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import spaces
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL, EulerAncestralDiscreteScheduler
from diffusers.utils import load_image
from PIL import Image
import torch
import numpy as np
import cv2
import gradio as gr
from torchvision import transforms
controlnet = ControlNetModel.from_pretrained(
"briaai/BRIA-2.2-ControlNet-Depth",
torch_dtype=torch.float16
).to('cuda')
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"briaai/BRIA-2.2",
controlnet=controlnet,
torch_dtype=torch.float16,
device_map='auto',
low_cpu_mem_usage=True,
offload_state_dict=True,
).to('cuda')
pipe.scheduler = EulerAncestralDiscreteScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
num_train_timesteps=1000,
steps_offset=1
)
# pipe.enable_freeu(b1=1.1, b2=1.1, s1=0.5, s2=0.7)
# pipe.enable_xformers_memory_efficient_attention()
pipe.force_zeros_for_empty_prompt = False
from transformers import DPTFeatureExtractor, DPTForDepthEstimation
depth_estimator = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas").to("cuda")
feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-hybrid-midas")
def resize_image(image):
image = image.convert('RGB')
current_size = image.size
if current_size[0] > current_size[1]:
center_cropped_image = transforms.functional.center_crop(image, (current_size[1], current_size[1]))
else:
center_cropped_image = transforms.functional.center_crop(image, (current_size[0], current_size[0]))
resized_image = transforms.functional.resize(center_cropped_image, (1024, 1024))
return resized_image
def get_depth_map(image):
image = feature_extractor(images=image, return_tensors="pt").pixel_values.to("cuda")
with torch.no_grad(), torch.autocast("cuda"):
depth_map = depth_estimator(image).predicted_depth
image = transforms.functional.center_crop(image, min(image.shape[-2:]))
depth_map = torch.nn.functional.interpolate(
depth_map.unsqueeze(1),
size=(1024, 1024),
mode="bicubic",
align_corners=False,
)
depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True)
depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True)
depth_map = (depth_map - depth_min) / (depth_max - depth_min)
image = torch.cat([depth_map] * 3, dim=1)
image = image.permute(0, 2, 3, 1).cpu().numpy()[0]
image = Image.fromarray((image * 255.0).clip(0, 255).astype(np.uint8))
return image
@spaces.GPU
def generate_(prompt, negative_prompt, canny_image, num_steps, controlnet_conditioning_scale, seed):
generator = torch.Generator("cuda").manual_seed(seed)
images = pipe(
prompt, negative_prompt=negative_prompt, image=canny_image, num_inference_steps=num_steps, controlnet_conditioning_scale=float(controlnet_conditioning_scale),
generator=generator,
).images
return images
@spaces.GPU
def process(input_image, prompt, negative_prompt, num_steps, controlnet_conditioning_scale, seed):
# resize input_image to 1024x1024
input_image = resize_image(input_image)
depth_image = get_depth_map(input_image)
images = generate_(prompt, negative_prompt, depth_image, num_steps, controlnet_conditioning_scale, seed)
return [depth_image, images[0]]
block = gr.Blocks().queue()
with block:
gr.Markdown("## BRIA 2.2 ControlNet Depth")
gr.HTML('''
<p style="margin-bottom: 10px; font-size: 94%">
This is a demo for ControlNet Depth that using
<a href="https://huggingface.co/briaai/BRIA-2.2" target="_blank">BRIA 2.2 text-to-image model</a> as backbone.
Trained on licensed data, BRIA 2.2 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") # 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="Run")
with gr.Column():
result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery", columns=[2], height='auto')
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) |