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# -*- coding: utf-8 -*-
"""
Hugging Face Space for Text-to-Video generation using the Wan 2.1 model,
enhanced with a base `FusionX` LoRA, dynamic user-selectable style LoRAs,
and an LLM-based prompt enhancer.
"""
# --- 1. Imports ---
import os
import re
import json
import random
import tempfile
import traceback
from functools import partial
import gradio as gr
import numpy as np
import torch
import spaces
from diffusers import DiffusionPipeline, AutoModel, AutoencoderKLWan
from diffusers.utils import export_to_video
from transformers import AutoTokenizer, AutoModelForCausalLM, UMT5EncoderModel, pipeline
from huggingface_hub import hf_hub_download, list_repo_files
# --- 2. Configuration & Constants ---
# --- Model & LoRA Identifiers ---
T2V_BASE_MODEL_ID = "Wan-AI/Wan2.1-T2V-14B-Diffusers"
# Base LoRA (always applied)
FUSIONX_LORA_REPO = "vrgamedevgirl84/Wan14BT2VFusioniX"
FUSIONX_LORA_FILE = "FusionX_LoRa/Wan2.1_T2V_14B_FusionX_LoRA.safetensors"
FUSIONX_ADAPTER_NAME = "fusionx_t2v"
FUSIONX_LORA_WEIGHT = 0.75
ENHANCER_MODEL_ID = "Qwen/Qwen2-1.5B-Instruct" # Using a smaller model to save space
# Dynamic LoRAs (user selectable)
DYNAMIC_LORA_REPO_ID = "DeepBeepMeep/Wan2.1"
DYNAMIC_LORA_SUBFOLDER = "loras_i2v"
DYNAMIC_LORA_ADAPTER_NAME = "dynamic_lora"
# --- Generation Parameters ---
MOD_VALUE = 8
T2V_FIXED_FPS = 16
MIN_FRAMES_MODEL = 8
MAX_FRAMES_MODEL = 81
MAX_SEED = np.iinfo(np.int64).max
# --- UI Defaults ---
DEFAULT_H_SLIDER_VALUE = 480
DEFAULT_W_SLIDER_VALUE = 640
DEFAULT_PROMPT_T2V = "A majestic lion surveying its kingdom from a rocky outcrop at sunrise, cinematic lighting, hyperrealistic."
DEFAULT_NEGATIVE_PROMPT = "static image, no motion, watermark, text, signature, jpeg artifacts, ugly, incomplete, disfigured, low quality, worst quality, messy background"
# --- System Prompt for LLM Enhancer ---
T2V_CINEMATIC_PROMPT_SYSTEM = (
"You are a prompt engineer for a generative AI video model. Your task is to rewrite user inputs into high-quality, "
"detailed, and cinematic prompts. Focus on visual details, camera movements, lighting, and mood. "
"Add natural motion attributes. The revised prompt should be around 80-100 words. "
"Directly output the rewritten prompt in English without any conversational text or quotation marks."
)
# --- 3. Helper Functions ---
def sanitize_prompt_for_filename(prompt: str) -> str:
"""Creates a filesystem-safe filename from a prompt."""
if not prompt:
return "no_prompt"
sanitized = re.sub(r'[^a-zA-Z0-9\s]', '', prompt)
sanitized = re.sub(r'\s+', '_', sanitized).lower()
return sanitized[:50]
def get_t2v_duration(
prompt: str, height: int, width: int, negative_prompt: str,
duration_seconds: float, steps: int, seed: int,
randomize_seed: bool, selected_lora: str,
lora_weight: float
) -> int:
"""
Estimates GPU time for Text-to-Video generation.
The logic is tiered and considers duration, steps, and resolution to prevent timeouts.
"""
# Calculate a resolution multiplier. A higher resolution will significantly increase generation time.
# Base resolution is considered 640x480 pixels.
base_pixels = DEFAULT_W_SLIDER_VALUE * DEFAULT_H_SLIDER_VALUE
current_pixels = width * height
# Check if the current resolution is significantly larger than the base.
is_high_res = current_pixels > (base_pixels * 1.5)
# Tiered duration based on video length and number of inference steps.
if steps > 10 or duration_seconds > 4:
# Longest generations (e.g., high step count or long duration).
base_duration = 600
elif steps > 10 or duration_seconds > 3:
# Medium-length generations.
base_duration = 400
else:
# Shortest/quickest generations.
base_duration = 250
# Apply a multiplier for high-resolution videos.
final_duration = base_duration * 2 if is_high_res else base_duration
# Cap the duration at a maximum value (900s = 15 minutes) to comply with typical free-tier limits.
final_duration = min(final_duration, 900)
print(f"Requesting {final_duration}s of GPU time for {steps} steps, {duration_seconds:.1f}s duration, and {width}x{height} resolution.")
return final_duration
def get_available_presets(repo_id, subfolder):
"""
Fetches the list of available LoRA presets by looking for .lset files.
"""
print(f"\nπ Discovering LoRA presets in {repo_id}...")
try:
all_files = list_repo_files(repo_id=repo_id, repo_type='model')
subfolder_path = f"{subfolder}/"
lset_files = [
os.path.splitext(f.split('/')[-1])[0]
for f in all_files
if f.startswith(subfolder_path) and f.endswith('.lset')
]
print(f"β
Discovered {len(lset_files)} LoRA presets.")
return ["None"] + sorted(lset_files)
except Exception as e:
print(f"β οΈ Warning: Could not fetch LoRA presets from {repo_id}. LoRA selection will be disabled. Error: {e}")
return ["None"]
def parse_lset_prompt(lset_prompt):
"""Parses a .lset prompt, resolving variables and highlighting them."""
variables = dict(re.findall(r'! \{(\w+)\}="([^"]+)"', lset_prompt))
prompt_template = re.sub(r'! \{\w+\}="[^"]+"\n?', '', lset_prompt).strip()
resolved_prompt = prompt_template
for key, value in variables.items():
highlighted_value = f"__{value}__"
resolved_prompt = resolved_prompt.replace(f"{{{key}}}", highlighted_value)
return resolved_prompt
def handle_lora_selection_change(preset_name: str, current_prompt: str):
"""
Appends the selected LoRA's trigger words to the current prompt text
and controls the visibility of the weight slider. Ensures slider is only
visible on success.
"""
# If "None" is selected, hide the slider and return the prompt unchanged.
if not preset_name or preset_name == "None":
gr.Info("LoRA cleared.")
return gr.update(value=current_prompt), gr.update(visible=False, interactive=False)
try:
# Fetch the trigger words from the LoRA's .lset file.
lset_filename = f"{preset_name}.lset"
lset_path = hf_hub_download(
repo_id=DYNAMIC_LORA_REPO_ID,
filename=lset_filename, subfolder=DYNAMIC_LORA_SUBFOLDER, repo_type='model'
)
with open(lset_path, 'r', encoding='utf-8') as f:
lset_content = f.read()
lset_prompt_raw = None
try:
lset_data = json.loads(lset_content)
lset_prompt_raw = lset_data.get("prompt")
except json.JSONDecodeError:
lset_prompt_raw = lset_content
# Only if we successfully get trigger words, we update the prompt and show the slider.
if lset_prompt_raw:
trigger_words = parse_lset_prompt(lset_prompt_raw)
separator = ", " if current_prompt and not current_prompt.endswith((",", " ")) else ""
new_prompt = f"{current_prompt}{separator}{trigger_words}".strip()
gr.Info(f"β
Appended triggers from '{preset_name}'. You can now edit them.")
return gr.update(value=new_prompt), gr.update(visible=True, interactive=True)
else:
# If the .lset file has no prompt, don't change the prompt and ensure the slider is hidden.
gr.Info(f"βΉοΈ No prompt found in '{preset_name}.lset'. Prompt unchanged.")
return gr.update(value=current_prompt), gr.update(visible=False, interactive=False)
except Exception as e:
print(f"Info: Could not process .lset for '{preset_name}'. Reason: {e}")
gr.Warning(f"β οΈ Could not load triggers for '{preset_name}'.")
# On any error, don't change the prompt and ensure the slider is hidden.
return gr.update(value=current_prompt), gr.update(visible=False, interactive=False)
def _manage_lora_state(pipe, selected_lora: str, lora_weight: float) -> bool:
"""
Handles the loading, setting, and cleanup of dynamic LoRA adapters.
Returns:
bool: True if a dynamic LoRA was loaded, False otherwise.
"""
# Pre-emptive cleanup of any previously loaded dynamic adapter.
try:
pipe.delete_adapters([DYNAMIC_LORA_ADAPTER_NAME])
print("π§Ό Pre-emptively unloaded previous dynamic LoRA.")
except Exception:
pass # No dynamic lora was present, which is a clean state.
if not selected_lora or selected_lora == "None":
pipe.set_adapters([FUSIONX_ADAPTER_NAME], adapter_weights=[FUSIONX_LORA_WEIGHT])
print("βΉοΈ No dynamic LoRA selected. Using base LoRA only.")
return False
# --- DYNAMIC LORA HANDLING ---
print(f"π Processing preset: {selected_lora} with weight {lora_weight}")
lora_filename = None
try:
lset_filename = f"{selected_lora}.lset"
lset_path = hf_hub_download(
repo_id=DYNAMIC_LORA_REPO_ID,
filename=lset_filename, subfolder=DYNAMIC_LORA_SUBFOLDER, repo_type='model'
)
with open(lset_path, 'r', encoding='utf-8') as f:
lset_content = f.read()
try:
lset_data = json.loads(lset_content)
loras_list = lset_data.get("loras")
if loras_list and isinstance(loras_list, list) and len(loras_list) > 0:
lora_filename = loras_list[0]
print(f" - Found LoRA file in preset: {lora_filename}")
except json.JSONDecodeError:
print(f" - Info: '{lset_filename}' is not JSON. Assuming filename matches preset name.")
except Exception as e:
print(f" - Warning: Could not process .lset file for '{selected_lora}'. Assuming filename matches preset. Error: {e}")
if not lora_filename:
lora_filename = f"{selected_lora}.safetensors"
pipe.load_lora_weights(
DYNAMIC_LORA_REPO_ID, weight_name=lora_filename,
subfolder=DYNAMIC_LORA_SUBFOLDER, adapter_name=DYNAMIC_LORA_ADAPTER_NAME,
)
pipe.set_adapters(
[FUSIONX_ADAPTER_NAME, DYNAMIC_LORA_ADAPTER_NAME],
adapter_weights=[FUSIONX_LORA_WEIGHT, lora_weight]
)
print("β
Dynamic LoRA activated alongside base LoRA.")
return True
# --- 4. Pipeline Loading ---
def load_pipelines():
"""Loads and configures the T2V and LLM pipelines."""
t2v_pipe, enhancer_pipe = None, None
print("\nπ Loading T2V pipeline with base LoRA...")
try:
t2v_pipe = DiffusionPipeline.from_pretrained(
T2V_BASE_MODEL_ID,
torch_dtype=torch.bfloat16,
)
print("β
Base pipeline loaded. Overriding VAE with float32 version...")
vae_fp32 = AutoencoderKLWan.from_pretrained(T2V_BASE_MODEL_ID, subfolder="vae", torch_dtype=torch.float32)
t2v_pipe.vae = vae_fp32
t2v_pipe.to("cuda")
print("β
Pipeline configured. Loading and activating base FusionX LoRA...")
t2v_pipe.load_lora_weights(FUSIONX_LORA_REPO, weight_name=FUSIONX_LORA_FILE, adapter_name=FUSIONX_ADAPTER_NAME)
t2v_pipe.set_adapters([FUSIONX_ADAPTER_NAME], adapter_weights=[FUSIONX_LORA_WEIGHT])
print("β
T2V pipeline with base LoRA is ready.")
except Exception as e:
print(f"β CRITICAL ERROR: Failed to load T2V pipeline. T2V will be disabled. Reason: {e}")
traceback.print_exc()
t2v_pipe = None
print("\nπ€ Loading LLM for Prompt Enhancement...")
try:
enhancer_pipe = pipeline("text-generation", model=ENHANCER_MODEL_ID, torch_dtype=torch.bfloat16, device="cpu")
print("β
LLM Prompt Enhancer loaded successfully (on CPU).")
except Exception as e:
print(f"β οΈ WARNING: Could not load the LLM prompt enhancer. The feature will be disabled. Error: {e}")
enhancer_pipe = None
return t2v_pipe, enhancer_pipe
# --- 5. Core Generation & UI Logic ---
@spaces.GPU()
def enhance_prompt_with_llm(prompt: str, enhancer_pipeline):
"""
Uses the loaded LLM to enhance a given prompt.
"""
if enhancer_pipeline is None:
gr.Warning("LLM enhancer is not available.")
return prompt, gr.update(), gr.update()
if enhancer_pipeline.model.device.type != 'cuda':
print("Moving enhancer model to CUDA for on-demand GPU execution...")
enhancer_pipeline.model.to("cuda")
messages = [{"role": "system", "content": T2V_CINEMATIC_PROMPT_SYSTEM}, {"role": "user", "content": prompt}]
print(f"Enhancing prompt: '{prompt}'")
try:
tokenizer = enhancer_pipeline.tokenizer
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
tokenized_inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
if isinstance(tokenized_inputs, torch.Tensor):
inputs_on_cuda = {"input_ids": tokenized_inputs.to("cuda")}
inputs_on_cuda["attention_mask"] = torch.ones_like(inputs_on_cuda["input_ids"])
else:
inputs_on_cuda = {k: v.to("cuda") for k, v in tokenized_inputs.items()}
generated_ids = enhancer_pipeline.model.generate(**inputs_on_cuda, max_new_tokens=256, do_sample=True, temperature=0.7, top_p=0.95)
input_token_length = inputs_on_cuda['input_ids'].shape[1]
newly_generated_ids = generated_ids[:, input_token_length:]
final_answer = tokenizer.decode(newly_generated_ids[0], skip_special_tokens=True)
print(f"Enhanced prompt: '{final_answer.strip()}'")
# The enhanced prompt overwrites the textbox. The LoRA selection is reset.
return final_answer.strip(), "None", gr.update(visible=False, interactive=False)
except Exception as e:
print(f"β Error during prompt enhancement: {e}")
traceback.print_exc()
gr.Warning(f"An error occurred during prompt enhancement. See console for details.")
return prompt, gr.update(), gr.update()
finally:
print("π§Ή Clearing CUDA cache after prompt enhancement...")
torch.cuda.empty_cache()
@spaces.GPU(duration_from_args=get_t2v_duration)
def generate_t2v_video(
prompt: str, height: int, width: int, negative_prompt: str,
duration_seconds: float, steps: int, seed: int,
randomize_seed: bool, selected_lora: str,
lora_weight: float,
progress=gr.Progress(track_tqdm=True)
):
"""Main function to generate a video from a text prompt."""
if t2v_pipe is None:
raise gr.Error("Text-to-Video pipeline is not available due to a loading error.")
if not prompt:
raise gr.Error("Please enter a prompt for Text-to-Video generation.")
# --- The prompt from the textbox is now the final prompt. No more combining. ---
final_prompt = prompt
target_h = max(MOD_VALUE, (height // MOD_VALUE) * MOD_VALUE)
target_w = max(MOD_VALUE, (width // MOD_VALUE) * MOD_VALUE)
requested_frames = int(round(duration_seconds * T2V_FIXED_FPS))
frames_minus_one = requested_frames - 1
valid_frames_minus_one = round(frames_minus_one / 4.0) * 4
num_frames = int(valid_frames_minus_one) + 1
if num_frames != requested_frames:
print(f"Info: Adjusted number of frames from {requested_frames} to {num_frames} to meet model constraints.")
num_frames = np.clip(num_frames, MIN_FRAMES_MODEL, MAX_FRAMES_MODEL)
current_seed = random.randint(0, MAX_SEED) if randomize_seed else int(seed)
lora_loaded = False
try:
lora_loaded = _manage_lora_state(pipe=t2v_pipe, selected_lora=selected_lora, lora_weight=lora_weight)
print("\n--- Starting T2V Generation ---")
print(f"Final Prompt: {final_prompt}")
print(f"Resolution: {target_w}x{target_h}, Frames: {num_frames}, Seed: {current_seed}")
print(f"Steps: {steps}, Guidance: 1.0 (fixed for FusionX)")
print("---------------------------------")
with torch.inference_mode():
output_frames_list = t2v_pipe(
prompt=final_prompt, negative_prompt=negative_prompt,
height=target_h, width=target_w, num_frames=num_frames,
guidance_scale=1.0, num_inference_steps=int(steps),
generator=torch.Generator(device="cuda").manual_seed(current_seed)
).frames[0]
sanitized_prompt = sanitize_prompt_for_filename(final_prompt)
filename = f"t2v_{sanitized_prompt}_{current_seed}.mp4"
temp_dir = tempfile.mkdtemp()
video_path = os.path.join(temp_dir, filename)
export_to_video(output_frames_list, video_path, fps=T2V_FIXED_FPS)
print(f"β
Video saved to: {video_path}")
download_label = f"π₯ Download: {filename}"
return video_path, current_seed, gr.File(value=video_path, visible=True, label=download_label)
except Exception as e:
print(f"β An error occurred during video generation: {e}")
traceback.print_exc()
raise gr.Error("Video generation failed. Please check the logs for details.")
finally:
if lora_loaded:
print(f"π§Ό Cleaning up dynamic LoRA: {selected_lora}")
try:
t2v_pipe.delete_adapters([DYNAMIC_LORA_ADAPTER_NAME])
t2v_pipe.set_adapters([FUSIONX_ADAPTER_NAME], adapter_weights=[FUSIONX_LORA_WEIGHT])
print("β
Cleanup complete. Pipeline reset to base LoRA state.")
except Exception as e:
print(f"β οΈ Error during LoRA cleanup: {e}. State may be inconsistent.")
print("π§Ή Clearing CUDA cache after video generation...")
torch.cuda.empty_cache()
# --- 6. Gradio UI Layout ---
def build_ui(t2v_pipe, enhancer_pipe, available_loras):
"""Creates and configures the Gradio UI."""
with gr.Blocks(theme=gr.themes.Soft(), css=".main-container { max-width: 1080px; margin: auto; }") as demo:
gr.Markdown("# β¨ Wan 2.1 Text-to-Video Suite with Dynamic LoRAs")
gr.Markdown("Generate videos from text. Edit the prompt below. Selecting a LoRA will append its triggers to your prompt.")
with gr.Tabs():
with gr.TabItem("βοΈ Text-to-Video", id="t2v_tab", interactive=t2v_pipe is not None):
if t2v_pipe is None:
gr.Markdown("<h3 style='color: #ff9999; text-align: center;'>β οΈ T2V Pipeline Failed to Load. Tab disabled.</h3>")
else:
with gr.Row():
with gr.Column(scale=2):
t2v_prompt = gr.Textbox(
label="βοΈ Prompt", value=DEFAULT_PROMPT_T2V, lines=4,
placeholder="e.g., A cinematic drone shot flying over a futuristic city at night..."
)
t2v_enhance_btn = gr.Button(
"π€ Enhance Prompt with AI",
interactive=enhancer_pipe is not None
)
with gr.Group():
t2v_lora_preset = gr.Dropdown(
label="π¨ Dynamic Style LoRA (Optional)",
choices=available_loras,
value="None",
info="Appends style triggers to the prompt text above."
)
t2v_lora_weight = gr.Slider(
label="πͺ LoRA Weight", minimum=0.0, maximum=2.0, step=0.05, value=0.8,
interactive=False, visible=False
)
t2v_duration = gr.Slider(
minimum=round(MIN_FRAMES_MODEL / T2V_FIXED_FPS, 1),
maximum=round(MAX_FRAMES_MODEL / T2V_FIXED_FPS, 1),
step=0.1, value=2.0, label="β±οΈ Duration (seconds)"
)
with gr.Accordion("βοΈ Advanced Settings", open=False):
t2v_neg_prompt = gr.Textbox(label="β Negative Prompt", value=DEFAULT_NEGATIVE_PROMPT, lines=3)
with gr.Row():
t2v_seed = gr.Slider(label="π² Seed", minimum=0, maximum=MAX_SEED, step=1, value=1234)
t2v_rand_seed = gr.Checkbox(label="π Randomize", value=True)
with gr.Row():
t2v_height = gr.Slider(minimum=256, maximum=896, step=MOD_VALUE, value=DEFAULT_H_SLIDER_VALUE, label="π Height")
t2v_width = gr.Slider(minimum=256, maximum=896, step=MOD_VALUE, value=DEFAULT_W_SLIDER_VALUE, label="π Width")
t2v_steps = gr.Slider(minimum=5, maximum=40, step=1, value=8, label="π Inference Steps")
t2v_generate_btn = gr.Button("π¬ Generate Video", variant="primary")
with gr.Column(scale=3):
t2v_output_video = gr.Video(label="π₯ Generated Video", autoplay=True, interactive=False)
t2v_download = gr.File(label="π₯ Download Video", visible=False)
if t2v_pipe is not None:
enhance_fn = partial(enhance_prompt_with_llm, enhancer_pipeline=enhancer_pipe)
# 1. When the user enhances the prompt with the LLM.
t2v_enhance_btn.click(
fn=enhance_fn,
inputs=[t2v_prompt],
outputs=[t2v_prompt, t2v_lora_preset, t2v_lora_weight]
)
# 2. When the user selects a LoRA from the dropdown.
t2v_lora_preset.change(
fn=handle_lora_selection_change,
# Pass the current prompt text in, get the new text back out.
inputs=[t2v_lora_preset, t2v_prompt],
outputs=[t2v_prompt, t2v_lora_weight]
)
# 3. When the user clicks the final generate button.
t2v_generate_btn.click(
fn=generate_t2v_video,
inputs=[
t2v_prompt, t2v_height, t2v_width, t2v_neg_prompt,
t2v_duration, t2v_steps, t2v_seed,
t2v_rand_seed, t2v_lora_preset, t2v_lora_weight
],
outputs=[t2v_output_video, t2v_seed, t2v_download]
)
return demo
# --- 7. Main Execution ---
if __name__ == "__main__":
t2v_pipe, enhancer_pipe = load_pipelines()
available_loras = []
if t2v_pipe:
available_loras = get_available_presets(DYNAMIC_LORA_REPO_ID, DYNAMIC_LORA_SUBFOLDER)
app_ui = build_ui(t2v_pipe, enhancer_pipe, available_loras)
app_ui.queue(max_size=10).launch()
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