mirror of
https://git.datalinker.icu/comfyanonymous/ComfyUI
synced 2025-12-08 21:44:33 +08:00
118 lines
4.7 KiB
Python
118 lines
4.7 KiB
Python
import node_helpers
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import comfy.utils
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import math
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from typing_extensions import override
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from comfy_api.latest import ComfyExtension, io
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class TextEncodeQwenImageEdit(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="TextEncodeQwenImageEdit",
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category="advanced/conditioning",
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inputs=[
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io.Clip.Input("clip"),
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io.String.Input("prompt", multiline=True, dynamic_prompts=True),
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io.Vae.Input("vae", optional=True),
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io.Image.Input("image", optional=True),
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],
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outputs=[
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io.Conditioning.Output(),
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],
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)
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@classmethod
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def execute(cls, clip, prompt, vae=None, image=None) -> io.NodeOutput:
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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 io.NodeOutput(conditioning)
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class TextEncodeQwenImageEditPlus(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="TextEncodeQwenImageEditPlus",
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category="advanced/conditioning",
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inputs=[
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io.Clip.Input("clip"),
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io.String.Input("prompt", multiline=True, dynamic_prompts=True),
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io.Vae.Input("vae", optional=True),
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io.Image.Input("image1", optional=True),
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io.Image.Input("image2", optional=True),
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io.Image.Input("image3", optional=True),
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],
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outputs=[
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io.Conditioning.Output(),
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],
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)
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@classmethod
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def execute(cls, clip, prompt, vae=None, image1=None, image2=None, image3=None) -> io.NodeOutput:
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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 io.NodeOutput(conditioning)
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class QwenExtension(ComfyExtension):
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@override
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async def get_node_list(self) -> list[type[io.ComfyNode]]:
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return [
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TextEncodeQwenImageEdit,
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TextEncodeQwenImageEditPlus,
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]
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async def comfy_entrypoint() -> QwenExtension:
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return QwenExtension()
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