Upload PLAN.md with huggingface_hub

PLAN.md

@@ -0,0 +1,103 @@
+# 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