Ai image generator

import argparse
import os
import math
import numpy as np
import torch
import safetensors.torch as sf
import db_examples

from PIL import Image
from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline
from diffusers import AutoencoderKL, UNet2DConditionModel, DDIMScheduler, EulerAncestralDiscreteScheduler, DPMSolverMultistepScheduler
from diffusers.models.attention_processor import AttnProcessor2_0
from transformers import CLIPTextModel, CLIPTokenizer
from briarmbg import BriaRMBG
from enum import Enum
from torch.hub import download_url_to_file

# 'stablediffusionapi/realistic-vision-v51'
# 'runwayml/stable-diffusion-v1-5'
sd15_name = 'stablediffusionapi/realistic-vision-v51'
tokenizer = CLIPTokenizer.from_pretrained(sd15_name, subfolder="tokenizer")
text_encoder = CLIPTextModel.from_pretrained(sd15_name, subfolder="text_encoder")
vae = AutoencoderKL.from_pretrained(sd15_name, subfolder="vae")
unet = UNet2DConditionModel.from_pretrained(sd15_name, subfolder="unet")
rmbg = BriaRMBG.from_pretrained("briaai/RMBG-1.4")

# Change UNet

with torch.no_grad():
    new_conv_in = torch.nn.Conv2d(8, unet.conv_in.out_channels, unet.conv_in.kernel_size, unet.conv_in.stride, unet.conv_in.padding)
    new_conv_in.weight.zero_()
    new_conv_in.weight[:, :4, :, :].copy_(unet.conv_in.weight)
    new_conv_in.bias = unet.conv_in.bias
    unet.conv_in = new_conv_in

unet_original_forward = unet.forward


def hooked_unet_forward(sample, timestep, encoder_hidden_states, **kwargs):
    c_concat = kwargs['cross_attention_kwargs']['concat_conds'].to(sample)
    c_concat = torch.cat([c_concat] * (sample.shape[0] // c_concat.shape[0]), dim=0)
    new_sample = torch.cat([sample, c_concat], dim=1)
    kwargs['cross_attention_kwargs'] = {}
    return unet_original_forward(new_sample, timestep, encoder_hidden_states, **kwargs)


unet.forward = hooked_unet_forward

# Load

model_path = './models/iclight_sd15_fc.safetensors'

if not os.path.exists(model_path):
    download_url_to_file(url='https://huggingface.co/lllyasviel/ic-light/resolve/main/iclight_sd15_fc.safetensors', dst=model_path)

sd_offset = sf.load_file(model_path)
sd_origin = unet.state_dict()
keys = sd_origin.keys()
sd_merged = {k: sd_origin[k] + sd_offset[k] for k in sd_origin.keys()}
unet.load_state_dict(sd_merged, strict=True)
del sd_offset, sd_origin, sd_merged, keys

# Device

device = torch.device('cuda')
text_encoder = text_encoder.to(device=device, dtype=torch.float16)
vae = vae.to(device=device, dtype=torch.bfloat16)
unet = unet.to(device=device, dtype=torch.float16)
rmbg = rmbg.to(device=device, dtype=torch.float32)

# SDP

unet.set_attn_processor(AttnProcessor2_0())
vae.set_attn_processor(AttnProcessor2_0())

# Samplers

ddim_scheduler = DDIMScheduler(
    num_train_timesteps=1000,
    beta_start=0.00085,
    beta_end=0.012,
    beta_schedule="scaled_linear",
    clip_sample=False,
    set_alpha_to_one=False,
    steps_offset=1,
)

euler_a_scheduler = EulerAncestralDiscreteScheduler(
    num_train_timesteps=1000,
    beta_start=0.00085,
    beta_end=0.012,
    steps_offset=1
)

dpmpp_2m_sde_karras_scheduler = DPMSolverMultistepScheduler(
    num_train_timesteps=1000,
    beta_start=0.00085,
    beta_end=0.012,
    algorithm_type="sde-dpmsolver++",
    use_karras_sigmas=True,
    steps_offset=1
)

# Pipelines

t2i_pipe = StableDiffusionPipeline(
    vae=vae,
    text_encoder=text_encoder,
    tokenizer=tokenizer,
    unet=unet,
    scheduler=dpmpp_2m_sde_karras_scheduler,
    safety_checker=None,
    requires_safety_checker=False,
    feature_extractor=None,
    image_encoder=None
)

i2i_pipe = StableDiffusionImg2ImgPipeline(
    vae=vae,
    text_encoder=text_encoder,
    tokenizer=tokenizer,
    unet=unet,
    scheduler=dpmpp_2m_sde_karras_scheduler,
    safety_checker=None,
    requires_safety_checker=False,
    feature_extractor=None,
    image_encoder=None
)


@torch.inference_mode()
def encode_prompt_inner(txt: str):
    max_length = tokenizer.model_max_length
    chunk_length = tokenizer.model_max_length - 2
    id_start = tokenizer.bos_token_id
    id_end = tokenizer.eos_token_id
    id_pad = id_end

    def pad(x, p, i):
        return x[:i] if len(x) >= i else x + [p] * (i - len(x))

    tokens = tokenizer(txt, truncation=False, add_special_tokens=False)["input_ids"]
    chunks = [[id_start] + tokens[i: i + chunk_length] + [id_end] for i in range(0, len(tokens), chunk_length)]
    chunks = [pad(ck, id_pad, max_length) for ck in chunks]

    token_ids = torch.tensor(chunks).to(device=device, dtype=torch.int64)
    conds = text_encoder(token_ids).last_hidden_state

    return conds


@torch.inference_mode()
def encode_prompt_pair(positive_prompt, negative_prompt):
    c = encode_prompt_inner(positive_prompt)
    uc = encode_prompt_inner(negative_prompt)

    c_len = float(len(c))
    uc_len = float(len(uc))
    max_count = max(c_len, uc_len)
    c_repeat = int(math.ceil(max_count / c_len))
    uc_repeat = int(math.ceil(max_count / uc_len))
    max_chunk = max(len(c), len(uc))

    c = torch.cat([c] * c_repeat, dim=0)[:max_chunk]
    uc = torch.cat([uc] * uc_repeat, dim=0)[:max_chunk]

    c = torch.cat([p[None, ...] for p in c], dim=1)
    uc = torch.cat([p[None, ...] for p in uc], dim=1)

    return c, uc


@torch.inference_mode()
def pytorch2numpy(imgs, quant=True):
    results = []
    for x in imgs:
        y = x.movedim(0, -1)

        if quant:
            y = y * 127.5 + 127.5
            y = y.detach().float().cpu().numpy().clip(0, 255).astype(np.uint8)
        else:
            y = y * 0.5 + 0.5
            y = y.detach().float().cpu().numpy().clip(0, 1).astype(np.float32)

        results.append(y)
    return results


@torch.inference_mode()
def numpy2pytorch(imgs):
    h = torch.from_numpy(np.stack(imgs, axis=0)).float() / 127.0 - 1.0  # so that 127 must be strictly 0.0
    h = h.movedim(-1, 1)
    return h


def resize_and_center_crop(image, target_width, target_height):
    pil_image = Image.fromarray(image)
    original_width, original_height = pil_image.size
    scale_factor = max(target_width / original_width, target_height / original_height)
    resized_width = int(round(original_width * scale_factor))
    resized_height = int(round(original_height * scale_factor))
    resized_image = pil_image.resize((resized_width, resized_height), Image.LANCZOS)
    left = (resized_width - target_width) / 2
    top = (resized_height - target_height) / 2
    right = (resized_width + target_width) / 2
    bottom = (resized_height + target_height) / 2
    cropped_image = resized_image.crop((left, top, right, bottom))
    return np.array(cropped_image)


def resize_without_crop(image, target_width, target_height):
    pil_image = Image.fromarray(image)
    resized_image = pil_image.resize((target_width, target_height), Image.LANCZOS)
    return np.array(resized_image)


@torch.inference_mode()
def run_rmbg(img, sigma=0.0):
    H, W, C = img.shape
    assert C == 3
    k = (256.0 / float(H * W)) ** 0.5
    feed = resize_without_crop(img, int(64 * round(W * k)), int(64 * round(H * k)))
    feed = numpy2pytorch([feed]).to(device=device, dtype=torch.float32)
    alpha = rmbg(feed)[0][0]
    alpha = torch.nn.functional.interpolate(alpha, size=(H, W), mode="bilinear")
    alpha = alpha.movedim(1, -1)[0]
    alpha = alpha.detach().float().cpu().numpy().clip(0, 1)
    result = 127 + (img.astype(np.float32) - 127 + sigma) * alpha
    return result.clip(0, 255).astype(np.uint8), alpha


@torch.inference_mode()
def process(input_fg, prompt, image_width, image_height, num_samples, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, lowres_denoise, bg_source):
    input_bg = None

    if bg_source == "NONE":
        pass
    elif bg_source == "LEFT":
        gradient = np.linspace(255, 0, image_width)
        image = np.tile(gradient, (image_height, 1))
        input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
    elif bg_source == "RIGHT":
        gradient = np.linspace(0, 255, image_width)
        image = np.tile(gradient, (image_height, 1))
        input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
    elif bg_source == "TOP":
        gradient = np.linspace(255, 0, image_height)[:, None]
        image = np.tile(gradient, (1, image_width))
        input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
    elif bg_source == "BOTTOM":
        gradient = np.linspace(0, 255, image_height)[:, None]
        image = np.tile(gradient, (1, image_width))
        input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
    else:
        raise 'Wrong initial latent!'

    rng = torch.Generator(device=device).manual_seed(int(seed))

    fg = resize_and_center_crop(input_fg, image_width, image_height)

    concat_conds = numpy2pytorch([fg]).to(device=vae.device, dtype=vae.dtype)
    concat_conds = vae.encode(concat_conds).latent_dist.mode() * vae.config.scaling_factor

    conds, unconds = encode_prompt_pair(positive_prompt=prompt + ', ' + a_prompt, negative_prompt=n_prompt)

    if input_bg is None:
        latents = t2i_pipe(
            prompt_embeds=conds,
            negative_prompt_embeds=unconds,
            width=image_width,
            height=image_height,
            num_inference_steps=steps,
            num_images_per_prompt=num_samples,
            generator=rng,
            output_type='latent',
            guidance_scale=cfg,
            cross_attention_kwargs={'concat_conds': concat_conds},
        ).images.to(vae.dtype) / vae.config.scaling_factor
    else:
        bg = resize_and_center_crop(input_bg, image_width, image_height)
        bg_latent = numpy2pytorch([bg]).to(device=vae.device, dtype=vae.dtype)
        bg_latent = vae.encode(bg_latent).latent_dist.mode() * vae.config.scaling_factor
        latents = i2i_pipe(
            image=bg_latent,
            strength=lowres_denoise,
            prompt_embeds=conds,
            negative_prompt_embeds=unconds,
            width=image_width,
            height=image_height,
            num_inference_steps=int(round(steps / lowres_denoise)),
            num_images_per_prompt=num_samples,
            generator=rng,
            output_type='latent',
            guidance_scale=cfg,
            cross_attention_kwargs={'concat_conds': concat_conds},
        ).images.to(vae.dtype) / vae.config.scaling_factor

    pixels = vae.decode(latents).sample
    pixels = pytorch2numpy(pixels)
    pixels = [resize_without_crop(
        image=p,
        target_width=int(round(image_width * highres_scale / 64.0) * 64),
        target_height=int(round(image_height * highres_scale / 64.0) * 64))
    for p in pixels]

    pixels = numpy2pytorch(pixels).to(device=vae.device, dtype=vae.dtype)
    latents = vae.encode(pixels).latent_dist.mode() * vae.config.scaling_factor
    latents = latents.to(device=unet.device, dtype=unet.dtype)

    image_height, image_width = latents.shape[2] * 8, latents.shape[3] * 8

    fg = resize_and_center_crop(input_fg, image_width, image_height)
    concat_conds = numpy2pytorch([fg]).to(device=vae.device, dtype=vae.dtype)
    concat_conds = vae.encode(concat_conds).latent_dist.mode() * vae.config.scaling_factor

    latents = i2i_pipe(
        image=latents,
        strength=highres_denoise,
        prompt_embeds=conds,
        negative_prompt_embeds=unconds,
        width=image_width,
        height=image_height,
        num_inference_steps=int(round(steps / highres_denoise)),
        num_images_per_prompt=num_samples,
        generator=rng,
        output_type='latent',
        guidance_scale=cfg,
        cross_attention_kwargs={'concat_conds': concat_conds},
    ).images.to(vae.dtype) / vae.config.scaling_factor

    pixels = vae.decode(latents).sample

    return pytorch2numpy(pixels)


@torch.inference_mode()
def process_relight(input_fg, prompt, image_width, image_height, num_samples, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, lowres_denoise, bg_source):
    input_fg, matting = run_rmbg(input_fg)
    results = process(input_fg, prompt, image_width, image_height, num_samples, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, lowres_denoise, bg_source)
    return input_fg, results


# quick_prompts = [
#     'sunshine from window',
#     'neon light, city',
#     'sunset over sea',
#     'golden time',
#     'sci-fi RGB glowing, cyberpunk',
#     'natural lighting',
#     'warm atmosphere, at home, bedroom',
#     'magic lit',
#     'evil, gothic, Yharnam',
#     'light and shadow',
#     'shadow from window',
#     'soft studio lighting',
#     'home atmosphere, cozy bedroom illumination',
#     'neon, Wong Kar-wai, warm'
# ]
# quick_prompts = [[x] for x in quick_prompts]


# quick_subjects = [
#     'beautiful woman, detailed face',
#     'handsome man, detailed face',
# ]
# quick_subjects = [[x] for x in quick_subjects]

def parse_args():
    parser = argparse.ArgumentParser(description="Process image generation parameters")
    parser.add_argument('--input_fg', type=str, required=True, help="Path to the foreground image")
    parser.add_argument('--prompt', type=str, required=True, help="Text prompt for the image generation")
    parser.add_argument('--image_width', type=int, default=970, help="Width of the generated image")
    parser.add_argument('--image_height', type=int, default=600, help="Height of the generated image")
    parser.add_argument('--num_samples', type=int, default=1, help="Number of images to generate")
    parser.add_argument('--seed', type=int, default=12345, help="Random seed for generation")
    parser.add_argument('--steps', type=int, default=25, help="Number of inference steps")
    parser.add_argument('--a_prompt', type=str, default="best quality", help="Additional prompt")
    parser.add_argument('--n_prompt', type=str, default="lowres, bad anatomy", help="Negative prompt")
    parser.add_argument('--cfg', type=float, default=7.5, help="CFG scale")
    parser.add_argument('--highres_scale', type=float, default=1.5, help="Highres scale")
    parser.add_argument('--highres_denoise', type=float, default=0.5, help="Highres denoise")
    parser.add_argument('--lowres_denoise', type=float, default=0.9, help="Lowres denoise")
    parser.add_argument('--bg_source', type=str, default="NONE", choices=["NONE", "LEFT", "RIGHT", "TOP", "BOTTOM"], help="Background source preference")

    return parser.parse_args()

if __name__ == "__main__":
    args = parse_args()

    input_fg = np.array(Image.open(args.input_fg).convert("RGB"))

    result = process(
        input_fg=input_fg,
        prompt=args.prompt,
        image_width=args.image_width,
        image_height=args.image_height,
        num_samples=args.num_samples,
        seed=args.seed,
        steps=args.steps,
        a_prompt=args.a_prompt,
        n_prompt=args.n_prompt,
        cfg=args.cfg,
        highres_scale=args.highres_scale,
        highres_denoise=args.highres_denoise,
        lowres_denoise=args.lowres_denoise,
        bg_source=args.bg_source
    )

    output_image = Image.fromarray(result[0])
    output_image.save("generated_image.png")