Update app.py

app.py

@@ -86,6 +86,154 @@ with gr.Blocks(theme=theme, elem_id="app-container") as app:
 
         submit_btn.click(feifeichat, [input_img], [output_text])
 
+# Project by Nymbo
+# Edited by DigiP-AI
+
+API_URL = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-3.5-large-turbo"
+API_TOKEN = os.getenv("HF_READ_TOKEN")
+headers = {"Authorization": f"Bearer {API_TOKEN}"}
+timeout = 100
+
+# Function to clear input and output
+def clear():
+    return None 
+
+# Function to query the API and return the generated image
+def query(prompt, is_negative=False, steps=35, cfg_scale=7, sampler="DPM++ 2M Karras", seed=-1, strength=0.7, width=896, height=1152):
+    if prompt == "" or prompt is None:
+        return None
+
+    key = random.randint(0, 999)
+    
+    API_TOKEN = random.choice([os.getenv("HF_READ_TOKEN")])
+    headers = {"Authorization": f"Bearer {API_TOKEN}"}
+    
+    # Translate the prompt from Russian to English if necessary
+    prompt = GoogleTranslator(source='ru', target='en').translate(prompt)
+    print(f'\033[1mGeneration {key} translation:\033[0m {prompt}')
+
+    # Add some extra flair to the prompt
+    prompt = f"{prompt} | ultra detail, ultra elaboration, ultra quality, perfect."
+    print(f'\033[1mGeneration {key}:\033[0m {prompt}')
+    
+    # Prepare the payload for the API call, including width and height
+    payload = {
+        "inputs": prompt,
+        "is_negative": is_negative,
+        "steps": steps,
+        "cfg_scale": cfg_scale,
+        "seed": seed if seed != -1 else random.randint(1, 1000000000),
+        "strength": strength,
+        "parameters": {
+            "width": width,  # Pass the width to the API
+            "height": height  # Pass the height to the API
+        }
+    }
+
+    # Send the request to the API and handle the response
+    response = requests.post(API_URL, headers=headers, json=payload, timeout=timeout)
+    if response.status_code != 200:
+        print(f"Error: Failed to get image. Response status: {response.status_code}")
+        print(f"Response content: {response.text}")
+        if response.status_code == 503:
+            raise gr.Error(f"{response.status_code} : The model is being loaded")
+        raise gr.Error(f"{response.status_code}")
+    
+    try:
+        # Convert the response content into an image
+        image_bytes = response.content
+        image = Image.open(io.BytesIO(image_bytes))
+        print(f'\033[1mGeneration {key} completed!\033[0m ({prompt})')
+        return image
+    except Exception as e:
+        print(f"Error when trying to open the image: {e}")
+        return None
+
+examples = [
+    "a beautiful woman with blonde hair and blue eyes",
+    "a beautiful woman with brown hair and grey eyes",
+    "a beautiful woman with black hair and brown eyes",
+]        
+
+# CSS to style the app
+css = """
+footer{display:none !important}
+#app-container {
+    max-width: 930px;
+    margin-left: auto;
+    margin-right: auto;
+}
+"""
+
+# Build the Gradio UI with Blocks
+with gr.Blocks(theme=theme, css=css) as app:
+    # Add a title to the app
+    gr.HTML("<center><h1>🎨 Stable Diffusion 3.5 large turbo 🇬🇧</h1></center>")
+    with gr.Tabs() as tabs:
+        with gr.TabItem("Text to Image"):
+            # Container for all the UI elements
+            with gr.Column(elem_id="app-container"):
+                # Add a text input for the main prompt
+                with gr.Row():
+                    with gr.Column(elem_id="prompt-container"):
+                        with gr.Group():
+                            with gr.Row():
+                                text_prompt = gr.Textbox(label="Image Prompt ✍️", placeholder="Enter a prompt here", lines=2, show_copy_button = True, elem_id="prompt-text-input")
+                            
+                            # Accordion for advanced settings
+                            with gr.Row():
+                                with gr.Accordion("Advanced Settings", open=False):
+                                    negative_prompt = gr.Textbox(label="Negative Prompt", lines=4, placeholder="What should not be in the image", value="(hands:-1.25), physical-defects:2, unhealthy-deformed-joints:2, unhealthy-hands:2, out of frame, bad face, (bad-image-v2-39000:1.3), (((out of frame))), deformed body features,  poor facial details, (poorly drawn face:1.3), jpeg artifacts, (missing arms:1.1), (missing legs:1.1), (extra arms:1.2), (extra legs:1.2)")
+                                    with gr.Row():
+                                        width = gr.Slider(label="ImageWidth", value=896, minimum=64, maximum=1216, step=32)
+                                        height = gr.Slider(label="Image Height", value=1152, minimum=64, maximum=1216, step=32)
+                                    steps = gr.Slider(label="Sampling steps", value=50, minimum=1, maximum=100, step=1)
+                                    cfg = gr.Slider(label="CFG Scale", value=3.5, minimum=1, maximum=20, step=1)
+                                    strength = gr.Slider(label="PromptStrength", value=100, minimum=0, maximum=100, step=1)
+                                    seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=1000000000, step=1) # Setting the seed to -1 will make it random
+                                    method = gr.Radio(label="Sampling method", value="DPM++ 2M Karras", choices=["DPM++ 2M Karras", "DPM++ 2S a Karras", "DPM2 a Karras", "DPM2 Karras", "DPM++ SDE Karras", "DEIS", "LMS", "DPM Adaptive", "DPM++ 2M", "DPM2 Ancestral", "DPM++ S", "DPM++ SDE", "DDPM", "DPM Fast", "dpmpp_2s_ancestral", "Euler", "Euler CFG PP", "Euler a", "Euler Ancestral", "Euler+beta", "Heun", "Heun PP2", "DDIM", "LMS Karras", "PLMS", "UniPC", "UniPC BH2"])
+                # Add a button to trigger the image generation
+                with gr.Row():
+                    text_button = gr.Button("Generate Image 🎨", variant='primary', elem_id="gen-button")
+                    clear_prompt =gr.Button("Clear Prompt 🗑️",variant="primary", elem_id="clear_button")
+                    clear_prompt.click(lambda: (None), None, [text_prompt], queue=False, show_api=False)
+
+                with gr.Group():
+                    # Image output area to display the generated image
+                    with gr.Row():
+                        image_output = gr.Image(type="pil", label="Image Output", format="png", show_share_button=False, elem_id="gallery")
+                        
+                    
+                        
+                    gr.Examples(
+                        examples = examples,    
+                        inputs = [text_prompt],    
+                    )
+
+                with gr.Row():
+                    clear_results = gr.Button(value="Clear Image 🗑️", variant="primary", elem_id="clear_button")
+                    clear_results.click(lambda: (None), None, [image_output], queue=False, show_api=False)
+                    
+                # Bind the button to the query function with the added width and height inputs
+                text_button.click(query, inputs=[text_prompt, negative_prompt, steps, cfg, method, seed, strength, width, height], outputs=image_output)
+
+        
+        with gr.TabItem("Tips", visible=True):
+            with gr.Row():
+                    gr.Markdown(
+            """
+            <div style="max-width: 650px; margin: 2rem auto; padding: 1rem; border-radius: 10px; background-color: #f0f0f0;">
+                <h2 style="font-size: 1.5rem; margin-bottom: 1rem;">How to Use</h2>
+                <ol style="padding-left: 1.5rem;">
+                    <li>Enter a detailed description of the image you want to create.</li>
+                    <li>Adjust advanced settings if desired (tap to expand).</li>
+                    <li>Tap "Generate Image" and wait for your creation!</li>
+                </ol>
+                <p style="margin-top: 1rem; font-style: italic;">Tip: Be specific in your description for best results!</p>
+            </div>
+            """
+            )        
+
         
 if __name__ == "__main__":
     app.launch()