Update app.py

app.py

@@ -10,12 +10,12 @@ import spaces
 device = "cuda" if torch.cuda.is_available() else "cpu"
 dtype = torch.float16
 
-repo = "dataautogpt3/OpenDalleV1.1"
+# repo = "dataautogpt3/OpenDalleV1.1"
 repo = "stabilityai/stable-diffusion-3-medium"
-repo="stabilityai/stable-diffusion-3-medium-tensorrt"
+# repo="stabilityai/stable-diffusion-3-medium-tensorrt"
 
-pipe = StableDiffusion3Pipeline.from_pretrained(repo, torch_dtype=torch.float16).to(device)
-# pipeline = AutoPipelineForText2Image.from_pretrained(repo, torch_dtype=torch.float16).to('cuda')        
+# pipe = StableDiffusion3Pipeline.from_pretrained(repo, torch_dtype=torch.float16).to(device)
+pipeline = AutoPipelineForText2Image.from_pretrained(repo, torch_dtype=torch.float16).to('cuda')        
 
 
 MAX_SEED = np.iinfo(np.int32).max
@@ -27,20 +27,20 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
         
-    # generator = torch.Generator().manual_seed(seed)
-    # image = pipeline(prompt).images[0]
+    generator = torch.Generator().manual_seed(seed)
+    image = pipeline(prompt).images[0]
 
 
     
-    image = pipe(
-        prompt = prompt, 
-        negative_prompt = negative_prompt,
-        guidance_scale = guidance_scale, 
-        num_inference_steps = num_inference_steps, 
-        width = width, 
-        height = height,
-        generator = generator
-    ).images[0] 
+    # image = pipe(
+    #     prompt = prompt, 
+    #     negative_prompt = negative_prompt,
+    #     guidance_scale = guidance_scale, 
+    #     num_inference_steps = num_inference_steps, 
+    #     width = width, 
+    #     height = height,
+    #     generator = generator
+    # ).images[0] 
     
     return image, seed