Update app.py

app.py

@@ -1,6 +1,6 @@
 import gradio as gr
 import torch
-from diffusers import StableDiffusionXLPipeline, EulerDiscreteScheduler
+from diffusers import DiffusionPipeline, UNet2DConditionModel, LCMScheduler
 from huggingface_hub import hf_hub_download
 import spaces
 from PIL import Image
@@ -22,7 +22,8 @@ CSS = """
 
 # Ensure model and scheduler are initialized in GPU-enabled function
 if torch.cuda.is_available():
-    pipe = StableDiffusionXLPipeline.from_pretrained(base, torch_dtype=torch.float16, variant="fp16").to("cuda")
+    unet = UNet2DConditionModel.from_config(base, subfolder="unet").to("cuda", torch.float16)
+    pipe = DiffusionPipeline.from_pretrained(base, torch_dtype=torch.float16, variant="fp16").to("cuda")
 
 
 # Function 
@@ -35,7 +36,7 @@ def generate_image(prompt, ckpt):
     num_inference_steps = checkpoints[ckpt][1]
 
     if loaded != num_inference_steps:
-        pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", prediction_type="sample" if num_inference_steps==1 else "epsilon")
+        pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", prediction_type="sample" if num_inference_steps==1 else "epsilon")
         pipe.unet.load_state_dict(torch.load(hf_hub_download(repo, checkpoint), map_location="cuda"))
         loaded = num_inference_steps
         
@@ -54,7 +55,7 @@ with gr.Blocks(css=CSS) as demo:
     with gr.Group():
         with gr.Row():
             prompt = gr.Textbox(label='Enter your prompt (English)', scale=8)
-            ckpt = gr.Dropdown(label='Select inference steps',choices=['1-Step', '2-Step', '4-Step', '8-Step'], value='4-Step', interactive=True)
+            ckpt = gr.Dropdown(label='Select inference steps',choices=['1-Step', '4-Step'], value='4-Step', interactive=True)
             submit = gr.Button(scale=1, variant='primary')
     img = gr.Image(label='DMD2 Generated Image')