126 lines
4.1 KiB
Python
126 lines
4.1 KiB
Python
from logging import getLogger
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from typing import Callable, List, Optional, Tuple
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import numpy as np
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import torch
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from PIL import Image
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from ..diffusers.load import load_pipeline
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from ..diffusers.utils import (
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encode_prompt,
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get_latents_from_seed,
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get_tile_latents,
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parse_prompt,
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)
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from ..image import mask_filter_none, noise_source_histogram
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from ..output import save_image
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from ..params import Border, ImageParams, Size, SizeChart, StageParams
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from ..server import ServerContext
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from ..utils import is_debug
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from ..worker import ProgressCallback, WorkerContext
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from .base import BaseStage
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logger = getLogger(__name__)
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class UpscaleOutpaintStage(BaseStage):
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max_tile = SizeChart.max
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def run(
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self,
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worker: WorkerContext,
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server: ServerContext,
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stage: StageParams,
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params: ImageParams,
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sources: List[Image.Image],
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*,
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border: Border,
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dims: Tuple[int, int, int],
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tile_mask: Image.Image,
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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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latents: Optional[np.ndarray] = None,
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callback: Optional[ProgressCallback] = None,
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stage_source: Optional[Image.Image] = None,
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stage_mask: Optional[Image.Image] = None,
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**kwargs,
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) -> List[Image.Image]:
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prompt_pairs, loras, inversions, (prompt, negative_prompt) = parse_prompt(
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params
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)
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pipe_type = params.get_valid_pipeline("inpaint", params.pipeline)
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pipe = load_pipeline(
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server,
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params,
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pipe_type,
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worker.get_device(),
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embeddings=inversions,
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loras=loras,
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)
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outputs = []
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for source in sources:
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if is_debug():
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save_image(server, "tile-source.png", source)
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save_image(server, "tile-mask.png", tile_mask)
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# if the tile mask is all black, skip processing this tile
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if not tile_mask.getbbox():
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outputs.append(source)
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continue
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tile_size = params.unet_tile
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size = Size(*source.size)
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latent_size = size.min(tile_size, tile_size)
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# generate new latents or slice existing
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if latents is None:
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latents = get_latents_from_seed(params.seed, latent_size, params.batch)
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else:
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latents = get_tile_latents(latents, params.seed, latent_size, dims)
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if params.is_lpw():
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logger.debug("using LPW pipeline for inpaint")
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rng = torch.manual_seed(params.seed)
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result = pipe.inpaint(
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source,
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tile_mask,
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prompt,
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negative_prompt=negative_prompt,
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height=latent_size.height,
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width=latent_size.width,
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num_inference_steps=params.steps,
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guidance_scale=params.cfg,
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generator=rng,
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latents=latents,
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callback=callback,
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)
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else:
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# encode and record alternative prompts outside of LPW
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if not params.is_xl():
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prompt_embeds = encode_prompt(
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pipe, prompt_pairs, params.batch, params.do_cfg()
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)
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pipe.unet.set_prompts(prompt_embeds)
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rng = np.random.RandomState(params.seed)
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result = pipe(
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prompt,
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source,
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tile_mask,
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negative_prompt=negative_prompt,
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height=latent_size.height,
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width=latent_size.width,
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num_inference_steps=params.steps,
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guidance_scale=params.cfg,
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generator=rng,
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latents=latents,
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callback=callback,
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)
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outputs.extend(result.images)
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return outputs
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