Modern diffusion systems such as [Flux](../api/pipelines/flux) are very large and have multiple models. For example, [Flux.1-Dev](https://hf.co/black-forest-labs/FLUX.1-dev) is made up of two text encoders - [T5-XXL](https://hf.co/google/t5-v1_1-xxl) and [CLIP-L](https://hf.co/openai/clip-vit-large-patch14) - a [diffusion transformer](../api/models/flux_transformer), and a [VAE](../api/models/autoencoderkl). With a model this size, it can be challenging to run inference on consumer GPUs.  
Model sharding is a technique that distributes models across GPUs when the models don't fit on a single GPU. The example below assumes two 16GB GPUs are available for inference.  
Start by computing the text embeddings with the text encoders. Keep the text encoders on two GPUs by setting `device_map="balanced"`. The `balanced` strategy evenly distributes the model on all available GPUs. Use the `max_memory` parameter to allocate the maximum amount of memory for each text encoder on each GPU.  
> [!TIP]
> **Only** load the text encoders for this step! The diffusion transformer and VAE are loaded in a later step to preserve memory.  
```py
from diffusers import FluxPipeline
import torch

prompt = "a photo of a dog with cat-like look"

pipeline = FluxPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev",
transformer=None,
vae=None,
device_map="balanced",
max_memory={0: "16GB", 1: "16GB"},
torch_dtype=torch.bfloat16
)
with torch.no_grad():
print("Encoding prompts.")
prompt_embeds, pooled_prompt_embeds, text_ids = pipeline.encode_prompt(
prompt=prompt, prompt_2=None, max_sequence_length=512
)
```  
Once the text embeddings are computed, remove them from the GPU to make space for the diffusion transformer.  
```py
import gc

def flush():
gc.collect()
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()

del pipeline.text_encoder
del pipeline.text_encoder_2
del pipeline.tokenizer
del pipeline.tokenizer_2
del pipeline

flush()
```  
Load the diffusion transformer next which has 12.5B parameters. This time, set `device_map="auto"` to automatically distribute the model across two 16GB GPUs. The `auto` strategy is backed by [Accelerate](https://hf.co/docs/accelerate/index) and available as a part of the [Big Model Inference](https://hf.co/docs/accelerate/concept_guides/big_model_inference) feature. It starts by distributing a model across the fastest device first (GPU) before moving to slower devices like the CPU and hard drive if needed. The trade-off of storing model parameters on slower devices is slower inference latency.  
```py
from diffusers import FluxTransformer2DModel
import torch

transformer = FluxTransformer2DModel.from_pretrained(
"black-forest-labs/FLUX.1-dev",
subfolder="transformer",
device_map="auto",
torch_dtype=torch.bfloat16
)
```  
> [!TIP]
> At any point, you can try `print(pipeline.hf_device_map)` to see how the various models are distributed across devices. This is useful for tracking the device placement of the models. You can also try `print(transformer.hf_device_map)` to see how the transformer model is sharded across devices.  
Add the transformer model to the pipeline for denoising, but set the other model-level components like the text encoders and VAE to `None` because you don't need them yet.  
```py
pipeline = FluxPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev",
text_encoder=None,
text_encoder_2=None,
tokenizer=None,
tokenizer_2=None,
vae=None,
transformer=transformer,
torch_dtype=torch.bfloat16
)

print("Running denoising.")
height, width = 768, 1360
latents = pipeline(
prompt_embeds=prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
num_inference_steps=50,
guidance_scale=3.5,
height=height,
width=width,
output_type="latent",
).images
```  
Remove the pipeline and transformer from memory as they're no longer needed.  
```py
del pipeline.transformer
del pipeline

flush()
```  
Finally, decode the latents with the VAE into an image. The VAE is typically small enough to be loaded on a single GPU.  
```py
from diffusers import AutoencoderKL
from diffusers.image_processor import VaeImageProcessor
import torch

vae = AutoencoderKL.from_pretrained(ckpt_id, subfolder="vae", torch_dtype=torch.bfloat16).to("cuda")
vae_scale_factor = 2 ** (len(vae.config.block_out_channels))
image_processor = VaeImageProcessor(vae_scale_factor=vae_scale_factor)

with torch.no_grad():
print("Running decoding.")
latents = FluxPipeline._unpack_latents(latents, height, width, vae_scale_factor)
latents = (latents / vae.config.scaling_factor) + vae.config.shift_factor

image = vae.decode(latents, return_dict=False)[0]
image = image_processor.postprocess(image, output_type="pil")
image[0].save("split_transformer.png")
```  
By selectively loading and unloading the models you need at a given stage and sharding the largest models across multiple GPUs, it is possible to run inference with large models on consumer GPUs.
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/distributed_inference.md