt<\/strong>: A full shot of a cute magical monster cryptid wearing a dress made of opals and tentacles. chibi. subsurface scattering. translucent skin. caustics. prismatic light. defined facial features, symmetrical facial features. opalescent surface. soft lighting. beautiful lighting. by giger and ruan jia and artgerm and wlop and william-adolphe bouguereau and loish and lisa frank. sailor moon. trending on artstation, featured on pixiv, award winning, sharp, details, intricate details, realistic, hyper-detailed, hd, hdr, 4k, 8k.<\/em><\/span><\/li>\n<li><span style=\"color: #808080;\"><em>The war by Robert Capa. <\/em><\/span><span style=\"color: #808080;\"><em><strong>Prompt <\/strong>: Complex 3 d render of a beautiful porcelain cyberpunk robot ai face, beautiful eyes. red gold and black, fractal veins. dragon cyborg, 1 5 0 mm, beautiful natural soft light, rim light, gold fractal details, fine lace, mandelbot fractal, anatomical, glass, facial muscles, elegant, ultra detailed, metallic armor, octane render, depth of field<\/em><\/span><\/li>\n<li><span style=\"color: #808080;\"><em><strong>Prompt:<\/strong> Complex 3 d render of a beautiful porcelain cyberpunk robot ai face, beautiful eyes. red gold and black, fractal veins. dragon cyborg, 1 5 0 mm, beautiful natural soft light, rim light, gold fractal details, fine lace, mandelbot fractal, anatomical, glass, facial muscles, elegant, ultra detailed, metallic armor, octane render, depth of field\u00a0<\/em><\/span><\/li>\n<\/ol>\n[\/vc_column_text][vc_column_text][\/vc_column_text][\/vc_column_inner][\/vc_row_inner][divider line_type=&#8221;No Line&#8221; custom_height=&#8221;30&#8243;][vc_column_text]Ces exemples ont \u00e9t\u00e9 tir\u00e9s au hasard depuis l\u2019excellent site : <a href=\"https:\/\/lexica.art\/\">https:\/\/lexica.art\/<\/a><\/p>\n<p>De nombreux projets de recherche ont attaqu\u00e9 ce sujet de g\u00e9n\u00e9ration d\u2019images : <a href=\"http:\/\/[https:\/\/imagen.research.google\/\" target=\"_blank\" rel=\"noopener\"><em>Imagen <\/em><\/a>de Google , <a href=\"https:\/\/openai.com\/dall-e-2\/\" target=\"_blank\" rel=\"noopener\">DALL.E<\/a> . Mais c\u2019est un travail plus r\u00e9cent qui a r\u00e9volutionn\u00e9 l\u2019Internet, notamment car les mod\u00e8les \u00e9taient, cette fois-ci, accessibles librement sous licence <em>Open Source <\/em>: les <a href=\"https:\/\/github.com\/CompVis\/stable-diffusion\" target=\"_blank\" rel=\"noopener\"><em>Latent Diffusion Models<\/em><\/a>. Depuis la diffusion de cet outil, un \u00e9norme d\u00e9bat a \u00e9merg\u00e9 au sein de la communaut\u00e9 des illustrateurs :<\/p>\n<ul>\n<li>Ces mod\u00e8les peuvent-ils \u00eatre vus comme des outils de cr\u00e9ation artistique ? Instinctivement, non, et pourtant, nous ne faisons qu&#8217;effleurer les utilisations possibles de ces mod\u00e8les\u2026<\/li>\n<li>Quid de la concurrence demain entre des illustrateurs qui passeront un temps certain sur leur travail, et l\u2019exploitation d\u2019un mod\u00e8le IA ? Il y a en effet fort \u00e0 parier que dans de nombreux cas, les donneurs d\u2019ordre ne seront pas forc\u00e9ment sensibles \u00e0 la valeur ajout\u00e9e d\u2019une r\u00e9elle cr\u00e9ation artistique ?<\/li>\n<li>Dans la mesure o\u00f9 ces mod\u00e8les ont \u00e9t\u00e9 entra\u00een\u00e9s sur une gigantesque base d\u2019images comprenant majoritairement des travaux de vrais artistes, comment consid\u00e9rer les r\u00e9sultats du mod\u00e8le, surtout quand les utilisateurs ne se privent pas d\u2019utiliser des noms d\u2019artistes encore en activit\u00e9 ( <a href=\"https:\/\/huggingface.co\/spaces\/stabilityai\/stable-diffusion\/discussions\/731\">https:\/\/huggingface.co\/spaces\/stabilityai\/stable-diffusion\/discussions\/731 <\/a>ou <a href=\"https:\/\/huggingface.co\/spaces\/stabilityai\/stable-diffusion\/discussions\/688\">https:\/\/huggingface.co\/spaces\/stabilityai\/stable-diffusion\/discussions\/688<\/a> ) ?<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3>Apparation des mod\u00e8les de diffusion<\/h3>\n<p>Au-del\u00e0 de ces d\u00e9bats, d\u2019un point de vue technique, nous pouvons constater l\u2019apparition d\u2019une nouvelle famille d\u2019outils d\u2019intelligence artificielle (<strong>les \u201cmod\u00e8les de diffusion\u201d<\/strong>).<br \/>\nCette famille d\u2019outils pr\u00e9sente d\u00e9j\u00e0 des r\u00e9sultats prodigieux sur les apprentissages d\u2019images.<\/p>\n<p>Nous vous proposons d\u2019\u00e9tudier ensemble, en restant \u00e0 haut niveau, la particularit\u00e9 de ces outils, comment ceux-ci peuvent s\u2019appliquer \u00e0 d\u2019autres probl\u00e8mes, et les opportunit\u00e9s g\u00e9n\u00e9rales apport\u00e9es par ces mod\u00e8les. Mais force est de reconna\u00eetre que la communaut\u00e9 scientifique du <a href=\"https:\/\/datascientest.com\/deep-learning-definition\" target=\"_blank\" rel=\"noopener\"><em>Deep Learning <\/em><\/a>s\u2019est empar\u00e9e largement de ce sujet avec une explosion des travaux de recherche.<\/p>\n<p>Par exemple, les \u00e9quipes de recherche de <a href=\"https:\/\/www.nvidia.com\/fr-fr\/\" target=\"_blank\" rel=\"noopener\">NVIDIA<\/a> (<em>leader <\/em>incontest\u00e9 des mod\u00e8les g\u00e9n\u00e9ratifs) ont r\u00e9cemment propos\u00e9 une <a href=\"https:\/\/openreview.net\/forum?id=k7FuTOWMOc7\" target=\"_blank\" rel=\"noopener\">publication<\/a> qui a \u00e9t\u00e9 reconnue comme \u201c<em>outstanding paper<\/em>\u201d de l\u2019incontournable conf\u00e9rence du <em>NEURIPS 2022<\/em>,<a href=\"https:\/\/openreview.net\/forum?id=k7FuTOWMOc7\" target=\"_blank\" rel=\"noopener\"> publication<\/a> dans laquelle ils approfondissent ces mod\u00e8les et en proposent une meilleure compr\u00e9hension.[\/vc_column_text][\/vc_column][\/vc_row][vc_row type=&#8221;in_container&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; scene_position=&#8221;center&#8221; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; id=&#8221;plus-que-des-images&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; shape_divider_position=&#8221;bottom&#8221; bg_image_animation=&#8221;none&#8221; shape_type=&#8221;&#8221;][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_spacing=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][vc_column_text]\n<h2>Au-del\u00e0 des images : audio, 3d, mouvement humain\u2026<\/h2>\n[\/vc_column_text][vc_row_inner column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; text_align=&#8221;left&#8221;][vc_column_inner column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_spacing=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/4&#8243; tablet_width_inherit=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][divider line_type=&#8221;Small Line&#8221; line_alignment=&#8221;default&#8221; line_thickness=&#8221;5&#8243; divider_color=&#8221;accent-color&#8221; custom_height=&#8221;30&#8243;][\/vc_column_inner][vc_column_inner column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_spacing=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/4&#8243; tablet_width_inherit=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][\/vc_column_inner][vc_column_inner column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_spacing=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/4&#8243; tablet_width_inherit=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][\/vc_column_inner][vc_column_inner column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_spacing=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/4&#8243; tablet_width_inherit=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][\/vc_column_inner][\/vc_row_inner][vc_column_text]Commen\u00e7ons peut-\u00eatre par l\u00e0. Si l\u2019application aux images a \u00e9t\u00e9 fortement diffus\u00e9e sur les r\u00e9seaux sociaux, il serait tr\u00e8s limit\u00e9 de s\u2019arr\u00eater l\u00e0. Nous sommes en effet face \u00e0 une nouvelle architecture qui s\u2019applique d\u00e9j\u00e0 \u00e0 de nombreux autres domaines, et qui demain pourrait s\u2019appliquer \u00e0 des probl\u00e8mes particuliers sur une donn\u00e9e m\u00e9tier.<\/p>\n<p>L\u2019application aux images en premier s\u2019explique facilement, et c\u2019est d\u2019ailleurs un ph\u00e9nom\u00e8ne courant en <em>Deep Learning<\/em>. En effet, la donn\u00e9e est beaucoup plus simple \u00e0 r\u00e9cup\u00e9rer, de nombreuses applications existent, et nous sommes tr\u00e8s tol\u00e9rants aux erreurs marginales dans une image g\u00e9n\u00e9r\u00e9e, beaucoup plus que nous ne le serions face \u00e0 de la musique g\u00e9n\u00e9r\u00e9e.<\/p>\n<p>Ceci dit, nous avons d\u00e9j\u00e0 pu observer des applications tr\u00e8s diff\u00e9rentes et passionnantes<\/p>\n<h3><\/h3>\n<h3>Les nouvelles applications<\/h3>\n<p>&nbsp;<\/p>\n<p>\u201c<a href=\"https:\/\/arxiv.org\/abs\/2206.05408\">Multi-instrument Music Synthesis with Spectrogram Diffusion<\/a>\u201d est une application \u00e0 la musique, o\u00f9 le mod\u00e8le de diffusion va ainsi g\u00e9n\u00e9rer des extraits de musique avec de multiples instruments, en travaillant directement sur les spectrogrammes. Un m\u00e9canisme de conditionnement va permettre de poursuivre un morceau de musique dans la dur\u00e9e. Ce travail n\u2019est pas encore bluffant sur le plan auditif, et nous ne sommes donc pas au niveau de la g\u00e9n\u00e9ration d\u2019image ou un simple amateur pourrait facilement croire que l\u2019oeuvre a \u00e9t\u00e9 g\u00e9n\u00e9r\u00e9e par un humain.<\/p>\n<p>N\u00e9anmoins, force est de constater que les choses vont vite. Le dernier travail en date de g\u00e9n\u00e9ration musicale int\u00e9ressant \u00e9tait l\u2019oeuvre d\u2019OpenAI avec leur <a href=\"https:\/\/openai.com\/blog\/jukebox\/\" target=\"_blank\" rel=\"noopener\">Jukebox<\/a> qui d\u00e9j\u00e0 \u00e9tait tr\u00e8s prometteur. La publication plus r\u00e9cente est l\u2019oeuvre de l\u2019\u00e9quipe<a href=\"https:\/\/magenta.tensorflow.org\/\" target=\"_blank\" rel=\"noopener\"> Magenta de Google<\/a> qui s\u2019est sp\u00e9cialis\u00e9e dans le Deep Learning adapt\u00e9 \u00e0 la musique, avec de nombreux travaux passionnants.<\/p>\n<p>Demain, ce type de travail peut s\u2019appliquer \u00e0 toute approche sur le domaine audio. En effet (nous en reparlerons par la suite), un tel mod\u00e8le ne permet pas juste de g\u00e9n\u00e9rer de la donn\u00e9e, mais d\u2019approximer ses variances et de les contr\u00f4ler.[\/vc_column_text][image_with_animation image_url=&#8221;15701&#8243; animation=&#8221;Fade In&#8221; hover_animation=&#8221;none&#8221; alignment=&#8221;center&#8221; border_radius=&#8221;none&#8221; box_shadow=&#8221;none&#8221; image_loading=&#8221;default&#8221; max_width=&#8221;100%&#8221; max_width_mobile=&#8221;default&#8221; margin_top=&#8221;4%&#8221; margin_bottom=&#8221;4%&#8221;][vc_column_text]\n<h3>Mod\u00e9lisation et g\u00e9n\u00e9ration de mouvements humains<\/h3>\n<p>Autre approche passionnante, \u201c<a href=\"https:\/\/github.com\/guytevet\/motion-diffusion-model\" target=\"_blank\" rel=\"noopener\">Human Motion Diffusion Models<\/a>\u201d<\/p>\n<p>Elle vise \u00e0 mod\u00e9liser et \u00e0 g\u00e9n\u00e9rer des mouvements humains, notamment \u00e0 des fins d\u2019animation. Ce travail vise \u00e0 pouvoir g\u00e9n\u00e9rer facilement le mouvement d\u2019un mod\u00e8le 3d repr\u00e9sentant un individu, via une simple phrase d\u00e9crivant l\u2019action \u00e0 r\u00e9aliser. Ce travail est important, car en apprenant \u00e0 g\u00e9n\u00e9rer un mouvement humain, il apprend implicitement \u00e0 \u201cr\u00e9sumer\u201d ou \u201ccompresser\u201d ces mouvements, et peut donc permettre de partir de mouvements d\u00e9tect\u00e9s pour les normaliser ou les qualifier.[\/vc_column_text][image_with_animation image_url=&#8221;15703&#8243; animation=&#8221;Fade In&#8221; hover_animation=&#8221;none&#8221; alignment=&#8221;center&#8221; border_radius=&#8221;none&#8221; box_shadow=&#8221;none&#8221; image_loading=&#8221;default&#8221; max_width=&#8221;100%&#8221; max_width_mobile=&#8221;default&#8221; margin_top=&#8221;4%&#8221; margin_bottom=&#8221;4%&#8221;][vc_column_text]\n<h3>G\u00e9n\u00e9ration de volumes en trois dimensions<\/h3>\n<p>Dernier exemple, la g\u00e9n\u00e9ration de volumes en trois dimensions directement depuis une phrase de g\u00e9n\u00e9ration. Plusieurs travaux existent d\u00e9j\u00e0, nous recommandons le travail de Google Research et Berkeley \u201c<a href=\"https:\/\/dreamfusion3d.github.io\/\" target=\"_blank\" rel=\"noopener\"><em>DreamFusion: Text-to-3D using 2D Diffusion<\/em><\/a>\u201d.<\/p>\n<p>La g\u00e9n\u00e9ration de volumes en trois dimensions est souvent un passage obligatoire en mod\u00e9lisation, et ce type d\u2019outils peut permettre d\u2019alimenter tr\u00e8s rapidement une architecture de traitement en nouveaux volumes avec un axe de g\u00e9n\u00e9ration tr\u00e8s simple \u00e0 utiliser. Au-del\u00e0, similairement au mod\u00e8le de g\u00e9n\u00e9ration d\u2019images, un tel mod\u00e8le apprend la correspondance entre certains termes et leur expression en trois dimensions, que l\u2019on parle du sujet, du style, de la position, etc. Les travaux futurs permettront (on l\u2019esp\u00e8re) de mieux contr\u00f4ler ce type de g\u00e9n\u00e9ration via une s\u00e9paration plus pertinente de la g\u00e9n\u00e9ration.<\/p>\n<p>Et la liste s\u2019allonge tous les jours. On a r\u00e9cemment vu des propositions d\u2019approches scientifiques pour faire de la d\u00e9tection localis\u00e9e par mod\u00e8les de diffusion, ou m\u00eame enrichissant des mod\u00e8les de langage de type BERT avec cette m\u00e9thode\u2026[\/vc_column_text][\/vc_column][\/vc_row][vc_row type=&#8221;in_container&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; scene_position=&#8221;center&#8221; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; id=&#8221;modeles-generatifs&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; shape_divider_position=&#8221;bottom&#8221; bg_image_animation=&#8221;none&#8221; shape_type=&#8221;&#8221;][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_spacing=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][divider line_type=&#8221;No Line&#8221; custom_height=&#8221;60&#8243;][vc_column_text el_id=&#8221;modele-generatif&#8221;]\n<h2><strong><span id=\"p30\"><b>Mod\u00e8les g\u00e9n\u00e9ratifs : pour quels usages ?<\/b><\/span><\/strong><\/h2>\n[\/vc_column_text][vc_row_inner column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; text_align=&#8221;left&#8221;][vc_column_inner column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_spacing=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/4&#8243; tablet_width_inherit=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][divider line_type=&#8221;Small Line&#8221; line_alignment=&#8221;default&#8221; line_thickness=&#8221;5&#8243; divider_color=&#8221;accent-color&#8221; custom_height=&#8221;30&#8243;][\/vc_column_inner][vc_column_inner column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_spacing=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/4&#8243; tablet_width_inherit=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][\/vc_column_inner][vc_column_inner column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_spacing=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/4&#8243; tablet_width_inherit=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][\/vc_column_inner][vc_column_inner column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_spacing=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/4&#8243; tablet_width_inherit=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][\/vc_column_inner][\/vc_row_inner][vc_column_text]\n<h3>Bref rappel<\/h3>\n<p>Les mod\u00e8les g\u00e9n\u00e9ratifs sont une famille de mod\u00e8les en Deep Learning (IA) sp\u00e9cialis\u00e9s dans la g\u00e9n\u00e9ration de donn\u00e9es. Entra\u00een\u00e9s sur un dataset compos\u00e9 de nombreux \u00e9l\u00e9ments, ces mod\u00e8les apprennent \u00e0 g\u00e9n\u00e9rer une donn\u00e9e qui ne soit pas directement pr\u00e9sente dans le dataset, mais qui corresponde \u00e0 la distribution de la donn\u00e9e telle qu\u2019elle est repr\u00e9sent\u00e9e par ce dataset. Dit autrement, un tel mod\u00e8le a pour objectif d\u2019apprendre les r\u00e8gles globales propres \u00e0 tous les \u00e9l\u00e9ments du dataset pour r\u00e9ussir \u00e0 g\u00e9n\u00e9rer une donn\u00e9e qui aurait pu se trouver dans ce dataset. On parle math\u00e9matiquement d\u2019apprentissage d\u2019une distribution. \u00c9videmment, un tel mod\u00e8le est tr\u00e8s d\u00e9pendant de la variance des donn\u00e9es pr\u00e9sentes dans ce dataset.<\/p>\n<p>Ces mod\u00e8les relevaient de la science-fiction jusqu\u2019\u00e0 2013\/2014 avec l\u2019apparition de deux grandes familles de mod\u00e8les g\u00e9n\u00e9ratifs.<\/p>\n<h3>Premi\u00e8re famille de mod\u00e8les g\u00e9n\u00e9ratifs<\/h3>\n<p>La premi\u00e8re famille est celle des <strong><em>Variational Autoencoders <\/em>(VAE) de <em>Kingma<\/em><\/strong>. \u00c0 tr\u00e8s haut niveau, ces mod\u00e8les apprennent \u00e0 simplifier une donn\u00e9e au maximum tout en apprenant la diversit\u00e9 (math\u00e9matiquement, la distribution) de cette donn\u00e9e. Ce sont donc des outils tr\u00e8s pr\u00e9cieux pour pouvoir approximer une donn\u00e9e dans sa complexit\u00e9, avec ensuite la possibilit\u00e9 d\u2019appliquer de nombreuses approches transversales : d\u00e9tection d\u2019anomalie, clustering, etc. Ces outils vont donc au-del\u00e0 du simple mod\u00e8le g\u00e9n\u00e9ratif, et ont permis notamment de cr\u00e9er des syst\u00e8mes IA avec une notion d\u2019incertitude dans les pr\u00e9dictions.<\/p>\n<h3>Deuxi\u00e8me famille de mod\u00e8les g\u00e9n\u00e9ratifs<\/h3>\n<p>La deuxi\u00e8me famille, un peu plus connue, est celle des <strong><em>Generative Adversarial Networks <\/em>(GAN) de Goodfellow.<\/strong> Cette approche est d\u00e9routante au premier abord, avec un \u201cduel\u201d entre un mod\u00e8le apprenant \u00e0 g\u00e9n\u00e9rer de la donn\u00e9e et un autre mod\u00e8le apprenant \u00e0 critiquer la g\u00e9n\u00e9ration. Elle permet n\u00e9anmoins de facilement cr\u00e9er des mod\u00e8les g\u00e9n\u00e9ratifs de bonne qualit\u00e9. Le c\u00e9l\u00e8bre site <a href=\"https:\/\/thispersondoesnotexist.com\/\" target=\"_blank\" rel=\"noopener\">\u201cThis person does not exist\u201d<\/a> pr\u00e9sente ainsi des portraits d\u2019individus qui n\u2019existent pas, mais ont \u00e9t\u00e9 g\u00e9n\u00e9r\u00e9 par le StyleGan de Nvidia.<\/p>\n<p><strong>Le consensus jusqu\u2019\u00e0 r\u00e9cemment \u00e9tait que les GAN pouvaient donner de meilleurs r\u00e9sultats sur le plan de la qualit\u00e9 visuelle, mais que les VAE apprenaient beaucoup mieux la diversit\u00e9 d\u2019une donn\u00e9e et repr\u00e9sentaient ainsi un outil plus puissant pour travailler sur une donn\u00e9e complexe. Evidemment, en Deep Learning, les choses ne restent jamais stables tr\u00e8s longtemps. Les \u201cVQ-VAE\u201d se sont impos\u00e9s ces trois derni\u00e8res ann\u00e9es, et les mod\u00e8les de diffusion sont ensuite arriv\u00e9s.<\/strong>[\/vc_column_text][vc_column_text]\n<h3>Int\u00e9r\u00eat dans les processus m\u00e9tier et\/ou industriels<\/h3>\n<p>Il ne faut pas sous-estimer l\u2019int\u00e9r\u00eat de ces outils dans des processus m\u00e9tier et\/ou industriels.<\/p>\n<p>Un mod\u00e8le g\u00e9n\u00e9ratif est un outil pr\u00e9cieux pour l\u2019ensemble des op\u00e9rations d\u2019analyse de la donn\u00e9e. On pourrait m\u00eame argumenter sur le fait que <strong>leur capacit\u00e9 \u00e0 g\u00e9n\u00e9rer une donn\u00e9e n\u2019est pas leur principal attrait sur un plan appliqu\u00e9, face \u00e0 la possibilit\u00e9 de pouvoir les exploiter comme des outils d\u2019exploration et d\u2019analyse de la donn\u00e9e. <\/strong><\/p>\n<p>Approximer la distribution d\u2019une donn\u00e9e, c\u2019est devenir capable d\u2019identifier les grandes variances de cette donn\u00e9e, seules ou combin\u00e9es, et de pouvoir ensuite questionner tout nouvel \u00e9l\u00e9ment face \u00e0 cette distribution.<\/p>\n<p>D\u00e9tection d\u2019anomalies, simplification de la donn\u00e9e, prise en compte de l\u2019incertitude dans une pr\u00e9diction ou dans une annotation ou clustering ne sont que la partie \u00e9merg\u00e9e de l\u2019iceberg. Au-del\u00e0, tous ces mod\u00e8les apprennent \u00e0 projeter la donn\u00e9e dans un espace beaucoup plus agr\u00e9able, dans lequel des op\u00e9rations arithm\u00e9tiques simples donnent lieu \u00e0 des modifications importantes de la donn\u00e9e. Pour peut \u00eatre rendre cette approche plus claire, reprenons l\u2019exemple des images, et du StyleGan de NVIDIA g\u00e9n\u00e9rant des visages. Il devient possible d\u2019\u00e9diter des images tr\u00e8s finement, non pas en manipulant les pixels de l\u2019image, mais en travaillant sur la projection de la donn\u00e9e effectu\u00e9e par le mod\u00e8le :<br \/>\n(from https:\/\/github.com\/yuval-alaluf\/hyperstyle)<br \/>\nLes mod\u00e8les de diffusion sont donc plus qu\u2019un coup d\u2019\u00e9clat r\u00e9serv\u00e9 \u00e0 la g\u00e9n\u00e9ration d\u2019images. En tant que mod\u00e8les g\u00e9n\u00e9ratifs, ces mod\u00e8les pr\u00e9sentent de nombreuses applications dont la majorit\u00e9 n\u2019a sans doute pas encore \u00e9t\u00e9 d\u00e9couverte.[\/vc_column_text][image_with_animation image_url=&#8221;15706&#8243; animation=&#8221;Fade In&#8221; hover_animation=&#8221;none&#8221; alignment=&#8221;&#8221; border_radius=&#8221;none&#8221; box_shadow=&#8221;none&#8221; image_loading=&#8221;default&#8221; max_width=&#8221;100%&#8221; max_width_mobile=&#8221;default&#8221; margin_top=&#8221;4%&#8221; margin_bottom=&#8221;4%&#8221;][\/vc_column][\/vc_row][vc_row type=&#8221;in_container&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; scene_position=&#8221;center&#8221; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; id=&#8221;necessite-developpement&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; shape_divider_position=&#8221;bottom&#8221; bg_image_animation=&#8221;none&#8221; shape_type=&#8221;&#8221;][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_spacing=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][divider line_type=&#8221;No Line&#8221; custom_height=&#8221;60&#8243;][vc_column_text]\n<h2><strong><span id=\"p30\"><b>Mod\u00e8les de diffusion, quelles opportunit\u00e9s ?<\/b><\/span><\/strong><\/h2>\n[\/vc_column_text][vc_row_inner column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; text_align=&#8221;left&#8221;][vc_column_inner column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_spacing=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/4&#8243; tablet_width_inherit=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][divider line_type=&#8221;Small Line&#8221; line_alignment=&#8221;default&#8221; line_thickness=&#8221;5&#8243; divider_color=&#8221;accent-color&#8221; custom_height=&#8221;30&#8243;][\/vc_column_inner][vc_column_inner column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_spacing=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/4&#8243; tablet_width_inherit=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][\/vc_column_inner][vc_column_inner column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_spacing=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/4&#8243; tablet_width_inherit=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][\/vc_column_inner][vc_column_inner column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_spacing=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/4&#8243; tablet_width_inherit=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][\/vc_column_inner][\/vc_row_inner][vc_row_inner column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; text_align=&#8221;left&#8221;][vc_column_inner column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_spacing=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][vc_column_text]\n<h3>Quels int\u00e9r\u00eats futurs ?<\/h3>\n<p>Les mod\u00e8les de diffusion ne font donc qu\u2019arriver, mais nous pouvons d\u00e9j\u00e0 r\u00e9fl\u00e9chir \u00e0 ce que ces nouveaux outils vont nous apporter en termes d\u2019usage, au-del\u00e0 du simple traitement d\u2019images. Pour cet exercice l\u00e9g\u00e8rement acrobatique, trois sources peuvent alimenter notre r\u00e9flexion :<\/p>\n<ul>\n<li>1\/ L\u2019exploitation des approches issues de l\u2019inf\u00e9rence variationnelle (VAE ou Normalizing Flows), qui ont pouss\u00e9 ces mod\u00e8les au-del\u00e0 de la simple g\u00e9n\u00e9ration de donn\u00e9es, et qui servent aujourd\u2019hui d\u2019outils<\/li>\n<li>2\/ L\u2019observation de ce que produit la communaut\u00e9 sur Internet \u00e0 partir de la r\u00e9cente diffusion du mod\u00e8le Stable Diffusion, ou de nouveaux usages apparaissent r\u00e9guli\u00e8rement.<\/li>\n<li>3\/ Nous voyons que cette approche s\u2019applique d\u00e9j\u00e0 \u00e0 d\u2019autres types de donn\u00e9es (audio, mouvement humain, volumes en 3 dimensions), et pouvons donc imaginer ce que ces nouvelles donn\u00e9es vont apporter<\/li>\n<li><\/li>\n<\/ul>\n<h3>D\u00e9tails des points \u00e9voqu\u00e9s<\/h3>\n<p><em>Le premier point<\/em> est sans doute le plus fondamental, mais le plus complexe \u00e0 pr\u00e9voir. Un mod\u00e8le de diffusion apprend \u00e0 approximer une donn\u00e9e dans sa distribution (au sens math\u00e9matique). Cela implique que l\u2019on peut s\u2019en servir pour des probl\u00e9matiques comme la d\u00e9tection d\u2019anomalie (comme par exemple la maintenance pr\u00e9dictive). Les prochains mois nous montreront si le monde acad\u00e9mique arrive \u00e0 produire des r\u00e9sultats \u00e0 ce sujet. \u00c9videmment, il faut garder un principe de pr\u00e9caution, et ne pas utiliser un outil sous pr\u00e9texte qu\u2019il est r\u00e9cent et \u201csexy\u201d. Il faut plut\u00f4t questionner dans quelle mesure ce nouvel outil pourrait am\u00e9liorer notre capacit\u00e9 \u00e0 adresser certains probl\u00e8mes. Hors, la d\u00e9tection d\u2019anomalie est un serpent de mer du domaine du <em>Machine Learning <\/em>: il englobe un grand nombre de sujets tr\u00e8s diff\u00e9rents avec des complexit\u00e9s tr\u00e8s variables. Un mod\u00e8le de diffusion offre une approche originale, car permet d\u2019it\u00e9rer sur diff\u00e9rentes versions de la donn\u00e9e, en la re-projetant dans l\u2019espace \u201cnormal\u201d qui a \u00e9t\u00e9 appris (via les it\u00e9rations de bruitage ou d\u00e9bruitage). Il est probable que certaines probl\u00e9matiques puissent ainsi \u00eatre adress\u00e9es d\u2019une nouvelle mani\u00e8re. Notons qu\u2019un int\u00e9r\u00eat ici pourrait \u00eatre de localiser l\u2019anomalie dans l\u2019image d\u2019une mani\u00e8re plus efficace, et d\u2019exposer une distance entre cette anomalie et une \u201cnorme\u201d apprise par le mod\u00e8le.<\/p>\n<p><em>Le second point<\/em> est moins scientifique, mais ne doit pas \u00eatre ignor\u00e9. Il y a autant de potentiel d\u2019innovation dans une d\u00e9couverte fondamentale, que dans l\u2019exploration de nouveaux usages. Un simple suivi des exp\u00e9rimentations r\u00e9alis\u00e9es avec Stable Diffusion montre de nouvelles approches toutes les semaines. Par exemple, si tout le monde sait que l\u2019on peut g\u00e9n\u00e9rer une image \u00e0 partir d\u2019un texte, peu savent qu\u2019il est aussi possible de g\u00e9n\u00e9rer une image en d\u00e9finissant les \u00e9l\u00e9ments qui doivent appara\u00eetre d\u2019une mani\u00e8re globale, via des rectangles de localisation (sch\u00e9ma issu <em>Rombach et al<\/em>) :<\/p>\n<p>Ou m\u00eame, via une \u201cesquisse\u201d en aplats color\u00e9s :[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][image_with_animation image_url=&#8221;15707&#8243; animation=&#8221;Fade In&#8221; hover_animation=&#8221;none&#8221; alignment=&#8221;&#8221; border_radius=&#8221;none&#8221; box_shadow=&#8221;none&#8221; image_loading=&#8221;default&#8221; max_width=&#8221;100%&#8221; max_width_mobile=&#8221;default&#8221; margin_top=&#8221;4%&#8221; margin_bottom=&#8221;4%&#8221;][image_with_animation image_url=&#8221;15708&#8243; animation=&#8221;Fade In&#8221; hover_animation=&#8221;none&#8221; alignment=&#8221;&#8221; border_radius=&#8221;none&#8221; box_shadow=&#8221;none&#8221; image_loading=&#8221;default&#8221; max_width=&#8221;100%&#8221; max_width_mobile=&#8221;default&#8221; margin_top=&#8221;4%&#8221; margin_bottom=&#8221;4%&#8221;][vc_column_text]Dernier exemple en \u201cinpainting\u201d, o\u00f9 nous supprimons une partie de l\u2019image et demandons au mod\u00e8le de diffusion de r\u00e9g\u00e9n\u00e9rer la partie manquante. Celle-ci le sera en respectant le reste de l\u2019image encore visible, produisant une image \u201ccr\u00e9dible\u201d en g\u00e9n\u00e9rant la partie manquante :[\/vc_column_text][image_with_animation image_url=&#8221;15709&#8243; animation=&#8221;Fade In&#8221; hover_animation=&#8221;none&#8221; alignment=&#8221;&#8221; border_radius=&#8221;none&#8221; box_shadow=&#8221;none&#8221; image_loading=&#8221;default&#8221; max_width=&#8221;100%&#8221; max_width_mobile=&#8221;default&#8221; margin_top=&#8221;4%&#8221; margin_bottom=&#8221;4%&#8221;][vc_column_text]Nous sommes donc face \u00e0 un outil tr\u00e8s polymorphe. Qui plus est, le m\u00e9canisme de conditionnement (permettant d\u2019apprendre un lien entre input et image g\u00e9n\u00e9r\u00e9e) est relativement libre et ne demande qu\u2019\u00e0 \u00eatre adapt\u00e9 \u00e0 de nouveaux concepts.<\/p>\n<p>Nous sommes donc face \u00e0 des outils qui vont au-del\u00e0 de la simple g\u00e9n\u00e9ration, pour effectuer de la conversion de domaines, avec de nombreuses applications. Face \u00e0 un type de donn\u00e9e sp\u00e9cifique mod\u00e9lisant un probl\u00e8me m\u00e9tier ou industriel, nous pouvons cartographier cette donn\u00e9e et la modifier d\u2019une mani\u00e8re stup\u00e9fiante en la conditionnant \u00e0 une information plus simple.<\/p>\n<p><em>Concluant sur le dernier point<\/em>, on observe que les mod\u00e8les de diffusion arrivent sur de nombreux types de donn\u00e9es diff\u00e9rents (audio, image, texte, etc.) Nous pouvons donc d\u00e9j\u00e0 observer que de nombreux types de signaux plus ou moins complexes pourront conna\u00eetre le m\u00eame type d\u2019application.<br \/>\nHors, la majorit\u00e9 des syst\u00e8mes industriels proposent un monitoring bas\u00e9 sur de nombreuses sondes, cam\u00e9ras, micros, dont la trop grande complexit\u00e9 est un frein pour des analyses pouss\u00e9es.<br \/>\nLe Deep Learning offre des outils pour r\u00e9duire cette complexit\u00e9 en minimisant la perte d\u2019informations (les features de haut niveau dans un mod\u00e8le entra\u00een\u00e9). Les mod\u00e8les de diffusion nous proposent une approche de ce type novatrice. Par exemple, pour qualifier le bruit pr\u00e9sent dans un signal, et d\u00e9cider si ce bruit est ext\u00e9rieur au syst\u00e8me \u00e9tudi\u00e9 ou si, \u00e0 l\u2019inverse, ce bruit est une nouvelle composante traduisant un probl\u00e8me important, les mod\u00e8les de diffusion pourraient \u00eatre un outil tr\u00e8s pertinent dans la mesure o\u00f9 ils apprennent pr\u00e9cis\u00e9ment \u00e0 bruiter ou d\u00e9bruiter la donn\u00e9e&#8230;<\/p>\n<p>&nbsp;<\/p>\n<p>Au-del\u00e0, rappelons que ces approches permettent aussi de cr\u00e9er des correspondances entre diff\u00e9rents types d\u2019information. C\u2019est en rapprochant un apprentissage sur le texte et un apprentissage sur les images qu\u2019OpenAI avait cr\u00e9\u00e9 DALL-E. On peut donc esp\u00e9rer disposer d\u2019outils permettant de convertir une information en une autre, par exemple, en transformant un signal temporel de bon fonctionnement m\u00e9canique vers un texte explicatif de ce bon fonctionnement. \u00c0 ce stade, il est grand temps d\u2019exp\u00e9rimenter en attendant de pied ferme les prochains travaux scientifiques.<\/p>\n<p>&nbsp;<\/p>\n<blockquote><p>\u00c9videmment, nous surveillons \u00e0 Kickmaker ces sujets avec attention, et exp\u00e9rimentons d\u00e9j\u00e0 afin de pouvoir demain vous proposer les meilleures solutions possibles, alliant la qualit\u00e9 d\u2019innovation de ces travaux \u00e0 notre rigueur d\u2019impl\u00e9mentation en ing\u00e9nierie. En effet, au-del\u00e0 de la r\u00e9volution scientifique, notre d\u00e9fi est de transformer ces essais en outils exploitables et contr\u00f4lables. Suivez-nous, et si vous d\u00e9sirez aller un peu plus loin, parlons-en! 2023 sera une ann\u00e9e exceptionnelle en suivant ces opportunit\u00e9s d\u2019application.<\/p><\/blockquote>\n<p style=\"text-align: center;\"><strong>Article \u00e9crit par <a href=\"https:\/\/www.linkedin.com\/in\/eric-debeir-a99bb994\/?originalSubdomain=fr\">Eric Debeir<\/a>, lead data scientist<\/strong><\/p>\n[\/vc_column_text][\/vc_column][\/vc_row][vc_row type=&#8221;in_container&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; scene_position=&#8221;center&#8221; top_padding=&#8221;3%&#8221; bottom_padding=&#8221;3%&#8221; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; 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column_border_style=&#8221;solid&#8221;][nectar_btn size=&#8221;jumbo&#8221; button_style=&#8221;regular&#8221; button_color_2=&#8221;Extra-Color-1&#8243; icon_family=&#8221;none&#8221; text=&#8221;Proposer un article !&#8221; url=&#8221;https:\/\/www.kickmaker.fr\/blog\/contact\/&#8221;][\/vc_column][\/vc_row][vc_row type=&#8221;full_width_content&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;default&#8221; equal_height=&#8221;yes&#8221; content_placement=&#8221;top&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; bg_color=&#8221;#222222&#8243; scene_position=&#8221;center&#8221; top_padding=&#8221;2%&#8221; bottom_padding=&#8221;2%&#8221; left_padding_desktop=&#8221;10%&#8221; right_padding_desktop=&#8221;10%&#8221; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; shape_divider_position=&#8221;bottom&#8221; bg_image_animation=&#8221;none&#8221; shape_type=&#8221;&#8221;][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_spacing=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/2&#8243; tablet_width_inherit=&#8221;default&#8221; tablet_text_alignment=&#8221;center&#8221; phone_text_alignment=&#8221;center&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][vc_column_text]\n<h4><span style=\"color: #ffffff;\">Pour ne rien louper des actus industrielles.\u00a0<\/span><\/h4>\n[\/vc_column_text][vc_column_text css=&#8221;.vc_custom_1620054670298{margin-top: 15px !important;}&#8221;]<span style=\"color: #ffffff;\">Inscrivez-vous \u00e0 notre newsletter. 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