DreamStyler: Paint by Style Inversion with Text-to-Image Diffusion Models
Authors: Namhyuk Ahn, Junsoo Lee, Chunggi Lee, Kunhee Kim, Daesik Kim, Seung-Hun Nam, Kibeom Hong
Abstract: Recent progresses in large-scale text-to-image models have yielded remarkable accomplishments, finding various applications in art domain. However, expressing unique characteristics of an artwork (e.g. brushwork, colortone, or composition) with text prompts alone may encounter limitations due to the inherent constraints of verbal description. To this end, we introduce DreamStyler, a novel framework designed for artistic image synthesis, proficient in both text-to-image synthesis and style transfer. DreamStyler optimizes a multi-stage textual embedding with a context-aware text prompt, resulting in prominent image quality. In addition, with content and style guidance, DreamStyler exhibits flexibility to accommodate a range of style references. Experimental results demonstrate its superior performance across multiple scenarios, suggesting its promising potential in artistic product creation.
What, Why and How
Here is a summary of the key points from the paper:
What:
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The paper proposes a new method called DreamStyler for artistic image synthesis. It can perform both text-to-image synthesis and style transfer by incorporating a reference style image.
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The method is based on textual inversion, where a style embedding vector is optimized to capture the style of a reference image.
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It introduces multi-stage textual inversion, which uses multiple style embedding vectors across different stages of the diffusion process. This provides more capacity to capture both global and local style attributes.
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It also uses a context-aware prompt to help disentangle style from contextual elements during training. This results in style embeddings that better isolate stylistic features.
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Style and content guidance terms are used during inference to allow flexible control over the balance of style replication and text/content alignment.
Why:
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Existing text-to-image models struggle to fully capture the intricate artistic styles found in paintings using only text prompts. Style embeddings can more effectively encapsulate artistic attributes.
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Standard textual inversion has limited capacity with a single embedding vector. Multi-stage inversion and context-aware prompts address this.
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Separate guidance terms for style and content allow better control over the image generation process.
How:
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During training, the model optimizes multiple style embedding vectors to represent a reference image, using a prompt that describes the image’s contextual elements.
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At inference time, content and style guidance strengths can be adjusted to control the balance between adhering to the text prompt and replicating the reference style.
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For style transfer, a content image is inverted to a latent code which is then denoised with guidance from the style embeddings and content image features.
In summary, DreamStyler introduces techniques to more effectively encapsulate artistic styles in embeddings for high quality artistic image synthesis controllable via guidance terms. The multi-stage design and context-aware prompts help improve style representation.
Main Contributions
Based on my reading, the main contributions of this paper are:
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Proposing multi-stage textual inversion to expand the capacity for representing artistic styles compared to standard single-token textual inversion.
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Using context-aware prompts during training to help disentangle style and content, resulting in style embeddings that better isolate stylistic features.
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Introducing separate style and content guidance terms during inference for flexible control over adhering to the text prompt versus replicating the reference style.
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Demonstrating high quality text-to-image synthesis and style transfer results by incorporating an artistic style reference image.
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Providing analysis and insights into when the proposed style/content guidance have more significant effects depending on complexity of style patterns and abstractness of content.
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Enabling style mixing by substituting different style tokens at each stage to create customized and novel styles.
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Showing that the proposed approach avoids overfitting issues that can occur with model optimization personalization techniques.
In summary, the main novelties are in the multi-stage design, context-aware prompts, separate guidance terms, and insights provided through analysis. Together these contributions improve the state-of-the-art in artistic image synthesis and style transfer through better style representation and control.
Method Section
Here is a summary of the method section from the paper:
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Multi-Stage Textual Inversion: Extends the textual inversion concept by using multiple style embedding tokens across different stages of the diffusion process. This provides more capacity to represent both global and local style attributes. The diffusion steps are split into T stages, and T style tokens are optimized, with each mapped to a different stage.
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Context-Aware Text Prompt: Uses a prompt during training that includes a description of the contextual elements in the style reference image. This helps disentangle style from content, resulting in style tokens that better isolate stylistic features. The contextual description is generated automatically using BLIP-2 and refined with human feedback.
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Style and Content Guidance: During inference, separate guidance strengths can be set for style (lambda_s) and content (lambda_c) to control adherence to the text prompt vs replication of style. The guidance terms are derived by decomposing the joint distribution into style/content components.
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Style Transfer: Inverts a content image into a latent code which is denoised towards the style domain using the style tokens. Additional conditioning from the content image helps preserve structure. Leverages powerful prior knowledge from text-to-image models.
In summary, the key ideas are:
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Multi-stage textual inversion for increased style representation capacity
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Contextual descriptions in prompts to disentangle style/content
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Separate guidance terms for flexible style/content control
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Image inversion with content conditioning for style transfer
Together these techniques allow high quality artistic image synthesis controllable via the guidance strengths.
High-Level Pseudo Code
Here is the high-level pseudocode for the key components of the paper:
# Multi-Stage Textual Inversion
# Split diffusion steps into T stages
num_stages = T
# Create T copies of style token s* as multi-stage tokens
style_tokens = [s*_1, s*_2, ..., s*_T]
# Optimize stage tokens to represent style image
for token in style_tokens:
optimize(token)
# Map each token to corresponding stage
for t in range(T):
stage_t_token = style_tokens[t]
# Context-Aware Prompt
# Automatically describe context of style image
context_desc = caption_style_image(style_img)
# Refine with human feedback
refined_context_desc = human_refine(context_desc)
# Construct prompt with context description
prompt = ["A painting of", refined_context_desc, "in", s*_style]# Style and Content Guidance
# Embed prompts for style and content
style_emb = embed(style_prompt)
content_emb = embed(content_prompt)
# Get diffusions for different prompts
style_diff = diffuse(style_emb)
content_diff = diffuse(content_emb)
joint_diff = diffuse(joint_emb)
# Compute guidance terms
guidance = null_diffusion
+ lambda_s * (joint_diff - content_diff)
+ lambda_c * (content_diff - null_diff)
+ lambda_s * (style_diff - null_diff)
+ lambda_c * (joint_diff - style_diff)# Style Transfer
# Invert content image to latent code
content_latent = invert(content_img)
# Denoise latent code with style tokens
denoised = denoise(content_latent, style_tokens)
# Additionally condition on content image
denoised = denoise(denoised, encode(content_img)) Detailed Pseudo Code
Here is a more detailed pseudocode outline for implementing the key components of the paper:
# Hyperparameters
num_stages = 6 # T
num_steps = 500 # training steps
lr = 0.002 # learning rate
# Create Style Tokens
style_tokens = []
for i in range(num_stages):
token = create_token("s*" + str(i))
style_tokens.append(token)
# Optimize Style Tokens
for step in range(num_steps):
for token in style_tokens:
# Get embedding for token
emb = text_encoder(token)
# Diffuse with embedding
noise = diffuse(emb)
# Calculate loss
loss = MSE(noise, noise_pred)
# Update token embedding
update(emb, lr, loss)
# Map Tokens to Stages
stage_tokens = {}
for i, token in enumerate(style_tokens):
stage_tokens[i] = token# Context-Aware Prompt
# Auto-generate context description
context_desc = caption(style_img)
# Refine description
print("Please refine this description:")
print(context_desc)
refined_desc = input("Refined description: ")
# Construct prompt
prompt = "A painting of " + refined_desc + " in " + s*_style# Style and Content Guidance
# Get style and content embeddings
style_emb = encode(style_prompt)
content_emb = encode(content_prompt)
# Diffuse with null, style, and content prompts
null_noise = diffuse(null_token)
style_noise = diffuse(style_emb)
content_noise = diffuse(content_emb)
joint_noise = diffuse(joint_emb)
# Compute guidance terms
guidance = null_noise
+ lambda_s * (joint_noise - content_noise)
+ lambda_c * (content_noise - null_noise)
+ lambda_s * (style_noise - null_noise)
+ lambda_c * (joint_noise - style_noise)
# Generate image
img = denoise(guidance) # Style Transfer
# Encode content image
content_enc = encoder(content_img)
# Invert content code to latent
content_lat = invert(content_enc)
# Denoise latent with style tokens
denoised = denoise(content_lat, stage_tokens)
# Get content encoding
content_feat = encode(content_img)
# Denoise again with content encoding
output = denoise(denoised, content_feat)