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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
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+conceptmix_results.png filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+language: en
+license: apache-2.0
+library_name: diffusers
+base_model: black-forest-labs/FLUX.1-dev
+tags:
+- flux
+- diffusers
+- lora
+- cmo
+- text-to-image
+pipeline_tag: text-to-image
+---
+
+# FLUX.1-dev-CMO
+
+<p align="center">
+  🤗 <a href="[https://huggingface.co/](https://huggingface.co/)Bruece/FLUX.1-dev-CMO"><b>Hugging Face</b></a> | 
+  📄 <a href="[https://arxiv.org/abs/2603.18528](https://arxiv.org/abs/2603.18528)"><b>arXiv</b></a> 
+</p>
+
+**🌟 Official LoRA Adapter for [Correlation-Weighted Multi-Reward Optimization for Compositional Generation](https://arxiv.org/abs/2603.18528)**
+
+This repository contains the official LoRA adapter for [black-forest-labs/FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev) fine-tuned using **CMO (Correlation-Weighted Multi-Reward Optimization)** to enhance compositional generation capabilities.
+
+## 🚀 Usage
+
+Below is the code to load and merge the LoRA adapter with the base FLUX.1-dev model.
+
+```python
+import torch
+from diffusers import FluxPipeline
+from peft import PeftModel
+
+model_id = "black-forest-labs/FLUX.1-dev"
+lora_ckpt_path = "Bruece/FLUX.1-dev-CMO"
+device = "cuda"
+
+pipe = FluxPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
+pipe.transformer = PeftModel.from_pretrained(pipe.transformer, lora_ckpt_path)
+pipe.transformer = pipe.transformer.merge_and_unload()
+pipe = pipe.to(device)
+
+prompt = 'a photo of a black kite and a green bear'
+image = pipe(prompt, height=512, width=512, num_inference_steps=40, guidance_scale=4.5).images[0]  
+image.save("flux_cmo_lora.png")
+```
+
+## 🖼️ Qualitative Results
+
+<details>
+<summary>ConceptMix (<a href="[https://arxiv.org/abs/2408.14339](https://arxiv.org/abs/2408.14339)">Link</a>)</summary>
+<br>
+<img src="./conceptmix_results.png" alt="ConceptMix Results">
+</details>
+
+<details>
+<summary>GenEval 2 (<a href="[https://arxiv.org/abs/2512.16853](https://arxiv.org/abs/2512.16853)">Link</a>)</summary>
+<br>
+<img src="./GenEval2_results.png" alt="GenEval 2 Results">
+</details>
+
+<details>
+<summary>T2I-CompBench (<a href="[https://arxiv.org/pdf/2307.06350v2](https://arxiv.org/pdf/2307.06350v2)">Link</a>)</summary>
+<br>
+<img src="./T2I-CompBench_results.png" alt="T2I-CompBench Results">
+</details>
+
+## 🛠️ Training Details
+- **Base Model:** FLUX.1-dev
+- **Algorithm:** Correlation-Weighted Multi-Reward Optimization (CMO)
+- **Precision:** bfloat16
+
+## 📜 Citation
+
+If you find this model useful for your research, please cite:
+
+```bibtex
+@article{wi2026correlation,
+  title={Correlation-Weighted Multi-Reward Optimization for Compositional Generation},
+  author={Wi, Jungmyung and Kim, Hyunsoo and Kim, Donghyun},
+  journal={arXiv preprint arXiv:2603.18528},
+  year={2026}
+}
+```

adapter_config.json

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+{
+  "alpha_pattern": {},
+  "auto_mapping": {
+    "base_model_class": "FluxTransformer2DModel",
+    "parent_library": "diffusers.models.transformers.transformer_flux"
+  },
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_dropout": 0.0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "attn.add_v_proj",
+    "ff_context.net.0.proj",
+    "attn.add_k_proj",
+    "attn.to_q",
+    "ff.net.0.proj",
+    "ff_context.net.2",
+    "attn.to_out.0",
+    "attn.to_v",
+    "ff.net.2",
+    "attn.add_q_proj",
+    "attn.to_k",
+    "attn.to_add_out"
+  ],
+  "task_type": null,
+  "use_dora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

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