Scriptyfai

README.md

@@ -1,13 +1,12 @@
 ---
-title: Scriptify AI
-emoji: 🎨
-colorFrom: indigo
-colorTo: pink
+title: Scriptyfa
+emoji: 🏃
+colorFrom: blue
+colorTo: red
 sdk: gradio
 sdk_version: 5.36.2
 app_file: app.py
 pinned: false
 ---
 
-# Scriptify AI – Ultra Image Generator
-Generate beautiful images from text using SDXL, voice input, and style presets.
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,47 +0,0 @@
-import gradio as gr
-from utils import load_model, generate_image, prompt_presets
-
-pipe = load_model()
-
-with gr.Blocks(css="style.css") as demo:
-    gr.Markdown("# 🚀 Scriptify AI - Ultra Image Generator with ControlNet + Voice")
-    gr.Markdown("Create beautiful, controlled images using AI. Supports voice prompts, styles, and ControlNet!")
-
-    with gr.Row():
-        prompt_input = gr.Textbox(placeholder="e.g., cinematic shot of a samurai in rain", label="📝 Text Prompt", lines=2)
-        voice_input = gr.Audio(source="microphone", type="filepath", label="🎤 Or Speak Prompt")
-
-    with gr.Accordion("🔧 Advanced Settings", open=False):
-        with gr.Row():
-            style_dropdown = gr.Dropdown(choices=list(prompt_presets.keys()), value="None", label="🎨 Style Presets")
-            guidance = gr.Slider(minimum=1, maximum=20, value=7.5, label="🎚️ Guidance Scale")
-            steps = gr.Slider(minimum=10, maximum=50, value=30, label="🧠 Inference Steps")
-
-        with gr.Row():
-            width = gr.Slider(minimum=512, maximum=1024, step=64, value=768, label="🖼️ Width")
-            height = gr.Slider(minimum=512, maximum=1024, step=64, value=768, label="🖼️ Height")
-
-    generate_button = gr.Button("✨ Generate Image")
-    output_image = gr.Image(type="pil", label="🖼️ Result Image")
-    status = gr.Textbox(visible=False)
-
-    def run(prompt, voice, style, guidance, steps, width, height):
-        import whisper
-        if voice:
-            model = whisper.load_model("base")
-            result = model.transcribe(voice)
-            prompt = result["text"]
-
-        if style != "None":
-            prompt = f"{prompt}, {prompt_presets[style]}"
-        try:
-            image = generate_image(pipe, prompt, guidance, steps, width, height)
-            return image, ""
-        except Exception as e:
-            return None, f"⚠️ {str(e)}"
-
-    generate_button.click(fn=run,
-                          inputs=[prompt_input, voice_input, style_dropdown, guidance, steps, width, height],
-                          outputs=[output_image, status])
-
-demo.launch()

requirements.txt

@@ -1,8 +0,0 @@
-gradio==4.24.0
-torch
-diffusers
-transformers
-accelerate
-safetensors
-whisper
-ffmpeg

style.css

@@ -1,15 +0,0 @@
-body {
-  background-color: #0f172a;
-  color: white;
-  font-family: 'Segoe UI', sans-serif;
-}
-
-h1 {
-  color: #38bdf8;
-}
-
-.gr-button {
-  background-color: #2563eb !important;
-  border-radius: 12px !important;
-  font-weight: bold !important;
-}

utils.py

@@ -1,27 +0,0 @@
-from diffusers import DiffusionPipeline
-import torch
-
-prompt_presets = {
-    "Cinematic": "cinematic lighting, epic composition, 8k",
-    "Realistic Portrait": "realistic face, shallow depth of field, photography",
-    "Anime Style": "anime, cel-shading, crisp lines, colorful",
-    "Fantasy": "mythical, magical light, detailed, fantasy world",
-    "None": ""
-}
-
-def load_model():
-    print("Loading SDXL model...")
-    pipe = DiffusionPipeline.from_pretrained(
-        "stabilityai/stable-diffusion-xl-base-1.0",
-        torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-        variant="fp16" if torch.cuda.is_available() else None
-    )
-    pipe.to("cuda" if torch.cuda.is_available() else "cpu")
-    return pipe
-
-def generate_image(pipe, prompt: str, guidance: float, steps: int, width: int, height: int):
-    if not prompt or len(prompt.strip()) < 5:
-        raise ValueError("Prompt too short. Please describe your idea better.")
-
-    result = pipe(prompt, guidance_scale=guidance, num_inference_steps=steps, height=height, width=width)
-    return result.images[0]