Update app.py
Browse files
app.py
CHANGED
@@ -1,347 +1,249 @@
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import gradio as gr
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import os
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from PIL import Image
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import
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import
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import
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import json
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load_dotenv()
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# Styles artistiques étendus
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ART_STYLES = {
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"Art Moderne": {
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"prompt_prefix": "modern art style poster, professional design",
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"negative_prompt": "traditional, photorealistic, cluttered, busy design"
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},
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"Neo Vintage": {
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"prompt_prefix": "vintage style advertising poster, retro design",
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"negative_prompt": "modern, digital, contemporary style"
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},
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"Pop Art": {
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"prompt_prefix": "pop art style poster, bold design",
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"negative_prompt": "subtle, realistic, traditional art"
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},
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"Minimaliste": {
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"prompt_prefix": "minimalist design poster, clean composition",
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"negative_prompt": "complex, detailed, ornate, busy"
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},
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"Cyberpunk": {
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"prompt_prefix": "cyberpunk style poster, neon lights, futuristic design",
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"negative_prompt": "vintage, natural, rustic, traditional"
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},
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"Aquarelle": {
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"prompt_prefix": "watercolor art style poster, fluid artistic design",
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"negative_prompt": "digital, sharp, photorealistic"
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},
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"Art Déco": {
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"prompt_prefix": "art deco style poster, geometric patterns, luxury design",
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"negative_prompt": "modern, minimalist, casual"
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},
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"Japonais": {
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"prompt_prefix": "japanese art style poster, ukiyo-e inspired design",
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"negative_prompt": "western, modern, photographic"
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}
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}
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# Paramètres de composition
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COMPOSITION_PARAMS = {
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"Layouts": {
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"Centré": "centered composition, balanced layout",
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"Asymétrique": "dynamic asymmetrical composition",
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"Grille": "grid-based layout, structured composition",
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"Diagonal": "diagonal dynamic composition",
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"Minimaliste": "minimal composition, lots of whitespace"
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},
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"Ambiances": {
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"Dramatique": "dramatic lighting, high contrast",
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"Doux": "soft lighting, gentle atmosphere",
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"Vibrant": "vibrant colors, energetic mood",
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"Mystérieux": "mysterious atmosphere, moody lighting",
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"Serein": "peaceful atmosphere, calm mood"
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},
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"Palette": {
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"Monochrome": "monochromatic color scheme",
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"Contrasté": "high contrast color palette",
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"Pastel": "soft pastel color palette",
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"Terre": "earthy color palette",
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"Néon": "neon color palette"
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}
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}
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class ImageGenerator:
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def __init__(self):
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self.
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def generate(self, params: Dict[str, Any]) -> Tuple[Optional[Image.Image], str]:
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try:
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prompt = self._build_prompt(params)
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#
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"inputs": prompt,
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"parameters": {
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"negative_prompt": ART_STYLES[params["style"]]["negative_prompt"],
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"num_inference_steps": min(int(35 * (params["quality"]/100)), 40),
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"guidance_scale": min(7.5 * (params["creativity"]/10), 10.0),
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"width": 768,
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"height": 768 if params["orientation"] == "Portrait" else 512
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}
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}
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else:
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except Exception as e:
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finally:
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css = """
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.container { max-width: 1200px; margin: auto; }
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.welcome { text-align: center; margin: 20px 0; padding: 20px; background: #1e293b; border-radius: 10px; }
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.controls-group { background: #2d3748; padding: 15px; border-radius: 5px; margin: 10px 0; }
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.advanced-controls { background: #374151; padding: 12px; border-radius: 5px; margin: 8px 0; }
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"""
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generator = ImageGenerator()
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with gr.Blocks(css=css) as app:
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gr.HTML("""
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<div class="welcome">
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<h1>🎨 Equity Artisan 3.0</h1>
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<p>Assistant de création d'affiches professionnelles</p>
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</div>
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""")
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format_size = gr.Dropdown(
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choices=["A4", "A3", "A2", "A1", "A0"],
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value="A4",
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label="Format"
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)
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orientation = gr.Radio(
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choices=["Portrait", "Paysage"],
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value="Portrait",
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label="Orientation"
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)
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gr.
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style = gr.Dropdown(
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choices=list(
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value="
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label="Style
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)
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layout = gr.Dropdown(
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choices=list(COMPOSITION_PARAMS["Layouts"].keys()),
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value="Centré",
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label="Composition"
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)
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)
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)
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# Ajustements fins
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with gr.Group(elem_classes="advanced-controls"):
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gr.Markdown("### 🎯 Ajustements Fins")
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with gr.Row():
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detail_level = gr.Slider(
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minimum=1,
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maximum=10,
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value=7,
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step=1,
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label="Niveau de Détail"
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)
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contrast = gr.Slider(
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minimum=1,
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maximum=10,
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value=5,
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step=1,
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label="Contraste"
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)
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saturation = gr.Slider(
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minimum=1,
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maximum=10,
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value=5,
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step=1,
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label="Saturation"
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)
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# Paramètres de génération
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with gr.Group(elem_classes="controls-group"):
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with gr.Row():
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quality = gr.Slider(
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minimum=30,
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maximum=50,
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value=35,
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label="Qualité"
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)
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creativity = gr.Slider(
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minimum=5,
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maximum=15,
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value=7.5,
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label="Créativité"
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)
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image_output = gr.Image(label="Aperçu")
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status = gr.Textbox(label="Statut", interactive=False)
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def generate(*args):
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logger.info("Démarrage d'une nouvelle génération")
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params = {
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"format_size": args[0],
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"orientation": args[1],
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"style": args[2],
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"layout": args[3],
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"ambiance": args[4],
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"palette": args[5],
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"subject": args[6],
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"title": args[7],
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"detail_level": args[8],
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"contrast": args[9],
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"saturation": args[10],
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"quality": args[11],
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"creativity": args[12]
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}
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result = generator.generate(params)
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logger.info(f"Génération terminée avec statut: {result[1]}")
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return result
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generate_btn.click(
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generate,
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inputs=[
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format_size,
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orientation,
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style,
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layout,
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ambiance,
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palette,
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subject,
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title,
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detail_level,
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contrast,
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saturation,
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quality,
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creativity
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],
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outputs=[image_output, status]
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)
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clear_btn.click(
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lambda: (None, "🗑️ Image effacée"),
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outputs=[image_output, status]
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)
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logger.info("Interface créée avec succès")
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return app
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if __name__ == "__main__":
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import gradio as gr
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import torch
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from diffusers import StableDiffusionPipeline
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import gc
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import os
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from PIL import Image
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import numpy as np
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from dataclasses import dataclass
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from typing import Optional, Dict, Any
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import json
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import time
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@dataclass
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class GenerationParams:
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prompt: str
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style: str = "realistic"
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steps: int = 20
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guidance_scale: float = 7.0
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seed: int = -1
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quality: str = "balanced"
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class GenerartSystem:
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def __init__(self):
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self.model = None
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self.styles = {
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"realistic": {
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"prompt_prefix": "professional photography, highly detailed, photorealistic quality",
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"negative_prompt": "cartoon, anime, illustration, painting, drawing, blurry, low quality",
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"params": {"guidance_scale": 7.5, "steps": 20}
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},
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"artistic": {
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"prompt_prefix": "artistic painting, impressionist style, vibrant colors",
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"negative_prompt": "photorealistic, digital art, 3d render, low quality",
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"params": {"guidance_scale": 6.5, "steps": 25}
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},
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"modern": {
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"prompt_prefix": "modern art, contemporary style, abstract qualities",
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"negative_prompt": "traditional, classic, photorealistic, low quality",
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"params": {"guidance_scale": 8.0, "steps": 15}
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}
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}
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self.quality_presets = {
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"speed": {"steps_multiplier": 0.8},
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"balanced": {"steps_multiplier": 1.0},
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"quality": {"steps_multiplier": 1.2}
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}
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self.performance_stats = {
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"total_generations": 0,
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"average_time": 0,
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"success_rate": 100,
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"last_error": None
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}
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def initialize_model(self):
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"""Initialize the model with memory optimizations"""
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if self.model is not None:
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return
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# Memory cleanup before model load
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gc.collect()
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torch.cuda.empty_cache() if torch.cuda.is_available() else None
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try:
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self.model = StableDiffusionPipeline.from_pretrained(
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"CompVis/stable-diffusion-v1-4",
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torch_dtype=torch.float32,
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safety_checker=None,
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requires_safety_checker=False
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)
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# Memory optimizations
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self.model.enable_attention_slicing()
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self.model.enable_vae_slicing()
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# Move to CPU - system doesn't have adequate GPU
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self.model = self.model.to("cpu")
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except Exception as e:
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print(f"Error initializing model: {str(e)}")
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raise
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def cleanup(self):
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"""Memory cleanup after generation"""
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gc.collect()
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torch.cuda.empty_cache() if torch.cuda.is_available() else None
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def update_performance_stats(self, generation_time: float, success: bool = True, error: Optional[str] = None):
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"""Update system performance statistics"""
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self.performance_stats["total_generations"] += 1
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# Update average time
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+
prev_avg = self.performance_stats["average_time"]
|
93 |
+
self.performance_stats["average_time"] = (prev_avg * (self.performance_stats["total_generations"] - 1) +
|
94 |
+
generation_time) / self.performance_stats["total_generations"]
|
95 |
+
|
96 |
+
# Update success rate
|
97 |
+
if not success:
|
98 |
+
self.performance_stats["success_rate"] = (self.performance_stats["success_rate"] *
|
99 |
+
(self.performance_stats["total_generations"] - 1) +
|
100 |
+
0) / self.performance_stats["total_generations"]
|
101 |
+
self.performance_stats["last_error"] = error
|
102 |
+
|
103 |
+
def get_system_stats(self):
|
104 |
+
"""Get current system statistics"""
|
105 |
+
return {
|
106 |
+
"total_generations": self.performance_stats["total_generations"],
|
107 |
+
"average_time": round(self.performance_stats["average_time"], 2),
|
108 |
+
"success_rate": round(self.performance_stats["success_rate"], 1),
|
109 |
+
"memory_usage": f"{torch.cuda.memory_allocated()/1024**2:.1f}MB" if torch.cuda.is_available()
|
110 |
+
else "CPU Mode"
|
111 |
+
}
|
112 |
+
|
113 |
+
def generate_image(self, params: GenerationParams) -> Image.Image:
|
114 |
+
"""Generate image with given parameters"""
|
115 |
+
try:
|
116 |
+
# Initialize model if needed
|
117 |
+
if self.model is None:
|
118 |
+
self.initialize_model()
|
119 |
+
|
120 |
+
# Prepare generation parameters
|
121 |
+
style_config = self.styles[params.style]
|
122 |
+
quality_config = self.quality_presets[params.quality]
|
123 |
+
|
124 |
+
# Construct final prompt
|
125 |
+
full_prompt = f"{style_config['prompt_prefix']}, {params.prompt}"
|
126 |
|
127 |
+
# Calculate final steps
|
128 |
+
final_steps = int(min(25, params.steps * quality_config["steps_multiplier"]))
|
129 |
+
|
130 |
+
# Set random seed if needed
|
131 |
+
if params.seed == -1:
|
132 |
+
generator = None
|
133 |
else:
|
134 |
+
generator = torch.manual_seed(params.seed)
|
135 |
+
|
136 |
+
start_time = time.time()
|
137 |
+
|
138 |
+
# Generate image
|
139 |
+
with torch.no_grad():
|
140 |
+
image = self.model(
|
141 |
+
prompt=full_prompt,
|
142 |
+
negative_prompt=style_config["negative_prompt"],
|
143 |
+
num_inference_steps=final_steps,
|
144 |
+
guidance_scale=params.guidance_scale,
|
145 |
+
generator=generator,
|
146 |
+
width=512,
|
147 |
+
height=512
|
148 |
+
).images[0]
|
149 |
+
|
150 |
+
generation_time = time.time() - start_time
|
151 |
+
self.update_performance_stats(generation_time, success=True)
|
152 |
+
|
153 |
+
return image
|
154 |
+
|
155 |
except Exception as e:
|
156 |
+
self.update_performance_stats(0, success=False, error=str(e))
|
157 |
+
raise RuntimeError(f"Generation error: {str(e)}")
|
158 |
+
|
159 |
finally:
|
160 |
+
self.cleanup()
|
161 |
|
162 |
+
class GenerartInterface:
|
163 |
+
def __init__(self):
|
164 |
+
self.system = GenerartSystem()
|
|
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|
|
165 |
|
166 |
+
def create_interface(self):
|
167 |
+
"""Create the Gradio interface"""
|
168 |
+
with gr.Blocks(theme=gr.themes.Soft()) as demo:
|
169 |
+
# Header
|
170 |
+
gr.Markdown("# 🎨 Generart Beta")
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
171 |
|
172 |
+
with gr.Row():
|
173 |
+
# Left column - Controls
|
174 |
+
with gr.Column(scale=1):
|
175 |
+
prompt = gr.Textbox(label="Description", placeholder="Décrivez l'image souhaitée...")
|
176 |
+
|
177 |
style = gr.Dropdown(
|
178 |
+
choices=list(self.system.styles.keys()),
|
179 |
+
value="realistic",
|
180 |
+
label="Style Artistique"
|
|
|
|
|
|
|
|
|
|
|
181 |
)
|
182 |
+
|
183 |
+
with gr.Group():
|
184 |
+
steps = gr.Slider(
|
185 |
+
minimum=15,
|
186 |
+
maximum=25,
|
187 |
+
value=20,
|
188 |
+
step=1,
|
189 |
+
label="Nombre d'étapes"
|
190 |
+
)
|
191 |
+
|
192 |
+
guidance = gr.Slider(
|
193 |
+
minimum=6.0,
|
194 |
+
maximum=8.0,
|
195 |
+
value=7.0,
|
196 |
+
step=0.1,
|
197 |
+
label="Guide Scale"
|
198 |
+
)
|
199 |
+
|
200 |
+
quality = gr.Dropdown(
|
201 |
+
choices=list(self.system.quality_presets.keys()),
|
202 |
+
value="balanced",
|
203 |
+
label="Qualité"
|
204 |
+
)
|
205 |
+
|
206 |
+
seed = gr.Number(
|
207 |
+
value=-1,
|
208 |
+
label="Seed (-1 pour aléatoire)",
|
209 |
+
precision=0
|
210 |
+
)
|
211 |
+
|
212 |
+
generate_btn = gr.Button("Générer", variant="primary")
|
213 |
+
|
214 |
+
# System Stats
|
215 |
+
with gr.Group():
|
216 |
+
gr.Markdown("### 📊 Statistiques Système")
|
217 |
+
stats_output = gr.JSON(value=self.system.get_system_stats())
|
218 |
+
|
219 |
+
# Right column - Output
|
220 |
+
with gr.Column(scale=1):
|
221 |
+
image_output = gr.Image(label="Image Générée", type="pil")
|
222 |
+
|
223 |
+
# Generation Event
|
224 |
+
def generate(prompt, style, steps, guidance_scale, quality, seed):
|
225 |
+
params = GenerationParams(
|
226 |
+
prompt=prompt,
|
227 |
+
style=style,
|
228 |
+
steps=steps,
|
229 |
+
guidance_scale=guidance_scale,
|
230 |
+
quality=quality,
|
231 |
+
seed=seed
|
232 |
)
|
233 |
+
|
234 |
+
image = self.system.generate_image(params)
|
235 |
+
return [image, self.system.get_system_stats()]
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
236 |
|
237 |
+
generate_btn.click(
|
238 |
+
fn=generate,
|
239 |
+
inputs=[prompt, style, steps, guidance, quality, seed],
|
240 |
+
outputs=[image_output, stats_output]
|
241 |
+
)
|
242 |
|
243 |
+
return demo
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
244 |
|
245 |
+
# Create and launch the interface
|
246 |
if __name__ == "__main__":
|
247 |
+
interface = GenerartInterface()
|
248 |
+
demo = interface.create_interface()
|
249 |
+
demo.launch()
|