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main.py
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from typing import List, Union
import numpy as np
# import onnxruntime
import torch
from PIL import Image
from transformers import CLIPTokenizer, CLIPTextModel, PreTrainedTokenizer, CLIPTextModelWithProjection
# from diffusers import UNet2DConditionModel, DiffusionPipeline, LCMScheduler, AutoencoderKL
from axengine import InferenceSession
import time
import argparse
def get_args():
parser = argparse.ArgumentParser(
prog="StableDiffusion",
description="Generate picture with the input prompt"
)
parser.add_argument("--prompt", "-p", type=str, required=False, default="Self-portrait oil painting, a beautiful cyborg with golden hair, 8k", help="the input text prompt")
parser.add_argument("--text_model_dir", "-e", type=str, required=False, default="./models/", help="the dir of text encoder and tokenizer files")
parser.add_argument("--unet_model", "-u", type=str, required=False, default="./models/unet.axmodel", help="the dir of unet.axmodel")
parser.add_argument("--vae_model", "-v", type=str, required=False, default="./models/vae.axmodel", help="the dir of vae.axmodel")
parser.add_argument("--time_input", "-t", type=str, required=False, default="./models/time_input.npy", help="the dir of time input file")
parser.add_argument("--save_dir", "-s", type=str, required=False, default="./lcm_lora_sdv1_5_axmodel.png", help="the save dir of the output image")
return parser.parse_args()
def maybe_convert_prompt(prompt: Union[str, List[str]], tokenizer: "PreTrainedTokenizer"): # noqa: F821
if not isinstance(prompt, List):
prompts = [prompt]
else:
prompts = prompt
prompts = [_maybe_convert_prompt(p, tokenizer) for p in prompts]
if not isinstance(prompt, List):
return prompts[0]
return prompts
def _maybe_convert_prompt(prompt: str, tokenizer: "PreTrainedTokenizer"): # noqa: F821
tokens = tokenizer.tokenize(prompt)
unique_tokens = set(tokens)
for token in unique_tokens:
if token in tokenizer.added_tokens_encoder:
replacement = token
i = 1
while f"{token}_{i}" in tokenizer.added_tokens_encoder:
replacement += f" {token}_{i}"
i += 1
prompt = prompt.replace(token, replacement)
return prompt
def get_embeds(prompt = "Portrait of a pretty girl", tokenizer_dir = "./models/tokenizer", text_encoder_dir = "./models/text_encoder"):
tokenizer = CLIPTokenizer.from_pretrained(tokenizer_dir)
text_encoder = CLIPTextModel.from_pretrained(text_encoder_dir,
torch_dtype=torch.float32,
variant="fp16")
text_inputs = tokenizer(
prompt,
padding="max_length",
max_length=77,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
prompt_embeds = text_encoder(text_input_ids.to("cpu"), attention_mask=None)
prompt_embeds_npy = prompt_embeds[0].detach().numpy()
return prompt_embeds_npy
def get_alphas_cumprod():
betas = torch.linspace(0.00085 ** 0.5, 0.012 ** 0.5, 1000, dtype=torch.float32) ** 2
alphas = 1.0 - betas
alphas_cumprod = torch.cumprod(alphas, dim=0).detach().numpy()
final_alphas_cumprod = alphas_cumprod[0]
self_timesteps = np.arange(0, 1000)[::-1].copy().astype(np.int64)
return alphas_cumprod, final_alphas_cumprod, self_timesteps
if __name__ == '__main__':
args = get_args()
prompt = args.prompt
tokenizer_dir = args.text_model_dir + 'tokenizer'
text_encoder_dir = args.text_model_dir + 'text_encoder'
unet_model = args.unet_model
vae_model = args.vae_model
time_input = args.time_input
save_dir = args.save_dir
print(f"prompt: {prompt}")
print(f"text_tokenizer: {tokenizer_dir}")
print(f"text_encoder: {text_encoder_dir}")
print(f"unet_model: {unet_model}")
print(f"vae_model: {vae_model}")
print(f"time_input: {time_input}")
print(f"save_dir: {save_dir}")
timesteps = np.array([999, 759, 499, 259]).astype(np.int64)
# text encoder
start = time.time()
# prompt = "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k"
prompt_embeds_npy = get_embeds(prompt, tokenizer_dir, text_encoder_dir)
print(f"text encoder take {1000 * (time.time() - start)}ms")
prompt_name = prompt.replace(" ", "_")
latents_shape = [1, 4, 64, 64]
latent = torch.randn(latents_shape, generator=None, device="cpu", dtype=torch.float32,
layout=torch.strided).detach().numpy()
alphas_cumprod, final_alphas_cumprod, self_timesteps = get_alphas_cumprod()
# load unet model and vae model
start = time.time()
unet_session_main = InferenceSession.load_from_model(unet_model)
vae_decoder = InferenceSession.load_from_model(vae_model)
print(f"load models take {1000 * (time.time() - start)}ms")
# load time input file
time_input = np.load(time_input)
# unet inference loop
unet_loop_start = time.time()
for i, timestep in enumerate(timesteps):
# print(i, timestep)
unet_start = time.time()
noise_pred = unet_session_main.run({"sample": latent, \
"/down_blocks.0/resnets.0/act_1/Mul_output_0": np.expand_dims(time_input[i], axis=0), \
"encoder_hidden_states": prompt_embeds_npy})['5771']
print(f"unet once take {1000 * (time.time() - unet_start)}ms")
sample = latent
model_output = noise_pred
if i < 3:
prev_timestep = timesteps[i + 1]
else:
prev_timestep = timestep
alpha_prod_t = alphas_cumprod[timestep]
alpha_prod_t_prev = alphas_cumprod[prev_timestep] if prev_timestep >= 0 else final_alphas_cumprod
beta_prod_t = 1 - alpha_prod_t
beta_prod_t_prev = 1 - alpha_prod_t_prev
# 3. Get scalings for boundary conditions
scaled_timestep = timestep * 10
c_skip = 0.5 ** 2 / (scaled_timestep ** 2 + 0.5 ** 2)
c_out = scaled_timestep / (scaled_timestep ** 2 + 0.5 ** 2) ** 0.5
predicted_original_sample = (sample - (beta_prod_t ** 0.5) * model_output) / (alpha_prod_t ** 0.5)
denoised = c_out * predicted_original_sample + c_skip * sample
if i != 3:
noise = torch.randn(model_output.shape, generator=None, device="cpu", dtype=torch.float32,
layout=torch.strided).to("cpu").detach().numpy()
prev_sample = (alpha_prod_t_prev ** 0.5) * denoised + (beta_prod_t_prev ** 0.5) * noise
else:
prev_sample = denoised
latent = prev_sample
print(f"unet loop take {1000 * (time.time() - unet_loop_start)}ms")
# vae inference
vae_start = time.time()
latent = latent / 0.18215
image = vae_decoder.run({"x": latent})['784']
print(f"vae inference take {1000 * (time.time() - vae_start)}ms")
# save result
save_start = time.time()
image = np.transpose(image, (0, 2, 3, 1)).squeeze(axis=0)
image_denorm = np.clip(image / 2 + 0.5, 0, 1)
image = (image_denorm * 255).round().astype("uint8")
pil_image = Image.fromarray(image[:, :, :3])
pil_image.save(save_dir)
print(f"save image take {1000 * (time.time() - vae_start)}ms")