make cpu compatible

inference.py

@@ -18,6 +18,7 @@ class DiffusionInference:
             provider="hf-inference",
             api_key=self.api_key,
         )
+        self.device = torch.device("cuda" if torch.cuda else "cpu")
 
     def text_to_image(self, prompt, model_name=None, negative_prompt=None, seed=None, **kwargs):
         """
@@ -154,17 +155,17 @@ class DiffusionInference:
         if seed is not None:
             try:
                 # Convert to integer and add to params
-                generator = torch.Generator(device="cuda").manual_seed(seed)
+                generator = torch.Generator(device=self.device).manual_seed(seed)
             except (ValueError, TypeError):
                 # Use random seed if conversion fails
                 random_seed = random.randint(0, 3999999999)  # Max 32-bit integer
-                generator = torch.Generator(device="cuda").manual_seed(random_seed)
+                generator = torch.Generator(device=self.device).manual_seed(random_seed)
                 print(f"Warning: Invalid seed value: {seed}, using random seed {random_seed} instead")
         else:
             # Generate random seed when none is provided
             random_seed = random.randint(0, 3999999999)  # Max 32-bit integer
-            generator = torch.Generator(device="cuda").manual_seed(random_seed)
+            generator = torch.Generator(device=self.device).manual_seed(random_seed)
             print(f"Using random seed: {random_seed}")
-        pipeline = AutoPipelineForText2Image.from_pretrained(model_name, generator=generator, torch_dtype=torch.float16).to("cuda")
+        pipeline = AutoPipelineForText2Image.from_pretrained(model_name, generator=generator, torch_dtype=torch.float16).to(self.device)
         image = pipeline(**kwargs).images[0]
         return image

requirements.txt

@@ -8,7 +8,6 @@ torch
 transformers
 diffusers
 spaces>=0.14.0
-xformers
 numpy
 accelerate
 sentencepiece