09e0d1cb3a
* Reworked SAG, removed unnecessary patch * Reworked anisotropic filters for faster compute. * Replaced with guided anisotropic filter for less distribution.
181 lines
6.0 KiB
Python
181 lines
6.0 KiB
Python
import modules.core as core
|
|
import os
|
|
import torch
|
|
import modules.path
|
|
|
|
from comfy.model_base import SDXL, SDXLRefiner
|
|
from modules.patch import cfg_patched
|
|
|
|
|
|
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 = ''
|
|
|
|
|
|
def refresh_base_model(name):
|
|
global xl_base, xl_base_hash, xl_base_patched, xl_base_patched_hash
|
|
if xl_base_hash == str(name):
|
|
return
|
|
|
|
filename = os.path.join(modules.path.modelfile_path, name)
|
|
|
|
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 = name
|
|
xl_base_patched = xl_base
|
|
xl_base_patched_hash = ''
|
|
return
|
|
|
|
xl_base_hash = name
|
|
xl_base_patched = xl_base
|
|
xl_base_patched_hash = ''
|
|
print(f'Base model loaded: {xl_base_hash}')
|
|
|
|
return
|
|
|
|
|
|
def refresh_refiner_model(name):
|
|
global xl_refiner, xl_refiner_hash
|
|
if xl_refiner_hash == str(name):
|
|
return
|
|
|
|
if name == 'None':
|
|
xl_refiner = None
|
|
xl_refiner_hash = ''
|
|
print(f'Refiner unloaded.')
|
|
return
|
|
|
|
filename = os.path.join(modules.path.modelfile_path, name)
|
|
|
|
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 = name
|
|
print(f'Refiner model loaded: {xl_refiner_hash}')
|
|
|
|
xl_refiner.vae.first_stage_model.to('meta')
|
|
xl_refiner.vae = None
|
|
return
|
|
|
|
|
|
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
|
|
|
|
filename = os.path.join(modules.path.lorafile_path, name)
|
|
model = core.load_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
|
|
|
|
|
|
refresh_base_model(modules.path.default_base_model_name)
|
|
refresh_refiner_model(modules.path.default_refiner_model_name)
|
|
refresh_loras([(modules.path.default_lora_name, 0.5), ('None', 0.5), ('None', 0.5), ('None', 0.5), ('None', 0.5)])
|
|
|
|
positive_conditions_cache = None
|
|
negative_conditions_cache = None
|
|
positive_conditions_refiner_cache = None
|
|
negative_conditions_refiner_cache = None
|
|
|
|
|
|
def clean_prompt_cond_caches():
|
|
global positive_conditions_cache, negative_conditions_cache, \
|
|
positive_conditions_refiner_cache, negative_conditions_refiner_cache
|
|
positive_conditions_cache = None
|
|
negative_conditions_cache = None
|
|
positive_conditions_refiner_cache = None
|
|
negative_conditions_refiner_cache = None
|
|
return
|
|
|
|
|
|
@torch.no_grad()
|
|
def process(positive_prompt, negative_prompt, steps, switch, width, height, image_seed, callback):
|
|
global positive_conditions_cache, negative_conditions_cache, \
|
|
positive_conditions_refiner_cache, negative_conditions_refiner_cache
|
|
|
|
if xl_base is not None:
|
|
xl_base.unet.model_options['sampler_cfg_function'] = cfg_patched
|
|
|
|
if xl_base_patched is not None:
|
|
xl_base_patched.unet.model_options['sampler_cfg_function'] = cfg_patched
|
|
|
|
if xl_refiner is not None:
|
|
xl_refiner.unet.model_options['sampler_cfg_function'] = cfg_patched
|
|
|
|
positive_conditions = core.encode_prompt_condition(clip=xl_base_patched.clip, prompt=positive_prompt) if positive_conditions_cache is None else positive_conditions_cache
|
|
negative_conditions = core.encode_prompt_condition(clip=xl_base_patched.clip, prompt=negative_prompt) if negative_conditions_cache is None else negative_conditions_cache
|
|
|
|
positive_conditions_cache = positive_conditions
|
|
negative_conditions_cache = negative_conditions
|
|
|
|
empty_latent = core.generate_empty_latent(width=width, height=height, batch_size=1)
|
|
|
|
if xl_refiner is not None:
|
|
positive_conditions_refiner = core.encode_prompt_condition(clip=xl_refiner.clip, prompt=positive_prompt) if positive_conditions_refiner_cache is None else positive_conditions_refiner_cache
|
|
negative_conditions_refiner = core.encode_prompt_condition(clip=xl_refiner.clip, prompt=negative_prompt) if negative_conditions_refiner_cache is None else negative_conditions_refiner_cache
|
|
|
|
positive_conditions_refiner_cache = positive_conditions_refiner
|
|
negative_conditions_refiner_cache = negative_conditions_refiner
|
|
|
|
sampled_latent = core.ksampler_with_refiner(
|
|
model=xl_base_patched.unet,
|
|
positive=positive_conditions,
|
|
negative=negative_conditions,
|
|
refiner=xl_refiner.unet,
|
|
refiner_positive=positive_conditions_refiner,
|
|
refiner_negative=negative_conditions_refiner,
|
|
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,
|
|
callback_function=callback
|
|
)
|
|
|
|
else:
|
|
sampled_latent = core.ksampler(
|
|
model=xl_base_patched.unet,
|
|
positive=positive_conditions,
|
|
negative=negative_conditions,
|
|
latent=empty_latent,
|
|
steps=steps, start_step=0, last_step=steps, disable_noise=False, force_full_denoise=True,
|
|
seed=image_seed,
|
|
callback_function=callback
|
|
)
|
|
|
|
decoded_latent = core.decode_vae(vae=xl_base_patched.vae, latent_image=sampled_latent)
|
|
|
|
images = core.image_to_numpy(decoded_latent)
|
|
|
|
return images
|