Flux.1-dev installation
I see all the time the same problem when i try to run this code. Do you have any ideas? It looks like it was stopped midway.import torchfrom diffusers import FluxPipelinepipe = FluxPipeline.from_pretrained(“black-forest-labs/FLUX.1-dev”, torch_dtype=torch.bfloat16)pipe.enable_model_cpu_offload()prompt = “a tiny astronaut hatching from an egg on the moon”out = pipe(prompt=prompt,guidance_scale=3.5,height=768,width=1360,num_inference_steps=50,).images[0]out.save(“image.png”)(venv) PS C:\Users\alf\Desktop> python generate_image.pyLoading pipeline components…:  43%|██████████████████████▎                             | 3/7 [00:00<00:00, 27.43it/s]You setadd_prefix_space. The tokenizer needs to be converted from the slow tokenizersLoading checkpoint shards: 100%|█████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 14.23it/s]Loading pipeline components…:  86%|████████████████████████████████████████████▌       | 6/7 [00:00<00:00,  8.61it/s](venv) PS C:\Users\alf\Desktop>
2024-08-31T00:15:18Z
Loading Flux into RAM or VRAM with bfloat16 requires a significant amount of RAM and VRAM, is it enough?I was experiencing a lack of RAM (probably) and freezing in the middle of the process (no error message, just stops).We could save a memory bit by loading it in float8, but right now the library is buggy and doesn’t seem to work.As a solution, if the cause was insufficient RAM or VRAM, it may be somehow sufficient to load each component separately and quantize it.Detailshuggingface.coFluxWe’re on a journey to advance and democratize artificial intelligence through open source and open science.gist.github.comhttps://gist.github.com/VvanGemert/ab9c3ce63f12d429cf6075dbd764e57cflux_on_potato.py# First, in your terminal.
#
# $ python3 -m virtualenv env
# $ source env/bin/activate
# $ pip install torch torchvision transformers sentencepiece protobuf accelerate
# $ pip install git+https://github.com/huggingface/diffusers.git
# $ pip install optimum-quanto
# $ pip install gradio

import torchThis file has been truncated.show originalgithub.comhuggingface/blog/blob/main/quanto-diffusers.md---
title: "Memory-efficient Diffusion Transformers with Quanto and Diffusers"
thumbnail: /blog/assets/quanto-diffusers/thumbnail.png
authors:
- user: sayakpaul
- user: dacorvo
---

# Memory-efficient Diffusion Transformers with Quanto and Diffusers

Over the past few months, we have seen an emergence in the use of Transformer-based diffusion backbones for high-resolution text-to-image (T2I) generation. These models use the transformer architecture as the building block for the diffusion process, instead of the UNet architecture that was prevalent in many of the initial diffusion models. Thanks to the nature of Transformers, these backbones show good scalability, with models ranging from 0.6B to 8B parameters. 

As models become larger, memory requirements increase. The problem intensifies because a diffusion pipeline usually consists of several components: a text encoder, a diffusion backbone, and an image decoder. Furthermore, modern diffusion pipelines use multiple text encoders – for example, there are three in the case of Stable Diffusion 3. It takes 18.765 GB of GPU memory to run SD3 inference using FP16 precision. 

These high memory requirements can make it difficult to use these models with consumer GPUs, slowing adoption and making experimentation harder. In this post, we show how to improve the memory efficiency of Transformer-based diffusion pipelines by leveraging Quanto's quantization utilities from the Diffusers library.

### Table of contents

- [Preliminaries](#preliminaries)
- [Quantizing a `DiffusionPipeline` with Quanto](#quantizing-a-diffusionpipeline-with-quanto)This file has been truncated.show originalAbout the Bughuggingface.coKijai/flux-fp8 · Diffusers?Can this somehow be used in Diffusers, since the FluxPipeline currently does not support from_single_file?