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import spaces | |
import gradio as gr | |
import torch | |
from diffusers import ( | |
AutoencoderKL, | |
EulerAncestralDiscreteScheduler, | |
) | |
from diffusers.utils import load_image | |
from replace_bg.model.pipeline_controlnet_sd_xl import StableDiffusionXLControlNetPipeline | |
from replace_bg.model.controlnet import ControlNetModel | |
from replace_bg.utilities import resize_image, remove_bg_from_image, paste_fg_over_image, get_control_image_tensor | |
controlnet = ControlNetModel.from_pretrained("briaai/BRIA-2.3-ControlNet-BG-Gen", torch_dtype=torch.float16) | |
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16) | |
pipe = StableDiffusionXLControlNetPipeline.from_pretrained("briaai/BRIA-2.3", controlnet=controlnet, torch_dtype=torch.float16, vae=vae).to('cuda:0') | |
pipe.scheduler = EulerAncestralDiscreteScheduler( | |
beta_start=0.00085, | |
beta_end=0.012, | |
beta_schedule="scaled_linear", | |
num_train_timesteps=1000, | |
steps_offset=1 | |
) | |
def generate_(prompt, negative_prompt, control_tensor, num_steps, controlnet_conditioning_scale, seed): | |
generator = torch.Generator("cuda").manual_seed(seed) | |
gen_img = pipe( | |
negative_prompt=negative_prompt, | |
prompt=prompt, | |
controlnet_conditioning_scale=float(controlnet_conditioning_scale), | |
num_inference_steps=num_steps, | |
image = control_tensor, | |
generator=generator | |
).images[0] | |
return gen_img | |
def process(input_image, prompt, negative_prompt, num_steps, controlnet_conditioning_scale, seed): | |
image = resize_image(input_image) | |
mask = remove_bg_from_image(image) | |
control_tensor = get_control_image_tensor(pipe.vae, image, mask) | |
gen_image = generate_(prompt, negative_prompt, control_tensor, num_steps, controlnet_conditioning_scale, seed) | |
result_image = paste_fg_over_image(gen_image, image, mask) | |
return result_image | |
block = gr.Blocks().queue() | |
with block: | |
gr.Markdown("## BRIA Generate Background") | |
gr.HTML(''' | |
<p style="margin-bottom: 10px; font-size: 94%"> | |
This is a demo for ControlNet Depth that using | |
<a href="briaai/BRIA-2.3-ControlNet-BG-Gen" target="_blank">BRIA 2.3 text-to-image model</a> as backbone. | |
Trained on licensed data, BRIA 2.3 provide full legal liability coverage for copyright and privacy infringement. | |
</p> | |
''') | |
with gr.Row(): | |
with gr.Column(): | |
input_image = gr.Image(sources=None, type="pil",label="Upload",height=600) # None for upload, ctrl+v and webcam | |
prompt = gr.Textbox(label="Prompt") | |
negative_prompt = gr.Textbox(label="Negative prompt", value="Logo,Watermark,Text,Ugly,Morbid,Extra fingers,Poorly drawn hands,Mutation,Blurry,Extra limbs,Gross proportions,Missing arms,Mutated hands,Long neck,Duplicate,Mutilated,Mutilated hands,Poorly drawn face,Deformed,Bad anatomy,Cloned face,Malformed limbs,Missing legs,Too many fingers") | |
num_steps = gr.Slider(label="Number of steps", minimum=25, maximum=100, value=50, step=1) | |
controlnet_conditioning_scale = gr.Slider(label="ControlNet conditioning scale", minimum=0.1, maximum=2.0, value=1.0, step=0.05) | |
seed = gr.Slider(label="Seed", minimum=0, maximum=2147483647, step=1, randomize=True,) | |
run_button = gr.Button(value="Generate") | |
with gr.Column(): | |
result_gallery = gr.Image(label='Output', show_label=True, elem_id="gallery", height=600) # None for upload, ctrl+v and webcam | |
# result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery", columns=[1], height=600) | |
ips = [input_image, prompt, negative_prompt, num_steps, controlnet_conditioning_scale, seed] | |
run_button.click(fn=process, inputs=ips, outputs=[result_gallery]) | |
block.launch(debug = True) |