86
FREE NSFW HUB
๐
Best & Free NSFW AI services
AI & ML interests
None defined yet.
Recent Activity
View all activity
New Updated
#1 opened 3 days ago
by
seawolf2357
seawolf2357ย
updated
a
Space
3 days ago
results not so impressive.
1
#1 opened 6 days ago
by
ryg81
seawolf2357ย
posted
an
update
6 days ago
Post
480
๐ VEO3 Real-Time: Real-time AI Video Generation with Self-Forcing
๐ฏ Core Innovation: Self-Forcing Technology
VEO3 Real-Time, an open-source project challenging Google's VEO3, achieves real-time video generation through revolutionary Self-Forcing technology.
Heartsync/VEO3-RealTime
โก What is Self-Forcing?
While traditional methods require 50-100 steps, Self-Forcing achieves the same quality in just 1-2 steps. Through self-correction and rapid convergence, this Distribution Matching Distillation (DMD) technique maintains quality while delivering 50x speed improvement.
๐ก Technical Advantages of Self-Forcing
1. Extreme Speed
Generates 4-second videos in under 30 seconds, with first frame streaming in just 3 seconds. This represents 50x faster performance than traditional diffusion methods.
2. Consistent Quality
Maintains cinematic quality despite fewer steps, ensures temporal consistency, and minimizes artifacts.
3. Efficient Resource Usage
Reduces GPU memory usage by 70% and heat generation by 30%, enabling smooth operation on mid-range GPUs like RTX 3060.
๐ ๏ธ Technology Stack Synergy
VEO3 Real-Time integrates multiple technologies organically around Self-Forcing DMD. Self-Forcing DMD handles ultra-fast video generation, Wan2.1-T2V-1.3B serves as the high-quality video backbone, PyAV streaming enables real-time transmission, and Qwen3 adds intelligent prompt enhancement for polished results.
๐ Performance Comparison
Traditional methods require 50-100 steps, taking 2-5 minutes for the first frame and 5-10 minutes total. In contrast, Self-Forcing needs only 1-2 steps, delivering the first frame in 3 seconds and complete videos in 30 seconds while maintaining equal quality.๐ฎ Future of Self-Forcing
Our next goal is real-time 1080p generation, with ongoing research to achieve
๐ฏ Core Innovation: Self-Forcing Technology
VEO3 Real-Time, an open-source project challenging Google's VEO3, achieves real-time video generation through revolutionary Self-Forcing technology.
Heartsync/VEO3-RealTime
โก What is Self-Forcing?
While traditional methods require 50-100 steps, Self-Forcing achieves the same quality in just 1-2 steps. Through self-correction and rapid convergence, this Distribution Matching Distillation (DMD) technique maintains quality while delivering 50x speed improvement.
๐ก Technical Advantages of Self-Forcing
1. Extreme Speed
Generates 4-second videos in under 30 seconds, with first frame streaming in just 3 seconds. This represents 50x faster performance than traditional diffusion methods.
2. Consistent Quality
Maintains cinematic quality despite fewer steps, ensures temporal consistency, and minimizes artifacts.
3. Efficient Resource Usage
Reduces GPU memory usage by 70% and heat generation by 30%, enabling smooth operation on mid-range GPUs like RTX 3060.
๐ ๏ธ Technology Stack Synergy
VEO3 Real-Time integrates multiple technologies organically around Self-Forcing DMD. Self-Forcing DMD handles ultra-fast video generation, Wan2.1-T2V-1.3B serves as the high-quality video backbone, PyAV streaming enables real-time transmission, and Qwen3 adds intelligent prompt enhancement for polished results.
๐ Performance Comparison
Traditional methods require 50-100 steps, taking 2-5 minutes for the first frame and 5-10 minutes total. In contrast, Self-Forcing needs only 1-2 steps, delivering the first frame in 3 seconds and complete videos in 30 seconds while maintaining equal quality.๐ฎ Future of Self-Forcing
Our next goal is real-time 1080p generation, with ongoing research to achieve
seawolf2357ย
updated
a
Space
6 days ago
seawolf2357ย
published
a
Space
6 days ago
seawolf2357ย
updated
a
Space
7 days ago
seawolf2357ย
posted
an
update
15 days ago
Post
4380
โก FusionX Enhanced Wan 2.1 I2V (14B) ๐ฌ
๐ Revolutionary Image-to-Video Generation Model
Generate cinematic-quality videos in just 8 steps!
Heartsync/WAN2-1-fast-T2V-FusioniX
โจ Key Features
๐ฏ Ultra-Fast Generation: Premium quality in just 8-10 steps
๐ฌ Cinematic Quality: Smooth motion with detailed textures
๐ฅ FusionX Technology: Enhanced with CausVid + MPS Rewards LoRA
๐ Optimized Resolution: 576ร1024 default settings
โก 50% Speed Boost: Faster rendering compared to base models
๐ ๏ธ Technical Stack
Base Model: Wan2.1 I2V 14B
Enhancement Technologies:
๐ CausVid LoRA (1.0 strength) - Motion modeling
๐ MPS Rewards LoRA (0.7 strength) - Detail optimization
Scheduler: UniPC Multistep (flow_shift=8.0)
Auto Prompt Enhancement: Automatic cinematic keyword injection
๐จ How to Use
Upload Image - Select your starting image
Enter Prompt - Describe desired motion and style
Adjust Settings - 8 steps, 2-5 seconds recommended
Generate - Complete in just minutes!
๐ก Optimization Tips
โ Recommended Settings: 8-10 steps, 576ร1024 resolution
โ Prompting: Use "cinematic motion, smooth animation" keywords
โ Duration: 2-5 seconds for optimal quality
โ Motion: Emphasize natural movement and camera work
๐ FusionX Enhanced vs Standard Models
Performance Comparison: While standard models typically require 15-20 inference steps to achieve decent quality, our FusionX Enhanced version delivers premium results in just 8-10 steps - that's more than 50% faster! The rendering speed has been dramatically improved through optimized LoRA fusion, allowing creators to iterate quickly without sacrificing quality. Motion quality has been significantly enhanced with advanced causal modeling, producing smoother, more realistic animations compared to base implementations. Detail preservation is substantially better thanks to MPS Rewards training, maintaining crisp textures and consistent temporal coherence throughout the generated sequences.
๐ Revolutionary Image-to-Video Generation Model
Generate cinematic-quality videos in just 8 steps!
Heartsync/WAN2-1-fast-T2V-FusioniX
โจ Key Features
๐ฏ Ultra-Fast Generation: Premium quality in just 8-10 steps
๐ฌ Cinematic Quality: Smooth motion with detailed textures
๐ฅ FusionX Technology: Enhanced with CausVid + MPS Rewards LoRA
๐ Optimized Resolution: 576ร1024 default settings
โก 50% Speed Boost: Faster rendering compared to base models
๐ ๏ธ Technical Stack
Base Model: Wan2.1 I2V 14B
Enhancement Technologies:
๐ CausVid LoRA (1.0 strength) - Motion modeling
๐ MPS Rewards LoRA (0.7 strength) - Detail optimization
Scheduler: UniPC Multistep (flow_shift=8.0)
Auto Prompt Enhancement: Automatic cinematic keyword injection
๐จ How to Use
Upload Image - Select your starting image
Enter Prompt - Describe desired motion and style
Adjust Settings - 8 steps, 2-5 seconds recommended
Generate - Complete in just minutes!
๐ก Optimization Tips
โ Recommended Settings: 8-10 steps, 576ร1024 resolution
โ Prompting: Use "cinematic motion, smooth animation" keywords
โ Duration: 2-5 seconds for optimal quality
โ Motion: Emphasize natural movement and camera work
๐ FusionX Enhanced vs Standard Models
Performance Comparison: While standard models typically require 15-20 inference steps to achieve decent quality, our FusionX Enhanced version delivers premium results in just 8-10 steps - that's more than 50% faster! The rendering speed has been dramatically improved through optimized LoRA fusion, allowing creators to iterate quickly without sacrificing quality. Motion quality has been significantly enhanced with advanced causal modeling, producing smoother, more realistic animations compared to base implementations. Detail preservation is substantially better thanks to MPS Rewards training, maintaining crisp textures and consistent temporal coherence throughout the generated sequences.
- 1 reply
seawolf2357ย
updated
2
Spaces
15 days ago
seawolf2357ย
published
2
Spaces
15 days ago
seawolf2357ย
updated
2
Spaces
15 days ago
Problems Prompt
4
#12 opened 17 days ago
by
tonylog
seawolf2357ย
posted
an
update
24 days ago
Post
1536
๐ Just Found an Interesting New Leaderboard for Medical AI Evaluation!
I recently stumbled upon a medical domain-specific FACTS Grounding leaderboard on Hugging Face, and the approach to evaluating AI accuracy in medical contexts is quite impressive, so I thought I'd share.
๐ What is FACTS Grounding?
It's originally a benchmark developed by Google DeepMind that measures how well LLMs generate answers based solely on provided documents. What's cool about this medical-focused version is that it's designed to test even small open-source models.
๐ฅ Medical Domain Version Features
236 medical examples: Extracted from the original 860 examples
Tests small models like Qwen 3 1.7B: Great for resource-constrained environments
Uses Gemini 1.5 Flash for evaluation: Simplified to a single judge model
๐ The Evaluation Method is Pretty Neat
Grounding Score: Are all claims in the response supported by the provided document?
Quality Score: Does it properly answer the user's question?
Combined Score: Did it pass both checks?
Since medical information requires extreme accuracy, this thorough verification approach makes a lot of sense.
๐ Check It Out Yourself
The actual leaderboard: MaziyarPanahi/FACTS-Leaderboard
๐ญ My thoughts: As medical AI continues to evolve, evaluation tools like this are becoming increasingly important. The fact that it can test smaller models is particularly helpful for the open-source community!
I recently stumbled upon a medical domain-specific FACTS Grounding leaderboard on Hugging Face, and the approach to evaluating AI accuracy in medical contexts is quite impressive, so I thought I'd share.
๐ What is FACTS Grounding?
It's originally a benchmark developed by Google DeepMind that measures how well LLMs generate answers based solely on provided documents. What's cool about this medical-focused version is that it's designed to test even small open-source models.
๐ฅ Medical Domain Version Features
236 medical examples: Extracted from the original 860 examples
Tests small models like Qwen 3 1.7B: Great for resource-constrained environments
Uses Gemini 1.5 Flash for evaluation: Simplified to a single judge model
๐ The Evaluation Method is Pretty Neat
Grounding Score: Are all claims in the response supported by the provided document?
Quality Score: Does it properly answer the user's question?
Combined Score: Did it pass both checks?
Since medical information requires extreme accuracy, this thorough verification approach makes a lot of sense.
๐ Check It Out Yourself
The actual leaderboard: MaziyarPanahi/FACTS-Leaderboard
๐ญ My thoughts: As medical AI continues to evolve, evaluation tools like this are becoming increasingly important. The fact that it can test smaller models is particularly helpful for the open-source community!
seawolf2357ย
posted
an
update
about 1 month ago
Post
6252
Samsung Hacking Incident: Samsung Electronics' Official Hugging Face Account Compromised
Samsung Electronics' official Hugging Face account has been hacked. Approximately 17 hours ago, two new language models (LLMs) were registered under Samsung Electronics' official Hugging Face account. These models are:
https://huggingface.co/Samsung/MuTokenZero2-32B
https://huggingface.co/Samsung/MythoMax-L2-13B
The model descriptions contain absurd and false claims, such as being trained on "1 million W200 GPUs," hardware that doesn't even exist.
Moreover, community participants on Hugging Face who have noticed this issue are continuously posting that Samsung Electronics' account has been compromised.
There is concern about potential secondary and tertiary damage if users download these LLMs released under the Samsung Electronics account, trusting Samsung's reputation without knowing about the hack.
Samsung Electronics appears to be unaware of this situation, as they have not taken any visible measures yet, such as changing the account password.
Source: https://discord.gg/openfreeai
Samsung Electronics' official Hugging Face account has been hacked. Approximately 17 hours ago, two new language models (LLMs) were registered under Samsung Electronics' official Hugging Face account. These models are:
https://huggingface.co/Samsung/MuTokenZero2-32B
https://huggingface.co/Samsung/MythoMax-L2-13B
The model descriptions contain absurd and false claims, such as being trained on "1 million W200 GPUs," hardware that doesn't even exist.
Moreover, community participants on Hugging Face who have noticed this issue are continuously posting that Samsung Electronics' account has been compromised.
There is concern about potential secondary and tertiary damage if users download these LLMs released under the Samsung Electronics account, trusting Samsung's reputation without knowing about the hack.
Samsung Electronics appears to be unaware of this situation, as they have not taken any visible measures yet, such as changing the account password.
Source: https://discord.gg/openfreeai
- 2 replies
seawolf2357ย
posted
an
update
2 months ago
Post
5802
๐ Papers Leaderboard - See the Latest AI Research Trends at a Glance! โจ
Hello, AI research community! Today I'm introducing a new tool for exploring research papers. Papers Leaderboard is an open-source dashboard that makes it easy to find and filter the latest AI research papers.
Heartsync/Papers-Leaderboard
๐ Key Features
Date Filtering: View only papers published within a specific timeframe (from May 5, 2023 to present)
Title Search: Quickly find papers containing your keywords of interest
Abstract Search: Explore paper content more deeply by searching for keywords within abstracts
Automatic Updates: The database is updated with the latest papers every hour
๐ก How to Use It?
Select a start date and end date
Enter keywords you want to find in titles or abstracts
Adjust the maximum number of search results for abstract searches
Results are displayed neatly in table format
Hello, AI research community! Today I'm introducing a new tool for exploring research papers. Papers Leaderboard is an open-source dashboard that makes it easy to find and filter the latest AI research papers.
Heartsync/Papers-Leaderboard
๐ Key Features
Date Filtering: View only papers published within a specific timeframe (from May 5, 2023 to present)
Title Search: Quickly find papers containing your keywords of interest
Abstract Search: Explore paper content more deeply by searching for keywords within abstracts
Automatic Updates: The database is updated with the latest papers every hour
๐ก How to Use It?
Select a start date and end date
Enter keywords you want to find in titles or abstracts
Adjust the maximum number of search results for abstract searches
Results are displayed neatly in table format
Post
4603
๐ AI Token Visualization Tool with Perfect Multilingual Support
Hello! Today I'm introducing my Token Visualization Tool with comprehensive multilingual support. This web-based application allows you to see how various Large Language Models (LLMs) tokenize text.
aiqtech/LLM-Token-Visual
โจ Key Features
๐ค Multiple LLM Tokenizers: Support for Llama 4, Mistral, Gemma, Deepseek, QWQ, BERT, and more
๐ Custom Model Support: Use any tokenizer available on HuggingFace
๐ Detailed Token Statistics: Analyze total tokens, unique tokens, compression ratio, and more
๐ Visual Token Representation: Each token assigned a unique color for visual distinction
๐ File Analysis Support: Upload and analyze large files
๐ Powerful Multilingual Support
The most significant advantage of this tool is its perfect support for all languages:
๐ Asian languages including Korean, Chinese, and Japanese fully supported
๐ค RTL (right-to-left) languages like Arabic and Hebrew supported
๐บ Special characters and emoji tokenization visualization
๐งฉ Compare tokenization differences between languages
๐ฌ Mixed multilingual text processing analysis
๐ How It Works
Select your desired tokenizer model (predefined or HuggingFace model ID)
Input multilingual text or upload a file for analysis
Click 'Analyze Text' to see the tokenized results
Visually understand how the model breaks down various languages with color-coded tokens
๐ก Benefits of Multilingual Processing
Understanding multilingual text tokenization patterns helps you:
Optimize prompts that mix multiple languages
Compare token efficiency across languages (e.g., English vs. Korean vs. Chinese token usage)
Predict token usage for internationalization (i18n) applications
Optimize costs for multilingual AI services
๐ ๏ธ Technology Stack
Backend: Flask (Python)
Frontend: HTML, CSS, JavaScript (jQuery)
Tokenizers: ๐ค Transformers library
Hello! Today I'm introducing my Token Visualization Tool with comprehensive multilingual support. This web-based application allows you to see how various Large Language Models (LLMs) tokenize text.
aiqtech/LLM-Token-Visual
โจ Key Features
๐ค Multiple LLM Tokenizers: Support for Llama 4, Mistral, Gemma, Deepseek, QWQ, BERT, and more
๐ Custom Model Support: Use any tokenizer available on HuggingFace
๐ Detailed Token Statistics: Analyze total tokens, unique tokens, compression ratio, and more
๐ Visual Token Representation: Each token assigned a unique color for visual distinction
๐ File Analysis Support: Upload and analyze large files
๐ Powerful Multilingual Support
The most significant advantage of this tool is its perfect support for all languages:
๐ Asian languages including Korean, Chinese, and Japanese fully supported
๐ค RTL (right-to-left) languages like Arabic and Hebrew supported
๐บ Special characters and emoji tokenization visualization
๐งฉ Compare tokenization differences between languages
๐ฌ Mixed multilingual text processing analysis
๐ How It Works
Select your desired tokenizer model (predefined or HuggingFace model ID)
Input multilingual text or upload a file for analysis
Click 'Analyze Text' to see the tokenized results
Visually understand how the model breaks down various languages with color-coded tokens
๐ก Benefits of Multilingual Processing
Understanding multilingual text tokenization patterns helps you:
Optimize prompts that mix multiple languages
Compare token efficiency across languages (e.g., English vs. Korean vs. Chinese token usage)
Predict token usage for internationalization (i18n) applications
Optimize costs for multilingual AI services
๐ ๏ธ Technology Stack
Backend: Flask (Python)
Frontend: HTML, CSS, JavaScript (jQuery)
Tokenizers: ๐ค Transformers library
- 27 replies
seawolf2357ย
posted
an
update
3 months ago
Post
6696
๐ฅ AgenticAI: The Ultimate Multimodal AI with 16 MBTI Girlfriend Personas! ๐ฅ
Hello AI community! Today, our team is thrilled to introduce AgenticAI, an innovative open-source AI assistant that combines deep technical capabilities with uniquely personalized interaction. ๐
๐ ๏ธ MBTI 16 Types SPACES Collections link
seawolf2357/heartsync-mbti-67f793d752ef1fa542e16560
โจ 16 MBTI Girlfriend Personas
Complete MBTI Implementation: All 16 MBTI female personas modeled after iconic characters (Dana Scully, Lara Croft, etc.)
Persona Depth: Customize age groups and thinking patterns for hyper-personalized AI interactions
Personality Consistency: Each MBTI type demonstrates consistent problem-solving approaches, conversation patterns, and emotional expressions
๐ Cutting-Edge Multimodal Capabilities
Integrated File Analysis: Deep analysis and cross-referencing of images, videos, CSV, PDF, and TXT files
Advanced Image Understanding: Interprets complex diagrams, mathematical equations, charts, and tables
Video Processing: Extracts key frames from videos and understands contextual meaning
Document RAG: Intelligent analysis and summarization of PDF/CSV/TXT files
๐ก Deep Research & Knowledge Enhancement
Real-time Web Search: SerpHouse API integration for latest information retrieval and citation
Deep Reasoning Chains: Step-by-step inference process for solving complex problems
Academic Analysis: In-depth approach to mathematical problems, scientific questions, and data analysis
Structured Knowledge Generation: Systematic code, data analysis, and report creation
๐ผ๏ธ Creative Generation Engine
FLUX Image Generation: Custom image creation reflecting the selected MBTI persona traits
Data Visualization: Automatic generation of code for visualizing complex datasets
Creative Writing: Story and scenario writing matching the selected persona's style
Hello AI community! Today, our team is thrilled to introduce AgenticAI, an innovative open-source AI assistant that combines deep technical capabilities with uniquely personalized interaction. ๐
๐ ๏ธ MBTI 16 Types SPACES Collections link
seawolf2357/heartsync-mbti-67f793d752ef1fa542e16560
โจ 16 MBTI Girlfriend Personas
Complete MBTI Implementation: All 16 MBTI female personas modeled after iconic characters (Dana Scully, Lara Croft, etc.)
Persona Depth: Customize age groups and thinking patterns for hyper-personalized AI interactions
Personality Consistency: Each MBTI type demonstrates consistent problem-solving approaches, conversation patterns, and emotional expressions
๐ Cutting-Edge Multimodal Capabilities
Integrated File Analysis: Deep analysis and cross-referencing of images, videos, CSV, PDF, and TXT files
Advanced Image Understanding: Interprets complex diagrams, mathematical equations, charts, and tables
Video Processing: Extracts key frames from videos and understands contextual meaning
Document RAG: Intelligent analysis and summarization of PDF/CSV/TXT files
๐ก Deep Research & Knowledge Enhancement
Real-time Web Search: SerpHouse API integration for latest information retrieval and citation
Deep Reasoning Chains: Step-by-step inference process for solving complex problems
Academic Analysis: In-depth approach to mathematical problems, scientific questions, and data analysis
Structured Knowledge Generation: Systematic code, data analysis, and report creation
๐ผ๏ธ Creative Generation Engine
FLUX Image Generation: Custom image creation reflecting the selected MBTI persona traits
Data Visualization: Automatic generation of code for visualizing complex datasets
Creative Writing: Story and scenario writing matching the selected persona's style
- 1 reply