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feat: allow to use both pixel upscale and hires generation steps from latent

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Dijkstra
2024-03-15 12:01:20 +01:00
parent f6d01dc544
commit 067417c14c
2 changed files with 70 additions and 1 deletions
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26
efficiency_nodes.py
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@@ -607,6 +607,19 @@ class TSC_KSampler:
store_ksampler_results("image", my_unique_id, images)
images = ImageUpscaleWithModel().upscale(pixel_upscale_model, images)[0]
images = ImageScaleBy().upscale(images, "nearest-exact", upscale_by/4)[0]
elif upscale_type == "both":
for _ in range(iterations):
if images is None:
images = vae_decode_latent(vae, samples, vae_decode)
store_ksampler_results("image", my_unique_id, images)
images = ImageUpscaleWithModel().upscale(pixel_upscale_model, images)[0]
images = ImageScaleBy().upscale(images, "nearest-exact", upscale_by/4)[0]
samples = vae_encode_image(vae, images, vae_decode)
upscaled_latent_image = latent_upscale_function().upscale(samples, latent_upscaler, 1)[0]
samples = KSampler().sample(latent_upscale_model, hires_seed, hires_steps, cfg, sampler_name, scheduler,
positive, negative, upscaled_latent_image, denoise=hires_denoise)[0]
images = None # set to None when samples is updated
# ------------------------------------------------------------------------------------------------------
# Check if "tile" exists in the script after main sampling has taken place
@@ -3999,7 +4012,7 @@ class TSC_HighRes_Fix:
@classmethod
def INPUT_TYPES(cls):
return {"required": {"upscale_type": (["latent","pixel"],),
return {"required": {"upscale_type": (["latent","pixel","both"],),
"hires_ckpt_name": (["(use same)"] + folder_paths.get_filename_list("checkpoints"),),
"latent_upscaler": (cls.latent_upscalers,),
"pixel_upscaler": (cls.pixel_upscalers,),
@@ -4099,6 +4112,17 @@ class TSC_HighRes_Fix:
elif upscale_type == "pixel":
pixel_upscale_model = UpscaleModelLoader().load_model(pixel_upscaler)[0]
elif upscale_type == "both":
latent_upscale_function = LatentUpscaleBy
latent_upscaler = self.default_latent_upscalers[0]
pixel_upscale_model = UpscaleModelLoader().load_model(pixel_upscaler)[0]
if hires_ckpt_name == "(use same)":
clear_cache(my_unique_id, 0, "ckpt")
else:
latent_upscale_model, _, _ = \
load_checkpoint(hires_ckpt_name, my_unique_id, output_vae=False, cache=1, cache_overwrite=True)
control_net = ControlNetLoader().load_controlnet(control_net_name)[0] if use_controlnet is True else None
# Construct the script output