Update app.py

app.py

@@ -454,33 +454,14 @@ def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, progress)
             output_type="pil",
             good_vae=good_vae,
         ):
-            print(f"Debug: Yielding image of type {type(img)}")  # Debugging: Check image type
+            print(f"Debug: Yielding image of type {type(img)}")  # Check type of each image
             if isinstance(img, float):
                 print("Error: A float was returned instead of an image.")  # Log if img is a float
                 raise ValueError("Expected an image, but got a float.")  # Raise error if a float is found
             yield img
             final_image = img  # Update final_image with the current image
         return final_image
-        
-def generate_image_to_image(prompt_mash, image_input_path, image_strength, steps, cfg_scale, width, height, seed):
-    pipe_i2i.to("cuda")
-    generator = torch.Generator(device="cuda").manual_seed(seed)
-    image_input = load_image(image_input_path)
-    final_image = pipe_i2i(
-        prompt=prompt_mash,
-        image=image_input,
-        strength=image_strength,
-        num_inference_steps=steps,
-        guidance_scale=cfg_scale,
-        width=width,
-        height=height,
-        generator=generator,
-        joint_attention_kwargs={"scale": 1.0},
-        output_type="pil",
-    ).images[0]
-    return final_image
-
-@spaces.GPU(duration=75)
+
 def run_lora(prompt, cfg_scale, steps, selected_info_1, selected_info_2, selected_info_3, selected_info_4, selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, lora_scale_4, randomize_seed, seed, width, height, loras_state, image_input=None, progress=gr.Progress(track_tqdm=True)):
     print("run_lora function called.")  # Debugging statement
     print(f"Inputs received - Prompt: {prompt}, CFG Scale: {cfg_scale}, Steps: {steps}, Seed: {seed}, Width: {width}, Height: {height}")  # Debugging statement
@@ -552,10 +533,9 @@ def run_lora(prompt, cfg_scale, steps, selected_info_1, selected_info_2, selecte
         if image_input is not None:
             final_image = generate_image_to_image(prompt_mash, image_input, image_strength, steps, cfg_scale, width, height, seed)
             print(f"Debug: generate_image_to_image output type: {type(final_image)}")  # Debugging
-            if not isinstance(final_image, (PIL.Image.Image, torch.Tensor)):  # Check if it's an image
-                print(f"Error: Expected an image, but got {type(final_image)}")
-                raise ValueError("Expected an image from generate_image_to_image, but got a non-image value.")
-            print(f"Debug: generate_image_to_image output value: {final_image}")  # Debugging
+            if isinstance(final_image, float):
+                print("Error: Expected an image but got a float.")  # Log if it's a float
+                raise ValueError("Expected an image, but got a float.")  # Raise error if a float is found
             yield final_image, seed, gr.update(visible=False)
         else:
             image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, progress)
@@ -581,7 +561,6 @@ def run_lora(prompt, cfg_scale, steps, selected_info_1, selected_info_2, selecte
 
 run_lora.zerogpu = True
 
-
 def get_huggingface_safetensors(link):
     split_link = link.split("/")
     if len(split_link) == 4: