Update app.py

app.py

@@ -15,8 +15,9 @@ API_TOKEN = os.getenv("HF_READ_TOKEN")
 headers = {"Authorization": f"Bearer {API_TOKEN}"}
 timeout = 100
 
-def query(prompt, is_negative=False, steps=30, cfg_scale=7, sampler="DPM++ 2M Karras", seed=-1, strength=0.7):
-    if prompt == "" or prompt == 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=1024, height=1024):
+    if prompt == "" or prompt is None:
         return None
 
     key = random.randint(0, 999)
@@ -24,21 +25,29 @@ def query(prompt, is_negative=False, steps=30, cfg_scale=7, sampler="DPM++ 2M Ka
     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
+        "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}")
@@ -48,6 +57,7 @@ def query(prompt, is_negative=False, steps=30, cfg_scale=7, sampler="DPM++ 2M Ka
         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})')
@@ -56,35 +66,51 @@ def query(prompt, is_negative=False, steps=30, cfg_scale=7, sampler="DPM++ 2M Ka
         print(f"Error when trying to open the image: {e}")
         return None
 
+# CSS to style the app
 css = """
 #app-container {
-    max-width: 600px;
+    max-width: 800px;
     margin-left: auto;
     margin-right: auto;
 }
 """
 
+# Build the Gradio UI with Blocks
 with gr.Blocks(theme='Nymbo/Nymbo_Theme', css=css) as app:
+    # Add a title to the app
     gr.HTML("<center><h1>FLUX.1-Dev</h1></center>")
+    
+    # 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.Row():
                     text_prompt = gr.Textbox(label="Prompt", placeholder="Enter a prompt here", lines=2, 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", placeholder="What should not be in the image", value="(deformed, distorted, disfigured), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, misspellings, typos", lines=3, elem_id="negative-prompt-text-input")
+                        with gr.Row():
+                            width = gr.Slider(label="Width", value=1024, minimum=64, maximum=1216, step=32)
+                            height = gr.Slider(label="Height", value=1024, minimum=64, maximum=1216, step=32)
                         steps = gr.Slider(label="Sampling steps", value=35, minimum=1, maximum=100, step=1)
                         cfg = gr.Slider(label="CFG Scale", value=7, minimum=1, maximum=20, step=1)
-                        method = gr.Radio(label="Sampling method", value="DPM++ 2M Karras", choices=["DPM++ 2M Karras", "DPM++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM"])
                         strength = gr.Slider(label="Strength", value=0.7, minimum=0, maximum=1, step=0.001)
-                        seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=1000000000, 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++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM"])
 
+        # Add a button to trigger the image generation
         with gr.Row():
             text_button = gr.Button("Run", variant='primary', elem_id="gen-button")
+        
+        # Image output area to display the generated image
         with gr.Row():
             image_output = gr.Image(type="pil", label="Image Output", elem_id="gallery")
         
-        text_button.click(query, inputs=[text_prompt, negative_prompt, steps, cfg, method, seed, strength], outputs=image_output)
+        # 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)
 
+# Launch the Gradio app
 app.launch(show_api=False, share=False)