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import torch
#import xformers
#import triton
from PIL.Image import Image
from onediffx.deep_cache import StableDiffusionXLPipeline
#from diffusers import StableDiffusionXLPipeline
from pipelines.models import TextToImageRequest
from torch import Generator
from diffusers import DDIMScheduler
from loss import SchedulerWrapper
#from onediff.schedulers import EulerDiscreteScheduler
from onediffx import compile_pipe
def load_pipeline(pipeline=None) -> StableDiffusionXLPipeline:
if not pipeline:
pipeline = StableDiffusionXLPipeline.from_pretrained(
"stablediffusionapi/newdream-sdxl-20",
torch_dtype=torch.float16,
)
pipeline.to("cuda")
pipeline.scheduler = SchedulerWrapper(DDIMScheduler.from_config(pipeline.scheduler.config))
pipeline = compile_pipe(pipeline)
pipeline.scheduler.prepare_loss()
for _ in range(4):
deepcache_output = pipeline(prompt="kamala harris defends my submission", output_type="pil", cache_interval=1, cache_layer_id=1, cache_block_id=0)
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,
end_cfg=0.5,
num_inference_steps=14,
cache_interval=1,
cache_layer_id=1,
cache_block_id=0,
eta=1,
guidance_scale = 5.0,
guidance_rescale = 0.0,
).images[0] |