Possible import error fix

nodes.py

@@ -81,7 +81,7 @@ class DownloadAndLoadDynamiCrafterModel:
             'ckpt_name': model,
             'fp8_unet': fp8_unet
         }
-        if not hasattr(self, 'model') or self.model == None or custom_config != self.current_config:
+        if not hasattr(self, 'model') or self.model is None or custom_config != self.current_config:
             self.current_config = custom_config
             download_path = os.path.join(folder_paths.models_dir, "checkpoints", "dynamicrafter")
             model_path = os.path.join(download_path, model)
@@ -557,8 +557,6 @@ class DynamiCrafterI2V:
             text_emb = positive[0][0].to(device)
 
             cond_images = clip_vision.encode_image(image.permute(0, 2, 3, 1))['last_hidden_state'].to(device)
-            cond_images = torch.sum(cond_images, dim=0).unsqueeze(0)
-            cond_images = torch.mean(cond_images, dim=0).unsqueeze(0)
 
             img_emb = self.model.image_proj_model(cond_images)
 
@@ -816,11 +814,10 @@ class ToonCrafterInterpolation:
         pbar = comfy.utils.ProgressBar(len(images) - 1)
         autocast_condition = (dtype != torch.float32) and not comfy.model_management.is_device_mps(device)
         with torch.autocast(comfy.model_management.get_autocast_device(device), dtype=dtype) if autocast_condition else nullcontext():
-            for i in range(len(images) - 1) if len(images) > 1 else range(len(images)):
+            for i in range(len(images) - 1):
                 videos, videos2 = None, None
                 mm.soft_empty_cache()
                 image = images[i].unsqueeze(0)
-                if len(images) !=1:
                 image2 = images[i+1].unsqueeze(0)
                 
                 B, C, H, W = image.shape
@@ -834,14 +831,10 @@ class ToonCrafterInterpolation:
 
                 encode_pixels = image.unsqueeze(2) * 2 - 1
                 videos = encode_pixels # bc1hw
-                videos = repeat(videos, 'b c t h w -> b c (repeat t) h w', repeat=frames // 2)
+                videos = repeat(videos, 'b c t h w -> b c (repeat t) h w', repeat=frames//2)
-
-                if len(images) == 1:
-                    videos = torch.cat([videos, videos], dim=2)
-                else:
                 encode_pixels = image2.unsqueeze(2) * 2 - 1
                 videos2 = encode_pixels # bc1hw
-                    videos2 = repeat(videos2, 'b c t h w -> b c (repeat t) h w', repeat=frames // 2)
+                videos2 = repeat(videos2, 'b c t h w -> b c (repeat t) h w', repeat=frames//2)
                 videos = torch.cat([videos, videos2], dim=2)                              
 
                 try:
@@ -854,25 +847,23 @@ class ToonCrafterInterpolation:
 
                 img_tensor_repeat = torch.zeros_like(z)
                 img_tensor_repeat[:,:,:1,:,:] = z[:,:,:1,:,:]
-                if len(images) !=1:
                 img_tensor_repeat[:,:,-1:,:,:] = z[:,:,-1:,:,:]
 
                 self.model.first_stage_model.to(offload_device)
 
                 text_emb = positive[0][0].to(device)
                 
-                self.model.image_proj_model.to(device)
                 cond_images = clip_vision.encode_image(image.permute(0, 2, 3, 1))["last_hidden_state"].to(device)
-                img_emb = self.model.image_proj_model(cond_images)
-                if len(images) !=1:
                 cond_images2 = clip_vision.encode_image(image2.permute(0, 2, 3, 1))["last_hidden_state"].to(device)
+
+                self.model.image_proj_model.to(device)
+
+                img_emb = self.model.image_proj_model(cond_images)
                 img_emb2 = self.model.image_proj_model(cond_images2)
                 img_embeds = img_emb * image_embed_ratio + img_emb2 * (1.0 - image_embed_ratio)
-                else:
-                    img_embeds = img_emb
 
                 imtext_cond = torch.cat([text_emb, img_embeds], dim=1)
-                del cond_images, img_emb, text_emb
+                del cond_images, img_emb, img_emb2, text_emb
 
                 if comfy.model_management.is_device_mps(device):
                     fs = torch.tensor([fs], dtype=torch.float32, device=self.model.device)

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-dynamicrafterwrapper"
 description = "Wrapper nodes to use Dynami/ToonCrafter image2video and frame interpolation models in ComfyUI"
-version = "1.0.2"
+version = "1.0.3"
 license = "Apache-2.0"
 dependencies = ["einops>=0.3.0", "numpy>=1.24.2", "omegaconf>=2.1.1", "pytorch_lightning>=2.2.1", "tqdm>=4.65.0", "transformers>=4.25.1", "timm"]
 

utils/utils.py

@@ -34,12 +34,15 @@ def instantiate_from_config(config):
 
 
 def get_obj_from_str(string, reload=False):
-    package_directory_name = os.path.basename(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
     module, cls = string.rsplit(".", 1)
     if reload:
         module_imp = importlib.import_module(module)
         importlib.reload(module_imp)
-    return getattr(importlib.import_module(module, package=package_directory_name), cls)
+    try:
+        obj = getattr(importlib.import_module(module, package=os.path.basename(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))), cls)
+    except:
+        obj = getattr(importlib.import_module(module, package=os.path.dirname(os.path.dirname(os.path.abspath( __file__ )))), cls)
+    return obj
 
 
 def load_npz_from_dir(data_dir):