bak.THU.08072025.app.py

# 🚀 Import all necessary libraries
import os
import argparse
from functools import partial
from pathlib import Path
import sys
import random
from omegaconf import OmegaConf
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 tqdm import trange
from transformers import CLIPProcessor, CLIPModel
from vqvae import VQVAE2  # Autoencoder replacement
from diffusion_models import Diffusion  # Swapped Diffusion model for DALL·E 2 based model
from huggingface_hub import hf_hub_url, cached_download
import gradio as gr  # 🎨 The magic canvas for AI-powered image generation!

# 🖼️ Download the necessary model files from HuggingFace
vqvae_model_path = cached_download(hf_hub_url("huggingface/vqvae-2", filename="vqvae_model.ckpt"))
diffusion_model_path = cached_download(hf_hub_url("huggingface/dalle-2", filename="diffusion_model.ckpt"))

# 📐 Utility Functions: Math and images, what could go wrong?
# These functions help parse prompts and resize/crop images to fit nicely

def parse_prompt(prompt, default_weight=3.):
    """
    🎯 Parses a prompt into text and weight.
    """
    vals = prompt.rsplit(':', 1)
    vals = vals + ['', default_weight][len(vals):]
    return vals[0], float(vals[1])

def resize_and_center_crop(image, size):
    """
    ✂️ Resize and crop image to center it beautifully.
    """
    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])

# 🧠 Model loading: the brain of our operation! 🔥

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

# Load CLIP model
clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32").to(device)
clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")

# Load VQ-VAE-2 Autoencoder
vqvae = VQVAE2()
vqvae.load_state_dict(torch.load(vqvae_model_path))
vqvae.eval().requires_grad_(False).to(device)

# Load Diffusion Model
diffusion_model = Diffusion()
diffusion_model.load_state_dict(torch.load(diffusion_model_path))
diffusion_model = diffusion_model.to(device).eval().requires_grad_(False)

# 🎨 The key function: Where the magic happens!
# This is where we generate images based on text and image prompts

def generate(n=1, prompts=['a red circle'], images=[], seed=42, steps=15, method='ddim', eta=None):
    """
    🖼️ Generates a list of PIL images based on given text and image prompts.
    """
    zero_embed = torch.zeros([1, clip_model.config.projection_dim], device=device)
    target_embeds, weights = [zero_embed], []

    # Parse text prompts and encode with CLIP
    for prompt in prompts:
        inputs = clip_processor(text=prompt, return_tensors="pt").to(device)
        text_embed = clip_model.get_text_features(**inputs).float()
        target_embeds.append(text_embed)
        weights.append(1.0)

    # Parse image prompts
    for prompt in images:
        path, weight = parse_prompt(prompt)
        img = Image.open(path).convert('RGB')
        img = resize_and_center_crop(img, (224, 224))
        inputs = clip_processor(images=img, return_tensors="pt").to(device)
        image_embed = clip_model.get_image_features(**inputs).float()
        target_embeds.append(image_embed)
        weights.append(weight)

    # Adjust weights and set seed
    weights = torch.tensor([1 - sum(weights), *weights], device=device)
    torch.manual_seed(seed)

    # 💡 Model function with classifier-free guidance
    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])
        embed_in = torch.cat([*target_embeds]).repeat_interleave(n, 0)
        vs = diffusion_model(x_in, t_in, embed_in).view([n_conds, n, *x.shape[1:]])
        v = vs.mul(weights[:, None, None, None, None]).sum(0)
        return v

    # 🎞️ Run the sampler to generate images
    def run(x, steps):
        if method == 'ddpm':
            return sampling.sample(cfg_model_fn, x, steps, 1., {})
        if method == 'ddim':
            return sampling.sample(cfg_model_fn, x, steps, eta, {})
        if method == 'plms':
            return sampling.plms_sample(cfg_model_fn, x, steps, {})
        assert False

    # 🏃‍♂️ Generate the output images
    batch_size = n
    x = torch.randn([n, 3, 64, 64], device=device)
    t = torch.linspace(1, 0, steps + 1, device=device)[:-1]
    pil_ims = []
    for i in trange(0, n, batch_size):
        cur_batch_size = min(n - i, batch_size)
        out_latents = run(x[i:i + cur_batch_size], steps)
        outs = vqvae.decode(out_latents)
        for j, out in enumerate(outs):
            pil_ims.append(transforms.ToPILImage()(out))

    return pil_ims

# 🖌️ Interface: Gradio's brush to paint the UI
def gen_ims(prompt, im_prompt=None, seed=None, n_steps=10, method='plms'):
    """
    💡 Gradio function to wrap image generation.
    """
    if seed is None:
        seed = random.randint(0, 10000)
    prompts = [prompt]
    im_prompts = []
    if im_prompt is not None:
        im_prompts = [im_prompt]
    pil_ims = generate(n=1, prompts=prompts, images=im_prompts, seed=seed, steps=n_steps, method=method)
    return pil_ims[0]

# 🖼️ Gradio UI: The interface where users can input text or image prompts
iface = gr.Interface(
    fn=gen_ims,
    inputs=[
        gr.Textbox(label="Text prompt"),
        gr.Image(optional=True, label="Image prompt", type='filepath')
    ],
    outputs=gr.Image(type="pil", label="Generated Image"),
    examples=[
        ["A beautiful sunset over the ocean"],
        ["A futuristic cityscape at night"],
        ["A surreal dream-like landscape"]
    ],
    title='CLIP + Diffusion Model Image Generator',
    description="Generate stunning images from text and image prompts using CLIP and a diffusion model.",
)

# 🚀 Launch the Gradio interface
iface.launch(enable_queue=True)