sd-turbo

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zeromodels/sd-turbo

Paper: High-Resolution Image Synthesis with Latent Diffusion Models (arXiv:2112.10752) | HF Papers

Pure-Keras 3 conversion of stabilityai/sd-turbo 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 EulerDiscreteScheduler 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

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/sd-turbo")
tokenizer = StableDiffusion2Tokenizer.from_weights("zeromodels/sd-turbo")

inputs = tokenizer("a photograph of an astronaut riding a horse")
images = model.generate(**inputs, num_inference_steps=1, guidance_scale=0.0, 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>"):

Tips

• Set KERAS_BACKEND before importing Keras / zeromodels.

• The graphs are built for 512px. Pass unet_sample_size=<px / 8>, vae_sample_size=<px> to from_weights to 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_last and channels_first are supported (keras.config.set_image_data_format before loading); generate always 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 Stability AI Community License of the upstream checkpoint, including its use-based restrictions. By using them you agree to those terms.

Notice

This Stability AI Model is licensed under the Stability AI Community License, Copyright © Stability AI Ltd. All Rights Reserved

Modifications by zeromodels (https://github.com/IMvision12/ZeroModels): the checkpoint released at https://huggingface.co/stabilityai/sd-turbo 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.

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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.