113 lines
3.5 KiB
Python
113 lines
3.5 KiB
Python
from logging import getLogger
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from typing import Callable, Optional, Tuple
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import numpy as np
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import torch
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from diffusers import OnnxStableDiffusionInpaintPipeline
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from PIL import Image
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from ..device_pool import JobContext, ProgressCallback
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from ..diffusion.load import get_latents_from_seed, load_pipeline
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from ..image import expand_image, mask_filter_none, noise_source_histogram
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from ..output import save_image
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from ..params import Border, ImageParams, Size, SizeChart, StageParams
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from ..utils import ServerContext, is_debug
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from .utils import process_tile_order
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logger = getLogger(__name__)
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def blend_inpaint(
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job: JobContext,
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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.Image,
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*,
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expand: Border,
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mask_image: Optional[Image.Image] = None,
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fill_color: str = "white",
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mask_filter: Callable = mask_filter_none,
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noise_source: Callable = noise_source_histogram,
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callback: ProgressCallback = None,
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**kwargs,
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) -> Image.Image:
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logger.info("upscaling image by expanding borders", expand)
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if mask_image is None:
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# if no mask was provided, keep the full source image
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mask_image = Image.new("RGB", source_image.size, "black")
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source_image, mask_image, noise_image, _full_dims = expand_image(
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source_image,
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mask_image,
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expand,
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fill=fill_color,
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noise_source=noise_source,
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mask_filter=mask_filter,
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)
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if is_debug():
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save_image(server, "last-source.png", source_image)
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save_image(server, "last-mask.png", mask_image)
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save_image(server, "last-noise.png", noise_image)
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def outpaint(image: Image.Image, dims: Tuple[int, int, int]):
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left, top, tile = dims
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size = Size(*image.size)
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mask = mask_image.crop((left, top, left + tile, top + tile))
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if is_debug():
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save_image(server, "tile-source.png", image)
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save_image(server, "tile-mask.png", mask)
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latents = get_latents_from_seed(params.seed, size)
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pipe = load_pipeline(
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OnnxStableDiffusionInpaintPipeline,
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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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)
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if params.lpw:
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logger.debug("using LPW pipeline for inpaint")
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rng = torch.manual_seed(params.seed)
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result = pipe.inpaint(
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params.prompt,
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generator=rng,
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guidance_scale=params.cfg,
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height=size.height,
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image=image,
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latents=latents,
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mask_image=mask,
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negative_prompt=params.negative_prompt,
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num_inference_steps=params.steps,
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width=size.width,
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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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height=size.height,
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image=image,
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latents=latents,
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mask_image=mask,
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negative_prompt=params.negative_prompt,
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num_inference_steps=params.steps,
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width=size.width,
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callback=callback,
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)
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return result.images[0]
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output = process_tile_order(
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stage.tile_order, source_image, SizeChart.auto, 1, [outpaint]
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)
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logger.info("final output image size", output.size)
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return output
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