Würstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models
Authors: Pablo Pernias
Dominic Rampas
Mats Leon Richter
Christopher Pal
Marc Aubreville
Venue: ICLR 2024 oral (2024)
Keywords: Latent Diffusion Model
Text-to-Image
Neural Architectures
Foundation Models
Primary area: generative models
TL;DR: We propose an efficient text-to-image model that only requires 1/8th of Stable Diffusion 2.1's compute budget for training and has comparable, if not better image quality with less than half the inference time.

Abstract: We introduce Würstchen, a novel architecture for text-to-image synthesis that combines competitive performance with unprecedented cost-effectiveness for large-scale text-to-image diffusion models.
A key contribution of our work is to develop a latent diffusion technique in which we learn a detailed but extremely compact semantic image representation used to guide the diffusion process. This highly compressed representation of an image provides much more detailed guidance compared to latent representations of language and this significantly reduces the computational requirements to achieve state-of-the-art results. Our approach also improves the quality of text-conditioned image generation based on our user preference study.
The training requirements of our approach consists of 24,602 A100-GPU hours - compared to Stable Diffusion 2.1's 200,000 GPU hours.  
Our approach also requires less training data to achieve these results. Furthermore, our compact latent representations allows us to perform inference over twice as fast, slashing the usual costs and carbon footprint of a state-of-the-art (SOTA) diffusion model significantly, without compromising the end performance. In a broader comparison against SOTA models our approach is substantially more efficient and compares favourably in terms of image quality.
We believe that this work motivates more emphasis on the prioritization of both performance and computational accessibility.

Published as a conference paper at ICLR 2024
WÜRSTCHEN:
AN EFFICIENT ARCHITECTURE FOR LARGE-SCALE TEXT-TO-IMAGE DIFFUSION MODELS
Pablo Pernías*
Dominic Rampas"
Mats L. Richter"
LAION e.V.
Technische Hochschule Ingolstadt Wand Technologies Inc., LAION V.
Mila, Quebec Al Institute
Christopher J. Pal
Mare Aubreville
Polytechnique Montréal, Canada CIFAR Al Chair
Technische Hochschule Ingolstadt
ABSTRACT
We introduce Würstchen, a novel architecture for text-to- ext-to-image synthesis that combines competitive performance with unprecedented eesttfffectiveeess for large- scale texl-to-image diffusion models, key contribution of our work is to develop latent diffusion technique in which we learn a detailed but extremely compact semantic image representation used to guide the diffusion process. This highly compressed representation of image provides much more detailed guidance compared to latent representations of language and this significantly reduces the computational requirements achieve state-of-the-art results. Our approach also improves the quality of text-conditioned image generation based on our user preference study. The training requirements of our approach consists of 24,602 A A100-GPU hours compared to Stable Diffusion 2.1's 200,000 GPU hours. Our approach also requires less training data to achieve these results. Furthermore, our compact latent representations allows us to perform inference over twice as fast, slashing the usual costs and carbon footprint of state-of-the-art (SOTA) diffusion model significantly, without compromising the end performance. In a broader comparison against SOTA models our approach substantially more efficient and compares favourably in terms of image quality. We believe that this work motivates more emphasis on the prioritization of both performance and computational accessibility.
ronman business man, realiiie, dramiti.
Painting of an Gogh.
GoPro young looking friendly look.
art the beach.
Mlach, standing horway, closeup.
psychedetc mood, monning light.claar extremely decalled.
planet the with advanced seeing supemova explosion accessories.
star.
Figure I: Text-conditional generations using Würstchen. Note the various art styles and aspect ratios.
INTRODUCTION
State-of-the-as diffusion models (oo et al. 2020: Saharia et al., 2022 Ramesh et al. 2022) have
advanced the field of image synthesis considerably, achieving remarkable results that closely approxi-
equal contribution
corresponding author

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Stage C
Stage
Stage
Model
f
Decoder Ai (14)
text tondi-ondi
Model do
16:4624
Figure 2: Inference architecture for text-conditional image generation.
mate photorealism. However, these foundation models, while impressive in their capabilities, carry a significant drawback: they are computationally demanding. For instance, Stable Diffusion (SD) 1.4, one of the most notable models in the field, used 150,000 GPU hours for training (Rombach &
Esser 2022). While more economical text-to-image models do exist (Ding et al.) [021] |022] Tao
al. 2023 2022), the image quality of these models can be considered inferior terms of lower resolution and overall aesthetic features.
The core dilemma for this discrepancy is that increasing the resolution also increases visual complexity and computational cost, making image synthesis more expensive and data-intensive to train. Encoder- based Latent Diffusion Models (LDMs) partially address this by operating on compressed latent space instead of directly on the pixel-space (Rombach et al. 2022). but are ultimately limited by how much the mncoder-decoder model can compress the image without degradation (Richter et al. 2021a).
Against this backdrop, we propose a novel three- architecture named "Würstchen", which drastically reduces the computational demands while maintaining competitive performance. We achieve this by training a diffusion model on very low dimensional latent space with high compression ratio of 42:1. This very low dimensional latent-space is used to condition the second generative latent model, effectively helping it to navigate higher dimensional latent space of Vector-quantized Generative Adversarial Network (VQGAN), which operates at a compression ratio of 4:1. More concretely, the approach uses three distinct stages for image synthesis (see Figure 2 initially, a text-conditional LDM is used to create a low dimensional latent representation of the image (Stage C). This latent representation is used to condition another LDM (Stage B), producing a latent image in a latent space of higher dimensionality. Finally, the latent image is decoded by a VQGAN-decoder to yield the full-resolution output image (Stage A).
Training is performed in reverse order to the inference (Figure 3): The initial training is carried out on Stage A and employs VOGAN to create a latent space. This compact representation facilitates learning and inference speed (Rombach et al., 2022 Chang et al. 2023; Rampas et al., 2023). The
next phase (Stage B) involves a first latent diffusion process (Rombach al. (022), conditioned on the outputs of Semantic Compressor (an encoder operating at very high spatial compression rate) and on text embeddings. This diffusion process is tasked to reconstruct the latent space established by the training of Stage A, which is strongly guided by the detailed semantic information provided by the Semantic Compressor. Finally, for the construction of Stage C. the strongly compressed latents of the Semantic Compressor from Stage B are used to project images into the condensed latent space where a text-conditional LDM (Rombach et al., 2022 is trained. The significant reduction in space dimensions in Stage C allows for more efficient training and inference of the diffusion model, considerably reducing both the computational resources required and the time taken for the process.
Our proposed Würstchen model thus introduces thoughtfully designed approach to address the high computational burden of current state-of-the-art models, providing a significant leap forward in text-to-image synthesis. With this approach we are able to train 1B parameter Stage C text- conditional diffusion model within approximately 24,602 GPU hours, resembling 8x reduction in computation compared to the amount SD 2.1 used for training (200,000 GPU hours), while showing similar fidelity both visually and numerically. Throughout this paper, we provide comprehensive evaluation of Würstchen's 's efficacy, demonstrating its potential democratize the deployment training of high-quality image synthesis models.
Our main contributions are the following:
1. We propose a novel three-stage architecture for text-to-image synthesis at strong compression ratio, consisting of two conditional latent diffusion stages and a latent image decoder.
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2. We show that by using text-conditional diffusion model in strongly compressed latent space we can achieve state-of- state-of-the-an cost and inference speed.
f-the-arr art model performance at significantly reduced training
3. We provide comprehensive experimental validation of the model's efficacy based on auto- mated metrics and human feedback.
4. We are publicly releasing the source code and the entire suite of model weights.
RELATED WORK
2.1 CONDITIONAL IMAGE GENERATION
The field of image generation guided by text prompts has undergone significant progression in recent years. Initial approaches predominantly leveraged Generative Adversarial Networks (GANs) (Reed et al. 2016 Zhang et al. 2017). More recently, however, paradigm shift in the field of image generation towards diffusion models (Sohl-Dickstein et al., 2013 Ho et al., 2020) has occurred. These approaches, in some cases, have not only met but even exceeded the performance of GANs in both conditional and unconditional image generation (hhriwwl & Nichol (02T). Diffusion models put forth score-based scheme that gradually eliminates perturbations (e.g., noise) from target image, with the training objective framed as a reweighted variational lower-bound. Next to diffusion models, another dominant choice for training text-to-image models is transformers. In their early stages, transformer-buse based models utilized an autoregressive approach, leading to a significant slowdown in inference due to the requirement for each token to be sampled individually. Current strategies, however, employ a bidirectional transformer (Ding et al. 2022 Chang et al.) 2022, 2023) to address the challenges that traditional autoregressive models present. As a result, image generation can be executed using fewer steps, while also benefiting from a global context during the generative phase. Other recent work has shown that convolution-ban approaches for image generation can yield similar results (Rampas et al. 2023
2.2 COMPRESSED LATENT SPACES
The majority of approaches in the visual modality of generative models use some way to train at smaller space, followed by upscaling to high resolutions, as training at large pixel resolutions can become exponentially more expensive with the size of images. For text-conditional image generation, there are two established categories of approaches: encoder-based and upsampler-
LDMs
(Rombach et al. 2022). DALL-E (Ramesh et al. 2021], View (Ding et al., 2021] (022). MUSE (Chang et al. 2023) belong to the first category and employ a two-stage training process. Initially, an autoencoder (Rumelhart et al. 1985) is trained to provide lower-dimensional, yet perceptually equivalent, representation of the data. This representation forms the basis for the subsequent training of diffusion transformer model. Eventually, generated latent representations can be decoded with the decoder branch of the autoencoder to the pixel space. The result is a significant reduction in computational complexity for the diffusionnssmpllng process and efficient image decoding from the latent space using single network pass. On the contrary, upsampler- -baed methods generate images at low resolution in the pixel space and subsequent models for upscaling the images to higher resolution. UnClip (Ramesh et al.) 2022). Matryoshka Gu et al. (2023) and Imagen (Saharia al. 2022) both generate images at 64x64 and upscale using two models 256 and 1024 pixels. The former model is the largest in terms of parameter count, while the latter models are smaller due to working higher resolution and only being responsible for upscaling.
2.3 CONDITIONAL GUIDANCE
The conditional guidance of models in text-based scenarios is typically facilitated through the encoding of textual prompts via pretrained language model. Two major categories text encoders employed: contrastive text encoders and uni-modal text encoders. Contrastive Language- Pretraining (CLIP) (Radford et al. (02T) is a representative of the contrastive multimodal models that strives to align text descriptions and images bearing semantic resemblance within common latent space. A host of image generation methodologies have adopted frozen CLIP model as their exclusive conditioning method in recent literature. The hierarchical DALL-E by Ramesh al.
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specifically harnesses CLIP image embeddings input for their diffusion model, while prior performs the conversion of CLIP text embeddings to image embeddings. SD (Rombach et al. 2022). the other hand, makes of un un-pooled CLIP text embeddings condition its LDM. In contrast, the works of Sahaaiaa et al. 2022). ). Liu et al. (2022a) and Chang et al. (2023) leverage a large, uni-modal language model such T5 (Raftel al. 2020) ByT5 (Xue al. 2022/ that can
2022
encode textual prompts with notable accuracy, leading to image generations of superior precision in terms of composition, style, and layout.
METHOD
Our method comprises three stages, all implemented as deep neural networks. For image generation, we first generate a latent image at a strong compression ratio using a texl-conditional LDM (Stage C). Subsequently, this representation is transformed to less-compressed latent space by the means of secondary model which is tasked for this reconstruction (Stage B). Finally, the tokens that comprise the latent image in this intermediate resolution
decoded to yield the output image (Stage A). The training of this architecture is performed in reverse order, starting with Stage A, then following up with Stage B and finally Stage C (see Figure 3). Text conditioning applied on Stage C using CLIP-H (llharoo et al. 202T) Details on the training procedure can be found in Appendix F
Training of Stage A: Image reconstructio with VQGAN
Encoder Ta (14)
Decader (
Treining of Stage Latent Image decoder
for
Model '
Iatent noliing
Play
text-condntoning
Training of Stage C:
Text canditioonll Image generation
LEGGRO4
cond-cnnditionngg
Model in
Figure 3: Training objectives of our model. Initially, a VQGAN is trained. Secondly, Stage B is trained diffusion model inside Stage A's latent space. Stage B is conditioned on tebt-embeddingg and the output of the Semantic Compressor, which produces strongly downsampled latent representations of the same image. Finally, Stage C is trained on the latents of the Semantic Compressor as text-conditional LDM, effectively operating on compression ratio of 42 1.
3.1 STAGE A AND B
a known and well-studied technique reduce the computational burden by compressing data into smaller representation this paradigm, too, and makes use of Stages & B to achieve a notably higher compression than usual. Let /! x W x C' be the dimensions of images. A spatial compression maps images to latent representation with a resolution of x w z with compression rate. Common approaches for modeling image synthesis between f4 and f16 (Esser et al., 202T Chang et al., 2023: Rombach et al. 2022), J. with higher factors usually resulting worse reconstructions. Our Stage A consists of /4 VQGAN (Esser al.) 2021)
et al. 2021al a b. Chang et al. 2022). Our approach follows
H/S. w = W/f. where f defines the
one-stage compression
with parameters θ and initially encodes images X E I 3x121xxx00 into 256 x 256 discrete tokens from learned codebook of size 8.192.
X, x,= fo(X)
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The network is trained described by Esser et al. and tries to reconstruct the image based on the quantized latents, so that:
(ffe (X)) fō (X₂) X
where f resembles the decoder part of the VQGAN.
Afterward, the quantization is dropped from Stage A, and Stage B is trained in the unquantized latent space of the Stage A-encoder as conditioned LDM. In Stage B, we utilize Semantic Compressor, i.e., an encoder-type network that is tasked create latent representations at strong spatial compression rate that can be used to create a latent representation to guide the diffusion process. The unquantized image embeddings are noised following an LDM training procedure. The noised representation X,, together with the visual embeddings from the Semantic Compressor, C.c. text conditioning Ctext and the timestep are given to the model.
The highly compressed visual embeddings extracted by the Semantic Compressor will act as an interface for Stage C, which will be trained to generate them. The embeddings will have a shape of 2280x22222 obtained by encoding images with shape X E 786 We simple bicubic interpolation for the resizing of the images from 1024 x 1024 to 786 x 786, which is a sufficiently high resolution to fully utilize the parameters of the Semantic Compressor (Richter al. 2023 Richter & Pal 2022). while also reducing the latent representation size. Moreover, we further compress the latents with a X 1 convolution that normalizes and projects the embeddings to C.c E 116x24x44
This compressed representation of the images is given to the Stage B decoder as conditioning to guide the decoding process.
X fX( Cec, Ctext, t)
By conditioning Stage B on low-dimensional latent representations, we can effectively decode images from a 16x22 latent space to a resolution of X E g3 1xx1122x1022 resulting in a total spatial compression of 42:1.
We initialized the Semantic Compressor with weights pre-trained on ImageNet, which, however, does capture the broad distribution of images present in large text-image datasets and is not well-suited for semantic image projection, since it was trained with an objective to discriminate the ImageNet categories. Hence updated the weights of the Semantic Compressor during training, establishing latent space with high-precision semantic information. We use Cross-Attention (Vaswani et al. 2017) for conditioning and project Csc (flattened) to the same dimension in each block of the model and concatenate them. Furthermore. during training Stage B, we intermittently add noise to the Semantic Compressor's s embeddings, to teach the model to understand non-perfect embeddings, which likely to be the case when generating these embeddings with Stage C. Lastly, we also randomly drop C.c to be able to sample with classifier-fi cetteer-rregguddeee grrednguidnnee (Ho & Salimans
2021 during sampling.
3.2 STAGE
After Stage A and Stage B were trained, training of the text-conditional last stage started. In implementation, Stage C consists 16 ConyNeXt-block (Liu et al. (022b) without downsampling. text and time step conditionings diffusion process, applied in the latent space of the finetuned Semantic Compressor. Images are encoded into their latent representation Xsc Csc. representing the target. The latents noised by using the following forward diffusion formula:
applied after each block via cross- cross-attention. We follow standard
X
X
Von- X V1 04
where represents noise from a zero mean unit variance normal distribution. We use cosine schedule Nichool & Dhariwal 2021) to generate a, and use continuous timesteps. The diffusion model takes in the noised embeddings X the text conditioning Ctext and the timestep The model returns the prediction for the noise in the following form:
X
A
E
B +
with
A, B So (x, Crexs, 1)
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We decided to formulate the objective such, since it made the training this occurs because the model parameters are initialized to predict at the beginning, enlarging the difference to timesteps with a lot of noise. By reformulating to the A & B objective, the model initially returns the input, making the loss small for very noised inputs. We use the standard mean- squared-error loss between the predicted noise and the ground truth noise. Additionally, employ the p2 loss weighting (Choi et al. 2022):
stable. We hypothesize
P2(t) 1| c
where (t) is defined as I+8," making higher noise levels contribute more to the loss. Text conditioning Crext are dropped randomly for 5% of the time and replaced with null-label in order use classffier-fre--uuidaae (Ho & Salimans 202T
1-6,,
3.3 IMAGE GENERATION (SAMPLING)
A depiction of the sampling pipeline can be seen in Figure Sampling starts at Stage C, which is primarily responsible for image-synthesis (see Appendix from initial random noise X (0.. I). We use the DDPM (Ho et al. 2020 algorithm to sample the Semantic Compressor latents conditioned on text-embeddings. To do so, we run the following operation for TO steps:
1 04-1 1- at 2) (1 a1)
X sc,-11
(x sc,
Vat
V1 ai
We denote the outcome as X5c which is of shape 16 x 24 x 24. This output is flattened to shape of 576 x and given as conditioning, along with the same text embeddings used sample X to Stage B. This stage operates at 4 x 256 x 256 unquantized VQGAN latent space. We initialize X g. to random tokens drawn from the VQGAN codebook. We sample X for steps using the standard I.DM scheme.
X,-1 1-1=fo(X, fo(X,, Cscy Croxt, t))
Finally X is projected back to the pixel space using the decoder fōt of the VQGAN (Stage A):
X= = x=fo (x)
3.4 MODEL DECISIONS
Theoretically, any feature extractor could be used as backbone for the Semantic Compressor. However, we hypothesize that it is beneficial to backbone that already has good feature representation of a wide variety of images. Furthermore, having a small Semantic Compressor makes training of Stage B & C faster. Finally, the feature dimension is vital. If it is excessively small, it may fail to capture sufficient image details or will underutilize parameters (Richter Pal 2022); conversely, it is overly large, it may unnecessarily increase computational requirements and extend training duration (Richter et al. 202 For this reason, we decided to use an ImageNet 1 pre-trained Efficient (S) as the backbone for our Semantic Compressor, as it combines high compression with well generalizing feature representations and computational efficiency.
Furthermore, deviate in Stage C from the U-Net standard architecture. As the image is already compressed by a factor of 42, and we find further compression harmful to the model quality. Instead, the model is a simple sequence of 16 ConvNeXt blocks (Liu et al. 2022b) without downsampling. Time and text conditioning is applied after each block.
4 EXPERIMENTS AND EVALUATION
To demonstrate Würstchen's s capabilities on text-to-image generation, we trained an 18M parameter Stage A, 1B parameter Stage B and a 1B parameter Stage C. We employed an EfficientNer2-Smen as Semantic Compressor (Tan & Le 2019) during training. Stage B and C are conditioned on un-pooled

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CLIP-H (Thaacoo et al. p021p text-embeddings.. The setup is designed to produce images of variable aspect ratio with up to 1538 pixels per side. All stages were trained on aggressively filtered (approx. 103M images) subsets of the umproved-aesthetii LAION-5B (Schubmann et al. 2022 dataset.
All the experiments use the standard DDPM (Ho ct al. 2020 algorithm to sample latents in Stage B and C. Both stages also make of classifier- freeggraarree (Ho & Salimans 2021 with guidance
scale w. We fix the hyperparameters for Stage B sampling to тB = 12 and w 4, Stage C uses
TC = 60 for sampling. Images are generated using a 1024 x 1024 resolution.
To better assess the efficacy of our architecture, we additionally train a U-Net-based 1B parameter LDM on SD 2.1 first stage and text-conditioning model. We refer to this model as Baseline LDM, it is trained for x 25,000 GPU-hours (same as Stage C) using an 512 x 512 input resolution.
Baselines
Additionally, we evaluate our model against various ofate-of-the-art models that were publicly available at the time of writing (see Tables [ and Table 2). All these models were used in their respective default configuration for text-to-image synthesis. Whenever possible, the evaluation metrics published by the original authors were used.
Evaluation Metrics We used the Fréchet Inception Distance (FID) (Heusel et al. 2017) and
Inception Score (IS) evaluate all our models on COCO-30K, similar to (Tao al. 2023 Ding
et al. 202 2022). For evaluating the FID score, all images were downsampled to 256 x 256 pixels
to allow for a fair comparison between other models in the literature. However, both metrics suffer from inconsistencies and are known to be not necessarily well correlated with the aesthetic quality perceived by humans (Podell et al. (2024); Ding et al. 2021 2022), , see also Appendix C For this reason, we chose PickScore (Kirstain et al. 2024) our primary automated metric. PickScore is designed to imitate human preferences, when selecting from set of images given the same prompt. We applied PickScore to compare Würstchen to various other models on various datasets. We provide the percentage of images, where PickScore preferred the image of Würstchen over the image of the other model. To also evaluate the environmental impact of our model estimated the carbon emitted during training based on the work of (Lacoste et al. 2019).
Finally, we also conducted a study with human participants, where the participants chose between two images from two different models given the prompt.
Datasets To assess the zero-shot textato- capabilities of our model, we use three distinct sets of captions alongside their corresponding images. The COCO-validation is the de-facto standard dataset evaluate the zero-shot performance for text-to-image models. For MS COCO we generate 30,000 images based on prompts randomly chosen from the validation set. We refer to this set of images as COCO30K. Since the prompts of MS COCO are quite short and frequently lack detail, we also generate 5,000 images from the Localized Narrative MS COCO subset, we refer to his dataset as Localized Narratives- Narratives-CCC--SK COCO-5K. Finally, we also use Parti-prompti (Yu el al., 2022), a highly diverse set of 1633 captions, which closely reflects the usage scenario we intend for our model.
4.1 AUTOMATED TEXT-TO-IMAS IMAGE EVALUATION
We evaluate the quality of the generated images using automated metrics in comparison to other, publicly available models (see Appendix D for random examples). The PickScores in Table [ paint a consistent picture over the three datasets the models were evaluated on. Würstchen is preferred very significantly over smaller models like DF-GAN and GALIP, which is expected. The LDM is outperformed dramatically in all cases, highlighting that the architecture had a significant impact on the model's computational training efficiency. Würstchen is also preferred in all three scenarios over SD 1.4 and 2.1, despite their significantly higher compute-budge at similar model- capacity. While SD XL is still superior in image quality, our inference speed is significantly faster (see Figure 4. This comparison is not entirely fair, as it's higher capacity model and its data and compute budget is unknown. For this reason, we are omitting SD XL from the following experiments.
While we achieve a higher Inception Score (IS) COCO30K compared to all other models in our broader comparison in Table 2 also shows a relatively high FID on the same dataset. While still outperforming larger models like Cog View2 (Ding et al., 2022 and our Baseline LDM, the FID is substantially lower compared to other state-of-the-art models. We attribute this discrepancy to
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Interence time breakdoww
300 marovere Wi
Stage &
Mtakee 2
sups
Sue
Batch
Figure 4: Inference time for 1024 x 1024 images on A100-GPUs for Würstchen and three competitive approaches, all evaluations without specific optimization (torch. compile ()). Right side shows breakdown of Würstchen according to the three stages.
Table Image quality evaluation on MS-COCO and Localized Narratives (Pont- et al. 2020
using the PickScore (Kirstain et al. 2024) to binary select images generated from the same captions by two different models (score für Würstchen shown). Würstchen outperforms all models of equal and smaller size, despite Stable Diffusion models using significantly higher compute budget.
PickScore for Würstchen against Competing Model
Model
Baseline LDM (ours)
DF-GAN
GALIP
SD 1.4
SD 2.1
SD XI.
Dataset
(=25,000 gpu-h)
(150.000 gpu-h)
(200,000 gpu-h)
COCO-30k
96.5%
99.8%
98.1%
78.1% %
64.4%
39.4%
Localized Narratives
96.6%
98.0%
95.5%
79.9%
70.0%
39.1%
COCO-5K
Parti-prompts
98.6%
99.6%
97.9%
82.1 %
74.6% %
39.0%
high- tigh-frequency features in the images. During visual inspections we find that images generates by Würstchen tend smoother than in other text-to-image models. This difference is most noticeable in real-world images like COCO, on which we compute the FID-metric.
4.2 HUMAN PREFERENCE EVALUATION
While most metrics evaluated in the previous section correlated with human preference (Kirstain et al. 2024 Heusel et al.) 2017; Salimans et al., 2016), we follow the example of other works human preference. To simplify the evaluation, we solely
and also conducted two brief studies compared Würstchen against SD 2.1, its closest capacity and performance competitor, and evaluated the human preference between these two models following the setup of Kirstain et al. (024). In total, we conducted two studies using the generated images from Parti-prompts and COCO30K images. Participants were presented randomly chosen pairs of images randomized order. For each pair the participants selected preference for one or neither of the images (see Appendix for details). In total, 3343 (Parti-pro parti-prompts) and 2262 (COCO Captions) comparisons by 90 participants were made.
We evaluate results in two distinct ways. First, by counting the total number of preferences indepen- dent of usse-identity. In Figure 5 (a) we can see that images generated by our model on Parti-prompts were clearly preferred. This is important to us, since Parti-prompt closely reflects the intended use case of the model. However, for MS-COCO this statistic inconclusive. We hypothesize that this
Table 2: Comparison other architectures. computed from own evaluation. f based official model cards (Rombach & Esser 2022 Rombach et al.
2023)
Param
Pamms (gssandd
Nampling Stops
RIB
CLP COCOSOK
Open Sourze
CIU Hoon
Tramn Sumples
'02y om. [s CO. eq.
(osal)
42202
92007
A100
Make
4B
1024
11.34
20.7
1024
7.25
CugVee
4B
1024
27.1
22.4
6B
24.0
25.2
I9M
100
19.3
19,3
13.6
GALIIP
340M
199
12.5
27.0"
96.9"
DAIl-11
M0a]
12B
356
17.80
13.0
LDM
Koo 40364
145B
500M
250
12.63
30.3
Basalize 100 (003)
1.30
0390
10
43.7
25.17
20.1
2157007
$2.X0
Winechm (ours)
Stage
0.998
1B
30
14A02
1,4211
2,276
1.088
Combishrr1i 2044
XL
2009
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300
100
beags femm furti prompb
Preferred Participans
krages from furk prompts
# Comparisors [Part-grompss
254+
200
% Preference
250
200-
pretasnos MARIOc
Comparisons
200
prstoures stolbu
150
300
10
20
Würstchen Stable, Both Equal Diffusion 2.1
Würstchen Stable Both Equal Diffusion 2.1
Individuals
Individuals
(a) Overall Preference
(b) Individual Preference
(c) Histogram (MS COCO) (d) Histogram (Parti)
Figure 5: Overall human preferences (left) and by users (middle). The preference by users considered only users with large number of comparisons (right).
due to the vague prompts generating more diverse set of images, making the preference more subject to personal taste, biasing this statistics towards users that completed more comparisons (Figure 5 (c. d)). For this reason, we conducted a second analysis, where we evaluated the personally preferred model for each individual. In an effort to only include participants that completed a representative number of comparisons, we only include users from the upper 50th percentile and above. By doing so, we include only individuals with at least 30 (MS-COCO) and 51 (Parti-prompee comparisons in the statistie. Under these circumstances, we observed light preference for MS-COCO in favor of Würstchen and strong overall preference for our model on Parti- (Figure 7 (b)), which we further break down in section of the appendix, showing that Würstchen has an edge over SD 2.1 in most categories and has limitations in fields where fine-grained composition is key. In summary, the human preference experiments confirm the observation made in the PickScore experiments. While the real-word worldwoldd results were in-part less decisive, the image generation quality of Würstchen was overall preferred by the participants of both studies over SD 2.1. Finally, a followup study in Appendix A indicates that the larger Würstchen v3 is even outperforming SDXL to a significant degree in terms of preference and alignment.
4.3 EFFICIENCY
Table Table[]shows the computational costs for training Würstchen compared to the original SD 1.4 and 2.1. Based on the evaluations in Section 4.1 it can be seen that the proposed setup of decoupling high-resolution image projection from the actual text-conditional generation can be leveraged even more done in the past (Esser et al. 2021 Saharia et al. 2022, Ramesh et al. 2022). while still staying on-par or outperforming in terms of quality, fidelity and alignment. Stage C, being the most expensive stage to train, required only 24.6K GPU hours, compared to 200K GPU hours (Rombach et al. 2023) for SD 2.1, a 8 x improvement. Additionally, SD 1.4 and 2.1 processed significantly more image samples. The latter metric is based the total number of steps of all trainings and finetunings and multiplied with the respective batch sizes. Even when accounting for 1 11,000 GPU hours and 318M train samples used for training Stage B, Würstchen is significantly more efficient to train than the SD models. Moreover, although needing to sample with both Stage A & B to generate the VQGAN latents Xq. the total inference is still much faster than SD 2.1 and XL (see Figure 4
CONCLUSION
In this work, we presented our text-conditional image generation model Würstchen, which employs three stage process of decoupling text-conditions image generation from high-resolution spaces. The proposed process enables train large-sces scale models efficiently, substantially reducing computational requirements, while at the same time providing high-fidelity images. Our trained model achieved comparable performance to models trained using significantly more computational resources, illus- trating the viability of this approach and suggesting potential efficient scalability to even larger model parameters. We hope our work can serve as a starting point for further research into a more sustainable and computationally more efficient domain of generative AI and open up more possibilities into training, finetuning & deploying large-scale models on consumer hardware. We will provide all of source code, including training-, and inference scripts and trained models GitHub.

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Published as a conference paper at ICLR 2024
ACKNOWLEDGEMENTS
The authors wish to express their thanks to Stability AI Inc. for providing generous computational resources for experiments and LAION gemeinnütziger V. for dataset access and support. This work was supported by a fellowship within the IFI program of the German Academic Exchange Service (DAAD).
AUTHOR CONTRIBUTIONS
The model architecture was designed by PP and DR. The model training was carried out by PP and DR. The baseline model was trained and implemented by MR. The evaluation was carried out by MR and MA. The manuscript was written by PP, DR, MR, CP and MA.
ETHICS STATEMENT
The studies on human preference discussed in Section 4.2 were conducted online, voluntary and anonymous. Participants were paid for their participation in the study. The purpose of the study was disclosed to the participants. No personal or identifiable information about the participants was requested or recorded. A detailed methodology the studies is described in Appendix D
This work uses the LAION 5-B dataset, which is sourced from the freely available Common Crawl web index and was recently criticized as containing problematic content. We aggressively filtered the dataset to 1.76% of its original size, to reduce the risk of harmful content being accidentally shown to the model during training (see Appendix
REPRODUCIBILITY STATEMENT
We release the entire source code of our pipeline, together with the model weights used to generate these results in our GitHub repository. We also include instructions on how to train the model and an inference notebook. As described in Appendix F only used deduplicated publicly available data to train the model. The methodology that was used to conduct the study on human preference can be found in Appendix D The setup of the comparison between other open-source baselines described in Section 4 We exclusively used open-source models with their official repositories and weights, when computing metrics for other models.
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Reviews:
[{"review_id": "QE5sUOsWuv", "reviewer": "Reviewer_CAkw", "summary": "The paper proposes an efficient architecture for large-scale text-to-image diffusion models. It presents a novel text-to-image generation model that utilizes a three-stage process for improved efficiency and superior output quality. With its unique ability to separate text-conditional generation from high-resolution projection, this model demonstrates superior performance over existing models, requiring fewer computational resources without compromising image quality. Evaluations with both automated metrics and human assessments substantiate its effectiveness.", "questions": "Please refer to the weakness section. I expect the authors to clarify the questions about the ablation study and evaluation metrics in the rebuttal.\n\n## Post-rebuttal:\nI have read the author feedback. The authors addressed my concerns by adding more ablation studies and evaluations, so I raised my score to accept.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "(1) This study tackles an important topic of reducing the computational cost of text-to-image diffusion models.\n(2) The method introduced in the study is both innovative and efficient, offering clear results and validating its effectiveness through extensive evaluations.\n(3) The paper is well written, and one can quickly grasp the main idea and technical designs.", "weaknesses": "(1) Ablation study is missing. An understanding of the impact of different model components on the final results is desired.\n(2) For automatic evaluation metrics in Section 4.1, only FID and Inception score are evaluated, and there are no metrics evaluating how well the generated images are aligned with the input text instructions, such as CLIPScore.\n(3) The paper does not elaborate on the possible limitations or potential failure cases of the proposed method. Could the authors clarify this aspect?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Tq2Za8aPzx", "reviewer": "Reviewer_b5h8", "summary": "This paper introduces a new latent representation for images that can serve as compact semantic guidance for the current denoising diffusion process. Specifically, the proposed Wurstchen framework employs three stages of decoupling text-conditional image generation from high-resolution spaces. This supports an efficient optimization, which significantly reduces computational requirements for large-scale training. This architecture also enables faster inference.", "questions": "Please see the Weakness", "limitations": "", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "+ This paper is well-written and easy to follow.\n+ The field of efficient training is less discussed than inference, which makes this draft more valuable.\n+ The Wurstchen framework can reduce ~9X GPU training hours yet maintain competitive T2I performance.\n+ They provide comprehensive qualitative examples in the supplementary. The released code and checkpoint can benefit generative AI research.", "weaknesses": "I am satisfied with the current draft. As it targets robust latent visual representations, there should be a detailed analysis (e.g., the quality of the latent features / the distribution of the compression space). This can make its claim more convincing.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "KsQL14YXIH", "reviewer": "Reviewer_ix2S", "summary": "The paper proposes a new text-to-image diffusion model architecture in which a base diffusion model is conditioned on a highly compressed 2D latent space obtained from a second diffusion model. Concretely, the \"main\" diffusion model denoises a higher-resolution image (e.g., 256x256 latent or pixel space) but is being conditioned on 24x24 feature map of the image that is to be generated. The 24x24 feature map is obtained by another diffusion model that is trained on that feature space. The resulting model is faster to train and faster to sample from, since both training and sampling of the 24x24 diffusion model is cheap and the large diffusion model at higher resolution benefits from the additional conditioning of the first diffusion model.", "questions": "Since the Semantic Compressor is one of the main novelties I wonder if you tested other feature extractors (e.g., could also use CLIP or Dino) and how that would affect training and quality. Or by simply training an autoencoder with strong compression rate instead of using a pretrained feature extractor?\nAlso, did you try other model architectures for the Stage C model (e.g., transformer based models) instead of only the ConvNext blocks?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The model architecture seems novel and based on the evaluation it seems to be faster to sample from while also being faster to train than other baseline models.\n\nThe paper builds on top of the latent diffusion architecture and outperforms similarly sized LDMs (and even Stable Diffusion 1 and 2) based on quantitative metrics and human user studies. Importantly, it does so while being faster to train and faster to sample from.\nThe evaluation is well done and compares against several strong baselines, performs severfal human user studies, and also highlights some weaknesses of the current model compared to other models (e.g. fewer high-frequency details).\n\nFurthermore, the model and code to reproduce will be released.", "weaknesses": "The approach is only tested on latent diffusion models. While there is no reason to believe it wouldn't work on pixel diffusion models it would be nice to verify this.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "7wpwhMqeGQ", "reviewer": "Reviewer_MvDK", "summary": "This study presents an architecture designed for efficient text-to-image generation. The first text-conditional LDM produces a low-resolution latent map (Stage C), which is used for the second LDM for a high-resolution latent map (Stage B). This map is fed into a VQGAN-based decoder to produce a final image (Stage A), as performed in other LDM and SD models.", "questions": "Please refer to the Weaknesses in the above.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The key distinction of this work from previous LDM and SD lies in the introduction of a two-stage latent diffusion process, facilitated by the Semantic Compressor. The authors argue that the additional guidance from low-resolution latent maps (Stage C) can help yield good results under a smaller training budget, compared to the conventional LDM framework's Stage B and Stage A.\n- I appreciate the efforts put into designing and training the Semantic Compressor and Stage C. This appears to be far from straightforward, representing methodological and empirical contributions.", "weaknesses": "- I'm uncertain about the inference efficiency of this approach, as it appears to add an \"extra\" computation (Stage C) on top of the conventional LDM and SD (Stage B and Stage A). In particular, how could the proposed method achieve better inference time than SD-v2.1 in Figure 4? A detailed computational comparison would be beneficial for different components in the system (the text encoder, LDM(s), and image decoder) instead of just an overall process. \n- I think the Baseline LDM (trained for 25,000 GPU-hours (same as Stage C)) needs to be trained for GPU hours of Stage B + Stage C, given that both stages contribute to the final latent representation of the proposed method. More importantly, a baseline with the same architecture of the upper part in Stage B, Figure 3 (i.e., a conventional LDM obtained by just removing Stage C and the below part of Stage B, Figure 3) seems necessary to show the benefit of the proposed approach.\n- The parameter values in Table 2 might confuse readers due to inconsistencies in their presentation. For some models, like LDM, the table seems to consider all the parameters, including the text encoder. Yet, for other models such as the proposed method and SD, only the diffusion parameters are listed. I strongly suggest presenting the \"total\" parameters (because several components work together for a single text-to-image system) or, preferably, detailing both the \"total\" and diffusion parameters separately.\n- The popular MS-COCO benchmark has been conducted at the resolution of 256x256. Why did the authors change the resolution for IS in Table 2? In my experience, the resolution affects the metric scores. Furthermore, for some models (LDM, DALL-E, CogView), the IS results at 256x256 were reported. I also highly recommend including CLIP score.\n- I think the description “By conditioning Stage B on low-dimensional latent representations, we can effectively decode images from a 16x24x24 latent space to a resolution of 3x1024x1024, resulting in a total spatial compression of 42:1” in page 5 looks incorrect or overclaimed, because Stage B also takes a high-resolution latent map, 4x256x256, as input.\n- The behavior of the proposed model seems less explored. The representative analysis with different classifier-free guidance scales to show the tradeoff between FID-CLIP score [SD, GLIDE, Imagen] is missing. Furthermore, it would be interesting to analyze the tradeoff between the number of sampling steps and generation quality.\n- Minors: The paper is fairly easy to follow, but I think a careful proofreading is necessary: many typos exist.\n  - x -> × (in many parts)\n  - In stage B, we utilize a -> Stage B\n  - Inception Score (IC) -> IS", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (oral)