103 lines
2.8 KiB
Python
103 lines
2.8 KiB
Python
from diffusers import (
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DiffusionPipeline,
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)
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from logging import getLogger
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from typing import Any, Optional, Tuple
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from ..params import (
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DeviceParams,
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Size,
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)
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from ..utils import (
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run_gc,
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)
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import numpy as np
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logger = getLogger(__name__)
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last_pipeline_instance = None
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last_pipeline_options = (None, None, None)
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last_pipeline_scheduler = None
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latent_channels = 4
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latent_factor = 8
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def get_latents_from_seed(seed: int, size: Size, batch: int = 1) -> np.ndarray:
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'''
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From https://www.travelneil.com/stable-diffusion-updates.html
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'''
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latents_shape = (batch, latent_channels, size.height // latent_factor,
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size.width // latent_factor)
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rng = np.random.default_rng(seed)
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image_latents = rng.standard_normal(latents_shape).astype(np.float32)
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return image_latents
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def get_tile_latents(full_latents: np.ndarray, dims: Tuple[int, int, int]) -> np.ndarray:
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x, y, tile = dims
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t = tile // latent_factor
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x = x // latent_factor
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y = y // latent_factor
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xt = x + t
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yt = y + t
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return full_latents[:, :, y:yt, x:xt]
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def load_pipeline(pipeline: DiffusionPipeline, model: str, scheduler: Any, device: DeviceParams):
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global last_pipeline_instance
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global last_pipeline_scheduler
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global last_pipeline_options
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options = (pipeline, model, device.provider)
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if last_pipeline_instance != None and last_pipeline_options == options:
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logger.debug('reusing existing diffusion pipeline')
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pipe = last_pipeline_instance
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else:
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logger.debug('unloading previous diffusion pipeline')
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last_pipeline_instance = None
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last_pipeline_scheduler = None
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run_gc()
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logger.debug('loading new diffusion pipeline from %s', model)
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scheduler = scheduler.from_pretrained(
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model,
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provider=device.provider,
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provider_options=device.options,
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subfolder='scheduler',
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)
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pipe = pipeline.from_pretrained(
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model,
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provider=device.provider,
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provider_options=device.options,
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safety_checker=None,
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scheduler=scheduler,
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)
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if device is not None and hasattr(pipe, 'to'):
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pipe = pipe.to(device)
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last_pipeline_instance = pipe
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last_pipeline_options = options
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last_pipeline_scheduler = scheduler
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if last_pipeline_scheduler != scheduler:
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logger.debug('loading new diffusion scheduler')
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scheduler = scheduler.from_pretrained(
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model,
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provider=device.provider,
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provider_options=device.options,
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subfolder='scheduler',
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)
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if device is not None and hasattr(scheduler, 'to'):
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scheduler = scheduler.to(device)
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pipe.scheduler = scheduler
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last_pipeline_scheduler = scheduler
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run_gc()
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return pipe
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