ComfyUI/comfy_extras/nodes_qwen.py
2025-09-27 12:25:35 -07:00

118 lines
4.7 KiB
Python

import node_helpers
import comfy.utils
import math
from typing_extensions import override
from comfy_api.latest import ComfyExtension, io
class TextEncodeQwenImageEdit(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="TextEncodeQwenImageEdit",
category="advanced/conditioning",
inputs=[
io.Clip.Input("clip"),
io.String.Input("prompt", multiline=True, dynamic_prompts=True),
io.Vae.Input("vae", optional=True),
io.Image.Input("image", optional=True),
],
outputs=[
io.Conditioning.Output(),
],
)
@classmethod
def execute(cls, clip, prompt, vae=None, image=None) -> io.NodeOutput:
ref_latent = None
if image is None:
images = []
else:
samples = image.movedim(-1, 1)
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")
image = s.movedim(1, -1)
images = [image[:, :, :, :3]]
if vae is not None:
ref_latent = vae.encode(image[:, :, :, :3])
tokens = clip.tokenize(prompt, images=images)
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 io.NodeOutput(conditioning)
class TextEncodeQwenImageEditPlus(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="TextEncodeQwenImageEditPlus",
category="advanced/conditioning",
inputs=[
io.Clip.Input("clip"),
io.String.Input("prompt", multiline=True, dynamic_prompts=True),
io.Vae.Input("vae", optional=True),
io.Image.Input("image1", optional=True),
io.Image.Input("image2", optional=True),
io.Image.Input("image3", optional=True),
],
outputs=[
io.Conditioning.Output(),
],
)
@classmethod
def execute(cls, clip, prompt, vae=None, image1=None, image2=None, image3=None) -> io.NodeOutput:
ref_latents = []
images = [image1, image2, image3]
images_vl = []
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"
image_prompt = ""
for i, image in enumerate(images):
if image is not None:
samples = image.movedim(-1, 1)
total = int(384 * 384)
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")
images_vl.append(s.movedim(1, -1))
if vae is not None:
total = int(1024 * 1024)
scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2]))
width = round(samples.shape[3] * scale_by / 8.0) * 8
height = round(samples.shape[2] * scale_by / 8.0) * 8
s = comfy.utils.common_upscale(samples, width, height, "area", "disabled")
ref_latents.append(vae.encode(s.movedim(1, -1)[:, :, :, :3]))
image_prompt += "Picture {}: <|vision_start|><|image_pad|><|vision_end|>".format(i + 1)
tokens = clip.tokenize(image_prompt + prompt, images=images_vl, llama_template=llama_template)
conditioning = clip.encode_from_tokens_scheduled(tokens)
if len(ref_latents) > 0:
conditioning = node_helpers.conditioning_set_values(conditioning, {"reference_latents": ref_latents}, append=True)
return io.NodeOutput(conditioning)
class QwenExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[io.ComfyNode]]:
return [
TextEncodeQwenImageEdit,
TextEncodeQwenImageEditPlus,
]
async def comfy_entrypoint() -> QwenExtension:
return QwenExtension()