app

birb-style

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+Subproject commit 421b470c1c55d204152be94d590aa4e5a2dcd715

herge-style

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+Subproject commit 6ddab2d3191715a679b9e6bbc38aa05baff1ffa0

indian-watercolor-portraits

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+Subproject commit 89ff0bc8ae9b4cb0731796be374c3f9efe2b54dc

midjourney-style

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+Subproject commit 4a3668469aedc8f883a88d11192156d0374bcce2

sd_utils.py

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+import os
+import numpy
+import torch
+from torch import autocast
+from torchvision import transforms as tfms
+import torch.nn.functional as F
+
+import PIL
+from PIL import Image
+
+from diffusers import StableDiffusionPipeline
+from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer, logging
+from diffusers import AutoencoderKL, LMSDiscreteScheduler, UNet2DConditionModel, KDPM2DiscreteScheduler
+
+# For video display:
+from IPython.display import HTML
+from matplotlib import pyplot as plt
+from pathlib import Path
+from tqdm.auto import tqdm
+import cv2
+
+bb = cv2.imread("./qr_code1.png")
+bb = cv2.cvtColor(bb, cv2.COLOR_BGR2RGB) 
+tfm2 = tfms.Compose([ 
+    tfms.ToTensor(),
+    tfms.Resize([512, 512]),
+    tfms.CenterCrop(512),
+    #tfms.Normalize((0.6813,0.6813, 0.6813), (0.4549, 0.4549, 0.4549))
+]) 
+img2 = tfm2(bb)
+device = "cuda" if torch.cuda.is_available() else "cpu"
+pretrained_model_name_or_path = "CompVis/stable-diffusion-v1-4"
+# Load the autoencoder model which will be used to decode the latents into image space.
+vae = AutoencoderKL.from_pretrained(pretrained_model_name_or_path, 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(pretrained_model_name_or_path, subfolder="unet")
+
+# The noise scheduler
+scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000)
+#scheduler = KDPM2DiscreteScheduler(num_train_timesteps=1000, beta_start=)
+
+# To the GPU we go!
+vae = vae.to(device)
+text_encoder = text_encoder.to(device)
+unet = unet.to(device)
+
+pipe = StableDiffusionPipeline.from_pretrained(pretrained_model_name_or_path,torch_dtype=torch.float16).to(device)
+
+
+
+# birb_embed = pipe.load_textual_inversion("sd-concepts-library/birb-style")
+# herge_embed = pipe.load_textual_inversion("sd-concepts-library/herge-style")
+# indian_water_color_embed = pipe.load_textual_inversion("sd-concepts-library/indian-watercolor-portraits")
+# midjourney_embed = pipe.load_textual_inversion("sd-concepts-library/midjourney-style")
+# marc_allante_embed = pipe.load_textual_inversion("sd-concepts-library/style-of-marc-allante")
+
+birb_embed = torch.load('./birb-style/learned_embeds.bin')
+herge_embed = torch.load('./herge-style/learned_embeds.bin')
+indian_water_color_embed = torch.load('./indian-watercolor-portraits/learned_embeds.bin')
+midjourney_embed = torch.load('./midjourney-style/learned_embeds.bin')
+marc_allante_embed = torch.load('./style-of-marc-allante/learned_embeds.bin')
+
+style_seeds = {
+    'birb': 321,
+    'herge': 1,
+    'indian_watercolor': 42,
+    'midjourney': 8081,
+    'marc_allante': 100
+}
+
+
+
+def qr_loss(images, qr_img):
+
+    #qr_img = 0.5 * qr_img
+    qr_img = qr_img.unsqueeze(0).to(device)
+    #error = F.mse_loss(images, qr_img, reduction='mean')
+    error = F.l1_loss(images, qr_img, reduction='mean')
+    
+    return error
+
+def set_timesteps(scheduler, num_inference_steps):
+    scheduler.set_timesteps(num_inference_steps)
+    scheduler.timesteps = scheduler.timesteps.to(torch.float32) # minor fix to ensure MPS compatibility, fixed in diffusers PR 3925
+
+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 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)
+    causal_attention_mask = 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(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 build_causal_attention_mask(bsz, seq_len, dtype):
+    # lazily create causal attention mask, with full attention between the vision tokens
+    # pytorch uses additive attention mask; fill with -inf
+    mask = torch.empty(bsz, seq_len, seq_len, dtype=dtype)
+    mask.fill_(torch.tensor(torch.finfo(dtype).min))
+    mask.triu_(1)  # zero out the lower diagonal
+    mask = mask.unsqueeze(1)  # expand mask
+    return mask
+
+def generate_with_embs_custom_loss(prompt, text_embeddings, seed):
+    #prompt = "A labrador dog in a car" #@param
+    height = 512                        # default height of Stable Diffusion
+    width = 512                         # default width of Stable Diffusion
+    num_inference_steps = 50  #@param           # Number of denoising steps
+    guidance_scale = 11 #@param               # Scale for classifier-free guidance
+    generator = torch.manual_seed(seed)   # Seed generator to create the inital latent noise
+    batch_size = 1
+    blue_loss_scale = 100 #@param
+
+    # Prep text
+    text_input = tokenizer([prompt], padding="max_length", max_length=tokenizer.model_max_length, truncation=True, return_tensors="pt")
+    with torch.no_grad():
+        text_embeddings = text_encoder(text_input.input_ids.to(device))[0]
+
+    # And the uncond. input as before:
+    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(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(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 CFG guidance
+        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%2 == 0:
+            # Requires grad on the latents
+            latents = latents.detach().requires_grad_()
+
+            # 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 # range (0, 1)
+
+            # Calculate loss
+            #loss = blue_loss(denoised_images) * blue_loss_scale
+            #loss = purple_loss(denoised_images) * blue_loss_scale
+            loss = qr_loss(denoised_images, img2) * blue_loss_scale
+
+            # Occasionally print it out
+            if i%10==0:
+                print(i, 'loss:', loss.item())
+
+            # Get gradient
+            cond_grad = torch.autograd.grad(loss, latents)[0]
+
+            # Modify the latents based on this gradient
+            latents = latents.detach() - cond_grad * sigma**2
+
+        # Now step with scheduler
+        latents = scheduler.step(noise_pred, t, latents).prev_sample
+
+    return latents_to_pil(latents)[0]
+

style-of-marc-allante

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+Subproject commit 6de4ce4972ae09f61d6e4caf377b9ca00898b8b4