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+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
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+                            Preamble
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+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

README.md

@@ -1,10 +1,10 @@
 ---
-title: ControlNet Preprocessors
+title: ImageGen7
 emoji: 🖼
 colorFrom: purple
 colorTo: red
 sdk: gradio
 sdk_version: "5.50.0"
 app_file: app.py
-short_description: ControlNet Auxiliary Preprocessors
----
+short_description: Multi-task image generator with dynamic, chainable workflows
+---

app.py

@@ -44,11 +44,11 @@ def dummy_gpu_for_startup():
     print("--- [GPU Startup] Startup check passed. ---")
     return "Startup check passed."
 
+
 def main():
-    from utils.app_utils import print_welcome_message
     from scripts import build_sage_attention
-
-    print_welcome_message()
+    from comfy_integration import setup as setup_comfyui
+    from utils.app_utils import load_ipadapter_presets
     
     print("--- [Setup] Attempting to build and install SageAttention... ---")
     try:
@@ -57,6 +57,9 @@ def main():
     except Exception as e:
         print(f"--- [Setup] ❌ SageAttention installation failed: {e}. Continuing with default attention. ---")
 
+    print("--- [Setup] Starting ComfyUI initialization ---")
+    setup_comfyui.initialize_comfyui()
+    
     print("--- [Setup] Reloading site-packages to detect newly installed packages... ---")
     try:
         site.main()
@@ -64,28 +67,18 @@ def main():
     except Exception as e:
         print(f"--- [Setup] ⚠️  Warning: Could not fully reload site-packages: {e} ---")
 
-    from comfy_integration import setup as setup_comfyui
-    from utils.app_utils import (
-        build_preprocessor_model_map,
-        build_preprocessor_parameter_map
-    )
-
-    print("--- Starting Application Setup ---")
-    
-    setup_comfyui.initialize_comfyui()
-
     print("--- Initiating GPU Startup Check & SageAttention Patch ---")
     try:
         dummy_gpu_for_startup()
     except Exception as e:
         print(f"--- [GPU Startup] ⚠️ Warning: Startup check failed: {e} ---")
 
-    print("--- Building ControlNet preprocessor maps ---")
-    from core.generation_logic import build_reverse_map
-    build_reverse_map()
-    build_preprocessor_model_map()
-    build_preprocessor_parameter_map()
-    print("--- ✅ ControlNet preprocessor setup complete. ---")
+    print("--- Starting Application Setup ---")
+    
+    print("--- Loading IPAdapter presets ---")
+    load_ipadapter_presets()
+    print("--- ✅ IPAdapter setup complete. ---")
+
 
     print("--- Environment configured. Proceeding with module imports. ---")
     from ui.layout import build_ui

chain_injectors/anima_controlnet_lllite_injector.py

@@ -0,0 +1,48 @@
+def inject(assembler, chain_definition, chain_items):
+    if not chain_items:
+        return
+
+    ksampler_name = chain_definition.get('ksampler_node', 'ksampler')
+    if ksampler_name not in assembler.node_map:
+        print(f"Warning: KSampler node '{ksampler_name}' not found for Anima LLLite chain. Skipping.")
+        return
+        
+    ksampler_id = assembler.node_map[ksampler_name]
+
+    if 'model' not in assembler.workflow[ksampler_id]['inputs']:
+        print(f"Warning: KSampler node '{ksampler_name}' is missing 'model' input. Skipping.")
+        return
+        
+    current_model_connection = assembler.workflow[ksampler_id]['inputs']['model']
+    
+    for item_data in chain_items:
+        image_loader_id = assembler._get_unique_id()
+        image_loader_node = assembler._get_node_template("LoadImage")
+        image_loader_node['inputs']['image'] = item_data['image']
+        assembler.workflow[image_loader_id] = image_loader_node
+
+        image_scaler_id = assembler._get_unique_id()
+        image_scaler_node = assembler._get_node_template("ImageScaleToTotalPixels")
+        image_scaler_node['inputs']['image'] = [image_loader_id, 0]
+        image_scaler_node['inputs']['upscale_method'] = 'nearest-exact'
+        image_scaler_node['inputs']['megapixels'] = 1.0
+        assembler.workflow[image_scaler_id] = image_scaler_node
+
+        apply_cn_id = assembler._get_unique_id()
+        apply_cn_node = assembler._get_node_template("AnimaLLLiteApply")
+        
+        apply_cn_node['inputs']['lllite_name'] = item_data['control_net_name']
+        apply_cn_node['inputs']['strength'] = item_data['strength']
+        apply_cn_node['inputs']['start_percent'] = item_data.get('start_percent', 0.0)
+        apply_cn_node['inputs']['end_percent'] = item_data.get('end_percent', 1.0)
+
+        apply_cn_node['inputs']['model'] = current_model_connection
+        apply_cn_node['inputs']['image'] = [image_scaler_id, 0]
+        
+        assembler.workflow[apply_cn_id] = apply_cn_node
+
+        current_model_connection = [apply_cn_id, 0]
+
+    assembler.workflow[ksampler_id]['inputs']['model'] = current_model_connection
+    
+    print(f"Anima LLLite injector applied. KSampler model input re-routed through {len(chain_items)} LLLite(s).")

chain_injectors/conditioning_injector.py

@@ -0,0 +1,81 @@
+def inject(assembler, chain_definition, chain_items):
+    if not chain_items:
+        return
+
+    ksampler_name = chain_definition.get('ksampler_node', 'ksampler')
+    
+    target_node_id = None
+    target_input_name = None
+
+    if ksampler_name in assembler.node_map:
+        ksampler_id = assembler.node_map[ksampler_name]
+        if 'positive' in assembler.workflow[ksampler_id]['inputs']:
+            target_node_id = ksampler_id
+            target_input_name = 'positive'
+            print(f"Conditioning injector targeting KSampler node '{ksampler_name}'.")
+    else:
+        print(f"Warning: KSampler node '{ksampler_name}' for Conditioning chain not found. Skipping.")
+        return
+    
+    if not target_node_id:
+        print("Warning: Conditioning chain could not find a valid injection point (KSampler may be missing 'positive' input). Skipping.")
+        return
+
+    clip_source_str = chain_definition.get('clip_source')
+    if not clip_source_str:
+        print("Warning: 'clip_source' definition missing in the recipe for the Conditioning chain. Skipping.")
+        return
+    clip_node_name, clip_idx_str = clip_source_str.split(':')
+    if clip_node_name not in assembler.node_map:
+        print(f"Warning: CLIP source node '{clip_node_name}' for Conditioning chain not found. Skipping.")
+        return
+    clip_connection = [assembler.node_map[clip_node_name], int(clip_idx_str)]
+    
+    original_positive_connection = assembler.workflow[target_node_id]['inputs'][target_input_name]
+    
+    area_conditioning_outputs = []
+    
+    for item_data in chain_items:
+        prompt = item_data.get('prompt', '')
+        if not prompt or not prompt.strip():
+            continue
+
+        text_encode_id = assembler._get_unique_id()
+        text_encode_node = assembler._get_node_template("CLIPTextEncode")
+        text_encode_node['inputs']['text'] = prompt
+        text_encode_node['inputs']['clip'] = clip_connection
+        assembler.workflow[text_encode_id] = text_encode_node
+
+        set_area_id = assembler._get_unique_id()
+        set_area_node = assembler._get_node_template("ConditioningSetArea")
+        set_area_node['inputs']['width'] = item_data.get('width', 1024)
+        set_area_node['inputs']['height'] = item_data.get('height', 1024)
+        set_area_node['inputs']['x'] = item_data.get('x', 0)
+        set_area_node['inputs']['y'] = item_data.get('y', 0)
+        set_area_node['inputs']['strength'] = item_data.get('strength', 1.0)
+        set_area_node['inputs']['conditioning'] = [text_encode_id, 0]
+        assembler.workflow[set_area_id] = set_area_node
+        
+        area_conditioning_outputs.append([set_area_id, 0])
+
+    if not area_conditioning_outputs:
+        return
+
+    current_combined_conditioning = area_conditioning_outputs[0]
+    if len(area_conditioning_outputs) > 1:
+        for i in range(1, len(area_conditioning_outputs)):
+            combine_id = assembler._get_unique_id()
+            combine_node = assembler._get_node_template("ConditioningCombine")
+            combine_node['inputs']['conditioning_1'] = current_combined_conditioning
+            combine_node['inputs']['conditioning_2'] = area_conditioning_outputs[i]
+            assembler.workflow[combine_id] = combine_node
+            current_combined_conditioning = [combine_id, 0]
+
+    final_combine_id = assembler._get_unique_id()
+    final_combine_node = assembler._get_node_template("ConditioningCombine")
+    final_combine_node['inputs']['conditioning_1'] = original_positive_connection
+    final_combine_node['inputs']['conditioning_2'] = current_combined_conditioning
+    assembler.workflow[final_combine_id] = final_combine_node
+
+    assembler.workflow[target_node_id]['inputs'][target_input_name] = [final_combine_id, 0]
+    print(f"Conditioning injector applied. Redirected '{target_input_name}' input with {len(area_conditioning_outputs)} regional prompts.")

chain_injectors/controlnet_injector.py

@@ -0,0 +1,60 @@
+def inject(assembler, chain_definition, chain_items):
+    if not chain_items:
+        return
+
+    ksampler_name = chain_definition.get('ksampler_node', 'ksampler')
+    if ksampler_name not in assembler.node_map:
+        print(f"Warning: Target node '{ksampler_name}' for ControlNet chain not found. Skipping chain injection.")
+        return
+        
+    ksampler_id = assembler.node_map[ksampler_name]
+
+    if 'positive' not in assembler.workflow[ksampler_id]['inputs'] or \
+       'negative' not in assembler.workflow[ksampler_id]['inputs']:
+        print(f"Warning: KSampler node '{ksampler_name}' is missing 'positive' or 'negative' inputs. Skipping ControlNet chain.")
+        return
+
+    vae_source_str = chain_definition.get('vae_source')
+    if not vae_source_str:
+        print("Warning: 'vae_source' definition missing in the recipe for the ControlNet chain. Skipping.")
+        return
+    vae_node_name, vae_idx_str = vae_source_str.split(':')
+    if vae_node_name not in assembler.node_map:
+        print(f"Warning: VAE source node '{vae_node_name}' for ControlNet chain not found. Skipping.")
+        return
+    vae_connection = [assembler.node_map[vae_node_name], int(vae_idx_str)]
+        
+    current_positive_connection = assembler.workflow[ksampler_id]['inputs']['positive']
+    current_negative_connection = assembler.workflow[ksampler_id]['inputs']['negative']
+    
+    for item_data in chain_items:
+        cn_loader_id = assembler._get_unique_id()
+        cn_loader_node = assembler._get_node_template("ControlNetLoader")
+        cn_loader_node['inputs']['control_net_name'] = item_data['control_net_name']
+        assembler.workflow[cn_loader_id] = cn_loader_node
+
+        image_loader_id = assembler._get_unique_id()
+        image_loader_node = assembler._get_node_template("LoadImage")
+        image_loader_node['inputs']['image'] = item_data['image']
+        assembler.workflow[image_loader_id] = image_loader_node
+
+        apply_cn_id = assembler._get_unique_id()
+        apply_cn_node = assembler._get_node_template(chain_definition['template'])
+        
+        apply_cn_node['inputs']['strength'] = item_data['strength']
+        
+        apply_cn_node['inputs']['positive'] = current_positive_connection
+        apply_cn_node['inputs']['negative'] = current_negative_connection
+        apply_cn_node['inputs']['control_net'] = [cn_loader_id, 0]
+        apply_cn_node['inputs']['image'] = [image_loader_id, 0]
+        apply_cn_node['inputs']['vae'] = vae_connection
+        
+        assembler.workflow[apply_cn_id] = apply_cn_node
+
+        current_positive_connection = [apply_cn_id, 0]
+        current_negative_connection = [apply_cn_id, 1]
+
+    assembler.workflow[ksampler_id]['inputs']['positive'] = current_positive_connection
+    assembler.workflow[ksampler_id]['inputs']['negative'] = current_negative_connection
+    
+    print(f"ControlNet injector applied. KSampler inputs redirected through {len(chain_items)} ControlNet nodes.")

chain_injectors/diffsynth_controlnet_injector.py

@@ -0,0 +1,75 @@
+def inject(assembler, chain_definition, chain_items):
+    if not chain_items:
+        return
+
+    model_sampler_name = chain_definition.get('model_sampler_node')
+    ksampler_name = chain_definition.get('ksampler_node', 'ksampler')
+    
+    target_node_id = None
+    target_input_name = 'model'
+    
+    if model_sampler_name and model_sampler_name in assembler.node_map:
+        model_sampler_id = assembler.node_map[model_sampler_name]
+        if target_input_name in assembler.workflow[model_sampler_id]['inputs']:
+            target_node_id = model_sampler_id
+            print(f"ControlNet Model Patch injector targeting ModelSamplingAuraFlow node '{model_sampler_name}'.")
+
+    if not target_node_id:
+        if ksampler_name in assembler.node_map:
+            ksampler_id = assembler.node_map[ksampler_name]
+            if target_input_name in assembler.workflow[ksampler_id]['inputs']:
+                target_node_id = ksampler_id
+                print(f"ControlNet Model Patch injector targeting KSampler node '{ksampler_name}'.")
+        else:
+            print(f"Warning: Neither ModelSamplingAuraFlow node '{model_sampler_name}' nor KSampler node '{ksampler_name}' found for ControlNet patch chain. Skipping.")
+            return
+            
+    if not target_node_id:
+        print(f"Warning: Could not find a valid 'model' input on target nodes. Skipping ControlNet patch chain.")
+        return
+
+    current_model_connection = assembler.workflow[target_node_id]['inputs'][target_input_name]
+    
+    vae_source_str = chain_definition.get('vae_source')
+    vae_connection = None
+    if vae_source_str:
+        try:
+            vae_node_name, vae_idx_str = vae_source_str.split(':')
+            if vae_node_name in assembler.node_map:
+                vae_connection = [assembler.node_map[vae_node_name], int(vae_idx_str)]
+            else:
+                print(f"Warning: VAE source node '{vae_node_name}' not found for ControlNet patch chain. VAE will not be connected.")
+        except ValueError:
+            print(f"Warning: Invalid 'vae_source' format '{vae_source_str}' for ControlNet patch chain. Expected 'node_name:index'. VAE will not be connected.")
+    else:
+        print(f"Warning: 'vae_source' not defined for ControlNet patch chain definition. VAE may not be connected.")
+
+    for item_data in chain_items:
+        patch_loader_id = assembler._get_unique_id()
+        patch_loader_node = assembler._get_node_template("ModelPatchLoader")
+        patch_loader_node['inputs']['name'] = item_data['control_net_name']
+        assembler.workflow[patch_loader_id] = patch_loader_node
+
+        image_loader_id = assembler._get_unique_id()
+        image_loader_node = assembler._get_node_template("LoadImage")
+        image_loader_node['inputs']['image'] = item_data['image']
+        assembler.workflow[image_loader_id] = image_loader_node
+
+        apply_cn_id = assembler._get_unique_id()
+        apply_cn_node = assembler._get_node_template(chain_definition['template'])
+        
+        apply_cn_node['inputs']['strength'] = item_data.get('strength', 1.0)
+        apply_cn_node['inputs']['model'] = current_model_connection
+        apply_cn_node['inputs']['model_patch'] = [patch_loader_id, 0]
+        apply_cn_node['inputs']['image'] = [image_loader_id, 0]
+        
+        if 'vae' in apply_cn_node['inputs'] and vae_connection:
+            apply_cn_node['inputs']['vae'] = vae_connection
+        
+        assembler.workflow[apply_cn_id] = apply_cn_node
+
+        current_model_connection = [apply_cn_id, 0]
+
+    assembler.workflow[target_node_id]['inputs'][target_input_name] = current_model_connection
+    
+    print(f"ControlNet Model Patch injector applied. Target 'model' input re-routed through {len(chain_items)} patch(es).")

chain_injectors/flux1_ipadapter_injector.py

@@ -0,0 +1,46 @@
+def inject(assembler, chain_definition, chain_items):
+    if not chain_items:
+        return
+
+    ksampler_name = chain_definition.get('ksampler_node', 'ksampler')
+    if ksampler_name not in assembler.node_map:
+        print(f"Warning: KSampler node '{ksampler_name}' not found for Flux1 IPAdapter chain. Skipping.")
+        return
+        
+    ksampler_id = assembler.node_map[ksampler_name]
+
+    if 'model' not in assembler.workflow[ksampler_id]['inputs']:
+        print(f"Warning: KSampler node '{ksampler_name}' is missing 'model' input. Skipping Flux1 IPAdapter chain.")
+        return
+        
+    current_model_connection = assembler.workflow[ksampler_id]['inputs']['model']
+    
+    for item_data in chain_items:
+        image_loader_id = assembler._get_unique_id()
+        image_loader_node = assembler._get_node_template("LoadImage")
+        image_loader_node['inputs']['image'] = item_data['image']
+        assembler.workflow[image_loader_id] = image_loader_node
+
+        ipadapter_loader_id = assembler._get_unique_id()
+        ipadapter_loader_node = assembler._get_node_template("IPAdapterFluxLoader")
+        ipadapter_loader_node['inputs']['ipadapter'] = "ip-adapter.bin"
+        ipadapter_loader_node['inputs']['clip_vision'] = "google/siglip-so400m-patch14-384"
+        ipadapter_loader_node['inputs']['provider'] = "cuda"
+        assembler.workflow[ipadapter_loader_id] = ipadapter_loader_node
+        
+        apply_ipa_id = assembler._get_unique_id()
+        apply_ipa_node = assembler._get_node_template("ApplyIPAdapterFlux")
+        
+        apply_ipa_node['inputs']['weight'] = item_data['weight']
+        apply_ipa_node['inputs']['start_percent'] = item_data.get('start_percent', 0.0)
+        apply_ipa_node['inputs']['end_percent'] = item_data.get('end_percent', 0.6)
+        
+        apply_ipa_node['inputs']['model'] = current_model_connection
+        apply_ipa_node['inputs']['ipadapter_flux'] = [ipadapter_loader_id, 0]
+        apply_ipa_node['inputs']['image'] = [image_loader_id, 0]
+        
+        assembler.workflow[apply_ipa_id] = apply_ipa_node
+        current_model_connection = [apply_ipa_id, 0]
+
+    assembler.workflow[ksampler_id]['inputs']['model'] = current_model_connection
+    print(f"Flux1 IPAdapter injector applied. KSampler model input re-routed through {len(chain_items)} IPAdapter(s).")

chain_injectors/ipadapter_injector.py

@@ -0,0 +1,106 @@
+def inject(assembler, chain_definition, chain_items):
+    if not chain_items:
+        return
+
+    final_settings = {}
+    if chain_items and isinstance(chain_items[-1], dict) and chain_items[-1].get('is_final_settings'):
+        final_settings = chain_items.pop()
+
+    if not chain_items:
+        return
+
+    end_node_name = chain_definition.get('end')
+    if not end_node_name or end_node_name not in assembler.node_map:
+        print(f"Warning: Target node '{end_node_name}' for IPAdapter chain not found. Skipping chain injection.")
+        return
+        
+    end_node_id = assembler.node_map[end_node_name]
+    
+    if 'model' not in assembler.workflow[end_node_id]['inputs']:
+        print(f"Warning: Target node '{end_node_name}' is missing 'model' input. Skipping IPAdapter chain.")
+        return
+    
+    current_model_connection = assembler.workflow[end_node_id]['inputs']['model']
+    
+    model_type = final_settings.get('model_type', 'sdxl')
+    megapixels = 1.05 if model_type == 'sdxl' else 0.39
+
+    pos_embed_outputs = []
+    neg_embed_outputs = []
+
+    for i, item_data in enumerate(chain_items):
+        loader_type = 'FaceID' if 'FACEID' in item_data.get('preset', '') else 'Unified'
+        
+        loader_template_name = "IPAdapterUnifiedLoader"
+        if loader_type == 'FaceID':
+            loader_template_name = "IPAdapterUnifiedLoaderFaceID"
+
+        image_loader_id = assembler._get_unique_id()
+        image_loader_node = assembler._get_node_template("LoadImage")
+        image_loader_node['inputs']['image'] = item_data['image']
+        assembler.workflow[image_loader_id] = image_loader_node
+        
+        image_scaler_id = assembler._get_unique_id()
+        image_scaler_node = assembler._get_node_template("ImageScaleToTotalPixels")
+        image_scaler_node['inputs']['image'] = [image_loader_id, 0]
+        image_scaler_node['inputs']['megapixels'] = megapixels
+        image_scaler_node['inputs']['upscale_method'] = "lanczos"
+        assembler.workflow[image_scaler_id] = image_scaler_node
+
+        ipadapter_loader_id = assembler._get_unique_id()
+        ipadapter_loader_node = assembler._get_node_template(loader_template_name)
+        ipadapter_loader_node['inputs']['model'] = current_model_connection
+        ipadapter_loader_node['inputs']['preset'] = item_data['preset']
+        if loader_type == 'FaceID':
+             ipadapter_loader_node['inputs']['lora_strength'] = item_data.get('lora_strength', 0.6)
+        assembler.workflow[ipadapter_loader_id] = ipadapter_loader_node
+        
+        encoder_id = assembler._get_unique_id()
+        encoder_node = assembler._get_node_template("IPAdapterEncoder")
+        encoder_node['inputs']['weight'] = item_data['weight']
+        encoder_node['inputs']['ipadapter'] = [ipadapter_loader_id, 1]
+        encoder_node['inputs']['image'] = [image_scaler_id, 0]
+        assembler.workflow[encoder_id] = encoder_node
+        
+        pos_embed_outputs.append([encoder_id, 0])
+        neg_embed_outputs.append([encoder_id, 1])
+
+    pos_combiner_id = assembler._get_unique_id()
+    pos_combiner_node = assembler._get_node_template("IPAdapterCombineEmbeds")
+    pos_combiner_node['inputs']['method'] = final_settings.get('final_combine_method', 'concat')
+    for i, conn in enumerate(pos_embed_outputs):
+        pos_combiner_node['inputs'][f'embed{i+1}'] = conn
+    assembler.workflow[pos_combiner_id] = pos_combiner_node
+
+    neg_combiner_id = assembler._get_unique_id()
+    neg_combiner_node = assembler._get_node_template("IPAdapterCombineEmbeds")
+    neg_combiner_node['inputs']['method'] = final_settings.get('final_combine_method', 'concat')
+    for i, conn in enumerate(neg_embed_outputs):
+        neg_combiner_node['inputs'][f'embed{i+1}'] = conn
+    assembler.workflow[neg_combiner_id] = neg_combiner_node
+    
+    final_loader_type = 'FaceID' if 'FACEID' in final_settings.get('final_preset', '') else 'Unified'
+    final_loader_template_name = "IPAdapterUnifiedLoader"
+    if final_loader_type == 'FaceID':
+        final_loader_template_name = "IPAdapterUnifiedLoaderFaceID"
+
+    final_loader_id = assembler._get_unique_id()
+    final_loader_node = assembler._get_node_template(final_loader_template_name)
+    final_loader_node['inputs']['model'] = current_model_connection
+    final_loader_node['inputs']['preset'] = final_settings.get('final_preset', 'STANDARD (medium strength)')
+    if final_loader_type == 'FaceID':
+        final_loader_node['inputs']['lora_strength'] = final_settings.get('final_lora_strength', 0.6)
+    assembler.workflow[final_loader_id] = final_loader_node
+
+    apply_embeds_id = assembler._get_unique_id()
+    apply_embeds_node = assembler._get_node_template("IPAdapterEmbeds")
+    apply_embeds_node['inputs']['weight'] = final_settings.get('final_weight', 1.0)
+    apply_embeds_node['inputs']['embeds_scaling'] = final_settings.get('final_embeds_scaling', 'V only')
+    apply_embeds_node['inputs']['model'] = [final_loader_id, 0]
+    apply_embeds_node['inputs']['ipadapter'] = [final_loader_id, 1]
+    apply_embeds_node['inputs']['pos_embed'] = [pos_combiner_id, 0]
+    apply_embeds_node['inputs']['neg_embed'] = [neg_combiner_id, 0]
+    assembler.workflow[apply_embeds_id] = apply_embeds_node
+
+    assembler.workflow[end_node_id]['inputs']['model'] = [apply_embeds_id, 0]
+    print(f"IPAdapter injector applied. Redirected '{end_node_name}' model input through {len(chain_items)} reference images.")

chain_injectors/lora_injector.py

@@ -0,0 +1,67 @@
+from copy import deepcopy
+
+def inject(assembler, chain_definition, chain_items):
+    if not chain_items:
+        return
+
+    start_node_name = chain_definition.get('start')
+    start_node_id = None
+    if start_node_name:
+        if start_node_name not in assembler.node_map:
+            print(f"Warning: Start node '{start_node_name}' for dynamic LoRA chain not found. Skipping chain.")
+            return
+        start_node_id = assembler.node_map[start_node_name]
+    
+    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: 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]
+        elif start_node_id:
+            start_output_idx = int(key)
+            current_connections[type_name] = [start_node_id, start_output_idx]
+        else:
+            print(f"Warning: LoRA chain has no 'start' node defined, and an output_map key '{key}' is not in 'node:index' format. Skipping this connection.")
+
+
+    input_map = chain_definition.get('input_map', {})
+    chain_output_map = chain_definition.get('template_output_map', { "0": "model", "1": "clip" })
+
+    for item_data in chain_items:
+        template_name = chain_definition['template']
+        template = assembler._get_node_template(template_name)
+        node_data = deepcopy(template)
+        
+        for param_name, value in item_data.items():
+            if param_name in node_data['inputs']:
+                node_data['inputs'][param_name] = value
+        
+        for type_name, input_name in input_map.items():
+            if type_name in current_connections:
+                node_data['inputs'][input_name] = current_connections[type_name]
+
+        new_node_id = assembler._get_unique_id()
+        assembler.workflow[new_node_id] = node_data
+        
+        for idx_str, type_name in chain_output_map.items():
+            current_connections[type_name] = [new_node_id, int(idx_str)]
+
+    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:
+                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: End node '{end_node_name}' for dynamic chain not found. Skipping connection.")

chain_injectors/newbie_lora_injector.py

@@ -0,0 +1,63 @@
+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/reference_latent_injector.py

@@ -0,0 +1,157 @@
+def inject(assembler, chain_definition, chain_items):
+    if not chain_items:
+        return
+
+    guider_node_name = chain_definition.get('guider_node')
+    guider_target_inputs = chain_definition.get('guider_target_inputs', [])
+    start_connections_map = chain_definition.get('start_connections', {})
+    vae_node_name = chain_definition.get('vae_node', 'vae_loader')
+
+    if guider_node_name and guider_node_name in assembler.node_map and guider_target_inputs:
+        guider_id = assembler.node_map[guider_node_name]
+        if vae_node_name not in assembler.node_map:
+            print(f"Warning: VAE node '{vae_node_name}' not found for Guider chain. Skipping.")
+            return
+        vae_node_id = assembler.node_map[vae_node_name]
+        
+        print(f"ReferenceLatent injector targeting DualCFGGuider node '{guider_node_name}'.")
+
+        current_connections = {}
+        for target_input in guider_target_inputs:
+            conn_str = start_connections_map.get(target_input)
+            if not conn_str:
+                print(f"Warning: No start connection defined for '{target_input}' in Guider chain. Skipping this input.")
+                continue
+            try:
+                node_name, idx_str = conn_str.split(':')
+                node_id = assembler.node_map[node_name]
+                current_connections[target_input] = [node_id, int(idx_str)]
+            except (ValueError, KeyError):
+                print(f"Warning: Invalid start connection '{conn_str}' for '{target_input}'. Skipping.")
+
+        encoded_latents = []
+        for i, img_filename in enumerate(chain_items):
+            load_id = assembler._get_unique_id()
+            load_node = assembler._get_node_template("LoadImage")
+            load_node['inputs']['image'] = img_filename
+            assembler.workflow[load_id] = load_node
+            
+            scale_id = assembler._get_unique_id()
+            scale_node = assembler._get_node_template("ImageScaleToTotalPixels")
+            scale_node['inputs']['megapixels'] = 1.0
+            scale_node['inputs']['upscale_method'] = "lanczos"
+            scale_node['inputs']['image'] = [load_id, 0]
+            assembler.workflow[scale_id] = scale_node
+            
+            vae_encode_id = assembler._get_unique_id()
+            vae_encode_node = assembler._get_node_template("VAEEncode")
+            vae_encode_node['inputs']['pixels'] = [scale_id, 0]
+            vae_encode_node['inputs']['vae'] = [vae_node_id, 0]
+            assembler.workflow[vae_encode_id] = vae_encode_node
+            encoded_latents.append([vae_encode_id, 0])
+        
+        for target_input_name, start_connection in current_connections.items():
+            current_chain_head = start_connection
+            for i, latent_conn in enumerate(encoded_latents):
+                ref_latent_id = assembler._get_unique_id()
+                ref_latent_node = assembler._get_node_template("ReferenceLatent")
+                ref_latent_node['inputs']['conditioning'] = current_chain_head
+                ref_latent_node['inputs']['latent'] = latent_conn
+                ref_latent_node['_meta']['title'] = f"{target_input_name} RefLatent {i+1}"
+                assembler.workflow[ref_latent_id] = ref_latent_node
+                current_chain_head = [ref_latent_id, 0]
+            
+            assembler.workflow[guider_id]['inputs'][target_input_name] = current_chain_head
+            print(f"  - Input '{target_input_name}' of node '{guider_node_name}' re-routed through {len(chain_items)} reference images.")
+
+        return
+
+    flux_guidance_name = chain_definition.get('flux_guidance_node')
+    ksampler_name = chain_definition.get('ksampler_node', 'ksampler')
+
+    if ksampler_name not in assembler.node_map:
+        print(f"Warning: KSampler node '{ksampler_name}' not found for ReferenceLatent chain. Skipping.")
+        return
+    if vae_node_name not in assembler.node_map:
+        print(f"Warning: VAE loader node '{vae_node_name}' not found for ReferenceLatent chain. Skipping.")
+        return
+
+    ksampler_id = assembler.node_map[ksampler_name]
+    vae_node_id = assembler.node_map[vae_node_name]
+
+    pos_target_node_id = None
+    pos_target_input_name = None
+    if flux_guidance_name and flux_guidance_name in assembler.node_map:
+        flux_guidance_id = assembler.node_map[flux_guidance_name]
+        if 'conditioning' in assembler.workflow[flux_guidance_id]['inputs']:
+            pos_target_node_id = flux_guidance_id
+            pos_target_input_name = 'conditioning'
+            print(f"ReferenceLatent injector targeting FluxGuidance node '{flux_guidance_name}' for positive chain.")
+    
+    if not pos_target_node_id:
+        if 'positive' in assembler.workflow[ksampler_id]['inputs']:
+            pos_target_node_id = ksampler_id
+            pos_target_input_name = 'positive'
+            print(f"ReferenceLatent injector targeting KSampler node '{ksampler_name}' for positive chain.")
+        else:
+            print(f"Warning: Could not find a valid positive injection point for ReferenceLatent chain. Skipping.")
+            return
+
+    current_pos_conditioning = assembler.workflow[pos_target_node_id]['inputs'][pos_target_input_name]
+
+    neg_target_node_id = ksampler_id
+    neg_target_input_name = 'negative'
+    if 'negative' not in assembler.workflow[neg_target_node_id]['inputs']:
+        print(f"Warning: KSampler node '{ksampler_name}' has no 'negative' input. Skipping negative ReferenceLatent chain.")
+        neg_target_node_id = None
+    
+    current_neg_conditioning = None
+    if neg_target_node_id:
+        current_neg_conditioning = assembler.workflow[neg_target_node_id]['inputs'][neg_target_input_name]
+
+    for i, img_filename in enumerate(chain_items):
+        load_id = assembler._get_unique_id()
+        load_node = assembler._get_node_template("LoadImage")
+        load_node['inputs']['image'] = img_filename
+        load_node['_meta']['title'] = f"Load Reference Image {i+1}"
+        assembler.workflow[load_id] = load_node
+        
+        scale_id = assembler._get_unique_id()
+        scale_node = assembler._get_node_template("ImageScaleToTotalPixels")
+        scale_node['inputs']['megapixels'] = 1.0
+        scale_node['inputs']['upscale_method'] = "lanczos"
+        scale_node['inputs']['image'] = [load_id, 0]
+        scale_node['_meta']['title'] = f"Scale Reference {i+1}"
+        assembler.workflow[scale_id] = scale_node
+        
+        vae_encode_id = assembler._get_unique_id()
+        vae_encode_node = assembler._get_node_template("VAEEncode")
+        vae_encode_node['inputs']['pixels'] = [scale_id, 0]
+        vae_encode_node['inputs']['vae'] = [vae_node_id, 0]
+        vae_encode_node['_meta']['title'] = f"VAE Encode Reference {i+1}"
+        assembler.workflow[vae_encode_id] = vae_encode_node
+        
+        latent_conn = [vae_encode_id, 0]
+
+        pos_ref_latent_id = assembler._get_unique_id()
+        pos_ref_latent_node = assembler._get_node_template("ReferenceLatent")
+        pos_ref_latent_node['inputs']['conditioning'] = current_pos_conditioning
+        pos_ref_latent_node['inputs']['latent'] = latent_conn
+        pos_ref_latent_node['_meta']['title'] = f"Positive ReferenceLatent {i+1}"
+        assembler.workflow[pos_ref_latent_id] = pos_ref_latent_node
+        current_pos_conditioning = [pos_ref_latent_id, 0]
+
+        if neg_target_node_id:
+            neg_ref_latent_id = assembler._get_unique_id()
+            neg_ref_latent_node = assembler._get_node_template("ReferenceLatent")
+            neg_ref_latent_node['inputs']['conditioning'] = current_neg_conditioning
+            neg_ref_latent_node['inputs']['latent'] = latent_conn
+            neg_ref_latent_node['_meta']['title'] = f"Negative ReferenceLatent {i+1}"
+            assembler.workflow[neg_ref_latent_id] = neg_ref_latent_node
+            current_neg_conditioning = [neg_ref_latent_id, 0]
+
+    assembler.workflow[pos_target_node_id]['inputs'][pos_target_input_name] = current_pos_conditioning
+    if neg_target_node_id:
+        assembler.workflow[neg_target_node_id]['inputs'][neg_target_input_name] = current_neg_conditioning
+    
+    print(f"ReferenceLatent injector applied. Re-routed inputs through {len(chain_items)} reference images.")

chain_injectors/sd3_ipadapter_injector.py

@@ -0,0 +1,66 @@
+def inject(assembler, chain_definition, chain_items):
+    if not chain_items:
+        return
+
+    ksampler_name = chain_definition.get('ksampler_node', 'ksampler')
+    if ksampler_name not in assembler.node_map:
+        print(f"Warning: KSampler node '{ksampler_name}' not found for SD3 IPAdapter chain. Skipping.")
+        return
+        
+    ksampler_id = assembler.node_map[ksampler_name]
+
+    if 'model' not in assembler.workflow[ksampler_id]['inputs']:
+        print(f"Warning: KSampler node '{ksampler_name}' is missing 'model' input. Skipping SD3 IPAdapter chain.")
+        return
+        
+    current_model_connection = assembler.workflow[ksampler_id]['inputs']['model']
+    
+    clip_vision_loader_id = assembler._get_unique_id()
+    clip_vision_loader_node = assembler._get_node_template("CLIPVisionLoader")
+    clip_vision_loader_node['inputs']['clip_name'] = "sigclip_vision_patch14_384.safetensors"
+    assembler.workflow[clip_vision_loader_id] = clip_vision_loader_node
+
+    ipadapter_loader_id = assembler._get_unique_id()
+    ipadapter_loader_node = assembler._get_node_template("IPAdapterSD3Loader")
+    ipadapter_loader_node['inputs']['ipadapter'] = "ip-adapter_sd35l_instantx.bin"
+    ipadapter_loader_node['inputs']['provider'] = "cuda"
+    assembler.workflow[ipadapter_loader_id] = ipadapter_loader_node
+
+    for item_data in chain_items:
+        image_loader_id = assembler._get_unique_id()
+        image_loader_node = assembler._get_node_template("LoadImage")
+        image_loader_node['inputs']['image'] = item_data['image']
+        assembler.workflow[image_loader_id] = image_loader_node
+
+        image_scaler_id = assembler._get_unique_id()
+        image_scaler_node = assembler._get_node_template("ImageScaleToTotalPixels")
+        image_scaler_node['inputs']['image'] = [image_loader_id, 0]
+        image_scaler_node['inputs']['upscale_method'] = 'nearest-exact'
+        image_scaler_node['inputs']['megapixels'] = 1.0
+        assembler.workflow[image_scaler_id] = image_scaler_node
+
+        clip_vision_encode_id = assembler._get_unique_id()
+        clip_vision_encode_node = assembler._get_node_template("CLIPVisionEncode")
+        clip_vision_encode_node['inputs']['crop'] = "center"
+        clip_vision_encode_node['inputs']['clip_vision'] = [clip_vision_loader_id, 0]
+        clip_vision_encode_node['inputs']['image'] = [image_scaler_id, 0]
+        assembler.workflow[clip_vision_encode_id] = clip_vision_encode_node
+        
+        apply_ipa_id = assembler._get_unique_id()
+        apply_ipa_node = assembler._get_node_template("ApplyIPAdapterSD3")
+        
+        apply_ipa_node['inputs']['weight'] = item_data.get('weight', 1.0)
+        apply_ipa_node['inputs']['start_percent'] = item_data.get('start_percent', 0.0)
+        apply_ipa_node['inputs']['end_percent'] = item_data.get('end_percent', 1.0)
+        
+        apply_ipa_node['inputs']['model'] = current_model_connection
+        apply_ipa_node['inputs']['ipadapter'] = [ipadapter_loader_id, 0]
+        apply_ipa_node['inputs']['image_embed'] = [clip_vision_encode_id, 0]
+        
+        assembler.workflow[apply_ipa_id] = apply_ipa_node
+        
+        current_model_connection = [apply_ipa_id, 0]
+
+    assembler.workflow[ksampler_id]['inputs']['model'] = current_model_connection
+    
+    print(f"SD3 IPAdapter injector applied. KSampler model input re-routed through {len(chain_items)} IPAdapter(s).")

chain_injectors/style_injector.py

@@ -0,0 +1,71 @@
+def inject(assembler, chain_definition, chain_items):
+    if not chain_items:
+        return
+
+    flux_guidance_name = chain_definition.get('flux_guidance_node')
+    ksampler_name = chain_definition.get('ksampler_node', 'ksampler')
+
+    target_node_id = None
+    target_input_name = None
+    
+    if flux_guidance_name and flux_guidance_name in assembler.node_map:
+        flux_guidance_id = assembler.node_map[flux_guidance_name]
+        if 'conditioning' in assembler.workflow[flux_guidance_id]['inputs']:
+            target_node_id = flux_guidance_id
+            target_input_name = 'conditioning'
+
+    if not target_node_id:
+        if ksampler_name in assembler.node_map:
+            ksampler_id = assembler.node_map[ksampler_name]
+            if 'positive' in assembler.workflow[ksampler_id]['inputs']:
+                target_node_id = ksampler_id
+                target_input_name = 'positive'
+        else:
+            return
+            
+    if not target_node_id:
+        return
+
+    current_conditioning = assembler.workflow[target_node_id]['inputs'][target_input_name]
+
+    style_model_loader_id = assembler._get_unique_id()
+    style_model_loader_node = assembler._get_node_template("StyleModelLoader")
+    style_model_loader_node['inputs']['style_model_name'] = "flux1-redux-dev.safetensors"
+    assembler.workflow[style_model_loader_id] = style_model_loader_node
+
+    clip_vision_loader_id = assembler._get_unique_id()
+    clip_vision_loader_node = assembler._get_node_template("CLIPVisionLoader")
+    clip_vision_loader_node['inputs']['clip_name'] = "sigclip_vision_patch14_384.safetensors"
+    assembler.workflow[clip_vision_loader_id] = clip_vision_loader_node
+
+    for item_data in chain_items:
+        image = item_data.get('image')
+        strength = item_data.get('strength', 1.0)
+        if not image or strength is None:
+            continue
+
+        load_image_id = assembler._get_unique_id()
+        clip_vision_encode_id = assembler._get_unique_id()
+        style_apply_id = assembler._get_unique_id()
+
+        load_image_node = assembler._get_node_template("LoadImage")
+        clip_vision_encode_node = assembler._get_node_template("CLIPVisionEncode")
+        style_apply_node = assembler._get_node_template("StyleModelApply")
+        
+        load_image_node['inputs']['image'] = image
+        clip_vision_encode_node['inputs']['crop'] = "center"
+        clip_vision_encode_node['inputs']['clip_vision'] = [clip_vision_loader_id, 0]
+        clip_vision_encode_node['inputs']['image'] = [load_image_id, 0]
+
+        style_apply_node['inputs']['strength'] = strength
+        style_apply_node['inputs']['strength_type'] = "multiply"
+        style_apply_node['inputs']['conditioning'] = current_conditioning
+        style_apply_node['inputs']['style_model'] = [style_model_loader_id, 0]
+        style_apply_node['inputs']['clip_vision_output'] = [clip_vision_encode_id, 0]
+        
+        assembler.workflow[load_image_id] = load_image_node
+        assembler.workflow[clip_vision_encode_id] = clip_vision_encode_node
+        assembler.workflow[style_apply_id] = style_apply_node
+        current_conditioning = [style_apply_id, 0]
+
+    assembler.workflow[target_node_id]['inputs'][target_input_name] = current_conditioning

chain_injectors/vae_injector.py

@@ -0,0 +1,30 @@
+def inject(assembler, chain_definition, chain_items):
+    if not chain_items:
+        return
+
+    vae_name = chain_items[0] if isinstance(chain_items, list) else chain_items
+    if not vae_name or vae_name == "None":
+        return
+
+    targets = chain_definition.get('targets', [])
+    if not targets:
+        return
+
+    vae_loader_id = assembler._get_unique_id()
+    vae_loader_node = assembler._get_node_template("VAELoader")
+    vae_loader_node['inputs']['vae_name'] = vae_name
+    assembler.workflow[vae_loader_id] = vae_loader_node
+
+    injected_count = 0
+    for target_str in targets:
+        try:
+            node_name, input_name = target_str.split(':')
+            if node_name in assembler.node_map:
+                node_id = assembler.node_map[node_name]
+                assembler.workflow[node_id]['inputs'][input_name] = [vae_loader_id, 0]
+                injected_count += 1
+        except ValueError:
+            print(f"Warning: Invalid VAE injector target format '{target_str}'. Expected 'node_name:input_name'.")
+    
+    if injected_count > 0:
+        print(f"VAE injector applied. Rerouted {injected_count} connection(s) to new VAELoader ({vae_name}).")

comfy_integration/nodes.py

@@ -1,7 +1,12 @@
 import asyncio
 import execution
 import server
-from nodes import init_extra_nodes, NODE_CLASS_MAPPINGS
+from nodes import (
+    init_extra_nodes, CheckpointLoaderSimple, EmptyLatentImage, KSampler, 
+    VAEDecode, SaveImage, NODE_CLASS_MAPPINGS, LoadImage, VAEEncode,
+    VAEEncodeForInpaint, ImagePadForOutpaint, LatentUpscaleBy, RepeatLatentBatch
+)
+
 
 def import_custom_nodes() -> None:
     loop = asyncio.new_event_loop()
@@ -13,4 +18,27 @@ def import_custom_nodes() -> None:
 
 import_custom_nodes()
 
+CLIPTextEncode = NODE_CLASS_MAPPINGS['CLIPTextEncode']
+CLIPTextEncodeSDXL = NODE_CLASS_MAPPINGS['CLIPTextEncodeSDXL']
+LoraLoader = NODE_CLASS_MAPPINGS['LoraLoader']
+CLIPSetLastLayer = NODE_CLASS_MAPPINGS['CLIPSetLastLayer']
+
+if 'EmptyHunyuanImageLatent' in NODE_CLASS_MAPPINGS:
+    EmptyHunyuanImageLatent = NODE_CLASS_MAPPINGS['EmptyHunyuanImageLatent']
+else:
+    print("⚠️ Warning: 'EmptyHunyuanImageLatent' not found in NODE_CLASS_MAPPINGS. HunyuanImage txt2img may fail if this node is required.")
+
+try:
+    KSamplerNode = NODE_CLASS_MAPPINGS['KSampler']
+    SAMPLER_CHOICES = KSamplerNode.INPUT_TYPES()["required"]["sampler_name"][0]
+    SCHEDULER_CHOICES = KSamplerNode.INPUT_TYPES()["required"]["scheduler"][0]
+except Exception:
+    print("⚠️ Could not dynamically get sampler/scheduler choices, using fallback list.")
+    SAMPLER_CHOICES = ['euler', 'dpmpp_2m_sde_gpu']
+    SCHEDULER_CHOICES = ['normal', 'karras']
+
+checkpointloadersimple = CheckpointLoaderSimple()
+loraloader = LoraLoader()
+
+
 print("✅ ComfyUI custom nodes and class mappings are ready.")

comfy_integration/setup.py

@@ -2,6 +2,8 @@ import os
 import sys
 import shutil
 
+from core.settings import *
+
 def move_and_overwrite(src, dst):
     if os.path.isdir(src):
         if os.path.exists(dst):
@@ -37,16 +39,60 @@ def initialize_comfyui():
     except OSError as e:
         print(f"⚠️ Could not remove temporary directory '{COMFYUI_TEMP_DIR}': {e}")
 
+
     print("--- Cloning third-party extensions for ComfyUI ---")
-    controlnet_aux_path = os.path.join(APP_DIR, "custom_nodes", "comfyui_controlnet_aux")
-    if not os.path.exists(controlnet_aux_path):
-        os.system(f"git clone https://github.com/Fannovel16/comfyui_controlnet_aux.git {controlnet_aux_path}")
-        print("✅ comfyui_controlnet_aux extension cloned.")
+
+    # 1. ComfyUI_IPAdapter_plus
+    ipadapter_plus_path = os.path.join(APP_DIR, "custom_nodes", "ComfyUI_IPAdapter_plus")
+    if not os.path.exists(ipadapter_plus_path):
+        os.system(f"git clone https://github.com/cubiq/ComfyUI_IPAdapter_plus.git {ipadapter_plus_path}")
+        print("✅ ComfyUI_IPAdapter_plus extension cloned.")
+    else:
+        print("✅ ComfyUI_IPAdapter_plus extension already exists.")
+
+    # 2. ComfyUI-InstantX-IPAdapter-SD3
+    ipadapter_plus_path = os.path.join(APP_DIR, "custom_nodes", "ComfyUI-InstantX-IPAdapter-SD3")
+    if not os.path.exists(ipadapter_plus_path):
+        os.system(f"git clone https://github.com/Slickytail/ComfyUI-InstantX-IPAdapter-SD3.git {ipadapter_plus_path}")
+        print("✅ ComfyUI-InstantX-IPAdapter-SD3 extension cloned.")
+    else:
+        print("✅ ComfyUI-InstantX-IPAdapter-SD3 extension already exists.")
+
+    # 3. ComfyUI-IPAdapter-Flux
+    ipadapter_flux_path = os.path.join(APP_DIR, "custom_nodes", "ComfyUI-IPAdapter-Flux")
+    if not os.path.exists(ipadapter_flux_path):
+        os.system(f"git clone https://github.com/Shakker-Labs/ComfyUI-IPAdapter-Flux.git {ipadapter_flux_path}")
+        print("✅ ComfyUI-IPAdapter-Flux extension cloned.")
     else:
-        print("✅ comfyui_controlnet_aux extension already exists.")
+        print("✅ ComfyUI-IPAdapter-Flux extension already exists.")
+
+    # 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-Anima-LLLite
+    anima_controlnet_lllite_nodes_path = os.path.join(APP_DIR, "custom_nodes", "ComfyUI-Anima-LLLite")
+    if not os.path.exists(anima_controlnet_lllite_nodes_path):
+        os.system(f"git clone https://github.com/kohya-ss/ComfyUI-Anima-LLLite.git {anima_controlnet_lllite_nodes_path}")
+        print("✅ ComfyUI-Anima-LLLite extension cloned.")
+    else:
+        print("✅ ComfyUI-Anima-LLLite extension already exists.")
 
     print(f"✅ Current working directory is: {os.getcwd()}")
     
     import comfy.model_management
     print("--- Environment Ready ---")
-    print("✅ ComfyUI initialized.")
+
+    print("✅ ComfyUI initialized with default attention mechanism.")
+
+    for dir_path in CATEGORY_TO_DIR_MAP.values():
+        os.makedirs(os.path.join(APP_DIR, dir_path), exist_ok=True)
+    
+    os.makedirs(os.path.join(APP_DIR, INPUT_DIR), exist_ok=True)
+    os.makedirs(os.path.join(APP_DIR, OUTPUT_DIR), exist_ok=True)
+    
+    print("✅ All required model directories are present.")

core/generation_logic.py

@@ -1,17 +1,10 @@
 from typing import Any, Dict
+import gradio as gr
 
-from core.pipelines.controlnet_preprocessor import ControlNetPreprocessorPipeline
+from core.pipelines.sd_image_pipeline import SdImagePipeline
 
-controlnet_preprocessor_pipeline = ControlNetPreprocessorPipeline()
+sd_image_pipeline = SdImagePipeline()
 
-def build_reverse_map():
-    from nodes import NODE_DISPLAY_NAME_MAPPINGS
-    import core.pipelines.controlnet_preprocessor as cn_module
-    
-    if cn_module.REVERSE_DISPLAY_NAME_MAP is None:
-        cn_module.REVERSE_DISPLAY_NAME_MAP = {v: k for k, v in NODE_DISPLAY_NAME_MAPPINGS.items()}
-        if "Semantic Segmentor (legacy, alias for UniFormer)" not in cn_module.REVERSE_DISPLAY_NAME_MAP:
-             cn_module.REVERSE_DISPLAY_NAME_MAP["Semantic Segmentor (legacy, alias for UniFormer)"] = "SemSegPreprocessor"
 
-def run_cn_preprocessor_entry(*args, **kwargs):
-    return controlnet_preprocessor_pipeline.run(*args, **kwargs)
+def generate_image_wrapper(ui_inputs: dict, progress=gr.Progress(track_tqdm=True)):
+    return sd_image_pipeline.run(ui_inputs=ui_inputs, progress=progress)

core/model_manager.py

@@ -0,0 +1,32 @@
+import gc
+from typing import List
+import gradio as gr
+from utils.app_utils import _ensure_model_downloaded
+from core.settings import ALL_MODEL_MAP
+
+class ModelManager:
+    _instance = None
+
+    def __new__(cls, *args, **kwargs):
+        if not cls._instance:
+            cls._instance = super(ModelManager, cls).__new__(cls, *args, **kwargs)
+        return cls._instance
+
+    def __init__(self):
+        if hasattr(self, 'initialized'):
+            return
+        self.initialized = True
+        print("✅ ModelManager initialized.")
+
+    def ensure_models_downloaded(self, required_models: List[str], progress):
+        print(f"--- [ModelManager] Ensuring models are downloaded: {required_models} ---")
+        for i, display_name in enumerate(required_models):
+            if progress and hasattr(progress, '__call__'):
+                progress(i / max(len(required_models), 1), desc=f"Checking file: {display_name}")
+            try:
+                _ensure_model_downloaded(display_name, progress)
+            except Exception as e:
+                raise gr.Error(f"Failed to download model '{display_name}'. Reason: {e}")
+        print(f"--- [ModelManager] ✅ All required models are present on disk. ---")
+    
+model_manager = ModelManager()

core/pipelines/base_pipeline.py

@@ -1,5 +1,5 @@
 from abc import ABC, abstractmethod
-from typing import Any
+from typing import List, Any, Dict
 import gradio as gr
 import spaces
 import tempfile
@@ -8,12 +8,22 @@ import numpy as np
 
 class BasePipeline(ABC):
     def __init__(self):
+        from core.model_manager import model_manager
+        self.model_manager = model_manager
+
+    @abstractmethod
+    def get_required_models(self, **kwargs) -> List[str]:
         pass
 
     @abstractmethod
     def run(self, *args, progress: gr.Progress, **kwargs) -> Any:
         pass
 
+    def _ensure_models_downloaded(self, progress: gr.Progress, **kwargs):
+        """Ensures model files are downloaded before requesting GPU."""
+        required_models = self.get_required_models(**kwargs)
+        self.model_manager.ensure_models_downloaded(required_models, progress=progress)
+
     def _execute_gpu_logic(self, gpu_function: callable, duration: int, default_duration: int, task_name: str, *args, **kwargs):
         final_duration = default_duration
         try:
@@ -28,7 +38,7 @@ class BasePipeline(ABC):
         
         return gpu_runner(*args, **kwargs)
 
-    def _encode_video_from_frames(self, frames_tensor_cpu, fps: int, progress: gr.Progress) -> str:
+    def _encode_video_from_frames(self, frames_tensor_cpu: 'torch.Tensor', fps: int, progress: gr.Progress) -> str:
         progress(0.9, desc="Encoding video on CPU...")
         frames_np = (frames_tensor_cpu.numpy() * 255.0).astype(np.uint8)
         

core/pipelines/sd_image_pipeline.py

@@ -0,0 +1,594 @@
+import os
+import random
+import shutil
+import torch
+import gradio as gr
+from PIL import Image, ImageChops
+from typing import List, Dict, Any
+from collections import defaultdict, deque
+import numpy as np
+
+from .base_pipeline import BasePipeline
+from core.settings import *
+from comfy_integration.nodes import *
+from utils.app_utils import get_value_at_index, sanitize_prompt, get_lora_path, get_embedding_path, ensure_controlnet_model_downloaded, ensure_ipadapter_models_downloaded, sanitize_filename
+from core.workflow_assembler import WorkflowAssembler
+
+class SdImagePipeline(BasePipeline):
+    def get_required_models(self, model_display_name: str, **kwargs) -> List[str]:
+        model_info = ALL_MODEL_MAP.get(model_display_name)
+        if not model_info:
+            return [model_display_name]
+
+        path_or_components = model_info[1]
+        if isinstance(path_or_components, dict):
+            return [v for v in path_or_components.values() if v and v != "pixel_space"]
+        else:
+            return [model_display_name]
+
+    def _topological_sort(self, workflow: Dict[str, Any]) -> List[str]:
+        graph = defaultdict(list)
+        in_degree = {node_id: 0 for node_id in workflow}
+
+        for node_id, node_info in workflow.items():
+            for input_value in node_info.get('inputs', {}).values():
+                if isinstance(input_value, list) and len(input_value) == 2 and isinstance(input_value[0], str):
+                    source_node_id = input_value[0]
+                    if source_node_id in workflow:
+                        graph[source_node_id].append(node_id)
+                        in_degree[node_id] += 1
+
+        queue = deque([node_id for node_id, degree in in_degree.items() if degree == 0])
+        
+        sorted_nodes = []
+        while queue:
+            current_node_id = queue.popleft()
+            sorted_nodes.append(current_node_id)
+
+            for neighbor_node_id in graph[current_node_id]:
+                in_degree[neighbor_node_id] -= 1
+                if in_degree[neighbor_node_id] == 0:
+                    queue.append(neighbor_node_id)
+        
+        if len(sorted_nodes) != len(workflow):
+            raise RuntimeError("Workflow contains a cycle and cannot be executed.")
+            
+        return sorted_nodes
+
+    def _execute_workflow(self, workflow: Dict[str, Any], initial_objects: Dict[str, Any]):
+        with torch.no_grad():
+            computed_outputs = initial_objects
+            
+            try:
+                sorted_node_ids = self._topological_sort(workflow)
+                print(f"--- [Workflow Executor] Execution order: {sorted_node_ids}")
+            except RuntimeError as e:
+                print("--- [Workflow Executor] ERROR: Failed to sort workflow. Dumping graph details. ---")
+                for node_id, node_info in workflow.items():
+                    print(f"  Node {node_id} ({node_info['class_type']}):")
+                    for input_name, input_value in node_info['inputs'].items():
+                         if isinstance(input_value, list) and len(input_value) == 2 and isinstance(input_value[0], str):
+                             print(f"    - {input_name} <- [{input_value[0]}, {input_value[1]}]")
+                raise e
+
+            for node_id in sorted_node_ids:
+                if node_id in computed_outputs:
+                    continue
+                    
+                node_info = workflow[node_id]
+                class_type = node_info['class_type']
+                
+                is_loader_with_filename = 'Loader' in class_type and any(key.endswith('_name') for key in node_info['inputs'])
+                if node_id in initial_objects and is_loader_with_filename:
+                    continue
+                
+                node_class = NODE_CLASS_MAPPINGS.get(class_type)
+                if node_class is None:
+                     raise RuntimeError(f"Could not find node class '{class_type}'. Is it imported in comfy_integration/nodes.py?")
+                
+                node_instance = node_class()
+                
+                kwargs = {}
+                for param_name, param_value in node_info['inputs'].items():
+                    if isinstance(param_value, list) and len(param_value) == 2 and isinstance(param_value[0], str):
+                        source_node_id, output_index = param_value
+                        if source_node_id not in computed_outputs:
+                            raise RuntimeError(f"Workflow integrity error: Output of node {source_node_id} needed for {node_id} but not yet computed.")
+                        
+                        source_output_tuple = computed_outputs[source_node_id]
+                        kwargs[param_name] = get_value_at_index(source_output_tuple, output_index)
+                    else:
+                        kwargs[param_name] = param_value
+
+                function_name = getattr(node_class, 'FUNCTION')
+                execution_method = getattr(node_instance, function_name)
+                
+                result = execution_method(**kwargs)
+                computed_outputs[node_id] = result
+            
+            final_node_id = None
+            for node_id in reversed(sorted_node_ids):
+                 if workflow[node_id]['class_type'] == 'SaveImage':
+                     final_node_id = node_id
+                     break
+            
+            if not final_node_id:
+                raise RuntimeError("Workflow does not contain a 'SaveImage' node as the output.")
+
+            save_image_inputs = workflow[final_node_id]['inputs']
+            image_source_node_id, image_source_index = save_image_inputs['images']
+            
+            return get_value_at_index(computed_outputs[image_source_node_id], image_source_index)
+
+    def _gpu_logic(self, ui_inputs: Dict, loras_string: str, workflow: Dict[str, Any], assembler: WorkflowAssembler, progress=gr.Progress(track_tqdm=True)):
+        model_display_name = ui_inputs['model_display_name']
+        
+        progress(0.4, desc="Executing workflow...")
+        
+        initial_objects = {}
+
+        decoded_images_tensor = self._execute_workflow(workflow, initial_objects=initial_objects)
+        
+        output_images = []
+        start_seed = ui_inputs['seed'] if ui_inputs['seed'] != -1 else random.randint(0, 2**64 - 1)
+        for i in range(decoded_images_tensor.shape[0]):
+            img_tensor = decoded_images_tensor[i]
+            pil_image = Image.fromarray((img_tensor.cpu().numpy() * 255.0).astype("uint8"))
+            current_seed = start_seed + i
+            
+            width_for_meta = ui_inputs.get('width', 'N/A')
+            height_for_meta = ui_inputs.get('height', 'N/A')
+
+            params_string = f"{ui_inputs['positive_prompt']}\nNegative prompt: {ui_inputs['negative_prompt']}\n"
+            params_string += f"Steps: {ui_inputs['num_inference_steps']}, Sampler: {ui_inputs['sampler']}, Scheduler: {ui_inputs['scheduler']}, CFG scale: {ui_inputs['guidance_scale']}, Seed: {current_seed}, Size: {width_for_meta}x{height_for_meta}, Base Model: {model_display_name}"
+            if ui_inputs['task_type'] != 'txt2img': params_string += f", Denoise: {ui_inputs['denoise']}"
+            if ui_inputs.get('clip_skip') and ui_inputs['clip_skip'] != 1: params_string += f", Clip skip: {abs(ui_inputs['clip_skip'])}"
+            if loras_string: params_string += f", {loras_string}"
+            
+            pil_image.info = {'parameters': params_string.strip()}
+            output_images.append(pil_image)
+            
+        return output_images
+
+    def run(self, ui_inputs: Dict, progress):
+        progress(0, desc="Preparing models...")
+        
+        task_type = ui_inputs['task_type']
+        model_display_name = ui_inputs['model_display_name']
+        model_type = MODEL_TYPE_MAP.get(model_display_name, 'sdxl')
+        
+        architectures_dict = ARCHITECTURES_CONFIG.get('architectures', {})
+        workflow_model_type = architectures_dict.get(model_type, {}).get("model_type", "sdxl")
+        
+        ui_inputs['positive_prompt'] = sanitize_prompt(ui_inputs.get('positive_prompt', ''))
+        ui_inputs['negative_prompt'] = sanitize_prompt(ui_inputs.get('negative_prompt', ''))
+        
+        if 'clip_skip' in ui_inputs and ui_inputs['clip_skip'] is not None:
+             ui_inputs['clip_skip'] = -int(ui_inputs['clip_skip'])
+        else:
+             ui_inputs['clip_skip'] = -1
+
+        required_models = self.get_required_models(model_display_name=model_display_name)
+        self.model_manager.ensure_models_downloaded(required_models, progress=progress)
+        
+        lora_data = ui_inputs.get('lora_data', [])
+        active_loras_for_gpu, active_loras_for_meta = [], []
+        if lora_data:
+            sources, ids, scales, files = lora_data[0::4], lora_data[1::4], lora_data[2::4], lora_data[3::4]
+            for i, (source, lora_id, scale, _) in enumerate(zip(sources, ids, scales, files)):
+                if scale > 0 and lora_id and lora_id.strip():
+                    lora_filename = None
+                    if source == "File":
+                        lora_filename = sanitize_filename(lora_id)
+                    elif source == "Civitai":
+                        local_path, status = get_lora_path(source, lora_id, os.environ.get("CIVITAI_API_KEY", ""), progress)
+                        if local_path: lora_filename = os.path.basename(local_path)
+                        else: raise gr.Error(f"Failed to prepare LoRA {lora_id}: {status}")
+                    
+                    if lora_filename:
+                        active_loras_for_gpu.append({"lora_name": lora_filename, "strength_model": scale, "strength_clip": scale})
+                        active_loras_for_meta.append(f"{source} {lora_id}:{scale}")
+
+        ui_inputs['denoise'] = 1.0
+        if task_type == 'img2img': ui_inputs['denoise'] = ui_inputs.get('img2img_denoise', 0.7)
+        elif task_type == 'hires_fix': ui_inputs['denoise'] = ui_inputs.get('hires_denoise', 0.55)
+        
+        temp_files_to_clean = []
+        if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
+
+        if task_type == 'img2img':
+            input_image_pil = ui_inputs.get('img2img_image')
+            if not input_image_pil:
+                raise gr.Error("Please upload an image for Image-to-Image.")
+            temp_file_path = os.path.join(INPUT_DIR, f"temp_input_{random.randint(1000, 9999)}.png")
+            input_image_pil.save(temp_file_path, "PNG")
+            ui_inputs['input_image'] = os.path.basename(temp_file_path)
+            temp_files_to_clean.append(temp_file_path)
+            ui_inputs['width'] = input_image_pil.width
+            ui_inputs['height'] = input_image_pil.height
+        
+        elif task_type == 'inpaint':
+            inpaint_dict = ui_inputs.get('inpaint_image_dict')
+            if not inpaint_dict or not inpaint_dict.get('background') or not inpaint_dict.get('layers'):
+                raise gr.Error("Inpainting requires an input image and a drawn mask.")
+            
+            background_img = inpaint_dict['background'].convert("RGBA")
+            composite_mask_pil = Image.new('L', background_img.size, 0)
+            for layer in inpaint_dict['layers']:
+                if layer:
+                    layer_alpha = layer.split()[-1]
+                    composite_mask_pil = ImageChops.lighter(composite_mask_pil, layer_alpha)
+
+            inverted_mask_alpha = Image.fromarray(255 - np.array(composite_mask_pil), mode='L')
+            r, g, b, _ = background_img.split()
+            composite_image_with_mask = Image.merge('RGBA', [r, g, b, inverted_mask_alpha])
+            
+            temp_file_path = os.path.join(INPUT_DIR, f"temp_inpaint_composite_{random.randint(1000, 9999)}.png")
+            composite_image_with_mask.save(temp_file_path, "PNG")
+            
+            ui_inputs['input_image'] = os.path.basename(temp_file_path)
+            temp_files_to_clean.append(temp_file_path)
+            ui_inputs.pop('inpaint_mask', None)
+
+        elif task_type == 'outpaint':
+            input_image_pil = ui_inputs.get('outpaint_image')
+            if not input_image_pil:
+                raise gr.Error("Please upload an image for Outpainting.")
+            temp_file_path = os.path.join(INPUT_DIR, f"temp_input_{random.randint(1000, 9999)}.png")
+            input_image_pil.save(temp_file_path, "PNG")
+            ui_inputs['input_image'] = os.path.basename(temp_file_path)
+            temp_files_to_clean.append(temp_file_path)
+            
+            ui_inputs['megapixels'] = 0.25
+            ui_inputs['grow_mask_by'] = ui_inputs.get('feathering', 10)
+
+        elif task_type == 'hires_fix':
+            input_image_pil = ui_inputs.get('hires_image')
+            if not input_image_pil:
+                raise gr.Error("Please upload an image for Hires Fix.")
+            temp_file_path = os.path.join(INPUT_DIR, f"temp_input_{random.randint(1000, 9999)}.png")
+            input_image_pil.save(temp_file_path, "PNG")
+            ui_inputs['input_image'] = os.path.basename(temp_file_path)
+            temp_files_to_clean.append(temp_file_path)
+
+        embedding_data = ui_inputs.get('embedding_data', [])
+        embedding_filenames = []
+        if embedding_data:
+            emb_sources, emb_ids, emb_files = embedding_data[0::3], embedding_data[1::3], embedding_data[2::3]
+            for i, (source, emb_id, _) in enumerate(zip(emb_sources, emb_ids, emb_files)):
+                if emb_id and emb_id.strip():
+                    emb_filename = None
+                    if source == "File":
+                        emb_filename = sanitize_filename(emb_id)
+                    elif source == "Civitai":
+                        local_path, status = get_embedding_path(source, emb_id, os.environ.get("CIVITAI_API_KEY", ""), progress)
+                        if local_path: emb_filename = os.path.basename(local_path)
+                        else: raise gr.Error(f"Failed to prepare Embedding {emb_id}: {status}")
+                    
+                    if emb_filename:
+                        embedding_filenames.append(emb_filename)
+        
+        if embedding_filenames:
+            embedding_prompt_text = " ".join([f"embedding:{f}" for f in embedding_filenames])
+            if ui_inputs['positive_prompt']:
+                ui_inputs['positive_prompt'] = f"{ui_inputs['positive_prompt']}, {embedding_prompt_text}"
+            else:
+                ui_inputs['positive_prompt'] = embedding_prompt_text
+
+        controlnet_data = ui_inputs.get('controlnet_data', [])
+        active_controlnets = []
+        if controlnet_data:
+            (cn_images, _, _, cn_strengths, cn_filepaths) = [controlnet_data[i::5] for i in range(5)]
+            for i in range(len(cn_images)):
+                if cn_images[i] and cn_strengths[i] > 0 and cn_filepaths[i] and cn_filepaths[i] != "None":
+                    ensure_controlnet_model_downloaded(cn_filepaths[i], progress)
+                    if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
+                    cn_temp_path = os.path.join(INPUT_DIR, f"temp_cn_{i}_{random.randint(1000, 9999)}.png")
+                    cn_images[i].save(cn_temp_path, "PNG")
+                    temp_files_to_clean.append(cn_temp_path)
+                    active_controlnets.append({
+                        "image": os.path.basename(cn_temp_path), "strength": cn_strengths[i],
+                        "start_percent": 0.0, "end_percent": 1.0, "control_net_name": cn_filepaths[i]
+                    })
+        
+        anima_controlnet_lllite_data = ui_inputs.get('anima_controlnet_lllite_data', [])
+        active_anima_controlnets = []
+        if anima_controlnet_lllite_data:
+            (cn_images, _, _, cn_strengths, cn_filepaths, cn_starts, cn_ends) = [anima_controlnet_lllite_data[i::7] for i in range(7)]
+            for i in range(len(cn_images)):
+                if cn_images[i] and cn_strengths[i] > 0 and cn_filepaths[i] and cn_filepaths[i] != "None":
+                    from utils.app_utils import _ensure_model_downloaded
+                    _ensure_model_downloaded(cn_filepaths[i], progress)
+                    if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
+                    cn_temp_path = os.path.join(INPUT_DIR, f"temp_anima_cn_{i}_{random.randint(1000, 9999)}.png")
+                    cn_images[i].save(cn_temp_path, "PNG")
+                    temp_files_to_clean.append(cn_temp_path)
+                    active_anima_controlnets.append({
+                        "image": os.path.basename(cn_temp_path), "strength": cn_strengths[i],
+                        "start_percent": cn_starts[i], "end_percent": cn_ends[i], "control_net_name": cn_filepaths[i]
+                    })
+
+        diffsynth_controlnet_data = ui_inputs.get('diffsynth_controlnet_data', [])
+        active_diffsynth_controlnets = []
+        if diffsynth_controlnet_data:
+            (cn_images, _, _, cn_strengths, cn_filepaths) = [diffsynth_controlnet_data[i::5] for i in range(5)]
+            for i in range(len(cn_images)):
+                if cn_images[i] and cn_strengths[i] > 0 and cn_filepaths[i] and cn_filepaths[i] != "None":
+                    ensure_controlnet_model_downloaded(cn_filepaths[i], progress)
+
+                    if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
+                    cn_temp_path = os.path.join(INPUT_DIR, f"temp_diffsynth_cn_{i}_{random.randint(1000, 9999)}.png")
+                    cn_images[i].save(cn_temp_path, "PNG")
+                    temp_files_to_clean.append(cn_temp_path)
+                    active_diffsynth_controlnets.append({
+                        "image": os.path.basename(cn_temp_path), "strength": cn_strengths[i],
+                        "control_net_name": cn_filepaths[i]
+                    })
+        
+        ipadapter_data = ui_inputs.get('ipadapter_data', [])
+        active_ipadapters = []
+        if ipadapter_data:
+            num_ipa_units = (len(ipadapter_data) - 5) // 3
+            final_preset, final_weight, final_lora_strength, final_embeds_scaling, final_combine_method = ipadapter_data[-5:]
+            ipa_images, ipa_weights, ipa_lora_strengths = [ipadapter_data[i*num_ipa_units:(i+1)*num_ipa_units] for i in range(3)]
+            all_presets_to_download = set()
+            for i in range(num_ipa_units):
+                if ipa_images[i] and ipa_weights[i] > 0 and final_preset:
+                    all_presets_to_download.add(final_preset)
+                    if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
+                    ipa_temp_path = os.path.join(INPUT_DIR, f"temp_ipa_{i}_{random.randint(1000, 9999)}.png")
+                    ipa_images[i].save(ipa_temp_path, "PNG")
+                    temp_files_to_clean.append(ipa_temp_path)
+                    active_ipadapters.append({
+                        "image": os.path.basename(ipa_temp_path), "preset": final_preset,
+                        "weight": ipa_weights[i], "lora_strength": ipa_lora_strengths[i]
+                    })
+            if active_ipadapters and final_preset:
+                all_presets_to_download.add(final_preset)
+            for preset in all_presets_to_download:
+                ensure_ipadapter_models_downloaded(preset, progress)
+                
+            model_type_key = 'sd15' if workflow_model_type == 'sd15' else 'sdxl'
+            if active_ipadapters:
+                active_ipadapters.append({
+                    'is_final_settings': True, 'model_type': model_type_key, 'final_preset': final_preset,
+                    'final_weight': final_weight, 'final_lora_strength': final_lora_strength,
+                    'final_embeds_scaling': final_embeds_scaling, 'final_combine_method': final_combine_method
+                })
+                
+        flux1_ipadapter_data = ui_inputs.get('flux1_ipadapter_data', [])
+        active_flux1_ipadapters = []
+        if flux1_ipadapter_data:
+            num_units = len(flux1_ipadapter_data) // 4
+            f_images = flux1_ipadapter_data[0*num_units : 1*num_units]
+            f_weights = flux1_ipadapter_data[1*num_units : 2*num_units]
+            f_starts = flux1_ipadapter_data[2*num_units : 3*num_units]
+            f_ends = flux1_ipadapter_data[3*num_units : 4*num_units]
+            for i in range(len(f_images)):
+                if f_images[i] and f_weights[i] > 0:
+                    from utils.app_utils import _ensure_model_downloaded
+                    for filename in ["ip-adapter.bin"]:
+                        _ensure_model_downloaded(filename, progress)
+                    
+                    from huggingface_hub import snapshot_download
+                    progress(0.5, desc="Caching HF SigLIP model...")
+                    snapshot_download(
+                        repo_id="google/siglip-so400m-patch14-384", 
+                        allow_patterns=["*.json", "*.safetensors", "*.txt"], 
+                        ignore_patterns=["*.msgpack", "*.h5", "*.bin"]
+                    )
+
+                    temp_path = os.path.join(INPUT_DIR, f"temp_fipa_{i}_{random.randint(1000, 9999)}.png")
+                    f_images[i].save(temp_path, "PNG")
+                    temp_files_to_clean.append(temp_path)
+                    active_flux1_ipadapters.append({
+                        "image": os.path.basename(temp_path),
+                        "weight": f_weights[i], "start_percent": f_starts[i], "end_percent": f_ends[i]
+                    })
+                    
+        sd3_ipadapter_data = ui_inputs.get('sd3_ipadapter_chain', [])
+        active_sd3_ipadapters = []
+        if sd3_ipadapter_data:
+            num_units = len(sd3_ipadapter_data) // 4
+            s_images = sd3_ipadapter_data[0*num_units : 1*num_units]
+            s_weights = sd3_ipadapter_data[1*num_units : 2*num_units]
+            s_starts = sd3_ipadapter_data[2*num_units : 3*num_units]
+            s_ends = sd3_ipadapter_data[3*num_units : 4*num_units]
+            sd3_ipa_downloaded = False
+            for i in range(len(s_images)):
+                if s_images[i] and s_weights[i] > 0:
+                    if not sd3_ipa_downloaded:
+                        from utils.app_utils import ensure_sd3_ipadapter_models_downloaded
+                        ensure_sd3_ipadapter_models_downloaded(progress)
+                        sd3_ipa_downloaded = True
+                    temp_path = os.path.join(INPUT_DIR, f"temp_s3ipa_{i}_{random.randint(1000, 9999)}.png")
+                    s_images[i].save(temp_path, "PNG")
+                    temp_files_to_clean.append(temp_path)
+                    active_sd3_ipadapters.append({
+                        "image": os.path.basename(temp_path),
+                        "weight": s_weights[i], "start_percent": s_starts[i], "end_percent": s_ends[i]
+                    })
+                    
+        style_data = ui_inputs.get('style_data', [])
+        active_styles = []
+        if style_data:
+            num_units = len(style_data) // 2
+            st_images = style_data[0*num_units : 1*num_units]
+            st_strengths = style_data[1*num_units : 2*num_units]
+            for i in range(len(st_images)):
+                if st_images[i] and st_strengths[i] > 0:
+                    from utils.app_utils import _ensure_model_downloaded
+                    _ensure_model_downloaded("sigclip_vision_patch14_384.safetensors", progress)
+                    temp_path = os.path.join(INPUT_DIR, f"temp_style_{i}_{random.randint(1000, 9999)}.png")
+                    st_images[i].save(temp_path, "PNG")
+                    temp_files_to_clean.append(temp_path)
+                    active_styles.append({
+                        "image": os.path.basename(temp_path), "strength": st_strengths[i]
+                    })
+        
+        reference_latent_data = ui_inputs.get('reference_latent_data', [])
+        active_reference_latents = []
+        if reference_latent_data:
+            for img in reference_latent_data:
+                if img:
+                    if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
+                    temp_path = os.path.join(INPUT_DIR, f"temp_ref_{random.randint(1000, 9999)}.png")
+                    img.save(temp_path, "PNG")
+                    temp_files_to_clean.append(temp_path)
+                    active_reference_latents.append(os.path.basename(temp_path))
+        
+        from utils.app_utils import get_vae_path
+        vae_source = ui_inputs.get('vae_source')
+        vae_id = ui_inputs.get('vae_id')
+        vae_name_override = None
+        if vae_source and vae_source != "None":
+            if vae_source == "File":
+                vae_name_override = sanitize_filename(vae_id)
+            elif vae_source == "Civitai" and vae_id and vae_id.strip():
+                local_path, status = get_vae_path(vae_source, vae_id, os.environ.get("CIVITAI_API_KEY", ""), progress)
+                if local_path: vae_name_override = os.path.basename(local_path)
+                else: raise gr.Error(f"Failed to prepare VAE {vae_id}: {status}")
+        if vae_name_override:
+            ui_inputs['vae_name'] = vae_name_override
+
+        conditioning_data = ui_inputs.get('conditioning_data', [])
+        active_conditioning = []
+        if conditioning_data:
+            num_units = len(conditioning_data) // 6
+            prompts, widths, heights, xs, ys, strengths = [conditioning_data[i*num_units : (i+1)*num_units] for i in range(6)]
+            for i in range(num_units):
+                if prompts[i] and prompts[i].strip():
+                    active_conditioning.append({
+                        "prompt": prompts[i], "width": int(widths[i]), "height": int(heights[i]),
+                        "x": int(xs[i]), "y": int(ys[i]), "strength": float(strengths[i])
+                    })
+
+        loras_string = f"LoRAs: [{', '.join(active_loras_for_meta)}]" if active_loras_for_meta else ""
+        
+        progress(0.8, desc="Assembling workflow...")
+        
+        if ui_inputs.get('seed') == -1:
+            ui_inputs['seed'] = random.randint(0, 2**32 - 1)
+        
+        model_info = ALL_MODEL_MAP[model_display_name]
+        path_or_components = model_info[1]
+        latent_type = model_info[3] if len(model_info) > 3 and model_info[3] else 'latent'
+        latent_generator_template = "EmptyLatentImage"
+        if latent_type == 'sd3_latent':
+            latent_generator_template = "EmptySD3LatentImage"
+        elif latent_type == 'chroma_radiance_latent':
+            latent_generator_template = "EmptyChromaRadianceLatentImage"
+        elif latent_type == 'hunyuan_latent':
+            latent_generator_template = "EmptyHunyuanImageLatent"
+
+        dynamic_values = {
+            'task_type': ui_inputs['task_type'], 
+            'model_type': workflow_model_type,
+            'latent_type': latent_type,
+            'latent_generator_template': latent_generator_template
+        }
+        
+        recipe_path = os.path.join(os.path.dirname(__file__), "workflow_recipes", "sd_unified_recipe.yaml")
+        assembler = WorkflowAssembler(recipe_path, dynamic_values=dynamic_values)
+        
+        workflow_inputs = {
+            **ui_inputs,
+            "positive_prompt": ui_inputs['positive_prompt'], "negative_prompt": ui_inputs['negative_prompt'],
+            "seed": ui_inputs['seed'], "steps": ui_inputs['num_inference_steps'], "cfg": ui_inputs['guidance_scale'],
+            "sampler_name": ui_inputs['sampler'], "scheduler": ui_inputs['scheduler'],
+            "batch_size": ui_inputs['batch_size'],
+            "clip_skip": ui_inputs['clip_skip'],
+            "denoise": ui_inputs['denoise'],
+            "vae_name": ui_inputs.get('vae_name'),
+            "guidance": ui_inputs.get('guidance', 3.5),
+            "lora_chain": active_loras_for_gpu,
+            "controlnet_chain": active_controlnets if not active_anima_controlnets else [],
+            "anima_controlnet_lllite_chain": active_anima_controlnets,
+            "diffsynth_controlnet_chain": active_diffsynth_controlnets,
+            "ipadapter_chain": active_ipadapters,
+            "flux1_ipadapter_chain": active_flux1_ipadapters,
+            "sd3_ipadapter_chain": active_sd3_ipadapters,
+            "style_chain": active_styles,
+            "conditioning_chain": active_conditioning,
+            "reference_latent_chain": active_reference_latents,
+            "vae_chain": [ui_inputs.get('vae_name')] if ui_inputs.get('vae_name') else [],
+        }
+
+        if isinstance(path_or_components, dict):
+            workflow_inputs.update({
+                'unet_name': path_or_components.get('unet'),
+                'vae_name': ui_inputs.get('vae_name') or path_or_components.get('vae'),
+                'clip_name': path_or_components.get('clip'),
+                'clip1_name': path_or_components.get('clip1'),
+                'clip2_name': path_or_components.get('clip2'),
+                'clip3_name': path_or_components.get('clip3'),
+                'clip4_name': path_or_components.get('clip4'),
+                'lora_name': path_or_components.get('lora'),
+            })
+        else:
+            workflow_inputs['model_name'] = path_or_components
+        
+        if task_type == 'txt2img':
+            workflow_inputs['width'] = ui_inputs['width']
+            workflow_inputs['height'] = ui_inputs['height']
+        
+        workflow = assembler.assemble(workflow_inputs)
+
+        progress(1.0, desc="All models ready. Requesting GPU for generation...")
+
+        try:
+            results = self._execute_gpu_logic(
+                self._gpu_logic,
+                duration=ui_inputs['zero_gpu_duration'],
+                default_duration=60,
+                task_name=f"ImageGen ({task_type})",
+                ui_inputs=ui_inputs,
+                loras_string=loras_string,
+                workflow=workflow,
+                assembler=assembler, 
+                progress=progress
+            )
+            
+            import json
+            import glob
+            from PIL import PngImagePlugin
+            
+            prompt_json = json.dumps(workflow)
+            
+            out_dir = os.path.abspath(OUTPUT_DIR)
+            os.makedirs(out_dir, exist_ok=True)
+            
+            try:
+                existing_files = glob.glob(os.path.join(out_dir, "gen_*.png"))
+                existing_files.sort(key=os.path.getmtime)
+                while len(existing_files) > 50:
+                    os.remove(existing_files.pop(0))
+            except Exception as e:
+                print(f"Warning: Failed to cleanup output dir: {e}")
+
+            final_results = []
+            for img in results:
+                if not isinstance(img, Image.Image):
+                    final_results.append(img)
+                    continue
+                    
+                metadata = PngImagePlugin.PngInfo()
+                params_string = img.info.get("parameters", "")
+                if params_string:
+                    metadata.add_text("parameters", params_string)
+                metadata.add_text("prompt", prompt_json)
+                
+                filename = f"gen_{random.randint(1000000, 9999999)}.png"
+                filepath = os.path.join(out_dir, filename)
+                img.save(filepath, "PNG", pnginfo=metadata)
+                final_results.append(filepath)
+            
+            results = final_results
+
+        finally:
+            for temp_file in temp_files_to_clean:
+                if temp_file and os.path.exists(temp_file):
+                    os.remove(temp_file)
+                    print(f"✅ Cleaned up temp file: {temp_file}")
+
+        return results

core/pipelines/workflow_recipes/_partials/_base_sampler_sd.yaml

@@ -0,0 +1,36 @@
+nodes:
+  pos_prompt:
+    class_type: CLIPTextEncode
+    title: "CLIP Text Encode (Positive)"
+  neg_prompt:
+    class_type: CLIPTextEncode
+    title: "CLIP Text Encode (Negative)"
+  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:
+  positive_prompt: "pos_prompt:text"
+  negative_prompt: "neg_prompt:text"
+  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/anima.yaml

@@ -0,0 +1,58 @@
+nodes:
+  unet_loader:
+    class_type: UNETLoader
+    title: "Load Diffusion Model"
+    params:
+      weight_dtype: "default"
+  vae_loader:
+    class_type: VAELoader
+    title: "Load VAE"
+  clip_loader:
+    class_type: CLIPLoader
+    title: "Load CLIP"
+    params:
+      type: "stable_diffusion"
+      device: "default"
+
+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: "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": ["ksampler:model"]
+      "clip": ["pos_prompt:clip", "neg_prompt:clip"]
+
+dynamic_anima_controlnet_lllite_chains:
+  anima_controlnet_lllite_chain:
+    ksampler_node: "ksampler"
+
+dynamic_conditioning_chains:
+  conditioning_chain:
+    ksampler_node: "ksampler"
+    clip_source: "clip_loader:0"
+
+ui_map:
+  unet_name: "unet_loader:unet_name"
+  vae_name: "vae_loader:vae_name"
+  clip_name: "clip_loader:clip_name"

core/pipelines/workflow_recipes/_partials/conditioning/chroma1-radiance.yaml

@@ -0,0 +1,59 @@
+nodes:
+  unet_loader:
+    class_type: UNETLoader
+    title: "Load Diffusion Model"
+    params:
+      weight_dtype: "default"
+  vae_loader:
+    class_type: VAELoader
+    title: "Load VAE"
+    params:
+      vae_name: "pixel_space"
+  clip_loader:
+    class_type: CLIPLoader
+    title: "Load CLIP"
+    params:
+      type: "chroma"
+      device: "default"
+  t5_tokenizer:
+    class_type: T5TokenizerOptions
+    title: "T5TokenizerOptions"
+    params:
+      min_padding: 0
+      min_length: 3
+  model_sampler:
+    class_type: ModelSamplingAuraFlow
+    params:
+      shift: 3.0
+
+connections:
+  - from: "unet_loader:0"
+    to: "model_sampler:model"
+  - from: "model_sampler:0"
+    to: "ksampler:model"
+  
+  - from: "clip_loader:0"
+    to: "t5_tokenizer:clip"
+  - from: "t5_tokenizer:0"
+    to: "pos_prompt:clip"
+  - from: "t5_tokenizer: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_conditioning_chains:
+  conditioning_chain:
+    ksampler_node: "ksampler"
+    clip_source: "t5_tokenizer:0"
+
+ui_map:
+  unet_name: "unet_loader:unet_name"
+  clip_name: "clip_loader:clip_name"

core/pipelines/workflow_recipes/_partials/conditioning/chroma1.yaml

@@ -0,0 +1,61 @@
+nodes:
+  unet_loader:
+    class_type: UNETLoader
+    title: "Load Diffusion Model"
+    params:
+      weight_dtype: "default"
+  vae_loader:
+    class_type: VAELoader
+    title: "Load VAE"
+  clip_loader:
+    class_type: CLIPLoader
+    title: "Load CLIP"
+    params:
+      type: "chroma"
+      device: "default"
+  t5_tokenizer:
+    class_type: T5TokenizerOptions
+    title: "T5TokenizerOptions"
+    params:
+      min_padding: 1
+      min_length: 0
+  fresca:
+    class_type: FreSca
+    title: "FreSca"
+    params:
+      scale_low: 1.0
+      scale_high: 2.5
+      freq_cutoff: 30
+
+connections:
+  - from: "unet_loader:0"
+    to: "fresca:model"
+  - from: "fresca:0"
+    to: "ksampler:model"
+  
+  - from: "clip_loader:0"
+    to: "t5_tokenizer:clip"
+  - from: "t5_tokenizer:0"
+    to: "pos_prompt:clip"
+  - from: "t5_tokenizer: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_conditioning_chains:
+  conditioning_chain:
+    ksampler_node: "ksampler"
+    clip_source: "t5_tokenizer:0"
+
+ui_map:
+  unet_name: "unet_loader:unet_name"
+  vae_name: "vae_loader:vae_name"
+  clip_name: "clip_loader:clip_name"

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

@@ -0,0 +1,54 @@
+nodes:
+  unet_loader:
+    class_type: UNETLoader
+    title: "Load Diffusion Model"
+    params:
+      weight_dtype: "default"
+  clip_loader:
+    class_type: CLIPLoader
+    title: "Load CLIP"
+    params:
+      type: "flux2"
+      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"
+
+ui_map:
+  unet_name: "unet_loader:unet_name"
+  clip_name: "clip_loader:clip_name"
+  vae_name: "vae_loader:vae_name"

core/pipelines/workflow_recipes/_partials/conditioning/flux1.yaml

@@ -0,0 +1,64 @@
+nodes:
+  unet_loader:
+    class_type: UNETLoader
+    title: "Load FLUX UNET"
+    params:
+      weight_dtype: "default"
+  vae_loader:
+    class_type: VAELoader
+    title: "Load FLUX VAE"
+  clip_loader:
+    class_type: DualCLIPLoader
+    title: "Load FLUX Dual CLIP"
+    params:
+      type: "flux"
+      device: "default"
+  flux_guidance:
+    class_type: FluxGuidance
+    title: "FluxGuidance"
+
+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: "vae_loader:0"
+    to: "vae_decode:vae"
+  - from: "vae_loader:0"
+    to: "vae_encode:vae"
+  - from: "pos_prompt:0"
+    to: "flux_guidance:conditioning"
+  - from: "flux_guidance:0"
+    to: "ksampler:positive"
+  - from: "neg_prompt:0"
+    to: "ksampler:negative"
+
+dynamic_controlnet_chains:
+  controlnet_chain:
+    template: "ControlNetApplyAdvanced"
+    ksampler_node: "ksampler"
+    vae_source: "vae_loader:0"
+
+dynamic_flux1_ipadapter_chains:
+  flux1_ipadapter_chain:
+    ksampler_node: "ksampler"
+
+dynamic_style_chains:
+  style_chain:
+    flux_guidance_node: "flux_guidance"
+    ksampler_node: "ksampler"
+
+dynamic_conditioning_chains:
+  conditioning_chain:
+    flux_guidance_node: "flux_guidance"
+    ksampler_node: "ksampler"
+    clip_source: "clip_loader:0"
+
+ui_map:
+  unet_name: "unet_loader:unet_name"
+  vae_name: "vae_loader:vae_name"
+  clip1_name: "clip_loader:clip_name1"
+  clip2_name: "clip_loader:clip_name2"
+  guidance: "flux_guidance:guidance"

core/pipelines/workflow_recipes/_partials/conditioning/flux2-kv.yaml

@@ -0,0 +1,104 @@
+nodes:
+  unet_loader:
+    class_type: UNETLoader
+    title: "Load Diffusion Model"
+    params:
+      weight_dtype: "default"
+  clip_loader:
+    class_type: CLIPLoader
+    title: "Load CLIP"
+    params:
+      type: "flux2"
+      device: "default"
+  vae_loader:
+    class_type: VAELoader
+    title: "Load VAE"
+    
+  flux_kv_cache:
+    class_type: FluxKVCache
+    title: "Flux KV Cache"
+  
+  pos_prompt:
+    class_type: CLIPTextEncode
+    title: "CLIP Text Encode (Positive)"
+  neg_prompt:
+    class_type: CLIPTextEncode
+    title: "CLIP Text Encode (Negative)"
+  
+  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"
+
+connections:
+  - from: "unet_loader:0"
+    to: "flux_kv_cache:model"
+  - from: "flux_kv_cache: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"
+    
+  - from: "latent_source:0"
+    to: "ksampler:latent_image"
+    
+  - from: "ksampler:0"
+    to: "vae_decode:samples"
+  - from: "vae_decode:0"
+    to: "save_image:images"
+
+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": ["flux_kv_cache:model"]
+      "clip": ["pos_prompt:clip", "neg_prompt:clip"]
+
+dynamic_reference_latent_chains:
+  reference_latent_chain:
+    ksampler_node: "ksampler"
+    vae_node: "vae_loader"
+
+ui_map:
+  unet_name: "unet_loader:unet_name"
+  clip_name: "clip_loader:clip_name"
+  vae_name: "vae_loader:vae_name"
+  
+  positive_prompt: "pos_prompt:text"
+  negative_prompt: "neg_prompt:text"
+  
+  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/flux2.yaml

@@ -0,0 +1,96 @@
+nodes:
+  unet_loader:
+    class_type: UNETLoader
+    title: "Load Diffusion Model"
+    params:
+      weight_dtype: "default"
+  clip_loader:
+    class_type: CLIPLoader
+    title: "Load CLIP"
+    params:
+      type: "flux2"
+      device: "default"
+  vae_loader:
+    class_type: VAELoader
+    title: "Load VAE"
+  
+  pos_prompt:
+    class_type: CLIPTextEncode
+    title: "CLIP Text Encode (Positive)"
+  neg_prompt:
+    class_type: CLIPTextEncode
+    title: "CLIP Text Encode (Negative)"
+  
+  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"
+
+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: "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"
+    
+  - from: "latent_source:0"
+    to: "ksampler:latent_image"
+    
+  - from: "ksampler:0"
+    to: "vae_decode:samples"
+  - from: "vae_decode:0"
+    to: "save_image:images"
+
+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_reference_latent_chains:
+  reference_latent_chain:
+    ksampler_node: "ksampler"
+    vae_node: "vae_loader"
+
+ui_map:
+  unet_name: "unet_loader:unet_name"
+  clip_name: "clip_loader:clip_name"
+  vae_name: "vae_loader:vae_name"
+  
+  positive_prompt: "pos_prompt:text"
+  negative_prompt: "neg_prompt:text"
+  
+  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/hidream.yaml

@@ -0,0 +1,53 @@
+nodes:
+  unet_loader:
+    class_type: UNETLoader
+    title: "Load HiDream UNET"
+    params:
+      weight_dtype: "default"
+  vae_loader:
+    class_type: VAELoader
+    title: "Load HiDream VAE"
+  clip_loader:
+    class_type: QuadrupleCLIPLoader
+    title: "Load HiDream Quadruple CLIP"
+  
+  model_sampler:
+    class_type: ModelSamplingSD3
+    title: "ModelSamplingSD3"
+    params:
+      shift: 6.0
+
+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: "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_conditioning_chains:
+  conditioning_chain:
+    ksampler_node: "ksampler"
+    clip_source: "clip_loader:0"
+
+ui_map:
+  unet_name: "unet_loader:unet_name"
+  vae_name: "vae_loader:vae_name"
+  clip1_name: "clip_loader:clip_name1"
+  clip2_name: "clip_loader:clip_name2"
+  clip3_name: "clip_loader:clip_name3"
+  clip4_name: "clip_loader:clip_name4"

core/pipelines/workflow_recipes/_partials/conditioning/hunyuanimage.yaml

@@ -0,0 +1,42 @@
+nodes:
+  unet_loader:
+    class_type: UNETLoader
+    title: "Load Hunyuan UNET"
+    params:
+      weight_dtype: "default"
+  vae_loader:
+    class_type: VAELoader
+    title: "Load Hunyuan VAE"
+  clip_loader:
+    class_type: DualCLIPLoader
+    title: "Load Hunyuan Dual CLIP"
+    params:
+      type: "hunyuan_image"
+      device: "default"
+
+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: "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_conditioning_chains:
+  conditioning_chain:
+    ksampler_node: "ksampler"
+    clip_source: "clip_loader:0"
+
+ui_map:
+  unet_name: "unet_loader:unet_name"
+  vae_name: "vae_loader:vae_name"
+  clip1_name: "clip_loader:clip_name1"
+  clip2_name: "clip_loader:clip_name2"

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

@@ -0,0 +1,83 @@
+nodes:
+  unet_loader:
+    class_type: UNETLoader
+    title: "Load Diffusion Model"
+    params:
+      weight_dtype: "default"
+  vae_loader:
+    class_type: VAELoader
+    title: "Load VAE"
+  clip_loader:
+    class_type: CLIPLoader
+    title: "Load CLIP"
+    params:
+      type: "longcat_image"
+      device: "default"
+  
+  cfg_norm:
+    class_type: CFGNorm
+    title: "CFGNorm"
+    params:
+      strength: 1.0
+
+  flux_guidance_pos:
+    class_type: FluxGuidance
+    title: "FluxGuidance (Positive)"
+    params:
+      guidance: 4.0
+
+  flux_guidance_neg:
+    class_type: FluxGuidance
+    title: "FluxGuidance (Negative)"
+    params:
+      guidance: 4.0
+
+connections:
+  - from: "unet_loader:0"
+    to: "cfg_norm:model"
+  - from: "cfg_norm: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: "flux_guidance_pos:conditioning"
+  - from: "neg_prompt:0"
+    to: "flux_guidance_neg:conditioning"
+    
+  - from: "flux_guidance_pos:0"
+    to: "ksampler:positive"
+  - from: "flux_guidance_neg: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": ["cfg_norm:model"]
+      "clip": ["pos_prompt:clip", "neg_prompt:clip"]
+
+dynamic_conditioning_chains:
+  conditioning_chain:
+    flux_guidance_node: "flux_guidance_pos"
+    ksampler_node: "ksampler"
+    clip_source: "clip_loader:0"
+
+ui_map:
+  unet_name: "unet_loader:unet_name"
+  vae_name: "vae_loader:vae_name"
+  clip_name: "clip_loader:clip_name"

core/pipelines/workflow_recipes/_partials/conditioning/lumina.yaml

@@ -0,0 +1,57 @@
+nodes:
+  ckpt_loader:
+    class_type: CheckpointLoaderSimple
+    title: "Load Checkpoint"
+  model_sampler:
+    class_type: ModelSamplingAuraFlow
+    title: "ModelSamplingAuraFlow"
+    params:
+      shift: 4.0
+
+connections:
+  - from: "ckpt_loader:0"
+    to: "model_sampler:model"
+  - from: "model_sampler:0"
+    to: "ksampler:model"
+  
+  - from: "ckpt_loader:1"
+    to: "pos_prompt:clip"
+  - from: "ckpt_loader:1"
+    to: "neg_prompt:clip"
+  - from: "pos_prompt:0"
+    to: "ksampler:positive"
+  - from: "neg_prompt:0"
+    to: "ksampler:negative"
+    
+  - from: "ckpt_loader:2"
+    to: "vae_decode:vae"
+  - from: "ckpt_loader:2"
+    to: "vae_encode:vae"
+
+dynamic_vae_chains:
+  vae_chain:
+    targets:
+      - "vae_decode:vae"
+      - "vae_encode:vae"
+
+dynamic_lora_chains:
+  lora_chain:
+    template: "LoraLoader"
+    start: "ckpt_loader" 
+    output_map:
+      "0": "model" 
+      "1": "clip"  
+    input_map:
+      "model": "model"
+      "clip": "clip"
+    end_input_map:
+      "model": ["model_sampler:model"]
+      "clip": ["pos_prompt:clip", "neg_prompt:clip"]
+
+dynamic_conditioning_chains:
+  conditioning_chain:
+    ksampler_node: "ksampler"
+    clip_source: "ckpt_loader:1"
+
+ui_map:
+  model_name: "ckpt_loader:ckpt_name"

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

@@ -0,0 +1,65 @@
+nodes:
+  unet_loader:
+    class_type: UNETLoader
+    title: "Load Diffusion Model"
+    params:
+      weight_dtype: "default"
+  vae_loader:
+    class_type: VAELoader
+    title: "Load VAE"
+  clip_loader:
+    class_type: DualCLIPLoader
+    title: "Load Dual CLIP"
+    params:
+      type: "newbie"
+      device: "default"
+  model_sampler:
+    class_type: ModelSamplingAuraFlow
+    title: "ModelSamplingAuraFlow"
+    params:
+      shift: 6
+
+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: "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_newbie_lora_chains:
+  lora_chain:
+    template: "NewBieLoraLoader"
+    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_conditioning_chains:
+  conditioning_chain:
+    ksampler_node: "ksampler"
+    clip_source: "clip_loader:0"
+
+ui_map:
+  unet_name: "unet_loader:unet_name"
+  vae_name: "vae_loader:vae_name"
+  clip1_name: "clip_loader:clip_name1"
+  clip2_name: "clip_loader:clip_name2"

core/pipelines/workflow_recipes/_partials/conditioning/omnigen2.yaml

@@ -0,0 +1,59 @@
+nodes:
+  unet_loader:
+    class_type: UNETLoader
+    title: "Load Diffusion Model"
+    params:
+      weight_dtype: "default"
+  vae_loader:
+    class_type: VAELoader
+    title: "Load VAE"
+  clip_loader:
+    class_type: CLIPLoader
+    title: "Load CLIP"
+    params:
+      type: "omnigen2"
+      device: "default"
+
+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_reference_latent_chains:
+  reference_latent_chain:
+    ksampler_node: "ksampler"
+    vae_node: "vae_loader"
+
+ui_map:
+  unet_name: "unet_loader:unet_name"
+  vae_name: "vae_loader:vae_name"
+  clip_name: "clip_loader:clip_name"

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

@@ -0,0 +1,50 @@
+nodes:
+  unet_loader:
+    class_type: UNETLoader
+    title: "Load Diffusion Model"
+    params:
+      weight_dtype: "default"
+  vae_loader:
+    class_type: VAELoader
+    title: "Load VAE"
+  clip_loader:
+    class_type: CLIPLoader
+    title: "Load CLIP"
+    params:
+      type: "ovis"
+      device: "default"
+  model_sampler:
+    class_type: ModelSamplingAuraFlow
+    params:
+      shift: 3.0
+
+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: "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_conditioning_chains:
+  conditioning_chain:
+    ksampler_node: "ksampler"
+    clip_source: "clip_loader:0"
+
+ui_map:
+  unet_name: "unet_loader:unet_name"
+  vae_name: "vae_loader:vae_name"
+  clip_name: "clip_loader:clip_name"

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

@@ -0,0 +1,80 @@
+nodes:
+  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"
+  
+  lora_loader:
+    class_type: LoraLoaderModelOnly
+    title: "Load Qwen Lightning LoRA"
+    params:
+      strength_model: 1.0
+  model_sampler:
+    class_type: ModelSamplingAuraFlow
+    title: "ModelSamplingAuraFlow"
+    params:
+      shift: 3.1
+
+connections:
+  - from: "unet_loader:0"
+    to: "lora_loader:model"
+  - from: "lora_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:
+      "lora_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"
+
+ui_map:
+  unet_name: "unet_loader:unet_name"
+  vae_name: "vae_loader:vae_name"
+  clip_name: "clip_loader:clip_name"
+  lora_name: "lora_loader:lora_name"

core/pipelines/workflow_recipes/_partials/conditioning/sd15.yaml

@@ -0,0 +1,69 @@
+nodes:
+  ckpt_loader:
+    class_type: CheckpointLoaderSimple
+    title: "Load Checkpoint"
+  clip_set_last_layer:
+    class_type: CLIPSetLastLayer
+    title: "CLIP Set Last Layer"
+
+connections:
+  - from: "ckpt_loader:0"
+    to: "ksampler:model"
+  - from: "ckpt_loader:1"
+    to: "clip_set_last_layer:clip"
+  - from: "clip_set_last_layer:0"
+    to: "pos_prompt:clip"
+  - from: "clip_set_last_layer:0"
+    to: "neg_prompt:clip"
+  - from: "pos_prompt:0"
+    to: "ksampler:positive"
+  - from: "neg_prompt:0"
+    to: "ksampler:negative"
+  - from: "ckpt_loader:2"
+    to: "vae_decode:vae"
+  - from: "ckpt_loader:2"
+    to: "vae_encode:vae"
+
+dynamic_vae_chains:
+  vae_chain:
+    targets:
+      - "vae_decode:vae"
+      - "vae_encode:vae"
+
+dynamic_lora_chains:
+  lora_chain:
+    template: "LoraLoader"
+    start: "clip_set_last_layer"
+    output_map:
+      "ckpt_loader:0": "model" 
+      "0": "clip"             
+    input_map:
+      "model": "model"
+      "clip": "clip"
+    end_input_map:
+      "model": ["ksampler:model"]
+      "clip": ["pos_prompt:clip", "neg_prompt:clip"]
+
+dynamic_controlnet_chains:
+  controlnet_chain:
+    template: "ControlNetApplyAdvanced"
+    ksampler_node: "ksampler"
+    vae_source: "ckpt_loader:2"
+
+dynamic_ipadapter_chains:
+  ipadapter_chain:
+    end: "ksampler"
+    final_preset: "{{ ipadapter_final_preset }}"
+    final_weight: "{{ ipadapter_final_weight }}"
+    final_embeds_scaling: "{{ ipadapter_embeds_scaling }}"
+    final_loader_type: "{{ ipadapter_final_loader_type }}"
+    final_lora_strength: "{{ ipadapter_final_lora_strength }}"
+
+dynamic_conditioning_chains:
+  conditioning_chain:
+    ksampler_node: "ksampler"
+    clip_source: "clip_set_last_layer:0"
+
+ui_map:
+  model_name: "ckpt_loader:ckpt_name"
+  clip_skip: "clip_set_last_layer:stop_at_clip_layer"

core/pipelines/workflow_recipes/_partials/conditioning/sd35.yaml

@@ -0,0 +1,58 @@
+nodes:
+  ckpt_loader:
+    class_type: CheckpointLoaderSimple
+    title: "Load Checkpoint"
+
+connections:
+  - from: "ckpt_loader:0"
+    to: "ksampler:model"
+  - from: "ckpt_loader:1"
+    to: "pos_prompt:clip"
+  - from: "ckpt_loader:1"
+    to: "neg_prompt:clip"
+  - from: "pos_prompt:0"
+    to: "ksampler:positive"
+  - from: "neg_prompt:0"
+    to: "ksampler:negative"
+  - from: "ckpt_loader:2"
+    to: "vae_decode:vae"
+  - from: "ckpt_loader:2"
+    to: "vae_encode:vae"
+
+dynamic_vae_chains:
+  vae_chain:
+    targets:
+      - "vae_decode:vae"
+      - "vae_encode:vae"
+
+dynamic_lora_chains:
+  lora_chain:
+    template: "LoraLoader"
+    start: "ckpt_loader" 
+    output_map:
+      "0": "model" 
+      "1": "clip"  
+    input_map:
+      "model": "model"
+      "clip": "clip"
+    end_input_map:
+      "model": ["ksampler:model"]
+      "clip": ["pos_prompt:clip", "neg_prompt:clip"]
+
+dynamic_controlnet_chains:
+  controlnet_chain:
+    template: "ControlNetApplyAdvanced"
+    ksampler_node: "ksampler"
+    vae_source: "ckpt_loader:2"
+
+dynamic_sd3_ipadapter_chains:
+  sd3_ipadapter_chain:
+    ksampler_node: "ksampler"
+
+dynamic_conditioning_chains:
+  conditioning_chain:
+    ksampler_node: "ksampler"
+    clip_source: "ckpt_loader:1"
+
+ui_map:
+  model_name: "ckpt_loader:ckpt_name"

core/pipelines/workflow_recipes/_partials/conditioning/sdxl.yaml

@@ -0,0 +1,63 @@
+nodes:
+  ckpt_loader:
+    class_type: CheckpointLoaderSimple
+    title: "Load Checkpoint"
+
+connections:
+  - from: "ckpt_loader:0"
+    to: "ksampler:model"
+  - from: "ckpt_loader:1"
+    to: "pos_prompt:clip"
+  - from: "ckpt_loader:1"
+    to: "neg_prompt:clip"
+  - from: "pos_prompt:0"
+    to: "ksampler:positive"
+  - from: "neg_prompt:0"
+    to: "ksampler:negative"
+  - from: "ckpt_loader:2"
+    to: "vae_decode:vae"
+  - from: "ckpt_loader:2"
+    to: "vae_encode:vae"
+
+dynamic_vae_chains:
+  vae_chain:
+    targets:
+      - "vae_decode:vae"
+      - "vae_encode:vae"
+
+dynamic_lora_chains:
+  lora_chain:
+    template: "LoraLoader"
+    start: "ckpt_loader" 
+    output_map:
+      "0": "model" 
+      "1": "clip"  
+    input_map:
+      "model": "model"
+      "clip": "clip"
+    end_input_map:
+      "model": ["ksampler:model"]
+      "clip": ["pos_prompt:clip", "neg_prompt:clip"]
+
+dynamic_controlnet_chains:
+  controlnet_chain:
+    template: "ControlNetApplyAdvanced"
+    ksampler_node: "ksampler"
+    vae_source: "ckpt_loader:2"
+
+dynamic_ipadapter_chains:
+  ipadapter_chain:
+    end: "ksampler"
+    final_preset: "{{ ipadapter_final_preset }}"
+    final_weight: "{{ ipadapter_final_weight }}"
+    final_embeds_scaling: "{{ ipadapter_embeds_scaling }}"
+    final_loader_type: "{{ ipadapter_final_loader_type }}"
+    final_lora_strength: "{{ ipadapter_final_lora_strength }}"
+
+dynamic_conditioning_chains:
+  conditioning_chain:
+    ksampler_node: "ksampler"
+    clip_source: "ckpt_loader:1"
+
+ui_map:
+  model_name: "ckpt_loader:ckpt_name"

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

@@ -0,0 +1,65 @@
+nodes:
+  unet_loader:
+    class_type: UNETLoader
+    title: "Load Diffusion Model"
+    params:
+      weight_dtype: "default"
+  vae_loader:
+    class_type: VAELoader
+    title: "Load VAE"
+  clip_loader:
+    class_type: CLIPLoader
+    title: "Load CLIP"
+    params:
+      type: "lumina2"
+      device: "default"
+  model_sampler:
+    class_type: ModelSamplingAuraFlow
+    params:
+      shift: 3.0
+
+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: "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": ["model_sampler:model"]
+      "clip": ["pos_prompt:clip", "neg_prompt:clip"]
+
+dynamic_diffsynth_controlnet_chains:
+  diffsynth_controlnet_chain:
+    template: "QwenImageDiffsynthControlnet"
+    model_sampler_node: "model_sampler"
+    ksampler_node: "ksampler"
+    vae_source: "vae_loader:0"
+
+ui_map:
+  unet_name: "unet_loader:unet_name"
+  vae_name: "vae_loader:vae_name"
+  clip_name: "clip_loader:clip_name"

core/pipelines/workflow_recipes/_partials/input/hires_fix.yaml

@@ -0,0 +1,27 @@
+nodes:
+  input_image_loader:
+    class_type: LoadImage
+    title: "Load Input Image"
+  vae_encode:
+    class_type: VAEEncode
+    title: "VAE Encode (Hires Pre-step)"
+  latent_upscaler:
+    class_type: LatentUpscaleBy
+    title: "Upscale Latent By"
+  latent_source:
+    class_type: RepeatLatentBatch
+    title: "Repeat Latent Batch for Hires"
+
+connections:
+  - from: "input_image_loader:0"
+    to: "vae_encode:pixels"
+  - from: "vae_encode:0"
+    to: "latent_upscaler:samples"
+  - from: "latent_upscaler:0"
+    to: "latent_source:samples"
+
+ui_map:
+  input_image: "input_image_loader:image"
+  hires_upscaler: "latent_upscaler:upscale_method"
+  hires_scale_by: "latent_upscaler:scale_by"
+  batch_size: "latent_source:amount"

core/pipelines/workflow_recipes/_partials/input/img2img.yaml

@@ -0,0 +1,20 @@
+nodes:
+  input_image_loader:
+    class_type: LoadImage
+    title: "Load Input Image"
+  vae_encode:
+    class_type: VAEEncode
+    title: "VAE Encode (Img2Img)"
+  latent_source:
+    class_type: RepeatLatentBatch
+    title: "Repeat Latent Batch"
+
+connections:
+  - from: "input_image_loader:0"
+    to: "vae_encode:pixels"
+  - from: "vae_encode:0"
+    to: "latent_source:samples"
+    
+ui_map:
+  input_image: "input_image_loader:image"
+  batch_size: "latent_source:amount"

core/pipelines/workflow_recipes/_partials/input/inpaint.yaml

@@ -0,0 +1,23 @@
+nodes:
+  inpaint_loader:
+    class_type: LoadImage
+    title: "Load Inpaint Image+Mask"
+  vae_encode:
+    class_type: VAEEncodeForInpaint
+    title: "VAE Encode (for Inpainting)"
+  latent_source:
+    class_type: RepeatLatentBatch
+    title: "Repeat Latent Batch"
+
+connections:
+  - from: "inpaint_loader:0"
+    to: "vae_encode:pixels"
+  - from: "inpaint_loader:1"
+    to: "vae_encode:mask"
+  - from: "vae_encode:0"
+    to: "latent_source:samples"
+
+ui_map:
+  input_image: "inpaint_loader:image"
+  batch_size: "latent_source:amount"
+  grow_mask_by: "vae_encode:grow_mask_by"

core/pipelines/workflow_recipes/_partials/input/outpaint.yaml

@@ -0,0 +1,41 @@
+nodes:
+  input_image_loader:
+    class_type: LoadImage
+    title: "Load Image for Outpaint"
+  scale_image:
+    class_type: ImageScaleToTotalPixels
+    title: "Scale Image to Total Pixels"
+    params:
+      upscale_method: "nearest-exact"
+  pad_image:
+    class_type: ImagePadForOutpaint
+    title: "Pad Image for Outpainting"
+  vae_encode:
+    class_type: VAEEncodeForInpaint
+    title: "VAE Encode (for Inpainting)"
+  latent_source:
+    class_type: RepeatLatentBatch
+    title: "Repeat Latent Batch"
+
+connections:
+  - from: "input_image_loader:0"
+    to: "scale_image:image"
+  - from: "scale_image:0"
+    to: "pad_image:image"
+  - from: "pad_image:0"
+    to: "vae_encode:pixels"
+  - from: "pad_image:1"
+    to: "vae_encode:mask"
+  - from: "vae_encode:0"
+    to: "latent_source:samples"
+
+ui_map:
+  input_image: "input_image_loader:image"
+  megapixels: "scale_image:megapixels"
+  left: "pad_image:left"
+  top: "pad_image:top"
+  right: "pad_image:right"
+  bottom: "pad_image:bottom"
+  feathering: "pad_image:feathering"
+  grow_mask_by: "vae_encode:grow_mask_by"
+  batch_size: "latent_source:amount"

core/pipelines/workflow_recipes/_partials/input/txt2img.yaml

@@ -0,0 +1,2 @@
+imports:
+  - "txt2img_{{ latent_type }}.yaml"

core/pipelines/workflow_recipes/_partials/input/txt2img_chroma_radiance_latent.yaml

@@ -0,0 +1,11 @@
+nodes:
+  latent_source:
+    class_type: "EmptyChromaRadianceLatentImage"
+    title: "EmptyChromaRadianceLatentImage"
+
+connections: []
+
+ui_map:
+  width: "latent_source:width"
+  height: "latent_source:height"
+  batch_size: "latent_source:batch_size"

core/pipelines/workflow_recipes/_partials/input/txt2img_flux2_latent.yaml

@@ -0,0 +1,11 @@
+nodes:
+  latent_source:
+    class_type: "EmptyFlux2LatentImage"
+    title: "Empty Flux 2 Latent"
+
+connections: []
+
+ui_map:
+  width: "latent_source:width"
+  height: "latent_source:height"
+  batch_size: "latent_source:batch_size"

core/pipelines/workflow_recipes/_partials/input/txt2img_hunyuan_latent.yaml

@@ -0,0 +1,11 @@
+nodes:
+  latent_source:
+    class_type: "EmptyHunyuanImageLatent"
+    title: "EmptyHunyuanImageLatent"
+
+connections: []
+
+ui_map:
+  width: "latent_source:width"
+  height: "latent_source:height"
+  batch_size: "latent_source:batch_size"