Upload dc.py

dc.py

@@ -360,8 +360,8 @@ class GuiSD:
             retain_task_model_in_cache=False,
             device="cpu",
         )
-        #self.model.load_beta_styles()
-        self.model.device = torch.device("cpu") #
+        self.model.load_beta_styles()
+        #self.model.device = torch.device("cpu") #
 
     def infer_short(self, model, pipe_params, progress=gr.Progress(track_tqdm=True)):
         #progress(0, desc="Start inference...")
@@ -679,7 +679,7 @@ class GuiSD:
         #progress(1, desc="Inference preparation completed. Starting inference...")
 
         info_state = "" # for yield version
-        return self.infer_short(self.model, pipe_params), info_state
+        return self.infer_short(self.model, pipe_params)
 ## END MOD
 
 def dynamic_gpu_duration(func, duration, *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, info = sd_gen_generate_pipeline(prompt, negative_prompt, 1, num_inference_steps,
+    images = 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,