import os import sys os.system('pip install gradio==2.3.0a0') 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'") # Imports #import gc import io import math import sys #from IPython import display 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]) # 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': '90', # 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): all_frames = [] prompts = [text] 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 = 16 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 HQ" description = "Gradio demo for CLIP Guided Diffusion. To use it, simply add your text, or click one of the examples to load them. Read more at the links below." article = "

By Katherine Crowson (https://github.com/crowsonkb, https://twitter.com/RiversHaveWings). It uses OpenAI's 256x256 unconditional ImageNet diffusion model (https://github.com/openai/guided-diffusion) together with CLIP (https://github.com/openai/CLIP) to connect text prompts with images. | Colab

" 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=0, label="skip_timesteps"), gr.inputs.Slider(minimum=0, maximum=1000, step=1, default=700, label="clip guidance scale (Controls how much the image should look like the prompt)"), gr.inputs.Slider(minimum=0, maximum=1000, step=1, default=150, label="tv_scale (Controls the smoothness of the final output)"), gr.inputs.Slider(minimum=0, maximum=1000, step=1, default=50, 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, a good value is 1000)"), gr.inputs.Number(default=0, label="Seed") ], outputs=["image","video"], title=title, description=description, article=article, examples=[["coral reef city by artistation artists", None, 0, 1000, 150, 50, 0, 0]], enable_queue=True) iface.launch()