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import torch
from diffusers.utils import load_image
from diffusers import FluxControlNetPipeline, FluxControlNetModel, FluxMultiControlNetModel
import gradio as gr
import spaces
base_model = 'black-forest-labs/FLUX.1-dev'
controlnet_model_union = 'InstantX/FLUX.1-dev-Controlnet-Union'
controlnet_union = FluxControlNetModel.from_pretrained(controlnet_model_union, torch_dtype=torch.bfloat16)
controlnet = FluxMultiControlNetModel([controlnet_union]) # we always recommend loading via FluxMultiControlNetModel
pipe = FluxControlNetPipeline.from_pretrained(base_model, controlnet=controlnet, torch_dtype=torch.bfloat16)
pipe.to("cuda")
control_modes = [
"canny",
"tile",
"depth",
"blur",
"pose",
"gray",
"lq"
]
@spaces.GPU
def generate_image(prompt, control_image_depth, control_mode_depth_index, control_image_canny, control_mode_canny_index):
control_mode_depth = control_modes.index(control_mode_depth_index)
control_mode_canny = control_modes.index(control_mode_canny_index)
width, height = control_image_depth.size
image = pipe(
prompt,
control_image=[control_image_depth, control_image_canny],
control_mode=[control_mode_depth, control_mode_canny],
width=width,
height=height,
controlnet_conditioning_scale=[0.2, 0.4],
num_inference_steps=24,
guidance_scale=3.5,
generator=torch.manual_seed(42),
).images[0]
return image
iface = gr.Interface(
fn=generate_image,
inputs=[
gr.Text(label="Prompt"),
gr.Image(label="Control Image (Depth)"),
gr.Dropdown(choices=control_modes, value="depth", label="Control Mode (Depth)"),
gr.Image(label="Control Image (Canny)"),
gr.Dropdown(choices=control_modes, value="canny", label="Control Mode (Canny)")
],
outputs=gr.Image(label="Generated Image"),
title="FluxControlNet Image Generation",
description="Generate an image using FluxControlNet with depth and canny control images.",
)
iface.launch() |