stable-diffusion-webui-two-shot/scripts/two_shot.py

from typing import List, Dict, Optional, Tuple
from dataclasses import dataclass

import torch

from modules import devices

import modules.scripts as scripts
import gradio as gr
# todo:
from modules.script_callbacks import CFGDenoisedParams, on_cfg_denoised

from modules.processing import StableDiffusionProcessing


@dataclass
class Division:
    y: float
    x: float


@dataclass
class Position:
    y: float
    x: float
    ey: float
    ex: float


class Filter:

    def __init__(self, division: Division, position: Position, weight: float):
        self.division = division
        self.position = position
        self.weight = weight

    def create_tensor(self, num_channels: int, height_b: int, width_b: int) -> torch.Tensor:

        x = torch.zeros(num_channels, height_b, width_b).to(devices.device)

        division_height = height_b / self.division.y
        division_width = width_b / self.division.x
        y1 = int(division_height * self.position.y)
        y2 = int(division_height * self.position.ey)
        x1 = int(division_width * self.position.x)
        x2 = int(division_width * self.position.ex)

        x[:, y1:y2, x1:x2] = self.weight

        return x


class Script(scripts.Script):

    def __init__(self):
        self.num_batches: int = 0
        self.end_at_step: int = 20
        self.filters: List[Filter] = []
        self.debug: bool = False

    def title(self):
        return "Latent Couple extension"

    def show(self, is_img2img):
        return scripts.AlwaysVisible

    def create_filters_from_ui_params(self, raw_divisions: str, raw_positions: str, raw_weights: str):

        divisions = []
        for division in raw_divisions.split(','):
            y, x = division.split(':')
            divisions.append(Division(float(y), float(x)))

        def start_and_end_position(raw: str):
            nums = [float(num) for num in raw.split('-')]
            if len(nums) == 1:
                return nums[0], nums[0] + 1.0
            else:
                return nums[0], nums[1]

        positions = []
        for position in raw_positions.split(','):
            y, x = position.split(':')
            y1, y2 = start_and_end_position(y)
            x1, x2 = start_and_end_position(x)
            positions.append(Position(y1, x1, y2, x2))

        weights = []
        for w in raw_weights.split(','):
            weights.append(float(w))

        # todo: assert len

        return [Filter(division, position, weight) for division, position, weight in zip(divisions, positions, weights)]

    def do_visualize(self, raw_divisions: str, raw_positions: str, raw_weights: str):

        self.filters = self.create_filters_from_ui_params(raw_divisions, raw_positions, raw_weights)

        return [f.create_tensor(1, 128, 128).squeeze(dim=0).cpu().numpy() for f in self.filters]

    def do_apply(self, extra_generation_params: str):
        #
        # parse "Latent Couple" extra_generation_params
        #
        raw_params = {}

        for assignment in extra_generation_params.split(' '):
            pair = assignment.split('=', 1)
            if len(pair) != 2:
                continue
            raw_params[pair[0]] = pair[1]

        return raw_params.get('divisions', '1:1,1:2,1:2'), raw_params.get('positions', '0:0,0:0,0:1'), raw_params.get('weights', '0.2,0.8,0.8'), int(raw_params.get('step', '20'))

    def ui(self, is_img2img):
        id_part = "img2img" if is_img2img else "txt2img"

        with gr.Group():
            with gr.Accordion("Latent Couple", open=False):
                enabled = gr.Checkbox(value=False, label="Enabled")
                with gr.Row():
                    divisions = gr.Textbox(label="Divisions", elem_id=f"cd_{id_part}_divisions", value="1:1,1:2,1:2")
                    positions = gr.Textbox(label="Positions", elem_id=f"cd_{id_part}_positions", value="0:0,0:0,0:1")
                with gr.Row():
                    weights = gr.Textbox(label="Weights", elem_id=f"cd_{id_part}_weights", value="0.2,0.8,0.8")
                    end_at_step = gr.Slider(minimum=0, maximum=150, step=1, label="end at this step", elem_id=f"cd_{id_part}_end_at_this_step", value=20)

                visualize_button = gr.Button(value="Visualize")
                visual_regions = gr.Gallery(label="Regions").style(grid=(4, 4, 4, 8), height="auto")

                visualize_button.click(fn=self.do_visualize, inputs=[divisions, positions, weights], outputs=[visual_regions])

                extra_generation_params = gr.Textbox(label="Extra generation params")
                apply_button = gr.Button(value="Apply")

                apply_button.click(fn=self.do_apply, inputs=[extra_generation_params], outputs=[divisions, positions, weights, end_at_step])

        self.infotext_fields = [
            (extra_generation_params, "Latent Couple")
        ]
        return enabled, divisions, positions, weights, end_at_step

    def denoised_callback(self, params: CFGDenoisedParams):

        if self.enabled and params.sampling_step < self.end_at_step:

            x = params.x
            # x.shape = [batch_size, C, H // 8, W // 8]

            num_batches = self.num_batches
            num_prompts = x.shape[0] // num_batches
            # ex. num_batches = 3
            # ex. num_prompts = 3 (tensor) + 1 (uncond)

            if self.debug:
                print(f"### Latent couple ###")
                print(f"denoised_callback x.shape={x.shape} num_batches={num_batches} num_prompts={num_prompts}")

            filters = [
                f.create_tensor(x.shape[1], x.shape[2], x.shape[3]) for f in self.filters
            ]
            neg_filters = [1.0 - f for f in filters]

            """
            batch #1
              subprompt #1
              subprompt #2
              subprompt #3
            batch #2
              subprompt #1
              subprompt #2
              subprompt #3
            uncond
              batch #1
              batch #2
            """

            tensor_off = 0
            uncond_off = num_batches * num_prompts - num_batches
            for b in range(num_batches):
                uncond = x[uncond_off, :, :, :]

                for p in range(num_prompts - 1):
                    if self.debug:
                        print(f"b={b} p={p}")
                    if p < len(filters):
                        tensor = x[tensor_off, :, :, :]
                        x[tensor_off, :, :, :] = tensor * filters[p] + uncond * neg_filters[p]

                    tensor_off += 1

                uncond_off += 1

    def process(self, p: StableDiffusionProcessing, enabled: bool, raw_divisions: str, raw_positions: str, raw_weights: str, raw_end_at_step: int):

        self.enabled = enabled

        if not self.enabled:
            return

        self.num_batches = p.batch_size

        self.filters = self.create_filters_from_ui_params(raw_divisions, raw_positions, raw_weights)

        self.end_at_step = raw_end_at_step

        #

        if self.end_at_step != 0:
            p.extra_generation_params["Latent Couple"] = f"divisions={raw_divisions} positions={raw_positions} weights={raw_weights} end at step={raw_end_at_step}"
            # save params into the output file as PNG textual data.

        if self.debug:
            print(f"### Latent couple ###")
            print(f"process num_batches={self.num_batches} end_at_step={self.end_at_step}")

        if not hasattr(self, 'callbacks_added'):
            on_cfg_denoised(self.denoised_callback)
            self.callbacks_added = True

        return

    def postprocess(self, *args):
        return