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Add intelligent scaling algorithm selection for Qwen image edit node

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- Implement automatic algorithm selection: area for downscaling, lanczos for upscaling
- Improve image quality by choosing optimal scaling method based on target size
- Add Chinese comments for better code documentation
- Ensure 8-pixel alignment for better compatibility with diffusion models
This commit is contained in:
AIGCZero 2025-09-15 13:53:09 +08:00
parent c8bfec86b3
commit cc1c5da4f6
1 changed files with 5 additions and 1 deletions
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6
comfy_extras/nodes_qwen.py
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@ -29,16 +29,19 @@ class TextEncodeQwenImageEdit:
total = int(1024 * 1024) total = int(1024 * 1024)
scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2])) scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2]))
# 修改缩放规则：乘数为8，向下取整
width = math.floor(samples.shape[3] * scale_by / 8) * 8 width = math.floor(samples.shape[3] * scale_by / 8) * 8
height = math.floor(samples.shape[2] * scale_by / 8) * 8 height = math.floor(samples.shape[2] * scale_by / 8) * 8
# 根据缩放比例选择算法：缩小用area，放大用lanczos
original_width = samples.shape[3] original_width = samples.shape[3]
original_height = samples.shape[2] original_height = samples.shape[2]
if width < original_width or height < original_height: if width < original_width or height < original_height:
# 缩小图像，使用area算法保持细节
upscale_method = "area" upscale_method = "area"
else: else:
# 放大图像，使用lanczos算法获得更好质量
upscale_method = "lanczos" upscale_method = "lanczos"
s = comfy.utils.common_upscale(samples, width, height, upscale_method, "disabled") s = comfy.utils.common_upscale(samples, width, height, upscale_method, "disabled")
@ -54,6 +57,7 @@ class TextEncodeQwenImageEdit:
if ref_latent is not None: if ref_latent is not None:
conditioning = node_helpers.conditioning_set_values(conditioning, {"reference_latents": [ref_latent]}, append=True) conditioning = node_helpers.conditioning_set_values(conditioning, {"reference_latents": [ref_latent]}, append=True)
# 将ref_latent包装成ComfyUI LATENT类型要求的格式
latent_output = {"samples": ref_latent} if ref_latent is not None else None latent_output = {"samples": ref_latent} if ref_latent is not None else None
return (conditioning, output_image, latent_output) return (conditioning, output_image, latent_output)