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it run in colab t4

from diffusers import FluxPipeline
import torch

ckpt_id = "black-forest-labs/FLUX.1-schnell"
prompt = [
"an astronaut riding a horse",
# more prompts here
]
height, width = 1024, 1024

denoising

pipe = FluxPipeline.from_pretrained(
ckpt_id,
torch_dtype=torch.bfloat16,
)
pipe.vae.enable_tiling()
pipe.vae.enable_slicing()
pipe.enable_sequential_cpu_offload() # offloads modules to CPU on a submodule level (rather than model level)

image = pipe(
prompt,
num_inference_steps=1,
guidance_scale=0.0,
height=height,
width=width,
).images[0]
print('Max mem allocated (GB) while denoising:', torch.cuda.max_memory_allocated() / (1024 ** 3))

import matplotlib.pyplot as plt
plt.imshow(image)
plt.show()