Update app.py

app.py

@@ -49,41 +49,29 @@ def process_images(image,prompt="a girl",strength=0.75,seed=0,inference_step=4,p
     progress(0, desc="Starting")
 
 
-    def process_img2img(image,prompt="a person",strength=0.75,seed=0,num_inference_steps=4):
-        #print("start  process_img2img")
-        if image == None:
+    def process_img2img(image, prompt="a person", strength=0.75, seed=0, num_inference_steps=4):
+        if image is None:
             print("empty input image returned")
             return None
-
-        generators = []
         generator = torch.Generator(device).manual_seed(seed)
-        generators.append(generator)
-        fit_width,fit_height = convert_to_fit_size(image.size)
-        #print(f"fit {width}x{height}")
-        width,height = adjust_to_multiple_of_32(fit_width,fit_height)
-        #print(f"multiple {width}x{height}")
+        fit_width, fit_height = convert_to_fit_size(image.size)
+        width, height = adjust_to_multiple_of_32(fit_width, fit_height)
         image = image.resize((width, height), Image.LANCZOS)
-        #mask_image = mask_image.resize((width, height), Image.NEAREST)
-
-        # more parameter see https://huggingface.co/docs/diffusers/api/pipelines/flux#diffusers.FluxInpaintPipeline
-        #print(prompt)
-        output = pipe(prompt=prompt, image=image,generator=generator,strength=strength,width=width,height=height
-                    ,guidance_scale=0,num_inference_steps=num_inference_steps,max_sequence_length=256)
-
-        pil_image = output.images[0]#Image.fromarray()
-        new_width,new_height = pil_image.size
-
-        # resize back multiple of 32
-        if (new_width!=fit_width) or (new_height!=fit_height):
-            resized_image= pil_image.resize((fit_width,fit_height),Image.LANCZOS)
+    
+        output = pipe(prompt=prompt, image=image, generator=generator, strength=strength, width=width, height=height,
+                    guidance_scale=0, num_inference_steps=num_inference_steps, max_sequence_length=256)
+    
+        pil_image = output.images[0]
+        new_width, new_height = pil_image.size
+    
+        if (new_width != fit_width) or (new_height != fit_height):
+            resized_image = pil_image.resize((fit_width, fit_height), Image.LANCZOS)
             return resized_image
-        
         return pil_image
-
-    output = process_img2img(image,prompt,strength,seed,inference_step)
-   
-    #print("end process_images")
+    
+    output = process_img2img(image, prompt, strength, seed, inference_step)
     return output
+
     
 
 def read_file(path: str) -> str: