Add Anima ControlNet-LLLite, HiDream-01 smoothing/reference & PID injectors

chain_injectors/ipadapter_injector.py

@@ -25,82 +25,127 @@ def inject(assembler, chain_definition, chain_items):
     model_type = final_settings.get('model_type', 'sdxl')
     megapixels = 1.05 if model_type == 'sdxl' else 0.39
 
-    pos_embed_outputs = []
-    neg_embed_outputs = []
+    first_preset = chain_items[0].get('preset', '')
+    is_faceid_chain = 'FACEID' in first_preset.upper()
 
-    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
+    if is_faceid_chain:
+        for i, item_data in enumerate(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']['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("IPAdapterUnifiedLoaderFaceID")
+            ipadapter_loader_node['inputs']['model'] = current_model_connection
+            ipadapter_loader_node['inputs']['preset'] = item_data['preset']
+            ipadapter_loader_node['inputs']['lora_strength'] = item_data.get('lora_strength', 0.6)
+            ipadapter_loader_node['inputs']['provider'] = "CUDA"
+            assembler.workflow[ipadapter_loader_id] = ipadapter_loader_node
+
+            apply_id = assembler._get_unique_id()
+            apply_node = assembler._get_node_template("IPAdapterFaceID")
+            apply_node['inputs']['model'] = [ipadapter_loader_id, 0]
+            apply_node['inputs']['ipadapter'] = [ipadapter_loader_id, 1]
+            apply_node['inputs']['image'] = [image_scaler_id, 0]
+            apply_node['inputs']['weight'] = item_data['weight']
+            apply_node['inputs']['weight_faceidv2'] = final_settings.get('final_lora_strength', 0.6)
+            apply_node['inputs']['weight_type'] = "linear"
+            apply_node['inputs']['combine_embeds'] = final_settings.get('final_combine_method', 'concat')
+            apply_node['inputs']['start_at'] = item_data.get('start_percent', 0.0)
+            apply_node['inputs']['end_at'] = item_data.get('end_percent', 1.0)
+            apply_node['inputs']['embeds_scaling'] = final_settings.get('final_embeds_scaling', 'V only')
+            
+            assembler.workflow[apply_id] = apply_node
+            current_model_connection = [apply_id, 0]
+
+        assembler.workflow[end_node_id]['inputs']['model'] = current_model_connection
+        print(f"IPAdapter FaceID injector applied (Direct Apply). Redirected '{end_node_name}' model input through {len(chain_items)} FaceID node(s).")
+        return
+
+    else:
+        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
         
-        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.")
+        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 Unified injector applied. Redirected '{end_node_name}' model input through {len(chain_items)} reference image(s).")

chain_injectors/pid_injector.py

@@ -270,8 +270,22 @@ def inject(assembler, chain_definition, chain_items):
                         if input_val[0] == original_vae_decode_id:
                             node_data['inputs'][input_name] = [pid_vae_decode_id, 0]
 
-    if original_vae_loader_id in assembler.workflow:
+    is_vae_loader_referenced = False
+    if original_vae_loader_id:
+        for node_id, node_data in assembler.workflow.items():
+            if node_id == original_vae_loader_id:
+                continue
+            for input_val in node_data.get('inputs', {}).values():
+                if isinstance(input_val, list) and len(input_val) == 2:
+                    if input_val[0] == original_vae_loader_id:
+                        is_vae_loader_referenced = True
+                        break
+            if is_vae_loader_referenced:
+                break
+
+    if original_vae_loader_id in assembler.workflow and not is_vae_loader_referenced:
         del assembler.workflow[original_vae_loader_id]
+
     if original_vae_decode_id in assembler.workflow:
         del assembler.workflow[original_vae_decode_id]
 

yaml/injectors.yaml

@@ -7,6 +7,8 @@ injector_definitions:
     module: "chain_injectors.newbie_lora_injector"
   dynamic_controlnet_chains: 
     module: "chain_injectors.controlnet_injector"
+  dynamic_krea2_controlnet_chains:
+    module: "chain_injectors.krea2_controlnet_injector"
   dynamic_anima_controlnet_lllite_chains:
     module: "chain_injectors.anima_controlnet_lllite_injector"
   dynamic_diffsynth_controlnet_chains:
@@ -42,6 +44,7 @@ injector_order:
   - dynamic_conditioning_chains
   - dynamic_style_chains
   - dynamic_controlnet_chains
+  - dynamic_krea2_controlnet_chains
   - dynamic_anima_controlnet_lllite_chains
   - dynamic_hidream_o1_smoothing_chains
   - dynamic_hidream_o1_reference_chains

yaml/pid.yaml

@@ -1,16 +1,16 @@
-PiD:
-  - filepath: "pid_flux2_1024_to_4096_4step_mxfp8.safetensors"
-    latent_format: "flux"
-    architectures: ["flux2", "flux2-kv", "lens", "ernie-image"]
-  - filepath: "pid_qwenimage_1024_to_4096_4step_bf16.safetensors"
-    latent_format: "qwenimage"
-    architectures: ["anima", "qwen-image"]
-  - filepath: "pid_flux1_1024_to_4096_4step_mxfp8.safetensors"
-    latent_format: "flux"
-    architectures: ["longcat-image", "newbie-image", "z-image", "ovis-image", "omnigen2", "chroma1", "hidream-i1", "flux1"]
-  - filepath: "pid_sd3_1024_to_4096_4step_bf16.safetensors"
-    latent_format: "sd3"
-    architectures: ["sd35"]
-  - filepath: "pid_sdxl_1024_to_4096_4step_bf16.safetensors"
-    latent_format: "sdxl"
+PiD:
+  - filepath: "pid_1.5_flux2_1024_to_4096_4step_int8_convrot.safetensors"
+    latent_format: "flux"
+    architectures: ["flux2", "flux2-kv", "ideogram-4", "lens", "ernie-image"]
+  - filepath: "pid_1.5_qwenimage_1024_to_4096_4step_int8_convrot.safetensors"
+    latent_format: "qwenimage"
+    architectures: ["anima", "qwen-image", "krea-2"]
+  - filepath: "pid_1.5_flux1_1024_to_4096_4step_int8_convrot.safetensors"
+    latent_format: "flux"
+    architectures: ["longcat-image", "newbie-image", "z-image", "kandinsky-5", "ovis-image", "omnigen2", "chroma1", "hidream-i1", "flux1", "boogu-image"]
+  - filepath: "pid_sd3_1024_to_4096_4step_bf16.safetensors"
+    latent_format: "sd3"
+    architectures: ["sd35"]
+  - filepath: "pid_sdxl_1024_to_4096_4step_bf16.safetensors"
+    latent_format: "sdxl"
     architectures: ["sdxl"]