Update app.py

app.py

@@ -1,43 +1,59 @@
 import gradio as gr
 import numpy as np
-import random
-#import spaces #[uncomment to use ZeroGPU]
+from optimum.intel import OVStableDiffusionPipeline, OVStableDiffusionXLPipeline, OVLatentConsistencyModelPipeline
+from optimum.intel.openvino.modeling_diffusion import OVModelVaeDecoder, OVBaseModel
+from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
 from diffusers import DiffusionPipeline
-import torch
+from diffusers.schedulers import EulerDiscreteScheduler
+import openvino.runtime as ov
+from typing import Optional, Dict
+from huggingface_hub import snapshot_download
 
-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 = "echarlaix/sdxl-turbo-openvino-int8"
+#model_id = "echarlaix/LCM_Dreamshaper_v7-openvino"
+#model_id = "OpenVINO/LCM_Dreamshaper_v7-int8-ov"
+model_id = "yujiepan/dreamshaper-8-lcm-openvino-w8a8"
 
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
+#safety_checker = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker")
 
-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
+#pipeline = OVLatentConsistencyModelPipeline.from_pretrained(model_id, compile=False, safety_checker=safety_checker)
+pipeline = OVLatentConsistencyModelPipeline.from_pretrained(model_id, compile=False, device='CPU',)
 
-#@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)):
+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)
 
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-        
-    generator = torch.Generator().manual_seed(seed)
-    
-    image = pipe(
+#不可用lora
+#pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
+#pipeline.set_adapters("pixel")
+
+
+# 选择采样方法（调度器） 可以新增但是跑就死
+#scheduler = EulerDiscreteScheduler()
+#pipeline.scheduler = scheduler
+
+#badhandv4
+#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, seed
+    return image
 
 examples = [
     "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
@@ -48,17 +64,18 @@ examples = [
 css="""
 #col-container {
     margin: 0 auto;
-    max-width: 640px;
+    max-width: 520px;
 }
 """
 
+
 with gr.Blocks(css=css) as demo:
     
     with gr.Column(elem_id="col-container"):
         gr.Markdown(f"""
-        # Text-to-Image Gradio Template
+        # Demo : [Fast LCM](https://huggingface.co/OpenVINO/LCM_Dreamshaper_v7-int8-ov) quantized with NNCF ⚡
         """)
-        
+
         with gr.Row():
             
             prompt = gr.Text(
@@ -74,69 +91,32 @@ 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=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
+                    value=4,
                 )
         
         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, num_inference_steps],
+        outputs = [result]
     )
 
-demo.queue().launch()
+demo.queue().launch(share=True)