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import gradio as gr
from diffusers import AutoPipelineForText2Image, AutoPipelineForImage2Image
import torch
import peft
# Initialize the pipelines
text2img_pipe = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16")
text2img_pipe.to("cuda")
text2img_pipe.load_lora_weights("AbdalrhmanRi/SDXL-Turbo-With-AppleVisionPro", weight_name="pytorch_lora_weights.safetensors")
img2img_pipe = AutoPipelineForImage2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16")
img2img_pipe.to("cuda")
img2img_pipe.load_lora_weights("AbdalrhmanRi/SDXL-Turbo-With-AppleVisionPro", weight_name="pytorch_lora_weights.safetensors")
def generate_image(prompt, init_image):
if init_image is None:
# Text-to-Image generation
output_image = text2img_pipe(
prompt,
num_inference_steps=40,
guidance_scale=2.0,
height=480
).images[0]
yield output_image, None
output_refiner_image = text2img_pipe(
prompt=prompt,
image=output_image,
guidance_scale=1.0,
height=480
).images[0]
yield output_image, output_refiner_image
else:
# Image-to-Image generation
init_image = init_image.resize((512, 512))
output_image = img2img_pipe(
prompt,
image=init_image,
num_inference_steps=40,
strength=0.5,
guidance_scale=2.0,
height=480
).images[0]
yield output_image, None
output_refiner_image = img2img_pipe(
prompt=prompt,
image=output_image,
strength=0.5,
guidance_scale=1.0,
height=480
).images[0]
yield output_image, output_refiner_image
# Define the Gradio interface
interface = gr.Interface(
fn=generate_image,
inputs=[
gr.Textbox(label="Prompt", lines=2, placeholder="Enter your prompt here..."),
gr.Image(type="pil", label="Initial Image (Optional)", height=360)
],
outputs=[gr.Image(type="pil", label="Generated Image"), gr.Image(type="pil", label="Refined Image")],
title="Genrate Image Using Generative AI",
theme=gr.themes.Default(primary_hue="green"),
description="Text-to-Image or Image-to-Image Generation with SDXL-Turbo."
)
# Launch the interface
interface.launch() |