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README.md

@@ -1,5 +1,5 @@
 ---
-title: ImageGen - SD1.5
+title: ImageGen - Anima
 emoji: 🖼
 colorFrom: purple
 colorTo: red
@@ -7,4 +7,4 @@ sdk: gradio
 sdk_version: "5.50.0"
 app_file: app.py
 short_description: Multi-task image generator with dynamic, chainable workflows
----
+---

app.py

@@ -58,7 +58,6 @@ def main():
     except Exception as e:
         print(f"--- [Setup] ❌ SageAttention installation failed: {e}. Continuing with default attention. ---")
 
-
     print("--- [Setup] Reloading site-packages to detect newly installed packages... ---")
     try:
         site.main()
@@ -69,8 +68,7 @@ def main():
     from comfy_integration import setup as setup_comfyui
     from utils.app_utils import (
         build_preprocessor_model_map,
-        build_preprocessor_parameter_map,
-        load_ipadapter_presets
+        build_preprocessor_parameter_map
     )
     from core import shared_state
     from core.settings import ALL_MODEL_MAP, ALL_FILE_DOWNLOAD_MAP
@@ -78,27 +76,39 @@ def main():
     def check_all_model_urls_on_startup():
         print("--- [Setup] Checking all model URL validity (one-time check) ---")
         for display_name, model_info in ALL_MODEL_MAP.items():
-            repo_id, filename, _, _ = model_info
-            if not repo_id: continue
-
-            download_info = ALL_FILE_DOWNLOAD_MAP.get(filename, {})
-            repo_file_path = download_info.get('repository_file_path', filename)
-            url = f"https://huggingface.co/{repo_id}/resolve/main/{repo_file_path}"
+            _, components, _, _ = model_info
+            if not components: continue
             
-            try:
-                response = requests.head(url, timeout=5, allow_redirects=True)
-                if response.status_code >= 400:
-                    print(f"❌ Invalid URL for '{display_name}': {url} (Status: {response.status_code})")
+            for filename in components.values():
+                download_info = ALL_FILE_DOWNLOAD_MAP.get(filename, {})
+                repo_id = download_info.get('repo_id')
+                if not repo_id: continue
+                
+                repo_file_path = download_info.get('repository_file_path', filename)
+                url = f"https://huggingface.co/{repo_id}/resolve/main/{repo_file_path}"
+                
+                try:
+                    response = requests.head(url, timeout=5, allow_redirects=True)
+                    if response.status_code >= 400:
+                        print(f"❌ Invalid URL for '{display_name}' component '{filename}': {url} (Status: {response.status_code})")
+                        shared_state.INVALID_MODEL_URLS[display_name] = True
+                        break 
+                except requests.RequestException as e:
+                    print(f"❌ URL check failed for '{display_name}' component '{filename}': {e}")
                     shared_state.INVALID_MODEL_URLS[display_name] = True
-            except requests.RequestException as e:
-                print(f"❌ URL check failed for '{display_name}': {e}")
-                shared_state.INVALID_MODEL_URLS[display_name] = True
+                    break
         print("--- [Setup] ✅ Finished checking model URLs. ---")
 
     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} ---")
+
     check_all_model_urls_on_startup()
     
     print("--- Building ControlNet preprocessor maps ---")
@@ -108,11 +118,6 @@ def main():
     build_preprocessor_parameter_map()
     print("--- ✅ ControlNet preprocessor setup complete. ---")
 
-    print("--- Loading IPAdapter presets ---")
-    load_ipadapter_presets()
-    print("--- ✅ IPAdapter setup complete. ---")
-
-
     print("--- Environment configured. Proceeding with module imports. ---")
     from ui.layout import build_ui
     from ui.events import attach_event_handlers

chain_injectors/conditioning_injector.py

@@ -48,8 +48,8 @@ def inject(assembler, chain_definition, chain_items):
 
         set_area_id = assembler._get_unique_id()
         set_area_node = assembler._get_node_template("ConditioningSetArea")
-        set_area_node['inputs']['width'] = item_data.get('width', 512)
-        set_area_node['inputs']['height'] = item_data.get('height', 512)
+        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)

chain_injectors/controlnet_injector.py

@@ -13,17 +13,13 @@ def inject(assembler, chain_definition, chain_items):
        '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)]
         
+    vae_source_name = chain_definition.get('vae_source_node')
+    if not vae_source_name or vae_source_name not in assembler.node_map:
+        print(f"Warning: VAE source node '{vae_source_name}' for ControlNet chain not found in recipe. Skipping chain injection.")
+        return
+    vae_connection = [assembler.node_map[vae_source_name], 0]
+
     current_positive_connection = assembler.workflow[ksampler_id]['inputs']['positive']
     current_negative_connection = assembler.workflow[ksampler_id]['inputs']['negative']
     
@@ -47,6 +43,7 @@ def inject(assembler, chain_definition, chain_items):
         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

chain_injectors/ipadapter_injector.py

@@ -1,106 +0,0 @@
-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', 'sd15')
-    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.")

comfy_integration/setup.py

@@ -48,13 +48,6 @@ def initialize_comfyui():
     else:
         print("✅ comfyui_controlnet_aux extension already exists.")
 
-    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.")
-
     print(f"✅ Current working directory is: {os.getcwd()}")
     
     import comfy.model_management

core/model_manager.py

@@ -1,14 +1,10 @@
 import gc
-from typing import Dict, List, Any, Set
+from typing import List
 
-import torch
 import gradio as gr
-from comfy import model_management
 
-from core.settings import ALL_MODEL_MAP, CHECKPOINT_DIR, LORA_DIR, DIFFUSION_MODELS_DIR, VAE_DIR, TEXT_ENCODERS_DIR
-from comfy_integration.nodes import checkpointloadersimple, LoraLoader
-from nodes import NODE_CLASS_MAPPINGS
-from utils.app_utils import get_value_at_index, _ensure_model_downloaded
+from core.settings import ALL_MODEL_MAP
+from utils.app_utils import _ensure_model_downloaded
 
 
 class ModelManager:
@@ -22,124 +18,31 @@ class ModelManager:
     def __init__(self):
         if hasattr(self, 'initialized'):
             return
-        self.loaded_models: Dict[str, Any] = {}
-        self.last_active_loras: List[Dict[str, Any]] = []
         self.initialized = True
         print("✅ ModelManager initialized.")
 
-    def get_loaded_model_names(self) -> Set[str]:
-        return set(self.loaded_models.keys())
-
-    def _load_single_model(self, display_name: str, progress) -> Any:
-        print(f"--- [ModelManager] Loading model: '{display_name}' ---")
-        
-        filename = _ensure_model_downloaded(display_name, progress)
+    def ensure_models_downloaded(self, required_models: List[str], progress):
+        print(f"--- [ModelManager] Ensuring models are downloaded: {required_models} ---")
         
-        _, _, model_type, _ = ALL_MODEL_MAP[display_name]
+        files_to_download = set()
+        for display_name in required_models:
+            if display_name in ALL_MODEL_MAP:
+                _, components, _, _ = ALL_MODEL_MAP[display_name]
+                for component_key, component_file in components.items():
+                    if component_key in ['unet', 'clip', 'vae', 'lora']:
+                        files_to_download.add(component_file)
         
-        loader_map = {
-            "SDXL": (checkpointloadersimple, "load_checkpoint", {"ckpt_name": filename}),
-            "SD1.5": (checkpointloadersimple, "load_checkpoint", {"ckpt_name": filename}),
-            "UNET": (NODE_CLASS_MAPPINGS["UNETLoader"](), "load_unet", {"unet_name": filename, "weight_dtype": "default"}),
-            "VAE": (NODE_CLASS_MAPPINGS["VAELoader"](), "load_vae", {"vae_name": filename}),
-            "TEXT_ENCODER": (NODE_CLASS_MAPPINGS["CLIPLoader"](), "load_clip", {"clip_name": filename, "type": "wan", "device": "default"}),
-        }
-
-        if model_type not in loader_map:
-            if model_type == "LORA":
-                print(f"--- [ModelManager] ✅ '{display_name}' is a LoRA. It will be loaded dynamically. ---")
-                return (filename,)
-            raise ValueError(f"[ModelManager] No loader configured for model type '{model_type}'")
-
-        loader_instance, method_name, kwargs = loader_map[model_type]
+        files_to_download = list(files_to_download)
+        total_files = len(files_to_download)
         
-        load_method = getattr(loader_instance, method_name)
-        loaded_tuple = load_method(**kwargs)
-
-        print(f"--- [ModelManager] ✅ Successfully loaded '{display_name}' to CPU/RAM ---")
-        return loaded_tuple
-
-    def move_models_to_gpu(self, required_models: List[str]):
-        print(f"--- [ModelManager] Moving models to GPU: {required_models} ---")
-        models_to_load_gpu = []
-        for name in required_models:
-            if name in self.loaded_models:
-                model_tuple = self.loaded_models[name]
-                _, _, model_type, _ = ALL_MODEL_MAP[name]
-                if model_type in ["SDXL", "SD1.5"]:
-                     models_to_load_gpu.append(get_value_at_index(model_tuple, 0))
-
-        if models_to_load_gpu:
-            model_management.load_models_gpu(models_to_load_gpu)
-            print("--- [ModelManager] ✅ Models successfully moved to GPU. ---")
-        else:
-            print("--- [ModelManager] ⚠️ No checkpoint models found to move to GPU. ---")
-
-    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):
+        for i, filename in enumerate(files_to_download):
             if progress and hasattr(progress, '__call__'):
-                progress(i / len(required_models), desc=f"Checking file: {display_name}")
+                progress(i / total_files, desc=f"Checking file: {filename}")
             try:
-                _ensure_model_downloaded(display_name, progress)
+                _ensure_model_downloaded(filename, progress)
             except Exception as e:
-                raise gr.Error(f"Failed to download model '{display_name}'. Reason: {e}")
+                raise gr.Error(f"Failed to download model component '{filename}'. Reason: {e}")
+
         print(f"--- [ModelManager] ✅ All required models are present on disk. ---")
     
-    def load_managed_models(self, required_models: List[str], active_loras: List[Dict[str, Any]], progress) -> Dict[str, Any]:
-        required_set = set(required_models)
-        current_set = set(self.loaded_models.keys())
-
-        loras_changed = self.last_active_loras != active_loras
-        models_to_unload = current_set - required_set
-        
-        must_reload = bool(models_to_unload) or loras_changed
-
-        if must_reload:
-            if models_to_unload:
-                print(f"--- [ModelManager] Models to unload: {models_to_unload} ---")
-            if loras_changed and not models_to_unload:
-                print(f"--- [ModelManager] LoRA configuration changed. Reloading base model(s): {current_set.intersection(required_set)} ---")
-                
-            model_management.unload_all_models()
-            self.loaded_models.clear()
-            gc.collect()
-            torch.cuda.empty_cache()
-            print("--- [ModelManager] All models unloaded to free RAM. ---")
-        
-        models_to_load = required_set if must_reload else (required_set - current_set)
-        
-        if models_to_load:
-            print(f"--- [ModelManager] Models to load: {models_to_load} ---")
-            for i, display_name in enumerate(models_to_load):
-                progress(i / len(models_to_load), desc=f"Loading model: {display_name}")
-                try:
-                    loaded_model_data = self._load_single_model(display_name, progress)
-                    
-                    if active_loras and ALL_MODEL_MAP[display_name][2] in ["SDXL", "SD1.5"]:
-                        print(f"--- [ModelManager] Applying {len(active_loras)} LoRAs on CPU... ---")
-                        lora_loader = LoraLoader()
-                        patched_model, patched_clip = loaded_model_data[0], loaded_model_data[1]
-
-                        for lora_info in active_loras:
-                            patched_model, patched_clip = lora_loader.load_lora(
-                                model=patched_model,
-                                clip=patched_clip,
-                                lora_name=lora_info["lora_name"],
-                                strength_model=lora_info["strength_model"],
-                                strength_clip=lora_info["strength_clip"]
-                            )
-                        
-                        loaded_model_data = (patched_model, patched_clip, loaded_model_data[2])
-                        print(f"--- [ModelManager] ✅ All LoRAs merged into the model on CPU. ---")
-                    
-                    self.loaded_models[display_name] = loaded_model_data
-                except Exception as e:
-                    raise gr.Error(f"Failed to load model or apply LoRA '{display_name}'. Reason: {e}")
-        else:
-             print(f"--- [ModelManager] All required models are already loaded. ---")
-        
-        self.last_active_loras = active_loras
-        return {name: self.loaded_models[name] for name in required_models}
-
 model_manager = ModelManager()

core/pipelines/sd_image_pipeline.py

@@ -11,7 +11,7 @@ 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 utils.app_utils import get_value_at_index, sanitize_prompt, get_lora_path, get_embedding_path, ensure_controlnet_model_downloaded, sanitize_filename
 from core.workflow_assembler import WorkflowAssembler
 
 class SdImagePipeline(BasePipeline):
@@ -71,6 +71,10 @@ class SdImagePipeline(BasePipeline):
                 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?")
@@ -109,24 +113,13 @@ class SdImagePipeline(BasePipeline):
             
             return get_value_at_index(computed_outputs[image_source_node_id], image_source_index)
 
-    def _gpu_logic(self, ui_inputs: Dict, loras_string: str, required_models_for_gpu: List[str], workflow: Dict[str, Any], assembler: WorkflowAssembler, progress=gr.Progress(track_tqdm=True)):
+    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(0.1, desc="Moving models to GPU...")
-        self.model_manager.move_models_to_gpu(required_models_for_gpu)
-        
         progress(0.4, desc="Executing workflow...")
         
-        loaded_model_tuple = self.model_manager.loaded_models[model_display_name]
-        
-        ckpt_loader_node_id = assembler.node_map.get("ckpt_loader")
-        if not ckpt_loader_node_id:
-            raise RuntimeError("Workflow is missing the 'ckpt_loader' node required for model injection.")
-        
-        initial_objects = {
-            ckpt_loader_node_id: loaded_model_tuple
-        }
-        
+        initial_objects = {}
+            
         decoded_images_tensor = self._execute_workflow(workflow, initial_objects=initial_objects)
         
         output_images = []
@@ -163,25 +156,23 @@ class SdImagePipeline(BasePipeline):
         
         lora_data = ui_inputs.get('lora_data', [])
         active_loras_for_gpu, active_loras_for_meta = [], []
-        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, ui_inputs['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}")
+        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, ui_inputs['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}")
         
-        progress(0.1, desc="Loading models into RAM...")
-        self.model_manager.load_managed_models(required_models, active_loras=active_loras_for_gpu, progress=progress)
-
         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)
@@ -266,54 +257,20 @@ class SdImagePipeline(BasePipeline):
 
         controlnet_data = ui_inputs.get('controlnet_data', [])
         active_controlnets = []
-        (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]
-                })
-        
-        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 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)
-                    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]
+                    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]
                     })
-            
-            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)
-            
-            if active_ipadapters:
-                active_ipadapters.append({
-                    'is_final_settings': True, 'model_type': 'sd15', '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
-                })
         
         from utils.app_utils import get_vae_path
         vae_source = ui_inputs.get('vae_source')
@@ -361,17 +318,22 @@ class SdImagePipeline(BasePipeline):
         if ui_inputs.get('seed') == -1:
             ui_inputs['seed'] = random.randint(0, 2**32 - 1)
 
-        dynamic_values = {'task_type': ui_inputs['task_type'], 'model_type': "sd15"}
+        dynamic_values = {'task_type': ui_inputs['task_type']}
         
         recipe_path = os.path.join(os.path.dirname(__file__), "workflow_recipes", "sd_unified_recipe.yaml")
         assembler = WorkflowAssembler(recipe_path, dynamic_values=dynamic_values)
         
+        model_display_name = ui_inputs['model_display_name']
+        if model_display_name not in ALL_MODEL_MAP:
+            raise gr.Error(f"Model '{model_display_name}' is not configured in model_list.yaml.")
+        
+        _, components, _, _ = ALL_MODEL_MAP[model_display_name]
+        
         workflow_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": -int(ui_inputs['clip_skip']),
             "denoise": ui_inputs['denoise'], 
             "input_image": ui_inputs.get('input_image'),
             "inpaint_image": ui_inputs.get('inpaint_image'),
@@ -379,10 +341,11 @@ class SdImagePipeline(BasePipeline):
             "left": ui_inputs.get('outpaint_left'), "top": ui_inputs.get('outpaint_top'),
             "right": ui_inputs.get('outpaint_right'), "bottom": ui_inputs.get('outpaint_bottom'),
             "hires_upscaler": ui_inputs.get('hires_upscaler'), "hires_scale_by": ui_inputs.get('hires_scale_by'),
-            "model_name": ALL_MODEL_MAP[ui_inputs['model_display_name']][1],
-            "vae_name": ui_inputs.get('vae_name'),
+            "unet_name": components['unet'],
+            "clip_name": components['clip'],
+            "vae_name": ui_inputs.get('vae_name', components['vae']),
+            "lora_chain": active_loras_for_gpu,
             "controlnet_chain": active_controlnets,
-            "ipadapter_chain": active_ipadapters,
             "conditioning_chain": active_conditioning,
         }
         
@@ -391,25 +354,6 @@ class SdImagePipeline(BasePipeline):
             workflow_inputs['height'] = ui_inputs['height']
         
         workflow = assembler.assemble(workflow_inputs)
-
-        if workflow_inputs.get("vae_name"):
-            print("--- [Workflow Patch] VAE override provided. Adding VAELoader and rewiring connections. ---")
-            vae_loader_id = assembler._get_unique_id()
-            vae_loader_node = assembler._get_node_template("VAELoader")
-            vae_loader_node['inputs']['vae_name'] = workflow_inputs["vae_name"]
-            workflow[vae_loader_id] = vae_loader_node
-
-            vae_decode_id = assembler.node_map.get("vae_decode")
-            if vae_decode_id and vae_decode_id in workflow:
-                workflow[vae_decode_id]['inputs']['vae'] = [vae_loader_id, 0]
-                print(f"  - Rewired 'vae_decode' (ID: {vae_decode_id}) to use new VAELoader.")
-
-            vae_encode_id = assembler.node_map.get("vae_encode")
-            if vae_encode_id and vae_encode_id in workflow:
-                workflow[vae_encode_id]['inputs']['vae'] = [vae_loader_id, 0]
-                print(f"  - Rewired 'vae_encode' (ID: {vae_encode_id}) to use new VAELoader.")
-        else:
-            print("--- [Workflow Info] No VAE override. Using VAE from checkpoint. ---")
         
         progress(1.0, desc="All models ready. Requesting GPU for generation...")
 
@@ -421,11 +365,47 @@ class SdImagePipeline(BasePipeline):
                 task_name=f"ImageGen ({task_type})",
                 ui_inputs=ui_inputs,
                 loras_string=loras_string,
-                required_models_for_gpu=required_models,
                 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):

core/pipelines/workflow_recipes/_partials/conditioning/{sd15.yaml → anima.yaml}

@@ -1,59 +1,72 @@
 nodes:
-  ckpt_loader:
-    class_type: CheckpointLoaderSimple
-    title: "Load SD1.5 Checkpoint"
-  
-  clip_set_last_layer:
-    class_type: CLIPSetLastLayer
-    title: "CLIP Set Last Layer"
+  unet_loader:
+    class_type: UNETLoader
+    title: "Load Anima UNET"
+    params:
+      weight_dtype: "default"
+  vae_loader:
+    class_type: VAELoader
+    title: "Load Anima VAE"
+  clip_loader:
+    class_type: CLIPLoader
+    title: "Load Anima CLIP"
+    params:
+      type: "stable_diffusion"
+      device: "default"
   
   pos_prompt:
     class_type: CLIPTextEncode
     title: "Positive Prompt Encoder"
-  
   neg_prompt:
     class_type: CLIPTextEncode
     title: "Negative Prompt Encoder"
 
 connections:
-  - from: "ckpt_loader:0"
+  - from: "unet_loader:0"
     to: "ksampler:model"
-
-  - from: "ckpt_loader:1"
-    to: "clip_set_last_layer:clip"
-  - from: "clip_set_last_layer:0"
+    
+  - from: "clip_loader:0"
     to: "pos_prompt:clip"
-  - from: "clip_set_last_layer:0"
+  - from: "clip_loader:0"
     to: "neg_prompt:clip"
-  
-  - from: "ckpt_loader:2"
+    
+  - 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"
-    
-ui_map:
-  model_name: "ckpt_loader:ckpt_name"
-  clip_skip: "clip_set_last_layer:stop_at_clip_layer"
-  positive_prompt: "pos_prompt:text"
-  negative_prompt: "neg_prompt:text"
-  
+
+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_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 }}"
+    vae_source_node: "vae_loader"
 
 dynamic_conditioning_chains:
   conditioning_chain:
     ksampler_node: "ksampler"
-    clip_source: "clip_set_last_layer:0"
+    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"
+  positive_prompt: "pos_prompt:text"
+  negative_prompt: "neg_prompt:text"

core/pipelines/workflow_recipes/sd_unified_recipe.yaml

@@ -1,10 +1,8 @@
 imports:
   - "_partials/_base_sampler.yaml"
   - "_partials/input/{{ task_type }}.yaml"
-  - "_partials/conditioning/sd15.yaml"
+  - "_partials/conditioning/anima.yaml"
 
 connections:
   - from: "latent_source:0"
-    to: "ksampler:latent_image"
-  - from: "ckpt_loader:2"
-    to: "vae_encode:vae"
+    to: "ksampler:latent_image"

core/settings.py

@@ -15,7 +15,6 @@ OUTPUT_DIR = "output"
 _PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 _MODEL_LIST_PATH = os.path.join(_PROJECT_ROOT, 'yaml', 'model_list.yaml')
 _FILE_LIST_PATH = os.path.join(_PROJECT_ROOT, 'yaml', 'file_list.yaml')
-_IPADAPTER_LIST_PATH = os.path.join(_PROJECT_ROOT, 'yaml', 'ipadapter.yaml')
 _CONSTANTS_PATH = os.path.join(_PROJECT_ROOT, 'yaml', 'constants.yaml')
 
 
@@ -66,15 +65,12 @@ def load_models_from_yaml(model_list_filepath=_MODEL_LIST_PATH, download_map=Non
             if not isinstance(models, list): continue
             for model in models:
                 display_name = model['display_name']
-                filename = model['path']
-                
-                download_info = download_map.get(filename, {})
-                repo_id = download_info.get('repo_id', '')
+                components = model.get('components', {})
 
                 model_tuple = (
-                    repo_id,
-                    filename,
-                    "SD1.5",
+                    None,
+                    components,
+                    "SDXL",
                     None
                 )
                 model_maps[map_name][display_name] = model_tuple
@@ -103,15 +99,13 @@ try:
     MAX_EMBEDDINGS = _constants.get('MAX_EMBEDDINGS', 5)
     MAX_CONDITIONINGS = _constants.get('MAX_CONDITIONINGS', 10)
     MAX_CONTROLNETS = _constants.get('MAX_CONTROLNETS', 5)
-    MAX_IPADAPTERS = _constants.get('MAX_IPADAPTERS', 5)
     LORA_SOURCE_CHOICES = _constants.get('LORA_SOURCE_CHOICES', ["Civitai", "Custom URL", "File"])
     RESOLUTION_MAP = _constants.get('RESOLUTION_MAP', {})
-    SAMPLER_MAP = _constants.get('SAMPLER_MAP', {})
 except Exception as e:
     print(f"FATAL: Could not load constants from YAML. Error: {e}")
-    MAX_LORAS, MAX_EMBEDDINGS, MAX_CONDITIONINGS, MAX_CONTROLNETS, MAX_IPADAPTERS = 5, 5, 10, 5, 5
+    MAX_LORAS, MAX_EMBEDDINGS, MAX_CONDITIONINGS, MAX_CONTROLNETS = 5, 5, 10, 5
     LORA_SOURCE_CHOICES = ["Civitai", "Custom URL", "File"]
-    RESOLUTION_MAP, SAMPLER_MAP = {}, {}
+    RESOLUTION_MAP = {}
 
 
-DEFAULT_NEGATIVE_PROMPT = "monochrome, (low quality, worst quality:1.2), 3d, watermark, signature, ugly, poorly drawn,"
+DEFAULT_NEGATIVE_PROMPT = ""

requirements.txt

@@ -1,5 +1,5 @@
-comfyui-frontend-package==1.41.20
-comfyui-workflow-templates==0.9.21
+comfyui-frontend-package==1.42.10
+comfyui-workflow-templates==0.9.47
 comfyui-embedded-docs==0.4.3
 torch
 torchsde

ui/events.py

@@ -10,7 +10,7 @@ from comfy_integration.nodes import SAMPLER_CHOICES, SCHEDULER_CHOICES
 
 from core.pipelines.controlnet_preprocessor import CPU_ONLY_PREPROCESSORS
 from utils.app_utils import PREPROCESSOR_MODEL_MAP, PREPROCESSOR_PARAMETER_MAP, save_uploaded_file_with_hash
-from ui.shared.ui_components import RESOLUTION_MAP, MAX_CONTROLNETS, MAX_IPADAPTERS, MAX_EMBEDDINGS, MAX_CONDITIONINGS, MAX_LORAS
+from ui.shared.ui_components import RESOLUTION_MAP, MAX_CONTROLNETS, MAX_EMBEDDINGS, MAX_CONDITIONINGS, MAX_LORAS
 
 
 @lru_cache(maxsize=1)
@@ -22,104 +22,11 @@ def load_controlnet_config():
         with open(_CN_MODEL_LIST_PATH, 'r', encoding='utf-8') as f:
             config = yaml.safe_load(f)
         print("--- ✅ controlnet_models.yaml loaded successfully ---")
-        return config.get("ControlNet", {}).get("SD1.5", [])
+        return config.get("ControlNet", {}).get("Anima", [])
     except Exception as e:
         print(f"Error loading controlnet_models.yaml: {e}")
         return []
 
-@lru_cache(maxsize=1)
-def load_ipadapter_config():
-    _PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-    _IPA_MODEL_LIST_PATH = os.path.join(_PROJECT_ROOT, 'yaml', 'ipadapter.yaml')
-    try:
-        print("--- Loading ipadapter.yaml ---")
-        with open(_IPA_MODEL_LIST_PATH, 'r', encoding='utf-8') as f:
-            config = yaml.safe_load(f)
-        print("--- ✅ ipadapter.yaml loaded successfully ---")
-        return config
-    except Exception as e:
-        print(f"Error loading ipadapter.yaml: {e}")
-        return {}
-
-
-def apply_data_to_ui(data, prefix, ui_components):
-    final_sampler = data.get('sampler') if data.get('sampler') in SAMPLER_CHOICES else SAMPLER_CHOICES[0]
-    default_scheduler = 'normal' if 'normal' in SCHEDULER_CHOICES else SCHEDULER_CHOICES[0]
-    final_scheduler = data.get('scheduler') if data.get('scheduler') in SCHEDULER_CHOICES else default_scheduler
-
-    updates = {}
-    base_model_name = data.get('base_model')
-    
-    model_map = MODEL_MAP_CHECKPOINT
-
-    if f'base_model_{prefix}' in ui_components:
-        model_dropdown_component = ui_components[f'base_model_{prefix}']
-        if base_model_name and base_model_name in model_map:
-            updates[model_dropdown_component] = base_model_name
-        else:
-            updates[model_dropdown_component] = gr.update()
-    
-    common_params = {
-        f'prompt_{prefix}': data.get('prompt', ''),
-        f'neg_prompt_{prefix}': data.get('negative_prompt', ''),
-        f'seed_{prefix}': data.get('seed', -1),
-        f'cfg_{prefix}': data.get('cfg_scale', 7.5),
-        f'steps_{prefix}': data.get('steps', 28),
-        f'sampler_{prefix}': final_sampler,
-        f'scheduler_{prefix}': final_scheduler,
-    }
-    
-    for comp_name, value in common_params.items():
-        if comp_name in ui_components:
-            updates[ui_components[comp_name]] = value
-    
-    if prefix == 'txt2img':
-         if f'width_{prefix}' in ui_components:
-             updates[ui_components[f'width_{prefix}']] = data.get('width', 1024)
-         if f'height_{prefix}' in ui_components:
-             updates[ui_components[f'height_{prefix}']] = data.get('height', 1024)
-
-    tab_indices = {"txt2img": 0, "img2img": 1, "inpaint": 2, "outpaint": 3, "hires_fix": 4}
-    tab_index = tab_indices.get(prefix, 0)
-
-    updates[ui_components['tabs']] = gr.Tabs(selected=0)
-    updates[ui_components['image_gen_tabs']] = gr.Tabs(selected=tab_index)
-    
-    return updates
-
-
-def send_info_to_tab(image, prefix, ui_components):
-    if not image or not image.info.get('parameters', ''):
-        all_comps = [comp for comp_or_list in ui_components.values() for comp in (comp_or_list if isinstance(comp_or_list, list) else [comp_or_list])]
-        return {comp: gr.update() for comp in all_comps}
-    
-    data = parse_parameters(image.info['parameters'])
-    
-    image_input_map = {
-        "img2img": 'input_image_img2img',
-        "inpaint": 'input_image_dict_inpaint',
-        "outpaint": 'input_image_outpaint',
-        "hires_fix": 'input_image_hires_fix'
-    }
-    
-    updates = apply_data_to_ui(data, prefix, ui_components)
-    
-    if prefix in image_input_map and image_input_map[prefix] in ui_components:
-        component_key = image_input_map[prefix]
-        updates[ui_components[component_key]] = gr.update(value=image)
-        
-    return updates
-
-
-def send_info_by_hash(image, ui_components):
-    if not image or not image.info.get('parameters', ''):
-        all_comps = [comp for comp_or_list in ui_components.values() for comp in (comp_or_list if isinstance(comp_or_list, list) else [comp_or_list])]
-        return {comp: gr.update() for comp in all_comps}
-
-    data = parse_parameters(image.info['parameters'])
-    
-    return apply_data_to_ui(data, "txt2img", ui_components)
-
 
 def attach_event_handlers(ui_components, demo):
     def update_cn_input_visibility(choice):
@@ -230,7 +137,7 @@ def attach_event_handlers(ui_components, demo):
         ] + all_dynamic_inputs,
         outputs=[ui_components["output_gallery_cn"]]
     )
-    
+
     def create_lora_event_handlers(prefix):
         lora_rows = ui_components[f'lora_rows_{prefix}']
         lora_ids = ui_components[f'lora_ids_{prefix}']
@@ -362,57 +269,6 @@ def attach_event_handlers(ui_components, demo):
             show_progress=False
         )
 
-    def create_ipadapter_event_handlers(prefix):
-        ipa_rows = ui_components[f'ipadapter_rows_{prefix}']
-        ipa_lora_strengths = ui_components[f'ipadapter_lora_strengths_{prefix}']
-        ipa_final_preset = ui_components[f'ipadapter_final_preset_{prefix}']
-        ipa_final_lora_strength = ui_components[f'ipadapter_final_lora_strength_{prefix}']
-        count_state = ui_components[f'ipadapter_count_state_{prefix}']
-        add_button = ui_components[f'add_ipadapter_button_{prefix}']
-        del_button = ui_components[f'delete_ipadapter_button_{prefix}']
-        accordion = ui_components[f'ipadapter_accordion_{prefix}']
-
-        def add_ipa_row(c):
-            c += 1
-            return {
-                count_state: c,
-                ipa_rows[c - 1]: gr.update(visible=True),
-                add_button: gr.update(visible=c < MAX_IPADAPTERS),
-                del_button: gr.update(visible=True),
-            }
-
-        def del_ipa_row(c):
-            c -= 1
-            return {
-                count_state: c,
-                ipa_rows[c]: gr.update(visible=False),
-                add_button: gr.update(visible=True),
-                del_button: gr.update(visible=c > 0),
-            }
-
-        add_outputs = [count_state, add_button, del_button] + ipa_rows
-        del_outputs = [count_state, add_button, del_button] + ipa_rows
-        add_button.click(fn=add_ipa_row, inputs=[count_state], outputs=add_outputs, show_progress=False)
-        del_button.click(fn=del_ipa_row, inputs=[count_state], outputs=del_outputs, show_progress=False)
-
-        def on_preset_change(preset_value):
-            config = load_ipadapter_config()
-            faceid_presets = []
-            if isinstance(config, list):
-                faceid_presets = [
-                    p.get('preset_name', '') for p in config 
-                    if 'FACE' in p.get('preset_name', '') or 'FACEID' in p.get('preset_name', '')
-                ]
-            is_visible = preset_value in faceid_presets
-            updates = [gr.update(visible=is_visible)] * (MAX_IPADAPTERS + 1)
-            return updates
-
-        all_lora_strength_sliders = [ipa_final_lora_strength] + ipa_lora_strengths
-        ipa_final_preset.change(fn=on_preset_change, inputs=[ipa_final_preset], outputs=all_lora_strength_sliders, show_progress=False)
-        
-        accordion.expand(fn=lambda *imgs: [gr.update() for _ in imgs], inputs=ui_components[f'ipadapter_images_{prefix}'], outputs=ui_components[f'ipadapter_images_{prefix}'], show_progress=False)
-
-
     def create_embedding_event_handlers(prefix):
         rows = ui_components[f'embedding_rows_{prefix}']
         ids = ui_components[f'embeddings_ids_{prefix}']
@@ -531,7 +387,6 @@ def attach_event_handlers(ui_components, demo):
         
         lora_data_components = ui_components.get(f'all_lora_components_flat_{prefix}', [])
         controlnet_data_components = ui_components.get(f'all_controlnet_components_flat_{prefix}', [])
-        ipadapter_data_components = ui_components.get(f'all_ipadapter_components_flat_{prefix}', [])
         embedding_data_components = ui_components.get(f'all_embedding_components_flat_{prefix}', [])
         conditioning_data_components = ui_components.get(f'all_conditioning_components_flat_{prefix}', [])
         
@@ -541,7 +396,7 @@ def attach_event_handlers(ui_components, demo):
 
         input_keys = list(run_inputs_map.keys())
         input_list_flat = [v for v in run_inputs_map.values() if v is not None]
-        input_list_flat += lora_data_components + controlnet_data_components + ipadapter_data_components + embedding_data_components + conditioning_data_components
+        input_list_flat += lora_data_components + controlnet_data_components + embedding_data_components + conditioning_data_components
 
         def create_ui_inputs_dict(*args):
             valid_keys = [k for k in input_keys if run_inputs_map[k] is not None]
@@ -552,8 +407,6 @@ def attach_event_handlers(ui_components, demo):
             arg_idx += len(lora_data_components)
             ui_dict['controlnet_data'] = list(args[arg_idx : arg_idx + len(controlnet_data_components)])
             arg_idx += len(controlnet_data_components)
-            ui_dict['ipadapter_data'] = list(args[arg_idx : arg_idx + len(ipadapter_data_components)])
-            arg_idx += len(ipadapter_data_components)
             ui_dict['embedding_data'] = list(args[arg_idx : arg_idx + len(embedding_data_components)])
             arg_idx += len(embedding_data_components)
             ui_dict['conditioning_data'] = list(args[arg_idx : arg_idx + len(conditioning_data_components)])
@@ -584,7 +437,6 @@ def attach_event_handlers(ui_components, demo):
                     show_progress=False
                 )
         if f'add_controlnet_button_{prefix}' in ui_components: create_controlnet_event_handlers(prefix)
-        if f'add_ipadapter_button_{prefix}' in ui_components: create_ipadapter_event_handlers(prefix)
         if f'add_embedding_button_{prefix}' in ui_components: 
             create_embedding_event_handlers(prefix)
             if f'embeddings_uploads_{prefix}' in ui_components:
@@ -615,29 +467,13 @@ def attach_event_handlers(ui_components, demo):
 
         create_run_event(prefix, task_type)
 
-
-    ui_components["info_get_button"].click(
-        get_png_info, 
-        [ui_components["info_image_input"]], 
-        [ui_components["info_prompt_output"], ui_components["info_neg_prompt_output"], ui_components["info_params_output"]]
-    )
-
-    flat_ui_list = [comp for comp_or_list in ui_components.values() for comp in (comp_or_list if isinstance(comp_or_list, list) else [comp_or_list])]
-            
-    ui_components["send_to_txt2img_button"].click(lambda img: send_info_by_hash(img, ui_components), [ui_components["info_image_input"]], flat_ui_list)
-    ui_components["send_to_img2img_button"].click(lambda img: send_info_to_tab(img, "img2img", ui_components), [ui_components["info_image_input"]], flat_ui_list)
-    ui_components["send_to_inpaint_button"].click(lambda img: send_info_to_tab(img, "inpaint", ui_components), [ui_components["info_image_input"]], flat_ui_list)
-    ui_components["send_to_outpaint_button"].click(lambda img: send_info_to_tab(img, "outpaint", ui_components), [ui_components["info_image_input"]], flat_ui_list)
-    ui_components["send_to_hires_fix_button"].click(lambda img: send_info_to_tab(img, "hires_fix", ui_components), [ui_components["info_image_input"]], flat_ui_list)
-
     def on_aspect_ratio_change(ratio_key, model_display_name):
-        model_type = MODEL_TYPE_MAP.get(model_display_name, 'sd1.5').lower()
-        if model_type == 'sd1.5': model_type = 'sd15'
-        res_map = RESOLUTION_MAP.get(model_type, RESOLUTION_MAP.get("sd15", {}))
-        w, h = res_map.get(ratio_key, (512, 512))
+        model_type = MODEL_TYPE_MAP.get(model_display_name, 'sdxl').lower()
+        res_map = RESOLUTION_MAP.get(model_type, RESOLUTION_MAP.get("sdxl", {}))
+        w, h = res_map.get(ratio_key, (1024, 1024))
         return w, h
 
-    for prefix in ["txt2img", "img2img", "inpaint", "outpaint", "hires_fix"]:
+    for prefix in ["txt2img", "img2img", "outpaint", "hires_fix"]:
         if f'aspect_ratio_{prefix}' in ui_components:
             aspect_ratio_dropdown = ui_components[f'aspect_ratio_{prefix}']
             width_component = ui_components[f'width_{prefix}']
@@ -695,41 +531,10 @@ def attach_event_handlers(ui_components, demo):
                     updates[filepath_state] = filepath
         return updates
 
-    def initialize_all_ipa_dropdowns():
-        config = load_ipadapter_config()
-        if not config or not isinstance(config, list): return {}
-        
-        unified_presets = []
-        faceid_presets = []
-        for preset_info in config:
-            name = preset_info.get("preset_name")
-            if not name:
-                continue
-            if "FACEID" in name or "FACE" in name:
-                faceid_presets.append(name)
-            else:
-                unified_presets.append(name)
-        
-        all_presets = unified_presets + faceid_presets
-        default_preset = all_presets[0] if all_presets else None
-        is_faceid_default = default_preset in faceid_presets
-
-        lora_strength_update = gr.update(visible=is_faceid_default)
-        
-        updates = {}
-        for prefix in ["txt2img", "img2img", "inpaint", "outpaint", "hires_fix"]:
-            if f'ipadapter_final_preset_{prefix}' in ui_components:
-                for lora_strength_slider in ui_components[f'ipadapter_lora_strengths_{prefix}']:
-                    updates[lora_strength_slider] = lora_strength_update
-                updates[ui_components[f'ipadapter_final_preset_{prefix}']] = gr.update(choices=all_presets, value=default_preset)
-                updates[ui_components[f'ipadapter_final_lora_strength_{prefix}']] = lora_strength_update
-        return updates
-
     def run_on_load():
+        all_updates = {}
         cn_updates = initialize_all_cn_dropdowns()
-        ipa_updates = initialize_all_ipa_dropdowns()
-        
-        all_updates = {**cn_updates, **ipa_updates}
+        all_updates.update(cn_updates)
         
         default_preprocessor = "Canny Edge" 
         model_update = update_preprocessor_models_dropdown(default_preprocessor)
@@ -752,11 +557,7 @@ def attach_event_handlers(ui_components, demo):
             all_load_outputs.extend(ui_components[f'controlnet_types_{prefix}'])
             all_load_outputs.extend(ui_components[f'controlnet_series_{prefix}'])
             all_load_outputs.extend(ui_components[f'controlnet_filepaths_{prefix}'])
-        if f'ipadapter_final_preset_{prefix}' in ui_components:
-            all_load_outputs.extend(ui_components[f'ipadapter_lora_strengths_{prefix}'])
-            all_load_outputs.append(ui_components[f'ipadapter_final_preset_{prefix}'])
-            all_load_outputs.append(ui_components[f'ipadapter_final_lora_strength_{prefix}'])
-
+            
     all_load_outputs.extend([
         ui_components["preprocessor_model_cn"],
         *ui_components["cn_sliders"],
@@ -765,8 +566,9 @@ def attach_event_handlers(ui_components, demo):
         ui_components["run_cn"],
         ui_components["zero_gpu_cn"]
     ])
-    
-    demo.load(
-        fn=run_on_load,
-        outputs=all_load_outputs
-    )
+
+    if all_load_outputs:
+        demo.load(
+            fn=run_on_load,
+            outputs=all_load_outputs
+        )

ui/layout.py

@@ -21,20 +21,19 @@ def build_ui(event_handler_function):
     ui_components = {}
 
     with gr.Blocks() as demo:
-        gr.Markdown("# ImageGen - SD1.5")
+        gr.Markdown("# ImageGen - Anima")
         gr.Markdown(
             "This demo is a streamlined version of the [Comfy web UI](https://github.com/RioShiina47/comfy-webui)'s ImgGen functionality. "
             "Other versions are also available: "
             "[FLUX.2](https://huggingface.co/spaces/RioShiina/ImageGen-FLUX.2), "
             "[Z-Image](https://huggingface.co/spaces/RioShiina/ImageGen-Z-Image), "
-            "[Qwen-Image](https://huggingface.co/spaces/RioShiina/ImageGen-Qwen-Image), "
             "[Illstrious](https://huggingface.co/spaces/RioShiina/ImageGen-Illstrious), "
             "[NoobAI](https://huggingface.co/spaces/RioShiina/ImageGen-NoobAI), "
             "[Pony](https://huggingface.co/spaces/RioShiina/ImageGen-Pony), "
             "[SDXL](https://huggingface.co/spaces/RioShiina/ImageGen-SDXL)"
         )
         with gr.Tabs(elem_id="tabs_container") as tabs:
-            with gr.TabItem("SD1.5", id=0):
+            with gr.TabItem("ImageGen", id=0):
                 with gr.Tabs(elem_id="image_gen_tabs") as image_gen_tabs:
                     with gr.TabItem("Txt2Img", id=0):
                         ui_components.update(txt2img_ui.create_ui())
@@ -81,34 +80,9 @@ def build_ui(event_handler_function):
                     "preprocessor_settings_ui": preprocessor_settings_ui, "cn_sliders": cn_sliders,
                     "cn_dropdowns": cn_dropdowns, "cn_checkboxes": cn_checkboxes
                 })
-            
-            with gr.TabItem("PNG Info", id=2):
-                with gr.Column():
-                    info_image_input = gr.Image(type="pil", label="Upload Image", height=512)
-                    with gr.Row():
-                        info_get_button = gr.Button("Get Info")
-                        send_to_txt2img_button = gr.Button("Send to Txt2Img", variant="primary")
-                    with gr.Row():
-                        send_to_img2img_button = gr.Button("Send to Img2Img")
-                        send_to_inpaint_button = gr.Button("Send to Inpaint")
-                        send_to_outpaint_button = gr.Button("Send to Outpaint")
-                        send_to_hires_fix_button = gr.Button("Send to Hires. Fix")
-                    gr.Markdown("### Positive Prompt"); info_prompt_output = gr.Textbox(lines=3, interactive=False, show_label=False)
-                    gr.Markdown("### Negative Prompt"); info_neg_prompt_output = gr.Textbox(lines=3, interactive=False, show_label=False)
-                    gr.Markdown("### Other Parameters"); info_params_output = gr.Textbox(lines=5, interactive=False, show_label=False)
-                ui_components.update({
-                    "info_image_input": info_image_input, "info_get_button": info_get_button,
-                    "send_to_txt2img_button": send_to_txt2img_button,
-                    "send_to_img2img_button": send_to_img2img_button,
-                    "send_to_inpaint_button": send_to_inpaint_button,
-                    "send_to_outpaint_button": send_to_outpaint_button,
-                    "send_to_hires_fix_button": send_to_hires_fix_button,
-                    "info_prompt_output": info_prompt_output, "info_neg_prompt_output": info_neg_prompt_output,
-                    "info_params_output": info_params_output
-                })
-
-            ui_components["tabs"] = tabs
         
+        ui_components['tabs'] = tabs
+
         gr.Markdown("<div style='text-align: center; margin-top: 20px;'>Made by RioShiina with ❤️<br><a href='https://github.com/RioShiina47' target='_blank'>GitHub</a> | <a href='https://huggingface.co/RioShiina' target='_blank'>Hugging Face</a> | <a href='https://civitai.com/user/RioShiina' target='_blank'>Civitai</a></div>")
         
         event_handler_function(ui_components, demo)

ui/shared/hires_fix_ui.py

@@ -3,7 +3,7 @@ from core.settings import MODEL_MAP_CHECKPOINT
 from comfy_integration.nodes import SAMPLER_CHOICES, SCHEDULER_CHOICES
 from .ui_components import (
     create_lora_settings_ui,
-    create_controlnet_ui, create_ipadapter_ui, create_embedding_ui,
+    create_controlnet_ui, create_embedding_ui,
     create_conditioning_ui, create_vae_override_ui, create_api_key_ui
 )
 
@@ -27,7 +27,7 @@ def create_ui():
                 components[f'input_image_{prefix}'] = gr.Image(type="pil", label="Input Image", height=255)
             with gr.Column(scale=2):
                 components[f'prompt_{prefix}'] = gr.Text(label="Prompt", lines=3, placeholder="Describe the final image...")
-                components[f'neg_prompt_{prefix}'] = gr.Text(label="Negative prompt", lines=3, value="monochrome, (low quality, worst quality:1.2), 3d, watermark, signature, ugly, poorly drawn,")
+                components[f'neg_prompt_{prefix}'] = gr.Text(label="Negative prompt", lines=3, value="")
 
         with gr.Row():
             with gr.Column(scale=1):
@@ -45,18 +45,18 @@ def create_ui():
                     components[f'denoise_{prefix}'] = gr.Slider(label="Denoise Strength", minimum=0.0, maximum=1.0, step=0.01, value=0.55)
                 
                 with gr.Row():
-                    components[f'sampler_{prefix}'] = gr.Dropdown(label="Sampler", choices=SAMPLER_CHOICES, value=SAMPLER_CHOICES[0])
-                    components[f'scheduler_{prefix}'] = gr.Dropdown(label="Scheduler", choices=SCHEDULER_CHOICES, value='normal' if 'normal' in SCHEDULER_CHOICES else SCHEDULER_CHOICES[0])
+                    components[f'sampler_{prefix}'] = gr.Dropdown(label="Sampler", choices=SAMPLER_CHOICES, value="er_sde")
+                    components[f'scheduler_{prefix}'] = gr.Dropdown(label="Scheduler", choices=SCHEDULER_CHOICES, value="simple")
                 with gr.Row():
-                    components[f'steps_{prefix}'] = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=28)
-                    components[f'cfg_{prefix}'] = gr.Slider(label="CFG Scale", minimum=1.0, maximum=20.0, step=0.1, value=7.5)
+                    components[f'steps_{prefix}'] = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=30)
+                    components[f'cfg_{prefix}'] = gr.Slider(label="CFG Scale", minimum=1.0, maximum=20.0, step=0.1, value=4.0)
                 with gr.Row():
                     components[f'seed_{prefix}'] = gr.Number(label="Seed (-1 for random)", value=-1, precision=0)
                     components[f'batch_size_{prefix}'] = gr.Slider(label="Batch Size", minimum=1, maximum=16, step=1, value=1)
                 with gr.Row():
-                    components[f'clip_skip_{prefix}'] = gr.Slider(label="Clip Skip", minimum=1, maximum=2, step=1, value=1)
                     components[f'zero_gpu_{prefix}'] = gr.Number(label="ZeroGPU Duration (s)", value=None, placeholder="Default: 60s, Max: 120s", info="Optional: Set how long to reserve the GPU.")
 
+                components[f'clip_skip_{prefix}'] = gr.State(value=1)
                 components[f'width_{prefix}'] = gr.State(value=512)
                 components[f'height_{prefix}'] = gr.State(value=512)
 
@@ -66,9 +66,8 @@ def create_ui():
         components.update(create_api_key_ui(prefix))
         components.update(create_lora_settings_ui(prefix))
         components.update(create_controlnet_ui(prefix))
-        components.update(create_ipadapter_ui(prefix))
-        components.update(create_embedding_ui(prefix))
+        # components.update(create_embedding_ui(prefix))
         components.update(create_conditioning_ui(prefix))
-        components.update(create_vae_override_ui(prefix))
+        # components.update(create_vae_override_ui(prefix))
             
     return components

ui/shared/img2img_ui.py

@@ -3,7 +3,7 @@ from core.settings import MODEL_MAP_CHECKPOINT
 from comfy_integration.nodes import SAMPLER_CHOICES, SCHEDULER_CHOICES
 from .ui_components import (
     create_lora_settings_ui,
-    create_controlnet_ui, create_ipadapter_ui, create_embedding_ui,
+    create_controlnet_ui, create_embedding_ui,
     create_conditioning_ui, create_vae_override_ui, create_api_key_ui
 )
 
@@ -23,34 +23,34 @@ def create_ui():
 
             with gr.Column(scale=2):
                 components[f'prompt_{prefix}'] = gr.Text(label="Prompt", lines=3, placeholder="Enter your prompt")
-                components[f'neg_prompt_{prefix}'] = gr.Text(label="Negative prompt", lines=3, value="monochrome, (low quality, worst quality:1.2), 3d, watermark, signature, ugly, poorly drawn,")
+                components[f'neg_prompt_{prefix}'] = gr.Text(label="Negative prompt", lines=3, value="")
 
         with gr.Row():
             with gr.Column(scale=1):
                 components[f'denoise_{prefix}'] = gr.Slider(label="Denoise Strength", minimum=0.0, maximum=1.0, step=0.01, value=0.7)
                 
                 with gr.Row():
-                    components[f'sampler_{prefix}'] = gr.Dropdown(label="Sampler", choices=SAMPLER_CHOICES, value=SAMPLER_CHOICES[0])
-                    components[f'scheduler_{prefix}'] = gr.Dropdown(label="Scheduler", choices=SCHEDULER_CHOICES, value='normal' if 'normal' in SCHEDULER_CHOICES else SCHEDULER_CHOICES[0])
+                    components[f'sampler_{prefix}'] = gr.Dropdown(label="Sampler", choices=SAMPLER_CHOICES, value="er_sde")
+                    components[f'scheduler_{prefix}'] = gr.Dropdown(label="Scheduler", choices=SCHEDULER_CHOICES, value="simple")
                 with gr.Row():
-                    components[f'steps_{prefix}'] = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=28)
-                    components[f'cfg_{prefix}'] = gr.Slider(label="CFG Scale", minimum=1.0, maximum=20.0, step=0.1, value=7.5)
+                    components[f'steps_{prefix}'] = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=30)
+                    components[f'cfg_{prefix}'] = gr.Slider(label="CFG Scale", minimum=1.0, maximum=20.0, step=0.1, value=4.0)
                 with gr.Row():
                     components[f'seed_{prefix}'] = gr.Number(label="Seed (-1 for random)", value=-1, precision=0)
                     components[f'batch_size_{prefix}'] = gr.Slider(label="Batch Size", minimum=1, maximum=16, step=1, value=1)
                 with gr.Row():
-                    components[f'clip_skip_{prefix}'] = gr.Slider(label="Clip Skip", minimum=1, maximum=2, step=1, value=1)
                     components[f'zero_gpu_{prefix}'] = gr.Number(label="ZeroGPU Duration (s)", value=None, placeholder="Default: 60s, Max: 120s", info="Optional: Set how long to reserve the GPU. Longer jobs may need more time.")
                 
+                components[f'clip_skip_{prefix}'] = gr.State(value=1)
+                
             with gr.Column(scale=1):
                 components[f'result_{prefix}'] = gr.Gallery(label="Result", show_label=False, columns=1, object_fit="contain", height=505)
         
         components.update(create_api_key_ui(prefix))
         components.update(create_lora_settings_ui(prefix))
         components.update(create_controlnet_ui(prefix))
-        components.update(create_ipadapter_ui(prefix))
-        components.update(create_embedding_ui(prefix))
+        # components.update(create_embedding_ui(prefix))
         components.update(create_conditioning_ui(prefix))
-        components.update(create_vae_override_ui(prefix))
+        # components.update(create_vae_override_ui(prefix))
             
     return components

ui/shared/inpaint_ui.py

@@ -2,7 +2,7 @@ import gradio as gr
 from core.settings import MODEL_MAP_CHECKPOINT
 from .ui_components import (
     create_base_parameter_ui, create_lora_settings_ui,
-    create_controlnet_ui, create_ipadapter_ui, create_embedding_ui,
+    create_controlnet_ui, create_embedding_ui,
     create_conditioning_ui, create_vae_override_ui, create_api_key_ui
 )
 
@@ -40,7 +40,7 @@ def create_ui():
 
             with gr.Column(scale=2) as prompts_column:
                 components[f'prompt_{prefix}'] = gr.Text(label="Prompt", lines=6, placeholder="Describe what to fill in the mask...")
-                components[f'neg_prompt_{prefix}'] = gr.Text(label="Negative prompt", lines=6, value="monochrome, (low quality, worst quality:1.2), 3d, watermark, signature, ugly, poorly drawn,")
+                components[f'neg_prompt_{prefix}'] = gr.Text(label="Negative prompt", lines=6, value="")
             components[f'prompts_column_{prefix}'] = prompts_column
 
         with gr.Row() as params_and_gallery_row:
@@ -48,18 +48,18 @@ def create_ui():
                 param_defaults = {'w': 1024, 'h': 1024, 'cs_vis': False, 'cs_val': 1}
                 from comfy_integration.nodes import SAMPLER_CHOICES, SCHEDULER_CHOICES
                 with gr.Row():
-                    components[f'sampler_{prefix}'] = gr.Dropdown(label="Sampler", choices=SAMPLER_CHOICES, value=SAMPLER_CHOICES[0])
-                    components[f'scheduler_{prefix}'] = gr.Dropdown(label="Scheduler", choices=SCHEDULER_CHOICES, value='normal' if 'normal' in SCHEDULER_CHOICES else SCHEDULER_CHOICES[0])
+                    components[f'sampler_{prefix}'] = gr.Dropdown(label="Sampler", choices=SAMPLER_CHOICES, value="er_sde")
+                    components[f'scheduler_{prefix}'] = gr.Dropdown(label="Scheduler", choices=SCHEDULER_CHOICES, value="simple")
                 with gr.Row():
-                    components[f'steps_{prefix}'] = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=28)
-                    components[f'cfg_{prefix}'] = gr.Slider(label="CFG Scale", minimum=1.0, maximum=20.0, step=0.1, value=7.5)
+                    components[f'steps_{prefix}'] = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=30)
+                    components[f'cfg_{prefix}'] = gr.Slider(label="CFG Scale", minimum=1.0, maximum=20.0, step=0.1, value=4.0)
                 with gr.Row():
                     components[f'seed_{prefix}'] = gr.Number(label="Seed (-1 for random)", value=-1, precision=0)
                     components[f'batch_size_{prefix}'] = gr.Slider(label="Batch Size", minimum=1, maximum=16, step=1, value=1)
                 with gr.Row():
-                    components[f'clip_skip_{prefix}'] = gr.Slider(label="Clip Skip", minimum=1, maximum=2, step=1, value=1)
                     components[f'zero_gpu_{prefix}'] = gr.Number(label="ZeroGPU Duration (s)", value=None, placeholder="Default: 60s, Max: 120s", info="Optional: Set how long to reserve the GPU.")
 
+                components[f'clip_skip_{prefix}'] = gr.State(value=1)
                 components[f'width_{prefix}'] = gr.State(value=512)
                 components[f'height_{prefix}'] = gr.State(value=512)
 
@@ -72,10 +72,9 @@ def create_ui():
             components.update(create_api_key_ui(prefix))
             components.update(create_lora_settings_ui(prefix))
             components.update(create_controlnet_ui(prefix))
-            components.update(create_ipadapter_ui(prefix))
-            components.update(create_embedding_ui(prefix))
+            # components.update(create_embedding_ui(prefix))
             components.update(create_conditioning_ui(prefix))
-            components.update(create_vae_override_ui(prefix))
+            # components.update(create_vae_override_ui(prefix))
         components[f'accordion_wrapper_{prefix}'] = accordion_wrapper
             
     return components

ui/shared/outpaint_ui.py

@@ -3,7 +3,7 @@ from core.settings import MODEL_MAP_CHECKPOINT
 from comfy_integration.nodes import SAMPLER_CHOICES, SCHEDULER_CHOICES
 from .ui_components import (
     create_lora_settings_ui,
-    create_controlnet_ui, create_ipadapter_ui, create_embedding_ui,
+    create_controlnet_ui, create_embedding_ui,
     create_conditioning_ui, create_vae_override_ui, create_api_key_ui
 )
 
@@ -27,7 +27,7 @@ def create_ui():
                 components[f'input_image_{prefix}'] = gr.Image(type="pil", label="Input Image", height=255)
             with gr.Column(scale=2):
                 components[f'prompt_{prefix}'] = gr.Text(label="Prompt", lines=3, placeholder="Describe the content for the expanded areas...")
-                components[f'neg_prompt_{prefix}'] = gr.Text(label="Negative prompt", lines=3, value="monochrome, (low quality, worst quality:1.2), 3d, watermark, signature, ugly, poorly drawn,")
+                components[f'neg_prompt_{prefix}'] = gr.Text(label="Negative prompt", lines=3, value="")
 
         with gr.Row():
             with gr.Column(scale=1):
@@ -39,18 +39,18 @@ def create_ui():
                     components[f'outpaint_bottom_{prefix}'] = gr.Slider(label="Pad Bottom", minimum=0, maximum=512, step=64, value=0)
                 
                 with gr.Row():
-                    components[f'sampler_{prefix}'] = gr.Dropdown(label="Sampler", choices=SAMPLER_CHOICES, value=SAMPLER_CHOICES[0])
-                    components[f'scheduler_{prefix}'] = gr.Dropdown(label="Scheduler", choices=SCHEDULER_CHOICES, value='normal' if 'normal' in SCHEDULER_CHOICES else SCHEDULER_CHOICES[0])
+                    components[f'sampler_{prefix}'] = gr.Dropdown(label="Sampler", choices=SAMPLER_CHOICES, value="er_sde")
+                    components[f'scheduler_{prefix}'] = gr.Dropdown(label="Scheduler", choices=SCHEDULER_CHOICES, value="simple")
                 with gr.Row():
-                    components[f'steps_{prefix}'] = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=28)
-                    components[f'cfg_{prefix}'] = gr.Slider(label="CFG Scale", minimum=1.0, maximum=20.0, step=0.1, value=7.5)
+                    components[f'steps_{prefix}'] = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=30)
+                    components[f'cfg_{prefix}'] = gr.Slider(label="CFG Scale", minimum=1.0, maximum=20.0, step=0.1, value=4.0)
                 with gr.Row():
                     components[f'seed_{prefix}'] = gr.Number(label="Seed (-1 for random)", value=-1, precision=0)
                     components[f'batch_size_{prefix}'] = gr.Slider(label="Batch Size", minimum=1, maximum=16, step=1, value=1)
                 with gr.Row():
-                    components[f'clip_skip_{prefix}'] = gr.Slider(label="Clip Skip", minimum=1, maximum=2, step=1, value=1)
                     components[f'zero_gpu_{prefix}'] = gr.Number(label="ZeroGPU Duration (s)", value=None, placeholder="Default: 60s, Max: 120s", info="Optional: Set how long to reserve the GPU.")
 
+                components[f'clip_skip_{prefix}'] = gr.State(value=1)
                 components[f'width_{prefix}'] = gr.State(value=512)
                 components[f'height_{prefix}'] = gr.State(value=512)
 
@@ -60,9 +60,8 @@ def create_ui():
         components.update(create_api_key_ui(prefix))
         components.update(create_lora_settings_ui(prefix))
         components.update(create_controlnet_ui(prefix))
-        components.update(create_ipadapter_ui(prefix))
-        components.update(create_embedding_ui(prefix))
+        # components.update(create_embedding_ui(prefix))
         components.update(create_conditioning_ui(prefix))
-        components.update(create_vae_override_ui(prefix))
+        # components.update(create_vae_override_ui(prefix))
             
     return components

ui/shared/txt2img_ui.py

@@ -2,7 +2,7 @@ import gradio as gr
 from core.settings import MODEL_MAP_CHECKPOINT
 from .ui_components import (
     create_base_parameter_ui, create_lora_settings_ui,
-    create_controlnet_ui, create_ipadapter_ui, create_embedding_ui,
+    create_controlnet_ui, create_embedding_ui,
     create_conditioning_ui, create_vae_override_ui, create_api_key_ui
 )
 
@@ -18,11 +18,11 @@ def create_ui():
                 components[f'run_{prefix}'] = gr.Button("Run", variant="primary")
         
         components[f'prompt_{prefix}'] = gr.Text(label="Prompt", lines=3, placeholder="Enter your prompt")
-        components[f'neg_prompt_{prefix}'] = gr.Text(label="Negative prompt", lines=3, value="monochrome, (low quality, worst quality:1.2), 3d, watermark, signature, ugly, poorly drawn,")
+        components[f'neg_prompt_{prefix}'] = gr.Text(label="Negative prompt", lines=3, value="")
         
         with gr.Row():
             with gr.Column(scale=1):
-                param_defaults = {'w': 512, 'h': 512, 'cs_vis': False, 'cs_val': 1}
+                param_defaults = {'w': 1024, 'h': 1024, 'cs_vis': False, 'cs_val': 1}
                 components.update(create_base_parameter_ui(prefix, param_defaults))
             with gr.Column(scale=1):
                 components[f'result_{prefix}'] = gr.Gallery(label="Result", show_label=False, columns=2, object_fit="contain", height=627)
@@ -30,9 +30,8 @@ def create_ui():
         components.update(create_api_key_ui(prefix))
         components.update(create_lora_settings_ui(prefix))
         components.update(create_controlnet_ui(prefix))
-        components.update(create_ipadapter_ui(prefix))
-        components.update(create_embedding_ui(prefix))
+        # components.update(create_embedding_ui(prefix))
         components.update(create_conditioning_ui(prefix))
-        components.update(create_vae_override_ui(prefix))
+        # components.update(create_vae_override_ui(prefix))
         
     return components

ui/shared/ui_components.py

@@ -2,30 +2,12 @@ import gradio as gr
 from comfy_integration.nodes import SAMPLER_CHOICES, SCHEDULER_CHOICES
 from core.settings import (
     MAX_LORAS, LORA_SOURCE_CHOICES, MAX_EMBEDDINGS, MAX_CONDITIONINGS,
-    MAX_CONTROLNETS, MAX_IPADAPTERS, RESOLUTION_MAP
+    MAX_CONTROLNETS, RESOLUTION_MAP
 )
 import yaml
 import os
 from functools import lru_cache
 
-@lru_cache(maxsize=1)
-def get_ipadapter_presets_from_yaml():
-    try:
-        _PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-        _IPADAPTER_LIST_PATH = os.path.join(_PROJECT_ROOT, 'yaml', 'ipadapter.yaml')
-        with open(_IPADAPTER_LIST_PATH, 'r', encoding='utf-8') as f:
-            config = yaml.safe_load(f)
-        if isinstance(config, list):
-            presets = [item.get('preset_name') for item in config if item.get('preset_name')]
-            if "Composition" not in presets:
-                pass
-            return presets
-        return []
-    except Exception as e:
-        print(f"Warning: Could not load ipadapter.yaml for UI components: {e}")
-        return ["STANDARD (medium strength)"]
-
-
 def create_base_parameter_ui(prefix, defaults=None):
     if defaults is None:
         defaults = {}
@@ -35,25 +17,26 @@ def create_base_parameter_ui(prefix, defaults=None):
     with gr.Row():
         components[f'aspect_ratio_{prefix}'] = gr.Dropdown(
             label="Aspect Ratio",
-            choices=list(RESOLUTION_MAP['sd15'].keys()),
+            choices=list(RESOLUTION_MAP['sdxl'].keys()),
             value="1:1 (Square)",
             interactive=True
         )
     with gr.Row():
-        components[f'width_{prefix}'] = gr.Number(label="Width", value=defaults.get('w', 512), interactive=True)
-        components[f'height_{prefix}'] = gr.Number(label="Height", value=defaults.get('h', 512), interactive=True)
+        components[f'width_{prefix}'] = gr.Number(label="Width", value=defaults.get('w', 1024), interactive=True)
+        components[f'height_{prefix}'] = gr.Number(label="Height", value=defaults.get('h', 1024), interactive=True)
     with gr.Row():
-        components[f'sampler_{prefix}'] = gr.Dropdown(label="Sampler", choices=SAMPLER_CHOICES, value=SAMPLER_CHOICES[0])
-        components[f'scheduler_{prefix}'] = gr.Dropdown(label="Scheduler", choices=SCHEDULER_CHOICES, value='normal' if 'normal' in SCHEDULER_CHOICES else SCHEDULER_CHOICES[0])
+        components[f'sampler_{prefix}'] = gr.Dropdown(label="Sampler", choices=SAMPLER_CHOICES, value="er_sde")
+        components[f'scheduler_{prefix}'] = gr.Dropdown(label="Scheduler", choices=SCHEDULER_CHOICES, value="simple")
     with gr.Row():
-        components[f'steps_{prefix}'] = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=28)
-        components[f'cfg_{prefix}'] = gr.Slider(label="CFG Scale", minimum=1.0, maximum=20.0, step=0.1, value=7.5)
+        components[f'steps_{prefix}'] = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=30)
+        components[f'cfg_{prefix}'] = gr.Slider(label="CFG Scale", minimum=1.0, maximum=20.0, step=0.1, value=4.0)
     with gr.Row():
         components[f'seed_{prefix}'] = gr.Number(label="Seed (-1 for random)", value=-1, precision=0)
         components[f'batch_size_{prefix}'] = gr.Slider(label="Batch Size", minimum=1, maximum=16, step=1, value=1)
     with gr.Row():
-        components[f'clip_skip_{prefix}'] = gr.Slider(label="Clip Skip", minimum=1, maximum=2, step=1, value=1)
         components[f'zero_gpu_{prefix}'] = gr.Number(label="ZeroGPU Duration (s)", value=None, placeholder="Default: 60s, Max: 120s", info="Optional: Set how long to reserve the GPU. Longer jobs may need more time.")
+    
+    components[f'clip_skip_{prefix}'] = gr.State(value=1)
             
     return components
 
@@ -154,77 +137,6 @@ def create_controlnet_ui(prefix: str, max_units=MAX_CONTROLNETS):
 
     return components
 
-def create_ipadapter_ui(prefix: str, max_units=MAX_IPADAPTERS):
-    components = {}
-    key = lambda name: f"{name}_{prefix}"
-
-    ipadapter_presets = get_ipadapter_presets_from_yaml()
-    default_preset = ipadapter_presets[0] if ipadapter_presets else None
-
-    with gr.Accordion("IPAdapter Settings", open=False) as accordion:
-        components[key('ipadapter_accordion')] = accordion
-        gr.Markdown("Powered by [cubiq/ComfyUI_IPAdapter_plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus).")
-        
-        with gr.Row():
-            components[key('ipadapter_final_preset')] = gr.Dropdown(
-                label="Preset (for all images)", 
-                choices=ipadapter_presets,
-                value=default_preset, 
-                interactive=True
-            )
-            components[key('ipadapter_embeds_scaling')] = gr.Dropdown(
-                label="Embeds Scaling", 
-                choices=['V only', 'K+V', 'K+V w/ C penalty', 'K+mean(V) w/ C penalty'],
-                value='V only',
-                interactive=True
-            )
-        
-        with gr.Row():
-            components[key('ipadapter_combine_method')] = gr.Dropdown(
-                label="Combine Method",
-                choices=["concat", "add", "subtract", "average", "norm average", "max", "min"],
-                value="concat",
-                interactive=True
-            )
-            components[key('ipadapter_final_weight')] = gr.Slider(label="Final Weight", minimum=0.0, maximum=2.0, step=0.05, value=1.0, interactive=True)
-            components[key('ipadapter_final_lora_strength')] = gr.Slider(label="Final LoRA Strength", minimum=0.0, maximum=2.0, step=0.05, value=0.6, interactive=True, visible=False)
-
-        gr.Markdown("---")
-        
-        ipa_rows, images, weights, lora_strengths = [], [], [], []
-        components.update({
-            key('ipadapter_rows'): ipa_rows,
-            key('ipadapter_images'): images,
-            key('ipadapter_weights'): weights,
-            key('ipadapter_lora_strengths'): lora_strengths
-        })
-        
-        for i in range(max_units):
-            with gr.Row(visible=(i < 1)) as row:
-                with gr.Column(scale=1):
-                    images.append(gr.Image(label=f"IPAdapter Image {i+1}", type="pil", sources="upload", height=256))
-                with gr.Column(scale=2):
-                    weights.append(gr.Slider(label="Weight", minimum=0.0, maximum=2.0, step=0.05, value=1.0, interactive=True))
-                    lora_strengths.append(gr.Slider(label="LoRA Strength", minimum=0.0, maximum=2.0, step=0.05, value=0.6, interactive=True, visible=False))
-                ipa_rows.append(row)
-
-        with gr.Row():
-            components[key('add_ipadapter_button')] = gr.Button("✚ Add IPAdapter")
-            components[key('delete_ipadapter_button')] = gr.Button("➖ Delete IPAdapter", visible=False)
-        components[key('ipadapter_count_state')] = gr.State(1)
-        
-        all_ipa_components_flat = images + weights + lora_strengths
-        all_ipa_components_flat += [
-            components[key('ipadapter_final_preset')],
-            components[key('ipadapter_final_weight')],
-            components[key('ipadapter_final_lora_strength')],
-            components[key('ipadapter_embeds_scaling')],
-            components[key('ipadapter_combine_method')],
-        ]
-        components[key('all_ipadapter_components_flat')] = all_ipa_components_flat
-
-    return components
-
 def create_embedding_ui(prefix: str):
     components = {}
     key = lambda name: f"{name}_{prefix}"

utils/app_utils.py

@@ -20,7 +20,6 @@ MODELS_ROOT_DIR = "ComfyUI/models"
 
 PREPROCESSOR_MODEL_MAP = None
 PREPROCESSOR_PARAMETER_MAP = None
-IPADAPTER_PRESETS = None
 
 
 def save_uploaded_file_with_hash(file_obj: gr.File, target_dir: str) -> str:
@@ -302,45 +301,44 @@ def get_vae_path(source: str, id_or_url: str, civitai_key: str, progress) -> tup
     return (local_path, status) if "Successfully" in status else (None, status)
 
 
-def _ensure_model_downloaded(display_name: str, progress=gr.Progress()):
-    if display_name not in ALL_MODEL_MAP:
-        raise ValueError(f"Model '{display_name}' not found in configuration.")
-
-    _, repo_filename, model_type, _ = ALL_MODEL_MAP[display_name]
-    
-    type_to_dir_map = {
-        "SDXL": CHECKPOINT_DIR, "SD1.5": CHECKPOINT_DIR, "UNET": DIFFUSION_MODELS_DIR,
-        "VAE": VAE_DIR, "TEXT_ENCODER": TEXT_ENCODERS_DIR, "LORA": LORA_DIR,
-        "IPADAPTER": os.path.join(os.path.dirname(LORA_DIR), "ipadapter"),
-        "CLIP_VISION": os.path.join(os.path.dirname(LORA_DIR), "clip_vision")
+def _ensure_model_downloaded(filename: str, progress=gr.Progress()):
+    download_info = ALL_FILE_DOWNLOAD_MAP.get(filename)
+    if not download_info:
+        raise gr.Error(f"Model component '{filename}' not found in file_list.yaml. Cannot download.")
+        
+    category_to_dir_map = {
+        "diffusion_models": DIFFUSION_MODELS_DIR,
+        "text_encoders": TEXT_ENCODERS_DIR,
+        "vae": VAE_DIR,
+        "checkpoints": CHECKPOINT_DIR,
+        "loras": LORA_DIR,
+        "controlnet": CONTROLNET_DIR,
+        "clip_vision": os.path.join(os.path.dirname(LORA_DIR), "clip_vision")
     }
-    dest_dir = type_to_dir_map.get(model_type)
+    
+    category = download_info.get('category')
+    dest_dir = category_to_dir_map.get(category)
     if not dest_dir:
-        raise ValueError(f"Unknown model type '{model_type}' for '{display_name}'.")
+        raise ValueError(f"Unknown model category '{category}' for file '{filename}'.")
     
-    base_filename = os.path.basename(repo_filename)
-    dest_path = os.path.join(dest_dir, base_filename)
+    dest_path = os.path.join(dest_dir, filename)
 
     if os.path.lexists(dest_path):
         if not os.path.exists(dest_path):
             print(f"⚠️ Found and removed broken symlink: {dest_path}")
             os.remove(dest_path)
         else:
-            return base_filename
-
-    download_info = ALL_FILE_DOWNLOAD_MAP.get(base_filename)
-    if not download_info:
-        raise gr.Error(f"Model '{base_filename}' not found in file_list.yaml. Cannot download.")
+            return filename
 
     source = download_info.get("source")
     try:
-        progress(0, desc=f"Downloading: {base_filename}")
+        progress(0, desc=f"Downloading: {filename}")
         
         if source == "hf":
             repo_id = download_info.get("repo_id")
-            hf_filename = download_info.get("repository_file_path", base_filename)
+            hf_filename = download_info.get("repository_file_path", filename)
             if not repo_id:
-                raise ValueError(f"repo_id is missing for HF model '{base_filename}'")
+                raise ValueError(f"repo_id is missing for HF model '{filename}'")
             
             cached_path = hf_hub_download(repo_id=repo_id, filename=hf_filename)
             os.makedirs(dest_dir, exist_ok=True)
@@ -350,173 +348,35 @@ def _ensure_model_downloaded(display_name: str, progress=gr.Progress()):
         elif source == "civitai":
             model_version_id = download_info.get("model_version_id")
             if not model_version_id:
-                raise ValueError(f"model_version_id is missing for Civitai model '{base_filename}'")
+                raise ValueError(f"model_version_id is missing for Civitai model '{filename}'")
             
             file_info = get_civitai_file_info(model_version_id)
             if not file_info or not file_info.get('downloadUrl'):
                 raise ConnectionError(f"Could not get download URL for Civitai model version ID {model_version_id}")
             
             status = download_file(
-                file_info['downloadUrl'], dest_path, progress=progress, desc=f"Downloading: {base_filename}"
+                file_info['downloadUrl'], dest_path, progress=progress, desc=f"Downloading: {filename}"
             )
             if "Failed" in status:
                 raise ConnectionError(status)
         else:
-            raise NotImplementedError(f"Download source '{source}' is not implemented for '{base_filename}'")
+            raise NotImplementedError(f"Download source '{source}' is not implemented for '{filename}'")
             
-        progress(1.0, desc=f"Downloaded: {base_filename}")
+        progress(1.0, desc=f"Downloaded: {filename}")
 
     except Exception as e:
         if os.path.lexists(dest_path):
             try:
                 os.remove(dest_path)
             except OSError: pass
-        raise gr.Error(f"Failed to download and link '{display_name}': {e}")
+        raise gr.Error(f"Failed to download and link '{filename}': {e}")
     
-    return base_filename
+    return filename
 
 def ensure_controlnet_model_downloaded(filename: str, progress):
     if not filename or filename == "None":
         return
-
-    dest_path = os.path.join(CONTROLNET_DIR, filename)
-    if os.path.exists(dest_path):
-        return
-
-    download_info = ALL_FILE_DOWNLOAD_MAP.get(filename)
-    if not download_info:
-        raise gr.Error(f"ControlNet model '{filename}' not found in configuration (file_list.yaml). Cannot download.")
-    
-    source = download_info.get("source")
-    
-    try:
-        if source == "hf":
-            repo_id = download_info.get("repo_id")
-            repo_filename = download_info.get("repository_file_path", filename)
-            if not repo_id:
-                raise ValueError("repo_id is missing for Hugging Face download.")
-            
-            progress(0, desc=f"Downloading CN: {filename}")
-            cached_path = hf_hub_download(repo_id=repo_id, filename=repo_filename)
-            os.makedirs(CONTROLNET_DIR, exist_ok=True)
-            os.symlink(cached_path, dest_path)
-            print(f"✅ Symlinked ControlNet '{cached_path}' to '{dest_path}'")
-            progress(1.0, desc=f"Downloaded CN: {filename}")
-
-        elif source == "civitai":
-            model_version_id = download_info.get("model_version_id")
-            if not model_version_id:
-                raise ValueError("model_version_id is missing for Civitai download.")
-            
-            file_info = get_civitai_file_info(model_version_id)
-            if not file_info or not file_info.get('downloadUrl'):
-                raise ConnectionError(f"Could not get download URL for Civitai model version ID {model_version_id}")
-
-            status = download_file(
-                file_info['downloadUrl'],
-                dest_path,
-                progress=progress,
-                desc=f"Downloading CN: {filename}"
-            )
-            if "Failed" in status:
-                raise ConnectionError(status)
-        else:
-            raise NotImplementedError(f"Download source '{source}' is not implemented for ControlNets.")
-
-    except Exception as e:
-        if os.path.lexists(dest_path):
-            try:
-                os.remove(dest_path)
-            except OSError:
-                pass
-        raise gr.Error(f"Failed to download ControlNet model '{filename}': {e}")
-
-def load_ipadapter_presets():
-    global IPADAPTER_PRESETS
-    if IPADAPTER_PRESETS is not None:
-        return
-    
-    _PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-    _IPADAPTER_LIST_PATH = os.path.join(_PROJECT_ROOT, 'yaml', 'ipadapter.yaml')
-    
-    try:
-        with open(_IPADAPTER_LIST_PATH, 'r', encoding='utf-8') as f:
-            presets_list = yaml.safe_load(f)
-        
-        IPADAPTER_PRESETS = {item['preset_name']: item for item in presets_list}
-        print("✅ IPAdapter presets loaded successfully.")
-    except Exception as e:
-        print(f"❌ FATAL: Could not load or parse ipadapter.yaml. IPAdapter will not work. Error: {e}")
-        IPADAPTER_PRESETS = {}
-
-def ensure_ipadapter_models_downloaded(preset_name: str, progress):
-    if not preset_name:
-        return
-        
-    if IPADAPTER_PRESETS is None:
-        raise RuntimeError("IPAdapter presets have not been loaded. `load_ipadapter_presets` must be called on startup.")
-    
-    preset_info = IPADAPTER_PRESETS.get(preset_name)
-    if not preset_info:
-        print(f"⚠️ Warning: IPAdapter preset '{preset_name}' not found in configuration. Skipping download.")
-        return
-
-    model_files_to_check = {
-        preset_info.get('vision_model'): 'CLIP_VISION',
-        preset_info.get('ipadapter_model'): 'IPADAPTER',
-        preset_info.get('lora_model'): 'LORA'
-    }
-
-    for filename, model_type in model_files_to_check.items():
-        if not filename:
-            continue
-        
-        temp_display_name = f"ipadapter_asset_{filename}"
-        
-        if temp_display_name not in ALL_MODEL_MAP:
-             ALL_MODEL_MAP[temp_display_name] = (None, filename, model_type, None)
-
-        try:
-            _ensure_model_downloaded(temp_display_name, progress)
-        except Exception as e:
-            print(f"❌ Error ensuring download for IPAdapter asset '{filename}': {e}")
-
-
-def parse_parameters(params_text: str) -> dict:
-    data = {}
-    lines = params_text.strip().split('\n')
-    data['prompt'] = lines[0]
-    data['negative_prompt'] = lines[1].replace("Negative prompt:", "").strip() if len(lines) > 1 and lines[1].startswith("Negative prompt:") else ""
-    
-    params_line = '\n'.join(lines[2:])
-
-    def find_param(key, default, cast_type=str):
-        match = re.search(fr"\b{key}: ([^,]+?)(,|$|\n)", params_line)
-        return cast_type(match.group(1).strip()) if match else default
-
-    data['steps'] = find_param("Steps", 28, int)
-    data['sampler'] = find_param("Sampler", "euler", str)
-    data['scheduler'] = find_param("Scheduler", "normal", str)
-    data['cfg_scale'] = find_param("CFG scale", 7.5, float)
-    data['seed'] = find_param("Seed", -1, int)
-    data['clip_skip'] = find_param("Clip skip", 1, int)
-    data['base_model'] = find_param("Base Model", list(ALL_MODEL_MAP.keys())[0] if ALL_MODEL_MAP else "", str)
-    data['model_hash'] = find_param("Model hash", None, str)
-    
-    size_match = re.search(r"Size: (\d+)x(\d+)", params_line)
-    data['width'], data['height'] = (int(size_match.group(1)), int(size_match.group(2))) if size_match else (512, 512)
-    
-    return data
-
-def get_png_info(image) -> tuple[str, str, str]:
-    if not image or not (params := image.info.get('parameters')):
-        return "", "", "No metadata found in the image."
-    
-    parsed_data = parse_parameters(params)
-    raw_params_list = '\n'.join(params.strip().split('\n')[2:]).split(',')
-    other_params_text = "\n".join([p.strip() for p in raw_params_list])
-    
-    return parsed_data.get('prompt', ''), parsed_data.get('negative_prompt', ''), other_params_text
+    _ensure_model_downloaded(filename, progress)
 
 
 def build_preprocessor_model_map():

yaml/constants.yaml

@@ -1,32 +1,15 @@
 MAX_LORAS: 5
 MAX_CONTROLNETS: 5
-MAX_IPADAPTERS: 5
 MAX_EMBEDDINGS: 5
 MAX_CONDITIONINGS: 10
 LORA_SOURCE_CHOICES: ["Civitai", "File"]
 
 RESOLUTION_MAP:
-  sd15:
-    "1:1 (Square)": [512, 512]
-    "16:9 (Landscape)": [896, 512]
-    "9:16 (Portrait)": [512, 896]
-    "4:3 (Classic Landscape)": [683, 512]
-    "3:4 (Classic Portrait)": [512, 683]
-    "3:2 (Landscape)": [768, 512]
-    "2:3 (Portrait)": [512, 768]
-
-SAMPLER_MAP:
-  euler a: "euler_ancestral"
-  dpm++ 2s a: "dpmpp_2s_ancestral"
-  dpm++ 2m: "dpmpp_2m"
-  dpm++ sde: "dpmpp_sde"
-  dpm++ 2m sde: "dpmpp_2m_sde"
-  dpm++ 3m sde: "dpmpp_3m_sde"
-  ddim: "ddim"
-  uni_pc: "uni_pc"
-  euler_a: "euler_ancestral"
-  dpm++ 2s a karras: "dpmpp_2s_ancestral"
-  dpm++ 2m karras: "dpmpp_2m"
-  dpm++ sde karras: "dpmpp_sde"
-  dpm++ 2m sde karras: "dpmpp_2m_sde"
-  dpm++ 3m sde karras: "dpmpp_3m_sde"
+  sdxl:
+    "1:1 (Square)": [1024, 1024]
+    "16:9 (Landscape)": [1344, 768]
+    "9:16 (Portrait)": [768, 1344]
+    "4:3 (Classic)": [1152, 896]
+    "3:4 (Classic Portrait)": [896, 1152]
+    "3:2 (Photography)": [1216, 832]
+    "2:3 (Photography Portrait)": [832, 1216]

yaml/controlnet_models.yaml

@@ -1,44 +1,5 @@
 ControlNet:
-  SD1.5:
-    - Filepath: "control_v11p_sd15_canny_fp16.safetensors"
-      Series: "Standard"
-      Type: ["Canny"]
-    - Filepath: "control_v11f1p_sd15_depth_fp16.safetensors"
-      Series: "Standard"
-      Type: ["Depth"]
-    - Filepath: "control_v11p_sd15_openpose_fp16.safetensors"
-      Series: "Standard"
-      Type: ["OpenPose"]
-    - Filepath: "control_v11p_sd15_lineart_fp16.safetensors"
-      Series: "Standard"
-      Type: ["Lineart"]
-    - Filepath: "control_v11p_sd15_softedge_fp16.safetensors"
-      Series: "Standard"
-      Type: ["SoftEdge"]
-    - Filepath: "control_v11p_sd15_scribble_fp16.safetensors"
-      Series: "Standard"
-      Type: ["Scribble"]
-    - Filepath: "control_v11p_sd15_seg_fp16.safetensors"
-      Series: "Standard"
-      Type: ["Segmentation"]
-    - Filepath: "control_v11p_sd15_normalbae_fp16.safetensors"
-      Series: "Standard"
-      Type: ["Normal BAE"]
-    - Filepath: "control_v11p_sd15_mlsd_fp16.safetensors"
-      Series: "Standard"
-      Type: ["MLSD"]
-    - Filepath: "control_v11p_sd15_inpaint_fp16.safetensors"
-      Series: "Standard"
-      Type: ["Inpaint"]
-    - Filepath: "control_v11f1e_sd15_tile_fp16.safetensors"
-      Series: "Standard"
-      Type: ["Tile"]
-    - Filepath: "control_v11e_sd15_shuffle_fp16.safetensors"
-      Series: "Standard"
-      Type: ["Shuffle"]
-    - Filepath: "control_v11e_sd15_ip2p_fp16.safetensors"
-      Series: "Standard"
-      Type: ["Instruct P2P"]
-    - Filepath: "control_v11p_sd15s2_lineart_anime_fp16.safetensors"
-      Series: "Standard"
-      Type: ["Anime Lineart"]
+  Anima:
+    - Filepath: "anima-preview-canny-v0.2.safetensors"
+      Series: "levzzz"
+      Type: ["Canny"]

yaml/file_list.yaml

@@ -1,174 +1,27 @@
 file:
-  checkpoints:
-    - filename: "AnythingV5V3_v5PrtRE.safetensors"
-      source: hf
-      repo_id: "ckpt/anything-v5.0"
-      repository_file_path: "AnythingV5V3_v5PrtRE.safetensors"
-    - filename: "Anything-V3.0-pruned.safetensors"
-      source: hf
-      repo_id: "ckpt/anything-v3.0"
-      repository_file_path: "Anything-V3.0-pruned.safetensors"
-    - filename: "blue_pencil-v10.safetensors"
-      source: hf
-      repo_id: "bluepen5805/blue_pencil"
-      repository_file_path: "blue_pencil-v10.safetensors"
-    - filename: "AOM3_orangemixs.safetensors"
-      source: hf
-      repo_id: "WarriorMama777/OrangeMixs"
-      repository_file_path: "Models/AbyssOrangeMix3/AOM3_orangemixs.safetensors"
-    - filename: "AOM3A1_orangemixs.safetensors"
-      source: hf
-      repo_id: "WarriorMama777/OrangeMixs"
-      repository_file_path: "Models/AbyssOrangeMix3/AOM3A1_orangemixs.safetensors"
-    - filename: "AOM3A2_orangemixs.safetensors"
-      source: hf
-      repo_id: "WarriorMama777/OrangeMixs"
-      repository_file_path: "Models/AbyssOrangeMix3/AOM3A2_orangemixs.safetensors"
-    - filename: "AOM3A3_orangemixs.safetensors"
-      source: hf
-      repo_id: "WarriorMama777/OrangeMixs"
-      repository_file_path: "Models/AbyssOrangeMix3/AOM3A3_orangemixs.safetensors"
-    - filename: "AOM3A1B_orangemixs.safetensors"
-      source: hf
-      repo_id: "WarriorMama777/OrangeMixs"
-      repository_file_path: "Models/AbyssOrangeMix3/AOM3A1B_orangemixs.safetensors"
-    - filename: "AOM3B2_orangemixs.safetensors"
-      source: hf
-      repo_id: "WarriorMama777/OrangeMixs"
-      repository_file_path: "Models/AbyssOrangeMix3/AOM3B2_orangemixs.safetensors"
-    - filename: "AOM3B3_orangemixs.safetensors"
-      source: hf
-      repo_id: "WarriorMama777/OrangeMixs"
-      repository_file_path: "Models/AbyssOrangeMix3/AOM3B3_orangemixs.safetensors"
-    - filename: "AOM3B4_orangemixs.safetensors"
-      source: hf
-      repo_id: "WarriorMama777/OrangeMixs"
-      repository_file_path: "Models/AbyssOrangeMix3/AOM3B4_orangemixs.safetensors"
-
-  clip_vision:
-    - filename: "CLIP-ViT-bigG-14-laion2B-39B-b160k.safetensors"
+  diffusion_models: 
+    - filename: "anima-preview3-base.safetensors"
       source: "hf"
-      repo_id: "h94/IP-Adapter"
-      repository_file_path: "sdxl_models/image_encoder/model.safetensors"
-    - filename: "CLIP-ViT-H-14-laion2B-s32B-b79K.safetensors"
+      repo_id: "circlestone-labs/Anima"
+      repository_file_path: "split_files/diffusion_models/anima-preview3-base.safetensors"
+    - filename: "AnimaYume_tuned_v04.safetensors"
       source: "hf"
-      repo_id: "h94/IP-Adapter"
-      repository_file_path: "models/image_encoder/model.safetensors"
+      repo_id: "duongve/AnimaYume"
+      repository_file_path: "split_files/diffusion_models/AnimaYume_tuned_v04.safetensors"
 
-  controlnet:
-    - filename: "control_v11e_sd15_ip2p_fp16.safetensors"
-      source: "hf"
-      repo_id: "comfyanonymous/ControlNet-v1-1_fp16_safetensors"
-      repository_file_path: "control_v11e_sd15_ip2p_fp16.safetensors"
-    - filename: "control_v11e_sd15_shuffle_fp16.safetensors"
-      source: "hf"
-      repo_id: "comfyanonymous/ControlNet-v1-1_fp16_safetensors"
-      repository_file_path: "control_v11e_sd15_shuffle_fp16.safetensors"
-    - filename: "control_v11f1e_sd15_tile_fp16.safetensors"
-      source: "hf"
-      repo_id: "comfyanonymous/ControlNet-v1-1_fp16_safetensors"
-      repository_file_path: "control_v11f1e_sd15_tile_fp16.safetensors"
-    - filename: "control_v11f1p_sd15_depth_fp16.safetensors"
-      source: "hf"
-      repo_id: "comfyanonymous/ControlNet-v1-1_fp16_safetensors"
-      repository_file_path: "control_v11f1p_sd15_depth_fp16.safetensors"
-    - filename: "control_v11p_sd15_canny_fp16.safetensors"
-      source: "hf"
-      repo_id: "comfyanonymous/ControlNet-v1-1_fp16_safetensors"
-      repository_file_path: "control_v11p_sd15_canny_fp16.safetensors"
-    - filename: "control_v11p_sd15_inpaint_fp16.safetensors"
-      source: "hf"
-      repo_id: "comfyanonymous/ControlNet-v1-1_fp16_safetensors"
-      repository_file_path: "control_v11p_sd15_inpaint_fp16.safetensors"
-    - filename: "control_v11p_sd15_lineart_fp16.safetensors"
-      source: "hf"
-      repo_id: "comfyanonymous/ControlNet-v1-1_fp16_safetensors"
-      repository_file_path: "control_v11p_sd15_lineart_fp16.safetensors"
-    - filename: "control_v11p_sd15_mlsd_fp16.safetensors"
-      source: "hf"
-      repo_id: "comfyanonymous/ControlNet-v1-1_fp16_safetensors"
-      repository_file_path: "control_v11p_sd15_mlsd_fp16.safetensors"
-    - filename: "control_v11p_sd15_normalbae_fp16.safetensors"
-      source: "hf"
-      repo_id: "comfyanonymous/ControlNet-v1-1_fp16_safetensors"
-      repository_file_path: "control_v11p_sd15_normalbae_fp16.safetensors"
-    - filename: "control_v11p_sd15_openpose_fp16.safetensors"
-      source: "hf"
-      repo_id: "comfyanonymous/ControlNet-v1-1_fp16_safetensors"
-      repository_file_path: "control_v11p_sd15_openpose_fp16.safetensors"
-    - filename: "control_v11p_sd15_scribble_fp16.safetensors"
-      source: "hf"
-      repo_id: "comfyanonymous/ControlNet-v1-1_fp16_safetensors"
-      repository_file_path: "control_v11p_sd15_scribble_fp16.safetensors"
-    - filename: "control_v11p_sd15_seg_fp16.safetensors"
-      source: "hf"
-      repo_id: "comfyanonymous/ControlNet-v1-1_fp16_safetensors"
-      repository_file_path: "control_v11p_sd15_seg_fp16.safetensors"
-    - filename: "control_v11p_sd15_softedge_fp16.safetensors"
-      source: "hf"
-      repo_id: "comfyanonymous/ControlNet-v1-1_fp16_safetensors"
-      repository_file_path: "control_v11p_sd15_softedge_fp16.safetensors"
-    - filename: "control_v11p_sd15s2_lineart_anime_fp16.safetensors"
+  text_encoders: 
+    - filename: "qwen_3_06b_base.safetensors"
       source: "hf"
-      repo_id: "comfyanonymous/ControlNet-v1-1_fp16_safetensors"
-      repository_file_path: "control_v11p_sd15s2_lineart_anime_fp16.safetensors"
-    - filename: "control_v11u_sd15_tile_fp16.safetensors"
-      source: "hf"
-      repo_id: "comfyanonymous/ControlNet-v1-1_fp16_safetensors"
-      repository_file_path: "control_v11u_sd15_tile_fp16.safetensors"
+      repo_id: "circlestone-labs/Anima"
+      repository_file_path: "split_files/text_encoders/qwen_3_06b_base.safetensors"
 
-  ipadapter:
-    - filename: "ip-adapter_sd15.safetensors"
-      source: "hf"
-      repo_id: "h94/IP-Adapter"
-      repository_file_path: "models/ip-adapter_sd15.safetensors"
-    - filename: "ip-adapter_sd15_light_v11.bin"
-      source: "hf"
-      repo_id: "h94/IP-Adapter"
-      repository_file_path: "models/ip-adapter_sd15_light_v11.bin"
-    - filename: "ip-adapter-plus_sd15.safetensors"
-      source: "hf"
-      repo_id: "h94/IP-Adapter"
-      repository_file_path: "models/ip-adapter-plus_sd15.safetensors"
-    - filename: "ip-adapter-plus-face_sd15.safetensors"
-      source: "hf"
-      repo_id: "h94/IP-Adapter"
-      repository_file_path: "models/ip-adapter-plus-face_sd15.safetensors"
-    - filename: "ip-adapter-full-face_sd15.safetensors"
-      source: "hf"
-      repo_id: "h94/IP-Adapter"
-      repository_file_path: "models/ip-adapter-full-face_sd15.safetensors"
-    - filename: "ip-adapter_sd15_vit-G.safetensors"
-      source: "hf"
-      repo_id: "h94/IP-Adapter"
-      repository_file_path: "models/ip-adapter_sd15_vit-G.safetensors"
-    - filename: "ip-adapter-faceid_sd15.bin"
+  vae: 
+    - filename: "qwen_image_vae.safetensors"
       source: "hf"
-      repo_id: "h94/IP-Adapter-FaceID"
-      repository_file_path: "ip-adapter-faceid_sd15.bin"
-    - filename: "ip-adapter-faceid-plus_sd15.bin"
-      source: "hf"
-      repo_id: "h94/IP-Adapter-FaceID"
-      repository_file_path: "ip-adapter-faceid-plus_sd15.bin"
-    - filename: "ip-adapter-faceid-plusv2_sd15.bin"
-      source: "hf"
-      repo_id: "h94/IP-Adapter-FaceID"
-      repository_file_path: "ip-adapter-faceid-plusv2_sd15.bin"
-    - filename: "ip-adapter-faceid-portrait-v11_sd15.bin"
-      source: "hf"
-      repo_id: "h94/IP-Adapter-FaceID"
-      repository_file_path: "ip-adapter-faceid-portrait-v11_sd15.bin"
+      repo_id: "Comfy-Org/Qwen-Image_ComfyUI"
+      repository_file_path: "split_files/vae/qwen_image_vae.safetensors"
 
-  loras:
-    - filename: "ip-adapter-faceid_sd15_lora.safetensors"
-      source: "hf"
-      repo_id: "h94/IP-Adapter-FaceID"
-      repository_file_path: "ip-adapter-faceid_sd15_lora.safetensors"
-    - filename: "ip-adapter-faceid-plus_sd15_lora.safetensors"
-      source: "hf"
-      repo_id: "h94/IP-Adapter-FaceID"
-      repository_file_path: "ip-adapter-faceid-plus_sd15_lora.safetensors"
-    - filename: "ip-adapter-faceid-plusv2_sd15_lora.safetensors"
-      source: "hf"
-      repo_id: "h94/IP-Adapter-FaceID"
-      repository_file_path: "ip-adapter-faceid-plusv2_sd15_lora.safetensors"
+  controlnet: 
+    - filename: "anima-preview-canny-v0.2.safetensors"
+      source: "civitai"
+      model_version_id: "2748244"

yaml/injectors.yaml

@@ -1,12 +1,12 @@
 injector_definitions:
+  dynamic_lora_chains: 
+    module: "chain_injectors.lora_injector"
   dynamic_controlnet_chains: 
     module: "chain_injectors.controlnet_injector"
-  dynamic_ipadapter_chains:
-    module: "chain_injectors.ipadapter_injector"
   dynamic_conditioning_chains:
     module: "chain_injectors.conditioning_injector"
   
 injector_order:
-  - dynamic_ipadapter_chains
+  - dynamic_lora_chains
   - dynamic_conditioning_chains
   - dynamic_controlnet_chains

yaml/ipadapter.yaml

@@ -1,42 +0,0 @@
-- preset_name: "LIGHT - SD1.5 only (low strength)"
-  vision_model: "CLIP-ViT-H-14-laion2B-s32B-b79K.safetensors"
-  ipadapter_model: "ip-adapter_sd15_light_v11.bin"
-
-- preset_name: "STANDARD (medium strength)"
-  vision_model: "CLIP-ViT-H-14-laion2B-s32B-b79K.safetensors"
-  ipadapter_model: "ip-adapter_sd15.safetensors"
-
-- preset_name: "VIT-G (medium strength)"
-  vision_model: "CLIP-ViT-bigG-14-laion2B-39B-b160k.safetensors"
-  ipadapter_model: "ip-adapter_sd15_vit-G.safetensors"
-
-- preset_name: "PLUS (high strength)"
-  vision_model: "CLIP-ViT-H-14-laion2B-s32B-b79K.safetensors"
-  ipadapter_model: "ip-adapter-plus_sd15.safetensors"
-
-- preset_name: "PLUS FACE (portraits)"
-  vision_model: "CLIP-ViT-H-14-laion2B-s32B-b79K.safetensors"
-  ipadapter_model: "ip-adapter-plus-face_sd15.safetensors"
-
-- preset_name: "FULL FACE - SD1.5 only (portraits stronger)"
-  vision_model: "CLIP-ViT-H-14-laion2B-s32B-b79K.safetensors"
-  ipadapter_model: "ip-adapter-full-face_sd15.safetensors"
-
-- preset_name: "FACEID"
-  vision_model: "CLIP-ViT-H-14-laion2B-s32B-b79K.safetensors"
-  ipadapter_model: "ip-adapter-faceid_sd15.bin"
-  lora_model: "ip-adapter-faceid_sd15_lora.safetensors"
-
-- preset_name: "FACEID PLUS - SD1.5 only"
-  vision_model: "CLIP-ViT-H-14-laion2B-s32B-b79K.safetensors"
-  ipadapter_model: "ip-adapter-faceid-plus_sd15.bin"
-  lora_model: "ip-adapter-faceid-plus_sd15_lora.safetensors"
-
-- preset_name: "FACEID PLUS V2"
-  vision_model: "CLIP-ViT-H-14-laion2B-s32B-b79K.safetensors"
-  ipadapter_model: "ip-adapter-faceid-plusv2_sd15.bin"
-  lora_model: "ip-adapter-faceid-plusv2_sd15_lora.safetensors"
-
-- preset_name: "FACEID PORTRAIT (style transfer)"
-  vision_model: "CLIP-ViT-H-14-laion2B-s32B-b79K.safetensors"
-  ipadapter_model: "ip-adapter-faceid-portrait-v11_sd15.bin"

yaml/model_list.yaml

@@ -1,23 +1,11 @@
 Checkpoint:
-  - display_name: "Yuno779/Anything V5"
-    path: "AnythingV5V3_v5PrtRE.safetensors"
-  - display_name: "Yuno779/Anything V3"
-    path: "Anything-V3.0-pruned.safetensors"
-  - display_name: "blue_pen5805/blue_pencil v10"
-    path: "blue_pencil-v10.safetensors"
-  - display_name: "WarriorMama777/AOM3"
-    path: "AOM3_orangemixs.safetensors"
-  - display_name: "WarriorMama777/AOM3A1"
-    path: "AOM3A1_orangemixs.safetensors"
-  - display_name: "WarriorMama777/AOM3A2"
-    path: "AOM3A2_orangemixs.safetensors"
-  - display_name: "WarriorMama777/AOM3A3"
-    path: "AOM3A3_orangemixs.safetensors"
-  - display_name: "WarriorMama777/AOM3A1B"
-    path: "AOM3A1B_orangemixs.safetensors"
-  - display_name: "WarriorMama777/AOM3B2"
-    path: "AOM3B2_orangemixs.safetensors"
-  - display_name: "WarriorMama777/AOM3B3"
-    path: "AOM3B3_orangemixs.safetensors"
-  - display_name: "WarriorMama777/AOM3B4"
-    path: "AOM3B4_orangemixs.safetensors"
+  - display_name: "circlestone-labs/Anima-preview3-base"
+    components:
+      unet: "anima-preview3-base.safetensors"
+      vae: "qwen_image_vae.safetensors"
+      clip: "qwen_3_06b_base.safetensors"
+  - display_name: "duongve/AnimaYume-v0.4"
+    components:
+      unet: "AnimaYume_tuned_v04.safetensors"
+      vae: "qwen_image_vae.safetensors"
+      clip: "qwen_3_06b_base.safetensors"