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Sponsored by
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zeromodels / stable-diffusion-2-base
Model's Last Updated: September 14 2026
text-to-image
Introduction of stable-diffusion-2-base
Model Details of stable-diffusion-2-base
See our collection for all Stable Diffusion 2.x checkpoints.
Run Stable Diffusion 2.x with Keras 3: JAX, PyTorch, or TensorFlow
zeromodels/stable-diffusion-2-base
Paper: High-Resolution Image Synthesis with Latent Diffusion Models (arXiv:2112.10752) | HF Papers
Pure-
Keras 3
conversion of
sd2-community/stable-diffusion-2-base
for
zeromodels
. One implementation runs unmodified on
TensorFlow / Torch / JAX
. The whole text-to-image model ships as
one container
:
the UNet denoiser, the VAE and the OpenCLIP ViT-H/14 text encoder (penultimate layer) in
model.weights.h5
(1.29B parameters, 4.81 GB), plus
zm_config.json
(the three
component configs, the checkpoint's
DDIMScheduler
schedule with its
epsilon
objective
and the default generation settings) and the tokenizer as
tokenizer.json
. Weights are stored in
float32
, exactly as released.
This checkpoint generates
512x512
images (a 64x64 latent).
For model details, intended use and limitations, see the upstream model card .
Architecture
| Component | zeromodels class | Details |
|---|---|---|
| Denoiser |
UNet2DConditionModel
|
(320, 640, 1280, 1280) channels, 2 ResNet blocks per level, (5, 10, 20, 20) attention heads on the 1024-d text context, linear token projection, 64x64x4 latent |
| Autoencoder |
AutoencoderKL
|
(128, 256, 512, 512) channels, x8 spatial compression to 4 latent channels,
scaling_factor
0.18215
|
| Text encoder |
CLIPTextModel
|
OpenCLIP ViT-H/14 text encoder (penultimate layer): 1024-d, 23 layers, 16 heads, 77 tokens,
gelu
|
| Scheduler |
DDIMScheduler
|
scaled_linear betas 0.00085 to 0.012 over 1000 steps,
epsilon
; DDIM / PNDM / Euler / Euler-ancestral are drop-in
|
Quick start
import os
os.environ["KERAS_BACKEND"] = "torch" # or "jax" / "tensorflow"
from PIL import Image
from zeromodels.models.stable_diffusion_2 import StableDiffusion2TextToImage, StableDiffusion2Tokenizer
model = StableDiffusion2TextToImage.from_weights("zeromodels/stable-diffusion-2-base")
tokenizer = StableDiffusion2Tokenizer.from_weights("zeromodels/stable-diffusion-2-base")
inputs = tokenizer("a photograph of an astronaut riding a horse")
images = model.generate(**inputs, num_inference_steps=50, guidance_scale=7.5, seed=0)
Image.fromarray(images[0]).save("astronaut.png") # (512, 512, 3) uint8
generate
takes the tokenizer's
input_ids
(batch them for several prompts), an optional
negative_input_ids
(tokenize the negative prompt),
num_inference_steps
,
guidance_scale
,
a
seed
, or explicit
latents
of shape
(batch, 64, 64, 4)
for results that are
identical across backends.
Load any Stable Diffusion 2.x checkpoint the same way with
from_weights("zeromodels/<variant>")
:
| Variant | Hub | Training |
|---|---|---|
stable-diffusion-2-base
|
zeromodels/stable-diffusion-2-base | 512px, epsilon: from scratch, 550k steps at 256px on LAION-5B (aesthetics >= 4.5), then 850k steps at 512px |
stable-diffusion-2
|
zeromodels/stable-diffusion-2 | 768px, v-prediction: 2-base + 150k steps at 768px |
stable-diffusion-2-1-base
|
zeromodels/stable-diffusion-2-1-base | 512px, epsilon: 2-base + 220k steps at 512px (punsafe 0.98) |
stable-diffusion-2-1
|
zeromodels/stable-diffusion-2-1 | 768px, v-prediction: 2 + 55k steps (punsafe 0.1) + 155k steps (punsafe 0.98) at 768px |
sd-turbo
|
zeromodels/sd-turbo | 512px, epsilon, Euler (trailing spacing), 1 to 4 steps, no guidance: SD 2.1 distilled with Adversarial Diffusion Distillation (Stability AI Community License) |
Tips
-
Set
KERAS_BACKENDbefore importing Keras / zeromodels. -
The graphs are built for 512px. Pass
unet_sample_size=<px / 8>, vae_sample_size=<px>tofrom_weightsto build for another multiple of 64px (the weights are resolution-independent). -
Swap the sampler any time:
model.scheduler = EulerDiscreteScheduler.from_config(model.config.scheduler_config)(zeromodels.base.base_scheduler). -
StableDiffusion2Model.from_weights(...)loads the same repo as the bare container (UNet / VAE / text encoder as.unet/.vae/.text_encoder) without the generation loop. -
Both
channels_lastandchannels_firstare supported (keras.config.set_image_data_formatbefore loading);generatealways returns(batch, H, W, 3)uint8. -
On-the-fly
hf:conversion is not supported for diffusion models; the checkpoints are hosted here, converted once. -
See the Stable Diffusion 2.x docs .
License
The weights are redistributed under the CreativeML Open RAIL++-M License of the upstream checkpoint, including its use-based restrictions. By using them you agree to those terms.
Notice
Modifications by zeromodels (
https://github.com/IMvision12/ZeroModels
): the checkpoint
released at
https://huggingface.co/sd2-community/stable-diffusion-2-base
was converted to
the Keras 3 weights layout of zeromodels (
model.weights.h5, zm_config.json, tokenizer.json
), stored in float32 as released. The model architecture and the parameter
values are unchanged; the weight names and the file format differ from the release.
Special Thanks
Thank you to Stability AI and the LAION / OpenCLIP teams for training and releasing Stable Diffusion, and to the Hugging Face diffusers team, whose implementation this port was verified against.
Runs of zeromodels stable-diffusion-2-base on huggingface.co
20
Total runs
0
24-hour runs
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3-day runs
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30-day runs
More Information About stable-diffusion-2-base huggingface.co Model
Url of stable-diffusion-2-base
stable-diffusion-2-base huggingface.co Url
Provider of stable-diffusion-2-base huggingface.co
Other API from zeromodels
Updated:August 27 2026
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