add model

README.md

@@ -1,3 +1,76 @@
 ---
 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="https://arxiv.org/abs/2406.04314"><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/1.png" alt="teaser example 0" width="200"/></td>
+    <td><img src="assets/2.png" alt="teaser example 1" width="200"/></td>
+    <td><img src="assets/3.png" alt="teaser example 2" width="200"/></td>
+    <td><img src="assets/4.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>
+
+## Model Description
+
+This model is fine-tuned from [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5). It has been trained on 4,000 prompts for 10 epochs.
+
+This is a merged checkpoint that combines the LoRA checkpoint with the base model [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5). If you want to access the LoRA checkpoint, please visit [SPO-SD-v1-5_4k-p_10ep_LoRA](https://huggingface.co/SPO-Diffusion-Models/SPO-SD-v1-5_4k-p_10ep_LoRA). We also provide a LoRA checkpoint compatible with [stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui), which can be accessed [here](https://civitai.com/models/526379/spo-sd-v1-54k-p10eplorawebui).
+
+
+## A quick example
+```python
+from diffusers import StableDiffusionPipeline
+import torch
+
+# load pipeline
+inference_dtype = torch.float16
+pipe = StableDiffusionPipeline.from_pretrained(
+    "SPO-Diffusion-Models/SPO-SD-v1-5_4k-p_10ep", 
+    torch_dtype=inference_dtype,
+)
+pipe.to('cuda')
+
+generator=torch.Generator(device='cuda').manual_seed(42)
+image = pipe(
+    prompt='an image of a beautiful lake',
+    generator=generator,
+    guidance_scale=7.5,
+    output_type='pil',
+).images[0]
+image.save('lake.png')
+```
+
+## Citation
+If you find our work or codebase useful, please consider giving us a star and citing our work.
+```
+@article{liang2024step,
+  title={Step-aware Preference Optimization: Aligning Preference with Denoising Performance at Each Step},
+  author={Liang, Zhanhao and Yuan, Yuhui and Gu, Shuyang and Chen, Bohan and Hang, Tiankai and Li, Ji and Zheng, Liang},
+  journal={arXiv preprint arXiv:2406.04314},
+  year={2024}
+}
+```

feature_extractor/preprocessor_config.json

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+{
+  "_valid_processor_keys": [
+    "images",
+    "do_resize",
+    "size",
+    "resample",
+    "do_center_crop",
+    "crop_size",
+    "do_rescale",
+    "rescale_factor",
+    "do_normalize",
+    "image_mean",
+    "image_std",
+    "do_convert_rgb",
+    "return_tensors",
+    "data_format",
+    "input_data_format"
+  ],
+  "crop_size": {
+    "height": 224,
+    "width": 224
+  },
+  "do_center_crop": true,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_processor_type": "CLIPImageProcessor",
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "shortest_edge": 224
+  }
+}

model_index.json

@@ -0,0 +1,38 @@
+{
+  "_class_name": "StableDiffusionPipeline",
+  "_diffusers_version": "0.26.1",
+  "_name_or_path": "runwayml/stable-diffusion-v1-5",
+  "feature_extractor": [
+    "transformers",
+    "CLIPImageProcessor"
+  ],
+  "image_encoder": [
+    null,
+    null
+  ],
+  "requires_safety_checker": true,
+  "safety_checker": [
+    "stable_diffusion",
+    "StableDiffusionSafetyChecker"
+  ],
+  "scheduler": [
+    "diffusers",
+    "PNDMScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "CLIPTextModel"
+  ],
+  "tokenizer": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "unet": [
+    "diffusers",
+    "UNet2DConditionModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}

safety_checker/config.json

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+{
+  "_name_or_path": "/detr_blob/liuzeyu/checkpoints/huggingface/models--runwayml--stable-diffusion-v1-5/snapshots/1d0c4ebf6ff58a5caecab40fa1406526bca4b5b9/safety_checker",
+  "architectures": [
+    "StableDiffusionSafetyChecker"
+  ],
+  "initializer_factor": 1.0,
+  "logit_scale_init_value": 2.6592,
+  "model_type": "clip",
+  "projection_dim": 768,
+  "text_config": {
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+    "hidden_size": 768,
+    "intermediate_size": 3072,
+    "model_type": "clip_text_model",
+    "num_attention_heads": 12
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+  "torch_dtype": "float16",
+  "transformers_version": "4.41.2",
+  "vision_config": {
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+    "hidden_size": 1024,
+    "intermediate_size": 4096,
+    "model_type": "clip_vision_model",
+    "num_attention_heads": 16,
+    "num_hidden_layers": 24,
+    "patch_size": 14
+  }
+}

safety_checker/model.safetensors

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scheduler/scheduler_config.json

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+{
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+  "_diffusers_version": "0.29.0",
+  "beta_end": 0.012,
+  "beta_schedule": "scaled_linear",
+  "beta_start": 0.00085,
+  "clip_sample": false,
+  "num_train_timesteps": 1000,
+  "prediction_type": "epsilon",
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+  "skip_prk_steps": true,
+  "steps_offset": 1,
+  "timestep_spacing": "leading",
+  "trained_betas": null
+}

text_encoder/config.json

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+  "architectures": [
+    "CLIPTextModel"
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+  "dropout": 0.0,
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+  "hidden_act": "quick_gelu",
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+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 77,
+  "model_type": "clip_text_model",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "projection_dim": 768,
+  "torch_dtype": "float16",
+  "transformers_version": "4.41.2",
+  "vocab_size": 49408
+}

text_encoder/model.safetensors

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tokenizer/special_tokens_map.json

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+{
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+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
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+    "single_word": false
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tokenizer/tokenizer_config.json

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+  "added_tokens_decoder": {
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+      "special": true
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+  "bos_token": "<|startoftext|>",
+  "clean_up_tokenization_spaces": true,
+  "do_lower_case": true,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "model_max_length": 77,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "CLIPTokenizer",
+  "unk_token": "<|endoftext|>"
+}

unet/config.json

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+{
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+  "_diffusers_version": "0.29.0",
+  "_name_or_path": "runwayml/stable-diffusion-v1-5",
+  "act_fn": "silu",
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unet/diffusion_pytorch_model.safetensors

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vae/config.json

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+{
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+  "_diffusers_version": "0.29.0",
+  "_name_or_path": "/detr_blob/liuzeyu/checkpoints/huggingface/models--runwayml--stable-diffusion-v1-5/snapshots/1d0c4ebf6ff58a5caecab40fa1406526bca4b5b9/vae",
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