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import os
import inspect
from typing import Any, Callable, Dict, List, Optional, Union
from PIL import Image
import numpy as np
import torch
from huggingface_hub import snapshot_download
from peft import LoraConfig, PeftModel
from diffusers.models import AutoencoderKL
from diffusers.utils import (
USE_PEFT_BACKEND,
is_torch_xla_available,
logging,
replace_example_docstring,
scale_lora_layers,
unscale_lora_layers,
)
from safetensors.torch import load_file
from OmniGen import OmniGen, OmniGenProcessor, OmniGenScheduler
import gc # For clearing unused objects
logger = logging.get_logger(__name__)
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> from OmniGen import OmniGenPipeline
>>> pipe = FluxControlNetPipeline.from_pretrained(
... base_model
... )
>>> prompt = "A woman holds a bouquet of flowers and faces the camera"
>>> image = pipe(
... prompt,
... guidance_scale=3.0,
... num_inference_steps=50,
... ).images[0]
>>> image.save("t2i.png")
```
"""
class OmniGenPipeline:
def __init__(
self,
vae: AutoencoderKL,
model: OmniGen,
processor: OmniGenProcessor,
):
self.vae = vae
self.model = model
self.processor = processor
self.device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
self.model.to(self.device)
self.model.eval()
self.vae.to(self.device)
@classmethod
def from_pretrained(cls, model_name, vae_path: str=None, Quantization: bool=False):
if not os.path.exists(model_name) or (not os.path.exists(os.path.join(model_name, 'model.safetensors')) and model_name == "Shitao/OmniGen-v1"):
logger.info("Model not found, downloading...")
cache_folder = os.getenv('HF_HUB_CACHE')
model_name = snapshot_download(repo_id=model_name,
cache_dir=cache_folder,
ignore_patterns=['flax_model.msgpack', 'rust_model.ot', 'tf_model.h5', 'model.pt'])
logger.info(f"Downloaded model to {model_name}")
# Pass Quantization parameter to OmniGen's from_pretrained
model = OmniGen.from_pretrained(model_name, quantize=Quantization)
processor = OmniGenProcessor.from_pretrained(model_name)
if os.path.exists(os.path.join(model_name, "vae")):
vae = AutoencoderKL.from_pretrained(os.path.join(model_name, "vae"))
elif vae_path is not None:
vae = AutoencoderKL.from_pretrained(vae_path)
else:
logger.info(f"No VAE found in {model_name}, downloading stabilityai/sdxl-vae from HF")
vae = AutoencoderKL.from_pretrained("stabilityai/sdxl-vae")
return cls(vae, model, processor)
def merge_lora(self, lora_path: str):
model = PeftModel.from_pretrained(self.model, lora_path)
model.merge_and_unload()
self.model = model
def to(self, device: Union[str, torch.device]):
if isinstance(device, str):
device = torch.device(device)
self.model.to(device)
self.vae.to(device)
def vae_encode(self, x, dtype):
if self.vae.config.shift_factor is not None:
x = self.vae.encode(x).latent_dist.sample()
x = (x - self.vae.config.shift_factor) * self.vae.config.scaling_factor
else:
x = self.vae.encode(x).latent_dist.sample().mul_(self.vae.config.scaling_factor)
x = x.to(dtype)
return x
def move_to_device(self, data):
if isinstance(data, list):
return [x.to(self.device) for x in data]
return data.to(self.device)
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]],
input_images: Union[List[str], List[List[str]]] = None,
height: int = 1024,
width: int = 1024,
num_inference_steps: int = 50,
guidance_scale: float = 3,
use_img_guidance: bool = True,
img_guidance_scale: float = 1.6,
separate_cfg_infer: bool = False,
use_kv_cache: bool = True,
dtype: torch.dtype = torch.bfloat16,
seed: int = None,
Quantization: bool = False,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
input_images (`List[str]` or `List[List[str]]`, *optional*):
The list of input images. We will replace the "<|image_i|>" in prompt with the 1-th image in list.
height (`int`, *optional*, defaults to 1024):
The height in pixels of the generated image. The number must be a multiple of 16.
width (`int`, *optional*, defaults to 1024):
The width in pixels of the generated image. The number must be a multiple of 16.
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 4.0):
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.
use_img_guidance (`bool`, *optional*, defaults to True):
Defined as equation 3 in [Instrucpix2pix](https://arxiv.org/pdf/2211.09800).
img_guidance_scale (`float`, *optional*, defaults to 1.6):
Defined as equation 3 in [Instrucpix2pix](https://arxiv.org/pdf/2211.09800).
separate_cfg_infer (`bool`, *optional*, defaults to False):
Perform inference on images with different guidance separately; this can save memory when generating images of large size at the expense of slower inference.
use_kv_cache (`bool`, *optional*, defaults to True): enable kv cache to speed up the inference
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
Examples:
Returns:
A list with the generated images.
"""
assert height%16 == 0 and width%16 == 0
if separate_cfg_infer:
use_kv_cache = False
# raise "Currently, don't support both use_kv_cache and separate_cfg_infer"
if input_images is None:
use_img_guidance = False
if isinstance(prompt, str):
prompt = [prompt]
input_images = [input_images] if input_images is not None else None
input_data = self.processor(prompt, input_images, height=height, width=width, use_img_cfg=use_img_guidance, separate_cfg_input=separate_cfg_infer)
num_prompt = len(prompt)
num_cfg = 2 if use_img_guidance else 1
latent_size_h, latent_size_w = height // 8, width // 8
if seed is not None:
generator = torch.Generator(device=self.device).manual_seed(seed)
else:
generator = None
latents = torch.randn(num_prompt, 4, latent_size_h, latent_size_w, device=self.device, generator=generator)
latents = torch.cat([latents] * (1 + num_cfg), 0).to(dtype)
# Load VAE into VRAM (GPU) in bfloat16
self.vae.to(self.device, dtype=torch.bfloat16)
input_img_latents = []
if separate_cfg_infer:
for temp_pixel_values in input_data['input_pixel_values']:
temp_input_latents = []
for img in temp_pixel_values:
img = self.vae_encode(img.to(self.device, dtype=torch.bfloat16), dtype)
temp_input_latents.append(img)
input_img_latents.append(temp_input_latents)
else:
for img in input_data['input_pixel_values']:
img = self.vae_encode(img.to(self.device, dtype=torch.bfloat16), dtype)
input_img_latents.append(img)
model_kwargs = dict(input_ids=self.move_to_device(input_data['input_ids']),
input_img_latents=input_img_latents,
input_image_sizes=input_data['input_image_sizes'],
attention_mask=self.move_to_device(input_data["attention_mask"]),
position_ids=self.move_to_device(input_data["position_ids"]),
cfg_scale=guidance_scale,
img_cfg_scale=img_guidance_scale,
use_img_cfg=use_img_guidance,
use_kv_cache=use_kv_cache)
#unlode vae to cpu
self.vae.to('cpu')
torch.cuda.empty_cache() # Clear VRAM
gc.collect() # Run garbage collection to free system RAM
if separate_cfg_infer:
func = self.model.forward_with_separate_cfg
else:
func = self.model.forward_with_cfg
#move main model to gpu
self.model.to(self.device, dtype=dtype)
scheduler = OmniGenScheduler(num_steps=num_inference_steps)
samples = scheduler(latents, func, model_kwargs, use_kv_cache=use_kv_cache)
samples = samples.chunk((1 + num_cfg), dim=0)[0]
if self.vae.config.shift_factor is not None:
samples = samples / self.vae.config.scaling_factor + self.vae.config.shift_factor
else:
samples = samples / self.vae.config.scaling_factor
#unlode main model to cpu
self.model.to('cpu')
torch.cuda.empty_cache() # Clear VRAM
gc.collect() # Run garbage collection to free system RAM
# Move samples to GPU and ensure they are in bfloat16 (for the VAE)
samples = samples.to(self.device, dtype=torch.bfloat16)
# Load VAE into VRAM (GPU) in bfloat16
self.vae.to(self.device, dtype=torch.bfloat16)
# Decode the samples using the VAE
samples = self.vae.decode(samples).sample
#unlode vae to cpu
self.vae.to('cpu')
torch.cuda.empty_cache() # Clear VRAM
gc.collect() # Run garbage collection to free system RAM
# Convert samples back to float32 for further processing
samples = samples.to(torch.float32)
# Convert samples to uint8 for final image output
output_samples = (samples * 0.5 + 0.5).clamp(0, 1) * 255
output_samples = output_samples.permute(0, 2, 3, 1).to("cpu", dtype=torch.uint8).numpy()
# Create output images
output_images = []
for i, sample in enumerate(output_samples):
output_images.append(Image.fromarray(sample))
# Return the generated images
return output_images
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