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core/pipelines/pipeline_input_processor.py

@@ -1,335 +1,335 @@
-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)
-    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 == "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
+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 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]
+                })
+    
+    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 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_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
     }

requirements.txt

@@ -22,7 +22,7 @@ alembic
 SQLAlchemy>=2.0.0
 filelock
 av>=16.0.0
-comfy-kitchen==0.2.11
+comfy-kitchen==0.2.12
 comfy-aimdo==0.4.10
 requests
 simpleeval>=1.0.0

ui/shared/ui_components.py

@@ -128,14 +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)
@@ -479,7 +479,7 @@ 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:** 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 from the example above will automatically save the file as \"civitai_456.safetensors\". You will need to manually enter embedding:civitai_456 in either your prompt or negative prompt to activate it.")
+        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({
@@ -493,7 +493,7 @@ 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)
@@ -565,7 +565,7 @@ def create_vae_override_ui(prefix: str):
 
     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", 
@@ -575,8 +575,7 @@ 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

@@ -178,30 +178,51 @@ def get_lora_path(source: str, id_or_url: str, civitai_key: str, progress) -> tu
             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:])
+            filename = sanitize_filename(parts[-1])
+            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":
+            file_ext = ".safetensors"
             version_id = sanitize_id(id_or_url)
             if not version_id:
                 return None, "Invalid Civitai ID. Must be numeric."
@@ -214,30 +235,51 @@ def get_embedding_path(source: str, id_or_url: str, civitai_key: str, progress)
             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":
+            file_ext = ".safetensors"
             version_id = sanitize_id(id_or_url)
             if not version_id:
                 return None, "Invalid Civitai ID. Must be numeric."
@@ -250,21 +292,43 @@ def get_vae_path(source: str, id_or_url: str, civitai_key: str, progress) -> tup
             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:])
+            filename = sanitize_filename(parts[-1])
+            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 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(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()):

yaml/constants.yaml

@@ -4,7 +4,7 @@ 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: