Update app.py

app.py

@@ -1,37 +1,197 @@
+from base64 import b64encode
+
+import numpy
 import torch
-import numpy as np
-from torchvision.transforms.functional import to_tensor
+from diffusers import AutoencoderKL, LMSDiscreteScheduler, UNet2DConditionModel
+from huggingface_hub import notebook_login
+
+# For video display:
+from matplotlib import pyplot as plt
+from pathlib import Path
 from PIL import Image
+from torch import autocast
+from torchvision import transforms as tfms
+from tqdm.auto import tqdm
+from transformers import CLIPTextModel, CLIPTokenizer, logging
+import os
+import numpy as np  
 
-def blue_loss(images):
-    """
-    Custom loss function to penalize or encourage the presence of blue hues in the images.
-    """
-    # Convert images to tensors
-    images_tensor = torch.tensor(images).float() / 255.0
-    
-    # Extract the blue channel (last channel in RGB)
-    blue_channel = images_tensor[:, :, :, 2]
-    
-    # Calculate variance of the blue channel
-    variance = torch.var(blue_channel)
-    
-    # Return negative variance as the loss (penalize less blue)
+torch.manual_seed(1)
+# if not (Path.home()/'.cache/huggingface'/'token').exists(): notebook_login()
+
+# Supress some unnecessary warnings when loading the CLIPTextModel
+logging.set_verbosity_error()
+
+# Set device
+torch_device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu" 
+
+# Load the autoencoder model which will be used to decode the latents into image space.
+vae = AutoencoderKL.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="vae")
+
+# Load the tokenizer and text encoder to tokenize and encode the text.
+tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
+text_encoder = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14")
+
+# The UNet model for generating the latents.
+unet = UNet2DConditionModel.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="unet")
+
+# The noise scheduler
+scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000)
+
+# To the GPU we go!
+vae = vae.to(torch_device)
+text_encoder = text_encoder.to(torch_device)
+unet = unet.to(torch_device)
+token_emb_layer = text_encoder.text_model.embeddings.token_embedding
+pos_emb_layer = text_encoder.text_model.embeddings.position_embedding
+
+position_ids = text_encoder.text_model.embeddings.position_ids[:, :77]
+position_embeddings = pos_emb_layer(position_ids)
+
+
+def get_output_embeds(input_embeddings):
+    # CLIP's text model uses causal mask, so we prepare it here:
+    bsz, seq_len = input_embeddings.shape[:2]
+    causal_attention_mask = text_encoder.text_model._build_causal_attention_mask(bsz, seq_len, dtype=input_embeddings.dtype)
+
+    # Getting the output embeddings involves calling the model with passing output_hidden_states=True
+    # so that it doesn't just return the pooled final predictions:
+    encoder_outputs = text_encoder.text_model.encoder(
+        inputs_embeds=input_embeddings,
+        attention_mask=None, # We aren't using an attention mask so that can be None
+        causal_attention_mask=causal_attention_mask.to(torch_device),
+        output_attentions=None,
+        output_hidden_states=True, # We want the output embs not the final output
+        return_dict=None,
+    )
+
+    # We're interested in the output hidden state only
+    output = encoder_outputs[0]
+
+    # There is a final layer norm we need to pass these through
+    output = text_encoder.text_model.final_layer_norm(output)
+
+    # And now they're ready!
+    return output
+
+
+def set_timesteps(scheduler, num_inference_steps):
+    scheduler.set_timesteps(num_inference_steps)
+    scheduler.timesteps = scheduler.timesteps.to(torch.float32)
+
+def pil_to_latent(input_im):
+    # Single image -> single latent in a batch (so size 1, 4, 64, 64)
+    with torch.no_grad():
+        latent = vae.encode(tfms.ToTensor()(input_im).unsqueeze(0).to(torch_device)*2-1) # Note scaling
+    return 0.18215 * latent.latent_dist.sample()
+
+def latents_to_pil(latents):
+    # bath of latents -> list of images
+    latents = (1 / 0.18215) * latents
+    with torch.no_grad():
+        image = vae.decode(latents).sample
+    image = (image / 2 + 0.5).clamp(0, 1)
+    image = image.detach().cpu().permute(0, 2, 3, 1).numpy()
+    images = (image * 255).round().astype("uint8")
+    pil_images = [Image.fromarray(image) for image in images]
+    return pil_images
+
+
+def generate_with_embs(text_embeddings, text_input, seed):
+
+    height = 512                        # default height of Stable Diffusion
+    width = 512                         # default width of Stable Diffusion
+    num_inference_steps = 10            # Number of denoising steps
+    guidance_scale = 7.5                # Scale for classifier-free guidance
+    generator = torch.manual_seed(seed)   # Seed generator to create the inital latent noise
+    batch_size = 1
+
+    max_length = text_input.input_ids.shape[-1]
+    uncond_input = tokenizer(
+      [""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
+    )
+    with torch.no_grad():
+        uncond_embeddings = text_encoder(uncond_input.input_ids.to(torch_device))[0]
+    text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+
+    # Prep Scheduler
+    set_timesteps(scheduler, num_inference_steps)
+
+    # Prep latents
+    latents = torch.randn(
+    (batch_size, unet.in_channels, height // 8, width // 8),
+    generator=generator,
+    )
+    latents = latents.to(torch_device)
+    latents = latents * scheduler.init_noise_sigma
+
+    # Loop
+    for i, t in tqdm(enumerate(scheduler.timesteps), total=len(scheduler.timesteps)):
+        # expand the latents if we are doing classifier-free guidance to avoid doing two forward passes.
+        latent_model_input = torch.cat([latents] * 2)
+        sigma = scheduler.sigmas[i]
+        latent_model_input = scheduler.scale_model_input(latent_model_input, t)
+
+        # predict the noise residual
+        with torch.no_grad():
+            noise_pred = unet(latent_model_input, t, encoder_hidden_states=text_embeddings)["sample"]
+
+        # perform guidance
+        noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+        noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+        # compute the previous noisy sample x_t -> x_t-1
+        latents = scheduler.step(noise_pred, t, latents).prev_sample
+
+    return latents_to_pil(latents)[0]
+
+
+def generate_with_prompt_style(prompt, style, seed = 42):
+
+    prompt = prompt + ' in style of s'
+    embed = torch.load(style)
+
+    text_input = tokenizer(prompt, padding="max_length", max_length=tokenizer.model_max_length, truncation=True, return_tensors="pt")
+    # for t in text_input['input_ids'][0][:20]: # We'll just look at the first 7 to save you from a wall of '<|endoftext|>'
+    #     print(t, tokenizer.decoder.get(int(t)))
+    input_ids = text_input.input_ids.to(torch_device)
+
+    token_embeddings = token_emb_layer(input_ids)
+    # The new embedding - our special birb word
+    replacement_token_embedding = embed[list(embed.keys())[0]].to(torch_device)
+
+    # Insert this into the token embeddings
+    token_embeddings[0, torch.where(input_ids[0]==338)] = replacement_token_embedding.to(torch_device)
+
+    # Combine with pos embs
+    input_embeddings = token_embeddings + position_embeddings
+
+    #  Feed through to get final output embs
+    modified_output_embeddings = get_output_embeds(input_embeddings)
+
+    # And generate an image with this:
+    return generate_with_embs(modified_output_embeddings, text_input, seed)
+
+
+import torch
+
+def contrast_loss(images):
+    variance = torch.var(images)
     return -variance
 
 def generate_with_prompt_style_guidance(prompt, style, seed=42):
+
     prompt = prompt + ' in style of s'
     
     embed = torch.load(style)
 
-    height = 512
-    width = 512
-    num_inference_steps = 10
-    guidance_scale = 8
-    generator = torch.manual_seed(seed)
+    height = 512                        # default height of Stable Diffusion
+    width = 512                         # default width of Stable Diffusion
+    num_inference_steps = 10  #           # Number of denoising steps
+    guidance_scale = 8 #               # Scale for classifier-free guidance
+    generator = torch.manual_seed(seed)   # Seed generator to create the inital latent noise
     batch_size = 1
-    contrast_loss_scale = 200
-    blue_loss_scale = 100  # Scale for blue loss
+    contrast_loss_scale = 200 #
 
     # Prep text
     text_input = tokenizer([prompt], padding="max_length", max_length=tokenizer.model_max_length, truncation=True, return_tensors="pt")
@@ -47,12 +207,12 @@ def generate_with_prompt_style_guidance(prompt, style, seed=42):
     replacement_token_embedding = embed[list(embed.keys())[0]].to(torch_device)
 
     # Insert this into the token embeddings
-    token_embeddings[0, torch.where(input_ids[0] == 338)] = replacement_token_embedding.to(torch_device)
+    token_embeddings[0, torch.where(input_ids[0]==338)] = replacement_token_embedding.to(torch_device)
 
     # Combine with pos embs
     input_embeddings = token_embeddings + position_embeddings
 
-    # Feed through to get final output embs
+    #  Feed through to get final output embs
     modified_output_embeddings = get_output_embeds(input_embeddings)
 
     # And the uncond. input as before:
@@ -70,47 +230,48 @@ def generate_with_prompt_style_guidance(prompt, style, seed=42):
 
     # Prep latents
     latents = torch.randn(
-        (batch_size, unet.config.in_channels, height // 8, width // 8),
-        generator=generator,
+    (batch_size, unet.config.in_channels, height // 8, width // 8),
+    generator=generator,
     )
     latents = latents.to(torch_device)
     latents = latents * scheduler.init_noise_sigma
 
     # Loop
     for i, t in tqdm(enumerate(scheduler.timesteps), total=len(scheduler.timesteps)):
-        # Expand the latents if we are doing classifier-free guidance to avoid doing two forward passes.
+        # expand the latents if we are doing classifier-free guidance to avoid doing two forward passes.
         latent_model_input = torch.cat([latents] * 2)
         sigma = scheduler.sigmas[i]
         latent_model_input = scheduler.scale_model_input(latent_model_input, t)
 
-        # Predict the noise residual
+        # predict the noise residual
         with torch.no_grad():
             noise_pred = unet(latent_model_input, t, encoder_hidden_states=text_embeddings)["sample"]
 
-        # Perform CFG
+        # perform CFG
         noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
         noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
 
-        # Additional Guidance
-        if i % 5 == 0:
+        #### ADDITIONAL GUIDANCE ###
+        if i%5 == 0:
             # Requires grad on the latents
             latents = latents.detach().requires_grad_()
 
-            # Get the predicted x0
+            # Get the predicted x0:
             latents_x0 = latents - sigma * noise_pred
+            # latents_x0 = scheduler.step(noise_pred, t, latents).pred_original_sample
 
             # Decode to image space
-            denoised_images = vae.decode((1 / 0.18215) * latents_x0).sample / 2 + 0.5
+            denoised_images = vae.decode((1 / 0.18215) * latents_x0).sample / 2 + 0.5 # range (0, 1)
 
-            # Calculate losses
-            contrast_loss_val = contrast_loss(denoised_images) * contrast_loss_scale
-            blue_loss_val = blue_loss(denoised_images) * blue_loss_scale
+            # Calculate loss
+            loss = contrast_loss(denoised_images) * contrast_loss_scale
 
-            # Combine losses
-            total_loss = contrast_loss_val + blue_loss_val
+            # # Occasionally print it out
+            # if i%10==0:
+            #     print(i, 'loss:', loss.item())
 
             # Get gradient
-            cond_grad = torch.autograd.grad(total_loss, latents)[0]
+            cond_grad = torch.autograd.grad(loss, latents)[0]
 
             # Modify the latents based on this gradient
             latents = latents.detach() - cond_grad * sigma**2
@@ -118,8 +279,10 @@ def generate_with_prompt_style_guidance(prompt, style, seed=42):
         # Now step with scheduler
         latents = scheduler.step(noise_pred, t, latents).prev_sample
 
+
     return latents_to_pil(latents)[0]
 
+
 import gradio as gr
 
 dict_styles = { 
@@ -128,8 +291,10 @@ dict_styles = {
     'Manga':'styles/learned_embeds_manga.bin', 
     'Pokemon':'styles/learned_embeds_pokemon.bin', 
 }
+# dict_styles.keys()
 
 def inference(prompt, style): 
+    
     if prompt is not None and style is not None: 
         style = dict_styles[style]
         result = generate_with_prompt_style_guidance(prompt, style)
@@ -139,13 +304,19 @@ def inference(prompt, style):
 
 title = "Stable Diffusion and Textual Inversion"
 description = "A simple Gradio interface to stylize Stable Diffusion outputs"
-examples = [['A man sipping wine wearing a spacesuit on the moon']]
+examples = [['A man sipping wine wearing a spacesuit on the moon', 'Stripes']]
 
 demo = gr.Interface(inference,
-                    inputs=[gr.Textbox(label='Prompt'), 
-                            gr.Dropdown(['Dr Strange', 'GTA-5', 'Manga', 'Pokemon'], label='Style')],
-                    outputs=[gr.Image(label="Stable Diffusion Output")],
-                    title=title,
-                    description=description,
-                    examples=examples)
-demo.launch()
+                    inputs = [gr.Textbox(label='Prompt'), 
+                              gr.Dropdown(['Dr Strange', 'GTA-5', 
+                                           'Manga', 'Pokemon'], label='Style')
+                              ],
+                    outputs = [
+                              gr.Image(label="Stable Diffusion Output"),
+                              ],
+                    title = title,
+                    description = description,
+                    # examples = examples, 
+                    # cache_examples=True
+                    )
+demo.launch()