import os
from dataclasses import dataclass
import cv2
import numpy as np
import torch
import torch.nn.functional as F
from controlnet_aux import CannyDetector, NormalBaeDetector
from diffusers import ControlNetModel, DDIMScheduler, StableDiffusionControlNetPipeline, DPMSolverMultistepScheduler
from diffusers.utils.import_utils import is_xformers_available
from tqdm import tqdm
import threestudio
from threestudio.models.prompt_processors.base import PromptProcessorOutput
from threestudio.utils.base import BaseObject
from threestudio.utils.misc import C, parse_version
from threestudio.utils.typing import *
@threestudio.register("stable-diffusion-controlnet-reg-guidance")
class ControlNetGuidance(BaseObject):
@dataclass
class Config(BaseObject.Config):
cache_dir: Optional[str] = None
local_files_only: Optional[bool] = False
pretrained_model_name_or_path: str = "SG161222/Realistic_Vision_V2.0"
ddim_scheduler_name_or_path: str = "runwayml/stable-diffusion-v1-5"
control_type: str = "normal" # normal/canny
enable_memory_efficient_attention: bool = False
enable_sequential_cpu_offload: bool = False
enable_attention_slicing: bool = False
enable_channels_last_format: bool = False
guidance_scale: float = 7.5
condition_scale: float = 1.5
grad_clip: Optional[Any] = None
half_precision_weights: bool = True
min_step_percent: float = 0.02
max_step_percent: float = 0.98
diffusion_steps: int = 20
use_sds: bool = False
# Canny threshold
canny_lower_bound: int = 50
canny_upper_bound: int = 100
cfg: Config
def configure(self) -> None:
threestudio.info(f"Loading ControlNet ...")
self.weights_dtype = torch.float16 if self.cfg.half_precision_weights else torch.float32
self.preprocessor, controlnet_name_or_path = self.get_preprocessor_and_controlnet()
pipe_kwargs = self.configure_pipeline()
self.load_models(pipe_kwargs, controlnet_name_or_path)
self.num_train_timesteps = self.scheduler.config.num_train_timesteps
self.scheduler.set_timesteps(self.cfg.diffusion_steps)
self.pipe.scheduler = DPMSolverMultistepScheduler.from_config(self.pipe.scheduler.config)
self.scheduler = self.pipe.scheduler
self.check_memory_efficiency_conditions()
self.set_min_max_steps()
self.alphas = self.scheduler.alphas_cumprod.to(self.device)
self.grad_clip_val = None
threestudio.info(f"Loaded ControlNet!")
def get_preprocessor_and_controlnet(self):
if self.cfg.control_type in ("normal", "input_normal"):
if self.cfg.pretrained_model_name_or_path == "SG161222/Realistic_Vision_V2.0":
controlnet_name_or_path = "lllyasviel/control_v11p_sd15_normalbae"
else:
controlnet_name_or_path = "thibaud/controlnet-sd21-normalbae-diffusers"
preprocessor = NormalBaeDetector.from_pretrained("lllyasviel/Annotators", cache_dir=self.cfg.cache_dir)
preprocessor.model.to(self.device)
elif self.cfg.control_type == "canny" or self.cfg.control_type == "canny2":
controlnet_name_or_path = self.get_canny_controlnet()
preprocessor = CannyDetector()
else:
raise ValueError(f"Unknown control type: {self.cfg.control_type}")
return preprocessor, controlnet_name_or_path
def get_canny_controlnet(self):
if self.cfg.control_type == "canny":
return "lllyasviel/control_v11p_sd15_canny"
elif self.cfg.control_type == "canny2":
return "thepowefuldeez/sd21-controlnet-canny"
def configure_pipeline(self):
return {
"safety_checker": None,
"feature_extractor": None,
"requires_safety_checker": False,
"torch_dtype": self.weights_dtype,
"cache_dir": self.cfg.cache_dir,
"local_files_only": self.cfg.local_files_only
}
def load_models(self, pipe_kwargs, controlnet_name_or_path):
controlnet = ControlNetModel.from_pretrained(
controlnet_name_or_path,
torch_dtype=self.weights_dtype,
cache_dir=self.cfg.cache_dir,
local_files_only=self.cfg.local_files_only
)
self.pipe = StableDiffusionControlNetPipeline.from_pretrained(
self.cfg.pretrained_model_name_or_path, controlnet=controlnet, **pipe_kwargs
).to(self.device)
self.scheduler = DDIMScheduler.from_pretrained(
self.cfg.ddim_scheduler_name_or_path,
subfolder="scheduler",
torch_dtype=self.weights_dtype,
cache_dir=self.cfg.cache_dir,
local_files_only=self.cfg.local_files_only
)
self.vae = self.pipe.vae.eval()
self.unet = self.pipe.unet.eval()
self.controlnet = self.pipe.controlnet.eval()
def check_memory_efficiency_conditions(self):
if self.cfg.enable_memory_efficient_attention:
self.memory_efficiency_status()
if self.cfg.enable_sequential_cpu_offload:
self.pipe.enable_sequential_cpu_offload()
if self.cfg.enable_attention_slicing:
self.pipe.enable_attention_slicing(1)
if self.cfg.enable_channels_last_format:
self.pipe.unet.to(memory_format=torch.channels_last)
def memory_efficiency_status(self):
if parse_version(torch.__version__) >= parse_version("2"):
threestudio.info("PyTorch2.0 uses memory efficient attention by default.")
elif not is_xformers_available():
threestudio.warn("xformers is not available, memory efficient attention is not enabled.")
else:
self.pipe.enable_xformers_memory_efficient_attention()
@torch.cuda.amp.autocast(enabled=False)
def set_min_max_steps(self, min_step_percent=0.02, max_step_percent=0.98):
self.min_step = int(self.num_train_timesteps * min_step_percent)
self.max_step = int(self.num_train_timesteps * max_step_percent)
@torch.cuda.amp.autocast(enabled=False)
def forward_controlnet(
self,
latents: Float[Tensor, "..."],
t: Float[Tensor, "..."],
image_cond: Float[Tensor, "..."],
condition_scale: float,
encoder_hidden_states: Float[Tensor, "..."],
) -> Float[Tensor, "..."]:
return self.controlnet(
latents.to(self.weights_dtype),
t.to(self.weights_dtype),
encoder_hidden_states=encoder_hidden_states.to(self.weights_dtype),
controlnet_cond=image_cond.to(self.weights_dtype),
conditioning_scale=condition_scale,
return_dict=False,
)
@torch.cuda.amp.autocast(enabled=False)
def forward_control_unet(
self,
latents: Float[Tensor, "..."],
t: Float[Tensor, "..."],
encoder_hidden_states: Float[Tensor, "..."],
cross_attention_kwargs,
down_block_additional_residuals,
mid_block_additional_residual,
) -> Float[Tensor, "..."]:
input_dtype = latents.dtype
return self.unet(
latents.to(self.weights_dtype),
t.to(self.weights_dtype),
encoder_hidden_states=encoder_hidden_states.to(self.weights_dtype),
cross_attention_kwargs=cross_attention_kwargs,
down_block_additional_residuals=down_block_additional_residuals,
mid_block_additional_residual=mid_block_additional_residual,
).sample.to(input_dtype)
@torch.cuda.amp.autocast(enabled=False)
def encode_images(
self, imgs: Float[Tensor, "B 3 512 512"]
) -> Float[Tensor, "B 4 64 64"]:
input_dtype = imgs.dtype
imgs = imgs * 2.0 - 1.0
posterior = self.vae.encode(imgs.to(self.weights_dtype)).latent_dist
latents = posterior.sample() * self.vae.config.scaling_factor
return latents.to(input_dtype)
@torch.cuda.amp.autocast(enabled=False)
def encode_cond_images(
self, imgs: Float[Tensor, "B 3 512 512"]
) -> Float[Tensor, "B 4 64 64"]:
input_dtype = imgs.dtype
imgs = imgs * 2.0 - 1.0
posterior = self.vae.encode(imgs.to(self.weights_dtype)).latent_dist
latents = posterior.mode()
uncond_image_latents = torch.zeros_like(latents)
latents = torch.cat([latents, latents, uncond_image_latents], dim=0)
return latents.to(input_dtype)
@torch.cuda.amp.autocast(enabled=False)
def decode_latents(
self,
latents: Float[Tensor, "B 4 H W"],
latent_height: int = 64,
latent_width: int = 64,
) -> Float[Tensor, "B 3 512 512"]:
input_dtype = latents.dtype
latents = F.interpolate(
latents, (latent_height, latent_width), mode="bilinear", align_corners=False
)
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.decode(latents.to(self.weights_dtype)).sample
image = (image * 0.5 + 0.5).clamp(0, 1)
return image.to(input_dtype)
def edit_latents(
self,
text_embeddings: Float[Tensor, "BB 77 768"],
latents: Float[Tensor, "B 4 64 64"],
image_cond: Float[Tensor, "B 3 512 512"],
t: Int[Tensor, "B"],
mask=None
) -> Float[Tensor, "B 4 64 64"]:
batch_size = t.shape[0]
self.scheduler.set_timesteps(num_inference_steps=self.cfg.diffusion_steps)
init_timestep = max(1, min(int(self.cfg.diffusion_steps * t[0].item() / self.num_train_timesteps), self.cfg.diffusion_steps))
t_start = max(self.cfg.diffusion_steps - init_timestep, 0)
latent_timestep = self.scheduler.timesteps[t_start : t_start + 1].repeat(batch_size)
B, _, DH, DW = latents.shape
origin_latents = latents.clone()
if mask is not None:
mask = F.interpolate(mask, (DH, DW), mode="bilinear", antialias=True)
with torch.no_grad():
# sections of code used from https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py
noise = torch.randn_like(latents)
latents = self.scheduler.add_noise(latents, noise, latent_timestep) # type: ignore
threestudio.debug("Start editing...")
for i, step in enumerate(range(t_start, self.cfg.diffusion_steps)):
timestep = self.scheduler.timesteps[step]
# predict the noise residual with unet, NO grad!
with torch.no_grad():
# pred noise
latent_model_input = torch.cat([latents] * 2)
(
down_block_res_samples,
mid_block_res_sample,
) = self.forward_controlnet(
latent_model_input,
timestep,
encoder_hidden_states=text_embeddings,
image_cond=image_cond,
condition_scale=self.cfg.condition_scale,
)
noise_pred = self.forward_control_unet(
latent_model_input,
timestep,
encoder_hidden_states=text_embeddings,
cross_attention_kwargs=None,
down_block_additional_residuals=down_block_res_samples,
mid_block_additional_residual=mid_block_res_sample,
)
# perform classifier-free guidance
noise_pred_text, noise_pred_uncond = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + self.cfg.guidance_scale * (
noise_pred_text - noise_pred_uncond
)
if mask is not None:
noise_pred = noise_pred * mask + (1-mask) * noise
latents = self.scheduler.step(noise_pred, timestep, latents).prev_sample
threestudio.debug("Editing finished.")
return latents
def prepare_image_cond(self, cond_rgb: Float[Tensor, "B H W C"]):
if self.cfg.control_type == "normal":
cond_rgb = (
(cond_rgb[0].detach().cpu().numpy() * 255).astype(np.uint8).copy()
)
detected_map = self.preprocessor(cond_rgb)
control = (
torch.from_numpy(np.array(detected_map)).float().to(self.device) / 255.0
)
control = control.unsqueeze(0)
control = control.permute(0, 3, 1, 2)
elif self.cfg.control_type == "canny" or self.cfg.control_type == "canny2":
cond_rgb = (
(cond_rgb[0].detach().cpu().numpy() * 255).astype(np.uint8).copy()
)
blurred_img = cv2.blur(cond_rgb, ksize=(5, 5))
detected_map = self.preprocessor(
blurred_img, self.cfg.canny_lower_bound, self.cfg.canny_upper_bound
)
control = (
torch.from_numpy(np.array(detected_map)).float().to(self.device) / 255.0
)
control = control.unsqueeze(-1).repeat(1, 1, 3)
control = control.unsqueeze(0)
control = control.permute(0, 3, 1, 2)
elif self.cfg.control_type == "input_normal":
cond_rgb[..., 0] = (
1 - cond_rgb[..., 0]
) # Flip the sign on the x-axis to match bae system
control = cond_rgb.permute(0, 3, 1, 2)
else:
raise ValueError(f"Unknown control type: {self.cfg.control_type}")
return F.interpolate(control, (512, 512), mode="bilinear", align_corners=False)
def compute_grad_sds(
self,
text_embeddings: Float[Tensor, "BB 77 768"],
latents: Float[Tensor, "B 4 64 64"],
image_cond: Float[Tensor, "B 3 512 512"],
t: Int[Tensor, "B"],
):
with torch.no_grad():
# add noise
noise = torch.randn_like(latents) # TODO: use torch generator
latents_noisy = self.scheduler.add_noise(latents, noise, t)
# pred noise
latent_model_input = torch.cat([latents_noisy] * 2)
down_block_res_samples, mid_block_res_sample = self.forward_controlnet(
latent_model_input,
t,
encoder_hidden_states=text_embeddings,
image_cond=image_cond,
condition_scale=self.cfg.condition_scale,
)
noise_pred = self.forward_control_unet(
latent_model_input,
t,
encoder_hidden_states=text_embeddings,
cross_attention_kwargs=None,
down_block_additional_residuals=down_block_res_samples,
mid_block_additional_residual=mid_block_res_sample,
)
# perform classifier-free guidance
noise_pred_text, noise_pred_uncond = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + self.cfg.guidance_scale * (
noise_pred_text - noise_pred_uncond
)
w = (1 - self.alphas[t]).view(-1, 1, 1, 1)
grad = w * (noise_pred - noise)
return grad
def __call__(
self,
rgb: Float[Tensor, "B H W C"],
cond_rgb: Float[Tensor, "B H W C"],
prompt_utils: PromptProcessorOutput,
mask: Float[Tensor, "B H W C"],
**kwargs,
):
batch_size, H, W, _ = rgb.shape
rgb_BCHW = rgb.permute(0, 3, 1, 2)
latents: Float[Tensor, "B 4 64 64"]
rgb_BCHW_512 = F.interpolate(
rgb_BCHW, (512, 512), mode="bilinear", align_corners=False
)
latents = self.encode_images(rgb_BCHW_512)
image_cond = self.prepare_image_cond(cond_rgb)
temp = torch.zeros(1).to(rgb.device)
text_embeddings = prompt_utils.get_text_embeddings(temp, temp, temp, False)
# timestep ~ U(0.02, 0.98) to avoid very high/low noise level
t = torch.randint(
self.min_step,
self.max_step + 1,
[batch_size],
dtype=torch.long,
device=self.device,
)
if self.cfg.use_sds:
grad = self.compute_grad_sds(text_embeddings, latents, image_cond, t)
grad = torch.nan_to_num(grad)
if self.grad_clip_val is not None:
grad = grad.clamp(-self.grad_clip_val, self.grad_clip_val)
target = (latents - grad).detach()
loss_sds = 0.5 * F.mse_loss(latents, target, reduction="sum") / batch_size
return {
"loss_sds": loss_sds,
"grad_norm": grad.norm(),
"min_step": self.min_step,
"max_step": self.max_step,
}
else:
if mask is not None: mask = mask.permute(0, 3, 1, 2)
edit_latents = self.edit_latents(text_embeddings, latents, image_cond, t, mask)
edit_images = self.decode_latents(edit_latents)
edit_images = F.interpolate(edit_images, (H, W), mode="bilinear")
return {"edit_images": edit_images.permute(0, 2, 3, 1),
"edit_latents": edit_latents}
def update_step(self, epoch: int, global_step: int, on_load_weights: bool = False):
# clip grad for stable training as demonstrated in
# Debiasing Scores and Prompts of 2D Diffusion for Robust Text-to-3D Generation
# http://arxiv.org/abs/2303.15413
if self.cfg.grad_clip is not None:
self.grad_clip_val = C(self.cfg.grad_clip, epoch, global_step)
self.set_min_max_steps(
min_step_percent=C(self.cfg.min_step_percent, epoch, global_step),
max_step_percent=C(self.cfg.max_step_percent, epoch, global_step),
)
if __name__ == "__main__":
from threestudio.utils.config import ExperimentConfig, load_config
from threestudio.utils.typing import Optional
cfg = load_config("configs/experimental/controlnet-normal.yaml")
guidance = threestudio.find(cfg.system.guidance_type)(cfg.system.guidance)
prompt_processor = threestudio.find(cfg.system.prompt_processor_type)(
cfg.system.prompt_processor
)
rgb_image = cv2.imread("assets/face.jpg")[:, :, ::-1].copy() / 255
rgb_image = cv2.resize(rgb_image, (512, 512))
rgb_image = torch.FloatTensor(rgb_image).unsqueeze(0).to(guidance.device)
prompt_utils = prompt_processor()
guidance_out = guidance(rgb_image, rgb_image, prompt_utils)
edit_image = (
(guidance_out["edit_images"][0].detach().cpu().clip(0, 1).numpy() * 255)
.astype(np.uint8)[:, :, ::-1]
.copy()
)
os.makedirs(".threestudio_cache", exist_ok=True)
cv2.imwrite(".threestudio_cache/edit_image.jpg", edit_image)