feat(api): collect progress from chain pipelines (#90)
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@ -5,7 +5,7 @@ from typing import Any, List, Optional, Protocol, Tuple
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from PIL import Image
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from ..device_pool import JobContext
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from ..device_pool import JobContext, ProgressCallback
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from ..output import save_image
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from ..params import ImageParams, StageParams
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from ..utils import ServerContext, is_debug
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@ -30,6 +30,24 @@ class StageCallback(Protocol):
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PipelineStage = Tuple[StageCallback, StageParams, Optional[dict]]
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class ChainProgress:
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def __init__(self, parent: ProgressCallback, start=0) -> None:
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self.parent = parent
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self.step = start
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self.total = 0
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def __call__(self, step: int, timestep: int, latents: Any) -> None:
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if step < self.step:
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# accumulate on resets
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self.total += self.step
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self.step = step
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self.parent(self.get_total(), timestep, latents)
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def get_total(self) -> int:
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return self.step + self.total
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class ChainPipeline:
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"""
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Run many stages in series, passing the image results from each to the next, and processing
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@ -57,11 +75,15 @@ class ChainPipeline:
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server: ServerContext,
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params: ImageParams,
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source: Image.Image,
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callback: ProgressCallback = None,
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**pipeline_kwargs
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) -> Image.Image:
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"""
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TODO: handle List[Image] outputs
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TODO: handle List[Image] inputs and outputs
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"""
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if callback is not None:
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callback = ChainProgress(callback, start=callback.step)
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start = monotonic()
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logger.info(
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"running pipeline on source image with dimensions %sx%s",
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@ -6,7 +6,7 @@ import torch
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from diffusers import OnnxStableDiffusionImg2ImgPipeline
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from PIL import Image
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from ..device_pool import JobContext
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from ..device_pool import JobContext, ProgressCallback
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from ..diffusion.load import load_pipeline
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from ..params import ImageParams, StageParams
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from ..utils import ServerContext
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@ -23,6 +23,7 @@ def blend_img2img(
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*,
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strength: float,
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prompt: Optional[str] = None,
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callback: ProgressCallback,
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**kwargs,
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) -> Image.Image:
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prompt = prompt or params.prompt
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@ -46,6 +47,7 @@ def blend_img2img(
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negative_prompt=params.negative_prompt,
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num_inference_steps=params.steps,
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strength=strength,
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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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@ -57,6 +59,7 @@ def blend_img2img(
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negative_prompt=params.negative_prompt,
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num_inference_steps=params.steps,
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strength=strength,
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callback=callback,
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)
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output = result.images[0]
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@ -6,7 +6,7 @@ import torch
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from diffusers import OnnxStableDiffusionInpaintPipeline
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from PIL import Image
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from ..device_pool import JobContext
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from ..device_pool import JobContext, ProgressCallback
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from ..diffusion.load import get_latents_from_seed, 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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@ -29,6 +29,7 @@ def blend_inpaint(
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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,
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**kwargs,
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) -> Image.Image:
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logger.info("upscaling image by expanding borders", expand)
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@ -83,6 +84,7 @@ def blend_inpaint(
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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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@ -97,6 +99,7 @@ def blend_inpaint(
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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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return result.images[0]
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@ -5,7 +5,7 @@ import torch
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from diffusers import OnnxStableDiffusionPipeline
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from PIL import Image
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from ..device_pool import JobContext
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from ..device_pool import JobContext, ProgressCallback
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from ..diffusion.load import get_latents_from_seed, load_pipeline
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from ..params import ImageParams, Size, StageParams
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from ..utils import ServerContext
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@ -22,6 +22,7 @@ def source_txt2img(
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*,
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size: Size,
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prompt: str = None,
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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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@ -53,6 +54,7 @@ def source_txt2img(
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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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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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@ -65,6 +67,7 @@ def source_txt2img(
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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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callback=callback,
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)
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output = result.images[0]
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@ -6,7 +6,7 @@ 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
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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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@ -30,6 +30,7 @@ def upscale_outpaint(
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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,
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**kwargs,
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) -> Image.Image:
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prompt = prompt or params.prompt
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@ -92,6 +93,7 @@ def upscale_outpaint(
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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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@ -106,6 +108,7 @@ def upscale_outpaint(
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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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@ -5,7 +5,7 @@ import torch
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from diffusers import StableDiffusionUpscalePipeline
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from PIL import Image
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from ..device_pool import JobContext
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from ..device_pool import JobContext, ProgressCallback
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from ..diffusion.pipeline_onnx_stable_diffusion_upscale import (
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OnnxStableDiffusionUpscalePipeline,
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)
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@ -67,6 +67,7 @@ def upscale_stable_diffusion(
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*,
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upscale: UpscaleParams,
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prompt: str = None,
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callback: ProgressCallback,
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**kwargs,
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) -> Image.Image:
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prompt = prompt or params.prompt
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@ -80,4 +81,5 @@ def upscale_stable_diffusion(
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source,
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generator=generator,
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num_inference_steps=params.steps,
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callback=callback,
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).images[0]
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@ -10,6 +10,8 @@ from .utils import run_gc
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logger = getLogger(__name__)
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ProgressCallback = Callable[[int, int, Any], None]
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class JobContext:
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cancel: Value = None
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@ -51,8 +53,9 @@ class JobContext:
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def get_progress(self) -> int:
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return self.progress.value
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def get_progress_callback(self) -> Callable[..., None]:
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def get_progress_callback(self) -> ProgressCallback:
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def on_progress(step: int, timestep: int, latents: Any):
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on_progress.step = step
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if self.is_cancelled():
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raise Exception("job has been cancelled")
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else:
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