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import torch
from PIL.Image import Image
from onediffx.deep_cache import StableDiffusionXLPipeline
from pipelines.models import TextToImageRequest
from torch import Generator
import oneflow as flow
from onediff.infer_compiler import oneflow_compile
from onediffx import compile_pipe, save_pipe, load_pipe
from diffusers import DDIMScheduler
from loss import SchedulerWrapper
def load_pipeline(pipeline=None) -> StableDiffusionXLPipeline:
if not pipeline:
pipeline = StableDiffusionXLPipeline.from_pretrained(
"./models/newdream-sdxl-20",
torch_dtype=torch.float16,
local_files_only=True,
)
pipeline.to("cuda")
pipeline.scheduler = SchedulerWrapper(DDIMScheduler.from_config(pipeline.scheduler.config))
pipeline = compile_pipe(pipeline)
pipeline.unet = oneflow_compile(pipeline.unet)
load_pipe(pipeline,dir="cached_pipe")
for _ in range(4):
deepcache_output = pipeline(prompt="make submissions great again", cache_interval=1, cache_layer_id=1, cache_block_id=0, num_inference_steps=20)
pipeline.scheduler.prepare_loss()
return pipeline
def infer(request: TextToImageRequest, pipeline: StableDiffusionXLPipeline) -> Image:
if request.seed is None:
generator = None
else:
generator = Generator(pipeline.device).manual_seed(request.seed)
return pipeline(
prompt=request.prompt,
negative_prompt=request.negative_prompt,
width=request.width,
height=request.height,
generator=generator,
num_inference_steps=1,
cache_interval=1,
cache_layer_id=1,
cache_block_id=0,
).images[0]
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