118 lines
3.2 KiB
Python
118 lines
3.2 KiB
Python
from diffusers import (
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OnnxStableDiffusionInpaintPipeline,
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)
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from logging import getLogger
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from PIL import Image, ImageDraw
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from typing import Callable, Tuple
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from ..diffusion.load import (
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get_latents_from_seed,
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get_tile_latents,
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load_pipeline,
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)
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from ..image import (
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expand_image,
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mask_filter_none,
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noise_source_histogram,
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)
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from ..params import (
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Border,
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ImageParams,
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Size,
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SizeChart,
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StageParams,
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)
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from ..output import (
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save_image,
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)
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from ..utils import (
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base_join,
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is_debug,
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ServerContext,
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)
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from .utils import (
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process_tile_spiral,
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)
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import numpy as np
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logger = getLogger(__name__)
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def upscale_outpaint(
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ctx: 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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**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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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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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(ctx, 'last-source.png', source_image)
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save_image(ctx, 'last-mask.png', mask_image)
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save_image(ctx, '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(ctx, 'tile-source.png', image)
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save_image(ctx, 'tile-mask.png', mask)
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# TODO: must use inpainting model here
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model = '../models/stable-diffusion-onnx-v1-inpainting'
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pipe = load_pipeline(OnnxStableDiffusionInpaintPipeline,
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model, params.provider, params.scheduler)
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latents = get_tile_latents(full_latents, dims)
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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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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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)
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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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output = process_tile_spiral(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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