DAAM-Image2Image is an extension for Diffusion Attentive Attribution Map which captures image self-attention attention whereas originally DAAM captured only text-image cross-attention. A guided self-attention significantly boost the heatmap qualites. Experiments can be found as updates and notebooks in the Text-Based Object Discovery Repo. Below are some comparisons.

• The heatmap comparision of DAAM-I2I to Original DAAM on the left. An immediate benefit of DAAM+DAAM-I2I over DAAM is segmentation and object detection tasks for the TITAN workflow shown on the right (DAAM-I2I results on left and DAAM on the right - detection and segmentation for the object woman)

• In addition to DAAM's Text-Guided Object Detection, DAAM-I2I offers even more capability to glide through the Modelled Vision space for Semantic Image Segmentation (sometimes even better than Segment Anything Model). It is achieved through a graph-inspired pixel attention diffusion (more methods are coming soon). More interesting being the fact that it is completely unsupervised - exploiting zero-shot capability of Diffusion Model. We get segmentation maps for each pixel so only a few are visualized below.

Semantic Segmentation (Only a Few visualized)	Pixel Attention Diffusion

In original DAAM paper, the author proposes diffusion attentive attribution maps (DAAM), a cross attention-based approach for interpreting Stable Diffusion for interpretability of token heatmap over latent images. Here, I use the same approach but extended for latent image self-attention heatmaps.

First, install PyTorch for your platform.

• You may check out the Notebook to get started with the basics.
• If you interested in various merging schemes for generate interesting heatmaps like the one shown above and use them for object detection leveraging TITAN, check out this Colab Tutorial

The following steps are useful for setting up daami2i package in Colab Environment.

!pip install git+https://github.com/RishiDarkDevil/daam-i2i.git

Import and use DAAMI2I as follows:

# Plotting
from matplotlib import pyplot as plt

# Data Handling
import numpy as np

# Image Processing
from PIL import Image

# Image Generation
from diffusers import StableDiffusionPipeline
import daami2i

DEVICE = 'cuda' # device

model = StableDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base')
model = model.to(DEVICE) # Set it to something else if needed, make sure DAAM supports that

prompt = 'Dinner table with chairs and flower pot'

# Image generation
with daami2i.trace(model) as trc:
  output_image = model(prompt).images
  global_heat_map = trc.compute_global_heat_map()
  
display(output_image[0]) # Output image

There types of heatmaps available are:

• Pixel-based: The pixels are numbered in row-major order i.e.

  • 0 1 .. 63
    4032 4033 .. 4095
    Only latent image height and width is valid (i.e. 64 x 64 for Stable Diffusion v2 base) so the pixels that can be mentioned is a list (i.e. from 0 ... 4095 for SDV2).

  # Example
  # Compute heatmap for latent pixel lists row-major. It will return the heatmap for pixels [0,1,..,1023]
  pixel_heatmap = global_heat_map.compute_pixel_heat_map(list(range(1024))).expand_as(output_image[0]).numpy()
  
  # Casting heatmap from 0-1 floating range to 0-255 unsigned 8 bit integer
  heatmap = np.array(pixel_heatmap * 255, dtype = np.uint8)
  
  plt.imshow(heatmap)

• BBox-based: The bounding box's upper left and bottom right corner needs to be specified. Again latent height and width are valid ranges (i.e. for SDV2 the top-left corner of the bbox cannot be more lefter or higher than 0,0 and the bottom-right corner of the bbox cannot be more righter or downer than 63,63).

  # Example
  # Compute heatmap for latent bbox pixels with corners specified
  pixel_heatmap = global_heat_map.compute_bbox_heat_map(0,10,25,64).expand_as(output_image[0]).numpy()
  
  # Casting heatmap from 0-1 floating range to 0-255 unsigned 8 bit integer
  heatmap = np.array(pixel_heatmap * 255, dtype = np.uint8)
  
  plt.imshow(heatmap)

• Contour-based:. The image height and width can be different from the latent height and width. Enter contour and attention map will be generated for that contour containing pixels i.e. if you have a contour for the generated image (say 512 x 512) then the contour lines will be on this bigger image and not on the latent image. While calling make sure to pass the image size as shown below.

  # Example
  # Compute heatmap for inner pixels for contour boundary specified
  pixel_heatmap = global_heat_map.compute_contour_heat_map([[0,300], [256, 100], [512, 300], [512, 400], [0, 400], [0, 300]], 512, 512).expand_as(output_image[0]).numpy()
  
  # Casting heatmap from 0-1 floating range to 0-255 unsigned 8 bit integer
  heatmap = np.array(pixel_heatmap * 255, dtype = np.uint8)
  
  plt.imshow(heatmap)

• Multi-Contour based: To handle merging pixel heatmaps for multiple contours. It is an extension for the compute_contour_heat_map which can handle only single contour. Function: compute_segmentation_heat_map

• Guided Heatmap-based: It merges all the pixel heatmaps based on some weighing scheme provided by another heatmap called the guide_heatmap. The guide_heatmap should be of the dimension of the latent image. Function: compute_guided_heat_map

• Pixel-Diffused Heatmap-based: It starts with the Pixel Heatmap of a given pixel and then iteratively reweighs all the other pixels based on this heatmap for given number of iteration. It enhances the parts which a pixel focuses the most. Currently it only has thresholding method to remove the noisy or unwanted (low-scored pixel heatmaps which if not removed will amplify giving worse diffused heatmap). Function: compute_pixel_diffused_heatmap.

• Diffused Heatmap-based: Parameters include method, n_iter and thresh. The diffused heatmap for all the pixels are calculated at once (faster than compute_pixel_diffsed_heatmap). Function: compute_diffused_heatmap

• track_all argument added to trace function, which not only tracks the attention heatmap for the attention layers of the size of the latent image but also throughout the UNet. This makes it possible to visualize the attention of each pixel at a better semantic level.

DAAM-I2I: Add Link to this project as citation in your work.

• Original DAAM