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rework refiner for some potential new features (#642)

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This commit is contained in:
lllyasviel
2023-10-11 03:07:43 -07:00
committed by GitHub
parent 777510dc9b
commit bbdf4bd120
10 changed files with 209 additions and 93 deletions
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modules/sample_hijack.py
+18 -7
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@@ -2,7 +2,8 @@ import torch
import comfy.samplers
import comfy.model_management
from comfy.sample import prepare_sampling, cleanup_additional_models, get_additional_models
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
@@ -14,11 +15,18 @@ refiner_switch_step = -1
@torch.no_grad()
@torch.inference_mode()
def clip_separate(cond):
def clip_separate(cond, target_model=None):
c, p = cond[0]
c = c[..., -1280:].clone()
p = p["pooled_output"].clone()
return [[c, {"pooled_output": p}]]
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()
@@ -54,8 +62,11 @@ def sample_hacked(model, noise, positive, negative, cfg, device, sampler, sigmas
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 = encode_adm(current_refiner.model, clip_separate(positive), noise.shape[0], noise.shape[3], noise.shape[2], device, "positive")
negative_refiner = encode_adm(current_refiner.model, clip_separate(negative), noise.shape[0], noise.shape[3], noise.shape[2], device, "negative")
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'])