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4977f203fa
* P2 of qwen edit model. * Typo. * Fix normal qwen. * Fix. * Make the TextEncodeQwenImageEdit also set the ref latent. If you don't want it to set the ref latent and want to use the ReferenceLatent node with your custom latent instead just disconnect the VAE.
64 lines
1.8 KiB
Python
64 lines
1.8 KiB
Python
import node_helpers
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import comfy.utils
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PREFERRED_QWENIMAGE_RESOLUTIONS = [
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(672, 1568),
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(688, 1504),
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(720, 1456),
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(752, 1392),
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(800, 1328),
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(832, 1248),
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(880, 1184),
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(944, 1104),
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(1024, 1024),
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(1104, 944),
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(1184, 880),
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(1248, 832),
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(1328, 800),
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(1392, 752),
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(1456, 720),
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(1504, 688),
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(1568, 672),
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]
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class TextEncodeQwenImageEdit:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"clip": ("CLIP", ),
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"prompt": ("STRING", {"multiline": True, "dynamicPrompts": True}),
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},
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"optional": {"vae": ("VAE", ),
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"image": ("IMAGE", ),}}
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RETURN_TYPES = ("CONDITIONING",)
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FUNCTION = "encode"
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CATEGORY = "advanced/conditioning"
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def encode(self, clip, prompt, vae=None, image=None):
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ref_latent = None
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if image is None:
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images = []
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else:
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images = [image]
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if vae is not None:
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width = image.shape[2]
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height = image.shape[1]
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aspect_ratio = width / height
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_, width, height = min((abs(aspect_ratio - w / h), w, h) for w, h in PREFERRED_QWENIMAGE_RESOLUTIONS)
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image = comfy.utils.common_upscale(image.movedim(-1, 1), width, height, "lanczos", "center").movedim(1, -1)
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ref_latent = vae.encode(image[:, :, :, :3])
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tokens = clip.tokenize(prompt, images=images)
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conditioning = clip.encode_from_tokens_scheduled(tokens)
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if ref_latent is not None:
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conditioning = node_helpers.conditioning_set_values(conditioning, {"reference_latents": [ref_latent]}, append=True)
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return (conditioning, )
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NODE_CLASS_MAPPINGS = {
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"TextEncodeQwenImageEdit": TextEncodeQwenImageEdit,
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}
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