Update app_diffusers.py

app_diffusers.py

@@ -13,7 +13,6 @@ from diffusers import OvisImagePipeline
 
 logging.set_verbosity_error()
 
-# DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 MAX_SEED = np.iinfo(np.int32).max
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -28,17 +27,16 @@ pipe = OvisImagePipeline.from_pretrained(
 pipe.to(device)
 
 # @spaces.GPU(duration=75)
-def generate(prompt, img_height=1024, img_width=1024, seed=42, steps=50, guidance_scale=5.0):
-    print(f'inference with prompt : {prompt}, size: {img_height}x{img_width}, seed : {seed}, step : {steps}, cfg : {guidance_scale}')
+def generate(prompt, img_height=1024, img_width=1024, seed=42, steps=50):
+    print(f'inference with prompt : {prompt}, size: {img_height}x{img_width}, seed : {seed}, step : {steps}')
     generator = torch.Generator().manual_seed(seed)
     image = pipe(
         prompt, 
         negative_prompt="", 
         height=img_height,
         width=img_width,
-        num_inference_steps=steps, 
-        true_cfg_scale=guidance_scale,
-        generator=generator
+        num_inference_steps=steps,
+        generator=generator,
     ).images[0]
     return image
 
@@ -100,14 +98,6 @@ Built upon [Ovis-U1](https://huggingface.co/spaces/AIDC-AI/Ovis-U1-3B), Ovis-Ima
                         
             with gr.Row():
 
-                guidance_scale = gr.Slider(
-                    label="Guidance Scale",
-                    minimum=1,
-                    maximum=14,
-                    step=0.1,
-                    value=5.0,
-                )
-  
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
                     minimum=1,
@@ -134,7 +124,7 @@ Built upon [Ovis-U1](https://huggingface.co/spaces/AIDC-AI/Ovis-U1-3B), Ovis-Ima
     gr.on(
         triggers=[run_button.click, prompt.submit],
         fn = generate,
-        inputs = [prompt, img_height, img_width, seed, num_inference_steps, guidance_scale],
+        inputs = [prompt, img_height, img_width, seed, num_inference_steps],
         outputs = [result]
     )