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from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
import torch
import gradio as gr
import spaces
lora_path = "OedoSoldier/detail-tweaker-lora"
@spaces.GPU
def generate_image(prompt, negative_prompt, num_inference_steps=30, guidance_scale=7.0,model="Real6.0"):
"""
Generate an image using Stable Diffusion based on the input prompt
"""
if model == "Real5.0":
model_id = "SG161222/Realistic_Vision_V5.0_noVAE"
elif model == "Real5.1":
model_id = "SG161222/Realistic_Vision_V5.1_noVAE"
else:
model_id = "SG161222/Realistic_Vision_V6.0_B1_noVAE"
pipe = DiffusionPipeline.from_pretrained(model_id).to("cuda")
if model == "Real6.0":
pipe.safety_checker = lambda images, **kwargs: (images, [False] * len(images))
pipe.load_lora_weights(lora_path)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(
pipe.scheduler.config,
algorithm_type="dpmsolver++",
use_karras_sigmas=True
)
# Generate the image
image = pipe(
prompt = prompt,
negative_prompt = negative_prompt,
cross_attention_kwargs = {"scale":1},
num_inference_steps = num_inference_steps,
guidance_scale = guidance_scale,
width = 960,
height = 960
).images[0]
return image
title = """<h1 align="center">ProFaker</h1>"""
# Create the Gradio interface
with gr.Blocks() as demo:
gr.HTML(title)
with gr.Row():
with gr.Column():
# Input components
prompt = gr.Textbox(
label="Prompt",
info="Enter your image description here...",
lines=3
)
negative_prompt = gr.Textbox(
label="Negative Prompt",
info="Enter what you don't want in Image...",
lines=3
)
generate_button = gr.Button("Generate Image")
with gr.Accordion("Advanced Options", open=False):
model = gr.Dropdown(
choices=["Real6.0","Real5.1","Real5.0"],
value="Real6.0",
label="Model",
)
steps_slider = gr.Slider(
minimum=1,
maximum=100,
value=30,
step=1,
label="Number of Steps"
)
guidance_slider = gr.Slider(
minimum=1,
maximum=10,
value=7.0,
step=0.5,
label="Guidance Scale"
)
with gr.Column():
# Output component
image_output = gr.Image(label="Generated Image")
# Connect the interface to the generation function
generate_button.click(
fn=generate_image,
inputs=[prompt, negative_prompt, steps_slider, guidance_slider, model],
outputs=image_output
)
demo.queue(max_size=10).launch(share=False)
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