DreamCraft3D/threestudio/models/guidance/controlnet_reg_guidance.py

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)