sayakpaul

import torch 

torch.set_float32_matmul_precision("high")

from diffusers import StableDiffusion3Pipeline
import time

id = "stabilityai/stable-diffusion-3-medium-diffusers"
pipeline = StableDiffusion3Pipeline.from_pretrained(
    id, 

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from diffusers import StableDiffusion3Pipeline
from transformers import T5EncoderModel
import torch 
import time
import gc

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

sayakpaul

import torch

torch.set_float32_matmul_precision("high")

from diffusers import HunyuanDiTPipeline
import argparse
import time


def load_pipeline(args):

sayakpaul

"""
Make sure you have `diffusers`, `accelerate`, `transformers`, and `bitsandbytes` installed.

You also set up PyTorch and CUDA.

Once the dependencies are installed, you can run `python run_hunyuan_dit_less_memory.py`. 
"""

from diffusers import HunyuanDiTPipeline
from transformers import T5EncoderModel

sayakpaul

sayakpaul

1. Get a Makefile from here: https://github.com/huggingface/diffusion-fast/blob/main/Makefile.
2. Install ruff: pip install ruff.
3. Get the ruff configuration file through pyproject.toml: https://github.com/huggingface/diffusion-fast/blob/main/pyproject.toml.

Keep both the Makefile and pyproject.toml files at the root level of your project.

Before staging your changes, run the following from the root of your project directory:

make style && make quality

sayakpaul

from datasets import Dataset, Features
from datasets import Image as ImageFeature
from datasets import Value
import pandas as pd 
import os

# CSV comes from the notebook above.
df = pd.read_csv("coco_30k_randomly_sampled_2014_val.csv")
root_path = "val2014"

sayakpaul

from huggingface_hub import (
    hf_hub_download,
    list_files_info,
    parse_safetensors_file_metadata,
)
import json
import argparse


def get_files_info(args):

sayakpaul

# SDXL: 0.613, 0.5566, 0.54, 0.4162, 0.4042, 0.4596, 0.5374, 0.5286, 0.5038
# SD: 0.5396, 0.5707, 0.477, 0.4665, 0.5419, 0.4594, 0.4857, 0.4741, 0.4804

from diffusers import DiffusionPipeline
from peft import LoraConfig
import argparse
import torch


def load_pipeline(pipeline_id):

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import argparse

import torch
import torch_tensorrt
import torch.utils.benchmark as benchmark

from diffusers import DiffusionPipeline

CKPT = "CompVis/stable-diffusion-v1-4"
PROMPT = "a majestic castle in the clouds"