73 lines
2.1 KiB
Python
73 lines
2.1 KiB
Python
from logging import getLogger
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from os import path
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from typing import List, Optional
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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 encode_prompt, parse_prompt
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from ..params import ImageParams, StageParams, UpscaleParams
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from ..server import ServerContext
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from ..worker import ProgressCallback, WorkerContext
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from .stage import BaseStage
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logger = getLogger(__name__)
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class UpscaleStableDiffusionStage(BaseStage):
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def run(
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self,
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job: 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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upscale: UpscaleParams,
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stage_source: Optional[Image.Image] = None,
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callback: Optional[ProgressCallback] = None,
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**kwargs,
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) -> List[Image.Image]:
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params = params.with_args(**kwargs)
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upscale = upscale.with_args(**kwargs)
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logger.info(
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"upscaling with Stable Diffusion, %s steps: %s", params.steps, params.prompt
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)
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prompt_pairs, _loras, _inversions = parse_prompt(params)
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pipeline = load_pipeline(
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server,
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params,
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"upscale",
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job.get_device(),
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model=path.join(server.model_path, upscale.upscale_model),
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)
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generator = torch.manual_seed(params.seed)
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prompt_embeds = encode_prompt(
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pipeline,
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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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pipeline.unet.set_prompts(prompt_embeds)
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outputs = []
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for source in sources:
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result = pipeline(
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params.prompt,
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source,
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generator=generator,
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guidance_scale=params.cfg,
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negative_prompt=params.negative_prompt,
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num_inference_steps=params.steps,
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eta=params.eta,
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noise_level=upscale.denoise,
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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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