ImageGen / chain_injectors /reference_image_injector.py
RioShiina's picture
refactor(injectors): consolidate boogu and qwen-2.1 into reference_image with flexible max_images
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def create_node(assembler, class_type, title):
try:
node = assembler._get_node_template(class_type)
except Exception:
node = {
"inputs": {},
"class_type": class_type,
"_meta": {"title": title}
}
if "_meta" not in node:
node["_meta"] = {}
node['_meta']['title'] = title
return node
def inject(assembler, chain_definition, chain_items):
if not chain_items:
return
valid_images = []
for item in chain_items:
if not item:
continue
img_path = item
if isinstance(item, dict):
img_path = item.get('image') or item.get('filename') or item.get('path')
if img_path:
valid_images.append(img_path)
if not valid_images:
return
max_images = chain_definition.get('max_images', 10)
if isinstance(max_images, int) and max_images > 0:
valid_images = valid_images[:max_images]
# Target prompt/text_encode node resolution
target_node_name = (
chain_definition.get('text_encode_node')
or chain_definition.get('prompt_node')
or chain_definition.get('boogu_prompt_node')
or 'prompt'
)
target_node_id = assembler.node_map.get(target_node_name)
if not target_node_id or target_node_id not in assembler.workflow:
supported_types = ('TextEncodeMageFlowEdit', 'TextEncodeQwenImage21', 'TextEncodeBooguEdit')
for node_id, node in assembler.workflow.items():
if isinstance(node, dict) and node.get('class_type') in supported_types:
target_node_id = node_id
break
if not target_node_id or target_node_id not in assembler.workflow:
print(f"Warning: Target text encode node '{target_node_name}' for Reference Image chain not found. Skipping.")
return
# VAE loader node resolution
vae_node_name = chain_definition.get('vae_node') or chain_definition.get('vae_loader_node', 'vae_loader')
vae_node_id = assembler.node_map.get(vae_node_name)
if not vae_node_id or vae_node_id not in assembler.workflow:
for node_id, node in assembler.workflow.items():
if isinstance(node, dict) and node.get('class_type') == 'VAELoader':
vae_node_id = node_id
break
if vae_node_id:
assembler.workflow[target_node_id]['inputs']['vae'] = [vae_node_id, 0]
upscale_method = chain_definition.get('upscale_method', 'nearest-exact')
megapixels = chain_definition.get('megapixels', 1.0)
resolution_steps = chain_definition.get('resolution_steps', 1)
for i, img_filename in enumerate(valid_images):
load_id = assembler._get_unique_id()
load_node = create_node(assembler, "LoadImage", f"Load Reference Image {i+1}")
load_node['inputs']['image'] = img_filename
assembler.workflow[load_id] = load_node
scale_id = assembler._get_unique_id()
scale_node = create_node(assembler, "ImageScaleToTotalPixels", f"Scale Reference {i+1}")
scale_node['inputs']['upscale_method'] = upscale_method
scale_node['inputs']['megapixels'] = megapixels
scale_node['inputs']['resolution_steps'] = resolution_steps
scale_node['inputs']['image'] = [load_id, 0]
assembler.workflow[scale_id] = scale_node
input_key = f"images.image_{i+1}"
assembler.workflow[target_node_id]['inputs'][input_key] = [scale_id, 0]
node_class = assembler.workflow[target_node_id].get('class_type', 'Unknown')
print(f"Reference Image injector applied: {len(valid_images)} image(s) injected into '{target_node_id}' ({node_class}), max_images={max_images}.")