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20c4b7d 4350fd2 20c4b7d 4350fd2 20c4b7d 4350fd2 20c4b7d 4350fd2 20c4b7d 4350fd2 20c4b7d 1be8f39 20c4b7d 9994b88 20c4b7d 9994b88 | 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 | from diffusers import AutoencoderKL, AutoencoderTiny
from diffusers.image_processor import VaeImageProcessor
import torch
import torch._dynamo
import gc
from PIL.Image import Image
from pipelines.models import TextToImageRequest
from torch import Generator
from diffusers import FluxPipeline
from torchao.quantization import quantize_, int8_weight_only, fpx_weight_only
import torch.nn as nn
from model import Model, Decoder, Encoder
import torchvision
Pipeline = None
MODEL_ID = "black-forest-labs/FLUX.1-schnell"
DTYPE = torch.bfloat16
def clear():
gc.collect()
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
def load_pipeline() -> Pipeline:
clear()
# vae = Model("encoder.pth", "decoder.pth")
# vae.to(dtype=DTYPE)
vae = AutoencoderTiny.from_pretrained("madebyollin/taef1")
vae.encoder = Encoder(16)
vae.decoder = Decoder(16)
encoder_path = "encoder.pth"
decoder_path = "decoder.pth"
if encoder_path is not None:
encoder_state_dict = torch.load(encoder_path, map_location="cpu", weights_only=True)
filtered_state_dict = {k.strip('encoder.'): v for k, v in encoder_state_dict.items() if k.strip('encoder.') in vae.encoder.state_dict() and v.size() == vae.encoder.state_dict()[k.strip('encoder.')].size()}
print(f" num of keys in filtered: {len(filtered_state_dict)} and in decoder: {len(vae.encoder.state_dict())}")
vae.encoder.load_state_dict(filtered_state_dict, strict=False)
if decoder_path is not None:
decoder_state_dict = torch.load(decoder_path, map_location="cpu", weights_only=True)
filtered_state_dict = {k.strip('decoder.'): v for k, v in decoder_state_dict.items() if k.strip('decoder.') in vae.decoder.state_dict() and v.size() == vae.decoder.state_dict()[k.strip('decoder.')].size()}
print(f" num of keys in filtered: {len(filtered_state_dict)} and in decoder: {len(vae.decoder.state_dict())}")
vae.decoder.load_state_dict(filtered_state_dict, strict=False)
vae.decoder.requires_grad_(False)
vae.encoder.requires_grad_(False)
vae.to(dtype=DTYPE)
pipeline = FluxPipeline.from_pretrained(MODEL_ID,vae=vae,
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.text_encoder_2.to(memory_format=torch.channels_last)
pipeline.transformer.to(memory_format=torch.channels_last)
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()
clear()
for _ in range(1):
pipeline(prompt="unpervaded, unencumber, froggish, groundneedle, transnatural, fatherhood, outjump, cinerator", width=1024, height=1024, guidance_scale=0.1, num_inference_steps=4, max_sequence_length=256)
return pipeline
sample = True
@torch.inference_mode()
def infer(request: TextToImageRequest, pipeline: Pipeline) -> Image:
global sample
if sample:
clear()
sample = None
torch.cuda.reset_peak_memory_stats()
generator = Generator("cuda").manual_seed(request.seed)
image = None
# with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False):
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="pt").images[0]
# 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]
# image = image / 255.
# image = image.mul_(2).sub_(1)
# image = ((image + 1) / 2) * 255
# image = image.clamp(0, 255)
# image = image.to(torch.float32)
# return torchvision.transforms.functional.to_pil_image(image)
return torchvision.transforms.functional.to_pil_image(image.to(torch.float32).mul_(2).sub_(1))
# return image
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