trying to fix mat1 and mat2

app.py

@@ -12,7 +12,7 @@ MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 2048
 MIN_IMAGE_SIZE = 256
 DEFAULT_IMAGE_SIZE = 1024
-MAX_PROMPT_LENGTH = 500
+MAX_PROMPT_LENGTH = 256  # Changed to 256 as per FLUX.1 schnell requirements
 
 # Check for GPU availability
 device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -23,7 +23,10 @@ dtype = torch.float16 if device == "cuda" else torch.float32
 
 def load_model():
     try:
-        return DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=dtype).to(device)
+        pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=dtype).to(device)
+        pipe.enable_model_cpu_offload()
+        pipe.enable_attention_slicing()
+        return pipe
     except Exception as e:
         raise RuntimeError(f"Failed to load the model: {str(e)}")
 
@@ -65,19 +68,23 @@ def infer(prompt, init_image=None, seed=42, randomize_seed=False, width=DEFAULT_
             seed = random.randint(0, MAX_SEED)
         generator = torch.Generator(device=device).manual_seed(seed)
         
+        # Ensure max_sequence_length is not more than 256
+        max_sequence_length = min(MAX_PROMPT_LENGTH, len(prompt))
+        
         if init_image is not None:
             init_image = init_image.convert("RGB")
             init_image = preprocess_image(init_image, (height, width))
             latents = encode_image(init_image, pipe.vae)
-            latents = torch.nn.functional.interpolate(latents, size=(height // 8, width // 8), mode='bilinear')
+            latents = torch.nn.functional.interpolate(latents, size=(height // 8, width // 8), mode='bilinear', align_corners=False)
             image = pipe(
                 prompt=prompt,
+                image=latents,  # Changed from latents=latents to image=latents
                 height=height,
                 width=width,
                 num_inference_steps=num_inference_steps,
                 generator=generator,
                 guidance_scale=0.0,
-                latents=latents
+                max_sequence_length=max_sequence_length
             ).images[0]
         else:
             image = pipe(
@@ -86,7 +93,8 @@ def infer(prompt, init_image=None, seed=42, randomize_seed=False, width=DEFAULT_
                 width=width,
                 num_inference_steps=num_inference_steps,
                 generator=generator,
-                guidance_scale=0.0
+                guidance_scale=0.0,
+                max_sequence_length=max_sequence_length
             ).images[0]
         
         return image, seed