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https://github.com/comfyanonymous/ComfyUI.git
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e96bd48e2d
Upstream merged native Qwen3-VL support (#14298), adding comfy/text_encoders/qwen3vl.py plus helpers in qwen_vl.py / llama.py / qwen35.py. The JoyImage port previously shipped its own duplicate Qwen3-VL implementation (comfy/text_encoders/qwen3_vl.py); that duplication is now removed and the JoyImage text encoder rides on the upstream stack. - Delete comfy/text_encoders/qwen3_vl.py. - Rewrite comfy/text_encoders/joyimage.py to subclass upstream comfy.text_encoders.qwen3vl. The JoyImage checkpoint is a stock qwen3vl_8b, so only JoyImage-specific behavior is overridden: * Qwen3VL8B_JoyImage.forward builds the 3D MRoPE position ids and injects deepstack visual features on the conditioning path. Upstream Qwen3VL only does this inside generate() via build_image_inputs; SDClipModel.forward never passes those kwargs. The JoyImage node feeds an image through the encoder (clip.tokenize(prompt, images=[..])), so the override reuses build_image_inputs to reproduce the multimodal conditioning that Llama2_.forward already accepts kwargs for. * preprocess_embed keeps JoyImage's bicubic+clamp image preprocessing (process_qwen3vl_image) instead of upstream's bilinear path, to preserve validated DiT numerics. * JoyImageTokenizer keeps the JoyImage system-prompt templates, suppresses the Qwen3 <think> block, and raises on image-placeholder count mismatch. * JoyImageTEModel keeps the drop_idx=34 system-prompt strip and the pre-final-norm layer tap (layer="hidden", layer_idx=-1). - sd.py QWEN3VL_8B_JOYIMAGE branch: apply the same state-dict prefix remap the sibling QWEN3VL branch uses (model.language_model.->model., model.visual.->visual., lm_head.->model.lm_head.) so the checkpoint loads into the upstream Qwen3VL namespace, then use the module-level llama_detect. Detection ordering is preserved: the JoyImage discriminator is checked before the generic Qwen3-VL deepstack key. No changes to llama.py / qwen3vl.py / qwen_vl.py / qwen35.py.
273 lines
11 KiB
Python
273 lines
11 KiB
Python
"""JoyImageEdit text encoder: a stock Qwen3-VL-8B multimodal stack feeding the
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JoyImageEdit DiT, built on `comfy.text_encoders.qwen3vl` with the
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JoyImage-specific prompt templates, system-prompt strip, image preprocessing,
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and conditioning-path multimodal handling.
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"""
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import math
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from typing import List, Optional
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import torch
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import torch.nn.functional as F
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from comfy import sd1_clip
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from comfy.text_encoders.qwen3vl import Qwen3VL, Qwen3VLTokenizer
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# Prompt templates for the text-only and image-conditioned modes. The
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# image-conditioned template wraps the user text with a single
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# `<|vision_start|><|image_pad|><|vision_end|>` block; one user turn per call.
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JOYIMAGE_TEMPLATE_TEXT = (
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"<|im_start|>system\n \\nDescribe the image by detailing the color, shape, size, texture, "
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"quantity, text, spatial relationships of the objects and background:<|im_end|>\n"
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"<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
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)
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JOYIMAGE_TEMPLATE_IMAGE = (
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"<|im_start|>system\n \\nDescribe the image by detailing the color, shape, size, texture, "
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"quantity, text, spatial relationships of the objects and background:<|im_end|>\n"
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"<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>{}<|im_end|>\n<|im_start|>assistant\n"
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)
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# Number of leading template tokens (system prompt + the user block's opening
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# `<|im_start|>`) stripped from the encoded output by
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# JoyImageTEModel.encode_token_weights, so the kept sequence begins at the
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# `user` token.
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JOYIMAGE_DROP_IDX = 34
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# Special-token ids (vocab shared with Qwen2.5 / Qwen3, vocab_size 151936).
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IMAGE_PAD_TOKEN = 151655
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PAD_TOKEN = 151643
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# ---------------------------------------------------------------------------
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# Image preprocessing
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# ---------------------------------------------------------------------------
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def process_qwen3vl_image(
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image: torch.Tensor,
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min_pixels: int = 65536,
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max_pixels: int = 16777216,
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patch_size: int = 16,
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temporal_patch_size: int = 2,
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merge_size: int = 2,
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image_mean: Optional[List[float]] = None,
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image_std: Optional[List[float]] = None,
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):
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"""Resize, normalize and patch-flatten a single (B=1, H, W, C) image tensor in [0, 1].
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Returns ``(flatten_patches, grid_thw)`` ready for the Qwen3-VL vision tower.
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Uses bicubic interpolation followed by ``clamp(0, 1)``.
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"""
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if image_mean is None:
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image_mean = [0.5, 0.5, 0.5]
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if image_std is None:
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image_std = [0.5, 0.5, 0.5]
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if image.dim() == 3:
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image = image.unsqueeze(0)
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batch, height, width, channels = image.shape
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if batch != 1:
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raise ValueError("process_qwen3vl_image expects one image (B=1) at a time.")
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device = image.device
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image = image.permute(0, 3, 1, 2) # (1, C, H, W)
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img = image[0]
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factor = patch_size * merge_size
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h_bar = round(height / factor) * factor
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w_bar = round(width / factor) * factor
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if h_bar * w_bar > max_pixels:
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beta = math.sqrt((height * width) / max_pixels)
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h_bar = max(factor, math.floor(height / beta / factor) * factor)
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w_bar = max(factor, math.floor(width / beta / factor) * factor)
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elif h_bar * w_bar < min_pixels:
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beta = math.sqrt(min_pixels / (height * width))
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h_bar = math.ceil(height * beta / factor) * factor
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w_bar = math.ceil(width * beta / factor) * factor
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img_resized = F.interpolate(
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img.unsqueeze(0), size=(h_bar, w_bar), mode="bicubic", align_corners=False,
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).squeeze(0).clamp(0.0, 1.0)
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normalized = img_resized.clone()
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for c in range(3):
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normalized[c] = (img_resized[c] - image_mean[c]) / image_std[c]
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grid_h = h_bar // patch_size
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grid_w = w_bar // patch_size
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grid_thw = torch.tensor([[1, grid_h, grid_w]], device=device, dtype=torch.long)
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# Single-frame inputs are duplicated along T to fill the 2-frame temporal
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# patch kernel; matches Qwen2VLImageProcessorFast for static images.
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pixel_values = normalized.unsqueeze(0).repeat(temporal_patch_size, 1, 1, 1)
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grid_t = 1
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channel = pixel_values.shape[1]
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patches = pixel_values.reshape(
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grid_t, temporal_patch_size, channel,
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grid_h // merge_size, merge_size, patch_size,
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grid_w // merge_size, merge_size, patch_size,
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)
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patches = patches.permute(0, 3, 6, 4, 7, 2, 1, 5, 8)
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flatten_patches = patches.reshape(
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grid_t * grid_h * grid_w,
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channel * temporal_patch_size * patch_size * patch_size,
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)
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return flatten_patches, grid_thw
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class Qwen3VL8B_JoyImage(Qwen3VL):
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"""JoyImage Qwen3-VL-8B encoder.
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Stock `qwen3vl_8b` config (text dims 4096 / 36L / 32H / 8 kv; interleaved
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3D MRoPE rope_dims=[24,20,20], rope_theta=5e6; vision 1152/4304, depth 27,
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patch_size 16, deepstack_visual_indexes=[8,16,24]).
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"""
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model_type = "qwen3vl_8b"
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def preprocess_embed(self, embed, device):
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# Run the vision tower with JoyImage's bicubic+clamp preprocessing and
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# return ``(merged, {"grid", "deepstack"})``.
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if embed["type"] == "image":
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image, grid = process_qwen3vl_image(
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embed["data"], patch_size=16, image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5],
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)
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merged, deepstack = self.visual(image.to(device, dtype=torch.float32), grid)
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return merged, {"grid": grid, "deepstack": deepstack}
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return None, None
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def forward(self, x, attention_mask=None, embeds=None, num_tokens=None,
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intermediate_output=None, final_layer_norm_intermediate=True,
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dtype=None, embeds_info=()):
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# The conditioning path must build the 3D MRoPE position ids for the
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# image-token block and inject the deepstack visual features.
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# `build_image_inputs` returns the kwargs the decoder expects:
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# (position_ids, visual_pos_masks, deepstack).
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if embeds is not None:
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position_ids, visual_pos_masks, deepstack = self.build_image_inputs(embeds, embeds_info)
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else:
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position_ids, visual_pos_masks, deepstack = None, None, None
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return self.model(
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x,
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attention_mask=attention_mask,
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embeds=embeds,
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num_tokens=num_tokens,
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intermediate_output=intermediate_output,
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final_layer_norm_intermediate=final_layer_norm_intermediate,
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dtype=dtype,
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position_ids=position_ids,
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deepstack_embeds=deepstack,
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visual_pos_masks=visual_pos_masks,
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)
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class JoyImageTokenizer(Qwen3VLTokenizer):
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"""JoyImageEdit tokenizer.
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``tokenize_with_weights(text, images=[...])`` selects the image-conditioned
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template when one or more image tensors are passed, otherwise the text-only
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template. Each ``<|image_pad|>`` token in the formatted prompt is replaced
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with an embedding marker so `SDClipModel.process_tokens` routes the image
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through `Qwen3VL8B_JoyImage.preprocess_embed`; ``drop_idx=34`` leading
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template tokens are stripped downstream by
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`JoyImageTEModel.encode_token_weights`. No ``<think>`` block is appended.
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"""
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def __init__(self, embedding_directory=None, tokenizer_data={}):
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super().__init__(
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embedding_directory=embedding_directory, tokenizer_data=tokenizer_data,
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model_type="qwen3vl_8b",
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)
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self.llama_template = JOYIMAGE_TEMPLATE_TEXT
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self.llama_template_images = JOYIMAGE_TEMPLATE_IMAGE
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def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None,
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images=[], **kwargs):
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if text.startswith("<|im_start|>"):
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llama_text = text
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elif llama_template is not None:
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llama_text = llama_template.format(text)
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elif len(images) > 0:
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llama_text = self.llama_template_images.format(text)
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else:
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llama_text = self.llama_template.format(text)
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# Tokenize the already-rendered template via the grandparent
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# (SD1Tokenizer); calling `super()` would re-apply the Qwen3VL template.
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tokens = sd1_clip.SD1Tokenizer.tokenize_with_weights(
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self, llama_text, return_word_ids=return_word_ids, disable_weights=True, **kwargs,
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)
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key_name = next(iter(tokens))
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embed_count = 0
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qwen_tokens = tokens[key_name]
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for r in qwen_tokens:
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for i in range(len(r)):
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if r[i][0] == IMAGE_PAD_TOKEN:
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if len(images) > embed_count:
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r[i] = ({"type": "image", "data": images[embed_count],
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"original_type": "image"},) + r[i][1:]
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embed_count += 1
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if embed_count != len(images):
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raise ValueError(
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f"JoyImageTokenizer: prompt had {embed_count} <|image_pad|> placeholders "
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f"but {len(images)} image(s) were supplied. Either pre-format the prompt "
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f"with `<|vision_start|><|image_pad|><|vision_end|>` per image or pass an "
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f"image-free prompt."
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)
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return tokens
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class _JoyImageClipModel(sd1_clip.SDClipModel):
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"""Qwen3-VL multimodal encoder wrapper.
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Conditions on the **pre-final-norm** output of the last decoder layer
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(``layer="hidden", layer_idx=-1, layer_norm_hidden_state=False``). The
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post-norm ``last_hidden_state`` differs by ~10x in scale and produces broken
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DiT outputs, so these flags must not be changed.
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"""
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def __init__(self, device="cpu", layer="hidden", layer_idx=-1, dtype=None,
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attention_mask=True, model_options={}):
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super().__init__(
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device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={},
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dtype=dtype, special_tokens={"pad": PAD_TOKEN}, layer_norm_hidden_state=False,
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model_class=Qwen3VL8B_JoyImage, enable_attention_masks=attention_mask,
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return_attention_masks=attention_mask, model_options=model_options,
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)
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class JoyImageTEModel(sd1_clip.SD1ClipModel):
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def __init__(self, device="cpu", dtype=None, model_options={}):
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super().__init__(
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device=device, dtype=dtype, name="qwen3vl_8b",
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clip_model=_JoyImageClipModel, model_options=model_options,
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)
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def encode_token_weights(self, token_weight_pairs):
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out, pooled, extra = super().encode_token_weights(token_weight_pairs)
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# Strip the JOYIMAGE_DROP_IDX-token system-prompt prefix from both the
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# embedding sequence and the attention mask.
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if out.shape[1] <= JOYIMAGE_DROP_IDX:
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raise ValueError(
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f"JoyImageTEModel: encoded sequence length {out.shape[1]} is shorter "
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f"than drop_idx={JOYIMAGE_DROP_IDX}; the prompt did not include the "
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f"template prefix."
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)
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out = out[:, JOYIMAGE_DROP_IDX:]
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if "attention_mask" in extra:
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extra["attention_mask"] = extra["attention_mask"][:, JOYIMAGE_DROP_IDX:]
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return out, pooled, extra
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def te(dtype_llama=None, llama_quantization_metadata=None):
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class JoyImageTEModel_(JoyImageTEModel):
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def __init__(self, device="cpu", dtype=None, model_options={}):
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if llama_quantization_metadata is not None:
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model_options = model_options.copy()
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model_options["quantization_metadata"] = llama_quantization_metadata
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if dtype_llama is not None:
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dtype = dtype_llama
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super().__init__(device=device, dtype=dtype, model_options=model_options)
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return JoyImageTEModel_
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