DiffSynth-Studio/docs/source_en/GetStarted/A_simple_example.md

A Simple Example: Text-to-Image Synthesis with Flux

The following example shows how to use the FLUX.1 model for text-to-image tasks. The script provides a simple setup for generating images from text descriptions. It covers downloading the necessary models, configuring the pipeline, and generating images with and without classifier-free guidance.

For other models supported by DiffSynth, see Models.md.

Setup

First, ensure you have the necessary models downloaded and configured:

import torch
from diffsynth import ModelManager, FluxImagePipeline, download_models

# Download the FLUX.1-dev model files
download_models(["FLUX.1-dev"])

For instructions on downloading models, see Download_models.md.

Loading Models

Initialize the model manager with your device and data type:

model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cuda")
model_manager.load_models([
    "models/FLUX/FLUX.1-dev/text_encoder/model.safetensors",
    "models/FLUX/FLUX.1-dev/text_encoder_2",
    "models/FLUX/FLUX.1-dev/ae.safetensors",
    "models/FLUX/FLUX.1-dev/flux1-dev.safetensors"
])

For instructions on loading models, see ModelManager.md.

Creating the Pipeline

Create an instance of the FluxImagePipeline from the loaded model manager:

pipe = FluxImagePipeline.from_model_manager(model_manager)

For instructions on using the Pipeline, see Pipeline.md.

Text-to-Image Synthesis

Generate an image using a short prompt. Below are examples of generating images with and without classifier-free guidance.

Basic Generation

prompt = "A cute little turtle"
negative_prompt = ""

torch.manual_seed(6)
image = pipe(
    prompt=prompt,
    num_inference_steps=30, embedded_guidance=3.5
)
image.save("image_1024.jpg")

Generation with Classifier-Free Guidance

torch.manual_seed(6)
image = pipe(
    prompt=prompt, negative_prompt=negative_prompt,
    num_inference_steps=30, cfg_scale=2.0, embedded_guidance=3.5
)
image.save("image_1024_cfg.jpg")

High-Resolution Fix

torch.manual_seed(7)
image = pipe(
    prompt=prompt,
    num_inference_steps=30, embedded_guidance=3.5,
    input_image=image.resize((2048, 2048)), height=2048, width=2048, denoising_strength=0.6, tiled=True
)
image.save("image_2048_highres.jpg")