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feat: add support for playground v2.5 (#3073)
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* feat: add support for playground v2.5

* feat: add preset for playground v2.5

* feat: change URL to mashb1t

* feat: optimize playground v2.5 preset
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mashb1t authored Jun 4, 2024
1 parent cb24c68 commit 2d55a5f
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Showing 11 changed files with 233 additions and 30 deletions.
3 changes: 0 additions & 3 deletions args_manager.py
Original file line number Diff line number Diff line change
@@ -1,7 +1,4 @@
import ldm_patched.modules.args_parser as args_parser
import os

from tempfile import gettempdir

args_parser.parser.add_argument("--share", action='store_true', help="Set whether to share on Gradio.")

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12 changes: 10 additions & 2 deletions ldm_patched/contrib/external_model_advanced.py
Original file line number Diff line number Diff line change
Expand Up @@ -108,7 +108,7 @@ class ModelSamplingContinuousEDM:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"sampling": (["v_prediction", "eps"],),
"sampling": (["v_prediction", "edm_playground_v2.5", "eps"],),
"sigma_max": ("FLOAT", {"default": 120.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
"sigma_min": ("FLOAT", {"default": 0.002, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
}}
Expand All @@ -121,17 +121,25 @@ def INPUT_TYPES(s):
def patch(self, model, sampling, sigma_max, sigma_min):
m = model.clone()

latent_format = None
sigma_data = 1.0
if sampling == "eps":
sampling_type = ldm_patched.modules.model_sampling.EPS
elif sampling == "v_prediction":
sampling_type = ldm_patched.modules.model_sampling.V_PREDICTION
elif sampling == "edm_playground_v2.5":
sampling_type = ldm_patched.modules.model_sampling.EDM
sigma_data = 0.5
latent_format = ldm_patched.modules.latent_formats.SDXL_Playground_2_5()

class ModelSamplingAdvanced(ldm_patched.modules.model_sampling.ModelSamplingContinuousEDM, sampling_type):
pass

model_sampling = ModelSamplingAdvanced(model.model.model_config)
model_sampling.set_sigma_range(sigma_min, sigma_max)
model_sampling.set_parameters(sigma_min, sigma_max, sigma_data)
m.add_object_patch("model_sampling", model_sampling)
if latent_format is not None:
m.add_object_patch("latent_format", latent_format)
return (m, )

class RescaleCFG:
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65 changes: 65 additions & 0 deletions ldm_patched/modules/latent_formats.py
Original file line number Diff line number Diff line change
@@ -1,3 +1,4 @@
import torch

class LatentFormat:
scale_factor = 1.0
Expand Down Expand Up @@ -34,6 +35,70 @@ def __init__(self):
]
self.taesd_decoder_name = "taesdxl_decoder"

class SDXL_Playground_2_5(LatentFormat):
def __init__(self):
self.scale_factor = 0.5
self.latents_mean = torch.tensor([-1.6574, 1.886, -1.383, 2.5155]).view(1, 4, 1, 1)
self.latents_std = torch.tensor([8.4927, 5.9022, 6.5498, 5.2299]).view(1, 4, 1, 1)

self.latent_rgb_factors = [
# R G B
[ 0.3920, 0.4054, 0.4549],
[-0.2634, -0.0196, 0.0653],
[ 0.0568, 0.1687, -0.0755],
[-0.3112, -0.2359, -0.2076]
]
self.taesd_decoder_name = "taesdxl_decoder"

def process_in(self, latent):
latents_mean = self.latents_mean.to(latent.device, latent.dtype)
latents_std = self.latents_std.to(latent.device, latent.dtype)
return (latent - latents_mean) * self.scale_factor / latents_std

def process_out(self, latent):
latents_mean = self.latents_mean.to(latent.device, latent.dtype)
latents_std = self.latents_std.to(latent.device, latent.dtype)
return latent * latents_std / self.scale_factor + latents_mean


class SD_X4(LatentFormat):
def __init__(self):
self.scale_factor = 0.08333
self.latent_rgb_factors = [
[-0.2340, -0.3863, -0.3257],
[ 0.0994, 0.0885, -0.0908],
[-0.2833, -0.2349, -0.3741],
[ 0.2523, -0.0055, -0.1651]
]

class SC_Prior(LatentFormat):
def __init__(self):
self.scale_factor = 1.0
self.latent_rgb_factors = [
[-0.0326, -0.0204, -0.0127],
[-0.1592, -0.0427, 0.0216],
[ 0.0873, 0.0638, -0.0020],
[-0.0602, 0.0442, 0.1304],
[ 0.0800, -0.0313, -0.1796],
[-0.0810, -0.0638, -0.1581],
[ 0.1791, 0.1180, 0.0967],
[ 0.0740, 0.1416, 0.0432],
[-0.1745, -0.1888, -0.1373],
[ 0.2412, 0.1577, 0.0928],
[ 0.1908, 0.0998, 0.0682],
[ 0.0209, 0.0365, -0.0092],
[ 0.0448, -0.0650, -0.1728],
[-0.1658, -0.1045, -0.1308],
[ 0.0542, 0.1545, 0.1325],
[-0.0352, -0.1672, -0.2541]
]

class SC_B(LatentFormat):
def __init__(self):
self.scale_factor = 1.0 / 0.43
self.latent_rgb_factors = [
[ 0.1121, 0.2006, 0.1023],
[-0.2093, -0.0222, -0.0195],
[-0.3087, -0.1535, 0.0366],
[ 0.0290, -0.1574, -0.4078]
]
93 changes: 80 additions & 13 deletions ldm_patched/modules/model_sampling.py
Original file line number Diff line number Diff line change
@@ -1,5 +1,4 @@
import torch
import numpy as np
from ldm_patched.ldm.modules.diffusionmodules.util import make_beta_schedule
import math

Expand All @@ -12,12 +11,28 @@ def calculate_denoised(self, sigma, model_output, model_input):
sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
return model_input - model_output * sigma

def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
if max_denoise:
noise = noise * torch.sqrt(1.0 + sigma ** 2.0)
else:
noise = noise * sigma

noise += latent_image
return noise

def inverse_noise_scaling(self, sigma, latent):
return latent

class V_PREDICTION(EPS):
def calculate_denoised(self, sigma, model_output, model_input):
sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) - model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5

class EDM(V_PREDICTION):
def calculate_denoised(self, sigma, model_output, model_input):
sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) + model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5


class ModelSamplingDiscrete(torch.nn.Module):
def __init__(self, model_config=None):
Expand All @@ -42,24 +57,23 @@ def _register_schedule(self, given_betas=None, beta_schedule="linear", timesteps
else:
betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
alphas = 1. - betas
alphas_cumprod = torch.tensor(np.cumprod(alphas, axis=0), dtype=torch.float32)
# alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
alphas_cumprod = torch.cumprod(alphas, dim=0)

timesteps, = betas.shape
self.num_timesteps = int(timesteps)
self.linear_start = linear_start
self.linear_end = linear_end

# self.register_buffer('betas', torch.tensor(betas, dtype=torch.float32))
# self.register_buffer('alphas_cumprod', torch.tensor(alphas_cumprod, dtype=torch.float32))
# self.register_buffer('alphas_cumprod_prev', torch.tensor(alphas_cumprod_prev, dtype=torch.float32))

sigmas = ((1 - alphas_cumprod) / alphas_cumprod) ** 0.5
self.set_sigmas(sigmas)
self.set_alphas_cumprod(alphas_cumprod.float())

def set_sigmas(self, sigmas):
self.register_buffer('sigmas', sigmas)
self.register_buffer('log_sigmas', sigmas.log())

def set_alphas_cumprod(self, alphas_cumprod):
self.register_buffer("alphas_cumprod", alphas_cumprod.float())
self.register_buffer('sigmas', sigmas.float())
self.register_buffer('log_sigmas', sigmas.log().float())

@property
def sigma_min(self):
Expand Down Expand Up @@ -94,18 +108,18 @@ def percent_to_sigma(self, percent):
class ModelSamplingContinuousEDM(torch.nn.Module):
def __init__(self, model_config=None):
super().__init__()
self.sigma_data = 1.0

if model_config is not None:
sampling_settings = model_config.sampling_settings
else:
sampling_settings = {}

sigma_min = sampling_settings.get("sigma_min", 0.002)
sigma_max = sampling_settings.get("sigma_max", 120.0)
self.set_sigma_range(sigma_min, sigma_max)
sigma_data = sampling_settings.get("sigma_data", 1.0)
self.set_parameters(sigma_min, sigma_max, sigma_data)

def set_sigma_range(self, sigma_min, sigma_max):
def set_parameters(self, sigma_min, sigma_max, sigma_data):
self.sigma_data = sigma_data
sigmas = torch.linspace(math.log(sigma_min), math.log(sigma_max), 1000).exp()

self.register_buffer('sigmas', sigmas) #for compatibility with some schedulers
Expand Down Expand Up @@ -134,3 +148,56 @@ def percent_to_sigma(self, percent):

log_sigma_min = math.log(self.sigma_min)
return math.exp((math.log(self.sigma_max) - log_sigma_min) * percent + log_sigma_min)

class StableCascadeSampling(ModelSamplingDiscrete):
def __init__(self, model_config=None):
super().__init__()

if model_config is not None:
sampling_settings = model_config.sampling_settings
else:
sampling_settings = {}

self.set_parameters(sampling_settings.get("shift", 1.0))

def set_parameters(self, shift=1.0, cosine_s=8e-3):
self.shift = shift
self.cosine_s = torch.tensor(cosine_s)
self._init_alpha_cumprod = torch.cos(self.cosine_s / (1 + self.cosine_s) * torch.pi * 0.5) ** 2

#This part is just for compatibility with some schedulers in the codebase
self.num_timesteps = 10000
sigmas = torch.empty((self.num_timesteps), dtype=torch.float32)
for x in range(self.num_timesteps):
t = (x + 1) / self.num_timesteps
sigmas[x] = self.sigma(t)

self.set_sigmas(sigmas)

def sigma(self, timestep):
alpha_cumprod = (torch.cos((timestep + self.cosine_s) / (1 + self.cosine_s) * torch.pi * 0.5) ** 2 / self._init_alpha_cumprod)

if self.shift != 1.0:
var = alpha_cumprod
logSNR = (var/(1-var)).log()
logSNR += 2 * torch.log(1.0 / torch.tensor(self.shift))
alpha_cumprod = logSNR.sigmoid()

alpha_cumprod = alpha_cumprod.clamp(0.0001, 0.9999)
return ((1 - alpha_cumprod) / alpha_cumprod) ** 0.5

def timestep(self, sigma):
var = 1 / ((sigma * sigma) + 1)
var = var.clamp(0, 1.0)
s, min_var = self.cosine_s.to(var.device), self._init_alpha_cumprod.to(var.device)
t = (((var * min_var) ** 0.5).acos() / (torch.pi * 0.5)) * (1 + s) - s
return t

def percent_to_sigma(self, percent):
if percent <= 0.0:
return 999999999.9
if percent >= 1.0:
return 0.0

percent = 1.0 - percent
return self.sigma(torch.tensor(percent))
2 changes: 1 addition & 1 deletion ldm_patched/modules/samplers.py
Original file line number Diff line number Diff line change
Expand Up @@ -523,7 +523,7 @@ def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=N

KSAMPLER_NAMES = ["euler", "euler_ancestral", "heun", "heunpp2","dpm_2", "dpm_2_ancestral",
"lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu",
"dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm", "tcd"]
"dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm", "tcd", "edm_playground_v2.5"]

class KSAMPLER(Sampler):
def __init__(self, sampler_function, extra_options={}, inpaint_options={}):
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27 changes: 22 additions & 5 deletions modules/async_worker.py
Original file line number Diff line number Diff line change
Expand Up @@ -828,16 +828,33 @@ def handler(async_task):

if scheduler_name in ['lcm', 'tcd']:
final_scheduler_name = 'sgm_uniform'
if pipeline.final_unet is not None:
pipeline.final_unet = core.opModelSamplingDiscrete.patch(

def patch_discrete(unet):
return core.opModelSamplingDiscrete.patch(
pipeline.final_unet,
sampling=scheduler_name,
zsnr=False)[0]

if pipeline.final_unet is not None:
pipeline.final_unet = patch_discrete(pipeline.final_unet)
if pipeline.final_refiner_unet is not None:
pipeline.final_refiner_unet = core.opModelSamplingDiscrete.patch(
pipeline.final_refiner_unet,
pipeline.final_refiner_unet = patch_discrete(pipeline.final_refiner_unet)
print(f'Using {scheduler_name} scheduler.')
elif scheduler_name == 'edm_playground_v2.5':
final_scheduler_name = 'karras'

def patch_edm(unet):
return core.opModelSamplingContinuousEDM.patch(
unet,
sampling=scheduler_name,
zsnr=False)[0]
sigma_max=120.0,
sigma_min=0.002)[0]

if pipeline.final_unet is not None:
pipeline.final_unet = patch_edm(pipeline.final_unet)
if pipeline.final_refiner_unet is not None:
pipeline.final_refiner_unet = patch_edm(pipeline.final_refiner_unet)

print(f'Using {scheduler_name} scheduler.')

async_task.yields.append(['preview', (flags.preparation_step_count, 'Moving model to GPU ...', None)])
Expand Down
4 changes: 2 additions & 2 deletions modules/core.py
Original file line number Diff line number Diff line change
Expand Up @@ -21,8 +21,7 @@
from modules.util import get_file_from_folder_list
from ldm_patched.modules.lora import model_lora_keys_unet, model_lora_keys_clip
from modules.config import path_embeddings
from ldm_patched.contrib.external_model_advanced import ModelSamplingDiscrete

from ldm_patched.contrib.external_model_advanced import ModelSamplingDiscrete, ModelSamplingContinuousEDM

opEmptyLatentImage = EmptyLatentImage()
opVAEDecode = VAEDecode()
Expand All @@ -32,6 +31,7 @@
opControlNetApplyAdvanced = ControlNetApplyAdvanced()
opFreeU = FreeU_V2()
opModelSamplingDiscrete = ModelSamplingDiscrete()
opModelSamplingContinuousEDM = ModelSamplingContinuousEDM()


class StableDiffusionModel:
Expand Down
3 changes: 1 addition & 2 deletions modules/flags.py
Original file line number Diff line number Diff line change
Expand Up @@ -48,8 +48,7 @@

KSAMPLER_NAMES = list(KSAMPLER.keys())

SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform", "lcm", "turbo",
"align_your_steps", "tcd"]
SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform", "lcm", "turbo", "align_your_steps", "tcd", "edm_playground_v2.5"]
SAMPLER_NAMES = KSAMPLER_NAMES + list(SAMPLER_EXTRA.keys())

sampler_list = SAMPLER_NAMES
Expand Down
2 changes: 0 additions & 2 deletions modules/patch_precision.py
Original file line number Diff line number Diff line change
Expand Up @@ -51,8 +51,6 @@ def patched_register_schedule(self, given_betas=None, beta_schedule="linear", ti
self.linear_end = linear_end
sigmas = torch.tensor(((1 - alphas_cumprod) / alphas_cumprod) ** 0.5, dtype=torch.float32)
self.set_sigmas(sigmas)
alphas_cumprod = torch.tensor(alphas_cumprod, dtype=torch.float32)
self.set_alphas_cumprod(alphas_cumprod)
return


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1 change: 1 addition & 0 deletions presets/.gitignore
Original file line number Diff line number Diff line change
Expand Up @@ -2,5 +2,6 @@
!anime.json
!default.json
!lcm.json
!playground_v2.5.json
!realistic.json
!sai.json
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