274 lines
7.1 KiB
Python
274 lines
7.1 KiB
Python
from logging import getLogger
|
|
from typing import Any, List
|
|
|
|
import numpy as np
|
|
import torch
|
|
from diffusers import OnnxStableDiffusionImg2ImgPipeline, OnnxStableDiffusionPipeline
|
|
from PIL import Image
|
|
|
|
from ..chain import blend_mask, upscale_outpaint
|
|
from ..chain.base import ChainProgress
|
|
from ..output import save_image, save_params
|
|
from ..params import Border, ImageParams, Size, StageParams, UpscaleParams
|
|
from ..server import ServerContext
|
|
from ..upscale import run_upscale_correction
|
|
from ..utils import run_gc
|
|
from ..worker import WorkerContext
|
|
from .load import get_latents_from_seed, load_pipeline
|
|
|
|
logger = getLogger(__name__)
|
|
|
|
|
|
def run_txt2img_pipeline(
|
|
job: WorkerContext,
|
|
server: ServerContext,
|
|
params: ImageParams,
|
|
size: Size,
|
|
outputs: List[str],
|
|
upscale: UpscaleParams,
|
|
) -> None:
|
|
latents = get_latents_from_seed(params.seed, size, batch=params.batch)
|
|
pipe = load_pipeline(
|
|
server,
|
|
OnnxStableDiffusionPipeline,
|
|
params.model,
|
|
params.scheduler,
|
|
job.get_device(),
|
|
params.lpw,
|
|
params.inversion,
|
|
)
|
|
progress = job.get_progress_callback()
|
|
|
|
if params.lpw:
|
|
logger.debug("using LPW pipeline for txt2img")
|
|
rng = torch.manual_seed(params.seed)
|
|
result = pipe.text2img(
|
|
params.prompt,
|
|
height=size.height,
|
|
width=size.width,
|
|
generator=rng,
|
|
guidance_scale=params.cfg,
|
|
latents=latents,
|
|
negative_prompt=params.negative_prompt,
|
|
num_images_per_prompt=params.batch,
|
|
num_inference_steps=params.steps,
|
|
eta=params.eta,
|
|
callback=progress,
|
|
)
|
|
else:
|
|
rng = np.random.RandomState(params.seed)
|
|
result = pipe(
|
|
params.prompt,
|
|
height=size.height,
|
|
width=size.width,
|
|
generator=rng,
|
|
guidance_scale=params.cfg,
|
|
latents=latents,
|
|
negative_prompt=params.negative_prompt,
|
|
num_images_per_prompt=params.batch,
|
|
num_inference_steps=params.steps,
|
|
eta=params.eta,
|
|
callback=progress,
|
|
)
|
|
|
|
for image, output in zip(result.images, outputs):
|
|
image = run_upscale_correction(
|
|
job,
|
|
server,
|
|
StageParams(),
|
|
params,
|
|
image,
|
|
upscale=upscale,
|
|
callback=progress,
|
|
)
|
|
|
|
dest = save_image(server, output, image)
|
|
save_params(server, output, params, size, upscale=upscale)
|
|
|
|
run_gc([job.get_device()])
|
|
|
|
logger.info("finished txt2img job: %s", dest)
|
|
|
|
|
|
def run_img2img_pipeline(
|
|
job: WorkerContext,
|
|
server: ServerContext,
|
|
params: ImageParams,
|
|
outputs: List[str],
|
|
upscale: UpscaleParams,
|
|
source: Image.Image,
|
|
strength: float,
|
|
) -> None:
|
|
pipe = load_pipeline(
|
|
server,
|
|
OnnxStableDiffusionImg2ImgPipeline,
|
|
params.model,
|
|
params.scheduler,
|
|
job.get_device(),
|
|
params.lpw,
|
|
params.inversion,
|
|
)
|
|
progress = job.get_progress_callback()
|
|
if params.lpw:
|
|
logger.debug("using LPW pipeline for img2img")
|
|
rng = torch.manual_seed(params.seed)
|
|
result = pipe.img2img(
|
|
source,
|
|
params.prompt,
|
|
generator=rng,
|
|
guidance_scale=params.cfg,
|
|
negative_prompt=params.negative_prompt,
|
|
num_images_per_prompt=params.batch,
|
|
num_inference_steps=params.steps,
|
|
strength=strength,
|
|
eta=params.eta,
|
|
callback=progress,
|
|
)
|
|
else:
|
|
rng = np.random.RandomState(params.seed)
|
|
result = pipe(
|
|
params.prompt,
|
|
source,
|
|
generator=rng,
|
|
guidance_scale=params.cfg,
|
|
negative_prompt=params.negative_prompt,
|
|
num_images_per_prompt=params.batch,
|
|
num_inference_steps=params.steps,
|
|
strength=strength,
|
|
eta=params.eta,
|
|
callback=progress,
|
|
)
|
|
|
|
for image, output in zip(result.images, outputs):
|
|
image = run_upscale_correction(
|
|
job,
|
|
server,
|
|
StageParams(),
|
|
params,
|
|
image,
|
|
upscale=upscale,
|
|
callback=progress,
|
|
)
|
|
|
|
dest = save_image(server, output, image)
|
|
size = Size(*source.size)
|
|
save_params(server, output, params, size, upscale=upscale)
|
|
|
|
run_gc([job.get_device()])
|
|
|
|
logger.info("finished img2img job: %s", dest)
|
|
|
|
|
|
def run_inpaint_pipeline(
|
|
job: WorkerContext,
|
|
server: ServerContext,
|
|
params: ImageParams,
|
|
size: Size,
|
|
outputs: List[str],
|
|
upscale: UpscaleParams,
|
|
source: Image.Image,
|
|
mask: Image.Image,
|
|
border: Border,
|
|
noise_source: Any,
|
|
mask_filter: Any,
|
|
fill_color: str,
|
|
tile_order: str,
|
|
) -> None:
|
|
progress = job.get_progress_callback()
|
|
stage = StageParams(tile_order=tile_order)
|
|
|
|
# calling the upscale_outpaint stage directly needs accumulating progress
|
|
progress = ChainProgress.from_progress(progress)
|
|
|
|
logger.debug("applying mask filter and generating noise source")
|
|
image = upscale_outpaint(
|
|
job,
|
|
server,
|
|
stage,
|
|
params,
|
|
source,
|
|
border=border,
|
|
stage_mask=mask,
|
|
fill_color=fill_color,
|
|
mask_filter=mask_filter,
|
|
noise_source=noise_source,
|
|
callback=progress,
|
|
)
|
|
|
|
image = run_upscale_correction(
|
|
job, server, stage, params, image, upscale=upscale, callback=progress
|
|
)
|
|
|
|
dest = save_image(server, outputs[0], image)
|
|
save_params(server, outputs[0], params, size, upscale=upscale, border=border)
|
|
|
|
del image
|
|
|
|
run_gc([job.get_device()])
|
|
|
|
logger.info("finished inpaint job: %s", dest)
|
|
|
|
|
|
def run_upscale_pipeline(
|
|
job: WorkerContext,
|
|
server: ServerContext,
|
|
params: ImageParams,
|
|
size: Size,
|
|
outputs: List[str],
|
|
upscale: UpscaleParams,
|
|
source: Image.Image,
|
|
) -> None:
|
|
progress = job.get_progress_callback()
|
|
stage = StageParams()
|
|
|
|
image = run_upscale_correction(
|
|
job, server, stage, params, source, upscale=upscale, callback=progress
|
|
)
|
|
|
|
dest = save_image(server, outputs[0], image)
|
|
save_params(server, outputs[0], params, size, upscale=upscale)
|
|
|
|
del image
|
|
|
|
run_gc([job.get_device()])
|
|
|
|
logger.info("finished upscale job: %s", dest)
|
|
|
|
|
|
def run_blend_pipeline(
|
|
job: WorkerContext,
|
|
server: ServerContext,
|
|
params: ImageParams,
|
|
size: Size,
|
|
outputs: List[str],
|
|
upscale: UpscaleParams,
|
|
sources: List[Image.Image],
|
|
mask: Image.Image,
|
|
) -> None:
|
|
progress = job.get_progress_callback()
|
|
stage = StageParams()
|
|
|
|
image = blend_mask(
|
|
job,
|
|
server,
|
|
stage,
|
|
params,
|
|
sources=sources,
|
|
stage_mask=mask,
|
|
callback=progress,
|
|
)
|
|
image = image.convert("RGB")
|
|
|
|
image = run_upscale_correction(
|
|
job, server, stage, params, image, upscale=upscale, callback=progress
|
|
)
|
|
|
|
dest = save_image(server, outputs[0], image)
|
|
save_params(server, outputs[0], params, size, upscale=upscale)
|
|
|
|
del image
|
|
|
|
run_gc([job.get_device()])
|
|
|
|
logger.info("finished blend job: %s", dest)
|