90 lines
2.3 KiB
Python
90 lines
2.3 KiB
Python
from diffusers import (
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OnnxStableDiffusionInpaintPipeline,
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)
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from PIL import Image
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from typing import Callable
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from ..diffusion import (
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get_latents_from_seed,
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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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StageParams,
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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_tiles,
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)
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import numpy as np
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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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expand: Border,
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mask_image: Image.Image,
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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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) -> Image:
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print('upscaling image by expanding borders', expand)
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output = expand_image(source_image, mask_image, expand)
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size = Size(*output.size)
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def outpaint():
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pipe = load_pipeline(OnnxStableDiffusionInpaintPipeline,
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params.model, params.provider, params.scheduler)
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latents = get_latents_from_seed(params.seed, size)
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rng = np.random.RandomState(params.seed)
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print('applying mask filter and generating noise source')
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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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if is_debug():
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source_image.save(base_join(ctx.output_path, 'last-source.png'))
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mask_image.save(base_join(ctx.output_path, 'last-mask.png'))
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noise_image.save(base_join(ctx.output_path, 'last-noise.png'))
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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=source_image,
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latents=latents,
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mask_image=mask_image,
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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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return result.images[0]
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output = process_tiles(output, 256, 4, [outpaint])
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print('final output image size', output.size)
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return output
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