File size: 1,739 Bytes
aeb3682 | 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 93 94 95 96 97 98 99 | import torch
from PIL.Image import Image
from onediffx.deep_cache import StableDiffusionXLPipeline
from pipelines.models import TextToImageRequest
from diffusers import DDIMScheduler # ,StableDiffusionXLPipeline
from torch import Generator
from loss import SchedulerWrapper
from onediffx import compile_pipe, save_pipe, load_pipe
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,
).to("cuda")
pipeline.scheduler = SchedulerWrapper(DDIMScheduler.from_config(pipeline.scheduler.config))
pipeline = compile_pipe(pipeline)
for _ in range(4):
deepcache_output = pipeline(prompt="mynki, robert, slon, simpleminer, crybit", num_inference_steps=20, negative_prompt="bloody, cruel, war, weapong", output_type="pil", cache_interval=2, cache_layer_id=1, cache_block_id=0)
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=20,
cache_interval=1,
cache_layer_id=1,
cache_block_id=0,
eta=1.0,
guidance_scale = 5.0,
guidance_rescale = 0.0,
).images[0]
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