607 lines
16 KiB
Python
607 lines
16 KiB
Python
from logging import getLogger
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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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from diffusers import OnnxStableDiffusionPipeline
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from PIL import Image
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from ..chain import blend_mask, upscale_outpaint
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from ..chain.base import ChainProgress
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from ..chain.utils import process_tile_order
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from ..output import save_image, save_params
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from ..params import (
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Border,
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HighresParams,
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ImageParams,
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Size,
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StageParams,
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TileOrder,
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UpscaleParams,
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)
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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 load_pipeline
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from .upscale import run_upscale_correction
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from .utils import (
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get_latents_from_seed,
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encode_prompt,
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parse_prompt,
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)
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logger = getLogger(__name__)
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def run_loopback(
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job: WorkerContext,
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server: ServerContext,
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params: ImageParams,
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strength: float,
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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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pipeline: Optional[Any] = None,
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) -> Image.Image:
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if params.loopback == 0:
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return image
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loopback_progress = ChainProgress.from_progress(progress)
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# load img2img pipeline once
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pipe_type = "lpw" if params.lpw() else "img2img"
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pipe = pipeline or load_pipeline(
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server,
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pipe_type,
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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 loopback_iteration(source: Image.Image):
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if params.lpw():
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logger.debug("using LPW pipeline for loopback")
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rng = torch.manual_seed(params.seed)
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result = pipe.img2img(
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source,
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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=params.steps,
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strength=strength,
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eta=params.eta,
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callback=loopback_progress,
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)
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return result.images[0]
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else:
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logger.debug("using img2img pipeline for loopback")
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rng = np.random.RandomState(params.seed)
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result = pipe(
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params.prompt,
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source,
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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=params.steps,
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strength=strength,
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eta=params.eta,
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callback=loopback_progress,
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)
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return result.images[0]
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for _i in range(params.loopback):
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image = loopback_iteration(image)
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return image
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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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pipeline: Optional[Any] = None,
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) -> Image.Image:
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if highres.scale <= 1:
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return image
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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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pipe_type = "lpw" if params.lpw() else "img2img"
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highres_pipe = pipeline or load_pipeline(
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server,
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pipe_type,
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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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logger.debug("using img2img pipeline for highres")
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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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params: ImageParams,
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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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) -> None:
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latents = get_latents_from_seed(params.seed, size, batch=params.batch)
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prompt_pairs, loras, inversions = parse_prompt(params)
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pipe_type = "lpw" if params.lpw() else "txt2img"
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pipe = load_pipeline(
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server,
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pipe_type,
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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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progress = job.get_progress_callback()
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prompt_embeds = encode_prompt(
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pipe,
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prompt_pairs,
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num_images_per_prompt=params.batch,
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do_classifier_free_guidance=params.do_cfg(),
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)
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pipe.unet.set_prompts(prompt_embeds)
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if params.lpw():
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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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result = pipe.text2img(
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params.prompt,
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height=size.height,
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width=size.width,
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generator=rng,
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guidance_scale=params.cfg,
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latents=latents,
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negative_prompt=params.negative_prompt,
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num_images_per_prompt=params.batch,
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num_inference_steps=params.steps,
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eta=params.eta,
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callback=progress,
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)
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else:
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rng = np.random.RandomState(params.seed)
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result = pipe(
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params.prompt,
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height=size.height,
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width=size.width,
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generator=rng,
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guidance_scale=params.cfg,
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latents=latents,
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negative_prompt=params.negative_prompt,
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num_images_per_prompt=params.batch,
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num_inference_steps=params.steps,
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eta=params.eta,
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callback=progress,
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)
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image_outputs = list(zip(result.images, outputs))
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del result
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del pipe
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for image, output in image_outputs:
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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,
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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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StageParams(),
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params,
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image,
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upscale=upscale,
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callback=progress,
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)
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dest = save_image(server, output, image)
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save_params(server, output, params, size, upscale=upscale, highres=highres)
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run_gc([job.get_device()])
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logger.info("finished txt2img job: %s", dest)
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def run_img2img_pipeline(
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job: WorkerContext,
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server: ServerContext,
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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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) -> None:
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prompt_pairs, loras, inversions = parse_prompt(params)
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# filter the source image
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if source_filter is not None:
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f = get_source_filters().get(source_filter, None)
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if f is not None:
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logger.debug("running source filter: %s", f.__name__)
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source = f(server, source)
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pipe = load_pipeline(
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server,
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params.pipeline, # this is one of the only places this can actually vary between different pipelines
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params.model,
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params.scheduler,
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job.get_device(),
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control=params.control,
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inversions=inversions,
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loras=loras,
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)
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prompt_embeds = encode_prompt(pipe, prompt_pairs, params.batch, params.do_cfg())
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pipe.unet.set_prompts(prompt_embeds)
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pipe_params = {}
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if params.pipeline == "controlnet":
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pipe_params["controlnet_conditioning_scale"] = strength
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elif params.pipeline == "img2img":
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pipe_params["strength"] = strength
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elif params.pipeline == "pix2pix":
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pipe_params["image_guidance_scale"] = strength
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progress = job.get_progress_callback()
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if params.lpw():
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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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result = pipe.img2img(
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source,
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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=params.batch,
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num_inference_steps=params.steps,
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eta=params.eta,
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callback=progress,
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**pipe_params,
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)
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else:
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rng = np.random.RandomState(params.seed)
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result = pipe(
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params.prompt,
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source,
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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=params.batch,
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num_inference_steps=params.steps,
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eta=params.eta,
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callback=progress,
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**pipe_params,
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)
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images = result.images
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if source_filter is not None and source_filter != "none":
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images.append(source)
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for image, output in zip(images, outputs):
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image = run_loopback(
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job,
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server,
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params,
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strength,
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image,
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progress,
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inversions,
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loras,
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pipeline=pipe,
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)
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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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pipeline=pipe,
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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,
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callback=progress,
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)
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dest = save_image(server, output, image)
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size = Size(*source.size)
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save_params(server, output, params, size, upscale=upscale)
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run_gc([job.get_device()])
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logger.info("finished img2img job: %s", dest)
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def run_inpaint_pipeline(
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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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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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noise_source: Any,
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mask_filter: Any,
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fill_color: str,
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tile_order: str,
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) -> None:
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progress = job.get_progress_callback()
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stage = StageParams(tile_order=tile_order)
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_prompt_pairs, loras, inversions = parse_prompt(params)
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# calling the upscale_outpaint stage directly needs accumulating progress
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progress = ChainProgress.from_progress(progress)
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logger.debug("applying mask filter and generating noise source")
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image = upscale_outpaint(
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job,
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server,
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stage,
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params,
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source,
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border=border,
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stage_mask=mask,
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fill_color=fill_color,
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mask_filter=mask_filter,
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noise_source=noise_source,
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callback=progress,
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)
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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,
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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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stage,
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params,
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image,
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upscale=upscale,
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callback=progress,
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)
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dest = save_image(server, outputs[0], image)
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save_params(server, outputs[0], params, size, upscale=upscale, border=border)
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del image
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run_gc([job.get_device()])
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logger.info("finished inpaint job: %s", dest)
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|
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def run_upscale_pipeline(
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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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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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stage = StageParams()
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_prompt_pairs, loras, inversions = parse_prompt(params)
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|
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image = run_upscale_correction(
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job, server, stage, params, source, upscale=upscale, callback=progress
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)
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# TODO: should this come first?
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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,
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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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dest = save_image(server, outputs[0], image)
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save_params(server, outputs[0], params, size, upscale=upscale)
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del image
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run_gc([job.get_device()])
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logger.info("finished upscale job: %s", dest)
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|
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def run_blend_pipeline(
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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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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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progress = job.get_progress_callback()
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stage = StageParams()
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|
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image = blend_mask(
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job,
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server,
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stage,
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params,
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sources=sources,
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stage_mask=mask,
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callback=progress,
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)
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image = image.convert("RGB")
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image = run_upscale_correction(
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job, server, stage, params, image, upscale=upscale, callback=progress
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)
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dest = save_image(server, outputs[0], image)
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save_params(server, outputs[0], params, size, upscale=upscale)
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del image
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run_gc([job.get_device()])
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logger.info("finished blend job: %s", dest)
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