Update tab_timeline_wip.py

This commit is contained in:
2026-01-03 01:25:46 +01:00
committed by GitHub
parent f25e264db9
commit 7e4cede82b

View File

@@ -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)