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# Stable Diffusion Dynamic Thresholding (CFG Scale Fix)
#
# Author: Alex 'mcmonkey' Goodwin
# GitHub URL: https://github.com/mcmonkeyprojects/sd-dynamic-thresholding
# Created: 2022/01/26
# Last updated: 2023/01/26
#
# For usage help, view the README.md file in the extension root, or via the GitHub page.
#
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import gradio as gr
import random
import torch
from copy import copy
from modules import sd_samplers, scripts
from modules.processing import process_images, Processed
from modules.shared import opts

######################### Script class entrypoint #########################
class Script(scripts.Script):

    def title(self):
        return "Dynamic Thresholding (CFG Scale Fix)"

    def show(self, is_img2img):
        return True

    def ui(self, is_img2img):
        help_info = gr.Markdown("### Dynamic Thresholding (CFG Scale Fix)  \nThresholds high CFG scales to make them work better.  \nSet your actual **CFG Scale** to the high value you want above (eg: 20).  \nThen set '**Mimic CFG Scale**' below to a (lower) CFG scale to mimic the effects of (eg: 10). Make sure it's not *too* different from your actual scale, it can only compensate so far.  \nSet '**Top percentile**' to how much clamping you want. 90% is good is normal, 100% clamps so hard it's like the mimic scale is the real scale. This scales as it approaches 100%, (eg 90% and 95% are much more similar than 98% and 99%).  \n...  \n")
        mimic_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='Mimic CFG Scale', value=7.0)
        threshold_percentile = gr.Slider(minimum=90.0, value=90.0, maximum=100.0, step=0.05, label='Top percentile of latents to clamp')
        return [help_info, mimic_scale, threshold_percentile]

    def run(self, p, help_info, mimic_scale, threshold_percentile):
        # Note: the random number is to protect the edge case of multiple simultaneous runs with different settings
        fixed_sampler_name = f"{p.sampler_name}_dynthres{random.randrange(100)}"
        try:
            # Percentage to portion
            threshold_percentile *= 0.01
            # Make a placeholder sampler
            sampler = sd_samplers.all_samplers_map[p.sampler_name]
            def newConstructor(model):
                result = sampler.constructor(model)
                cfg = CustomCFGDenoiser(result.model_wrap_cfg.inner_model, mimic_scale, threshold_percentile)
                result.model_wrap_cfg = cfg
                return result
            newSampler = sd_samplers.SamplerData(fixed_sampler_name, newConstructor, sampler.aliases, sampler.options)
            sd_samplers.all_samplers_map[fixed_sampler_name] = newSampler
            # Prep data
            p = copy(p)
            p.sampler_name = fixed_sampler_name
            # Run
            proc = process_images(p)
            # Cleanup
            del sd_samplers.all_samplers_map[fixed_sampler_name]
            return proc
        except Exception as e:
            del sd_samplers.all_samplers_map[fixed_sampler_name]
            raise e

######################### Implementation logic #########################

class CustomCFGDenoiser(sd_samplers.CFGDenoiser):
    def __init__(self, model, mimic_scale, threshold_percentile):
        super().__init__(model)
        self.mimic_scale = mimic_scale
        self.threshold_percentile = threshold_percentile
        
    def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
        denoised_uncond = x_out[-uncond.shape[0]:]
        return dynthresh(x_out[:-uncond.shape[0]], denoised_uncond, cond_scale, conds_list, self.mimic_scale, self.threshold_percentile)

def dynthresh(cond, uncond, cond_scale, conds_list, mimic_scale, threshold_percentile):
    # uncond shape is (batch, 4, height, width)
    conds_per_batch = cond.shape[0] / uncond.shape[0]
    assert conds_per_batch == int(conds_per_batch), "Expected # of conds per batch to be constant across batches"
    cond_stacked = cond.reshape((-1, int(conds_per_batch)) + uncond.shape[1:])
    diff = cond_stacked - uncond.unsqueeze(1)
    # conds_list shape is (batch, cond, 2)
    weights = torch.tensor(conds_list).select(2, 1)
    weights = weights.reshape(*weights.shape, 1, 1, 1).to(diff.device)
    diff_weighted = (diff * weights).sum(1)
    dynthresh_target = uncond + diff_weighted * mimic_scale

    dt_flattened = dynthresh_target.flatten(2)
    dt_means = dt_flattened.mean(dim=2).unsqueeze(2)
    dt_recentered = dt_flattened - dt_means
    dt_max = dt_recentered.abs().max(dim=2).values.unsqueeze(2)

    ut = uncond + diff_weighted * cond_scale
    ut_flattened = ut.flatten(2)
    ut_means = ut_flattened.mean(dim=2).unsqueeze(2)
    ut_centered = ut_flattened - ut_means

    ut_q = torch.quantile(ut_centered.abs(), threshold_percentile, dim=2).unsqueeze(2)
    s = torch.maximum(ut_q, dt_max)
    t_clamped = ut_centered.clamp(-s, s)
    t_normalized = t_clamped / s
    t_renormalized = t_normalized * dt_max

    uncentered = t_renormalized + ut_means
    unflattened = uncentered.unflatten(2, dynthresh_target.shape[2:])
    return unflattened