apply lint, fix shadowed names
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parent
20c7fb8b33
commit
4d93c13431
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@ -1,5 +1,4 @@
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from logging import getLogger
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from typing import Optional
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import numpy as np
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import torch
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@ -25,7 +24,9 @@ def blend_img2img(
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**kwargs,
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) -> Image.Image:
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params = params.with_args(**kwargs)
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logger.info("blending image using img2img, %s steps: %s", params.steps, params.prompt)
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logger.info(
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"blending image using img2img, %s steps: %s", params.steps, params.prompt
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)
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pipe = load_pipeline(
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server,
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@ -56,14 +56,14 @@ def blend_inpaint(
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save_image(server, "last-mask.png", mask)
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save_image(server, "last-noise.png", noise)
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def outpaint(image: Image.Image, dims: Tuple[int, int, int]):
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def outpaint(tile_source: 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.crop((left, top, left + tile, top + tile))
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size = Size(*tile_source.size)
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tile_mask = mask.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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save_image(server, "tile-source.png", tile_source)
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save_image(server, "tile-mask.png", tile_mask)
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latents = get_latents_from_seed(params.seed, size)
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pipe = load_pipeline(
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@ -83,9 +83,9 @@ def blend_inpaint(
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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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image=tile_source,
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latents=latents,
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mask=mask,
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mask=tile_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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@ -98,7 +98,7 @@ def blend_inpaint(
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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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image=tile_source,
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latents=latents,
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mask=mask,
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negative_prompt=params.negative_prompt,
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@ -109,9 +109,7 @@ def blend_inpaint(
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return result.images[0]
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output = process_tile_order(
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stage.tile_order, source, SizeChart.auto, 1, [outpaint]
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)
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output = process_tile_order(stage.tile_order, source, SizeChart.auto, 1, [outpaint])
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logger.info("final output image size", output.size)
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return output
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@ -26,7 +26,9 @@ def source_txt2img(
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) -> Image.Image:
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params = params.with_args(**kwargs)
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size = size.with_args(**kwargs)
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logger.info("generating image using txt2img, %s steps: %s", params.steps, params.prompt)
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logger.info(
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"generating image using txt2img, %s steps: %s", params.steps, params.prompt
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)
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if source is not None:
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logger.warn(
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@ -62,14 +62,14 @@ def upscale_outpaint(
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save_image(server, "last-mask.png", mask)
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save_image(server, "last-noise.png", noise)
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def outpaint(image: Image.Image, dims: Tuple[int, int, int]):
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def outpaint(tile_source: 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.crop((left, top, left + tile, top + tile))
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size = Size(*tile_source.size)
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tile_mask = mask.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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save_image(server, "tile-source.png", tile_source)
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save_image(server, "tile-mask.png", tile_mask)
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latents = get_tile_latents(full_latents, dims)
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pipe = load_pipeline(
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@ -84,8 +84,8 @@ def upscale_outpaint(
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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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tile_source,
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tile_mask,
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prompt,
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generator=rng,
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guidance_scale=params.cfg,
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@ -100,12 +100,12 @@ def upscale_outpaint(
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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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tile_source,
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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=mask,
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mask=tile_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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@ -74,7 +74,9 @@ def upscale_stable_diffusion(
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) -> Image.Image:
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params = params.with_args(**kwargs)
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upscale = upscale.with_args(**kwargs)
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logger.info("upscaling with Stable Diffusion, %s steps: %s", params.steps, params.prompt)
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logger.info(
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"upscaling with Stable Diffusion, %s steps: %s", params.steps, params.prompt
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)
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pipeline = load_stable_diffusion(server, upscale, job.get_device())
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generator = torch.manual_seed(params.seed)
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