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Add TextEncodeQwenImageEdit node with intelligent scaling

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- Implements Qwen image editing functionality with CLIP text encoding
- Features intelligent scaling algorithm selection:
  - Uses 'area' method for downscaling to preserve details
  - Uses 'lanczos' method for upscaling for better quality
- Supports optional VAE encoding for reference latents
- Maintains aspect ratio with 'disabled' crop method
- Scales images to target resolution (1024x1024 pixels) with 8-pixel alignment
This commit is contained in:
AIGCZero 2025-09-15 13:50:49 +08:00
parent 4449e14769
commit c8bfec86b3
1 changed files with 22 additions and 6 deletions
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28
comfy_extras/nodes_qwen.py
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@ -1,4 +1,4 @@
import node_helpers
import node_helpers
import comfy.utils
import math
@ -13,13 +13,15 @@ class TextEncodeQwenImageEdit:
"optional": {"vae": ("VAE", ),
"image": ("IMAGE", ),}}
RETURN_TYPES = ("CONDITIONING",)
RETURN_TYPES = ("CONDITIONING", "IMAGE", "LATENT")
FUNCTION = "encode"
CATEGORY = "advanced/conditioning"
def encode(self, clip, prompt, vae=None, image=None):
ref_latent = None
output_image = None
if image is None:
images = []
else:
@ -27,12 +29,23 @@ class TextEncodeQwenImageEdit:
total = int(1024 * 1024)
scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2]))
width = round(samples.shape[3] * scale_by)
height = round(samples.shape[2] * scale_by)
s = comfy.utils.common_upscale(samples, width, height, "area", "disabled")
width = math.floor(samples.shape[3] * scale_by / 8) * 8
height = math.floor(samples.shape[2] * scale_by / 8) * 8
original_width = samples.shape[3]
original_height = samples.shape[2]
if width < original_width or height < original_height:
upscale_method = "area"
else:
upscale_method = "lanczos"
s = comfy.utils.common_upscale(samples, width, height, upscale_method, "disabled")
image = s.movedim(1, -1)
images = [image[:, :, :, :3]]
output_image = image[:, :, :, :3]
if vae is not None:
ref_latent = vae.encode(image[:, :, :, :3])
@ -40,7 +53,10 @@ class TextEncodeQwenImageEdit:
conditioning = clip.encode_from_tokens_scheduled(tokens)
if ref_latent is not None:
conditioning = node_helpers.conditioning_set_values(conditioning, {"reference_latents": [ref_latent]}, append=True)
return (conditioning, )
latent_output = {"samples": ref_latent} if ref_latent is not None else None
return (conditioning, output_image, latent_output)
NODE_CLASS_MAPPINGS = {