File size: 2,483 Bytes
a7b0378 2fb2757 a7b0378 2fb2757 a7b0378 2fb2757 a7b0378 2fb2757 a7b0378 2fb2757 a7b0378 2fb2757 a7b0378 2fb2757 a7b0378 2fb2757 a7b0378 2fb2757 a7b0378 2fb2757 a7b0378 2fb2757 a7b0378 2fb2757 a7b0378 | 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 | import torch
from PIL.Image import Image
from diffusers import StableDiffusionXLPipeline
from pipelines.models import TextToImageRequest
from diffusers import DDIMScheduler
from torch import Generator
from loss import SchedulerWrapper
from onediffx import compile_pipe, save_pipe, load_pipe
def callback_dynamic_cfg(pipe, step_index, timestep, callback_kwargs):
if step_index == int(pipe.num_timesteps * 0.78):
callback_kwargs['prompt_embeds'] = callback_kwargs['prompt_embeds'].chunk(2)[-1]
callback_kwargs['add_text_embeds'] = callback_kwargs['add_text_embeds'].chunk(2)[-1]
callback_kwargs['add_time_ids'] = callback_kwargs['add_time_ids'].chunk(2)[-1]
pipe._guidance_scale = 0.1
return callback_kwargs
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)
load_pipe(pipeline, dir="/home/sandbox/.cache/huggingface/hub/models--slobers--make_me_happy/cucumber")
for _ in range(1):
deepcache_output = pipeline(prompt="insensible, timbale, pothery, electrovital, actinogram, taxis, intracerebellar, centrodesmus", output_type="pil", cache_interval=1, cache_layer_id=1, cache_block_id=0)
pipeline.scheduler.prepare_loss()
for _ in range(2):
pipeline(prompt="insensible, timbale, pothery, electrovital, actinogram, taxis, intracerebellar, centrodesmus", output_type="pil", num_inference_steps=20)
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=14,
cache_interval=1,
cache_layer_id=1,
cache_block_id=0,
eta=1.0,
guidance_scale = 5.2,
guidance_rescale = 0.0,
callback_on_step_end=callback_dynamic_cfg,
callback_on_step_end_tensor_inputs=['prompt_embeds', 'add_text_embeds', 'add_time_ids'],
).images[0]
|