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Running
on
Zero
File size: 3,684 Bytes
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from __future__ import annotations
import PIL.Image
import torch
from diffusers import UniDiffuserPipeline
class Model:
def __init__(self):
self.device = torch.device(
'cuda:0' if torch.cuda.is_available() else 'cpu')
if self.device.type == 'cuda':
self.pipe = UniDiffuserPipeline.from_pretrained(
'thu-ml/unidiffuser-v1', torch_dtype=torch.float16)
self.pipe.to(self.device)
else:
self.pipe = UniDiffuserPipeline.from_pretrained(
'thu-ml/unidiffuser-v1')
def run(
self,
mode: str,
prompt: str,
image: PIL.Image.Image | None,
seed: int = 0,
num_steps: int = 20,
guidance_scale: float = 8.0,
) -> tuple[PIL.Image.Image | None, str]:
generator = torch.Generator(device=self.device).manual_seed(seed)
if mode == 't2i':
self.pipe.set_text_to_image_mode()
sample = self.pipe(prompt=prompt,
num_inference_steps=num_steps,
guidance_scale=guidance_scale,
generator=generator)
return sample.images[0], ''
elif mode == 'i2t':
self.pipe.set_image_to_text_mode()
sample = self.pipe(image=image,
num_inference_steps=num_steps,
guidance_scale=guidance_scale,
generator=generator)
return None, sample.text[0]
elif mode == 'joint':
self.pipe.set_joint_mode()
sample = self.pipe(num_inference_steps=num_steps,
guidance_scale=guidance_scale,
generator=generator)
return sample.images[0], sample.text[0]
elif mode == 'i':
self.pipe.set_image_mode()
sample = self.pipe(num_inference_steps=num_steps,
guidance_scale=guidance_scale,
generator=generator)
return sample.images[0], ''
elif mode == 't':
self.pipe.set_text_mode()
sample = self.pipe(num_inference_steps=num_steps,
guidance_scale=guidance_scale,
generator=generator)
return None, sample.text[0]
elif mode == 'i2t2i':
self.pipe.set_image_to_text_mode()
sample = self.pipe(image=image,
num_inference_steps=num_steps,
guidance_scale=guidance_scale,
generator=generator)
self.pipe.set_text_to_image_mode()
sample = self.pipe(prompt=sample.text[0],
num_inference_steps=num_steps,
guidance_scale=guidance_scale,
generator=generator)
return sample.images[0], ''
elif mode == 't2i2t':
self.pipe.set_text_to_image_mode()
sample = self.pipe(prompt=prompt,
num_inference_steps=num_steps,
guidance_scale=guidance_scale,
generator=generator)
self.pipe.set_image_to_text_mode()
sample = self.pipe(image=sample.images[0],
num_inference_steps=num_steps,
guidance_scale=guidance_scale,
generator=generator)
return None, sample.text[0]
else:
raise ValueError
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