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