Update app.py

testing interm yielding

app.py

@@ -5,10 +5,317 @@ from io import BytesIO
 from diffusers import StableDiffusionPipeline
 from diffusers import DDIMScheduler
 from utils import *
-from inversion_utils import *
+# from inversion_utils import *
 from torch import autocast, inference_mode
 import re
 
+
+############################################################################################################################################################################
+import torch
+import os
+from tqdm import tqdm
+from PIL import Image, ImageDraw ,ImageFont
+from matplotlib import pyplot as plt
+import torchvision.transforms as T
+import os
+import yaml
+import numpy as np
+import gradio as gr
+
+
+def load_512(image_path, left=0, right=0, top=0, bottom=0, device=None):
+    if type(image_path) is str:
+        image = np.array(Image.open(image_path).convert('RGB'))[:, :, :3]
+    else:
+        image = image_path
+    h, w, c = image.shape
+    left = min(left, w-1)
+    right = min(right, w - left - 1)
+    top = min(top, h - left - 1)
+    bottom = min(bottom, h - top - 1)
+    image = image[top:h-bottom, left:w-right]
+    h, w, c = image.shape
+    if h < w:
+        offset = (w - h) // 2
+        image = image[:, offset:offset + h]
+    elif w < h:
+        offset = (h - w) // 2
+        image = image[offset:offset + w]
+    image = np.array(Image.fromarray(image).resize((512, 512)))
+    image = torch.from_numpy(image).float() / 127.5 - 1
+    image = image.permute(2, 0, 1).unsqueeze(0).to(device)
+
+    return image 
+
+
+def load_real_image(folder = "data/", img_name = None, idx = 0, img_size=512, device='cuda'):
+    from PIL import Image
+    from glob import glob
+    if img_name is not None:
+        path = os.path.join(folder, img_name)
+    else:
+        path = glob(folder + "*")[idx]
+
+    img = Image.open(path).resize((img_size,
+                                    img_size))
+
+    img = pil_to_tensor(img).to(device)
+
+    if img.shape[1]== 4:
+        img = img[:,:3,:,:]
+    return img
+
+def mu_tilde(model, xt,x0, timestep):
+    "mu_tilde(x_t, x_0) DDPM paper eq. 7"
+    prev_timestep = timestep - model.scheduler.config.num_train_timesteps // model.scheduler.num_inference_steps
+    alpha_prod_t_prev = model.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else model.scheduler.final_alpha_cumprod
+    alpha_t = model.scheduler.alphas[timestep]
+    beta_t = 1 - alpha_t 
+    alpha_bar = model.scheduler.alphas_cumprod[timestep]
+    return ((alpha_prod_t_prev ** 0.5 * beta_t) / (1-alpha_bar)) * x0 +  ((alpha_t**0.5 *(1-alpha_prod_t_prev)) / (1- alpha_bar))*xt
+
+def sample_xts_from_x0(model, x0, num_inference_steps=50):
+    """
+    Samples from P(x_1:T|x_0)
+    """
+    # torch.manual_seed(43256465436)
+    alpha_bar = model.scheduler.alphas_cumprod
+    sqrt_one_minus_alpha_bar = (1-alpha_bar) ** 0.5
+    alphas = model.scheduler.alphas
+    betas = 1 - alphas
+    variance_noise_shape = (
+            num_inference_steps,
+            model.unet.in_channels, 
+            model.unet.sample_size,
+            model.unet.sample_size)
+    
+    timesteps = model.scheduler.timesteps.to(model.device)
+    t_to_idx = {int(v):k for k,v in enumerate(timesteps)}
+    xts = torch.zeros(variance_noise_shape).to(x0.device)
+    for t in reversed(timesteps):
+        idx = t_to_idx[int(t)]
+        xts[idx] = x0 * (alpha_bar[t] ** 0.5) + torch.randn_like(x0) * sqrt_one_minus_alpha_bar[t]
+    xts = torch.cat([xts, x0 ],dim = 0)
+
+    return xts
+
+def encode_text(model, prompts):
+    text_input = model.tokenizer(
+        prompts,
+        padding="max_length",
+        max_length=model.tokenizer.model_max_length, 
+        truncation=True,
+        return_tensors="pt",
+    )
+    with torch.no_grad():
+        text_encoding = model.text_encoder(text_input.input_ids.to(model.device))[0]
+    return text_encoding
+
+def forward_step(model, model_output, timestep, sample):
+    next_timestep = min(model.scheduler.config.num_train_timesteps - 2,
+                        timestep + model.scheduler.config.num_train_timesteps // model.scheduler.num_inference_steps)
+
+    # 2. compute alphas, betas
+    alpha_prod_t = model.scheduler.alphas_cumprod[timestep]
+    # alpha_prod_t_next = self.scheduler.alphas_cumprod[next_timestep] if next_ltimestep >= 0 else self.scheduler.final_alpha_cumprod
+
+    beta_prod_t = 1 - alpha_prod_t
+
+    # 3. compute predicted original sample from predicted noise also called
+    # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
+    pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
+
+    # 5. TODO: simple noising implementatiom
+    next_sample = model.scheduler.add_noise(pred_original_sample,
+                                    model_output,
+                                    torch.LongTensor([next_timestep]))
+    return next_sample
+
+
+def get_variance(model, timestep): #, prev_timestep):
+    prev_timestep = timestep - model.scheduler.config.num_train_timesteps // model.scheduler.num_inference_steps
+    alpha_prod_t = model.scheduler.alphas_cumprod[timestep]
+    alpha_prod_t_prev = model.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else model.scheduler.final_alpha_cumprod
+    beta_prod_t = 1 - alpha_prod_t
+    beta_prod_t_prev = 1 - alpha_prod_t_prev
+    variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
+    return variance
+
+def inversion_forward_process(model, x0, 
+                            etas = None,    
+                            prog_bar = False,
+                            prompt = "",
+                            cfg_scale = 3.5,
+                            num_inference_steps=50, eps = None
+                             ):
+
+    if not prompt=="":
+        text_embeddings = encode_text(model, prompt)
+    uncond_embedding = encode_text(model, "")
+    timesteps = model.scheduler.timesteps.to(model.device)
+    variance_noise_shape = (
+        num_inference_steps,
+        model.unet.in_channels, 
+        model.unet.sample_size,
+        model.unet.sample_size)
+    if etas is None or (type(etas) in [int, float] and etas == 0):
+        eta_is_zero = True
+        zs = None
+    else:
+        eta_is_zero = False
+        if type(etas) in [int, float]: etas = [etas]*model.scheduler.num_inference_steps
+        xts = sample_xts_from_x0(model, x0, num_inference_steps=num_inference_steps)
+        alpha_bar = model.scheduler.alphas_cumprod
+        zs = torch.zeros(size=variance_noise_shape, device=model.device)
+        
+    t_to_idx = {int(v):k for k,v in enumerate(timesteps)}
+    xt = x0
+    op = tqdm(reversed(timesteps)) if prog_bar else reversed(timesteps)
+
+    for t in op:
+        idx = t_to_idx[int(t)]
+        # 1. predict noise residual
+        if not eta_is_zero:
+            xt = xts[idx][None]
+                    
+        with torch.no_grad():
+            out = model.unet.forward(xt, timestep =  t, encoder_hidden_states = uncond_embedding)
+            if not prompt=="":
+                cond_out = model.unet.forward(xt, timestep=t, encoder_hidden_states = text_embeddings)
+
+        if not prompt=="":
+            ## classifier free guidance
+            noise_pred = out.sample + cfg_scale * (cond_out.sample - out.sample)
+        else:
+            noise_pred = out.sample
+
+        if eta_is_zero:
+            # 2. compute more noisy image and set x_t -> x_t+1
+            xt = forward_step(model, noise_pred, t, xt)
+
+        else: 
+            xtm1 =  xts[idx+1][None]
+            # pred of x0
+            pred_original_sample = (xt - (1-alpha_bar[t])  ** 0.5 * noise_pred ) / alpha_bar[t] ** 0.5
+            
+            # direction to xt
+            prev_timestep = t - model.scheduler.config.num_train_timesteps // model.scheduler.num_inference_steps
+            alpha_prod_t_prev = model.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else model.scheduler.final_alpha_cumprod
+            
+            variance = get_variance(model, t)
+            pred_sample_direction = (1 - alpha_prod_t_prev - etas[idx] * variance ) ** (0.5) * noise_pred
+
+            mu_xt = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction
+            
+            z = (xtm1 - mu_xt ) / ( etas[idx] * variance ** 0.5 )
+            zs[idx] = z
+
+            # correction to avoid error accumulation
+            xtm1 = mu_xt + ( etas[idx] * variance ** 0.5 )*z
+            xts[idx+1] = xtm1
+
+    if not zs is None: 
+        zs[-1] = torch.zeros_like(zs[-1]) 
+
+    return xt, zs, xts
+
+
+def reverse_step(model, model_output, timestep, sample, eta = 0, variance_noise=None):
+    # 1. get previous step value (=t-1)
+    prev_timestep = timestep - model.scheduler.config.num_train_timesteps // model.scheduler.num_inference_steps
+    # 2. compute alphas, betas
+    alpha_prod_t = model.scheduler.alphas_cumprod[timestep]
+    alpha_prod_t_prev = model.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else model.scheduler.final_alpha_cumprod
+    beta_prod_t = 1 - alpha_prod_t
+    # 3. compute predicted original sample from predicted noise also called
+    # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
+    pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
+    # 5. compute variance: "sigma_t(η)" -> see formula (16)
+    # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)    
+    # variance = self.scheduler._get_variance(timestep, prev_timestep)
+    variance = get_variance(model, timestep) #, prev_timestep)
+    std_dev_t = eta * variance ** (0.5)
+    # Take care of asymetric reverse process (asyrp)
+    model_output_direction = model_output
+    # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
+    # pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * model_output_direction
+    pred_sample_direction = (1 - alpha_prod_t_prev - eta * variance) ** (0.5) * model_output_direction
+    # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
+    prev_sample = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction
+    # 8. Add noice if eta > 0
+    if eta > 0:
+        if variance_noise is None:
+            variance_noise = torch.randn(model_output.shape, device=model.device)
+        sigma_z =  eta * variance ** (0.5) * variance_noise
+        prev_sample = prev_sample + sigma_z
+
+    return prev_sample
+
+def inversion_reverse_process(model,
+                    xT, 
+                    etas = 0,
+                    prompts = "",
+                    cfg_scales = None,
+                    prog_bar = False,
+                    zs = None,
+                    controller=None,
+                    asyrp = False
+                    ):
+
+    batch_size = len(prompts)
+
+    cfg_scales_tensor = torch.Tensor(cfg_scales).view(-1,1,1,1).to(model.device)
+
+    text_embeddings = encode_text(model, prompts)
+    uncond_embedding = encode_text(model, [""] * batch_size)
+
+    if etas is None: etas = 0
+    if type(etas) in [int, float]: etas = [etas]*model.scheduler.num_inference_steps
+    assert len(etas) == model.scheduler.num_inference_steps
+    timesteps = model.scheduler.timesteps.to(model.device)
+
+    xt = xT.expand(batch_size, -1, -1, -1)
+    op = tqdm(timesteps[-zs.shape[0]:]) if prog_bar else timesteps[-zs.shape[0]:] 
+
+    t_to_idx = {int(v):k for k,v in enumerate(timesteps[-zs.shape[0]:])}
+
+    for t in op:
+        idx = t_to_idx[int(t)]        
+        ## Unconditional embedding
+        with torch.no_grad():
+            uncond_out = model.unet.forward(xt, timestep =  t, 
+                                            encoder_hidden_states = uncond_embedding)
+
+            ## Conditional embedding  
+        if prompts:  
+            with torch.no_grad():
+                cond_out = model.unet.forward(xt, timestep =  t, 
+                                                encoder_hidden_states = text_embeddings)
+            
+        
+        z = zs[idx] if not zs is None else None
+        z = z.expand(batch_size, -1, -1, -1)
+        if prompts:
+            ## classifier free guidance
+            noise_pred = uncond_out.sample + cfg_scales_tensor * (cond_out.sample - uncond_out.sample)
+        else: 
+            noise_pred = uncond_out.sample
+        # 2. compute less noisy image and set x_t -> x_t-1  
+        xt = reverse_step(model, noise_pred, t, xt, eta = etas[idx], variance_noise = z)
+
+        # interm denoised img
+            with autocast("cuda"), inference_mode():
+                x0_dec = sd_pipe.vae.decode(1 / 0.18215 * xt).sample
+                if x0_dec.dim()<4:
+                    x0_dec = x0_dec[None,:,:,:]
+                interm_img = image_grid(x0_dec)
+                yield interm_img
+            
+        if controller is not None:
+            xt = controller.step_callback(xt)        
+    return xt, zs
+############################################################################################################################################################################
+
 def invert(x0, prompt_src="", num_diffusion_steps=100, cfg_scale_src = 3.5, eta = 1):
 
   #  inverts a real image according to Algorihm 1 in https://arxiv.org/pdf/2304.06140.pdf, 
@@ -34,7 +341,7 @@ def invert(x0, prompt_src="", num_diffusion_steps=100, cfg_scale_src = 3.5, eta
 def sample(wt, zs, wts, prompt_tar="", cfg_scale_tar=15, skip=36, eta = 1):
 
     # reverse process (via Zs and wT)
-    w0, _ = inversion_reverse_process(sd_pipe, xT=wts[skip], etas=eta, prompts=[prompt_tar], cfg_scales=[cfg_scale_tar], prog_bar=True, zs=zs[skip:])
+    w0, _ = inversion_reverse_process(sd_pipe, xT=wts[skip], etas=eta, prompts=[prompt_tar], cfg_scales=[cfg_scale_tar], prog_bar=False, zs=zs[skip:])
     
     # vae decode image
     with autocast("cuda"), inference_mode():
@@ -91,9 +398,76 @@ def edit(input_image,
     wt, zs, wts = invert(x0 =x0 , prompt_src=src_prompt, num_diffusion_steps=steps, cfg_scale_src=src_cfg_scale)
 
     #
-    output = sample(wt, zs, wts, prompt_tar=tar_prompt)
+    xT=wts[skip]
+    etas=eta
+    prompts=[prompt_tar]
+    cfg_scales=[cfg_scale_tar]
+    prog_bar=False
+    zs=zs[skip:]
+
+
+    batch_size = len(prompts)
+
+    cfg_scales_tensor = torch.Tensor(cfg_scales).view(-1,1,1,1).to(model.device)
+
+    text_embeddings = encode_text(model, prompts)
+    uncond_embedding = encode_text(model, [""] * batch_size)
+
+    if etas is None: etas = 0
+    if type(etas) in [int, float]: etas = [etas]*model.scheduler.num_inference_steps
+    assert len(etas) == model.scheduler.num_inference_steps
+    timesteps = model.scheduler.timesteps.to(model.device)
+
+    xt = xT.expand(batch_size, -1, -1, -1)
+    op = tqdm(timesteps[-zs.shape[0]:]) if prog_bar else timesteps[-zs.shape[0]:] 
+
+    t_to_idx = {int(v):k for k,v in enumerate(timesteps[-zs.shape[0]:])}
+
+    for t in op:
+        idx = t_to_idx[int(t)]        
+        ## Unconditional embedding
+        with torch.no_grad():
+            uncond_out = model.unet.forward(xt, timestep =  t, 
+                                            encoder_hidden_states = uncond_embedding)
+
+            ## Conditional embedding  
+        if prompts:  
+            with torch.no_grad():
+                cond_out = model.unet.forward(xt, timestep =  t, 
+                                                encoder_hidden_states = text_embeddings)
+            
+        
+        z = zs[idx] if not zs is None else None
+        z = z.expand(batch_size, -1, -1, -1)
+        if prompts:
+            ## classifier free guidance
+            noise_pred = uncond_out.sample + cfg_scales_tensor * (cond_out.sample - uncond_out.sample)
+        else: 
+            noise_pred = uncond_out.sample
+        # 2. compute less noisy image and set x_t -> x_t-1  
+        xt = reverse_step(model, noise_pred, t, xt, eta = etas[idx], variance_noise = z)
+
+        # interm denoised img
+        with autocast("cuda"), inference_mode():
+            x0_dec = sd_pipe.vae.decode(1 / 0.18215 * xt).sample
+            if x0_dec.dim()<4:
+                x0_dec = x0_dec[None,:,:,:]
+                interm_img = image_grid(x0_dec)
+                yield interm_img
+      
+    return interm_img
+    
+    # # vae decode image
+    # with autocast("cuda"), inference_mode():
+    #     x0_dec = sd_pipe.vae.decode(1 / 0.18215 * w0).sample
+    # if x0_dec.dim()<4:
+    #     x0_dec = x0_dec[None,:,:,:]
+    # img = image_grid(x0_dec)
+    # return img
+
+    # output = sample(wt, zs, wts, prompt_tar=tar_prompt)
 
-    return output
+    # return output