HunyuanCustom — Multi-Modal Video Generation With Perfect Subject Consistency

TL;DR
HunyuanCustom just solved the identity consistency problem that breaks most AI video workflows.
Feed it images, audio, video clips, and text → get perfectly consistent characters across every frame without identity drift.
The text-image fusion module powered by LLaVA + AudioNet spatial cross-attention + video-driven injection create the first truly multi-modal video generation system.
8GB GPU support as of June 2025 — finally, professional-grade customized video without datacenter hardware.

1 The customization breakthrough that changes everything

May 8, 2025 brought us HunyuanCustom — Tencent's answer to the biggest problem in AI video: subject consistency. While other models struggle to maintain character identity across frames, HunyuanCustom delivers rock-solid consistency across image, audio, video, and text conditions simultaneously.

1.1 The consistency challenge solved

2 Core architectural innovations

2.1 Text-Image Fusion Module (LLaVA-powered)

Unlike basic concatenation approaches, HunyuanCustom uses LLaVA-based multi-modal understanding for enhanced text-image alignment:

Multi-Modal Processing Pipeline:

• Text Prompt → LLaVA semantic embedding
• Reference Image → Visual feature extraction
• Fusion Layer → Cross-modal attention
• Unified Representation → Video generation

Why this matters: Traditional methods treat text and images as separate inputs, causing inconsistencies. LLaVA's joint understanding prevents modal conflicts.

2.2 Image ID Enhancement Module

The breakthrough temporal concatenation technique reinforces identity features across frame sequences:

ID Enhancement Workflow:

Identity Feature Extraction:

• Input → extract_id_features(reference_image)
• Output → identity_features

Frame-by-Frame Enhancement:

• For Each Frame Index → range(len(video_frames))
• Temporal Concatenation → concat_temporal_features(video_frames[frame_idx], identity_features, temporal_weight=calculate_temporal_decay(frame_idx))
• Frame Update → video_frames[frame_idx] = enhanced_frame

Return → enhanced video_frames

Result: Character faces remain pixel-perfect consistent even in complex motion sequences.

2.3 AudioNet Spatial Cross-Attention

For audio-conditioned generation, AudioNet achieves hierarchical alignment via spatial cross-attention mechanisms:

• Low-level audio features → Basic lip-sync and movement
• Mid-level semantic content → Emotion and expression mapping
• High-level audio style → Overall character animation consistency
• Spatial cross-attention → Frame-by-frame audio-visual alignment

2.4 Video-Driven Injection Network

The patchify-based feature-alignment network handles video conditioning by:

1. Latent compression of conditional video input
2. Patch-level feature extraction for fine-grained control
3. Feature alignment with target generation space
4. Injection into the diffusion process at optimal layers

3 Multi-modal conditioning workflows

3.1 Image + Text conditioning

Use case: Brand mascot in different scenarios

Example Configuration:

Conditioning Inputs:

• Image → "brand_mascot.jpg"
• Text → "The friendly mascot waves hello in a sunny park setting"

Expected Output:

• Design Consistency → Maintains exact mascot design
• Environment Application → Applies park environment
• Color Preservation → Preserves brand color palette
• Proportions → Consistent character proportions

3.2 Audio + Video conditioning

Use case: Product demo with custom spokesperson

Multi-Modal Setup:

Condition Inputs:

• Reference Video → "spokesperson_sample.mp4" (3-second reference)
• Audio Track → "product_script.wav" (30-second narration)
• Style Image → "corporate_headshot.jpg" (Professional look)

Generation Parameters:

• Duration → 30 seconds
• Resolution → "720p"
• Consistency Strength → 0.95

Output → result = hunyuan_custom.generate(conditions)

3.3 All-modality conditioning

Advanced scenario: Interactive training video with multiple characters

Input Stack:

• Character Images → 3 people
• Dialogue Audio Tracks → Synchronized
• Reference Video Style → Corporate training
• Text Descriptions → Scene-by-scene
• Emotion References → Professional, friendly

Expected Output:

• Duration → 5-minute training video
• Character Consistency → Perfect identity preservation
• Dialogue Quality → Natural lip-sync
• Visual Style → Corporate appearance
• Transitions → Smooth scene changes

4 Production capabilities & performance

4.1 Technical specifications

4.2 Benchmark performance vs competitors

Professional evaluation across 500+ test scenarios:

5 Real-world applications dominating

5.1 Brand content automation

Scenario: E-commerce brand with 500+ products

Traditional Workflow:

• Model Hiring → $50,000 (each product category)
• Studio Shoots → $25,000 (10 days)
• Post-Production → $40,000 (2 months)
• Seasonal Reshoots → +$30,000
• Total Cost → $145,000 + 3 months

HunyuanCustom Workflow:

• Reference Photos → 1 hour (brand spokesperson)
• Script Templates → 1 day (product categories)
• Video Generation → 3 days GPU time (500 videos)
• Quality Review → 2 days (edits)
• Total Cost → $500 + 1 week

ROI: 290x cost reduction + 12x speed improvement

5.2 Educational content scaling

Use case: Online course with consistent instructor across 100+ lessons

Before: Record all lessons in person = 3 months of instructor time
After: Record 10 reference lessons + generate remaining 90 = 1 week total

Consistency benefits:

• Same instructor appearance across all lessons
• Consistent lighting and framing
• Professional audio quality maintained
• Easy content updates without re-recording

5.3 Personalized marketing campaigns

Campaign: Insurance company with 50 regional representatives

Automated Regional Campaign Generation:

Setup:

• Representatives Database → load_rep_database() (50 people)
• Campaign Script Template → "Welcome to [REGION] insurance coverage..."

For Each Representative:

• Image Input → rep.headshot
• Audio Generation → synthesize_voice(campaign_script, rep.voice_sample)
• Text Description → "Professional insurance presentation for {rep.region}"
• Style Reference → corporate_template

Deployment:

• Output → deploy_to_region(personalized_video, rep.region)

Results: 50 personalized videos in 4 hours vs 2 months of individual recordings

6 Advanced customization techniques

6.1 Identity reinforcement strategies

Strong consistency (brand mascots, spokescharacters):

Strong Consistency Configuration:

• Temporal Weight = 0.95
• Feature Injection Layers = [2, 4, 6, 8]
• Consistency Loss Multiplier = 2.0

Natural variation (human characters, realistic scenarios):

Natural Variation Configuration:

• Temporal Weight = 0.85
• Feature Injection Layers = [3, 6]
• Consistency Loss Multiplier = 1.2

6.2 Multi-character scene management

Challenge: Maintaining multiple character identities simultaneously

Advanced Multi-Character Setup:

Host Character:

• ID → "host"
• Reference Image → "tv_host.jpg"
• Audio Track → "host_dialogue.wav"
• Consistency Priority → "high"

Guest Character:

• ID → "guest"
• Reference Image → "expert_guest.jpg"
• Audio Track → "guest_responses.wav"
• Consistency Priority → "high"

Scene Configuration:

• Description → "Professional interview setup with corporate backdrop"
• Interaction Style → "conversational"

Generation:

• Output → interview_video = hunyuan_custom.generate_scene(scene_config)

7 Production optimization & deployment

7.1 Hardware scaling recommendations

7.2 Quality optimization workflow

Production Quality Pipeline:

Phase 1 - Quick Preview Generation:

• Quality → "preview"
• Duration → 5 seconds
• Resolution → "480p"
• Output → preview = hunyuan_custom.generate(config)

Phase 2 - Client Approval Workflow:

• Condition → if client_approves(preview)

Phase 3 - Full Quality Generation:

• Quality → "production"
• Duration → 30 seconds
• Resolution → "720p"
• Consistency Strength → 0.95
• Return → final_video or request_revisions(preview)

7.3 Content pipeline automation

Automated brand content factory:

Content Factory Pipeline:

Input Sources:

• Brand Assets → brand_assets/spokespersons/
• Audio Scripts → audio_scripts/product_categories/
• Style References → style_references/seasonal_campaigns/

Processing Rules:

• Spokesperson Matching → match_spokesperson_to_product_category
• Style Updates → apply_seasonal_style_updates
• Multi-Resolution → generate_multi_resolution_outputs

Output Destinations:

• Social Media → social_media/instagram_reels/
• Website → website/product_pages/
• Email Campaigns → email_campaigns/video_headers/

8 Integration with existing workflows

8.1 CMS & marketing automation platforms

WordPress/Drupal integration:

WordPress/Drupal Plugin Integration:

Function: generate_product_video($product_id)

Data Retrieval:

• Product Data → $product = get_product($product_id)
• Spokesperson → $spokesperson = get_brand_spokesperson()

Video Configuration:

• Image → $spokesperson['headshot']
• Text → generate_product_script($product)
• Audio → synthesize_product_narration($product)
• Style → get_brand_style_template()

API Call:

• Return → hunyuan_custom_api_call($video_config)

Shopify app integration:

• Auto-generate product videos when new items are added
• Batch update existing products with video content
• A/B test different spokesperson/style combinations
• Performance tracking with conversion analytics

8.2 Video editing suite plugins

Adobe Premiere Pro extension:

• Import HunyuanCustom directly into timeline
• Real-time preview with different conditioning inputs
• Batch processing for multi-video projects
• Color correction presets for consistency

Final Cut Pro workflow:

• Custom effects library for HunyuanCustom integration
• Template projects with placeholders for quick generation
• Multi-cam editing for multi-character scenarios

9 Cost analysis & ROI calculations

9.1 Enterprise cost comparison

Scenario: Technology company creating 200 product demo videos annually

HunyuanCustom ROI:

• Setup payback: 2.1 months
• Annual savings: $530,000 vs traditional
• Quality advantage: Superior to stock, competitive with custom

9.2 Agency business model transformation

Before HunyuanCustom:

• 5 video editors × $75/hour × 40 hours/week = $15,000/week capacity
• Average project: 3 days = $3,600 revenue
• Weekly capacity: 6.6 projects = $23,760 revenue

After HunyuanCustom:

• Same 5 editors manage 4x more projects with AI assistance
• Average project time: 6 hours = same $3,600 revenue
• Weekly capacity: 26 projects = $95,040 revenue

Business impact: 4x revenue increase with same team size

10 Advanced features & upcoming developments

10.1 Current capabilities (June 2025)

✅ Audio-driven generation via OmniV2V integration
✅ Video-driven features for style transfer
✅ Single GPU support (8GB VRAM minimum)
✅ Batch processing for production workflows
✅ API endpoints for programmatic access

10.2 Roadmap features

Q3 2025:

• Real-time generation for interactive applications
• 4K resolution support with optimized models
• Extended duration (up to 2 minutes per generation)
• Advanced emotion control with micro-expression mapping

Q4 2025:

• Multi-language consistency across generated content
• Brand safety filters for automated content screening
• Integration APIs for major marketing platforms
• Mobile optimization for on-device generation

11 Getting started: implementation guide

11.1 Technical setup (Week 1)

Installation and Setup:

Repository Setup:

• Clone → git clone https://github.com/Tencent-Hunyuan/HunyuanCustom.git
• Navigate → cd HunyuanCustom

Dependencies:

• Install Requirements → pip install -r requirements.txt
• Install PyTorch → pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118

Model Weights:

• Download → wget https://huggingface.co/tencent/HunyuanCustom/resolve/main/custom-model.safetensors

Verification:

• Test → python test_generation.py --config sample_config.yaml

11.2 Content preparation (Week 2)

Asset organization:

Asset Organization Structure:

Characters Directory:

• Spokesperson 01 → spokesperson_01.jpg
• Spokesperson 02 → spokesperson_02.jpg
• Brand Mascot → brand_mascot.png

Audio Templates:

• Product Intro → product_intro_script.wav
• Testimonial Template → testimonial_template.wav
• Call to Action → call_to_action.wav

Style References:

• Corporate Clean → corporate_clean.mp4
• Energetic Youth → energetic_youth.mp4
• Luxury Elegant → luxury_elegant.mp4

Text Prompts:

• Product Categories → product_categories.json
• Campaign Descriptions → campaign_descriptions.json

11.3 Production workflow (Week 3-4)

Day-by-day implementation:

• Week 3: Single-video generation testing + quality optimization
• Week 4: Batch processing setup + team training
• Month 2: Full production integration + performance monitoring
• Month 3: Advanced features + custom fine-tuning

12 Community resources & support

12.1 Official resources

• GitHub Repository: Tencent-Hunyuan/HunyuanCustom
• Model Hub: Hugging Face
• Research Paper: ArXiv
• Online Demo: hunyuancustom.online

12.2 Professional services

Ready to implement HunyuanCustom for enterprise-scale customized video production? Our team specializes in AI video infrastructure for marketing and content teams.

Production teams:
DM us "CUSTOM DEPLOY" for a consultation on building your automated customized video pipeline with perfect subject consistency.

Last updated 25 Jul 2025. Model version: v1.0 (May 2025 release)