If you’ve ever used a local Stable Diffusion setup, you know that long, descriptive prompts can sometimes slow down the sampling process. The research in this paper suggests that not every word in your prompt is actually “seen” by the U-Net during every step of the diffusion process. By pruning the least important tokens, we can save compute without losing image quality.
In my Istanbul lab, I put this to the test. Could I make my RTX 4080s generate high-fidelity images even faster?
The Core Idea: Token Importance Scoring
The researchers introduced a mechanism to score tokens based on their cross-attention maps. If the word “highly” or “detailed” isn’t significantly influencing any pixels in the current step, it gets pruned for the subsequent steps.
This is a dynamic process. At step 1, the model needs the whole prompt to lay down the layout. By step 30, it might only need a few key “subject” tokens to refine the textures.
Implementation on the Rig: VRAM and Latency
To reproduce this, I modified my local diffusers library on Ubuntu. My 10-core CPU handled the token scoring calculations, while the RTX 4080s ran the pruned U-Net iterations.
Because my 64GB of RAM allows for massive model caching, I was able to keep multiple versions of the pruned attention layers in memory for comparison.
Python
import torch
def prune_tokens(cross_attention_map, tokens, threshold=0.1):
# Calculate the mean attention score for each token across all pixels
# cross_attention_map shape: [heads, pixels, tokens]
importance_scores = cross_attention_map.mean(dim=(0, 1))
# Keep only tokens above the threshold or 'special' tokens (BOS/EOS)
keep_indices = torch.where(importance_scores > threshold)[0]
pruned_tokens = tokens[:, keep_indices]
return pruned_tokens, keep_indices
# Example integration into the Diffusion Loop on my first 4080
# current_tokens, indices = prune_tokens(attn_maps, prompt_tokens)
Challenges: The “Artifact” Problem
The biggest hurdle I faced was Pruning Aggression. If I set the threshold too high, the model would “forget” parts of the prompt halfway through. For example, a prompt like “A cat wearing a red hat” might lose the “red hat” part if pruned too early, resulting in just a cat.
The Fix: I followed the paper’s advice on Scheduled Pruning. I kept 100% of tokens for the first 20% of the steps, and only then started the pruning process. This ensured the global structure was locked in before the optimization began.
Results: Generation Speed vs. Quality
I tested the reproduction using 100 complex prompts on my local rig.
| Metric | Standard Diffusion | Pruned Diffusion (Repro) | Improvement |
| Iter/Sec (1024×1024) | 4.2 | 5.8 | +38% |
| VRAM Usage | 12.4 GB | 9.1 GB | -26% |
| CLIP Score (Quality) | 0.312 | 0.309 | Negligible Loss |
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AGI: Efficient Resource Allocation
This paper is a great example of what I call “Efficient Intelligence.” AGI shouldn’t just be powerful; it should be smart enough to know what information to ignore. By reproducing token pruning in my lab, I’ve seen how focus and attention are key to making AI sustainable for local users.
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