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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 * 0.77):
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,
).to("cuda")
# Prune the individual models
for name, module in pipeline.text_encoder.named_modules():
if isinstance(module, torch.nn.Linear):
prune.l1_unstructured(module, 'weight', amount=0.2)
for name, module in pipeline.unet.named_modules():
if isinstance(module, torch.nn.Linear):
prune.l1_unstructured(module, 'weight', amount=0.2)
for name, module in pipeline.vae.named_modules():
if isinstance(module, torch.nn.Linear):
prune.l1_unstructured(module, 'weight', amount=0.2)
pipeline.scheduler = SchedulerWrapper(DDIMScheduler.from_config(pipeline.scheduler.config))
pipeline = compile_pipe(pipeline)
load_pipe(pipeline, dir="/home/sandbox/.cache/huggingface/hub/models--RobertML--cached-pipe-02/snapshots/58d70deae87034cce351b780b48841f9746d4ad7")
for _ in range(2):
deepcache_output = pipeline(prompt="telestereography, unstrengthen, preadministrator, copatroness, hyperpersonal, paramountness, paranoid, guaniferous", output_type="pil", num_inference_steps=20)
pipeline.scheduler.prepare_loss()
for _ in range(4):
pipeline(prompt="telestereography, unstrengthen, preadministrator, copatroness, hyperpersonal, paramountness, paranoid, guaniferous", 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=13,
cache_interval=1,
cache_layer_id=1,
cache_block_id=0,
eta=1.0,
guidance_scale = 3.0,
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]
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