MosaicML Diffusion
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Databricks
- Organisation type
- Industry
- Country
- United States of America
- Published
- 28 April 2023
- Authors
- Mihir Patel, Erica Ji Yuen, Cory Stephenson, Landan Seguin
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Image generation
- Task
- Image generation, Text-to-image
- Approach
- Self-supervised learning
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Parameters
- 1.3B
- Training data
- 790,000,000 tokens
- Epochs
- 1.38
Manually loaded model with following code snippet: from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler import torch repo_id = "stabilityai/stable-diffusion-2" pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.float16, revision="fp16") n_params_vae = sum(p.numel() for p in pipe.components['vae'].parameters()) n_params_text_encoder = sum(p.numel() for p in pipe.components['text_encoder'].parameters()) n_params_unet = sum(p.numel() for p in pipe.components['une…
For the first phase of training, we used all images with resolution >=256x256, amounting to 790 million image-caption samples. For the second phase of training, we only used images with resolution >=512x512, amounting to 300 million image-caption samples. Note that second phase data is a strict subset of first stage.
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 1.1 × 10²² FLOP
- How it was established
- Hardware
23,835 A100-hours * 3.12e14 FLOP/GPU-sec * 3600 sec/hour * 0.4 (assumed utilization) = 1.07e22 "Both phases of training ran on 128 NVIDIA A100 GPUs. The first training phase was run for 550k iterations in 1.6 days while the second phase was run for 850k iterations in 4.9 days, for a total of 20,051 A100 hours for training. In addition to the training time, we pre-computed the latents for the VAE and CLIP model to reduce training time and cost when making multiple passes over the dataset. Pre-co…
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA A100
- Chips used
- 128
- Wall-clock time
- 186 hours (7.8 days)
- Power draw
- 102.0 kW
"Both phases of training ran on 128 NVIDIA A100 GPUs. The first training phase was run for 550k iterations in 1.6 days while the second phase was run for 850k iterations in 4.9 days, for a total of 20,051 A100 hours for training. In addition to the training time, we pre-computed the latents for the VAE and CLIP model to reduce training time and cost when making multiple passes over the dataset. Pre-computing the latents required an additional 3,784 A100 hours, resulting in 23,835 A100 hours in t…
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Training Stable Diffusion from Scratch for <$50k with MosaicML
- Last updated
- 28 November 2025
What the numbers mean
About this model
MosaicML Diffusion was published by Databricks, in United States of America, in April 2023. The organisation is categorised as industry.
It works in Image generation, and is recorded as doing image generation, Text-to-image.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
Training it took roughly 1.1 × 10²² FLOP of computation, on NVIDIA A100 — a measure of what producing the model cost, not of how fast it answers.
The training set ran to roughly 790,000,000 tokens.
Answers
MosaicML Diffusion — common questions
When was MosaicML Diffusion released?
MosaicML Diffusion was published in April 2023. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is MosaicML Diffusion used for?
MosaicML Diffusion works in Image generation, and is recorded as handling image generation, Text-to-image. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
How much compute was used to train MosaicML Diffusion?
Around 1.1 × 10²² FLOP, on NVIDIA A100. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
What GPU do I need to run MosaicML Diffusion?
None. MosaicML Diffusion is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Is MosaicML Diffusion open source?
No. MosaicML Diffusion has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does MosaicML Diffusion have?
MosaicML Diffusion has 1.3B parameters. Manually loaded model with following code snippet: from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler import torch repo_id = "stabilityai/stable-diffusion-2" pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.float16, revision="fp16") n_params_vae = sum(p.numel() for p in pipe.components['vae'].parameters()) n_params_text_encoder = sum(p.numel() for p in pipe.components['text_encoder'].parameters()) n_params_unet = sum(p.numel() for p in pipe.components['unet'].parameters()) n_params = n_params_vae + n_params_text_encoder + n_params_unet print(f"Total number of parameters: {n_params}"). That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
Who created MosaicML Diffusion?
MosaicML Diffusion was published by Databricks, based in United States of America, categorised as industry.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.