140 lines
4.7 KiB
Python
140 lines
4.7 KiB
Python
from logging import getLogger
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from typing import Callable, 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, ImageDraw
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from ..device_pool import JobContext, ProgressCallback
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from ..diffusion.load import get_latents_from_seed, get_tile_latents, 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_grid, process_tile_order
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logger = getLogger(__name__)
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def upscale_outpaint(
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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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border: Border,
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prompt: str = None,
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mask_image: 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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prompt = prompt or params.prompt
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logger.info("upscaling image by expanding borders: %s", border)
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margin_x = float(max(border.left, border.right))
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margin_y = float(max(border.top, border.bottom))
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overlap = min(margin_x / source_image.width, margin_y / source_image.height)
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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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border,
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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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draw_mask = ImageDraw.Draw(mask_image)
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full_size = Size(*full_dims)
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full_latents = get_latents_from_seed(params.seed, full_size)
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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_tile_latents(full_latents, dims)
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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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image,
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mask,
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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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latents=latents,
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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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prompt,
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image,
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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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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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# once part of the image has been drawn, keep it
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draw_mask.rectangle((left, top, left + tile, top + tile), fill="black")
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return result.images[0]
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if overlap == 0:
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logger.debug("outpainting with 0 margin, using grid tiling")
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output = process_tile_grid(source_image, SizeChart.auto, 1, [outpaint])
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elif border.left == border.right and border.top == border.bottom:
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logger.debug(
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"outpainting with an even border, using spiral tiling with %s overlap",
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overlap,
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)
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output = process_tile_order(
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stage.tile_order,
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source_image,
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SizeChart.auto,
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1,
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[outpaint],
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overlap=overlap,
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)
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else:
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logger.debug("outpainting with an uneven border, using grid tiling")
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output = process_tile_grid(source_image, SizeChart.auto, 1, [outpaint])
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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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