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onnx-web/api/onnx_web/chain/source_txt2img.py

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from logging import getLogger
import numpy as np
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import torch
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from diffusers import OnnxStableDiffusionPipeline
from PIL import Image
from ..device_pool import JobContext
from ..diffusion.load import get_latents_from_seed, load_pipeline
from ..params import ImageParams, Size, StageParams
from ..utils import ServerContext
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logger = getLogger(__name__)
def source_txt2img(
job: JobContext,
server: ServerContext,
stage: StageParams,
params: ImageParams,
source_image: Image.Image,
*,
size: Size,
prompt: str = None,
**kwargs,
) -> Image.Image:
prompt = prompt or params.prompt
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logger.info("generating image using txt2img, %s steps: %s", params.steps, prompt)
if source_image is not None:
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logger.warn(
"a source image was passed to a txt2img stage, but will be discarded"
)
latents = get_latents_from_seed(params.seed, size)
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pipe = load_pipeline(
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OnnxStableDiffusionPipeline,
params.model,
params.scheduler,
job.get_device(),
params.lpw,
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)
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if params.lpw:
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logger.debug("using LPW pipeline for txt2img")
rng = torch.manual_seed(params.seed)
result = pipe.text2img(
prompt,
height=size.height,
width=size.width,
generator=rng,
guidance_scale=params.cfg,
latents=latents,
negative_prompt=params.negative_prompt,
num_inference_steps=params.steps,
)
else:
rng = np.random.RandomState(params.seed)
result = pipe(
prompt,
height=size.height,
width=size.width,
generator=rng,
guidance_scale=params.cfg,
latents=latents,
negative_prompt=params.negative_prompt,
num_inference_steps=params.steps,
)
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output = result.images[0]
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logger.info("final output image size: %sx%s", output.width, output.height)
return output