81 lines
3.2 KiB
Python
81 lines
3.2 KiB
Python
"""
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Texture Alchemy- Qwen Image Edit Plus (Batch) – same behaviour as the built-in
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Text Encode Qwen Image Edit Plus but accepts a single IMAGE batch and chains
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each image in the batch.
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"""
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import math
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import node_helpers
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import comfy.utils
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class TextEncodeQwenImageEditPlusBatchNode:
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"""
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Same as Text Encode Qwen Image Edit Plus but takes one IMAGE batch and
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chains every image in the batch (Picture 1, Picture 2, ...) into one
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conditioning.
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"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"clip": ("CLIP",),
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"prompt": ("STRING", {"default": "", "multiline": True}),
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},
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"optional": {
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"vae": ("VAE", {"default": None}),
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"images": ("IMAGE", {"default": None}),
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},
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}
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RETURN_TYPES = ("CONDITIONING",)
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RETURN_NAMES = ("conditioning",)
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FUNCTION = "encode_batch"
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CATEGORY = "Conditioning"
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def encode_batch(self, clip, prompt, vae=None, images=None):
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ref_latents = []
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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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if images is not None:
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batch_size = images.shape[0]
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for i in range(batch_size):
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image = images[i : i + 1]
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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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"TextEncodeQwenImageEditPlusBatchNode": TextEncodeQwenImageEditPlusBatchNode,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"TextEncodeQwenImageEditPlusBatchNode": "Text Encode Qwen Image Edit Plus (Batch)",
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}
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