kand spe

app.py

@@ -58,11 +58,11 @@ MODEL_CONFIGS = {
         "pipeline_class": AuraFlowPipeline,
         #"cache_dir" : cache_dir
     },
-   # "Kandinsky": {
-   #     "repo_id": "kandinsky-community/kandinsky-3",
-   #     "pipeline_class": Kandinsky3Pipeline,
-   #     #"cache_dir" : cache_dir
-   # },
+    "Kandinsky": {
+        "repo_id": "kandinsky-community/kandinsky-3",
+        "pipeline_class": Kandinsky3Pipeline,
+        #"cache_dir" : cache_dir
+    },
     "Hunyuan": {
         "repo_id": "Tencent-Hunyuan/HunyuanDiT-Diffusers",
         "pipeline_class": HunyuanDiTPipeline,
@@ -150,11 +150,19 @@ def load_pipeline(model_name):
     initial_memory = get_process_memory()
     config = MODEL_CONFIGS[model_name]
 
-    pipe = config["pipeline_class"].from_pretrained(
-        config["repo_id"],
-        torch_dtype=TORCH_DTYPE,
-        #cache_dir=cache_dir
-    )
+
+    pipe = None
+    if model_name == "Kandinsky":
+        print("Kandinsky Special")
+        pipe = AutoPipelineForText2Image.from_pretrained(
+            "kandinsky-community/kandinsky-3", variant="fp16", torch_dtype=torch.float16
+        )
+    else:
+        pipe = config["pipeline_class"].from_pretrained(
+            config["repo_id"],
+            torch_dtype=TORCH_DTYPE,
+            # cache_dir=cache_dir
+        )
     pipe = pipe.to(DEVICE)
 
     if hasattr(pipe, 'enable_model_cpu_offload'):