diff --git a/tab_timeline_wip.py b/tab_timeline_wip.py index 3a2a2c9..f2be61a 100644 --- a/tab_timeline_wip.py +++ b/tab_timeline_wip.py @@ -75,13 +75,11 @@ def render_timeline_wip(data, file_path): st.subheader("✨ Interactive Timeline") st.caption("Click a node to view its settings below.") - # Render Graph - selected_id = agraph(nodes=nodes, edges=edges, config=config) + selected_id = agraph(nodes=nodes, edges=edges, config=config, key="interactive_timeline") st.markdown("---") # --- 2. DETERMINE TARGET --- - # Default to HEAD if nothing clicked target_node_id = selected_id if selected_id else htree.head_id if target_node_id and target_node_id in htree.nodes: @@ -110,52 +108,67 @@ def render_timeline_wip(data, file_path): st.toast(f"Restored {target_node_id}!", icon="🔄") st.rerun() - # --- 3. PREVIEW PANELS (DYNAMIC KEYS FIX) --- - # We append target_node_id to every key to force a hard refresh + # --- 3. PREVIEW LOGIC (BATCH VS SINGLE) --- - # A. Prompts - p_col1, p_col2 = st.columns(2) - with p_col1: - val_gp = node_data.get("general_prompt", "") - st.text_area("General Positive", value=val_gp, height=80, disabled=True, key=f"p_gp_{target_node_id}") - - val_sp = node_data.get("current_prompt", "") or node_data.get("prompt", "") - st.text_area("Specific Positive", value=val_sp, height=80, disabled=True, key=f"p_sp_{target_node_id}") - - with p_col2: - val_gn = node_data.get("general_negative", "") - st.text_area("General Negative", value=val_gn, height=80, disabled=True, key=f"p_gn_{target_node_id}") - - val_sn = node_data.get("negative", "") - st.text_area("Specific Negative", value=val_sn, height=80, disabled=True, key=f"p_sn_{target_node_id}") + # Helper to render one set of inputs + def render_preview_fields(item_data, prefix): + # A. Prompts + p_col1, p_col2 = st.columns(2) + with p_col1: + val_gp = item_data.get("general_prompt", "") + st.text_area("General Positive", value=val_gp, height=80, disabled=True, key=f"{prefix}_gp") + + val_sp = item_data.get("current_prompt", "") or item_data.get("prompt", "") + st.text_area("Specific Positive", value=val_sp, height=80, disabled=True, key=f"{prefix}_sp") + with p_col2: + val_gn = item_data.get("general_negative", "") + st.text_area("General Negative", value=val_gn, height=80, disabled=True, key=f"{prefix}_gn") + + val_sn = item_data.get("negative", "") + st.text_area("Specific Negative", value=val_sn, height=80, disabled=True, key=f"{prefix}_sn") - # B. Key Settings - st.caption("⚙️ Core Settings") - s_col1, s_col2, s_col3 = st.columns(3) - s_col1.text_input("Camera", value=str(node_data.get("camera", "static")), disabled=True, key=f"p_cam_{target_node_id}") - s_col2.text_input("FLF", value=str(node_data.get("flf", "0.0")), disabled=True, key=f"p_flf_{target_node_id}") - s_col3.text_input("Seed", value=str(node_data.get("seed", "-1")), disabled=True, key=f"p_seed_{target_node_id}") + # B. Settings + s_col1, s_col2, s_col3 = st.columns(3) + s_col1.text_input("Camera", value=str(item_data.get("camera", "static")), disabled=True, key=f"{prefix}_cam") + s_col2.text_input("FLF", value=str(item_data.get("flf", "0.0")), disabled=True, key=f"{prefix}_flf") + s_col3.text_input("Seed", value=str(item_data.get("seed", "-1")), disabled=True, key=f"{prefix}_seed") - # C. LoRAs - with st.expander("💊 LoRA Configuration", expanded=False): - l1, l2, l3 = st.columns(3) - with l1: - st.text_input("LoRA 1 Name", value=node_data.get("lora 1 high", ""), disabled=True, key=f"p_l1h_{target_node_id}") - st.text_input("LoRA 1 Str", value=str(node_data.get("lora 1 low", "")), disabled=True, key=f"p_l1l_{target_node_id}") - with l2: - st.text_input("LoRA 2 Name", value=node_data.get("lora 2 high", ""), disabled=True, key=f"p_l2h_{target_node_id}") - st.text_input("LoRA 2 Str", value=str(node_data.get("lora 2 low", "")), disabled=True, key=f"p_l2l_{target_node_id}") - with l3: - st.text_input("LoRA 3 Name", value=node_data.get("lora 3 high", ""), disabled=True, key=f"p_l3h_{target_node_id}") - st.text_input("LoRA 3 Str", value=str(node_data.get("lora 3 low", "")), disabled=True, key=f"p_l3l_{target_node_id}") + # C. LoRAs + with st.expander("💊 LoRA Configuration", expanded=False): + l1, l2, l3 = st.columns(3) + with l1: + st.text_input("L1 Name", value=item_data.get("lora 1 high", ""), disabled=True, key=f"{prefix}_l1h") + st.text_input("L1 Str", value=str(item_data.get("lora 1 low", "")), disabled=True, key=f"{prefix}_l1l") + with l2: + st.text_input("L2 Name", value=item_data.get("lora 2 high", ""), disabled=True, key=f"{prefix}_l2h") + st.text_input("L2 Str", value=str(item_data.get("lora 2 low", "")), disabled=True, key=f"{prefix}_l2l") + with l3: + st.text_input("L3 Name", value=item_data.get("lora 3 high", ""), disabled=True, key=f"{prefix}_l3h") + st.text_input("L3 Str", value=str(item_data.get("lora 3 low", "")), disabled=True, key=f"{prefix}_l3l") + + # D. VACE + vace_keys = ["frame_to_skip", "vace schedule", "video file path"] + has_vace = any(k in item_data for k in vace_keys) + if has_vace: + with st.expander("🎞️ VACE / I2V Settings", expanded=False): + v1, v2, v3 = st.columns(3) + v1.text_input("Skip Frames", value=str(item_data.get("frame_to_skip", "")), disabled=True, key=f"{prefix}_fts") + v2.text_input("Schedule", value=str(item_data.get("vace schedule", "")), disabled=True, key=f"{prefix}_vsc") + v3.text_input("Video Path", value=str(item_data.get("video file path", "")), disabled=True, key=f"{prefix}_vid") - # D. VACE / I2V Specifics - vace_keys = ["frame_to_skip", "vace schedule", "video file path"] - has_vace = any(k in node_data for k in vace_keys) + # --- DETECT BATCH VS SINGLE --- + batch_list = node_data.get("batch_data", []) - if has_vace: - with st.expander("🎞️ VACE / I2V Settings", expanded=True): - v1, v2, v3 = st.columns(3) - v1.text_input("Skip Frames", value=str(node_data.get("frame_to_skip", "")), disabled=True, key=f"p_fts_{target_node_id}") - v2.text_input("Schedule", value=str(node_data.get("vace schedule", "")), disabled=True, key=f"p_vsc_{target_node_id}") - v3.text_input("Video Path", value=str(node_data.get("video file path", "")), disabled=True, key=f"p_vid_{target_node_id}") + if batch_list and isinstance(batch_list, list) and len(batch_list) > 0: + st.info(f"📚 This snapshot contains {len(batch_list)} sequences.") + + for i, seq_data in enumerate(batch_list): + seq_num = seq_data.get("sequence_number", i+1) + with st.expander(f"🎬 Sequence #{seq_num}", expanded=(i==0)): + # Unique prefix for every sequence in every node + prefix = f"p_{target_node_id}_s{i}" + render_preview_fields(seq_data, prefix) + else: + # Single File Preview + prefix = f"p_{target_node_id}_single" + render_preview_fields(node_data, prefix)