109 lines
3.2 KiB
Python
109 lines
3.2 KiB
Python
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from logging import getLogger
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from typing import Any, Optional
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import numpy as np
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import torch
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from PIL import Image
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from ..diffusers.load import load_pipeline
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from ..diffusers.upscale import append_upscale_correction
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from ..diffusers.utils import parse_prompt
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from ..params import HighresParams, ImageParams, Size, StageParams, UpscaleParams
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from ..server import ServerContext
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from ..worker import WorkerContext
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from ..worker.context import ProgressCallback
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logger = getLogger(__name__)
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def upscale_highres(
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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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highres: HighresParams,
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upscale: UpscaleParams,
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size: Size,
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stage_source: Optional[Image.Image] = None,
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pipeline: Optional[Any] = None,
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callback: Optional[ProgressCallback] = None,
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**kwargs,
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) -> Image.Image:
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image = stage_source or source
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if highres.scale <= 1:
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return image
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# load img2img pipeline once
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pipe_type = params.get_valid_pipeline("img2img")
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logger.debug("using %s pipeline for highres", pipe_type)
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_prompt_pairs, loras, inversions = parse_prompt(params)
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highres_pipe = pipeline or load_pipeline(
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server,
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params,
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pipe_type,
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job.get_device(),
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inversions=inversions,
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loras=loras,
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)
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scaled_size = (source.width * highres.scale, source.height * highres.scale)
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if highres.method == "bilinear":
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logger.debug("using bilinear interpolation for highres")
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source = source.resize(scaled_size, resample=Image.Resampling.BILINEAR)
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elif highres.method == "lanczos":
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logger.debug("using Lanczos interpolation for highres")
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source = source.resize(scaled_size, resample=Image.Resampling.LANCZOS)
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else:
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logger.debug("using upscaling pipeline for highres")
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upscale = append_upscale_correction(
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StageParams(),
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params,
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upscale=upscale.with_args(
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faces=False,
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scale=highres.scale,
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outscale=highres.scale,
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),
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)
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source = upscale(
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job,
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server,
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source,
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callback=callback,
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)
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if pipe_type == "lpw":
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rng = torch.manual_seed(params.seed)
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result = highres_pipe.img2img(
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source,
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params.prompt,
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generator=rng,
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guidance_scale=params.cfg,
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negative_prompt=params.negative_prompt,
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num_images_per_prompt=1,
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num_inference_steps=highres.steps,
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strength=highres.strength,
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eta=params.eta,
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callback=callback,
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)
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return result.images[0]
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else:
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rng = np.random.RandomState(params.seed)
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result = highres_pipe(
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params.prompt,
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source,
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generator=rng,
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guidance_scale=params.cfg,
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negative_prompt=params.negative_prompt,
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num_images_per_prompt=1,
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num_inference_steps=highres.steps,
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strength=highres.strength,
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eta=params.eta,
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callback=callback,
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)
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return result.images[0]
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