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# -*- coding: utf-8 -*-
# ===========================================================================================
#
# Copyright (c) Beijing Academy of Artificial Intelligence (BAAI). All rights reserved.
#
# Author : Fan Zhang
# Email : zhangfan@baai.ac.cn
# Institute : Beijing Academy of Artificial Intelligence (BAAI)
# Create On : 2023-12-19 10:45
# Last Modified : 2023-12-25 07:59
# File Name : pipeline_emu2_gen.py
# Description :
#
# ===========================================================================================
from dataclasses import dataclass
from typing import List, Optional
from PIL import Image
import numpy as np
import torch
from torchvision import transforms as TF
from tqdm import tqdm
import pdb
from diffusers import DiffusionPipeline
from diffusers.utils import BaseOutput
from diffusers import UNet2DConditionModel, EulerDiscreteScheduler, AutoencoderKL
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from transformers import CLIPImageProcessor
from transformers import AutoModelForCausalLM, AutoTokenizer
EVA_IMAGE_SIZE = 448
OPENAI_DATASET_MEAN = (0.48145466, 0.4578275, 0.40821073)
OPENAI_DATASET_STD = (0.26862954, 0.26130258, 0.27577711)
DEFAULT_IMG_PLACEHOLDER = "<image>"
from transformers import AutoProcessor
image_processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-3B-Instruct").image_processor
@dataclass
class EmuVisualGenerationPipelineOutput(BaseOutput):
image: Image.Image
nsfw_content_detected: Optional[bool]
class EmuVisualGenerationPipeline(DiffusionPipeline):
def __init__(
self,
tokenizer: AutoTokenizer,
multimodal_encoder: AutoModelForCausalLM,
scheduler: EulerDiscreteScheduler,
unet: UNet2DConditionModel,
vae: AutoencoderKL,
feature_extractor: CLIPImageProcessor,
safety_checker: StableDiffusionSafetyChecker,
eva_size=EVA_IMAGE_SIZE,
eva_mean=OPENAI_DATASET_MEAN,
eva_std=OPENAI_DATASET_STD,
):
super().__init__()
self.register_modules(
tokenizer=tokenizer,
multimodal_encoder=multimodal_encoder,
scheduler=scheduler,
unet=unet,
vae=vae,
feature_extractor=feature_extractor,
safety_checker=None,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.transform = TF.Compose([
TF.Resize((eva_size, eva_size), interpolation=TF.InterpolationMode.BICUBIC),
TF.ToTensor(),
TF.Normalize(mean=eva_mean, std=eva_std),
])
self.negative_prompt = {}
def device(self, module):
return next(module.parameters()).device
def dtype(self, module):
return next(module.parameters()).dtype
@torch.no_grad()
def __call__(
self,
inputs: List[Image.Image | str] | str | Image.Image,
height: int = 1024,
width: int = 1024,
num_inference_steps: int = 50,
guidance_scale: float = 3.0,
crop_info: List[int] = [0, 0],
original_size: List[int] = [1024, 1024],
):
if not isinstance(inputs, list):
inputs = [inputs]
# 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
device = self.device(self.unet)
dtype = self.dtype(self.unet)
do_classifier_free_guidance = guidance_scale > 1.0
# 1. Encode input prompt
prompt_embeds = self._prepare_and_encode_inputs(
inputs,
do_classifier_free_guidance,
).to(dtype).to(device)
batch_size = prompt_embeds.shape[0] // 2 if do_classifier_free_guidance else prompt_embeds.shape[0]
unet_added_conditions = {}
time_ids = torch.LongTensor(original_size + crop_info + [height, width]).to(device)
if do_classifier_free_guidance:
unet_added_conditions["time_ids"] = torch.cat([time_ids, time_ids], dim=0)
else:
unet_added_conditions["time_ids"] = time_ids
unet_added_conditions["text_embeds"] = torch.mean(prompt_embeds, dim=1)
# 2. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 3. Prepare latent variables
shape = (
batch_size,
self.unet.config.in_channels,
height // self.vae_scale_factor,
width // self.vae_scale_factor,
)
latents = torch.randn(shape, device=device, dtype=dtype)
latents = latents * self.scheduler.init_noise_sigma
# 4. Denoising loop
for t in tqdm(timesteps):
# Expand the latents if doing classifier free guidance: 2B x 4 x H x W
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)
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
added_cond_kwargs=unet_added_conditions,
).sample
# Perform guidance
if do_classifier_free_guidance:
noise_pred_cond, noise_pred_uncond = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond)
# Compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents).prev_sample
# 5. Post-processing
images = self.decode_latents(latents)
# 6. Run safety checker
# images, has_nsfw_concept = self.run_safety_checker(images)
# 7. Convert to PIL
images = self.numpy_to_pil(images)
# return EmuVisualGenerationPipelineOutput(
# image=images[0],
# nsfw_content_detected=None if has_nsfw_concept is None else has_nsfw_concept[0],
# )
return EmuVisualGenerationPipelineOutput(
image=images[0],
nsfw_content_detected=None
)
def _prepare_and_encode_inputs(
self,
inputs: List[str | Image.Image],
do_classifier_free_guidance: bool = False,
placeholder: str = DEFAULT_IMG_PLACEHOLDER,
):
# pdb.set_trace()
device = self.device(self.multimodal_encoder.model)
dtype = self.dtype(self.multimodal_encoder.model)
has_image, has_text = False, False
text_prompt, image_prompt, image_grid_thw = "", [], []
for x in inputs:
if isinstance(x, str):
has_text = True
text_prompt += x
else:
has_image = True
text_prompt = text_prompt.replace(
"<image>",
"<|vision_start|>" + "<|image_pad|>" * 256 + "<|vision_end|>"
)
resized_images = x.resize((448, 448))
image_inputs = image_processor(resized_images, return_tensors="pt")
image_prompt.append(image_inputs.pixel_values)
image_grid_thw.append(image_inputs.image_grid_thw)
if len(image_prompt) == 0:
image_prompt = None
image_grid_thw = None
else:
image_prompt = torch.cat(image_prompt, dim=0)
image_grid_thw = torch.cat(image_grid_thw, dim=0)
# breakpoint()
if has_image and not has_text:
prompt = self.multimodal_encoder.model.encode_image(image=image_prompt)
if do_classifier_free_guidance:
key = "[NULL_IMAGE]"
if key not in self.negative_prompt:
negative_image = torch.zeros_like(image_prompt)
self.negative_prompt[key] = self.multimodal_encoder.model.encode_image(image=negative_image)
prompt = torch.cat([prompt, self.negative_prompt[key]], dim=0)
elif has_text and not has_image:
prompt = self.multimodal_encoder.generate_image(
text=[text_prompt], tokenizer=self.tokenizer
)
if do_classifier_free_guidance:
key = ""
if key not in self.negative_prompt:
self.negative_prompt[key] = self.multimodal_encoder.generate_image(
text=[" "],
tokenizer=self.tokenizer
)
prompt = torch.cat([prompt, self.negative_prompt[key]], dim=0)
elif has_text and has_image:
prompt = self.multimodal_encoder.generate_image(
text=[text_prompt],
pixel_values=image_prompt.cuda(),
image_grid_thw=image_grid_thw.cuda(),
tokenizer=self.tokenizer
)
if do_classifier_free_guidance:
key = ""
if key not in self.negative_prompt:
self.negative_prompt[key] = self.multimodal_encoder.generate_image(
text=[" "],
tokenizer=self.tokenizer
)
prompt = torch.cat([prompt, self.negative_prompt[key]], dim=0)
return prompt
def decode_latents(self, latents: torch.Tensor) -> np.ndarray:
latents = 1 / self.vae.config.scaling_factor * 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 numpy_to_pil(self, images: np.ndarray) -> List[Image.Image]:
"""
Convert a numpy image or a batch of images to a PIL image.
"""
if images.ndim == 3:
images = images[None, ...]
images = (images * 255).round().astype("uint8")
if images.shape[-1] == 1:
# Special case for grayscale (single channel) images.
pil_images = [Image.fromarray(image.squeeze(), mode="L") for image in images]
else:
pil_images = [Image.fromarray(image) for image in images]
return pil_images
def run_safety_checker(self, images: np.ndarray):
if self.safety_checker is not None:
device = self.device(self.safety_checker)
dtype = self.dtype(self.safety_checker)
safety_checker_input = self.feature_extractor(
self.numpy_to_pil(images), return_tensors="pt"
).to(device)
images, has_nsfw_concept = self.safety_checker(
images=images, clip_input=safety_checker_input.pixel_values.to(dtype)
)
else:
has_nsfw_concept = None
return images, has_nsfw_concept
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