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from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from diffusers import StableDiffusionImg2ImgPipeline
from internals.data.result import Result
from internals.pipelines.twoStepPipeline import two_step_pipeline
from internals.util.commons import disable_safety_checker, download_image
from internals.util.config import get_hf_token, num_return_sequences
class AbstractPipeline:
def load(self, model_dir: str):
pass
def create(self, pipe):
pass
class Text2Img(AbstractPipeline):
@dataclass
class Params:
prompt: List[str] = None
modified_prompt: List[str] = None
prompt_left: List[str] = None
prompt_right: List[str] = None
def load(self, model_dir: str):
self.pipe = two_step_pipeline.from_pretrained(
model_dir, torch_dtype=torch.float16, use_auth_token=get_hf_token()
).to("cuda")
self.__patch()
def is_loaded(self):
if hasattr(self, "pipe"):
return True
return False
def create(self, pipeline: AbstractPipeline):
self.pipe = two_step_pipeline(**pipeline.pipe.components).to("cuda")
self.__patch()
def __patch(self):
self.pipe.enable_xformers_memory_efficient_attention()
@torch.inference_mode()
def process(
self,
params: Params,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[str] = None,
num_images_per_prompt: int = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
iteration: float = 3.0,
):
prompt = params.prompt
if params.prompt_left and params.prompt_right:
# multi-character pipelines
prompt = [params.prompt[0], params.prompt_left[0], params.prompt_right[0]]
result = self.pipe.multi_character_diffusion(
prompt=prompt,
pos=["1:1-0:0", "1:2-0:0", "1:2-0:1"],
mix_val=[0.2, 0.8, 0.8],
height=height,
width=width,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
negative_prompt=[negative_prompt or ""] * len(prompt),
num_images_per_prompt=num_return_sequences,
eta=eta,
# generator=generator,
output_type=output_type,
return_dict=return_dict,
callback=callback,
callback_steps=callback_steps,
)
else:
# two step pipeline
modified_prompt = params.modified_prompt
result = self.pipe.two_step_pipeline(
prompt=prompt,
modified_prompts=modified_prompt,
height=height,
width=width,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
negative_prompt=[negative_prompt or ""] * num_return_sequences,
num_images_per_prompt=num_images_per_prompt,
eta=eta,
generator=generator,
latents=latents,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
output_type=output_type,
return_dict=return_dict,
callback=callback,
callback_steps=callback_steps,
cross_attention_kwargs=cross_attention_kwargs,
iteration=iteration,
)
return Result.from_result(result)
class Img2Img(AbstractPipeline):
__loaded = False
def load(self, model_dir: str):
if self.__loaded:
return
self.pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
model_dir, torch_dtype=torch.float16, use_auth_token=get_hf_token()
).to("cuda")
self.__patch()
self.__loaded = True
def create(self, pipeline: AbstractPipeline):
self.pipe = StableDiffusionImg2ImgPipeline(**pipeline.pipe.components).to(
"cuda"
)
self.__patch()
self.__loaded = True
def __patch(self):
self.pipe.enable_xformers_memory_efficient_attention()
@torch.inference_mode()
def process(
self,
prompt: List[str],
imageUrl: str,
negative_prompt: List[str],
strength: float,
guidance_scale: float,
steps: int,
width: int,
height: int,
):
image = download_image(imageUrl).resize((width, height))
result = self.pipe.__call__(
prompt=prompt,
image=image,
strength=strength,
negative_prompt=negative_prompt,
guidance_scale=guidance_scale,
num_images_per_prompt=1,
num_inference_steps=steps,
)
return Result.from_result(result)
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