Update src/pipeline.py

60d5f94 verified almost 2 years ago

2.15 kB

from diffusers import DiffusionPipeline, AutoencoderKL
from transformers import T5EncoderModel
import torch
import gc
from PIL import Image
from pipelines.models import TextToImageRequest  # Assuming this defines your request object
import os
from torch import Generator

Pipeline = None
# Consistent environment variable setting
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:128"  # More robust memory management

ckpt_id = "black-forest-labs/FLUX.1-schnell"

def load_pipeline() -> Pipeline:
    gc.collect()
    # torch.cuda.empty_cache()

    dtype = torch.bfloat16
    text_encoder_2 = T5EncoderModel.from_pretrained(
        "city96/t5-v1_1-xxl-encoder-bf16", torch_dtype=dtype
    ).to(memory_format=torch.channels_last)
    vae = AutoencoderKL.from_pretrained(ckpt_id, subfolder="vae", torch_dtype=dtype).to(memory_format=torch.channels_last)


    pipeline = DiffusionPipeline.from_pretrained(
        ckpt_id,
        vae=vae,
        text_encoder_2=text_encoder_2,
        torch_dtype=dtype,
    )#.to("cuda")
    pipeline.transformer.to(memory_format=torch.channels_last)
    # pipeline.text_encoder.to(memory_format=torch.channels_last)
    

    # Optimize after moving to GPU
    pipeline.vae = torch.compile(pipeline.vae) # compile after moving to device

    # It's unclear if offloading helped in the originals. Test with and without!
    pipeline._exclude_from_cpu_offload = ["vae"]
    pipeline.enable_sequential_cpu_offload()

    # Warmup on GPU
    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)

	from diffusers import DiffusionPipeline, AutoencoderKL
	from transformers import T5EncoderModel
	import torch
	import gc
	from PIL import Image
	from pipelines.models import TextToImageRequest # Assuming this defines your request object
	import os
	from torch import Generator

	Pipeline = None
	# Consistent environment variable setting
	os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:128" # More robust memory management

	ckpt_id = "black-forest-labs/FLUX.1-schnell"

	def load_pipeline() -> Pipeline:
	gc.collect()
	# torch.cuda.empty_cache()

	dtype = torch.bfloat16
	text_encoder_2 = T5EncoderModel.from_pretrained(
	"city96/t5-v1_1-xxl-encoder-bf16", torch_dtype=dtype
	).to(memory_format=torch.channels_last)
	vae = AutoencoderKL.from_pretrained(ckpt_id, subfolder="vae", torch_dtype=dtype).to(memory_format=torch.channels_last)


	pipeline = DiffusionPipeline.from_pretrained(
	ckpt_id,
	vae=vae,
	text_encoder_2=text_encoder_2,
	torch_dtype=dtype,
	)#.to("cuda")
	pipeline.transformer.to(memory_format=torch.channels_last)
	# pipeline.text_encoder.to(memory_format=torch.channels_last)


	# Optimize after moving to GPU
	pipeline.vae = torch.compile(pipeline.vae) # compile after moving to device

	# It's unclear if offloading helped in the originals. Test with and without!
	pipeline._exclude_from_cpu_offload = ["vae"]
	pipeline.enable_sequential_cpu_offload()

	# Warmup on GPU
	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)