controlnet_module.py

import torch
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
from controlnet_aux import OpenposeDetector
from PIL import Image
import random
import cv2
import numpy as np
import gradio as gr


class ControlNetProgressCallback:
    def __init__(self, progress, total_steps):
        self.progress = progress
        self.total_steps = total_steps
        self.current_step = 0

    def __call__(self, pipe, step_index, timestep, callback_kwargs):
        self.current_step = step_index + 1
        progress_percentage = self.current_step / self.total_steps

        # Fortschritt aktualisieren
        if self.progress is not None:
            self.progress(progress_percentage, desc=f"ControlNet: Schritt {self.current_step}/{self.total_steps}")

        print(f"ControlNet Fortschritt: {self.current_step}/{self.total_steps} ({progress_percentage:.1%})")
        return callback_kwargs


class ControlNetProcessor:
    def __init__(self, device="cuda", torch_dtype=torch.float32):
        self.device = device
        self.torch_dtype = torch_dtype
        self.pose_detector = None
        self.controlnet_openpose = None
        self.controlnet_canny = None
        self.pipe_openpose = None
        self.pipe_canny = None

    def load_pose_detector(self):
        """Lädt nur den Pose-Detector"""
        if self.pose_detector is None:
            print("Loading Pose Detector...")
            try:
                self.pose_detector = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")
            except Exception as e:
                print(f"Warnung: Pose-Detector konnte nicht geladen werden: {e}")
        return self.pose_detector

    def extract_pose_simple(self, image):
        """Einfache Pose-Extraktion ohne komplexe Abhängigkeiten"""
        try:
            img_array = np.array(image.convert("RGB"))
            edges = cv2.Canny(img_array, 100, 200)
            pose_image = Image.fromarray(edges).convert("RGB")
            print("⚠️ Verwende Kanten-basierte Pose-Approximation")
            return pose_image
        except Exception as e:
            print(f"Fehler bei einfacher Pose-Extraktion: {e}")
            return image.convert("RGB").resize((512, 512))

    def extract_pose(self, image):
        """Extrahiert Pose-Map aus Bild mit Fallback"""
        try:
            detector = self.load_pose_detector()
            if detector is None:
                return self.extract_pose_simple(image)

            pose_image = detector(image, hand_and_face=True)
            return pose_image
        except Exception as e:
            print(f"Fehler bei Pose-Extraktion: {e}")
            return self.extract_pose_simple(image)

    def extract_canny_edges(self, image):
        """Extrahiert Canny Edges für Umgebungserhaltung"""
        try:
            img_array = np.array(image.convert("RGB"))
            
            # Canny Edge Detection
            gray = cv2.cvtColor(img_array, cv2.COLOR_RGB2GRAY)
            edges = cv2.Canny(gray, 100, 200)
            
            # Zu 3-Kanal Bild konvertieren
            edges_rgb = cv2.cvtColor(edges, cv2.COLOR_GRAY2RGB)
            edges_image = Image.fromarray(edges_rgb)
            
            print("✅ Canny Edge für Umgebungserhaltung erstellt")
            return edges_image
        except Exception as e:
            print(f"Fehler bei Canny Edge Extraction: {e}")
            return image.convert("RGB").resize((512, 512))

    def load_controlnet_pipeline(self, controlnet_type="openpose"):
        """Lädt die passende ControlNet Pipeline"""
        if controlnet_type == "openpose":
            if self.pipe_openpose is None:
                print("Loading OpenPose ControlNet pipeline...")
                try:
                    self.controlnet_openpose = ControlNetModel.from_pretrained(
                        "lllyasviel/sd-controlnet-openpose",
                        torch_dtype=self.torch_dtype
                    )
                    self.pipe_openpose = StableDiffusionControlNetPipeline.from_pretrained(
                        "runwayml/stable-diffusion-v1-5",
                        controlnet=self.controlnet_openpose,
                        torch_dtype=self.torch_dtype,
                        safety_checker=None,
                        requires_safety_checker=False
                    ).to(self.device)

                    from diffusers import EulerAncestralDiscreteScheduler
                    self.pipe_openpose.scheduler = EulerAncestralDiscreteScheduler.from_config(self.pipe_openpose.scheduler.config)
                    self.pipe_openpose.enable_attention_slicing()
                    print("✅ OpenPose ControlNet pipeline loaded successfully!")
                except Exception as e:
                    print(f"Fehler beim Laden von OpenPose ControlNet: {e}")
                    raise
            return self.pipe_openpose

        elif controlnet_type == "canny":
            if self.pipe_canny is None:
                print("Loading Canny ControlNet pipeline...")
                try:
                    self.controlnet_canny = ControlNetModel.from_pretrained(
                        "lllyasviel/sd-controlnet-canny",
                        torch_dtype=self.torch_dtype
                    )
                    self.pipe_canny = StableDiffusionControlNetPipeline.from_pretrained(
                        "runwayml/stable-diffusion-v1-5",
                        controlnet=self.controlnet_canny,
                        torch_dtype=self.torch_dtype,
                        safety_checker=None,
                        requires_safety_checker=False
                    ).to(self.device)

                    from diffusers import EulerAncestralDiscreteScheduler
                    self.pipe_canny.scheduler = EulerAncestralDiscreteScheduler.from_config(self.pipe_canny.scheduler.config)
                    self.pipe_canny.enable_attention_slicing()
                    print("✅ Canny ControlNet pipeline loaded successfully!")
                except Exception as e:
                    print(f"Fehler beim Laden von Canny ControlNet: {e}")
                    raise
            return self.pipe_canny

    def generate_with_controlnet(
        self, image, prompt, negative_prompt,
        steps, guidance_scale, controlnet_strength,
        progress=None, keep_environment=False
    ):
        """Generiert Bild mit ControlNet und Fortschrittsanzeige"""
        try:
            # --- KORREKTE LOGIK ---
            if keep_environment:
                # UMGEBUNG BEIBEHALTEN, PERSON ÄNDERN
                controlnet_type = "canny"  # ✅ Canny behält Umgebung
                print("🎯 ControlNet Modus: Umgebung beibehalten (Canny Edge)")
                conditioning_image = self.extract_canny_edges(image)
                inpaint_input = image  # ✅ ORIGINALBILD für Inpaint!
            else:
                # PERSON BEIBEHALTEN, UMGEBUNG ÄNDERN  
                controlnet_type = "openpose"  # ✅ OpenPose behält Person
                print("🎯 ControlNet Modus: Person beibehalten (OpenPose)")
                conditioning_image = self.extract_pose(image)
                inpaint_input = conditioning_image  # ✅ POSE-MAP für Inpaint

            pipe = self.load_controlnet_pipeline(controlnet_type)

            # Zufälliger Seed
            seed = random.randint(0, 2**32 - 1)
            generator = torch.Generator(device=self.device).manual_seed(seed)
            print(f"ControlNet Seed: {seed}")

            # Fortschritt-Callback
            callback = ControlNetProgressCallback(progress, int(steps)) if progress is not None else None

            print("🔄 ControlNet: Starte Pipeline...")

            # ControlNet Generierung
            result = pipe(
                prompt=prompt,
                image=conditioning_image,
                negative_prompt=negative_prompt,
                num_inference_steps=int(steps),
                guidance_scale=guidance_scale,
                generator=generator,
                controlnet_conditioning_scale=controlnet_strength,
                height=512,
                width=512,
                output_type="pil",
                callback_on_step_end=callback,
                callback_on_step_end_tensor_inputs=[],
            )

            # Debug-Ausgabe Scheduler Steps
            try:
                scheduler = pipe.scheduler
                if hasattr(scheduler, "timesteps"):
                    actual_steps = len(scheduler.timesteps)
                    print(f"🎯 CONTROLNET TATSÄCHLICHE STEPS: {actual_steps} (von {steps} angefordert)")
            except Exception as e:
                print(f"⚠️ Konnte ControlNet Scheduler-Info nicht auslesen: {e}")

            print("✅ ControlNet abgeschlossen!")
            
            # DREI Werte zurückgeben
            return result.images[0], conditioning_image, inpaint_input

        except Exception as e:
            print(f"❌ Fehler in ControlNet: {e}")
            import traceback
            traceback.print_exc()
            error_image = image.convert("RGB").resize((512, 512))
            return error_image, error_image, error_image

    def prepare_inpaint_input(self, image, keep_environment=False):
        """
        Bereitet das Input-Bild für Inpaint vor
        Rückgabe: (image_für_inpaint, conditioning_info)
        """
        if keep_environment:
            # PERSON ÄNDERN: Originalbild an Inpaint übergeben
            print("🎯 Inpaint: Übergebe Originalbild (Person ändern)")
            return image, {"type": "original", "image": image}
        else:
            # UMGEBUNG ÄNDERN: Pose-Map an Inpaint übergeben  
            print("🎯 Inpaint: Übergebe Pose-Map (Umgebung ändern)")
            pose_image = self.extract_pose(image)
            return pose_image, {"type": "pose", "image": pose_image}


# Globale Instanz
device = "cuda" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if device == "cuda" else torch.float32
controlnet_processor = ControlNetProcessor(device=device, torch_dtype=torch_dtype)