????????????

no idea, I don't use kagle, this model is big and using two GPUs is for very advanced users which you don't seem to be if you're asking, you probably should just rent some bigger instance or use group offloading if kagle has the RAM needed. In the future we will provide some kind of guide to use multiple GPUs but I repeat, this is for advanced users.

diffusers/FLUX.2-dev-bnb-4bit
2*16gb t4
g30 ram

kagle

In general, how can the model
diffusers/FLUX.2-dev-bnb-4bit
work on two gpu?

group offloading

?????????????????????????

with parallelism

في Google Colab Pro مع 2 T4 GPUs

import torch
from diffusers import Flux2Pipeline, ContextParallelConfig
from diffusers.hooks import apply_context_parallel

التحقق من عدد الأجهزة

print(f"عدد GPUs: {torch.cuda.device_count()}")

إذا كان لديك جهازان فقط

if torch.cuda.device_count() >= 2:
config = ContextParallelConfig(
ring_degree=2,
convert_to_fp32=True
)

pipe = Flux2Pipeline.from_pretrained(
    "diffusers/FLUX.2-dev-bnb-4bit",
    torch_dtype=torch.float16,  # T4 لا يدعم bf16
)

# تطبيق التوازي
apply_context_parallel(pipe.transformer, config)

# تشغيل
prompt = "A futuristic city at night with flying cars"
image = pipe(prompt, num_inference_steps=28).images[0]
image.save("colab_ring_parallel.png")

else:
print("⚠️ هذا الكود يتطلب جهازين على الأقل")

RuntimeError: Failed to import diffusers.pipelines.flux2.pipeline_flux2 because of the following error (look up to see its traceback):
Could not import module 'AutoProcessor'. Are this object's requirements defined correctly?
add Codeadd Markdown

KAgl 2 T4

kagle 2*t4

code
https://huggingface.co/docs/diffusers/main/en/api/parallel

https://huggingface.co/diffusers/FLUX.2-dev-bnb-4bit/discussions/3#69d4c4443f29c59da80cb052

with parallelism
how??????????????????????????

https://github.com/ayttop/xflux2gpu/blob/main/xxxxxx%20(1).ipynb

not run on two gpu never