81 lines
2.4 KiB
Python
81 lines
2.4 KiB
Python
from logging import getLogger
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from typing import List, Optional
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import numpy as np
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from diffusers import OnnxRuntimeModel
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from ...server import ServerContext
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logger = getLogger(__name__)
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class UNetWrapper(object):
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prompt_embeds: Optional[List[np.ndarray]] = None
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prompt_index: int = 0
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server: ServerContext
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wrapped: OnnxRuntimeModel
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xl: bool
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def __init__(
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self,
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server: ServerContext,
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wrapped: OnnxRuntimeModel,
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xl: bool,
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):
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self.server = server
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self.wrapped = wrapped
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self.xl = xl
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def __call__(
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self,
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sample: Optional[np.ndarray] = None,
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timestep: Optional[np.ndarray] = None,
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encoder_hidden_states: Optional[np.ndarray] = None,
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**kwargs,
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):
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logger.trace(
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"UNet parameter types: %s, %s, %s",
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sample.dtype,
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timestep.dtype,
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encoder_hidden_states.dtype,
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)
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if self.prompt_embeds is not None:
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step_index = self.prompt_index % len(self.prompt_embeds)
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logger.trace("multiple prompt embeds found, using step: %s", step_index)
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encoder_hidden_states = self.prompt_embeds[step_index]
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self.prompt_index += 1
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if self.xl:
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if sample.dtype != encoder_hidden_states.dtype:
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logger.trace(
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"converting UNet sample to hidden state dtype for XL: %s",
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encoder_hidden_states.dtype,
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)
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sample = sample.astype(encoder_hidden_states.dtype)
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else:
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if sample.dtype != timestep.dtype:
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logger.trace("converting UNet sample to timestep dtype")
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sample = sample.astype(timestep.dtype)
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if encoder_hidden_states.dtype != timestep.dtype:
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logger.trace("converting UNet hidden states to timestep dtype")
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encoder_hidden_states = encoder_hidden_states.astype(timestep.dtype)
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return self.wrapped(
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sample=sample,
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timestep=timestep,
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encoder_hidden_states=encoder_hidden_states,
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**kwargs,
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)
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def __getattr__(self, attr):
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return getattr(self.wrapped, attr)
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def set_prompts(self, prompt_embeds: List[np.ndarray]):
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logger.debug(
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"setting prompt embeds for UNet: %s", [p.shape for p in prompt_embeds]
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)
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self.prompt_embeds = prompt_embeds
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self.prompt_index = 0
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