Added original code

app.py

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+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 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 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 = "<p style='text-align: center'> 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. | <a href='https://colab.research.google.com/drive/12a_Wrfi2_gwwAuN3VvMTwVMz9TfqctNj' target='_blank'>Colab</a></p>"
+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, description=description, article=article, examples=[["coral reef city by artistation artists", None, 0, 1000, 150, 50, 0, 0, None, 90, 32]],
+    enable_queue=True)
+iface.launch()

requirements.txt

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+torch
+torchvision
+kornia
+tqdm
+clip-anytorch
+requests
+lpips
+numpy
+imageio
+imageio-ffmpeg