src/mgd_pipelines/mgd_pipe_disentangled.py

import logging
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)

logger.info("This is an informational message.")
logger.warning("This is a warning message.")


import inspect
from typing import Callable, List, Optional, Union
import PIL
import logging
import torch
import torchvision
from diffusers.utils import is_accelerate_available
from packaging import version
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers import DiffusionPipeline
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers import StableDiffusionInpaintPipeline






class MGDPipeDisentangled(DiffusionPipeline):
    """
    Pipeline for text and posemap-guided image inpainting using Stable Diffusion.
    """
    _optional_components = ["safety_checker"]

    def __init__(
        self,
        vae: AutoencoderKL,
        text_encoder: CLIPTextModel,
        tokenizer: CLIPTokenizer,
        unet: UNet2DConditionModel,
        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
        safety_checker=None,
        feature_extractor=None,
        requires_safety_checker: bool = False,
    ):
        super().__init__()

        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
            new_config = dict(scheduler.config)
            new_config["steps_offset"] = 1
            scheduler._internal_dict = FrozenDict(new_config)

        if hasattr(scheduler.config, "skip_prk_steps") and scheduler.config.skip_prk_steps is False:
            new_config = dict(scheduler.config)
            new_config["skip_prk_steps"] = True
            scheduler._internal_dict = FrozenDict(new_config)

        if safety_checker is None and requires_safety_checker:
            logger.warning(
                "Safety checker is disabled. Ensure adherence to license conditions when using this pipeline."
            )

        if safety_checker is not None and feature_extractor is None:
            raise ValueError(
                "Feature extractor must be defined if safety checker is used."
            )

        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
            version.parse(unet.config._diffusers_version).base_version
        ) < version.parse("0.9.0.dev0")
        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
        if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
            new_config = dict(unet.config)
            new_config["sample_size"] = 64
            unet._internal_dict = FrozenDict(new_config)

        self.register_modules(
            vae=vae,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            unet=unet,
            scheduler=scheduler,
            safety_checker=safety_checker,
            feature_extractor=feature_extractor,
        )
        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
        self.register_to_config(requires_safety_checker=requires_safety_checker)

    def enable_sequential_cpu_offload(self, gpu_id=0):
        if is_accelerate_available():
            from accelerate import cpu_offload
        else:
            raise ImportError("Install accelerate via `pip install accelerate`")

        device = torch.device(f"cuda:{gpu_id}")

        for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:
            if cpu_offloaded_model is not None:
                cpu_offload(cpu_offloaded_model, device)

        if self.safety_checker is not None:
            cpu_offload(self.safety_checker.vision_model, device)

    @property
    def _execution_device(self):
        if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
            return self.device
        for module in self.unet.modules():
            if (
                hasattr(module, "_hf_hook")
                and hasattr(module._hf_hook, "execution_device")
                and module._hf_hook.execution_device is not None
            ):
                return torch.device(module._hf_hook.execution_device)
        return self.device

    def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt):
        batch_size = len(prompt) if isinstance(prompt, list) else 1

        text_inputs = self.tokenizer(
            prompt,
            padding="max_length",
            max_length=self.tokenizer.model_max_length,
            truncation=True,
            return_tensors="pt",
        )
        text_input_ids = text_inputs.input_ids
        untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids

        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
            removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
            logger.warning(
                "Part of your input was truncated due to token length limits: {removed_text}"
            )

        if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
            attention_mask = text_inputs.attention_mask.to(device)
        else:
            attention_mask = None

        text_embeddings = self.text_encoder(
            text_input_ids.to(device),
            attention_mask=attention_mask,
        )[0]

        bs_embed, seq_len, _ = text_embeddings.shape
        text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
        text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)

        if do_classifier_free_guidance:
            if negative_prompt is None:
                uncond_tokens = [""] * batch_size
            elif type(prompt) is not type(negative_prompt):
                raise TypeError("`negative_prompt` must match the type of `prompt`.")
            elif isinstance(negative_prompt, str):
                uncond_tokens = [negative_prompt]
            elif batch_size != len(negative_prompt):
                raise ValueError("`negative_prompt` batch size must match `prompt` batch size.")
            else:
                uncond_tokens = negative_prompt

            max_length = text_input_ids.shape[-1]
            uncond_input = self.tokenizer(
                uncond_tokens,
                padding="max_length",
                max_length=max_length,
                truncation=True,
                return_tensors="pt",
            )

            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
                attention_mask = uncond_input.attention_mask.to(device)
            else:
                attention_mask = None

            uncond_embeddings = self.text_encoder(
                uncond_input.input_ids.to(device),
                attention_mask=attention_mask,
            )[0]

            seq_len = uncond_embeddings.shape[1]
            uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
            uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)

            text_embeddings = torch.cat([uncond_embeddings, text_embeddings, uncond_embeddings, uncond_embeddings])

        return text_embeddings

    def prepare_extra_step_kwargs(self, generator, eta):
        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
        extra_step_kwargs = {}
        if accepts_eta:
            extra_step_kwargs["eta"] = eta

        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
        if accepts_generator:
            extra_step_kwargs["generator"] = generator
        return extra_step_kwargs

    def decode_latents(self, latents):
        latents = 1 / 0.18215 * latents
        image = self.vae.decode(latents).sample
        image = (image / 2 + 0.5).clamp(0, 1)
        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
        return image

    def check_inputs(self, prompt, height, width, callback_steps):
        if not isinstance(prompt, str) and not isinstance(prompt, list):
            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")

        if height % 8 != 0 or width % 8 != 0:
            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")

        if (callback_steps is None) or (
            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
        ):
            raise ValueError(
                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
                f" {type(callback_steps)}."
            )

    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
        if isinstance(generator, list) and len(generator) != batch_size:
            raise ValueError(
                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
            )

        if latents is None:
            rand_device = "cpu" if device.type == "mps" else device

            if isinstance(generator, list):
                shape = (1,) + shape[1:]
                latents = [
                    torch.randn(shape, generator=generator[i], device=rand_device, dtype=dtype)
                    for i in range(batch_size)
                ]
                latents = torch.cat(latents, dim=0).to(device)
            else:
                latents = torch.randn(shape, generator=generator, device=rand_device, dtype=dtype).to(device)
        else:
            if latents.shape != shape:
                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
            latents = latents.to(device)

        latents = latents * self.scheduler.init_noise_sigma
        return latents

    def prepare_mask_latents(
        self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance
    ):
        mask = torch.nn.functional.interpolate(
            mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor)
        )
        mask = mask.to(device=device, dtype=dtype)

        masked_image = masked_image.to(device=device, dtype=dtype)

        if isinstance(generator, list):
            masked_image_latents = [
                self.vae.encode(masked_image[i : i + 1]).latent_dist.sample(generator=generator[i])
                for i in range(batch_size)
            ]
            masked_image_latents = torch.cat(masked_image_latents, dim=0)
        else:
            masked_image_latents = self.vae.encode(masked_image).latent_dist.sample(generator=generator)
        masked_image_latents = 0.18215 * masked_image_latents

        if mask.shape[0] < batch_size:
            if not batch_size % mask.shape[0] == 0:
                raise ValueError(
                    "The passed mask and the required batch size don't match. Masks are supposed to be duplicated to"
                    f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number"
                    " of masks that you pass is divisible by the total requested batch size."
                )
            mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)
        if masked_image_latents.shape[0] < batch_size:
            if not batch_size % masked_image_latents.shape[0] == 0:
                raise ValueError(
                    "The passed images and the required batch size don't match. Images are supposed to be duplicated"
                    f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed."
                    " Make sure the number of images that you pass is divisible by the total requested batch size."
                )
            masked_image_latents = masked_image_latents.repeat(batch_size // masked_image_latents.shape[0], 1, 1, 1)

        mask = torch.cat([mask] * 4) if do_classifier_free_guidance else mask
        masked_image_latents = (
            torch.cat([masked_image_latents] * 4) if do_classifier_free_guidance else masked_image_latents
        )

        masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)
        return mask, masked_image_latents

    @torch.no_grad()
    def __call__(
        self,
        prompt: Union[str, List[str]],
        image: Union[torch.FloatTensor, PIL.Image.Image],
        mask_image: Union[torch.FloatTensor, PIL.Image.Image],
        pose_map: torch.FloatTensor,
        sketch: torch.FloatTensor,
        height: Optional[int] = None,
        width: Optional[int] = None,
        num_inference_steps: int = 50,
        guidance_scale: float = 7.5,
        guidance_scale_pose: float = 7.5,
        guidance_scale_sketch: 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,
        sketch_cond_rate: float = 1.0,
        start_cond_rate: float = 0,
        no_pose: bool = False,
    ):
        height = height or self.unet.config.sample_size * self.vae_scale_factor
        width = width or self.unet.config.sample_size * self.vae_scale_factor

        self.check_inputs(prompt, height, width, callback_steps)

        batch_size = 1 if isinstance(prompt, str) else len(prompt)
        device = self._execution_device
        do_classifier_free_guidance = True

        text_embeddings = self._encode_prompt(
            prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
        )

        mask, masked_image_latents = self.prepare_mask_latents(
            mask=mask_image,  # Assuming mask_image is the mask input
            masked_image=image,  # Assuming image is the masked image input
            batch_size=batch_size * num_images_per_prompt,
            height=height,
            width=width,
            dtype=text_embeddings.dtype,
            device=device,
            generator=generator,
            do_classifier_free_guidance=do_classifier_free_guidance,
        )

        pose_map = torch.nn.functional.interpolate(
            pose_map, size=(pose_map.shape[2] // 8, pose_map.shape[3] // 8), mode="bilinear"
        )
        if no_pose:
            pose_map = torch.zeros_like(pose_map)

        sketch = torchvision.transforms.functional.resize(
            sketch, size=(sketch.shape[2] // 8, sketch.shape[3] // 8),
            interpolation=torchvision.transforms.InterpolationMode.BILINEAR,
            antialias=True)

        self.scheduler.set_timesteps(num_inference_steps, device=device)
        timesteps = self.scheduler.timesteps

        start_cond_step = int(num_inference_steps * start_cond_rate)

        sketch_start = start_cond_step
        sketch_end = sketch_cond_rate * num_inference_steps + start_cond_step

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

        pose_map = torch.cat([torch.zeros_like(pose_map), torch.zeros_like(pose_map), pose_map, torch.zeros_like(pose_map)]) if do_classifier_free_guidance else pose_map
        sketch = torch.cat([torch.zeros_like(sketch), torch.zeros_like(sketch), torch.zeros_like(sketch), sketch]) if do_classifier_free_guidance else sketch

        num_channels_mask = mask.shape[1]
        num_channels_masked_image = masked_image_latents.shape[1]
        if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:
           raise ValueError(
           f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
           f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
           f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}"
           f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of"
            " `pipeline.unet` or your `mask_image` or `image` input."
          )

        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)

        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):
                latent_model_input = torch.cat([latents] * 4) if do_classifier_free_guidance else latents

                if i < sketch_start or i > sketch_end:
                   local_sketch = torch.zeros_like(sketch)
                else:
                  local_sketch = sketch

                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
                latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents, pose_map.to(mask.dtype), local_sketch.to(mask.dtype)], dim=1)

                noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample

                if do_classifier_free_guidance:
                   noise_pred_uncond, noise_pred_text, noise_pred_pose, noise_pred_sketch = noise_pred.chunk(4)
                   noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) + guidance_scale_pose * (noise_pred_pose - noise_pred_uncond) + guidance_scale_sketch * (noise_pred_sketch - noise_pred_uncond)

                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample

                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)

        if output_type == "pil":
           image = self.numpy_to_pil(image)

        if not return_dict:
           return (image, None)

        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=None)