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import torch
from time import perf_counter
from PIL.Image import Image
from diffusers import StableDiffusionXLPipeline, DPMSolverMultistepScheduler, AutoencoderTiny, UNet2DConditionModel
from pipelines.models import TextToImageRequest
from torch import Generator
from sfast.compilers.diffusion_pipeline_compiler import (compile,
CompilationConfig)
def load_pipeline() -> StableDiffusionXLPipeline:
pipeline = StableDiffusionXLPipeline.from_pretrained(
"./models/newdream-sdxl-20/",
torch_dtype=torch.float16,
use_safetensors=True,
variant="fp16",
local_files_only=True,)
pipeline.vae = AutoencoderTiny.from_pretrained("madebyollin/taesdxl", torch_dtype=torch.float16)
pipeline.scheduler = DPMSolverMultistepScheduler.from_config(
pipeline.scheduler.config)
pipeline.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2)
pipeline.to("cuda")
config = CompilationConfig.Default()
try:
import xformers
config.enable_xformers = True
except ImportError:
print('xformers not installed, skip')
try:
import triton
config.enable_triton = True
except ImportError:
print('Triton not installed, skip')
pipeline = compile(pipeline, config)
for _ in range(4):
pipeline(prompt="", num_inference_steps=15,)
return pipeline
def infer(request: TextToImageRequest, pipeline: StableDiffusionXLPipeline) -> Image:
generator = Generator(pipeline.device).manual_seed(request.seed) if request.seed else None
return pipeline(
prompt=request.prompt,
negative_prompt=request.negative_prompt,
width=request.width,
height=request.height,
generator=generator,
num_inference_steps=8,
).images[0]
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