Upload dc.py

dc.py

@@ -679,14 +679,14 @@ class GuiSD:
         #progress(1, desc="Inference preparation completed. Starting inference...")
 
         info_state = "" # for yield version
-        return self.infer_short(self.model, pipe_params)
+        return self.infer_short(self.model, pipe_params), info_state
 ## END MOD
 
 def dynamic_gpu_duration(func, duration, *args):
 
     @spaces.GPU(duration=duration)
     def wrapped_func():
-        yield from func(*args)
+        return func(*args)
 
     return wrapped_func()
 
@@ -712,8 +712,8 @@ def sd_gen_generate_pipeline(*args):
     if load_lora_cpu:
         msg_load_lora = "Updating LoRAs in CPU (Slow but saves GPU usage)..."
 
-    if lora_list != sd_gen.model.lora_memory and lora_list != [None] * 5:
-        yield None, msg_load_lora
+    #if lora_list != sd_gen.model.lora_memory and lora_list != [None] * 5:
+    #    yield None, msg_load_lora
 
     # Load lora in CPU
     if load_lora_cpu:
@@ -747,7 +747,7 @@ def sd_gen_generate_pipeline(*args):
 
     start_time = time.time()
 
-    yield from dynamic_gpu_duration(
+    return dynamic_gpu_duration(
         sd_gen.generate_pipeline,
         gpu_duration_arg,
         *generation_args,
@@ -817,7 +817,7 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
     sd_gen.load_new_model(model_name, vae, TASK_MODEL_LIST[0])
     progress(1, desc="Model loaded.")
     progress(0, desc="Starting Inference...")
-    images = sd_gen_generate_pipeline(prompt, negative_prompt, 1, num_inference_steps,
+    images, info = sd_gen_generate_pipeline(prompt, negative_prompt, 1, num_inference_steps,
         guidance_scale, True, generator, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt,
         lora4, lora4_wt, lora5, lora5_wt, sampler,
         height, width, model_name, vae, TASK_MODEL_LIST[0], None, "Canny", 512, 1024,