Update app.py

app.py

@@ -1,38 +1,33 @@
 import gradio as gr
 import numpy as np
-import random
-from diffusers import DiffusionPipeline
-import torch
+from optimum.intel import OVStableDiffusionPipeline, OVStableDiffusionXLPipeline, OVLatentConsistencyModelPipeline
+from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
 
-if torch.cuda.is_available():
-    torch.cuda.max_memory_allocated(device=device)
-    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
-    pipe.enable_xformers_memory_efficient_attention()
-    pipe = pipe.to(device)
-else: 
-    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
-    pipe = pipe.to(device)
+# model_id = "echarlaix/sdxl-turbo-openvino-int8"
+# model_id = "echarlaix/LCM_Dreamshaper_v7-openvino"
 
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
+#safety_checker = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker")
 
-def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
+model_id = "OpenVINO/LCM_Dreamshaper_v7-int8-ov"
+#pipeline = OVLatentConsistencyModelPipeline.from_pretrained(model_id, compile=False, safety_checker=safety_checker)
+pipeline = OVLatentConsistencyModelPipeline.from_pretrained(model_id, compile=False)
 
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-        
-    generator = torch.Generator().manual_seed(seed)
-    
-    image = pipe(
+
+batch_size, num_images, height, width = 1, 1, 512, 512
+pipeline.reshape(batch_size=batch_size, height=height, width=width, num_images_per_prompt=num_images)
+pipeline.compile()
+
+def infer(prompt, num_inference_steps):
+
+    image = pipeline(
         prompt = prompt, 
         negative_prompt = negative_prompt,
-        guidance_scale = guidance_scale, 
+        # guidance_scale = guidance_scale,
         num_inference_steps = num_inference_steps, 
-        width = width, 
+        width = width,
         height = height,
-        generator = generator
+        num_images_per_prompt=num_images,
     ).images[0] 
     
     return image
@@ -50,19 +45,14 @@ css="""
 }
 """
 
-if torch.cuda.is_available():
-    power_device = "GPU"
-else:
-    power_device = "CPU"
 
 with gr.Blocks(css=css) as demo:
     
     with gr.Column(elem_id="col-container"):
         gr.Markdown(f"""
-        # Text-to-Image Gradio Template
-        Currently running on {power_device}.
+        # Demo : [Fast LCM](https://huggingface.co/OpenVINO/LCM_Dreamshaper_v7-int8-ov) quantized with NNCF ⚡
         """)
-        
+
         with gr.Row():
             
             prompt = gr.Text(
@@ -79,57 +69,21 @@ with gr.Blocks(css=css) as demo:
 
         with gr.Accordion("Advanced Settings", open=False):
             
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-            
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-            
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            
-            with gr.Row():
-                
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=512,
-                )
-                
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=512,
-                )
+             negative_prompt = gr.Text(
+                 label="Negative prompt",
+                 max_lines=1,
+                 placeholder="Enter a negative prompt",
+                 visible=True,
+             )
             
             with gr.Row():
                 
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,
-                )
-                
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
                     minimum=1,
-                    maximum=12,
+                    maximum=10,
                     step=1,
-                    value=2,
+                    value=5,
                 )
         
         gr.Examples(
@@ -139,7 +93,7 @@ with gr.Blocks(css=css) as demo:
 
     run_button.click(
         fn = infer,
-        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        inputs = [prompt, num_inference_steps],
         outputs = [result]
     )