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# Install Gradio and dependencies
# --- Imports ---
from diffusers import StableDiffusionPipeline
import torch
import gradio as gr
import uuid
# --- Configuration ---
class CGF:
device = "cuda" if torch.cuda.is_available() else "cpu"
seed = 42
generator = torch.Generator(device=device).manual_seed(seed)
image_gen_model_id = "my_text_to_image_saved_sd_model"
image_gen_steps = 50
image_gen_guidance_scale = 10
image_gen_size = (768, 768)
# --- Load Model ---
pipe = StableDiffusionPipeline.from_pretrained(
CGF.image_gen_model_id,
torch_dtype=torch.float16,
use_safetensors=True,
revision="fp16",
).to(CGF.device)
# --- Generation Function ---
def generate_image(prompt):
result = pipe(
prompt,
num_inference_steps=CGF.image_gen_steps,
guidance_scale=CGF.image_gen_guidance_scale,
generator=CGF.generator,
).images[0]
filename = f"{prompt.replace(' ', '_')}_{uuid.uuid4().hex[:6]}.png"
result.save(filename)
return result, filename
# --- Gradio UI ---
examples = [
"A majestic mountain landscape during sunrise",
"A futuristic cyberpunk city at night with neon lights",
"A photorealistic portrait of a happy Indian woman in a green saree",
"A fantasy dragon flying over a castle at sunset",
"A cat astronaut floating in space",
]
with gr.Blocks(theme=gr.themes.Soft()) as demo:
gr.Markdown("## 🎨 Text-to-Image Generator with Stable Diffusion 2.1")
gr.Markdown("Type your prompt or select an example below to generate high-quality images.")
with gr.Row():
prompt = gr.Textbox(label="Enter your prompt", placeholder="e.g. A girl sitting on a hill in a summer breeze 🌼")
generate_btn = gr.Button("🚀 Generate Image")
with gr.Row():
with gr.Column():
gallery = gr.Image(label="Generated Image", type="filepath", interactive=False)
download = gr.File(label="Download Image")
with gr.Column():
gr.Examples(examples=examples, inputs=prompt)
generate_btn.click(fn=generate_image, inputs=prompt, outputs=[gallery, download])
# Launch Gradio app with a public link
# --- Launch App ---
demo.launch(share=True)
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