Update app.py

app.py

@@ -1,39 +1,43 @@
 import gradio as gr
 import numpy as np
-import random
+from optimum.intel import OVStableDiffusionPipeline, OVStableDiffusionXLPipeline, OVLatentConsistencyModelPipeline
+from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
 from diffusers import DiffusionPipeline
-import torch
 
-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"
+#model_id = "OpenVINO/LCM_Dreamshaper_v7-int8-ov"
+model_id = "stabilityai/stable-diffusion-xl-base-1.0"
 
-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):
 
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-        
-    generator = torch.Generator().manual_seed(seed)
-    
-    image = pipe(
+#pipeline = OVLatentConsistencyModelPipeline.from_pretrained(model_id, compile=False, safety_checker=safety_checker)
+#pipeline = OVLatentConsistencyModelPipeline.from_pretrained(model_id, compile=False)
+pipeline = OVStableDiffusionXLPipeline.from_pretrained(model_id, compile=False, export=True)
+
+batch_size, num_images, height, width = 1, 1, 1024, 512
+pipeline.reshape(batch_size=batch_size, height=height, width=width, num_images_per_prompt=num_images)
+#pipeline.load_textual_inversion("./badhandv4.pt", "badhandv4")
+#hiten1
+pipeline.load_textual_inversion("./hiten1.pt", "hiten1")
+pipeline.compile()
+
+#TypeError: LatentConsistencyPipelineMixin.__call__() got an unexpected keyword argument 'negative_prompt'
+negative_prompt="easynegative,bad anatomy, bad hands, missing fingers, extra fingers, three hands, three legs, bad arms, missing legs, missing arms, poorly drawn face, bad face, fused face, cloned face, three crus, fused feet, fused thigh, extra crus, ugly fingers, horn, cartoon, cg, 3d, unreal, animate, amputation, disconnected limbs, nsfw, nude, censored,  "
+
+def infer(prompt, num_inference_steps):
+
+    image = pipeline(
         prompt = prompt, 
-        negative_prompt = negative_prompt,
-        guidance_scale = guidance_scale, 
+        #negative_prompt = negative_prompt,
+        guidance_scale = 7.0,
         num_inference_steps = num_inference_steps, 
-        width = width, 
+        width = width,
         height = height,
-        generator = generator
-    ).images[0] 
+        num_images_per_prompt=num_images,
+    ).images[0]
     
     return image
 
@@ -50,19 +54,14 @@ 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(
@@ -78,58 +77,21 @@ with gr.Blocks(css=css) as demo:
         result = gr.Image(label="Result", show_label=False)
 
         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():
+            # negative_prompt = gr.Text(
+            #     label="Negative prompt",
+            #     max_lines=1,
+            #     placeholder="Enter a negative prompt",
+            # )
             
             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,
-                )
-            
-            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=30,
                 )
         
         gr.Examples(
@@ -139,8 +101,8 @@ 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]
     )
 
-demo.queue().launch()
+demo.queue().launch(share=True)