inversion.py

'''
Copyright (c) 2025 Bytedance Ltd. and/or its affiliates

Licensed under the Apache License, Version 2.0 (the "License"); 
you may not use this file except in compliance with the License. 
You may obtain a copy of the License at 

    http://www.apache.org/licenses/LICENSE-2.0 

Unless required by applicable law or agreed to in writing, software 
distributed under the License is distributed on an "AS IS" BASIS, 
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 
See the License for the specific language governing permissions and 
limitations under the License. 

'''

import pyrallis
import torch
from PIL import Image
from diffusers.utils.torch_utils import randn_tensor

from src.config import RunConfig
from src.utils.enums_utils import model_type_to_size, is_stochastic
from src.frequency_utils import freq_exp

def create_noise_list(model_type, length, generator=None):
    img_size = model_type_to_size(model_type)
    VQAE_SCALE = 8
    latents_size = (1, 4, img_size[0] // VQAE_SCALE, img_size[1] // VQAE_SCALE)
    return [randn_tensor(latents_size, dtype=torch.float16, device=torch.device("cuda:0"), generator=generator) for i in range(length)]

@pyrallis.wrap()
def main(cfg: RunConfig):
    run(cfg)

def run(init_image: Image,
        prompt: str,
        cfg: RunConfig,
        pipe_inversion,
        pipe_inference,
        latents = None,
        edit_prompt = None,
        edit_cfg = 1.0,
        noise = None,
        do_reconstruction = True,
        feature_extractor = None,
        style_embedding = None,
        content_embedding = None,
        neg_style_embedding = None,
        neg_content_embedding = None,
        enable_guidance = True,
        used_NPI_guidance = True):
    
    generator = torch.Generator().manual_seed(cfg.seed)

    if is_stochastic(cfg.scheduler_type):
        if latents is None:
            noise = create_noise_list(cfg.model_type, cfg.num_inversion_steps, generator=generator)
        pipe_inversion.scheduler.set_noise_list(noise)
        pipe_inference.scheduler.set_noise_list(noise)

    pipe_inversion.cfg = cfg
    pipe_inference.cfg = cfg
    all_latents = None

    if latents is None:
        print("Inverting...")
        res = pipe_inversion(prompt = prompt,
                        num_inversion_steps = cfg.num_inversion_steps,
                        num_inference_steps = cfg.num_inference_steps,
                        generator = generator,
                        image = init_image,
                        guidance_scale = cfg.inv_guidance_scale,
                        strength = cfg.inversion_max_step,
                        denoising_start = 1.0-cfg.inversion_max_step,
                        num_renoise_steps = cfg.num_renoise_steps,
                        pipe_inf=pipe_inference,
                        feature_extractor=feature_extractor,
                        style_embedding=style_embedding,
                        content_embedding=content_embedding,
                        neg_style_embedding=neg_style_embedding,
                        neg_content_embedding=neg_content_embedding,
                        enable_guidance=enable_guidance,
                        used_NPI_guidance = used_NPI_guidance)
        latents = res[0][0]
        all_latents = res[1]
    
    inv_latent = latents.clone()
    # latent_h, latent_l, latent_sum = freq_exp(inv_latent,d_s=0.5,d_t=0.5,alpha=0.7)

    if do_reconstruction:
        print("Generating...")
        edit_prompt = prompt if edit_prompt is None else edit_prompt
        guidance_scale = edit_cfg
        img = pipe_inference(prompt = edit_prompt,
                            num_inference_steps = 1, #cfg.num_inference_steps, #
                            negative_prompt = prompt,
                            image =inv_latent,
                            strength = cfg.inversion_max_step,
                            denoising_start = 1.0-cfg.inversion_max_step,
                            guidance_scale = guidance_scale).images[0]
    else:
        img = None
                    
    return img, inv_latent, noise, all_latents

if __name__ == "__main__":
    main()