124 lines
3.7 KiB
Python
124 lines
3.7 KiB
Python
from logging import getLogger
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from typing import 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 ..params import ImageParams, Size, SizeChart, StageParams
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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 SourceTxt2ImgStage(BaseStage):
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max_tile = SizeChart.unlimited
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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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_source: Image.Image,
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*,
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dims: Tuple[int, int, int],
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size: Size,
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callback: Optional[ProgressCallback] = None,
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latents: Optional[np.ndarray] = None,
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**kwargs,
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) -> Image.Image:
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params = params.with_args(**kwargs)
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size = size.with_args(**kwargs)
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logger.info(
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"generating image using txt2img, %s steps: %s", params.steps, params.prompt
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)
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if "stage_source" in kwargs:
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logger.warn(
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"a source image was passed to a txt2img stage, and will be discarded"
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)
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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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tile_size = params.tiles
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if max(size) > tile_size:
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latent_size = Size(tile_size, tile_size)
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pipe_width = pipe_height = tile_size
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else:
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latent_size = Size(size.width, size.height)
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pipe_width = size.width
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pipe_height = size.height
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# generate new latents or slice existing
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if latents is None:
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# generate new latents
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latents = get_latents_from_seed(params.seed, latent_size, params.batch)
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else:
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# slice existing latents
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latents = get_tile_latents(latents, dims, Size(tile_size, tile_size))
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pipe_width = pipe_height = tile_size
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pipe_type = params.get_valid_pipeline("txt2img")
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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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job.get_device(),
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inversions=inversions,
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loras=loras,
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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=pipe_height,
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width=pipe_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=negative_prompt,
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num_images_per_prompt=params.batch,
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num_inference_steps=params.steps,
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eta=params.eta,
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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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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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height=pipe_height,
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width=pipe_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=negative_prompt,
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num_images_per_prompt=params.batch,
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num_inference_steps=params.steps,
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eta=params.eta,
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callback=callback,
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)
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return result.images
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