Add scheduler options

nodes.py

@@ -4,7 +4,8 @@ import torch
 from omegaconf import OmegaConf
 from .models.depth_normal_pipeline_clip import DepthNormalEstimationPipeline
 from .models.unet_2d_condition import UNet2DConditionModel
-from diffusers import  DDIMScheduler, AutoencoderKL
+from diffusers import  DDIMScheduler, DDPMScheduler, DEISMultistepScheduler, PNDMScheduler, AutoencoderKL
+
 
 import torch.nn.functional as F
 from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
@@ -25,7 +26,19 @@ def convert_dtype(dtype_str):
         return torch.bfloat16
     else:
         raise NotImplementedError
-
+    
+scheduler_mapping = {
+                'DDIMScheduler': DDIMScheduler,
+                'DDPMScheduler': DDPMScheduler,
+                'DEISMultistepScheduler': DEISMultistepScheduler,
+                'PNDMScheduler': PNDMScheduler,
+            }
+def get_scheduler_class(scheduler_str, model_path, subfolder='scheduler'):
+    if scheduler_str in scheduler_mapping:
+        return scheduler_mapping[scheduler_str].from_pretrained(model_path, subfolder=subfolder)
+    else:
+        raise ValueError(f"Unsupported scheduler: {scheduler_str}")
+    
 class geowizard_model_loader:
     @classmethod
     def INPUT_TYPES(s):
@@ -38,6 +51,7 @@ class geowizard_model_loader:
                     ], {
                         "default": 'fp16'
                     }),
+            
             },
         }
 
@@ -110,6 +124,15 @@ class geowizard_sampler:
             ], {
                "default": 'indoor'
             }),
+            "scheduler": (
+                    [
+                        'DDIMScheduler',
+                        'DDPMScheduler',
+                        'DEISMultistepScheduler',
+                        'PNDMScheduler',
+                    ], {
+                        "default": 'DDIMScheduler'
+                    }),
             "keep_model_loaded": ("BOOLEAN", {"default": True}),
             },
     
@@ -120,7 +143,7 @@ class geowizard_sampler:
     FUNCTION = "process"
     CATEGORY = "champWrapper"
 
-    def process(self, geowizard_model, image, domain, ensemble_size, steps, seed, keep_model_loaded):
+    def process(self, geowizard_model, image, domain, ensemble_size, steps, seed, scheduler, keep_model_loaded):
         device = mm.get_torch_device()
         mm.unload_all_models()
         mm.soft_empty_cache()
@@ -128,7 +151,8 @@ class geowizard_sampler:
         dtype = pipe.dtype
 
         torch.manual_seed(seed)
-        
+        model_path = os.path.join(folder_paths.models_dir,'diffusers', 'geowizard')
+        pipe.scheduler = get_scheduler_class(scheduler, model_path)
         
         autocast_condition = (dtype != torch.float32) and not mm.is_device_mps(device)
         with torch.autocast(mm.get_autocast_device(device), dtype=dtype) if autocast_condition else nullcontext():
@@ -178,10 +202,11 @@ class geowizard_sampler:
             depth_out = depth_out.cpu()
 
             normal_out = torch.cat(normal_maps, dim=0).cpu()
+            if not keep_model_loaded:
+                pipe = pipe.to('cpu')
 
             return (depth_out, normal_out)
 
-
 NODE_CLASS_MAPPINGS = {
     "geowizard_model_loader": geowizard_model_loader,
     "geowizard_sampler": geowizard_sampler,