Update app.py

app.py

@@ -1,146 +1,68 @@
+# -*- coding: utf-8 -*-
+# @时间       : 2024/5/15 18:03
+# @作者       : caishilong
+# @文件名      : main.py
+# @项目名      : ai-platform
+# @Software   : PyCharm
+import io
 import gradio as gr
 import numpy as np
-import random
-from diffusers import DiffusionPipeline
-import torch
+import requests
+from PIL import Image
 
-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)
+API_BASE_URL = "https://api.cloudflare.com/client/v4/accounts/0c76db6b77f9629c9e3f19d037c937c0/ai/run/"
+headers = {"Authorization": "Bearer qOn7C354fZtjY7SiOFWmwN9g0rtbtLqflUPMS5JU"}
 
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
 
-def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
+def run(model, inputs):
+    input = inputs
+    response = requests.post(f"{API_BASE_URL}{model}", headers=headers, json=input)
+    # 返回的是"content-type": "image/png"
+    try:
+        print(response.json())
+    except:
+        pass
+    image = Image.open(io.BytesIO(response.content))
+    image_array = np.array(image)
+    return image_array
 
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-        
-    generator = torch.Generator().manual_seed(seed)
-    
-    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] 
-    
-    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",
-]
+# output = run("@cf/stabilityai/stable-diffusion-xl-base-1.0", inputs)
+#
+# with open("output.png", "wb") as f:
+#     f.write(output)
 
-css="""
-#col-container {
-    margin: 0 auto;
-    max-width: 520px;
-}
-"""
 
-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}.
-        """)
-        
-        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)
+def fake_diffusion(model, prompt, guidance, image: np.ndarray):
+    # image = Image.fromarray(image)
+    #
+    # image = np.array(image)
+    # print(image)
+    # plt.plot(121)
+    # plt.imshow(image)
+    # plt.show()
+    data = {"prompt": prompt,
+            "guidance": guidance,
+            # "image": image.tolist()
+            }
+    output = run(model, data)
+    yield output
 
-        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,
-                )
-            
-            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,
-                    step=1,
-                    value=2,
-                )
-        
-        gr.Examples(
-            examples = examples,
-            inputs = [prompt]
-        )
 
-    run_button.click(
-        fn = infer,
-        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs = [result]
-    )
+demo = gr.Interface(fake_diffusion,
+                    [
+                        gr.Dropdown(["@cf/stabilityai/stable-diffusion-xl-base-1.0",
+                                     "@cf/lykon/dreamshaper-8-lcm",
+                                     '@cf/bytedance/stable-diffusion-xl-lightning',
+                                     '@cf/runwayml/stable-diffusion-v1-5-img2img'],
+                                    label="Model", value='@cf/bytedance/stable-diffusion-xl-lightning'),
+                        gr.Textbox(label='prompt', value='human playing with a dog'),
+                        gr.Slider(1, 20, 20, label='guidance'),
+                        # gr.Image()
+                    ],
+                    outputs="image")
 
-demo.queue().launch()
+if __name__ == "__main__":
+    demo.launch(server_name='0.0.0.0',auth= ("admin", r'qwe[]\123'),)