Commit cdac5ace authored by AUTOMATIC's avatar AUTOMATIC
Browse files

suppress ENSD infotext for samplers that don't use it

parent 3d76eabb

modules/processing.py

+7 −4
Original line number Diff line number Diff line
@@ -13,7 +13,7 @@ from skimage import exposure 
from typing import Any, Dict, List



import modules.sd_hijack

from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts

from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common

from modules.sd_hijack import model_hijack

from modules.shared import opts, cmd_opts, state

import modules.shared as shared
@@ -480,6 +480,10 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter 
    clip_skip = getattr(p, 'clip_skip', opts.CLIP_stop_at_last_layers)

    enable_hr = getattr(p, 'enable_hr', False)



    uses_ensd = opts.eta_noise_seed_delta != 0

    if uses_ensd:

        uses_ensd = sd_samplers_common.is_sampler_using_eta_noise_seed_delta(p)



    generation_params = {

        "Steps": p.steps,

        "Sampler": p.sampler_name,
@@ -496,17 +500,16 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter 
        "Denoising strength": getattr(p, 'denoising_strength', None),

        "Conditional mask weight": getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None,

        "Clip skip": None if clip_skip <= 1 else clip_skip,

        "ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta,

        "ENSD": opts.eta_noise_seed_delta if uses_ensd else None,

        "Token merging ratio": None if opts.token_merging_ratio == 0 else opts.token_merging_ratio,

        "Token merging ratio hr": None if not enable_hr or opts.token_merging_ratio_hr == 0 else opts.token_merging_ratio_hr,

        "Init image hash": getattr(p, 'init_img_hash', None),

        "RNG": opts.randn_source if opts.randn_source != "GPU" else None,

        "NGMS": None if p.s_min_uncond == 0 else p.s_min_uncond,

        **p.extra_generation_params,

        "Version": program_version() if opts.add_version_to_infotext else None,

    }



    generation_params.update(p.extra_generation_params)



    generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])



    negative_prompt_text = f"\nNegative prompt: {p.all_negative_prompts[index]}" if p.all_negative_prompts[index] else ""

modules/sd_samplers.py

+7 −1
Original line number Diff line number Diff line
@@ -14,12 +14,18 @@ samplers_for_img2img = [] 
samplers_map = {}





def create_sampler(name, model):

def find_sampler_config(name):

    if name is not None:

        config = all_samplers_map.get(name, None)

    else:

        config = all_samplers[0]



    return config





def create_sampler(name, model):

    config = find_sampler_config(name)



    assert config is not None, f'bad sampler name: {name}'



    sampler = config.constructor(model)

modules/sd_samplers_common.py

+20 −1
Original line number Diff line number Diff line
@@ -2,7 +2,7 @@ from collections import namedtuple 
import numpy as np

import torch

from PIL import Image

from modules import devices, processing, images, sd_vae_approx

from modules import devices, processing, images, sd_vae_approx, sd_samplers



from modules.shared import opts, state

import modules.shared as shared
@@ -58,6 +58,25 @@ def store_latent(decoded): 
            shared.state.assign_current_image(sample_to_image(decoded))





def is_sampler_using_eta_noise_seed_delta(p):

    """returns whether sampler from config will use eta noise seed delta for image creation"""



    sampler_config = sd_samplers.find_sampler_config(p.sampler_name)



    eta = p.eta



    if eta is None and p.sampler is not None:

        eta = p.sampler.eta



    if eta is None and sampler_config is not None:

        eta = 0 if sampler_config.options.get("default_eta_is_0", False) else 1.0



    if eta == 0:

        return False



    return sampler_config.options.get("uses_ensd", False)





class InterruptedException(BaseException):

    pass

modules/sd_samplers_compvis.py

+6 −2
Original line number Diff line number Diff line
@@ -11,7 +11,7 @@ import modules.models.diffusion.uni_pc 




samplers_data_compvis = [

    sd_samplers_common.SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {}),

    sd_samplers_common.SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {"default_eta_is_0": True, "uses_ensd": True}),

    sd_samplers_common.SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {}),

    sd_samplers_common.SamplerData('UniPC', lambda model: VanillaStableDiffusionSampler(modules.models.diffusion.uni_pc.UniPCSampler, model), [], {}),

]
@@ -134,7 +134,11 @@ class VanillaStableDiffusionSampler: 
        self.update_step(x)



    def initialize(self, p):

        if self.is_ddim:

            self.eta = p.eta if p.eta is not None else shared.opts.eta_ddim

        else:

            self.eta = 0.0



        if self.eta != 0.0:

            p.extra_generation_params["Eta DDIM"] = self.eta

modules/sd_samplers_kdiffusion.py

+8 −8
Original line number Diff line number Diff line
@@ -11,21 +11,21 @@ from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback 
from modules.script_callbacks import AfterCFGCallbackParams, cfg_after_cfg_callback



samplers_k_diffusion = [

    ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {}),

    ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {"uses_ensd": True}),

    ('Euler', 'sample_euler', ['k_euler'], {}),

    ('LMS', 'sample_lms', ['k_lms'], {}),

    ('Heun', 'sample_heun', ['k_heun'], {}),

    ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'discard_next_to_last_sigma': True}),

    ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True}),

    ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),

    ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True, "uses_ensd": True}),

    ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {"uses_ensd": True}),

    ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),

    ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {}),

    ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}),

    ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}),

    ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {"uses_ensd": True}),

    ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {"uses_ensd": True}),

    ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),

    ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True}),

    ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True}),

    ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),

    ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True}),

    ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True}),

    ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras', "uses_ensd": True}),

    ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),

    ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras'}),

]

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