boomerang.py

import inspect
from PIL import Image
import os
import torch
from torch import autocast
from torchvision import transforms as T
from types import MethodType
from typing import List, Optional, Tuple, Union

from diffusers import StableDiffusionPipeline
from diffusers.models.unet_2d_condition import UNet2DConditionOutput
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput

COLAB = False
if COLAB:
  pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16, use_auth_token=os.environ.get('HF_TOKEN_SD')) 
else: 
  pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=os.environ.get('HF_TOKEN_SD'))  

#pipe = pipe.to('cuda')




# Overriding the U-Net forward pass
def forward(
    self,
    sample: torch.FloatTensor,
    timestep: Union[torch.Tensor, float, int],
    encoder_hidden_states: torch.Tensor,
    return_dict: bool = True,
) -> Union[UNet2DConditionOutput, Tuple]:
    """r
    Args:
        sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor
        timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps
        encoder_hidden_states (`torch.FloatTensor`): (batch, channel, height, width) encoder hidden states
        return_dict (`bool`, *optional*, defaults to `True`):
            Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple.

    Returns:
        [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
        [`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When
        returning a tuple, the first element is the sample tensor.
    """
    # 0. center input if necessary
    if self.config.center_input_sample:
        sample = 2 * sample - 1.0

    # 1. time
    timesteps = timestep
    if not torch.is_tensor(timesteps):
        timesteps = torch.tensor([timesteps], dtype=torch.long, device=sample.device)
    elif torch.is_tensor(timesteps) and len(timesteps.shape) == 0:
        timesteps = timesteps.to(dtype=torch.float32)
        timesteps = timesteps[None].to(device=sample.device)

    # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
    timesteps = timesteps.expand(sample.shape[0])

    t_emb = self.time_proj(timesteps)
    #emb = self.time_embedding(t_emb)
    emb = self.time_embedding(t_emb.to(sample.dtype))

    # 2. pre-process
    sample = self.conv_in(sample)

    # 3. down
    down_block_res_samples = (sample,)
    for downsample_block in self.down_blocks:
        if hasattr(downsample_block, "attentions") and downsample_block.attentions is not None:
            sample, res_samples = downsample_block(
                hidden_states=sample, temb=emb, encoder_hidden_states=encoder_hidden_states
            )
        else:
            sample, res_samples = downsample_block(hidden_states=sample, temb=emb)

        down_block_res_samples += res_samples

    # 4. mid
    sample = self.mid_block(sample, emb, encoder_hidden_states=encoder_hidden_states)

    # 5. up
    for upsample_block in self.up_blocks:
        res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
        down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]

        if hasattr(upsample_block, "attentions") and upsample_block.attentions is not None:
            sample = upsample_block(
                hidden_states=sample,
                temb=emb,
                res_hidden_states_tuple=res_samples,
                encoder_hidden_states=encoder_hidden_states,
            )
        else:
            sample = upsample_block(hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples)

    # 6. post-process
    # make sure hidden states is in float32
    # when running in half-precision
    #sample = self.conv_norm_out(sample.float()).type(sample.dtype)
    sample = self.conv_norm_out(sample)
    sample = self.conv_act(sample)
    sample = self.conv_out(sample)

    if not return_dict:
        return (sample,)

    return UNet2DConditionOutput(sample=sample)


def safety_forward(self, clip_input, images):
  return images, False


# Overriding the Stable Diffusion call method
@torch.no_grad()
def call(
    self,
    prompt: Union[str, List[str]],
    height: Optional[int] = 512,
    width: Optional[int] = 512,
    num_inference_steps: Optional[int] = 50,
    guidance_scale: Optional[float] = 7.5,
    eta: Optional[float] = 0.0,
    generator: Optional[torch.Generator] = None,
    latents: Optional[torch.FloatTensor] = None,
    output_type: Optional[str] = "pil",
    return_dict: bool = True,
    percent_noise: float = 0.7,
    **kwargs,
):
    if isinstance(prompt, str):
        batch_size = 1
    elif isinstance(prompt, list):
        batch_size = len(prompt)
    else:
        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}.")

    # get prompt text embeddings
    text_input = self.tokenizer(
        prompt,
        padding="max_length",
        max_length=self.tokenizer.model_max_length,
        truncation=True,
        return_tensors="pt",
    )
    text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0]

    # 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
    # get unconditional embeddings for classifier free guidance
    if do_classifier_free_guidance:
        max_length = text_input.input_ids.shape[-1]
        uncond_input = self.tokenizer(
            [""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
        )
        uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]

        # For classifier free guidance, we need to do two forward passes.
        # Here we concatenate the unconditional and text embeddings into a single batch
        # to avoid doing two forward passes
        text_embeddings = torch.cat([uncond_embeddings, text_embeddings])

    # get the initial random noise unless the user supplied it

    # Unlike in other pipelines, latents need to be generated in the target device
    # for 1-to-1 results reproducibility with the CompVis implementation.
    # However this currently doesn't work in `mps`.
    latents_device = "cpu" if self.device.type == "mps" else self.device
    latents_shape = (batch_size, self.unet.in_channels, height // 8, width // 8)
    if latents is None:
        latents = torch.randn(
            latents_shape,
            generator=generator,
            device=latents_device,
        )
    else:
        if latents.shape != latents_shape:
            raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
    latents = latents.to(self.device)

    # set timesteps
    self.scheduler.set_timesteps(num_inference_steps)

    # if we use LMSDiscreteScheduler, let's make sure latents are multiplied by sigmas
    #if isinstance(self.scheduler, LMSDiscreteScheduler):
    #    latents = latents * self.scheduler.sigmas[0]

    # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
    # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
    # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
    # and should be between [0, 1]
    accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
    extra_step_kwargs = {}
    if accepts_eta:
        extra_step_kwargs["eta"] = eta

    
    for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):

        if t - 1 > 1000 * percent_noise:
            continue

        #print(t)

        # expand the latents if we are doing classifier free guidance
        latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
        #if isinstance(self.scheduler, LMSDiscreteScheduler):
        #    sigma = self.scheduler.sigmas[i]
        #    # the model input needs to be scaled to match the continuous ODE formulation in K-LMS
        #    latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5)

        # 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
        #if isinstance(self.scheduler, LMSDiscreteScheduler):
        #    latents = self.scheduler.step(noise_pred, i, latents, **extra_step_kwargs).prev_sample
        #else:
        latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
    

    # scale and decode the image latents with vae
    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).numpy()

    # run safety checker
    safety_cheker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(self.device)
    image, has_nsfw_concept = self.safety_checker(images=image, clip_input=safety_cheker_input.pixel_values)

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

    if not return_dict:
        return (image, has_nsfw_concept)

    return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)


if COLAB:
    pipe.unet.forward = MethodType(forward, pipe.unet)
    pipe.safety_checker.forward = MethodType(safety_forward, pipe.safety_checker)
type(pipe).__call__ = call