mirror of
https://git.datalinker.icu/comfyanonymous/ComfyUI
synced 2025-12-09 22:14:34 +08:00
104 lines
4.2 KiB
Python
104 lines
4.2 KiB
Python
import node_helpers
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import comfy.utils
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import math
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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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samples = image.movedim(-1, 1)
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total = int(1024 * 1024)
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scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2]))
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width = round(samples.shape[3] * scale_by)
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height = round(samples.shape[2] * scale_by)
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s = comfy.utils.common_upscale(samples, width, height, "area", "disabled")
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image = s.movedim(1, -1)
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images = [image[:, :, :, :3]]
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if vae is not None:
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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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class TextEncodeQwenImageEditPlus:
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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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"image1": ("IMAGE", ),
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"image2": ("IMAGE", ),
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"image3": ("IMAGE", ),
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}}
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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, image1=None, image2=None, image3=None):
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ref_latents = []
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images = [image1, image2, image3]
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images_vl = []
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llama_template = "<|im_start|>system\nDescribe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
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image_prompt = ""
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for i, image in enumerate(images):
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if image is not None:
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samples = image.movedim(-1, 1)
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total = int(384 * 384)
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scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2]))
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width = round(samples.shape[3] * scale_by)
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height = round(samples.shape[2] * scale_by)
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s = comfy.utils.common_upscale(samples, width, height, "area", "disabled")
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images_vl.append(s.movedim(1, -1))
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if vae is not None:
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total = int(1024 * 1024)
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scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2]))
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width = round(samples.shape[3] * scale_by / 8.0) * 8
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height = round(samples.shape[2] * scale_by / 8.0) * 8
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s = comfy.utils.common_upscale(samples, width, height, "area", "disabled")
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ref_latents.append(vae.encode(s.movedim(1, -1)[:, :, :, :3]))
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image_prompt += "Picture {}: <|vision_start|><|image_pad|><|vision_end|>".format(i + 1)
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tokens = clip.tokenize(image_prompt + prompt, images=images_vl, llama_template=llama_template)
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conditioning = clip.encode_from_tokens_scheduled(tokens)
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if len(ref_latents) > 0:
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conditioning = node_helpers.conditioning_set_values(conditioning, {"reference_latents": ref_latents}, 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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"TextEncodeQwenImageEditPlus": TextEncodeQwenImageEditPlus,
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}
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