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import gradio as gr
import torch
from PIL import Image
from diffusers import DiffusionPipeline
import os
import spaces
# Constants
#SAFETY_CHECKER = os.environ.get("SAFETY_CHECKER", "0") == "1"
# Initialize the model
pipe = DiffusionPipeline.from_pretrained(
"playgroundai/playground-v2.5-1024px-aesthetic",
torch_dtype=torch.float16,
variant="fp16",
).to("cuda")
# Safety Checker (if necessary)
#if SAFETY_CHECKER:
# Implement or import the safety checker code here
@spaces.GPU(enable_queue=True)
def generate_image(prompt, num_inference_steps=50, guidance_scale=7):
# Generate image
results = pipe(prompt=prompt, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale)
# Safety check (if necessary)
if SAFETY_CHECKER:
# Implement the safety check logic here
pass
return results.images[0]
# Gradio Interface
description = """
This demo utilizes the playgroundai/playground-v2.5-1024px-aesthetic by Playground, which is a text-to-image generative model capable of producing high-quality images.
As a community effort, this demo was put together by AngryPenguin. Link to model: https://huggingface.co/playgroundai/playground-v2.5-1024px-aesthetic
"""
with gr.Blocks() as demo:
gr.Markdown("## Playground-V2.5 Demo")
with gr.Row():
prompt = gr.Textbox(label='Enter your image prompt')
num_inference_steps = gr.Slider(minimum=1, maximum=75, step=1, label='Number of Inference Steps', value=50)
guidance_scale = gr.Slider(minimum=1, maximum=20, step=0.1, label='Guidance Scale', value=7)
submit = gr.Button('Generate Image')
img = gr.Image(label='Generated Image')
submit.click(
fn=generate_image,
inputs=[prompt, num_inference_steps, guidance_scale],
outputs=img,
)
demo.queue().launch() |