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Add an image_interleave option to the Hunyuan image to video encode node.

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See the tooltip for what it does.
This commit is contained in:
comfyanonymous 2025-03-07 19:56:11 -05:00
parent c3d9cc4592
commit be4e760648
2 changed files with 20 additions and 13 deletions
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26
comfy/text_encoders/hunyuan_video.py
View File

@ -42,7 +42,7 @@ class HunyuanVideoTokenizer:
self.llama_template = """<|start_header_id|>system<|end_header_id|>\n\nDescribe the video by detailing the following aspects: 1. The main content and theme of the video.2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects.3. Actions, events, behaviors temporal relationships, physical movement changes of the objects.4. background environment, light, style and atmosphere.5. camera angles, movements, and transitions used in the video:<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>""" # 95 tokens
self.llama = LLAMA3Tokenizer(embedding_directory=embedding_directory, min_length=1)
def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, image_embeds=None, **kwargs):
def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, image_embeds=None, image_interleave=1, **kwargs):
out = {}
out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids)
@ -56,7 +56,7 @@ class HunyuanVideoTokenizer:
for i in range(len(r)):
if r[i][0] == 128257:
if image_embeds is not None and embed_count < image_embeds.shape[0]:
r[i] = ({"type": "embedding", "data": image_embeds[embed_count], "original_type": "image"},) + r[i][1:]
r[i] = ({"type": "embedding", "data": image_embeds[embed_count], "original_type": "image", "image_interleave": image_interleave},) + r[i][1:]
embed_count += 1
out["llama"] = llama_text_tokens
return out
@ -92,10 +92,10 @@ class HunyuanVideoClipModel(torch.nn.Module):
llama_out, llama_pooled, llama_extra_out = self.llama.encode_token_weights(token_weight_pairs_llama)
template_end = 0
image_start = None
image_end = None
extra_template_end = 0
extra_sizes = 0
user_end = 9999999999999
images = []
tok_pairs = token_weight_pairs_llama[0]
for i, v in enumerate(tok_pairs):
@ -112,22 +112,28 @@ class HunyuanVideoClipModel(torch.nn.Module):
else:
if elem.get("original_type") == "image":
elem_size = elem.get("data").shape[0]
if image_start is None:
if template_end > 0:
if user_end == -1:
extra_template_end += elem_size - 1
else:
image_start = i + extra_sizes
image_end = i + elem_size + extra_sizes
images.append((image_start, image_end, elem.get("image_interleave", 1)))
extra_sizes += elem_size - 1
if llama_out.shape[1] > (template_end + 2):
if tok_pairs[template_end + 1][0] == 271:
template_end += 2
llama_output = llama_out[:, template_end + extra_sizes:user_end + extra_sizes]
llama_extra_out["attention_mask"] = llama_extra_out["attention_mask"][:, template_end + extra_sizes:user_end + extra_sizes]
llama_output = llama_out[:, template_end + extra_sizes:user_end + extra_sizes + extra_template_end]
llama_extra_out["attention_mask"] = llama_extra_out["attention_mask"][:, template_end + extra_sizes:user_end + extra_sizes + extra_template_end]
if llama_extra_out["attention_mask"].sum() == torch.numel(llama_extra_out["attention_mask"]):
llama_extra_out.pop("attention_mask") # attention mask is useless if no masked elements
if image_start is not None:
image_output = llama_out[:, image_start: image_end]
llama_output = torch.cat([image_output[:, ::2], llama_output], dim=1)
if len(images) > 0:
out = []
for i in images:
out.append(llama_out[:, i[0]: i[1]: i[2]])
llama_output = torch.cat(out + [llama_output], dim=1)
l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l)
return llama_output, l_pooled, llama_extra_out

5
comfy_extras/nodes_hunyuan.py
View File

@ -57,14 +57,15 @@ class TextEncodeHunyuanVideo_ImageToVideo:
"clip": ("CLIP", ),
"clip_vision_output": ("CLIP_VISION_OUTPUT", ),
"prompt": ("STRING", {"multiline": True, "dynamicPrompts": True}),
"image_interleave": ("INT", {"default": 2, "min": 1, "max": 512, "tooltip": "How much the image influences things vs the text prompt. Higher number means more influence from the text prompt."}),
}}
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "encode"
CATEGORY = "advanced/conditioning"
def encode(self, clip, clip_vision_output, prompt):
tokens = clip.tokenize(prompt, llama_template=PROMPT_TEMPLATE_ENCODE_VIDEO_I2V, image_embeds=clip_vision_output.mm_projected)
def encode(self, clip, clip_vision_output, prompt, image_interleave):
tokens = clip.tokenize(prompt, llama_template=PROMPT_TEMPLATE_ENCODE_VIDEO_I2V, image_embeds=clip_vision_output.mm_projected, image_interleave=image_interleave)
return (clip.encode_from_tokens_scheduled(tokens), )