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improve: IterativeLatentUpscale - use geometric progression for upscale steps (#523)

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* improve: IterativeLatentUpscale - use geometric progression for upscale steps

This is a small change to IterativeLatentUpscale that makes the sequence of the upscale steps to use geometric progression rather than arithmetic. This has the advantage that every step scales the intermediate image by the same factor.

* feat: Iterative Upscale - support `step_mode`

---------

Co-authored-by: Dr.Lt.Data <dr.lt.data@gmail.com>
Co-authored-by: Dr.Lt.Data <128333288+ltdrdata@users.noreply.github.com>
This commit is contained in:
combolek
2024-03-19 23:10:10 +09:00
committed by GitHub
co-authored by Dr.Lt.Data Dr.Lt.Data
parent 6b50c18336
commit 6f7d55cc86
2 changed files with 21 additions and 11 deletions
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modules/impact/config.py
+1 -1
View File
@@ -2,7 +2,7 @@ import configparser
import os
version_code = [4, 83, 6]
version_code = [4, 84]
version = f"V{version_code[0]}.{version_code[1]}" + (f'.{version_code[2]}' if len(version_code) > 2 else '')
dependency_version = 20
modules/impact/impact_pack.py
+20 -10
View File
@@ -1094,10 +1094,11 @@ class IterativeLatentUpscale:
"upscale_factor": ("FLOAT", {"default": 1.5, "min": 1, "max": 10000, "step": 0.1}),
"steps": ("INT", {"default": 3, "min": 1, "max": 10000, "step": 1}),
"temp_prefix": ("STRING", {"default": ""}),
"upscaler": ("UPSCALER",)
},
"upscaler": ("UPSCALER",),
"step_mode": (["simple", "geometric"], {"default": "simple"})
},
"hidden": {"unique_id": "UNIQUE_ID"},
}
}
RETURN_TYPES = ("LATENT", "VAE")
RETURN_NAMES = ("latent", "vae")
@@ -1105,19 +1106,27 @@ class IterativeLatentUpscale:
CATEGORY = "ImpactPack/Upscale"
def doit(self, samples, upscale_factor, steps, temp_prefix, upscaler, unique_id):
def doit(self, samples, upscale_factor, steps, temp_prefix, upscaler, step_mode="simple", unique_id=None):
w = samples['samples'].shape[3]*8 # image width
h = samples['samples'].shape[2]*8 # image height
if temp_prefix == "":
temp_prefix = None
upscale_factor_unit = max(0, (upscale_factor-1.0)/steps)
if step_mode == "geometric":
upscale_factor_unit = pow(upscale_factor, 1.0/steps)
else: # simple
upscale_factor_unit = max(0, (upscale_factor - 1.0) / steps)
current_latent = samples
scale = 1
for i in range(steps-1):
scale += upscale_factor_unit
if step_mode == "geometric":
scale *= upscale_factor_unit
else: # simple
scale += upscale_factor_unit
new_w = w*scale
new_h = h*scale
core.update_node_status(unique_id, f"{i+1}/{steps} steps | x{scale:.2f}", (i+1)/steps)
@@ -1148,9 +1157,10 @@ class IterativeImageUpscale:
"temp_prefix": ("STRING", {"default": ""}),
"upscaler": ("UPSCALER",),
"vae": ("VAE",),
},
"step_mode": (["simple", "geometric"], {"default": "simple"})
},
"hidden": {"unique_id": "UNIQUE_ID"}
}
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("image",)
@@ -1158,7 +1168,7 @@ class IterativeImageUpscale:
CATEGORY = "ImpactPack/Upscale"
def doit(self, pixels, upscale_factor, steps, temp_prefix, upscaler, vae, unique_id):
def doit(self, pixels, upscale_factor, steps, temp_prefix, upscaler, vae, step_mode="simple", unique_id=None):
if temp_prefix == "":
temp_prefix = None
@@ -1168,7 +1178,7 @@ class IterativeImageUpscale:
else:
latent = nodes.VAEEncode().encode(vae, pixels)[0]
refined_latent = IterativeLatentUpscale().doit(latent, upscale_factor, steps, temp_prefix, upscaler, unique_id)
refined_latent = IterativeLatentUpscale().doit(latent, upscale_factor, steps, temp_prefix, upscaler, step_mode, unique_id)
core.update_node_status(unique_id, "VAEDecode (final)", 1.0)
if upscaler.is_tiled: