inference_models/class_guidance_inference.py

# -*- coding: UTF-8 -*-
"""
@Time : 30/05/2025 19:24
@Author : xiaoguangliang
@File : class_guidance_inference.py
@Project : Faice_text2face
"""
import torch
import random
import numpy as np
from inference_models.ccddpm_pipeline import CCDDPMPipeline
from accelerate import Accelerator
import gradio as gr
import spaces
from loguru import logger

from utils import timer

model_path = 'Ngene787/Faice_class_guidance'

if torch.backends.mps.is_available():
    accelerator = Accelerator(gradient_accumulation_steps=1)
else:
    accelerator = Accelerator(mixed_precision="fp16", gradient_accumulation_steps=1)

logger.info("Loading model ...")
device = "cuda" if torch.cuda.is_available() else "cpu"
if torch.cuda.is_available():
    torch_dtype = torch.float16
else:
    torch_dtype = torch.float32
pipe = CCDDPMPipeline.from_pretrained(model_path, torch_dtype=torch_dtype,
                                      low_cpu_mem_usage=True
                                      )
pipe = pipe.to(device)

pipe = accelerator.prepare(pipe)
# Enable memory-efficient attention
# pipe.enable_xformers_memory_efficient_attention()


MAX_SEED = np.iinfo(np.int32).max

GENDER_CHOICES = [
    "Female",
    "Male"
]


@spaces.GPU(duration=65)
def inference_class_guidance(label_name,
                             seed=0,
                             randomize_seed=False,
                             num_inference_steps=20,
                             progress=gr.Progress(track_tqdm=True), ):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

    generator = torch.Generator().manual_seed(seed)
    label_id = 1 if label_name == "Male" else 0

    logger.info('Generating image ...')
    batch_size = 1
    with timer("inference"):
        class_labels = torch.full(
            (batch_size,), label_id, dtype=torch.long, device=device
        )
        encoder_hidden_states = torch.zeros(
            batch_size,
            1,
            pipe.unet.config.cross_attention_dim,
            device=device,
        )
        image = pipe(
            batch_size=batch_size,
            generator=generator,
            num_inference_steps=num_inference_steps,
            class_labels=class_labels,
            encoder_hidden_states=encoder_hidden_states,
        ).images[0]
    return image