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import gradio as gr
from huggingface_hub import login
import os
import spaces
from diffusers.schedulers import UniPCMultistepScheduler
from diffusers import StableDiffusionXLAdapterPipeline, T2IAdapter, EulerAncestralDiscreteScheduler, AutoencoderKL
from diffusers.utils import load_image, make_image_grid
import torch
# Log in to Hugging Face with your token
token = os.getenv("HF_TOKEN")
login(token=token)
model_id = 'stabilityai/sdxl-turbo'
adapter = StableDiffusionXLAdapterPipeline.load_ip_adapter("TencentARC/T2I-Adapter",
subfolder="models",
weight_name="t2iadapter_style_sd14v1.pth")
euler_a = EulerAncestralDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
vae=AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix",)
pipe = StableDiffusionXLAdapterPipeline.from_pretrained(
model_id, vae=vae, adapter=adapter, scheduler=euler_a, variant="fp16",
)
pipe.enable_xformers_memory_efficient_attention()
pipe.to("cuda", torch.float16)
# controlnet = SD3ControlNetModel.from_pretrained("alimama-creative/SD3-Controlnet-Softedge", torch_dtype=torch.float16)
#
# pipe = StableDiffusion3ControlNetPipeline.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers", controlnet=controlnet)
# pipe.to("cuda", torch.float16)
@spaces.GPU
def generate_image(prompt, reference_image, controlnet_conditioning_scale):
# Generate the image with ControlNet conditioning
generated_image = pipe(
prompt=prompt,
ip_adapter_image=load_image(reference_image),
controlnet_conditioning_scale=controlnet_conditioning_scale,
).images[0]
return generated_image
# Set up Gradio interface
interface = gr.Interface(
fn=generate_image,
inputs=[
gr.Textbox(label="Prompt"),
gr.Image( type= "filepath",label="Reference Image (Style)"),
gr.Slider(label="Control Net Conditioning Scale", minimum=0, maximum=1.0, step=0.1, value=0.6),
],
outputs="image",
title="Image Generation with Stable Diffusion 3 medium and ControlNet",
description="Generates an image based on a text prompt and a reference image using Stable Diffusion 3 medium with ControlNet."
)
interface.launch()
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