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from __future__ import annotations
import gc
import numpy as np
import PIL.Image
import torch
from diffusers import (
ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL, EulerAncestralDiscreteScheduler
)
from cv_utils import resize_image
from preprocessor import Preprocessor
from settings import MAX_IMAGE_RESOLUTION, MAX_NUM_IMAGES
CONTROLNET_MODEL_IDS = {
"Canny": "briaai/BRIA-2.2-ControlNet-Canny",
"Depth": "briaai/BRIA-2.2-ControlNet-Depth",
"Recoloring": "briaai/BRIA-2.2-ControlNet-Recoloring",
}
def download_all_controlnet_weights() -> None:
for model_id in CONTROLNET_MODEL_IDS.values():
ControlNetModel.from_pretrained(model_id)
class Model:
def __init__(self, base_model_id: str = "briaai/BRIA-2.2", task_name: str = "Canny"):
self.device = torch.device("cuda:0")
self.base_model_id = ""
self.task_name = ""
self.pipe = self.load_pipe(base_model_id, task_name)
self.preprocessor = Preprocessor()
def load_pipe(self, base_model_id: str, task_name) -> DiffusionPipeline:
if (
base_model_id == self.base_model_id
and task_name == self.task_name
and hasattr(self, "pipe")
and self.pipe is not None
):
return self.pipe
model_id = CONTROLNET_MODEL_IDS[task_name]
controlnet = ControlNetModel.from_pretrained(model_id, torch_dtype=torch.float16).to('cuda')
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
base_model_id,
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
torch.cuda.empty_cache()
gc.collect()
self.base_model_id = base_model_id
self.task_name = task_name
return pipe
def set_base_model(self, base_model_id: str) -> str:
if not base_model_id or base_model_id == self.base_model_id:
return self.base_model_id
del self.pipe
torch.cuda.empty_cache()
gc.collect()
try:
self.pipe = self.load_pipe(base_model_id, self.task_name)
except Exception:
self.pipe = self.load_pipe(self.base_model_id, self.task_name)
return self.base_model_id
def load_controlnet_weight(self, task_name: str) -> None:
if task_name == self.task_name:
return
if self.pipe is not None and hasattr(self.pipe, "controlnet"):
del self.pipe.controlnet
torch.cuda.empty_cache()
gc.collect()
model_id = CONTROLNET_MODEL_IDS[task_name]
controlnet = ControlNetModel.from_pretrained(model_id, torch_dtype=torch.float16)
controlnet.to(self.device)
torch.cuda.empty_cache()
gc.collect()
self.pipe.controlnet = controlnet
self.task_name = task_name
def get_prompt(self, prompt: str, additional_prompt: str) -> str:
if not prompt:
prompt = additional_prompt
else:
prompt = f"{prompt}, {additional_prompt}"
return prompt
@torch.autocast("cuda")
def run_pipe(
self,
prompt: str,
negative_prompt: str,
control_image: PIL.Image.Image,
num_images: int,
num_steps: int,
controlnet_conditioning_scale: float,
seed: int,
) -> list[PIL.Image.Image]:
generator = torch.Generator().manual_seed(seed)
return self.pipe(
prompt=prompt,
negative_prompt=negative_prompt,
controlnet_conditioning_scale=controlnet_conditioning_scale,
num_images_per_prompt=num_images,
num_inference_steps=num_steps,
generator=generator,
image=control_image,
).images
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_canny_filter(image):
low_threshold = 100
high_threshold = 200
if not isinstance(image, np.ndarray):
image = np.array(image)
image = cv2.Canny(image, low_threshold, high_threshold)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
canny_image = Image.fromarray(image)
return canny_image
@torch.inference_mode()
def process_canny(
self,
image: np.ndarray,
prompt: str,
negative_prompt: str,
image_resolution: int,
num_steps: int,
controlnet_conditioning_scale: float,
seed: int,
) -> list[PIL.Image.Image]:
# resize input_image to 1024x1024
input_image = resize_image(image)
canny_image = get_canny_filter(input_image)
self.load_controlnet_weight("Canny")
results = self.run_pipe(
prompt, negative_prompt=negative_prompt, image=canny_image, num_inference_steps=num_steps, controlnet_conditioning_scale=float(controlnet_conditioning_scale)
)
return [control_image] + results
----------------------------------------------------------------------------
# from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
# 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-Canny",
# 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
# low_threshold = 100
# high_threshold = 200
# 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_canny_filter(image):
# if not isinstance(image, np.ndarray):
# image = np.array(image)
# image = cv2.Canny(image, low_threshold, high_threshold)
# image = image[:, :, None]
# image = np.concatenate([image, image, image], axis=2)
# canny_image = Image.fromarray(image)
# return canny_image
# def process(input_image, prompt, negative_prompt, num_steps, controlnet_conditioning_scale, seed):
# generator = torch.manual_seed(seed)
# # resize input_image to 1024x1024
# input_image = resize_image(input_image)
# canny_image = get_canny_filter(input_image)
# 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 [canny_image,images[0]]
# block = gr.Blocks().queue()
# with block:
# gr.Markdown("## BRIA 2.2 ControlNet Canny")
# gr.HTML('''
# <p style="margin-bottom: 10px; font-size: 94%">
# This is a demo for ControlNet Canny 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) |