Add custom nodes, Civitai loras (LFS), and vast.ai setup script

Includes 30 custom nodes committed directly, 7 Civitai-exclusive
loras stored via Git LFS, and a setup script that installs all
dependencies and downloads HuggingFace-hosted models on vast.ai.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

custom_nodes/ComfyUI-Easy-Use/py/libs/conditioning.py

@@ -0,0 +1,57 @@
+from .utils import find_wildcards_seed, find_nearest_steps, is_linked_styles_selector
+from .log import log_node_warn
+from .translate import zh_to_en, has_chinese
+from .wildcards import process_with_loras
+from .adv_encode import advanced_encode
+
+from nodes import ConditioningConcat, ConditioningCombine, ConditioningAverage, ConditioningSetTimestepRange, CLIPTextEncode
+
+def prompt_to_cond(type, model, clip, clip_skip, lora_stack, text, prompt_token_normalization, prompt_weight_interpretation, a1111_prompt_style ,my_unique_id, prompt, easyCache, can_load_lora=True, steps=None, model_type=None):
+    styles_selector = is_linked_styles_selector(prompt, my_unique_id, type)
+    title = "Positive encoding" if type == 'positive' else "Negative encoding"
+
+    # Translate cn to en
+    if model_type not in ['hydit'] and text is not None and has_chinese(text):
+        text = zh_to_en([text])[0]
+
+    if model_type in ['hydit', 'flux', 'mochi']:
+        log_node_warn(title + "...")
+        embeddings_final, = CLIPTextEncode().encode(clip, text) if text is not None else (None,)
+
+        return (embeddings_final, "", model, clip)
+
+    log_node_warn(title + "...")
+
+    positive_seed = find_wildcards_seed(my_unique_id, text, prompt)
+    model, clip, text, cond_decode, show_prompt, pipe_lora_stack = process_with_loras(
+        text, model, clip, type, positive_seed, can_load_lora, lora_stack, easyCache)
+    wildcard_prompt = cond_decode if show_prompt or styles_selector else ""
+
+    clipped = clip.clone()
+    # 当clip模型不存在t5xxl时，可执行跳过层
+    if not hasattr(clip.cond_stage_model, 't5xxl'):
+        if clip_skip != 0:
+            clipped.clip_layer(clip_skip)
+
+    steps = steps if steps is not None else find_nearest_steps(my_unique_id, prompt)
+    return (advanced_encode(clipped, text, prompt_token_normalization,
+                            prompt_weight_interpretation, w_max=1.0,
+                            apply_to_pooled='enable',
+                            a1111_prompt_style=a1111_prompt_style, steps=steps) if text is not None else None, wildcard_prompt, model, clipped)
+
+def set_cond(old_cond, new_cond, mode, average_strength, old_cond_start, old_cond_end, new_cond_start, new_cond_end):
+    if not old_cond:
+        return new_cond
+    else:
+        if mode == "replace":
+            return new_cond
+        elif mode == "concat":
+            return ConditioningConcat().concat(new_cond, old_cond)[0]
+        elif mode == "combine":
+            return ConditioningCombine().combine(old_cond, new_cond)[0]
+        elif mode == 'average':
+            return ConditioningAverage().addWeighted(new_cond, old_cond, average_strength)[0]
+        elif mode == 'timestep':
+            cond_1 = ConditioningSetTimestepRange().set_range(old_cond, old_cond_start, old_cond_end)[0]
+            cond_2 = ConditioningSetTimestepRange().set_range(new_cond, new_cond_start, new_cond_end)[0]
+            return ConditioningCombine().combine(cond_1, cond_2)[0]