77 lines
2.1 KiB
Python
77 lines
2.1 KiB
Python
from logging import getLogger
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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 ..device_pool import JobContext, ProgressCallback
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from ..diffusion.load import get_latents_from_seed, load_pipeline
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from ..params import ImageParams, Size, StageParams
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from ..utils import ServerContext
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logger = getLogger(__name__)
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def source_txt2img(
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job: JobContext,
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server: ServerContext,
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stage: StageParams,
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params: ImageParams,
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source_image: Image.Image,
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*,
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size: Size,
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prompt: str = None,
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callback: ProgressCallback = None,
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**kwargs,
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) -> Image.Image:
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prompt = prompt or params.prompt
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logger.info("generating image using txt2img, %s steps: %s", params.steps, prompt)
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if source_image is not None:
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logger.warn(
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"a source image was passed to a txt2img stage, but will be discarded"
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)
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latents = get_latents_from_seed(params.seed, size)
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pipe = load_pipeline(
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OnnxStableDiffusionPipeline,
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params.model,
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params.scheduler,
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job.get_device(),
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params.lpw,
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)
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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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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_inference_steps=params.steps,
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callback=callback,
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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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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_inference_steps=params.steps,
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callback=callback,
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)
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output = result.images[0]
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logger.info("final output image size: %sx%s", output.width, output.height)
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return output
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