pytorch_svgrender/painter/diffsketcher/ASDS_pipeline.py

# -*- coding: utf-8 -*-
# Copyright (c) XiMing Xing. All rights reserved.
# Author: XiMing Xing
# Description:

import PIL
from PIL import Image
from typing import Callable, List, Optional, Union, Tuple, AnyStr

import numpy as np
import torch
import torch.nn.functional as F
from torch.cuda.amp import custom_bwd, custom_fwd
from torchvision import transforms
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion import StableDiffusionPipeline

from pytorch_svgrender.token2attn.attn_control import AttentionStore
from pytorch_svgrender.token2attn.ptp_utils import text_under_image, view_images


class Token2AttnMixinASDSPipeline(StableDiffusionPipeline):
    r"""
    Pipeline for text-to-image generation using Stable Diffusion.
    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
    Args:
        vae ([`AutoencoderKL`]):
            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
        text_encoder ([`CLIPTextModel`]):
            Frozen text-encoder. Stable Diffusion uses the text portion of
            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
        tokenizer (`CLIPTokenizer`):
            Tokenizer of class
            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
        scheduler ([`SchedulerMixin`]):
            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
        feature_extractor ([`CLIPFeatureExtractor`]):
            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
    """
    _optional_components = ["safety_checker", "feature_extractor"]

    @torch.no_grad()
    def __call__(
            self,
            prompt: Union[str, List[str]],
            height: Optional[int] = None,
            width: Optional[int] = None,
            controller: AttentionStore = None,  # feed attention_store as control of ptp
            num_inference_steps: int = 50,
            guidance_scale: float = 7.5,
            negative_prompt: Optional[Union[str, List[str]]] = None,
            num_images_per_prompt: Optional[int] = 1,
            eta: float = 0.0,
            generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
            latents: Optional[torch.FloatTensor] = None,
            output_type: Optional[str] = "pil",
            return_dict: bool = True,
            callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
            callback_steps: Optional[int] = 1,
    ):
        r"""
        Function invoked when calling the pipeline for generation.

        Args:
            prompt (`str` or `List[str]`):
                The prompt or prompts to guide the image generation.
            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                The height in pixels of the generated image.
            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                The width in pixels of the generated image.
            num_inference_steps (`int`, *optional*, defaults to 50):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            guidance_scale (`float`, *optional*, defaults to 7.5):
                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
                `guidance_scale` is defined as `w` of equation 2. of [Imagen
                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
                usually at the expense of lower image quality.
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
                if `guidance_scale` is less than `1`).
            num_images_per_prompt (`int`, *optional*, defaults to 1):
                The number of images to generate per prompt.
            eta (`float`, *optional*, defaults to 0.0):
                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
                [`schedulers.DDIMScheduler`], will be ignored for others.
            generator (`torch.Generator`, *optional*):
                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
                to make generation deterministic.
            latents (`torch.FloatTensor`, *optional*):
                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
                tensor will ge generated by sampling using the supplied random `generator`.
            output_type (`str`, *optional*, defaults to `"pil"`):
                The output format of the generate image. Choose between
                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
                plain tuple.
            callback (`Callable`, *optional*):
                A function that will be called every `callback_steps` steps during inference. The function will be
                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
            callback_steps (`int`, *optional*, defaults to 1):
                The frequency at which the `callback` function will be called. If not specified, the callback will be
                called at every step.

        Returns:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
            When returning a tuple, the first element is a list with the generated images, and the second element is a
            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
            (nsfw) content, according to the `safety_checker`.
        """

        self.register_attention_control(controller)  # add attention controller

        # 0. Default height and width to unet
        height = height or self.unet.config.sample_size * self.vae_scale_factor
        width = width or self.unet.config.sample_size * self.vae_scale_factor

        # 1. Check inputs. Raise error if not correct
        self.check_inputs(prompt, height, width, callback_steps)

        # 2. Define call parameters
        batch_size = 1 if isinstance(prompt, str) else len(prompt)
        device = self._execution_device
        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
        # corresponds to doing no classifier free guidance.
        do_classifier_free_guidance = guidance_scale > 1.0

        # 3. Encode input prompt
        text_embeddings = self._encode_prompt(
            prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
        )

        # 4. Prepare timesteps
        self.scheduler.set_timesteps(num_inference_steps, device=device)
        timesteps = self.scheduler.timesteps

        # 5. Prepare latent variables
        try:
            num_channels_latents = self.unet.config.in_channels
        except Exception or Warning:
            num_channels_latents = self.unet.in_channels

        latents = self.prepare_latents(
            batch_size * num_images_per_prompt,
            num_channels_latents,
            height,
            width,
            text_embeddings.dtype,
            device,
            generator,
            latents,
        )

        # 6. Prepare extra step kwargs. inherit TODO: Logic should ideally just be moved out of the pipeline
        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)

        # 7. Denoising loop
        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                # expand the latents if we are doing classifier free guidance
                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)

                # predict the noise residual
                noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample

                # perform guidance
                if do_classifier_free_guidance:
                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                # compute the previous noisy sample x_t -> x_t-1
                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample

                # step callback
                latents = controller.step_callback(latents)

                # call the callback, if provided
                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                    progress_bar.update()
                    if callback is not None and i % callback_steps == 0:
                        callback(i, t, latents)

        # image = self.decode_latents(latents)

        # 8. Post-processing
        # 9. Run safety checker
        if not output_type == "latent":
            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
            image, has_nsfw_concept = self.run_safety_checker(image, device, text_embeddings.dtype)
        else:
            image = latents
            has_nsfw_concept = None

        if has_nsfw_concept is None:
            do_denormalize = [True] * image.shape[0]
        else:
            do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]

        # 10. Convert to output_type
        image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)

        if not return_dict:
            return (image, has_nsfw_concept)

        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

    def encode_(self, images):
        images = (2 * images - 1).clamp(-1.0, 1.0)  # images: [B, 3, H, W]

        # encode images
        latents = self.vae.encode(images).latent_dist.sample()
        latents = self.vae.config.scaling_factor * latents

        # scale the initial noise by the standard deviation required by the scheduler
        latents = latents * self.scheduler.init_noise_sigma

        return latents

    @staticmethod
    def S_aug(sketch: torch.Tensor,
              crop_size: int = 512,
              augments: str = "affine_contrast"):
        # init augmentations
        augment_list = []
        if "affine" in augments:
            augment_list.append(
                transforms.RandomPerspective(fill=0, p=1.0, distortion_scale=0.5)
            )
            augment_list.append(
                transforms.RandomResizedCrop(crop_size, scale=(0.8, 0.8), ratio=(1.0, 1.0))
            )
        if "contrast" in augments:
            # 2: increases the sharpness by a factor of 2.
            augment_list.append(
                transforms.RandomAdjustSharpness(sharpness_factor=2)
            )
        augment_compose = transforms.Compose(augment_list)

        return augment_compose(sketch)

    def score_distillation_sampling(self,
                                    pred_rgb: torch.Tensor,
                                    crop_size: int,
                                    augments: str,
                                    prompt: Union[List, str],
                                    negative_prompt: Union[List, str] = None,
                                    guidance_scale: float = 100,
                                    as_latent: bool = False,
                                    grad_scale: float = 1,
                                    t_range: Union[List[float], Tuple[float]] = (0.02, 0.98)):
        num_train_timesteps = self.scheduler.config.num_train_timesteps
        min_step = int(num_train_timesteps * t_range[0])
        max_step = int(num_train_timesteps * t_range[1])
        alphas = self.scheduler.alphas_cumprod.to(self.device)  # for convenience

        # sketch augmentation
        pred_rgb_a = self.S_aug(pred_rgb, crop_size, augments)

        # interp to crop_size x crop_size to be fed into vae.
        if as_latent:
            latents = F.interpolate(pred_rgb_a, (64, 64), mode='bilinear', align_corners=False) * 2 - 1
        else:
            # encode image into latents with vae, requires grad!
            latents = self.encode_(pred_rgb_a)

        #  Encode input prompt
        num_images_per_prompt = 1  # the number of images to generate per prompt
        do_classifier_free_guidance = guidance_scale > 1.0
        text_embeddings = self._encode_prompt(
            prompt, self.device, num_images_per_prompt, do_classifier_free_guidance,
            negative_prompt=negative_prompt,
        )

        # timestep ~ U(0.02, 0.98) to avoid very high/low noise level
        t = torch.randint(min_step, max_step + 1, [1], dtype=torch.long, device=self.device)

        # predict the noise residual with unet, stop gradient
        with torch.no_grad():
            # add noise
            noise = torch.randn_like(latents)
            latents_noisy = self.scheduler.add_noise(latents, noise, t)
            # pred noise
            latent_model_input = torch.cat([latents_noisy] * 2) if do_classifier_free_guidance else latents_noisy
            noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample

        # perform guidance (high scale from paper!)
        if do_classifier_free_guidance:
            noise_pred_uncond, noise_pred_pos = noise_pred.chunk(2)
            noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_pos - noise_pred_uncond)

        # w(t), sigma_t^2
        w = (1 - alphas[t])
        grad = grad_scale * w * (noise_pred - noise)
        grad = torch.nan_to_num(grad)

        # since we omitted an item in grad, we need to use the custom function to specify the gradient
        loss = SpecifyGradient.apply(latents, grad)

        return loss, grad.mean()

    def register_attention_control(self, controller):
        attn_procs = {}
        cross_att_count = 0
        for name in self.unet.attn_processors.keys():
            cross_attention_dim = None if name.endswith("attn1.processor") else self.unet.config.cross_attention_dim
            if name.startswith("mid_block"):
                hidden_size = self.unet.config.block_out_channels[-1]
                place_in_unet = "mid"
            elif name.startswith("up_blocks"):
                block_id = int(name[len("up_blocks.")])
                hidden_size = list(reversed(self.unet.config.block_out_channels))[block_id]
                place_in_unet = "up"
            elif name.startswith("down_blocks"):
                block_id = int(name[len("down_blocks.")])
                hidden_size = self.unet.config.block_out_channels[block_id]
                place_in_unet = "down"
            else:
                continue
            cross_att_count += 1
            attn_procs[name] = P2PCrossAttnProcessor(
                controller=controller, place_in_unet=place_in_unet
            )

        self.unet.set_attn_processor(attn_procs)
        controller.num_att_layers = cross_att_count

    @staticmethod
    def aggregate_attention(prompts,
                            attention_store: AttentionStore,
                            res: int,
                            from_where: List[str],
                            is_cross: bool,
                            select: int):
        if isinstance(prompts, str):
            prompts = [prompts]
        assert isinstance(prompts, list)

        out = []
        attention_maps = attention_store.get_average_attention()
        num_pixels = res ** 2
        for location in from_where:
            for item in attention_maps[f"{location}_{'cross' if is_cross else 'self'}"]:
                if item.shape[1] == num_pixels:
                    cross_maps = item.reshape(len(prompts), -1, res, res, item.shape[-1])[select]
                    out.append(cross_maps)
        out = torch.cat(out, dim=0)
        out = out.sum(0) / out.shape[0]
        return out.cpu()

    def get_cross_attention(self,
                            prompts,
                            attention_store: AttentionStore,
                            res: int,
                            from_where: List[str],
                            select: int = 0,
                            save_path=None):
        tokens = self.tokenizer.encode(prompts[select])
        decoder = self.tokenizer.decode
        # shape: [res ** 2, res ** 2, seq_len]
        attention_maps = self.aggregate_attention(prompts, attention_store, res, from_where, True, select)

        images = []
        for i in range(len(tokens)):
            image = attention_maps[:, :, i]
            image = 255 * image / image.max()
            image = image.unsqueeze(-1).expand(*image.shape, 3)
            image = image.numpy().astype(np.uint8)
            image = np.array(Image.fromarray(image).resize((256, 256)))
            image = text_under_image(image, decoder(int(tokens[i])))
            images.append(image)
        image_array = np.stack(images, axis=0)
        view_images(image_array, save_image=True, fp=save_path)

        return attention_maps, tokens

    def get_self_attention_comp(self,
                                prompts,
                                attention_store: AttentionStore,
                                res: int,
                                from_where: List[str],
                                img_size: int = 224,
                                max_com=10,
                                select: int = 0,
                                save_path: AnyStr = None):
        attention_maps = self.aggregate_attention(prompts, attention_store, res, from_where, False, select)
        attention_maps = attention_maps.numpy().reshape((res ** 2, res ** 2))
        # shape: [res ** 2, res ** 2]
        u, s, vh = np.linalg.svd(attention_maps - np.mean(attention_maps, axis=1, keepdims=True))
        print(f"self-attention_maps: {attention_maps.shape}, "
              f"u: {u.shape}, "
              f"s: {s.shape}, "
              f"vh: {vh.shape}")

        images = []
        vh_returns = []
        for i in range(max_com):
            image = vh[i].reshape(res, res)
            image = (image - image.min()) / (image.max() - image.min())
            image = 255 * image

            ret_ = Image.fromarray(image).resize((img_size, img_size), resample=PIL.Image.Resampling.BILINEAR)
            vh_returns.append(np.array(ret_))

            image = np.repeat(np.expand_dims(image, axis=2), 3, axis=2).astype(np.uint8)
            image = Image.fromarray(image).resize((256, 256))
            image = np.array(image)
            images.append(image)
        image_array = np.stack(images, axis=0)
        view_images(image_array, num_rows=max_com // 10, offset_ratio=0,
                    save_image=True, fp=save_path / "self-attn-vh.png")

        return attention_maps, (u, s, vh), np.stack(vh_returns, axis=0)


class P2PCrossAttnProcessor:

    def __init__(self, controller, place_in_unet):
        super().__init__()
        self.controller = controller
        self.place_in_unet = place_in_unet

    def __call__(self, attn, hidden_states, encoder_hidden_states=None, attention_mask=None):
        batch_size, sequence_length, _ = hidden_states.shape
        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size=batch_size)

        query = attn.to_q(hidden_states)

        is_cross = encoder_hidden_states is not None
        encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states
        key = attn.to_k(encoder_hidden_states)
        value = attn.to_v(encoder_hidden_states)

        query = attn.head_to_batch_dim(query)
        key = attn.head_to_batch_dim(key)
        value = attn.head_to_batch_dim(value)

        attention_probs = attn.get_attention_scores(query, key, attention_mask)

        # one line change
        self.controller(attention_probs, is_cross, self.place_in_unet)

        hidden_states = torch.bmm(attention_probs, value)
        hidden_states = attn.batch_to_head_dim(hidden_states)

        # linear proj
        hidden_states = attn.to_out[0](hidden_states)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        return hidden_states


class SpecifyGradient(torch.autograd.Function):

    @staticmethod
    @custom_fwd
    def forward(ctx, input_tensor, gt_grad):
        ctx.save_for_backward(gt_grad)
        # we return a dummy value 1, which will be scaled by amp's scaler so we get the scale in backward.
        return torch.ones([1], device=input_tensor.device, dtype=input_tensor.dtype)

    @staticmethod
    @custom_bwd
    def backward(ctx, grad_scale):
        gt_grad, = ctx.saved_tensors
        gt_grad = gt_grad * grad_scale
        return gt_grad, None