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from __future__ import annotations
import gc
import numpy as np
import PIL.Image
import spaces
import torch
from controlnet_aux.util import HWC3
from diffusers import (
ControlNetModel,
DiffusionPipeline,
StableDiffusionControlNetPipeline,
UniPCMultistepScheduler,
)
from cv_utils import resize_image
from preprocessor import Preprocessor
from settings import MAX_IMAGE_RESOLUTION, MAX_NUM_IMAGES
CONTROLNET_MODEL_IDS = {
"Canny": "checkpoints/canny/controlnet",
"softedge": "checkpoints/hed/controlnet",
"segmentation": "checkpoints/seg/controlnet",
"depth": "checkpoints/depth/controlnet",
"lineart": "checkpoints/lineart/controlnet",
}
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 = "benjamin-paine/stable-diffusion-v1-5", task_name: str = "Canny"):
self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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.float32)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
base_model_id, safety_checker=None, controlnet=controlnet, torch_dtype=torch.float32
)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
# if self.device.type == "cuda":
# pipe.disable_xformers_memory_efficient_attention()
pipe.to(self.device)
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.float32)
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,
guidance_scale: float,
seed: int,
) -> list[PIL.Image.Image]:
self.pipe.to(self.device)
self.pipe.controlnet.to(self.device)
generator = torch.Generator().manual_seed(seed)
return self.pipe(
prompt=prompt,
negative_prompt=negative_prompt,
guidance_scale=guidance_scale,
num_images_per_prompt=num_images,
num_inference_steps=num_steps,
generator=generator,
image=control_image,
).images
@torch.no_grad()
@spaces.GPU(enable_queue=True)
def process_canny(
self,
image: np.ndarray,
prompt: str,
additional_prompt: str,
negative_prompt: str,
num_images: int,
image_resolution: int,
num_steps: int,
guidance_scale: float,
seed: int,
low_threshold: int,
high_threshold: int,
) -> list[PIL.Image.Image]:
if image is None:
raise ValueError
if image_resolution > MAX_IMAGE_RESOLUTION:
raise ValueError
if num_images > MAX_NUM_IMAGES:
raise ValueError
self.preprocessor.load("Canny")
control_image = self.preprocessor(
image=image, low_threshold=low_threshold, high_threshold=high_threshold, detect_resolution=image_resolution
)
self.load_controlnet_weight("Canny")
results = self.run_pipe(
prompt=self.get_prompt(prompt, additional_prompt),
negative_prompt=negative_prompt,
control_image=control_image,
num_images=num_images,
num_steps=num_steps,
guidance_scale=guidance_scale,
seed=seed,
)
conditions_of_generated_imgs = [
self.preprocessor(
image=x, low_threshold=low_threshold, high_threshold=high_threshold, detect_resolution=image_resolution
) for x in results
]
return [control_image] * num_images + results + conditions_of_generated_imgs
@torch.no_grad()
@spaces.GPU(enable_queue=True)
def process_softedge(
self,
image: np.ndarray,
prompt: str,
additional_prompt: str,
negative_prompt: str,
num_images: int,
image_resolution: int,
preprocess_resolution: int,
num_steps: int,
guidance_scale: float,
seed: int,
preprocessor_name: str,
) -> list[PIL.Image.Image]:
if image is None:
raise ValueError
if image_resolution > MAX_IMAGE_RESOLUTION:
raise ValueError
if num_images > MAX_NUM_IMAGES:
raise ValueError
if preprocessor_name == "None":
image = HWC3(image)
image = resize_image(image, resolution=image_resolution)
control_image = PIL.Image.fromarray(image)
elif preprocessor_name in ["HED", "HED safe"]:
safe = "safe" in preprocessor_name
self.preprocessor.load("HED")
control_image = self.preprocessor(
image=image,
image_resolution=image_resolution,
detect_resolution=preprocess_resolution,
scribble=safe,
)
elif preprocessor_name in ["PidiNet", "PidiNet safe"]:
safe = "safe" in preprocessor_name
self.preprocessor.load("PidiNet")
control_image = self.preprocessor(
image=image,
image_resolution=image_resolution,
detect_resolution=preprocess_resolution,
safe=safe,
)
else:
raise ValueError
self.load_controlnet_weight("softedge")
results = self.run_pipe(
prompt=self.get_prompt(prompt, additional_prompt),
negative_prompt=negative_prompt,
control_image=control_image,
num_images=num_images,
num_steps=num_steps,
guidance_scale=guidance_scale,
seed=seed,
)
conditions_of_generated_imgs = [
self.preprocessor(
image=x,
image_resolution=image_resolution,
detect_resolution=preprocess_resolution,
scribble=safe,
) for x in results
]
return [control_image] * num_images + results + conditions_of_generated_imgs
@torch.no_grad()
@spaces.GPU(enable_queue=True)
def process_segmentation(
self,
image: np.ndarray,
prompt: str,
additional_prompt: str,
negative_prompt: str,
num_images: int,
image_resolution: int,
preprocess_resolution: int,
num_steps: int,
guidance_scale: float,
seed: int,
preprocessor_name: str,
) -> list[PIL.Image.Image]:
if image is None:
raise ValueError
if image_resolution > MAX_IMAGE_RESOLUTION:
raise ValueError
if num_images > MAX_NUM_IMAGES:
raise ValueError
if preprocessor_name == "None":
image = HWC3(image)
image = resize_image(image, resolution=image_resolution)
control_image = PIL.Image.fromarray(image)
else:
self.preprocessor.load(preprocessor_name)
control_image = self.preprocessor(
image=image,
image_resolution=image_resolution,
detect_resolution=preprocess_resolution,
)
self.load_controlnet_weight("segmentation")
results = self.run_pipe(
prompt=self.get_prompt(prompt, additional_prompt),
negative_prompt=negative_prompt,
control_image=control_image,
num_images=num_images,
num_steps=num_steps,
guidance_scale=guidance_scale,
seed=seed,
)
self.preprocessor.load('UPerNet')
conditions_of_generated_imgs = [
self.preprocessor(
image=np.array(x),
image_resolution=image_resolution,
detect_resolution=preprocess_resolution,
) for x in results
]
return [control_image] * num_images + results + conditions_of_generated_imgs
@torch.no_grad()
@spaces.GPU(enable_queue=True)
def process_depth(
self,
image: np.ndarray,
prompt: str,
additional_prompt: str,
negative_prompt: str,
num_images: int,
image_resolution: int,
preprocess_resolution: int,
num_steps: int,
guidance_scale: float,
seed: int,
preprocessor_name: str,
) -> list[PIL.Image.Image]:
if image is None:
raise ValueError
if image_resolution > MAX_IMAGE_RESOLUTION:
raise ValueError
if num_images > MAX_NUM_IMAGES:
raise ValueError
if preprocessor_name == "None":
image = HWC3(image)
image = resize_image(image, resolution=image_resolution)
control_image = PIL.Image.fromarray(image)
else:
self.preprocessor.load(preprocessor_name)
control_image = self.preprocessor(
image=image,
image_resolution=image_resolution,
detect_resolution=preprocess_resolution,
)
self.load_controlnet_weight("depth")
results = self.run_pipe(
prompt=self.get_prompt(prompt, additional_prompt),
negative_prompt=negative_prompt,
control_image=control_image,
num_images=num_images,
num_steps=num_steps,
guidance_scale=guidance_scale,
seed=seed,
)
conditions_of_generated_imgs = [
self.preprocessor(
image=x,
image_resolution=image_resolution,
detect_resolution=preprocess_resolution,
) for x in results
]
return [control_image] * num_images + results + conditions_of_generated_imgs
@torch.no_grad()
@spaces.GPU(enable_queue=True)
def process_lineart(
self,
image: np.ndarray,
prompt: str,
additional_prompt: str,
negative_prompt: str,
num_images: int,
image_resolution: int,
preprocess_resolution: int,
num_steps: int,
guidance_scale: float,
seed: int,
preprocessor_name: str,
) -> list[PIL.Image.Image]:
if image is None:
raise ValueError
if image_resolution > MAX_IMAGE_RESOLUTION:
raise ValueError
if num_images > MAX_NUM_IMAGES:
raise ValueError
if preprocessor_name in ["None", "None (anime)"]:
image = 255 - HWC3(image)
image = resize_image(image, resolution=image_resolution)
control_image = PIL.Image.fromarray(image)
elif preprocessor_name in ["Lineart", "Lineart coarse"]:
coarse = "coarse" in preprocessor_name
self.preprocessor.load("Lineart")
control_image = self.preprocessor(
image=image,
image_resolution=image_resolution,
detect_resolution=preprocess_resolution,
coarse=coarse,
)
elif preprocessor_name == "Lineart (anime)":
self.preprocessor.load("LineartAnime")
control_image = self.preprocessor(
image=image,
image_resolution=image_resolution,
detect_resolution=preprocess_resolution,
)
# NOTE: We still use the general lineart model
if "anime" in preprocessor_name:
self.load_controlnet_weight("lineart_anime")
else:
self.load_controlnet_weight("lineart")
results = self.run_pipe(
prompt=self.get_prompt(prompt, additional_prompt),
negative_prompt=negative_prompt,
control_image=control_image,
num_images=num_images,
num_steps=num_steps,
guidance_scale=guidance_scale,
seed=seed,
)
self.preprocessor.load("Lineart")
conditions_of_generated_imgs = [
self.preprocessor(
image=x,
image_resolution=image_resolution,
detect_resolution=preprocess_resolution,
) for x in results
]
control_image = PIL.Image.fromarray((255 - np.array(control_image)).astype(np.uint8))
conditions_of_generated_imgs = [PIL.Image.fromarray((255 - np.array(x)).astype(np.uint8)) for x in conditions_of_generated_imgs]
return [control_image] * num_images + results + conditions_of_generated_imgs
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