Update app.py

app.py

@@ -11,10 +11,10 @@ device = "cuda" if torch.cuda.is_available() else "cpu"
 dtype = torch.float16
 
 repo = "dataautogpt3/OpenDalleV1.1"
-# repo = "stabilityai/stable-diffusion-3-medium"
+repo = "stabilityai/stable-diffusion-3-medium"
 
-# 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
@@ -27,19 +27,19 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
         seed = random.randint(0, MAX_SEED)
         
     # generator = torch.Generator().manual_seed(seed)
-    image = pipeline(prompt).images[0]
+    # 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