Add Krea-2 architecture and Krea-2 ControlNet injector

.gitattributes

@@ -1,35 +1,4 @@
-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
+/web/assets/** linguist-generated
+/web/** linguist-vendored
+comfy_api_nodes/apis/__init__.py linguist-generated
+comfy/text_encoders/t5_pile_tokenizer/tokenizer.model filter=lfs diff=lfs merge=lfs -text

chain_injectors/krea2_controlnet_injector.py

@@ -0,0 +1,78 @@
+def inject(assembler, chain_definition, chain_items):
+    if not chain_items:
+        return
+
+    ksampler_name = chain_definition.get('ksampler_node', 'ksampler')
+    if ksampler_name not in assembler.node_map:
+        print(f"Warning: Target node '{ksampler_name}' for Krea2 ControlNet chain not found. Skipping.")
+        return
+        
+    ksampler_id = assembler.node_map[ksampler_name]
+
+    if 'model' not in assembler.workflow[ksampler_id]['inputs']:
+        print(f"Warning: KSampler node '{ksampler_name}' is missing 'model' input. Skipping.")
+        return
+
+    vae_source_str = chain_definition.get('vae_source')
+    vae_connection = None
+    if vae_source_str:
+        vae_node_name, vae_idx_str = vae_source_str.split(':')
+        if vae_node_name in assembler.node_map:
+            vae_connection = [assembler.node_map[vae_node_name], int(vae_idx_str)]
+            
+    latent_connection = assembler.workflow[ksampler_id]['inputs'].get('latent_image')
+    if not latent_connection:
+        print(f"Warning: KSampler node '{ksampler_name}' is missing 'latent_image' input. Krea2 ControlNet requires it. Skipping.")
+        return
+
+    current_model_connection = assembler.workflow[ksampler_id]['inputs']['model']
+    
+    for item_data in chain_items:
+        image_loader_id = assembler._get_unique_id()
+        image_loader_node = assembler._get_node_template("LoadImage")
+        image_loader_node['inputs']['image'] = item_data['image']
+        assembler.workflow[image_loader_id] = image_loader_node
+
+        image_scaler_id = assembler._get_unique_id()
+        image_scaler_node = assembler._get_node_template("ImageScaleToTotalPixels")
+        image_scaler_node['inputs']['image'] = [image_loader_id, 0]
+        image_scaler_node['inputs']['upscale_method'] = 'nearest-exact'
+        image_scaler_node['inputs']['megapixels'] = 1.0
+        image_scaler_node['inputs']['resolution_steps'] = 1
+        assembler.workflow[image_scaler_id] = image_scaler_node
+
+        lora_loader_id = assembler._get_unique_id()
+        lora_loader_node = assembler._get_node_template("Krea2ControlLoRALoader")
+        lora_loader_node['inputs']['lora_name'] = item_data['control_net_name']
+        lora_loader_node['inputs']['strength'] = item_data.get('strength', 1.0)
+        lora_loader_node['inputs']['model'] = current_model_connection
+        assembler.workflow[lora_loader_id] = lora_loader_node
+
+        img_encode_id = assembler._get_unique_id()
+        img_encode_node = assembler._get_node_template("Krea2ControlImageEncode")
+        img_encode_node['inputs']['resize'] = "match_latent_size"
+        img_encode_node['inputs']['upscale_method'] = "lanczos"
+        img_encode_node['inputs']['crop'] = "center"
+        img_encode_node['inputs']['channel_mode'] = "rgb"
+        img_encode_node['inputs']['normalize'] = "none"
+        img_encode_node['inputs']['invert'] = False
+        img_encode_node['inputs']['batch_mode'] = "independent_images"
+        img_encode_node['inputs']['control_image'] = [image_scaler_id, 0]
+        if vae_connection:
+            img_encode_node['inputs']['vae'] = vae_connection
+        if latent_connection:
+            img_encode_node['inputs']['latent'] = latent_connection
+        assembler.workflow[img_encode_id] = img_encode_node
+
+        apply_cn_id = assembler._get_unique_id()
+        apply_cn_node = assembler._get_node_template("Krea2ControlApply")
+        apply_cn_node['inputs']['model'] = [lora_loader_id, 0]
+        apply_cn_node['inputs']['control_latent'] = [img_encode_id, 0]
+        
+        assembler.workflow[apply_cn_id] = apply_cn_node
+
+        current_model_connection = [apply_cn_id, 0]
+
+    assembler.workflow[ksampler_id]['inputs']['model'] = current_model_connection
+    
+    print(f"Krea2 ControlNet injector applied. KSampler model input redirected through {len(chain_items)} Krea2 ControlNet nodes.")

comfy_integration/setup.py

@@ -65,6 +65,12 @@ def initialize_comfyui():
         print("✅ ComfyUI-IPAdapter-Flux extension cloned.")
     else:
         print("✅ ComfyUI-IPAdapter-Flux extension already exists.")
+    try:
+        print("--- [Setup] Applying PR #108 compatibility patch for ComfyUI-IPAdapter-Flux ---")
+        os.system(f"git -C {ipadapter_flux_path} fetch origin pull/108/head && git -C {ipadapter_flux_path} checkout -f FETCH_HEAD")
+        print("✅ Successfully applied PR #108 compatibility patch.")
+    except Exception as e:
+        print(f"⚠️ Warning: Could not apply PR #108 compatibility patch for ComfyUI-IPAdapter-Flux: {e}")
 
     # 4. ComfyUI-Newbie-Nodes
     newbie_nodes_path = os.path.join(APP_DIR, "custom_nodes", "ComfyUI-Newbie-Nodes")
@@ -82,6 +88,14 @@ def initialize_comfyui():
     else:
         print("✅ ComfyUI-Anima-LLLite extension already exists.")
 
+    # 6. comfyui-krea2-controlnet
+    krea2_controlnet_nodes_path = os.path.join(APP_DIR, "custom_nodes", "comfyui-krea2-controlnet")
+    if not os.path.exists(krea2_controlnet_nodes_path):
+        os.system(f"git clone https://github.com/facok/comfyui-krea2-controlnet.git {krea2_controlnet_nodes_path}")
+        print("✅ comfyui-krea2-controlnet extension cloned.")
+    else:
+        print("✅ comfyui-krea2-controlnet extension already exists.")
+
     print(f"✅ Current working directory is: {os.getcwd()}")
     
     import comfy.model_management

core/pipelines/pipeline_input_processor.py

@@ -1,334 +1,383 @@
-import os
-import random
-import numpy as np
-import gradio as gr
-from PIL import Image, ImageChops
-from typing import Dict, Any, List
-
-from core.settings import INPUT_DIR
-from utils.app_utils import (
-    sanitize_filename,
-    get_lora_path,
-    get_embedding_path,
-    ensure_controlnet_model_downloaded,
-    ensure_ipadapter_models_downloaded,
-    _ensure_model_downloaded,
-    ensure_sd3_ipadapter_models_downloaded,
-    get_vae_path,
-)
-
-def process_pipeline_inputs(ui_inputs: Dict[str, Any], progress: gr.Progress, workflow_model_type: str) -> Dict[str, Any]:
-    task_type = ui_inputs['task_type']
-    temp_files_to_clean = []
-    
-    lora_data = ui_inputs.get('lora_data', [])
-    active_loras_for_gpu, active_loras_for_meta = [], []
-    if lora_data:
-        sources, ids, scales, files = lora_data[0::4], lora_data[1::4], lora_data[2::4], lora_data[3::4]
-        for i, (source, lora_id, scale, _) in enumerate(zip(sources, ids, scales, files)):
-            if scale > 0 and lora_id and lora_id.strip():
-                lora_filename = None
-                if source == "File":
-                    lora_filename = sanitize_filename(lora_id)
-                elif source == "Civitai":
-                    local_path, status = get_lora_path(source, lora_id, os.environ.get("CIVITAI_API_KEY", ""), progress)
-                    if local_path: lora_filename = os.path.basename(local_path)
-                    else: raise gr.Error(f"Failed to prepare LoRA {lora_id}: {status}")
-                
-                if lora_filename:
-                    active_loras_for_gpu.append({"lora_name": lora_filename, "strength_model": scale, "strength_clip": scale})
-                    active_loras_for_meta.append(f"{source} {lora_id}:{scale}")
-
-    ui_inputs['denoise'] = 1.0
-    if task_type == 'img2img': ui_inputs['denoise'] = ui_inputs.get('img2img_denoise', 0.7)
-    elif task_type == 'hires_fix': ui_inputs['denoise'] = ui_inputs.get('hires_denoise', 0.55)
-    
-    if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
-
-    if task_type == 'img2img':
-        input_image_pil = ui_inputs.get('img2img_image')
-        if not input_image_pil:
-            raise gr.Error("Please upload an image for Image-to-Image.")
-        temp_file_path = os.path.join(INPUT_DIR, f"temp_input_{random.randint(1000, 9999)}.png")
-        input_image_pil.save(temp_file_path, "PNG")
-        ui_inputs['input_image'] = os.path.basename(temp_file_path)
-        temp_files_to_clean.append(temp_file_path)
-        ui_inputs['width'] = input_image_pil.width
-        ui_inputs['height'] = input_image_pil.height
-    
-    elif task_type == 'inpaint':
-        inpaint_dict = ui_inputs.get('inpaint_image_dict')
-        if not inpaint_dict or not inpaint_dict.get('background') or not inpaint_dict.get('layers'):
-            raise gr.Error("Inpainting requires an input image and a drawn mask.")
-        
-        background_img = inpaint_dict['background'].convert("RGBA")
-        composite_mask_pil = Image.new('L', background_img.size, 0)
-        for layer in inpaint_dict['layers']:
-            if layer:
-                layer_alpha = layer.split()[-1]
-                composite_mask_pil = ImageChops.lighter(composite_mask_pil, layer_alpha)
-
-        inverted_mask_alpha = Image.fromarray(255 - np.array(composite_mask_pil), mode='L')
-        r, g, b, _ = background_img.split()
-        composite_image_with_mask = Image.merge('RGBA', [r, g, b, inverted_mask_alpha])
-        
-        temp_file_path = os.path.join(INPUT_DIR, f"temp_inpaint_composite_{random.randint(1000, 9999)}.png")
-        composite_image_with_mask.save(temp_file_path, "PNG")
-        
-        ui_inputs['input_image'] = os.path.basename(temp_file_path)
-        temp_files_to_clean.append(temp_file_path)
-        ui_inputs.pop('inpaint_mask', None)
-
-    elif task_type == 'outpaint':
-        input_image_pil = ui_inputs.get('outpaint_image')
-        if not input_image_pil:
-            raise gr.Error("Please upload an image for Outpainting.")
-        temp_file_path = os.path.join(INPUT_DIR, f"temp_input_{random.randint(1000, 9999)}.png")
-        input_image_pil.save(temp_file_path, "PNG")
-        ui_inputs['input_image'] = os.path.basename(temp_file_path)
-        temp_files_to_clean.append(temp_file_path)
-        
-        ui_inputs['megapixels'] = 0.25
-        ui_inputs['grow_mask_by'] = ui_inputs.get('feathering', 10)
-
-    elif task_type == 'hires_fix':
-        input_image_pil = ui_inputs.get('hires_image')
-        if not input_image_pil:
-            raise gr.Error("Please upload an image for Hires Fix.")
-        temp_file_path = os.path.join(INPUT_DIR, f"temp_input_{random.randint(1000, 9999)}.png")
-        input_image_pil.save(temp_file_path, "PNG")
-        ui_inputs['input_image'] = os.path.basename(temp_file_path)
-        temp_files_to_clean.append(temp_file_path)
-
-    embedding_data = ui_inputs.get('embedding_data', [])
-    embedding_filenames = []
-    if embedding_data:
-        emb_sources, emb_ids, emb_files = embedding_data[0::3], embedding_data[1::3], embedding_data[2::3]
-        for i, (source, emb_id, _) in enumerate(zip(emb_sources, emb_ids, emb_files)):
-            if emb_id and emb_id.strip():
-                emb_filename = None
-                if source == "File":
-                    emb_filename = sanitize_filename(emb_id)
-                elif source == "Civitai":
-                    local_path, status = get_embedding_path(source, emb_id, os.environ.get("CIVITAI_API_KEY", ""), progress)
-                    if local_path: emb_filename = os.path.basename(local_path)
-                    else: raise gr.Error(f"Failed to prepare Embedding {emb_id}: {status}")
-                
-                if emb_filename:
-                    embedding_filenames.append(emb_filename)
-    
-    if embedding_filenames:
-        embedding_prompt_text = " ".join([f"embedding:{f}" for f in embedding_filenames])
-        if ui_inputs['positive_prompt']:
-            ui_inputs['positive_prompt'] = f"{ui_inputs['positive_prompt']}, {embedding_prompt_text}"
-        else:
-            ui_inputs['positive_prompt'] = embedding_prompt_text
-
-    controlnet_data = ui_inputs.get('controlnet_data', [])
-    active_controlnets = []
-    if controlnet_data:
-        (cn_images, _, _, cn_strengths, cn_filepaths) = [controlnet_data[i::5] for i in range(5)]
-        for i in range(len(cn_images)):
-            if cn_images[i] and cn_strengths[i] > 0 and cn_filepaths[i] and cn_filepaths[i] != "None":
-                ensure_controlnet_model_downloaded(cn_filepaths[i], progress)
-                if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
-                cn_temp_path = os.path.join(INPUT_DIR, f"temp_cn_{i}_{random.randint(1000, 9999)}.png")
-                cn_images[i].save(cn_temp_path, "PNG")
-                temp_files_to_clean.append(cn_temp_path)
-                active_controlnets.append({
-                    "image": os.path.basename(cn_temp_path), "strength": cn_strengths[i],
-                    "start_percent": 0.0, "end_percent": 1.0, "control_net_name": cn_filepaths[i]
-                })
-    
-    anima_controlnet_lllite_data = ui_inputs.get('anima_controlnet_lllite_data', [])
-    active_anima_controlnets = []
-    if anima_controlnet_lllite_data:
-        (cn_images, _, _, cn_strengths, cn_filepaths, cn_starts, cn_ends) = [anima_controlnet_lllite_data[i::7] for i in range(7)]
-        for i in range(len(cn_images)):
-            if cn_images[i] and cn_strengths[i] > 0 and cn_filepaths[i] and cn_filepaths[i] != "None":
-                _ensure_model_downloaded(cn_filepaths[i], progress)
-                if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
-                cn_temp_path = os.path.join(INPUT_DIR, f"temp_anima_cn_{i}_{random.randint(1000, 9999)}.png")
-                cn_images[i].save(cn_temp_path, "PNG")
-                temp_files_to_clean.append(cn_temp_path)
-                active_anima_controlnets.append({
-                    "image": os.path.basename(cn_temp_path), "strength": cn_strengths[i],
-                    "start_percent": cn_starts[i], "end_percent": cn_ends[i], "control_net_name": cn_filepaths[i]
-                })
-
-    diffsynth_controlnet_data = ui_inputs.get('diffsynth_controlnet_data', [])
-    active_diffsynth_controlnets = []
-    if diffsynth_controlnet_data:
-        (cn_images, _, _, cn_strengths, cn_filepaths) = [diffsynth_controlnet_data[i::5] for i in range(5)]
-        for i in range(len(cn_images)):
-            if cn_images[i] and cn_strengths[i] > 0 and cn_filepaths[i] and cn_filepaths[i] != "None":
-                ensure_controlnet_model_downloaded(cn_filepaths[i], progress)
-                if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
-                cn_temp_path = os.path.join(INPUT_DIR, f"temp_diffsynth_cn_{i}_{random.randint(1000, 9999)}.png")
-                cn_images[i].save(cn_temp_path, "PNG")
-                temp_files_to_clean.append(cn_temp_path)
-                active_diffsynth_controlnets.append({
-                    "image": os.path.basename(cn_temp_path), "strength": cn_strengths[i],
-                    "control_net_name": cn_filepaths[i]
-                })
-    
-    ipadapter_data = ui_inputs.get('ipadapter_data', [])
-    active_ipadapters = []
-    if ipadapter_data:
-        num_ipa_units = (len(ipadapter_data) - 5) // 3
-        final_preset, final_weight, final_lora_strength, final_embeds_scaling, final_combine_method = ipadapter_data[-5:]
-        ipa_images, ipa_weights, ipa_lora_strengths = [ipadapter_data[i*num_ipa_units:(i+1)*num_ipa_units] for i in range(3)]
-        all_presets_to_download = set()
-        for i in range(num_ipa_units):
-            if ipa_images[i] and ipa_weights[i] > 0 and final_preset:
-                all_presets_to_download.add(final_preset)
-                if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
-                ipa_temp_path = os.path.join(INPUT_DIR, f"temp_ipa_{i}_{random.randint(1000, 9999)}.png")
-                ipa_images[i].save(ipa_temp_path, "PNG")
-                temp_files_to_clean.append(ipa_temp_path)
-                active_ipadapters.append({
-                    "image": os.path.basename(ipa_temp_path), "preset": final_preset,
-                    "weight": ipa_weights[i], "lora_strength": ipa_lora_strengths[i]
-                })
-        if active_ipadapters and final_preset:
-            all_presets_to_download.add(final_preset)
-        for preset in all_presets_to_download:
-            ensure_ipadapter_models_downloaded(preset, progress)
-            
-        model_type_key = 'sd15' if workflow_model_type == 'sd15' else 'sdxl'
-        if active_ipadapters:
-            active_ipadapters.append({
-                'is_final_settings': True, 'model_type': model_type_key, 'final_preset': final_preset,
-                'final_weight': final_weight, 'final_lora_strength': final_lora_strength,
-                'final_embeds_scaling': final_embeds_scaling, 'final_combine_method': final_combine_method
-            })
-            
-    flux1_ipadapter_data = ui_inputs.get('flux1_ipadapter_data', [])
-    active_flux1_ipadapters = []
-    if flux1_ipadapter_data:
-        num_units = len(flux1_ipadapter_data) // 4
-        f_images = flux1_ipadapter_data[0*num_units : 1*num_units]
-        f_weights = flux1_ipadapter_data[1*num_units : 2*num_units]
-        f_starts = flux1_ipadapter_data[2*num_units : 3*num_units]
-        f_ends = flux1_ipadapter_data[3*num_units : 4*num_units]
-        for i in range(len(f_images)):
-            if f_images[i] and f_weights[i] > 0:
-                for filename in ["ip-adapter.bin"]:
-                    _ensure_model_downloaded(filename, progress)
-                
-                from huggingface_hub import snapshot_download
-                progress(0.5, desc="Caching HF SigLIP model...")
-                snapshot_download(
-                    repo_id="google/siglip-so400m-patch14-384", 
-                    allow_patterns=["*.json", "*.safetensors", "*.txt"], 
-                    ignore_patterns=["*.msgpack", "*.h5", "*.bin"]
-                )
-
-                temp_path = os.path.join(INPUT_DIR, f"temp_fipa_{i}_{random.randint(1000, 9999)}.png")
-                f_images[i].save(temp_path, "PNG")
-                temp_files_to_clean.append(temp_path)
-                active_flux1_ipadapters.append({
-                    "image": os.path.basename(temp_path),
-                    "weight": f_weights[i], "start_percent": f_starts[i], "end_percent": f_ends[i]
-                })
-                
-    sd3_ipadapter_data = ui_inputs.get('sd3_ipadapter_chain', [])
-    active_sd3_ipadapters = []
-    if sd3_ipadapter_data:
-        num_units = len(sd3_ipadapter_data) // 4
-        s_images = sd3_ipadapter_data[0*num_units : 1*num_units]
-        s_weights = sd3_ipadapter_data[1*num_units : 2*num_units]
-        s_starts = sd3_ipadapter_data[2*num_units : 3*num_units]
-        s_ends = sd3_ipadapter_data[3*num_units : 4*num_units]
-        sd3_ipa_downloaded = False
-        for i in range(len(s_images)):
-            if s_images[i] and s_weights[i] > 0:
-                if not sd3_ipa_downloaded:
-                    ensure_sd3_ipadapter_models_downloaded(progress)
-                    sd3_ipa_downloaded = True
-                temp_path = os.path.join(INPUT_DIR, f"temp_s3ipa_{i}_{random.randint(1000, 9999)}.png")
-                s_images[i].save(temp_path, "PNG")
-                temp_files_to_clean.append(temp_path)
-                active_sd3_ipadapters.append({
-                    "image": os.path.basename(temp_path),
-                    "weight": s_weights[i], "start_percent": s_starts[i], "end_percent": s_ends[i]
-                })
-                
-    style_data = ui_inputs.get('style_data', [])
-    active_styles = []
-    if style_data:
-        num_units = len(style_data) // 2
-        st_images = style_data[0*num_units : 1*num_units]
-        st_strengths = style_data[1*num_units : 2*num_units]
-        for i in range(len(st_images)):
-            if st_images[i] and st_strengths[i] > 0:
-                _ensure_model_downloaded("sigclip_vision_patch14_384.safetensors", progress)
-                temp_path = os.path.join(INPUT_DIR, f"temp_style_{i}_{random.randint(1000, 9999)}.png")
-                st_images[i].save(temp_path, "PNG")
-                temp_files_to_clean.append(temp_path)
-                active_styles.append({
-                    "image": os.path.basename(temp_path), "strength": st_strengths[i]
-                })
-    
-    reference_latent_data = ui_inputs.get('reference_latent_data', [])
-    active_reference_latents = []
-    if reference_latent_data:
-        for img in reference_latent_data:
-            if img:
-                if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
-                temp_path = os.path.join(INPUT_DIR, f"temp_ref_{random.randint(1000, 9999)}.png")
-                img.save(temp_path, "PNG")
-                temp_files_to_clean.append(temp_path)
-                active_reference_latents.append(os.path.basename(temp_path))
-                
-    hidream_o1_reference_data = ui_inputs.get('hidream_o1_reference_data', [])
-    active_hidream_o1_reference = []
-    if hidream_o1_reference_data:
-        for img in hidream_o1_reference_data:
-            if img:
-                if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
-                temp_path = os.path.join(INPUT_DIR, f"temp_ho1_ref_{random.randint(1000, 9999)}.png")
-                img.save(temp_path, "PNG")
-                temp_files_to_clean.append(temp_path)
-                active_hidream_o1_reference.append(os.path.basename(temp_path))
-    
-    vae_source = ui_inputs.get('vae_source')
-    vae_id = ui_inputs.get('vae_id')
-    vae_name_override = None
-    if vae_source and vae_source != "None":
-        if vae_source == "File":
-            vae_name_override = sanitize_filename(vae_id)
-        elif vae_source == "Civitai" and vae_id and vae_id.strip():
-            local_path, status = get_vae_path(vae_source, vae_id, os.environ.get("CIVITAI_API_KEY", ""), progress)
-            if local_path: vae_name_override = os.path.basename(local_path)
-            else: raise gr.Error(f"Failed to prepare VAE {vae_id}: {status}")
-    if vae_name_override:
-        ui_inputs['vae_name'] = vae_name_override
-
-    conditioning_data = ui_inputs.get('conditioning_data', [])
-    active_conditioning = []
-    if conditioning_data:
-        num_units = len(conditioning_data) // 6
-        prompts, widths, heights, xs, ys, strengths = [conditioning_data[i*num_units : (i+1)*num_units] for i in range(6)]
-        for i in range(num_units):
-            if prompts[i] and prompts[i].strip():
-                active_conditioning.append({
-                    "prompt": prompts[i], "width": int(widths[i]), "height": int(heights[i]),
-                    "x": int(xs[i]), "y": int(ys[i]), "strength": float(strengths[i])
-                })
-
-    return {
-        "active_loras_for_gpu": active_loras_for_gpu,
-        "active_loras_for_meta": active_loras_for_meta,
-        "active_controlnets": active_controlnets,
-        "active_anima_controlnets": active_anima_controlnets,
-        "active_diffsynth_controlnets": active_diffsynth_controlnets,
-        "active_ipadapters": active_ipadapters,
-        "active_flux1_ipadapters": active_flux1_ipadapters,
-        "active_sd3_ipadapters": active_sd3_ipadapters,
-        "active_styles": active_styles,
-        "active_reference_latents": active_reference_latents,
-        "active_hidream_o1_reference": active_hidream_o1_reference,
-        "active_conditioning": active_conditioning,
-        "temp_files_to_clean": temp_files_to_clean
-    }
+import os
+import random
+import numpy as np
+import gradio as gr
+from PIL import Image, ImageChops
+from typing import Dict, Any, List
+
+from core.settings import INPUT_DIR, MULTIPLIERS_MAP
+from utils.app_utils import (
+    sanitize_filename,
+    get_lora_path,
+    get_embedding_path,
+    ensure_controlnet_model_downloaded,
+    ensure_ipadapter_models_downloaded,
+    _ensure_model_downloaded,
+    ensure_sd3_ipadapter_models_downloaded,
+    get_vae_path,
+)
+
+def process_pipeline_inputs(ui_inputs: Dict[str, Any], progress: gr.Progress, workflow_model_type: str) -> Dict[str, Any]:
+    task_type = ui_inputs['task_type']
+    temp_files_to_clean = []
+    
+    multiplier = MULTIPLIERS_MAP.get(workflow_model_type, 8)
+    img_w, img_h = 0, 0
+    if task_type == 'txt2img':
+        img_w = int(ui_inputs.get('width', 0))
+        img_h = int(ui_inputs.get('height', 0))
+    elif task_type == 'img2img':
+        input_image_pil = ui_inputs.get('img2img_image')
+        if input_image_pil:
+            img_w, img_h = input_image_pil.width, input_image_pil.height
+    elif task_type == 'inpaint':
+        inpaint_dict = ui_inputs.get('inpaint_image_dict')
+        if inpaint_dict and inpaint_dict.get('background'):
+            img_w, img_h = inpaint_dict['background'].width, inpaint_dict['background'].height
+    elif task_type == 'outpaint':
+        input_image_pil = ui_inputs.get('outpaint_image')
+        if input_image_pil:
+            img_w, img_h = input_image_pil.width, input_image_pil.height
+    elif task_type == 'hires_fix':
+        input_image_pil = ui_inputs.get('hires_image')
+        if input_image_pil:
+            img_w, img_h = input_image_pil.width, input_image_pil.height
+
+    if img_w > 0 and img_h > 0:
+        if (img_w % multiplier != 0) or (img_h % multiplier != 0):
+            warning_msg = f"Width and height must be multiples of {multiplier} for this model."
+            raise gr.Error(warning_msg)
+    
+    lora_data = ui_inputs.get('lora_data', [])
+    active_loras_for_gpu, active_loras_for_meta = [], []
+    if lora_data:
+        sources, ids, scales, files = lora_data[0::4], lora_data[1::4], lora_data[2::4], lora_data[3::4]
+        for i, (source, lora_id, scale, _) in enumerate(zip(sources, ids, scales, files)):
+            if scale > 0 and lora_id and lora_id.strip():
+                lora_filename = None
+                if source == "File":
+                    lora_filename = sanitize_filename(lora_id)
+                elif source in ("Civitai", "Hugging Face"):
+                    local_path, status = get_lora_path(source, lora_id, os.environ.get("CIVITAI_API_KEY", ""), progress)
+                    if local_path: lora_filename = os.path.basename(local_path)
+                    else: raise gr.Error(f"Failed to prepare LoRA {lora_id}: {status}")
+                
+                if lora_filename:
+                    active_loras_for_gpu.append({"lora_name": lora_filename, "strength_model": scale, "strength_clip": scale})
+                    active_loras_for_meta.append(f"{source} {lora_id}:{scale}")
+
+    ui_inputs['denoise'] = 1.0
+    if task_type == 'img2img': ui_inputs['denoise'] = ui_inputs.get('img2img_denoise', 0.7)
+    elif task_type == 'hires_fix': ui_inputs['denoise'] = ui_inputs.get('hires_denoise', 0.55)
+    elif task_type == 'inpaint': ui_inputs['denoise'] = ui_inputs.get('inpaint_denoise', 1.0)
+    
+    if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
+
+    if task_type == 'img2img':
+        input_image_pil = ui_inputs.get('img2img_image')
+        if not input_image_pil:
+            raise gr.Error("Please upload an image for Image-to-Image.")
+        temp_file_path = os.path.join(INPUT_DIR, f"temp_input_{random.randint(1000, 9999)}.png")
+        input_image_pil.save(temp_file_path, "PNG")
+        ui_inputs['input_image'] = os.path.basename(temp_file_path)
+        temp_files_to_clean.append(temp_file_path)
+        ui_inputs['width'] = input_image_pil.width
+        ui_inputs['height'] = input_image_pil.height
+    
+    elif task_type == 'inpaint':
+        inpaint_dict = ui_inputs.get('inpaint_image_dict')
+        if not inpaint_dict or not inpaint_dict.get('background') or not inpaint_dict.get('layers'):
+            raise gr.Error("Inpainting requires an input image and a drawn mask.")
+        
+        background_img = inpaint_dict['background'].convert("RGBA")
+        composite_mask_pil = Image.new('L', background_img.size, 0)
+        for layer in inpaint_dict['layers']:
+            if layer:
+                layer_alpha = layer.split()[-1]
+                composite_mask_pil = ImageChops.lighter(composite_mask_pil, layer_alpha)
+
+        inverted_mask_alpha = Image.fromarray(255 - np.array(composite_mask_pil), mode='L')
+        r, g, b, _ = background_img.split()
+        composite_image_with_mask = Image.merge('RGBA', [r, g, b, inverted_mask_alpha])
+        
+        temp_file_path = os.path.join(INPUT_DIR, f"temp_inpaint_composite_{random.randint(1000, 9999)}.png")
+        composite_image_with_mask.save(temp_file_path, "PNG")
+        
+        ui_inputs['input_image'] = os.path.basename(temp_file_path)
+        temp_files_to_clean.append(temp_file_path)
+        ui_inputs.pop('inpaint_mask', None)
+
+    elif task_type == 'outpaint':
+        input_image_pil = ui_inputs.get('outpaint_image')
+        if not input_image_pil:
+            raise gr.Error("Please upload an image for Outpainting.")
+        temp_file_path = os.path.join(INPUT_DIR, f"temp_input_{random.randint(1000, 9999)}.png")
+        input_image_pil.save(temp_file_path, "PNG")
+        ui_inputs['input_image'] = os.path.basename(temp_file_path)
+        temp_files_to_clean.append(temp_file_path)
+        
+        ui_inputs['megapixels'] = 0.25
+        ui_inputs['grow_mask_by'] = ui_inputs.get('feathering', 10)
+
+    elif task_type == 'hires_fix':
+        input_image_pil = ui_inputs.get('hires_image')
+        if not input_image_pil:
+            raise gr.Error("Please upload an image for Hires Fix.")
+        temp_file_path = os.path.join(INPUT_DIR, f"temp_input_{random.randint(1000, 9999)}.png")
+        input_image_pil.save(temp_file_path, "PNG")
+        ui_inputs['input_image'] = os.path.basename(temp_file_path)
+        temp_files_to_clean.append(temp_file_path)
+
+    embedding_data = ui_inputs.get('embedding_data', [])
+    embedding_filenames = []
+    if embedding_data:
+        emb_sources, emb_ids, emb_files = embedding_data[0::3], embedding_data[1::3], embedding_data[2::3]
+        for i, (source, emb_id, _) in enumerate(zip(emb_sources, emb_ids, emb_files)):
+            if emb_id and emb_id.strip():
+                emb_filename = None
+                if source == "File":
+                    emb_filename = sanitize_filename(emb_id)
+                elif source in ("Civitai", "Hugging Face"):
+                    local_path, status = get_embedding_path(source, emb_id, os.environ.get("CIVITAI_API_KEY", ""), progress)
+                    if local_path: emb_filename = os.path.basename(local_path)
+                    else: raise gr.Error(f"Failed to prepare Embedding {emb_id}: {status}")
+                
+                if emb_filename:
+                    embedding_filenames.append(emb_filename)
+    
+    if embedding_filenames:
+        embedding_prompt_text = " ".join([f"embedding:{f}" for f in embedding_filenames])
+        if ui_inputs['positive_prompt']:
+            ui_inputs['positive_prompt'] = f"{ui_inputs['positive_prompt']}, {embedding_prompt_text}"
+        else:
+            ui_inputs['positive_prompt'] = embedding_prompt_text
+
+    controlnet_data = ui_inputs.get('controlnet_data', [])
+    active_controlnets = []
+    if controlnet_data:
+        (cn_images, _, _, cn_strengths, cn_filepaths) = [controlnet_data[i::5] for i in range(5)]
+        for i in range(len(cn_images)):
+            if cn_images[i] and cn_strengths[i] > 0 and cn_filepaths[i] and cn_filepaths[i] != "None":
+                ensure_controlnet_model_downloaded(cn_filepaths[i], progress)
+                if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
+                cn_temp_path = os.path.join(INPUT_DIR, f"temp_cn_{i}_{random.randint(1000, 9999)}.png")
+                cn_images[i].save(cn_temp_path, "PNG")
+                temp_files_to_clean.append(cn_temp_path)
+                active_controlnets.append({
+                    "image": os.path.basename(cn_temp_path), "strength": cn_strengths[i],
+                    "start_percent": 0.0, "end_percent": 1.0, "control_net_name": cn_filepaths[i]
+                })
+    
+    anima_controlnet_lllite_data = ui_inputs.get('anima_controlnet_lllite_data', [])
+    active_anima_controlnets = []
+    if anima_controlnet_lllite_data:
+        (cn_images, _, _, cn_strengths, cn_filepaths, cn_starts, cn_ends) = [anima_controlnet_lllite_data[i::7] for i in range(7)]
+        for i in range(len(cn_images)):
+            if cn_images[i] and cn_strengths[i] > 0 and cn_filepaths[i] and cn_filepaths[i] != "None":
+                _ensure_model_downloaded(cn_filepaths[i], progress)
+                if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
+                cn_temp_path = os.path.join(INPUT_DIR, f"temp_anima_cn_{i}_{random.randint(1000, 9999)}.png")
+                cn_images[i].save(cn_temp_path, "PNG")
+                temp_files_to_clean.append(cn_temp_path)
+                active_anima_controlnets.append({
+                    "image": os.path.basename(cn_temp_path), "strength": cn_strengths[i],
+                    "start_percent": cn_starts[i], "end_percent": cn_ends[i], "control_net_name": cn_filepaths[i]
+                })
+
+    diffsynth_controlnet_data = ui_inputs.get('diffsynth_controlnet_data', [])
+    active_diffsynth_controlnets = []
+    if diffsynth_controlnet_data:
+        (cn_images, _, _, cn_strengths, cn_filepaths) = [diffsynth_controlnet_data[i::5] for i in range(5)]
+        for i in range(len(cn_images)):
+            if cn_images[i] and cn_strengths[i] > 0 and cn_filepaths[i] and cn_filepaths[i] != "None":
+                ensure_controlnet_model_downloaded(cn_filepaths[i], progress)
+                if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
+                cn_temp_path = os.path.join(INPUT_DIR, f"temp_diffsynth_cn_{i}_{random.randint(1000, 9999)}.png")
+                cn_images[i].save(cn_temp_path, "PNG")
+                temp_files_to_clean.append(cn_temp_path)
+                active_diffsynth_controlnets.append({
+                    "image": os.path.basename(cn_temp_path), "strength": cn_strengths[i],
+                    "control_net_name": cn_filepaths[i]
+                })
+                
+    krea2_controlnet_data = ui_inputs.get('krea2_controlnet_data', [])
+    active_krea2_controlnets = []
+    if krea2_controlnet_data:
+        (cn_images, _, _, cn_strengths, cn_filepaths) = [krea2_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_krea2_cn_{i}_{random.randint(1000, 9999)}.png")
+                cn_images[i].save(cn_temp_path, "PNG")
+                temp_files_to_clean.append(cn_temp_path)
+                active_krea2_controlnets.append({
+                    "image": os.path.basename(cn_temp_path), "strength": cn_strengths[i],
+                    "control_net_name": cn_filepaths[i]
+                })
+    
+    ipadapter_data = ui_inputs.get('ipadapter_data', [])
+    active_ipadapters = []
+    if ipadapter_data:
+        num_ipa_units = (len(ipadapter_data) - 5) // 3
+        final_preset, final_weight, final_lora_strength, final_embeds_scaling, final_combine_method = ipadapter_data[-5:]
+        ipa_images, ipa_weights, ipa_lora_strengths = [ipadapter_data[i*num_ipa_units:(i+1)*num_ipa_units] for i in range(3)]
+        all_presets_to_download = set()
+        for i in range(num_ipa_units):
+            if ipa_images[i] and ipa_weights[i] > 0 and final_preset:
+                all_presets_to_download.add(final_preset)
+                if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
+                ipa_temp_path = os.path.join(INPUT_DIR, f"temp_ipa_{i}_{random.randint(1000, 9999)}.png")
+                ipa_images[i].save(ipa_temp_path, "PNG")
+                temp_files_to_clean.append(ipa_temp_path)
+                active_ipadapters.append({
+                    "image": os.path.basename(ipa_temp_path), "preset": final_preset,
+                    "weight": ipa_weights[i], "lora_strength": ipa_lora_strengths[i]
+                })
+        if active_ipadapters and final_preset:
+            all_presets_to_download.add(final_preset)
+        for preset in all_presets_to_download:
+            ensure_ipadapter_models_downloaded(preset, progress)
+            
+        model_type_key = 'sd15' if workflow_model_type == 'sd15' else 'sdxl'
+        if active_ipadapters:
+            active_ipadapters.append({
+                'is_final_settings': True, 'model_type': model_type_key, 'final_preset': final_preset,
+                'final_weight': final_weight, 'final_lora_strength': final_lora_strength,
+                'final_embeds_scaling': final_embeds_scaling, 'final_combine_method': final_combine_method
+            })
+            
+    flux1_ipadapter_data = ui_inputs.get('flux1_ipadapter_data', [])
+    active_flux1_ipadapters = []
+    if flux1_ipadapter_data:
+        num_units = len(flux1_ipadapter_data) // 4
+        f_images = flux1_ipadapter_data[0*num_units : 1*num_units]
+        f_weights = flux1_ipadapter_data[1*num_units : 2*num_units]
+        f_starts = flux1_ipadapter_data[2*num_units : 3*num_units]
+        f_ends = flux1_ipadapter_data[3*num_units : 4*num_units]
+        for i in range(len(f_images)):
+            if f_images[i] and f_weights[i] > 0:
+                for filename in ["ip-adapter.bin"]:
+                    _ensure_model_downloaded(filename, progress)
+                
+                from huggingface_hub import snapshot_download
+                progress(0.5, desc="Caching HF SigLIP model...")
+                snapshot_download(
+                    repo_id="google/siglip-so400m-patch14-384", 
+                    allow_patterns=["*.json", "*.safetensors", "*.txt"], 
+                    ignore_patterns=["*.msgpack", "*.h5", "*.bin"]
+                )
+
+                temp_path = os.path.join(INPUT_DIR, f"temp_fipa_{i}_{random.randint(1000, 9999)}.png")
+                f_images[i].save(temp_path, "PNG")
+                temp_files_to_clean.append(temp_path)
+                active_flux1_ipadapters.append({
+                    "image": os.path.basename(temp_path),
+                    "weight": f_weights[i], "start_percent": f_starts[i], "end_percent": f_ends[i]
+                })
+                
+    sd3_ipadapter_data = ui_inputs.get('sd3_ipadapter_chain', [])
+    active_sd3_ipadapters = []
+    if sd3_ipadapter_data:
+        num_units = len(sd3_ipadapter_data) // 4
+        s_images = sd3_ipadapter_data[0*num_units : 1*num_units]
+        s_weights = sd3_ipadapter_data[1*num_units : 2*num_units]
+        s_starts = sd3_ipadapter_data[2*num_units : 3*num_units]
+        s_ends = sd3_ipadapter_data[3*num_units : 4*num_units]
+        sd3_ipa_downloaded = False
+        for i in range(len(s_images)):
+            if s_images[i] and s_weights[i] > 0:
+                if not sd3_ipa_downloaded:
+                    ensure_sd3_ipadapter_models_downloaded(progress)
+                    sd3_ipa_downloaded = True
+                temp_path = os.path.join(INPUT_DIR, f"temp_s3ipa_{i}_{random.randint(1000, 9999)}.png")
+                s_images[i].save(temp_path, "PNG")
+                temp_files_to_clean.append(temp_path)
+                active_sd3_ipadapters.append({
+                    "image": os.path.basename(temp_path),
+                    "weight": s_weights[i], "start_percent": s_starts[i], "end_percent": s_ends[i]
+                })
+                
+    style_data = ui_inputs.get('style_data', [])
+    active_styles = []
+    if style_data:
+        num_units = len(style_data) // 2
+        st_images = style_data[0*num_units : 1*num_units]
+        st_strengths = style_data[1*num_units : 2*num_units]
+        style_models_downloaded = False
+        for i in range(len(st_images)):
+            if st_images[i] and st_strengths[i] > 0:
+                if not style_models_downloaded:
+                    _ensure_model_downloaded("sigclip_vision_patch14_384.safetensors", progress)
+                    _ensure_model_downloaded("flux1-redux-dev.safetensors", progress)
+                    style_models_downloaded = True
+                temp_path = os.path.join(INPUT_DIR, f"temp_style_{i}_{random.randint(1000, 9999)}.png")
+                st_images[i].save(temp_path, "PNG")
+                temp_files_to_clean.append(temp_path)
+                active_styles.append({
+                    "image": os.path.basename(temp_path), "strength": st_strengths[i]
+                })
+            
+    reference_latent_data = ui_inputs.get('reference_latent_data', [])
+    active_reference_latents = []
+    if reference_latent_data:
+        for img in reference_latent_data:
+            if img:
+                if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
+                temp_path = os.path.join(INPUT_DIR, f"temp_ref_{random.randint(1000, 9999)}.png")
+                img.save(temp_path, "PNG")
+                temp_files_to_clean.append(temp_path)
+                active_reference_latents.append(os.path.basename(temp_path))
+                
+    hidream_o1_reference_data = ui_inputs.get('hidream_o1_reference_data', [])
+    active_hidream_o1_reference = []
+    if hidream_o1_reference_data:
+        for img in hidream_o1_reference_data:
+            if img:
+                if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
+                temp_path = os.path.join(INPUT_DIR, f"temp_ho1_ref_{random.randint(1000, 9999)}.png")
+                img.save(temp_path, "PNG")
+                temp_files_to_clean.append(temp_path)
+                active_hidream_o1_reference.append(os.path.basename(temp_path))
+            
+    vae_source = ui_inputs.get('vae_source')
+    vae_id = ui_inputs.get('vae_id')
+    vae_name_override = None
+    if vae_source and vae_source != "None":
+        if vae_source == "File":
+            vae_name_override = sanitize_filename(vae_id)
+        elif vae_source in ("Civitai", "Hugging Face") and vae_id and vae_id.strip():
+            local_path, status = get_vae_path(vae_source, vae_id, os.environ.get("CIVITAI_API_KEY", ""), progress)
+            if local_path: vae_name_override = os.path.basename(local_path)
+            else: raise gr.Error(f"Failed to prepare VAE {vae_id}: {status}")
+    if vae_name_override:
+        ui_inputs['vae_name'] = vae_name_override
+
+    conditioning_data = ui_inputs.get('conditioning_data', [])
+    active_conditioning = []
+    if conditioning_data:
+        num_units = len(conditioning_data) // 6
+        prompts, widths, heights, xs, ys, strengths = [conditioning_data[i*num_units : (i+1)*num_units] for i in range(6)]
+        for i in range(num_units):
+            if prompts[i] and prompts[i].strip():
+                active_conditioning.append({
+                    "prompt": prompts[i], "width": int(widths[i]), "height": int(heights[i]),
+                    "x": int(xs[i]), "y": int(ys[i]), "strength": float(strengths[i])
+                })
+
+    return {
+        "active_loras_for_gpu": active_loras_for_gpu,
+        "active_loras_for_meta": active_loras_for_meta,
+        "active_controlnets": active_controlnets,
+        "active_anima_controlnets": active_anima_controlnets,
+        "active_diffsynth_controlnets": active_diffsynth_controlnets,
+        "active_krea2_controlnets": active_krea2_controlnets,
+        "active_ipadapters": active_ipadapters,
+        "active_flux1_ipadapters": active_flux1_ipadapters,
+        "active_sd3_ipadapters": active_sd3_ipadapters,
+        "active_styles": active_styles,
+        "active_reference_latents": active_reference_latents,
+        "active_hidream_o1_reference": active_hidream_o1_reference,
+        "active_conditioning": active_conditioning,
+        "temp_files_to_clean": temp_files_to_clean
+    }

core/pipelines/sd_image_pipeline.py

@@ -1,254 +1,259 @@
-import os
-import random
-import shutil
-import torch
-import gradio as gr
-from PIL import Image
-from typing import List, Dict, Any
-
-from .base_pipeline import BasePipeline
-from core.settings import *
-from utils.app_utils import sanitize_prompt
-from core.workflow_assembler import WorkflowAssembler
-from .workflow_executor import WorkflowExecutor
-from .pipeline_input_processor import process_pipeline_inputs
-
-class SdImagePipeline(BasePipeline):
-    def get_required_models(self, model_display_name: str, **kwargs) -> List[str]:
-        model_info = ALL_MODEL_MAP.get(model_display_name)
-        if not model_info:
-            return [model_display_name]
-
-        path_or_components = model_info[1]
-        if isinstance(path_or_components, dict):
-            return [v for v in path_or_components.values() if v and v != "pixel_space"]
-        else:
-            return [model_display_name]
-
-    def _gpu_logic(self, ui_inputs: Dict, loras_string: str, workflow: Dict[str, Any], assembler: WorkflowAssembler, progress=gr.Progress(track_tqdm=True)):
-        model_display_name = ui_inputs['model_display_name']
-        
-        progress(0.4, desc="Executing workflow...")
-        
-        initial_objects = {}
-
-        decoded_images_tensor = WorkflowExecutor.execute_workflow(workflow, initial_objects=initial_objects)
-        
-        output_images = []
-        start_seed = ui_inputs['seed'] if ui_inputs['seed'] != -1 else random.randint(0, 2**64 - 1)
-        for i in range(decoded_images_tensor.shape[0]):
-            img_tensor = decoded_images_tensor[i]
-            pil_image = Image.fromarray((img_tensor.cpu().numpy() * 255.0).astype("uint8"))
-            current_seed = start_seed + i
-            
-            width_for_meta = ui_inputs.get('width', 'N/A')
-            height_for_meta = ui_inputs.get('height', 'N/A')
-
-            params_string = f"{ui_inputs['positive_prompt']}\nNegative prompt: {ui_inputs['negative_prompt']}\n"
-            params_string += f"Steps: {ui_inputs['num_inference_steps']}, Sampler: {ui_inputs['sampler']}, Scheduler: {ui_inputs['scheduler']}, CFG scale: {ui_inputs['guidance_scale']}, Seed: {current_seed}, Size: {width_for_meta}x{height_for_meta}, Base Model: {model_display_name}"
-            if ui_inputs['task_type'] != 'txt2img': params_string += f", Denoise: {ui_inputs['denoise']}"
-            if ui_inputs.get('clip_skip') and ui_inputs['clip_skip'] != 1: params_string += f", Clip skip: {abs(ui_inputs['clip_skip'])}"
-            if loras_string: params_string += f", {loras_string}"
-            
-            pil_image.info = {'parameters': params_string.strip()}
-            output_images.append(pil_image)
-            
-        return output_images
-
-    def run(self, ui_inputs: Dict, progress):
-        progress(0, desc="Preparing models...")
-        
-        task_type = ui_inputs['task_type']
-        model_display_name = ui_inputs['model_display_name']
-        model_type = MODEL_TYPE_MAP.get(model_display_name, 'sdxl')
-        
-        architectures_dict = ARCHITECTURES_CONFIG.get('architectures', {})
-        workflow_model_type = architectures_dict.get(model_type, {}).get("model_type", model_type.lower().replace(" ", "").replace(".", ""))
-        
-        ui_inputs['positive_prompt'] = sanitize_prompt(ui_inputs.get('positive_prompt', ''))
-        ui_inputs['negative_prompt'] = sanitize_prompt(ui_inputs.get('negative_prompt', ''))
-        
-        if 'clip_skip' in ui_inputs and ui_inputs['clip_skip'] is not None:
-             ui_inputs['clip_skip'] = -int(ui_inputs['clip_skip'])
-        else:
-             ui_inputs['clip_skip'] = -1
-
-        required_models = self.get_required_models(model_display_name=model_display_name)
-
-        is_pid_enabled = (ui_inputs.get('pid_settings', 'OFF') == 'ON' and task_type == 'txt2img')
-        if is_pid_enabled:
-            import yaml
-            pid_config_path = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))), 'yaml', 'pid.yaml')
-            pid_unet_name = "pid_flux1_1024_to_4096_4step_mxfp8.safetensors"
-            try:
-                with open(pid_config_path, 'r', encoding='utf-8') as f:
-                    pid_config = yaml.safe_load(f) or {}
-                pid_items = pid_config.get("PiD", [])
-                for item in pid_items:
-                    archs = item.get("architectures", [])
-                    if workflow_model_type in archs:
-                        pid_unet_name = item.get("filepath")
-                        break
-            except Exception as e:
-                print(f"Error loading PiD config for download: {e}")
-
-            if pid_unet_name not in required_models:
-                required_models.append(pid_unet_name)
-            if "gemma_2_2b_it_elm_fp8_scaled.safetensors" not in required_models:
-                required_models.append("gemma_2_2b_it_elm_fp8_scaled.safetensors")
-
-        self.model_manager.ensure_models_downloaded(required_models, progress=progress)
-        
-        temp_files_to_clean = []
-        try:
-            processed = process_pipeline_inputs(ui_inputs, progress, workflow_model_type)
-            temp_files_to_clean.extend(processed["temp_files_to_clean"])
-            
-            active_loras_for_gpu = processed["active_loras_for_gpu"]
-            active_loras_for_meta = processed["active_loras_for_meta"]
-            active_controlnets = processed["active_controlnets"]
-            active_anima_controlnets = processed["active_anima_controlnets"]
-            active_diffsynth_controlnets = processed["active_diffsynth_controlnets"]
-            active_ipadapters = processed["active_ipadapters"]
-            active_flux1_ipadapters = processed["active_flux1_ipadapters"]
-            active_sd3_ipadapters = processed["active_sd3_ipadapters"]
-            active_styles = processed["active_styles"]
-            active_reference_latents = processed["active_reference_latents"]
-            active_hidream_o1_reference = processed["active_hidream_o1_reference"]
-            active_conditioning = processed["active_conditioning"]
-
-            loras_string = f"LoRAs: [{', '.join(active_loras_for_meta)}]" if active_loras_for_meta else ""
-            
-            progress(0.8, desc="Assembling workflow...")
-            
-            if ui_inputs.get('seed') == -1:
-                ui_inputs['seed'] = random.randint(0, 2**32 - 1)
-            
-            model_info = ALL_MODEL_MAP[model_display_name]
-            path_or_components = model_info[1]
-            latent_type = model_info[3] if len(model_info) > 3 and model_info[3] else 'latent'
-            latent_generator_template = "EmptyLatentImage"
-            if latent_type == 'sd3_latent':
-                latent_generator_template = "EmptySD3LatentImage"
-            elif latent_type == 'chroma_radiance_latent':
-                latent_generator_template = "EmptyChromaRadianceLatentImage"
-            elif latent_type == 'hunyuan_latent':
-                latent_generator_template = "EmptyHunyuanImageLatent"
-
-            dynamic_values = {
-                'task_type': ui_inputs['task_type'], 
-                'model_type': workflow_model_type,
-                'latent_type': latent_type,
-                'latent_generator_template': latent_generator_template
-            }
-            
-            recipe_path = os.path.join(os.path.dirname(__file__), "workflow_recipes", "sd_unified_recipe.yaml")
-            assembler = WorkflowAssembler(recipe_path, dynamic_values=dynamic_values)
-            
-            hidream_o1_smoothing_data = []
-            if workflow_model_type == 'hidream-o1' and model_display_name == "HiDream-O1-Image":
-                hidream_o1_smoothing_data.append({})
-
-            workflow_inputs = {
-                **ui_inputs,
-                "positive_prompt": ui_inputs['positive_prompt'], "negative_prompt": ui_inputs['negative_prompt'],
-                "seed": ui_inputs['seed'], "steps": ui_inputs['num_inference_steps'], "cfg": ui_inputs['guidance_scale'],
-                "sampler_name": ui_inputs['sampler'], "scheduler": ui_inputs['scheduler'],
-                "batch_size": ui_inputs['batch_size'],
-                "clip_skip": ui_inputs['clip_skip'],
-                "denoise": ui_inputs['denoise'],
-                "vae_name": ui_inputs.get('vae_name'),
-                "guidance": ui_inputs.get('guidance', 3.5),
-                "lora_chain": active_loras_for_gpu,
-                "controlnet_chain": active_controlnets if not active_anima_controlnets else [],
-                "anima_controlnet_lllite_chain": active_anima_controlnets,
-                "diffsynth_controlnet_chain": active_diffsynth_controlnets,
-                "ipadapter_chain": active_ipadapters,
-                "flux1_ipadapter_chain": active_flux1_ipadapters,
-                "sd3_ipadapter_chain": active_sd3_ipadapters,
-                "style_chain": active_styles,
-                "conditioning_chain": active_conditioning,
-                "reference_latent_chain": active_reference_latents,
-                "hidream_o1_reference_chain": active_hidream_o1_reference,
-                "vae_chain": [ui_inputs.get('vae_name')] if ui_inputs.get('vae_name') else [],
-                "hidream_o1_smoothing_chain": hidream_o1_smoothing_data,
-                "pid_chain": [ui_inputs.get('pid_settings', 'OFF')] if is_pid_enabled else [],
-            }
-
-            if isinstance(path_or_components, dict):
-                workflow_inputs.update({
-                    'unet_name': path_or_components.get('unet'),
-                    'vae_name': ui_inputs.get('vae_name') or path_or_components.get('vae'),
-                    'clip_name': path_or_components.get('clip'),
-                    'clip1_name': path_or_components.get('clip1'),
-                    'clip2_name': path_or_components.get('clip2'),
-                    'clip3_name': path_or_components.get('clip3'),
-                    'clip4_name': path_or_components.get('clip4'),
-                    'lora_name': path_or_components.get('lora'),
-                })
-            else:
-                workflow_inputs['model_name'] = path_or_components
-            
-            if task_type == 'txt2img':
-                workflow_inputs['width'] = ui_inputs['width']
-                workflow_inputs['height'] = ui_inputs['height']
-            
-            workflow = assembler.assemble(workflow_inputs)
-
-            progress(1.0, desc="All models ready. Requesting GPU for generation...")
-
-            results = self._execute_gpu_logic(
-                self._gpu_logic,
-                duration=ui_inputs['zero_gpu_duration'],
-                default_duration=60,
-                task_name=f"ImageGen ({task_type})",
-                ui_inputs=ui_inputs,
-                loras_string=loras_string,
-                workflow=workflow,
-                assembler=assembler, 
-                progress=progress
-            )
-            
-            import json
-            import glob
-            from PIL import PngImagePlugin
-            
-            prompt_json = json.dumps(workflow)
-            
-            out_dir = os.path.abspath(OUTPUT_DIR)
-            os.makedirs(out_dir, exist_ok=True)
-            
-            try:
-                existing_files = glob.glob(os.path.join(out_dir, "gen_*.png"))
-                existing_files.sort(key=os.path.getmtime)
-                while len(existing_files) > 50:
-                    os.remove(existing_files.pop(0))
-            except Exception as e:
-                print(f"Warning: Failed to cleanup output dir: {e}")
-
-            final_results = []
-            for img in results:
-                if not isinstance(img, Image.Image):
-                    final_results.append(img)
-                    continue
-                    
-                metadata = PngImagePlugin.PngInfo()
-                params_string = img.info.get("parameters", "")
-                if params_string:
-                    metadata.add_text("parameters", params_string)
-                metadata.add_text("prompt", prompt_json)
-                
-                filename = f"gen_{random.randint(1000000, 9999999)}.png"
-                filepath = os.path.join(out_dir, filename)
-                img.save(filepath, "PNG", pnginfo=metadata)
-                final_results.append(filepath)
-            
-            results = final_results
-
-        finally:
-            for temp_file in temp_files_to_clean:
-                if temp_file and os.path.exists(temp_file):
-                    os.remove(temp_file)
-                    print(f"✅ Cleaned up temp file: {temp_file}")
-
+import os
+import random
+import shutil
+import torch
+import gradio as gr
+from PIL import Image
+from typing import List, Dict, Any
+
+from .base_pipeline import BasePipeline
+from core.settings import *
+from utils.app_utils import sanitize_prompt
+from core.workflow_assembler import WorkflowAssembler
+from .workflow_executor import WorkflowExecutor
+from .pipeline_input_processor import process_pipeline_inputs
+
+class SdImagePipeline(BasePipeline):
+    def get_required_models(self, model_display_name: str, **kwargs) -> List[str]:
+        model_info = ALL_MODEL_MAP.get(model_display_name)
+        if not model_info:
+            return [model_display_name]
+
+        path_or_components = model_info[1]
+        if isinstance(path_or_components, dict):
+            return [v for v in path_or_components.values() if v and v != "pixel_space"]
+        else:
+            return [model_display_name]
+
+    def _gpu_logic(self, ui_inputs: Dict, loras_string: str, workflow: Dict[str, Any], assembler: WorkflowAssembler, progress=gr.Progress(track_tqdm=True)):
+        model_display_name = ui_inputs['model_display_name']
+        
+        progress(0.4, desc="Executing workflow...")
+        
+        initial_objects = {}
+
+        decoded_images_tensor = WorkflowExecutor.execute_workflow(workflow, initial_objects=initial_objects)
+        
+        output_images = []
+        start_seed = ui_inputs['seed'] if ui_inputs['seed'] != -1 else random.randint(0, 2**64 - 1)
+        for i in range(decoded_images_tensor.shape[0]):
+            img_tensor = decoded_images_tensor[i]
+            pil_image = Image.fromarray((img_tensor.cpu().numpy() * 255.0).astype("uint8"))
+            current_seed = start_seed + i
+            
+            width_for_meta = ui_inputs.get('width', 'N/A')
+            height_for_meta = ui_inputs.get('height', 'N/A')
+
+            params_string = f"{ui_inputs['positive_prompt']}\nNegative prompt: {ui_inputs['negative_prompt']}\n"
+            params_string += f"Steps: {ui_inputs['num_inference_steps']}, Sampler: {ui_inputs['sampler']}, Scheduler: {ui_inputs['scheduler']}, CFG scale: {ui_inputs['guidance_scale']}, Seed: {current_seed}, Size: {width_for_meta}x{height_for_meta}, Base Model: {model_display_name}"
+            if ui_inputs['task_type'] != 'txt2img': params_string += f", Denoise: {ui_inputs['denoise']}"
+            if ui_inputs.get('clip_skip') and ui_inputs['clip_skip'] != 1: params_string += f", Clip skip: {abs(ui_inputs['clip_skip'])}"
+            if loras_string: params_string += f", {loras_string}"
+            
+            pil_image.info = {'parameters': params_string.strip()}
+            output_images.append(pil_image)
+            
+        return output_images
+
+    def run(self, ui_inputs: Dict, progress):
+        progress(0, desc="Preparing models...")
+        
+        task_type = ui_inputs['task_type']
+        model_display_name = ui_inputs['model_display_name']
+        model_type = MODEL_TYPE_MAP.get(model_display_name, 'sdxl')
+        
+        architectures_dict = ARCHITECTURES_CONFIG.get('architectures', {})
+        workflow_model_type = architectures_dict.get(model_type, {}).get("model_type", model_type.lower().replace(" ", "").replace(".", ""))
+        
+        ui_inputs['positive_prompt'] = sanitize_prompt(ui_inputs.get('positive_prompt', ''))
+        ui_inputs['negative_prompt'] = sanitize_prompt(ui_inputs.get('negative_prompt', ''))
+        
+        if 'clip_skip' in ui_inputs and ui_inputs['clip_skip'] is not None:
+             ui_inputs['clip_skip'] = -int(ui_inputs['clip_skip'])
+        else:
+             ui_inputs['clip_skip'] = -1
+
+        required_models = self.get_required_models(model_display_name=model_display_name)
+
+        is_pid_enabled = (ui_inputs.get('pid_settings', 'OFF') == 'ON' and task_type == 'txt2img')
+        if is_pid_enabled:
+            import yaml
+            pid_config_path = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))), 'yaml', 'pid.yaml')
+            pid_unet_name = "pid_flux1_1024_to_4096_4step_mxfp8.safetensors"
+            try:
+                with open(pid_config_path, 'r', encoding='utf-8') as f:
+                    pid_config = yaml.safe_load(f) or {}
+                pid_items = pid_config.get("PiD", [])
+                for item in pid_items:
+                    archs = item.get("architectures", [])
+                    if workflow_model_type in archs:
+                        pid_unet_name = item.get("filepath")
+                        break
+            except Exception as e:
+                print(f"Error loading PiD config for download: {e}")
+
+            if pid_unet_name not in required_models:
+                required_models.append(pid_unet_name)
+            if "gemma_2_2b_it_elm_fp8_scaled.safetensors" not in required_models:
+                required_models.append("gemma_2_2b_it_elm_fp8_scaled.safetensors")
+
+        self.model_manager.ensure_models_downloaded(required_models, progress=progress)
+        
+        temp_files_to_clean = []
+        try:
+            processed = process_pipeline_inputs(ui_inputs, progress, workflow_model_type)
+            temp_files_to_clean.extend(processed["temp_files_to_clean"])
+            
+            active_loras_for_gpu = processed["active_loras_for_gpu"]
+            active_loras_for_meta = processed["active_loras_for_meta"]
+            active_controlnets = processed["active_controlnets"]
+            active_anima_controlnets = processed["active_anima_controlnets"]
+            active_diffsynth_controlnets = processed["active_diffsynth_controlnets"]
+            active_krea2_controlnets = processed.get("active_krea2_controlnets", [])
+            active_ipadapters = processed["active_ipadapters"]
+            active_flux1_ipadapters = processed["active_flux1_ipadapters"]
+            active_sd3_ipadapters = processed["active_sd3_ipadapters"]
+            active_styles = processed["active_styles"]
+            active_reference_latents = processed["active_reference_latents"]
+            active_hidream_o1_reference = processed["active_hidream_o1_reference"]
+            active_conditioning = processed["active_conditioning"]
+
+            loras_string = f"LoRAs: [{', '.join(active_loras_for_meta)}]" if active_loras_for_meta else ""
+            
+            progress(0.8, desc="Assembling workflow...")
+            
+            if ui_inputs.get('seed') == -1:
+                ui_inputs['seed'] = random.randint(0, 2**32 - 1)
+            
+            model_info = ALL_MODEL_MAP[model_display_name]
+            path_or_components = model_info[1]
+            latent_type = model_info[3] if len(model_info) > 3 and model_info[3] else 'latent'
+            latent_generator_template = "EmptyLatentImage"
+            if latent_type == 'sd3_latent':
+                latent_generator_template = "EmptySD3LatentImage"
+            elif latent_type == 'chroma_radiance_latent':
+                latent_generator_template = "EmptyChromaRadianceLatentImage"
+            elif latent_type == 'hunyuan_latent':
+                latent_generator_template = "EmptyHunyuanImageLatent"
+
+            dynamic_values = {
+                'task_type': ui_inputs['task_type'], 
+                'model_type': workflow_model_type,
+                'latent_type': latent_type,
+                'latent_generator_template': latent_generator_template
+            }
+            
+            recipe_path = os.path.join(os.path.dirname(__file__), "workflow_recipes", "sd_unified_recipe.yaml")
+            assembler = WorkflowAssembler(recipe_path, dynamic_values=dynamic_values)
+            
+            hidream_o1_smoothing_data = []
+            if workflow_model_type == 'hidream-o1' and model_display_name == "HiDream-O1-Image":
+                hidream_o1_smoothing_data.append({})
+
+            workflow_inputs = {
+                **ui_inputs,
+                "positive_prompt": ui_inputs['positive_prompt'], "negative_prompt": ui_inputs['negative_prompt'],
+                "seed": ui_inputs['seed'], "steps": ui_inputs['num_inference_steps'], "cfg": ui_inputs['guidance_scale'],
+                "sampler_name": ui_inputs['sampler'], "scheduler": ui_inputs['scheduler'],
+                "batch_size": ui_inputs['batch_size'],
+                "clip_skip": ui_inputs['clip_skip'],
+                "denoise": ui_inputs['denoise'],
+                "vae_name": ui_inputs.get('vae_name'),
+                "guidance": ui_inputs.get('guidance', 3.5),
+                "lora_chain": active_loras_for_gpu,
+                "controlnet_chain": active_controlnets if not active_anima_controlnets else [],
+                "anima_controlnet_lllite_chain": active_anima_controlnets,
+                "diffsynth_controlnet_chain": active_diffsynth_controlnets,
+                "krea2_controlnet_chain": active_krea2_controlnets,
+                "ipadapter_chain": active_ipadapters,
+                "flux1_ipadapter_chain": active_flux1_ipadapters,
+                "sd3_ipadapter_chain": active_sd3_ipadapters,
+                "style_chain": active_styles,
+                "conditioning_chain": active_conditioning,
+                "reference_latent_chain": active_reference_latents,
+                "hidream_o1_reference_chain": active_hidream_o1_reference,
+                "vae_chain": [ui_inputs.get('vae_name')] if ui_inputs.get('vae_name') else [],
+                "hidream_o1_smoothing_chain": hidream_o1_smoothing_data,
+                "pid_chain": [ui_inputs.get('pid_settings', 'OFF')] if is_pid_enabled else [],
+                "scheduler_width": ui_inputs.get('width', 1024),
+                "scheduler_height": ui_inputs.get('height', 1024),
+            }
+
+            if isinstance(path_or_components, dict):
+                workflow_inputs.update({
+                    'unet_name': path_or_components.get('unet'),
+                    'unet_uncond_name': path_or_components.get('unet_uncond'),
+                    'vae_name': ui_inputs.get('vae_name') or path_or_components.get('vae'),
+                    'clip_name': path_or_components.get('clip'),
+                    'clip1_name': path_or_components.get('clip1'),
+                    'clip2_name': path_or_components.get('clip2'),
+                    'clip3_name': path_or_components.get('clip3'),
+                    'clip4_name': path_or_components.get('clip4'),
+                    'lora_name': path_or_components.get('lora'),
+                })
+            else:
+                workflow_inputs['model_name'] = path_or_components
+            
+            if task_type == 'txt2img':
+                workflow_inputs['width'] = ui_inputs['width']
+                workflow_inputs['height'] = ui_inputs['height']
+            
+            workflow = assembler.assemble(workflow_inputs)
+
+            progress(1.0, desc="All models ready. Requesting GPU for generation...")
+
+            results = self._execute_gpu_logic(
+                self._gpu_logic,
+                duration=ui_inputs['zero_gpu_duration'],
+                default_duration=60,
+                task_name=f"ImageGen ({task_type})",
+                ui_inputs=ui_inputs,
+                loras_string=loras_string,
+                workflow=workflow,
+                assembler=assembler, 
+                progress=progress
+            )
+            
+            import json
+            import glob
+            from PIL import PngImagePlugin
+            
+            prompt_json = json.dumps(workflow)
+            
+            out_dir = os.path.abspath(OUTPUT_DIR)
+            os.makedirs(out_dir, exist_ok=True)
+            
+            try:
+                existing_files = glob.glob(os.path.join(out_dir, "gen_*.png"))
+                existing_files.sort(key=os.path.getmtime)
+                while len(existing_files) > 50:
+                    os.remove(existing_files.pop(0))
+            except Exception as e:
+                print(f"Warning: Failed to cleanup output dir: {e}")
+
+            final_results = []
+            for img in results:
+                if not isinstance(img, Image.Image):
+                    final_results.append(img)
+                    continue
+                    
+                metadata = PngImagePlugin.PngInfo()
+                params_string = img.info.get("parameters", "")
+                if params_string:
+                    metadata.add_text("parameters", params_string)
+                metadata.add_text("prompt", prompt_json)
+                
+                filename = f"gen_{random.randint(1000000, 9999999)}.png"
+                filepath = os.path.join(out_dir, filename)
+                img.save(filepath, "PNG", pnginfo=metadata)
+                final_results.append(filepath)
+            
+            results = final_results
+
+        finally:
+            for temp_file in temp_files_to_clean:
+                if temp_file and os.path.exists(temp_file):
+                    os.remove(temp_file)
+                    print(f"✅ Cleaned up temp file: {temp_file}")
+
         return results

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

@@ -0,0 +1,63 @@
+nodes:
+  unet_loader:
+    class_type: UNETLoader
+    title: "Load Diffusion Model"
+    params:
+      weight_dtype: "default"
+  clip_loader:
+    class_type: CLIPLoader
+    title: "Load CLIP"
+    params:
+      type: "krea2"
+      device: "default"
+  vae_loader:
+    class_type: VAELoader
+    title: "Load VAE"
+
+connections:
+  - from: "unet_loader:0"
+    to: "ksampler:model"
+  - from: "clip_loader:0"
+    to: "pos_prompt:clip"
+  - from: "clip_loader:0"
+    to: "neg_prompt:clip"
+  - from: "pos_prompt:0"
+    to: "ksampler:positive"
+  - from: "neg_prompt:0"
+    to: "ksampler:negative"
+  - from: "vae_loader:0"
+    to: "vae_decode:vae"
+  - from: "vae_loader:0"
+    to: "vae_encode:vae"
+
+dynamic_lora_chains:
+  lora_chain:
+    template: "LoraLoader"
+    output_map:
+      "unet_loader:0": "model"
+      "clip_loader:0": "clip"
+    input_map:
+      "model": "model"
+      "clip": "clip"
+    end_input_map:
+      "model": ["ksampler:model"]
+      "clip": ["pos_prompt:clip", "neg_prompt:clip"]
+
+dynamic_krea2_controlnet_chains:
+  krea2_controlnet_chain:
+    ksampler_node: "ksampler"
+    vae_source: "vae_loader:0"
+
+dynamic_conditioning_chains:
+  conditioning_chain:
+    ksampler_node: "ksampler"
+    clip_source: "clip_loader:0"
+
+dynamic_pid_chains:
+  pid_chain:
+    ksampler_node: "ksampler"
+
+ui_map:
+  unet_name: "unet_loader:unet_name"
+  clip_name: "clip_loader:clip_name"
+  vae_name: "vae_loader:vae_name"

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

@@ -13,12 +13,6 @@ nodes:
     params:
       type: "qwen_image"
       device: "default"
-  
-  lora_loader:
-    class_type: LoraLoaderModelOnly
-    title: "Load Qwen Lightning LoRA"
-    params:
-      strength_model: 1.0
   model_sampler:
     class_type: ModelSamplingAuraFlow
     title: "ModelSamplingAuraFlow"
@@ -27,8 +21,6 @@ nodes:
 
 connections:
   - from: "unet_loader:0"
-    to: "lora_loader:model"
-  - from: "lora_loader:0"
     to: "model_sampler:model"
 
   - from: "model_sampler:0"
@@ -53,7 +45,7 @@ dynamic_lora_chains:
   lora_chain:
     template: "LoraLoader"
     output_map:
-      "lora_loader:0": "model"
+      "unet_loader:0": "model"
       "clip_loader:0": "clip"
     input_map:
       "model": "model"
@@ -80,5 +72,4 @@ dynamic_pid_chains:
 ui_map:
   unet_name: "unet_loader:unet_name"
   vae_name: "vae_loader:vae_name"
-  clip_name: "clip_loader:clip_name"
-  lora_name: "lora_loader:lora_name"
+  clip_name: "clip_loader:clip_name"

core/settings.py

@@ -192,6 +192,7 @@ try:
     MAX_IPADAPTERS = _constants.get('MAX_IPADAPTERS', 5)
     LORA_SOURCE_CHOICES = _constants.get('LORA_SOURCE_CHOICES', ["Civitai", "File"])
     RESOLUTION_MAP = _constants.get('RESOLUTION_MAP', {})
+    MULTIPLIERS_MAP = _constants.get('MULTIPLIERS_MAP', {})
     ARCHITECTURES_CONFIG = load_architectures_config()
     FEATURES_CONFIG = load_features_config()
     MODEL_DEFAULTS_CONFIG = load_model_defaults()
@@ -200,6 +201,7 @@ except Exception as e:
     MAX_LORAS, MAX_EMBEDDINGS, MAX_CONDITIONINGS, MAX_CONTROLNETS, MAX_IPADAPTERS = 5, 5, 10, 5, 5
     LORA_SOURCE_CHOICES = ["Civitai", "File"]
     RESOLUTION_MAP = {}
+    MULTIPLIERS_MAP = {}
     ARCHITECTURES_CONFIG = {}
     FEATURES_CONFIG = {}
     MODEL_DEFAULTS_CONFIG = {}

core/workflow_assembler.py

@@ -36,7 +36,6 @@ class WorkflowAssembler:
                     module = importlib.import_module(module_path)
                     if hasattr(module, 'inject'):
                         self.global_injectors[chain_type] = module.inject
-                        print(f"✅ Successfully registered global injector: {chain_type} from {module_path}")
                     else:
                         print(f"⚠️ Warning: Module '{module_path}' for injector '{chain_type}' does not have an 'inject' function.")
                 except ImportError as e:

requirements.txt

@@ -1,6 +1,6 @@
-comfyui-frontend-package==1.45.15
-comfyui-workflow-templates==0.9.98
-comfyui-embedded-docs==0.5.2
+comfyui-frontend-package==1.45.21
+comfyui-workflow-templates==0.11.9
+comfyui-embedded-docs==0.5.8
 torch
 torchsde
 torchvision
@@ -22,8 +22,8 @@ alembic
 SQLAlchemy>=2.0.0
 filelock
 av>=16.0.0
-comfy-kitchen==0.2.10
-comfy-aimdo==0.4.8
+comfy-kitchen==0.2.20
+comfy-aimdo==0.4.10
 requests
 simpleeval>=1.0.0
 blake3
@@ -33,12 +33,13 @@ kornia>=0.7.1
 spandrel
 pydantic~=2.0
 pydantic-settings~=2.0
-PyOpenGL
-glfw
+PyOpenGL>=3.1.8
+comfy-angle
 
 
 diffusers
 protobuf
+insightface
 huggingface-hub
 imageio
 spaces

ui/events/__init__.py

@@ -6,5 +6,7 @@ from .config_loaders import (
     get_anima_cn_defaults,
     load_diffsynth_controlnet_config,
     get_diffsynth_cn_defaults,
+    load_krea2_controlnet_config,
+    get_krea2_cn_defaults,
     load_ipadapter_config
 )

ui/events/chain_handlers.py

@@ -12,6 +12,7 @@ from .config_loaders import (
     load_controlnet_config,
     load_anima_controlnet_lllite_config,
     load_diffsynth_controlnet_config,
+    load_krea2_controlnet_config,
     load_ipadapter_config
 )
 
@@ -191,6 +192,99 @@ def create_controlnet_event_handlers(prefix, ui_components):
     )
 
 
+def create_krea2_controlnet_event_handlers(prefix, ui_components):
+    cn_rows = ui_components.get(f'krea2_controlnet_rows_{prefix}')
+    if not cn_rows: return
+    cn_types = ui_components[f'krea2_controlnet_types_{prefix}']
+    cn_series = ui_components[f'krea2_controlnet_series_{prefix}']
+    cn_filepaths = ui_components[f'krea2_controlnet_filepaths_{prefix}']
+    cn_images = ui_components[f'krea2_controlnet_images_{prefix}']
+    cn_strengths = ui_components[f'krea2_controlnet_strengths_{prefix}']
+    
+    count_state = ui_components[f'krea2_controlnet_count_state_{prefix}']
+    add_button = ui_components[f'add_krea2_controlnet_button_{prefix}']
+    del_button = ui_components[f'delete_krea2_controlnet_button_{prefix}']
+    accordion = ui_components[f'krea2_controlnet_accordion_{prefix}']
+
+    def add_cn_row(c):
+        c += 1
+        updates = {
+            count_state: c,
+            cn_rows[c-1]: gr.update(visible=True),
+            add_button: gr.update(visible=c < MAX_CONTROLNETS),
+            del_button: gr.update(visible=True)
+        }
+        return updates
+
+    def del_cn_row(c):
+        c -= 1
+        updates = {
+            count_state: c,
+            cn_rows[c]: gr.update(visible=False),
+            cn_images[c]: None,
+            cn_strengths[c]: 1.0,
+            add_button: gr.update(visible=True),
+            del_button: gr.update(visible=c > 0)
+        }
+        return updates
+        
+    add_outputs = [count_state, add_button, del_button] + cn_rows
+    del_outputs = [count_state, add_button, del_button] + cn_rows + cn_images + cn_strengths
+    add_button.click(fn=add_cn_row, inputs=[count_state], outputs=add_outputs, show_progress=False)
+    del_button.click(fn=del_cn_row, inputs=[count_state], outputs=del_outputs, show_progress=False)
+    
+    def on_cn_type_change(selected_type):
+        cn_config = load_krea2_controlnet_config()
+        series_choices = []
+        if selected_type:
+            series_choices = sorted(list(set(
+                model.get("Series", "Default") for model in cn_config
+                if selected_type in model.get("Type", [])
+            )))
+        default_series = series_choices[0] if series_choices else None
+        filepath = "None"
+        if default_series:
+            for model in cn_config:
+                if model.get("Series") == default_series and selected_type in model.get("Type", []):
+                    filepath = model.get("Filepath")
+                    break
+        return gr.update(choices=series_choices, value=default_series), filepath
+
+    def on_cn_series_change(selected_series, selected_type):
+        cn_config = load_krea2_controlnet_config()
+        filepath = "None"
+        if selected_series and selected_type:
+            for model in cn_config:
+                if model.get("Series") == selected_series and selected_type in model.get("Type", []):
+                    filepath = model.get("Filepath")
+                    break
+        return filepath
+        
+    for i in range(MAX_CONTROLNETS):
+        cn_types[i].change(
+            fn=on_cn_type_change,
+            inputs=[cn_types[i]],
+            outputs=[cn_series[i], cn_filepaths[i]],
+            show_progress=False
+        )
+        cn_series[i].change(
+            fn=on_cn_series_change,
+            inputs=[cn_series[i], cn_types[i]],
+            outputs=[cn_filepaths[i]],
+            show_progress=False
+        )
+
+    def on_accordion_expand(*images):
+        return [gr.update() for _ in images]
+    
+    accordion.expand(
+        fn=on_accordion_expand,
+        inputs=cn_images,
+        outputs=cn_images,
+        show_progress=False
+    )
+
+
 def create_anima_controlnet_lllite_event_handlers(prefix, ui_components):
     cn_rows = ui_components.get(f'anima_controlnet_lllite_rows_{prefix}')
     if not cn_rows: return
@@ -677,4 +771,4 @@ def create_conditioning_event_handlers(prefix, ui_components):
     add_outputs = [count_state, add_button, del_button] + rows
     del_outputs = [count_state, add_button, del_button] + rows + prompts
     add_button.click(fn=add_row, inputs=[count_state], outputs=add_outputs, show_progress=False)
-    del_button.click(fn=del_row, inputs=[count_state], outputs=del_outputs, show_progress=False)
+    del_button.click(fn=del_row, inputs=[count_state], outputs=del_outputs, show_progress=False)

ui/events/change_handlers.py

@@ -13,10 +13,11 @@ from .config_loaders import (
     get_cn_defaults,
     get_anima_cn_defaults,
     get_diffsynth_cn_defaults,
+    get_krea2_cn_defaults,
     load_ipadapter_config
 )
 
-def make_update_fn(m_comp, cat_comp, cs_comp, ar_comp, width_comp, height_comp, cn_types, cn_series, cn_filepaths, anima_cn_types, anima_cn_series, anima_cn_filepaths, diffsynth_cn_types, diffsynth_cn_series, diffsynth_cn_filepaths, ipa_preset, lora_acc, cn_acc, anima_cn_acc, diffsynth_cn_acc, ipa_acc, sd3_ipa_acc, flux1_ipa_acc, style_acc, embed_acc, cond_acc, ref_latent_acc, hidream_o1_ref_acc, guidance_comp, prompt_comp, neg_prompt_comp, steps_comp, cfg_comp, sampler_comp, scheduler_comp, pid_acc=None):
+def make_update_fn(m_comp, cat_comp, cs_comp, ar_comp, width_comp, height_comp, cn_types, cn_series, cn_filepaths, anima_cn_types, anima_cn_series, anima_cn_filepaths, diffsynth_cn_types, diffsynth_cn_series, diffsynth_cn_filepaths, krea2_cn_types, krea2_cn_series, krea2_cn_filepaths, ipa_preset, lora_acc, cn_acc, anima_cn_acc, diffsynth_cn_acc, krea2_cn_acc, ipa_acc, sd3_ipa_acc, flux1_ipa_acc, style_acc, embed_acc, cond_acc, ref_latent_acc, hidream_o1_ref_acc, guidance_comp, prompt_comp, neg_prompt_comp, steps_comp, cfg_comp, sampler_comp, scheduler_comp, pid_acc=None, vae_acc=None):
     def update_fn(*args):
         arch = args[0]
         category = args[1]
@@ -58,6 +59,7 @@ def make_update_fn(m_comp, cat_comp, cs_comp, ar_comp, width_comp, height_comp,
         if cn_acc: updates[cn_acc] = gr.update(visible=('controlnet' in enabled_chains))
         if anima_cn_acc: updates[anima_cn_acc] = gr.update(visible=('anima_controlnet_lllite' in enabled_chains))
         if diffsynth_cn_acc: updates[diffsynth_cn_acc] = gr.update(visible=('controlnet_model_patch' in enabled_chains))
+        if krea2_cn_acc: updates[krea2_cn_acc] = gr.update(visible=('krea2_controlnet' in enabled_chains))
         if ipa_acc: updates[ipa_acc] = gr.update(visible=('ipadapter' in enabled_chains))
         if flux1_ipa_acc: updates[flux1_ipa_acc] = gr.update(visible=('flux1_ipadapter' in enabled_chains))
         if sd3_ipa_acc: updates[sd3_ipa_acc] = gr.update(visible=('sd3_ipadapter' in enabled_chains))
@@ -67,6 +69,7 @@ def make_update_fn(m_comp, cat_comp, cs_comp, ar_comp, width_comp, height_comp,
         if ref_latent_acc: updates[ref_latent_acc] = gr.update(visible=('reference_latent' in enabled_chains))
         if hidream_o1_ref_acc: updates[hidream_o1_ref_acc] = gr.update(visible=('hidream_o1_reference' in enabled_chains))
         if pid_acc: updates[pid_acc] = gr.update(visible=('pid' in enabled_chains))
+        if vae_acc: updates[vae_acc] = gr.update(visible=('vae' in enabled_chains))
 
         if cs_comp:
             updates[cs_comp] = gr.update(visible=(arch_model_type == "sd15"))
@@ -109,6 +112,14 @@ def make_update_fn(m_comp, cat_comp, cs_comp, ar_comp, width_comp, height_comp,
             updates[s_comp] = gr.update(choices=diffsynth_series_choices, value=diffsynth_default_series)
         for f_comp in diffsynth_cn_filepaths:
             updates[f_comp] = diffsynth_filepath
+
+        krea2_all_types, krea2_default_type, krea2_series_choices, krea2_default_series, krea2_filepath = get_krea2_cn_defaults()
+        for t_comp in krea2_cn_types:
+            updates[t_comp] = gr.update(choices=krea2_all_types, value=krea2_default_type)
+        for s_comp in krea2_cn_series:
+            updates[s_comp] = gr.update(choices=krea2_series_choices, value=krea2_default_series)
+        for f_comp in krea2_cn_filepaths:
+            updates[f_comp] = krea2_filepath
         
         if ipa_preset and (arch_model_type in ["sdxl", "sd15", "sd35"]):
             config = load_ipadapter_config()
@@ -131,7 +142,7 @@ def make_update_fn(m_comp, cat_comp, cs_comp, ar_comp, width_comp, height_comp,
     return update_fn
 
 
-def make_model_change_fn(cat_comp_ref, cs_comp, ar_comp, width_comp, height_comp, cn_types, cn_series, cn_filepaths, anima_cn_types, anima_cn_series, anima_cn_filepaths, diffsynth_cn_types, diffsynth_cn_series, diffsynth_cn_filepaths, arch_comp_ref, ipa_preset, lora_acc, cn_acc, anima_cn_acc, diffsynth_cn_acc, ipa_acc, sd3_ipa_acc, flux1_ipa_acc, style_acc, embed_acc, cond_acc, ref_latent_acc, hidream_o1_ref_acc, guidance_comp, prompt_comp, neg_prompt_comp, steps_comp, cfg_comp, sampler_comp, scheduler_comp, pid_acc=None):
+def make_model_change_fn(cat_comp_ref, cs_comp, ar_comp, width_comp, height_comp, cn_types, cn_series, cn_filepaths, anima_cn_types, anima_cn_series, anima_cn_filepaths, diffsynth_cn_types, diffsynth_cn_series, diffsynth_cn_filepaths, krea2_cn_types, krea2_cn_series, krea2_cn_filepaths, arch_comp_ref, ipa_preset, lora_acc, cn_acc, anima_cn_acc, diffsynth_cn_acc, krea2_cn_acc, ipa_acc, sd3_ipa_acc, flux1_ipa_acc, style_acc, embed_acc, cond_acc, ref_latent_acc, hidream_o1_ref_acc, guidance_comp, prompt_comp, neg_prompt_comp, steps_comp, cfg_comp, sampler_comp, scheduler_comp, pid_acc=None, vae_acc=None):
     def change_fn(*args):
         model_name = args[0]
         idx = 1
@@ -178,6 +189,7 @@ def make_model_change_fn(cat_comp_ref, cs_comp, ar_comp, width_comp, height_comp
         if cn_acc: updates[cn_acc] = gr.update(visible=('controlnet' in enabled_chains))
         if anima_cn_acc: updates[anima_cn_acc] = gr.update(visible=('anima_controlnet_lllite' in enabled_chains))
         if diffsynth_cn_acc: updates[diffsynth_cn_acc] = gr.update(visible=('controlnet_model_patch' in enabled_chains))
+        if krea2_cn_acc: updates[krea2_cn_acc] = gr.update(visible=('krea2_controlnet' in enabled_chains))
         if ipa_acc: updates[ipa_acc] = gr.update(visible=('ipadapter' in enabled_chains))
         if flux1_ipa_acc: updates[flux1_ipa_acc] = gr.update(visible=('flux1_ipadapter' in enabled_chains))
         if sd3_ipa_acc: updates[sd3_ipa_acc] = gr.update(visible=('sd3_ipadapter' in enabled_chains))
@@ -187,6 +199,7 @@ def make_model_change_fn(cat_comp_ref, cs_comp, ar_comp, width_comp, height_comp
         if ref_latent_acc: updates[ref_latent_acc] = gr.update(visible=('reference_latent' in enabled_chains))
         if hidream_o1_ref_acc: updates[hidream_o1_ref_acc] = gr.update(visible=('hidream_o1_reference' in enabled_chains))
         if pid_acc: updates[pid_acc] = gr.update(visible=('pid' in enabled_chains))
+        if vae_acc: updates[vae_acc] = gr.update(visible=('vae' in enabled_chains))
 
         if cs_comp:
             updates[cs_comp] = gr.update(visible=(arch_model_type == "sd15"))
@@ -229,6 +242,14 @@ def make_model_change_fn(cat_comp_ref, cs_comp, ar_comp, width_comp, height_comp
             updates[s_comp] = gr.update(choices=diffsynth_series_choices, value=diffsynth_default_series)
         for f_comp in diffsynth_cn_filepaths:
             updates[f_comp] = diffsynth_filepath
+
+        krea2_all_types, krea2_default_type, krea2_series_choices, krea2_default_series, krea2_filepath = get_krea2_cn_defaults()
+        for t_comp in krea2_cn_types:
+            updates[t_comp] = gr.update(choices=krea2_all_types, value=krea2_default_type)
+        for s_comp in krea2_cn_series:
+            updates[s_comp] = gr.update(choices=krea2_series_choices, value=krea2_default_series)
+        for f_comp in krea2_cn_filepaths:
+            updates[f_comp] = krea2_filepath
         
         if ipa_preset and (arch_model_type in ["sdxl", "sd15", "sd35"]):
             config = load_ipadapter_config()
@@ -260,6 +281,7 @@ def initialize_all_cn_dropdowns(ui_components):
     all_types, default_type, series_choices, default_series, filepath = get_cn_defaults(controlnet_key)
     anima_all_types, anima_default_type, anima_series_choices, anima_default_series, anima_filepath = get_anima_cn_defaults()
     diffsynth_all_types, diffsynth_default_type, diffsynth_series_choices, diffsynth_default_series, diffsynth_filepath = get_diffsynth_cn_defaults(controlnet_key)
+    krea2_all_types, krea2_default_type, krea2_series_choices, krea2_default_series, krea2_filepath = get_krea2_cn_defaults()
     
     updates = {}
     for prefix in ["txt2img", "img2img", "inpaint", "outpaint", "hires_fix"]:
@@ -286,6 +308,14 @@ def initialize_all_cn_dropdowns(ui_components):
                 updates[series_dd] = gr.update(choices=diffsynth_series_choices, value=default_series)
             for filepath_state in ui_components[f'diffsynth_controlnet_filepaths_{prefix}']:
                 updates[filepath_state] = diffsynth_filepath
+
+        if f'krea2_controlnet_types_{prefix}' in ui_components:
+            for type_dd in ui_components[f'krea2_controlnet_types_{prefix}']:
+                updates[type_dd] = gr.update(choices=krea2_all_types, value=krea2_default_type)
+            for series_dd in ui_components[f'krea2_controlnet_series_{prefix}']:
+                updates[series_dd] = gr.update(choices=krea2_series_choices, value=krea2_default_series)
+            for filepath_state in ui_components[f'krea2_controlnet_filepaths_{prefix}']:
+                updates[filepath_state] = krea2_filepath
                 
     return updates
 

ui/events/config_loaders.py

@@ -116,6 +116,43 @@ def get_diffsynth_cn_defaults(arch_val):
     return all_types, default_type, series_choices, default_series, filepath
 
 
+@lru_cache(maxsize=1)
+def load_krea2_controlnet_config():
+    _PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+    _CN_MODEL_LIST_PATH = os.path.join(_PROJECT_ROOT, 'yaml', 'krea2_controlnet_models.yaml')
+    try:
+        print("--- Loading krea2_controlnet_models.yaml ---")
+        with open(_CN_MODEL_LIST_PATH, 'r', encoding='utf-8') as f:
+            config = yaml.safe_load(f)
+        print("--- ✅ krea2_controlnet_models.yaml loaded successfully ---")
+        return config.get("Krea2_ControlNet", [])
+    except Exception as e:
+        print(f"Error loading krea2_controlnet_models.yaml: {e}")
+        return []
+
+def get_krea2_cn_defaults():
+    cn_config = load_krea2_controlnet_config()
+    if not cn_config:
+        return [], None, [], None, "None"
+        
+    all_types = sorted(list(set(t for model in cn_config for t in model.get("Type", []))))
+    default_type = all_types[0] if all_types else None
+    
+    series_choices = []
+    if default_type:
+        series_choices = sorted(list(set(model.get("Series", "Default") for model in cn_config if default_type in model.get("Type", []))))
+    default_series = series_choices[0] if series_choices else None
+    
+    filepath = "None"
+    if default_series and default_type:
+        for model in cn_config:
+            if model.get("Series") == default_series and default_type in model.get("Type", []):
+                filepath = model.get("Filepath")
+                break
+                
+    return all_types, default_type, series_choices, default_series, filepath
+
+
 @lru_cache(maxsize=1)
 def load_ipadapter_config():
     _PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
@@ -128,4 +165,4 @@ def load_ipadapter_config():
         return config
     except Exception as e:
         print(f"Error loading ipadapter.yaml: {e}")
-        return {}
+        return {}

ui/events/main.py

@@ -4,6 +4,7 @@ from .chain_handlers import (
     create_controlnet_event_handlers,
     create_anima_controlnet_lllite_event_handlers,
     create_diffsynth_controlnet_event_handlers,
+    create_krea2_controlnet_event_handlers,
     create_ipadapter_event_handlers,
     create_embedding_event_handlers,
     create_conditioning_event_handlers,
@@ -47,11 +48,16 @@ def attach_event_handlers(ui_components, demo):
         diffsynth_cn_types_list = ui_components.get(f'diffsynth_controlnet_types_{prefix}', [])
         diffsynth_cn_series_list = ui_components.get(f'diffsynth_controlnet_series_{prefix}', [])
         diffsynth_cn_filepaths_list = ui_components.get(f'diffsynth_controlnet_filepaths_{prefix}', [])
+
+        krea2_cn_types_list = ui_components.get(f'krea2_controlnet_types_{prefix}', [])
+        krea2_cn_series_list = ui_components.get(f'krea2_controlnet_series_{prefix}', [])
+        krea2_cn_filepaths_list = ui_components.get(f'krea2_controlnet_filepaths_{prefix}', [])
         
         lora_accordion = ui_components.get(f'lora_accordion_{prefix}')
         cn_accordion = ui_components.get(f'controlnet_accordion_{prefix}')
         anima_cn_accordion = ui_components.get(f'anima_controlnet_lllite_accordion_{prefix}')
         diffsynth_cn_accordion = ui_components.get(f'diffsynth_controlnet_accordion_{prefix}')
+        krea2_cn_accordion = ui_components.get(f'krea2_controlnet_accordion_{prefix}')
         ipa_accordion = ui_components.get(f'ipadapter_accordion_{prefix}')
         sd3_ipa_accordion = ui_components.get(f'sd3_ipadapter_accordion_{prefix}')
         flux1_ipa_accordion = ui_components.get(f'flux1_ipadapter_accordion_{prefix}')
@@ -61,6 +67,7 @@ def attach_event_handlers(ui_components, demo):
         ref_latent_accordion = ui_components.get(f'reference_latent_accordion_{prefix}')
         hidream_o1_ref_accordion = ui_components.get(f'hidream_o1_reference_accordion_{prefix}')
         pid_accordion = ui_components.get(f'pid_accordion_{prefix}')
+        vae_accordion = ui_components.get(f'vae_accordion_{prefix}')
         
         ipa_preset_list = ui_components.get(f'ipadapter_final_preset_{prefix}')
         
@@ -82,10 +89,12 @@ def attach_event_handlers(ui_components, demo):
             outputs.extend(cn_types_list + cn_series_list + cn_filepaths_list)
             outputs.extend(anima_cn_types_list + anima_cn_series_list + anima_cn_filepaths_list)
             outputs.extend(diffsynth_cn_types_list + diffsynth_cn_series_list + diffsynth_cn_filepaths_list)
+            outputs.extend(krea2_cn_types_list + krea2_cn_series_list + krea2_cn_filepaths_list)
             if lora_accordion: outputs.append(lora_accordion)
             if cn_accordion: outputs.append(cn_accordion)
             if anima_cn_accordion: outputs.append(anima_cn_accordion)
             if diffsynth_cn_accordion: outputs.append(diffsynth_cn_accordion)
+            if krea2_cn_accordion: outputs.append(krea2_cn_accordion)
             if ipa_accordion: outputs.append(ipa_accordion)
             if sd3_ipa_accordion: outputs.append(sd3_ipa_accordion)
             if flux1_ipa_accordion: outputs.append(flux1_ipa_accordion)
@@ -95,6 +104,7 @@ def attach_event_handlers(ui_components, demo):
             if ref_latent_accordion: outputs.append(ref_latent_accordion)
             if hidream_o1_ref_accordion: outputs.append(hidream_o1_ref_accordion)
             if pid_accordion: outputs.append(pid_accordion)
+            if vae_accordion: outputs.append(vae_accordion)
             if ipa_preset_list: outputs.append(ipa_preset_list)
             
             outputs.extend(valid_extra_comps)
@@ -104,9 +114,10 @@ def attach_event_handlers(ui_components, demo):
                 cn_types_list, cn_series_list, cn_filepaths_list, 
                 anima_cn_types_list, anima_cn_series_list, anima_cn_filepaths_list,
                 diffsynth_cn_types_list, diffsynth_cn_series_list, diffsynth_cn_filepaths_list, 
-                ipa_preset_list, lora_accordion, cn_accordion, anima_cn_accordion, diffsynth_cn_accordion, ipa_accordion, sd3_ipa_accordion, flux1_ipa_accordion, style_accordion, embedding_accordion, conditioning_accordion,
+                krea2_cn_types_list, krea2_cn_series_list, krea2_cn_filepaths_list,
+                ipa_preset_list, lora_accordion, cn_accordion, anima_cn_accordion, diffsynth_cn_accordion, krea2_cn_accordion, ipa_accordion, sd3_ipa_accordion, flux1_ipa_accordion, style_accordion, embedding_accordion, conditioning_accordion,
                 ref_latent_accordion, hidream_o1_ref_accordion, guidance_comp, prompt_comp, neg_prompt_comp, steps_comp, cfg_comp, sampler_comp, scheduler_comp,
-                pid_acc=pid_accordion
+                pid_acc=pid_accordion, vae_acc=vae_accordion
             )
             inputs = [arch_comp, cat_comp]
             if aspect_ratio_comp:
@@ -124,10 +135,12 @@ def attach_event_handlers(ui_components, demo):
             outputs2.extend(cn_types_list + cn_series_list + cn_filepaths_list)
             outputs2.extend(anima_cn_types_list + anima_cn_series_list + anima_cn_filepaths_list)
             outputs2.extend(diffsynth_cn_types_list + diffsynth_cn_series_list + diffsynth_cn_filepaths_list)
+            outputs2.extend(krea2_cn_types_list + krea2_cn_series_list + krea2_cn_filepaths_list)
             if lora_accordion: outputs2.append(lora_accordion)
             if cn_accordion: outputs2.append(cn_accordion)
             if anima_cn_accordion: outputs2.append(anima_cn_accordion)
             if diffsynth_cn_accordion: outputs2.append(diffsynth_cn_accordion)
+            if krea2_cn_accordion: outputs2.append(krea2_cn_accordion)
             if ipa_accordion: outputs2.append(ipa_accordion)
             if sd3_ipa_accordion: outputs2.append(sd3_ipa_accordion)
             if flux1_ipa_accordion: outputs2.append(flux1_ipa_accordion)
@@ -137,6 +150,7 @@ def attach_event_handlers(ui_components, demo):
             if ref_latent_accordion: outputs2.append(ref_latent_accordion)
             if hidream_o1_ref_accordion: outputs2.append(hidream_o1_ref_accordion)
             if pid_accordion: outputs2.append(pid_accordion)
+            if vae_accordion: outputs2.append(vae_accordion)
             if ipa_preset_list: outputs2.append(ipa_preset_list)
             
             outputs2.extend(valid_extra_comps)
@@ -151,9 +165,10 @@ def attach_event_handlers(ui_components, demo):
                     cn_types_list, cn_series_list, cn_filepaths_list, 
                     anima_cn_types_list, anima_cn_series_list, anima_cn_filepaths_list,
                     diffsynth_cn_types_list, diffsynth_cn_series_list, diffsynth_cn_filepaths_list, 
-                    arch_comp, ipa_preset_list, lora_accordion, cn_accordion, anima_cn_accordion, diffsynth_cn_accordion, ipa_accordion, sd3_ipa_accordion, flux1_ipa_accordion, style_accordion, embedding_accordion, conditioning_accordion,
+                    krea2_cn_types_list, krea2_cn_series_list, krea2_cn_filepaths_list,
+                    arch_comp, ipa_preset_list, lora_accordion, cn_accordion, anima_cn_accordion, diffsynth_cn_accordion, krea2_cn_accordion, ipa_accordion, sd3_ipa_accordion, flux1_ipa_accordion, style_accordion, embedding_accordion, conditioning_accordion,
                     ref_latent_accordion, hidream_o1_ref_accordion, guidance_comp, prompt_comp, neg_prompt_comp, steps_comp, cfg_comp, sampler_comp, scheduler_comp,
-                    pid_acc=pid_accordion
+                    pid_acc=pid_accordion, vae_acc=vae_accordion
                 )
                 model_comp.change(fn=change_fn, inputs=inputs2, outputs=outputs2)
 
@@ -161,6 +176,7 @@ def attach_event_handlers(ui_components, demo):
         create_controlnet_event_handlers(prefix, ui_components)
         create_anima_controlnet_lllite_event_handlers(prefix, ui_components)
         create_diffsynth_controlnet_event_handlers(prefix, ui_components)
+        create_krea2_controlnet_event_handlers(prefix, ui_components)
         create_ipadapter_event_handlers(prefix, ui_components)
         create_embedding_event_handlers(prefix, ui_components)
         create_conditioning_event_handlers(prefix, ui_components)
@@ -225,6 +241,10 @@ def attach_event_handlers(ui_components, demo):
             all_load_outputs.extend(ui_components[f'diffsynth_controlnet_types_{prefix}'])
             all_load_outputs.extend(ui_components[f'diffsynth_controlnet_series_{prefix}'])
             all_load_outputs.extend(ui_components[f'diffsynth_controlnet_filepaths_{prefix}'])
+        if f'krea2_controlnet_types_{prefix}' in ui_components:
+            all_load_outputs.extend(ui_components[f'krea2_controlnet_types_{prefix}'])
+            all_load_outputs.extend(ui_components[f'krea2_controlnet_series_{prefix}'])
+            all_load_outputs.extend(ui_components[f'krea2_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}'])

ui/events/run_handlers.py

@@ -1,103 +1,105 @@
-import gradio as gr
-from core.generation_logic import generate_image_wrapper
-
-def create_run_event(prefix: str, task_type: str, ui_components: dict):
-    run_inputs_map = {
-        'model_display_name': ui_components[f'base_model_{prefix}'],
-        'positive_prompt': ui_components.get(f'prompt_{prefix}') or ui_components.get(f'{prefix}_positive_prompt'),
-        'negative_prompt': ui_components.get(f'neg_prompt_{prefix}') or ui_components.get(f'{prefix}_negative_prompt'),
-        'seed': ui_components.get(f'seed_{prefix}') or ui_components.get(f'{prefix}_seed'),
-        'batch_size': ui_components.get(f'batch_size_{prefix}') or ui_components.get(f'{prefix}_batch_size'),
-        'guidance_scale': ui_components.get(f'cfg_{prefix}') or ui_components.get(f'{prefix}_cfg'),
-        'num_inference_steps': ui_components.get(f'steps_{prefix}') or ui_components.get(f'{prefix}_steps'),
-        'sampler': ui_components.get(f'sampler_{prefix}') or ui_components.get(f'{prefix}_sampler_name'),
-        'scheduler': ui_components.get(f'scheduler_{prefix}') or ui_components.get(f'{prefix}_scheduler'),
-        'zero_gpu_duration': ui_components.get(f'zero_gpu_{prefix}'),
-
-        'clip_skip': ui_components.get(f'clip_skip_{prefix}'),
-        'guidance': ui_components.get(f'guidance_{prefix}'),
-        'task_type': gr.State(task_type)
-    }
-    
-    if ui_components.get(f'pid_settings_{prefix}'):
-        run_inputs_map['pid_settings'] = ui_components[f'pid_settings_{prefix}']
-    
-    if task_type not in ['img2img', 'inpaint']:
-        run_inputs_map.update({
-            'width': ui_components.get(f'width_{prefix}') or ui_components.get(f'{prefix}_width'), 
-            'height': ui_components.get(f'height_{prefix}') or ui_components.get(f'{prefix}_height')
-        })
-    
-    task_specific_map = {
-        'img2img': {'img2img_image': f'input_image_{prefix}', 'img2img_denoise': f'denoise_{prefix}'},
-        'inpaint': {'inpaint_image_dict': f'input_image_dict_{prefix}', 'grow_mask_by': f'grow_mask_by_{prefix}'},
-        'outpaint': {'outpaint_image': f'input_image_{prefix}', 'left': f'left_{prefix}', 'top': f'top_{prefix}', 'right': f'right_{prefix}', 'bottom': f'bottom_{prefix}', 'feathering': f'feathering_{prefix}'},
-        'hires_fix': {'hires_image': f'input_image_{prefix}', 'hires_upscaler': f'hires_upscaler_{prefix}', 'hires_scale_by': f'hires_scale_by_{prefix}', 'hires_denoise': f'denoise_{prefix}'}
-    }
-    if task_type in task_specific_map:
-        for key, comp_name in task_specific_map[task_type].items():
-            if comp_name in ui_components:
-                run_inputs_map[key] = ui_components[comp_name]
-    
-    lora_data_components = ui_components.get(f'all_lora_components_flat_{prefix}', [])
-    controlnet_data_components = ui_components.get(f'all_controlnet_components_flat_{prefix}', [])
-    anima_controlnet_lllite_data_components = ui_components.get(f'all_anima_controlnet_lllite_components_flat_{prefix}', [])
-    diffsynth_controlnet_data_components = ui_components.get(f'all_diffsynth_controlnet_components_flat_{prefix}', [])
-    ipadapter_data_components = ui_components.get(f'all_ipadapter_components_flat_{prefix}', [])
-    sd3_ipadapter_data_components = ui_components.get(f'all_sd3_ipadapter_components_flat_{prefix}', [])
-    flux1_ipadapter_data_components = ui_components.get(f'all_flux1_ipadapter_components_flat_{prefix}', [])
-    style_data_components = ui_components.get(f'all_style_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}', [])
-    reference_latent_data_components = ui_components.get(f'all_reference_latent_components_flat_{prefix}', [])
-    hidream_o1_reference_data_components = ui_components.get(f'all_hidream_o1_reference_components_flat_{prefix}', [])
-    
-    run_inputs_map['vae_source'] = ui_components.get(f'vae_source_{prefix}')
-    run_inputs_map['vae_id'] = ui_components.get(f'vae_id_{prefix}')
-    run_inputs_map['vae_file'] = ui_components.get(f'vae_file_{prefix}')
-
-    input_keys = list(run_inputs_map.keys())
-    input_list_flat = [v for v in run_inputs_map.values() if v is not None]
-    all_chains = [
-        lora_data_components, controlnet_data_components, anima_controlnet_lllite_data_components, diffsynth_controlnet_data_components, ipadapter_data_components,
-        sd3_ipadapter_data_components, flux1_ipadapter_data_components, style_data_components,
-        embedding_data_components, conditioning_data_components, reference_latent_data_components, hidream_o1_reference_data_components
-    ]
-    for chain in all_chains:
-        if chain:
-            input_list_flat.extend(chain)
-
-    def create_ui_inputs_dict(*args):
-        valid_keys = [k for k in input_keys if run_inputs_map[k] is not None]
-        ui_dict = dict(zip(valid_keys, args[:len(valid_keys)]))
-        arg_idx = len(valid_keys)
-
-        def assign_chain_data(chain_key, components_list):
-            nonlocal arg_idx
-            if components_list:
-                ui_dict[chain_key] = list(args[arg_idx : arg_idx + len(components_list)])
-                arg_idx += len(components_list)
-
-        assign_chain_data('lora_data', lora_data_components)
-        assign_chain_data('controlnet_data', controlnet_data_components)
-        assign_chain_data('anima_controlnet_lllite_data', anima_controlnet_lllite_data_components)
-        assign_chain_data('diffsynth_controlnet_data', diffsynth_controlnet_data_components)
-        assign_chain_data('ipadapter_data', ipadapter_data_components)
-        assign_chain_data('sd3_ipadapter_chain', sd3_ipadapter_data_components)
-        assign_chain_data('flux1_ipadapter_data', flux1_ipadapter_data_components)
-        assign_chain_data('style_data', style_data_components)
-        assign_chain_data('embedding_data', embedding_data_components)
-        assign_chain_data('conditioning_data', conditioning_data_components)
-        assign_chain_data('reference_latent_data', reference_latent_data_components)
-        assign_chain_data('hidream_o1_reference_data', hidream_o1_reference_data_components)
-
-        return ui_dict
-
-    run_btn = ui_components.get(f'run_{prefix}') or ui_components.get(f'{prefix}_run_button')
-    res_gal = ui_components.get(f'result_{prefix}') or ui_components.get(f'{prefix}_output_gallery')
-    if run_btn and res_gal:
-        run_btn.click(
-            fn=lambda *args, progress=gr.Progress(track_tqdm=True): generate_image_wrapper(create_ui_inputs_dict(*args), progress),
-            inputs=input_list_flat,
-            outputs=[res_gal]
+import gradio as gr
+from core.generation_logic import generate_image_wrapper
+
+def create_run_event(prefix: str, task_type: str, ui_components: dict):
+    run_inputs_map = {
+        'model_display_name': ui_components[f'base_model_{prefix}'],
+        'positive_prompt': ui_components.get(f'prompt_{prefix}') or ui_components.get(f'{prefix}_positive_prompt'),
+        'negative_prompt': ui_components.get(f'neg_prompt_{prefix}') or ui_components.get(f'{prefix}_negative_prompt'),
+        'seed': ui_components.get(f'seed_{prefix}') or ui_components.get(f'{prefix}_seed'),
+        'batch_size': ui_components.get(f'batch_size_{prefix}') or ui_components.get(f'{prefix}_batch_size'),
+        'guidance_scale': ui_components.get(f'cfg_{prefix}') or ui_components.get(f'{prefix}_cfg'),
+        'num_inference_steps': ui_components.get(f'steps_{prefix}') or ui_components.get(f'{prefix}_steps'),
+        'sampler': ui_components.get(f'sampler_{prefix}') or ui_components.get(f'{prefix}_sampler_name'),
+        'scheduler': ui_components.get(f'scheduler_{prefix}') or ui_components.get(f'{prefix}_scheduler'),
+        'zero_gpu_duration': ui_components.get(f'zero_gpu_{prefix}'),
+
+        'clip_skip': ui_components.get(f'clip_skip_{prefix}'),
+        'guidance': ui_components.get(f'guidance_{prefix}'),
+        'task_type': gr.State(task_type)
+    }
+    
+    if ui_components.get(f'pid_settings_{prefix}'):
+        run_inputs_map['pid_settings'] = ui_components[f'pid_settings_{prefix}']
+    
+    if task_type not in ['img2img', 'inpaint']:
+        run_inputs_map.update({
+            'width': ui_components.get(f'width_{prefix}') or ui_components.get(f'{prefix}_width'), 
+            'height': ui_components.get(f'height_{prefix}') or ui_components.get(f'{prefix}_height')
+        })
+    
+    task_specific_map = {
+        'img2img': {'img2img_image': f'input_image_{prefix}', 'img2img_denoise': f'denoise_{prefix}'},
+        'inpaint': {'inpaint_image_dict': f'input_image_dict_{prefix}', 'grow_mask_by': f'grow_mask_by_{prefix}', 'inpaint_denoise': f'denoise_{prefix}'},
+        'outpaint': {'outpaint_image': f'input_image_{prefix}', 'left': f'left_{prefix}', 'top': f'top_{prefix}', 'right': f'right_{prefix}', 'bottom': f'bottom_{prefix}', 'feathering': f'feathering_{prefix}'},
+        'hires_fix': {'hires_image': f'input_image_{prefix}', 'hires_upscaler': f'hires_upscaler_{prefix}', 'hires_scale_by': f'hires_scale_by_{prefix}', 'hires_denoise': f'denoise_{prefix}'}
+    }
+    if task_type in task_specific_map:
+        for key, comp_name in task_specific_map[task_type].items():
+            if comp_name in ui_components:
+                run_inputs_map[key] = ui_components[comp_name]
+    
+    lora_data_components = ui_components.get(f'all_lora_components_flat_{prefix}', [])
+    controlnet_data_components = ui_components.get(f'all_controlnet_components_flat_{prefix}', [])
+    anima_controlnet_lllite_data_components = ui_components.get(f'all_anima_controlnet_lllite_components_flat_{prefix}', [])
+    diffsynth_controlnet_data_components = ui_components.get(f'all_diffsynth_controlnet_components_flat_{prefix}', [])
+    krea2_controlnet_data_components = ui_components.get(f'all_krea2_controlnet_components_flat_{prefix}', [])
+    ipadapter_data_components = ui_components.get(f'all_ipadapter_components_flat_{prefix}', [])
+    sd3_ipadapter_data_components = ui_components.get(f'all_sd3_ipadapter_components_flat_{prefix}', [])
+    flux1_ipadapter_data_components = ui_components.get(f'all_flux1_ipadapter_components_flat_{prefix}', [])
+    style_data_components = ui_components.get(f'all_style_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}', [])
+    reference_latent_data_components = ui_components.get(f'all_reference_latent_components_flat_{prefix}', [])
+    hidream_o1_reference_data_components = ui_components.get(f'all_hidream_o1_reference_components_flat_{prefix}', [])
+    
+    run_inputs_map['vae_source'] = ui_components.get(f'vae_source_{prefix}')
+    run_inputs_map['vae_id'] = ui_components.get(f'vae_id_{prefix}')
+    run_inputs_map['vae_file'] = ui_components.get(f'vae_file_{prefix}')
+
+    input_keys = list(run_inputs_map.keys())
+    input_list_flat = [v for v in run_inputs_map.values() if v is not None]
+    all_chains = [
+        lora_data_components, controlnet_data_components, anima_controlnet_lllite_data_components, diffsynth_controlnet_data_components, krea2_controlnet_data_components, ipadapter_data_components,
+        sd3_ipadapter_data_components, flux1_ipadapter_data_components, style_data_components,
+        embedding_data_components, conditioning_data_components, reference_latent_data_components, hidream_o1_reference_data_components
+    ]
+    for chain in all_chains:
+        if chain:
+            input_list_flat.extend(chain)
+
+    def create_ui_inputs_dict(*args):
+        valid_keys = [k for k in input_keys if run_inputs_map[k] is not None]
+        ui_dict = dict(zip(valid_keys, args[:len(valid_keys)]))
+        arg_idx = len(valid_keys)
+
+        def assign_chain_data(chain_key, components_list):
+            nonlocal arg_idx
+            if components_list:
+                ui_dict[chain_key] = list(args[arg_idx : arg_idx + len(components_list)])
+                arg_idx += len(components_list)
+
+        assign_chain_data('lora_data', lora_data_components)
+        assign_chain_data('controlnet_data', controlnet_data_components)
+        assign_chain_data('anima_controlnet_lllite_data', anima_controlnet_lllite_data_components)
+        assign_chain_data('diffsynth_controlnet_data', diffsynth_controlnet_data_components)
+        assign_chain_data('krea2_controlnet_data', krea2_controlnet_data_components)
+        assign_chain_data('ipadapter_data', ipadapter_data_components)
+        assign_chain_data('sd3_ipadapter_chain', sd3_ipadapter_data_components)
+        assign_chain_data('flux1_ipadapter_data', flux1_ipadapter_data_components)
+        assign_chain_data('style_data', style_data_components)
+        assign_chain_data('embedding_data', embedding_data_components)
+        assign_chain_data('conditioning_data', conditioning_data_components)
+        assign_chain_data('reference_latent_data', reference_latent_data_components)
+        assign_chain_data('hidream_o1_reference_data', hidream_o1_reference_data_components)
+
+        return ui_dict
+
+    run_btn = ui_components.get(f'run_{prefix}') or ui_components.get(f'{prefix}_run_button')
+    res_gal = ui_components.get(f'result_{prefix}') or ui_components.get(f'{prefix}_output_gallery')
+    if run_btn and res_gal:
+        run_btn.click(
+            fn=lambda *args, progress=gr.Progress(track_tqdm=True): generate_image_wrapper(create_ui_inputs_dict(*args), progress),
+            inputs=input_list_flat,
+            outputs=[res_gal]
         )

ui/shared/hires_fix_ui.py

@@ -1,15 +1,23 @@
 import gradio as gr
-from core.settings import MODEL_MAP_CHECKPOINT
+from core.settings import MODEL_MAP_CHECKPOINT, MODEL_DEFAULTS_CONFIG
 from comfy_integration.nodes import SAMPLER_CHOICES, SCHEDULER_CHOICES
 from .ui_components import (
     create_lora_settings_ui,
-    create_controlnet_ui, create_anima_controlnet_lllite_ui, create_ipadapter_ui, create_embedding_ui,
+    create_controlnet_ui, create_anima_controlnet_lllite_ui, create_diffsynth_controlnet_ui, create_krea2_controlnet_ui, create_ipadapter_ui, create_embedding_ui,
     create_conditioning_ui, create_vae_override_ui,
     create_model_architecture_filter_ui, create_category_filter_ui,
     create_sd3_ipadapter_ui, create_flux1_ipadapter_ui, create_style_ui,
     create_reference_latent_ui, create_hidream_o1_reference_ui
 )
 
+default_vals = MODEL_DEFAULTS_CONFIG.get('Default', {})
+DEFAULT_STEPS = default_vals.get('steps', 20)
+DEFAULT_CFG = default_vals.get('cfg', 5.0)
+DEFAULT_SAMPLER = default_vals.get('sampler_name', 'euler')
+DEFAULT_SCHEDULER = default_vals.get('scheduler', 'simple')
+DEFAULT_POS_PROMPT = default_vals.get('positive_prompt', '')
+DEFAULT_NEG_PROMPT = default_vals.get('negative_prompt', '')
+
 def create_ui():
     prefix = "hires_fix"
     components = {}
@@ -33,8 +41,8 @@ def create_ui():
             with gr.Column(scale=1):
                 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)
-                components[f'neg_prompt_{prefix}'] = gr.Text(label="Negative prompt", lines=3)
+                components[f'prompt_{prefix}'] = gr.Text(label="Prompt", lines=3, value=DEFAULT_POS_PROMPT)
+                components[f'neg_prompt_{prefix}'] = gr.Text(label="Negative prompt", lines=3, value=DEFAULT_NEG_PROMPT)
 
         with gr.Row():
             with gr.Column(scale=1):
@@ -52,11 +60,19 @@ 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='er_sde' if 'er_sde' in SAMPLER_CHOICES else SAMPLER_CHOICES[0])
-                    components[f'scheduler_{prefix}'] = gr.Dropdown(label="Scheduler", choices=SCHEDULER_CHOICES, value='simple' if 'simple' in SCHEDULER_CHOICES else SCHEDULER_CHOICES[0])
+                    components[f'sampler_{prefix}'] = gr.Dropdown(
+                        label="Sampler", 
+                        choices=SAMPLER_CHOICES, 
+                        value=DEFAULT_SAMPLER if DEFAULT_SAMPLER in SAMPLER_CHOICES else (SAMPLER_CHOICES[0] if SAMPLER_CHOICES else 'euler')
+                    )
+                    components[f'scheduler_{prefix}'] = gr.Dropdown(
+                        label="Scheduler", 
+                        choices=SCHEDULER_CHOICES, 
+                        value=DEFAULT_SCHEDULER if DEFAULT_SCHEDULER in SCHEDULER_CHOICES else (SCHEDULER_CHOICES[0] if SCHEDULER_CHOICES else 'simple')
+                    )
                 with gr.Row():
-                    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)
+                    components[f'steps_{prefix}'] = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=DEFAULT_STEPS)
+                    components[f'cfg_{prefix}'] = gr.Slider(label="CFG Scale", minimum=1.0, maximum=20.0, step=0.1, value=DEFAULT_CFG)
                 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)
@@ -75,6 +91,8 @@ def create_ui():
         components.update(create_lora_settings_ui(prefix))
         components.update(create_controlnet_ui(prefix))
         components.update(create_anima_controlnet_lllite_ui(prefix))
+        components.update(create_diffsynth_controlnet_ui(prefix))
+        components.update(create_krea2_controlnet_ui(prefix))
         components.update(create_ipadapter_ui(prefix))
         components.update(create_flux1_ipadapter_ui(prefix))
         components.update(create_sd3_ipadapter_ui(prefix))

ui/shared/img2img_ui.py

@@ -1,15 +1,23 @@
 import gradio as gr
-from core.settings import MODEL_MAP_CHECKPOINT
+from core.settings import MODEL_MAP_CHECKPOINT, MODEL_DEFAULTS_CONFIG
 from comfy_integration.nodes import SAMPLER_CHOICES, SCHEDULER_CHOICES
 from .ui_components import (
     create_lora_settings_ui,
-    create_controlnet_ui, create_anima_controlnet_lllite_ui, create_diffsynth_controlnet_ui, create_ipadapter_ui, create_embedding_ui,
+    create_controlnet_ui, create_anima_controlnet_lllite_ui, create_diffsynth_controlnet_ui, create_krea2_controlnet_ui, create_ipadapter_ui, create_embedding_ui,
     create_conditioning_ui, create_vae_override_ui,
     create_model_architecture_filter_ui, create_category_filter_ui,
     create_sd3_ipadapter_ui, create_flux1_ipadapter_ui, create_style_ui,
     create_reference_latent_ui, create_hidream_o1_reference_ui
 )
 
+default_vals = MODEL_DEFAULTS_CONFIG.get('Default', {})
+DEFAULT_STEPS = default_vals.get('steps', 20)
+DEFAULT_CFG = default_vals.get('cfg', 5.0)
+DEFAULT_SAMPLER = default_vals.get('sampler_name', 'euler')
+DEFAULT_SCHEDULER = default_vals.get('scheduler', 'simple')
+DEFAULT_POS_PROMPT = default_vals.get('positive_prompt', '')
+DEFAULT_NEG_PROMPT = default_vals.get('negative_prompt', '')
+
 def create_ui():
     prefix = "img2img"
     components = {}
@@ -28,19 +36,27 @@ 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)
-                components[f'neg_prompt_{prefix}'] = gr.Text(label="Negative prompt", lines=3)
+                components[f'prompt_{prefix}'] = gr.Text(label="Prompt", lines=3, value=DEFAULT_POS_PROMPT)
+                components[f'neg_prompt_{prefix}'] = gr.Text(label="Negative prompt", lines=3, value=DEFAULT_NEG_PROMPT)
 
         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='er_sde' if 'er_sde' in SAMPLER_CHOICES else SAMPLER_CHOICES[0])
-                    components[f'scheduler_{prefix}'] = gr.Dropdown(label="Scheduler", choices=SCHEDULER_CHOICES, value='simple' if 'simple' in SCHEDULER_CHOICES else SCHEDULER_CHOICES[0])
+                    components[f'sampler_{prefix}'] = gr.Dropdown(
+                        label="Sampler", 
+                        choices=SAMPLER_CHOICES, 
+                        value=DEFAULT_SAMPLER if DEFAULT_SAMPLER in SAMPLER_CHOICES else (SAMPLER_CHOICES[0] if SAMPLER_CHOICES else 'euler')
+                    )
+                    components[f'scheduler_{prefix}'] = gr.Dropdown(
+                        label="Scheduler", 
+                        choices=SCHEDULER_CHOICES, 
+                        value=DEFAULT_SCHEDULER if DEFAULT_SCHEDULER in SCHEDULER_CHOICES else (SCHEDULER_CHOICES[0] if SCHEDULER_CHOICES else 'simple')
+                    )
                 with gr.Row():
-                    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)
+                    components[f'steps_{prefix}'] = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=DEFAULT_STEPS)
+                    components[f'cfg_{prefix}'] = gr.Slider(label="CFG Scale", minimum=1.0, maximum=20.0, step=0.1, value=DEFAULT_CFG)
                 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)
@@ -57,6 +73,7 @@ def create_ui():
         components.update(create_controlnet_ui(prefix))
         components.update(create_anima_controlnet_lllite_ui(prefix))
         components.update(create_diffsynth_controlnet_ui(prefix))
+        components.update(create_krea2_controlnet_ui(prefix))
         components.update(create_ipadapter_ui(prefix))
         components.update(create_flux1_ipadapter_ui(prefix))
         components.update(create_sd3_ipadapter_ui(prefix))

ui/shared/inpaint_ui.py

@@ -1,14 +1,22 @@
 import gradio as gr
-from core.settings import MODEL_MAP_CHECKPOINT
+from core.settings import MODEL_MAP_CHECKPOINT, MODEL_DEFAULTS_CONFIG
 from .ui_components import (
     create_base_parameter_ui, create_lora_settings_ui,
-    create_controlnet_ui, create_anima_controlnet_lllite_ui, create_diffsynth_controlnet_ui, create_ipadapter_ui, create_embedding_ui,
+    create_controlnet_ui, create_anima_controlnet_lllite_ui, create_diffsynth_controlnet_ui, create_krea2_controlnet_ui, create_ipadapter_ui, create_embedding_ui,
     create_conditioning_ui, create_vae_override_ui,
     create_model_architecture_filter_ui, create_category_filter_ui,
     create_sd3_ipadapter_ui, create_flux1_ipadapter_ui, create_style_ui,
     create_reference_latent_ui, create_hidream_o1_reference_ui
 )
 
+default_vals = MODEL_DEFAULTS_CONFIG.get('Default', {})
+DEFAULT_STEPS = default_vals.get('steps', 20)
+DEFAULT_CFG = default_vals.get('cfg', 5.0)
+DEFAULT_SAMPLER = default_vals.get('sampler_name', 'euler')
+DEFAULT_SCHEDULER = default_vals.get('scheduler', 'simple')
+DEFAULT_POS_PROMPT = default_vals.get('positive_prompt', '')
+DEFAULT_NEG_PROMPT = default_vals.get('negative_prompt', '')
+
 def create_ui():
     prefix = "inpaint"
     components = {}
@@ -47,8 +55,8 @@ def create_ui():
             components[f'editor_column_{prefix}'] = editor_column
 
             with gr.Column(scale=2) as prompts_column:
-                components[f'prompt_{prefix}'] = gr.Text(label="Prompt", lines=6)
-                components[f'neg_prompt_{prefix}'] = gr.Text(label="Negative prompt", lines=6)
+                components[f'prompt_{prefix}'] = gr.Text(label="Prompt", lines=6, value=DEFAULT_POS_PROMPT)
+                components[f'neg_prompt_{prefix}'] = gr.Text(label="Negative prompt", lines=6, value=DEFAULT_NEG_PROMPT)
             components[f'prompts_column_{prefix}'] = prompts_column
 
         with gr.Row() as params_and_gallery_row:
@@ -62,11 +70,19 @@ def create_ui():
                         label="Grow Mask By", minimum=0, maximum=64, step=1, value=6
                     )
                 with gr.Row():
-                    components[f'sampler_{prefix}'] = gr.Dropdown(label="Sampler", choices=SAMPLER_CHOICES, value='er_sde' if 'er_sde' in SAMPLER_CHOICES else SAMPLER_CHOICES[0])
-                    components[f'scheduler_{prefix}'] = gr.Dropdown(label="Scheduler", choices=SCHEDULER_CHOICES, value='simple' if 'simple' in SCHEDULER_CHOICES else SCHEDULER_CHOICES[0])
+                    components[f'sampler_{prefix}'] = gr.Dropdown(
+                        label="Sampler", 
+                        choices=SAMPLER_CHOICES, 
+                        value=DEFAULT_SAMPLER if DEFAULT_SAMPLER in SAMPLER_CHOICES else (SAMPLER_CHOICES[0] if SAMPLER_CHOICES else 'euler')
+                    )
+                    components[f'scheduler_{prefix}'] = gr.Dropdown(
+                        label="Scheduler", 
+                        choices=SCHEDULER_CHOICES, 
+                        value=DEFAULT_SCHEDULER if DEFAULT_SCHEDULER in SCHEDULER_CHOICES else (SCHEDULER_CHOICES[0] if SCHEDULER_CHOICES else 'simple')
+                    )
                 with gr.Row():
-                    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)
+                    components[f'steps_{prefix}'] = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=DEFAULT_STEPS)
+                    components[f'cfg_{prefix}'] = gr.Slider(label="CFG Scale", minimum=1.0, maximum=20.0, step=0.1, value=DEFAULT_CFG)
                 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)
@@ -89,6 +105,7 @@ def create_ui():
             components.update(create_controlnet_ui(prefix))
             components.update(create_anima_controlnet_lllite_ui(prefix))
             components.update(create_diffsynth_controlnet_ui(prefix))
+            components.update(create_krea2_controlnet_ui(prefix))
             components.update(create_ipadapter_ui(prefix))
             components.update(create_flux1_ipadapter_ui(prefix))
             components.update(create_sd3_ipadapter_ui(prefix))

ui/shared/outpaint_ui.py

@@ -1,15 +1,23 @@
 import gradio as gr
-from core.settings import MODEL_MAP_CHECKPOINT
+from core.settings import MODEL_MAP_CHECKPOINT, MODEL_DEFAULTS_CONFIG
 from comfy_integration.nodes import SAMPLER_CHOICES, SCHEDULER_CHOICES
 from .ui_components import (
     create_lora_settings_ui,
-    create_controlnet_ui, create_anima_controlnet_lllite_ui, create_diffsynth_controlnet_ui, create_ipadapter_ui, create_embedding_ui,
+    create_controlnet_ui, create_anima_controlnet_lllite_ui, create_diffsynth_controlnet_ui, create_krea2_controlnet_ui, create_ipadapter_ui, create_embedding_ui,
     create_conditioning_ui, create_vae_override_ui,
     create_model_architecture_filter_ui, create_category_filter_ui,
     create_sd3_ipadapter_ui, create_flux1_ipadapter_ui, create_style_ui,
     create_reference_latent_ui, create_hidream_o1_reference_ui
 )
 
+default_vals = MODEL_DEFAULTS_CONFIG.get('Default', {})
+DEFAULT_STEPS = default_vals.get('steps', 20)
+DEFAULT_CFG = default_vals.get('cfg', 5.0)
+DEFAULT_SAMPLER = default_vals.get('sampler_name', 'euler')
+DEFAULT_SCHEDULER = default_vals.get('scheduler', 'simple')
+DEFAULT_POS_PROMPT = default_vals.get('positive_prompt', '')
+DEFAULT_NEG_PROMPT = default_vals.get('negative_prompt', '')
+
 def create_ui():
     prefix = "outpaint"
     components = {}
@@ -33,8 +41,8 @@ def create_ui():
             with gr.Column(scale=1):
                 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)
-                components[f'neg_prompt_{prefix}'] = gr.Text(label="Negative prompt", lines=3)
+                components[f'prompt_{prefix}'] = gr.Text(label="Prompt", lines=3, value=DEFAULT_POS_PROMPT)
+                components[f'neg_prompt_{prefix}'] = gr.Text(label="Negative prompt", lines=3, value=DEFAULT_NEG_PROMPT)
 
         with gr.Row():
             with gr.Column(scale=1):
@@ -48,11 +56,19 @@ def create_ui():
                 components[f'feathering_{prefix}'] = gr.Slider(label="Feathering / Grow Mask", minimum=0, maximum=100, step=1, value=10)
                 
                 with gr.Row():
-                    components[f'sampler_{prefix}'] = gr.Dropdown(label="Sampler", choices=SAMPLER_CHOICES, value='er_sde' if 'er_sde' in SAMPLER_CHOICES else SAMPLER_CHOICES[0])
-                    components[f'scheduler_{prefix}'] = gr.Dropdown(label="Scheduler", choices=SCHEDULER_CHOICES, value='simple' if 'simple' in SCHEDULER_CHOICES else SCHEDULER_CHOICES[0])
+                    components[f'sampler_{prefix}'] = gr.Dropdown(
+                        label="Sampler", 
+                        choices=SAMPLER_CHOICES, 
+                        value=DEFAULT_SAMPLER if DEFAULT_SAMPLER in SAMPLER_CHOICES else (SAMPLER_CHOICES[0] if SAMPLER_CHOICES else 'euler')
+                    )
+                    components[f'scheduler_{prefix}'] = gr.Dropdown(
+                        label="Scheduler", 
+                        choices=SCHEDULER_CHOICES, 
+                        value=DEFAULT_SCHEDULER if DEFAULT_SCHEDULER in SCHEDULER_CHOICES else (SCHEDULER_CHOICES[0] if SCHEDULER_CHOICES else 'simple')
+                    )
                 with gr.Row():
-                    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)
+                    components[f'steps_{prefix}'] = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=DEFAULT_STEPS)
+                    components[f'cfg_{prefix}'] = gr.Slider(label="CFG Scale", minimum=1.0, maximum=20.0, step=0.1, value=DEFAULT_CFG)
                 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)
@@ -72,6 +88,7 @@ def create_ui():
         components.update(create_controlnet_ui(prefix))
         components.update(create_anima_controlnet_lllite_ui(prefix))
         components.update(create_diffsynth_controlnet_ui(prefix))
+        components.update(create_krea2_controlnet_ui(prefix))
         components.update(create_ipadapter_ui(prefix))
         components.update(create_flux1_ipadapter_ui(prefix))
         components.update(create_sd3_ipadapter_ui(prefix))

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_anima_controlnet_lllite_ui, create_diffsynth_controlnet_ui, create_ipadapter_ui, create_embedding_ui,
+    create_controlnet_ui, create_anima_controlnet_lllite_ui, create_diffsynth_controlnet_ui, create_krea2_controlnet_ui, create_ipadapter_ui, create_embedding_ui,
     create_conditioning_ui, create_vae_override_ui,
     create_model_architecture_filter_ui, create_category_filter_ui,
     create_sd3_ipadapter_ui, create_flux1_ipadapter_ui, create_style_ui,
@@ -45,6 +45,7 @@ def create_ui():
         components.update(create_controlnet_ui(prefix))
         components.update(create_anima_controlnet_lllite_ui(prefix))
         components.update(create_diffsynth_controlnet_ui(prefix))
+        components.update(create_krea2_controlnet_ui(prefix))
         components.update(create_ipadapter_ui(prefix))
         components.update(create_flux1_ipadapter_ui(prefix))
         components.update(create_sd3_ipadapter_ui(prefix))

ui/shared/ui_components.py

@@ -4,7 +4,7 @@ from core.settings import (
     MAX_LORAS, LORA_SOURCE_CHOICES, MAX_EMBEDDINGS, MAX_CONDITIONINGS,
     MAX_CONTROLNETS, MAX_IPADAPTERS, RESOLUTION_MAP, ARCHITECTURES_CONFIG,
     MODEL_MAP_CHECKPOINT, MODEL_TYPE_MAP, FEATURES_CONFIG, ARCH_CATEGORIES_MAP,
-    VAE_DIR
+    VAE_DIR, MODEL_DEFAULTS_CONFIG
 )
 import yaml
 import os
@@ -18,6 +18,13 @@ default_arch_model_type = default_architectures_dict.get(default_m_type, {}).get
 default_arch_features = FEATURES_CONFIG.get(default_arch_model_type, FEATURES_CONFIG.get('default', {}))
 default_enabled_chains = default_arch_features.get('enabled_chains', [])
 
+default_vals = MODEL_DEFAULTS_CONFIG.get('Default', {})
+DEFAULT_STEPS = default_vals.get('steps', 20)
+DEFAULT_CFG = default_vals.get('cfg', 5.0)
+DEFAULT_SAMPLER = default_vals.get('sampler_name', 'euler')
+DEFAULT_SCHEDULER = default_vals.get('scheduler', 'simple')
+DEFAULT_POS_PROMPT = default_vals.get('positive_prompt', '')
+DEFAULT_NEG_PROMPT = default_vals.get('negative_prompt', '')
 
 @lru_cache(maxsize=1)
 def get_ipadapter_config_from_yaml():
@@ -90,11 +97,19 @@ def create_base_parameter_ui(prefix, defaults=None):
         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='er_sde' if 'er_sde' in SAMPLER_CHOICES else SAMPLER_CHOICES[0])
-        components[f'scheduler_{prefix}'] = gr.Dropdown(label="Scheduler", choices=SCHEDULER_CHOICES, value='simple' if 'simple' in SCHEDULER_CHOICES else SCHEDULER_CHOICES[0])
+        components[f'sampler_{prefix}'] = gr.Dropdown(
+            label="Sampler", 
+            choices=SAMPLER_CHOICES, 
+            value=DEFAULT_SAMPLER if DEFAULT_SAMPLER in SAMPLER_CHOICES else (SAMPLER_CHOICES[0] if SAMPLER_CHOICES else 'euler')
+        )
+        components[f'scheduler_{prefix}'] = gr.Dropdown(
+            label="Scheduler", 
+            choices=SCHEDULER_CHOICES, 
+            value=DEFAULT_SCHEDULER if DEFAULT_SCHEDULER in SCHEDULER_CHOICES else (SCHEDULER_CHOICES[0] if SCHEDULER_CHOICES else 'simple')
+        )
     with gr.Row():
-                    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)
+        components[f'steps_{prefix}'] = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=DEFAULT_STEPS)
+        components[f'cfg_{prefix}'] = gr.Slider(label="CFG Scale", minimum=1.0, maximum=20.0, step=0.1, value=DEFAULT_CFG)
     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)
@@ -113,14 +128,14 @@ def create_lora_settings_ui(prefix: str):
 
     with gr.Accordion("LoRA Settings", open=False, visible=('lora' in default_enabled_chains)) as lora_accordion:
         components[f'lora_accordion_{prefix}'] = lora_accordion
-        gr.Markdown("💡 **Tip:** When downloading from Civitai, please use the **Version ID**, not the Model ID. You can find the Version ID in the URL (e.g., `civitai.com/models/123?modelVersionId=456`) or under the model's download button.")
+        gr.Markdown("💡 **Tip:** When downloading from Civitai, please use the **Version ID**, not the Model ID. You can find the Version ID in the URL (e.g., `civitai.com/models/123?modelVersionId=456`) or under the model's download button. When downloading from Hugging Face, please use the format: `repo_id/filename.extension` or `repo_id/folder_path/filename.extension` (e.g., `lightx2v/Qwen-Image-Lightning/Qwen-Image-Lightning-4steps-V2.0-bf16.safetensors`).")
         components[f'lora_count_state_{prefix}'] = gr.State(1)
 
         for i in range(MAX_LORAS):
             with gr.Row(visible=i==0) as row:
                 source = gr.Dropdown(label=f"LoRA Source {i+1}", choices=LORA_SOURCE_CHOICES, value=LORA_SOURCE_CHOICES[0], scale=1)
-                lora_id = gr.Textbox(label=f"Civitai Version ID / File", placeholder="Civitai Version ID or Filename", scale=2, type="text")
-                scale = gr.Slider(label=f"Scale", minimum=0.0, maximum=2.0, step=0.05, value=0.8, scale=1)
+                lora_id = gr.Textbox(label="Civitai Version ID / HF file / Upload File", scale=2, type="text")
+                scale = gr.Slider(label=f"Scale", minimum=0.0, maximum=2.0, step=0.05, value=1.0, scale=1)
                 upload = gr.UploadButton(label="Upload", file_types=[".safetensors"], scale=1)
             
             lora_rows.append(row)
@@ -188,6 +203,49 @@ def create_controlnet_ui(prefix: str, max_units=MAX_CONTROLNETS):
 
     return components
 
+def create_krea2_controlnet_ui(prefix: str, max_units=MAX_CONTROLNETS):
+    components = {}
+    key = lambda name: f"{name}_{prefix}"
+
+    with gr.Accordion("Krea2 ControlNet Settings", open=False, visible=('krea2_controlnet' in default_enabled_chains)) as accordion:
+        components[key('krea2_controlnet_accordion')] = accordion
+        gr.Markdown("💡 **Tip:** Processed using the [facok/comfyui-krea2-controlnet](https://github.com/facok/comfyui-krea2-controlnet) node.")       
+        
+        cn_rows, images, series, types, strengths, filepaths = [], [], [], [], [], []
+        components.update({
+            key('krea2_controlnet_rows'): cn_rows,
+            key('krea2_controlnet_images'): images,
+            key('krea2_controlnet_series'): series,
+            key('krea2_controlnet_types'): types,
+            key('krea2_controlnet_strengths'): strengths,
+            key('krea2_controlnet_filepaths'): filepaths
+        })
+        
+        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"Control Image {i+1}", type="pil", sources=["upload"], height=256))
+                with gr.Column(scale=2):
+                    types.append(gr.Dropdown(label="Type", choices=[], interactive=True, allow_custom_value=True))
+                    series.append(gr.Dropdown(label="Series", choices=[], interactive=True, allow_custom_value=True))
+                    strengths.append(gr.Slider(label="Strength", minimum=0.0, maximum=2.0, step=0.05, value=1.0, interactive=True))
+                    filepaths.append(gr.State(None))
+                cn_rows.append(row)
+
+        with gr.Row():
+            components[key('add_krea2_controlnet_button')] = gr.Button("✚ Add Krea2 ControlNet")
+            components[key('delete_krea2_controlnet_button')] = gr.Button("➖ Delete Krea2 ControlNet", visible=False)
+        components[key('krea2_controlnet_count_state')] = gr.State(1)
+        
+        all_cn_components_flat = []
+        for i in range(max_units):
+            all_cn_components_flat.extend([
+                images[i], types[i], series[i], strengths[i], filepaths[i]
+            ])
+        components[key('all_krea2_controlnet_components_flat')] = all_cn_components_flat
+
+    return components
+
 def create_anima_controlnet_lllite_ui(prefix: str, max_units=MAX_CONTROLNETS):
     components = {}
     key = lambda name: f"{name}_{prefix}"
@@ -464,7 +522,7 @@ def create_embedding_ui(prefix: str):
 
     with gr.Accordion("Embedding Settings", open=False, visible=('embedding' in default_enabled_chains)) as accordion:
         components[key('embedding_accordion')] = accordion
-        gr.Markdown("💡 **Tip:** Embeddings are automatically added to your prompt using `embedding:filename` syntax. When downloading from Civitai, please use the **Version ID**, not the Model ID. You can find the Version ID in the URL (e.g., `civitai.com/models/123?modelVersionId=456`) or under the model's download button. For instance, using the Version ID `456` from the example above would automatically append `embedding:civitai_456` to your positive prompt.")
+        gr.Markdown("💡 **Tip:** When downloading from Civitai, please use the **Version ID**, not the Model ID. You can find the Version ID in the URL (e.g., `civitai.com/models/123?modelVersionId=456`) or under the model's download button. For example, entering the Version ID 456 will automatically save the file as \"civitai_456.safetensors\", and you will need to manually enter `embedding:civitai_456` in either your prompt or negative prompt to activate it.When downloading from Hugging Face, please use the format: repo_id/filename.extension or repo_id/folder_path/filename.extension (e.g., ilikebigturtles/lazypos/lazypos.safetensors or ilikebigturtles/lazyneg/lazyneg.safetensors). For Hugging Face files, you will need to enter embedding:filename (e.g., entering embedding:lazypos in your positive prompt, or embedding:lazyneg in your negative prompt) to activate it.")
         
         embedding_rows, sources, ids, files, upload_buttons = [], [], [], [], []
         components.update({
@@ -478,7 +536,7 @@ def create_embedding_ui(prefix: str):
         for i in range(MAX_EMBEDDINGS):
             with gr.Row(visible=(i < 1)) as row:
                 sources.append(gr.Dropdown(label=f"Embedding Source {i+1}", choices=LORA_SOURCE_CHOICES, value="Civitai", scale=1, interactive=True))
-                ids.append(gr.Textbox(label="Civitai Version ID / File", placeholder="Civitai Version ID or Filename", scale=3, interactive=True, type="text"))
+                ids.append(gr.Textbox(label="Civitai Version ID / HF file / Upload File", scale=3, interactive=True, type="text"))
                 upload_btn = gr.UploadButton("Upload", file_types=[".safetensors"], scale=1)
                 files.append(gr.State(None))
                 upload_buttons.append(upload_btn)
@@ -548,9 +606,9 @@ def create_vae_override_ui(prefix: str):
     key = lambda name: f"{name}_{prefix}"
     source_choices = ["None"] + LORA_SOURCE_CHOICES
 
-    with gr.Accordion("VAE Settings (Override)", open=False) as vae_accordion:
+    with gr.Accordion("VAE Settings (Override)", open=False, visible=('vae' in default_enabled_chains)) as vae_accordion:
         components[key('vae_accordion')] = vae_accordion
-        gr.Markdown("💡 **Tip:** When downloading from Civitai, please use the **Version ID**, not the Model ID. You can find the Version ID in the URL (e.g., `civitai.com/models/123?modelVersionId=456`) or under the model's download button.")
+        gr.Markdown("💡 **Tip:** When downloading from Civitai, please use the **Version ID**, not the Model ID. You can find the Version ID in the URL (e.g., `civitai.com/models/123?modelVersionId=456`) or under the model's download button. When downloading from Hugging Face, please use the format: `repo_id/filename.extension` or `repo_id/folder_path/filename.extension` (e.g., `madebyollin/sdxl-vae-fp16-fix/sdxl_vae.safetensors`).")
         with gr.Row():
             components[key('vae_source')] = gr.Dropdown(
                 label="VAE Source", 
@@ -560,8 +618,7 @@ def create_vae_override_ui(prefix: str):
                 interactive=True
             )
             components[key('vae_id')] = gr.Textbox(
-                label="Civitai Version ID / File", 
-                placeholder="Civitai Version ID or Filename", 
+                label="Civitai Version ID / HF file / Upload File", 
                 scale=3, 
                 interactive=True,
                 type="text"

utils/app_utils.py

@@ -105,6 +105,14 @@ def sanitize_prompt(prompt: str) -> str:
 def sanitize_id(input_id: str) -> str:
     if not isinstance(input_id, str):
         return ""
+    input_id = input_id.strip()
+    if "civitai" in input_id.lower():
+        version_match = re.search(r'modelVersionId=(\d+)', input_id)
+        if version_match:
+            return version_match.group(1)
+        model_match = re.search(r'/models/(\d+)', input_id)
+        if model_match:
+            return model_match.group(1)
     return re.sub(r'[^0-9]', '', input_id)
 
 def sanitize_url(url: str) -> str:
@@ -129,12 +137,20 @@ def get_civitai_file_info(version_id: str) -> dict | None:
         response.raise_for_status()
         data = response.json()
         
+        model_type = data.get('model', {}).get('type')
+        
+        result_file = None
         for file_data in data.get('files', []):
             if file_data.get('type') == 'Model' and file_data['name'].endswith(('.safetensors', '.pt', '.bin')):
-                return file_data
+                result_file = file_data.copy()
+                break
         
-        if data.get('files'):
-            return data['files'][0]
+        if not result_file and data.get('files'):
+            result_file = data['files'][0].copy()
+            
+        if result_file:
+            result_file['model_type'] = model_type
+            return result_file
     except Exception:
         return None
 
@@ -173,99 +189,180 @@ def get_lora_path(source: str, id_or_url: str, civitai_key: str, progress) -> tu
             version_id = sanitize_id(id_or_url)
             if not version_id:
                 return None, "Invalid Civitai ID provided. Must be numeric."
+            
+            file_info = get_civitai_file_info(version_id)
+            if file_info:
+                model_type = file_info.get('model_type')
+                if model_type and model_type.lower() == 'checkpoint':
+                    return None, f"Invalid Civitai model type '{model_type}' for LoRA. Checkpoint models are not allowed."
+            
             filename = sanitize_filename(f"civitai_{version_id}.safetensors")
             local_path = os.path.join(LORA_DIR, filename)
-            file_info = get_civitai_file_info(version_id)
             api_key_to_use = civitai_key
             source_name = f"Civitai ID {version_id}"
+        elif source == "Hugging Face":
+            parts = id_or_url.strip().split('/')
+            if len(parts) < 3:
+                return None, "Invalid Hugging Face path. Format: repo_owner/repo_name/filename"
+            repo_id = f"{parts[0]}/{parts[1]}"
+            repo_file_path = "/".join(parts[2:])
+            unique_name = id_or_url.strip().replace('/', '_')
+            filename = sanitize_filename(unique_name)
+            local_path = os.path.join(LORA_DIR, filename)
+            source_name = f"HF {repo_file_path}"
         else:
             return None, "Invalid source."
 
     except ValueError as e:
         return None, f"Input validation failed: {e}"
 
-    if os.path.exists(local_path):
-        return local_path, "File already exists."
+    if os.path.lexists(local_path):
+        if not os.path.exists(local_path):
+            os.remove(local_path)
+        else:
+            return local_path, "File already exists."
 
-    if not file_info or not file_info.get('downloadUrl'):
-        return None, f"Could not get download link for {source_name}."
+    if source == "Civitai":
+        if not file_info or not file_info.get('downloadUrl'):
+            return None, f"Could not get download link for {source_name}."
 
-    status = download_file(file_info['downloadUrl'], local_path, api_key_to_use, progress=progress, desc=f"Downloading {source_name}")
-    
-    return (local_path, status) if "Successfully" in status else (None, status)
+        status = download_file(file_info['downloadUrl'], local_path, api_key_to_use, progress=progress, desc=f"Downloading {source_name}")
+        return (local_path, status) if "Successfully" in status else (None, status)
+    elif source == "Hugging Face":
+        try:
+            if progress: progress(0, desc=f"Downloading {source_name}")
+            cached_path = hf_hub_download(repo_id=repo_id, filename=repo_file_path, token=os.environ.get("HF_TOKEN"))
+            os.makedirs(LORA_DIR, exist_ok=True)
+            os.symlink(cached_path, local_path)
+            if progress: progress(1.0, desc=f"Downloaded {source_name}")
+            return local_path, f"Successfully downloaded: {filename}"
+        except Exception as e:
+            return None, f"Hugging Face download failed: {e}"
 
 def get_embedding_path(source: str, id_or_url: str, civitai_key: str, progress) -> tuple[str | None, str]:
     if not id_or_url or not id_or_url.strip():
         return None, "No ID/URL provided."
 
     try:
-        file_ext = ".safetensors"
-
         if source == "Civitai":
             version_id = sanitize_id(id_or_url)
             if not version_id:
                 return None, "Invalid Civitai ID. Must be numeric."
             
             file_info = get_civitai_file_info(version_id)
-            if file_info and file_info['name'].lower().endswith(('.pt', '.bin')):
+            if file_info:
+                model_type = file_info.get('model_type')
+                if model_type and model_type.lower() == 'checkpoint':
+                    return None, f"Invalid Civitai model type '{model_type}' for Embedding. Checkpoint models are not allowed."
+            
+            file_ext = ".safetensors"
+            if file_info and file_info.get('name') and file_info['name'].lower().endswith(('.pt', '.bin')):
                 file_ext = os.path.splitext(file_info['name'])[1]
 
             filename = sanitize_filename(f"civitai_{version_id}{file_ext}")
             local_path = os.path.join(EMBEDDING_DIR, filename)
             api_key_to_use = civitai_key
             source_name = f"Embedding Civitai ID {version_id}"
+        elif source == "Hugging Face":
+            parts = id_or_url.strip().split('/')
+            if len(parts) < 3:
+                return None, "Invalid Hugging Face path. Format: repo_owner/repo_name/filename"
+            repo_id = f"{parts[0]}/{parts[1]}"
+            repo_file_path = "/".join(parts[2:])
+            filename = sanitize_filename(parts[-1])
+            local_path = os.path.join(EMBEDDING_DIR, filename)
+            source_name = f"Embedding HF {repo_file_path}"
         else:
             return None, "Invalid source."
 
     except ValueError as e:
         return None, f"Input validation failed: {e}"
 
-    if os.path.exists(local_path):
-        return local_path, "File already exists."
+    if os.path.lexists(local_path):
+        if not os.path.exists(local_path):
+            os.remove(local_path)
+        else:
+            return local_path, "File already exists."
 
-    if not file_info or not file_info.get('downloadUrl'):
-        return None, f"Could not get download link for {source_name}."
+    if source == "Civitai":
+        if not file_info or not file_info.get('downloadUrl'):
+            return None, f"Could not get download link for {source_name}."
 
-    status = download_file(file_info['downloadUrl'], local_path, api_key_to_use, progress=progress, desc=f"Downloading {source_name}")
-    
-    return (local_path, status) if "Successfully" in status else (None, status)
+        status = download_file(file_info['downloadUrl'], local_path, api_key_to_use, progress=progress, desc=f"Downloading {source_name}")
+        return (local_path, status) if "Successfully" in status else (None, status)
+    elif source == "Hugging Face":
+        try:
+            if progress: progress(0, desc=f"Downloading {source_name}")
+            cached_path = hf_hub_download(repo_id=repo_id, filename=repo_file_path, token=os.environ.get("HF_TOKEN"))
+            os.makedirs(EMBEDDING_DIR, exist_ok=True)
+            os.symlink(cached_path, local_path)
+            if progress: progress(1.0, desc=f"Downloaded {source_name}")
+            return local_path, f"Successfully downloaded: {filename}"
+        except Exception as e:
+            return None, f"Hugging Face download failed: {e}"
 
 def get_vae_path(source: str, id_or_url: str, civitai_key: str, progress) -> tuple[str | None, str]:
     if not id_or_url or not id_or_url.strip():
         return None, "No ID/URL provided."
 
     try:
-        file_ext = ".safetensors"
-
         if source == "Civitai":
             version_id = sanitize_id(id_or_url)
             if not version_id:
                 return None, "Invalid Civitai ID. Must be numeric."
             
             file_info = get_civitai_file_info(version_id)
-            if file_info and file_info['name'].lower().endswith(('.pt', '.bin')):
+            if file_info:
+                model_type = file_info.get('model_type')
+                if model_type and model_type.lower() == 'checkpoint':
+                    return None, f"Invalid Civitai model type '{model_type}' for VAE. Checkpoint models are not allowed."
+            
+            file_ext = ".safetensors"
+            if file_info and file_info.get('name') and file_info['name'].lower().endswith(('.pt', '.bin')):
                 file_ext = os.path.splitext(file_info['name'])[1]
 
             filename = sanitize_filename(f"civitai_{version_id}{file_ext}")
             local_path = os.path.join(VAE_DIR, filename)
             api_key_to_use = civitai_key
             source_name = f"VAE Civitai ID {version_id}"
+        elif source == "Hugging Face":
+            parts = id_or_url.strip().split('/')
+            if len(parts) < 3:
+                return None, "Invalid Hugging Face path. Format: repo_owner/repo_name/filename"
+            repo_id = f"{parts[0]}/{parts[1]}"
+            repo_file_path = "/".join(parts[2:])  
+            unique_name = id_or_url.strip().replace('/', '_')
+            filename = sanitize_filename(unique_name)
+            local_path = os.path.join(VAE_DIR, filename)
+            source_name = f"VAE HF {repo_file_path}"
         else:
             return None, "Invalid source."
 
     except ValueError as e:
         return None, f"Input validation failed: {e}"
 
-    if os.path.exists(local_path):
-        return local_path, "File already exists."
-
-    if not file_info or not file_info.get('downloadUrl'):
-        return None, f"Could not get download link for {source_name}."
+    if os.path.lexists(local_path):
+        if not os.path.exists(local_path):
+            os.remove(local_path)
+        else:
+            return local_path, "File already exists."
 
-    status = download_file(file_info['downloadUrl'], local_path, api_key_to_use, progress=progress, desc=f"Downloading {source_name}")
-    
-    return (local_path, status) if "Successfully" in status else (None, status)
+    if source == "Civitai":
+        if not file_info or not file_info.get('downloadUrl'):
+            return None, f"Could not get download link for {source_name}."
 
+        status = download_file(file_info['downloadUrl'], local_path, api_key_to_use, progress=progress, desc=f"Downloading {source_name}")
+        return (local_path, status) if "Successfully" in status else (None, status)
+    elif source == "Hugging Face":
+        try:
+            if progress: progress(0, desc=f"Downloading {source_name}")
+            cached_path = hf_hub_download(repo_id=repo_id, filename=repo_file_path, token=os.environ.get("HF_TOKEN"))
+            os.makedirs(VAE_DIR, exist_ok=True)
+            os.symlink(cached_path, local_path)
+            if progress: progress(1.0, desc=f"Downloaded {source_name}")
+            return local_path, f"Successfully downloaded: {filename}"
+        except Exception as e:
+            return None, f"Hugging Face download failed: {e}"
 
 def _ensure_model_downloaded(display_name: str, progress=gr.Progress()):
     if display_name not in ALL_MODEL_MAP:
@@ -459,7 +556,6 @@ def ensure_ipadapter_models_downloaded(preset_name: str, progress):
         except Exception as e:
             print(f"❌ Error ensuring download for IPAdapter asset '{filename}': {e}")
 
-
 def ensure_sd3_ipadapter_models_downloaded(progress):
     _PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
     yaml_path = os.path.join(_PROJECT_ROOT, 'yaml', 'ipadapter_sd3_models.yaml')
@@ -474,8 +570,6 @@ def ensure_sd3_ipadapter_models_downloaded(progress):
     except Exception as e:
         print(f"Warning: Failed to load or download sd3 ipadapter models: {e}")
 
-
-
 def get_model_generation_defaults(model_display_name: str, model_type: str, defaults_config: dict):
     final_defaults = {
         'steps': 25, 'cfg': 7.0, 'sampler_name': 'euler', 'scheduler': 'simple',

yaml/constants.yaml

@@ -4,9 +4,25 @@ MAX_IPADAPTERS: 5
 MAX_EMBEDDINGS: 5
 MAX_CONDITIONINGS: 10
 MAX_REFERENCE_LATENTS: 10
-LORA_SOURCE_CHOICES: ["Civitai", "File"]
+LORA_SOURCE_CHOICES: ["Civitai", "Hugging Face", "File"]
 
 RESOLUTION_MAP:
+  krea-2:
+    "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]
+  boogu-image:
+    "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]
   pixeldit:
     "1:1 (Square)": [1024, 1024]
     "16:9 (Landscape)": [1344, 768]
@@ -15,6 +31,14 @@ RESOLUTION_MAP:
     "3:4 (Classic Portrait)": [896, 1152]
     "3:2 (Photography)": [1216, 832]
     "2:3 (Photography Portrait)": [832, 1216]
+  ideogram-4:
+    "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]
   lens:
     "1:1 (Square)": [1024, 1024]
     "16:9 (Landscape)": [1344, 768]
@@ -23,7 +47,7 @@ RESOLUTION_MAP:
     "3:4 (Classic Portrait)": [896, 1152]
     "3:2 (Photography)": [1216, 832]
     "2:3 (Photography Portrait)": [832, 1216]
-  ernie-image:
+  flux2-kv:
     "1:1 (Square)": [1024, 1024]
     "16:9 (Landscape)": [1344, 768]
     "9:16 (Portrait)": [768, 1344]
@@ -39,7 +63,15 @@ RESOLUTION_MAP:
     "3:4 (Classic Portrait)": [896, 1152]
     "3:2 (Photography)": [1216, 832]
     "2:3 (Photography Portrait)": [832, 1216]
-  flux2-kv:
+  ernie-image:
+    "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]
+  z-image:
     "1:1 (Square)": [1024, 1024]
     "16:9 (Landscape)": [1344, 768]
     "9:16 (Portrait)": [768, 1344]
@@ -63,6 +95,14 @@ RESOLUTION_MAP:
     "3:4 (Classic Portrait)": [896, 1152]
     "3:2 (Photography)": [1216, 832]
     "2:3 (Photography Portrait)": [832, 1216]
+  cosmos-predict2:
+    "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]
   anima:
     "1:1 (Square)": [1024, 1024]
     "16:9 (Landscape)": [1344, 768]
@@ -79,7 +119,7 @@ RESOLUTION_MAP:
     "3:4 (Classic Portrait)": [896, 1152]
     "3:2 (Photography)": [1216, 832]
     "2:3 (Photography Portrait)": [832, 1216]
-  omnigen2:
+  kandinsky-5:
     "1:1 (Square)": [1024, 1024]
     "16:9 (Landscape)": [1344, 768]
     "9:16 (Portrait)": [768, 1344]
@@ -87,7 +127,7 @@ RESOLUTION_MAP:
     "3:4 (Classic Portrait)": [896, 1152]
     "3:2 (Photography)": [1216, 832]
     "2:3 (Photography Portrait)": [832, 1216]
-  lumina:
+  ovis-image:
     "1:1 (Square)": [1024, 1024]
     "16:9 (Landscape)": [1344, 768]
     "9:16 (Portrait)": [768, 1344]
@@ -95,7 +135,15 @@ RESOLUTION_MAP:
     "3:4 (Classic Portrait)": [896, 1152]
     "3:2 (Photography)": [1216, 832]
     "2:3 (Photography Portrait)": [832, 1216]
-  ovis-image:
+  hunyuanimage:
+    "1:1 (Square)": [2048, 2048]
+    "16:9 (Landscape)": [2728, 1536]
+    "9:16 (Portrait)": [1536, 2728]
+    "4:3 (Classic)": [2368, 1776]
+    "3:4 (Classic Portrait)": [1776, 2368]
+    "3:2 (Photography)": [2504, 1672]
+    "2:3 (Photography Portrait)": [1672, 2504]
+  chroma1-radiance:
     "1:1 (Square)": [1024, 1024]
     "16:9 (Landscape)": [1344, 768]
     "9:16 (Portrait)": [768, 1344]
@@ -103,7 +151,7 @@ RESOLUTION_MAP:
     "3:4 (Classic Portrait)": [896, 1152]
     "3:2 (Photography)": [1216, 832]
     "2:3 (Photography Portrait)": [832, 1216]
-  flux1:
+  chroma1:
     "1:1 (Square)": [1024, 1024]
     "16:9 (Landscape)": [1344, 768]
     "9:16 (Portrait)": [768, 1344]
@@ -111,7 +159,7 @@ RESOLUTION_MAP:
     "3:4 (Classic Portrait)": [896, 1152]
     "3:2 (Photography)": [1216, 832]
     "2:3 (Photography Portrait)": [832, 1216]
-  hidream-o1:
+  omnigen2:
     "1:1 (Square)": [1024, 1024]
     "16:9 (Landscape)": [1344, 768]
     "9:16 (Portrait)": [768, 1344]
@@ -119,7 +167,7 @@ RESOLUTION_MAP:
     "3:4 (Classic Portrait)": [896, 1152]
     "3:2 (Photography)": [1216, 832]
     "2:3 (Photography Portrait)": [832, 1216]
-  hidream-i1:
+  lumina:
     "1:1 (Square)": [1024, 1024]
     "16:9 (Landscape)": [1344, 768]
     "9:16 (Portrait)": [768, 1344]
@@ -127,7 +175,7 @@ RESOLUTION_MAP:
     "3:4 (Classic Portrait)": [896, 1152]
     "3:2 (Photography)": [1216, 832]
     "2:3 (Photography Portrait)": [832, 1216]
-  sd35:
+  hidream-o1:
     "1:1 (Square)": [1024, 1024]
     "16:9 (Landscape)": [1344, 768]
     "9:16 (Portrait)": [768, 1344]
@@ -135,7 +183,7 @@ RESOLUTION_MAP:
     "3:4 (Classic Portrait)": [896, 1152]
     "3:2 (Photography)": [1216, 832]
     "2:3 (Photography Portrait)": [832, 1216]
-  sdxl:
+  hidream-i1:
     "1:1 (Square)": [1024, 1024]
     "16:9 (Landscape)": [1344, 768]
     "9:16 (Portrait)": [768, 1344]
@@ -143,15 +191,7 @@ RESOLUTION_MAP:
     "3:4 (Classic Portrait)": [896, 1152]
     "3:2 (Photography)": [1216, 832]
     "2:3 (Photography Portrait)": [832, 1216]
-  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]
-  chroma1-radiance:
+  flux1:
     "1:1 (Square)": [1024, 1024]
     "16:9 (Landscape)": [1344, 768]
     "9:16 (Portrait)": [768, 1344]
@@ -159,7 +199,7 @@ RESOLUTION_MAP:
     "3:4 (Classic Portrait)": [896, 1152]
     "3:2 (Photography)": [1216, 832]
     "2:3 (Photography Portrait)": [832, 1216]
-  chroma1:
+  sd35:
     "1:1 (Square)": [1024, 1024]
     "16:9 (Landscape)": [1344, 768]
     "9:16 (Portrait)": [768, 1344]
@@ -167,11 +207,48 @@ RESOLUTION_MAP:
     "3:4 (Classic Portrait)": [896, 1152]
     "3:2 (Photography)": [1216, 832]
     "2:3 (Photography Portrait)": [832, 1216]
-  hunyuanimage:
-    "1:1 (Square)": [2048, 2048]
-    "16:9 (Landscape)": [2728, 1536]
-    "9:16 (Portrait)": [1536, 2728]
-    "4:3 (Classic)": [2368, 1776]
-    "3:4 (Classic Portrait)": [1776, 2368]
-    "3:2 (Photography)": [2504, 1672]
-    "2:3 (Photography Portrait)": [1672, 2504]
+  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]
+  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]
+
+MULTIPLIERS_MAP:
+  krea-2: 1
+  boogu-image: 1
+  pixeldit: 1
+  ideogram-4: 1
+  lens: 1
+  flux2-kv: 1
+  flux2: 1
+  ernie-image: 1
+  z-image: 1
+  qwen-image: 1
+  longcat-image: 1
+  cosmos-predict2: 1
+  anima: 1
+  newbie-image: 1
+  kandinsky-5: 32
+  ovis-image: 1
+  hunyuanimage: 1
+  chroma1-radiance: 64
+  chroma1: 1
+  omnigen2: 1
+  lumina: 1
+  hidream-o1: 32
+  hidream-i1: 1
+  flux1: 1
+  sd35: 1
+  sdxl: 1
+  sd15: 1

yaml/file_list.yaml

@@ -403,6 +403,33 @@ file:
       repo_id: "comfyanonymous/ControlNet-v1-1_fp16_safetensors"
       repository_file_path: "control_v11u_sd15_tile_fp16.safetensors"
   diffusion_models: 
+    # Krea-2
+    - filename: "krea2_turbo_nvfp4.safetensors"
+      source: "hf"
+      repo_id: "Comfy-Org/Krea-2"
+      repository_file_path: "diffusion_models/krea2_turbo_nvfp4.safetensors"
+    - filename: "krea2_raw_fp8_scaled.safetensors"
+      source: "hf"
+      repo_id: "Comfy-Org/Krea-2"
+      repository_file_path: "diffusion_models/krea2_raw_fp8_scaled.safetensors"
+    # Boogu-Image
+    - filename: "boogu_image_base_nvfp4.safetensors"
+      source: "hf"
+      repo_id: "Comfy-Org/Boogu-Image"
+      repository_file_path: "diffusion_models/boogu_image_base_nvfp4.safetensors"
+    - filename: "boogu_image_turbo_hotfix_nvfp4.safetensors"
+      source: "hf"
+      repo_id: "Comfy-Org/Boogu-Image"
+      repository_file_path: "diffusion_models/boogu_image_turbo_hotfix_nvfp4.safetensors"
+    # Ideogram-4
+    - filename: "ideogram4_nvfp4_mixed.safetensors"
+      source: "hf"
+      repo_id: "Comfy-Org/Ideogram-4"
+      repository_file_path: "diffusion_models/ideogram4_nvfp4_mixed.safetensors"
+    - filename: "ideogram4_unconditional_nvfp4_mixed.safetensors"
+      source: "hf"
+      repo_id: "Comfy-Org/Ideogram-4"
+      repository_file_path: "diffusion_models/ideogram4_unconditional_nvfp4_mixed.safetensors"
     # PixelDiT
     - filename: "pixeldit_1300m_1024px_mxfp8.safetensors"
       source: "hf"
@@ -417,18 +444,18 @@ file:
       source: "hf"
       repo_id: "Comfy-Org/PixelDiT"
       repository_file_path: "diffusion_models/pid_sd3_1024_to_4096_4step_bf16.safetensors"
-    - filename: "pid_flux1_1024_to_4096_4step_mxfp8.safetensors"
+    - filename: "pid_1.5_flux1_1024_to_4096_4step_int8_convrot.safetensors"
       source: "hf"
       repo_id: "Comfy-Org/PixelDiT"
-      repository_file_path: "diffusion_models/pid_flux1_1024_to_4096_4step_mxfp8.safetensors"
-    - filename: "pid_qwenimage_1024_to_4096_4step_bf16.safetensors"
+      repository_file_path: "diffusion_models/pid_1.5_flux1_1024_to_4096_4step_int8_convrot.safetensors"
+    - filename: "pid_1.5_qwenimage_1024_to_4096_4step_int8_convrot.safetensors"
       source: "hf"
       repo_id: "Comfy-Org/PixelDiT"
-      repository_file_path: "diffusion_models/pid_qwenimage_1024_to_4096_4step_bf16.safetensors"
-    - filename: "pid_flux2_1024_to_4096_4step_mxfp8.safetensors"
+      repository_file_path: "diffusion_models/pid_1.5_qwenimage_1024_to_4096_4step_int8_convrot.safetensors"
+    - filename: "pid_1.5_flux2_1024_to_4096_4step_int8_convrot.safetensors"
       source: "hf"
       repo_id: "Comfy-Org/PixelDiT"
-      repository_file_path: "diffusion_models/pid_flux2_1024_to_4096_4step_mxfp8.safetensors"
+      repository_file_path: "diffusion_models/pid_1.5_flux2_1024_to_4096_4step_int8_convrot.safetensors"
     # Lens
     - filename: "lens_mxfp8.safetensors"
       source: "hf"
@@ -438,23 +465,48 @@ file:
       source: "hf"
       repo_id: "Comfy-Org/Lens"
       repository_file_path: "diffusion_models/lens_turbo_mxfp8.safetensors"
+    # Cosmos-Predict2
+    - filename: "cosmos_predict2_2B_t2i.pt"
+      source: "hf"
+      repo_id: "nvidia/Cosmos-Predict2-2B-Text2Image"
+      repository_file_path: "model.pt"
+    - filename: "cosmos_predict2_14B_t2i.pt"
+      source: "hf"
+      repo_id: "nvidia/Cosmos-Predict2-14B-Text2Image"
+      repository_file_path: "model.pt"
     # Anima
-    - filename: "waiANIMA_v10.safetensors"
+    - filename: "anima-turbo-v1.0.safetensors"
       source: "hf"
-      repo_id: "diffusionmodels1254ani/waiANIMA"
-      repository_file_path: "waiANIMA_v10.safetensors"
+      repo_id: "circlestone-labs/Anima"
+      repository_file_path: "split_files/diffusion_models/anima-turbo-v1.0.safetensors"
+    - filename: "anima-aesthetic-v1.1.safetensors"
+      source: "hf"
+      repo_id: "circlestone-labs/Anima"
+      repository_file_path: "split_files/diffusion_models/anima-aesthetic-v1.1.safetensors"
     - filename: "anima-base-v1.0.safetensors"
       source: "hf"
       repo_id: "circlestone-labs/Anima"
       repository_file_path: "split_files/diffusion_models/anima-base-v1.0.safetensors"
-    - filename: "AnimaYume_tuned_v05.safetensors"
+    - filename: "waiANIMA_v10Base10.safetensors"
+      source: "hf"
+      repo_id: "diffusionmodels1254ani/waiANIMA"
+      repository_file_path: "waiANIMA_v10Base10.safetensors"
+    - filename: "AnimaYume_v10_final_base.safetensors"
       source: "hf"
       repo_id: "duongve/AnimaYume"
-      repository_file_path: "split_files/diffusion_models/AnimaYume_tuned_v05.safetensors"
-    - filename: "anima_pencil-v1.0.0.safetensors"
+      repository_file_path: "split_files/diffusion_models/AnimaYume_v10_final_base.safetensors"
+    - filename: "hassakuAnima_v1Style.safetensors"
+      source: "hf"
+      repo_id: "diffusionmodels1254ani/hassakuAnima"
+      repository_file_path: "hassakuAnima_v1Style.safetensors"
+    - filename: "kirazuriAnima_v30AnimaBase1.safetensors"
+      source: "hf"
+      repo_id: "diffusionmodels1254ani/kirazuriAnima_v30AnimaBase1"
+      repository_file_path: "kirazuriAnima_v30AnimaBase1.safetensors"
+    - filename: "anima_pencil-v2.1.0.safetensors"
       source: "hf"
       repo_id: "bluepen5805/anima-models"
-      repository_file_path: "anima_pencil-v1.0.0.safetensors"
+      repository_file_path: "anima_pencil-v2.1.0.safetensors"
     # NewBie-Image
     - filename: "NewBie-Image-Exp0.1-bf16.safetensors"
       source: "hf"
@@ -523,23 +575,24 @@ file:
       source: "hf"
       repo_id: "Comfy-Org/Qwen-Image_ComfyUI"
       repository_file_path: "split_files/diffusion_models/qwen_image_2512_fp8_e4m3fn.safetensors"
-    - filename: "qwen_image_fp8_e4m3fn.safetensors"
+    - filename: "qwen_image_nvfp4.safetensors"
       source: "hf"
       repo_id: "Comfy-Org/Qwen-Image_ComfyUI"
-      repository_file_path: "split_files/diffusion_models/qwen_image_fp8_e4m3fn.safetensors"
+      repository_file_path: "split_files/diffusion_models/qwen_image_nvfp4.safetensors"
+    # Kandinsky-5
+    - filename: "kandinsky5lite_t2i.safetensors"
+      source: "hf"
+      repo_id: "kandinskylab/Kandinsky-5.0-T2I-Lite"
+      repository_file_path: "model/kandinsky5lite_t2i.safetensors"
     # Flux.1
-    - filename: "flux1-dev-fp8-e4m3fn.safetensors"
+    - filename: "flux1-dev-nvfp4.safetensors"
       source: "hf"
-      repo_id: "Kijai/flux-fp8"
-      repository_file_path: "flux1-dev-fp8-e4m3fn.safetensors"
+      repo_id: "black-forest-labs/FLUX.1-dev-NVFP4"
+      repository_file_path: "flux1-dev-nvfp4.safetensors"
     - filename: "flux1-schnell-fp8-e4m3fn.safetensors"
       source: "hf"
       repo_id: "Kijai/flux-fp8"
       repository_file_path: "flux1-schnell-fp8-e4m3fn.safetensors"
-    - filename: "flux1-dev-kontext_fp8_scaled.safetensors"
-      source: "hf"
-      repo_id: "Comfy-Org/flux1-kontext-dev_ComfyUI"
-      repository_file_path: "split_files/diffusion_models/flux1-dev-kontext_fp8_scaled.safetensors"
     - filename: "flux1-krea-dev_fp8_scaled.safetensors"
       source: "hf"
       repo_id: "Comfy-Org/FLUX.1-Krea-dev_ComfyUI"
@@ -557,6 +610,7 @@ file:
       source: "hf"
       repo_id: "Comfy-Org/HiDream-I1_ComfyUI"
       repository_file_path: "split_files/diffusion_models/hidream_i1_full_fp8.safetensors"
+    # HunyuanImage-2.1
     - filename: "hunyuanimage2.1_fp8_e4m3fn.safetensors"
       source: "hf"
       repo_id: "Comfy-Org/HunyuanImage_2.1_ComfyUI"
@@ -579,6 +633,7 @@ file:
       source: "hf"
       repo_id: "Clybius/Chroma-fp8-scaled"
       repository_file_path: "Chroma1-HD/Chroma1-HD_float8_e4m3fn_scaled_learned_topk8_svd.safetensors"
+    # Omnigen2
     - filename: "omnigen2_fp16.safetensors"
       source: "hf"
       repo_id: "Comfy-Org/Omnigen2_ComfyUI_repackaged"
@@ -671,15 +726,11 @@ file:
       repo_id: "black-forest-labs/FLUX.1-Redux-dev"
       repository_file_path: "flux1-redux-dev.safetensors"
   loras: 
-    # Qwen-Image
-    - filename: "Qwen-Image-2512-Lightning-4steps-V1.0-bf16.safetensors"
-      source: "hf"
-      repo_id: "lightx2v/Qwen-Image-2512-Lightning"
-      repository_file_path: "Qwen-Image-2512-Lightning-4steps-V1.0-bf16.safetensors"
-    - filename: "Qwen-Image-fp8-e4m3fn-Lightning-4steps-V1.0-bf16.safetensors"
+    # Krea2 ControlNet
+    - filename: "depth-control-lora.safetensors"
       source: "hf"
-      repo_id: "lightx2v/Qwen-Image-Lightning"
-      repository_file_path: "Qwen-Image-fp8-e4m3fn-Lightning-4steps-V1.0-bf16.safetensors"
+      repo_id: "Patil/Krea-2-depth-controlnet"
+      repository_file_path: "depth-control-lora.safetensors"
     # SD1.5 FaceID
     - filename: "ip-adapter-faceid_sd15_lora.safetensors"
       source: "hf"
@@ -708,6 +759,21 @@ file:
       repo_id: "alibaba-pai/Z-Image-Turbo-Fun-Controlnet-Union-2.1"
       repository_file_path: "Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.safetensors"
   text_encoders: 
+    # Krea-2
+    - filename: "qwen3vl_4b_fp8_scaled.safetensors"
+      source: "hf"
+      repo_id: "Comfy-Org/Krea-2"
+      repository_file_path: "text_encoders/qwen3vl_4b_fp8_scaled.safetensors"
+    # Cosmos-Predict2
+    - filename: "oldt5_xxl_fp8_e4m3fn_scaled.safetensors"
+      source: "hf"
+      repo_id: "comfyanonymous/cosmos_1.0_text_encoder_and_VAE_ComfyUI"
+      repository_file_path: "text_encoders/oldt5_xxl_fp8_e4m3fn_scaled.safetensors"
+    # Ideogram-4 & Boogu-Image
+    - filename: "qwen3vl_8b_nvfp4.safetensors"
+      source: "hf"
+      repo_id: "Comfy-Org/Ideogram-4"
+      repository_file_path: "text_encoders/qwen3vl_8b_nvfp4.safetensors"
     # PixelDiT
     - filename: "gemma_2_2b_it_elm_fp8_scaled.safetensors"
       source: "hf"
@@ -782,10 +848,10 @@ file:
       source: "hf"
       repo_id: "Comfy-Org/HiDream-I1_ComfyUI"
       repository_file_path: "split_files/text_encoders/llama_3.1_8b_instruct_fp8_scaled.safetensors"
-    - filename: "qwen_2.5_vl_7b_fp8_scaled.safetensors"
+    - filename: "qwen_2.5_vl_7b_nvfp4.safetensors"
       source: "hf"
       repo_id: "Comfy-Org/Qwen-Image_ComfyUI"
-      repository_file_path: "split_files/text_encoders/qwen_2.5_vl_7b_fp8_scaled.safetensors"
+      repository_file_path: "split_files/text_encoders/qwen_2.5_vl_7b_nvfp4.safetensors"
     - filename: "byt5_small_glyphxl_fp16.safetensors"
       source: "hf"
       repo_id: "Comfy-Org/HunyuanImage_2.1_ComfyUI"
@@ -795,6 +861,10 @@ file:
       repo_id: "Comfy-Org/Omnigen2_ComfyUI_repackaged"
       repository_file_path: "split_files/text_encoders/qwen_2.5_vl_fp16.safetensors"  
   vae: 
+    - filename: "wan_2.1_vae.safetensors"
+      source: "hf"
+      repo_id: "Comfy-Org/Wan_2.1_ComfyUI_repackaged"
+      repository_file_path: "split_files/vae/wan_2.1_vae.safetensors"
     - filename: "qwen_image_vae.safetensors"
       source: "hf"
       repo_id: "Comfy-Org/Qwen-Image_ComfyUI"

yaml/image_gen_features.yaml

@@ -8,38 +8,54 @@ default:
   - conditioning
   - vae
 
-pixeldit:
+krea-2:
+  enabled_chains:
+    - lora
+    - krea2_controlnet
+    - conditioning
+    - pid
+
+boogu-image:
   enabled_chains:
+  - lora
   - conditioning
+  - pid
 
-lens:
+pixeldit:
   enabled_chains:
   - conditioning
+
+ideogram-4:
+  enabled_chains:
+  - vae
   - pid
 
-ernie-image:
+lens:
   enabled_chains:
   - conditioning
   - pid
-anima:
+
+flux2-kv:
   enabled_chains:
   - lora
-  - anima_controlnet_lllite
   - conditioning
+  - reference_latent
   - vae
   - pid
-longcat-image:
+
+flux2:
   enabled_chains:
   - lora
   - conditioning
+  - reference_latent
+  - vae
   - pid
-newbie-image:
+
+ernie-image:
   enabled_chains:
-  - lora
-  - embedding
   - conditioning
-  - vae
   - pid
+
 z-image:
   enabled_chains:
   - lora
@@ -47,97 +63,130 @@ z-image:
   - controlnet_model_patch
   - vae
   - pid
-lumina:
+
+qwen-image:
   enabled_chains:
   - lora
-  - embedding
+  - controlnet
   - conditioning
   - vae
   - pid
-sd35:
+
+longcat-image:
   enabled_chains:
   - lora
-  - controlnet
-  - embedding
   - conditioning
-  - sd3_ipadapter
-  - vae
   - pid
-sdxl:
+
+cosmos-predict2:
+  enabled_chains:
+  - conditioning
+  - vae
+
+anima:
   enabled_chains:
   - lora
-  - controlnet
-  - ipadapter
-  - embedding
+  - anima_controlnet_lllite
   - conditioning
   - vae
   - pid
-sd15:
+
+newbie-image:
   enabled_chains:
   - lora
-  - controlnet
-  - ipadapter
   - embedding
   - conditioning
   - vae
-flux2:
+  - pid
+
+kandinsky-5:
   enabled_chains:
-  - lora
   - conditioning
-  - reference_latent
   - vae
   - pid
-flux2-kv:
+
+ovis-image:
   enabled_chains:
-  - lora
   - conditioning
-  - reference_latent
   - vae
   - pid
-flux1:
+
+hunyuanimage:
+  enabled_chains:
+  - conditioning
+  - vae
+
+chroma1-radiance:
+  enabled_chains:
+  - conditioning
+
+chroma1:
   enabled_chains:
-  - lora
-  - controlnet
-  - style
   - conditioning
-  - flux1_ipadapter
   - vae
   - pid
+
 omnigen2:
   enabled_chains:
   - conditioning
   - reference_latent
   - pid
-qwen-image:
+
+lumina:
   enabled_chains:
   - lora
-  - controlnet
+  - embedding
   - conditioning
   - vae
   - pid
+
 hidream-o1:
   enabled_chains:
   - lora
   - conditioning
   - hidream_o1_reference
+
 hidream-i1:
   enabled_chains:
   - lora
   - conditioning
   - pid
-hunyuanimage:
+
+flux1:
   enabled_chains:
+  - lora
+  - controlnet
+  - style
   - conditioning
+  - flux1_ipadapter
   - vae
+  - pid
 
-ovis-image:
+sd35:
   enabled_chains:
+  - lora
+  - controlnet
+  - embedding
   - conditioning
+  - sd3_ipadapter
   - vae
   - pid
 
-chroma1:
+sdxl:
   enabled_chains:
+  - lora
+  - controlnet
+  - ipadapter
+  - embedding
   - conditioning
   - vae
-  - pid
+  - pid
+
+sd15:
+  enabled_chains:
+  - lora
+  - controlnet
+  - ipadapter
+  - embedding
+  - conditioning
+  - vae

yaml/krea2_controlnet_models.yaml

@@ -0,0 +1,4 @@
+Krea2_ControlNet:
+  - Filepath: "depth-control-lora.safetensors"
+    Series: "Patil"
+    Type: ["Depth"]

yaml/model_architectures.yaml

@@ -1,5 +1,8 @@
 architecture_order:
+  - "Krea-2"
+  - "Boogu-Image"
   - "PixelDiT"
+  - "Ideogram-4"
   - "Lens"
   - "FLUX.2-KV"
   - "FLUX.2"
@@ -7,8 +10,10 @@ architecture_order:
   - "Z-Image"
   - "Qwen-Image"
   - "LongCat-Image"
+  - "Cosmos-Predict2"
   - "Anima"
   - "NewBie-Image"
+  - "Kandinsky-5"
   - "Ovis-Image"
   - "HunyuanImage"
   - "Chroma1-Radiance"
@@ -23,15 +28,18 @@ architecture_order:
   - "SD1.5"
 
 architectures:
+  Krea-2:
+    model_type: "krea-2"
+  "Boogu-Image":
+    model_type: "boogu-image"
   "PixelDiT":
     model_type: "pixeldit"
-    controlnet_key: "PixelDiT"
+  "Ideogram-4":
+    model_type: "ideogram-4"
   "Lens":
     model_type: "lens"
-    controlnet_key: "Lens"
   "ERNIE-Image":
     model_type: "ernie-image"
-    controlnet_key: "ERNIE-Image"
   "FLUX.2-KV":
     model_type: "flux2-kv"
     controlnet_key: "FLUX.2"
@@ -47,6 +55,9 @@ architectures:
   "LongCat-Image":
     model_type: "longcat-image"
     controlnet_key: "LongCat-Image"
+  "Cosmos-Predict2":
+    model_type: "cosmos-predict2"
+    controlnet_key: "Cosmos-Predict2"
   "Anima":
     model_type: "anima"
     controlnet_key: "Anima"
@@ -62,6 +73,9 @@ architectures:
   "Lumina":
     model_type: "lumina"
     controlnet_key: "Lumina"
+  "Kandinsky-5":
+    model_type: "kandinsky-5"
+    controlnet_key: "Kandinsky-5"
   "Ovis-Image":
     model_type: "ovis-image"
     controlnet_key: "Ovis-Image"

yaml/model_defaults.yaml

@@ -1,17 +1,50 @@
 Default:
-  steps: 20
-  cfg: 5.0
+  steps: 8
+  cfg: 1.0
   sampler_name: "euler"
   scheduler: "simple"
   positive_prompt: ""
   negative_prompt: ""
 
+Krea-2:
+  _defaults:
+    steps: 52
+    cfg: 3.5
+    sampler_name: "euler"
+    scheduler: "simple"
+  "Krea-2-Turbo":
+    steps: 8
+    cfg: 1.0
+    sampler_name: "euler"
+    scheduler: "simple"
+
+Boogu-Image:
+  _defaults:
+    steps: 25
+    cfg: 3.5
+    sampler_name: "dpmpp_2m"
+    scheduler: "simple"
+  "Boogu-Image-Turbo":
+    steps: 4
+    cfg: 1.0
+    sampler_name: "lcm"
+    scheduler: "sgm_uniform"
+
 PixelDiT:
   _defaults:
     steps: 30
     cfg: 4.0
     sampler_name: "er_sde"
     scheduler: "simple"
+    negative_prompt: "low quality, worst quality, over-saturated, blurry, deformed, watermark"
+
+Ideogram-4:
+  _defaults:
+    steps: 20
+    cfg: 7.0
+    sampler_name: "res_multistep"
+    scheduler: "simple"
+    positive_prompt: "NOTE: If you see \"Image blocked by safety filter\" it is because of safety training in the model itself, ImageGen does not have any safety filter."
 
 Lens:
   _defaults:
@@ -23,14 +56,9 @@ Lens:
     steps: 4
     cfg: 1.0
 
-ERNIE-Image:
+FLUX.2-KV:
   _defaults:
-    steps: 20
-    cfg: 4.0
-    sampler_name: "euler"
-    scheduler: "simple"
-  "baidu/ERNIE-Image-Turbo":
-    steps: 8
+    steps: 4
     cfg: 1.0
     sampler_name: "euler"
     scheduler: "simple"
@@ -48,14 +76,14 @@ FLUX.2:
     steps: 4
     cfg: 1.0
 
-FLUX.2-KV:
+ERNIE-Image:
   _defaults:
     steps: 20
     cfg: 4.0
     sampler_name: "euler"
     scheduler: "simple"
-  "black-forest-labs/FLUX.2-klein-9B-KV":
-    steps: 4
+  "baidu/ERNIE-Image-Turbo":
+    steps: 8
     cfg: 1.0
 
 Z-Image:
@@ -67,13 +95,11 @@ Z-Image:
   "Tongyi-MAI/Z Image Turbo":
     steps: 9
     cfg: 1.0
-    sampler_name: "euler"
-    scheduler: "simple"
 
 Qwen-Image:
   _defaults:
-    steps: 4
-    cfg: 1.0
+    steps: 20
+    cfg: 4.0
     sampler_name: "euler"
     scheduler: "simple"
 
@@ -85,6 +111,13 @@ LongCat-Image:
     sampler_name: "euler"
     scheduler: "simple"
 
+Cosmos-Predict2:
+  _defaults:
+    steps: 35
+    cfg: 4.0
+    sampler_name: "euler"
+    scheduler: "karras"
+
 Anima:
   _defaults:
     steps: 30
@@ -93,6 +126,9 @@ Anima:
     scheduler: "simple"
     positive_prompt: "masterpiece, best quality, score_7, safe. "
     negative_prompt: "worst quality, low quality, score_1, score_2, score_3, blurry, jpeg artifacts, sepia"
+  "circlestone-labs/Anima-Turbo-v1.0":
+    steps: 10
+    cfg: 1.0
 
 NewBie-Image:
   _defaults:
@@ -103,33 +139,29 @@ NewBie-Image:
     positive_prompt: "You are an assistant designed to generate high-quality anime images with the highest degree of image-text alignment based on xml format textual prompts. <Prompt Start>"
     negative_prompt: "You are an assistant designed to generate low-quality images based on textual prompts. <Prompt Start>"
 
-Ovis-Image:
+Kandinsky-5:
   _defaults:
-    steps: 20
-    cfg: 5.0
+    steps: 50
+    cfg: 3.5
     sampler_name: "euler"
     scheduler: "simple"
 
-OmniGen2:
+Ovis-Image:
   _defaults:
     steps: 20
     cfg: 5.0
     sampler_name: "euler"
     scheduler: "simple"
-    positive_prompt: ""
-    negative_prompt: ""
 
-Chroma1:
+HunyuanImage:
   _defaults:
-    steps: 30
-    cfg: 4.0
+    steps: 20
+    cfg: 3.5
     sampler_name: "euler"
     scheduler: "simple"
-    negative_prompt: "low quality, bad anatomy, extra digits, missing digits, extra limbs, missing limbs"
-  "lodestones/Chroma1-HD-Flash":
+  "HunyuanImage-2.1-Distilled":
     steps: 8
     cfg: 1.0
-    scheduler: "beta"
 
 Chroma1-Radiance:
   _defaults:
@@ -139,40 +171,35 @@ Chroma1-Radiance:
     scheduler: "simple"
     negative_prompt: "low quality, bad anatomy, extra digits, missing digits, extra limbs, missing limbs, hands, fingers"
 
-SD3.5:
+Chroma1:
   _defaults:
-    steps: 20
+    steps: 30
     cfg: 4.0
     sampler_name: "euler"
-    scheduler: "sgm_uniform"
+    scheduler: "simple"
+    negative_prompt: "low quality, bad anatomy, extra digits, missing digits, extra limbs, missing limbs"
+  "lodestones/Chroma1-HD-Flash":
+    steps: 8
+    cfg: 1.0
+    scheduler: "beta"
 
-SDXL:
+OmniGen2:
   _defaults:
-    steps: 25
-    cfg: 7.0
+    steps: 20
+    cfg: 5.0
     sampler_name: "euler"
     scheduler: "simple"
     positive_prompt: ""
     negative_prompt: ""
 
-SD1.5:
-  _defaults:
-    steps: 47
-    cfg: 7.0
-    sampler_name: "euler_ancestral"
-    scheduler: "simple"
-
-FLUX.1:
+Lumina:
   _defaults:
     steps: 20
-    cfg: 1.0
-    sampler_name: "euler"
-    scheduler: "simple"
-  "flux1-schnell":
-    steps: 4
-    cfg: 1.0
-    sampler_name: "euler"
+    cfg: 4.0
+    sampler_name: "res_multistep"
     scheduler: "simple"
+    positive_prompt: ""
+    negative_prompt: ""
 
 HiDream-O1:
   _defaults:
@@ -204,12 +231,37 @@ HiDream-I1:
     sampler_name: "lcm"
     scheduler: "normal"
 
-HunyuanImage:
+FLUX.1:
   _defaults:
     steps: 20
-    cfg: 3.5
+    cfg: 1.0
     sampler_name: "euler"
     scheduler: "simple"
-  "HunyuanImage-2.1-Distilled":
-    steps: 8
-    cfg: 1.0
+  "flux1-schnell":
+    steps: 4
+    cfg: 1.0
+    sampler_name: "euler"
+    scheduler: "simple"
+
+SD3.5:
+  _defaults:
+    steps: 20
+    cfg: 4.0
+    sampler_name: "euler"
+    scheduler: "sgm_uniform"
+
+SDXL:
+  _defaults:
+    steps: 25
+    cfg: 7.0
+    sampler_name: "euler"
+    scheduler: "simple"
+    positive_prompt: ""
+    negative_prompt: ""
+
+SD1.5:
+  _defaults:
+    steps: 47
+    cfg: 7.0
+    sampler_name: "euler_ancestral"
+    scheduler: "simple"

yaml/model_list.yaml

@@ -1,4 +1,30 @@
 Checkpoint:
+  Krea-2:
+    latent_type: latent
+    models:
+      - display_name: "Krea-2-Turbo"
+        components:
+          unet: "krea2_turbo_nvfp4.safetensors"
+          clip: "qwen3vl_4b_fp8_scaled.safetensors"
+          vae: "qwen_image_vae.safetensors"
+      - display_name: "Krea-2-Raw"
+        components:
+          unet: "krea2_raw_fp8_scaled.safetensors"
+          clip: "qwen3vl_4b_fp8_scaled.safetensors"
+          vae: "qwen_image_vae.safetensors"
+  Boogu-Image:
+    latent_type: latent
+    models:
+      - display_name: "Boogu-Image-Turbo"
+        components:
+          unet: "boogu_image_turbo_hotfix_nvfp4.safetensors"
+          clip: "qwen3vl_8b_nvfp4.safetensors"
+          vae: "ae.safetensors"
+      - display_name: "Boogu-Image-Base"
+        components:
+          unet: "boogu_image_base_nvfp4.safetensors"
+          clip: "qwen3vl_8b_nvfp4.safetensors"
+          vae: "ae.safetensors"
   PixelDiT:
     latent_type: chroma_radiance_latent
     models:
@@ -7,6 +33,15 @@ Checkpoint:
           unet: "pixeldit_1300m_1024px_mxfp8.safetensors"
           clip: "gemma_2_2b_it_elm_fp8_scaled.safetensors"
           vae: "pixel_space"
+  Ideogram-4:
+    latent_type: flux2_latent
+    models:
+      - display_name: "ideogram-ai/ideogram-4"
+        components:
+          unet: "ideogram4_nvfp4_mixed.safetensors"
+          unet_uncond: "ideogram4_unconditional_nvfp4_mixed.safetensors"
+          clip: "qwen3vl_8b_nvfp4.safetensors"
+          vae: "flux2-vae.safetensors"
   Lens:
     latent_type: flux2_latent
     models:
@@ -85,18 +120,16 @@ Checkpoint:
   Qwen-Image:
     latent_type: sd3_latent
     models:
-      - display_name: "Qwen/Qwen-Image-2512 + Lightning-4steps-V1.0 LoRA"
+      - display_name: "Qwen-Image-2512"
         components:
           unet: "qwen_image_2512_fp8_e4m3fn.safetensors"
           vae: "qwen_image_vae.safetensors"
-          clip: "qwen_2.5_vl_7b_fp8_scaled.safetensors"
-          lora: "Qwen-Image-2512-Lightning-4steps-V1.0-bf16.safetensors"
-      - display_name: "Qwen/Qwen-Image + Lightning-4steps-V1.0 LoRA"
+          clip: "qwen_2.5_vl_7b_nvfp4.safetensors"
+      - display_name: "Qwen-Image"
         components:
-          unet: "qwen_image_fp8_e4m3fn.safetensors"
+          unet: "qwen_image_nvfp4.safetensors"
           vae: "qwen_image_vae.safetensors"
-          clip: "qwen_2.5_vl_7b_fp8_scaled.safetensors"
-          lora: "Qwen-Image-fp8-e4m3fn-Lightning-4steps-V1.0-bf16.safetensors"
+          clip: "qwen_2.5_vl_7b_nvfp4.safetensors"
   LongCat-Image:
     latent_type: sd3_latent
     models:
@@ -104,28 +137,61 @@ Checkpoint:
         components:
           unet: "longcat_image_bf16.safetensors"
           vae: "ae.safetensors"
-          clip: "qwen_2.5_vl_7b_fp8_scaled.safetensors"
+          clip: "qwen_2.5_vl_7b_nvfp4.safetensors"
+  Cosmos-Predict2:
+    latent_type: sd3_latent
+    models:
+      - display_name: "Cosmos-Predict2-2B-T2I"
+        components:
+          unet: "cosmos_predict2_2B_t2i.pt"
+          clip: "oldt5_xxl_fp8_e4m3fn_scaled.safetensors"
+          vae: "wan_2.1_vae.safetensors"
+      - display_name: "Cosmos-Predict2-14B-T2I"
+        components:
+          unet: "cosmos_predict2_14B_t2i.pt"
+          clip: "oldt5_xxl_fp8_e4m3fn_scaled.safetensors"
+          vae: "wan_2.1_vae.safetensors"
   Anima:
     latent_type: latent
     models:
+      - display_name: "circlestone-labs/Anima-Turbo-v1.0"
+        components:
+          unet: "anima-turbo-v1.0.safetensors"
+          vae: "qwen_image_vae.safetensors"
+          clip: "qwen_3_06b_base.safetensors"
+      - display_name: "circlestone-labs/Anima-Aesthetic-v1.1"
+        components:
+          unet: "anima-aesthetic-v1.1.safetensors"
+          vae: "qwen_image_vae.safetensors"
+          clip: "qwen_3_06b_base.safetensors"
+      - display_name: "circlestone-labs/Anima-Base-v1.0"
+        components:
+          unet: "anima-base-v1.0.safetensors"
+          vae: "qwen_image_vae.safetensors"
+          clip: "qwen_3_06b_base.safetensors"
       - display_name: "WAI0731/waiANIMA-v1.0"
         components:
-          unet: "waiANIMA_v10.safetensors"
+          unet: "waiANIMA_v10Base10.safetensors"
           vae: "qwen_image_vae.safetensors"
           clip: "qwen_3_06b_base.safetensors"
-      - display_name: "duongve/AnimaYume-v0.5"
+      - display_name: "duongve/AnimaYume-v1.0"
         components:
-          unet: "AnimaYume_tuned_v05.safetensors"
+          unet: "AnimaYume_v10_final_base.safetensors"
           vae: "qwen_image_vae.safetensors"
           clip: "qwen_3_06b_base.safetensors"
-      - display_name: "bluepen5805/Anima-pencil-v1.0"
+      - display_name: "bluepen5805/Anima-pencil-v2.1"
         components:
-          unet: "anima_pencil-v1.0.0.safetensors"
+          unet: "anima_pencil-v2.1.0.safetensors"
           vae: "qwen_image_vae.safetensors"
           clip: "qwen_3_06b_base.safetensors"
-      - display_name: "circlestone-labs/Anima-base-v1.0"
+      - display_name: "Ikena/Hassaku-Anima-v1-Style"
         components:
-          unet: "anima-base-v1.0.safetensors"
+          unet: "hassakuAnima_v1Style.safetensors"
+          vae: "qwen_image_vae.safetensors"
+          clip: "qwen_3_06b_base.safetensors"
+      - display_name: "motimalu/Kirazuri (Anima)-v3.0"
+        components:
+          unet: "hassakuAnima_v1Style.safetensors"
           vae: "qwen_image_vae.safetensors"
           clip: "qwen_3_06b_base.safetensors"
   NewBie-Image:
@@ -137,6 +203,15 @@ Checkpoint:
           vae: "ae.safetensors"
           clip1: "gemma_3_4b_it_bf16.safetensors"
           clip2: "jina_clip_v2_bf16.safetensors"
+  Kandinsky-5:
+    latent_type: latent
+    models:
+      - display_name: "Kandinsky-5.0-T2I-Lite"
+        components:
+          unet: "kandinsky5lite_t2i.safetensors"
+          vae: "ae.safetensors"
+          clip1: "qwen_2.5_vl_7b_nvfp4.safetensors"
+          clip2: "clip_l.safetensors"
   Ovis-Image:
     latent_type: sd3_latent
     models:
@@ -152,13 +227,13 @@ Checkpoint:
         components:
           unet: "hunyuanimage2.1_fp8_e4m3fn.safetensors"
           vae: "hunyuan_image_2.1_vae_fp16.safetensors"
-          clip1: "qwen_2.5_vl_7b_fp8_scaled.safetensors"
+          clip1: "qwen_2.5_vl_7b_nvfp4.safetensors"
           clip2: "byt5_small_glyphxl_fp16.safetensors"
       - display_name: "HunyuanImage-2.1-Distilled"
         components:
           unet: "hunyuanimage2.1_distilled_fp8_e4m3fn.safetensors"
           vae: "hunyuan_image_2.1_vae_fp16.safetensors"
-          clip1: "qwen_2.5_vl_7b_fp8_scaled.safetensors"
+          clip1: "qwen_2.5_vl_7b_nvfp4.safetensors"
           clip2: "byt5_small_glyphxl_fp16.safetensors"
   Chroma1-Radiance:
     latent_type: chroma_radiance_latent
@@ -233,7 +308,7 @@ Checkpoint:
     models:
       - display_name: "flux1-dev"
         components:
-          unet: "flux1-dev-fp8-e4m3fn.safetensors"
+          unet: "flux1-dev-nvfp4.safetensors"
           vae: "ae.safetensors"
           clip1: "clip_l.safetensors"
           clip2: "t5xxl_fp8_e4m3fn_scaled.safetensors"