Add batch processing features to tab_batch.py

Implement batch processing functionality in Streamlit app with options to create, modify, and save batch sequences.
This commit is contained in:
2025-12-31 14:44:09 +01:00
committed by GitHub
parent f7495d4d74
commit 2964101782

169
tab_batch.py Normal file
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import streamlit as st
from utils import DEFAULTS, save_json, load_json
# Callback for creating batch file
def create_batch_callback(original_filename, current_data, current_dir):
new_name = f"batch_{original_filename}"
new_path = current_dir / new_name
if new_path.exists():
st.toast(f"File {new_name} already exists!", icon="⚠️")
return
first_item = current_data.copy()
if "prompt_history" in first_item: del first_item["prompt_history"]
first_item["sequence_number"] = 1
new_data = {
"batch_data": [first_item],
"prompt_history": current_data.get("prompt_history", [])
}
save_json(new_path, new_data)
st.toast(f"Created {new_name}", icon="")
st.session_state.file_selector = new_name
def render_batch_processor(data, file_path, json_files, current_dir, selected_file_name):
is_batch_file = "batch_data" in data or isinstance(data, list)
if not is_batch_file:
st.warning("This is a Single file. To use Batch mode, create a copy.")
st.button("✨ Create Batch Copy", on_click=create_batch_callback, args=(selected_file_name, data, current_dir))
return
batch_list = data.get("batch_data", [])
# --- ADD NEW SEQUENCE AREA ---
st.subheader("Add New Sequence")
ac1, ac2 = st.columns(2)
with ac1:
file_options = [f.name for f in json_files]
d_idx = file_options.index(selected_file_name) if selected_file_name in file_options else 0
src_name = st.selectbox("Source File:", file_options, index=d_idx, key="batch_src_file")
src_data, _ = load_json(current_dir / src_name)
with ac2:
src_hist = src_data.get("prompt_history", [])
h_opts = [f"#{i+1}: {h.get('note', 'No Note')}" for i, h in enumerate(src_hist)] if src_hist else []
sel_hist = st.selectbox("History Entry:", h_opts, key="batch_src_hist")
bc1, bc2, bc3 = st.columns(3)
# Helper to add sequence
def add_sequence(new_item):
max_seq = 0
for s in batch_list:
if "sequence_number" in s: max_seq = max(max_seq, int(s["sequence_number"]))
new_item["sequence_number"] = max_seq + 1
# Cleanup
for k in ["prompt_history", "note", "loras"]:
if k in new_item: del new_item[k]
batch_list.append(new_item)
data["batch_data"] = batch_list
save_json(file_path, data)
st.rerun()
if bc1.button(" Add Empty", use_container_width=True):
add_sequence(DEFAULTS.copy())
if bc2.button(" From File", use_container_width=True, help=f"Copy {src_name}"):
item = DEFAULTS.copy()
# Flatten logic
flat = src_data["batch_data"][0] if "batch_data" in src_data and src_data["batch_data"] else src_data
item.update(flat)
add_sequence(item)
if bc3.button(" From History", use_container_width=True, disabled=not src_hist):
if sel_hist:
idx = int(sel_hist.split(":")[0].replace("#", "")) - 1
item = DEFAULTS.copy()
h_item = src_hist[idx]
item.update(h_item)
if "loras" in h_item and isinstance(h_item["loras"], dict):
item.update(h_item["loras"])
add_sequence(item)
# --- RENDER LIST ---
st.markdown("---")
st.info(f"Batch contains {len(batch_list)} sequences.")
for i, seq in enumerate(batch_list):
seq_num = seq.get("sequence_number", i+1)
# Unique prefix for this sequence in this file
prefix = f"{selected_file_name}_seq{i}"
with st.expander(f"🎬 Sequence #{seq_num}", expanded=False):
# Header Buttons
b1, b2, b3 = st.columns([1, 1, 2])
if b1.button(f"📥 Copy {src_name}", key=f"{prefix}_copy"):
item = DEFAULTS.copy()
flat = src_data["batch_data"][0] if "batch_data" in src_data and src_data["batch_data"] else src_data
item.update(flat)
item["sequence_number"] = seq_num
if "prompt_history" in item: del item["prompt_history"]
batch_list[i] = item
data["batch_data"] = batch_list
save_json(file_path, data)
st.toast("Copied!", icon="📥")
st.rerun()
if b2.button("↖️ Promote to Single", key=f"{prefix}_prom"):
single_data = seq.copy()
single_data["prompt_history"] = data.get("prompt_history", [])
if "sequence_number" in single_data: del single_data["sequence_number"]
save_json(file_path, single_data)
st.toast("Converted to Single!", icon="")
st.rerun()
if b3.button("🗑️ Remove", key=f"{prefix}_del"):
batch_list.pop(i)
data["batch_data"] = batch_list
save_json(file_path, data)
st.rerun()
# Fields
st.markdown("---")
c1, c2 = st.columns([2, 1])
with c1:
seq["general_prompt"] = st.text_area("General Prompt", value=seq.get("general_prompt", ""), height=60, key=f"{prefix}_gp")
seq["general_negative"] = st.text_area("General Negative", value=seq.get("general_negative", ""), height=60, key=f"{prefix}_gn")
seq["current_prompt"] = st.text_area("Specific Prompt", value=seq.get("current_prompt", ""), height=100, key=f"{prefix}_sp")
seq["negative"] = st.text_area("Specific Negative", value=seq.get("negative", ""), height=60, key=f"{prefix}_sn")
with c2:
seq["sequence_number"] = st.number_input("Seq Num", value=int(seq_num), key=f"{prefix}_sn_val")
seq["seed"] = st.number_input("Seed", value=int(seq.get("seed", 0)), key=f"{prefix}_seed")
seq["camera"] = st.text_input("Camera", value=seq.get("camera", ""), key=f"{prefix}_cam")
seq["flf"] = st.text_input("FLF", value=str(seq.get("flf", DEFAULTS["flf"])), key=f"{prefix}_flf")
# Dynamic Paths
if "video file path" in seq or "vace" in selected_file_name:
seq["video file path"] = st.text_input("Video Path", value=seq.get("video file path", ""), key=f"{prefix}_vid")
with st.expander("VACE Settings"):
seq["frame_to_skip"] = st.number_input("Skip", value=int(seq.get("frame_to_skip", 81)), key=f"{prefix}_fts")
seq["input_a_frames"] = st.number_input("In A", value=int(seq.get("input_a_frames", 0)), key=f"{prefix}_ia")
seq["input_b_frames"] = st.number_input("In B", value=int(seq.get("input_b_frames", 0)), key=f"{prefix}_ib")
seq["reference switch"] = st.number_input("Switch", value=int(seq.get("reference switch", 1)), key=f"{prefix}_rsw")
seq["vace schedule"] = st.number_input("Sched", value=int(seq.get("vace schedule", 1)), key=f"{prefix}_vsc")
seq["reference path"] = st.text_input("Ref Path", value=seq.get("reference path", ""), key=f"{prefix}_rp")
seq["reference image path"] = st.text_input("Ref Img", value=seq.get("reference image path", ""), key=f"{prefix}_rip")
if "i2v" in selected_file_name and "vace" not in selected_file_name:
seq["reference image path"] = st.text_input("Ref Img", value=seq.get("reference image path", ""), key=f"{prefix}_ri2")
seq["flf image path"] = st.text_input("FLF Img", value=seq.get("flf image path", ""), key=f"{prefix}_flfi")
with st.expander("LoRA Settings"):
lc1, lc2 = st.columns(2)
lkeys = ["lora 1 high", "lora 1 low", "lora 2 high", "lora 2 low", "lora 3 high", "lora 3 low"]
for li, lk in enumerate(lkeys):
with (lc1 if li % 2 == 0 else lc2):
seq[lk] = st.text_input(lk.title(), value=seq.get(lk, ""), key=f"{prefix}_{lk}")
st.markdown("---")
if st.button("💾 Save Batch Changes"):
data["batch_data"] = batch_list
save_json(file_path, data)
st.toast("Batch saved!", icon="🚀")