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import gradio as gr
from diffusers import StableDiffusionPipeline
from diffusers import FluxPipeline
import torch
import spaces
@spaces.GPU
def generate_image(prompt, num_inference_steps, guidance_scale):
# Load the base model
base_model_id = "black-forest-labs/FLUX.1-dev"
#pipe = StableDiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16)
#model_path = "MegaTronX/SuicideGirl-FLUX"
model_path = "https://huggingface.co/MegaTronX/SuicideGirl-FLUX/blob/main/SuicideGirls.safetensors"
'''pipe = StableDiffusionPipeline.from_single_file(
model_path,
torch_dtype=torch.float16,
use_safetensors=True
'''
# Load the base model and apply LoRA weights using from_single_file
pipe = FluxPipeline.from_single_file(
"https://huggingface.co/MegaTronX/SuicideGirl-FLUX/blob/main/SuicideGirls.safetensors",
torch_dtype=torch.float16,
use_safetensors=True,
load_safety_checker=False,
variant="fp16",
original_config_file=base_model_id
)
# Load the LoRA weights
#pipe.unet.load_attn_procs("MegaTronX/SuicideGirl-FLUX")
pipe = pipe.to("cuda")
# Generate the image
image = pipe(
prompt,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale
).images[0]
return image
# Create the Gradio interface
iface = gr.Interface(
fn=generate_image,
inputs=[
gr.Textbox(label="Prompt"),
gr.Slider(minimum=1, maximum=100, value=50, label="Number of Inference Steps"),
gr.Slider(minimum=1, maximum=20, value=7.5, label="Guidance Scale")
],
outputs=gr.Image(label="Generated Image"),
title="Image Generation with Custom LoRA",
description="Generate images using a custom LoRA model trained on Flux Dev."
)
iface.launch()
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