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import gradio as gr
import random
model1 = gr.load("models/pimpilikipilapi1/NSFW_master")
model2 = gr.load("models/prashanth970/flux-lora-uncensored")
model3 = gr.load("models/DiegoJR1973/NSFW-TrioHMH-Flux")
def generate_images(text, seed, width, height, guidance_scale, num_inference_steps):
if seed is not None:
random.seed(seed)
result_image1 = model1(
text,
width=width,
height=height,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps
)
result_image2 = model2(
text,
width=width - 128 if width > 640 else width,
height=height - 128 if height > 640 else height,
guidance_scale=guidance_scale * 1.2,
num_inference_steps=max(1, num_inference_steps - 5)
)
result_image3 = model3(
text,
width=width + 128 if width < 1920 else width,
height=height + 128 if height < 1920 else height,
guidance_scale=max(0.1, guidance_scale * 0.8),
num_inference_steps=min(40, num_inference_steps + 5)
)
print(f"Model 1: Width={width}, Height={height}, Guidance Scale={guidance_scale}, Steps={num_inference_steps}")
print(f"Model 2: Width={width - 128}, Height={height - 128}, Guidance Scale={guidance_scale * 1.2}, Steps={max(1, num_inference_steps - 5)}")
print(f"Model 3: Width={width + 128}, Height={height + 128}, Guidance Scale={max(0.1, guidance_scale * 0.8)}, Steps={min(40, num_inference_steps + 5)}")
return result_image1, result_image2, result_image3
def randomize_parameters():
seed = random.randint(0, 999999)
width = random.randint(512, 2048)
height = random.randint(512, 2048)
guidance_scale = round(random.uniform(0.1, 20.0), 1)
num_inference_steps = random.randint(1, 40)
return seed, width, height, guidance_scale, num_inference_steps
interface = gr.Interface(
fn=generate_images,
inputs=[
gr.Textbox(label="Type here your imagination:", placeholder="Type or click an example..."),
gr.Slider(label="Seed", minimum=0, maximum=999999, step=1),
gr.Slider(label="Width", minimum=512, maximum=2048, step=64, value=1024),
gr.Slider(label="Height", minimum=512, maximum=2048, step=64, value=1024),
gr.Slider(label="Guidance Scale", minimum=0.1, maximum=20.0, step=0.1, value=3.0),
gr.Slider(label="Number of inference steps", minimum=1, maximum=40, step=1, value=28),
],
outputs=[
gr.Image(label="Generated Image 01"),
gr.Image(label="Generated Image 02"),
gr.Image(label="Generated Image 03")
],
description="Generate images with three different models, each with slight variations. Please note that the models are running on the CPU, which might affect performance. Thank you for your patience!",
)
interface.launch() |