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README.md

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+---
+license: apache-2.0
+datasets:
+- yuvalkirstain/pickapic_v1
+language:
+- en
+pipeline_tag: text-to-image
+---
+# Step-aware Preference Optimization: Aligning Preference with Denoising Performance at Each Step
+
+<a href=""><img src="https://img.shields.io/badge/Paper-arXiv-red?style=for-the-badge" height=22.5></a>
+<a href="https://github.com/RockeyCoss/SPO"><img src="https://img.shields.io/badge/Gihub-Code-succees?style=for-the-badge&logo=GitHub" height=22.5></a>
+<a href="https://rockeycoss.github.io/spo.github.io/"><img src="https://img.shields.io/badge/Project-Page-blue?style=for-the-badge" height=22.5></a>
+
+<table>
+  <tr>
+    <td><img src="assets/imgs/0.png" alt="teaser example 0" width="200"/></td>
+    <td><img src="assets/imgs/1.png" alt="teaser example 1" width="200"/></td>
+    <td><img src="assets/imgs/2.png" alt="teaser example 2" width="200"/></td>
+    <td><img src="assets/imgs/3.png" alt="teaser example 3" width="200"/></td>
+  </tr>
+</table>
+
+## Abstract
+<p>
+Recently, Direct Preference Optimization (DPO) has extended its success from aligning large language models (LLMs) to aligning text-to-image diffusion models with human preferences.
+Unlike most existing DPO methods that assume all diffusion steps share a consistent preference order with the final generated images, we argue that this assumption neglects step-specific denoising performance and that preference labels should be tailored to each step's contribution.
+</p>
+<p> 
+To address this limitation, we propose Step-aware Preference Optimization (SPO), a novel post-training approach that independently evaluates and adjusts the denoising performance at each step, using a <em>step-aware preference model</em> and a <em>step-wise resampler</em> to ensure accurate step-aware supervision.
+Specifically, at each denoising step, we sample a pool of images, find a suitable win-lose pair, and, most importantly, randomly select a single image from the pool to initialize the next denoising step. This step-wise resampler process ensures the next win-lose image pair comes from the same image, making the win-lose comparison independent of the previous step. To assess the preferences at each step, we train a separate step-aware preference model that can be applied to both noisy and clean images.
+</p> 
+<p>
+Our experiments with Stable Diffusion v1.5 and SDXL demonstrate that SPO significantly outperforms the latest Diffusion-DPO in aligning generated images with complex, detailed prompts and enhancing aesthetics, while also achieving more than 20&times; times faster in training efficiency. Code and model: <a ref="https://rockeycoss.github.io/spo.github.io/">https://rockeycoss.github.io/spo.github.io/</a>
+</p>
+
+This model is fine-tuned from [stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0). It has been trained on 4,000 prompts for 10 epochs.
+
+## A quick example
+```python
+from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel
+import torch
+
+# load pipeline
+inference_dtype = torch.float16
+pipe = StableDiffusionXLPipeline.from_pretrained(
+    "SPO-Diffusion-Models/SPO-SDXL_4k-p_10ep", 
+    torch_dtype=inference_dtype,
+)
+vae = AutoencoderKL.from_pretrained(
+    'madebyollin/sdxl-vae-fp16-fix',
+    torch_dtype=inference_dtype,
+)
+pipe.vae = vae
+pipe.to('cuda')
+
+generator=torch.Generator(device='cuda').manual_seed(42)
+image = pipe(
+    prompt='a child and a penguin sitting in front of the moon',
+    guidance_scale=5.0,
+    generator=generator,
+    output_type='pil',
+).images[0]
+image.save('moon.png')
+```
+
+## Citation
+If you find our work or codebase useful, please consider giving us a star and citing our work.
+```
+
+```

model_index.json

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+{
+  "_class_name": "StableDiffusionXLPipeline",
+  "_diffusers_version": "0.26.1",
+  "_name_or_path": "stabilityai/stable-diffusion-xl-base-1.0",
+  "feature_extractor": [
+    null,
+    null
+  ],
+  "force_zeros_for_empty_prompt": true,
+  "image_encoder": [
+    null,
+    null
+  ],
+  "scheduler": [
+    "diffusers",
+    "EulerDiscreteScheduler"
+  ],
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+    "CLIPTextModel"
+  ],
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+    "CLIPTextModelWithProjection"
+  ],
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+    "CLIPTokenizer"
+  ],
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+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "unet": [
+    "diffusers",
+    "UNet2DConditionModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}

scheduler/scheduler_config.json

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+  "use_karras_sigmas": false
+}

text_encoder/config.json

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+  "vocab_size": 49408
+}

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tokenizer/special_tokens_map.json

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