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import torch |
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from diffusers import StableDiffusionControlNetInpaintPipeline, ControlNetModel |
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from controlnet_aux import ZoeDetector |
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from PIL import Image |
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import time |
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print("Lade Face-Fix System (Depth-basiert)...") |
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try: |
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depth_processor = ZoeDetector.from_pretrained("lllyasviel/ControlNet") |
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print("✅ Depth Processor geladen") |
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controlnet_depth = ControlNetModel.from_pretrained( |
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"lllyasviel/control_v11f1e_sd15_depth", |
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torch_dtype=torch.float16 |
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).to("cuda") |
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print("✅ ControlNet Depth geladen") |
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_facefix_pipe = None |
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def _get_facefix_pipeline(model_id: str): |
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global _facefix_pipe |
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if _facefix_pipe is None: |
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print(f"Lade Face-Fix-Pipeline für Modell: {model_id}") |
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_facefix_pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained( |
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model_id, |
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controlnet=[controlnet_depth], |
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torch_dtype=torch.float16, |
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safety_checker=None, |
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).to("cuda") |
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return _facefix_pipe |
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def apply_facefix(image: Image.Image, prompt: str, negative_prompt: str, seed: int, model_id: str): |
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try: |
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pipe = _get_facefix_pipeline(model_id) |
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depth_img = depth_processor(image) |
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face_prompt = prompt + ", perfect face, detailed skin" |
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face_negative = negative_prompt + ", deformed face" |
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fixed = pipe( |
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prompt=face_prompt, |
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negative_prompt=face_negative, |
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image=image, |
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mask_image=None, |
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control_image=[depth_img], |
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controlnet_conditioning_scale=[0.7], |
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strength=0.35, |
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num_inference_steps=20, |
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guidance_scale=7.0, |
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generator=torch.Generator("cuda").manual_seed(seed), |
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).images[0] |
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return fixed |
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except Exception as e: |
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print(f"Face-Fix Error: {e}") |
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return image |
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except Exception as e: |
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print(f"❌ Face-Fix Setup fehlgeschlagen: {e}") |
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def apply_facefix(image: Image.Image, prompt: str, negative_prompt: str, seed: int, model_id: str): |
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print("⚠️ Face-Fix nicht verfügbar, gebe Original zurück") |
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return image |
