I MAGEN V IDEO : H IGH D EFINITION V IDEO
G ENERATION WITH D IFFUSION M ODELS
Jonathan Ho∗, William Chan∗, Chitwan Saharia∗, Jay Whang∗, Ruiqi Gao, Alexey Gritsenko,
Diederik P. Kingma, Ben Poole, Mohammad Norouzi, David J. Fleet, Tim Salimans∗
Google Research, Brain Team{jonathanho,williamchan,sahariac,jwhang,ruiqig,agritsenko,
durk,pooleb,mnorouzi,davidfleet,salimans}@google.com

A BSTRACTWe present Imagen Video, a text-conditional video generation system based on acascade of video diffusion models. Given a text prompt, Imagen Video generates
high definition videos using a base video generation model and a sequence of interleaved spatial and temporal video super-resolution models. We describe how
we scale up the system as a high definition text-to-video model including design
decisions such as the choice of fully-convolutional temporal and spatial superresolution models at certain resolutions, and the choice of the v-parameterizationof diffusion models. In addition, we confirm and transfer findings from previous
work on diffusion-based image generation to the video generation setting. Finally, we apply progressive distillation to our video models with classifier-free
guidance for fast, high quality sampling. We find Imagen Video not only capableof generating videos of high fidelity, but also having a high degree of controllability and world knowledge, including the ability to generate diverse videos andtext animations in various artistic styles and with 3D object understanding. See
imagen.research.google/video for samples.

Figure 1: Imagen Video sample for the prompt: “A bunch of autumn leaves falling on a calm lake toform the text ‘Imagen Video’. Smooth.” The generated video is at 1280×768 resolution, 5.3 second
duration and 24 frames per second.

1

I NTRODUCTION

Generative modeling has made tremendous progress with recent text-to-image systems like
DALL-E 2 (Ramesh et al., 2022), Imagen (Saharia et al., 2022b), Parti (Yu et al., 2022), CogView(Ding et al., 2021) and Latent Diffusion (Rombach et al., 2022). Diffusion models (Sohl-Dickstein
et al., 2015; Ho et al., 2020) in particular have found considerable success in multiple generativemodeling tasks (Nichol & Dhariwal, 2021; Ho et al., 2022a; Dhariwal & Nichol, 2022) including
density estimation (Kingma et al., 2021), text-to-speech (Chen et al., 2021a; Kong et al., 2021; Chenet al., 2021b), image-to-image (Saharia et al., 2022c;a; Whang et al., 2022), text-to-image (Rombach
et al., 2022; Nichol et al., 2021; Ramesh et al., 2022; Saharia et al., 2022b) and 3D synthesis (Pooleet al., 2022; Watson et al., 2022).
∗

Equal contribution.

1

A colorful professional animated logo for ’Imagen Video’ written using paint brush in cursive. Smooth animation.

Blue flame transforming into the text “Imagen”. Smooth animation

Wooden figurine surfing on a surfboard in space.

Balloon full of water exploding in extreme slow motion.

Melting pistachio ice cream dripping down the cone.

A british shorthair jumping over a couch.

Coffee pouring into a cup.

Figure 2: Videos generated from various text prompts. Imagen Video produces diverse andtemporally-coherent videos that are well-aligned with the given prompt.

2

A small hand-crafted wooden boat taking off to space.

A person riding a bike in the sunset.

Drone flythrough interior of Sagrada Familia cathedral

Wooden figurine walking on a treadmill made out of exercise mat.

Origami dancers in white paper, 3D render, ultra-detailed, on white background, studio shot, dancing modern dance.

Campfire at night in a snowy forest with starry sky in the background.

An astronaut riding a horse.

Figure 3: Videos generated from various text prompts. Imagen Video produces diverse andtemporally-coherent videos that are well-aligned with the given prompt.

3

A person riding a horse in the sunrise.

A happy elephant wearing a birthday hat walking under the sea.

Studio shot of minimal kinetic sculpture made from thin wire shaped like a bird on white background.

A bunch of colorful candies falling into a tray in the shape of text ’Imagen Video’. Smooth video.

A group of people hiking in a forest.

A goldendoodle playing in a park by a lake.

Incredibly detailed science fiction scene set on an alien planet, view of a marketplace. Pixel art.

Figure 4: Videos generated from various text prompts. Imagen Video produces diverse andtemporally-coherent videos that are well-aligned with the given prompt.

4

A bunch of autumn leaves falling on a calm lake to form the text ’Imagen Video’. Smooth.

Pouring latte art into a silver cup with a golden spoon next to it.

Shoveling snow.

Drone flythrough of a tropical jungle covered in snow

A beautiful sunrise on mars, Curiosity rover. High definition, timelapse, dramatic colors

A shark swimming in clear Carribean ocean.

A hand lifts a cup.

Figure 5: Videos generated from various text prompts. Imagen Video produces diverse andtemporally-coherent videos that are well-aligned with the given prompt.

5Our work aims to generate videos from text. Prior work on video generation has focused on more restricted datasets with autoregressive models (Ranzato et al., 2014; Shi et al., 2015; Finn et al., 2016;
Kalchbrenner et al., 2017; Babaeizadeh et al., 2021), latent-variable models with autoregressive priors (Mathieu et al., 2016; Vondrick et al., 2016; Babaeizadeh et al., 2018; Kumar et al., 2020), andmore recently non-autoregressive latent-variable approaches (Gupta et al., 2022). Diffusion models
have also shown promise for video generation (Ho et al., 2022b) at moderate resolution. Yang et al.
(2022) showed autoregressive generation with a RNN-based model with conditional diffusion observations. The concurrent work of Singer et al. (2022) also applied text-to-video modelling withdiffusion models, but built on a pretrained text-to-image model. Harvey et al. (2022) generates
videos up to 25 minutes in length with video diffusion models, however the domain is restricted.
In this work, we introduce Imagen Video, a text-to-video generation system based on video diffusion
models (Ho et al., 2022b) that is capable of generating high definition videos with high frame fidelity,
strong temporal consistency, and deep language understanding. Imagen Video scales from priorwork of 64-frame 128×128 videos at 24 frames per second to 128 frame 1280×768 high-definition
video at 24 frames per second. Imagen Video has a simple architecture: The model consists of afrozen T5 text encoder (Raffel et al., 2020), a base video diffusion model, and interleaved spatial
and temporal super-resolution diffusion models. Our key contributions are as follows:
1. We demonstrate the simplicity and effectiveness of cascaded diffusion video models forhigh definition video generation.
2. We confirm that recent findings in the text-to-image setting transfer to video generation,such as the effectiveness of frozen encoder text conditioning and classifier-free guidance.
3. We show new findings for video diffusion models that have implications for diffusion models in general, such as the effectiveness of the v-prediction parameterization for sample
quality and the effectiveness of progressive distillation of guided diffusion models for the
text-conditioned video generation setting.
4. We demonstrate qualitative controllability in Imagen Video, such as 3D object understanding, generation of text animations, and generation of videos in various artistic styles.

2

I MAGEN V IDEO

Our model, Imagen Video, is a cascade of video diffusion models (Ho et al., 2022a;b). It consistsof 7 sub-models which perform text-conditional video generation, spatial super-resolution, and temporal super-resolution. With the entire cascade, Imagen Video generates high definition 1280×768(width × height) videos at 24 frames per second, for 128 frames (≈ 5.3 seconds)—approximately
126 million pixels. We describe the components and techniques that constitute Imagen Video in thefollowing sections.
2.1

D IFFUSION MODELS

Imagen Video is built from diffusion models (Sohl-Dickstein et al., 2015; Song & Ermon, 2019; Hoet al., 2020) specified in continuous time (Tzen & Raginsky, 2019; Song et al., 2021; Kingma et al.,2021). We use the formulation of Kingma et al. (2021): the model is a latent variable model withlatents z = {zt | t ∈ [0, 1]} following a forward process q(z|x) starting at data x ∼ p(x). The
forward process is a Gaussian process that satisfies the Markovian structure:2
q(zt |x) = N (zt ; αt x, σt2 I), q(zt |zs ) = N (zt ; (αt /αs )zs , σt|sI)
(1)2
where 0 ≤ s < t ≤ 1, σt|s
= (1 − eλt −λs )σt2 , and αt , σt specify a noise schedule whose log signalto-noise-ratio λt = log[αt2 /σt2 ] decreases monotonically with t until q(z1 ) ≈ N (0, I). We use acontinuous time version of the cosine noise schedule (Nichol & Dhariwal, 2021). The generativemodel is a learned model that matches this forward process in the reverse time direction, generating
zt starting from t = 1 and ending at t = 0.

Learning to reverse the forward process for generation can be reduced to learning to denoise zt ∼q(zt |x) into an estimate x̂θ (zt , λt ) ≈ x for all t. Like (Song & Ermon, 2019; Ho et al., 2020) andmost follow-up work, we optimize the model by minimizing a simple noise-prediction loss:L(x) = E∼N (0,I),t∼U (0,1) kˆθ (zt , λt ) − k22(2)
6

where zt = αt x + σt , and ˆθ (zt , λt ) = σt−1 (zt − αt x̂θ (zt , λt )). We will drop the dependence on
λt to simplify notation. In practice, we parameterize our models in terms of the v-parameterization(Salimans & Ho, 2022), rather than predicting  or x directly; see Section 2.4.
For conditional generative modeling, we provide the conditioning information c drawn jointly withx to the model as x̂θ (zt , ct ). We use these conditional diffusion models for spatial and temporalsuper-resolution in our pipeline of diffusion models: in these cases, c includes both the text and theprevious stage low resolution video as well as a signal λ0t that describes the strength of conditioning
augmentation added to c. Saharia et al. (2022b) found it critical to condition all the super-resolution
models with the text embedding, and we follow this approach.
We use the discrete time ancestral sampler (Ho et al., 2020), with sampling variances derived fromlower and upper bounds on reverse process entropy (Sohl-Dickstein et al., 2015; Ho et al., 2020;
Nichol & Dhariwal, 2021). This sampler can be formulated by using a reversed description of the2
forward process as q(zs |zt , x) = N (zs ; µ̃s|t (zt , x), σ̃s|tI) (noting s < t), whereµ̃s|t (zt , x) = eλt −λs (αs /αt ) zt + (1 − eλt −λs )αs x

2and σ̃s|t= (1 − eλt −λs )σs2 .

Starting at z1 ∼ N (0, I), the ancestral sampler follows the ruleq2 )1−γ (σ 2 )γ 
zs = µ̃s|t (zt , x̂θ (zt )) + (σ̃s|t
t|s

(3)

(4)

where  is standard Gaussian noise, γ is a hyperparameter that controls the stochasticity of thesampler (Nichol & Dhariwal, 2021), and s, t follow a uniformly spaced sequence from 1 to 0. See
Section 3 for sampler hyperparameter settings.
Alternatively, the deterministic DDIM sampler (Song et al., 2020) can be used for sampling. This
sampler is a numerical integration rule for the probability flow ODE (Song et al., 2021; Salimans& Ho, 2022), which describes how a sample from a standard normal distribution can be deterministically transformed into a sample from the video data distribution using the denoising model. The
DDIM sampler is useful for progressive distillation for fast sampling, as described in Section 2.7.
2.2

C ASCADED D IFFUSION M ODELS AND TEXT CONDITIONING

Cascaded Diffusion Models (Ho et al., 2022a) are an effective method for scaling diffusion models tohigh resolution outputs, finding considerable success in both class-conditional ImageNet (Ho et al.,2022a) and text-to-image generation (Ramesh et al., 2022; Saharia et al., 2022b). Cascaded diffusion
models generate an image or video at a low resolution, then sequentially increase the resolutionof the image or video through a series of super-resolution diffusion models. Cascaded Diffusion
Models can model very high dimensional problems while still keeping each sub-model relatively
simple. Imagen (Saharia et al., 2022b) also showed that by conditioning on text embeddings from alarge frozen language model in conjunction with cascaded diffusion models, one can generate highquality 1024 × 1024 images from text descriptions. In this work we extend this approach to video
generation.
Figure 6 summarizes the entire cascading pipeline of Imagen Video. In total, we have 1 frozen text
encoder, 1 base video diffusion model, 3 SSR (spatial super-resolution), and 3 TSR (temporal superresolution) models – for a total of 7 video diffusion models, with a total of 11.6B diffusion model
parameters. The data used to train these models is processed to the appropriate spatial and temporal
resolutions by spatial resizing and frame skipping. At generation time, the SSR models increase
spatial resolution for all input frames, whereas the TSR models increase temporal resolution byfilling in intermediate frames between input frames. All models generate an entire block of frames
simultaneously – so for instance, our SSR models do not suffer from obvious artifacts that would
occur from naively running super-resolution on independent frames.
One benefit of cascaded models is that each diffusion model can be trained independently, allowing
one to train all 7 models in parallel. Additionally, our super-resolution models are general purpose
video super-resolution models, and they can be applied to real videos or samples from generativemodels other than the ones presented in this paper. This is similar to how Imagen’s super-resolution
models helped improve the fidelity of the images generated by Parti (Yu et al., 2022), which is an
autoregressive text-to-image model. We intend to explore hybrid pipelines of multiple model classes
further in future work.
7

Input Text Prompt

Base (5.6B)

T5-XXL (4.6B)

16x40x24 3fps

SSR (1.2B)

SSR (1.4B)

TSR (1.7B)

32x320x192 6fps

32x80x48 6fps

32x40x24 6fps

TSR (780M)

TSR (630M)

SSR (340M)

64x320x192 12fps

128x320x192 24fps

128x1280x768 24fps

Figure 6: The cascaded sampling pipeline starting from a text prompt input to generating a 5.3second, 1280×768 video at 24fps. “SSR” and “TSR” denote spatial and temporal super-resolution
respectively, and videos are labeled as frames×width×height. In practice, the text embeddings are
injected into all models, not just the base model.
Frame 1

Frame 2

Frame 3

Spatial Conv

Spatial Conv

Spatial Conv

Spatial Attention

Spatial Attention

Spatial Attention

Frame N...

Spatial Conv

Spatial Attention

Temporal Attention / Convolution

Figure 7: Video U-Net space-time separable block. Spatial operations are performed independently
over frames with shared parameters, whereas the temporal operation mixes activations over frames.
Our base model uses spatial convolutions, spatial self-attention and temporal self-attention. For
memory efficiency, our spatial and temporal super-resolution models use temporal convolutions
instead of attention, and our models at the highest spatial resolution do not have spatial attention.

Similar to Saharia et al. (2022b), we utilize contextual embeddings from a frozen T5-XXL text
encoder (Raffel et al., 2020) for conditioning on the input text prompt. We find these embeddings
to be critical for alignment between generated video and the text prompt. Similar to the findingsof Saharia et al. (2022b), we observe evidence of deeper language understanding, enabling us togenerate the videos displayed in Figs. 2 to 5.
2.3

V IDEO DIFFUSION ARCHITECTURES

Diffusion models for image generation typically use a 2D U-Net architecture (Ronneberger et al.,2015; Salimans et al., 2017; Ho et al., 2020) to represent the denoising model x̂θ . This is a multiscale model consisting of multiple layers of spatial attention and convolution at each resolution,
combined with shortcuts between layers at the same resolution. In earlier work on Video Diffusion
Models, Ho et al. (2022b) introduced the Video U-Net , which generalizes the 2D diffusion modelarchitecture to 3D in a space-time separable fashion using temporal attention and convolution layersinterleaved within spatial attention and convolution layers to capture dependencies between video
frames. Our work builds on the Video U-Net architecture: see Figure 7. Following Video Diffusion Models, each of our denoising models x̂θ operate on multiple video frames simultaneously andthereby generate entire blocks of video frames at a time, which we find to be important to capture
the temporal coherence of the generated video compared to frame-autoregressive approaches. Our
spatial super-resolution (SSR) and temporal super-resolution (TSR) models condition on their input videos by concatenating an upsampled conditioning input channelwise to the noisy data zt , thesame mechanism as SR3 (Saharia et al., 2022c) and Palette (Saharia et al., 2022a): spatial upsampling before concatenation is performed using bilinear resizing, and temporal upsampling before
concatenation is performed by repeating frames or by filling in blank frames.
8Our base video model, which is the first model in the pipeline that generates data at the lowest frame
count and spatial resolution, uses temporal attention to mix information across time. Our SSR and
TSR models, on the other hand, use temporal convolutions instead of temporal attention. The temporal attention in the base model enables Imagen Video to model long term temporal dependencies,
while the temporal convolutions in the SSR and TSR models allow Imagen Video to maintain local
temporal consistency during upsampling. The use of temporal convolutions lowers memory andcomputation costs over temporal attention—this is crucial because the very purpose of the TSR andSSR models is to operate at high frame rates and spatial resolutions. In our initial experiments,
we did not find any significant improvements when using temporal attention over temporal convolutions in our SSR and TSR models, which we hypothesize is due to the significant amount of
temporal correlation already present in the conditioning input to these models.
Our models also use spatial attention and spatial convolutions. The base model and the first two
spatial super-resolution models have spatial attention in addition to spatial convolutions. We foundthis to improve sample fidelity. However, as we move to higher resolutions, we switch to fully
convolutional architectures, like Saharia et al. (2022b), to minimize memory and compute costs inorder to generate 1280×768 resolution data. The highest resolution SSR model in our pipeline is afully convolutional model trained on random lower resolution spatial crops for training time memory
efficiency, and we find that the model easily generalizes to the full resolution during sampling time.
2.4

v- PREDICTION

We follow Salimans & Ho (2022) and use v-prediction parameterization (vt ≡ αt −σt x) for all ourmodels. The v-parameterization is particularly useful for numerical stability throughout the diffusion process to enable progressive distillation for our models. For models that operate at higher resolution in our pipeline, we also discovered that the v-parameterization avoids color shifting artifacts
that are known to affect high resolution diffusion models, and in the video setting it avoids temporalcolor shifting that sometimes appears with -prediction models. Our use of v-parameterization also
has the benefit of faster convergence of sample quality metrics: see Section 3.3.
2.5

C ONDITIONING AUGMENTATION

We use noise conditioning augmentation (Ho et al., 2022a) for all our temporal and spatial superresolution models. Noise conditioning augmentation has been found to be critical for cascaded
diffusion models for class-conditional generation (Ho et al., 2022a) as well as text-to-image models(Saharia et al., 2022b). In particular, it facilitates parallel training of different models in the cascade,as it reduces the sensitivity to domain gaps between the output of one stage of the cascade and the
inputs used in training the subsequent stage.
Following Ho et al. (2022a), we apply Gaussian noise augmentation with a random signal-to-noise
ratio to the conditioning input video during training, and this sampled signal-to-noise ratio is provided to the model as well. At sampling time we use a fixed signal-to-noise ratio such as 3 or 5,
representing a small amount of augmentation that aids in removing artifacts in the samples from theprevious stage while preserving most of the structure.
2.6

V IDEO -I MAGE J OINT T RAINING

We follow Ho et al. (2022b) in jointly training all the models in the Imagen Video pipeline on images
and videos. During training, individual images are treated as single frame videos. We achieve thisby packing individual independent images into a sequence of the same length as a video, and bypass
the temporal convolution residual blocks by masking out their computation path. Similarly, wedisable cross-frame temporal attention by applying masking to the temporal attention maps. This
strategy allows us to use to train our video models on image-text datasets that are significantly larger
and more diverse than available video-text datasets. Consistent with Ho et al. (2022b), we observethat joint training with images significantly increases the overall quality of video samples. Anotherinteresting artifact of joint training is the knowledge transfer from images to videos. For instance,
while training on natural video data only enables the model to learn dynamics in natural settings, themodel can learn about different image styles (such as sketch, painting, etc.) by training on images.
9As a result, this joint training enables the model to generate interesting video dynamics in different
styles. See Fig. 8 for such examples.
2.6.1

C LASSIFIER F REE G UIDANCE

We found classifier free guidance (Ho & Salimans, 2021) to be critical for generating high fidelity
samples which respect a given text prompt. This is consistent with earlier results on text-to-image
models (Nichol et al., 2021; Ramesh et al., 2022; Saharia et al., 2022b; Yu et al., 2022).
In the conditional generation setting, the data x is generated conditional on a signal c, which here
represents a contextualized embedding of the text prompt, and a conditional diffusion model canbe trained by using this signal c as an additional input to the denoising model x̂θ (zt , c). After
training, Ho & Salimans (2021) find that sample quality can be improved by adjusting the denoisingprediction x̂θ (zt , c) usingx̃θ (zt , c) = (1 + w)x̂θ (zt , c) − wx̂θ (zt ),(5)

where w is the guidance strength, x̂θ (zt , c) is the conditional model, and x̂θ (zt ) = x̂θ (zt , c = ∅)is an unconditional model. The unconditional model is jointly trained with the conditional modelby dropping out the conditioning input c. The predictions of the adjusted denoising model x̃θ (zt , c)
are clipped to respect the range of possible pixel values, which we discuss in more detail in the next
section. Note that the linear transformation in Equation 5 can equivalently be performed in v-space(ṽθ (zt , c) = (1 + w) v̂θ (zt , c) − w v̂θ (zt )) or -space (˜θ (zt , c) = (1 + w) ˆθ (zt , c) − w ˆθ (zt )).
For w > 0 this adjustment has the effect of over-emphasizing the effect of conditioning on the signalc, which tends to produce samples of lower diversity but higher quality compared to sampling fromthe regular conditional model (Ho & Salimans, 2021). The method can be interpreted as a way to
guide the samples towards areas where an implicit classifier p(c|zt ) has high likelihood; as such, it
is an adaptation of the explicit classifier guidance method proposed by Dhariwal & Nichol (2022).
2.6.2

L ARGE G UIDANCE W EIGHTS

When using large guidance weights, the resulting x̃θ (zt , c) must be projected back to the possible range of pixel values at every sampling step to prevent train-test mismatch. When usinglarge guidance weights, the standard approach, i.e., clipping the values to the right range (e.g.,
np.clip(x, -1, 1)), leads to significant saturation artifacts in the generated videos. A similar effect was observed in Saharia et al. (2022b) for text-to-image generation. Saharia et al.
(2022b) use dynamic thresholding to alleviate this saturation issue. Specifically, dynamic clipping
involves clipping the image to a dynamically chosen threshold s followed by scaling by s (i.e.,
np.clip(x, -s, s) / s) (Saharia et al., 2022b).
Although dynamic clipping can help with over-saturation, we did not find it sufficient in initial experiments. We therefore also experiment with letting w oscillate between a high and a low guidance
weight at each alternating sampling step, which we find significantly helps with these saturation
issues. We call this sampling technique oscillating guidance. Specifically, we use a constant highguidance weight for a certain number of initial sampling steps, followed by oscillation between highand low guidance weights: this oscillation is implemented simply by alternating between a large
weight (such as 15) and a small weight (such as 1) over the course of sampling. We hypothesizethat a constant high guidance weight at the start of sampling helps break modes with heavy emphasis on text, while oscillating between high and low guidance weights helps maintain a strong textalignment (via high guidance sampling step) while limiting saturation artifacts (via low guidance
sampling step). We however observed no improvement in sample fidelity and more visual artifacts
when applying oscillating guidance to models past the 80×48 spatial resolution. Thus we only apply
oscillating guidance to the base and the first two SR models.
2.7

P ROGRESSIVE D ISTILLATION WITH G UIDANCE AND S TOCHASTIC S AMPLERS

Salimans & Ho (2022) proposed progressive distillation to enable fast sampling of diffusion models.
This method distills a trained deterministic DDIM sampler (Song et al., 2020) to a diffusion modelthat takes many fewer sampling steps, without losing much perceptual quality. At each iterationof the distillation process, an N -step DDIM sampler is distilled to a new model with N/2-steps.
10This procedure is repeated by halving the required sampling steps each iteration. Meng et al. (2022)
extend this approach to samplers with guidance, and propose a new stochastic sampler for use with
distilled models. Here we show that this approach also works very well for video generation.
We use a two-stage distillation approach to distill a DDIM sampler (Song et al., 2020) with classifierfree guidance. At the first stage, we learn a single diffusion model that matches the combined output
from the jointly trained conditional and unconditional diffusion models, where the combination
coefficients are determined by the guidance weight. Then we apply progressive distillation to thatsingle model to produce models requiring fewer sampling steps at the second stage.
After distillation, we use a stochastic N -step sampler: At each step, we first apply one deterministic
DDIM update with twice the original step size (i.e., the same step size as a N/2-step sampler),and then we perform one stochastic step backward (i.e., perturbed with noise following the forward
diffusion process) with the original step size, inspired by Karras et al. (2022). See Meng et al. (2022)
for more details. Using this approach, we are able to distill all 7 video diffusion models down to just
8 sampling steps per model without any noticeable loss in perceptual quality.

3

E XPERIMENTS

We train our models on a combination of an internal dataset consisting of 14 million video-text pairs
and 60 million image-text pairs, and the publicly available LAION-400M image-text dataset. To
process the data into a form suitable for training our cascading pipeline, we spatially resize images
and videos using antialiased bilinear resizing, and we temporally resize videos by skipping frames.
Throughout our model development process, we evaluated Imagen Video on several different metrics, such as FID on individual frames (Heusel et al., 2017), FVD (Unterthiner et al., 2019) for
temporal consistency, and frame-wise CLIP scores (Hessel et al., 2021; Park et al., 2021) for videotext alignment. Below, we explore the capabilities of our model and investigate its performance inregards to 1) scaling up the number of parameters in our model, 2) changing the parameterizationof our model, and 3) distilling our models so that they are fast to sample from.
3.1

U NIQUE VIDEO GENERATION CAPABILITIESWe find that Imagen Video is capable of generating high fidelity video, and that it possesses several
unique capabilities that are not traditionally found in unstructured generative models learned purely
from data. For example, Fig. 8 shows that our model is capable of generating videos with artisticstyles learned from image information, such as videos in the style of van Gogh paintings or watercolor paintings. Fig. 9 shows that Imagen Video possesses an understanding of 3D structure, as itis capable of generating videos of objects rotating while roughly preserving structure. While the3D consistency over the course of rotation is not exact, we believe Imagen Video shows that video
models can serve as effective priors for methods that do force 3D consistency. Fig. 10 shows that
Imagen Video is also reliably capable of generating text in a wide variety of animation styles, someof which would be difficult to animate using traditional tools. We see results such as these as an exciting indication of how general purpose generative models such as Imagen Video can significantly
decrease the difficulty of high quality content generation.
3.2

S CALING

In Figure 11 we show that our base video model strongly benefits from scaling up the parametercount of the video U-Net. We performed this scaling by increasing the base channel count and depthof the network. This result is contrary to the text-to-image U-Net scaling results by Saharia et al.
(2022b), which found limited benefit from diffusion model scaling when measured by image-text
sample quality scores. We conclude that video modeling is a harder task for which performance is
not yet saturated at current model sizes, implying future benefits to further model scaling for video
generation.
3.3

C OMPARING PREDICTION PARAMETERIZATIONS

In early experiments we found that training -prediction models (Ho et al., 2020) performed worse
than v-prediction (Salimans & Ho, 2022) especially at high resolutions. Specifically, for high res11

A cat eating food out of a bowl, in style of Van Gogh.

A drone flythrough over a watercolor painting of a forest.

Drone flythrough of a pixel art of futuristic city.

Figure 8: Snapshots of frames from videos generated by Imagen Video demonstrating the ability ofthe model to generate dynamics in different artistic styles.

A 3D model of a 1800s victorian house. Studio lighting.

A 3D model of a car made out of sushi. Studio lighting.

A 3D model of an elephant origami. Studio lighting.

Figure 9: Snapshots of frames from videos generated by Imagen Video demonstrating the model’sunderstanding of 3D structures.

A colorful professional animated logo for ’Diffusion’ written using paint brush in cursive. Smooth animation.

Sprouts in the shape of text ’Imagen’ coming out of a fairytale book.

Thousands of fast brush strokes slowly forming the text ’Imagen Video’ on a light beige canvas. Smooth animation.

Figure 10: Snapshots of frames from videos generated by Imagen Video demonstrating the abilityof the model to render a variety of text with different style and dynamics.

12

25
CLIP Score

FVD

20
15
500M Params

10

24.5
24

500M Params

1.6B Params

1.6B Params

5.6B Params

01

5.6B Params

2

3

4

0
·105

Training Steps(a) Scaling Comparison on FVD scores.

1

2

3

Training Steps

4·105(b) Scaling Comparison on CLIP scores.

Figure 11: Scaling Comparison for the base 16×40×24 video model on FVD and CLIP scores (on0-100 scale). Both FVD and CLIP scores are computed on 4096 video samples. We see clear signsof improvement on both metrics when scaling from 500M to 1.6B to 5.6B parameters.
80×48 input video frames

SSR to 320×192 with -prediction

SSR to 320×192 with v-prediction

Figure 12: Comparison between -prediction (middle row) and v-prediction (bottom row) for a
8×80×48→8×320×192 spatial super-resolution architecture at 200k training steps. The frames
from the -prediction model are generally worse, suffering from unnatural global color shifts acrossframes. The frames from the v-prediction model do not and are more consistent.
olution SSR models, we observed that -prediction converges relatively slowly in terms of sample
quality metrics and suffers from color shift and color inconsistency across frames in the generated videos. Fig. 12 shows the comparison between -prediction and v-prediction on a 80×48 →320×192 video spatial super-resolution task. It is clear that -parameterization produces worse
generations than v-parameterization. Fig. 13 shows the quantitative comparison between the twoparameterizations as a function of training steps. We observe that v parameterization converges
much more faster than  parameterization.
3.4

P ERCEPTUAL QUALITY AND DISTILLATIONIn Table 1 we report perceptual quality metrics (CLIP score and CLIP R-Precision) for our model
samples, as well as for their distilled version. Samples are generated and evaluated at 192×320
resolution for 128 frames at 24 frames per second. For CLIP score, we take the average score over
all frames. For CLIP R-Precision (Park et al., 2021) we compute the top-1 accuracy (i.e. R = 1),
treating the frames of a video sample as images sharing the same text label (the prompt). We repeat
these over four different runs and report the mean and standard error.
13

First Frame FID

-prediction

30

v-prediction

20

10

0

0.5

1

1.5

2

2.5
·105

Training Steps

Figure 13: Comparison between 80×48 → 320×192 SSR models trained with - and v-prediction
parameterizations. We report FID evaluated on the first upsampled frame; FVD score is excessively
noisy for the -prediction model. We observe that the sample quality of the -prediction model
converges much more slowly than that of the v-prediction model.
We find that distillation provides a very favorable trade-off between sampling time and perceptual
quality: the distilled cascade is about 18× faster, while producing videos of similar quality to thesamples from the original models. In terms of FLOPs, the distilled models are about 36× more efficient: The original cascade evaluates each model twice (in parallel) to apply classifier-free guidance,
while our distilled models do not, since they distilled the effect of guidance into a single model. We
provide samples from our original and distilled cascade in Figure 14 for illustration.
Guidance w

Base Steps

SR Steps

CLIP Score

CLIP R-Precision

Sampling Time

constant=6
oscillate(15,1)
constant=6
oscillate(15,1)
constant=6
oscillate(15,1)

256
256
256
256
8
8

128
128
8
8
8
8

25.19±.03
25.02±.08
25.29±.05
25.15±.09
25.03±.05
25.12±.07

92.12±.53
89.91±.96
90.88±.50
88.78±.69
89.68±.38
90.97±.46618 sec
618 sec
135 sec
135 sec
35 sec35 sec

24.27

86.18

ground truth

Table 1: CLIP scores and CLIP R-Precision (Park et al., 2021) values for generated samples andground truth videos on prompts from our test set. Cells highlighted in green represent distilledmodels. We compare three different combinations: original pipeline, distilled SR models on topof original base model, and fully distilled pipeline. The original base models use 256 sampling
steps, and original SR models use 128 steps. All distilled models use 8 sampling steps per stage.
Sampling from the original pipeline takes 618 seconds for one batch of samples, while samplingfrom the distilled pipeline takes 35 seconds, making the distilled pipeline about 18× faster. We also
explored two different classifier-free guidance settings for the base models: constant guidance with
w = 6 and oscillating guidance which alternates between w = 15 and w = 1, following Saharia
et al. (2022b). When using oscillating guidance, the fully distilled pipeline performs the same as theoriginal model, or even slightly better. When using fixed guidance, our fully distilled pipeline scores
slightly lower than the original model, though the difference is minor. Combining the original basemodel with fixed guidance and distilled super-resolution models produced the highest CLIP score.
For all models, generated samples obtain better perceptual quality metrics than the original groundtruth data: By using classifier-free guidance our models sample from a distribution tilted towards
these quality metrics, rather than from an accurate approximation of the original data distribution.

4

L IMITATIONS AND S OCIETAL I MPACT

Generative modeling has made tremendous progress, especially in recent text-to-image models (Saharia et al., 2022b; Ramesh et al., 2022; Rombach et al., 2022). Imagen Video is another step
forward in generative modelling capabilities, advancing text-to-video AI systems. Video generativemodels can be used to positively impact society, for example by amplifying and augmenting humancreativity. However, these generative models may also be misused, for example to generate fake,
hateful, explicit or harmful content. We have taken multiple steps to minimize these concerns, for14

Figure 14: Frames from videos generated by Imagen Video for the text prompt “A teddy bear wearing sunglasses playing guitar next to a cactus.” The samples on the left are produced by our originalmodel cascade, while the samples on the right are from our distilled cascade with 8 sampling steps
per stage. Both used constant guidance with w = 6 and static clipping.
example in internal trials, we apply input text prompt filtering, and output video content filtering.
However, there are several important safety and ethical challenges remaining. Imagen Video and its
frozen T5-XXL text encoder were trained on problematic data (Bordia & Bowman, 2017; Birhane
et al., 2021; Bender et al., 2021). While our internal testing suggests much of explicit and violent
content can be filtered out, there still exists social biases and stereotypes which are challenging todetect and filter. We have decided not to release the Imagen Video model or its source code until
these concerns are mitigated.

5

C ONCLUSION

We presented Imagen Video: a text-conditional video generation system based on a cascade of video
diffusion models. By extending the text-to-image diffusion models of Imagen (Saharia et al., 2022b)
to the time domain, and training jointly on video and images, we obtained a model capable of generating high fidelity videos with good temporal consistency while maintaining the strong featuresof the original image system, such as the ability to accurately spell text. We transferred multiple
methods from the image domain to video, such as v-parameterization (Salimans & Ho, 2022), conditioning augmentation (Ho et al., 2022a), and classifier-free guidance (Ho & Salimans, 2021), andfound that these are also useful in the video setting. Video modeling is computationally demanding,
and we found that progressive distillation (Salimans & Ho, 2022; Meng et al., 2022) is a valuable
technique for speeding up video diffusion models at sampling time. Given the tremendous recent
progress in generative modeling, we believe there is ample scope for further improvements in video
generation capabilities in future work.

6

ACKNOWLEDGEMENTS

We give special thanks to Jordi Pont-Tuset and Shai Noy for engineering support. We also give
thanks to our artist friends, Alexander Chen, Irina Blok, Ian Muldoon, Daniel Smith, and Pedro Vergani for helping us test Imagen Video and lending us their amazing creativity. We are extremely
grateful for the support from Erica Moreira for compute resources. Finally, we give thanks to
Elizabeth Adkison, James Bradbury, Nicole Brichtova, Tom Duerig, Douglas Eck, Dumitru Erhan,
Zoubin Ghahramani, Kamyar Ghasemipour, Victor Gomes, Blake Hechtman, Jonathan Heek, Yash
Katariya, Sarah Laszlo, Sara Mahdavi, Anusha Ramesh, Tom Small, and Tris Warkentin for theirsupport.

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