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aggressive downscaling should not be performed

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This commit is contained in:
bigcat88
2026-01-21 18:12:07 +02:00
parent 83d4ce576b
commit c1675825b4
1 changed files with 62 additions and 16 deletions
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78
comfy_api_nodes/nodes_magnific.py
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@@ -1,3 +1,5 @@
import math
from typing_extensions import override
from comfy_api.latest import IO, ComfyExtension, Input
@@ -36,7 +38,8 @@ class MagnificImageUpscalerCreativeNode(IO.ComfyNode):
node_id="MagnificImageUpscalerCreativeNode",
display_name="Magnific Image Upscale (Creative)",
category="api node/image/Magnific",
description="Prompt‑guided enhancement, stylization, and 2x/4x/8x/16x upscaling.",
description="Prompt‑guided enhancement, stylization, and 2x/4x/8x/16x upscaling. "
"Maximum output: 25.3 megapixels.",
inputs=[
IO.Image.Input("image"),
IO.String.Input("prompt", multiline=True, default=""),
@@ -131,16 +134,36 @@ class MagnificImageUpscalerCreativeNode(IO.ComfyNode):
max_output_pixels = 25_300_000
height, width = get_image_dimensions(image)
scale = int(scale_factor.rstrip("x"))
output_pixels = height * width * scale * scale
requested_scale = int(scale_factor.rstrip("x"))
output_pixels = height * width * requested_scale * requested_scale
if output_pixels > max_output_pixels:
if auto_downscale:
# Calculate max input pixels to fit within output pixel limit
max_input_pixels = max_output_pixels // (scale * scale)
image = downscale_image_tensor(image, total_pixels=max_input_pixels)
# Find optimal scale factor that doesn't require >2x downscale.
# Server upscales in 2x steps, so aggressive downscaling degrades quality.
input_pixels = width * height
scale = 2
max_input_pixels = max_output_pixels // 4
for candidate in [16, 8, 4, 2]:
if candidate > requested_scale:
continue
scale_output_pixels = input_pixels * candidate * candidate
if scale_output_pixels <= max_output_pixels:
scale = candidate
max_input_pixels = None
break
downscale_ratio = math.sqrt(scale_output_pixels / max_output_pixels)
if downscale_ratio <= 2.0:
scale = candidate
max_input_pixels = max_output_pixels // (candidate * candidate)
break
if max_input_pixels is not None:
image = downscale_image_tensor(image, total_pixels=max_input_pixels)
scale_factor = f"{scale}x"
else:
raise ValueError(
f"Output size ({width * scale}x{height * scale} = {output_pixels:,} pixels) "
f"Output size ({width * requested_scale}x{height * requested_scale} = {output_pixels:,} pixels) "
f"exceeds maximum allowed size of {max_output_pixels:,} pixels. "
f"Use a smaller input image or lower scale factor."
)
@@ -179,7 +202,8 @@ class MagnificImageUpscalerPreciseV2Node(IO.ComfyNode):
node_id="MagnificImageUpscalerPreciseV2Node",
display_name="Magnific Image Upscale (Precise V2)",
category="api node/image/Magnific",
description="High-fidelity upscaling with fine control over sharpness, grain, and detail.",
description="High-fidelity upscaling with fine control over sharpness, grain, and detail. "
"Maximum output: 10060×10060 pixels.",
inputs=[
IO.Image.Input("image"),
IO.Combo.Input("scale_factor", options=["2x", "4x", "8x", "16x"]),
@@ -258,16 +282,38 @@ class MagnificImageUpscalerPreciseV2Node(IO.ComfyNode):
max_output_dimension = 10060
height, width = get_image_dimensions(image)
scale = int(scale_factor.strip("x"))
output_width = width * scale
output_height = height * scale
requested_scale = int(scale_factor.strip("x"))
output_width = width * requested_scale
output_height = height * requested_scale
if output_width > max_output_dimension or output_height > max_output_dimension:
if auto_downscale:
# Calculate max input pixels based on the largest dimension
max_input_dim = max_output_dimension // scale
scale_ratio = max_input_dim / max(width, height)
# Find optimal scale factor that doesn't require >2x downscale.
# Server upscales in 2x steps, so aggressive downscaling degrades quality.
max_dim = max(width, height)
scale = 2
max_input_dim = max_output_dimension // 2
scale_ratio = max_input_dim / max_dim
max_input_pixels = int(width * height * scale_ratio * scale_ratio)
image = downscale_image_tensor(image, total_pixels=max_input_pixels)
for candidate in [16, 8, 4, 2]:
if candidate > requested_scale:
continue
output_dim = max_dim * candidate
if output_dim <= max_output_dimension:
scale = candidate
max_input_pixels = None
break
downscale_ratio = output_dim / max_output_dimension
if downscale_ratio <= 2.0:
scale = candidate
max_input_dim = max_output_dimension // candidate
scale_ratio = max_input_dim / max_dim
max_input_pixels = int(width * height * scale_ratio * scale_ratio)
break
if max_input_pixels is not None:
image = downscale_image_tensor(image, total_pixels=max_input_pixels)
requested_scale = scale
else:
raise ValueError(
f"Output dimensions ({output_width}x{output_height}) exceed maximum allowed "
@@ -281,7 +327,7 @@ class MagnificImageUpscalerPreciseV2Node(IO.ComfyNode):
response_model=TaskResponse,
data=ImageUpscalerPrecisionV2Request(
image=(await upload_images_to_comfyapi(cls, image, max_images=1, total_pixels=None))[0],
scale_factor=scale,
scale_factor=requested_scale,
flavor=flavor,
sharpen=sharpen,
smart_grain=smart_grain,