app.py

import os
import sys
import gradio as gr
os.system('git clone https://github.com/openai/CLIP')
os.system('git clone https://github.com/crowsonkb/guided-diffusion')
os.system('pip install -e ./CLIP')
os.system('pip install -e ./guided-diffusion')
os.system('pip install lpips')
os.system("curl -OL 'https://openaipublic.blob.core.windows.net/diffusion/jul-2021/256x256_diffusion_uncond.pt'")
import io
import math
import sys
import lpips
from PIL import Image
import requests
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 tqdm.notebook import tqdm
sys.path.append('./CLIP')
sys.path.append('./guided-diffusion')
import clip
from guided_diffusion.script_util import create_model_and_diffusion, model_and_diffusion_defaults
import numpy as np
import imageio
# Define necessary functions
def fetch(url_or_path):
    if str(url_or_path).startswith('http://') or str(url_or_path).startswith('https://'):
        r = requests.get(url_or_path)
        r.raise_for_status()
        fd = io.BytesIO()
        fd.write(r.content)
        fd.seek(0)
        return fd
    return open(url_or_path, 'rb')
def parse_prompt(prompt):
    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 + ['', '1'][len(vals):]
    return vals[0], float(vals[1])
class MakeCutouts(nn.Module):
    def __init__(self, cut_size, cutn, cut_pow=1.):
        super().__init__()
        self.cut_size = cut_size
        self.cutn = cutn
        self.cut_pow = cut_pow
    def forward(self, input):
        sideY, sideX = input.shape[2:4]
        max_size = min(sideX, sideY)
        min_size = min(sideX, sideY, self.cut_size)
        cutouts = []
        for _ in range(self.cutn):
            size = int(torch.rand([])**self.cut_pow * (max_size - min_size) + min_size)
            offsetx = torch.randint(0, sideX - size + 1, ())
            offsety = torch.randint(0, sideY - size + 1, ())
            cutout = input[:, :, offsety:offsety + size, offsetx:offsetx + size]
            cutouts.append(F.adaptive_avg_pool2d(cutout, self.cut_size))
        return torch.cat(cutouts)
def spherical_dist_loss(x, y):
    x = F.normalize(x, dim=-1)
    y = F.normalize(y, dim=-1)
    return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2)
def tv_loss(input):
    """L2 total variation loss, as in Mahendran et al."""
    input = F.pad(input, (0, 1, 0, 1), 'replicate')
    x_diff = input[..., :-1, 1:] - input[..., :-1, :-1]
    y_diff = input[..., 1:, :-1] - input[..., :-1, :-1]
    return (x_diff**2 + y_diff**2).mean([1, 2, 3])
def range_loss(input):
    return (input - input.clamp(-1, 1)).pow(2).mean([1, 2, 3])
    
def inference(text, init_image, skip_timesteps, clip_guidance_scale, tv_scale, range_scale, init_scale, seed, image_prompts,timestep_respacing, cutn):
    # Model settings
    model_config = model_and_diffusion_defaults()
    model_config.update({
        'attention_resolutions': '32, 16, 8',
        'class_cond': False,
        'diffusion_steps': 1000,
        'rescale_timesteps': True,
        'timestep_respacing': str(timestep_respacing),  # Modify this value to decrease the number of
                                       # timesteps.
        'image_size': 256,
        'learn_sigma': True,
        'noise_schedule': 'linear',
        'num_channels': 256,
        'num_head_channels': 64,
        'num_res_blocks': 2,
        'resblock_updown': True,
        'use_fp16': True,
        'use_scale_shift_norm': True,
    })
    # Load models
    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
    print('Using device:', device)
    model, diffusion = create_model_and_diffusion(**model_config)
    model.load_state_dict(torch.load('256x256_diffusion_uncond.pt', map_location='cpu'))
    model.requires_grad_(False).eval().to(device)
    for name, param in model.named_parameters():
        if 'qkv' in name or 'norm' in name or 'proj' in name:
            param.requires_grad_()
    if model_config['use_fp16']:
        model.convert_to_fp16()
    clip_model = clip.load('ViT-B/16', jit=False)[0].eval().requires_grad_(False).to(device)
    clip_size = clip_model.visual.input_resolution
    normalize = transforms.Normalize(mean=[0.48145466, 0.4578275, 0.40821073],
                                     std=[0.26862954, 0.26130258, 0.27577711])
    lpips_model = lpips.LPIPS(net='vgg').to(device)
    
#def inference(text, init_image, skip_timesteps, clip_guidance_scale, tv_scale, range_scale, init_scale, seed, image_prompt):
    all_frames = []
    prompts = [text]
    if image_prompts:
        image_prompts = [image_prompts.name]
    else:
        image_prompts = []
    batch_size = 1
    clip_guidance_scale = clip_guidance_scale  # Controls how much the image should look like the prompt.
    tv_scale = tv_scale             # Controls the smoothness of the final output.
    range_scale = range_scale            # Controls how far out of range RGB values are allowed to be.
    cutn = cutn
    n_batches = 1
    if init_image:
        init_image = init_image.name
    else:
        init_image = None   # This can be an URL or Colab local path and must be in quotes.
    skip_timesteps = skip_timesteps  # This needs to be between approx. 200 and 500 when using an init image.
                        # Higher values make the output look more like the init.
    init_scale = init_scale      # This enhances the effect of the init image, a good value is 1000.
    seed = seed
   
    if seed is not None:
        torch.manual_seed(seed)
    make_cutouts = MakeCutouts(clip_size, cutn)
    side_x = side_y = model_config['image_size']
    target_embeds, weights = [], []
    for prompt in 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 image_prompts:
        path, weight = parse_prompt(prompt)
        img = Image.open(fetch(path)).convert('RGB')
        img = TF.resize(img, min(side_x, side_y, *img.size), transforms.InterpolationMode.LANCZOS)
        batch = make_cutouts(TF.to_tensor(img).unsqueeze(0).to(device))
        embed = clip_model.encode_image(normalize(batch)).float()
        target_embeds.append(embed)
        weights.extend([weight / cutn] * cutn)
    target_embeds = torch.cat(target_embeds)
    weights = torch.tensor(weights, device=device)
    if weights.sum().abs() < 1e-3:
        raise RuntimeError('The weights must not sum to 0.')
    weights /= weights.sum().abs()
    init = None
    if init_image is not None:
        init = Image.open(fetch(init_image)).convert('RGB')
        init = init.resize((side_x, side_y), Image.LANCZOS)
        init = TF.to_tensor(init).to(device).unsqueeze(0).mul(2).sub(1)
    cur_t = None
    def cond_fn(x, t, y=None):
        with torch.enable_grad():
            x = x.detach().requires_grad_()
            n = x.shape[0]
            my_t = torch.ones([n], device=device, dtype=torch.long) * cur_t
            out = diffusion.p_mean_variance(model, x, my_t, clip_denoised=False, model_kwargs={'y': y})
            fac = diffusion.sqrt_one_minus_alphas_cumprod[cur_t]
            x_in = out['pred_xstart'] * fac + x * (1 - fac)
            clip_in = normalize(make_cutouts(x_in.add(1).div(2)))
            image_embeds = clip_model.encode_image(clip_in).float()
            dists = spherical_dist_loss(image_embeds.unsqueeze(1), target_embeds.unsqueeze(0))
            dists = dists.view([cutn, n, -1])
            losses = dists.mul(weights).sum(2).mean(0)
            tv_losses = tv_loss(x_in)
            range_losses = range_loss(out['pred_xstart'])
            loss = losses.sum() * clip_guidance_scale + tv_losses.sum() * tv_scale + range_losses.sum() * range_scale
            if init is not None and init_scale:
                init_losses = lpips_model(x_in, init)
                loss = loss + init_losses.sum() * init_scale
            return -torch.autograd.grad(loss, x)[0]
    if model_config['timestep_respacing'].startswith('ddim'):
        sample_fn = diffusion.ddim_sample_loop_progressive
    else:
        sample_fn = diffusion.p_sample_loop_progressive
    for i in range(n_batches):
        cur_t = diffusion.num_timesteps - skip_timesteps - 1
        samples = sample_fn(
            model,
            (batch_size, 3, side_y, side_x),
            clip_denoised=False,
            model_kwargs={},
            cond_fn=cond_fn,
            progress=True,
            skip_timesteps=skip_timesteps,
            init_image=init,
            randomize_class=True,
        )
        for j, sample in enumerate(samples):
            cur_t -= 1
            if j % 1 == 0 or cur_t == -1:
                print()
                for k, image in enumerate(sample['pred_xstart']):
                    #filename = f'progress_{i * batch_size + k:05}.png'
                    img = TF.to_pil_image(image.add(1).div(2).clamp(0, 1))
                    all_frames.append(img)
                    tqdm.write(f'Batch {i}, step {j}, output {k}:')
                    #display.display(display.Image(filename))
    writer = imageio.get_writer('video.mp4', fps=5)
    for im in all_frames:
        writer.append_data(np.array(im))
    writer.close()
    return img, 'video.mp4'
    
title = "CLIP Guided Diffusion"
iface = gr.Interface(inference, inputs=["text",gr.inputs.Image(type="file", label='initial image (optional)', optional=True),gr.inputs.Slider(minimum=0, maximum=45, step=1, default=10, label="skip_timesteps"), gr.inputs.Slider(minimum=0, maximum=3000, step=1, default=600, label="clip guidance scale (Controls how much the image should look like the prompt)"), gr.inputs.Slider(minimum=0, maximum=1000, step=1, default=0, label="tv_scale (Controls the smoothness of the final output)"), gr.inputs.Slider(minimum=0, maximum=1000, step=1, default=0, label="range_scale (Controls how far out of range RGB values are allowed to be)"), gr.inputs.Slider(minimum=0, maximum=1000, step=1, default=0, label="init_scale (This enhances the effect of the init image)"), gr.inputs.Number(default=0, label="Seed"), gr.inputs.Image(type="file", label='image prompt (optional)', optional=True), gr.inputs.Slider(minimum=50, maximum=500, step=1, default=50, label="timestep respacing"),gr.inputs.Slider(minimum=1, maximum=64, step=1, default=32, label="cutn")], outputs=["image","video"], title=title, examples=[["coral reef city by artistation artists", None, 0, 1000, 150, 50, 0, 0, None, 90, 32]],
    enable_queue=True)
iface.launch()