Update app.py

app.py

@@ -214,6 +214,7 @@ def load_and_prepare_model():
     
 # Preload and compile both models
 pipe = load_and_prepare_model()
+ip_model = IPAdapterXL(pipe, local_folder, ip_ckpt, device)
 
 MAX_SEED = np.iinfo(np.int32).max
 
@@ -270,7 +271,19 @@ def generate_30(
     samples=1,
     progress=gr.Progress(track_tqdm=True)  # Add progress as a keyword argument
 ):
-    ip_model = IPAdapterXL(pipe, local_folder, ip_ckpt, device)
+    prompt: str = "",
+    negative_prompt: str = "",
+    use_negative_prompt: bool = False,
+    style_selection: str = "",
+    width: int = 768,
+    height: int = 768,
+    guidance_scale: float = 4,
+    num_inference_steps: int = 125,
+    latent_file = gr.File(),  # Add latents file input
+    latent_file_2 = gr.File(),  # Add latents file input
+    samples=1,
+    progress=gr.Progress(track_tqdm=True)  # Add progress as a keyword argument
+):
     seed = random.randint(0, MAX_SEED)
     generator = torch.Generator(device='cuda').manual_seed(seed)
     if latent_file is not None:  # Check if a latent file is provided
@@ -288,10 +301,12 @@ def generate_30(
                 pil_image=sd_image_a,
                 pil_image_2=sd_image_b,
                 prompt=prompt,
+                negative_prompt=negative_prompt,
+                scale=1.0,
                 num_samples=samples,
+                seed=seed,
                 num_inference_steps=num_inference_steps,
                 guidance_scale=guidance_scale,
-                seed=seed
         )
         sd_image[0].save(filename,optimize=False,compress_level=0)
         upload_to_ftp(filename)
@@ -323,7 +338,6 @@ def generate_60(
     samples=1,
     progress=gr.Progress(track_tqdm=True)  # Add progress as a keyword argument
 ):
-    ip_model = IPAdapterXL(pipe, local_folder, ip_ckpt, device)
     seed = random.randint(0, MAX_SEED)
     generator = torch.Generator(device='cuda').manual_seed(seed)
     if latent_file is not None:  # Check if a latent file is provided
@@ -341,10 +355,12 @@ def generate_60(
                 pil_image=sd_image_a,
                 pil_image_2=sd_image_b,
                 prompt=prompt,
+                negative_prompt=negative_prompt,
+                scale=1.0,
                 num_samples=samples,
+                seed=seed,
                 num_inference_steps=num_inference_steps,
                 guidance_scale=guidance_scale,
-                seed=seed
         )
         sd_image[0].save(filename,optimize=False,compress_level=0)
         upload_to_ftp(filename)
@@ -376,7 +392,6 @@ def generate_90(
     samples=1,
     progress=gr.Progress(track_tqdm=True)  # Add progress as a keyword argument
 ):
-    ip_model = IPAdapterXL(pipe, local_folder, ip_ckpt, device)
     seed = random.randint(0, MAX_SEED)
     generator = torch.Generator(device='cuda').manual_seed(seed)
     if latent_file is not None:  # Check if a latent file is provided
@@ -394,10 +409,12 @@ def generate_90(
                 pil_image=sd_image_a,
                 pil_image_2=sd_image_b,
                 prompt=prompt,
+                negative_prompt=negative_prompt,
+                scale=1.0,
                 num_samples=samples,
+                seed=seed,
                 num_inference_steps=num_inference_steps,
                 guidance_scale=guidance_scale,
-                seed=seed
         )
         sd_image[0].save(filename,optimize=False,compress_level=0)
         upload_to_ftp(filename)