| 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 |
| |
| 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_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), |
| |
| '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, |
| }) |
| |
| 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) |
| |
| |
| all_frames = [] |
| prompts = [text] |
| if image_prompts: |
| image_prompts = [image_prompts.name] |
| else: |
| image_prompts = [] |
| batch_size = 1 |
| clip_guidance_scale = clip_guidance_scale |
| tv_scale = tv_scale |
| range_scale = range_scale |
| cutn = cutn |
| n_batches = 1 |
| if init_image: |
| init_image = init_image.name |
| else: |
| init_image = None |
| skip_timesteps = skip_timesteps |
| |
| init_scale = init_scale |
| 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']): |
| |
| 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}:') |
| |
| 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() |
|
|
