cfg_sample.py

#!/usr/bin/env python3

"""Classifier-free guidance sampling from a diffusion model."""

import argparse
from functools import partial
from pathlib import Path
import os
from types import SimpleNamespace
from PIL import Image
import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from torchvision.transforms import functional as TF
from torchvision.utils import save_image
from tqdm.auto import trange

from CLIP import clip
from diffusion import get_model, get_models, sampling, utils, download_model

def isnotebook():
    try:
        shell = get_ipython().__class__.__name__
        return shell=='ZMQInteractiveShell' or shell=='Shell'
    except NameError:
        return False      
IS_NOTEBOOK = isnotebook()
if IS_NOTEBOOK:
    from IPython import display


MODULE_DIR = Path(__file__).resolve().parent


def parse_prompt(prompt, default_weight=3.):
    if prompt.startswith('http://') or prompt.startswith('https://'):
        vals = prompt.rsplit(':', 2)
        vals = [vals[0] + ':' + vals[1], *vals[2:]]
    else:
        vals = prompt.rsplit(':', 1)
    vals = vals + ['', default_weight][len(vals):]
    return vals[0], float(vals[1])


def resize_and_center_crop(image, size):
    fac = max(size[0] / image.size[0], size[1] / image.size[1])
    image = image.resize((int(fac * image.size[0]), int(fac * image.size[1])), Image.LANCZOS)
    return TF.center_crop(image, size[::-1])

def callback_fn(info):
    if info['i'] % 50==0:
        out = info['pred'].add(1).div(2)
        save_image(out, f"interm_output_{info['i']:05d}.png")
        if IS_NOTEBOOK:
            display.display(display.Image(f"interm_output_{info['i']:05d}.png",height=300))

def main():
    p = argparse.ArgumentParser(description=__doc__,
                                formatter_class=argparse.ArgumentDefaultsHelpFormatter)
    p.add_argument('prompts', type=str, default=[], nargs='*',
                   help='the text prompts to use')
    p.add_argument('--images', type=str, default=[], nargs='*', metavar='IMAGE',
                   help='the image prompts')
    p.add_argument('--batch-size', '-bs', type=int, default=1,
                   help='the number of images per batch')
    p.add_argument('--checkpoint', type=str,
                   help='the checkpoint to use')
    p.add_argument('--device', type=str,
                   help='the device to use')
    p.add_argument('--eta', type=float, default=1.,
                   help='the amount of noise to add during sampling (0-1)')
    p.add_argument('--init', type=str,
                   help='the init image')
    p.add_argument('--model', type=str, default='cc12m_1_cfg', choices=['cc12m_1_cfg'],
                   help='the model to use')
    p.add_argument('-n', type=int, default=1,
                   help='the number of images to sample')
    p.add_argument('--seed', type=int, default=0,
                   help='the random seed')
    p.add_argument('--size', type=int, nargs=2,
                   help='the output image size')
    p.add_argument('--starting-timestep', '-st', type=float, default=0.9,
                   help='the timestep to start at (used with init images)')
    p.add_argument('--steps', type=int, default=500,
                   help='the number of timesteps')
    args = p.parse_args()

    if args.device:
        device = torch.device(args.device)
    else:
        device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
    print('Using device:', device)

    model = get_model(args.model)()
    _, side_y, side_x = model.shape
    if args.size:
        side_x, side_y = args.size
    checkpoint = args.checkpoint
    if not checkpoint:
        checkpoint = MODULE_DIR / f'checkpoints/{args.model}.pth'
    if not os.path.isfile(checkpoint):
        download_model(args.model, checkpoint)
    model.load_state_dict(torch.load(checkpoint, map_location='cpu'))
    if device.type == 'cuda':
        model = model.half()
    model = model.to(device).eval().requires_grad_(False)
    clip_model_name = model.clip_model if hasattr(model, 'clip_model') else 'ViT-B/16'
    clip_model = clip.load(clip_model_name, jit=False, device=device)[0]
    clip_model.eval().requires_grad_(False)
    normalize = transforms.Normalize(mean=[0.48145466, 0.4578275, 0.40821073],
                                     std=[0.26862954, 0.26130258, 0.27577711])

    if args.init:
        init = Image.open(utils.fetch(args.init)).convert('RGB')
        init = resize_and_center_crop(init, (side_x, side_y))
        init = utils.from_pil_image(init).cuda()[None].repeat([args.n, 1, 1, 1])

    zero_embed = torch.zeros([1, clip_model.visual.output_dim], device=device)
    target_embeds, weights = [zero_embed], []

    for prompt in args.prompts:
        txt, weight = parse_prompt(prompt)
        target_embeds.append(clip_model.encode_text(clip.tokenize(txt).to(device)).float())
        weights.append(weight)

    for prompt in args.images:
        path, weight = parse_prompt(prompt)
        img = Image.open(utils.fetch(path)).convert('RGB')
        clip_size = clip_model.visual.input_resolution
        img = resize_and_center_crop(img, (clip_size, clip_size))
        batch = TF.to_tensor(img)[None].to(device)
        embed = F.normalize(clip_model.encode_image(normalize(batch)).float(), dim=-1)
        target_embeds.append(embed)
        weights.append(weight)

    weights = torch.tensor([1 - sum(weights), *weights], device=device)

    torch.manual_seed(args.seed)

    def cfg_model_fn(x, t):
        n = x.shape[0]
        n_conds = len(target_embeds)
        x_in = x.repeat([n_conds, 1, 1, 1])
        t_in = t.repeat([n_conds])
        clip_embed_in = torch.cat([*target_embeds]).repeat_interleave(n, 0)
        vs = model(x_in, t_in, clip_embed_in).view([n_conds, n, *x.shape[1:]])
        v = vs.mul(weights[:, None, None, None, None]).sum(0)
        return v

    def run(x, steps):
        return sampling.sample(cfg_model_fn, x, steps, args.eta, {}, callback=callback_fn)

    def run_all(n, batch_size):
        x = torch.randn([args.n, 3, side_y, side_x], device=device)
        t = torch.linspace(1, 0, args.steps + 1, device=device)[:-1]
        steps = utils.get_spliced_ddpm_cosine_schedule(t)
        if args.init:
            steps = steps[steps < args.starting_timestep]
            alpha, sigma = utils.t_to_alpha_sigma(steps[0])
            x = init * alpha + x * sigma
        for i in trange(0, n, batch_size):
            cur_batch_size = min(n - i, batch_size)
            outs = run(x[i:i+cur_batch_size], steps)
            for j, out in enumerate(outs):
                utils.to_pil_image(out).save(f'out_{i + j:05}.png')

    try:
        run_all(args.n, args.batch_size)
    except KeyboardInterrupt:
        pass


def run_diffusion_cfg(prompts,images=None,steps=1000,init=None,model="cc12m_1_cfg",size=[512,512], checkpoint=None, device=None, eta=1.0, n=1, seed=42,starting_timestep=0.9, batch_size=1,display_freq=50):

    args = SimpleNamespace(prompts=prompts,images=images,steps=steps,init=init,model=model,size=size, checkpoint=checkpoint, device=device, eta=eta, n=n, seed=seed,starting_timestep=starting_timestep, batch_size=batch_size)
    print(args)

    if args.device:
        device = torch.device(args.device)
    else:
        device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
    print('Using device:', device)

    model = get_model(args.model)()
    _, side_y, side_x = model.shape
    if args.size:
        side_x, side_y = args.size
    checkpoint = args.checkpoint
    if not checkpoint:
        checkpoint = MODULE_DIR / f'checkpoints/{args.model}.pth'
    if not os.path.isfile(checkpoint):
        download_model(args.model, checkpoint)
    model.load_state_dict(torch.load(checkpoint, map_location='cpu'))
    if device.type == 'cuda':
        model = model.half()
    model = model.to(device).eval().requires_grad_(False)
    clip_model_name = model.clip_model if hasattr(model, 'clip_model') else 'ViT-B/16'
    clip_model = clip.load(clip_model_name, jit=False, device=device)[0]
    clip_model.eval().requires_grad_(False)
    normalize = transforms.Normalize(mean=[0.48145466, 0.4578275, 0.40821073],
                                     std=[0.26862954, 0.26130258, 0.27577711])

    if args.init:
        init = Image.open(utils.fetch(args.init)).convert('RGB')
        init = resize_and_center_crop(init, (side_x, side_y))
        init = utils.from_pil_image(init).cuda()[None].repeat([args.n, 1, 1, 1])

    zero_embed = torch.zeros([1, clip_model.visual.output_dim], device=device)
    target_embeds, weights = [zero_embed], []

    if args.prompts:
        if isinstance(args.prompts, str):
            args.prompts = [args.prompts,]
        for prompt in args.prompts:
            txt, weight = parse_prompt(prompt)
            target_embeds.append(clip_model.encode_text(clip.tokenize(txt).to(device)).float())
            weights.append(weight)

    if args.images:
        if isinstance(args.images, str):
            args.images = [args.images,]
        for prompt in args.images:
            path, weight = parse_prompt(prompt)
            img = Image.open(utils.fetch(path)).convert('RGB')
            clip_size = clip_model.visual.input_resolution
            img = resize_and_center_crop(img, (clip_size, clip_size))
            batch = TF.to_tensor(img)[None].to(device)
            embed = F.normalize(clip_model.encode_image(normalize(batch)).float(), dim=-1)
            target_embeds.append(embed)
            weights.append(weight)

    weights = torch.tensor([1 - sum(weights), *weights], device=device)

    torch.manual_seed(args.seed)

    def cfg_model_fn(x, t):
        n = x.shape[0]
        n_conds = len(target_embeds)
        x_in = x.repeat([n_conds, 1, 1, 1])
        t_in = t.repeat([n_conds])
        clip_embed_in = torch.cat([*target_embeds]).repeat_interleave(n, 0)
        vs = model(x_in, t_in, clip_embed_in).view([n_conds, n, *x.shape[1:]])
        v = vs.mul(weights[:, None, None, None, None]).sum(0)
        return v

    def run(x, steps):
        return sampling.sample(cfg_model_fn, x, steps, args.eta, {}, callback=callback_fn)

    def run_all(n, batch_size):
        x = torch.randn([n, 3, side_y, side_x], device=device)
        t = torch.linspace(1, 0, args.steps + 1, device=device)[:-1]
        steps = utils.get_spliced_ddpm_cosine_schedule(t)
        if args.init:
            steps = steps[steps < args.starting_timestep]
            alpha, sigma = utils.t_to_alpha_sigma(steps[0])
            x = init * alpha + x * sigma
        for i in trange(0, n, batch_size):
            cur_batch_size = min(n - i, batch_size)
            outs = run(x[i:i+cur_batch_size], steps)
            for j, out in enumerate(outs):
                utils.to_pil_image(out).save(f'out_{i + j:05}.png')

    try:
        run_all(args.n, args.batch_size)
    except KeyboardInterrupt:
        pass


if __name__ == '__main__':
    main()