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import gradio as gr
import torch
from diffusers import AutoPipelineForImage2Image, DPMSolverMultistepScheduler
base_model = "SG161222/RealVisXL_V4.0"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
pipeline = AutoPipelineForImage2Image.from_pretrained(
base_model, torch_dtype=torch.float16, use_safetensors=True
)
pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
pipeline.to(device)
generator = torch.Generator(device).manual_seed(0)
def image_to_image(input_image, prompt, guidance_scale, num_inference_steps):
# Generate the output image
output_image = pipeline(
generator=generator,
prompt=prompt, image=input_image,
guidance_scale=guidance_scale, num_inference_steps = num_inference_steps
).images[0]
return output_image
with gr.Blocks() as grApp:
input_image = gr.Image(label="Input Image")
prompt = gr.Textbox(lines=3, label="Prompt")
guidance_scale = gr.Slider(minimum=0, maximum=1, value=0.75, label="Guidance Scale")
num_inference_steps = gr.Slider(minimum=10, maximum=100, value=25, label="Number of Inference Steps")
output_image = gr.Image()
generate_btn = gr.Button("Generate Image")
generate_btn.click(
fn=image_to_image,
inputs=[input_image, prompt, guidance_scale, num_inference_steps],
outputs=output_image,
)
grApp.launch()
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