Model reference · open weights
AlbedoBase is an open-weight image model from openart-custom. AlbedoBase (BF16) weighs 6.9 GB; the smallest configuration that runs it is RTX 3060 12 GB.
What it is
| Released by | openart-custom |
|---|---|
| Type | Image models |
| Task | Text→image |
| Parameters (lead) | 2.6B |
| Runs with | diffusers |
| Released | 2024-09-13 |
| Popularity | 4k downloads / month |
| Weights | 6.9 GB (AlbedoBase (BF16), file size) |
| Licence | Licence not stated |
What it runs on
Weights 6.9 GB (file size) · its biggest part 5.1 GB · working memory for one 1024×1024 image about 5.0 GB · overhead about 537 MB.
| Card | One 1024×1024 image | Counted memory |
|---|---|---|
| RTX 3060 12 GB | tight (encoders offloaded) | 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 |
Estimates, not measurements: the weights are the build's file size; one 1024×1024 image needs about 5 GB of working memory (larger images more); "encoders offloaded" means only the biggest part is on the card at once — diffusers' model offload, or ComfyUI unloading the text encoder. diffusers can also place a pipeline's parts on separate cards (device_map) — not estimated here. Counted memory is 92 % of what CUDA reports for the card.
From the model card
This is the model card of a 🧨 diffusers pipeline that has been pushed on the Hub. This model card has been automatically generated.
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
Use the code below to get started with the model.
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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BibTeX:
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APA:
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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("openart-custom/AlbedoBase", torch_dtype=torch.bfloat16).to("cuda")
image = pipe(prompt="a red bicycle on a cobbled street").images[0]
image.save("/workspace/out.png")