Multiverse Origin Studio AI

All rights reserved — Multiverse Origin Studio Built from scratch by a solo developer. No pretrained image generation backbone. Single-file architecture (~10,000 lines). Verified training on consumer hardware.

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Multiverse Origin Studio AI is a complete end-to-end anime and manga image generation pipeline — architecture, loss functions, data pipeline, and resource management built entirely from scratch — designed to produce high-quality output on hardware that existing systems treat as insufficient.

This is not a wrapper, fine-tune, or derivative of Stable Diffusion, Midjourney, or any existing image generation model. Every component was designed with awareness of what every other component requires.

Verified result: Successfully trained on 3,500+ curated images using 16GB RAM + RTX 3060 12GB VRAM. Estimated training time: 3–4 days continuous. Quality-filtered data with domain-specific losses outperforms brute-force approaches requiring 10,000–50,000+ images.

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Tested Hardware

Component	Specification
CPU	AMD Ryzen 5 5500 — 6 cores / 10 threads, 16MB L3 cache
GPU	NVIDIA RTX 3060 — 12GB VRAM
RAM	16 GB
Storage	M.2 NVMe SSD — persistent preprocessed tensor cache

Every allocation limit, cache budget, and batch scaling threshold references these numbers directly. This is not a generic configuration — it was derived from measured hardware capacity.

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Architecture Overview

System Controller

MultiverseOriginStudioAI — Top-level orchestrator. Initializes, wires, and coordinates all subsystems. Device placement is explicit and intentional:

GPU (quality-critical, performance-sensitive):

• EnhancedGNN v1–v4, EnhancedMoE (14 experts)
• EmotionalArtisticUnderstanding, DeepUnderstandingSystem
• DiffusionModel, VisionEncoder
• AdvancedColorExpert, AdvancedLineArtExpert, BackgroundExpert
• AdvancedCameraExpert, StructureGenerator, AdvancedEditingExpert

CPU (VRAM-constrained, deferred):

• QualityEnhancer — final post-processing stage, runs CPU-side to preserve VRAM throughout generation
• AdvancedCompressionSystem — embedding compression, optional
• DesignStyleSupportSystem — style conditioning, CPU-placed

Dimension bridge: nn.Linear(1024→512) → LayerNorm → ReLU — bridges MoE output to the 512-dim internal modules without restructuring any model.

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Mixture of Experts — 14 Specialists

EnhancedMoE routes each generation task through 14 specialized expert agents rather than a single monolithic model.

Router architecture:

Linear(768→1024) → LayerNorm → GELU → Dropout(0.1)
  → Linear(1024→512) → LayerNorm → GELU
  → Linear(512→14)
  → softmax(logits / temperature)

temperature is a learned nn.Parameter — the router learns how confident to be in its routing decisions, not just which expert to select.

Each expert (EnhancedExpertAgent) — full architecture:

input_proj: Linear(768→512)

layer1: LayerNorm → Linear(512→512) → GELU → Dropout(0.1) + residual from input_proj
layer2: LayerNorm → Linear(512→512) → GELU → Dropout(0.1) + residual from layer1

# Context integration (when context tensor provided):
attention: MultiheadAttention(512, heads=8, batch_first=True, dropout=0.1)
  h = h + attention(h, context, context).squeeze(1) × 0.5

comm_layer: Linear(512→512) → LayerNorm → GELU, added × 0.3

output_proj: Linear(512→512) × expert_weights (nn.Parameter)

Per-expert performance tracking — call_count, avg_time (EMA: α=0.1), success_rate (EMA: α=0.05/0.1) — logged live.

14 expert domains:

Expert	Domain
line_art	Edge structure, stroke continuity, Laplacian thickness matching
color	Palette generation, color harmony, distribution and shading
light	Lighting direction, intensity, shadow and specular modeling
anatomy	Body proportion, bilateral symmetry enforcement
scene	Spatial composition, layout reasoning
camera	Camera angle, projection matrix, perspective transformation
story	Multi-language narrative conditioning (10 languages via MultiLanguageStorySystem)
structure	High-level compositional structure and form
precision	Detail refinement, local sharpness
memory	Temporal consistency across panels and sequences
safety	Output constraint enforcement
editing	Region-based modification and inpainting
quality	Output quality scoring and per-sample enhancement
style	Style fingerprinting, artist-aware generation

Two-pass execution — how experts actually communicate:

Pass 1: All 14 experts run independently on input x, producing expert_hiddens[].

Pass 2: Expert outputs are stacked into [batch, 14, 512] and passed through:

cross_attention: MultiheadAttention(512, heads=16, batch_first=True, dropout=0.1)
expert_tensor = expert_tensor + attended × 0.5   # cross-expert residual

coordinator: Linear(512→512) → LayerNorm → GELU
expert_tensor = expert_tensor + coordinated × 0.3

# Weighted combination
weighted_outputs = (expert_tensor × routing_weights.unsqueeze(-1)).sum(dim=1)

# Fusion head
fusion: Linear(512×14 → 512×4) → LayerNorm → GELU → Dropout(0.1)
     → Linear(512×4 → 512×2) → LayerNorm → GELU → Dropout(0.1)
     → Linear(512×2 → 512)

# Final
final_output = weighted_outputs + fused × 0.5

Routing history tracked in deque(maxlen=1000) per forward call.

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Graph Neural Network — Four Versions

EnhancedGNN — 6-layer network connecting expert outputs as graph nodes. Enables relational reasoning between experts: the output of one expert can attend to and modify the representation of another through message passing.

Architecture (version 4 — active):

node_embedding: Linear(768→512)

Per layer (v4):
  MultiheadAttention(512, heads=8, batch_first=True)
  LayerNorm + residual (from v3+)
  ReLU → Dropout(0.1)

output_proj: Linear(512→512)

• v4: MultiheadAttention per layer + residual + LayerNorm
• v3: Linear per layer + residual + LayerNorm
• v1–v2: Linear per layer + LayerNorm, no residual

All four versions are initialized and live. gnn_v4 is registered in DataFlow as the active graph module. gnn_v1–gnn_v3 are retained for ablation and fallback. DataFlow.connect('gnn', 'graph_features', 'structure', 'embedding') wires graph features directly into structure generation.

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Computation Graph — DataFlow

DataFlow is a thread-safe pipeline registry backed by threading.Lock(). Modules declare their names, input keys, and output keys. DataFlow.forward() resolves and executes them in declared order, passing results through the dependency graph.

Module registration:

data_flow.register_module('line_art', line_art_expert,
    inputs=['embedding', 'structure'],
    outputs=['line_art'])
data_flow.connect('gnn', 'graph_features', 'structure', 'embedding')
data_flow.connect('structure', 'structure', 'line_art', 'structure')
data_flow.connect('structure', 'structure', 'background', 'structure')

New processing stages can be inserted by registration alone — no existing module code is modified.

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Diffusion Model

DiffusionModel — U-Net with sinusoidal time embedding and cross-attention text conditioning (768-dim). Text embeddings are injected at multiple scales via cross-attention, influencing both structure and fine detail rather than only at the bottleneck.

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Encoders

TextEncoder — sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 (384-dim output). In ArtDataset.__getitem__, the 384-dim output is duplicated to 768 via torch.cat([text_embed, text_embed]). Falls back to a deterministic SHA-256 hash embedding (not random) when the transformer is unavailable — _create_hash_embedding maps 96 bytes of the hash to [-1, 1] bit-level values and fills remaining dimensions from character frequency statistics. Reproducibility is preserved without the transformer dependency.

VisionEncoder — 4-layer CNN backbone → AdaptiveAvgPool2d(1) → 512-dim → 4 separate output heads:

• embedding: Linear(512→768) → LayerNorm → ReLU → Linear(768→768)
• structure: Linear(512→256) → ReLU → Linear(256→128)
• color: Linear(512→256) → ReLU → Linear(256→128)
• composition: Linear(512→256) → ReLU → Linear(256→128)

Important: VisionEncoder is not used inside ArtDataset.__getitem__ — this was an explicit design decision to avoid device mismatch between DataLoader worker processes and the GPU. Instead, _create_image_stat_embedding always generates exactly 768 dimensions from per-channel statistics (mean, std, min, max), 50-bin histograms per channel, and cross-channel correlations. VisionEncoder is used during inference only, when device context is controlled.

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Emotional and Artistic Understanding

EmotionalArtisticUnderstanding — GPU-placed system conditioning generation on emotional and artistic analysis. Architecture:

emotion_encoder:
  Linear(768→1536) → LayerNorm → GELU → Dropout(0.1)
  → Linear(1536→768) → LayerNorm

emotion_classifier:
  Linear(768→512) → ReLU → Linear(512→256) → ReLU → Linear(256→10) → Softmax

10 emotion categories: joy, sadness, anger, fear, surprise, disgust, neutral, love, nostalgia, melancholy.

Five artistic element extractors (composition, color_harmony, lighting_mood, perspective_emotion, line_quality), each Linear(768→256) → ReLU → Linear(256→128).

artistic_style_encoder:
  Linear(768→1536) → LayerNorm → GELU → Dropout(0.1) → Linear(1536→768)

artistic_fusion:
  Linear(768 + 128×5 → 1536) → LayerNorm → GELU → Dropout(0.1) → Linear(1536→768)

emotion_to_visual bridge:
  Linear(768+10 → 1536) → LayerNorm → GELU → Linear(1536→768)

Emotion scores (10-dim) are concatenated directly to the generation embedding and passed through the bridge — emotional analysis directly modulates the visual representation, not just a soft conditioning signal.

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Deep Understanding System

DeepUnderstandingSystem — GPU-placed multi-level understanding stack:

semantic_understanding:
  Linear(768→1536) → LayerNorm → GELU → Dropout(0.1) → Linear(1536→768)

contextual_encoder:
  TransformerEncoder(d_model=768, nhead=12, dim_ff=3072, layers=6,
                     batch_first=True, dropout=0.1)
  → mean over sequence

hierarchical_layers: 4 × [Linear(768→768) → LayerNorm → GELU]

relationship_net:
  Linear(1536→1536) → LayerNorm → GELU → Linear(1536→768)

understanding_fusion:
  Linear(768×3 → 1536) → LayerNorm → GELU → Dropout(0.1) → Linear(1536→768)

attention: MultiheadAttention(768, heads=12, batch_first=True)

Semantic, contextual (TransformerEncoder), and hierarchical representations are fused together before generation.

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Advanced Compression System

AdvancedCompressionSystem — CPU-placed. Compresses 768-dim embeddings at compression_ratio=0.5 → 384-dim for VRAM-critical paths.

importance_net:  Linear(768→384) → ReLU → Linear(384→768) → Sigmoid
  (importance scores [0,1] weight embedding before compression)

compressor:
  Linear(768→1536) → LayerNorm → GELU → Linear(1536→768) → LayerNorm → GELU → Linear(768→384)

decompressor:
  Linear(384→768) → LayerNorm → GELU → Linear(768→1536) → LayerNorm → GELU → Linear(1536→768)

8-bit quantization applied at inference (scale = abs_max / 127.0; quantized = round(compressed / scale) * scale). Reconstruction loss is F.mse_loss(decompressed, original) for training.

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Post-Processing Pipeline

QualityEnhancer — CPU-placed final stage. Orchestrates the full post-processing chain with 4 modes: full, resolution, detail, color. In full mode: MultiScaleProcessor → AdvancedDetailEnhancer → ColorVibrancySystem → base enhancer → final blend enhanced + 0.3×details + 0.2×vibrant. Staged quality enhancement (_apply_staged_quality_enhance) downscales to max_side (VRAM-based: 320–896px) before processing and upscales back — VRAM-neutral even for large outputs.

SuperResolutionSystem — 8-block residual SRGAN with PixelShuffle upsampling:

feature_extractor: Conv2d(3→64) → PReLU → Conv2d(64→64) → PReLU
residual_blocks (×8): Conv2d(64→64) → BN → PReLU → Conv2d(64→64) → BN + residual
upsampler: Conv2d(64→256) → PixelShuffle(2) → PReLU  [+ second stage for 4×]
reconstructor: Conv2d(64→64) → PReLU → Conv2d(64→3)

Bicubic fallback applied if spatial dimensions don't match target after PixelShuffle.

AdvancedDetailEnhancer — Three parallel detail branches (3×3 fine / 5×5 medium / 7×7 coarse), each Conv2d(3→128) → BN → ReLU → Conv2d(128→128). Concatenated → detail_fusion (128×3→256→256→128). Added to edge_enhancer (3×3) and texture_enhancer (3×3) outputs: combined = image + 0.3×fused_mean + 0.2×edge + 0.2×texture. Final final_enhancer (3→256→256→128→3) added residually × 0.4.

ColorVibrancySystem — Color analysis CNN (3→128→256→128) feeding three separate Tanh-activated branches for saturation (× vibrancy_strength=0.3), contrast (× 0.5×strength), and brightness (× 0.3×strength). Branches are decoupled to prevent coupling artifacts. Color harmony CNN (3→128→256→3) applied post-adjustment. Final color_enhancement (3→256→256→128→3) added × 0.4.

MultiScaleProcessor — Three scale branches at original / ½ / ¼ resolution, each with different kernel sizes (3×3 / 5×5 / 7×7). Upsampled to original → concatenated → scale_fusion (128×3→256→256→128) → output_generator (128→256→256→3). Residual connection: image + 0.5×output.

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Loss Functions

Every expert is trained with a loss matched to its domain constraint — not a generic pixel reconstruction objective applied uniformly.

AdvancedLossFunctions assembles the full loss stack.

VGG Perceptual Loss — VGG feature-space loss. Produces sharper, more visually coherent output than pixel MSE by matching perceptual features (texture, style, high-level structure).

Combined Loss (training path) — Used for MoE and generator joint training:

• Mixed precision path: pure L1 loss (scale-stabilized)
• Standard precision path: 0.5×MSE + 0.5×L1 (configurable via weights dict)

GAN Loss — BCE loss with label smoothing: real_label=0.9, fake_label=0.1. Generator optimizer: AdamW lr=5e-6, betas=(0.9, 0.999), AMSGrad. Discriminator optimizer: AdamW lr=2.5e-6 (half generator LR), betas=(0.5, 0.999), AMSGrad.

SSIM Loss:

ssim_map = ((2*mu1_mu2 + C1)*(2*sigma12 + C2)) /
           ((mu1_sq + mu2_sq + C1)*(sigma1_sq + sigma2_sq + C2))
loss = 1 - ssim_map.mean()

Anatomical Loss (AnatomicalDetector) — Three region-specific CNN detectors:

• Face: Conv2d(3→64, k=7, stride=2) → BN → ReLU → MaxPool → Conv2d(64→128) → BN → ReLU → Conv2d(128→256) → BN → ReLU → AdaptiveAvgPool(1,1)
• Hands: Conv2d(3→64, k=5, stride=2) → BN → ReLU → Conv2d(64→128) → BN → ReLU → AdaptiveAvgPool(1,1)
• Eyes: Conv2d(3→32) → ReLU → Conv2d(32→64) → ReLU → AdaptiveAvgPool(1,1)

Loss weighting: 0.5×face + 0.3×hands + 0.2×eyes

Style Consistency Loss — Gram matrix matching between generated and target style embeddings. Weight: 0.2 when style loss is enabled.

Temporal Consistency Loss (panel generation mode):

0.5×feature_consistency + character_weight × character_consistency
                        + scene_weight × scene_consistency

Outpainting overlap loss: 32-pixel border zones on all 4 sides, MSE between generated border and reference border. Attention-based edge loss: Sobel edge magnitude map of reference used as attention weight for generated edge alignment. Combined: loss + 0.2×overlap_loss + 0.1×attention_edge_loss.

AnimeStyleDiscriminator — CNN trained for anime/manga aesthetics specifically:

Conv2d(3→48, k=3, stride=2) → LeakyReLU(0.2) → Dropout2d(0.25)
Conv2d(48→96, k=3, stride=2) → BN → LeakyReLU(0.2) → Dropout2d(0.25)
Conv2d(96→192, k=3, stride=2) → BN → LeakyReLU(0.2)
Conv2d(192→384, k=3, stride=2) → BN → LeakyReLU(0.2)
anime_features: Conv2d(384→192, k=1) → LeakyReLU → Conv2d(192→96, k=1) → LeakyReLU
classifier: AdaptiveAvgPool2d(1) → Flatten → Linear(96→1) → Sigmoid

Base channels = 48. Prevents drift toward photorealistic features.

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Dataset Pipeline

ArtDataset — Lazy loading: __init__ stores file paths only. On first access per sample, image is decoded, transformed, and saved to ./cache/preprocessed/<name>.pt. All subsequent accesses load the .pt directly — no image decoding or transform overhead after first epoch. torch.save / torch.load(weights_only=False) with corrupted-cache fallback.

Text embedding: sentence-transformers → 384-dim → duplicated to 768. Fallback: _create_hash_embedding (SHA-256 → 768-dim deterministic, not random).

Image embedding inside DataLoader workers: Always _create_image_stat_embedding (768-dim from channel statistics + histograms + correlations) — never VisionEncoder inside workers, to avoid device mismatch. VisionEncoder is used only in the generate() path when device context is controlled.

smart_collate_fn — Handles variable-length embeddings with zero-padding to batch max. Stacks style fingerprints separately when style extraction is enabled.

Style pipeline: StyleAnalyzer runs on each sample when extract_style=True, keyed by folder name or filename prefix into ArtistStyleDatabase.

_create_dummy_samples is disabled — raises ValueError with instructions rather than generating synthetic data. Real images are required.

Supported formats: PNG, JPG, JPEG, WEBP, BMP.

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Quality Filters (Image Download)

Applied to every image in download_training_images. Target: 3,500 images. Parallel hash computation via ThreadPoolExecutor(max_workers=8).

Filter	Threshold
Minimum long edge	512px
Minimum short edge	256px
Aspect ratio	0.33–3.0 (stricter than 0.25–4.0 to exclude ultra-wide)
Color std	> 15 (raised from 10)
Sharpness (Laplacian variance)	> 30 (raised from 20)
Contrast (pixel std)	> 20
Unique color check	> 100 unique colors if near pure black/white
Duplicate detection	Full-file MD5 hash

NSFW: 40+ keyword blacklist applied to tag strings. Anime validation: tag string must contain at least one of anime, manga, cartoon, illustration, drawing, art, character. Danbooru score filter: ≥ 5.

If dataset already meets target → skip. If over target → deterministic trim (sorted reverse, remove last).

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Resource Management

EnhancedResourceManager — Monitors RAM, VRAM, CPU via psutil and torch.cuda. Distinguishes OS page cache from process memory. Computes dynamic batch size from live available resources and current resolution.

Hard allocation budget:

max_ram_gb     = 14.4   # 16GB − 1.6GB OS reserve
max_vram_gb    = 11.4   # 12GB − 0.6GB reserve
cpu_threads    = 10     # 2 reserved for OS and background
available_threads = 8   # used for DataLoader workers

Cleanup thresholds:

RAM:   soft=90%   aggressive=95%
VRAM:  soft=92%   aggressive=95%
Aggressive: gc.collect() × 2 + cuda.empty_cache() + cuda.synchronize()

L3CacheManager — LRU eviction over CPU L3 via OrderedDict. Budget: 15MB (1MB reserved from 16MB). Max entries: 1,500 (~10MB each). Tensors larger than 50% of budget are not cached (anti-thrash). Tracks hit rate, miss rate, eviction count. Used at inference time to cache gnn_edge_index and GNN outputs (< 1MB threshold).

SSDOptimizer — NVMe read cache. First access: decode → transform → torch.save('./cache/preprocessed/<name>.pt'). Subsequent accesses: torch.load() direct. Eliminates all image decoding and transform overhead after first epoch.

DataLoader: 8 workers (num_workers = min(8, available_threads)), multiprocessing_context='spawn', pin_memory=True when CUDA available, persistent_workers=True, prefetch_factor=2 when RAM > 2GB.

Resolution-aware batch scaling:

512px  → full batch
768px  → batch // 2
1024px → batch // 4
2048px → batch // 8
minimum: 1

Dynamic resolution selection (get_optimal_resolution) — based on live vram_available_gb:

> 8GB  → 2048px max
> 5GB  → 1024px max
> 2GB  → 768px max
else   → 512px

Further constrained if ram_available_gb < 4.

OOM recovery: On RuntimeError containing out of memory or oom:

1. Save emergency checkpoint to ./checkpoints/emergency_oom_epoch_<N>.pt
2. Reduce batch size by 1 and recreate DataLoader
3. Recompute grad_accum_steps based on available VRAM (2/3/4)
4. If already at batch size 2, stop training

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Training System

TrainingSystem — Full training orchestration.

Optimizer: AdamW, lr=5e-6, betas=(0.9, 0.999), weight_decay=1e-5, eps=1e-8, amsgrad=True. Parameters: MoE + structure_generator + line_art_expert + background_expert + color_expert + camera_expert + story_expert.

Discriminator optimizer: AdamW, lr=2.5e-6 (half generator LR), betas=(0.5, 0.999), amsgrad=True.

Scheduler: CosineAnnealingWarmRestarts(T_0=65, T_mult=2, eta_min=1e-8). Warmup: 44 epochs (~17% of 260-epoch run). LR schedule is handed to the scheduler only after epoch >= warmup_epochs.

Gradient clipping: max_grad_norm=0.5 (with ai_system), 1.0 (MoE only). clip_grad_norm_ applied before each optimizer step to all params with .grad is not None.

Mixed precision: torch.cuda.amp.GradScaler() + autocast. scaler.unscale_() before gradient clipping. scaler.step() + scaler.update() per step.

Gradient accumulation: configurable grad_accum_steps (2/3/4 based on VRAM). Optimizer step only when (batch_idx + 1) % grad_accum_steps == 0.

Loss NaN/Inf guard (safe_loss): Clamps loss to max_val=50.0 before backward. If NaN/Inf after clamping, batch is skipped with no gradient update.

AdaptiveTrainingSystem — Monitors loss variance and convergence rate over a 10-step window:

• convergence_rate < 0.01 → LR × 0.9
• convergence_rate > 0.05 → LR × 1.05 (capped at 1e-3)
• stability_score < 0.5 → LR × 0.85

AutomaticHyperparameterOptimizer — Bayesian-style search over lr (log scale, 1e-5–1e-3), batch_size (2/4/8/16), weight_decay (log, 1e-6–1e-4), beta1 (0.85–0.95), beta2 (0.99–0.9999), dropout (0–0.3). Explore/exploit: random search for first 5 trials, then weighted average of top-5 performers with variance-adaptive perturbation (±15% high-variance, ±5% low-variance).

AdvancedContinuousLearning — Updates expert weights based on observed performance without full retraining.

Checkpointing:

• Every 5 epochs (raised from 10 for OOM protection)
• Milestones: 40, 80, 120, 160, 200, 230, 260
• Keep last 30 checkpoints
• Emergency checkpoint on every OOM or unrecoverable error
• --auto_resume flag reads find_latest_checkpoint() (searches epoch_*.pt, emergency_epoch_*.pt, emergency_oom_epoch_*.pt, best_model*.pt, checkpoint_*.pt patterns)

# Basic
python ai_system_core.py --train --epochs 100 --batch_size 6

# Full configuration
python ai_system_core.py \
  --train \
  --data_dir ./data/train \
  --val_dir  ./data/val \
  --epochs   260 \
  --batch_size 2 \
  --save_path ./models/best_model.pt

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Inference

python ai_system_core.py \
  --generate \
  --model   ./models/best_model.pt \
  --prompt  "manga girl, forest background" \
  --output  result.png

Quality enhance modes: full / resolution / detail / color. Staged enhancement (full_quality_enhance=True) downscales to VRAM-safe max_side before QualityEnhancer, upscales back. Blend factor (full_enhance_blend) mixes enhanced and original.

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Monitoring and Logging

visualize_training_progress(ai_system, save_path='progress.png')  # 3×3 metric chart
print_progress_dashboard(ai_system)                                # Live console dashboard

TensorBoard: tensorboard --logdir=runs/multiverse_origin

Tracked metrics: total loss, per-expert losses (line art / color / background), art theory losses (anatomy / perspective / color harmony), self-critique score, creativity score, PSNR, SSIM, accuracy.

Validation metrics (ValidationMetrics): rolling deque(maxlen=1000) for PSNR, SSIM, accuracy. Running average and latest value accessible per metric.

Log	Content
main.log	Training events and system status
performance.log	Per-batch timing and resource utilization
error.log	Exceptions with full context and recovery status
diagnosis.log	Per-component health check results

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Project Structure

./
├── ai_system_core.py           # Complete system — single file, ~10,000 lines
├── data/
│   └── train/
│       ├── image001.png
│       ├── image001.txt        # Optional caption — paired by filename stem
│       └── ...
├── models/
│   └── best_model.pt
├── cache/
│   └── preprocessed/           # Auto-generated NVMe tensor cache (.pt files)
├── checkpoints/                # Per-epoch and emergency checkpoints
│   ├── epoch_0040.pt
│   ├── emergency_oom_epoch_0085.pt
│   └── ...
├── runs/
│   └── multiverse_origin/      # TensorBoard logs
└── logs/
    ├── main.log
    ├── performance.log
    ├── error.log
    └── diagnosis.log

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Dependencies

torch
torchvision
Pillow
numpy
requests
sentence-transformers
transformers
psutil
matplotlib
seaborn
tensorboard
python-dotenv
tqdm

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Intellectual Property

© 2025–2026 Multiverse Origin Studio. All rights reserved.

Developed independently by a solo developer. No pretrained image generation backbone.

The following are claimed as original work:

• The complete end-to-end architecture as implemented: 14-expert MoE with two-pass cross-expert attention (16 heads), GNN v4 with 8-head attention per layer connecting expert outputs as graph nodes, DataFlow runtime computation graph wiring, full post-processing chain, and all integration as a single-file training and inference system
• EmotionalArtisticUnderstanding — direct emotion score (10-category) to generation embedding bridge via concatenation and learned projection, combined with five artistic element extractors and full fusion
• DeepUnderstandingSystem — three-level (semantic + 6-layer TransformerEncoder + 4-level hierarchical) understanding stack with relationship network and multi-head attention fusion
• AdvancedCompressionSystem — importance-weighted embedding compression with 8-bit inference quantization
• EnhancedResourceManager with L3CacheManager — hardware-specific allocation budgets, staged quality enhancement (VRAM-neutral for large outputs), and GNN output caching at inference time
• Training methodology as implemented: GAN with label smoothing on an anime-specific discriminator, AMSGrad on both generator and discriminator, temporal consistency loss with outpainting overlap zones and attention-weighted edge loss for panel generation, composite loss stack with NaN/inf guard, OOM-recovery batch-size reduction with automatic checkpoint and resume
• ArtDataset with _create_image_stat_embedding as a device-safe DataLoader-compatible image representation, _create_hash_embedding as a reproducible non-random text fallback, and lazy NVMe tensor caching

This repository is private. Access does not grant any license to use, reproduce, modify, or build upon any part of this system.

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Multiverse Origin Studio — All rights reserved