black-forest-labs

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FLUX.1-schnell

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I am not on windows, but this works for me, maybe it works for you too:
https://gist.github.com/jason-kane/130b531ac68d9aa932181fbd530b3fba

#!/usr/bin/env -S uv run --script
# /// script
# requires-python = ">=3.12"
# dependencies = [
#     "torch>=2.8.0",
#     "diffusers@git+https://github.com/huggingface/diffusers.git",
#     "transformers==4.55.4",
#     "accelerate>=0.26.0",
#     "bitsandbytes==0.45.5",
#     "protobuf==5.29.4",
#     "sentencepiece",
# ]
# ///
"""
12GB VRAM FLUX.1-schnell example
After the first run it takes 38-41 seconds on a 12GB 3060 GTX 
to generate a 1024x1024 image

If you have UV installed, you can just run this file and it will
self-install.
"""
import gc
import logging
import random

import torch
from diffusers import DiffusionPipeline
from transformers import (
    BitsAndBytesConfig,
    T5EncoderModel,
)

logging.basicConfig(level=logging.INFO)
log = logging.getLogger(__name__)


def disabled_safety_checker(images, clip_input):
    if len(images.shape)==4:
        num_images = images.shape[0]
        return images, [False]*num_images
    else:
        return images, False


def local_flux_schnell(
        clip_prompt,  # short prompt 
        t5_prompt=None,  # longer, more detailed prompt
    ):

    quantization_config = BitsAndBytesConfig(load_in_8bit=True)
    if t5_prompt is None:
        t5_prompt = clip_prompt

    model_id = "black-forest-labs/FLUX.1-schnell"    #needs 4 steps only - it is faster than the dev version as the name implies
    
    text_encoder = T5EncoderModel.from_pretrained(
        model_id,
        subfolder="text_encoder_2",
        quantization_config=quantization_config,
        torch_dtype=torch.bfloat16   #bfloat16 and normal float16 both work - former gives a warning but seems to work                                
    )

    pipe = DiffusionPipeline.from_pretrained(
        model_id, 
        torch_dtype=torch.bfloat16,   #bfloat16 and float16 both work, must match the T5               
        text_encoder_2=text_encoder,
        device_map="balanced", 
        max_memory={0:"11GiB", "cpu":"48GiB"},
    )

    pipe.safety_checker = disabled_safety_checker
    pipe.vae.enable_tiling()   #less memory usage at VAE time

    log.info(f'Using {clip_prompt=} and {t5_prompt=} to generate a new image...')
    image = pipe(
        clip_prompt,
        prompt_2=t5_prompt,
        num_images_per_prompt=1,
        guidance_scale=0.0,    #must be 0.0 for schnell version, dev version can be as per SD                                                         
        num_inference_steps=4,  #only need 4 for schnell version, dev version needs 50 or so                                                      
        max_sequence_length=256,  #relates to the T5 encoder - text_encoder_2 - max 256 for schnell                                                   
        generator=torch.Generator("cpu").manual_seed(int(random.randrange(4294967294)))
    ).images[0]
    
    del pipe
    del text_encoder
    gc.collect()

    torch.cuda.empty_cache()
    torch.cuda.ipc_collect()
    return image

if __name__ == "__main__":
    prompt = "A beautiful landscape, trending on artstation"
    image = local_flux_schnell(prompt)
    image.save("flux_schnell_example.png")