VividFlow
🌊
27
Bring your images to life
Eric Chung PRO
DawnC
AI & ML interests
Computer Vision, LLM, Hybrid Architectures, MultiModel, Reinforcement Learning
Recent Activity
updated
a Space
2 days ago
DawnC/VividFlow
updated
a Space
5 days ago
DawnC/PawMatchAI
updated
a Space
9 days ago
DawnC/VisionScout
Organizations
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updated
a
Space
2 days ago
updated
a
Space
5 days ago
updated
a
Space
9 days ago
replied to
their
post
10 days ago
Thanks for letting me know! This is likely due to ZeroGPU limitations rather than a bug in the Space.
I’ve just increased the GPU duration from 240s to 300s and restarted the Space to improve stability.
If it still happens, trying again later usually helps. Thanks!
replied to
their
post
10 days ago
Thanks, Please stay tuned to this project !
liked
a
Space
11 days ago
posted
an
update
11 days ago
Post
2584
VividFlow: AI Image-to-Video Generation 🎬✨
Bring your images to life with cinematic motion! VividFlow transforms any static image—portraits, artwork, products, or landscapes, into dynamic videos with professional animation quality.
The system supports both curated motion templates and custom natural language prompts, giving you complete creative freedom to describe camera movements, subject actions, and atmospheric effects in your own words.
What's Inside?
🎭 Smart Motion Templates — 8 curated categories from fashion cinematography to wildlife animations, each with tested prompts that prevent common artifacts like phantom hands in portraits
⚡ Optimized Engine — Powered by Wan2.2-I2V-A14B with Lightning LoRA distillation and FP8 quantization for memory-efficient inference
🎯 Full Creative Control — Seed-based reproducibility for consistent results, adjustable duration from half a second to five seconds, optional AI prompt expansion with Qwen2.5 for enhanced descriptions, and real-time resolution preview
Current Performance & Development Roadmap
VividFlow runs on ZeroGPU with generation taking about 3-4 minutes for 3-second videos. While I am actively optimizing the pipeline to reduce this time, the current version prioritizes output stability and quality, results are worth the wait!
Future development focuses on dedicated GPU deployment for faster processing, batch generation to create multiple variations at once, and expanding our motion template library based on what the community wants to see.
👉 Try it now: DawnC/VividFlow
If VividFlow brings motion to your creative vision, please show your support with a ❤️, your engagement influences future development priorities!
#AI #ImageToVideo #GenerativeAI #VideoGeneration #DeepLearning
Bring your images to life with cinematic motion! VividFlow transforms any static image—portraits, artwork, products, or landscapes, into dynamic videos with professional animation quality.
The system supports both curated motion templates and custom natural language prompts, giving you complete creative freedom to describe camera movements, subject actions, and atmospheric effects in your own words.
What's Inside?
🎭 Smart Motion Templates — 8 curated categories from fashion cinematography to wildlife animations, each with tested prompts that prevent common artifacts like phantom hands in portraits
⚡ Optimized Engine — Powered by Wan2.2-I2V-A14B with Lightning LoRA distillation and FP8 quantization for memory-efficient inference
🎯 Full Creative Control — Seed-based reproducibility for consistent results, adjustable duration from half a second to five seconds, optional AI prompt expansion with Qwen2.5 for enhanced descriptions, and real-time resolution preview
Current Performance & Development Roadmap
VividFlow runs on ZeroGPU with generation taking about 3-4 minutes for 3-second videos. While I am actively optimizing the pipeline to reduce this time, the current version prioritizes output stability and quality, results are worth the wait!
Future development focuses on dedicated GPU deployment for faster processing, batch generation to create multiple variations at once, and expanding our motion template library based on what the community wants to see.
👉 Try it now: DawnC/VividFlow
If VividFlow brings motion to your creative vision, please show your support with a ❤️, your engagement influences future development priorities!
#AI #ImageToVideo #GenerativeAI #VideoGeneration #DeepLearning
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- 4 replies
published
a
Space
13 days ago
replied to
their
post
18 days ago
Sorry to hear that. Could you let me know which Indian breed your dog is?
The model is currently trained on 124 specific breeds, so if your dog’s breed isn’t in that list, it won’t be recognized. I’m working on expanding the coverage to include more regional breeds based on user feedback like yours.
Thanks for testing and letting me know.
posted
an
update
19 days ago
Post
3851
PawMatchAI — Smarter, Safer, and More Thoughtful Recommendations 🐕✨
🐾 Recommendation system update — deeper reasoning, safer decisions
Over the past weeks, user feedback led me to rethink how PawMatchAI handles description-based breed recommendations. Instead of only matching surface-level preferences, the system now implements a multi-dimensional semantic reasoning architecture that emphasizes real-life compatibility and risk awareness.
Key technical improvements:
- SBERT-powered semantic understanding with dynamic weight allocation across six constraint dimensions (space, activity, noise, grooming, experience, family)
- Hierarchical constraint management distinguishing critical safety constraints from flexible preferences, with progressive relaxation when needed
-Multi-head scoring system combining semantic matching (15%), lifestyle compatibility (70%), constraint adherence (10%), and confidence calibration (5%)
-Intelligent risk filtering that applies graduated penalties (-10% to -40%) for genuine incompatibilities while preserving user choice
The goal: 👉 Not just dogs that sound good on paper, but breeds people will actually thrive with long-term.
What's improved?
- 🎯 Clearer separation of must-have safety constraints versus flexible preferences
- 🧠 Bidirectional semantic matching evaluating compatibility from both user and breed perspectives
- 🔍 Context-aware prioritization where critical factors (safety, space, noise) automatically receive higher weighting
What's next?
- 🐕 Expanding behavioral and temperament analysis dimensions
- 🐾 Extension to additional species with transfer learning
- 📱 Mobile-optimized deployment for easier access
- 🧩 Enhanced explainability showing why specific breeds are recommended
👉 Try PawMatchAI: DawnC/PawMatchAI
#AIProduct #SBERT #RecommendationSystems #DeepLearning #MachineLearning #NLP
🐾 Recommendation system update — deeper reasoning, safer decisions
Over the past weeks, user feedback led me to rethink how PawMatchAI handles description-based breed recommendations. Instead of only matching surface-level preferences, the system now implements a multi-dimensional semantic reasoning architecture that emphasizes real-life compatibility and risk awareness.
Key technical improvements:
- SBERT-powered semantic understanding with dynamic weight allocation across six constraint dimensions (space, activity, noise, grooming, experience, family)
- Hierarchical constraint management distinguishing critical safety constraints from flexible preferences, with progressive relaxation when needed
-Multi-head scoring system combining semantic matching (15%), lifestyle compatibility (70%), constraint adherence (10%), and confidence calibration (5%)
-Intelligent risk filtering that applies graduated penalties (-10% to -40%) for genuine incompatibilities while preserving user choice
The goal: 👉 Not just dogs that sound good on paper, but breeds people will actually thrive with long-term.
What's improved?
- 🎯 Clearer separation of must-have safety constraints versus flexible preferences
- 🧠 Bidirectional semantic matching evaluating compatibility from both user and breed perspectives
- 🔍 Context-aware prioritization where critical factors (safety, space, noise) automatically receive higher weighting
What's next?
- 🐕 Expanding behavioral and temperament analysis dimensions
- 🐾 Extension to additional species with transfer learning
- 📱 Mobile-optimized deployment for easier access
- 🧩 Enhanced explainability showing why specific breeds are recommended
👉 Try PawMatchAI: DawnC/PawMatchAI
#AIProduct #SBERT #RecommendationSystems #DeepLearning #MachineLearning #NLP
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- 2 replies
updated
a
Space
19 days ago
updated
a
Space
24 days ago
posted
an
update
25 days ago
Post
5555
Intelligent Inpainting for Precise Creative Control 🎨✨
Transform your images with AI-powered precision! SceneWeaver delivers professional-quality image composition with intelligent background replacement and advanced object manipulation.
What's New in This Update?
🖌️ Object Replacement — Select and transform any element in your scene with natural language prompts while maintaining perfect visual consistency with surrounding content
🗑️ Object Removal — Intelligently remove unwanted objects with context-aware generation that preserves natural lighting, shadows, and scene coherence
🎯 Context-Aware Processing — Advanced inpainting technology ensures seamless integration across all regenerated regions
Core Capabilities
⚡ One-click transformation with smart subject detection, 24 curated professional backgrounds, custom scene generation through text prompts, and studio-quality results powered by BiRefNet, Stable Diffusion XL, and ControlNet Inpainting.
Current Infrastructure & Future Vision
SceneWeaver operates on ZeroGPU with dynamic resource allocation, resulting in extended processing times during peak usage. Based on community demand, I am exploring cloud deployment with dedicated GPU resources for enhanced speed and batch processing capabilities.
Active development focuses on expanding background variety, refining edge quality, and advancing toward intelligent object addition with automatic shadows and reflections—making professional image composition accessible to everyone without technical expertise.
👉 Try it here: DawnC/SceneWeaver
If SceneWeaver helps bring your creative vision to life, please give it a ❤️ — your support influences future development and infrastructure investments!
#AI #Inpainting #DeepLearning #ComputerVision #StableDiffusion #Photography
Transform your images with AI-powered precision! SceneWeaver delivers professional-quality image composition with intelligent background replacement and advanced object manipulation.
What's New in This Update?
🖌️ Object Replacement — Select and transform any element in your scene with natural language prompts while maintaining perfect visual consistency with surrounding content
🗑️ Object Removal — Intelligently remove unwanted objects with context-aware generation that preserves natural lighting, shadows, and scene coherence
🎯 Context-Aware Processing — Advanced inpainting technology ensures seamless integration across all regenerated regions
Core Capabilities
⚡ One-click transformation with smart subject detection, 24 curated professional backgrounds, custom scene generation through text prompts, and studio-quality results powered by BiRefNet, Stable Diffusion XL, and ControlNet Inpainting.
Current Infrastructure & Future Vision
SceneWeaver operates on ZeroGPU with dynamic resource allocation, resulting in extended processing times during peak usage. Based on community demand, I am exploring cloud deployment with dedicated GPU resources for enhanced speed and batch processing capabilities.
Active development focuses on expanding background variety, refining edge quality, and advancing toward intelligent object addition with automatic shadows and reflections—making professional image composition accessible to everyone without technical expertise.
👉 Try it here: DawnC/SceneWeaver
If SceneWeaver helps bring your creative vision to life, please give it a ❤️ — your support influences future development and infrastructure investments!
#AI #Inpainting #DeepLearning #ComputerVision #StableDiffusion #Photography
updated
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Space
25 days ago