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lllyasviel
2023-10-18 23:54:40 -07:00
parent 9660daff94
commit 906dfa1331
5 changed files with 28 additions and 22 deletions
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modules/default_pipeline.py
+10 -14
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@@ -335,8 +335,6 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
# Disable refiner to avoid SD15 in joint swap
final_refiner_unet = None
print(f'[Sampler] refiner_swap_method = {refiner_swap_method}')
if latent is None:
empty_latent = core.generate_empty_latent(width=width, height=height, batch_size=1)
else:
@@ -353,6 +351,12 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
sigma_min, sigma_max, seed=image_seed, cpu=False)
decoded_latent = None
refiner_use_different_vae = final_refiner_vae is not None and final_refiner_unet is not None
if refiner_swap_method == 'upscale' and not refiner_use_different_vae:
refiner_swap_method = 'joint'
print(f'[Sampler] refiner_swap_method = {refiner_swap_method}')
if refiner_swap_method == 'joint':
sampled_latent = core.ksampler(
@@ -375,14 +379,10 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
decoded_latent = core.decode_vae(vae=final_vae, latent_image=sampled_latent, tiled=tiled)
if refiner_swap_method == 'upscale':
target_model = final_refiner_unet
if target_model is None:
target_model = final_unet
sampled_latent = core.ksampler(
model=target_model,
positive=clip_separate(positive_cond, target_model=target_model.model, target_clip=final_clip),
negative=clip_separate(negative_cond, target_model=target_model.model, target_clip=final_clip),
model=final_refiner_unet,
positive=clip_separate(positive_cond, target_model=final_refiner_unet.model, target_clip=final_clip),
negative=clip_separate(negative_cond, target_model=final_refiner_unet.model, target_clip=final_clip),
latent=empty_latent,
steps=steps, start_step=0, last_step=steps, disable_noise=False, force_full_denoise=True,
seed=image_seed,
@@ -394,11 +394,7 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
previewer_start=0,
previewer_end=steps,
)
target_model = final_refiner_vae
if target_model is None:
target_model = final_vae
decoded_latent = core.decode_vae(vae=target_model, latent_image=sampled_latent, tiled=tiled)
decoded_latent = core.decode_vae(vae=final_refiner_vae, latent_image=sampled_latent, tiled=tiled)
if refiner_swap_method == 'separate':
sampled_latent = core.ksampler(