Image Generation | Techlist.io

figmaJun 24, 20263 min readCurated summary

Connecting Figma and Weave | Figma Blog

Figma is integrating Weave’s AI-powered creative workflows directly into Figma Design, bringing image, video, animation, audio, and 3D production closer to the collaborative design canvas. The initial release provides more than 20 prebuilt AI image tools for tasks such as style transfer, product photography, material extraction, and art direction. Figma’s broader goal is to make creative workflows inspectable, repeatable, shareable, and eventually publishable through the Figma Community. ## Figma Weave and the Open Creative Canvas - Figma’s acquisition of Weavy, now Figma Weave, is intended to combine generative AI with professional creative-editing tools. - Weave uses node-based workflows, allowing creators to: - Connect image, video, audio, text, and 3D-generation steps. - Inspect how creative outputs are produced. - Tweak individual stages and compare alternate approaches. - Run multiple creative explorations simultaneously. - The company sees this as a way to bring creative production into the same collaborative environment where teams already design and review work. ## Weave Tools in Figma Design - More than 20 Weave tools are available from Figma Design’s left panel. - Each tool packages a prebuilt Weave workflow behind a simpler interface. - Supported use cases include: - Transferring a visual style from one image to another. - Generating e-commerce and product-shoot imagery. - Extracting or applying material qualities. - Rendering artwork in different visual languages, including Art Nouveau. - Adjusting image aspect ratios and developing visual directions. - Users can provide inputs and generate production-quality results without writing freeform prompts. - Predefined workflows produce more consistent results for recurring tasks while still allowing designers to guide the creative direction. ## Reusable and Shareable Workflows - Weave is designed for users who want to build complex workflows as well as those who prefer ready-made tools. - Figma plans to let designers publish their own workflows as Weave tools. - A team member could define a creative process once and share it with colleagues or the broader Figma Community. - This makes the logic behind a workflow reusable instead of keeping it confined to the person who created it. ## OutSystems Customer Example - OutSystems designer Bruno Figueiredo uses Weave for presentation visuals, animation, event graphics, merchandise, and 3D assets. - He created a 3D model of the company’s mascot, Neo, for manufacturing without relying on an outside specialist. - Weave’s node-based structure lets him experiment with: - Illustration styles. - Costume colors. - Body proportions. - Multiple AI models and parallel flows. - He describes the tool as especially useful for exploratory work because several variations can run at once and be reviewed later. Figma’s recommendation is to use Weave tools for fast, repeatable creative tasks while using the underlying node-based canvas when deeper experimentation and customization are needed. Future integration, including a planned Figma node in Weave, should reduce the need to translate assets and instructions between design and creative-production tools.

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Image Generation Figma Generative AI Figma Weave+2

figmaJun 10, 20262 min readCurated summary

Steal This Template: Bring a User Persona to Life with Figma Weave | Figma Blog

The post shows how Figma Weave can make Ideal Customer Profiles (ICPs) more vivid and useful by turning static personas into realistic visual scenes. Dropbox designer Sara Clayton used a headshot, prompts, and reference images to depict media professionals in authentic work environments. The result was faster iteration, greater emotional connection, and more effective storytelling for product strategy. ## Static Personas Lack Context - Dropbox’s ICPs represented video editors, audio engineers, and production managers. - Traditional diagrams, user stories, and repeated headshots failed to show these users in their real working environments. - Story-driven slide decks worked better, but lacked visual variety and realism. - Clayton wanted personas to feel more human and connected to tools such as timelines, mixing boards, and editing software. ## Creating Realistic Persona Scenes with Figma Weave - Clayton used Figma Weave to transform a profile picture into an image of a media producer working from home. - A single prompt placed the persona in front of a Premiere Pro screen. - She uploaded a reference image to revise the character’s outfit when the initial style was not appropriate. - Weave provided more control over visual variables and iterations than general-purpose chatbots such as Gemini or ChatGPT. - The workflow took less than two minutes. ## Figma Weave’s Role in AI-Native Creation - Figma Weave emerged from Figma’s acquisition of Weavy. - The platform combines generative AI with professional editing tools on an open canvas. - Its intended capabilities include image, video, animation, motion design, and VFX generation and editing. - The post also highlights persona-focused templates for car scenes, character sheets, outfits, and character variations. ## Impact on Product Strategy - More realistic visuals help teams connect with personas and understand their contexts. - Higher-fidelity storytelling can make ICPs more influential in strategic decisions. - Clayton’s broader goal is to “humanize” product work by showing users as real people rather than static profile images. - Readers can use the featured Figma Weave template to create a more engaging ICP quickly. Figma Weave is best suited for teams that want to supplement traditional persona documentation with realistic, editable visual narratives that make customer needs easier to understand and remember.

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Image Generation Figma Generative AI Motion Design+2

figmaApr 9, 20262 min readCurated summary

Turning Prompts into Five Scalable Workflows with Figma Weave | Figma Blog

Figma Weave presents AI creation as a scalable, editable workflow rather than a one-off prompt. Its canvas connects AI models and processing nodes so creators can branch, refine, and reuse each step while maintaining control over imagery, video, audio, and 3D output. The article introduces five workflows, beginning with a method for deriving a reusable visual style from multiple reference images. ## Figma Weave as a Creative Workflow Canvas - Figma Weave evolved from Weavy, which Figma acquired to expand its capabilities in: - Image and video generation - Animation and motion design - Audio and 3D creation - VFX and professional editing - Users can chain prompts and AI nodes together, moving from references to finished assets without losing the ability to revise intermediate steps. - Figma has published more than 20 Community templates covering tasks such as: - Turning images into videos - Generating 3D models - Combining visual references - Comparing image-generation models ## Why Workflows Are More Scalable Than Single Prompts - A single prompt produces one interpretation of a style. - A workflow lets creators independently adjust how strongly each reference influences the result. - Individual stages can be reshaped, reused, and applied across multiple assets and channels. - The example brand, Epoch, demonstrates how the system can support a consistent visual identity based on distorted textures and 3D natural forms. ## Combining Two Images into a Reusable Style Guide - The first workflow combines a hibiscus flower and a rock face from Epoch’s existing visual references. - Each image is processed through an **Image Describer node**, which extracts attributes such as: - Texture - Color - Lighting - Composition - The resulting text descriptions can be edited and merged into a new style definition. - The balance between the two references can be adjusted until the desired blend is achieved. - The combined style can then be tested across different image-generation models, helping the team validate the look at scale. - The output is treated as a reusable style system rather than a single prompt for one image. The practical recommendation is to build visual direction as a modular workflow: analyze existing references, combine and tune their characteristics, and preserve the resulting style definition for reuse in future assets.

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Image Generation Generative AI Prompt Engineering Figma Weave+2

figmaNov 19, 20252 min readCurated summary

Creativity meets precision with Gemini 3 Pro Image Pro | Figma Blog

Google’s Nano Banana Pro, part of Gemini 3 Pro, brings more precise and context-aware image editing to creative workflows. The model can generate variations while preserving a design’s visual identity, including its palette, typography, composition, and subject likeness. Figma presents it as a tool for refining and extending ideas across products rather than simply regenerating images. ## Design Coherence Across Variations - Nano Banana Pro retains a design’s “visual DNA,” including color, texture, type, composition, and imagery. - In Figma Buzz, it creates branded social-asset variations while preserving logos, simple illustrations, and overall composition. - It can adapt illustrations for different contexts, such as converting a set of winter-themed images into dark-mode versions with minimal prompting. ## Extending Existing Work - The model can place illustrations into new environments while matching lighting and mood-board references. - In Figma Slides, it embedded an astrology-app illustration into a cozy reading scene and added complementary details such as a star-shaped light. - It can reframe portraits, change camera angles, and update backgrounds while maintaining a person’s likeness. - This makes it useful for keeping employee headshots and other brand imagery visually consistent. ## Building Composite Scenes - Figma Weave enables users to combine graphics, copy, photography, and other visual elements into unified scenes. - Nano Banana Pro helps maintain coherence when disparate assets are composited together. - These scenes can be extended with Google’s Veo for motion, as well as 3D models, upscalers, background-removal tools, and prompt-refinement models. ## Editing in Context Across Figma - The model is integrated into Figma’s products, including Buzz, Design, Slides, and Weave. - Users can refine existing images instead of starting over, making targeted changes to text, spot colors, and other details. - Typography can be localized, faces remain natural, and the surrounding visual context is preserved. Nano Banana Pro is most valuable when creative teams need fast iteration without sacrificing brand consistency or image integrity. Figma recommends experimenting with it directly as a flexible tool for refining, remixing, and expanding visual concepts.

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Image Generation Figma Large Language Models Figma Weave+3

figmaOct 29, 20252 min readCurated summary

Introducing Figma Weave: The Next Generation of AI-Native Creation at Figma | Figma Blog

Figma has acquired Weavy, bringing its AI media-generation and professional editing capabilities into Figma as **Figma Weave**. The new product aims to treat AI outputs as starting points rather than finished results, combining generative models with human editing and creative judgment. Figma’s goal is to support more sophisticated image, video, animation, motion design, and VFX workflows within its collaborative platform. ## Generative Craft - Weavy combines multiple AI models with professional editing tools in a browser-based canvas. - Creators can select models for different purposes, including: - Seedance, Sora, and Veo for cinematic video - Flux and Ideogram for realistic imagery - Nano-Banana and Seedream for precision-focused work - AI outputs can be refined through hands-on techniques such as: - Lighting adjustments - Object masking - Color grading - Its node-based workflow allows users to branch, remix, and refine outputs. - Each result can become an input for the next stage, creating a flexible media pipeline that balances experimentation with control. ## A Platform for Creative Professionals - Weavy has attracted independent creators, startups, and Fortune 100 companies in less than a year. - Its users include: - Architects creating staged environments - Visual-effects artists producing content for games, television, and film - Marketers developing social videos and banners - Designers creating product mockups, branding assets, and other media - Figma emphasizes that the platform is designed for process and craft rather than one-click generation. ## The Weavy Team and Figma’s Roadmap - Weavy’s founders and team bring experience in product development, engineering, visual effects, animation, and creative production. - The companies share a focus on community, making, and professional-quality creative work. - Figma is expanding the team in Tel Aviv and elsewhere to support Figma Weave’s development. - The acquisition is intended to broaden Figma from a digital-product design platform into a more comprehensive AI-powered creative environment. Figma Weave’s central promise is to make AI-generated media more editable, collaborative, and expressive. Rather than replacing creative expertise, Figma plans to use Weavy’s tools to help users push beyond generic AI results and retain control over the final work.

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Image Generation Generative AI Figma Weave Ai Models+4

lineAug 12, 2025Original article

The Current State of LY Corporation (opens in new tab)

Tech-Verse 2025 showcased LY Corporation’s strategic shift toward an AI-integrated ecosystem following the merger of LINE and Yahoo Japan. The event focused on the practical hurdles of deploying generative AI, concluding that the transition from experimental models to production-ready services requires sophisticated evaluation frameworks and deep contextual integration into developer workflows. ## AI-Driven Engineering with Ark Developer LY Corporation’s internal "Ark Developer" solution demonstrates how AI can be embedded directly into the software development life cycle. * The system utilizes a Retrieval-Augmented Generation (RAG) based code assistant to handle tasks such as code completion, security reviews, and automated test generation. * Rather than treating codebases as simple text documents, the tool performs graph analysis on directory structures to maintain structural context during code synthesis. * Real-world application includes a seamless integration with GitHub for automated Pull Request (PR) creation, with internal users reporting higher satisfaction compared to off-the-shelf tools like GitHub Copilot. ## Quantifying Quality in Generative AI A significant portion of the technical discussion centered on moving away from subjective "vibes-based" assessments toward rigorous, multi-faceted evaluation of AI outputs. * To measure the quality of generated images, developers utilized traditional metrics like Fréchet Inception Distance (FID) and Inception Score (IS) alongside LAION’s Aesthetic Score. * Advanced evaluation techniques were introduced, including CLIP-IQA, Q-Align, and Visual Question Answering (VQA) based on video-language models to analyze image accuracy. * Technical challenges in image translation and inpainting were highlighted, specifically the difficulty of restoring layout and text structures naturally after optical character recognition (OCR) and translation. ## Global Technical Exchange and Implementation The conference served as a collaborative hub for engineers across Japan, Taiwan, and Korea to discuss the implementation of emerging standards like the Model Context Protocol (MCP). * Sessions emphasized the "how-to" of overcoming deployment hurdles rather than just following technical trends. * Poster sessions (Product Street) and interactive Q&A segments allowed developers to share localized insights on LLM agent performance and agentic workflows. * The recurring theme across diverse teams was that the "evaluation and verification" stage is now the primary driver of quality in generative AI services. For organizations looking to scale AI, the key recommendation is to move beyond simple implementation and invest in "evaluation-driven development." By building internal tools that leverage graph-based context and quantitative metrics like Aesthetic Scores and VQA, teams can ensure that generative outputs meet professional service standards.

Image Generation AI Large Language Models Model Context Protocol+5