269 lines
7.6 KiB
Python
269 lines
7.6 KiB
Python
import modules.core as core
|
|
import os
|
|
import torch
|
|
import modules.path
|
|
import modules.virtual_memory as virtual_memory
|
|
import comfy.model_management
|
|
|
|
from comfy.model_base import SDXL, SDXLRefiner
|
|
from modules.patch import cfg_patched, patched_model_function
|
|
from modules.expansion import FooocusExpansion
|
|
|
|
|
|
xl_base: core.StableDiffusionModel = None
|
|
xl_base_hash = ''
|
|
|
|
xl_refiner: core.StableDiffusionModel = None
|
|
xl_refiner_hash = ''
|
|
|
|
xl_base_patched: core.StableDiffusionModel = None
|
|
xl_base_patched_hash = ''
|
|
|
|
|
|
@torch.no_grad()
|
|
@torch.inference_mode()
|
|
def refresh_base_model(name):
|
|
global xl_base, xl_base_hash, xl_base_patched, xl_base_patched_hash
|
|
|
|
filename = os.path.abspath(os.path.realpath(os.path.join(modules.path.modelfile_path, name)))
|
|
model_hash = filename
|
|
|
|
if xl_base_hash == model_hash:
|
|
return
|
|
|
|
if xl_base is not None:
|
|
xl_base.to_meta()
|
|
xl_base = None
|
|
|
|
xl_base = core.load_model(filename)
|
|
if not isinstance(xl_base.unet.model, SDXL):
|
|
print('Model not supported. Fooocus only support SDXL model as the base model.')
|
|
xl_base = None
|
|
xl_base_hash = ''
|
|
refresh_base_model(modules.path.default_base_model_name)
|
|
xl_base_hash = model_hash
|
|
xl_base_patched = xl_base
|
|
xl_base_patched_hash = ''
|
|
return
|
|
|
|
xl_base_hash = model_hash
|
|
xl_base_patched = xl_base
|
|
xl_base_patched_hash = ''
|
|
print(f'Base model loaded: {model_hash}')
|
|
return
|
|
|
|
|
|
@torch.no_grad()
|
|
@torch.inference_mode()
|
|
def refresh_refiner_model(name):
|
|
global xl_refiner, xl_refiner_hash
|
|
|
|
filename = os.path.abspath(os.path.realpath(os.path.join(modules.path.modelfile_path, name)))
|
|
model_hash = filename
|
|
|
|
if xl_refiner_hash == model_hash:
|
|
return
|
|
|
|
if name == 'None':
|
|
xl_refiner = None
|
|
xl_refiner_hash = ''
|
|
print(f'Refiner unloaded.')
|
|
return
|
|
|
|
if xl_refiner is not None:
|
|
xl_refiner.to_meta()
|
|
xl_refiner = None
|
|
|
|
xl_refiner = core.load_model(filename)
|
|
if not isinstance(xl_refiner.unet.model, SDXLRefiner):
|
|
print('Model not supported. Fooocus only support SDXL refiner as the refiner.')
|
|
xl_refiner = None
|
|
xl_refiner_hash = ''
|
|
print(f'Refiner unloaded.')
|
|
return
|
|
|
|
xl_refiner_hash = model_hash
|
|
print(f'Refiner model loaded: {model_hash}')
|
|
|
|
xl_refiner.vae.first_stage_model.to('meta')
|
|
xl_refiner.vae = None
|
|
return
|
|
|
|
|
|
@torch.no_grad()
|
|
@torch.inference_mode()
|
|
def refresh_loras(loras):
|
|
global xl_base, xl_base_patched, xl_base_patched_hash
|
|
if xl_base_patched_hash == str(loras):
|
|
return
|
|
|
|
model = xl_base
|
|
for name, weight in loras:
|
|
if name == 'None':
|
|
continue
|
|
|
|
if os.path.exists(name):
|
|
filename = name
|
|
else:
|
|
filename = os.path.join(modules.path.lorafile_path, name)
|
|
|
|
assert os.path.exists(filename), 'Lora file not found!'
|
|
|
|
model = core.load_sd_lora(model, filename, strength_model=weight, strength_clip=weight)
|
|
xl_base_patched = model
|
|
xl_base_patched_hash = str(loras)
|
|
print(f'LoRAs loaded: {xl_base_patched_hash}')
|
|
|
|
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 clip_encode(sd, texts, pool_top_k=1):
|
|
if sd is None:
|
|
return None
|
|
if sd.clip is None:
|
|
return None
|
|
if not isinstance(texts, list):
|
|
return None
|
|
if len(texts) == 0:
|
|
return None
|
|
|
|
clip = sd.clip
|
|
cond_list = []
|
|
pooled_acc = 0
|
|
|
|
for i, text in enumerate(texts):
|
|
cond, pooled = clip_encode_single(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_sd_cond_cache(sd):
|
|
if sd is None:
|
|
return None
|
|
if sd.clip is None:
|
|
return None
|
|
sd.clip.fcs_cond_cache = {}
|
|
return
|
|
|
|
|
|
@torch.no_grad()
|
|
@torch.inference_mode()
|
|
def clear_all_caches():
|
|
clear_sd_cond_cache(xl_base_patched)
|
|
clear_sd_cond_cache(xl_refiner)
|
|
|
|
|
|
@torch.no_grad()
|
|
@torch.inference_mode()
|
|
def refresh_everything(refiner_model_name, base_model_name, loras):
|
|
refresh_refiner_model(refiner_model_name)
|
|
if xl_refiner is not None:
|
|
virtual_memory.try_move_to_virtual_memory(xl_refiner.unet.model)
|
|
virtual_memory.try_move_to_virtual_memory(xl_refiner.clip.cond_stage_model)
|
|
|
|
refresh_base_model(base_model_name)
|
|
virtual_memory.load_from_virtual_memory(xl_base.unet.model)
|
|
|
|
refresh_loras(loras)
|
|
clear_all_caches()
|
|
return
|
|
|
|
|
|
refresh_everything(
|
|
refiner_model_name=modules.path.default_refiner_model_name,
|
|
base_model_name=modules.path.default_base_model_name,
|
|
loras=[(modules.path.default_lora_name, 0.5), ('None', 0.5), ('None', 0.5), ('None', 0.5), ('None', 0.5)]
|
|
)
|
|
|
|
expansion = FooocusExpansion()
|
|
|
|
|
|
@torch.no_grad()
|
|
@torch.inference_mode()
|
|
def patch_all_models():
|
|
assert xl_base is not None
|
|
assert xl_base_patched is not None
|
|
|
|
xl_base.unet.model_options['sampler_cfg_function'] = cfg_patched
|
|
xl_base.unet.model_options['model_function_wrapper'] = patched_model_function
|
|
|
|
xl_base_patched.unet.model_options['sampler_cfg_function'] = cfg_patched
|
|
xl_base_patched.unet.model_options['model_function_wrapper'] = patched_model_function
|
|
|
|
if xl_refiner is not None:
|
|
xl_refiner.unet.model_options['sampler_cfg_function'] = cfg_patched
|
|
xl_refiner.unet.model_options['model_function_wrapper'] = patched_model_function
|
|
|
|
return
|
|
|
|
|
|
@torch.no_grad()
|
|
@torch.inference_mode()
|
|
def process_diffusion(positive_cond, negative_cond, steps, switch, width, height, image_seed, callback, latent=None, denoise=1.0, tiled=False):
|
|
patch_all_models()
|
|
|
|
if xl_refiner is not None:
|
|
virtual_memory.try_move_to_virtual_memory(xl_refiner.unet.model)
|
|
virtual_memory.load_from_virtual_memory(xl_base.unet.model)
|
|
|
|
if latent is None:
|
|
empty_latent = core.generate_empty_latent(width=width, height=height, batch_size=1)
|
|
else:
|
|
empty_latent = latent
|
|
|
|
if xl_refiner is not None:
|
|
sampled_latent = core.ksampler_with_refiner(
|
|
model=xl_base_patched.unet,
|
|
positive=positive_cond[0],
|
|
negative=negative_cond[0],
|
|
refiner=xl_refiner.unet,
|
|
refiner_positive=positive_cond[1],
|
|
refiner_negative=negative_cond[1],
|
|
refiner_switch_step=switch,
|
|
latent=empty_latent,
|
|
steps=steps, start_step=0, last_step=steps, disable_noise=False, force_full_denoise=True,
|
|
seed=image_seed,
|
|
denoise=denoise,
|
|
callback_function=callback
|
|
)
|
|
else:
|
|
sampled_latent = core.ksampler(
|
|
model=xl_base_patched.unet,
|
|
positive=positive_cond[0],
|
|
negative=negative_cond[0],
|
|
latent=empty_latent,
|
|
steps=steps, start_step=0, last_step=steps, disable_noise=False, force_full_denoise=True,
|
|
seed=image_seed,
|
|
denoise=denoise,
|
|
callback_function=callback
|
|
)
|
|
|
|
decoded_latent = core.decode_vae(vae=xl_base_patched.vae, latent_image=sampled_latent, tiled=tiled)
|
|
images = core.pytorch_to_numpy(decoded_latent)
|
|
|
|
comfy.model_management.soft_empty_cache()
|
|
return images
|