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lllyasviel
2023-10-19 06:32:38 -07:00
parent 6f02c797c6
commit f553d0fb18
6 changed files with 93 additions and 72 deletions
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modules/sample_hijack.py
+10 -22
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@@ -6,7 +6,7 @@ from fcbh.model_base import SDXLRefiner, SDXL
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, \
create_cond_with_same_area_if_none, pre_run_control, apply_empty_x_to_equal_area, encode_adm, \
blank_inpaint_image_like
encode_cond
current_refiner = None
@@ -77,10 +77,19 @@ def sample_hacked(model, noise, positive, negative, cfg, device, sampler, sigmas
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 latent_image is not None:
latent_image = model.process_latent_in(latent_image)
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 hasattr(model, 'cond_concat'):
positive = encode_cond(model.cond_concat, "concat", positive, device, noise=noise, latent_image=latent_image, denoise_mask=denoise_mask)
negative = encode_cond(model.cond_concat, "concat", negative, device, noise=noise, latent_image=latent_image, denoise_mask=denoise_mask)
extra_args = {"cond":positive, "uncond":negative, "cond_scale": cfg, "model_options": model_options, "seed":seed}
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)
@@ -91,27 +100,6 @@ def sample_hacked(model, noise, positive, negative, cfg, device, sampler, sigmas
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():
cleanup_additional_models(set(get_models_from_cond(positive, "control") + get_models_from_cond(negative, "control")))