app.py

import os
os.system("pip uninstall -y gradio") 
os.system('pip install gradio==3.43.1')
import torch
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import Dataset, DataLoader
import gradio as gr
import sys
import tqdm
sys.path.append(os.path.abspath(os.path.join("", "..")))
import gc
import warnings
warnings.filterwarnings("ignore")
from PIL import Image
import numpy as np
from utils import load_models
from editing import get_direction, debias
from sampling import sample_weights
from lora_w2w import LoRAw2w
from huggingface_hub import snapshot_download




global device
global generator 
global unet
global vae 
global text_encoder
global tokenizer
global noise_scheduler
global network
device = "cuda:0"
generator = torch.Generator(device=device)



models_path = snapshot_download(repo_id="Snapchat/w2w")

mean = torch.load(f"{models_path}/files/mean.pt").bfloat16().to(device)
std = torch.load(f"{models_path}/files/std.pt").bfloat16().to(device)
v = torch.load(f"{models_path}/files/V.pt").bfloat16().to(device)
proj = torch.load(f"{models_path}/files/proj_1000pc.pt").bfloat16().to(device)
df = torch.load(f"{models_path}/files/identity_df.pt")
weight_dimensions = torch.load(f"{models_path}/files/weight_dimensions.pt")
pinverse = torch.load(f"{models_path}/files/pinverse_1000pc.pt").bfloat16().to(device)


unet, vae, text_encoder, tokenizer, noise_scheduler = load_models(device)


def sample_model():
    global unet
    del unet
    global network

    unet, _, _, _, _ = load_models(device)
    network = sample_weights(unet, proj, mean, std, v[:, :1000], device, factor = 1.00)
 
@torch.no_grad()
def inference( prompt, negative_prompt, guidance_scale, ddim_steps, seed):
    global device
    global generator 
    global unet
    global vae 
    global text_encoder
    global tokenizer
    global noise_scheduler
    generator = generator.manual_seed(seed)
    latents = torch.randn(
        (1, unet.in_channels, 512 // 8, 512 // 8),
        generator = generator,
        device = device
    ).bfloat16()
   

    text_input = tokenizer(prompt, padding="max_length", max_length=tokenizer.model_max_length, truncation=True, return_tensors="pt")

    text_embeddings = text_encoder(text_input.input_ids.to(device))[0]

    max_length = text_input.input_ids.shape[-1]
    uncond_input = tokenizer(
                            [negative_prompt], padding="max_length", max_length=max_length, return_tensors="pt"
                        )
    uncond_embeddings = text_encoder(uncond_input.input_ids.to(device))[0]
    text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
    noise_scheduler.set_timesteps(ddim_steps) 
    latents = latents * noise_scheduler.init_noise_sigma
    
    for i,t in enumerate(tqdm.tqdm(noise_scheduler.timesteps)):
        latent_model_input = torch.cat([latents] * 2)
        latent_model_input = noise_scheduler.scale_model_input(latent_model_input, timestep=t)
        with network:
            noise_pred = unet(latent_model_input, t, encoder_hidden_states=text_embeddings, timestep_cond= None).sample
        #guidance
        noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
        noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
        latents = noise_scheduler.step(noise_pred, t, latents).prev_sample
    
    latents = 1 / 0.18215 * latents
    image = vae.decode(latents).sample
    image = (image / 2 + 0.5).clamp(0, 1)
    image = image.detach().cpu().float().permute(0, 2, 3, 1).numpy()[0]

    image = Image.fromarray((image * 255).round().astype("uint8"))

    return image


@torch.no_grad()
def edit_inference(prompt, negative_prompt, guidance_scale, ddim_steps, seed, start_noise, a1, a2, a3, a4):

    global device
    global generator 
    global unet
    global vae 
    global text_encoder
    global tokenizer
    global noise_scheduler
    global young
    global pointy
    global wavy
    global large
    
    original_weights = network.proj.clone()
    
    #pad to same number of PCs
    pcs_original = original_weights.shape[1]
    pcs_edits = young.shape[1]
    padding =  torch.zeros((1,pcs_original-pcs_edits)).to(device)
    young_pad = torch.cat((young, padding), 1)
    pointy_pad = torch.cat((pointy, padding), 1)
    wavy_pad = torch.cat((wavy, padding), 1)
    large_pad = torch.cat((large, padding), 1)
    

    edited_weights = original_weights+a1*1e6*young_pad+a2*1e6*pointy_pad+a3*1e6*wavy_pad+a4*2e6*large_pad
   
    generator = generator.manual_seed(seed)
    latents = torch.randn(
        (1, unet.in_channels, 512 // 8, 512 // 8),
        generator = generator,
        device = device
    ).bfloat16()
   

    text_input = tokenizer(prompt, padding="max_length", max_length=tokenizer.model_max_length, truncation=True, return_tensors="pt")

    text_embeddings = text_encoder(text_input.input_ids.to(device))[0]

    max_length = text_input.input_ids.shape[-1]
    uncond_input = tokenizer(
                            [negative_prompt], padding="max_length", max_length=max_length, return_tensors="pt"
                        )
    uncond_embeddings = text_encoder(uncond_input.input_ids.to(device))[0]
    text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
    noise_scheduler.set_timesteps(ddim_steps) 
    latents = latents * noise_scheduler.init_noise_sigma
    

 
    for i,t in enumerate(tqdm.tqdm(noise_scheduler.timesteps)):
        latent_model_input = torch.cat([latents] * 2)
        latent_model_input = noise_scheduler.scale_model_input(latent_model_input, timestep=t)
        
        if t>start_noise:
            pass
        elif t<=start_noise:
            network.proj = torch.nn.Parameter(edited_weights)
            network.reset()


        with network:
            noise_pred = unet(latent_model_input, t, encoder_hidden_states=text_embeddings, timestep_cond= None).sample
            
        
        #guidance
        noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
        noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
        latents = noise_scheduler.step(noise_pred, t, latents).prev_sample
    
    latents = 1 / 0.18215 * latents
    image = vae.decode(latents).sample
    image = (image / 2 + 0.5).clamp(0, 1)

    image = image.detach().cpu().float().permute(0, 2, 3, 1).numpy()[0]

    image = Image.fromarray((image * 255).round().astype("uint8"))

    #reset weights back to original 
    network.proj = torch.nn.Parameter(original_weights)
    network.reset()

    return image
    
def sample_then_run():
    sample_model()    
    prompt = "sks person"
    negative_prompt = "low quality, blurry, unfinished, nudity, weapon"
    seed = 5
    cfg = 3.0
    steps = 50
    image = inference( prompt, negative_prompt, cfg, steps, seed)
    torch.save(network.proj, "model.pt" )
    return image, "model.pt"


global young
global pointy
global wavy 
global large

young = get_direction(df, "Young", pinverse, 1000, device)
young = debias(young, "Male", df, pinverse, device)
young = debias(young, "Pointy_Nose", df, pinverse, device)
young = debias(young, "Wavy_Hair", df, pinverse, device)
young = debias(young, "Chubby", df, pinverse, device)


pointy = get_direction(df, "Pointy_Nose", pinverse, 1000, device)
pointy = debias(pointy, "Young", df, pinverse, device)
pointy = debias(pointy, "Male", df, pinverse, device)
pointy = debias(pointy, "Wavy_Hair", df, pinverse, device)
pointy = debias(pointy, "Chubby", df, pinverse, device)
pointy = debias(pointy, "Heavy_Makeup", df, pinverse, device)



wavy = get_direction(df, "Wavy_Hair", pinverse, 1000, device)
wavy = debias(wavy, "Young", df, pinverse, device)
wavy = debias(wavy, "Male", df, pinverse, device)
wavy = debias(wavy, "Pointy_Nose", df, pinverse, device)
wavy = debias(wavy, "Chubby", df, pinverse, device)
wavy = debias(wavy, "Heavy_Makeup", df, pinverse, device)


large = get_direction(df, "Bushy_Eyebrows", pinverse, 1000, device)
large = debias(large, "Male", df, pinverse, device)
large = debias(large, "Young", df, pinverse, device)
large = debias(large, "Pointy_Nose", df, pinverse, device)
large = debias(large, "Wavy_Hair", df, pinverse, device)
large = debias(large, "Mustache", df, pinverse, device)
large = debias(large, "No_Beard", df, pinverse, device)
large = debias(large, "Sideburns", df, pinverse, device)
large = debias(large, "Big_Nose", df, pinverse, device)
large = debias(large, "Big_Lips", df, pinverse, device)
large = debias(large, "Black_Hair", df, pinverse, device)
large = debias(large, "Brown_Hair", df, pinverse, device)
large = debias(large, "Pale_Skin", df, pinverse, device)
large = debias(large, "Heavy_Makeup", df, pinverse, device)



class CustomImageDataset(Dataset):
    def __init__(self, images, transform=None):
        self.images = images
        self.transform = transform

    def __len__(self):
        return len(self.images)

    def __getitem__(self, idx):
        image = self.images[idx]
        if self.transform:
            image = self.transform(image)
        return image
    
def invert(image, mask, pcs=10000, epochs=400, weight_decay = 1e-10, lr=1e-1):
    global unet
    del unet
    global network
    unet, _, _, _, _ = load_models(device)
    
    proj = torch.zeros(1,pcs).bfloat16().to(device)
    network = LoRAw2w( proj, mean, std, v[:, :pcs], 
                        unet,
                        rank=1,
                        multiplier=1.0,
                        alpha=27.0,
                        train_method="xattn-strict"
                    ).to(device, torch.bfloat16)

    ### load mask
    mask = transforms.Resize((64,64), interpolation=transforms.InterpolationMode.BILINEAR)(mask)
    mask = torchvision.transforms.functional.pil_to_tensor(mask).unsqueeze(0).to(device).bfloat16()[:,0,:,:].unsqueeze(1)
    ### check if an actual mask was draw, otherwise mask is just all ones
    if torch.sum(mask) == 0:
        mask = torch.ones((1,1,64,64)).to(device).bfloat16()
        
    ### single image dataset
    image_transforms = transforms.Compose([transforms.Resize(512, interpolation=transforms.InterpolationMode.BILINEAR),
                                                transforms.RandomCrop(512),
                                                transforms.ToTensor(),
                                                transforms.Normalize([0.5], [0.5])])


    train_dataset = CustomImageDataset(image, transform=image_transforms)
    train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=1, shuffle=True) 

    ### optimizer 
    optim = torch.optim.Adam(network.parameters(), lr=lr, weight_decay=weight_decay)    

    ### training loop
    unet.train()
    for epoch in tqdm.tqdm(range(epochs)):
        for batch in train_dataloader:
            ### prepare inputs
            batch = batch.to(device).bfloat16()
            latents = vae.encode(batch).latent_dist.sample()
            latents = latents*0.18215
            noise = torch.randn_like(latents)
            bsz = latents.shape[0]
         
            timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
            timesteps = timesteps.long()
            noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
            text_input = tokenizer("sks person", padding="max_length", max_length=tokenizer.model_max_length, truncation=True, return_tensors="pt")
            text_embeddings = text_encoder(text_input.input_ids.to(device))[0]

            ### loss + sgd step
            with network:
                model_pred = unet(noisy_latents, timesteps, text_embeddings).sample
                loss = torch.nn.functional.mse_loss(mask*model_pred.float(), mask*noise.float(), reduction="mean")
                optim.zero_grad()
                loss.backward()
                optim.step()

    ### return optimized network
    return network



def run_inversion(dict, pcs, epochs, weight_decay,lr):
    global network
    init_image = dict["image"].convert("RGB").resize((512, 512))
    mask = dict["mask"].convert("RGB").resize((512, 512))
    network = invert([init_image], mask, pcs, epochs, weight_decay,lr)


    #sample an image
    prompt = "sks person"
    negative_prompt = "low quality, blurry, unfinished, nudity"
    seed = 5
    cfg = 3.0
    steps = 50
    image = inference( prompt, negative_prompt, cfg, steps, seed)
    torch.save(network.proj, "model.pt" )
    return image, "model.pt"
    
    

def file_upload(file):
    global unet
    del unet
    global network
    global device

    
    
    proj = torch.load(file.name).to(device)

    #pad to 10000 Principal components to keep everything consistent
    pcs = proj.shape[1]
    padding =  torch.zeros((1,10000-pcs)).to(device)
    proj = torch.cat((proj, padding), 1)

    unet, _, _, _, _ = load_models(device)


    network = LoRAw2w( proj, mean, std, v[:, :pcs], 
                        unet,
                        rank=1,
                        multiplier=1.0,
                        alpha=27.0,
                        train_method="xattn-strict"
                    ).to(device, torch.bfloat16)
        
    
    prompt = "sks person"
    negative_prompt = "low quality, blurry, unfinished, nudity"
    seed = 5
    cfg = 3.0
    steps = 50
    image = inference( prompt, negative_prompt, cfg, steps, seed)
    return image
    
    

    
intro = """
<div style="display: flex;align-items: center;justify-content: center">
    <h1 style="margin-left: 12px;text-align: center;margin-bottom: 7px;display: inline-block">weights2weights</h1>
    <h3 style="display: inline-block;margin-left: 10px;margin-top: 6px;font-weight: 500">Interpreting the Weight Space of Customized Diffusion Models</h3>
</div>
<p style="font-size: 0.95rem;margin: 0rem;line-height: 1.2em;margin-top:1em;display: inline-block">
    <a href="https://snap-research.github.io/weights2weights/" target="_blank">project page</a> | <a href="https://arxiv.org/abs/2406.09413" target="_blank">paper</a>
     | 
    <a href="https://huggingface.co./spaces/Snapchat/w2w-demo?duplicate=true" target="_blank" style="
        display: inline-block;
    ">
    <img style="margin-top: -1em;margin-bottom: 0em;position: absolute;" src="https://bit.ly/3CWLGkA" alt="Duplicate Space"></a>
</p>
"""



with gr.Blocks(css="style.css") as demo:
    gr.HTML(intro)
    
    gr.Markdown("""<div style="text-align: justify;"> In this demo, you can get an identity-encoding model by sampling or inverting. To use a model previously downloaded from this demo see \"Uploading a model\" in the Advanced options. Next, you can generate new samples from it, or edit the identity encoded in the model and generate samples from the edited model. We provide detailed instructions and tips at the bottom of the page.""")
    with gr.Column():
            with gr.Row():
                with gr.Column(): 
                    gr.Markdown("""1) Either sample a new model, or upload an image (optionally draw a mask over the face) and click `invert`. """)
                    input_image = gr.Image(source='upload', elem_id="image_upload", tool='sketch', type='pil', label="Upload image and draw to define mask",
                                           width=512, height=512, brush_color='#00FFFF', mask_opacity=0.4)    
                  
                    with gr.Row():
                        sample = gr.Button("🎲 Sample New Model")
                        invert_button = gr.Button("⬆️ Invert")



                with gr.Column():
                    gr.Markdown("""2) Generate images of the sampled/inverted identity or edit the identity and generate new images. """)
                    gallery = gr.Image(label="Image",height=512, width=512, interactive=False)
                    submit = gr.Button("Generate")

                    
            prompt = gr.Textbox(label="Prompt",
                                            info="Make sure to include 'sks person'" ,
                                            placeholder="sks person", 
                                            value="sks person")
            
            seed = gr.Number(value=5, label="Seed", precision=0, interactive=True)
                
            # Editing 
            with gr.Column():
                with gr.Row():
                    a1 = gr.Slider(label="- Young +", value=0, step=0.001, minimum=-1, maximum=1, interactive=True)
                    a2 = gr.Slider(label="- Pointy Nose +", value=0, step=0.001, minimum=-1, maximum=1, interactive=True)       
                with gr.Row():        
                    a3 = gr.Slider(label="- Curly Hair +", value=0, step=0.001, minimum=-1, maximum=1, interactive=True)
                    a4 = gr.Slider(label="- Thick Eyebrows +", value=0, step=0.001, minimum=-1, maximum=1, interactive=True)

            
            with gr.Accordion("Advanced Options", open=False):
                with gr.Tab("Inversion"):
                    with gr.Row():     
                        lr = gr.Number(value=1e-1, label="Learning Rate", interactive=True)
                        pcs = gr.Slider(label="# Principal Components", value=10000, step=1, minimum=1, maximum=10000, interactive=True)
                    with gr.Row():
                        epochs = gr.Slider(label="Epochs", value=800, step=1, minimum=1, maximum=2000, interactive=True)
                        weight_decay = gr.Number(value=1e-10, label="Weight Decay", interactive=True)
                with gr.Tab("Sampling"):
                    with gr.Row():     
                            cfg= gr.Slider(label="CFG", value=3.0, step=0.1, minimum=0, maximum=10, interactive=True)
                            steps = gr.Slider(label="Inference Steps",  value=50, step=1, minimum=0, maximum=100, interactive=True)
                    with gr.Row():
                            negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="low quality, blurry, unfinished, nudity, weapon", value="low quality, blurry, unfinished, nudity, weapon")
                            injection_step = gr.Slider(label="Injection Step",  value=800, step=1, minimum=0, maximum=1000, interactive=True)
                            
                with gr.Tab("Uploading a model"):
                    gr.Markdown("""<div style="text-align: justify;">Upload a model below downloaded from this demo.""")

                    file_input = gr.File(label="Upload Model", container=True, interactive=False)

     


    gr.Markdown("""<div style="text-align: justify;"> After sampling a new model or inverting, you can download the model below.""")

    with gr.Row():
        file_output = gr.File(label="Download Sampled/Inverted Model", container=True, interactive=False)
        



        invert_button.click(fn=run_inversion,
                    inputs=[input_image, pcs, epochs, weight_decay,lr], 
                    outputs = [gallery, file_output])
        
            
        sample.click(fn=sample_then_run, outputs=[gallery, file_output])

        
        submit.click(
        fn=edit_inference, inputs=[prompt, negative_prompt, cfg, steps, seed, injection_step, a1, a2, a3, a4], outputs=[gallery]
    )
        file_input.change(fn=file_upload, inputs=file_input, outputs = input_image)

demo.queue().launch()