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import gradio as gr | |
import torch | |
from diffusers import StableDiffusion3ControlNetPipeline, SD3ControlNetModel, UniPCMultistepScheduler | |
from huggingface_hub import login | |
import os | |
import spaces | |
from diffusers.utils import load_image, make_image_grid | |
import torch | |
token = os.getenv("HF_TOKEN") | |
login(token=token) | |
# # Load the T2I-Style Adapter and the SDXL pipeline | |
# adapter = T2IAdapter.from_pretrained("TencentARC/t2i-adapter-style-sdxl") | |
# pipe = StableDiffusionXLAdapterPipeline.from_pretrained( | |
# "stabilityai/stable-diffusion-xl-base-1.0", | |
# adapter=adapter, | |
# ) | |
# | |
# # Set up the scheduler and device | |
# pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) | |
# 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) | |
def generate_image(prompt, reference_image, controlnet_conditioning_scale): | |
# Generate the image with ControlNet conditioning | |
generated_image = pipe( | |
prompt=prompt, | |
control_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() | |