Model reference · open weights
playground is an open-weight image model from playgroundai. playground-v1 (BF16) weighs 5.1 GB; the smallest configuration that runs it is RTX 3060 12 GB.
For self-hosting, compare this build's requirements below with the GPU you plan to rent.
Summary of the playgroundai/playground-v1 model card, 2026-10-01
What it is
| Released by | playgroundai |
|---|---|
| Released | 2023-11-27 |
| VRAM | 5.1 GB for the weights |
What it runs on
| Card | One image | Memory |
|---|---|---|
| RTX 3060 12 GB | tight | 11.6 GB |
| RTX 4060 Ti 16 GB | fits | 15.4 GB |
| RTX 3090 24 GB | fits | 23.4 GB |
| RTX 4090 24 GB | fits | 23.4 GB |
| RTX 5090 32 GB | fits | 31.0 GB |
| L40S 48 GB | fits | 44.0 GB |
| A100 80 GB | fits | 78.2 GB |
| H100 80 GB | fits | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | fits | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | fits | 107 GB |
| H200 141 GB | fits | 138 GB |
| B200 180 GB | fits | 176 GB |
From the model card
A model from Playground.
Playground v1 is a latent diffusion model that improves the overall HDR quality to get more stunning images.
Status: no longer active on playground.com, no updates expected
First, install dependencies:
pip install diffusers torch accelerate transformers
Run the model:
from diffusers import DiffusionPipeline
import torch
pipe = DiffusionPipeline.from_pretrained("playgroundai/playground-v1")
if torch.cuda.is_available():
pipe.to("cuda")
prompt = "An Astronaut in a jungle, photorealistic"
image = pipe(prompt=prompt).images[0]
image.save("playground-v1.png")
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.
Running it yourself
Rent a machine by the hour. Runs as it is with diffusers, on the machine, in Python.
# on your rented machine: pip install diffusers transformers accelerate
import torch
from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained("playgroundai/playground-v1", torch_dtype=torch.bfloat16).to("cuda")
image = pipe(prompt="a red bicycle on a cobbled street").images[0]
image.save("/workspace/out.png")