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https://github.com/openvinotoolkit/stable-diffusion-webui.git
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resolve some of circular import issues for kohaku
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09165916fa
commit
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@ -10,7 +10,7 @@ import torch
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import tqdm
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import tqdm
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from einops import rearrange, repeat
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from einops import rearrange, repeat
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from ldm.util import default
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from ldm.util import default
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from modules import devices, processing, sd_models, shared, sd_samplers, hashes, sd_hijack_checkpoint, errors
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from modules import devices, sd_models, shared, sd_samplers, hashes, sd_hijack_checkpoint, errors
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from modules.textual_inversion import textual_inversion, logging
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from modules.textual_inversion import textual_inversion, logging
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from modules.textual_inversion.learn_schedule import LearnRateScheduler
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from modules.textual_inversion.learn_schedule import LearnRateScheduler
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from torch import einsum
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from torch import einsum
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@ -469,8 +469,7 @@ def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None,
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def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_hypernetwork_every, template_filename, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
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def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_hypernetwork_every, template_filename, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
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# images allows training previews to have infotext. Importing it at the top causes a circular import problem.
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from modules import images, processing
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from modules import images
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save_hypernetwork_every = save_hypernetwork_every or 0
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save_hypernetwork_every = save_hypernetwork_every or 0
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create_image_every = create_image_every or 0
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create_image_every = create_image_every or 0
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@ -30,6 +30,7 @@ from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion
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from einops import repeat, rearrange
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from einops import repeat, rearrange
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from blendmodes.blend import blendLayers, BlendType
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from blendmodes.blend import blendLayers, BlendType
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decode_first_stage = sd_samplers_common.decode_first_stage
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# some of those options should not be changed at all because they would break the model, so I removed them from options.
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# some of those options should not be changed at all because they would break the model, so I removed them from options.
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opt_C = 4
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opt_C = 4
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@ -572,12 +573,6 @@ def decode_latent_batch(model, batch, target_device=None, check_for_nans=False):
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return samples
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return samples
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def decode_first_stage(model, x):
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x = model.decode_first_stage(x.to(devices.dtype_vae))
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return x
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def get_fixed_seed(seed):
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def get_fixed_seed(seed):
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if seed is None or seed == '' or seed == -1:
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if seed is None or seed == '' or seed == -1:
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return int(random.randrange(4294967294))
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return int(random.randrange(4294967294))
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@ -2,7 +2,6 @@ import torch
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from torch.nn.functional import silu
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from torch.nn.functional import silu
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from types import MethodType
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from types import MethodType
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import modules.textual_inversion.textual_inversion
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from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors, sd_unet
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from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors, sd_unet
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from modules.hypernetworks import hypernetwork
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from modules.hypernetworks import hypernetwork
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from modules.shared import cmd_opts
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from modules.shared import cmd_opts
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@ -164,12 +163,13 @@ class StableDiffusionModelHijack:
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clip = None
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clip = None
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optimization_method = None
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optimization_method = None
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embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase()
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def __init__(self):
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def __init__(self):
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import modules.textual_inversion.textual_inversion
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self.extra_generation_params = {}
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self.extra_generation_params = {}
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self.comments = []
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self.comments = []
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self.embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase()
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self.embedding_db.add_embedding_dir(cmd_opts.embeddings_dir)
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self.embedding_db.add_embedding_dir(cmd_opts.embeddings_dir)
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def apply_optimizations(self, option=None):
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def apply_optimizations(self, option=None):
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@ -2,7 +2,7 @@ from collections import namedtuple
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import numpy as np
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import numpy as np
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import torch
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import torch
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from PIL import Image
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from PIL import Image
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from modules import devices, processing, images, sd_vae_approx, sd_samplers, sd_vae_taesd, shared
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from modules import devices, images, sd_vae_approx, sd_samplers, sd_vae_taesd, shared
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from modules.shared import opts, state
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from modules.shared import opts, state
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SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
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SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
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@ -35,7 +35,7 @@ def single_sample_to_image(sample, approximation=None):
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x_sample = sample * 1.5
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x_sample = sample * 1.5
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x_sample = sd_vae_taesd.model()(x_sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
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x_sample = sd_vae_taesd.model()(x_sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
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else:
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else:
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x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0] * 0.5 + 0.5
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x_sample = decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0] * 0.5 + 0.5
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x_sample = torch.clamp(x_sample, min=0.0, max=1.0)
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x_sample = torch.clamp(x_sample, min=0.0, max=1.0)
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x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
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x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
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@ -44,6 +44,12 @@ def single_sample_to_image(sample, approximation=None):
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return Image.fromarray(x_sample)
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return Image.fromarray(x_sample)
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def decode_first_stage(model, x):
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x = model.decode_first_stage(x.to(devices.dtype_vae))
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return x
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def sample_to_image(samples, index=0, approximation=None):
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def sample_to_image(samples, index=0, approximation=None):
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return single_sample_to_image(samples[index], approximation)
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return single_sample_to_image(samples[index], approximation)
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@ -13,7 +13,7 @@ import numpy as np
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from PIL import Image, PngImagePlugin
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from PIL import Image, PngImagePlugin
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from torch.utils.tensorboard import SummaryWriter
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from torch.utils.tensorboard import SummaryWriter
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from modules import shared, devices, sd_hijack, processing, sd_models, images, sd_samplers, sd_hijack_checkpoint, errors, hashes
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from modules import shared, devices, sd_hijack, sd_models, images, sd_samplers, sd_hijack_checkpoint, errors, hashes
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import modules.textual_inversion.dataset
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import modules.textual_inversion.dataset
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from modules.textual_inversion.learn_schedule import LearnRateScheduler
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from modules.textual_inversion.learn_schedule import LearnRateScheduler
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@ -387,6 +387,8 @@ def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, dat
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def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_embedding_every, template_filename, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
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def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_embedding_every, template_filename, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
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from modules import processing
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save_embedding_every = save_embedding_every or 0
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save_embedding_every = save_embedding_every or 0
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create_image_every = create_image_every or 0
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create_image_every = create_image_every or 0
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template_file = textual_inversion_templates.get(template_filename, None)
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template_file = textual_inversion_templates.get(template_filename, None)
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