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Efficient Loader:
- Added Baked VAE as an option on VAE select.
- Node will no longer reload unnecessarily on changes.

Efficient KSampler:
- Added a 'Preview Image' toggle on the Efficient Ksampler.
- Fixed mistakes in console messaging text.
- Fixed bugs and errors associated with the 'Hold' state.
- Added messages to the console for when a sampler output is being held.
- Refactored node code.
Image Overlay:
- Fixed rotation of overlay cropping the overlay image.
- Fixed an issue with the opacity slider not applying transparency correctly.
This commit is contained in:
TSC
2023-04-07 22:16:34 -05:00
committed by GitHub
parent de81cead21
commit 8b7800ad5b
1 changed files with 52 additions and 27 deletions
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efficiency_nodes.py
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@@ -129,9 +129,9 @@ class TSC_EfficientLoader:
# TSC KSampler (Efficient)
last_helds = {
"results": [[] for _ in range(15)],
"latent": [[] for _ in range(15)],
"images": [[] for _ in range(15)]
"results": [None for _ in range(15)],
"latent": [None for _ in range(15)],
"images": [None for _ in range(15)]
}
class TSC_KSampler:
@@ -169,29 +169,51 @@ class TSC_KSampler:
def sample(self, sampler_state, my_unique_id, model, seed, steps, cfg, sampler_name, scheduler, positive, negative,
latent_image, preview_image, denoise=1.0, prompt=None, extra_pnginfo=None, optional_vae=(None,)):
global last_helds
last_results = last_helds["results"][my_unique_id]
last_latent = last_helds["latent"][my_unique_id]
last_images = last_helds["images"][my_unique_id]
vae = optional_vae
empty_image = pil2tensor(Image.new('RGBA', (1, 1), (0, 0, 0, 0)))
vae = optional_vae
# Preview check
preview = True
if vae == (None,) or preview_image == "Disabled":
preview = False
last_helds["results"][my_unique_id] = None
last_helds["images"][my_unique_id] = None
if vae == (None,):
print('\033[32mKSampler(Efficient)[{}]:\033[0m No vae input detected, preview image disabled'.format(my_unique_id))
# Init last_results
if last_helds["results"][my_unique_id] == None:
last_results = list()
else:
last_results = last_helds["results"][my_unique_id]
# Init last_latent
if last_helds["latent"][my_unique_id] == None:
last_latent = latent_image
else:
last_latent = {"samples": None}
last_latent["samples"] = last_helds["latent"][my_unique_id]
# Init last_images
if last_helds["images"][my_unique_id] == None:
last_images = empty_image
else:
last_images = last_helds["images"][my_unique_id]
if sampler_state == "Sample":
samples = common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise)
latent = samples[0]["samples"]
last_helds["latent"][my_unique_id] = latent
if preview == False:
return {"ui": {"images": list()}, "result": (model, positive, negative, {"samples": latent}, vae, empty_image,)}
# Adjust for KSampler states
if sampler_state == "Hold":
if last_results is not None or last_latent is not None:
return {"ui": {"images": last_results}, "result": (model, positive, negative, {"samples": last_latent}, vae, last_images, )}
elif sampler_state == "Hold":
print('\033[32mKSampler(Efficient)[{}] outputs on hold\033[0m'.format(my_unique_id))
if preview == False:
return {"ui": {"images": last_results}, "result": (model, positive, negative, last_latent, vae, last_images,)}
else:
return (model, positive, negative, latent_image, vae, empty_image, )
samples = common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise)
latent = samples[0]["samples"]
last_helds["latent"][my_unique_id] = latent
if vae == (None,) or preview_image == "Disabled":
last_results = None
print('\033[38;2;62;116;77mTSC_Nodes:\033[0m TSC_KSampler({}): no vae input detected, preview disabled'.format(my_unique_id))
return {"ui": {"images": list()}, "result": (model, positive, negative, {"samples": latent}, vae, empty_image, )}
latent = last_latent["samples"]
images = vae.decode(latent).cpu()
last_helds["images"][my_unique_id] = images
@@ -251,8 +273,11 @@ class TSC_KSampler:
counter += 1
last_helds["results"][my_unique_id] = results
#if sampler_state == "Sample":
# Output results to ui and node outputs
return {"ui": {"images": results}, "result": (model, positive, negative, {"samples":latent}, vae, images, )}
#if sampler_state == "Hold":
# return {"ui": {"images": last_results}, "result": (model, positive, negative, last_latent, vae, last_images,)}
# TSC Image Overlay
@@ -266,13 +291,13 @@ class TSC_ImageOverlay:
"overlay_image": ("IMAGE",),
"overlay_resize": (["None", "Fit", "Resize by rescale_factor", "Resize to width & heigth"],),
"resize_method": (["nearest-exact", "bilinear", "area"],),
"rescale_factor": ("FLOAT", {"default": 1, "min": 0.01, "max": 16.0, "step": 0.01}),
"rescale_factor": ("FLOAT", {"default": 1, "min": 0.01, "max": 16.0, "step": 0.1}),
"width": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 64}),
"height": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 64}),
"x_offset": ("INT", {"default": 0, "min": -48000, "max": 48000, "step": 1}),
"y_offset": ("INT", {"default": 0, "min": -48000, "max": 48000, "step": 1}),
"rotation": ("INT", {"default": 0, "min": -180, "max": 180, "step": 1}),
"opacity": ("FLOAT", {"default": 0, "min": 0, "max": 100, "step": .5}),
"x_offset": ("INT", {"default": 0, "min": -48000, "max": 48000, "step": 10}),
"y_offset": ("INT", {"default": 0, "min": -48000, "max": 48000, "step": 10}),
"rotation": ("INT", {"default": 0, "min": -180, "max": 180, "step": 5}),
"opacity": ("FLOAT", {"default": 0, "min": 0, "max": 100, "step": 5}),
},
"optional": {"optional_mask": ("MASK",),}
}