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import gradio as gr
import random
# Load each model separately
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)
# Generate images using each model with the text prompt only
result_image1 = model1(text)
result_image2 = model2(text)
result_image3 = model3(text)
# Print parameters for debugging
print(f"Text: {text}, Seed: {seed}, Width: {width}, Height: {height}, Guidance Scale: {guidance_scale}, Steps: {num_inference_steps}")
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 from NSFW_master"),
gr.Image(label="Generated Image from flux-lora-uncensored"),
gr.Image(label="Generated Image from NSFW-TrioHMH-Flux")
],
description="Generate images with three different models based on your prompt. Note: the models run on the CPU, which may affect performance.",
)
interface.launch()
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