feat: add support for playground v2.5 (#3073)
* 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
This commit is contained in:
Manuel Schmid
GitHub
parent
cb24c686b0
commit
2d55a5f257
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@ -1,7 +1,4 @@
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import ldm_patched.modules.args_parser as args_parser
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import os
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from tempfile import gettempdir
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args_parser.parser.add_argument("--share", action='store_true', help="Set whether to share on Gradio.")
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@ -108,7 +108,7 @@ class ModelSamplingContinuousEDM:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "model": ("MODEL",),
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"sampling": (["v_prediction", "eps"],),
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"sampling": (["v_prediction", "edm_playground_v2.5", "eps"],),
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"sigma_max": ("FLOAT", {"default": 120.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
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"sigma_min": ("FLOAT", {"default": 0.002, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
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}}
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@ -121,17 +121,25 @@ class ModelSamplingContinuousEDM:
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def patch(self, model, sampling, sigma_max, sigma_min):
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m = model.clone()
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latent_format = None
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sigma_data = 1.0
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if sampling == "eps":
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sampling_type = ldm_patched.modules.model_sampling.EPS
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elif sampling == "v_prediction":
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sampling_type = ldm_patched.modules.model_sampling.V_PREDICTION
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elif sampling == "edm_playground_v2.5":
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sampling_type = ldm_patched.modules.model_sampling.EDM
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sigma_data = 0.5
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latent_format = ldm_patched.modules.latent_formats.SDXL_Playground_2_5()
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class ModelSamplingAdvanced(ldm_patched.modules.model_sampling.ModelSamplingContinuousEDM, sampling_type):
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pass
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model_sampling = ModelSamplingAdvanced(model.model.model_config)
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model_sampling.set_sigma_range(sigma_min, sigma_max)
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model_sampling.set_parameters(sigma_min, sigma_max, sigma_data)
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m.add_object_patch("model_sampling", model_sampling)
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if latent_format is not None:
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m.add_object_patch("latent_format", latent_format)
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return (m, )
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class RescaleCFG:
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@ -1,3 +1,4 @@
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import torch
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class LatentFormat:
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scale_factor = 1.0
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@ -34,6 +35,70 @@ class SDXL(LatentFormat):
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]
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self.taesd_decoder_name = "taesdxl_decoder"
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class SDXL_Playground_2_5(LatentFormat):
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def __init__(self):
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self.scale_factor = 0.5
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self.latents_mean = torch.tensor([-1.6574, 1.886, -1.383, 2.5155]).view(1, 4, 1, 1)
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self.latents_std = torch.tensor([8.4927, 5.9022, 6.5498, 5.2299]).view(1, 4, 1, 1)
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self.latent_rgb_factors = [
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# R G B
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[ 0.3920, 0.4054, 0.4549],
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[-0.2634, -0.0196, 0.0653],
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[ 0.0568, 0.1687, -0.0755],
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[-0.3112, -0.2359, -0.2076]
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]
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self.taesd_decoder_name = "taesdxl_decoder"
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def process_in(self, latent):
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latents_mean = self.latents_mean.to(latent.device, latent.dtype)
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latents_std = self.latents_std.to(latent.device, latent.dtype)
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return (latent - latents_mean) * self.scale_factor / latents_std
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def process_out(self, latent):
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latents_mean = self.latents_mean.to(latent.device, latent.dtype)
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latents_std = self.latents_std.to(latent.device, latent.dtype)
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return latent * latents_std / self.scale_factor + latents_mean
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class SD_X4(LatentFormat):
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def __init__(self):
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self.scale_factor = 0.08333
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self.latent_rgb_factors = [
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[-0.2340, -0.3863, -0.3257],
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[ 0.0994, 0.0885, -0.0908],
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[-0.2833, -0.2349, -0.3741],
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[ 0.2523, -0.0055, -0.1651]
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]
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class SC_Prior(LatentFormat):
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def __init__(self):
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self.scale_factor = 1.0
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self.latent_rgb_factors = [
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[-0.0326, -0.0204, -0.0127],
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[-0.1592, -0.0427, 0.0216],
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[ 0.0873, 0.0638, -0.0020],
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[-0.0602, 0.0442, 0.1304],
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[ 0.0800, -0.0313, -0.1796],
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[-0.0810, -0.0638, -0.1581],
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[ 0.1791, 0.1180, 0.0967],
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[ 0.0740, 0.1416, 0.0432],
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[-0.1745, -0.1888, -0.1373],
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[ 0.2412, 0.1577, 0.0928],
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[ 0.1908, 0.0998, 0.0682],
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[ 0.0209, 0.0365, -0.0092],
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[ 0.0448, -0.0650, -0.1728],
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[-0.1658, -0.1045, -0.1308],
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[ 0.0542, 0.1545, 0.1325],
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[-0.0352, -0.1672, -0.2541]
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]
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class SC_B(LatentFormat):
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def __init__(self):
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self.scale_factor = 1.0 / 0.43
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self.latent_rgb_factors = [
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[ 0.1121, 0.2006, 0.1023],
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[-0.2093, -0.0222, -0.0195],
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[-0.3087, -0.1535, 0.0366],
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[ 0.0290, -0.1574, -0.4078]
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]
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@ -1,5 +1,4 @@
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import torch
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import numpy as np
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from ldm_patched.ldm.modules.diffusionmodules.util import make_beta_schedule
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import math
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@ -12,12 +11,28 @@ class EPS:
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sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
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return model_input - model_output * sigma
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def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
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if max_denoise:
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noise = noise * torch.sqrt(1.0 + sigma ** 2.0)
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else:
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noise = noise * sigma
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noise += latent_image
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return noise
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def inverse_noise_scaling(self, sigma, latent):
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return latent
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class V_PREDICTION(EPS):
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def calculate_denoised(self, sigma, model_output, model_input):
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sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
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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
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class EDM(V_PREDICTION):
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def calculate_denoised(self, sigma, model_output, model_input):
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sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
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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
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class ModelSamplingDiscrete(torch.nn.Module):
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def __init__(self, model_config=None):
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@ -42,24 +57,23 @@ class ModelSamplingDiscrete(torch.nn.Module):
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else:
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betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
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alphas = 1. - betas
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alphas_cumprod = torch.tensor(np.cumprod(alphas, axis=0), dtype=torch.float32)
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# alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
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alphas_cumprod = torch.cumprod(alphas, dim=0)
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timesteps, = betas.shape
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self.num_timesteps = int(timesteps)
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self.linear_start = linear_start
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self.linear_end = linear_end
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# self.register_buffer('betas', torch.tensor(betas, dtype=torch.float32))
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# self.register_buffer('alphas_cumprod', torch.tensor(alphas_cumprod, dtype=torch.float32))
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# self.register_buffer('alphas_cumprod_prev', torch.tensor(alphas_cumprod_prev, dtype=torch.float32))
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sigmas = ((1 - alphas_cumprod) / alphas_cumprod) ** 0.5
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self.set_sigmas(sigmas)
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self.set_alphas_cumprod(alphas_cumprod.float())
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def set_sigmas(self, sigmas):
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self.register_buffer('sigmas', sigmas)
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self.register_buffer('log_sigmas', sigmas.log())
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def set_alphas_cumprod(self, alphas_cumprod):
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self.register_buffer("alphas_cumprod", alphas_cumprod.float())
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self.register_buffer('sigmas', sigmas.float())
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self.register_buffer('log_sigmas', sigmas.log().float())
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@property
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def sigma_min(self):
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@ -94,8 +108,6 @@ class ModelSamplingDiscrete(torch.nn.Module):
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class ModelSamplingContinuousEDM(torch.nn.Module):
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def __init__(self, model_config=None):
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super().__init__()
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self.sigma_data = 1.0
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if model_config is not None:
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sampling_settings = model_config.sampling_settings
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else:
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@ -103,9 +115,11 @@ class ModelSamplingContinuousEDM(torch.nn.Module):
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sigma_min = sampling_settings.get("sigma_min", 0.002)
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sigma_max = sampling_settings.get("sigma_max", 120.0)
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self.set_sigma_range(sigma_min, sigma_max)
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sigma_data = sampling_settings.get("sigma_data", 1.0)
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self.set_parameters(sigma_min, sigma_max, sigma_data)
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def set_sigma_range(self, sigma_min, sigma_max):
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def set_parameters(self, sigma_min, sigma_max, sigma_data):
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self.sigma_data = sigma_data
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sigmas = torch.linspace(math.log(sigma_min), math.log(sigma_max), 1000).exp()
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self.register_buffer('sigmas', sigmas) #for compatibility with some schedulers
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@ -134,3 +148,56 @@ class ModelSamplingContinuousEDM(torch.nn.Module):
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log_sigma_min = math.log(self.sigma_min)
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return math.exp((math.log(self.sigma_max) - log_sigma_min) * percent + log_sigma_min)
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class StableCascadeSampling(ModelSamplingDiscrete):
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def __init__(self, model_config=None):
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super().__init__()
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if model_config is not None:
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sampling_settings = model_config.sampling_settings
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else:
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sampling_settings = {}
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self.set_parameters(sampling_settings.get("shift", 1.0))
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def set_parameters(self, shift=1.0, cosine_s=8e-3):
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self.shift = shift
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self.cosine_s = torch.tensor(cosine_s)
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self._init_alpha_cumprod = torch.cos(self.cosine_s / (1 + self.cosine_s) * torch.pi * 0.5) ** 2
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#This part is just for compatibility with some schedulers in the codebase
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self.num_timesteps = 10000
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sigmas = torch.empty((self.num_timesteps), dtype=torch.float32)
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for x in range(self.num_timesteps):
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t = (x + 1) / self.num_timesteps
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sigmas[x] = self.sigma(t)
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self.set_sigmas(sigmas)
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def sigma(self, timestep):
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alpha_cumprod = (torch.cos((timestep + self.cosine_s) / (1 + self.cosine_s) * torch.pi * 0.5) ** 2 / self._init_alpha_cumprod)
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if self.shift != 1.0:
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var = alpha_cumprod
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logSNR = (var/(1-var)).log()
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logSNR += 2 * torch.log(1.0 / torch.tensor(self.shift))
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alpha_cumprod = logSNR.sigmoid()
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alpha_cumprod = alpha_cumprod.clamp(0.0001, 0.9999)
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return ((1 - alpha_cumprod) / alpha_cumprod) ** 0.5
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def timestep(self, sigma):
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var = 1 / ((sigma * sigma) + 1)
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var = var.clamp(0, 1.0)
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s, min_var = self.cosine_s.to(var.device), self._init_alpha_cumprod.to(var.device)
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t = (((var * min_var) ** 0.5).acos() / (torch.pi * 0.5)) * (1 + s) - s
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return t
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def percent_to_sigma(self, percent):
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if percent <= 0.0:
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return 999999999.9
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if percent >= 1.0:
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return 0.0
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percent = 1.0 - percent
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return self.sigma(torch.tensor(percent))
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@ -523,7 +523,7 @@ class UNIPCBH2(Sampler):
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KSAMPLER_NAMES = ["euler", "euler_ancestral", "heun", "heunpp2","dpm_2", "dpm_2_ancestral",
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"lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu",
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"dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm", "tcd"]
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"dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm", "tcd", "edm_playground_v2.5"]
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class KSAMPLER(Sampler):
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def __init__(self, sampler_function, extra_options={}, inpaint_options={}):
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@ -828,16 +828,33 @@ def worker():
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if scheduler_name in ['lcm', 'tcd']:
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final_scheduler_name = 'sgm_uniform'
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if pipeline.final_unet is not None:
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pipeline.final_unet = core.opModelSamplingDiscrete.patch(
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def patch_discrete(unet):
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return core.opModelSamplingDiscrete.patch(
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pipeline.final_unet,
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sampling=scheduler_name,
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zsnr=False)[0]
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if pipeline.final_unet is not None:
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pipeline.final_unet = patch_discrete(pipeline.final_unet)
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if pipeline.final_refiner_unet is not None:
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pipeline.final_refiner_unet = core.opModelSamplingDiscrete.patch(
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pipeline.final_refiner_unet,
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pipeline.final_refiner_unet = patch_discrete(pipeline.final_refiner_unet)
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print(f'Using {scheduler_name} scheduler.')
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elif scheduler_name == 'edm_playground_v2.5':
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final_scheduler_name = 'karras'
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def patch_edm(unet):
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return core.opModelSamplingContinuousEDM.patch(
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unet,
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sampling=scheduler_name,
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zsnr=False)[0]
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sigma_max=120.0,
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sigma_min=0.002)[0]
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if pipeline.final_unet is not None:
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pipeline.final_unet = patch_edm(pipeline.final_unet)
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if pipeline.final_refiner_unet is not None:
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pipeline.final_refiner_unet = patch_edm(pipeline.final_refiner_unet)
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print(f'Using {scheduler_name} scheduler.')
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async_task.yields.append(['preview', (flags.preparation_step_count, 'Moving model to GPU ...', None)])
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@ -21,8 +21,7 @@ from modules.lora import match_lora
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from modules.util import get_file_from_folder_list
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from ldm_patched.modules.lora import model_lora_keys_unet, model_lora_keys_clip
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from modules.config import path_embeddings
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from ldm_patched.contrib.external_model_advanced import ModelSamplingDiscrete
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from ldm_patched.contrib.external_model_advanced import ModelSamplingDiscrete, ModelSamplingContinuousEDM
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opEmptyLatentImage = EmptyLatentImage()
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opVAEDecode = VAEDecode()
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@ -32,6 +31,7 @@ opVAEEncodeTiled = VAEEncodeTiled()
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opControlNetApplyAdvanced = ControlNetApplyAdvanced()
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opFreeU = FreeU_V2()
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opModelSamplingDiscrete = ModelSamplingDiscrete()
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opModelSamplingContinuousEDM = ModelSamplingContinuousEDM()
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class StableDiffusionModel:
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@ -48,8 +48,7 @@ SAMPLERS = KSAMPLER | SAMPLER_EXTRA
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KSAMPLER_NAMES = list(KSAMPLER.keys())
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SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform", "lcm", "turbo",
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"align_your_steps", "tcd"]
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SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform", "lcm", "turbo", "align_your_steps", "tcd", "edm_playground_v2.5"]
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SAMPLER_NAMES = KSAMPLER_NAMES + list(SAMPLER_EXTRA.keys())
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sampler_list = SAMPLER_NAMES
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@ -51,8 +51,6 @@ def patched_register_schedule(self, given_betas=None, beta_schedule="linear", ti
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self.linear_end = linear_end
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sigmas = torch.tensor(((1 - alphas_cumprod) / alphas_cumprod) ** 0.5, dtype=torch.float32)
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self.set_sigmas(sigmas)
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alphas_cumprod = torch.tensor(alphas_cumprod, dtype=torch.float32)
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self.set_alphas_cumprod(alphas_cumprod)
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return
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@ -2,5 +2,6 @@
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!anime.json
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!default.json
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!lcm.json
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!playground_v2.5.json
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!realistic.json
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!sai.json
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@ -0,0 +1,51 @@
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{
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"default_model": "playground-v2.5-1024px-aesthetic.fp16.safetensors",
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"default_refiner": "None",
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"default_refiner_switch": 0.5,
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"default_loras": [
|
||||
[
|
||||
true,
|
||||
"None",
|
||||
1.0
|
||||
],
|
||||
[
|
||||
true,
|
||||
"None",
|
||||
1.0
|
||||
],
|
||||
[
|
||||
true,
|
||||
"None",
|
||||
1.0
|
||||
],
|
||||
[
|
||||
true,
|
||||
"None",
|
||||
1.0
|
||||
],
|
||||
[
|
||||
true,
|
||||
"None",
|
||||
1.0
|
||||
]
|
||||
],
|
||||
"default_cfg_scale": 3,
|
||||
"default_sample_sharpness": 2.0,
|
||||
"default_sampler": "dpmpp_2m",
|
||||
"default_scheduler": "edm_playground_v2.5",
|
||||
"default_performance": "Speed",
|
||||
"default_prompt": "",
|
||||
"default_prompt_negative": "",
|
||||
"default_styles": [
|
||||
"Fooocus V2",
|
||||
"Fooocus Enhance",
|
||||
"Fooocus Sharp"
|
||||
],
|
||||
"default_aspect_ratio": "1024*1024",
|
||||
"checkpoint_downloads": {
|
||||
"playground-v2.5-1024px-aesthetic.fp16.safetensors": "https://huggingface.co/mashb1t/fav_models/resolve/main/fav/playground-v2.5-1024px-aesthetic.fp16.safetensors"
|
||||
},
|
||||
"embeddings_downloads": {},
|
||||
"lora_downloads": {},
|
||||
"previous_default_models": []
|
||||
}
|
||||
Loading…
Reference in New Issue