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.
JoyImageEdit is an image-edit diffusion transformer from JD (jd-opensource),
Apache 2.0. This adds native ComfyUI support so it loads and runs like other
edit models (load checkpoint -> TextEncode + ReferenceLatent -> KSampler ->
VAEDecode), with no diffusers dependency.
Architecture:
- Transformer (comfy/ldm/joyimage/model.py): dual-stream (img/txt) DiT with a
Conv3d patch embed (patch_size [1,2,2]), Wan-style learnable modulation,
and 3D RoPE (rope_dim_list [16,56,56]). All attention goes through
comfy.ldm.modules.attention.optimized_attention.
- Text encoder (comfy/text_encoders/{qwen3_vl,joyimage}.py): a reusable
Qwen3-VL multimodal stack (vision tower + LM) in qwen3_vl.py, plus a thin
JoyImage-specific layer (prompt templates, drop_idx, tokenizer, te() factory)
in joyimage.py that depends on it. text_dim 4096.
- VAE: reuses the existing Wan 2.1 latent format (AutoencoderKLWan), no new
latent format.
- Edit conditioning: reuses the reference_latents mechanism. Reference and
noise latents are stacked on a new n-slot dimension and rotated at the model
boundary (model_base.JoyImage), so the transformer stays 5D-in/5D-out.
Guidance-rescale is built into the CFG path.
Model wiring:
- model_base.JoyImage uses ModelType.FLOW with sampling_settings
multiplier=1000 (the time embedding is trained on t in [0,1000]) and
shift=1.5; FLOW's linear time_snr_shift matches the diffusers
FlowMatchEuler sigma schedule.
- model_detection sniffs the transformer state-dict (double_blocks.*,
condition_embedder.*, 5D img_in Conv3d) to route image_model="joyimage".
- supported_models.JoyImage and the CLIPLoader "joyimage" type register it.
User-facing node TextEncodeJoyImageEdit (comfy_extras/nodes_joyimage.py)
bucket-resizes the input image to the nearest 1024-base bucket, encodes the
prompt with the image, and emits both the conditioning and the bucketed image
so the same pixels feed VAEEncode and the negative encode (JoyImage requires
noise and reference latents to share spatial dims).
