KB / modules /default_pipeline.py
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import modules.core as core
import os
import torch
import modules.patch
import modules.config
import ldm_patched.modules.model_management
import ldm_patched.modules.latent_formats
import modules.inpaint_worker
import extras.vae_interpose as vae_interpose
from extras.expansion import FooocusExpansion
from ldm_patched.modules.model_base import SDXL, SDXLRefiner
from modules.sample_hijack import clip_separate
from modules.util import get_file_from_folder_list
model_base = core.StableDiffusionModel()
model_refiner = core.StableDiffusionModel()
final_expansion = None
final_unet = None
final_clip = None
final_vae = None
final_refiner_unet = None
final_refiner_vae = None
loaded_ControlNets = {}
@torch.no_grad()
@torch.inference_mode()
def refresh_controlnets(model_paths):
global loaded_ControlNets
cache = {}
for p in model_paths:
if p is not None:
if p in loaded_ControlNets:
cache[p] = loaded_ControlNets[p]
else:
cache[p] = core.load_controlnet(p)
loaded_ControlNets = cache
return
@torch.no_grad()
@torch.inference_mode()
def assert_model_integrity():
error_message = None
if not isinstance(model_base.unet_with_lora.model, SDXL):
error_message = 'You have selected base model other than SDXL. This is not supported yet.'
if error_message is not None:
raise NotImplementedError(error_message)
return True
@torch.no_grad()
@torch.inference_mode()
def refresh_base_model(name):
global model_base
filename = get_file_from_folder_list(name, modules.config.paths_checkpoints)
if model_base.filename == filename:
return
model_base = core.StableDiffusionModel()
model_base = core.load_model(filename)
print(f'Base model loaded: {model_base.filename}')
return
@torch.no_grad()
@torch.inference_mode()
def refresh_refiner_model(name):
global model_refiner
filename = get_file_from_folder_list(name, modules.config.paths_checkpoints)
if model_refiner.filename == filename:
return
model_refiner = core.StableDiffusionModel()
if name == 'None':
print(f'Refiner unloaded.')
return
model_refiner = core.load_model(filename)
print(f'Refiner model loaded: {model_refiner.filename}')
if isinstance(model_refiner.unet.model, SDXL):
model_refiner.clip = None
model_refiner.vae = None
elif isinstance(model_refiner.unet.model, SDXLRefiner):
model_refiner.clip = None
model_refiner.vae = None
else:
model_refiner.clip = None
return
@torch.no_grad()
@torch.inference_mode()
def synthesize_refiner_model():
global model_base, model_refiner
print('Synthetic Refiner Activated')
model_refiner = core.StableDiffusionModel(
unet=model_base.unet,
vae=model_base.vae,
clip=model_base.clip,
clip_vision=model_base.clip_vision,
filename=model_base.filename
)
model_refiner.vae = None
model_refiner.clip = None
model_refiner.clip_vision = None
return
@torch.no_grad()
@torch.inference_mode()
def refresh_loras(loras, base_model_additional_loras=None):
global model_base, model_refiner
if not isinstance(base_model_additional_loras, list):
base_model_additional_loras = []
model_base.refresh_loras(loras + base_model_additional_loras)
model_refiner.refresh_loras(loras)
return
@torch.no_grad()
@torch.inference_mode()
def clip_encode_single(clip, text, verbose=False):
cached = clip.fcs_cond_cache.get(text, None)
if cached is not None:
if verbose:
print(f'[CLIP Cached] {text}')
return cached
tokens = clip.tokenize(text)
result = clip.encode_from_tokens(tokens, return_pooled=True)
clip.fcs_cond_cache[text] = result
if verbose:
print(f'[CLIP Encoded] {text}')
return result
@torch.no_grad()
@torch.inference_mode()
def clone_cond(conds):
results = []
for c, p in conds:
p = p["pooled_output"]
if isinstance(c, torch.Tensor):
c = c.clone()
if isinstance(p, torch.Tensor):
p = p.clone()
results.append([c, {"pooled_output": p}])
return results
@torch.no_grad()
@torch.inference_mode()
def clip_encode(texts, pool_top_k=1):
global final_clip
if final_clip is None:
return None
if not isinstance(texts, list):
return None
if len(texts) == 0:
return None
cond_list = []
pooled_acc = 0
for i, text in enumerate(texts):
cond, pooled = clip_encode_single(final_clip, text)
cond_list.append(cond)
if i < pool_top_k:
pooled_acc += pooled
return [[torch.cat(cond_list, dim=1), {"pooled_output": pooled_acc}]]
@torch.no_grad()
@torch.inference_mode()
def clear_all_caches():
final_clip.fcs_cond_cache = {}
@torch.no_grad()
@torch.inference_mode()
def prepare_text_encoder(async_call=True):
if async_call:
# TODO: make sure that this is always called in an async way so that users cannot feel it.
pass
assert_model_integrity()
ldm_patched.modules.model_management.load_models_gpu([final_clip.patcher, final_expansion.patcher])
return
@torch.no_grad()
@torch.inference_mode()
def refresh_everything(refiner_model_name, base_model_name, loras,
base_model_additional_loras=None, use_synthetic_refiner=False):
global final_unet, final_clip, final_vae, final_refiner_unet, final_refiner_vae, final_expansion
final_unet = None
final_clip = None
final_vae = None
final_refiner_unet = None
final_refiner_vae = None
if use_synthetic_refiner and refiner_model_name == 'None':
print('Synthetic Refiner Activated')
refresh_base_model(base_model_name)
synthesize_refiner_model()
else:
refresh_refiner_model(refiner_model_name)
refresh_base_model(base_model_name)
refresh_loras(loras, base_model_additional_loras=base_model_additional_loras)
assert_model_integrity()
final_unet = model_base.unet_with_lora
final_clip = model_base.clip_with_lora
final_vae = model_base.vae
final_refiner_unet = model_refiner.unet_with_lora
final_refiner_vae = model_refiner.vae
if final_expansion is None:
final_expansion = FooocusExpansion()
prepare_text_encoder(async_call=True)
clear_all_caches()
return
refresh_everything(
refiner_model_name=modules.config.default_refiner_model_name,
base_model_name=modules.config.default_base_model_name,
loras=modules.config.default_loras
)
@torch.no_grad()
@torch.inference_mode()
def vae_parse(latent):
if final_refiner_vae is None:
return latent
result = vae_interpose.parse(latent["samples"])
return {'samples': result}
@torch.no_grad()
@torch.inference_mode()
def calculate_sigmas_all(sampler, model, scheduler, steps):
from ldm_patched.modules.samplers import calculate_sigmas_scheduler
discard_penultimate_sigma = False
if sampler in ['dpm_2', 'dpm_2_ancestral']:
steps += 1
discard_penultimate_sigma = True
sigmas = calculate_sigmas_scheduler(model, scheduler, steps)
if discard_penultimate_sigma:
sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
return sigmas
@torch.no_grad()
@torch.inference_mode()
def calculate_sigmas(sampler, model, scheduler, steps, denoise):
if denoise is None or denoise > 0.9999:
sigmas = calculate_sigmas_all(sampler, model, scheduler, steps)
else:
new_steps = int(steps / denoise)
sigmas = calculate_sigmas_all(sampler, model, scheduler, new_steps)
sigmas = sigmas[-(steps + 1):]
return sigmas
@torch.no_grad()
@torch.inference_mode()
def get_candidate_vae(steps, switch, denoise=1.0, refiner_swap_method='joint'):
assert refiner_swap_method in ['joint', 'separate', 'vae']
if final_refiner_vae is not None and final_refiner_unet is not None:
if denoise > 0.9:
return final_vae, final_refiner_vae
else:
if denoise > (float(steps - switch) / float(steps)) ** 0.834: # karras 0.834
return final_vae, None
else:
return final_refiner_vae, None
return final_vae, final_refiner_vae
@torch.no_grad()
@torch.inference_mode()
def process_diffusion(positive_cond, negative_cond, steps, switch, width, height, image_seed, callback, sampler_name, scheduler_name, latent=None, denoise=1.0, tiled=False, cfg_scale=7.0, refiner_swap_method='joint', disable_preview=False):
target_unet, target_vae, target_refiner_unet, target_refiner_vae, target_clip \
= final_unet, final_vae, final_refiner_unet, final_refiner_vae, final_clip
assert refiner_swap_method in ['joint', 'separate', 'vae']
if final_refiner_vae is not None and final_refiner_unet is not None:
# Refiner Use Different VAE (then it is SD15)
if denoise > 0.9:
refiner_swap_method = 'vae'
else:
refiner_swap_method = 'joint'
if denoise > (float(steps - switch) / float(steps)) ** 0.834: # karras 0.834
target_unet, target_vae, target_refiner_unet, target_refiner_vae \
= final_unet, final_vae, None, None
print(f'[Sampler] only use Base because of partial denoise.')
else:
positive_cond = clip_separate(positive_cond, target_model=final_refiner_unet.model, target_clip=final_clip)
negative_cond = clip_separate(negative_cond, target_model=final_refiner_unet.model, target_clip=final_clip)
target_unet, target_vae, target_refiner_unet, target_refiner_vae \
= final_refiner_unet, final_refiner_vae, None, None
print(f'[Sampler] only use Refiner because of partial denoise.')
print(f'[Sampler] refiner_swap_method = {refiner_swap_method}')
if latent is None:
initial_latent = core.generate_empty_latent(width=width, height=height, batch_size=1)
else:
initial_latent = latent
minmax_sigmas = calculate_sigmas(sampler=sampler_name, scheduler=scheduler_name, model=final_unet.model, steps=steps, denoise=denoise)
sigma_min, sigma_max = minmax_sigmas[minmax_sigmas > 0].min(), minmax_sigmas.max()
sigma_min = float(sigma_min.cpu().numpy())
sigma_max = float(sigma_max.cpu().numpy())
print(f'[Sampler] sigma_min = {sigma_min}, sigma_max = {sigma_max}')
modules.patch.BrownianTreeNoiseSamplerPatched.global_init(
initial_latent['samples'].to(ldm_patched.modules.model_management.get_torch_device()),
sigma_min, sigma_max, seed=image_seed, cpu=False)
decoded_latent = None
if refiner_swap_method == 'joint':
sampled_latent = core.ksampler(
model=target_unet,
refiner=target_refiner_unet,
positive=positive_cond,
negative=negative_cond,
latent=initial_latent,
steps=steps, start_step=0, last_step=steps, disable_noise=False, force_full_denoise=True,
seed=image_seed,
denoise=denoise,
callback_function=callback,
cfg=cfg_scale,
sampler_name=sampler_name,
scheduler=scheduler_name,
refiner_switch=switch,
previewer_start=0,
previewer_end=steps,
disable_preview=disable_preview
)
decoded_latent = core.decode_vae(vae=target_vae, latent_image=sampled_latent, tiled=tiled)
if refiner_swap_method == 'separate':
sampled_latent = core.ksampler(
model=target_unet,
positive=positive_cond,
negative=negative_cond,
latent=initial_latent,
steps=steps, start_step=0, last_step=switch, disable_noise=False, force_full_denoise=False,
seed=image_seed,
denoise=denoise,
callback_function=callback,
cfg=cfg_scale,
sampler_name=sampler_name,
scheduler=scheduler_name,
previewer_start=0,
previewer_end=steps,
disable_preview=disable_preview
)
print('Refiner swapped by changing ksampler. Noise preserved.')
target_model = target_refiner_unet
if target_model is None:
target_model = target_unet
print('Use base model to refine itself - this may because of developer mode.')
sampled_latent = core.ksampler(
model=target_model,
positive=clip_separate(positive_cond, target_model=target_model.model, target_clip=target_clip),
negative=clip_separate(negative_cond, target_model=target_model.model, target_clip=target_clip),
latent=sampled_latent,
steps=steps, start_step=switch, last_step=steps, disable_noise=True, force_full_denoise=True,
seed=image_seed,
denoise=denoise,
callback_function=callback,
cfg=cfg_scale,
sampler_name=sampler_name,
scheduler=scheduler_name,
previewer_start=switch,
previewer_end=steps,
disable_preview=disable_preview
)
target_model = target_refiner_vae
if target_model is None:
target_model = target_vae
decoded_latent = core.decode_vae(vae=target_model, latent_image=sampled_latent, tiled=tiled)
if refiner_swap_method == 'vae':
modules.patch.patch_settings[os.getpid()].eps_record = 'vae'
if modules.inpaint_worker.current_task is not None:
modules.inpaint_worker.current_task.unswap()
sampled_latent = core.ksampler(
model=target_unet,
positive=positive_cond,
negative=negative_cond,
latent=initial_latent,
steps=steps, start_step=0, last_step=switch, disable_noise=False, force_full_denoise=True,
seed=image_seed,
denoise=denoise,
callback_function=callback,
cfg=cfg_scale,
sampler_name=sampler_name,
scheduler=scheduler_name,
previewer_start=0,
previewer_end=steps,
disable_preview=disable_preview
)
print('Fooocus VAE-based swap.')
target_model = target_refiner_unet
if target_model is None:
target_model = target_unet
print('Use base model to refine itself - this may because of developer mode.')
sampled_latent = vae_parse(sampled_latent)
k_sigmas = 1.4
sigmas = calculate_sigmas(sampler=sampler_name,
scheduler=scheduler_name,
model=target_model.model,
steps=steps,
denoise=denoise)[switch:] * k_sigmas
len_sigmas = len(sigmas) - 1
noise_mean = torch.mean(modules.patch.patch_settings[os.getpid()].eps_record, dim=1, keepdim=True)
if modules.inpaint_worker.current_task is not None:
modules.inpaint_worker.current_task.swap()
sampled_latent = core.ksampler(
model=target_model,
positive=clip_separate(positive_cond, target_model=target_model.model, target_clip=target_clip),
negative=clip_separate(negative_cond, target_model=target_model.model, target_clip=target_clip),
latent=sampled_latent,
steps=len_sigmas, start_step=0, last_step=len_sigmas, disable_noise=False, force_full_denoise=True,
seed=image_seed+1,
denoise=denoise,
callback_function=callback,
cfg=cfg_scale,
sampler_name=sampler_name,
scheduler=scheduler_name,
previewer_start=switch,
previewer_end=steps,
sigmas=sigmas,
noise_mean=noise_mean,
disable_preview=disable_preview
)
target_model = target_refiner_vae
if target_model is None:
target_model = target_vae
decoded_latent = core.decode_vae(vae=target_model, latent_image=sampled_latent, tiled=tiled)
images = core.pytorch_to_numpy(decoded_latent)
modules.patch.patch_settings[os.getpid()].eps_record = None
return images