apply lint fixes again
This commit is contained in:
parent
20467aafac
commit
7462c96616
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@ -1,7 +1,7 @@
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from logging import getLogger
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from logging import getLogger
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import torch
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import numpy as np
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import numpy as np
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import torch
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from diffusers import OnnxStableDiffusionImg2ImgPipeline
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from diffusers import OnnxStableDiffusionImg2ImgPipeline
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from PIL import Image
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from PIL import Image
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@ -35,7 +35,7 @@ def blend_img2img(
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params.lpw,
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params.lpw,
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)
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)
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if params.lpw:
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if params.lpw:
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logger.debug('using LPW pipeline for img2img')
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logger.debug("using LPW pipeline for img2img")
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rng = torch.manual_seed(params.seed)
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rng = torch.manual_seed(params.seed)
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result = pipe.img2img(
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result = pipe.img2img(
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prompt,
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prompt,
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@ -1,8 +1,8 @@
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from logging import getLogger
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from logging import getLogger
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from typing import Callable, Tuple
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from typing import Callable, Tuple
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import torch
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import numpy as np
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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 diffusers import OnnxStableDiffusionInpaintPipeline
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from PIL import Image
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from PIL import Image
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@ -70,7 +70,7 @@ def blend_inpaint(
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)
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)
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if params.lpw:
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if params.lpw:
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logger.debug('using LPW pipeline for inpaint')
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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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rng = torch.manual_seed(params.seed)
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result = pipe.inpaint(
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result = pipe.inpaint(
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params.prompt,
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params.prompt,
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@ -1,7 +1,7 @@
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from logging import getLogger
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from logging import getLogger
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import torch
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import numpy as np
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import numpy as np
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import torch
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from diffusers import OnnxStableDiffusionPipeline
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from diffusers import OnnxStableDiffusionPipeline
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from PIL import Image
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from PIL import Image
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@ -34,11 +34,15 @@ def source_txt2img(
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latents = get_latents_from_seed(params.seed, size)
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latents = get_latents_from_seed(params.seed, size)
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pipe = load_pipeline(
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pipe = load_pipeline(
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OnnxStableDiffusionPipeline, params.model, params.scheduler, job.get_device(), params.lpw
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OnnxStableDiffusionPipeline,
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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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)
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if params.lpw:
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if params.lpw:
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logger.debug('using LPW pipeline for txt2img')
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logger.debug("using LPW pipeline for txt2img")
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rng = torch.manual_seed(params.seed)
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rng = torch.manual_seed(params.seed)
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result = pipe.text2img(
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result = pipe.text2img(
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prompt,
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prompt,
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@ -1,8 +1,8 @@
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from logging import getLogger
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from logging import getLogger
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from typing import Callable, Tuple
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from typing import Callable, Tuple
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import torch
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import numpy as np
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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 diffusers import OnnxStableDiffusionInpaintPipeline
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from PIL import Image, ImageDraw
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from PIL import Image, ImageDraw
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@ -75,7 +75,7 @@ def upscale_outpaint(
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params.lpw,
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params.lpw,
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)
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)
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if params.lpw:
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if params.lpw:
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logger.debug('using LPW pipeline for inpaint')
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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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rng = torch.manual_seed(params.seed)
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result = pipe.inpaint(
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result = pipe.inpaint(
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image,
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image,
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@ -102,10 +102,8 @@ def upscale_outpaint(
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negative_prompt=params.negative_prompt,
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negative_prompt=params.negative_prompt,
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num_inference_steps=params.steps,
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num_inference_steps=params.steps,
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width=size.width,
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width=size.width,
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)
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)
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# once part of the image has been drawn, keep it
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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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draw_mask.rectangle((left, top, left + tile, top + tile), fill="black")
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return result.images[0]
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return result.images[0]
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@ -116,7 +114,9 @@ def upscale_outpaint(
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if border.left == border.right and border.top == border.bottom:
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if border.left == border.right and border.top == border.bottom:
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logger.debug("outpainting with an even border, using spiral tiling")
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logger.debug("outpainting with an even border, using spiral tiling")
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output = process_tile_spiral(source_image, SizeChart.auto, 1, [outpaint], overlap=overlap)
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output = process_tile_spiral(
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source_image, SizeChart.auto, 1, [outpaint], overlap=overlap
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)
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else:
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else:
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logger.debug("outpainting with an uneven border, using grid tiling")
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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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output = process_tile_grid(source_image, SizeChart.auto, 1, [outpaint])
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@ -60,7 +60,7 @@ base_models: Models = {
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"correction-codeformer",
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"correction-codeformer",
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"https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth",
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"https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth",
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1,
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1,
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)
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),
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],
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],
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"upscaling": [
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"upscaling": [
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(
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(
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@ -47,7 +47,11 @@ def get_tile_latents(
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def load_pipeline(
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def load_pipeline(
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pipeline: DiffusionPipeline, model: str, scheduler: Any, device: DeviceParams, lpw: bool
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pipeline: DiffusionPipeline,
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model: str,
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scheduler: Any,
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device: DeviceParams,
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lpw: bool,
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):
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):
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global last_pipeline_instance
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global last_pipeline_instance
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global last_pipeline_scheduler
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global last_pipeline_scheduler
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@ -1,8 +1,8 @@
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from logging import getLogger
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from logging import getLogger
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from typing import Any
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from typing import Any
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import torch
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import numpy as np
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import numpy as np
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import torch
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from diffusers import OnnxStableDiffusionImg2ImgPipeline, OnnxStableDiffusionPipeline
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from diffusers import OnnxStableDiffusionImg2ImgPipeline, OnnxStableDiffusionPipeline
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from PIL import Image, ImageChops
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from PIL import Image, ImageChops
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@ -27,12 +27,16 @@ def run_txt2img_pipeline(
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) -> None:
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) -> None:
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latents = get_latents_from_seed(params.seed, size)
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latents = get_latents_from_seed(params.seed, size)
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pipe = load_pipeline(
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pipe = load_pipeline(
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OnnxStableDiffusionPipeline, params.model, params.scheduler, job.get_device(), params.lpw
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OnnxStableDiffusionPipeline,
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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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)
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progress = job.get_progress_callback()
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progress = job.get_progress_callback()
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if params.lpw:
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if params.lpw:
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logger.debug('using LPW pipeline for txt2img')
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logger.debug("using LPW pipeline for txt2img")
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rng = torch.manual_seed(params.seed)
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rng = torch.manual_seed(params.seed)
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result = pipe.text2img(
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result = pipe.text2img(
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params.prompt,
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params.prompt,
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@ -59,7 +63,6 @@ def run_txt2img_pipeline(
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callback=progress,
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callback=progress,
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)
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)
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image = result.images[0]
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image = result.images[0]
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image = run_upscale_correction(
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image = run_upscale_correction(
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job, server, StageParams(), params, image, upscale=upscale
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job, server, StageParams(), params, image, upscale=upscale
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@ -89,11 +92,11 @@ def run_img2img_pipeline(
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params.model,
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params.model,
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params.scheduler,
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params.scheduler,
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job.get_device(),
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job.get_device(),
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params.lpw
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params.lpw,
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)
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)
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progress = job.get_progress_callback()
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progress = job.get_progress_callback()
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if params.lpw:
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if params.lpw:
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logger.debug('using LPW pipeline for img2img')
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logger.debug("using LPW pipeline for img2img")
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rng = torch.manual_seed(params.seed)
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rng = torch.manual_seed(params.seed)
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result = pipe.img2img(
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result = pipe.img2img(
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source_image,
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source_image,
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callback=progress,
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callback=progress,
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)
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)
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image = result.images[0]
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image = result.images[0]
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image = run_upscale_correction(
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image = run_upscale_correction(
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job, server, StageParams(), params, image, upscale=upscale
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job, server, StageParams(), params, image, upscale=upscale
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@ -143,7 +143,7 @@ correction_models = []
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upscaling_models = []
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upscaling_models = []
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def get_config_value(key: str, subkey: str = "default", default = None):
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def get_config_value(key: str, subkey: str = "default", default=None):
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return config_params.get(key, {}).get(subkey, default)
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return config_params.get(key, {}).get(subkey, default)
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@ -234,7 +234,14 @@ def pipeline_from_request() -> Tuple[DeviceParams, ImageParams, Size]:
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)
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)
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params = ImageParams(
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params = ImageParams(
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model_path, scheduler, prompt, cfg, steps, seed, lpw=lpw, negative_prompt=negative_prompt
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model_path,
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scheduler,
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prompt,
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cfg,
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steps,
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seed,
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lpw=lpw,
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negative_prompt=negative_prompt,
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)
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)
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size = Size(width, height)
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size = Size(width, height)
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return (device, params, size)
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return (device, params, size)
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