ighoshsubho
/

lora-grpo-flux-dev

@@ -6,202 +6,146 @@ tags:
   - flux
   - text-to-image
   - grpo
 ---
-# Model Card for Model ID
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-## Model Details
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-### Framework versions
-- PEFT 0.10.0

   - flux
   - text-to-image
   - grpo
+  - reinforcement-learning
+  - flow-matching
+  - pickscore
+license: apache-2.0
+language:
+  - en
+pipeline_tag: text-to-image
 ---
+# FLUX.1-dev LoRA Fine-tuned with Flow-GRPO
+This LoRA (Low-Rank Adaptation) model is a fine-tuned version of [FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev) using **Flow-GRPO** (Flow-based Group Relative Policy Optimization), a novel reinforcement learning technique for flow matching models.
+## Model Description
+This model was trained using the Flow-GRPO methodology described in the paper ["Flow-GRPO: Training Flow Matching Models via Online RL"](https://arxiv.org/abs/2505.05470). Flow-GRPO integrates online reinforcement learning into flow matching models by:
+1. **ODE-to-SDE conversion**: Transforms deterministic flow matching into stochastic sampling for RL exploration
+2. **Denoising reduction**: Uses fewer denoising steps during training while maintaining full quality at inference
+3. **Human preference optimization**: Trained with PickScore reward to align with human preferences
 ## Training Details
+### Core Configuration
+- **Base Model**: FLUX.1-dev
+- **Training Method**: Flow-GRPO with PickScore reward
+- **Resolution**: 512×512
+- **Mixed Precision**: bfloat16
+- **Seed**: 42
+### LoRA Configuration
+- **LoRA Enabled**: True
+- **Rank**: Not specified in config (typically 32-64)
+- **Target Modules**: Transformer layers
+### Training Hyperparameters
+- **Learning Rate**: 5e-5
+- **Batch Size**: 1 (with gradient accumulation: 32 steps)
+- **Optimizer**: 8-bit AdamW
+  - β₁: 0.9
+  - β₂: 0.999
+  - Weight Decay: 1e-4
+  - Epsilon: 1e-8
+- **Gradient Clipping**: Max norm 1.0
+- **Max Epochs**: 100,000
+- **Save Frequency**: Every 100 steps
+### Flow-GRPO Specific
+- **Reward Function**: PickScore (human preference)
+- **Beta (KL penalty)**: 0.001
+- **Clip Range**: 0.2
+- **Advantage Clipping**: Max 5.0
+- **Timestep Fraction**: 0.2
+- **Guidance Scale**: 3.5
+### Sampling Configuration
+- **Training Steps**: 2 (denoising reduction)
+- **Evaluation Steps**: 4
+- **Images per Prompt**: 4
+- **Batches per Epoch**: 4
+## Usage
+### With Diffusers
+```python
+import torch
+from diffusers import FluxPipeline
+# Load the base model
+pipe = FluxPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-dev",
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+# Load the LoRA weights
+pipe.load_lora_weights("ighoshsubho/lora-grpo-flux-dev")
+# Generate an image
+prompt = "A serene landscape with mountains and a lake at sunset"
+image = pipe(
+    prompt,
+    height=512,
+    width=512,
+    guidance_scale=3.5,
+    num_inference_steps=20,
+    max_sequence_length=256,
+).images[0]
+image.save("generated_image.png")
+```
+### Adjusting LoRA Strength
+```python
+# You can adjust the LoRA influence
+pipe.set_adapters(["default"], adapter_weights=[0.8])  # 80% LoRA influence
+```
+## Training Data & Objectives
+- **Dataset**: Custom PickScore dataset for human preference alignment
+- **Prompt Function**: General OCR prompts
+- **Optimization Target**: Maximizing PickScore while maintaining image quality
+- **KL Regularization**: Prevents reward hacking and maintains model stability
+## Performance Improvements
+This model demonstrates improvements in:
+- **Human preference alignment** through PickScore optimization
+- **Text rendering quality** via OCR-focused training
+- **Compositional understanding** enhanced by Flow-GRPO's exploration mechanism
+- **Stable training** with minimal reward hacking due to KL regularization
+## Technical Notes
+- Uses **denoising reduction** during training (2 steps) for efficiency
+- Maintains full quality with standard inference steps (20-50)
+- Trained with **mixed precision** (bfloat16) for memory efficiency
+- **8-bit AdamW** optimizer reduces memory footprint
+- **Gradient accumulation** (32 steps) enables effective large batch training
+## Limitations
+- Optimized for 512×512 resolution
+- Focused on PickScore preferences (may not generalize to all aesthetic preferences)
+- LoRA adaptation may have reduced capacity compared to full fine-tuning
+## Citation
+If you use this model, please cite the Flow-GRPO paper:
+```bibtex
+@article{liu2025flow,
+  title={Flow-GRPO: Training Flow Matching Models via Online RL},
+  author={Liu, Jie and Liu, Gongye and Liang, Jiajun and Li, Yangguang and Liu, Jiaheng and Wang, Xintao and Wan, Pengfei and Zhang, Di and Ouyang, Wanli},
+  journal={arXiv preprint arXiv:2505.05470},
+  year={2025}
+}
+```
+## License
+This model is released under the Apache 2.0 License, following the base FLUX.1-dev model license.