Update app.py

app.py

@@ -1,3 +1,140 @@
+from diffusers import DiffusionPipeline
 import gradio as gr
+import numpy as np
+import random
+import torch
 
-gr.load("models/Chan-Y/Cyber-Stable-Realistic").launch()
+device = "cuda" if torch.cuda.is_available() else "cpu"
+dtype = torch.float16
+
+pipe = DiffusionPipeline.from_pretrained("Chan-Y/Chan-Y-Cyber-Stable-Realistic", 
+                                             torch_dtype=torch.float16).to(device)
+
+MAX_SEED = 999999999999999
+MAX_IMAGE_SIZE = 1344
+
+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)
+    
+    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, seed
+
+
+examples = [
+    ["Batman, cute modern Disney style, Pixar 3d portrait, ultra detailed, gorgeous, 3d zbrush, trending on dribbble, 8k render.",
+     "",       
+     ,
+     50]
+]
+
+
+css="""
+#col-container {
+    margin: 0 auto;
+    max-width: 580px;
+}
+"""
+
+
+with gr.Blocks(css=css) as demo:
+    
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown(f"""
+        # Demo [Chan-Y/Stable-Flash-Lightning](https://huggingface.co/Chan-Y/Stable-Flash-Lightning)
+        by Cihan Yalçın | My [LinkedIn](https://www.linkedin.com/in/chanyalcin/) My [GitHub](https://github.com/g-hano)
+        """)
+        
+        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",
+            )
+            
+            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=64,
+                    value=1024,
+                )
+                
+                height = gr.Slider(
+                    label="Height",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=64,
+                    value=1024,
+                )
+            
+            with gr.Row():
+                
+                guidance_scale = gr.Slider(
+                    label="Guidance scale",
+                    minimum=0.0,
+                    maximum=10.0,
+                    step=0.1,
+                    value=5.0,
+                )
+                
+                num_inference_steps = gr.Slider(
+                    label="Number of inference steps",
+                    minimum=1,
+                    maximum=50,
+                    step=1,
+                    value=28,
+                )
+        
+    with gr.Accordion("Examples", open=False):
+        gr.Examples(
+            examples=examples,
+            inputs=[prompt, negative_prompt, seed, num_inference_steps]
+        )
+    gr.on(
+        triggers=[run_button.click, prompt.submit, negative_prompt.submit],
+        fn = infer,
+        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        outputs = [result, seed]
+    )
+
+demo.launch()