import subprocess
subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
from huggingface_hub import snapshot_download, hf_hub_download
snapshot_download(
repo_id="Wan-AI/Wan2.1-T2V-1.3B",
local_dir="wan_models/Wan2.1-T2V-1.3B",
local_dir_use_symlinks=False,
resume_download=True,
repo_type="model"
)
hf_hub_download(
repo_id="gdhe17/Self-Forcing",
filename="checkpoints/self_forcing_dmd.pt",
local_dir=".",
local_dir_use_symlinks=False
)
import os
import re
import random
import argparse
import hashlib
import urllib.request
import time
from PIL import Image
import spaces
import numpy as np
import torch
import gradio as gr
from omegaconf import OmegaConf
from tqdm import tqdm
import imageio
# Original project imports
from pipeline import CausalInferencePipeline
from demo_utils.constant import ZERO_VAE_CACHE
from demo_utils.vae_block3 import VAEDecoderWrapper
from utils.wan_wrapper import WanDiffusionWrapper, WanTextEncoder
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
device = "cuda" if torch.cuda.is_available() else "cpu"
model_checkpoint = "meta-llama/Meta-Llama-3-8B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_quant_type="nf4",
bnb_4bit_use_double_quant=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_checkpoint,
torch_dtype=torch.bfloat16,
attn_implementation="flash_attention_2",
quantization_config=quantization_config,
device_map="auto"
)
enhancer = pipeline(
'text-generation',
model=model,
tokenizer=tokenizer,
repetition_penalty=1.2,
)
T2V_CINEMATIC_PROMPT = """You are an expert cinematic director with many award winning movies, When writing prompts based on the user input, focus on detailed, chronological descriptions of actions and scenes.
Include specific movements, appearances, camera angles, and environmental details - all in a single flowing paragraph.
Start directly with the action, and keep descriptions literal and precise.
Think like a cinematographer describing a shot list.
Do not change the user input intent, just enhance it.
Keep within 150 words.
For best results, build your prompts using this structure:
Start with main action in a single sentence
Add specific details about movements and gestures
Describe character/object appearances precisely
Include background and environment details
Specify camera angles and movements
Describe lighting and colors
Note any changes or sudden events
Do not exceed the 150 word limit!
Output the enhanced prompt only.
"""
@spaces.GPU
def enhance_prompt(prompt):
messages = [
{"role": "system", "content": T2V_CINEMATIC_PROMPT},
{"role": "user", "content": f"user_prompt: {prompt}"},
]
answer = enhancer(
messages,
max_new_tokens=256,
return_full_text=False,
pad_token_id=tokenizer.eos_token_id
)
final_answer = answer[0]['generated_text']
return final_answer.strip()
# --- Argument Parsing ---
parser = argparse.ArgumentParser(description="Gradio Demo for Self-Forcing with Frame Streaming")
parser.add_argument('--port', type=int, default=7860, help="Port to run the Gradio app on.")
parser.add_argument('--host', type=str, default='0.0.0.0', help="Host to bind the Gradio app to.")
parser.add_argument("--checkpoint_path", type=str, default='./checkpoints/self_forcing_dmd.pt', help="Path to the model checkpoint.")
parser.add_argument("--config_path", type=str, default='./configs/self_forcing_dmd.yaml', help="Path to the model config.")
parser.add_argument('--share', action='store_true', help="Create a public Gradio link.")
parser.add_argument('--trt', action='store_true', help="Use TensorRT optimized VAE decoder.")
parser.add_argument('--fps', type=float, default=15.0, help="Playback FPS for frame streaming.")
args = parser.parse_args()
gpu = "cuda"
try:
config = OmegaConf.load(args.config_path)
default_config = OmegaConf.load("configs/default_config.yaml")
config = OmegaConf.merge(default_config, config)
except FileNotFoundError as e:
print(f"Error loading config file: {e}\n. Please ensure config files are in the correct path.")
exit(1)
# Initialize Models
print("Initializing models...")
text_encoder = WanTextEncoder()
transformer = WanDiffusionWrapper(is_causal=True)
try:
state_dict = torch.load(args.checkpoint_path, map_location="cpu")
transformer.load_state_dict(state_dict.get('generator_ema', state_dict.get('generator')))
except FileNotFoundError as e:
print(f"Error loading checkpoint: {e}\nPlease ensure the checkpoint '{args.checkpoint_path}' exists.")
exit(1)
text_encoder.eval().to(dtype=torch.float16).requires_grad_(False)
transformer.eval().to(dtype=torch.float16).requires_grad_(False)
text_encoder.to(gpu)
transformer.to(gpu)
APP_STATE = {
"torch_compile_applied": False,
"fp8_applied": False,
"current_use_taehv": False,
"current_vae_decoder": None,
}
def initialize_vae_decoder(use_taehv=False, use_trt=False):
if use_trt:
from demo_utils.vae import VAETRTWrapper
print("Initializing TensorRT VAE Decoder...")
vae_decoder = VAETRTWrapper()
APP_STATE["current_use_taehv"] = False
elif use_taehv:
print("Initializing TAEHV VAE Decoder...")
from demo_utils.taehv import TAEHV
taehv_checkpoint_path = "checkpoints/taew2_1.pth"
if not os.path.exists(taehv_checkpoint_path):
print(f"Downloading TAEHV checkpoint to {taehv_checkpoint_path}...")
os.makedirs("checkpoints", exist_ok=True)
download_url = "https://github.com/madebyollin/taehv/raw/main/taew2_1.pth"
try:
urllib.request.urlretrieve(download_url, taehv_checkpoint_path)
except Exception as e:
raise RuntimeError(f"Failed to download taew2_1.pth: {e}")
class DotDict(dict): __getattr__ = dict.get
class TAEHVDiffusersWrapper(torch.nn.Module):
def __init__(self):
super().__init__()
self.dtype = torch.float16
self.taehv = TAEHV(checkpoint_path=taehv_checkpoint_path).to(self.dtype)
self.config = DotDict(scaling_factor=1.0)
def decode(self, latents, return_dict=None):
return self.taehv.decode_video(latents, parallel=not LOW_MEMORY).mul_(2).sub_(1)
vae_decoder = TAEHVDiffusersWrapper()
APP_STATE["current_use_taehv"] = True
else:
print("Initializing Default VAE Decoder...")
vae_decoder = VAEDecoderWrapper()
try:
vae_state_dict = torch.load('wan_models/Wan2.1-T2V-1.3B/Wan2.1_VAE.pth', map_location="cpu")
decoder_state_dict = {k: v for k, v in vae_state_dict.items() if 'decoder.' in k or 'conv2' in k}
vae_decoder.load_state_dict(decoder_state_dict)
except FileNotFoundError:
print("Warning: Default VAE weights not found.")
APP_STATE["current_use_taehv"] = False
vae_decoder.eval().to(dtype=torch.float16).requires_grad_(False).to(gpu)
APP_STATE["current_vae_decoder"] = vae_decoder
print(f"✅ VAE decoder initialized: {'TAEHV' if use_taehv else 'Default VAE'}")
# Initialize with default VAE
initialize_vae_decoder(use_taehv=False, use_trt=args.trt)
pipeline = CausalInferencePipeline(
config, device=gpu, generator=transformer, text_encoder=text_encoder,
vae=APP_STATE["current_vae_decoder"]
)
pipeline.to(dtype=torch.float16).to(gpu)
# --- Frame Streaming Video Generation Handler ---
@torch.no_grad()
@spaces.GPU
def video_generation_handler(prompt, seed=42, fps=15):
"""
Generator function that yields RGB frames for display in gr.Image.
Includes timing delays for smooth playback.
"""
if seed == -1:
seed = random.randint(0, 2**32 - 1)
print(f"🎬 Starting video generation with prompt: '{prompt}' and seed: {seed}")
# Calculate frame delay based on FPS
frame_delay = 1.0 / fps if fps > 0 else 1.0 / 15.0
print("🔤 Encoding text prompt...")
conditional_dict = text_encoder(text_prompts=[prompt])
for key, value in conditional_dict.items():
conditional_dict[key] = value.to(dtype=torch.float16)
# --- Generation Loop ---
rnd = torch.Generator(gpu).manual_seed(int(seed))
pipeline._initialize_kv_cache(1, torch.float16, device=gpu)
pipeline._initialize_crossattn_cache(1, torch.float16, device=gpu)
noise = torch.randn([1, 21, 16, 60, 104], device=gpu, dtype=torch.float16, generator=rnd)
vae_cache, latents_cache = None, None
if not APP_STATE["current_use_taehv"] and not args.trt:
vae_cache = [c.to(device=gpu, dtype=torch.float16) for c in ZERO_VAE_CACHE]
num_blocks = 7
current_start_frame = 0
all_num_frames = [pipeline.num_frame_per_block] * num_blocks
total_frames_yielded = 0
all_frames_for_video = []
for idx, current_num_frames in enumerate(all_num_frames):
print(f"📦 Processing block {idx+1}/{num_blocks} with {current_num_frames} frames")
noisy_input = noise[:, current_start_frame : current_start_frame + current_num_frames]
for step_idx, current_timestep in enumerate(pipeline.denoising_step_list):
timestep = torch.ones([1, current_num_frames], device=noise.device, dtype=torch.int64) * current_timestep
_, denoised_pred = pipeline.generator(
noisy_image_or_video=noisy_input, conditional_dict=conditional_dict,
timestep=timestep, kv_cache=pipeline.kv_cache1,
crossattn_cache=pipeline.crossattn_cache,
current_start=current_start_frame * pipeline.frame_seq_length
)
if step_idx < len(pipeline.denoising_step_list) - 1:
next_timestep = pipeline.denoising_step_list[step_idx + 1]
noisy_input = pipeline.scheduler.add_noise(
denoised_pred.flatten(0, 1), torch.randn_like(denoised_pred.flatten(0, 1)),
next_timestep * torch.ones([1 * current_num_frames], device=noise.device, dtype=torch.long)
).unflatten(0, denoised_pred.shape[:2])
if idx < len(all_num_frames) - 1:
pipeline.generator(
noisy_image_or_video=denoised_pred, conditional_dict=conditional_dict,
timestep=torch.zeros_like(timestep), kv_cache=pipeline.kv_cache1,
crossattn_cache=pipeline.crossattn_cache,
current_start=current_start_frame * pipeline.frame_seq_length,
)
# Decode to pixels
if args.trt:
pixels, vae_cache = pipeline.vae.forward(denoised_pred.half(), *vae_cache)
elif APP_STATE["current_use_taehv"]:
if latents_cache is None:
latents_cache = denoised_pred
else:
denoised_pred = torch.cat([latents_cache, denoised_pred], dim=1)
latents_cache = denoised_pred[:, -3:]
pixels = pipeline.vae.decode(denoised_pred)
else:
pixels, vae_cache = pipeline.vae(denoised_pred.half(), *vae_cache)
# Handle frame skipping for first block
if idx == 0 and not args.trt:
pixels = pixels[:, 3:]
elif APP_STATE["current_use_taehv"] and idx > 0:
pixels = pixels[:, 12:]
print(f"📹 Decoded pixels shape: {pixels.shape}")
# Calculate actual frames that will be yielded for this block
actual_frames_this_block = pixels.shape[1]
# Yield individual frames with timing delays
for frame_idx in range(actual_frames_this_block):
frame_tensor = pixels[0, frame_idx] # Get single frame [C, H, W]
# Normalize from [-1, 1] to [0, 255]
frame_np = torch.clamp(frame_tensor.float(), -1., 1.) * 127.5 + 127.5
frame_np = frame_np.to(torch.uint8).cpu().numpy()
# Convert from CHW to HWC format (RGB)
frame_np = np.transpose(frame_np, (1, 2, 0)) # CHW -> HWC
all_frames_for_video.append(frame_np)
total_frames_yielded += 1
# Calculate progress based on blocks completed + current block progress
blocks_completed = idx
current_block_progress = (frame_idx + 1) / actual_frames_this_block
total_block_progress = (blocks_completed + current_block_progress) / num_blocks
frame_progress_percent = total_block_progress * 100
# Cap at 100% to avoid going over
frame_progress_percent = min(frame_progress_percent, 100.0)
print(f"📺 Yielding frame {total_frames_yielded}: shape {frame_np.shape}")
# Create HTML status update
if frame_idx == actual_frames_this_block - 1 and idx + 1 == num_blocks: # Last frame
status_html = (
f""
f"
🎉 Generation Complete!
"
f"
"
f" Total frames: {total_frames_yielded}. The final video is now available."
f"
"
f"
"
f"
Generating Video...
"
f"
"
f"
"
f" Block {idx+1}/{num_blocks} | Frame {total_frames_yielded} | {frame_progress_percent:.1f}%"
f"
"
f"
"
f"
🎉 Generation Complete!
"
f"
"
f" Video saved successfully with {total_frames_yielded} frames at {fps} FPS."
f"
"
f"
"
f"
⚠️ Video Save Error
"
f"
"
f" Could not save final video: {str(e)}"
f"
"
f"
Ready to start generation...
",
label="Generation Status"
)
with gr.Blocks(title="Self-Forcing Frame Streaming Demo") as demo:
gr.Markdown("# 🚀 Self-Forcing Video Generation with Frame Streaming")
gr.Markdown("Real-time video generation with frame-by-frame display. [[Model]](https://huggingface.co/gdhe17/Self-Forcing), [[Project page]](https://self-forcing.github.io), [[Paper]](https://huggingface.co/papers/2506.08009)")
with gr.Row():
with gr.Column(scale=2):
gr.Markdown("### 📝 Configure Generation")
prompt = gr.Textbox(
label="Prompt",
placeholder="A stylish woman walks down a Tokyo street...",
lines=4,
)
enhance_button = gr.Button("Enhance prompt")
gr.Examples(
examples=[
"A close-up shot of a ceramic teacup slowly pouring water into a glass mug. The water flows smoothly from the spout of the teacup into the mug, creating gentle ripples as it fills up. Both cups have detailed textures, with the teacup having a matte finish and the glass mug showcasing clear transparency. The background is a blurred kitchen countertop, adding context without distracting from the central action. The pouring motion is fluid and natural, emphasizing the interaction between the two cups.",
"A playful cat is seen playing an electronic guitar, strumming the strings with its front paws. The cat has distinctive black facial markings and a bushy tail. It sits comfortably on a small stool, its body slightly tilted as it focuses intently on the instrument. The setting is a cozy, dimly lit room with vintage posters on the walls, adding a retro vibe. The cat's expressive eyes convey a sense of joy and concentration. Medium close-up shot, focusing on the cat's face and hands interacting with the guitar.",
"A dynamic over-the-shoulder perspective of a chef meticulously plating a dish in a bustling kitchen. The chef, a middle-aged woman, deftly arranges ingredients on a pristine white plate. Her hands move with precision, each gesture deliberate and practiced. The background shows a crowded kitchen with steaming pots, whirring blenders, and the clatter of utensils. Bright lights highlight the scene, casting shadows across the busy workspace. The camera angle captures the chef's detailed work from behind, emphasizing his skill and dedication.",
],
inputs=[prompt],
fn=video_generation_handler_example,
outputs=[final_video],
cache_examples="lazy"
)
with gr.Row():
seed = gr.Number(label="Seed", value=-1, info="Use -1 for a random seed.")
fps = gr.Slider(
label="Playback FPS",
minimum=1,
maximum=30,
value=args.fps,
step=1,
visible=False,
info="Frames per second for playback"
)
start_btn = gr.Button("🎬 Start Generation", variant="primary", size="lg")
with gr.Column(scale=3):
gr.Markdown("### 📺 Live Frame Stream")
gr.Markdown("*Click 'Start Generation' to begin frame streaming*")
final_video.render()
frame_display.render()
status_html.render()
# Connect the generator to the image display
start_btn.click(
fn=video_generation_handler,
inputs=[prompt, seed, fps],
outputs=[frame_display, final_video, status_html]
)
enhance_button.click(
fn=enhance_prompt,
inputs=[prompt],
outputs=[prompt]
)
# --- Launch App ---
if __name__ == "__main__":
if os.path.exists("gradio_tmp"):
import shutil
shutil.rmtree("gradio_tmp")
os.makedirs("gradio_tmp", exist_ok=True)
demo.queue().launch(
server_name=args.host,
server_port=args.port,
share=args.share,
show_error=True
)