Update app.py

app.py

@@ -11,94 +11,9 @@ import json
 # Project by Nymbo
 from diffusers import DiffusionPipeline
 
-pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-video-diffusion-img2vid-xt")
 
-prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
-image = pipe(prompt).images[0]
+pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-video-diffusion-img2vid")
 
-API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-schnell"
-API_TOKEN = os.getenv("HF_READ_TOKEN")
-headers = {"Authorization": f"Bearer {API_TOKEN}"}
-timeout = 100
+image = Image.open("19616f3c-1419-43a1-baa7-c3c6ee8b0980.png")  # 
 
-# 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)
-    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
-
-# ... (CSS and other code remains the same)
-title="<title>FluxiFloXStrot</title>"
-# Build the Gradio UI with Blocks
-with gr.Blocks() as app:
-    gr.HTML(title)
-
-    with gr.Row():
-        gr.HTML('<div id="neon-cursor" class="neon-cursor"></div>')
-        
-    with gr.Column(elem_id="app-container"):
-        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")
-                
-                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)
-                        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)
-                        method = gr.Radio(label="Sampling method", value="DPM++ 2M Karras", choices=["DPM++ 2M Karras", "DPM++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM"])
-
-        with gr.Row():
-            text_button = gr.Button("Run", variant='primary', elem_id="gen-button")
-        
-        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, width, height], outputs=image_output)
-
-app.launch(show_api=True, share=False)
+image = pipe(image=image).images[0]