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add TextInversion for lpw_stable_diffusion.py #3106

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add TextInversion for lpw_stable_diffusion.py #3106

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1e19e0f
add TextInversion for neg/prompt
Apr 14, 2023
70a9568
fix code format
Apr 14, 2023
5cbf0c4
Merge branch 'huggingface:main' into main
NormXU Apr 24, 2023
9a26cac
fix type & add text inversion for lpw_stable_diffusion_onnx.py
Apr 24, 2023
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7 changes: 6 additions & 1 deletion examples/community/lpw_stable_diffusion.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,11 +10,11 @@

import diffusers
from diffusers import SchedulerMixin, StableDiffusionPipeline
from diffusers.loaders import TextualInversionLoaderMixin
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker
from diffusers.utils import logging


try:
from diffusers.utils import PIL_INTERPOLATION
except ImportError:
Expand Down Expand Up @@ -539,6 +539,11 @@ def _encode_prompt(
" the batch size of `prompt`."
)

# textual inversion: process multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
negative_prompt = self.maybe_convert_prompt(negative_prompt, self.tokenizer)

text_embeddings, uncond_embeddings = get_weighted_text_embeddings(
pipe=self,
prompt=prompt,
Expand Down
6 changes: 6 additions & 0 deletions examples/community/lpw_stable_diffusion_onnx.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,6 +10,7 @@

import diffusers
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, SchedulerMixin
from diffusers.loaders import TextualInversionLoaderMixin
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import logging

Expand Down Expand Up @@ -526,6 +527,11 @@ def _encode_prompt(
" the batch size of `prompt`."
)

# textual inversion: process multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
negative_prompt = self.maybe_convert_prompt(negative_prompt, self.tokenizer)

text_embeddings, uncond_embeddings = get_weighted_text_embeddings(
pipe=self,
prompt=prompt,
Expand Down