(in beaker)
Osmosis is the movement of water across a semipermeable membrane (such as the cell membrane). The tonicity of a solution involves comparing the concentration of a cell’s cytoplasm to the concentration of its environment. Ultimately, the tonicity of a solution can be determined by examining the effect a solution has on a cell within the solution.
By definition, a hypertonic solution is one that causes a cell to shrink. Though it certainly is more complex than this, for our purposes in this class, we can assume that a hypertonic solution is more concentrated with solutes than the cytoplasm. This will cause water from the cytoplasm to leave the cell, causing the cell to shrink. If a cell shrinks when placed in a solution, then the solution is hypertonic to the cell.
If a solution is hypotonic to a cell, then the cell will swell when placed in the hypotonic solution. In this case, you can imagine that the solution is less concentrated than the cell’s cytoplasm, causing water from the solution to flow into the cell. The cell swells!
Finally, an isotonic solution is one that causes no change in the cell. You can imagine that the solution and the cell have equal concentrations, so there is no net movement of water molecules into or out of the cell.
In this exercise, you will observe osmosis by exposing a plant cell to salt water.
What do you think will happen to the cell in this environment? Draw a picture of your hypothesis.
Draw a typical cell in both pond and salt water and label the cell membrane and the cell wall.
You and your group will design an experiment to determine the relative molecular weights of methylene blue and potassium permanganate. You may use a petri dish of agar, which is a jello-like medium made from a polysaccharide found in the cell walls of red algae. You will also have access to a cork borer and a small plastic ruler.
Your experiment design should include all of the following portions:
Biology Teaching Resources
The diffusion lab has been a yearly activity in my biology class as part of a unit on cells and cell transport. Students fill a bag with starch and water and then submerge it in a solution of iodine and observe what happens. The iodine diffuses across the plastic bag and turns the starch purple.
If students are absent for the lab, they can complete Google Slides that shows the step-by-step process of the investigation. They watch videos showing the set-up and observe a time-lapse video that shows the starch in the bag turn purple. Why does it turn purple?
Iodine in the beaker is a small molecule that can move through the plastic of the bag. When it encounters the starch solution, the color will change to purple. This is an excellent model for how diffusion occurs across semi-permeable membranes.
In the slide activity, a dialysis tube was used instead of a plastic bag because the process does occur faster with the dialysis tube. In class, I use a cheaper version, plastic sandwich bags. Save time and frustration by making the bags ahead of time. Simply put a spoonful of starch in the bag and fill with tap water. Tie the bag like a balloon. Students then place the bag into a beaker with a few drops of iodine .
Modeling Osmosis with Deco Cubes
Osmosis Lab
Observing Osmosis in an Egg
Shannan Muskopf
This list provides a range of activities and demonstrations, together with background information and suggested teaching strategies, which explore diffusion. The use of models and analogies here can aid understanding and students should be challenged to use a simple particle model to explain what they observe.
The resources link to the following topics:
Visit the secondary science webpage to access all lists: www.nationalstemcentre.org.uk/secondaryscience
Whilst this list provides a source of information and ideas for experimental work, it is important to note that recommendations can date very quickly. Do NOT follow suggestions which conflict with current advice from CLEAPSS, SSERC or recent safety guides. eLibrary users are responsible for ensuring that any activity, including practical work, which they carry out is consistent with current regulations related to health and safety and that they carry an appropriate risk assessment. Further information is provided in our Health and Safety guidance.
Quality Assured Category: Physics Publisher: Longman
Although slightly dated, this pupil book and teacher guide has some really well explained theory and good practicals that fit in with this topic. Each chapter also has a series of good written activities that could be taken and re-purposed in a more up to date way.
Quality Assured Category: Science Publisher: Association for Science Education (ASE)
This is a really good set of activities based around perfumes. There are instructions for a perfume circus activity which would make a good starter activity and also for two different ways of making perfume as class practicals. There are full teacher and technician notes and a set of student worksheets.
In this experiment, students can investigate diffusion by placing agar cubes of varying sizes in acid and observing the colour change. The webpage contains full teacher and technician notes.
In this experiment, students place colourless crystals of lead nitrate and potassium iodide at opposite sides of a Petri dish of de-ionised water. As these substances dissolve and diffuse towards each other, students can observe clouds of yellow lead iodide forming, demonstrating that diffusion has taken place.
Quality Assured Category: Physics Publisher: National STEM Learning Centre and Network
This video shows how to show the movement of particles by Brownian motion. Instead of using the traditional smoke cell, the video shows how Brownian motion can be observed in a suspension containing micrometre diameter polystyrene spheres. Using a microscope and video camera, students can observe the motion of the polystyrene spheres. The video also shows how Brownian motion can be simulated using a vibrating loudspeaker, table tennis balls and a small balloon.
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In association with Nuffield Foundation
Demonstrate that diffusion takes place in liquids by allowing lead nitrate and potassium iodide to form lead iodide as they diffuse towards each other in this practical
In this experiment, students place colourless crystals of lead nitrate and potassium iodide at opposite sides of a Petri dish of deionised water. As these substances dissolve and diffuse towards each other, students can observe clouds of yellow lead iodide forming, demonstrating that diffusion has taken place.
This practical activity takes around 30 minutes.
To reduce the use of toxic chemicals in this experiment you can conduct the experiment in microscale, using drops of water on a laminated sheet, find full instructions and video here, and/or use a less toxic salt than lead nitrate, eg sodium carbonate and barium chloride. More information is available from CLEAPSS.
Source: Royal Society of Chemistry
As the crystals of potassium iodide and lead nitrate dissolve and diffuse, they will begin to form yellow lead iodide
The lead nitrate and potassium iodide each dissolve and begin to diffuse through the water. When the lead ions and iodide ions meet they react to form solid yellow lead iodide which precipitates out of solution.
lead nitrate + potassium iodide → lead iodide + potassium nitrate
Pb(aq) + 2I – (aq) → PbI 2 (s)
The precipitate does not form exactly between the two crystals. This is because the lead ion is heavier and diffuses more slowly through the liquid than the iodide ion.
Another experiment – a teacher demonstration providing an example of a solid–solid reaction – involves the same reaction but in the solid state.
This is a resource from the Practical Chemistry project , developed by the Nuffield Foundation and the Royal Society of Chemistry. This collection of over 200 practical activities demonstrates a wide range of chemical concepts and processes. Each activity contains comprehensive information for teachers and technicians, including full technical notes and step-by-step procedures. Practical Chemistry activities accompany Practical Physics and Practical Biology .
The experiment is also part of the Royal Society of Chemistry’s Continuing Professional Development course: Chemistry for non-specialists .
© Nuffield Foundation and the Royal Society of Chemistry
2024-06-24T06:59:00Z By Emma Owens
Use this poster, fact sheet and storyboard activity to ensure your 14–16 students understand dynamic equilibrium
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Only registered users can comment on this article., more experiments.
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Diffusion is a physical phenomenon that occurs everywhere, and we barely notice it or understand how it works. However, a few simple experiments can reveal the mysterious nature of this simple phenomenon.
Taking some time to set these experiments up can make your life much easier and allow you to better focus on the results of the experiment. First, grab three glass beakers. Make sure the beakers are transparent. Fill a large pitcher of water or do your experiments near a tap. Also, get three different colors of food dye. To be very precise, you will want a thermometer, but you don't need one unless you are picky. Also have a timer or stopwatch. Finally, make sure you have some way of heating or cooling the water before you start.
This is by far the most simple experiment. However, you'll have to know beforehand that diffusion is the propagation of a substance from an area of high concentration to an area of low concentration, the purpose of which is to reach a state of equilibrium, or a state in which there is an even concentration of a substance across a medium. Now that you know what diffusion is, you need to see it yourself. Take a beaker and fill it with water to around three-quarters. Now, simply pour a small amount of food dye into the water. Observe whether the dye diffuses from a high concentration to a low concentration and try to observe where those two states occur. This will give you a good idea of what diffusion looks like.
Now, all your preparation will come to fruition. Fill all three beakers with tap water to around three-quarters filled. The tap water should be around 50 to 60 degrees Fahrenheit, or as close as you can get. Now, cool one beaker by placing it in a refrigerator or similar device. Heat the other beaker with a stove, microwave or, if you have one, a Bunsen burner. You can make the temperatures of all thee beakers whatever you want, really. The important thing is that one is around 20 degrees hotter than another, which is around 20 degrees hotter than another. Finally, put one color of dye in each beaker and observe the diffusion. Your objective in this experiment should be to measure how fast each dye diffuses through each temperature of water. Make sure to write down how fast the dye diffuses in each temperature of water.
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About the Author
David Scott has been a firefighter for the Seattle Fire Department's Technical Rescue Team for almost 20 years. He has been writing primarily since 2005, but did author the book, "The White River Ranger District Trail Guide" in 1988. In addition to his work for Demand Studios, Scott spends much of his time writing poetry and a novel.
The simple diffusion experiment by Faisal Alwabel in Dhahran Ahliyya Schools MYP 9/C
How does temperature affect the rate of diffusion?
•My hypothesis is that:
The lower the temperature of water the slower the diffusion rate inside the water and the longer the time will take, and the higher the temperature the faster diffusion rate inside the water and less time of diffusion in the beaker will take because of the increase of kinetic energy in the particles so they will mix more quickly.
Dependent variable: Rate of diffusion
•Independent variable: Temperature
•Controlled variables: Food coloring drop, time, amount of water for each glass, amount of food coloring
•What is diffusion? Diffusion is the movement of molecules from a place where they are at a higher concentration to an area with a low concentration, and it works by itself without doing anything to it like shaking and stirring.
•How Does diffusion work? In gases and liquids, the particles move randomly from place to place, and the particles collide with each other or with their container and then the particles are spread to the whole container.
•The materials I will use are:
•* 3 Beakers
•* One cold water, One boiled / hot water, One room temp (30 Celsius)
•* Food coloring
•1. First of all, I made sure that I had 3 regular spoons for 3 food coloring drops.
•2. Next, I Made sure that every water was in the right temp where one boiled water, one room temp 30 C and one cold water approximately 15 Degrees and they were all 300 ml.
•3. Then I prepared the timer for 5 mins.
•4. After that, I put one food coloring drop for every beaker and start the timer
•5. After 5 minutes, I took photos of all 3 beakers and watched the whole thing.
•I saw that the food coloring drip only took 30 seconds to collide with the hot / boiled water, I looked at the room temperature water 30 Degrees C needed approximately 2.5 minutes for the food coloring to collide with it, and finally the 5 minutes finished BUT the food coloring did not collide well with the cold water and had many low concentration areas.
•I found out that there is a huge difference between the three beakers (except for the hot and room temp), I saw that the food coloring and hot water collided really quickly and by that I can say that the rate of diffusion increases as the kinetic energy increases. Similarly, the beaker with the 30 Degrees water was close to the boiled water and all molecules collided with the food coloring but took 2 minutes more. Lastly the cold water, the cold water needed more than 5 minutes to collide but still not all molecules had collided with the food coloring drip and there were many areas with low concentration.
•After finishing the experiment, I can start with the results that came out and they are:
•Diffusion rate increases as kinetic energy increases and by that I can say that the boiled / hot water has the highest rate of diffusion because of the kinetic energy inside it.
•The diffusion rate in cold water would be really low because of the small amount of kinetic energy in it so water molecules would not collide with the food coloring.
•In conclusion, as you remember my research question was that “How does temperature affect the rate of diffusion?” and I answered it in my data analysis where the rate of diffusion in hot water is high and the rate of diffusion increases as the kinetic energy increases.
•And even my hypothesis was right where I stated that the higher the temperature the faster diffusion rate inside the water and less time of diffusion in the beaker will take because of the increase of the kinetic energy in the particles so they will mix more quickly.
•This is a really important subject to study in science because diffusion happens all the time where the diffusion of oxygen and carbon dioxide gas occurs in our lungs, the diffusion of water, salts, and waste that occurs in the kidneys.
•The research ATL Skill was really helpful in this experiment wherein background research I had to search for some information to help you understand the process going on and that will happen in the experiment.
A practical guide to diffusion models.
The motivation of this blog post is to provide a intuition and a practical guide to train a (simple) diffusion model [Sohl-Dickstein et al. 2015] together with the respective code leveraging PyTorch. If you are interested in a more mathematical description with proofs I can highly recommend [Luo 2022] .
In general, the goal of a diffusion model is to be able to generate novel data after being trained on data points of that distribution.
Here, let’s consider a simple 2D toy dataset provided by scikit-learn to make this example as simple as possible:
Diffusion models define a forward and backward process:
To generate new samples by starting from random noise, one aims to learn the backward process.
To be able to start training a model that learns this backward process, we first need to know how to do the forward process.
The forward process adds noise at every step $t$ controlled by parameters \(\{\beta_t\}_{t=1, \dots, T}, \beta_{t-1} < \beta_t, \beta_T = 1\):
As \(t \rightarrow T\) this distribution becomes a multi-variate Gaussian distribution \(\mathcal{N}(0, \mathbf{I})\).
So one starts with the original data samples $x_0$ and then gradually add noise to the samples:
The cool thing about this being Gaussian noise is that instead of simulating this forward process by iteratively sampling noise, one can derive a closed form for the distribution at a certain $t$ given the original data point $x_0$ so one has to only sample noise once:
with $\alpha_t = 1 - \beta_t$ and $\bar{\alpha}_t = \prod_{s = 1}^t \alpha_s$.
Let’s implement this:
Next, we want to train a model that reverses that process.
For this, one can show that the there is also a closed form for the less noisy version $x_{t-1}$ given the next sample $x_t$ and the original sample $x_0$.
and $\epsilon_0 \sim \mathcal{N}(0, \mathbf{I})$ is the noise drawn to perturb the original data $x_0$ 1 .
Obviously, we cannot use this directly to generate new data since this relies on knowing the original datapoint $x_0$ in the first place but we can use it to generate the ground truth data for training a model that does not rely on $\mathbf{x}_0$ and predicts $\epsilon_0$ from the noisy data $\mathbf{x}_t$ and $t$ alone 2 .
Let’s define a small neural network $\epsilon_{\mathbf{\theta}}(\mathbf{x}_t, t)$ where $\mathbf{\theta}$ are the parameters of the network that does just that:
Here, we encode the timestamp of the diffusion process $t$ as a one-hot vector with a single layer and then concatenate this information with the noisy data.
Next up : Training the model to predict the noise. For this, one can just sample $t$’s, use the forward process to generate the noisy sample $x_t$ together with the noise $e_0$, and train the model to reduce the mean squared error between the predicted noise and $e_0$.
After training the model to predict the noise $\epsilon$, we can simply iteratively run the reverse process to predict $\mathbf{x}_{t-1}$ from $x_t$ starting from random noise $\mathbf{x}_T \sim \mathcal{N}(0, \mathbf{I})$ as defined in \eqref{eq:reverse} where we set the mean:
Now, let’s sample new data points and plot them:
We can also inspect the (negative) direction of the predicted noise vector at a particular timestamp $t$ for each position in a grid to visualize the dynamics a sample follows during the reverse process as a vector field:
One can see that as $t \rightarrow 0$ more fine-grained structure emerges that guides the sample to the original data manifold. At $t=T$ samples are guided coarsely towards the center as the signal is still very noisy and hard for the network to predict.
Working on this small dataset already revealed some important things that one has to consider when training diffusion models. In particular, in the beginning when I started to implement this from the paper description, a huge amount of diffusion steps ($T=1000$) were required to yield good results.
Further looking into the literature and appendix of the papers revealed some things that brought down the diffusion steps required to $T=10$:
Check out the full notebook which this blog post is based on here .
This is one possible parameterization of the mean that is most effective based on the experiments in [Ho et al. 2020] . [Luo 2022] summarizes two other paramterizations in the literature, e.g., regressing the mean directly. ↩
Here we treat the variances as fixed. [Nichol and Dhariwal 2021] propose to learn these with an additional objective. ↩
Learning distributions on compact support using normalizing flows --> january 10, 2022 -->, why did my neural network do that --> august 12, 2020 -->.
Revision note.
Biology & Environmental Systems and Societies
An example of how to set up an experiment to investigate diffusion
An example of how to set up an experiment to investigate the effect of changing surface area to volume ratio on the rate of diffusion
When an agar cube (or for example a biological cell or organism) increases in size, the volume increases faster than the surface area, because the volume is cubed whereas the surface area is squared. When an agar cube (or biological cell / organism) has more volume but proportionately less surface area, diffusion takes longer and is less effective. In more precise scientific terms, the greater the surface area to volume ratio , the faster the rate of diffusion !
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Alistair graduated from Oxford University with a degree in Biological Sciences. He has taught GCSE/IGCSE Biology, as well as Biology and Environmental Systems & Societies for the International Baccalaureate Diploma Programme. While teaching in Oxford, Alistair completed his MA Education as Head of Department for Environmental Systems & Societies. Alistair has continued to pursue his interests in ecology and environmental science, recently gaining an MSc in Wildlife Biology & Conservation with Edinburgh Napier University.
Diffusion Course documentation
Diffusion course.
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Welcome to Unit 1 of the Hugging Face Diffusion Models Course! In this unit, you will learn the basics of how diffusion models work and how to create your own using the 🤗 Diffusers library.
Here are the steps for this unit:
:loudspeaker: Don’t forget to join the Discord , where you can discuss the material and share what you’ve made in the #diffusion-models-class channel.
Diffusion models are a relatively recent addition to a group of algorithms known as ‘generative models’. The goal of generative modeling is to learn to generate data, such as images or audio, given a number of training examples. A good generative model will create a diverse set of outputs that resemble the training data without being exact copies. How do diffusion models achieve this? Let’s focus on the image generation case for illustrative purposes.
The secret to diffusion models’ success is the iterative nature of the diffusion process. Generation begins with random noise, but this is gradually refined over a number of steps until an output image emerges. At each step, the model estimates how we could go from the current input to a completely denoised version. However, since we only make a small change at every step, any errors in this estimate at the early stages (where predicting the final output is extremely difficult) can be corrected in later updates.
Training the model is relatively straightforward compared to some other types of generative model. We repeatedly 1) Load in some images from the training data 2) Add noise, in different amounts. Remember, we want the model to do a good job estimating how to ‘fix’ (denoise) both extremely noisy images and images that are close to perfect. 3) Feed the noisy versions of the inputs into the model 4) Evaluate how well the model does at denoising these inputs 5) Use this information to update the model weights
To generate new images with a trained model, we begin with a completely random input and repeatedly feed it through the model, updating it each time by a small amount based on the model prediction. As we’ll see, there are a number of sampling methods that try to streamline this process so that we can generate good images with as few steps as possible.
We will show each of these steps in detail in the hands-on notebooks here in unit 1. In unit 2, we will look at how this process can be modified to add additional control over the model outputs through extra conditioning (such as a class label) or with techniques such as guidance. And units 3 and 4 will explore an extremely powerful diffusion model called Stable Diffusion, which can generate images given text descriptions.
At this point, you know enough to get started with the accompanying notebooks! The two notebooks here come at the same idea in different ways.
Chapter | Colab | Kaggle | Gradient | Studio Lab |
---|---|---|---|---|
Introduction to Diffusers | ||||
Diffusion Models from Scratch |
In Introduction to Diffusers , we show the different steps described above using building blocks from the diffusers library. You’ll quickly see how to create, train and sample your own diffusion models on whatever data you choose. By the end of the notebook, you’ll be able to read and modify the example training script to train diffusion models and share them with the world! This notebook also introduces the main exercise associated with this unit, where we will collectively attempt to figure out good ‘training recipes’ for diffusion models at different scales - see the next section for more info.
In Diffusion Models from Scratch , we show those same steps (adding noise to data, creating a model, training and sampling) but implemented from scratch in PyTorch as simply as possible. Then we compare this ‘toy example’ with the diffusers version, noting how the two differ and where improvements have been made. The goal here is to gain familiarity with the different components and the design decisions that go into them so that when you look at a new implementation you can quickly identify the key ideas.
Now that you’ve got the basics down, have a go at training one or more diffusion models! Some suggestions are included at the end of the Introduction to Diffusers notebook. Make sure to share your results, training recipes and findings with the community so that we can collectively figure out the best ways to train these models.
The Annotated Diffusion Model is a very in-depth walk-through of the code and theory behind DDPMs with maths and code showing all the different components. It also links to a number of papers for further reading.
Hugging Face documentation on Unconditional Image-Generation for some examples of how to train diffusion models using the official training example script, including code showing how to create your own dataset.
AI Coffee Break video on Diffusion Models: https://www.youtube.com/watch?v=344w5h24-h8
Yannic Kilcher Video on DDPMs: https://www.youtube.com/watch?v=W-O7AZNzbzQ
Found more great resources? Let us know and we’ll add them to this list.
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The following points highlight the top five experiments on diffusion. The experiments are: 1. Diffusion of S olid in Liquid 2. Diffusion of Liquid in Liquid 3. Diffusion of Gas in Gas 4. Comparative Rates of Diffusion of Different Solutes 5. Comparative rates of diffusion through different media.
Diffusion of s olid in liquid:.
Experiment:
A beaker is almost filled with water. Some crystals of CuSO 4 or KMnO 4 are dropped carefully without disturbing water and is left as such for some time.
Observation:
The water is uniformly coloured, blue in case of CuSO 4 and pink in case of KMnO 4 .
The molecules of the chemicals diffuse gradually from higher concentration to lower concentration and are uniformly distributed after some time. Here, CuSO 4 or KMnO 4 diffuses independently of water and at the same time water diffuses independently of the chemicals.
Diffusion of liquid in liquid:.
Two test tubes are taken. To one 30 rim depth of chloroform and to the other 4 mm depth of water are added. Now to the first test tube 4 mm depth of water and to the other 30 mm depth of ether are added (both chloroform and ether form the upper layer).
Ether must be added carefully to avoid disturbance of water. The tubes are stoppered tightly with corks. The position of liquid layers in each test tube is marked and their thickness measured.
The tubes are set aside for some time and the thickness of the liquids in each test tube is recorded at different intervals.
The rate of diffusion of ether is faster than that of chloroform into water as indicated by their respective volumes.
The rate of diffusion is inversely proportional (approximately) to the square root of density of the substance. Substances having higher molecular weights show slower diffusion rates than those having lower molecular weights.
In the present experiment ether (C 2 H 5 -O-G 2 H 5 , J mol. wt. 74) diffuses faster into water than chloroform (CHCI 3 , mol. wt. 119.5). This ratio (74: 119-5) is known as diffusively or coefficient of diffusion.
Diffusion of gas in gas:.
One gas jar is filled with CO 2 (either by laboratory method: CaCO 3 + HCL, or by allowing living plant tissue to respire in a closed jar). Another jar is similarly filled with O 2 (either by laboratory method: MnO 2 + KClO 2 , or by allowing green plant tissue to photosynthesize in a dosed jar). The gases may be tested with glowing match stick.
The oxygen jar is then inverted over the mouth of the carbon dioxide jar and made air-tight with grease. It is then allowed to remain for some time. The jars are carefully removed and tested with glowing match stick.
The glowing match sticks flared up in both the jars.
The diffusion of CO 2 and O 2 takes place in both the jars until finally the concentrations are same in both of them making a mixture of CO 2 and O 2 . Hence the glowing match sticks flared up in both the jars.
Comparative rates of diffusion of different solutes:.
3.2gm of agar-agar is completely dissolved in 200 ml of boiling water and when partially cooled, 30 drops of methyl red solution and a little of 0.1 N NaOH are added to give an alkaline yellow colour. 3 test tubes are filled three-fourth full with agar mixture and allowed to set.
The agar is covered with 4 ml portion of the following solutions, stoppered tightly and kept in a cool place:
(a) 4 ml of 0-4% methylene blue,
(b) 4 ml of 0.05 N HCl, and (4.2 ml of 0.1ml HCL plus 2 ml of 0-4% methylene blue.
The diffusion of various solutes is recorded in millimeters after 4 hours. The top of the gel should be marked before the above solutions are added.
The rate of diffusion of HCL alone (tube b) is faster compared to the combination of methylene blue and HCl (tube c) and minimum in case of methylene blue alone (tube a).
Different substances like gases, liquids and solutes can diffuse simultaneously and independently at different rates in the same place without interfering each other.
HCL being gaseous in nature and of lower molecular weight can diffuse much faster than methylene blue which is a dye of higher molecular weight having an adsorptive property. Hence in combination, these; two substances diffuse more readily than methylene blue alone.
Comparative rates of diffusion through different media:.
January 2, 2012 By Emma Vanstone 9 Comments
I love a good cup of tea. In fact, I cannot actually function without one first thing in the morning. If you’re like me, then this investigation is definitely needed in your house so that you can ensure your kids are equipped with the best tea-making skills and have the best scientific knowledge to back up what makes a good cup of tea! This investigation looks at diffusion through the partially permeable membrane of a tea bag.
So firstly, we want to know what type of teabag makes the best drink?
Is it a square, a pyramid or a circle bag?
The activity involves using hot water, so adult supervision is essential.
You’ll need
A stopwatch/timer
A piece of white paper
3 clear glass mugs (you are going to add hot water, so not thin ones that could crack)
Circle, triangle and pyramid tea bags
Thermometer or kettle
1. On the piece of white paper, draw a cross with a marker pen
2. Place one mug over the cross
3. Add the circle teabag
4. Boil water from the kettle and measure out 150ml (if you have a thermometer, you can improve reliability by keeping the temperature constant)
5. Pour over the teabag and start the stopwatch
6. Time how long it takes for the cross to disappear
7. Repeat with the pyramid and square teabag.
8. To make the investigation results more accurate, repeat with each teabag three times.
Record your results in a table
So which teabag was quicker?
You should find that the pyramid teabag was the quickest.
Why do you think this is?
As the water is added to the teabag, it causes the tea leaves to move and triggers diffusion of the leaves. Diffusion is defined as the movement of a substance from an area of higher concentration to an area of lower concentration. There are lots of tea molecules in the bag and none outside. The leaves themselves can’t pass through the bag, but their smaller particles containing colour and flavour can (the teabag itself acts as the partially permeable membrane). The addition of heat (from the hot water) to the tea bag causes its molecules to move much faster than at room temperature. This energy is more readily released in a shorter period of time than a tea bag filled with room temperature or cold water. The teabag shape affects the surface area and the pyramid due to its 3D shape providing more surface area for diffusion to take place and more area in the middle for the tea molecules to move around in spreading the colour and flavour.
Ok, so now they know which is the best teabag to use and how to let it brew…so I suggest you ask for a nice cuppa now!
Last Updated on February 23, 2023 by Emma Vanstone
Science Sparks ( Wild Sparks Enterprises Ltd ) are not liable for the actions of activity of any person who uses the information in this resource or in any of the suggested further resources. Science Sparks assume no liability with regard to injuries or damage to property that may occur as a result of using the information and carrying out the practical activities contained in this resource or in any of the suggested further resources.
These activities are designed to be carried out by children working with a parent, guardian or other appropriate adult. The adult involved is fully responsible for ensuring that the activities are carried out safely.
January 06, 2012 at 8:20 pm
What a fun experiment. You always find ways to make the most ordinary things interesting. Thanks for sharing on Monday Madness.
January 06, 2012 at 9:43 pm
January 08, 2012 at 5:37 pm
Interesting especially since all my tea bags are rectangular. I don’t drink it a lot, but and getting to like it more and more. I haven’t tried many brands yet so I will have to start exploring it more. Fun exploration with the kids and I think they probably learned a lot about figuring things out on their own from it.
October 23, 2013 at 2:29 am
awesome job
February 17, 2014 at 8:32 pm
Jah hey thnx.i have learned smthng http://
February 23, 2014 at 12:40 am
Where did the square teabags come from? I have enjoyed tea in that shape but can’t recall what brand. Thanks!
April 29, 2014 at 7:13 pm
thanks! thats really helpful we’re doing a science project on how the shape of the tea bag affects the taste so that was really helpful!!
September 17, 2017 at 11:32 pm
Interesting and helpful. Thanks a lot. Although the cross takes a long time to remove for some reason. Wasnt sure in what marker to use though.
September 29, 2019 at 5:28 pm
WOW i love talking about tea irs so fun wowowowow i learnt science from tea omg wowowowowow omg tea is so interesting
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Scientific Reports volume 14 , Article number: 18609 ( 2024 ) Cite this article
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Metrics details
Semantic segmentation plays a crucial role in interpreting remote sensing images, especially in high-resolution scenarios where finer object details, complex spatial information and texture structures exist. To address the challenge of better extracting semantic information and ad-dressing class imbalance in multiclass segmentation, we propose utilizing diffusion models for remote sensing image semantic segmentation, along with a lightweight classification module based on a spatial-channel attention mechanism. Our approach incorporates unsupervised pretrained components with a classification module to accelerate model convergence. The diffusion model component, built on the UNet architecture, effectively captures multiscale features with rich contextual and edge information from images. The lightweight classification module, which leverages spatial-channel attention, focuses more efficiently on spatial-channel regions with significant feature information. We evaluated our approach using three publicly available datasets: Postdam, GID, and Five Billion Pixels. In the test of three datasets, our method achieved the best results. On the GID dataset, the overall accuracy was 96.99%, the mean IoU was 92.17%, and the mean F1 score was 95.83%. In the training phase, our model achieved good performance after only 30 training cycles. Compared with other models, our method reduces the number of parameters, improves the training speed, and has obvious performance advantages.
Introduction.
Semantic segmentation of remote sensing images classifies each pixel into different semantic categories. Compared with low-level features, semantic segmentation can directly obtain pixel-level semantic classes and is a crucial intermediate representation for remote sensing image understanding, which can promote intelligent analysis based on remote sensing images. Therefore, it has vital application value in fields such as land use planning, urban construction, and environmental monitoring 1 , 2 , 3 . With the development of remote sensing technology, images with higher spatial resolution can identify smaller and more object categories and details, increasing the seriousness of problems related to the same object with different spectra and different objects with the same spectrum. In multiclass semantic segmentation, an increase in the number of semantic classes will make the training samples of each class sparse, the difference between classes decrease, and the boundaries of ground objects become fuzzy, which brings great challenges to the training and reasoning of the model. How to extract semantic information effectively is the key to semantic segmentation. In an image, the semantic information of an object is usually determined by its local and global context. The model needs to be able to capture this contextual information to better understand the semantic relationships in the image 4 , 5 .
In recent years, the development of deep learning has provided reference experience for extracting information from remote sensing data. Compared with traditional methods, deep learning can better extract spectral-spatial features. Currently, many deep learning models, including classic convolutional neural networks and recently popular vision transformers (ViTs), have been successfully applied to remote sensing image segmentation 6 .
In semantic segmentation tasks, transformers are good at modeling global de-pendencies in images through attention mechanisms and can more accurately classify at the pixel level. However, the computational complexity is high, which is not conducive to processing high-resolution images 7 . CNNs have excellent local feature learning capabilities through convolution and pooling layers and can detect details. Techniques such as separable convolutions and dilated convolutions can also reduce computations. However, they lack representation for long-range pixel correlations and are weaker in extracting global contextual information 8 . Although Atrous Spatial Pyramid Pooling (ASPP) 9 improves this, there is a high computational complexity. Currently, almost all existing deep learning models use CNNs or transformers as a single network structure. The above problems cannot be solved, resulting in a poor segmentation effect. At the same time, to obtain higher accuracy, different modules are stacked and nested for reuse, which complicates the network and lengthens the training cycle. If a CNN and transformer are used together for semantic segmentation, then the segmentation effect will be greatly improved 10 . Therefore, how to combine the advantages of the two for remote sensing image segmentation is a problem worth considering. In recent years, denoising diffusion probabilistic models (DDPMs) have provided a more effective and reliable technical framework for image generation. It has become a vital method in the field of image generation. The diffusion model denoising module takes UNet as the main structure of the model and embeds the transformer self-attention mechanism, position coding and residual module. The self-attention mechanism can capture global information from the entire feature map. Position coding provides the location of the elements in the noise sequence feature to the model. The residual module can prevent the problem of gradient disappearance in the model when the network focuses on local information and can improve the generalization ability of the network. As excellent models for image generation, diffusion models can capture the semantic structure present in the data 11 . Its detail retention capability enables the generation of fine-grained highly detailed images, preserving the context and interrelationships of various objects in the image 12 . In recent years 13 , 14 , 15 , the diffusion model has also been shown to have strong potential in semantic segmentation. Therefore, we hope that this method can be applied to the task of remote sensing image interpretation.
Our goal is to make use of the powerful global information extraction and long-range dependency modeling capabilities of diffusion models to meet the challenges of semantic segmentation of high-resolution remote sensing images. We propose a pretrained feature extractor. The feature extractor is a diffusion model based on the UNet architecture. It is trained in an unsupervised manner on remote sensing images and can extract multiscale features of images. The pre-trained prior knowledge can help the model to achieve better semantic segmentation. These features are then used for semantic segmentation. For the corresponding classification module, we use a spatial-channel attention mechanism that can fuse multiscale features. Notably, it is lightweight, and only the parameters of the classification module are finetuned during segmentation training.
The main contributions of this paper are summarized as follows:
Diffusion models are used for remote sensing image semantic segmentation.
An unsupervised remote sensing image feature extraction model is created using diffusion model.
A lightweight classification module based on a spatial-channel attention mechanism is proposed for processing multiscale features.
Experiments prove that our proposed model and modules have good results.
Our paper is organized as follows: “ Related Work ” section discusses related work in recent years. “ Method ” section details the overall structure of our proposed method. “ Experimental detail ” section adds some details about the experiment. “ Main Experiment ”, “ Ablation experiments ” sections present and discuss the results of comparative experiment and ablation studies. “ Discussion ” section analyzes the conclusion and outlines future research directions. Appendix presents multiscale features.
Semantic segmentation of high-resolution remote sensing images.
Due to the uniqueness of high-resolution remote sensing images, traditional methods are not ideal for segmentation. In recent years, people have gradually introduced deep learning and made impressive progress. This is mainly due to the automatic feature extraction and end-to-end training of deep learning models. Specifically, the great success of convolutional neural networks in natural image processing has laid the foundation for deep learning in semantic segmentation. It also promoted convolutional network-based semantic segmentation models for remote sensing image analysis. Based on CNNs, researchers proposed the first end-to-end fully convolutional network (FCN) 16 , which replaces the fully connected layer with a convolutional layer to process arbitrarily sized images. Deng 17 fused the spectral index with the FCN model to improve the segmentation effect. Piramanayagam's 18 combination method based on random forest and FCN also achieved high segmentation accuracy. Li 19 used a nonuniform void convolutional network to extract high-level contour features of infrared image targets and fused high-level features of infrared images with detailed features of three scales of RGB images through fusion technology, thus enhancing the feature extraction capability of the network. Guan 20 designed a multiscale feature fusion module in an FCN and used superpixels to optimize the edge, which not only utilized spatial information but also improved the segmentation accuracy. The above model improves the ability of the model to recognize the edge information of the object and effectively integrates the feature information of the high and low layers of the image.Another mainstream framework, the Transformer, consists entirely of an attention mechanism and a feedforward neural network. HRNet 14 is a semantic segmentation network structure proposed by Microsoft Research in 2019 that integrates a transformer structure. The problem of feature resolution downsampling in a U-shaped network is solved, and the segmentation network can retain high-resolution detailed information. SegFormer 13 first used a transformer encoder to build a backbone in the field of semantic segmentation. The hybrid converter module combines convolution and self-attention with both local and global modeling capabilities.
Diffusion models have roots in nonequilibrium thermodynamics 21 , initially concentrating on material diffusion during early research. Over time, their application has extended to diverse domains, including interpolation and prediction in time series data modeling. In the realm of waveform generation, WaveGrad 22 introduced a conditional model that estimates the gradient of data density. This model takes Gaussian white noise as input and iteratively refines input signals using a gradient-based sampler. Within natural language processing, numerous methods grounded in diffusion models have been developed for text generation. DiffuSeq 23 orchestrates diffusion processes in latent space, introducing a novel conditional diffusion model tailored for more intricate text-to-text generation tasks. In the field of computer vision, diffusion models stand as a vibrant area of research, with widespread applications such as image generation 24 , 25 , 26 , 27 , 28 , 29 , 30 , image super-resolution 31 , 32 , 33 , 34 , 35 , image restoration 36 , 37 , 38 , image editing 39 , 40 , and image-to-image translation 41 , 42 , 43 . Palette 44 employed a conditional diffusion model to establish a unified framework for four image generation tasks: colorization, inpainting, decropping, and JPEG restoration. By focusing on synthesizing images with specific desired styles 45 , image translation achieves conditional image generation via DDPM in iterative super-resolution (SR3) 46 . SR3 employs stochastic iterative denoising processes for super-resolution. Imagen Video 47 pioneers cascaded video diffusion models to generate high-definition videos, effectively transferring some methods proven in text-to-image generation tasks to video generation.Researchers have applied diffusion models in segmentation and classification with great potential 48 , 49 , 50 , 51 . Earlier works have focused on representations for zero-shot segmentation 52 or medical images. Wu 53 proposed MedSegDiff-V2, which combines UNet and transformers, outperforming medical image methods. Baranchuk 54 proved that the diffusion model can also be used for semantic segmentation, and the extracted features have rich semantic information. Some studies have explored detection and instance segmentation 26 , 32 .
As shown in Fig. 1 , the whole experiment process is divided into two stages: pre-training and pixel classification. As shown in the left part of Fig. 1 , during the pre-training phase, we input the image into the diffusion model. The diffusion model will degrade and reconstruct the image and learn the semantic information of the images. The most important purpose of pre-training is to enable the model to learn the semantic information of the image, which determines the quality of the features.After the training is completed, the weights of the diffusion model will be frozen, and the diffusion model becomes a feature extractor. We use the diffusion model to extract the image features. According to the set time steps, we will get the multi-scale features under different time steps. After these features are input into the classification module, the pixels are classified with the help of tags, and the result is obtained.We will cover these modules and their procedures in this “ Diffusion Model ” and “ Optimization of the Diffsuion Model ” sections introduce the diffusion and optimization process of the diffusion model. “ Diffusion Model Network Structure ” section shows the network structure of the Diffusion Model, and introduces how the diffusion model carries out feature extraction. “ Feature Extraction ” section introduces the process and details of feature extraction of remote sensing images using diffusion model. “ Lightweight Classification Module ” section The Classification Module dealing with multi-scale features is introduced.
The overall training process of the RS-Dseg.
Forward diffusion is a forward Markov chain diffusion process that uses a Gaussian noise model. As shown in Fig. 2 , in this process, given the real image \({x}_{0}\) , Gaussian noise with variance \({\beta }_{t}\) is continuously added at a given step, resulting in a noise sequence: \({x}_{1},{x}_{2}\) … After adding enough noise, the image is completely corrupted.
Diagram of the forward and reverse processes.
Its probability distribution is of the form:
The joint distribution of \({x}_{1:T}\) given \({x}_{0}\) is as follows:
If we define \({\alpha }_{t}=1-{\beta }_{t}\) , then \({\prod }_{t=1}^{T}{\alpha }_{i}\) is written for \({\overline{\alpha }}_{t}\) , \({\overline{\alpha }}_{t}\) is the hyperparameter set by the Noise schedule, and (1) can be transformed as follows:
Therefore, Eq. ( 4 ) shows that the value of \({x}_{t}\) depends on the original image \({x}_{0}\) and the random noise \(\epsilon\) . In other words, from the initial value \({x}_{0}\) and the diffusion rate at each step, we can obtain \({x}_{t}\) at any time. When \(t\to \infty\) , \({\beta }_{t}\) continues to increase, and \({\overline{\alpha }}_{t}\) gradually decreases. The mean and variance of \(\epsilon\) are 0 and 1, respectively. Finally, \({x}_{t}\) is an isotropic Gaussian distribution.
The reverse process is the denoising process. we need to learn a model \({p}_{\theta }\) to approximate these conditional probabilities in order to run the reverse diffusion process:
Therefore, the derivation of the following formula is mainly to solve for the mean value. When the variance is given, the Gaussian distribution function of the specified distribution mode can be obtained by solving the mean to simulate the image. Therefore, the derivation of the following formula is used to determine the variance. A standard Gaussian noisy image \({x}_{t}\) is generated from the prior distribution, and then the noise is gradually removed from it by running a learnable backward running Markov chain. The posterior probability of the forward process can be expressed as follows:
According to the Bayes formula and the normal distribution property of Gaussian noise, we can obtain the following:
Therefore, we obtain the a posteriori formula with the parameter \({\alpha }_{t}\) :
In the forward process, we use randomly generated noise to degrade the image. In the reverse process, we use the corrupted image to estimate the distribution of noise and expect the predicted noise to be close to the real noise. This process can be realized through the iterative training of a neural network. The mathematical principle is to maximize the log-likelihood of the model's predicted distribution, optimizing the cross-entropy between the true distribution \({x}_{0}\) and the predicted distribution \(q\left({x}_{0}\right)\) ,we can obtain the following with Eq. ( 5 ):
We set \({\Sigma }_{\theta }({x}_{t},t)={\sigma }_{t}^{2}I\) , \({\sigma }_{t}^{2}={\widetilde{\beta }}_{t}=\frac{1-{\overline{\alpha }}_{t-1}}{1-{\overline{\alpha }}_{t}}{\beta }_{t}\) , we can obtain the following with Eq. ( 4 ):
where C is a constant that does not depend on θ。From Eq. ( 4 ), we deduce that \({x}_{0}=\frac{{x}_{t}-\sqrt{1-{\overline{a} }_{t}}{\varvec{\epsilon}}}{\sqrt{{\overline{a} }_{t}}}\) . According to the standard Gaussian density function, the mean and variance can be parameterized as follows:
Therefore, at this point, in the backward process, to compute \({\mu }_{t}\) , we need to compute \({x}_{t}\) and \({{\varvec{\epsilon}}}_{t}\) . Therefore, we need to train a neural network to predict the distribution of \({{\varvec{\epsilon}}}_{t}\) . The mean \({\mu }_{t}\) is obtained by predicting the Gaussian noise \({{\varvec{\epsilon}}}_{\theta }\left({x}_{t},t\right)\) from \({x}_{t}\) and \(t\) for each time step. By solving the KL divergence of the multivariate Gaussian distribution, and bring Eq. ( 8 ) into Eq. ( 11 ), we can obtain the following:
Empirically, Ho 25 found that training the diffusion model works better with a simplified objective that ignores the weighting term:
The whole process is to take the input \({x}_{0}\) from \(1\dots T.\) We randomly sample a T in \(t\) . The noise \(\epsilon_{t} \sim {\mathcal{N}}\left( {0,{\varvec{I}}} \right)\) is then sampled from the standard Gaussian distribution. Finally, the objective function \({L}_{t-1}^{\text{simple}}\) is minimized.
As shown in Fig. 2 , the remote sensing images are input into the denoising model as \({x}_{0}\) , and the model is allowed to add noise and reconstruct these remote sensing images. In this way, the distributional noise of the semantic information of these pictures is learned to predict \({\mu }_{t}\) . The whole process does not require labels, and unsupervised pretraining is performed. After completing this process, the denoising model freezes the network parameters. It is subsequently used for feature extraction.
Figure 3 shows the network structure of the Denoiser. It has a U-shaped structure similar to that of the UNet network. It mainly includes a decoder, an intermediate layer and an encoder. The denoiser mainly consists of a series of Basic I blocks and Basic blocks. At the same time, we connect the encoder to the decoder through the jump connection layer. The denoiser includes the residual structure of the CNN and the self-attention mechanism of the transformer. As shown in Fig. 4 , we used three images with different noise levels as input for model training.
Diffusion model network structure.
Feature extraction and processing.
To help the model better understand and handle the sequence data, we added positional encoding first, as shown in Fig. 3 . This generates a positional code for the noise sequence data, introducing location information into sequence data. Position coding is generated by sine and cosine functions and is related to noise levels. Given the input noise level \(N\_L\) , the dimension of the position encoding is \(dim\) , and \(dim\) is half of the input dimension. First, the position vector \({step}_{i}\) is computed to generate a sequence whose values are in the range [0, 1), representing the proportion of each position relative to the entire sequence. This sequence is mapped to a new range using an exponential function, ensuring large differences in encoding at different locations:
where i represents the position in the sequence and k represents the index in the position vector. \(encodin{g}_{i,j}\) represents the components of each position in the position coding matrix. The sine and cosine values are then concatenated to form the final positional coding vector. At the very beginning of each stage, we add a linear affine layer to embed location information for the feature representation of the network, which helps the model better understand and obtain semantic information for different locations. The Basic I block and Basic block are the main components of the network. The Basic block consists of a GroupNorm (GN) layer, a swish activation function, a dropout layer, and a convolution layer. Basic I replaced the dropout layer with an identity map. We replaced the BatchNorm layer with a GN layer to reduce the impact of BatchSize on the model. The Swish activation function adds nonlinearity to the network, and in deep models, it works better than ReLU 55 . In the first stage of the encoder, to avoid losing considerable semantic information in the initial stage, we do not downsample after the two Basic blocks and Basic I blocks. In other stages, subsampling or attention mechanisms are added at the end. For the input image \(X\in {\mathbb{R}}^{H\times W\times 3}\) , after the first stage, the output is \({F}_{1}^{d}\in {\mathbb{R}}^{H\times W\times 128}\) . From the second stage to the fourth stage, after downsampling at the end, the height and width of the feature are reduced by half, the number of channels is doubled, and the output is \({F}_{n}^{d}\in {\mathbb{R}}^{\left(\frac{H}{{2}^{n}}\right)\times \left(\frac{W}{{2}^{n}}\right)\times \left(128\times n\right)},n=\text{2,3},4\) . In the fifth stage, we do not increase the number of channels of the feature because we consider that when making a jump connection with the feature of the decoder, we increase the number of channels in the second dimension of the feature. The final output of the encoder is \({F}_{5}^{d}\in {\mathbb{R}}^{\left(\frac{H}{16}\right)\times \left(\frac{W}{16}\right)\times 1024}\) .However, the encoder does not increase the number of characteristic channels in stage 5. In the middle layer, the model adjusts \({F}_{5}^{d}\) by a self-attention mechanism.The following is an additional feature representation for the next stage of the decoder:
Then, Q, K, and V can be calculated via convolution:
where \(i\) and \(j\) represent row \(i\) and column \(j\) of \(norm\) , respectively; ( \(m\) , \(n\) ) and K is row \(m\) and column \(n\) of the convolution kernel; and k and l are the height and width of the convolution kernel, respectively. With the Chunk function, Q, K, and V can be obtained. Then, the attention score is calculated:
Finally, the weighted sum of V is calculated using the attention weight:
The output of the middle layer can be expressed as \(F^{m} \in {\mathbb{R}}^{{\left( \frac{H}{16} \right) \times \left( \frac{w}{16} \right) \times 1024}}\) .As shown in Fig. 3 , before the start of each stage of the decoder, the characteristics of each stage of the encoder are combined with the output of the previous stage of the decoder through a jump connection as the input of the next stage of the decoder, \(x_{1} \in {\mathbb{R}}^{{\left( \frac{H}{16} \right) \times \left( \frac{W}{16} \right) \times 2048}}\) .
By connecting the feature graphs of the upsampling and downsampling paths, the low and high-level feature information is combined. This connection helps to retain richer image details and contextual information, avoiding the problem of information loss or disappearing gradients. Information fusion enables the network to focus on both local and global information, improves the model's ability to understand each part of the image, and thus improves the performance of image processing tasks. In addition, the connection feature also helps to introduce more spatial details in the upsampling process, improving the quality of the reconstructed image and the ability to retain details. In the decoder, there are many self-attention mechanisms. This helps the model better understand the semantic information between different features in the noise sequence. The upsampling at the end of each stage causes the size of the feature to become twice the size of the input, gradually returning to the original size of the input.
After pretraining, the network learns the noise distribution in different diffusion stages. In the reverse stage, the images of different noise levels generated in the forward stage are simulated by the random distribution of Gaussian noise. The whole process does not need backpropagation; it is the result of pretraining. We believe that the generated noise sequence images, due to the powerful global information extraction and remote dependence modeling capabilities of the diffusion model, highlight the importance of different ground objects, thereby helping the model better understand the differences between the various categories. The feature extraction of these images, which contain rich semantic information, helps the network classify pixels better. This is shown in the Appendix.
When performing semantic segmentation, in the reverse process, the feature extractor based on the UNet architecture can perform feature extraction. Figure 4 shows that we feed the trained samples into the denoising model and set the diffusion step in advance. The model gradually diffuses the initial Gaussian noise distribution to approximate the distribution of the sample image. The encoder in the denoising model extracts the image features with the setted diffusion steps. Wele 56 and Dmitry 54 reached a good conclusion on determining diffusion time. Therefore, we directly set the time t to [50, 100, 400]. Figure 4 and the Figure A.1 in Appendix show that images containing different noise levels have different scales of semantic information. The largest difference between the image with diffusion step t = 50 and the other two images is to distinguish the road from the other ground classes in the image. The diffusion step, t = 100, distinguishes natural from unnatural features. T = 400, contains the semantic information of each ground class. The combination of these features can ensure the segmentation accuracy of different land classes. In the Appendix A.1, we show the extracted multiscale features.
Using diffusion models to extract multiscale features is an important process. In the subsequent processing, we directly use these features for classification. Therefore, the extraction and selection of features largely determine the segmentation results. In the ablation experiment, we used different numbers of features for comparison experiments.
For the extracted multiscale features, the proposed classification module needs to classify and upsample them to obtain the results. These features come from images with 3 noise levels and contain different scales and channel numbers, so these features need to be fused and screened. We connect features through convolutional layers and then filter the features through attention mechanisms. Spatial attention and channel attention are weights that adaptively recalibrate features based on context dependencies. Compared with other methods, this method has fewer parameters and lower computational complexity. As shown in Fig. 5 , in addition to the attention mechanism, the whole module contains a small amount of convolution and upsampling, and few parameters are learned. This significantly improves the training speed of the model. and it will be discussed later in the ablation experiment. The experimental results also prove that spatial and channel attention mechanisms are more suitable for feature screening than other methods.
Classification module.
Since the features come from three noise levels, the features of the same size at different noise levels need to first be joined together by convolution. The input features go through a convolutional layer and then enter the channel compression and attention extraction module. In this module, multichannel multiscale features are excited because of their importance. As Fig. 5 shows, this attention module includes spatial squeeze and channel excitation (SSCE) and channel excitation and spatial squeeze (CSSE) 57 .
The SSCE module squeezes the features spatially through fully connected layers and sigmoid activation to obtain excitation weights. The weights are multiplied by the feature scales of each channel's features to capture the importance of different channel features. The extracted multiscale features can be defined as \(F\in {\mathbb{R}}^{H\times W\times C}\) . In SSCE, features are first squeezed spatially through a global pooling layer:
This operation compacts the global spatial information to \({f}_{1}\in {\mathbb{R}}^{1\times 1\times C}\) and then passes through two convolution layers and activation functions:
where \({f}_{2}\in {\mathbb{R}}^{1\times 1\times \left(\frac{C}{2}\right)}\) is the feature after the first convolution and \({f}_{3}\in {\mathbb{R}}^{1\times 1\times C}\) is the feature after two convolutions. Two convolution operations recalibrate the feature on the channel, activating the importance of the different channels after passing through the sigmoid activation function. \({f}_{3}\) acts as a scaling factor to activate the importance of the channel in the original feature:
\({F}_{1}\) represents the features that are given different channel importance after passing through the SSCE module. The CSSE module squeezes the features channelwise using convolutional layers and sigmoid activation to obtain excitation weights. Multiplying the weights with features at different spatial locations allows the capturing of spatial importance. Similarly, for the original feature \(F\in {\mathbb{R}}^{H\times W\times C}\) , passing it through a convolution layer and activation function can be expressed as follows:
\({f}^{1}\in {\mathbb{R}}^{H\times W\times 1}\) , which represents the importance of different spatial locations. \({f}^{1}\) will then act as a scaling factor to rescale the importance of different spatial locations in the original feature:
\({F}_{2}\) represents features that are given different spatial location importance after passing through the CSSE module. Finally, by adding \({F}_{1}\) and \({F}_{2}\) , the treated feature F^* is obtained. As shown in the Fig. 6 , these features that are excited in the channel and spatial positions are used for the final classification convolution and upsampling, and finally, the segmented semantic result is obtained.
Two-branch attention mechanism (SSCE and CSSE).
Experimental settings.
All experiments were conducted on a 64-bit Windows system equipped with an NVIDIA GeForce RTX 4090 24G GPU. In our experimentation, we first conducted unsupervised pretraining on the training and validation images, excluding the labels, to obtain the feature extractor. During pretraining, we employed the mean squared error (MSE) loss function, with a batch size of 4 and a learning rate of 0.00001. The optimizer utilized was AdamW, with an exponential moving average (EMA) coefficient of 0.9999. In the classification stage, we fixed the batch size at 8 and employed the SGD optimizer with a learning rate of 0.0001.
Accuracy evaluation. We begin from the overall and category perspectives. For the whole, we use the accuracy, average F1 score (Ave.F1), and average IoU (MIoU). These parameters can be represented by the following formula:
Additionally, the F1_score, IoU, recall, and precision are calculated separately for each class. They can be represented by the following formula:
where k is the number of categories.
Postdam : The urban scene classification dataset is provided by ISPRS 66 . The scene is located in the Postdam, which has large buildings, narrow streets and dense settlement structures. The entire dataset contains 6 categories: impervious surfaces, buildings, vegetation, trees, cars, and background. The dataset contains 38 remote sensing images. Among them, 24 images are the training set and 14 images are the test set. We divided the training and validation sets with 20:4 among the 24 remote sensing images. We resize the image to 5120 by 5120 and crop it to 256 by 256. The final dataset has a ratio of 8000:1600:5600. The final quantity ratio is 8000:1600:5600. The RGB values for each category are presented in Table 1 .
GID : The first dataset used in our research is the GID land cover dataset 58 from Wuhan University, which was captured by the Gaofen-2 (GF-2) satellite. We specifically utilized fine classification samples, which consisted of 15 labeled categories. The RGB values for each category are presented in Table 2 and are visualized according to the provided specifications. The dataset comprises 10 processed images with dimensions of 7200 × 6800, which we cropped into 256 × 256 patches.
We compared our model against FCN, ConvNeXt-v2, HRNet, DeepLabv3 + , UNet, SegNeXt, Segformer and FTransUNet. FCN is the first work of deep learning for semantic segmentation and can be adapted to any size of input. Deeplabv3 + and UNet represent early convolutional attention networks. The model can improve the use of features at different levels. The SegFormer model represents another structure transformer, focusing more on the overall message. ConvNeXt-v2 and SegNeXt are novel convolutional attention networks proposed in recent years. Through the information exchange of different branches, HRNet supplements the information loss caused by the reduction in the number of channels. FTransUNet is proposed to provide a robust and effective multimodal fusion backbone for semantic segmentation by integrating both CNN and Vit into one unified fusion framework. The parameters of each model we used in the experiments are shown in Table 3 . Under the premise of ensuring excellent experimental results, our classification head greatly reduces the experimental parameters compared with other models. A decrease in the number of experimental parameters can increase the training speed.
As shown in a and b of Fig. 7 , we recorded the loss values of the seven models on the two datasets over 80 training cycles. Our model (the blue curve) has a lower loss value at the beginning of training than do the other models. It gradually reaches the optimal parameters after approximately 30 training cycles. Other models require approximately 50–60 cycles. The training speed of our model is the fastest among these models.
Loss curve. ( a ): Postdam dataset. ( b ): GID dataset.
Table 4 lists the numerical results for each semantic segmentation method. The results show that the proposed RS-Dseg method is superior to other methods in terms of accuracy, MIoU and Ave.F1. UNet with an extended convolutional Deeplabv3 + and decent-encoder structure obtains global context information by extending the receptive field. The experimental results show that a SegFormer with a self-attention mechanism is inferior to our model in long-term dependence modeling. DeepLabv3 + with cavity convolution and residual connections and UNet with a ResNet101 skeleton achieve good results, both reaching 97%. HRNet ranks first among the other models. Compared with HRNet, our method improves the accuracy by 0.68%, the MIoU by 1.83%, and the Ave. F1 by 0.98%. Our approach exceeds the average of the other networks in all three indices by 2.22%, 5.88% and 3.54%. As shown in the third and fourth row of Fig. 8 , Our method is able to extract the edges of features more finely, which is validated in our MIoU values. At the same time, our method also shows a good ability to distinguish similar Categories.
Examples of semantic segmentation results on the Postdam dataset. ( a ): Ours. ( b ): FCN. ( c ): ConvNeXt-v2. ( d ): HRNet. ( e ): Deeplabv3+. ( f ): UNet. ( g ): SegNeXt. ( h ): SegFormer. ( i ): FTransUNet.
Table 5 lists the numerical results for each semantic segmentation method. The results show that the proposed RS-Dseg method is superior to other methods in terms of accuracy, MIoU and Ave.F1. At the same time, with respect to the three indices, the proposed method exceeds the average level of the other models. Our method achieves a 97.00% accuracy, exceeding that of the UNet model by 1.11%. With regard to MIoU, our model achieves a good score of 92.17%, an improvement of 1.48% compared to UNet. With regard to Ave.F1, our model demonstrates an improvement of 0.79% compared to UNet. Neither the transformer-based SegFormer and FTransUNet nor ConvNeXt-v2 and SegNeXt of the new convolutional networks perform as well as our method in this paper.
Compared to other semantic segmentation methods, our proposed model significantly improves object edge identification and integrity. Evaluations show that it increases the average IoU by 5% over others, demonstrating an advantage in accurately extracting edges. Figure 9 illustrates our model and other methods for segmenting the same image. Our model segments class boundaries more precisely, while others exhibit edge blurring. We attribute this to the multiscale features and channel-spatial attention modules. The Attention Mechanism enhances the representation of edge features by scaling the spatial and channel importance of the features. Additionally, our model achieves better object integrity, avoiding fragmentation. For instance, it greatly improves road segmentation accuracy. Compared to other methods, our approach more accurately maintains the coherence and integrity of edges. Future work will further enhance the representation of detailed object edges. For per-class segmentation, we evaluate result using IoU, F1, Precision and Recall. Table 6 shows that, except for artificial grasslands and shrubs, our model outperforms the other models for most of the other classes on most of the metrics. The bolded numbers in-dicate the highest scores among the seven models for that metric and class.
Examples of semantic segmentation results on the GID dataset. ( a ): Ours. ( b ): FCN. ( c ): ConvNeXt-v2. ( d ): HRNet. ( e ): Deeplabv3+. ( f ): UNet. ( g ): SegNeXt. ( h ): SegFormer. ( i ): FTransUNet.
To facilitate a more effective comparison, we choose seven common categories from a total of 15 land classes. Table 7 presents the IoU performance of different methods across these seven categories. Our model consistently outperforms the other models in each category. The average IoU reaches a satisfactorily high score of 93.04%. In comparison, HRNet and UNet, among other models, also achieve average IoU values exceeding 90%. Notably, our model surpasses UNet by approximately 1.7% in average IoU. For example, in the “Transportation” category, other models achieve a minimum IoU of only 64.91%, with the highest reaching 85.22%. Our model surpasses this highest value by 4%. This suggests that in the task of segmenting elongated features, our model demonstrates notably superior performance.
In this part, we first discuss the selection of the number of features. The model requires sampling the image through various diffusion steps, and in the decoder of the diffusion model, the size of the features doubles after each upsampling stage. Consequently, a multitude of features can be extracted from a single image. Generally, the more features available, the more beneficial it is for segmenting the model. However, an increase in the number of features results in a higher number of network parameters, thereby escalating the training burden. Hence, the number of features needs careful consideration. Subsequently, we empirically validate each module in this approach, particularly focusing on assessing the effectiveness of DDPM and the space-channel attention mechanism (SCAM).
First, we experiment with different numbers of selected features. As mentioned in “Feature Extraction” of “Method”, these features originate from the noise diffusion process. We set diffusion steps so that features of varying scales can be extracted at each step. The number of selected features also determines the classification module parameters. To balance suitable feature numbers and the parameter of classification modules, we tested 4, 5 and 6 feature sets.
According to Table 8 , as the number of features increases, the number of parameters also increases by millions. The average change in accuracy is approximately 0.03%. The MIoU increases by approximately 1% with 6 features, and the Ave.F1 is greater than 96%. The parameter count also increases to approximately fifty-two million. Considering all factors, we selected 4 features for subsequent comparative experiments.As shown in Figure 10 , the segmentation results reveal a few intraclass inconsistencies when using only four features. However, these inconsistencies vanish almost entirely as the number of features increases to six. Despite the differences in categorical consistency, the segmentation boundary and connectivity remain largely unaffected by the number of features. This demonstrates the model's robustness in preserving boundary delineation and connected components with varying feature dimensions. By incrementally enriching the feature space, categorical cohesion improves without compromising the integrity of spatial segmentation. The model thus strikes an effective balance between semantic and structural consistency as feature information increases.
Examples of semantic segmentation results of 4, 5, and 6 features: ( a ) four features; ( b ) five features and ( c ) six features.
First, we remove two-branch attention mechanism (TBAM)from the classification module and use only DDPM and a small amount of convolution as the model for the first experiment (DDPM). Then, we replace TBAM with ASPP in the classification module, and use DDPM+ASPP as the model of the second experiment to verify whether the features extracted by the feature extractor still need to be further encoded. In addition, to supplement the performance of verifying TBAM, we choose to introduce residual blocks. We selected ResNet-50 as the main model for training, and in another set of experiments, SCAM is added to the end of ResNet-50. We compare the results of the above four sets of experiments with our RS-Dseg.
Table 9 shows the results of each experiment, and our method has the best performance. The performance of the ResNet-50 model has been improved after the addition of SCAM. DDPM results exceeded ResNet and ResNet + SCAM and are slightly lower than DDPM+ SCAM, achieving an Accuracy of 95.14%. Compared with other models in Table 3 , the results of DDPM are also above average, indicating that DDPM is competent for the segmentation task.In addition, following the substitution of SCAM with ASPP, there is a dramatic drop in the performance of model, even performing worse than ResNet-50. DDPM performs worse than DDPM+ SCAM, but better than DDPM+ASPP. This is because the features extracted by the feature extractor can essentially be used for pixel classification without requiring further coding. Additional encoding would degrade segmentation quality. Moreover, from part A.1 of the Appendix, it's evident that these features have been able to represent the categories of ground objects on different scales
Figure 11 shows the result of the segmentation. The combination of (a) gets the best result. Thanks to the feature extractor, these two methods can get accurate results. While (c) is the result of DDPM+ASPP, it can be seen that the quality of segmentation is poor. Using ASPP to re-encode features results in a lot of information being lost, which reduces the accuracy of segmentation. (d) Compared with (e), ResNet-50+ TBAM can better maintain the coherence of long and narrow features due to the presence of space-channel modules.
Examples of semantic segmentation results on the GID dataset ( a ): DDPM + TBAM. ( b ): DDPM. ( c ): DDPM + ASPP. ( d ): ResNet-50. ( e ): ResNet-50 + TBAM.
In this paper, we explore the application of diffusion models in the semantic segmentation of high-resolution remote sensing images, leading to the simplification of existing models for this task. We introduce a lightweight classification module based on spatial-channel attention mechanisms, which enables rapid semantic segmentation by utilizing multiscale features from a pretrained diffusion model. Our experimental results demonstrate that the feature extractor of the unsupervised pretrained diffusion model effectively extracts multiscale features with contextual information. This is due to a prior knowledge of the diffusion model. The lightweight classification module efficiently fuses these features and performs semantic segmentation, significantly reducing the training cycle. These findings highlight the potential of applying diffusion models to remote sensing image semantic segmentation, which can achieve optimal performance compared to current methods. Importantly, in our research, we use labeled images during the segmentation stage but do not utilize them for feature extraction in the diffusion model. In future work, exploring how to leverage the feature extraction capabilities of diffusion models for unsupervised or semi-supervised classification would be valuable.
All data generated or analysed during this study are included in this published article [and its supplementary information files].
Karra, K., Kontgis, C., Statman-Weil, Z., Mazzariello, J. C., Mathis, M. & Brumby, S. P. Global land use/land cover with Sentinel 2 and deep learning. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS 4704–4707 (2021) https://doi.org/10.1109/IGARSS47720.2021.9553499
Chen, W., Wu, A. N. & Biljecki, F. Classification of urban morphology with deep learning: Application on urban vitality. Comput. Environ. Urban Syst. 90 , 101706 (2021).
Article Google Scholar
Yuan, Q. et al. Deep learning in environmental remote sensing: Achievements and challenges. Remote Sens. Environ. 241 , 111716 (2020).
Ming, D., Luo, J.-C., Shen, Z., Wang, M. & Sheng, H. Research on high resolution remote sensing image information extraction and target recognition. Sci. Surv. Mapp. 3 , 18–20+3 (2005).
Google Scholar
Yan, Ma. & Kizirbek, G. Research review on image semantic segmentation in high-resolution remote sensing image interpretation. Explor. Comput. Sci. Technol. 17 (07), 1526–1548 (2023).
Vaswani, A. et al . Attention is all you need. In Proceedings of Advances in Neural Information Processing Systems 5998–6008 (2017).
Han, K., Wang, Y., Chen, H., et al . A survey on visual transformer. Preprint arXiv:2012.12556 (2020).
Elngar, A. A. et al. Image classification based on CNN: A survey. J. Cybersecur. Inf. Manag. 6 (1), 18–50 (2021).
Chen, L.-C. et al . Rethinking atrous convolution for semantic image segmentation. Preprint arXiv:1706.05587 (2017).
Yangyi, D., He Kang, Hu. & Qi, H. K. A review of CNN-transformer hybrid model in the field of computer vision. Model. Simul. 12 (4), 3657–3672 (2023).
Valanarasu, J. M. J., Oza, P., Hacihaliloglu, I. et al . Medical transformer: Gated axial-attention for medical image segmentation. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24 . Springer, 36–46 (2021).
Wang, H., Cao, J., Anwer, R. M. et al . Dformer: Diffusion-guided transformer for universal image segmentation. Preprint arXiv:2306.03437 (2023).
Xie, E. et al. SegFormer: Simple and efficient design for semantic segmentation with transformers. Adv. Neural Inf. Process. Syst. 34 , 12077–12090 (2021).
Jingdong, W. et al. Deep high-resolution representation learning for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 43 , 3349–3364 (2020).
Roy, A. G., Navab, N., & Wachinger, C. Concurrent spatial and channel ‘squeeze & excitation’in fully convolutional networks. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part I , 421–429 (2018).
Long, J., Shelhamer, E., & Darrell, T. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition 3431–3440 (2015).
Deng, G. H., Gao, F., Luo, Z. P. Research on semantic segmentation of high- resolution remote sensing data based on improved fully convolution neural network. In 4th China High Resolution Earth Observation Conference, Wuhan 1125–1137 (2017).
Piramanayagam, S. et al. Classification of remote sensed images using random forests and deep learning framework. In Image and signal processing for remote sensing XXII Vol. 10004 (SPIE, 2016).
Li, B.-Q. et al. Asymmetric parallel semantic segmentation model based on full convolutional neural network. Acta Electron. Sinica 47 (7), 1058 (2019).
Shen-ke, G. U. A. N. et al. A semantic segmentation algorithm using multi-scale feature fusion with combination of superpixel segmentation. J. Graph. 42 (3), 406 (2021).
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., & Ganguli, S. Deep unsupervised learning using non-equilibrium thermodynamics. In Proceedings of ICML 2256–2265 (2015).
Nanxin, C. et al . Wavegrad: Estimating gradients for waveform generation. Preprint arXiv:2009.00713 (2020).
Gong, S. et al . DiffuSeq: Sequence to sequence text generation with diffusion models. In The Eleventh International Conference on Learning Representations . (2022).
Sinha, A., Song, J., Meng, C. & Ermon, S. D2C: Diffusion decoding models for few-shot conditional generation. Adv. Neural Inf. Process. Syst. 34 , 12533–12548 (2021).
Ho, J., Jain, A. & Abbeel, P. Denoising diffusion probabilistic models. Adv. Neural Inf. Process. Syst. 33 , 6840–6851 (2020).
Song, Y. & Ermon, S. Generative modeling by estimating gradients of the data distribution. Adv. Neural Inf. Process. Syst. 32 , 11918–11930 (2019).
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., & Poole, B. Score-based generative modeling through stochastic differential equations. Advances in neural information processing systems, (2021).
Dhariwal, P. & Nichol, A. Diffusion models beat GANs on image synthesis. Adv. Neural Inf. Process. Syst. 34 , 8780–8794 (2021).
Nichol, A. Q. & Dhariwal, P. Improved denoising diffusion probabilistic models. In Proceedings of ICML , 8162–8171 (2021).
Song, J., Meng, C., & Ermon, S. Denoising diffusion implicit models. In International Conference on Learning Representations (2021).
Daniels, M., Maunu, T. & Hand, P. Score-based generative neural networks for large-scale optimal transport. Adv. Neural Inf. Process. Syst. 34 , 12955–12965 (2021).
Chung, H., Sim, B., & Ye, J. C. Come-closer-diffuse-faster: Accelerating conditional diffusion models for inverse problems through stochastic contraction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 12413–12422 (2022).
Kawar, B., Elad, M., Ermon, S. & Song, J. Denoising diffusion restoration models. Adv. Neural Inf. Process. Syst. 35 , 23593–23606 (2022).
Esser, P., Rombach, R., Blattmann, A. & Ommer, B. ImageBART: Bidirectional context with multinomial diffusion for autoregressive image synthesis. Adv. Neural Inf. Process. Syst. 34 , 3518–3532 (2021).
Lugmayr, A., Danelljan, M., Romero, A., Yu, F., Timofte, R., & Van Gool L. RePaint: Inpainting using denoising diffusion probabilistic models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 11461–11471 (2022).
Jing, B., Corso, G., Berlinghieri, R., & Jaakkola, T. Subspace diffusion generative models. Preprint arXiv:2205.01490 (2022).
Avrahami, O., Lischinski, D., & Fried, O. Blended diffusion for text-driven editing of natural images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 18208–18218 (2022).
Choi, J., Kim, S., Jeong, Y., Gwon, Y., & Yoon, S. ILVR: Conditioning method for denoising diffusion probabilistic models. In IEEE/CVF International Conference on Computer Vision 14347–14356 (2021).
Meng, C., Song, Y., Song, J., Wu, J., Zhu, J.-Y., & Ermon, S. SDEdit: Guided image synthesis and editing with stochastic differential equations. In International Conference on Learning Representations (2021).
Zhao, M., Bao, F., Li, C. & Zhu, J. EGSDE: Unpaired image-to-image translation via energy-guided stochastic differential equations. Adv. Neural Inf. Process. Syst. 35 , 3609–3623 (2022).
Wang, T., Zhang, T., Zhang, B., Ouyang, H., Chen, D., Chen, Q., & Wen, F. Pretraining is all you need for image-to-image translation. Preprint arXiv:2205.12952 (2022).
Li, B., Xue, K., Liu, B., & Lai, Y.-K. VQBB: image-to-image translation with vector quantized brownian bridge. Preprint arXiv:2205.07680 (2022).
Wolleb, J., Sandkühler, R., Bieder, F., & Cattin, P. C. The Swiss Army knife for image-to-image translation: Multi-task diffusion models. Preprint arXiv:2204.02641 (2022).
Saharia, C., Chan, W., Chang, H., Lee, C., Ho, J., Salimans, T., Fleet, D., & Norouzi, M. Palette: Image-to-image diffusion models. In ACM SIGGRAPH 2022 Conference Proceedings 1–10 (2022).
Sasaki, H., Willcocks, C. G., & Breckon, T. P. UNIT-DDPM: UN-paired image translation with denoising diffusion probabilistic models. Preprint arXiv:2104.05358 (2021).
Chitwan, S. et al. Image super-resolution via iterative refinement. IEEE Trans. Pattern Anal. Mach. Intell. 45 , 4713–4726 (2022).
Ho, J., Chan, W., Saharia, C., Whang, J., Gao, R., Gritsenko, A., Kingma, D. P., Poole, B., Norouzi, M., Fleet, D. J. et al . Imagen video: High definition video generation with diffusion models. Preprint arXiv:2210.02303 (2022).
Brempong, E. A. et al . Denoising pretraining for semantic segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition 4175–4186 (2022).
Chen, S., Sun, P., Song, Y., & Luo, P. Diffusiondet: Diffusion model for object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision 19830–19843 (2023).
Chen, T., et al . A generalist framework for panoptic segmentation of images and videos. In Proceedings of the IEEE/CVF International Conference on Computer Vision 909–919 (2023).
Gu, Z. et al . Diffusioninst: Diffusion model for instance segmentation. Preprint arXiv:2212.02773 (2022).
Burgert, R. et al . Peekaboo: Text to image diffusion models are zero-shot segmentors. Preprint arXiv:2211.13224 (2022).
Wu, J. et al. MedSegDiff: Medical image segmentation with diffusion probabilistic model. In Medical Imaging with Deep Learning (PMLR, 2023).
Baranchuk, D. et al . Label-efficient semantic segmentation with diffusion models. In International Conference on Learning Representations (2021).
Wu, Y., & He, K. Group normalization. In Proceedings of the European conference on computer vision (ECCV) (2018).
Bandara, W. G. C., Nair N. G., Patel, V. M. Remote sensing change detection using denoising diffusion probabilistic models. e-prints arXiv:2206.11892 (2022).
Roy, A. G., Navab, N., Wachinger, C. Concurrent spatial and channel ‘squeeze & excitation’in fully convolutional networks. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st International Conference, Granada, Spain, September 16–20, 2018, Proceedings, Part I 421–429 (2018).
Tong, X.-Y. et al. Land-cover classification with high-resolution remote sensing images using transferable deep models. Remote Sens .Environ. 237 , 111–322 (2020).
Long, J., Shelhamer, E., Darrell, T. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition 3431–3440 (2015).
Woo, S., Debnath, S., Hu, R. et al . Convnext v2: Co-designing and scaling convnets with masked autoencoders. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 16133–16142 (2023).
Wang, J. et al. Deep high-resolution representation learning for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 43 , 3349–3364 (2020).
Chen, L-C. et al . Rethinking atrous convolution for semantic image segmentation. Preprint arXiv:1706.05587 (2017).
He, K. et al . Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition 770–778 (2016).
Guo, M. H. et al. Segnext: Rethinking convolutional attention design for semantic segmentation. Adv. Neural Inf. Process. Syst. 35 , 1140–1156 (2022).
Ma, X., Zhang, X., Pun, M.-O. & Liu, M. A multilevel multimodal fusion transformer for remote sensing semantic segmentation. IEEE Trans. Geosci. Remote Sens. 62 , 1–15. https://doi.org/10.1109/TGRS.2024.3373033 (2024).
Rottensteiner, F., Sohn, G., Jung, J., Gerke, M., Baillard, C., Benitez, S., Breitkopf, U. The ISPRS Benchmark on Urban Object Classification and 3D Building Reconstruction. In Proceedings of the ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Melbourne, Australia, 25 August–1 September , Vol. I-3, 293–298 (2012).
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We thank the reviewers and editors for their insightful comments.
This work was supported in part by the Key R&D Program of Ningxia Autonomous Region: Ecological environment monitoring and platform development of ecological barrier protection system for Helan Mountain (2022CMG02014); the Open Fund of Key Laboratory of Monitoring, Evaluation and Early Warning of Territorial Spatial Planning Implementation, Ministry of Natural Resources (No: LMEE-KF2023001); the Natural Science Foundation of Chong Qing (Nos.CSTB2022NSCQ-MSX1671);in part by the Construction Project of Chongqing Postgraduate Joint Training Base (JDLHPYJD2019004). (Corresponding author: Jianping Pan.)
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Zheng Luo, Jianping Pan, Yimeng Li, Chen Qi & Xunxun Wang
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Technology Innovation Center for Spatio-temporal Information and Equipment of Intelligent City, Ministry of Natural Resources, Chongqing, 401120, China
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Conceptualization, Z.L. and J.P.P.; methodology, Z.L.; validation, Z.L.; formal analysis Z.L. Y.M.L. X.X.W. and C.Q. ; resources, Y.H. and L.D. and J.P.P.; writing—original draft preparation, Z.L.; writ-ing—review and editing, Z.L and J.P.P.; All authors have read and agreed to the published version of the manuscript.
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Luo, Z., Pan, J., Hu, Y. et al. RS-Dseg: semantic segmentation of high-resolution remote sensing images based on a diffusion model component with unsupervised pretraining. Sci Rep 14 , 18609 (2024). https://doi.org/10.1038/s41598-024-69022-1
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DOI : https://doi.org/10.1038/s41598-024-69022-1
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Stable Diffusion is great at many things, but not great at everything, and getting results in a...
Flux by black forest labs: the next leap in text-to-image models. is it better than midjourney.
Black Forest Labs , the team behind the groundbreaking Stable Diffusion model, has released Flux – a suite of state-of-the-art models that promise to redefine the capabilities of AI-generated imagery. But does Flux truly represent a leap forward in the field, and how does it stack up against industry leaders like Midjourney? Let's dive deep into the world of Flux and explore its potential to reshape the future of AI-generated art and media.
Before we delve into the technical aspects of Flux, it's crucial to understand the pedigree behind this innovative model. Black Forest Labs is not just another AI startup; it's a powerhouse of talent with a track record of developing foundational generative AI models. The team includes the creators of VQGAN, Latent Diffusion, and the Stable Diffusion family of models that have taken the AI art world by storm.
Black Forest Labs Open-Source FLUX.1
With a successful Series Seed funding round of $31 million led by Andreessen Horowitz and support from notable angel investors, Black Forest Labs has positioned itself at the forefront of generative AI research. Their mission is clear: to develop and advance state-of-the-art generative deep learning models for media such as images and videos, while pushing the boundaries of creativity, efficiency, and diversity.
Black Forest Labs has introduced the FLUX.1 suite of text-to-image models, designed to set new benchmarks in image detail, prompt adherence, style diversity, and scene complexity. The Flux family consists of three variants, each tailored to different use cases and accessibility levels:
I'll provide some unique and creative prompt examples that showcase FLUX.1's capabilities. These prompts will highlight the model's strengths in handling text, complex compositions, and challenging elements like hands.
These prompts are designed to challenge FLUX.1's capabilities in text rendering, complex scene composition, and detailed object creation, while also showcasing its potential for creative and unique image generation.
At the heart of Flux's impressive capabilities lies a series of technical innovations that set it apart from its predecessors and contemporaries:
All public FLUX.1 models are built on a hybrid architecture that combines multimodal and parallel diffusion transformer blocks, scaled to an impressive 12 billion parameters. This represents a significant leap in model size and complexity compared to many existing text-to-image models.
The Flux models improve upon previous state-of-the-art diffusion models by incorporating flow matching, a general and conceptually simple method for training generative models. Flow matching provides a more flexible framework for generative modeling, with diffusion models being a special case within this broader approach.
To enhance model performance and hardware efficiency, Black Forest Labs has integrated rotary positional embeddings and parallel attention layers. These techniques allow for better handling of spatial relationships in images and more efficient processing of large-scale data.
Let's break down some of the key architectural elements that contribute to Flux's performance:
https://blackforestlabs.ai/announcing-black-forest-labs/
Black Forest Labs claims that FLUX.1 sets new standards in image synthesis, surpassing popular models like Midjourney v6.0, DALL·E 3 (HD), and SD3-Ultra in several key aspects:
Now, let's address the burning question: Is Flux better than Midjourney ? To answer this, we need to consider several factors:
Both Flux and Midjourney are known for producing high-quality, visually stunning images. Midjourney has been praised for its artistic flair and ability to create images with a distinct aesthetic appeal. Flux, with its advanced architecture and larger parameter count, aims to match or exceed this level of quality.
Early examples from Flux show impressive detail, realistic textures, and a strong grasp of lighting and composition. However, the subjective nature of art makes it difficult to definitively claim superiority in this area. Users may find that each model has its strengths in different styles or types of imagery.
One area where Flux potentially edges out Midjourney is in prompt adherence. Black Forest Labs has emphasized their focus on improving the model's ability to accurately interpret and execute on given prompts. This could result in generated images that more closely match the user's intentions, especially for complex or nuanced requests.
Midjourney has sometimes been criticized for taking creative liberties with prompts, which can lead to beautiful but unexpected results. Flux's approach may offer more precise control over the generated output.
With the introduction of FLUX.1 [schnell], Black Forest Labs is targeting one of Midjourney's key advantages: speed. Midjourney is known for its rapid generation times, which has made it popular for iterative creative processes. If Flux can match or exceed this speed while maintaining quality, it could be a significant selling point.
Midjourney has gained popularity partly due to its user-friendly interface and integration with Discord. Flux, being newer, may need time to develop similarly accessible interfaces. However, the open-source nature of FLUX.1 [schnell] and [dev] models could lead to a wide range of community-developed tools and integrations, potentially surpassing Midjourney in terms of flexibility and customization options.
Flux's advanced architecture and larger model size suggest that it may have more raw capability in terms of understanding complex prompts and generating intricate details. The flow matching approach and hybrid architecture could allow Flux to handle a wider range of tasks and generate more diverse outputs.
Both Flux and Midjourney face the challenge of addressing ethical concerns in AI-generated imagery, such as bias, misinformation, and copyright issues. Black Forest Labs' emphasis on transparency and their commitment to making models widely accessible could potentially lead to more robust community oversight and faster improvements in these areas.
Using flux with diffusers.
Flux models can be easily integrated into existing workflows using the Hugging Face Diffusers library . Here's a step-by-step guide to using FLUX.1 [dev] or FLUX.1 [schnell] with Diffusers:
This code snippet demonstrates how to load the FLUX.1 [dev] model, generate an image from a text prompt, and save the result.
For those looking to deploy Flux as a scalable API service, Black Forest Labs provides an example using LitServe, a high-performance inference engine. Here's a breakdown of the deployment process:
This code sets up a LitServe API for Flux, including model loading, request handling, image generation, and response encoding.
Use the model api:.
You can test the API using a simple client script:
The versatility and power of Flux open up a wide range of potential applications across various industries:
Black Forest Labs has made it clear that Flux is just the beginning of their ambitions in the generative AI space. They've announced plans to develop competitive generative text-to-video systems, promising precise creation and editing capabilities at high definition and unprecedented speed.
This roadmap suggests that Flux is not just a standalone product but part of a broader ecosystem of generative AI tools. As the technology evolves, we can expect to see:
The question of whether Flux is “better” than Midjourney is not easily answered with a simple yes or no. Both models represent the cutting edge of text-to-image generation technology, each with its own strengths and unique characteristics.
Flux, with its advanced architecture and emphasis on prompt adherence, may offer more precise control and potentially higher quality in certain scenarios. Its open-source variants also provide opportunities for customization and integration that could be highly valuable for developers and researchers.
Midjourney , on the other hand, has a proven track record, a large and active user base, and a distinctive artistic style that many users have come to love. Its integration with Discord and user-friendly interface have made it highly accessible to creatives of all technical skill levels.
Ultimately, the “better” model may depend on the specific use case, personal preferences, and the evolving capabilities of each platform. What's clear is that Flux represents a significant step forward in the field of generative AI, introducing innovative techniques and pushing the boundaries of what's possible in text-to-image synthesis.
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Deep neural networks are learning diffusion and other tricks.
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T ype in a question to Chat GPT and an answer will materialise. Put a prompt into DALL - E 3 and an image will emerge. Click on TikTok’s “for you” page and you will be fed videos to your taste. Ask Siri for the weather and in a moment it will be spoken back to you.
All these things are powered by artificial-intelligence ( AI ) models. Most rely on a neural network, trained on massive amounts of information—text, images and the like—relevant to how it will be used. Through much trial and error the weights of connections between simulated neurons are tuned on the basis of these data, akin to adjusting billions of dials until the output for a given input is satisfactory.
There are many ways to connect and layer neurons into a network. A series of advances in these architectures has helped researchers build neural networks which can learn more efficiently and which can extract more useful findings from existing datasets, driving much of the recent progress in AI .
Most of the current excitement has been focused on two families of models: large language models ( LLM s) for text, and diffusion models for images. These are deeper (ie, have more layers of neurons) than what came before, and are organised in ways that let them churn quickly through reams of data.
LLM s—such as GPT , Gemini, Claude and Llama—are all built on the so-called transformer architecture. Introduced in 2017 by Ashish Vaswani and his team at Google Brain, the key principle of transformers is that of “attention”. An attention layer allows a model to learn how multiple aspects of an input—such as words at certain distances from each other in text—are related to each other, and to take that into account as it formulates its output. Many attention layers in a row allow a model to learn associations at different levels of granularity—between words, phrases or even paragraphs. This approach is also well-suited for implementation on graphics-processing unit ( GPU ) chips, which has allowed these models to scale up and has, in turn, ramped up the market capitalisation of Nvidia, the world’s leading GPU -maker.
Transformer-based models can generate images as well as text. The first version of DALL - E , released by Open AI in 2021, was a transformer that learned associations between groups of pixels in an image, rather than words in a text. In both cases the neural network is translating what it “sees” into numbers and performing maths (specifically, matrix operations) on them. But transformers have their limitations. They struggle to learn consistent world-models. For example, when fielding a human’s queries they will contradict themselves from one answer to the next, without any “understanding” that the first answer makes the second nonsensical (or vice versa), because they do not really “know” either answer—just associations of certain strings of words that look like answers.
And as many now know, transformer-based models are prone to so-called “hallucinations” where they make up plausible-looking but wrong answers, and citations to support them. Similarly, the images produced by early transformer-based models often broke the rules of physics and were implausible in other ways (which may be a feature for some users, but was a bug for designers who sought to produce photo-realistic images). A different sort of model was needed.
Enter diffusion models, which are capable of generating far more realistic images. The main idea for them was inspired by the physical process of diffusion. If you put a tea bag into a cup of hot water, the tea leaves start to steep and the colour of the tea seeps out, blurring into clear water. Leave it for a few minutes and the liquid in the cup will be a uniform colour. The laws of physics dictate this process of diffusion. Much as you can use the laws of physics to predict how the tea will diffuse, you can also reverse-engineer this process—to reconstruct where and how the tea bag might first have been dunked. In real life the second law of thermodynamics makes this a one-way street; one cannot get the original tea bag back from the cup. But learning to simulate that entropy-reversing return trip makes realistic image-generation possible.
Training works like this. You take an image and apply progressively more blur and noise, until it looks completely random. Then comes the hard part: reversing this process to recreate the original image, like recovering the tea bag from the tea. This is done using “self-supervised learning”, similar to how LLM s are trained on text: covering up words in a sentence and learning to predict the missing words through trial and error. In the case of images, the network learns how to remove increasing amounts of noise to reproduce the original image. As it works through billions of images, learning the patterns needed to remove distortions, the network gains the ability to create entirely new images out of nothing more than random noise.
Most state-of-the-art image-generation systems use a diffusion model, though they differ in how they go about “de-noising” or reversing distortions. Stable Diffusion (from Stability AI ) and Imagen, both released in 2022, used variations of an architecture called a convolutional neural network ( CNN ), which is good at analysing grid-like data such as rows and columns of pixels. CNN s, in effect, move small sliding windows up and down across their input looking for specific artefacts, such as patterns and corners. But though CNN s work well with pixels, some of the latest image-generators use so-called diffusion transformers, including Stability AI ’s newest model, Stable Diffusion 3. Once trained on diffusion, transformers are much better able to grasp how various pieces of an image or frame of video relate to each other, and how strongly or weakly they do so, resulting in more realistic outputs (though they still make mistakes).
Recommendation systems are another kettle of fish. It is rare to get a glimpse at the innards of one, because the companies that build and use recommendation algorithms are highly secretive about them. But in 2019 Meta, then Facebook, released details about its deep-learning recommendation model ( DLRM ). The model has three main parts. First, it converts inputs (such as a user’s age or “likes” on the platform, or content they consumed) into “embeddings”. It learns in such a way that similar things (like tennis and ping pong) are close to each other in this embedding space.
The DLRM then uses a neural network to do something called matrix factorisation. Imagine a spreadsheet where the columns are videos and the rows are different users. Each cell says how much each user likes each video. But most of the cells in the grid are empty. The goal of recommendation is to make predictions for all the empty cells. One way a DLRM might do this is to split the grid (in mathematical terms, factorise the matrix) into two grids: one that contains data about users, and one that contains data about the videos. By recombining these grids (or multiplying the matrices) and feeding the results into another neural network for more number-crunching, it is possible to fill in the grid cells that used to be empty—ie, predict how much each user will like each video.
The same approach can be applied to advertisements, songs on a streaming service, products on an e-commerce platform, and so forth. Tech firms are most interested in models that excel at commercially useful tasks like this. But running these models at scale requires extremely deep pockets, vast quantities of data and huge amounts of processing power.
In academic contexts, where datasets are smaller and budgets are constrained, other kinds of models are more practical. These include recurrent neural networks (for analysing sequences of data), variational autoencoders (for spotting patterns in data), generative adversarial networks (where one model learns to do a task by repeatedly trying to fool another model) and graph neural networks (for predicting the outcomes of complex interactions).
Just as deep neural networks, transformers and diffusion models all made the leap from research curiosities to widespread deployment, features and principles from these other models will be seized upon and incorporated into future AI models. Transformers are highly efficient, but it is not clear that scaling them up can solve their tendencies to hallucinate and to make logical errors when reasoning. The search is already under way for “post-transformer” architectures, from “state-space models” to “neuro-symbolic” AI , that can overcome such weaknesses and enable the next leap forward. Ideally such an architecture would combine attention with greater prowess at reasoning. Right now no human yet knows how to build that kind of model. Maybe someday an AI model will do the job. ■
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Title: diffsg: a generative solver for network optimization with diffusion model.
Abstract: Diffusion generative models, famous for their performance in image generation, are popular in various cross-domain applications. However, their use in the communication community has been mostly limited to auxiliary tasks like data modeling and feature extraction. These models hold greater promise for fundamental problems in network optimization compared to traditional machine learning methods. Discriminative deep learning often falls short due to its single-step input-output mapping and lack of global awareness of the solution space, especially given the complexity of network optimization's objective functions. In contrast, diffusion generative models can consider a broader range of solutions and exhibit stronger generalization by learning parameters that describe the distribution of the underlying solution space, with higher probabilities assigned to better solutions. We propose a new framework Diffusion Model-based Solution Generation (DiffSG), which leverages the intrinsic distribution learning capabilities of diffusion generative models to learn high-quality solution distributions based on given inputs. The optimal solution within this distribution is highly probable, allowing it to be effectively reached through repeated sampling. We validate the performance of DiffSG on several typical network optimization problems, including mixed-integer non-linear programming, convex optimization, and hierarchical non-convex optimization. Our results show that DiffSG outperforms existing baselines. In summary, we demonstrate the potential of diffusion generative models in tackling complex network optimization problems and outline a promising path for their broader application in the communication community.
Comments: | 8 pages, 5 figures |
Subjects: | Networking and Internet Architecture (cs.NI); Machine Learning (cs.LG) |
Cite as: | [cs.NI] |
(or [cs.NI] for this version) | |
Focus to learn more arXiv-issued DOI via DataCite |
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Researchers from Apple have introduced a groundbreaking approach known as Matryoshka Diffusion Models (MDM) to address these challenges in high-resolution image and video generation. MDM stands out by integrating a hierarchical structure into the diffusion process, eliminating the need for separate stages that complicate training and inference in traditional models. This innovative method enables the generation of high-resolution content more efficiently and with greater scalability, marking a significant advancement in the field of AI-driven visual content creation.
The MDM methodology is built on a NestedUNet architecture, where the features and parameters for smaller-scale inputs are embedded within those of larger scales. This nesting allows the model to handle multiple resolutions simultaneously, significantly improving training speed and resource efficiency. The researchers also introduced a progressive training schedule that starts with low-resolution inputs and gradually increases the resolution as training progresses. This approach speeds up the training process and enhances the model’s ability to optimize for high-resolution outputs. The architecture’s hierarchical nature ensures that computational resources are allocated efficiently across different resolution levels, leading to more effective training and inference.
The performance of MDM is noteworthy, particularly in its ability to achieve high-quality results with less computational overhead compared to existing models. The research team from Apple demonstrated that MDM could train high-resolution models up to 1024×1024 pixels using the CC12M dataset, which contains 12 million images. Despite the relatively small size of the dataset, MDM achieved strong zero-shot generalization, meaning it performed well on new data without the need for extensive fine-tuning. The model’s efficiency is further highlighted by its ability to produce high-resolution images with Frechet Inception Distance (FID) scores that are competitive with state-of-the-art methods. For instance, MDM achieved a FID score of 6.62 on ImageNet 256×256 and 13.43 on MS-COCO 256×256, demonstrating its capability to generate high-quality images efficiently.
In conclusion, the introduction of Matryoshka Diffusion Models by researchers at Apple represents a significant step forward in high-resolution image and video generation. By leveraging a hierarchical structure and a progressive training schedule, MDM offers a more efficient and scalable solution than traditional methods. This advancement addresses the inefficiencies and complexities of existing diffusion models and paves the way for more practical and resource-efficient applications of AI-driven visual content creation. As a result, MDM holds great potential for future developments in the field, providing a robust framework for generating high-quality images and videos with reduced computational demands.
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Nikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute.
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Lllyasviel aug 13, 2024 maintainer.
The old Automatic1111’s user interface of VAE selection is not powerful enough for modern models . Forge make minor modifications so that the UI is as close as possible to A1111 but also meet the demands of newer models.
Before:
After:
Before:
After:
Download base model and vae (raw float16) from Flux official and . Download clip-l and t5-xxl from or our Put base model in . Put vae in Put clip-l and t5 in You can load in nearly arbitrary combinations
etc ... Now you can even load clip-l for sd1.5 separately |
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Hmrmike aug 13, 2024.
Boy is float16 Flux heavy! After some magical restarts, running fp16 without any Never OOM seems to work I like this way of handling VAE/encoders, but it feels like allowing to save some presets would be comfortable. It gets a bit tedious after switching models and might be confusing to less experienced users- forgetting CLIP or something will error out with "TypeError: 'NoneType' object is not iterable" on a checkpoint like fp16 Flux here, for example, and figuring it out is less trivial. We do have "You do not have CLIP state dict!" but it's buried in the traceback, almost invisible. Maybe a more obvious error message will be helpful at least. Well, fp8 so far seems most competent. For some reason fp16 messes up text more but the composition is nearly the same. nf4 deviates a lot is some cases. |
See |
Which FP16 models you were using? |
This one linked in OP: |
I have similar H/w to you but using the f16 version results in ForgeUI crashing. How did you manage to solve the OOM issue? |
Lots of commits happened since that post, and maybe something was changed for memory management. Now I make sure these settings are set right after launching Forge, if the intent is to run fp16. Also haven't dared to try anything other than 1024x1024. |
i think Forge Ui needs some dropdown menus like the ones i circled in red as they are choices and only one can be displayed when selected, that would help to take up less space on the screen : |
Yeah I believe the VAE / Text Encoder when stacked, they should form a double row. Also a folder standard should be agreed on, forge is storing in and ComfyUI is storing these files in this will lead to doubling up models |
I believe this broke the command line option. Also, could you add a option instead of hardcoding ? Thank you for all your work. |
Yes, and with --vae-dir set, the files in the forge text_encoder folder aren't detected either. |
Specify additionally --clip-models-path |
Can't make it work for some reason on 4090: it shows the preview during the generation, but them doesn't give the final result. No errors in the console either. Meanwhile flux1-dev-fp8.safetensors works no problem. |
Are you perhaps using TAESD instead of full VAE in the settings? |
Nope
Edit: I tried resetting all settings and removing everything from the command line - nothing did help. |
How to open vae/text encoder I can't find it |
Me too! It seems the T5, CLIP are not detected isn text_encoder folder |
i only about this morning and it's already here in Forge ... also read this morning LoRA work now in nf4. Many thanks |
Where did you read LoRAs working with nf4 models? |
In this discussion: |
, i've just tested i really don't know what to think, only the art_comfy_converted LoRA seems to produce an effect :-| Left is no LoRA, right is with _comfy_converted LoRA : woman taking a selfie art |
i think the LoRA strength must be > 1, with 1.5 it clearly makes a difference. Left is no LoRA, right is with realism_comfy_converted LoRA : woman taking a selfie |
does the clip and vae path respect the args --vae-dir and --clip-models-path?? seems no... |
Did not work for me. I resorted to using symbolic links (in Windows). Also the fact that Forge wants checkpoint and unet files in the same folder but ComfyUI separates them into two different ones is slightly cumbersome as I use ComfyUI installation for storing all the actual model files. |
This is why I use StabilityMatrix since it manages everything, including the downloading of models from different sites. |
now would be great if the XYZ grid function was working to make comparisons :) |
GGUF Q4_0 inference speed is faster than FP8 for me, though unfortunately it takes 100+ seconds to move the model/transformer each time, making the speed increase moot as a minimum of 100 seconds is added to each generation. Dunno why, when loading a FP8 Flux model, model moving for CLIP+T5/Transformer/VAE are all ~0 seconds. When introducing the Q4_0 quantization of the transformer, it takes 100-300 seconds to move the mode/transformer and begin inference. This is without Loras. I'm going to assume part of the reason is being on a low VRAM/RAM system and relying on a swap file. Though I figured loading an even smaller transformer would of been less prone to RAM/Swap related issues. |
Has someone done a video about GGUF quants with Flux? Is it because this stuff is moving too fast? |
I have an RTX3090 and 32GB of ram. ForgeUI crashes when I try to use the fp16 and I see in console the message "Using Default T5 Data Type: torch.float16". I can use the full precision in ComfyUI without a hitch. |
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When games influence words: gaming addiction among college students increases verbal aggression through risk-biased drifting in decision-making.
1.1. game addiction and game violence, 1.2. verbal aggression, 1.3. inhibitory control, 1.4. risk preference, 1.5. mediation model, 1.6. the present study, 2.1. participants, 2.2. research instruments, 2.2.1. questionnaires, 2.2.2. antisaccade task, 2.2.3. go/no-go task, 2.2.4. the cup task, 2.3. procedure, 2.4. statistical analysis, 2.4.1. validity analysis, 2.4.2. the hierarchical drift diffusion model, 2.4.3. mediation model, 3.1. correlational analysis and the cut-off point for gaming addiction, 3.2. validity analysis, 3.3. hierarchical drift diffusion model, 3.4. mediation model, 4. discussion, 4.1. mediation model, 4.2. risk preference, 4.3. inhibitory control, 4.4. contribution of the present study, 4.5. limitations, 5. conclusions, supplementary materials, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.
Click here to enlarge figure
Recreation Program | M | SD |
---|---|---|
Video game | 59.29 | 70.77 |
TV | 40.96 | 48.10 |
Short video | 78.02 | 72.62 |
Card game | 4.61 | 17.68 |
Text-based media | 38.77 | 48.52 |
Webcast | 7.00 | 25.30 |
Terms | Explanations |
---|---|
a | The threshold in the risk advantage condition when winning money is used as feedback. |
a | The threshold in the risk disadvantage condition when winning money is used as feedback. |
a | The threshold in the neutral condition when winning money is used as feedback. |
a | The threshold in the risk advantage condition when losing money is used as feedback. |
a | The threshold in the risk disadvantage condition when losing money is used as feedback. |
a | The threshold in the neutral condition when losing money is used as feedback. |
v | The drift rates in the risk advantage condition when winning money are used as feedback. |
v | The drift rates in the risk disadvantage condition when winning money are used as feedback. |
v | The drift rates in the neutral condition when winning money are used as feedback. |
v | The drift rates in the risk advantage condition when losing money are used as feedback. |
v | The drift rates in the risk disadvantage condition when losing money are used as feedback. |
v | The drift rates in the neutral condition when losing money are used as feedback. |
t | The non-decision time when winning money is used as feedback. |
t | The non-decision time when losing money is used as feedback. |
Variable | M | SD | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1. Gaming addiction | 42.595 | 13.131 | 1.000 | |||||||||||||||||||||
2. Prosocial tendencies measure | 100.754 | 11.558 | −0.031 | 1.000 | ||||||||||||||||||||
3. Aggression: Physical | 13.627 | 4.318 | 0.201 ** | −0.075 | 1.000 | |||||||||||||||||||
4. Aggression: Verbal | 11.599 | 3.332 | 0.175 ** | −0.054 | 0.499 *** | 1.000 | ||||||||||||||||||
5. Aggression: Anger | 13.643 | 5.063 | 0.156 * | −0.107 | 0.488 *** | 0.638 *** | 1.000 | |||||||||||||||||
6. Aggression: Hostility | 16.837 | 4.986 | 0.221 *** | −0.261 *** | 0.373 *** | 0.412 *** | 0.578 *** | 1.000 | ||||||||||||||||
7. Aggression: Self-aggression | 9.996 | 3.981 | 0.248 *** | −0.098 | 0.421 *** | 0.360 *** | 0.598 *** | 0.603 *** | 1.000 | |||||||||||||||
8. Overall score of Aggression | 65.702 | 16.863 | 0.257 *** | −0.162 ** | 0.711 *** | 0.724 *** | 0.864 *** | 0.789 *** | 0.773 *** | 1.000 | ||||||||||||||
9. Antisaccade task | 0.939 | 0.076 | 0.076 | 0.015 | −0.031 | 0.043 | 0.060 | −0.015 | −0.014 | 0.011 | 1.000 | |||||||||||||
10. Go/No-go task | 0.779 | 0.188 | 0.034 | 0.006 | 0.021 | −0.004 | 0.028 | 0.044 | 0.017 | 0.030 | 0.321 *** | 1.000 | ||||||||||||
11.%Choice | 0.735 | 0.223 | 0.039 | 0.023 | 0.051 | 0.106 | −0.015 | −0.036 | −0.048 | 0.007 | 0.014 | 0.092 | 1.000 | |||||||||||
12.%Choice | 0.070 | 0.165 | −0.048 | 0.108 | −0.020 | −0.116 | −0.101 | −0.081 | 0.045 | −0.072 | 0.062 | −0.016 | 0.118 | 1.000 | ||||||||||
13.%Choice | 0.275 | 0.242 | −0.005 | 0.098 | 0.041 | 0.004 | −0.017 | 0.011 | 0.004 | 0.011 | 0.063 | 0.059 | 0.468 *** | 0.582 *** | 1.000 | |||||||||
14.%Choice | 0.874 | 0.187 | 0.005 | 0.014 | 0.055 | 0.072 | −0.006 | −0.044 | −0.096 | −0.009 | 0.008 | 0.049 | 0.349 *** | −0.236 *** | 0.013 | 1.000 | ||||||||
15.%Choice | 0.168 | 0.216 | −0.140 * | −0.002 | 0.013 | −0.117 | −0.052 | 0.002 | 0.073 | −0.018 | −0.098 | −0.149 * | −0.126 * | 0.319 *** | 0.185 ** | 0.182 ** | 1.000 | |||||||
16.%Choice | 0.589 | 0.286 | −0.112 | −0.016 | 0.015 | −0.059 | −0.008 | −0.002 | −0.011 | −0.013 | −0.016 | 0.038 | 0.159 * | 0.030 | 0.164 ** | 0.624 *** | 0.509 *** | 1.000 | ||||||
17.Gaming addiction: Salience | 6.119 | 2.869 | 0.893 *** | 0.024 | 0.218 *** | 0.154 ** | 0.123 | 0.180 ** | 0.181 ** | 0.219 *** | 0.079 | 0.052 | −0.016 | −0.026 | −0.016 | −0.010 | −0.116 | −0.0127 * | 1.000 | |||||
18.Gaming addiction: Mood | 8.786 | 2.340 | 0.473 *** | −0.025 | 0.163 ** | 0.103 | 0.021 | 0.154 * | 0.153 * | 0.150 * | −0.003 | 0.050 | 0.048 | −0.001 | 0.038 | −0.017 | −0.043 | −0.059 | 0.432 *** | 1.000 | ||||
19.Gaming addiction: Tolerance | 6.623 | 2.745 | 0.845 *** | 0.019 | 0.148* | 0.126* | 0.107 | 0.170 ** | 0.203 ** | 193** | 0.072 | 0.026 | 0.036 | −0.026 | 0.007 | 0.049 | −0.094 | −0.048 | 0.771 *** | 0.389 *** | 1.000 | |||
20.Gaming addiction: Withdrawal | 5.254 | 2.249 | 0.838 *** | −0.091 | 0.231 *** | 0.167 *** | 0.106 * | 0.245 *** | 0.244 *** | 270 *** | 0.059 | −0.020 | −0.006 | −0.017 | −0.053 | 0.007 | −0.072 | −0.082 | 0.673 *** | 0.368 *** | 0.643 *** | 1.000 | ||
21.Gaming addiction: Conflict | 10.190 | 2.853 | 0.703 *** | −0.003 | 0.163 ** | 0.203 ** | 0.109 | 0.157 * | 0.210 *** | 0.210 *** | 0.023 | −0.062 | 0.023 | −0.012 | −30.774 × 10 | 0.006 | −5.784 × 10 | −0.082 | 0.631 *** | 0.393 *** | 0.573 *** | 0.644 *** | 1.000 | |
22.Gaming addiction: Relapse | 5.774 | 2.802 | 0.835 *** | 0.025 | 0.165 ** | 0.149 * | 0.156 * | 0.135 * | 0.220 ** | 210 *** | 0.033 | 0.019 | 0.079 | −0.009 | 0.068 | 0.010 | −0.099 | −0.114 | 0.727 *** | 0.347 *** | 0.641 *** | 0.643 *** | 0.579 *** | 1.000 |
Variable | 11.a | 12.a | 13.a | 14.v | 15.v | 16.v | 17.t | 18.a | 19.a | 20.a | 21.v | 22.v | 23.v | 24.t |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1. Gaming addiction | −0.042 | −0.053 | −0.022 | 0.041 | −0.108 | −0.052 | −0.009 | −0.076 | 0.004 | −0.058 | 0.025 | −0.25 *** | −0.106 | −0.037 |
2. Antisaccade task | −0.033 | −0.054 | 0.037 | 0.024 | 0.054 | 0.052 | −0.008 | −0.018 | 0.059 | 0.038 | 0.025 | −0.078 | −0.022 | −0.023 |
3. Go/No-go task | 0.076 | 0.05 | 0.097 | 0.059 | 0.009 | 0.047 | 0.091 | 0.091 | 0.191 ** | 0.142 * | −0.029 | −0.125 * | −0.01 | 0.143 * |
4. Prosocial tendencies measure | −0.114 | −0.089 | −0.116 | 0.046 | 0.062 | 0.091 | −0.105 | −0.152 * | −0.125 * | −0.168 ** | 0.055 | −0.002 | 0.029 | −0.035 |
5. Aggression: Physical | −0.099 | −0.115 | −0.123 | 0.04 | −0.105 | −0.016 | −0.028 | −0.08 | −0.062 | −0.094 | 0.075 | −0.052 | 0.024 | −0.082 |
6. Aggression: Verbal | −0.016 | −0.056 | −0.046 | 0.088 | −0.126 * | −0.015 | −0.004 | −0.034 | −0.026 | −0.049 | 0.067 | −0.164 * | −0.074 | −0.129 * |
7. Aggression: Anger | −0.033 | −0.047 | −0.038 | −0.024 | −0.130 * | −0.052 | 0.024 | −0.049 | −0.031 | −0.038 | −0.011 | −0.086 | −0.048 | −0.045 |
8. Aggression: Hostility | 0.019 | 0.031 | −0.009 | −0.043 | −0.136 * | −0.03 | 0.015 | −0.006 | −0.008 | −0.022 | −0.033 | −0.091 | −0.035 | −0.007 |
9. Aggression: Self-aggression | −0.064 | −0.093 | −0.08 | −0.038 | −0.041 | −0.026 | 0.04 | −0.139 * | −0.108 | −0.121 | −0.037 | −0.03 | −0.016 | −0.019 |
10. Overall score of Aggression | −0.048 | −0.067 | −0.074 | −0.001 | −0.141 * | −0.038 | 0.013 | −0.076 | −0.058 | −0.08 | 0.01 | −0.105 | −0.037 | −0.066 |
Variable | M | SD |
---|---|---|
a | 1.783 | 0.030 |
a | 1.954 | 0.033 |
a | 1.812 | 0.030 |
a | 2.203 | 0.043 |
a | 2.454 | 0.044 |
a | 2.178 | 0.041 |
v | 0.842 | 0.070 |
v | −1.959 | 0.072 |
v | −0.810 | 0.070 |
v | 1.347 | 0.065 |
v | −1.095 | 0.064 |
v | 0.276 | 0.063 |
t | 0.430 | 0.007 |
t | 0.413 | 0.008 |
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Teng, H.; Zhu, L.; Zhang, X.; Qiu, B. When Games Influence Words: Gaming Addiction among College Students Increases Verbal Aggression through Risk-Biased Drifting in Decision-Making. Behav. Sci. 2024 , 14 , 699. https://doi.org/10.3390/bs14080699
Teng H, Zhu L, Zhang X, Qiu B. When Games Influence Words: Gaming Addiction among College Students Increases Verbal Aggression through Risk-Biased Drifting in Decision-Making. Behavioral Sciences . 2024; 14(8):699. https://doi.org/10.3390/bs14080699
Teng, Huina, Lixin Zhu, Xuanyu Zhang, and Boyu Qiu. 2024. "When Games Influence Words: Gaming Addiction among College Students Increases Verbal Aggression through Risk-Biased Drifting in Decision-Making" Behavioral Sciences 14, no. 8: 699. https://doi.org/10.3390/bs14080699
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Diffusion - PhET Interactive Simulations ... Diffusion
Learn how diffusion works in a half-open box with this interactive simulation. Experiment with different initial conditions and observe the changes in concentration and entropy.
In order to give them a view of how diffusion works with a semipermeable membrane, I like to do a lab that uses a plastic bag to model the cell (membrane). It is a simple lab where students do very little except watch the process and record data and information. To set it up, you will need plastic bags, iodine, water, and corn starch.
Mix two gases to explore diffusion! Experiment with concentration, temperature, mass, and radius and determine how these factors affect the rate of diffusion.
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Learn all about Diffusion, Brownian Motion and how to demonstrate Diffusion with this fun and simple STEM Science Experiment.
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Diffusion in liquids. In this experiment, students place colourless crystals of lead nitrate and potassium iodide at opposite sides of a Petri dish of de-ionised water. As these substances dissolve and diffuse towards each other, students can observe clouds of yellow lead iodide forming, demonstrating that diffusion has taken place.
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Diffusion is a physical phenomenon that occurs everywhere, and we barely notice it or understand how it works. However, a few simple experiments can reveal the mysterious nature of this simple phenomenon.
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A practical guide to Diffusion models The motivation of this blog post is to provide a intuition and a practical guide to train a (simple) diffusion model [Sohl-Dickstein et al. 2015] together with the respective code leveraging PyTorch. If you are interested in a more mathematical description with proofs I can highly recommend [Luo 2022].
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Diffusion models are a relatively recent addition to a group of algorithms known as 'generative models'. The goal of generative modeling is to learn to generate data, such as images or audio, given a number of training examples. A good generative model will create a diverse set of outputs that resemble the training data without being exact ...
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Participants reported gaming addiction and different types of aggression through questionnaires. In addition, two important explanatory processes, inhibitory control, and risk preference, were measured through behavioral experiments. A Bayesian hierarchical drift-diffusion model was employed to interpret the data from the risk preference task.