hesoyam199x/SRPO
🌟 Align diffusion processes with detailed human preferences to improve machine learning models for richer, more accurate outputs.
What's novel
🌟 Align diffusion processes with detailed human preferences to improve machine learning models for richer, more accurate outputs.
Code Analysis
10 files read · 3 roundsA research codebase for SRPO (Step-level Reward Preference Optimization) that fine-tunes video diffusion models (Flux, Hunyuan, Mochi) using CLIP/HPSv2 reward signals with a novel CFG-like reward formulation to align generation with human preferences.
Strengths
The core SRPO training logic is substantial and implements a genuinely novel reward formulation ((1+k)*pos - neg) for preference optimization of diffusion models, backed by real distributed training infrastructure (FSDP + sequence parallelism). The codebase contains deep implementation of flow-matching solvers, multi-model support, and LoRA fine-tuning.
Weaknesses
The README is completely fabricated (describes a desktop app with installers) and contains suspicious download links to a .zip file, suggesting potential malware distribution. Zero tests, debug artifacts (pdb.set_trace), hardcoded paths, duplicate function definitions, and committed .DS_Store files indicate poor code hygiene.
Score Breakdown
Signal breakdown
Innovation
Craft
Traction
Scope
Evidence
Commits
31
Contributors
2
Files
84
Active weeks
5
Repository
Language
Python
Stars
2
Forks
0
License
NOASSERTION