diff --git a/model_loading.py b/model_loading.py
index 436212e..1123a4f 100644
--- a/model_loading.py
+++ b/model_loading.py
@@ -727,6 +727,8 @@ class CogVideoXModelLoader:
             model_type = "5b_I2V_1_5"
         elif sd["patch_embed.proj.weight"].shape == (1920, 33, 2, 2):
             model_type = "fun_2b"
+        elif sd["patch_embed.proj.weight"].shape == (1920, 32, 2, 2):
+            model_type = "cogvideox-2b-img2vid"
         elif sd["patch_embed.proj.weight"].shape == (1920, 16, 2, 2):
             model_type = "2b"
         elif sd["patch_embed.proj.weight"].shape == (3072, 32, 2, 2):
@@ -748,7 +750,7 @@ class CogVideoXModelLoader:
         with open(transformer_config_path) as f:
             transformer_config = json.load(f)
 
-            if model_type in ["I2V", "I2V_5b", "fun_5b_pose", "5b_I2V_1_5"]:
+            if model_type in ["I2V", "I2V_5b", "fun_5b_pose", "5b_I2V_1_5", "cogvideox-2b-img2vid"]:
                 transformer_config["in_channels"] = 32
                 if "1_5" in model_type:
                     transformer_config["ofs_embed_dim"] = 512
@@ -774,6 +776,10 @@ class CogVideoXModelLoader:
             #dtype_to_use = base_dtype if any(keyword in name for keyword in params_to_keep) else dtype
             set_module_tensor_to_device(transformer, name, device=transformer_load_device, dtype=base_dtype, value=sd[name])
         del sd
+        # TODO fix for transformer model patch_embed.pos_embedding dtype 
+        #   or at add line  ComfyUI-CogVideoXWrapper/embeddings.py:129 code
+        #   pos_embedding = pos_embedding.to(embeds.device, dtype=embeds.dtype)
+        transformer = transformer.to(base_dtype).to(transformer_load_device)
 
         #scheduler
         with open(scheduler_config_path) as f:
@@ -797,7 +803,8 @@ class CogVideoXModelLoader:
             dtype=base_dtype, 
             is_fun_inpaint="fun" in model.lower() and not ("pose" in model.lower() or "control" in model.lower())
             )
-
+        if "cogvideox-2b-img2vid" == model_type:
+            pipe.input_with_padding = False
         if enable_sequential_cpu_offload:
             pipe.enable_sequential_cpu_offload()