| --- |
| frameworks: |
| - Pytorch |
| license: Apache License 2.0 |
| tags: [] |
| tasks: |
| - image-to-image |
| base_model: |
| - Qwen/Qwen-Image-Edit |
| base_model_relation: adapter |
| --- |
| |
| # Qwen-Image-Edit 人脸生成图像模型 |
| ## 模型介绍 |
|
|
| 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。 |
|
|
| ## 效果展示 |
|
|
| |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 |
| ``` |
| git clone https://github.com/modelscope/DiffSynth-Studio.git |
| cd DiffSynth-Studio |
| pip install -e . |
| ``` |
|
|
| ```python |
| from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig |
| import torch |
| from modelscope import snapshot_download, dataset_snapshot_download |
| from PIL import Image |
| |
| pipe = QwenImagePipeline.from_pretrained( |
| torch_dtype=torch.bfloat16, |
| device="cuda", |
| model_configs=[ |
| ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), |
| ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), |
| ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), |
| ], |
| tokenizer_config=None, |
| processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), |
| ) |
| snapshot_download("DiffSynth-Studio/Qwen-Image-Edit-F2P", local_dir="models/DiffSynth-Studio/Qwen-Image-Edit-F2P", allow_file_pattern="model.safetensors") |
| pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-Edit-F2P/model.safetensors") |
| dataset_snapshot_download( |
| dataset_id="DiffSynth-Studio/example_image_dataset", |
| local_dir="./data/example_image_dataset", |
| allow_file_pattern="f2p/qwen_woman_face_crop.png" |
| ) |
| face_image = Image.open("data/example_image_dataset/f2p/qwen_woman_face_crop.png").convert("RGB") |
| |
| 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。" |
| image = pipe(prompt, edit_image=face_image, seed=42, num_inference_steps=40, height=1152, width=864) |
| image.save(f"image.jpg") |
| ``` |
| 人脸自动裁剪 |
| ```python |
| import torch |
| from PIL import Image |
| import numpy as np |
| from insightface.app import FaceAnalysis |
| import cv2 |
| |
| class FaceDetector(torch.nn.Module): |
| |
| def __init__(self): |
| super().__init__() |
| providers = ["CUDAExecutionProvider", "CPUExecutionProvider"] |
| provider_options = [{"device_id": 0}, {}] |
| self.app_640 = FaceAnalysis(name='antelopev2', providers=providers, provider_options=provider_options) |
| self.app_640.prepare(ctx_id=0, det_size=(640, 640)) |
| self.app_320 = FaceAnalysis(name='antelopev2', providers=providers, provider_options=provider_options) |
| self.app_320.prepare(ctx_id=0, det_size=(320, 320)) |
| self.app_160 = FaceAnalysis(name='antelopev2', providers=providers, provider_options=provider_options) |
| self.app_160.prepare(ctx_id=0, det_size=(160, 160)) |
| |
| def _detect_face(self, id_image_cv2): |
| face_info = self.app_640.get(id_image_cv2) |
| if len(face_info) > 0: |
| return face_info |
| face_info = self.app_320.get(id_image_cv2) |
| if len(face_info) > 0: |
| return face_info |
| face_info = self.app_160.get(id_image_cv2) |
| return face_info |
| |
| def crop_face(self, id_image): |
| face_info = self._detect_face(cv2.cvtColor(np.array(id_image), cv2.COLOR_RGB2BGR)) |
| if len(face_info) == 0: |
| return None |
| else: |
| bbox = sorted(face_info, key=lambda x: (x['bbox'][2] - x['bbox'][0]) * (x['bbox'][3] - x['bbox'][1]))[-1]['bbox'] |
| return id_image.crop(list(map(int, bbox))) |
| |
| |
| face_detector = FaceDetector() |
| face_image = face_detector.crop_face(Image.open("image_2.jpg")) |
| face_image.save("face_crop.jpg") |
| |
| ``` |
