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feat(diffusers): overcome prompt limit (#904)

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Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
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Ettore Di Giacinto 2023-08-16 22:24:52 +02:00 committed by GitHub
parent 0c73a637f1
commit ede71d398c
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2 changed files with 20 additions and 7 deletions
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14
extra/grpc/diffusers/backend_diffusers.py
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@ -14,8 +14,10 @@ import torch
from torch import autocast from torch import autocast
from diffusers import StableDiffusionXLPipeline, DPMSolverMultistepScheduler, StableDiffusionPipeline, DiffusionPipeline, EulerAncestralDiscreteScheduler from diffusers import StableDiffusionXLPipeline, DPMSolverMultistepScheduler, StableDiffusionPipeline, DiffusionPipeline, EulerAncestralDiscreteScheduler
from diffusers.pipelines.stable_diffusion import safety_checker from diffusers.pipelines.stable_diffusion import safety_checker
from compel import Compel
_ONE_DAY_IN_SECONDS = 60 * 60 * 24 _ONE_DAY_IN_SECONDS = 60 * 60 * 24
COMPEL=os.environ.get("COMPEL", "1") == "1"
# https://github.com/CompVis/stable-diffusion/issues/239#issuecomment-1627615287 # https://github.com/CompVis/stable-diffusion/issues/239#issuecomment-1627615287
def sc(self, clip_input, images) : return images, [False for i in images] def sc(self, clip_input, images) : return images, [False for i in images]
@ -99,6 +101,8 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
self.pipe.scheduler = DPMSolverMultistepScheduler.from_config(self.pipe.scheduler.config, algorithm_type="sde-dpmsolver++") self.pipe.scheduler = DPMSolverMultistepScheduler.from_config(self.pipe.scheduler.config, algorithm_type="sde-dpmsolver++")
if request.CUDA: if request.CUDA:
self.pipe.to('cuda') self.pipe.to('cuda')
self.compel = Compel(tokenizer=self.pipe.tokenizer, text_encoder=self.pipe.text_encoder)
except Exception as err: except Exception as err:
return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}") return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
# Implement your logic here for the LoadModel service # Implement your logic here for the LoadModel service
@ -127,7 +131,15 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
# create a dictionary of parameters by using the keys from EnableParameters and the values from defaults # create a dictionary of parameters by using the keys from EnableParameters and the values from defaults
kwargs = {key: options[key] for key in keys} kwargs = {key: options[key] for key in keys}
image = {}
if COMPEL:
conditioning = self.compel.build_conditioning_tensor(prompt)
kwargs["prompt_embeds"]= conditioning
# pass the kwargs dictionary to the self.pipe method
image = self.pipe(
**kwargs
).images[0]
else:
# pass the kwargs dictionary to the self.pipe method # pass the kwargs dictionary to the self.pipe method
image = self.pipe( image = self.pipe(
prompt, prompt,

1
extra/requirements.txt
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@ -4,3 +4,4 @@ google
protobuf protobuf
six six
omegaconf omegaconf
compel