106 lines
3.2 KiB
Python
106 lines
3.2 KiB
Python
from logging import getLogger
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from typing import List, Optional
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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 encode_prompt, parse_prompt, slice_prompt
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from ..params import ImageParams, 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 BlendImg2ImgStage(BaseStage):
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max_tile = SizeChart.unlimited
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def run(
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self,
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worker: 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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strength: float,
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callback: Optional[ProgressCallback] = None,
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stage_source: Optional[Image.Image] = None,
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prompt_index: Optional[int] = 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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# multi-stage prompting
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if prompt_index is not None:
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params = params.with_args(prompt=slice_prompt(params.prompt, prompt_index))
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logger.info(
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"blending image using img2img, %s steps: %s", params.steps, params.prompt
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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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pipe_type = params.get_valid_pipeline("img2img")
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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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worker.get_device(),
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inversions=inversions,
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loras=loras,
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)
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pipe_params = {}
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if params.is_pix2pix():
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pipe_params["image_guidance_scale"] = strength
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elif params.is_control():
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pipe_params["controlnet_conditioning_scale"] = strength
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else:
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pipe_params["strength"] = strength
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outputs = []
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for source in sources:
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if params.is_lpw():
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logger.debug("using LPW pipeline for img2img")
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rng = torch.manual_seed(params.seed)
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result = pipe.img2img(
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source,
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prompt,
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generator=rng,
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guidance_scale=params.cfg,
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negative_prompt=negative_prompt,
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num_inference_steps=params.steps,
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callback=callback,
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**pipe_params,
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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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if not params.is_xl():
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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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generator=rng,
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guidance_scale=params.cfg,
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image=source,
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negative_prompt=negative_prompt,
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num_inference_steps=params.steps,
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callback=callback,
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**pipe_params,
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)
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outputs.extend(result.images)
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return outputs
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