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--- |
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frameworks: |
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- Pytorch |
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license: Apache License 2.0 |
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tags: [] |
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tasks: |
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- image-to-image |
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base_model: |
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- Qwen/Qwen-Image-Edit |
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base_model_relation: adapter |
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--- |
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# Qwen-Image-Edit 人脸生成图像模型 |
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## 模型介绍 |
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本模型是基于 [Qwen-Image-Edit](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit) 人脸控制图像生成模型。输入裁剪下的人脸图像，输出该人的人像图片。 |
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## 效果展示 |
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|人脸|生成图1|生成图2|生成图3|生成图4| |
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## 推理代码 |
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``` |
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git clone https://github.com/modelscope/DiffSynth-Studio.git |
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cd DiffSynth-Studio |
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pip install -e . |
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``` |
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```python |
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from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig |
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import torch |
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from modelscope import snapshot_download, dataset_snapshot_download |
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from PIL import Image |
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pipe = QwenImagePipeline.from_pretrained( |
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torch_dtype=torch.bfloat16, |
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device="cuda", |
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model_configs=[ |
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ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), |
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ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), |
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ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), |
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], |
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tokenizer_config=None, |
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processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), |
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) |
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snapshot_download("DiffSynth-Studio/Qwen-Image-Edit-F2P", local_dir="models/DiffSynth-Studio/Qwen-Image-Edit-F2P", allow_file_pattern="model.safetensors") |
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pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-Edit-F2P/model.safetensors") |
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dataset_snapshot_download( |
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dataset_id="DiffSynth-Studio/example_image_dataset", |
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local_dir="./data/example_image_dataset", |
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allow_file_pattern="f2p/qwen_woman_face_crop.png" |
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) |
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face_image = Image.open("data/example_image_dataset/f2p/qwen_woman_face_crop.png").convert("RGB") |
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prompt = "摄影。一个年轻女性穿着黄色连衣裙，站在花田中，背景是五颜六色的花朵和绿色的草地。" |
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image = pipe(prompt, edit_image=face_image, seed=42, num_inference_steps=40, height=1152, width=864) |
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image.save(f"image.jpg") |
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``` |
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人脸自动裁剪 |
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```python |
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import torch |
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from PIL import Image |
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import numpy as np |
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from insightface.app import FaceAnalysis |
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import cv2 |
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class FaceDetector(torch.nn.Module): |
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def __init__(self): |
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super().__init__() |
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providers = ["CUDAExecutionProvider", "CPUExecutionProvider"] |
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provider_options = [{"device_id": 0}, {}] |
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self.app_640 = FaceAnalysis(name='antelopev2', providers=providers, provider_options=provider_options) |
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self.app_640.prepare(ctx_id=0, det_size=(640, 640)) |
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self.app_320 = FaceAnalysis(name='antelopev2', providers=providers, provider_options=provider_options) |
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self.app_320.prepare(ctx_id=0, det_size=(320, 320)) |
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self.app_160 = FaceAnalysis(name='antelopev2', providers=providers, provider_options=provider_options) |
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self.app_160.prepare(ctx_id=0, det_size=(160, 160)) |
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def _detect_face(self, id_image_cv2): |
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face_info = self.app_640.get(id_image_cv2) |
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if len(face_info) > 0: |
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return face_info |
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face_info = self.app_320.get(id_image_cv2) |
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if len(face_info) > 0: |
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return face_info |
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face_info = self.app_160.get(id_image_cv2) |
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return face_info |
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def crop_face(self, id_image): |
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face_info = self._detect_face(cv2.cvtColor(np.array(id_image), cv2.COLOR_RGB2BGR)) |
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if len(face_info) == 0: |
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return None |
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else: |
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bbox = sorted(face_info, key=lambda x: (x['bbox'][2] - x['bbox'][0]) * (x['bbox'][3] - x['bbox'][1]))[-1]['bbox'] |
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return id_image.crop(list(map(int, bbox))) |
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face_detector = FaceDetector() |
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face_image = face_detector.crop_face(Image.open("image_2.jpg")) |
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face_image.save("face_crop.jpg") |
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``` |
