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from __future__ import annotations
import PIL.Image
import torch
from diffusers import StableDiffusionAttendAndExcitePipeline, StableDiffusionPipeline
class Model:
def __init__(self):
self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model_id = "CompVis/stable-diffusion-v1-4"
self.ax_pipe = StableDiffusionAttendAndExcitePipeline.from_pretrained(model_id)
self.ax_pipe.to(self.device)
self.sd_pipe = StableDiffusionPipeline.from_pretrained(model_id)
self.sd_pipe.to(self.device)
def get_token_table(self, prompt: str):
tokens = [self.ax_pipe.tokenizer.decode(t) for t in self.ax_pipe.tokenizer(prompt)["input_ids"]]
tokens = tokens[1:-1]
return list(enumerate(tokens, start=1))
def run(
self,
prompt: str,
indices_to_alter_str: str,
seed: int = 0,
apply_attend_and_excite: bool = True,
num_steps: int = 50,
guidance_scale: float = 7.5,
scale_factor: int = 20,
thresholds: dict[int, float] = {
10: 0.5,
20: 0.8,
},
max_iter_to_alter: int = 25,
) -> PIL.Image.Image:
generator = torch.Generator(device=self.device).manual_seed(seed)
if apply_attend_and_excite:
try:
token_indices = list(map(int, indices_to_alter_str.split(",")))
except Exception:
raise ValueError("Invalid token indices.")
out = self.ax_pipe(
prompt=prompt,
token_indices=token_indices,
guidance_scale=guidance_scale,
generator=generator,
num_inference_steps=num_steps,
max_iter_to_alter=max_iter_to_alter,
thresholds=thresholds,
scale_factor=scale_factor,
)
else:
out = self.sd_pipe(
prompt=prompt,
guidance_scale=guidance_scale,
generator=generator,
num_inference_steps=num_steps,
)
return out.images[0]
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