File size: 10,179 Bytes
d3841a2 6551d57 d3841a2 b7d193e 9ae3cc2 d3841a2 6551d57 d3841a2 6551d57 d3841a2 6551d57 d3841a2 6551d57 d3841a2 6551d57 d3841a2 6551d57 d3841a2 957bbbe 6551d57 d3841a2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 |
# https://github.com/sayakpaul/diffusers-torchao
# https://github.com/pytorch/ao/releases
# https://developer.nvidia.com/cuda-gpus
import os
from typing import Any, Dict
import gc
from PIL import Image
from huggingface_hub import hf_hub_download
import torch
from torchao.quantization import quantize_, autoquant, int8_dynamic_activation_int8_weight, int8_dynamic_activation_int4_weight, float8_dynamic_activation_float8_weight
from torchao.quantization.quant_api import PerRow
from diffusers import FluxPipeline, FluxTransformer2DModel, AutoencoderKL, TorchAoConfig
from transformers import T5EncoderModel, BitsAndBytesConfig
from optimum.quanto import freeze, qfloat8, quantize
from para_attn.first_block_cache.diffusers_adapters import apply_cache_on_pipe
from huggingface_inference_toolkit.logging import logger
import subprocess
subprocess.run("pip list", shell=True)
print(torch.cuda.get_device_name())
print(torch.cuda.get_device_capability())
print(torch.cuda.get_arch_list())
IS_NEW_GPU = False if torch.cuda.get_device_capability() < (8, 9) else True
IS_TURBO = False
IS_4BIT = True
IS_COMPILE = False
IS_AUTOQ = False
IS_PARA = True
IS_LVRAM = True
# Set high precision for float32 matrix multiplications.
# This setting optimizes performance on NVIDIA GPUs with Ampere architecture (e.g., A100, RTX 30 series) or newer.
torch.set_float32_matmul_precision("high")
if IS_COMPILE:
import torch._dynamo
torch._dynamo.config.suppress_errors = True
def load_te2(repo_id: str, dtype: torch.dtype) -> Any:
if IS_4BIT:
nf4_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=torch.bfloat16)
text_encoder_2 = T5EncoderModel.from_pretrained(repo_id, subfolder="text_encoder_2", torch_dtype=dtype, quantization_config=nf4_config)
else:
text_encoder_2 = T5EncoderModel.from_pretrained(repo_id, subfolder="text_encoder_2", torch_dtype=dtype)
quantize(text_encoder_2, weights=qfloat8)
freeze(text_encoder_2)
return text_encoder_2
def load_pipeline_stable(repo_id: str, dtype: torch.dtype) -> Any:
quantization_config = TorchAoConfig("int4dq" if IS_4BIT else "float8dq" if IS_NEW_GPU else "int8wo")
vae = AutoencoderKL.from_pretrained(repo_id, subfolder="vae", torch_dtype=dtype)
pipe = FluxPipeline.from_pretrained(repo_id, vae=vae, text_encoder_2=load_te2(repo_id, dtype), torch_dtype=dtype, quantization_config=quantization_config)
pipe.transformer.fuse_qkv_projections()
pipe.vae.fuse_qkv_projections()
return pipe
def load_pipeline_lowvram(repo_id: str, dtype: torch.dtype) -> Any:
#int4_config = TorchAoConfig("int4dq")
#float8_config = TorchAoConfig("float8dq")
vae = AutoencoderKL.from_pretrained(repo_id, subfolder="vae", torch_dtype=dtype)
quantize_(vae, float8_dynamic_activation_float8_weight(granularity=PerRow()), device="cuda")
transformer = FluxTransformer2DModel.from_pretrained(repo_id, subfolder="transformer", torch_dtype=dtype)
quantize_(transformer, float8_dynamic_activation_float8_weight(granularity=PerRow()), device="cuda")
pipe = FluxPipeline.from_pretrained(repo_id, vae=None, transformer=None, text_encoder_2=load_te2(repo_id, dtype), torch_dtype=dtype, quantization_config=int4_config)
pipe.transformer = transformer
pipe.vae = vae
#pipe.transformer.fuse_qkv_projections()
#pipe.vae.fuse_qkv_projections()
pipe.to("cuda")
return pipe
def load_pipeline_compile(repo_id: str, dtype: torch.dtype) -> Any:
quantization_config = TorchAoConfig("int4dq" if IS_4BIT else "float8dq" if IS_NEW_GPU else "int8wo")
vae = AutoencoderKL.from_pretrained(repo_id, subfolder="vae", torch_dtype=dtype)
pipe = FluxPipeline.from_pretrained(repo_id, vae=vae, text_encoder_2=load_te2(repo_id, dtype), torch_dtype=dtype, quantization_config=quantization_config)
pipe.transformer.fuse_qkv_projections()
pipe.vae.fuse_qkv_projections()
pipe.transformer.to(memory_format=torch.channels_last)
pipe.transformer = torch.compile(pipe.transformer, mode="max-autotune", fullgraph=True)
pipe.vae.to(memory_format=torch.channels_last)
pipe.vae = torch.compile(pipe.vae, mode="max-autotune", fullgraph=True)
return pipe
def load_pipeline_autoquant(repo_id: str, dtype: torch.dtype) -> Any:
pipe = FluxPipeline.from_pretrained(repo_id, torch_dtype=dtype)
pipe.transformer.fuse_qkv_projections()
pipe.vae.fuse_qkv_projections()
pipe.transformer.to(memory_format=torch.channels_last)
pipe.transformer = torch.compile(pipe.transformer, mode="max-autotune", fullgraph=True)
pipe.vae.to(memory_format=torch.channels_last)
pipe.vae = torch.compile(pipe.vae, mode="max-autotune", fullgraph=True)
pipe.transformer = autoquant(pipe.transformer, error_on_unseen=False)
pipe.vae = autoquant(pipe.vae, error_on_unseen=False)
return pipe
def load_pipeline_turbo(repo_id: str, dtype: torch.dtype) -> Any:
pipe = FluxPipeline.from_pretrained(repo_id, torch_dtype=dtype)
pipe.load_lora_weights(hf_hub_download("ByteDance/Hyper-SD", "Hyper-FLUX.1-dev-8steps-lora.safetensors"), adapter_name="hyper-sd")
pipe.set_adapters(["hyper-sd"], adapter_weights=[0.125])
pipe.fuse_lora()
pipe.unload_lora_weights()
pipe.transformer.fuse_qkv_projections()
pipe.vae.fuse_qkv_projections()
weight = int8_dynamic_activation_int4_weight() if IS_4BIT else int8_dynamic_activation_int8_weight()
quantize_(pipe.transformer, weight, device="cuda")
quantize_(pipe.vae, weight, device="cuda")
return pipe
def load_pipeline_turbo_compile(repo_id: str, dtype: torch.dtype) -> Any:
pipe = FluxPipeline.from_pretrained(repo_id, torch_dtype=dtype)
pipe.load_lora_weights(hf_hub_download("ByteDance/Hyper-SD", "Hyper-FLUX.1-dev-8steps-lora.safetensors"), adapter_name="hyper-sd")
pipe.set_adapters(["hyper-sd"], adapter_weights=[0.125])
pipe.fuse_lora()
pipe.unload_lora_weights()
pipe.transformer.fuse_qkv_projections()
pipe.vae.fuse_qkv_projections()
weight = int8_dynamic_activation_int4_weight() if IS_4BIT else int8_dynamic_activation_int8_weight()
quantize_(pipe.transformer, weight, device="cuda")
quantize_(pipe.vae, weight, device="cuda")
pipe.transformer.to(memory_format=torch.channels_last)
pipe.transformer = torch.compile(pipe.transformer, mode="max-autotune", fullgraph=True)
pipe.vae.to(memory_format=torch.channels_last)
pipe.vae = torch.compile(pipe.vae, mode="max-autotune", fullgraph=True)
return pipe
def load_pipeline_opt(repo_id: str, dtype: torch.dtype) -> Any:
quantization_config = TorchAoConfig("int4dq" if IS_4BIT else "float8dq" if IS_NEW_GPU else "int8wo")
weight = int8_dynamic_activation_int4_weight() if IS_4BIT else int8_dynamic_activation_int8_weight()
transformer = FluxTransformer2DModel.from_pretrained(repo_id, subfolder="transformer", torch_dtype=dtype)
transformer.fuse_qkv_projections()
if IS_NEW_GPU: quantize_(transformer, float8_dynamic_activation_float8_weight(granularity=PerRow()), device="cuda")
else: quantize_(transformer, weight, device="cuda")
transformer.to(memory_format=torch.channels_last)
transformer = torch.compile(transformer, mode="max-autotune", fullgraph=True)
vae = AutoencoderKL.from_pretrained(repo_id, subfolder="vae", torch_dtype=dtype)
vae.fuse_qkv_projections()
if IS_NEW_GPU: quantize_(vae, float8_dynamic_activation_float8_weight(granularity=PerRow()), device="cuda")
else: quantize_(vae, weight, device="cuda")
vae.to(memory_format=torch.channels_last)
vae = torch.compile(vae, mode="max-autotune", fullgraph=True)
pipe = FluxPipeline.from_pretrained(repo_id, transformer=None, vae=None, text_encoder_2=load_te2(repo_id, dtype), torch_dtype=dtype, quantization_config=quantization_config)
pipe.transformer = transformer
pipe.vae = vae
return pipe
class EndpointHandler:
def __init__(self, path=""):
repo_id = "NoMoreCopyrightOrg/flux-dev-8step" if IS_TURBO else "NoMoreCopyrightOrg/flux-dev"
dtype = torch.bfloat16
#dtype = torch.float16 # for older nVidia GPUs
print("Loading pipeline...")
if IS_AUTOQ: self.pipeline = load_pipeline_autoquant(repo_id, dtype)
elif IS_COMPILE: self.pipeline = load_pipeline_opt(repo_id, dtype)
elif IS_LVRAM and IS_NEW_GPU: self.pipeline = load_pipeline_lowvram(repo_id, dtype)
else: self.pipeline = load_pipeline_stable(repo_id, dtype)
if IS_PARA: apply_cache_on_pipe(self.pipeline, residual_diff_threshold=0.12)
gc.collect()
torch.cuda.empty_cache()
self.pipeline.enable_vae_slicing()
self.pipeline.enable_vae_tiling()
self.pipeline.to("cuda")
print(self.pipeline)
def __call__(self, data: Dict[str, Any]) -> Image.Image:
logger.info(f"Received incoming request with {data=}")
if "inputs" in data and isinstance(data["inputs"], str):
prompt = data.pop("inputs")
elif "prompt" in data and isinstance(data["prompt"], str):
prompt = data.pop("prompt")
else:
raise ValueError(
"Provided input body must contain either the key `inputs` or `prompt` with the"
" prompt to use for the image generation, and it needs to be a non-empty string."
)
parameters = data.pop("parameters", {})
num_inference_steps = parameters.get("num_inference_steps", 8 if IS_TURBO else 28)
width = parameters.get("width", 1024)
height = parameters.get("height", 1024)
guidance_scale = parameters.get("guidance_scale", 3.5)
# seed generator (seed cannot be provided as is but via a generator)
seed = parameters.get("seed", 0)
generator = torch.manual_seed(seed)
return self.pipeline( # type: ignore
prompt,
height=height,
width=width,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
generator=generator,
output_type="pil",
).images[0]
|