RT Santiment (@santimentfeed)🧑‍💻 Here are crypto's top BSC & Binance Chain projects by development. Directional indicators represent each project's ranking rise or fall since last month:➡️ 1) @binance $BTCB 🥇📈 2) @runonflux $FLUX 🥈📈 3) @binance $BNB 🥉📉 4) @zcash $ZEC➡️ 5) @trustwallet $TWT📈 6) @duskfoundation $DUSK➡️ 7) @bandprotocol $BAND📈 8) @beefyfinance $BIFI📈 9) @0xproject $ZRX📉 10) @saito $SAITO📖 Read about @santimentfeed's methodology for covering development activity for each project, objectively, u ...

Core Viewpoint - The article introduces FreeMorph, a novel training-free image morphing method that enables high-quality and smooth transitions between two input images without the need for pre-training or additional annotations [5][32]. Group 1: Background and Challenges - Image morphing is a creative task that allows for smooth transitions between two distinct images, commonly seen in animations and photo editing [3]. - Traditional methods relied on complex algorithms and faced challenges with high training costs, data dependency, and instability in real-world applications [4]. - Recent advancements in deep learning methods like GANs and VAEs have improved image morphing but still struggle with training costs and adaptability [4][5]. Group 2: FreeMorph Methodology - FreeMorph addresses the challenges of image morphing by eliminating the need for training, achieving effective morphing with just two images [5]. - The method incorporates two key innovations: spherical feature aggregation and prior-driven self-attention mechanisms, enhancing the model's ability to maintain identity features and ensure smooth transitions [11][32]. - A step-oriented motion flow is introduced to control the transition direction, allowing for a coherent and gradual morphing process [21][32]. Group 3: Experimental Results - FreeMorph has been evaluated against existing methods, demonstrating superior performance in generating high-fidelity results across diverse scenarios, including images with varying semantics and layouts [27][30]. - The method effectively captures subtle changes, such as color variations in objects or nuanced facial expressions, showcasing its versatility [27][30]. Group 4: Limitations - Despite its advancements, FreeMorph has limitations, particularly when handling images with significant semantic or layout differences, which may result in less smooth transitions [34]. - The method inherits biases from the underlying Stable Diffusion model, affecting accuracy in specific contexts, such as human limb structures [34].

Core Insights - Poe's latest report analyzes AI model usage trends from January to May 2025, focusing on user engagement across text, reasoning, image, video, and audio domains [1][2] Group 1: Model Performance and Market Trends - The popularity of the DeepSeek model has declined, with its market share dropping from a peak of 7% in mid-February to 3% by the end of April [4][7] - New flagship models from the same provider tend to capture market share from their predecessors, leading to a rapid shift in user preferences towards newer models [4][7] - The share of text messages sent to reasoning models increased from approximately 2% to about 10%, peaking during DeepSeek's popularity [9][11] Group 2: Reasoning Models - The number of reasoning models has significantly increased, reflecting a growing trend towards more precise and reliable handling of complex tasks [8] - Gemini 2.5 Pro gained approximately 30% of reasoning message share within six weeks of its release [11] - Users are quickly transitioning to OpenAI's latest reasoning models, indicating a strong preference for newer, more powerful options [12] Group 3: Image Generation Models - The GPT image generation model, GPT-Image-1, achieved a usage rate of 17% within two weeks of its API launch [17] - Google's Imagen 3 series saw its usage grow from about 10% to 30%, while Black Forest Labs' FLUX series maintained a market share of approximately 35% [17][18] Group 4: Video Generation Models - Kuaishou's Kling video generation model rapidly captured about 30% of the market share, with Kling-2.0-Master accounting for 21% of all video generation requests within three weeks of its release [21][22] - Runway, a pioneer in video generation, experienced a 40% decline in usage share, dropping to around 20% [23] Group 5: Audio Generation Models - ElevenLabs dominated the audio generation space, handling about 80% of TTS requests from subscribers [24] - The audio generation market is becoming increasingly competitive, with new players offering unique voice options and performance features [24]