init test

app.py

@@ -1,51 +1,75 @@
+import os
+import threading
+import time
+from datetime import datetime, timedelta
+
 import gradio as gr
 import numpy as np
 import random
-#import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
+import spaces  # [uncomment to use ZeroGPU]
+from diffusers import FluxPipeline
 import torch
 
 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_repo_id = "black-forest-labs/FLUX.1-dev"
 
 if torch.cuda.is_available():
     torch_dtype = torch.float16
 else:
     torch_dtype = torch.float32
 
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
+pipe = FluxPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
 pipe = pipe.to(device)
 
 MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
 
-#@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)):
 
+def delete_old_files():
+    while True:
+        now = datetime.now()
+        cutoff = now - timedelta(minutes=10)
+        directories = ["./gradio_tmp"]
+
+        for directory in directories:
+            for filename in os.listdir(directory):
+                file_path = os.path.join(directory, filename)
+                if os.path.isfile(file_path):
+                    file_mtime = datetime.fromtimestamp(os.path.getmtime(file_path))
+                    if file_mtime < cutoff:
+                        os.remove(file_path)
+        time.sleep(600)
+
+
+threading.Thread(target=delete_old_files, daemon=True).start()
+
+
+@spaces.GPU  # [uncomment to use ZeroGPU]
+def infer(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)
-    
+
     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,
+        guidance_scale=guidance_scale,
+        num_images_per_prompt=1,
+        num_inference_steps=num_inference_steps,
+        width=width,
+        height=height,
+        generator=generator
+    ).images[0]
     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",
+    "A vintage pink convertible with glossy chrome finishes and whitewall tires sits parked on an open road, surrounded by a field of wildflowers under a clear blue sky. The car's body is a delicate pastel pink, complementing the vibrant greens and colors of the meadow. Its interior boasts cream leather seats and a polished wooden dashboard, evoking a sense of classic elegance. The sun casts a soft light on the vehicle, highlighting its curves and shiny surfaces, creating a picture of nostalgia mixed with dreamy escapism.",
+    "A noble black Labrador retriever sits serenely in a sunlit meadow, its glossy coat absorbing the golden rays of a late afternoon sun. The dog's intelligent eyes sparkle with a mixture of curiosity and loyalty, as it gazes off into the distance where the meadow meets a line of tall, slender birch trees. The dog's posture is regal, yet approachable, with its tongue playfully hanging out to the side slightly, suggesting a friendly disposition. The idyllic setting is filled with the vibrant greens of lush grass and the soft colors of wildflowers speckled throughout, creating a peaceful harmony between the dog and its natural surroundings.",
+    "A vibrant red-colored dog of medium build stands attentively in an autumn forest setting. Its fur is a deep, rich red, reminiscent of autumn leaves, contrasting with its bright, intelligent eyes, a clear sky blue. The dog's ears perk up, and its tail wags slightly as it looks off into the distance, its posture suggesting alertness and curiosity. Golden sunlight filters through the canopy of russet and gold leaves above, casting dappled light onto the forest floor and the glossy coat of the canine, creating a serene and heartwarming scene."
 ]
 
-css="""
+css = """
 #col-container {
     margin: 0 auto;
     max-width: 640px;
@@ -53,14 +77,28 @@ css="""
 """
 
 with gr.Blocks(css=css) as demo:
-    
     with gr.Column(elem_id="col-container"):
         gr.Markdown(f"""
-        # Text-to-Image Gradio Template
+            <div style="text-align: center; font-size: 32px; font-weight: bold; margin-bottom: 20px;">
+             CogView3-Plus Huggingface Space🤗
+           </div>
+           <div style="text-align: center;">
+               <a href="https://huggingface.co/THUDM/CogView3-Plus">🤗 Model Hub | 
+               <a href="https://github.com/THUDM/CogView3">🌐 Github</a> |
+               <a href="https://arxiv.org/abs/2403.05121">📜 arxiv </a>
+           </div>
+           <div style="text-align: center;display: flex;justify-content: center;align-items: center;margin-top: 1em;margin-bottom: .5em;">
+              <span>If the Space is too busy, duplicate it to use privately</span>
+              <a href="https://huggingface.co/spaces/THUDM-HF-SPACE/CogView-3-Plus?duplicate=true"><img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/duplicate-this-space-lg.svg" width="160" style="
+                margin-left: .75em;
+            "></a>
+           </div>
+           <div style="text-align: center; font-size: 15px; font-weight: bold; color: red; margin-bottom: 20px;">
+            ⚠️ This demo is for academic research and experiential use only. 
+            </div>
         """)
-        
+
         with gr.Row():
-            
             prompt = gr.Text(
                 label="Prompt",
                 show_label=False,
@@ -68,20 +106,11 @@ with gr.Blocks(css=css) as demo:
                 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,
@@ -89,54 +118,52 @@ with gr.Blocks(css=css) as demo:
                 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,
+                    minimum=512,
+                    maximum=2048,
                     step=32,
-                    value=1024, #Replace with defaults that work for your model
+                    value=1024,  # 替换为你的模型默认值
                 )
-                
+
                 height = gr.Slider(
                     label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
+                    minimum=512,
+                    maximum=2048,
                     step=32,
-                    value=1024, #Replace with defaults that work for your model
+                    value=1024,
                 )
-            
+
             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
+                    value=7.0,
                 )
-                
+
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
+                    minimum=10,
+                    maximum=100,
                     step=1,
-                    value=2, #Replace with defaults that work for your model
+                    value=50,
                 )
-        
+
         gr.Examples(
-            examples = examples,
-            inputs = [prompt]
+            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]
+        fn=infer,
+        inputs=[prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        outputs=[result, seed]
     )
 
-demo.queue().launch()
+demo.queue().launch(server_name="127.0.0.1")

requirements.txt

@@ -1,6 +1,6 @@
-accelerate
-diffusers
-invisible_watermark
+accelerate>=0.34.2
+diffusers>=0.30.3
 torch
-transformers
+gradio>=4.44
+transformers>=4.45.2
 xformers