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lllyasviel
2023-12-12 11:38:05 -08:00
parent 0d878b0282
commit e8d88d3e25
167 changed files with 135010 additions and 265 deletions
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modules/sample_hijack.py
+37 -8
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@@ -1,11 +1,15 @@
import torch
import fcbh.samplers
import fcbh.model_management
import ldm_patched.modules.samplers
import ldm_patched.modules.model_management
from fcbh.model_base import SDXLRefiner, SDXL
from fcbh.conds import CONDRegular
from fcbh.sample import get_additional_models, get_models_from_cond, cleanup_additional_models
from fcbh.samplers import resolve_areas_and_cond_masks, wrap_model, calculate_start_end_timesteps, \
from collections import namedtuple
from ldm_patched.contrib.external_custom_sampler import SDTurboScheduler
from ldm_patched.k_diffusion import sampling as k_diffusion_sampling
from ldm_patched.modules.samplers import normal_scheduler, simple_scheduler, ddim_scheduler
from ldm_patched.modules.model_base import SDXLRefiner, SDXL
from ldm_patched.modules.conds import CONDRegular
from ldm_patched.modules.sample import get_additional_models, get_models_from_cond, cleanup_additional_models
from ldm_patched.modules.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_model_conds
@@ -133,7 +137,9 @@ def sample_hacked(model, noise, positive, negative, cfg, device, sampler, sigmas
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())
fcbh.model_management.load_models_gpu([current_refiner] + models, current_refiner.memory_required(noise.shape) + inference_memory)
ldm_patched.modules.model_management.load_models_gpu(
[current_refiner] + models,
model.memory_required([noise.shape[0] * 2] + list(noise.shape[1:])) + inference_memory)
model_wrap.inner_model = current_refiner.model
print('Refiner Swapped')
@@ -152,4 +158,27 @@ def sample_hacked(model, noise, positive, negative, cfg, device, sampler, sigmas
return model.process_latent_out(samples.to(torch.float32))
fcbh.samplers.sample = sample_hacked
@torch.no_grad()
@torch.inference_mode()
def calculate_sigmas_scheduler_hacked(model, scheduler_name, steps):
if scheduler_name == "karras":
sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=float(model.model_sampling.sigma_min), sigma_max=float(model.model_sampling.sigma_max))
elif scheduler_name == "exponential":
sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=float(model.model_sampling.sigma_min), sigma_max=float(model.model_sampling.sigma_max))
elif scheduler_name == "normal":
sigmas = normal_scheduler(model, steps)
elif scheduler_name == "simple":
sigmas = simple_scheduler(model, steps)
elif scheduler_name == "ddim_uniform":
sigmas = ddim_scheduler(model, steps)
elif scheduler_name == "sgm_uniform":
sigmas = normal_scheduler(model, steps, sgm=True)
elif scheduler_name == "turbo":
sigmas = SDTurboScheduler().get_sigmas(namedtuple('Patcher', ['model'])(model=model), steps)[0]
else:
raise TypeError("error invalid scheduler")
return sigmas
ldm_patched.modules.samplers.calculate_sigmas_scheduler = calculate_sigmas_scheduler_hacked
ldm_patched.modules.samplers.sample = sample_hacked