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import gradio as gr
from utils import generate_images_simulated
# --- Constants ---
ANYCODER_LINK = "https://huggingface.co/spaces/akhaliq/anycoder"
MODEL_CHOICES = {
"Stable Diffusion v1.4 (CompVis)": "CompVis/stable-diffusion-v1-4",
"Stable Diffusion 2.1 Base (Manojb)": "Manojb/stable-diffusion-2-1-base"
}
DEFAULT_MODEL_KEY = list(MODEL_CHOICES.keys())[0]
# --- Gradio Logic Wrapper ---
def process_generation(prompt: str, negative_prompt: str, model_key: str, steps: float, guidance_scale: float, num_images: float):
"""
Wrapper function to call the simulated generation logic, translating model key.
Args:
prompt: The text prompt for generation.
negative_prompt: Text prompt to guide the model away from.
model_key: The user-friendly name of the selected model.
steps: Inference steps.
guidance_scale: CFG scale.
num_images: Number of images to generate (1-4).
Returns:
A list of PIL Images for the Gradio Gallery.
"""
model_name_id = MODEL_CHOICES[model_key]
# Note: steps and guidance_scale are floats from the slider, cast to appropriate types
results = generate_images_simulated(
prompt=prompt,
negative_prompt=negative_prompt,
model_name=model_name_id,
steps=int(steps),
guidance_scale=guidance_scale,
num_images=int(num_images)
)
return results
# --- Gradio UI Definition ---
with gr.Blocks(title="Stable Diffusion Model Comparison Demo") as demo:
# Custom Header
gr.HTML(
f"""
<div style="text-align: center; max-width: 800px; margin: 0 auto;">
<h1>Stable Diffusion Model Comparison Demo</h1>
<p>Compare outputs from Stable Diffusion v1.4 and 2.1 Base. The application handles empty prompts by using a default prompt internally.</p>
<p>Built with <a href="{ANYCODER_LINK}" target="_blank" style="color: blue; text-decoration: underline;">anycoder</a></p>
</div>
"""
)
with gr.Row():
with gr.Column(scale=2):
# --- Inputs ---
prompt = gr.Textbox(
label="Prompt",
placeholder="e.g., A photorealistic image of a vintage robot reading a book in a cozy library, cinematic lighting",
value="A majestic portrait of a futuristic cyborg cat, digital art, highly detailed",
lines=3
)
negative_prompt = gr.Textbox(
label="Negative Prompt (Optional)",
placeholder="e.g., blurry, low quality, distorted, bad anatomy, text",
lines=2
)
model_selector = gr.Radio(
label="Select Model",
choices=list(MODEL_CHOICES.keys()),
value=DEFAULT_MODEL_KEY,
interactive=True
)
with gr.Accordion("Generation Parameters", open=True):
steps = gr.Slider(
minimum=10, maximum=100, step=5, value=50, label="Inference Steps"
)
guidance_scale = gr.Slider(
minimum=1.0, maximum=15.0, step=0.5, value=7.5, label="Guidance Scale (CFG)"
)
num_images = gr.Slider(
minimum=1, maximum=4, step=1, value=4, label="Number of Images to Generate",
info="Uploads up to 4 images."
)
generate_btn = gr.Button("Generate Images", variant="primary")
with gr.Column(scale=3):
# --- Outputs ---
output_gallery = gr.Gallery(
label="Generated Images (512x512)",
columns=2,
rows=2,
height="auto",
preview=True
)
gr.Examples(
examples=[
["A hyper-detailed oil painting of a lighthouse during a storm", "ugly, blurry, low resolution", DEFAULT_MODEL_KEY],
["", "text, watermark, poor rendering", "Stable Diffusion 2.1 Base (Manojb)"], # Empty prompt example
["A 1950s sci-fi movie poster featuring a giant insect attacking Paris", "modern, photorealistic, color photo", DEFAULT_MODEL_KEY]
],
inputs=[prompt, negative_prompt, model_selector],
label="Examples (Note the second example uses an empty prompt)"
)
# --- Logic Binding ---
generate_btn.click(
fn=process_generation,
inputs=[prompt, negative_prompt, model_selector, steps, guidance_scale, num_images],
outputs=[output_gallery]
)
# Run initial generation on load to populate the gallery immediately
demo.load(
fn=process_generation,
inputs=[prompt, negative_prompt, model_selector, steps, guidance_scale, num_images],
outputs=[output_gallery]
)
if __name__ == "__main__":
demo.launch() |