Stable Diffusion

Stable Diffusion is an open-source diffusion-based text-to-image generation model developed by Stability AI, supporting 512x512 resolution text-to-image generation.

Installation

Before performing model inference and training, please install DiffSynth-Studio first.

git clone https://github.com/modelscope/DiffSynth-Studio.git
cd DiffSynth-Studio
pip install -e .

For more information on installation, please refer to Setup Dependencies.

Quick Start

Running the following code will quickly load the AI-ModelScope/stable-diffusion-v1-5 model for inference. VRAM management is enabled, the framework automatically controls parameter loading based on available VRAM, requiring a minimum of 2GB VRAM.

import torch
from diffsynth.core import ModelConfig
from diffsynth.pipelines.stable_diffusion import StableDiffusionPipeline

vram_config = {
    "offload_dtype": torch.float32,
    "offload_device": "cpu",
    "onload_dtype": torch.float32,
    "onload_device": "cpu",
    "preparing_dtype": torch.float32,
    "preparing_device": "cuda",
    "computation_dtype": torch.float32,
    "computation_device": "cuda",
}
pipe = StableDiffusionPipeline.from_pretrained(
    torch_dtype=torch.float32,
    model_configs=[
        ModelConfig(model_id="AI-ModelScope/stable-diffusion-v1-5", origin_file_pattern="text_encoder/model.safetensors", **vram_config),
        ModelConfig(model_id="AI-ModelScope/stable-diffusion-v1-5", origin_file_pattern="unet/diffusion_pytorch_model.safetensors", **vram_config),
        ModelConfig(model_id="AI-ModelScope/stable-diffusion-v1-5", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
    ],
    tokenizer_config=ModelConfig(model_id="AI-ModelScope/stable-diffusion-v1-5", origin_file_pattern="tokenizer/"),
    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
)

image = pipe(
    prompt="a photo of an astronaut riding a horse on mars, high quality, detailed",
    negative_prompt="blurry, low quality, deformed",
    cfg_scale=7.5,
    height=512,
    width=512,
    seed=42,
    rand_device="cuda",
    num_inference_steps=50,
)
image.save("image.jpg")

Model Overview

Model ID	Inference	Low VRAM Inference	Full Training	Full Training Validation	LoRA Training	LoRA Training Validation
AI-ModelScope/stable-diffusion-v1-5	code	code	code	code	code	code

Model Inference

The model is loaded via StableDiffusionPipeline.from_pretrained, see Loading Models for details.

The input parameters for StableDiffusionPipeline inference include:

prompt: Text prompt.
negative_prompt: Negative prompt, defaults to an empty string.
cfg_scale: Classifier-Free Guidance scale factor, default 7.5.
height: Output image height, default 512.
width: Output image width, default 512.
seed: Random seed, defaults to a random value if not set.
rand_device: Noise generation device, defaults to "cpu".
num_inference_steps: Number of inference steps, default 50.
eta: DDIM scheduler eta parameter, default 0.0.
guidance_rescale: Guidance rescale factor, default 0.0.
progress_bar_cmd: Progress bar callback function.

Model Training

Models in the stable_diffusion series are trained via examples/stable_diffusion/model_training/train.py. The script parameters include:

General Training Parameters
- Dataset Configuration
  - --dataset_base_path: Root directory of the dataset.
  - --dataset_metadata_path: Path to the dataset metadata file.
  - --dataset_repeat: Number of dataset repeats per epoch.
  - --dataset_num_workers: Number of processes per DataLoader.
  - --data_file_keys: Field names to load from metadata, typically paths to image or video files, separated by ,.
- Model Loading Configuration
  - --model_paths: Paths to load models from, in JSON format.
  - --model_id_with_origin_paths: Model IDs with original paths, separated by commas.
  - --extra_inputs: Additional input parameters required by the model Pipeline, separated by ,.
  - --fp8_models: Models to load in FP8 format, currently only supported for models whose parameters are not updated by gradients.
- Basic Training Configuration
  - --learning_rate: Learning rate.
  - --num_epochs: Number of epochs.
  - --trainable_models: Trainable models, e.g., dit, vae, text_encoder.
  - --find_unused_parameters: Whether unused parameters exist in DDP training.
  - --weight_decay: Weight decay magnitude.
  - --task: Training task, defaults to sft.
- Output Configuration
  - --output_path: Path to save the model.
  - --remove_prefix_in_ckpt: Remove prefix in the model's state dict.
  - --save_steps: Interval in training steps to save the model.
- LoRA Configuration
  - --lora_base_model: Which model to add LoRA to.
  - --lora_target_modules: Which layers to add LoRA to.
  - --lora_rank: Rank of LoRA.
  - --lora_checkpoint: Path to LoRA checkpoint.
  - --preset_lora_path: Path to preset LoRA checkpoint for LoRA differential training.
  - --preset_lora_model: Which model to integrate preset LoRA into, e.g., dit.
- Gradient Configuration
  - --use_gradient_checkpointing: Whether to enable gradient checkpointing.
  - --use_gradient_checkpointing_offload: Whether to offload gradient checkpointing to CPU memory.
  - --gradient_accumulation_steps: Number of gradient accumulation steps.
- Resolution Configuration
  - --height: Height of the image/video. Leave empty to enable dynamic resolution.
  - --width: Width of the image/video. Leave empty to enable dynamic resolution.
  - --max_pixels: Maximum pixel area, images larger than this will be scaled down during dynamic resolution.
  - --num_frames: Number of frames for video (video generation models only).
Stable Diffusion Specific Parameters
- --tokenizer_path: Tokenizer path, defaults to AI-ModelScope/stable-diffusion-v1-5:tokenizer/.

Example dataset download:

modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "stable_diffusion/*" --local_dir ./data/diffsynth_example_dataset

stable-diffusion-v1-5 training scripts

We provide recommended training scripts for each model, please refer to the table in "Model Overview" above. For guidance on writing model training scripts, see Model Training; for more advanced training algorithms, see Training Framework Overview.

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