SUR-adapter: Enhancing Text-to-Image Pre-trained Diffusion Models with Large Language Models
Authors: Shanshan Zhong, Zhongzhan Huang, Wushao Wen, Jinghui Qin, Liang Lin
Abstract: Diffusion models, which have emerged to become popular text-to-image generation models, can produce high-quality and content-rich images guided by textual prompts. However, there are limitations to semantic understanding and commonsense reasoning in existing models when the input prompts are concise narrative, resulting in low-quality image generation. To improve the capacities for narrative prompts, we propose a simple-yet-effective parameter-efficient fine-tuning approach called the Semantic Understanding and Reasoning adapter (SUR-adapter) for pre-trained diffusion models. To reach this goal, we first collect and annotate a new dataset SURD which consists of more than 57,000 semantically corrected multi-modal samples. Each sample contains a simple narrative prompt, a complex keyword-based prompt, and a high-quality image. Then, we align the semantic representation of narrative prompts to the complex prompts and transfer knowledge of large language models (LLMs) to our SUR-adapter via knowledge distillation so that it can acquire the powerful semantic understanding and reasoning capabilities to build a high-quality textual semantic representation for text-to-image generation. We conduct experiments by integrating multiple LLMs and popular pre-trained diffusion models to show the effectiveness of our approach in enabling diffusion models to understand and reason concise natural language without image quality degradation. Our approach can make text-to-image diffusion models easier to use with better user experience, which demonstrates our approach has the potential for further advancing the development of user-friendly text-to-image generation models by bridging the semantic gap between simple narrative prompts and complex keyword-based prompts. The code is released at SUR-adapter.
What, Why and How
Here is a summary of the key points from this paper:
What This paper proposes a new method called SUR-adapter to improve the semantic understanding and reasoning (SUR) capabilities of pretrained text-to-image diffusion models when using simple narrative prompts as input.
Why Existing diffusion models rely on complex keyword prompts to generate high-quality and semantically accurate images. However, when using simple narrative prompts, these models often fail to understand the semantics and generate low-quality images. This is because the text encoder of diffusion models lacks sufficient SUR abilities for concise natural language prompts.
How
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The authors collect a new dataset called SURD with 57k image-text pairs, each containing a simple prompt, complex prompt and image.
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They propose the SUR-adapter module to transfer SUR knowledge from large language models (LLMs) to diffusion models via distillation.
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The SUR-adapter aligns simple and complex prompt representations and maintains generation quality.
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Experiments on SURD show SUR-adapter enhances semantic accuracy of diffusion models for simple prompts without compromising image quality.
In summary, this paper enables diffusion models to generate high-quality and semantically accurate images from simple narrative prompts by improving their SUR capabilities using the proposed SUR-adapter and SURD dataset.
Main Contributions
Based on my understanding, the main contributions of this paper are:
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The authors collected and annotated a new dataset called SURD with over 57,000 semantically corrected image-text pairs. Each pair contains a simple narrative prompt, complex keyword prompt, and high-quality image.
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They proposed a simple yet effective fine-tuning approach called SUR-adapter to transfer semantic understanding and reasoning abilities from large language models to pretrained diffusion models.
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The SUR-adapter aligns the semantic representation between simple and complex prompts and maintains the image generation quality of diffusion models.
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Extensive experiments on SURD demonstrate that the proposed SUR-adapter can enhance the semantic accuracy of diffusion models for simple narrative prompts without compromising image quality.
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This work enables diffusion models to generate high-quality and semantically coherent images from simple natural language prompts, improving their ease of use.
In summary, the key contributions are:
- Construction of the SURD dataset
- Proposal of the SUR-adapter module
- Transferring SUR abilities of LLMs to diffusion models
- Improving semantic accuracy for simple prompts
- Maintaining image generation quality
- Enhancing user-friendliness of diffusion models
Method Section
Here is a summary of the method section from the paper:
The authors propose a fine-tuning approach called SUR-adapter to transfer semantic understanding and reasoning (SUR) abilities from large language models (LLMs) to pretrained diffusion models.
The SUR-adapter module contains two learnable components:
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A fully-connected network (FCN)
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An adapter module
The adapter module has three transformations - two for constructing query and key vectors for attention, and one for calibrating the text features.
During training, the SUR-adapter leverages the SURD dataset which contains simple prompts, complex prompts, and images.
It employs the following techniques:
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Knowledge distillation from LLMs: The adapter distills SUR knowledge from LLMs into the query vectors through KL divergence loss. This embeds LLM abilities into the adapter.
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Performance maintenance of diffusion models: Noise is added to images from SURD and denoising loss is minimized to maintain generation quality.
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Representation alignment: KL divergence loss between simple and complex prompts is minimized to align their representations.
The overall loss function is a weighted sum of the above losses. After training, the fine-tuned diffusion model with SUR-adapter can generate images from simple prompts.
In summary, the SUR-adapter leverages knowledge distillation and representation alignment on the SURD dataset to improve SUR abilities of diffusion models while maintaining image quality.
High-Level Pseudo Code
Here is the high-level pseudo code for the method proposed in this paper:
# Input:
# SURD dataset (simple prompts, complex prompts, images)
# Pretrained diffusion model
# Pretrained LLM
# Initialize SUR-adapter module
adapter = SURAdapter()
# Load pretrained diffusion model
diffusion_model = DiffusionModel()
# Freeze diffusion model parameters
diffusion_model.freeze_parameters()
for batch in SURD_dataloader:
simple_prompts, complex_prompts, images = batch
# Get LLM embeddings for simple prompts
llm_embeddings = LLM(simple_prompts)
# Get text encoder embeddings
text_embeddings = diffusion_model.text_encoder(simple_prompts)
# Knowledge distillation loss
llm_distill_loss = KL_divergence(llm_embeddings,
adapter(text_embeddings))
# Add noise to images
noisy_images = add_noise(images)
# Denoising loss
denoising_loss = diffusion_model(noisy_images, adapter(text_embeddings))
# Representation alignment loss
align_loss = KL_divergence(diffusion_model.text_encoder(complex_prompts),
adapter(text_embeddings))
# Total loss
loss = llm_distill_loss + denoising_loss + align_loss
# Update SUR-adapter parameters
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Finetuned diffusion model
finetuned_model = diffusion_model + adapter
The key steps are using the adapter to distill LLM knowledge, maintain denoising performance, and align representations between simple and complex prompts. The trained adapter enhances the semantic abilities of the diffusion model.
Detailed Pseudo Code
Here is a more detailed pseudo code to implement the method proposed in this paper:
# SURD dataset
surd_dataset = SURDDataset(simple_prompts, complex_prompts, images)
# DataLoader
surd_dataloader = DataLoader(surd_dataset, batch_size=16)
# Pretrained diffusion model
diffusion_model = DiffusionModel.from_pretrained(model_name)
diffusion_model.freeze_parameters()
text_encoder = diffusion_model.text_encoder
# Pretrained LLM
llm = LLAMA.from_pretrained(llama_model_name)
# SUR-adapter
class SURAdapter(nn.Module):
def __init__(self):
super().__init__()
# Learnable transformations
self.h1 = nn.Linear(...)
self.h2 = nn.Linear(...)
self.h3 = nn.Linear(...)
self.g = nn.Linear(...)
def forward(self, text_embeddings):
# Attention
q = self.h3(text_embeddings)
k = self.h2(text_embeddings)
v = text_embeddings
attn = softmax(q @ k.T / sqrt(d))
# Calibrate
calibrated = v * attn
# Output
output = self.g(calibrated + v + self.h1(calibrated + v))
return output
adapter = SURAdapter()
# Optimizer
optimizer = torch.optim.Adam(adapter.parameters(), lr=1e-5)
# Training
for x, y, z in surd_dataloader:
# x: simple prompts
# y: complex prompts
# z: images
# Get LLM embeddings
llm_emb = llm(x)
# Get text encoder embeddings
text_emb = text_encoder(x)
# Forward pass
adapter_emb = adapter(text_emb)
# Losses
llm_loss = KL_div(llm_emb, adapter.h3(text_emb))
denoise_loss = diffusion_model(add_noise(z), adapter_emb)
align_loss = KL_div(text_encoder(y), adapter_emb)
# Total loss
loss = w1*llm_loss + w2*denoise_loss + w3*align_loss
# Update
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Finetuned model
finetuned_model = diffusion_model + adapter
The key aspects include:
- SURD dataset and dataloader
- Pretrained diffusion model and text encoder
- Pretrained LLM
- SUR-adapter module with transformations
- Loss functions and training loop
- Getting finetuned diffusion model
This shows how to implement the proposed method in a PyTorch-style code.