asomoza

import torch
from diffusers import FlowMatchLCMScheduler, HiDreamImagePipeline
from transformers import LlamaForCausalLM, PreTrainedTokenizerFast

device = torch.device("cuda:0")
repo_id = "HiDream-ai/HiDream-I1-Dev"
llama_repo = "meta-llama/Llama-3.1-8B-Instruct"
torch_dtype = torch.bfloat16

prompt = "Ultra-realistic, high-quality photo of an anthropomorphic capybara with a tough, streetwise attitude, wearing a worn black leather jacket, dark sunglasses, and ripped jeans. The capybara is leaning casually against a gritty urban wall covered in vibrant graffiti. Behind it, in bold, dripping yellow spray paint, the word “HuggingFace” is scrawled in large street-art style letters. The scene is set in a dimly lit alleyway with moody lighting, scattered trash, and an edgy, rebellious vibe — like a character straight out of an underground comic book."

asomoza

import gc
import threading
import time

import psutil
import torch
from diffusers import HiDreamImagePipeline
from transformers import (
    CLIPTextModelWithProjection,
    CLIPTokenizer,

asomoza

import gc

import torch
from transformers import (
    BitsAndBytesConfig as BitsAndBytesConfig,
)
from transformers import (
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    LlamaForCausalLM,

asomoza

import torch
from optimum.quanto import QuantizedDiffusersModel, freeze, qfloat8, quantize

from diffusers import FluxPipeline, FluxTransformer2DModel


class QuantizedFluxTransformer2DModel(QuantizedDiffusersModel):
    base_class = FluxTransformer2DModel

asomoza

#!/usr/bin/env python
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#