Update app.py

app.py

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 import gradio as gr
 import numpy as np
 import random
-
-# import spaces #[uncomment to use ZeroGPU]
 from diffusers import DiffusionPipeline
+from optimum.intel.openvino.modeling_diffusion import OVModelVaeDecoder, OVBaseModel, OVStableDiffusionPipeline
 import torch
+from huggingface_hub import snapshot_download
+import openvino.runtime as ov
+from typing import Optional, Dict
+
+
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
+model_id = "stabilityai/sdxl-turbo"
+#model_id = "Disty0/sotediffusion-v2" #不可
 
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
+#1024*512 記憶體不足
+HIGH=512
+WIDTH=512
 
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
+batch_size = -1
 
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
+class CustomOVModelVaeDecoder(OVModelVaeDecoder):
+    def __init__(
+        self, model: ov.Model, parent_model: OVBaseModel, ov_config: Optional[Dict[str, str]] = None, model_dir: str = None,
+    ):
+        super(OVModelVaeDecoder, self).__init__(model, parent_model, ov_config, "vae_decoder", model_dir)
 
 
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
-):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
+pipe = OVStableDiffusionPipeline.from_pretrained(
+        model_id, 
+        compile = False, 
+        ov_config = {"CACHE_DIR":""},
+        torch_dtype=torch.int8, #快
+        #torch_dtype=torch.bfloat16, #中
+        #variant="fp16", 
+        #torch_dtype=torch.IntTensor, #慢
+        use_safetensors=False,
+        )
 
-    generator = torch.Generator().manual_seed(seed)
+taesd_dir = snapshot_download(repo_id="deinferno/taesd-openvino")
+
+pipe.vae_decoder = CustomOVModelVaeDecoder(model = OVBaseModel.load_model(f"{taesd_dir}/vae_decoder/openvino_model.xml"), 
+                                           parent_model = pipe, 
+                                           model_dir = taesd_dir
+                                          )
+
+
+
+pipe.reshape( batch_size=-1, height=HIGH, width=WIDTH, num_images_per_prompt=1)
+#pipe.load_textual_inversion("./badhandv4.pt", "badhandv4")
+#pipe.load_textual_inversion("./Konpeto.pt", "Konpeto")
+#<shigure-ui-style>
+#pipe.load_textual_inversion("sd-concepts-library/shigure-ui-style")
+#pipe.load_textual_inversion("sd-concepts-library/ruan-jia")
+#pipe.load_textual_inversion("sd-concepts-library/agm-style-nao")
+
+
+pipe.compile()
+
+prompt=""
+negative_prompt="(worst quality, low quality, lowres, loli, kid, child), zombie, interlocked fingers, large breasts, username, watermark,"
+
+def infer(prompt,negative_prompt):
 
     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]
+        prompt = prompt, 
+        negative_prompt = negative_prompt,
+        width = WIDTH, 
+        height = HIGH,
+        guidance_scale=1.0,
+        num_inference_steps=8,
+        num_images_per_prompt=1,
+    ).images[0] 
+    
+    return image
 
-    return image, seed
 
 
 examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
+    "(Digital art, highres, best quality, 8K, masterpiece, anime screencap, perfect eyes:1.4, ultra detailed:1.5),1girl,flat chest,short messy pink hair,blue eyes,tall,thick thighs,light blue hoodie,collar,light blue shirt,black sport shorts,bulge,black thigh highs,femboy,okoto no ko,smiling,blushing,looking at viewer,inside,livingroom,sitting on couch,nighttime,dark,hand_to_mouth,",
+    "1girl, silver hair, symbol-shaped pupils, yellow eyes, smiling, light particles, light rays, wallpaper, star guardian, serious face, red inner hair, power aura, grandmaster1, golden and white clothes",
+    "masterpiece, best quality, highres booru, 1girl, solo, depth of field, rim lighting, flowers, petals, from above, crystals, butterfly, vegetation, aura, magic, hatsune miku, blush, slight smile, close-up, against wall,",
+    "((colofrul:1.7)),((best quality)), ((masterpiece)), ((ultra-detailed)), (illustration), (detailed light), (an extremely delicate and beautiful),incredibly_absurdres,(glowing),(1girl:1.7),solo,a beautiful girl,(((cowboy shot))),standding,((Hosiery)),((beautiful off-shoulder lace-trimmed layered strapless dress+white stocking):1.25),((Belts)),(leg loops),((Hosiery)),((flower headdress)),((long white hair)),(((beautiful eyes))),BREAK,((english text)),(flower:1.35),(garden),(((border:1.75))),",
 ]
 
-css = """
+css="""
 #col-container {
     margin: 0 auto;
-    max-width: 640px;
+    max-width: 520px;
 }
 """
 
+
+power_device = "CPU"
+
 with gr.Blocks(css=css) as demo:
+    
     with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
+        gr.Markdown(f"""
+        # Disty0/LCM_SoteMix {WIDTH}x{HIGH}
+        Currently running on {power_device}.
+        """)
+        
         with gr.Row():
             prompt = gr.Text(
                 label="Prompt",
@@ -75,80 +155,22 @@ with gr.Blocks(css=css) as demo:
                 max_lines=1,
                 placeholder="Enter your prompt",
                 container=False,
-            )
-
-            run_button = gr.Button("Run", scale=0, variant="primary")
-
+            )         
+            run_button = gr.Button("Run", scale=0)
+        
         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():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
+        gr.Examples(
+            examples = examples,
+            fn = infer,
+            inputs = [prompt],
+            outputs = [result]
+        )
+
+    run_button.click(
+        fn = infer,
+        inputs = [prompt],
+        outputs = [result]
     )
 
-if __name__ == "__main__":
-    demo.launch()
+demo.queue().launch()