Optimize workflows and configuration files, and standardize file naming.

chain_injectors/__init__.py

@@ -0,0 +1,50 @@
+import os
+import importlib
+import pkgutil
+
+def discover_injectors():
+    injectors = {}
+    package_dir = os.path.dirname(__file__)
+
+    for _, module_name, is_pkg in pkgutil.iter_modules([package_dir]):
+        if is_pkg or module_name.startswith('_'):
+            continue
+
+        full_module_name = f"chain_injectors.{module_name}"
+        try:
+            module = importlib.import_module(full_module_name)
+            if hasattr(module, 'inject') and callable(module.inject):
+                feature_name = getattr(module, 'FEATURE_NAME', None)
+                if not feature_name:
+                    feature_name = module_name[:-9] if module_name.endswith('_injector') else module_name
+
+                chain_type = getattr(module, 'CHAIN_TYPE', None)
+                if not chain_type:
+                    chain_type = f"dynamic_{feature_name}_chains"
+
+                injectors[chain_type] = module.inject
+            else:
+                print(f"Warning: Module '{full_module_name}' does not have a callable 'inject' function.")
+        except Exception as e:
+            print(f"Error importing injector module '{full_module_name}': {e}")
+
+    return injectors
+
+def get_registered_features():
+    features = {}
+    package_dir = os.path.dirname(__file__)
+
+    for _, module_name, is_pkg in pkgutil.iter_modules([package_dir]):
+        if is_pkg or module_name.startswith('_'):
+            continue
+
+        feature_name = module_name[:-9] if module_name.endswith('_injector') else module_name
+        full_module_name = f"chain_injectors.{module_name}"
+        chain_type = f"dynamic_{feature_name}_chains"
+
+        features[feature_name] = {
+            'module': full_module_name,
+            'chain_type': chain_type
+        }
+
+    return features

chain_injectors/{boogu_edit_injector.py → boogu_image_edit_injector.py}

@@ -1,5 +1,3 @@
-import os
-
 def create_node(assembler, class_type, title):
     try:
         node = assembler._get_node_template(class_type)
@@ -31,64 +29,30 @@ def inject(assembler, chain_definition, chain_items):
 
     valid_images = valid_images[:10]
 
-    ksampler_name = chain_definition.get('ksampler_node', 'ksampler')
-    pos_prompt_name = chain_definition.get('pos_prompt_node', 'pos_prompt')
-    neg_prompt_name = chain_definition.get('neg_prompt_node', 'neg_prompt')
-    clip_loader_name = chain_definition.get('clip_loader_node', 'clip_loader')
+    boogu_prompt_name = chain_definition.get('boogu_prompt_node', 'boogu_prompt')
     vae_loader_name = chain_definition.get('vae_loader_node', 'vae_loader')
 
-    if ksampler_name not in assembler.node_map:
-        print(f"Warning: Target node '{ksampler_name}' for Boogu Edit chain not found. Skipping.")
-        return
-
-    ksampler_id = assembler.node_map[ksampler_name]
+    boogu_prompt_id = assembler.node_map.get(boogu_prompt_name)
+    if not boogu_prompt_id or boogu_prompt_id not in assembler.workflow:
+        for node_id, node in assembler.workflow.items():
+            if isinstance(node, dict) and node.get('class_type') == 'TextEncodeBooguEdit':
+                boogu_prompt_id = node_id
+                break
 
-    if 'model' not in assembler.workflow[ksampler_id]['inputs']:
-        print(f"Warning: KSampler node '{ksampler_name}' is missing 'model' input. Skipping.")
+    if not boogu_prompt_id:
+        print(f"Warning: Target node '{boogu_prompt_name}' (TextEncodeBooguEdit) for Boogu Edit chain not found. Skipping.")
         return
 
-    vae_connection = None
-    if vae_loader_name in assembler.node_map:
-        vae_connection = [assembler.node_map[vae_loader_name], 0]
-    else:
+    vae_id = assembler.node_map.get(vae_loader_name)
+    if not vae_id:
         for node_id, node in assembler.workflow.items():
             if isinstance(node, dict) and node.get('class_type') == 'VAELoader':
-                vae_connection = [node_id, 0]
+                vae_id = node_id
                 break
 
-    clip_connection = None
-    if clip_loader_name in assembler.node_map:
-        clip_connection = [assembler.node_map[clip_loader_name], 0]
-    elif pos_prompt_name in assembler.node_map:
-        pos_id = assembler.node_map[pos_prompt_name]
-        clip_connection = assembler.workflow[pos_id]['inputs'].get('clip')
-
-    pos_prompt_id = assembler.node_map.get(pos_prompt_name)
-    neg_prompt_id = assembler.node_map.get(neg_prompt_name)
-
-    pos_text = ""
-    if pos_prompt_id and pos_prompt_id in assembler.workflow:
-        pos_text = assembler.workflow[pos_prompt_id]['inputs'].get('text', '')
-    elif hasattr(assembler, 'ui_values') and isinstance(assembler.ui_values, dict):
-        pos_text = assembler.ui_values.get('positive_prompt') or assembler.ui_values.get('prompt') or ''
+    if vae_id:
+        assembler.workflow[boogu_prompt_id]['inputs']['vae'] = [vae_id, 0]
 
-    if not pos_text:
-        for node_id, node in assembler.workflow.items():
-            if isinstance(node, dict):
-                cls = node.get('class_type', '')
-                if cls in ['TextEncodeBooguEdit', 'Krea2EditGroundedEncode', 'TextEncodeQwenImageEditPlus', 'CLIPTextEncode']:
-                    t = node.get('inputs', {}).get('prompt') or node.get('inputs', {}).get('text')
-                    if t:
-                        pos_text = t
-                        break
-
-    neg_text = ""
-    if neg_prompt_id and neg_prompt_id in assembler.workflow:
-        neg_text = assembler.workflow[neg_prompt_id]['inputs'].get('text', '')
-    elif hasattr(assembler, 'ui_values') and isinstance(assembler.ui_values, dict):
-        neg_text = assembler.ui_values.get('negative_prompt') or assembler.ui_values.get('neg_prompt') or ''
-
-    scaled_image_ids = []
     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}")
@@ -102,27 +66,8 @@ def inject(assembler, chain_definition, chain_items):
         scale_node['inputs']['resolution_steps'] = 1
         scale_node['inputs']['image'] = [load_id, 0]
         assembler.workflow[scale_id] = scale_node
-        scaled_image_ids.append(scale_id)
-
-    boogu_encode_id = assembler._get_unique_id()
-    boogu_encode_node = create_node(assembler, "TextEncodeBooguEdit", "TextEncodeBooguEdit")
-    boogu_encode_node['inputs']['prompt'] = pos_text
-    boogu_encode_node['inputs']['negative_prompt'] = neg_text
-    if clip_connection:
-        boogu_encode_node['inputs']['clip'] = clip_connection
-    if vae_connection:
-        boogu_encode_node['inputs']['vae'] = vae_connection
-    for idx, s_id in enumerate(scaled_image_ids):
-        boogu_encode_node['inputs'][f"images.image_{idx+1}"] = [s_id, 0]
-    assembler.workflow[boogu_encode_id] = boogu_encode_node
-
-    assembler.workflow[ksampler_id]['inputs']['positive'] = [boogu_encode_id, 0]
-    assembler.workflow[ksampler_id]['inputs']['negative'] = [boogu_encode_id, 1]
-
-    if pos_prompt_id and pos_prompt_id in assembler.workflow:
-        del assembler.workflow[pos_prompt_id]
 
-    if neg_prompt_id and neg_prompt_id in assembler.workflow:
-        del assembler.workflow[neg_prompt_id]
+        image_key = f"images.image_{i+1}"
+        assembler.workflow[boogu_prompt_id]['inputs'][image_key] = [scale_id, 0]
 
-    print(f"Boogu Edit injector applied with {len(valid_images)} reference image(s). Original CLIPTextEncode nodes replaced.")
+    print(f"Boogu Edit injector applied with {len(valid_images)} reference image(s). Connected VAE dynamically.")

chain_injectors/newbie_lora_injector.py

@@ -1,63 +0,0 @@
-from copy import deepcopy
-
-def inject(assembler, chain_definition, chain_items):
-    if not chain_items:
-        return
-
-    output_map = chain_definition.get('output_map', {})
-    current_connections = {}
-    for key, type_name in output_map.items():
-        if ':' in str(key):
-            node_name, idx_str = key.split(':')
-            if node_name not in assembler.node_map:
-                print(f"Warning: [NewBie LoRA Injector] Node '{node_name}' in chain's output_map not found. Skipping.")
-                continue
-            node_id = assembler.node_map[node_name]
-            start_output_idx = int(idx_str)
-            current_connections[type_name] = [node_id, start_output_idx]
-        else:
-            print(f"Warning: [NewBie LoRA Injector] output_map key '{key}' is not in 'node:index' format. Skipping this connection.")
-
-    template_name = chain_definition.get('template')
-    if not template_name:
-        print(f"Warning: [NewBie LoRA Injector] No 'template' defined for chain. Skipping.")
-        return
-
-    for item_data in chain_items:
-        template = assembler._get_node_template(template_name)
-        node_data = deepcopy(template)
-        
-        node_data['inputs']['lora_name'] = item_data.get('lora_name')
-        node_data['inputs']['strength'] = item_data.get('strength_model', 1.0)
-        node_data['inputs']['enabled'] = True
-        
-        if 'model' in current_connections:
-            node_data['inputs']['model'] = current_connections['model']
-        if 'clip' in current_connections:
-            node_data['inputs']['clip'] = current_connections['clip']
-
-        new_node_id = assembler._get_unique_id()
-        assembler.workflow[new_node_id] = node_data
-        
-        current_connections['model'] = [new_node_id, 0]
-        current_connections['clip'] = [new_node_id, 1]
-
-    end_input_map = chain_definition.get('end_input_map', {})
-    for type_name, targets in end_input_map.items():
-        if type_name in current_connections:
-            if not isinstance(targets, list):
-                targets = [targets]
-            
-            for target_str in targets:
-                try:
-                    end_node_name, end_input_name = target_str.split(':')
-                    if end_node_name in assembler.node_map:
-                        end_node_id = assembler.node_map[end_node_name]
-                        assembler.workflow[end_node_id]['inputs'][end_input_name] = current_connections[type_name]
-                    else:
-                        print(f"Warning: [NewBie LoRA Injector] End node '{end_node_name}' for dynamic chain not found. Skipping connection.")
-                except ValueError:
-                    print(f"Warning: [NewBie LoRA Injector] Invalid target format '{target_str}' in end_input_map. Skipping.")
-
-    if chain_items:
-        print(f"NewBie LoRA injector applied. Re-routed model and clip through {len(chain_items)} LoRA(s).")

chain_injectors/qwen_image_edit_injector.py

@@ -1,5 +1,3 @@
-import os
-
 def create_node(assembler, class_type, title):
     try:
         node = assembler._get_node_template(class_type)
@@ -34,7 +32,6 @@ def inject(assembler, chain_definition, chain_items):
     ksampler_name = chain_definition.get('ksampler_node', 'ksampler')
     pos_prompt_name = chain_definition.get('pos_prompt_node', 'pos_prompt')
     neg_prompt_name = chain_definition.get('neg_prompt_node', 'neg_prompt')
-    clip_loader_name = chain_definition.get('clip_loader_node', 'clip_loader')
     vae_loader_name = chain_definition.get('vae_loader_node', 'vae_loader')
     model_sampler_name = chain_definition.get('model_sampler_node', 'model_sampler')
 
@@ -43,53 +40,24 @@ def inject(assembler, chain_definition, chain_items):
         return
 
     ksampler_id = assembler.node_map[ksampler_name]
+    pos_prompt_id = assembler.node_map.get(pos_prompt_name)
+    neg_prompt_id = assembler.node_map.get(neg_prompt_name)
 
-    if 'model' not in assembler.workflow[ksampler_id]['inputs']:
-        print(f"Warning: KSampler node '{ksampler_name}' is missing 'model' input. Skipping.")
+    if not pos_prompt_id or not neg_prompt_id:
+        print("Warning: Positive or negative prompt node not found for Qwen-Image Edit chain. Skipping.")
         return
 
-    vae_connection = None
-    if vae_loader_name in assembler.node_map:
-        vae_connection = [assembler.node_map[vae_loader_name], 0]
-    else:
+    vae_id = assembler.node_map.get(vae_loader_name)
+    if not vae_id:
         for node_id, node in assembler.workflow.items():
             if isinstance(node, dict) and node.get('class_type') == 'VAELoader':
-                vae_connection = [node_id, 0]
+                vae_id = node_id
                 break
 
-    clip_connection = None
-    if clip_loader_name in assembler.node_map:
-        clip_connection = [assembler.node_map[clip_loader_name], 0]
-    elif pos_prompt_name in assembler.node_map:
-        pos_id = assembler.node_map[pos_prompt_name]
-        clip_connection = assembler.workflow[pos_id]['inputs'].get('clip')
-
-    pos_prompt_id = assembler.node_map.get(pos_prompt_name)
-    neg_prompt_id = assembler.node_map.get(neg_prompt_name)
+    if vae_id:
+        assembler.workflow[pos_prompt_id]['inputs']['vae'] = [vae_id, 0]
+        assembler.workflow[neg_prompt_id]['inputs']['vae'] = [vae_id, 0]
 
-    pos_text = ""
-    if pos_prompt_id and pos_prompt_id in assembler.workflow:
-        pos_text = assembler.workflow[pos_prompt_id]['inputs'].get('text', '')
-    elif hasattr(assembler, 'ui_values') and isinstance(assembler.ui_values, dict):
-        pos_text = assembler.ui_values.get('positive_prompt') or assembler.ui_values.get('prompt') or ''
-
-    if not pos_text:
-        for node_id, node in assembler.workflow.items():
-            if isinstance(node, dict):
-                cls = node.get('class_type', '')
-                if cls in ['Krea2EditGroundedEncode', 'TextEncodeQwenImageEditPlus', 'CLIPTextEncode']:
-                    t = node.get('inputs', {}).get('prompt') or node.get('inputs', {}).get('text')
-                    if t:
-                        pos_text = t
-                        break
-
-    neg_text = ""
-    if neg_prompt_id and neg_prompt_id in assembler.workflow:
-        neg_text = assembler.workflow[neg_prompt_id]['inputs'].get('text', '')
-    elif hasattr(assembler, 'ui_values') and isinstance(assembler.ui_values, dict):
-        neg_text = assembler.ui_values.get('negative_prompt') or assembler.ui_values.get('neg_prompt') or ''
-
-    scaled_image_ids = []
     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}")
@@ -103,40 +71,21 @@ def inject(assembler, chain_definition, chain_items):
         scale_node['inputs']['resolution_steps'] = 1
         scale_node['inputs']['image'] = [load_id, 0]
         assembler.workflow[scale_id] = scale_node
-        scaled_image_ids.append(scale_id)
-
-    pos_encode_id = assembler._get_unique_id()
-    pos_encode_node = create_node(assembler, "TextEncodeQwenImageEditPlus", "TextEncodeQwenImageEditPlus (Positive)")
-    pos_encode_node['inputs']['prompt'] = pos_text
-    if clip_connection:
-        pos_encode_node['inputs']['clip'] = clip_connection
-    if vae_connection:
-        pos_encode_node['inputs']['vae'] = vae_connection
-    for idx, s_id in enumerate(scaled_image_ids):
-        pos_encode_node['inputs'][f"image{idx+1}"] = [s_id, 0]
-    assembler.workflow[pos_encode_id] = pos_encode_node
-
-    neg_encode_id = assembler._get_unique_id()
-    neg_encode_node = create_node(assembler, "TextEncodeQwenImageEditPlus", "TextEncodeQwenImageEditPlus")
-    neg_encode_node['inputs']['prompt'] = neg_text
-    if clip_connection:
-        neg_encode_node['inputs']['clip'] = clip_connection
-    if vae_connection:
-        neg_encode_node['inputs']['vae'] = vae_connection
-    for idx, s_id in enumerate(scaled_image_ids):
-        neg_encode_node['inputs'][f"image{idx+1}"] = [s_id, 0]
-    assembler.workflow[neg_encode_id] = neg_encode_node
+
+        image_key = f"image{i+1}"
+        assembler.workflow[pos_prompt_id]['inputs'][image_key] = [scale_id, 0]
+        assembler.workflow[neg_prompt_id]['inputs'][image_key] = [scale_id, 0]
 
     pos_ref_id = assembler._get_unique_id()
     pos_ref_node = create_node(assembler, "FluxKontextMultiReferenceLatentMethod", "Edit Model Reference Method")
     pos_ref_node['inputs']['reference_latents_method'] = "index_timestep_zero"
-    pos_ref_node['inputs']['conditioning'] = [pos_encode_id, 0]
+    pos_ref_node['inputs']['conditioning'] = [pos_prompt_id, 0]
     assembler.workflow[pos_ref_id] = pos_ref_node
 
     neg_ref_id = assembler._get_unique_id()
     neg_ref_node = create_node(assembler, "FluxKontextMultiReferenceLatentMethod", "Edit Model Reference Method")
     neg_ref_node['inputs']['reference_latents_method'] = "index_timestep_zero"
-    neg_ref_node['inputs']['conditioning'] = [neg_encode_id, 0]
+    neg_ref_node['inputs']['conditioning'] = [neg_prompt_id, 0]
     assembler.workflow[neg_ref_id] = neg_ref_node
 
     assembler.workflow[ksampler_id]['inputs']['positive'] = [pos_ref_id, 0]
@@ -161,10 +110,4 @@ def inject(assembler, chain_definition, chain_items):
     assembler.workflow[cfg_norm_id] = cfg_norm_node
     assembler.workflow[ksampler_id]['inputs']['model'] = [cfg_norm_id, 0]
 
-    if pos_prompt_id and pos_prompt_id in assembler.workflow:
-        del assembler.workflow[pos_prompt_id]
-
-    if neg_prompt_id and neg_prompt_id in assembler.workflow:
-        del assembler.workflow[neg_prompt_id]
-
-    print(f"Qwen-Image Edit injector applied with {len(valid_images)} reference image(s). Original CLIPTextEncode nodes replaced.")
+    print(f"Qwen-Image Edit injector applied with {len(valid_images)} reference image(s). Connected VAE dynamically.")

comfy_integration/setup.py

@@ -72,15 +72,7 @@ def initialize_comfyui():
     except Exception as e:
         print(f"⚠️ Warning: Could not apply PR #108 compatibility patch for ComfyUI-IPAdapter-Flux: {e}")
 
-    # 4. ComfyUI-Newbie-Nodes
-    newbie_nodes_path = os.path.join(APP_DIR, "custom_nodes", "ComfyUI-Newbie-Nodes")
-    if not os.path.exists(newbie_nodes_path):
-        os.system(f"git clone https://github.com/NewBieAI-Lab/ComfyUI-Newbie-Nodes.git {newbie_nodes_path}")
-        print("✅ ComfyUI-Newbie-Nodes extension cloned.")
-    else:
-        print("✅ ComfyUI-Newbie-Nodes extension already exists.")
-
-    # 5. comfyui-krea2-controlnet
+    # 4. comfyui-krea2-controlnet
     krea2_controlnet_nodes_path = os.path.join(APP_DIR, "custom_nodes", "comfyui-krea2-controlnet")
     if not os.path.exists(krea2_controlnet_nodes_path):
         os.system(f"git clone https://github.com/facok/comfyui-krea2-controlnet.git {krea2_controlnet_nodes_path}")
@@ -88,7 +80,7 @@ def initialize_comfyui():
     else:
         print("✅ comfyui-krea2-controlnet extension already exists.")
 
-    # 6. comfyui-krea2edit
+    # 5. comfyui-krea2edit
     krea2edit_nodes_path = os.path.join(APP_DIR, "custom_nodes", "comfyui-krea2edit")
     if not os.path.exists(krea2edit_nodes_path):
         os.system(f"git clone https://github.com/lbouaraba/comfyui-krea2edit.git {krea2edit_nodes_path}")

core/pipelines/sd_image_pipeline.py

@@ -151,7 +151,7 @@ class SdImagePipeline(BasePipeline):
                 'latent_generator_template': latent_generator_template
             }
             
-            recipe_path = os.path.join(os.path.dirname(__file__), "workflow_recipes", "sd_unified_recipe.yaml")
+            recipe_path = os.path.join(os.path.dirname(__file__), "workflow_recipes", "unified_recipe.yaml")
             assembler = WorkflowAssembler(recipe_path, dynamic_values=dynamic_values)
             
             hidream_o1_smoothing_data = []
@@ -180,11 +180,11 @@ class SdImagePipeline(BasePipeline):
                 "conditioning_chain": active_conditioning,
                 "reference_latent_chain": active_reference_latents,
                 "hidream_o1_reference_chain": active_hidream_o1_reference,
-                "joyai_reference_chain": active_joyai_reference,
+                "joyai_image_chain": active_joyai_reference,
                 "krea2_identity_edit_chain": active_krea2_identity_edit,
-                "krea2_reference_edit_chain": active_krea2_reference_edit,
+                "krea2_style_reference_chain": active_krea2_reference_edit,
                 "qwen_image_edit_chain": active_qwen_image_edit,
-                "boogu_edit_chain": active_boogu_edit,
+                "boogu_image_edit_chain": active_boogu_edit,
                 "reference_image_chain": active_reference_images,
                 "vae_chain": [ui_inputs.get('vae_name')] if ui_inputs.get('vae_name') else [],
                 "hidream_o1_smoothing_chain": hidream_o1_smoothing_data,

core/pipelines/workflow_recipes/_partials/{_base_sampler_sd.yaml → _base_sampler.yaml}

@@ -1,28 +1,28 @@
-nodes:
-  ksampler:
-    class_type: KSampler
-    title: "KSampler"
-    params:
-      denoise: 1.0
-  vae_decode:
-    class_type: VAEDecode
-    title: "VAE Decode"
-  save_image:
-    class_type: SaveImage
-    title: "Save Image"
-    params: {}
-
-connections:
-  - from: "ksampler:0"
-    to: "vae_decode:samples"
-  - from: "vae_decode:0"
-    to: "save_image:images"
-
-ui_map:
-  seed: "ksampler:seed"
-  steps: "ksampler:steps"
-  cfg: "ksampler:cfg"
-  sampler_name: "ksampler:sampler_name"
-  scheduler: "ksampler:scheduler"
-  denoise: "ksampler:denoise"
+nodes:
+  ksampler:
+    class_type: KSampler
+    title: "KSampler"
+    params:
+      denoise: 1.0
+  vae_decode:
+    class_type: VAEDecode
+    title: "VAE Decode"
+  save_image:
+    class_type: SaveImage
+    title: "Save Image"
+    params: {}
+
+connections:
+  - from: "ksampler:0"
+    to: "vae_decode:samples"
+  - from: "vae_decode:0"
+    to: "save_image:images"
+
+ui_map:
+  seed: "ksampler:seed"
+  steps: "ksampler:steps"
+  cfg: "ksampler:cfg"
+  sampler_name: "ksampler:sampler_name"
+  scheduler: "ksampler:scheduler"
+  denoise: "ksampler:denoise"
   filename_prefix: "save_image:filename_prefix"

core/pipelines/workflow_recipes/_partials/conditioning/boogu-image.yaml

@@ -1,74 +1,67 @@
-nodes:
-  pos_prompt:
-    class_type: CLIPTextEncode
-    title: "CLIP Text Encode (Positive)"
-  neg_prompt:
-    class_type: CLIPTextEncode
-    title: "CLIP Text Encode (Negative)"
-  unet_loader:
-    class_type: UNETLoader
-    title: "Load Diffusion Model"
-    params:
-      weight_dtype: "default"
-  clip_loader:
-    class_type: CLIPLoader
-    title: "Load CLIP"
-    params:
-      type: "boogu"
-      device: "default"
-  vae_loader:
-    class_type: VAELoader
-    title: "Load VAE"
-
-connections:
-  - from: "unet_loader:0"
-    to: "ksampler:model"
-  - from: "clip_loader:0"
-    to: "pos_prompt:clip"
-  - from: "clip_loader:0"
-    to: "neg_prompt:clip"
-  - from: "pos_prompt:0"
-    to: "ksampler:positive"
-  - from: "neg_prompt:0"
-    to: "ksampler:negative"
-  - from: "vae_loader:0"
-    to: "vae_decode:vae"
-  - from: "vae_loader:0"
-    to: "vae_encode:vae"
-
-dynamic_lora_chains:
-  lora_chain:
-    template: "LoraLoader"
-    output_map:
-      "unet_loader:0": "model"
-      "clip_loader:0": "clip"
-    input_map:
-      "model": "model"
-      "clip": "clip"
-    end_input_map:
-      "model": ["ksampler:model"]
-      "clip": ["pos_prompt:clip", "neg_prompt:clip"]
-
-dynamic_conditioning_chains:
-  conditioning_chain:
-    ksampler_node: "ksampler"
-    clip_source: "clip_loader:0"
-
-dynamic_boogu_edit_chains:
-  boogu_edit_chain:
-    ksampler_node: "ksampler"
-    pos_prompt_node: "pos_prompt"
-    neg_prompt_node: "neg_prompt"
-    clip_loader_node: "clip_loader"
-    vae_loader_node: "vae_loader"
-
-dynamic_pid_chains:
-  pid_chain:
-    ksampler_node: "ksampler"
-
-ui_map:
-  positive_prompt: "pos_prompt:text"
-  negative_prompt: "neg_prompt:text"
-  unet_name: "unet_loader:unet_name"
-  clip_name: "clip_loader:clip_name"
-  vae_name: "vae_loader:vae_name"
+nodes:
+  boogu_prompt:
+    class_type: TextEncodeBooguEdit
+    title: "Text Encode Boogu Edit"
+  unet_loader:
+    class_type: UNETLoader
+    title: "Load Diffusion Model"
+    params:
+      weight_dtype: "default"
+  clip_loader:
+    class_type: CLIPLoader
+    title: "Load CLIP"
+    params:
+      type: "boogu"
+      device: "default"
+  vae_loader:
+    class_type: VAELoader
+    title: "Load VAE"
+
+connections:
+  - from: "unet_loader:0"
+    to: "ksampler:model"
+  - from: "clip_loader:0"
+    to: "boogu_prompt:clip"
+  - from: "boogu_prompt:0"
+    to: "ksampler:positive"
+  - from: "boogu_prompt:1"
+    to: "ksampler:negative"
+  - from: "vae_loader:0"
+    to: "vae_decode:vae"
+  - from: "vae_loader:0"
+    to: "vae_encode:vae"
+
+dynamic_lora_chains:
+  lora_chain:
+    template: "LoraLoader"
+    output_map:
+      "unet_loader:0": "model"
+      "clip_loader:0": "clip"
+    input_map:
+      "model": "model"
+      "clip": "clip"
+    end_input_map:
+      "model": ["ksampler:model"]
+      "clip": ["boogu_prompt:clip"]
+
+dynamic_conditioning_chains:
+  conditioning_chain:
+    ksampler_node: "ksampler"
+    clip_source: "clip_loader:0"
+
+dynamic_boogu_image_edit_chains:
+  boogu_image_edit_chain:
+    ksampler_node: "ksampler"
+    boogu_prompt_node: "boogu_prompt"
+    vae_loader_node: "vae_loader"
+
+dynamic_pid_chains:
+  pid_chain:
+    ksampler_node: "ksampler"
+
+ui_map:
+  positive_prompt: "boogu_prompt:prompt"
+  negative_prompt: "boogu_prompt:negative_prompt"
+  unet_name: "unet_loader:unet_name"
+  clip_name: "clip_loader:clip_name"
+  vae_name: "vae_loader:vae_name"

core/pipelines/workflow_recipes/_partials/conditioning/joyai-image.yaml

@@ -71,8 +71,8 @@ dynamic_pid_chains:
   pid_chain:
     ksampler_node: "ksampler"
 
-dynamic_joyai_reference_chains:
-  joyai_reference_chain:
+dynamic_joyai_image_chains:
+  joyai_image_chain:
     pos_prompt_node: "pos_prompt"
     neg_prompt_node: "neg_prompt"
     vae_node: "vae_loader"

core/pipelines/workflow_recipes/_partials/conditioning/krea-2.yaml

@@ -62,8 +62,8 @@ dynamic_krea2_identity_edit_chains:
     clip_loader_node: "clip_loader"
     vae_loader_node: "vae_loader"
 
-dynamic_krea2_reference_edit_chains:
-  krea2_reference_edit_chain:
+dynamic_krea2_style_reference_chains:
+  krea2_style_reference_chain:
     ksampler_node: "ksampler"
     pos_prompt_node: "pos_prompt"
     neg_prompt_node: "neg_prompt"

core/pipelines/workflow_recipes/_partials/conditioning/newbie-image.yaml

@@ -46,9 +46,9 @@ connections:
   - from: "vae_loader:0"
     to: "vae_encode:vae"
 
-dynamic_newbie_lora_chains:
+dynamic_lora_chains:
   lora_chain:
-    template: "NewBieLoraLoader"
+    template: "LoraLoader"
     output_map:
       "unet_loader:0": "model"
       "clip_loader:0": "clip"

core/pipelines/workflow_recipes/_partials/conditioning/qwen-image.yaml

@@ -1,92 +1,91 @@
-nodes:
-  pos_prompt:
-    class_type: CLIPTextEncode
-    title: "CLIP Text Encode (Positive)"
-  neg_prompt:
-    class_type: CLIPTextEncode
-    title: "CLIP Text Encode (Negative)"
-  unet_loader:
-    class_type: UNETLoader
-    title: "Load Qwen UNET"
-    params:
-      weight_dtype: "default"
-  vae_loader:
-    class_type: VAELoader
-    title: "Load Qwen VAE"
-  clip_loader:
-    class_type: CLIPLoader
-    title: "Load Qwen CLIP"
-    params:
-      type: "qwen_image"
-      device: "default"
-  model_sampler:
-    class_type: ModelSamplingAuraFlow
-    title: "ModelSamplingAuraFlow"
-    params:
-      shift: 3.1
-
-connections:
-  - from: "unet_loader:0"
-    to: "model_sampler:model"
-
-  - from: "model_sampler:0"
-    to: "ksampler:model"
-    
-  - from: "clip_loader:0"
-    to: "pos_prompt:clip"
-  - from: "clip_loader:0"
-    to: "neg_prompt:clip"
-    
-  - from: "vae_loader:0"
-    to: "vae_decode:vae"
-  - from: "vae_loader:0"
-    to: "vae_encode:vae"
-
-  - from: "pos_prompt:0"
-    to: "ksampler:positive"
-  - from: "neg_prompt:0"
-    to: "ksampler:negative"
-
-dynamic_lora_chains:
-  lora_chain:
-    template: "LoraLoader"
-    output_map:
-      "unet_loader:0": "model"
-      "clip_loader:0": "clip"
-    input_map:
-      "model": "model"
-      "clip": "clip"
-    end_input_map:
-      "model": ["model_sampler:model"]
-      "clip": ["pos_prompt:clip", "neg_prompt:clip"]
-
-dynamic_controlnet_chains:
-  controlnet_chain:
-    template: "ControlNetApplyAdvanced"
-    ksampler_node: "ksampler"
-    vae_source: "vae_loader:0"
-
-dynamic_conditioning_chains:
-  conditioning_chain:
-    ksampler_node: "ksampler"
-    clip_source: "clip_loader:0"
-
-dynamic_qwen_image_edit_chains:
-  qwen_image_edit_chain:
-    ksampler_node: "ksampler"
-    pos_prompt_node: "pos_prompt"
-    neg_prompt_node: "neg_prompt"
-    clip_loader_node: "clip_loader"
-    vae_loader_node: "vae_loader"
-    model_sampler_node: "model_sampler"
-
-dynamic_pid_chains:
-  pid_chain:
-    ksampler_node: "ksampler"
-
-ui_map:
-  positive_prompt: "pos_prompt:text"
-  negative_prompt: "neg_prompt:text"
-  unet_name: "unet_loader:unet_name"
-  vae_name: "vae_loader:vae_name"
+nodes:
+  pos_prompt:
+    class_type: TextEncodeQwenImageEditPlus
+    title: "Text Encode Qwen Image Edit Plus (Positive)"
+  neg_prompt:
+    class_type: TextEncodeQwenImageEditPlus
+    title: "Text Encode Qwen Image Edit Plus (Negative)"
+  unet_loader:
+    class_type: UNETLoader
+    title: "Load Qwen UNET"
+    params:
+      weight_dtype: "default"
+  vae_loader:
+    class_type: VAELoader
+    title: "Load Qwen VAE"
+  clip_loader:
+    class_type: CLIPLoader
+    title: "Load Qwen CLIP"
+    params:
+      type: "qwen_image"
+      device: "default"
+  model_sampler:
+    class_type: ModelSamplingAuraFlow
+    title: "ModelSamplingAuraFlow"
+    params:
+      shift: 3.1
+
+connections:
+  - from: "unet_loader:0"
+    to: "model_sampler:model"
+
+  - from: "model_sampler:0"
+    to: "ksampler:model"
+    
+  - from: "clip_loader:0"
+    to: "pos_prompt:clip"
+  - from: "clip_loader:0"
+    to: "neg_prompt:clip"
+    
+  - from: "vae_loader:0"
+    to: "vae_decode:vae"
+  - from: "vae_loader:0"
+    to: "vae_encode:vae"
+
+  - from: "pos_prompt:0"
+    to: "ksampler:positive"
+  - from: "neg_prompt:0"
+    to: "ksampler:negative"
+
+dynamic_lora_chains:
+  lora_chain:
+    template: "LoraLoader"
+    output_map:
+      "unet_loader:0": "model"
+      "clip_loader:0": "clip"
+    input_map:
+      "model": "model"
+      "clip": "clip"
+    end_input_map:
+      "model": ["model_sampler:model"]
+      "clip": ["pos_prompt:clip", "neg_prompt:clip"]
+
+dynamic_controlnet_chains:
+  controlnet_chain:
+    template: "ControlNetApplyAdvanced"
+    ksampler_node: "ksampler"
+    vae_source: "vae_loader:0"
+
+dynamic_conditioning_chains:
+  conditioning_chain:
+    ksampler_node: "ksampler"
+    clip_source: "clip_loader:0"
+
+dynamic_qwen_image_edit_chains:
+  qwen_image_edit_chain:
+    ksampler_node: "ksampler"
+    pos_prompt_node: "pos_prompt"
+    neg_prompt_node: "neg_prompt"
+    vae_loader_node: "vae_loader"
+    model_sampler_node: "model_sampler"
+
+dynamic_pid_chains:
+  pid_chain:
+    ksampler_node: "ksampler"
+
+ui_map:
+  positive_prompt: "pos_prompt:prompt"
+  negative_prompt: "neg_prompt:prompt"
+  unet_name: "unet_loader:unet_name"
+  vae_name: "vae_loader:vae_name"
   clip_name: "clip_loader:clip_name"

core/pipelines/workflow_recipes/{sd_unified_recipe.yaml → unified_recipe.yaml}

@@ -1,8 +1,8 @@
-imports:
-  - "_partials/_base_sampler_sd.yaml"
-  - "_partials/input/{{ task_type }}.yaml"
-  - "_partials/conditioning/{{ model_type }}.yaml"
-
-connections:
-  - from: "latent_source:0"
+imports:
+  - "_partials/_base_sampler.yaml"
+  - "_partials/input/{{ task_type }}.yaml"
+  - "_partials/conditioning/{{ model_type }}.yaml"
+
+connections:
+  - from: "latent_source:0"
     to: "ksampler:latent_image"

core/workflow_assembler.py

@@ -1,179 +1,165 @@
-import yaml
-import os
-import importlib
-from copy import deepcopy
-from comfy_integration.nodes import NODE_CLASS_MAPPINGS
-
-class WorkflowAssembler:
-    def __init__(self, recipe_path, dynamic_values=None):
-        self.base_path = os.path.dirname(recipe_path)
-        self.node_counter = 0
-        self.workflow = {}
-        self.node_map = {}
-        
-        self._load_injector_config()
-        
-        self.recipe = self._load_and_merge_recipe(os.path.basename(recipe_path), dynamic_values or {})
-
-    def _load_injector_config(self):
-        try:
-            project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-            injectors_path = os.path.join(project_root, 'yaml', 'injectors.yaml')
-            
-            with open(injectors_path, 'r', encoding='utf-8') as f:
-                injector_config = yaml.safe_load(f)
-
-            definitions = injector_config.get("injector_definitions", {})
-            self.injector_order = injector_config.get("injector_order", [])
-            self.global_injectors = {}
-
-            for chain_type, config in definitions.items():
-                module_path = config.get("module")
-                if not module_path:
-                    print(f"Warning: Injector '{chain_type}' in injectors.yaml is missing 'module' path.")
-                    continue
-                try:
-                    module = importlib.import_module(module_path)
-                    if hasattr(module, 'inject'):
-                        self.global_injectors[chain_type] = module.inject
-                    else:
-                        print(f"⚠️ Warning: Module '{module_path}' for injector '{chain_type}' does not have an 'inject' function.")
-                except ImportError as e:
-                    print(f"❌ Error importing module '{module_path}' for injector '{chain_type}': {e}")
-            
-            if not self.injector_order:
-                 print("⚠️ Warning: 'injector_order' is not defined in injectors.yaml. Using definition order.")
-                 self.injector_order = list(definitions.keys())
-
-        except FileNotFoundError:
-            print(f"❌ FATAL: Could not find injectors.yaml at {injectors_path}. Dynamic chains will not work.")
-            self.injector_order = []
-            self.global_injectors = {}
-        except Exception as e:
-            print(f"❌ FATAL: Could not load or parse injectors.yaml. Dynamic chains will not work. Error: {e}")
-            self.injector_order = []
-            self.global_injectors = {}
-
-    def _get_unique_id(self):
-        self.node_counter += 1
-        return str(self.node_counter)
-
-    def _get_node_template(self, class_type):
-        if class_type not in NODE_CLASS_MAPPINGS:
-            raise ValueError(f"Node class '{class_type}' not found. Ensure it's correctly imported in comfy_integration/nodes.py.")
-        
-        node_class = NODE_CLASS_MAPPINGS[class_type]
-        input_types = node_class.INPUT_TYPES()
-        
-        template = {
-            "inputs": {},
-            "class_type": class_type,
-            "_meta": {"title": node_class.NODE_NAME if hasattr(node_class, 'NODE_NAME') else class_type}
-        }
-        
-        all_inputs = {**input_types.get('required', {}), **input_types.get('optional', {})}
-        for name, details in all_inputs.items():
-            config = details[1] if len(details) > 1 and isinstance(details[1], dict) else {}
-            template["inputs"][name] = config.get("default")
-            
-        return template
-
-    def _load_and_merge_recipe(self, recipe_filename, dynamic_values, search_context_dir=None):
-        search_path = search_context_dir or self.base_path
-        recipe_path_to_use = os.path.join(search_path, recipe_filename)
-
-        if not os.path.exists(recipe_path_to_use):
-            raise FileNotFoundError(f"Recipe file not found: {recipe_path_to_use}")
-
-        with open(recipe_path_to_use, 'r', encoding='utf-8') as f:
-            content = f.read()
-
-        for key, value in dynamic_values.items():
-            if value is not None:
-                content = content.replace(f"{{{{ {key} }}}}", str(value))
-        
-        main_recipe = yaml.safe_load(content)
-        
-        merged_recipe = {'nodes': {}, 'connections': [], 'ui_map': {}}
-        for key in self.injector_order:
-            if key.startswith('dynamic_'):
-                merged_recipe[key] = {}
-
-        parent_recipe_dir = os.path.dirname(recipe_path_to_use)
-        for import_path_template in main_recipe.get('imports', []):
-            import_path = import_path_template
-            for key, value in dynamic_values.items():
-                if value is not None:
-                    import_path = import_path.replace(f"{{{{ {key} }}}}", str(value))
-            
-            try:
-                imported_recipe = self._load_and_merge_recipe(import_path, dynamic_values, search_context_dir=parent_recipe_dir)
-                merged_recipe['nodes'].update(imported_recipe.get('nodes', {}))
-                merged_recipe['connections'].extend(imported_recipe.get('connections', []))
-                merged_recipe['ui_map'].update(imported_recipe.get('ui_map', {}))
-                for key in self.injector_order:
-                    if key in imported_recipe and key.startswith('dynamic_'):
-                        merged_recipe[key].update(imported_recipe.get(key, {}))
-            except FileNotFoundError:
-                print(f"Warning: Optional recipe partial '{import_path}' not found. Skipping.")
-
-        merged_recipe['nodes'].update(main_recipe.get('nodes', {}))
-        merged_recipe['connections'].extend(main_recipe.get('connections', []))
-        merged_recipe['ui_map'].update(main_recipe.get('ui_map', {}))
-        for key in self.injector_order:
-            if key in main_recipe and key.startswith('dynamic_'):
-                merged_recipe[key].update(main_recipe.get(key, {}))
-        
-        return merged_recipe
-
-    def assemble(self, ui_values):
-        self.ui_values = ui_values
-        for name, details in self.recipe['nodes'].items():
-            class_type = details['class_type']
-            template = self._get_node_template(class_type)
-            node_data = deepcopy(template)
-            
-            unique_id = self._get_unique_id()
-            self.node_map[name] = unique_id
-            
-            if 'params' in details:
-                for param, value in details['params'].items():
-                    if param in node_data['inputs']:
-                        node_data['inputs'][param] = value
-            
-            self.workflow[unique_id] = node_data
-
-        for ui_key, target in self.recipe.get('ui_map', {}).items():
-            if ui_key in ui_values and ui_values[ui_key] is not None:
-                target_list = target if isinstance(target, list) else [target]
-                for t in target_list:
-                    target_name, target_param = t.split(':')
-                    if target_name in self.node_map:
-                        self.workflow[self.node_map[target_name]]['inputs'][target_param] = ui_values[ui_key]
-
-        for conn in self.recipe.get('connections', []):
-            from_name, from_output_idx = conn['from'].split(':')
-            to_name, to_input_name = conn['to'].split(':')
-            
-            from_id = self.node_map.get(from_name)
-            to_id = self.node_map.get(to_name)
-            
-            if from_id and to_id:
-                self.workflow[to_id]['inputs'][to_input_name] = [from_id, int(from_output_idx)]
-        
-        print("--- [Assembler] Applying dynamic injectors ---")
-        recipe_chain_types = {key for key in self.recipe if key.startswith('dynamic_')}
-        processing_order = [key for key in self.injector_order if key in recipe_chain_types]
-
-        for chain_type in processing_order:
-            injector_func = self.global_injectors.get(chain_type)
-            if injector_func:
-                for chain_key, chain_def in self.recipe.get(chain_type, {}).items():
-                    if chain_key in ui_values and ui_values[chain_key]:
-                        print(f"  -> Injecting '{chain_type}' for '{chain_key}'...")
-                        chain_items = ui_values[chain_key]
-                        injector_func(self, chain_def, chain_items)
-        
-        print("--- [Assembler] Finished applying injectors ---")
-        
+import yaml
+import os
+import importlib
+from copy import deepcopy
+from comfy_integration.nodes import NODE_CLASS_MAPPINGS
+from chain_injectors import discover_injectors, get_registered_features
+from core.settings import FEATURES_CONFIG
+
+class WorkflowAssembler:
+    def __init__(self, recipe_path, dynamic_values=None):
+        self.base_path = os.path.dirname(recipe_path)
+        self.dynamic_values = dynamic_values or {}
+        self.node_counter = 0
+        self.workflow = {}
+        self.node_map = {}
+        
+        model_type = self.dynamic_values.get('model_type')
+        self._load_injector_config(model_type=model_type)
+        
+        self.recipe = self._load_and_merge_recipe(os.path.basename(recipe_path), self.dynamic_values)
+
+    def _load_injector_config(self, model_type=None):
+        self.global_injectors = discover_injectors()
+        registered_features = get_registered_features()
+
+        order = []
+        if model_type and model_type in FEATURES_CONFIG:
+            enabled_features = FEATURES_CONFIG[model_type].get('enabled_chains', [])
+            for feat in enabled_features:
+                if feat in registered_features:
+                    chain_key = registered_features[feat]['chain_type']
+                else:
+                    chain_key = f"dynamic_{feat}_chains"
+                if chain_key in self.global_injectors and chain_key not in order:
+                    order.append(chain_key)
+
+        for chain_key in self.global_injectors.keys():
+            if chain_key not in order:
+                order.append(chain_key)
+
+        self.injector_order = order
+
+    def _get_unique_id(self):
+        self.node_counter += 1
+        return str(self.node_counter)
+
+    def _get_node_template(self, class_type):
+        if class_type not in NODE_CLASS_MAPPINGS:
+            raise ValueError(f"Node class '{class_type}' not found. Ensure it's correctly imported in comfy_integration/nodes.py.")
+        
+        node_class = NODE_CLASS_MAPPINGS[class_type]
+        input_types = node_class.INPUT_TYPES()
+        
+        template = {
+            "inputs": {},
+            "class_type": class_type,
+            "_meta": {"title": node_class.NODE_NAME if hasattr(node_class, 'NODE_NAME') else class_type}
+        }
+        
+        all_inputs = {**input_types.get('required', {}), **input_types.get('optional', {})}
+        for name, details in all_inputs.items():
+            config = details[1] if len(details) > 1 and isinstance(details[1], dict) else {}
+            template["inputs"][name] = config.get("default")
+            
+        return template
+
+    def _load_and_merge_recipe(self, recipe_filename, dynamic_values, search_context_dir=None):
+        search_path = search_context_dir or self.base_path
+        recipe_path_to_use = os.path.join(search_path, recipe_filename)
+
+        if not os.path.exists(recipe_path_to_use):
+            raise FileNotFoundError(f"Recipe file not found: {recipe_path_to_use}")
+
+        with open(recipe_path_to_use, 'r', encoding='utf-8') as f:
+            content = f.read()
+
+        for key, value in dynamic_values.items():
+            if value is not None:
+                content = content.replace(f"{{{{ {key} }}}}", str(value))
+        
+        main_recipe = yaml.safe_load(content)
+        
+        merged_recipe = {'nodes': {}, 'connections': [], 'ui_map': {}}
+        for key in self.injector_order:
+            if key.startswith('dynamic_'):
+                merged_recipe[key] = {}
+
+        parent_recipe_dir = os.path.dirname(recipe_path_to_use)
+        for import_path_template in main_recipe.get('imports', []):
+            import_path = import_path_template
+            for key, value in dynamic_values.items():
+                if value is not None:
+                    import_path = import_path.replace(f"{{{{ {key} }}}}", str(value))
+            
+            try:
+                imported_recipe = self._load_and_merge_recipe(import_path, dynamic_values, search_context_dir=parent_recipe_dir)
+                merged_recipe['nodes'].update(imported_recipe.get('nodes', {}))
+                merged_recipe['connections'].extend(imported_recipe.get('connections', []))
+                merged_recipe['ui_map'].update(imported_recipe.get('ui_map', {}))
+                for key in self.injector_order:
+                    if key in imported_recipe and key.startswith('dynamic_'):
+                        merged_recipe[key].update(imported_recipe.get(key, {}))
+            except FileNotFoundError:
+                print(f"Warning: Optional recipe partial '{import_path}' not found. Skipping.")
+
+        merged_recipe['nodes'].update(main_recipe.get('nodes', {}))
+        merged_recipe['connections'].extend(main_recipe.get('connections', []))
+        merged_recipe['ui_map'].update(main_recipe.get('ui_map', {}))
+        for key in self.injector_order:
+            if key in main_recipe and key.startswith('dynamic_'):
+                merged_recipe[key].update(main_recipe.get(key, {}))
+        
+        return merged_recipe
+
+    def assemble(self, ui_values):
+        self.ui_values = ui_values
+        for name, details in self.recipe['nodes'].items():
+            class_type = details['class_type']
+            template = self._get_node_template(class_type)
+            node_data = deepcopy(template)
+            
+            unique_id = self._get_unique_id()
+            self.node_map[name] = unique_id
+            
+            if 'params' in details:
+                for param, value in details['params'].items():
+                    if param in node_data['inputs']:
+                        node_data['inputs'][param] = value
+            
+            self.workflow[unique_id] = node_data
+
+        for ui_key, target in self.recipe.get('ui_map', {}).items():
+            if ui_key in ui_values and ui_values[ui_key] is not None:
+                target_list = target if isinstance(target, list) else [target]
+                for t in target_list:
+                    target_name, target_param = t.split(':')
+                    if target_name in self.node_map:
+                        self.workflow[self.node_map[target_name]]['inputs'][target_param] = ui_values[ui_key]
+
+        for conn in self.recipe.get('connections', []):
+            from_name, from_output_idx = conn['from'].split(':')
+            to_name, to_input_name = conn['to'].split(':')
+            
+            from_id = self.node_map.get(from_name)
+            to_id = self.node_map.get(to_name)
+            
+            if from_id and to_id:
+                self.workflow[to_id]['inputs'][to_input_name] = [from_id, int(from_output_idx)]
+        
+        print("--- [Assembler] Applying dynamic injectors ---")
+        recipe_chain_types = {key for key in self.recipe if key.startswith('dynamic_')}
+        processing_order = [key for key in self.injector_order if key in recipe_chain_types]
+
+        for chain_type in processing_order:
+            injector_func = self.global_injectors.get(chain_type)
+            if injector_func:
+                for chain_key, chain_def in self.recipe.get(chain_type, {}).items():
+                    if chain_key in ui_values and ui_values[chain_key]:
+                        print(f"  -> Injecting '{chain_type}' for '{chain_key}'...")
+                        chain_items = ui_values[chain_key]
+                        injector_func(self, chain_def, chain_items)
+        
+        print("--- [Assembler] Finished applying injectors ---")
+        
         return self.workflow

ui/events/change_handlers.py

@@ -52,13 +52,13 @@ def make_update_fn(m_comp, cat_comp, cs_comp, ar_comp, width_comp, height_comp,
         architectures_dict = ARCHITECTURES_CONFIG.get('architectures', {})
         arch_model_type = architectures_dict.get(m_type, {}).get("model_type", m_type.lower().replace(" ", "").replace(".", ""))
 
-        arch_features = FEATURES_CONFIG.get(arch_model_type, FEATURES_CONFIG.get('default', {}))
+        arch_features = FEATURES_CONFIG.get(arch_model_type, {})
         enabled_chains = arch_features.get('enabled_chains', [])
 
         if lora_acc: updates[lora_acc] = gr.update(visible=('lora' in enabled_chains))
         if cn_acc: updates[cn_acc] = gr.update(visible=('controlnet' in enabled_chains))
         if anima_cn_acc: updates[anima_cn_acc] = gr.update(visible=('anima_controlnet_lllite' in enabled_chains))
-        if diffsynth_cn_acc: updates[diffsynth_cn_acc] = gr.update(visible=('controlnet_model_patch' in enabled_chains))
+        if diffsynth_cn_acc: updates[diffsynth_cn_acc] = gr.update(visible=('diffsynth_controlnet' in enabled_chains))
         if krea2_cn_acc: updates[krea2_cn_acc] = gr.update(visible=('krea2_controlnet' in enabled_chains))
         if ipa_acc: updates[ipa_acc] = gr.update(visible=('ipadapter' in enabled_chains))
         if flux1_ipa_acc: updates[flux1_ipa_acc] = gr.update(visible=('flux1_ipadapter' in enabled_chains))
@@ -68,11 +68,11 @@ def make_update_fn(m_comp, cat_comp, cs_comp, ar_comp, width_comp, height_comp,
         if cond_acc: updates[cond_acc] = gr.update(visible=('conditioning' in enabled_chains))
         if ref_latent_acc: updates[ref_latent_acc] = gr.update(visible=('reference_latent' in enabled_chains))
         if hidream_o1_ref_acc: updates[hidream_o1_ref_acc] = gr.update(visible=('hidream_o1_reference' in enabled_chains))
-        if joyai_ref_acc: updates[joyai_ref_acc] = gr.update(visible=('joyai_reference' in enabled_chains))
+        if joyai_ref_acc: updates[joyai_ref_acc] = gr.update(visible=('joyai_image' in enabled_chains))
         if krea2_identity_edit_acc: updates[krea2_identity_edit_acc] = gr.update(visible=('krea2_identity_edit' in enabled_chains))
-        if krea2_reference_edit_acc: updates[krea2_reference_edit_acc] = gr.update(visible=('krea2_reference_edit' in enabled_chains))
+        if krea2_reference_edit_acc: updates[krea2_reference_edit_acc] = gr.update(visible=('krea2_style_reference' in enabled_chains))
         if qwen_image_edit_acc: updates[qwen_image_edit_acc] = gr.update(visible=('qwen_image_edit' in enabled_chains))
-        if boogu_edit_acc: updates[boogu_edit_acc] = gr.update(visible=('boogu_edit' in enabled_chains))
+        if boogu_edit_acc: updates[boogu_edit_acc] = gr.update(visible=('boogu_image_edit' in enabled_chains))
         if ref_img_acc: updates[ref_img_acc] = gr.update(visible=('reference_image' in enabled_chains))
         if pid_acc: updates[pid_acc] = gr.update(visible=('pid' in enabled_chains))
         if vae_acc: updates[vae_acc] = gr.update(visible=('vae' in enabled_chains))
@@ -188,13 +188,13 @@ def make_model_change_fn(cat_comp_ref, cs_comp, ar_comp, width_comp, height_comp
         architectures_dict = ARCHITECTURES_CONFIG.get('architectures', {})
         arch_model_type = architectures_dict.get(m_type, {}).get("model_type", m_type.lower().replace(" ", "").replace(".", ""))
 
-        arch_features = FEATURES_CONFIG.get(arch_model_type, FEATURES_CONFIG.get('default', {}))
+        arch_features = FEATURES_CONFIG.get(arch_model_type, {})
         enabled_chains = arch_features.get('enabled_chains', [])
 
         if lora_acc: updates[lora_acc] = gr.update(visible=('lora' in enabled_chains))
         if cn_acc: updates[cn_acc] = gr.update(visible=('controlnet' in enabled_chains))
         if anima_cn_acc: updates[anima_cn_acc] = gr.update(visible=('anima_controlnet_lllite' in enabled_chains))
-        if diffsynth_cn_acc: updates[diffsynth_cn_acc] = gr.update(visible=('controlnet_model_patch' in enabled_chains))
+        if diffsynth_cn_acc: updates[diffsynth_cn_acc] = gr.update(visible=('diffsynth_controlnet' in enabled_chains))
         if krea2_cn_acc: updates[krea2_cn_acc] = gr.update(visible=('krea2_controlnet' in enabled_chains))
         if ipa_acc: updates[ipa_acc] = gr.update(visible=('ipadapter' in enabled_chains))
         if flux1_ipa_acc: updates[flux1_ipa_acc] = gr.update(visible=('flux1_ipadapter' in enabled_chains))
@@ -204,11 +204,11 @@ def make_model_change_fn(cat_comp_ref, cs_comp, ar_comp, width_comp, height_comp
         if cond_acc: updates[cond_acc] = gr.update(visible=('conditioning' in enabled_chains))
         if ref_latent_acc: updates[ref_latent_acc] = gr.update(visible=('reference_latent' in enabled_chains))
         if hidream_o1_ref_acc: updates[hidream_o1_ref_acc] = gr.update(visible=('hidream_o1_reference' in enabled_chains))
-        if joyai_ref_acc: updates[joyai_ref_acc] = gr.update(visible=('joyai_reference' in enabled_chains))
+        if joyai_ref_acc: updates[joyai_ref_acc] = gr.update(visible=('joyai_image' in enabled_chains))
         if krea2_identity_edit_acc: updates[krea2_identity_edit_acc] = gr.update(visible=('krea2_identity_edit' in enabled_chains))
-        if krea2_reference_edit_acc: updates[krea2_reference_edit_acc] = gr.update(visible=('krea2_reference_edit' in enabled_chains))
+        if krea2_reference_edit_acc: updates[krea2_reference_edit_acc] = gr.update(visible=('krea2_style_reference' in enabled_chains))
         if qwen_image_edit_acc: updates[qwen_image_edit_acc] = gr.update(visible=('qwen_image_edit' in enabled_chains))
-        if boogu_edit_acc: updates[boogu_edit_acc] = gr.update(visible=('boogu_edit' in enabled_chains))
+        if boogu_edit_acc: updates[boogu_edit_acc] = gr.update(visible=('boogu_image_edit' in enabled_chains))
         if ref_img_acc: updates[ref_img_acc] = gr.update(visible=('reference_image' in enabled_chains))
         if pid_acc: updates[pid_acc] = gr.update(visible=('pid' in enabled_chains))
         if vae_acc: updates[vae_acc] = gr.update(visible=('vae' in enabled_chains))

ui/shared/ui_components.py

@@ -15,7 +15,7 @@ default_model_name = list(MODEL_MAP_CHECKPOINT.keys())[0] if MODEL_MAP_CHECKPOIN
 default_m_type = MODEL_TYPE_MAP.get(default_model_name, "SDXL") if default_model_name else "SDXL"
 default_architectures_dict = ARCHITECTURES_CONFIG.get('architectures', {})
 default_arch_model_type = default_architectures_dict.get(default_m_type, {}).get("model_type", default_m_type.lower().replace(" ", "").replace(".", ""))
-default_arch_features = FEATURES_CONFIG.get(default_arch_model_type, FEATURES_CONFIG.get('default', {}))
+default_arch_features = FEATURES_CONFIG.get(default_arch_model_type, {})
 default_enabled_chains = default_arch_features.get('enabled_chains', [])
 
 default_vals = MODEL_DEFAULTS_CONFIG.get('Default', {})
@@ -297,7 +297,7 @@ def create_diffsynth_controlnet_ui(prefix: str, max_units=MAX_CONTROLNETS):
     components = {}
     key = lambda name: f"{name}_{prefix}"
 
-    with gr.Accordion("DiffSynth ControlNet Settings", open=False, visible=('controlnet_model_patch' in default_enabled_chains)) as accordion:
+    with gr.Accordion("DiffSynth ControlNet Settings", open=False, visible=('diffsynth_controlnet' in default_enabled_chains)) as accordion:
         components[key('diffsynth_controlnet_accordion')] = accordion
         
         cn_rows, images, series, types, strengths, filepaths = [], [], [], [], [], []
@@ -700,7 +700,7 @@ def create_joyai_reference_ui(prefix: str, max_units=2):
     components = {}
     key = lambda name: f"{name}_{prefix}"
 
-    with gr.Accordion("JoyAI Reference Edit Settings", open=False, visible=('joyai_reference' in default_enabled_chains)) as ref_accordion:
+    with gr.Accordion("JoyAI Reference Edit Settings", open=False, visible=('joyai_image' in default_enabled_chains)) as ref_accordion:
         components[key('joyai_reference_accordion')] = ref_accordion
         gr.Markdown("💡 **Tip:** For multimodal models, this feature enables powerful editing and combining capabilities. In txt2img mode, adding a single reference image performs an **Image Edit** (JoyAI-Image-Edit recommended), while adding multiple images performs an **Image Combine** (JoyAI-Image-Edit-Plus recommended with ZeroGPU Duration (s) set to 120).")
         
@@ -833,7 +833,7 @@ def create_krea2_reference_edit_ui(prefix: str, max_units=3):
     components = {}
     key = lambda name: f"{name}_{prefix}"
 
-    with gr.Accordion("Krea2 Style Reference Edit Settings", open=False, visible=('krea2_reference_edit' in default_enabled_chains)) as ref_accordion:
+    with gr.Accordion("Krea2 Style Reference Edit Settings", open=False, visible=('krea2_style_reference' in default_enabled_chains)) as ref_accordion:
         components[key('krea2_reference_edit_accordion')] = ref_accordion
         gr.Markdown("💡 **Tip:** (Krea-2-Turbo recommended) Add style reference images to perform style reference editing.")
         
@@ -862,7 +862,7 @@ def create_boogu_edit_ui(prefix: str, max_units=10):
     components = {}
     key = lambda name: f"{name}_{prefix}"
 
-    with gr.Accordion("Boogu-Image Edit Settings", open=False, visible=('boogu_edit' in default_enabled_chains)) as ref_accordion:
+    with gr.Accordion("Boogu-Image Edit Settings", open=False, visible=('boogu_image_edit' in default_enabled_chains)) as ref_accordion:
         components[key('boogu_edit_accordion')] = ref_accordion
         gr.Markdown("💡 **Tip:** (Boogu-Image-Edit-Turbo/Boogu-Image-Edit recommended, Boogu-Image-Edit need set ZeroGPU Duration (s) to 120 ) In txt2img mode, adding a single reference image performs an **Image Edit**, while adding multiple images performs an **Image Combine**.")
         

yaml/image_gen_features.yaml

@@ -1,208 +1,200 @@
-default:
-  enabled_chains:
-  - lora
-  - controlnet
-  - ipadapter
-  - embedding
-  - style
-  - conditioning
-  - vae
-
-mage-flow:
-  enabled_chains:
-  - reference_image
-
-joyai-image:
-  enabled_chains:
-  - joyai_reference
-
-krea-2:
-  enabled_chains:
-    - lora
-    - krea2_controlnet
-    - krea2_identity_edit
-    - krea2_reference_edit
-    - pid
-
-boogu-image:
-  enabled_chains:
-  - lora
-  - boogu_edit
-  - pid
-
-pixeldit:
-  enabled_chains:
-  - conditioning
-
-ideogram-4:
-  enabled_chains:
-  - vae
-  - pid
-
-lens:
-  enabled_chains:
-  - conditioning
-  - pid
-
-flux2-kv:
-  enabled_chains:
-  - lora
-  - conditioning
-  - reference_latent
-  - vae
-  - pid
-
-flux2:
-  enabled_chains:
-  - lora
-  - conditioning
-  - reference_latent
-  - vae
-  - pid
-
-ernie-image:
-  enabled_chains:
-  - conditioning
-  - pid
-
-z-image:
-  enabled_chains:
-  - lora
-  - conditioning
-  - controlnet_model_patch
-  - vae
-  - pid
-
-qwen-image:
-  enabled_chains:
-  - lora
-  - controlnet
-  - conditioning
-  - qwen_image_edit
-  - vae
-  - pid
-
-longcat-image:
-  enabled_chains:
-  - lora
-  - conditioning
-  - pid
-
-cosmos-predict2:
-  enabled_chains:
-  - conditioning
-  - vae
-
-anima:
-  enabled_chains:
-  - lora
-  - anima_controlnet_lllite
-  - conditioning
-  - vae
-  - pid
-
-newbie-image:
-  enabled_chains:
-  - lora
-  - embedding
-  - conditioning
-  - vae
-  - pid
-
-kandinsky-5:
-  enabled_chains:
-  - conditioning
-  - vae
-  - pid
-
-ovis-image:
-  enabled_chains:
-  - conditioning
-  - vae
-  - pid
-
-hunyuanimage:
-  enabled_chains:
-  - conditioning
-  - vae
-
-chroma1-radiance:
-  enabled_chains:
-  - conditioning
-
-chroma1:
-  enabled_chains:
-  - conditioning
-  - vae
-  - pid
-
-omnigen2:
-  enabled_chains:
-  - conditioning
-  - reference_latent
-  - pid
-
-lumina:
-  enabled_chains:
-  - lora
-  - embedding
-  - conditioning
-  - vae
-  - pid
-
-hidream-o1:
-  enabled_chains:
-  - lora
-  - conditioning
-  - hidream_o1_reference
-
-hidream-i1:
-  enabled_chains:
-  - lora
-  - conditioning
-  - pid
-
-flux1:
-  enabled_chains:
-  - lora
-  - controlnet
-  - style
-  - conditioning
-  - flux1_ipadapter
-  - vae
-  - pid
-
-auraflow:
-  enabled_chains:
-  - lora
-  - conditioning
-  - vae
-
-sd35:
-  enabled_chains:
-  - lora
-  - controlnet
-  - embedding
-  - conditioning
-  - sd3_ipadapter
-  - vae
-  - pid
-
-sdxl:
-  enabled_chains:
-  - lora
-  - controlnet
-  - ipadapter
-  - embedding
-  - conditioning
-  - vae
-  - pid
-
-sd15:
-  enabled_chains:
-  - lora
-  - controlnet
-  - ipadapter
-  - embedding
-  - conditioning
+# Feature names in enabled_chains correspond 1-to-1 with chain_injectors/<name>_injector.py
+krea-2:
+  enabled_chains:
+  - lora
+  - krea2_controlnet
+  - krea2_identity_edit
+  - krea2_style_reference
+  - pid
+
+mage-flow:
+  enabled_chains:
+  - reference_image
+
+joyai-image:
+  enabled_chains:
+  - joyai_image
+
+boogu-image:
+  enabled_chains:
+  - lora
+  - boogu_image_edit
+  - pid
+
+pixeldit:
+  enabled_chains:
+  - conditioning
+
+ideogram-4:
+  enabled_chains:
+  - vae
+  - pid
+
+lens:
+  enabled_chains:
+  - conditioning
+  - pid
+
+flux2-kv:
+  enabled_chains:
+  - lora
+  - reference_latent
+  - conditioning
+  - vae
+  - pid
+
+flux2:
+  enabled_chains:
+  - lora
+  - reference_latent
+  - conditioning
+  - vae
+  - pid
+
+ernie-image:
+  enabled_chains:
+  - conditioning
+  - pid
+
+z-image:
+  enabled_chains:
+  - lora
+  - diffsynth_controlnet
+  - conditioning
+  - vae
+  - pid
+
+qwen-image:
+  enabled_chains:
+  - lora
+  - conditioning
+  - controlnet
+  - qwen_image_edit
+  - vae
+  - pid
+
+longcat-image:
+  enabled_chains:
+  - lora
+  - conditioning
+  - pid
+
+cosmos-predict2:
+  enabled_chains:
+  - conditioning
+  - vae
+
+anima:
+  enabled_chains:
+  - lora
+  - conditioning
+  - anima_controlnet_lllite
+  - vae
+  - pid
+
+newbie-image:
+  enabled_chains:
+  - lora
+  - embedding
+  - conditioning
+  - vae
+  - pid
+
+kandinsky-5:
+  enabled_chains:
+  - conditioning
+  - vae
+  - pid
+
+ovis-image:
+  enabled_chains:
+  - conditioning
+  - vae
+  - pid
+
+hunyuanimage:
+  enabled_chains:
+  - conditioning
+  - vae
+
+chroma1-radiance:
+  enabled_chains:
+  - conditioning
+
+chroma1:
+  enabled_chains:
+  - conditioning
+  - vae
+  - pid
+
+omnigen2:
+  enabled_chains:
+  - reference_latent
+  - conditioning
+  - pid
+
+lumina:
+  enabled_chains:
+  - lora
+  - embedding
+  - conditioning
+  - vae
+  - pid
+
+hidream-o1:
+  enabled_chains:
+  - lora
+  - conditioning
+  - hidream_o1_smoothing
+  - hidream_o1_reference
+
+hidream-i1:
+  enabled_chains:
+  - lora
+  - conditioning
+  - pid
+
+flux1:
+  enabled_chains:
+  - lora
+  - flux1_ipadapter
+  - conditioning
+  - style
+  - controlnet
+  - vae
+  - pid
+
+auraflow:
+  enabled_chains:
+  - lora
+  - conditioning
+  - vae
+
+sd35:
+  enabled_chains:
+  - lora
+  - sd3_ipadapter
+  - embedding
+  - conditioning
+  - controlnet
+  - vae
+  - pid
+
+sdxl:
+  enabled_chains:
+  - lora
+  - ipadapter
+  - embedding
+  - conditioning
+  - controlnet
+  - vae
+  - pid
+
+sd15:
+  enabled_chains:
+  - lora
+  - ipadapter
+  - embedding
+  - conditioning
+  - controlnet
   - vae

yaml/injectors.yaml

@@ -1,69 +0,0 @@
-injector_definitions:
-  dynamic_vae_chains:
-    module: "chain_injectors.vae_injector"
-  dynamic_lora_chains: 
-    module: "chain_injectors.lora_injector"
-  dynamic_newbie_lora_chains:
-    module: "chain_injectors.newbie_lora_injector"
-  dynamic_controlnet_chains: 
-    module: "chain_injectors.controlnet_injector"
-  dynamic_krea2_controlnet_chains:
-    module: "chain_injectors.krea2_controlnet_injector"
-  dynamic_anima_controlnet_lllite_chains:
-    module: "chain_injectors.anima_controlnet_lllite_injector"
-  dynamic_diffsynth_controlnet_chains:
-    module: "chain_injectors.diffsynth_controlnet_injector"
-  dynamic_ipadapter_chains:
-    module: "chain_injectors.ipadapter_injector"
-  dynamic_flux1_ipadapter_chains:
-    module: "chain_injectors.flux1_ipadapter_injector"
-  dynamic_sd3_ipadapter_chains:
-    module: "chain_injectors.sd3_ipadapter_injector"
-  dynamic_reference_latent_chains:
-    module: "chain_injectors.reference_latent_injector"
-  dynamic_conditioning_chains:
-    module: "chain_injectors.conditioning_injector"
-  dynamic_style_chains:
-    module: "chain_injectors.style_injector"
-  dynamic_hidream_o1_smoothing_chains:
-    module: "chain_injectors.hidream_o1_smoothing_injector"
-  dynamic_hidream_o1_reference_chains:
-    module: "chain_injectors.hidream_o1_reference_injector"
-  dynamic_joyai_reference_chains:
-    module: "chain_injectors.joyai_reference_injector"
-  dynamic_krea2_identity_edit_chains:
-    module: "chain_injectors.krea2_identity_edit_injector"
-  dynamic_krea2_reference_edit_chains:
-    module: "chain_injectors.krea2_reference_edit_injector"
-  dynamic_qwen_image_edit_chains:
-    module: "chain_injectors.qwen_image_edit_injector"
-  dynamic_boogu_edit_chains:
-    module: "chain_injectors.boogu_edit_injector"
-  dynamic_reference_image_chains:
-    module: "chain_injectors.reference_image_injector"
-  dynamic_pid_chains:
-    module: "chain_injectors.pid_injector"
-  
-injector_order:
-  - dynamic_vae_chains
-  - dynamic_lora_chains
-  - dynamic_newbie_lora_chains
-  - dynamic_diffsynth_controlnet_chains
-  - dynamic_ipadapter_chains
-  - dynamic_flux1_ipadapter_chains
-  - dynamic_sd3_ipadapter_chains
-  - dynamic_reference_latent_chains
-  - dynamic_conditioning_chains
-  - dynamic_style_chains
-  - dynamic_controlnet_chains
-  - dynamic_krea2_controlnet_chains
-  - dynamic_krea2_identity_edit_chains
-  - dynamic_krea2_reference_edit_chains
-  - dynamic_qwen_image_edit_chains
-  - dynamic_boogu_edit_chains
-  - dynamic_anima_controlnet_lllite_chains
-  - dynamic_hidream_o1_smoothing_chains
-  - dynamic_hidream_o1_reference_chains
-  - dynamic_joyai_reference_chains
-  - dynamic_reference_image_chains
-  - dynamic_pid_chains