Plan: Train Model Tạo Ảnh 4K

Mục tiêu

Tạo model tạo ảnh chất lượng cao native 2K/4K, self-host trên VPS H100, bán API.

Infra

VPS: Azure, 80 vCPU, 629GB RAM, 2x NVIDIA H100 NVL 96GB
Số lượng VPS: 3+ (có thể scale thêm)
GPU Driver: 580.95.05, CUDA 13.0

Architecture: Cascaded Pipeline

Stage 1: Flux (fine-tuned) → Generate 1024px
Stage 2: SUPIR/StableSR (fine-tuned) → Upscale 1K → 2K
Stage 3: SUPIR/StableSR (fine-tuned) → Upscale 2K → 4K

Cost ước tính

Data collection: $0 (crawl free) + ~$20K (Opus caption, tuỳ deal)
Training: $0 (VPS free)
Tổng: $0 - $20K

Timeline: ~8-9 tuần

Phase	Công việc	Thời gian
Phase 1	Setup môi trường	2-3 ngày
Phase 2	Data collection	1-2 tuần
Phase 3	Training (3 stages song song)	4-6 tuần
Phase 4	Evaluation & iteration	1-2 tuần
Phase 5	Serving & API	3-5 ngày

Phase 1: Setup môi trường

Mỗi VPS cài:

Python 3.10+
PyTorch 2.x (CUDA 13.0)
diffusers, transformers, accelerate, deepspeed
img2dataset, webdataset
SUPIR / StableSR dependencies

Phân VPS:

VPS-1: Data collection + Train Stage 1 (Flux)
VPS-2: Train Stage 2 (SR 1K→2K)
VPS-3: Train Stage 3 (SR 2K→4K)

Phase 2: Data Collection

2A — Dataset Stage 1 (1-2M ảnh 1024px + caption)

Crawl: Unsplash, Pexels, LAION-Aesthetics (score > 6.0)
Caption: Opus 4.6 API
Format: WebDataset (tar shards)

2B — Dataset Stage 2-3 (200K-500K ảnh 4K pairs)

Crawl: Unsplash 4K, Flickr CC high-res
Tạo pairs: downscale 4K → 2K → 1K
Augmentation: crop, flip, color jitter, degradation

Phase 3: Training

Stage 1: Fine-tune Flux

Base: Flux Dev / Schnell
Method: LoRA rank 128 hoặc full fine-tune (DeepSpeed ZeRO-2)
Batch size: 8-16 (2x H100)
LR: 1e-5, cosine decay
Steps: 100K-500K
Resolution: 1024x1024

Stage 2: SR 1K→2K

Base: SUPIR / StableSR
Loss: L1 + perceptual + GAN
Batch size: 2-4 per GPU
Steps: 200K-500K

Stage 3: SR 2K→4K

Base: SUPIR / StableSR
Loss: L1 + perceptual + GAN
Batch size: 1-2 per GPU
Steps: 200K-500K

Phase 4: Evaluation

FID score, CLIP score
Human eval
So sánh với Midjourney, DALL-E 3, Flux Pro
Iterate nếu chưa đạt

Phase 5: Serving

FastAPI + Triton Inference Server
Queue: Redis/RabbitMQ
Pipeline: Stage 1 → 2 → 3
API: POST /generate, GET /status, GET /result
TensorRT optimize, target < 30s/ảnh 4K