feat(api): add highres to img2img mode for all pipelines
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c2f8ce5814
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GPG Key ID:
3EED7B957D362AF1
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@ -1,5 +1,5 @@
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from logging import getLogger
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from typing import Any, List, Optional
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from typing import Any, List, Optional, Tuple
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import numpy as np
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import torch
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@ -22,6 +22,7 @@ from ..server import ServerContext
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from ..server.load import get_source_filters
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from ..utils import run_gc
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from ..worker import WorkerContext
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from ..worker.context import ProgressCallback
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from .load import get_latents_from_seed, load_pipeline
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from .upscale import run_upscale_correction
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from .utils import get_inversions_from_prompt, get_loras_from_prompt
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@ -29,6 +30,126 @@ from .utils import get_inversions_from_prompt, get_loras_from_prompt
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logger = getLogger(__name__)
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def run_highres(
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job: WorkerContext,
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server: ServerContext,
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params: ImageParams,
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size: Size,
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upscale: UpscaleParams,
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highres: HighresParams,
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image: Image.Image,
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progress: ProgressCallback,
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inversions: List[Tuple[str, float]],
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loras: List[Tuple[str, float]],
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) -> None:
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highres_progress = ChainProgress.from_progress(progress)
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if upscale.faces and (
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upscale.upscale_order == "correction-both"
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or upscale.upscale_order == "correction-first"
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):
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image = run_upscale_correction(
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job,
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server,
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StageParams(),
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params,
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image,
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upscale=upscale.with_args(
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scale=1,
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outscale=1,
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),
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callback=highres_progress,
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)
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# load img2img pipeline once
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highres_pipe = load_pipeline(
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server,
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"img2img",
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params.model,
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params.scheduler,
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job.get_device(),
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inversions=inversions,
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loras=loras,
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)
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def highres_tile(tile: Image.Image, dims):
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if highres.method == "bilinear":
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logger.debug("using bilinear interpolation for highres")
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tile = tile.resize(
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(size.height, size.width), resample=Image.Resampling.BILINEAR
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)
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elif highres.method == "lanczos":
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logger.debug("using Lanczos interpolation for highres")
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tile = tile.resize(
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(size.height, size.width), resample=Image.Resampling.LANCZOS
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)
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else:
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logger.debug("using upscaling pipeline for highres")
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tile = run_upscale_correction(
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job,
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server,
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StageParams(),
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params,
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tile,
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upscale=upscale.with_args(
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faces=False,
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scale=highres.scale,
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outscale=highres.scale,
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),
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callback=highres_progress,
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)
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if params.lpw():
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logger.debug("using LPW pipeline for highres")
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rng = torch.manual_seed(params.seed)
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result = highres_pipe.img2img(
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tile,
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params.prompt,
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generator=rng,
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guidance_scale=params.cfg,
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negative_prompt=params.negative_prompt,
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num_images_per_prompt=1,
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num_inference_steps=highres.steps,
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strength=highres.strength,
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eta=params.eta,
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callback=highres_progress,
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)
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return result.images[0]
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else:
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rng = np.random.RandomState(params.seed)
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result = highres_pipe(
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params.prompt,
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tile,
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generator=rng,
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guidance_scale=params.cfg,
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negative_prompt=params.negative_prompt,
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num_images_per_prompt=1,
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num_inference_steps=highres.steps,
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strength=highres.strength,
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eta=params.eta,
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callback=highres_progress,
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)
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return result.images[0]
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logger.info(
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"running highres fix for %s iterations at %s scale",
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highres.iterations,
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highres.scale,
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)
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for _i in range(highres.iterations):
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image = process_tile_order(
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TileOrder.grid,
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image,
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size.height // highres.scale,
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highres.scale,
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[highres_tile],
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overlap=0,
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)
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return image
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def run_txt2img_pipeline(
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job: WorkerContext,
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server: ServerContext,
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@ -93,110 +214,19 @@ def run_txt2img_pipeline(
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for image, output in image_outputs:
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if highres.scale > 1:
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highres_progress = ChainProgress.from_progress(progress)
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if upscale.faces and (
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upscale.upscale_order == "correction-both"
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or upscale.upscale_order == "correction-first"
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):
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image = run_upscale_correction(
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job,
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server,
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StageParams(),
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params,
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image,
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upscale=upscale.with_args(
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scale=1,
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outscale=1,
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),
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callback=highres_progress,
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)
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# load img2img pipeline once
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highres_pipe = load_pipeline(
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image = run_highres(
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job,
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server,
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"img2img",
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params.model,
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params.scheduler,
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job.get_device(),
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inversions=inversions,
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loras=loras,
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params,
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size,
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upscale,
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highres,
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image,
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progress,
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inversions,
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loras,
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)
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def highres_tile(tile: Image.Image, dims):
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if highres.method == "bilinear":
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logger.debug("using bilinear interpolation for highres")
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tile = tile.resize(
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(size.height, size.width), resample=Image.Resampling.BILINEAR
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)
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elif highres.method == "lanczos":
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logger.debug("using Lanczos interpolation for highres")
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tile = tile.resize(
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(size.height, size.width), resample=Image.Resampling.LANCZOS
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)
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else:
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logger.debug("using upscaling pipeline for highres")
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tile = run_upscale_correction(
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job,
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server,
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StageParams(),
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params,
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tile,
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upscale=upscale.with_args(
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faces=False,
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scale=highres.scale,
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outscale=highres.scale,
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),
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callback=highres_progress,
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)
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if params.lpw():
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logger.debug("using LPW pipeline for highres")
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rng = torch.manual_seed(params.seed)
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result = highres_pipe.img2img(
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tile,
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params.prompt,
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generator=rng,
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guidance_scale=params.cfg,
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negative_prompt=params.negative_prompt,
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num_images_per_prompt=1,
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num_inference_steps=highres.steps,
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strength=highres.strength,
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eta=params.eta,
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callback=highres_progress,
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)
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return result.images[0]
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else:
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rng = np.random.RandomState(params.seed)
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result = highres_pipe(
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params.prompt,
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tile,
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generator=rng,
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guidance_scale=params.cfg,
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negative_prompt=params.negative_prompt,
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num_images_per_prompt=1,
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num_inference_steps=highres.steps,
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strength=highres.strength,
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eta=params.eta,
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callback=highres_progress,
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)
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return result.images[0]
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logger.info(
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"running highres fix for %s iterations at %s scale",
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highres.iterations,
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highres.scale,
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)
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for _i in range(highres.iterations):
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image = process_tile_order(
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TileOrder.grid,
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image,
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size.height // highres.scale,
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highres.scale,
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[highres_tile],
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overlap=0,
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)
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image = run_upscale_correction(
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job,
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server,
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@ -221,6 +251,7 @@ def run_img2img_pipeline(
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params: ImageParams,
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outputs: List[str],
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upscale: UpscaleParams,
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highres: HighresParams,
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source: Image.Image,
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strength: float,
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source_filter: Optional[str] = None,
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@ -290,6 +321,20 @@ def run_img2img_pipeline(
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images.append(source)
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for image, output in zip(images, outputs):
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if highres.scale > 1:
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image = run_highres(
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job,
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server,
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params,
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Size(source.width, source.height),
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upscale,
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highres,
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image,
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progress,
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inversions,
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loras,
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)
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image = run_upscale_correction(
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job,
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server,
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@ -316,6 +361,7 @@ def run_inpaint_pipeline(
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size: Size,
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outputs: List[str],
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upscale: UpscaleParams,
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highres: HighresParams,
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source: Image.Image,
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mask: Image.Image,
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border: Border,
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@ -372,6 +418,7 @@ def run_upscale_pipeline(
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size: Size,
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outputs: List[str],
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upscale: UpscaleParams,
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highres: HighresParams,
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source: Image.Image,
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) -> None:
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progress = job.get_progress_callback()
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@ -398,6 +445,7 @@ def run_blend_pipeline(
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size: Size,
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outputs: List[str],
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upscale: UpscaleParams,
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highres: HighresParams,
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sources: List[Image.Image],
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mask: Image.Image,
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) -> None:
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@ -180,7 +180,9 @@ def img2img(server: ServerContext, pool: DevicePoolExecutor):
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if source_filter is not None:
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output_count += 1
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output = make_output_name(server, "img2img", params, size, extras=[strength], count=output_count)
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output = make_output_name(
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server, "img2img", params, size, extras=[strength], count=output_count
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)
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job_name = output[0]
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logger.info("img2img job queued for: %s", job_name)
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