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Fooocus
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This commit is contained in:
lllyasviel 2023-10-19 00:17:17 -07:00
parent 906dfa1331
commit 296b1da278
2 changed files with 17 additions and 17 deletions
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2
fooocus_version.py
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@ -1 +1 @@
version = '2.1.704' version = '2.1.705'

32
modules/default_pipeline.py
View File

@ -320,20 +320,26 @@ def calculate_sigmas(sampler, model, scheduler, steps, denoise):
@torch.no_grad() @torch.no_grad()
@torch.inference_mode() @torch.inference_mode()
def process_diffusion(positive_cond, negative_cond, steps, switch, width, height, image_seed, callback, sampler_name, scheduler_name, latent=None, denoise=1.0, tiled=False, cfg_scale=7.0, refiner_swap_method='joint'): def process_diffusion(positive_cond, negative_cond, steps, switch, width, height, image_seed, callback, sampler_name, scheduler_name, latent=None, denoise=1.0, tiled=False, cfg_scale=7.0, refiner_swap_method='joint'):
global final_unet, final_refiner_unet global final_unet, final_refiner_unet, final_vae, final_refiner_vae
assert refiner_swap_method in ['joint', 'separate', 'vae', 'upscale'] assert refiner_swap_method in ['joint', 'separate', 'vae', 'upscale']
if final_refiner_unet is not None: refiner_use_different_vae = final_refiner_vae is not None and final_refiner_unet is not None
if isinstance(final_refiner_unet.model.latent_format, fcbh.latent_formats.SD15) \
and refiner_swap_method != 'upscale':
refiner_swap_method = 'vae'
if refiner_swap_method == 'vae' and denoise < 0.95: if refiner_swap_method == 'upscale':
# VAE swap only support full denoise if not refiner_use_different_vae:
refiner_swap_method = 'joint' refiner_swap_method = 'joint'
# Disable refiner to avoid SD15 in joint swap else:
final_refiner_unet = None if refiner_use_different_vae:
if denoise > 0.95:
refiner_swap_method = 'vae'
else:
# VAE swap only support full denoise
# Disable refiner to avoid SD15 in joint/separate swap
final_refiner_unet = None
final_refiner_vae = None
print(f'[Sampler] refiner_swap_method = {refiner_swap_method}')
if latent is None: if latent is None:
empty_latent = core.generate_empty_latent(width=width, height=height, batch_size=1) empty_latent = core.generate_empty_latent(width=width, height=height, batch_size=1)
@ -351,12 +357,6 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
sigma_min, sigma_max, seed=image_seed, cpu=False) sigma_min, sigma_max, seed=image_seed, cpu=False)
decoded_latent = None 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': if refiner_swap_method == 'joint':
sampled_latent = core.ksampler( sampled_latent = core.ksampler(