继续完善逻辑

app.py

@@ -121,6 +121,25 @@ def generate(
         torch.cuda.empty_cache()
         gc.collect()
 
+         # Input validation
+        prompt = validate_prompt(prompt)
+        if negative_prompt:
+            negative_prompt = negative_prompt.encode('utf-8').decode('utf-8')
+        
+        validate_dimensions(width, height)
+
+        # Set up generation
+        generator = utils.seed_everything(seed)
+
+        width, height = utils.preprocess_image_dimensions(width, height)
+
+        # Set up pipeline
+        backup_scheduler = pipe.scheduler
+        pipe.scheduler = utils.get_scheduler(pipe.scheduler.config, scheduler)
+
+        upscaler_pipe = StableDiffusionXLImg2ImgPipeline(**pipe.components)
+        
+
         return None
     except GenerationError as e:
         logger.warning(f"Generation validation error: {str(e)}")

utils.py

@@ -42,3 +42,37 @@ def load_pipeline(model_name: str, device: torch.device, hf_token: Optional[str]
     except Exception as e:
         logging.error(f"Failed to load pipeline: {str(e)}", exc_info=True)
         raise
+
+def seed_everything(seed: int) -> torch.Generator:
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    np.random.seed(seed)
+    generator = torch.Generator()
+    generator.manual_seed(seed)
+    return generator
+
+def preprocess_image_dimensions(width, height):
+    if width % 8 != 0:
+        width = width - (width % 8)
+    if height % 8 != 0:
+        height = height - (height % 8)
+    return width, height
+
+def get_scheduler(scheduler_config: Dict, name: str) -> Optional[Callable]:
+    scheduler_factory_map = {
+        "DPM++ 2M Karras": lambda: DPMSolverMultistepScheduler.from_config(
+            scheduler_config, use_karras_sigmas=True
+        ),
+        "DPM++ SDE Karras": lambda: DPMSolverSinglestepScheduler.from_config(
+            scheduler_config, use_karras_sigmas=True
+        ),
+        "DPM++ 2M SDE Karras": lambda: DPMSolverMultistepScheduler.from_config(
+            scheduler_config, use_karras_sigmas=True, algorithm_type="sde-dpmsolver++"
+        ),
+        "Euler": lambda: EulerDiscreteScheduler.from_config(scheduler_config),
+        "Euler a": lambda: EulerAncestralDiscreteScheduler.from_config(
+            scheduler_config
+        ),
+        "DDIM": lambda: DDIMScheduler.from_config(scheduler_config),
+    }
+    return scheduler_factory_map.get(name, lambda: None)()