Update app.py

app.py

@@ -1,142 +1,107 @@
 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 = "Disty0/LCM_SoteMixV3"
+#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)
 
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
+batch_size = -1
+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)
-        
-    generator = torch.Generator().manual_seed(seed)
-    
+pipe = OVStableDiffusionPipeline.from_pretrained(model_id, compile = False, ov_config = {"CACHE_DIR":""})
+
+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), zombie, interlocked fingers,"
+
+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
+        width = HIGH, 
+        height = WIDTH,
+        guidance_scale=1.0,
+        num_inference_steps=4,
+        num_images_per_prompt=1,
     ).images[0] 
     
-    return image, seed
+    return image
+
 
 examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
+    "A cute kitten, Japanese cartoon style.",
+    "A sweet family, dad stands next to mom, mom holds baby girl.",
+    "(illustration, 8k CG, extremely detailed),(whimsical),catgirl,teenage girl,playing in the snow,winter wonderland,snow-covered trees,soft pastel colors,gentle lighting,sparkling snow,joyful,magical atmosphere,highly detailed,fluffy cat ears and tail,intricate winter clothing,shallow depth of field,watercolor techniques,close-up shot,slightly tilted angle,fairy tale architecture,nostalgic,playful,winter magic,(masterpiece:2),best quality,ultra highres,original,extremely detailed,perfect lighting,",
 ]
 
 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(f"""
-        # Text-to-Image Gradio Template
+        # Disty0/LCM_SoteMix {HIGH}x{WIDTH}
+        Currently running on {power_device}.
         """)
         
         with gr.Row():
-            
             prompt = gr.Text(
                 label="Prompt",
                 show_label=False,
                 max_lines=1,
                 placeholder="Enter your prompt",
                 container=False,
-            )
-            
+            )         
             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],
+
+    run_button.click(
         fn = infer,
-        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs = [result, seed]
+        inputs = [prompt],
+        outputs = [result]
     )
 
 demo.queue().launch()