Update app.py

app.py

@@ -10,10 +10,10 @@ from huggingface_hub import InferenceClient
 dtype = torch.bfloat16
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
-pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to(device)
+#pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to(device)
 sdxl = InferenceClient(model="stabilityai/stable-diffusion-xl-base-1.0", token=os.environ['HF_TOKEN'])
 
-#pipeline2Image = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtypes=torch.bfloat16).to("cpu")
+pipeline2Image = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtypes=torch.bfloat16).to("cpu")
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 2048
@@ -23,18 +23,18 @@ MAX_IMAGE_SIZE = 2048
 def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=5.0, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
-    generator = torch.Generator().manual_seed(seed)
-    image = pipe(
-        prompt=prompt, 
-        width=width,
-        height=height,
-        num_inference_steps=num_inference_steps, 
-        generator=generator,
-        guidance_scale=guidance_scale
-    ).images[0] 
+#    generator = torch.Generator().manual_seed(seed)
+#    image = pipe(
+#        prompt=prompt, 
+#        width=width,
+#        height=height,
+#        num_inference_steps=num_inference_steps, 
+#        generator=generator,
+#        guidance_scale=guidance_scale
+#    ).images[0] 
     image = sdxl.text_to_image(
         "Dark gothic city in a misty night, lit by street lamps. A man in a cape is walking away from us",
-        guidance_scale=9, num_inference_steps=50
+        guidance_scale=9, num_inference_steps=num_inference_steps, seed=seed,width=width, height=height)
     )
     
     return image, seed