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33100d5 796bc4c 33100d5 796bc4c 33100d5 38fc09e 33100d5 38fc09e e18b8f9 38fc09e e18b8f9 38fc09e 33100d5 71265b3 33100d5 | 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 | from diffusers import FluxPipeline, AutoencoderKL, AutoencoderTiny
from diffusers.image_processor import VaeImageProcessor
from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
from torch.ao.quantization import quantize_dynamic
from transformers import T5EncoderModel, T5TokenizerFast, CLIPTokenizer, CLIPTextModel
import torch
import torch._dynamo
import gc
from PIL import Image as img
from PIL.Image import Image
from pipelines.models import TextToImageRequest
from torch import Generator
import time
from diffusers import FluxTransformer2DModel, DiffusionPipeline
# from torchao.quantization import quantize_,int8_weight_only
import os
from torch.ao.quantization import prepare, convert
from torch.ao.quantization import QConfig
from torch.ao.quantization.observer import MinMaxObserver
from torch.ao.quantization.quantize import quantize_dynamic
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:False,garbage_collection_threshold:0.01"
Pipeline = None
ckpt_id = "black-forest-labs/FLUX.1-schnell"
def empty_cache():
start = time.time()
gc.collect()
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
print(f"Flush took: {time.time() - start}")
def load_pipeline() -> Pipeline:
empty_cache()
dtype, device = torch.bfloat16, "cuda"
text_encoder_2 = T5EncoderModel.from_pretrained(
"city96/t5-v1_1-xxl-encoder-bf16", torch_dtype=torch.bfloat16
)
vae=AutoencoderKL.from_pretrained(ckpt_id, subfolder="vae", torch_dtype=dtype)
pipeline = DiffusionPipeline.from_pretrained(
ckpt_id,
vae=vae,
text_encoder_2 = text_encoder_2,
torch_dtype=dtype,
)
torch.backends.cudnn.benchmark = True
torch.backends.cuda.matmul.allow_tf32 = True
torch.cuda.set_per_process_memory_fraction(0.99)
pipeline.text_encoder.to(memory_format=torch.channels_last)
# pipeline.transformer.to(memory_format=torch.channels_last)
# quantize_dynamic(pipeline.transformer, dtype=torch.float8_e5m2fnuz, inplace=True)
# Define a custom qconfig for float8_e5m2fnuz
float8_observer = MinMaxObserver.with_args(dtype=torch.qint8)
custom_qconfig = QConfig(
activation=float8_observer,
weight=float8_observer
)
qconfig_spec = {
"linear": custom_qconfig,
"linear_1": custom_qconfig,
"linear_2": custom_qconfig,
"to_q": custom_qconfig,
"to_k": custom_qconfig,
"to_v": custom_qconfig,
"add_k_proj": custom_qconfig,
"add_v_proj": custom_qconfig,
"add_q_proj": custom_qconfig,
"proj": custom_qconfig,
"proj_mlp": custom_qconfig,
"proj_out": custom_qconfig
}
# Apply dynamic quantization to Transformer
pipeline.transformer = quantize_dynamic(
pipeline.transformer,
qconfig_spec=qconfig_spec, # Apply qconfig only to transformer layers
dtype=torch.qint8, #torch.float8_e5m2fnuz
inplace=True,
)
pipeline.vae.to(memory_format=torch.channels_last)
pipeline.vae = torch.compile(pipeline.vae)
pipeline._exclude_from_cpu_offload = ["vae"]
# pipeline.enable_sequential_cpu_offload()
def custom_cpu_offload(model, device, offload_buffers=True):
state_dict = model.state_dict()
filtered_state_dict = {k: v for k, v in state_dict.items() if isinstance(v, torch.Tensor)}
for name, param in filtered_state_dict.items():
param.data = param.to(device)
custom_cpu_offload(pipeline.text_encoder, "cpu")
custom_cpu_offload(pipeline.text_encoder_2, "cpu")
custom_cpu_offload(pipeline.transformer, "cpu")
for _ in range(2):
pipeline(prompt="onomancy, aftergo, spirantic, Platyhelmia, modificator, drupaceous, jobbernowl, hereness", width=1024, height=1024, guidance_scale=0.0, num_inference_steps=4, max_sequence_length=256)
return pipeline
@torch.inference_mode()
def infer(request: TextToImageRequest, pipeline: Pipeline) -> Image:
torch.cuda.reset_peak_memory_stats()
generator = Generator("cuda").manual_seed(request.seed)
image=pipeline(request.prompt,generator=generator, guidance_scale=0.0, num_inference_steps=4, max_sequence_length=256, height=request.height, width=request.width, output_type="pil").images[0]
return(image)
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