bbdf4bd120
* sync * sync * sync * sync * sync * sync * sync * sync * sync * sync * sync * sync * sync * sync * sync * sync * sync * sync * sync * sync * sync * sync * sync * sync
121 lines
5.1 KiB
Python
121 lines
5.1 KiB
Python
import torch
|
|
import comfy.samplers
|
|
import comfy.model_management
|
|
|
|
from comfy.model_base import SDXLRefiner, SDXL
|
|
from comfy.sample import get_additional_models
|
|
from comfy.samplers import resolve_areas_and_cond_masks, wrap_model, calculate_start_end_timesteps, \
|
|
create_cond_with_same_area_if_none, pre_run_control, apply_empty_x_to_equal_area, encode_adm, \
|
|
blank_inpaint_image_like
|
|
|
|
|
|
current_refiner = None
|
|
refiner_switch_step = -1
|
|
|
|
|
|
@torch.no_grad()
|
|
@torch.inference_mode()
|
|
def clip_separate(cond, target_model=None):
|
|
c, p = cond[0]
|
|
if target_model is None or isinstance(target_model, SDXLRefiner):
|
|
c = c[..., -1280:].clone()
|
|
p = {"pooled_output": p["pooled_output"].clone()}
|
|
elif isinstance(target_model, SDXL):
|
|
c = c.clone()
|
|
p = {"pooled_output": p["pooled_output"].clone()}
|
|
else:
|
|
c = c[..., :768].clone()
|
|
p = {}
|
|
return [[c, p]]
|
|
|
|
|
|
@torch.no_grad()
|
|
@torch.inference_mode()
|
|
def sample_hacked(model, noise, positive, negative, cfg, device, sampler, sigmas, model_options={}, latent_image=None, denoise_mask=None, callback=None, disable_pbar=False, seed=None):
|
|
global current_refiner
|
|
|
|
positive = positive[:]
|
|
negative = negative[:]
|
|
|
|
resolve_areas_and_cond_masks(positive, noise.shape[2], noise.shape[3], device)
|
|
resolve_areas_and_cond_masks(negative, noise.shape[2], noise.shape[3], device)
|
|
|
|
model_wrap = wrap_model(model)
|
|
|
|
calculate_start_end_timesteps(model_wrap, negative)
|
|
calculate_start_end_timesteps(model_wrap, positive)
|
|
|
|
#make sure each cond area has an opposite one with the same area
|
|
for c in positive:
|
|
create_cond_with_same_area_if_none(negative, c)
|
|
for c in negative:
|
|
create_cond_with_same_area_if_none(positive, c)
|
|
|
|
# pre_run_control(model_wrap, negative + positive)
|
|
pre_run_control(model_wrap, positive) # negative is not necessary in Fooocus, 0.5s faster.
|
|
|
|
apply_empty_x_to_equal_area(list(filter(lambda c: c[1].get('control_apply_to_uncond', False) == True, positive)), negative, 'control', lambda cond_cnets, x: cond_cnets[x])
|
|
apply_empty_x_to_equal_area(positive, negative, 'gligen', lambda cond_cnets, x: cond_cnets[x])
|
|
|
|
if model.is_adm():
|
|
positive = encode_adm(model, positive, noise.shape[0], noise.shape[3], noise.shape[2], device, "positive")
|
|
negative = encode_adm(model, negative, noise.shape[0], noise.shape[3], noise.shape[2], device, "negative")
|
|
|
|
if current_refiner is not None and current_refiner.model.is_adm():
|
|
positive_refiner = clip_separate(positive, target_model=current_refiner.model)
|
|
negative_refiner = clip_separate(negative, target_model=current_refiner.model)
|
|
|
|
positive_refiner = encode_adm(current_refiner.model, positive_refiner, noise.shape[0], noise.shape[3], noise.shape[2], device, "positive")
|
|
negative_refiner = encode_adm(current_refiner.model, negative_refiner, noise.shape[0], noise.shape[3], noise.shape[2], device, "negative")
|
|
|
|
positive_refiner[0][1]['adm_encoded'].to(positive[0][1]['adm_encoded'])
|
|
negative_refiner[0][1]['adm_encoded'].to(negative[0][1]['adm_encoded'])
|
|
|
|
if latent_image is not None:
|
|
latent_image = model.process_latent_in(latent_image)
|
|
|
|
extra_args = {"cond": positive, "uncond": negative, "cond_scale": cfg, "model_options": model_options, "seed": seed}
|
|
|
|
cond_concat = None
|
|
if hasattr(model, 'concat_keys'): # inpaint
|
|
cond_concat = []
|
|
for ck in model.concat_keys:
|
|
if denoise_mask is not None:
|
|
if ck == "mask":
|
|
cond_concat.append(denoise_mask[:,:1])
|
|
elif ck == "masked_image":
|
|
cond_concat.append(latent_image) #NOTE: the latent_image should be masked by the mask in pixel space
|
|
else:
|
|
if ck == "mask":
|
|
cond_concat.append(torch.ones_like(noise)[:, :1])
|
|
elif ck == "masked_image":
|
|
cond_concat.append(blank_inpaint_image_like(noise))
|
|
extra_args["cond_concat"] = cond_concat
|
|
|
|
def refiner_switch():
|
|
extra_args["cond"] = positive_refiner
|
|
extra_args["uncond"] = negative_refiner
|
|
|
|
# clear ip-adapter for refiner
|
|
extra_args['model_options'] = {k: {} if k == 'transformer_options' else v for k, v in extra_args['model_options'].items()}
|
|
|
|
models, inference_memory = get_additional_models(positive_refiner, negative_refiner, current_refiner.model_dtype())
|
|
comfy.model_management.load_models_gpu([current_refiner] + models, comfy.model_management.batch_area_memory(
|
|
noise.shape[0] * noise.shape[2] * noise.shape[3]) + inference_memory)
|
|
|
|
model_wrap.inner_model.inner_model = current_refiner.model
|
|
print('Refiner Swapped')
|
|
return
|
|
|
|
def callback_wrap(step, x0, x, total_steps):
|
|
if step == refiner_switch_step and current_refiner is not None:
|
|
refiner_switch()
|
|
if callback is not None:
|
|
callback(step, x0, x, total_steps)
|
|
|
|
samples = sampler.sample(model_wrap, sigmas, extra_args, callback_wrap, noise, latent_image, denoise_mask, disable_pbar)
|
|
return model.process_latent_out(samples.to(torch.float32))
|
|
|
|
|
|
comfy.samplers.sample = sample_hacked
|