70 lines
1.9 KiB
Python
70 lines
1.9 KiB
Python
from logging import getLogger
|
|
from typing import Optional
|
|
|
|
import numpy as np
|
|
import torch
|
|
from diffusers import OnnxStableDiffusionImg2ImgPipeline
|
|
from PIL import Image
|
|
|
|
from ..diffusion.load import load_pipeline
|
|
from ..params import ImageParams, StageParams
|
|
from ..server.device_pool import JobContext, ProgressCallback
|
|
from ..utils import ServerContext
|
|
|
|
logger = getLogger(__name__)
|
|
|
|
|
|
def blend_img2img(
|
|
job: JobContext,
|
|
server: ServerContext,
|
|
_stage: StageParams,
|
|
params: ImageParams,
|
|
source_image: Image.Image,
|
|
*,
|
|
strength: float,
|
|
prompt: Optional[str] = None,
|
|
callback: ProgressCallback = None,
|
|
**kwargs,
|
|
) -> Image.Image:
|
|
prompt = prompt or params.prompt
|
|
logger.info("blending image using img2img, %s steps: %s", params.steps, prompt)
|
|
|
|
pipe = load_pipeline(
|
|
server,
|
|
OnnxStableDiffusionImg2ImgPipeline,
|
|
params.model,
|
|
params.scheduler,
|
|
job.get_device(),
|
|
params.lpw,
|
|
)
|
|
if params.lpw:
|
|
logger.debug("using LPW pipeline for img2img")
|
|
rng = torch.manual_seed(params.seed)
|
|
result = pipe.img2img(
|
|
prompt,
|
|
generator=rng,
|
|
guidance_scale=params.cfg,
|
|
image=source_image,
|
|
negative_prompt=params.negative_prompt,
|
|
num_inference_steps=params.steps,
|
|
strength=strength,
|
|
callback=callback,
|
|
)
|
|
else:
|
|
rng = np.random.RandomState(params.seed)
|
|
result = pipe(
|
|
prompt,
|
|
generator=rng,
|
|
guidance_scale=params.cfg,
|
|
image=source_image,
|
|
negative_prompt=params.negative_prompt,
|
|
num_inference_steps=params.steps,
|
|
strength=strength,
|
|
callback=callback,
|
|
)
|
|
|
|
output = result.images[0]
|
|
|
|
logger.info("final output image size: %sx%s", output.width, output.height)
|
|
return output
|