[//]: # ((reference from [hugging Face](https://github.com/huggingface/diffusers/blob/docs/8bit-inference-pixart/docs/source/en/api/pipelines/pixart.md))) ## Running the `PixArtAlphaPipeline` in under 8GB GPU VRAM It is possible to run the [`PixArtAlphaPipeline`] under 8GB GPU VRAM by loading the text encoder in 8-bit numerical precision. Let's walk through a full-fledged example. First, install the `bitsandbytes` library: ```bash pip install -U bitsandbytes ``` Then load the text encoder in 8-bit: ```python from transformers import T5EncoderModel from diffusers import PixArtAlphaPipeline text_encoder = T5EncoderModel.from_pretrained( "PixArt-alpha/PixArt-XL-2-1024-MS", subfolder="text_encoder", load_in_8bit=True, device_map="auto", ) pipe = PixArtAlphaPipeline.from_pretrained( "PixArt-alpha/PixArt-XL-2-1024-MS", text_encoder=text_encoder, transformer=None, device_map="auto" ) ``` Now, use the `pipe` to encode a prompt: ```python with torch.no_grad(): prompt = "cute cat" prompt_embeds, prompt_attention_mask, negative_embeds, negative_prompt_attention_mask = pipe.encode_prompt(prompt) del text_encoder del pipe flush() ``` `flush()` is just a utility function to clear the GPU VRAM and is implemented like so: ```python import gc def flush(): gc.collect() torch.cuda.empty_cache() ``` Then compute the latents providing the prompt embeddings as inputs: ```python pipe = PixArtAlphaPipeline.from_pretrained( "PixArt-alpha/PixArt-XL-2-1024-MS", text_encoder=None, torch_dtype=torch.float16, ).to("cuda") latents = pipe( negative_prompt=None, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds, prompt_attention_mask=prompt_attention_mask, negative_prompt_attention_mask=negative_prompt_attention_mask, num_images_per_prompt=1, output_type="latent", ).images del pipe.transformer flush() ``` Notice that while initializing `pipe`, you're setting `text_encoder` to `None` so that it's not loaded. Once the latents are computed, pass it off the VAE to decode into a real image: ```python with torch.no_grad(): image = pipe.vae.decode(latents / pipe.vae.config.scaling_factor, return_dict=False)[0] image = pipe.image_processor.postprocess(image, output_type="pil") image.save("cat.png") ``` All of this, put together, should allow you to run [`PixArtAlphaPipeline`] under 8GB GPU VRAM.  Find the script [here](https://gist.github.com/sayakpaul/3ae0f847001d342af27018a96f467e4e) that can be run end-to-end to report the memory being used. Text embeddings computed in 8-bit can have an impact on the quality of the generated images because of the information loss in the representation space induced by the reduced precision. It's recommended to compare the outputs with and without 8-bit.