from diffusers_helper.hf_login import login
import os
os.environ['HF_HOME'] = os.path.abspath(os.path.realpath(os.path.join(os.path.dirname(__file__), './hf_download')))
import spaces
import gradio as gr
import torch
import traceback
import einops
import safetensors.torch as sf
import numpy as np
import random
import math
# 20250506 pftq: Added for video input loading
import decord
# 20250506 pftq: Added for progress bars in video_encode
from tqdm import tqdm
# 20250506 pftq: Normalize file paths for Windows compatibility
import pathlib
# 20250506 pftq: for easier to read timestamp
from datetime import datetime
# 20250508 pftq: for saving prompt to mp4 comments metadata
import imageio_ffmpeg
import tempfile
import shutil
import subprocess
from PIL import Image
from diffusers import AutoencoderKLHunyuanVideo
from transformers import LlamaModel, CLIPTextModel, LlamaTokenizerFast, CLIPTokenizer
from diffusers_helper.hunyuan import encode_prompt_conds, vae_decode, vae_encode, vae_decode_fake
from diffusers_helper.utils import save_bcthw_as_mp4, crop_or_pad_yield_mask, soft_append_bcthw, resize_and_center_crop, state_dict_weighted_merge, state_dict_offset_merge, generate_timestamp
from diffusers_helper.models.hunyuan_video_packed import HunyuanVideoTransformer3DModelPacked
from diffusers_helper.pipelines.k_diffusion_hunyuan import sample_hunyuan
if torch.cuda.device_count() > 0:
from diffusers_helper.memory import cpu, gpu, get_cuda_free_memory_gb, move_model_to_device_with_memory_preservation, offload_model_from_device_for_memory_preservation, fake_diffusers_current_device, DynamicSwapInstaller, unload_complete_models, load_model_as_complete
from diffusers_helper.thread_utils import AsyncStream, async_run
from diffusers_helper.gradio.progress_bar import make_progress_bar_css, make_progress_bar_html
from transformers import SiglipImageProcessor, SiglipVisionModel
from diffusers_helper.clip_vision import hf_clip_vision_encode
from diffusers_helper.bucket_tools import find_nearest_bucket
from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig, HunyuanVideoTransformer3DModel, HunyuanVideoPipeline
import pillow_heif
pillow_heif.register_heif_opener()
high_vram = False
free_mem_gb = 0
if torch.cuda.device_count() > 0:
free_mem_gb = get_cuda_free_memory_gb(gpu)
high_vram = free_mem_gb > 60
print(f'Free VRAM {free_mem_gb} GB')
print(f'High-VRAM Mode: {high_vram}')
text_encoder = LlamaModel.from_pretrained("hunyuanvideo-community/HunyuanVideo", subfolder='text_encoder', torch_dtype=torch.float16).cpu()
text_encoder_2 = CLIPTextModel.from_pretrained("hunyuanvideo-community/HunyuanVideo", subfolder='text_encoder_2', torch_dtype=torch.float16).cpu()
tokenizer = LlamaTokenizerFast.from_pretrained("hunyuanvideo-community/HunyuanVideo", subfolder='tokenizer')
tokenizer_2 = CLIPTokenizer.from_pretrained("hunyuanvideo-community/HunyuanVideo", subfolder='tokenizer_2')
vae = AutoencoderKLHunyuanVideo.from_pretrained("hunyuanvideo-community/HunyuanVideo", subfolder='vae', torch_dtype=torch.float16).cpu()
feature_extractor = SiglipImageProcessor.from_pretrained("lllyasviel/flux_redux_bfl", subfolder='feature_extractor')
image_encoder = SiglipVisionModel.from_pretrained("lllyasviel/flux_redux_bfl", subfolder='image_encoder', torch_dtype=torch.float16).cpu()
transformer = HunyuanVideoTransformer3DModelPacked.from_pretrained('lllyasviel/FramePack_F1_I2V_HY_20250503', torch_dtype=torch.bfloat16).cpu()
vae.eval()
text_encoder.eval()
text_encoder_2.eval()
image_encoder.eval()
transformer.eval()
if not high_vram:
vae.enable_slicing()
vae.enable_tiling()
transformer.high_quality_fp32_output_for_inference = True
print('transformer.high_quality_fp32_output_for_inference = True')
transformer.to(dtype=torch.bfloat16)
vae.to(dtype=torch.float16)
image_encoder.to(dtype=torch.float16)
text_encoder.to(dtype=torch.float16)
text_encoder_2.to(dtype=torch.float16)
vae.requires_grad_(False)
text_encoder.requires_grad_(False)
text_encoder_2.requires_grad_(False)
image_encoder.requires_grad_(False)
transformer.requires_grad_(False)
if not high_vram:
# DynamicSwapInstaller is same as huggingface's enable_sequential_offload but 3x faster
DynamicSwapInstaller.install_model(transformer, device=gpu)
DynamicSwapInstaller.install_model(text_encoder, device=gpu)
else:
text_encoder.to(gpu)
text_encoder_2.to(gpu)
image_encoder.to(gpu)
vae.to(gpu)
transformer.to(gpu)
stream = AsyncStream()
outputs_folder = './outputs/'
os.makedirs(outputs_folder, exist_ok=True)
default_local_storage = {
"generation-mode": "image",
}
@spaces.GPU()
@torch.no_grad()
def video_encode(video_path, resolution, no_resize, vae, vae_batch_size=16, device="cuda", width=None, height=None):
"""
Encode a video into latent representations using the VAE.
Args:
video_path: Path to the input video file.
vae: AutoencoderKLHunyuanVideo model.
height, width: Target resolution for resizing frames.
vae_batch_size: Number of frames to process per batch.
device: Device for computation (e.g., "cuda").
Returns:
start_latent: Latent of the first frame (for compatibility with original code).
input_image_np: First frame as numpy array (for CLIP vision encoding).
history_latents: Latents of all frames (shape: [1, channels, frames, height//8, width//8]).
fps: Frames per second of the input video.
"""
# 20250506 pftq: Normalize video path for Windows compatibility
video_path = str(pathlib.Path(video_path).resolve())
print(f"Processing video: {video_path}")
# 20250506 pftq: Check CUDA availability and fallback to CPU if needed
if device == "cuda" and not torch.cuda.is_available():
print("CUDA is not available, falling back to CPU")
device = "cpu"
try:
# 20250506 pftq: Load video and get FPS
print("Initializing VideoReader...")
vr = decord.VideoReader(video_path)
fps = vr.get_avg_fps() # Get input video FPS
num_real_frames = len(vr)
print(f"Video loaded: {num_real_frames} frames, FPS: {fps}")
# Truncate to nearest latent size (multiple of 4)
latent_size_factor = 4
num_frames = (num_real_frames // latent_size_factor) * latent_size_factor
if num_frames != num_real_frames:
print(f"Truncating video from {num_real_frames} to {num_frames} frames for latent size compatibility")
num_real_frames = num_frames
# 20250506 pftq: Read frames
print("Reading video frames...")
frames = vr.get_batch(range(num_real_frames)).asnumpy() # Shape: (num_real_frames, height, width, channels)
print(f"Frames read: {frames.shape}")
# 20250506 pftq: Get native video resolution
native_height, native_width = frames.shape[1], frames.shape[2]
print(f"Native video resolution: {native_width}x{native_height}")
# 20250506 pftq: Use native resolution if height/width not specified, otherwise use provided values
target_height = native_height if height is None else height
target_width = native_width if width is None else width
# 20250506 pftq: Adjust to nearest bucket for model compatibility
if not no_resize:
target_height, target_width = find_nearest_bucket(target_height, target_width, resolution=resolution)
print(f"Adjusted resolution: {target_width}x{target_height}")
else:
print(f"Using native resolution without resizing: {target_width}x{target_height}")
# 20250506 pftq: Preprocess frames to match original image processing
processed_frames = []
for i, frame in enumerate(frames):
#print(f"Preprocessing frame {i+1}/{num_frames}")
frame_np = resize_and_center_crop(frame, target_width=target_width, target_height=target_height)
processed_frames.append(frame_np)
processed_frames = np.stack(processed_frames) # Shape: (num_real_frames, height, width, channels)
print(f"Frames preprocessed: {processed_frames.shape}")
# 20250506 pftq: Save first frame for CLIP vision encoding
input_image_np = processed_frames[0]
# 20250506 pftq: Convert to tensor and normalize to [-1, 1]
print("Converting frames to tensor...")
frames_pt = torch.from_numpy(processed_frames).float() / 127.5 - 1
frames_pt = frames_pt.permute(0, 3, 1, 2) # Shape: (num_real_frames, channels, height, width)
frames_pt = frames_pt.unsqueeze(0) # Shape: (1, num_real_frames, channels, height, width)
frames_pt = frames_pt.permute(0, 2, 1, 3, 4) # Shape: (1, channels, num_real_frames, height, width)
print(f"Tensor shape: {frames_pt.shape}")
# 20250507 pftq: Save pixel frames for use in worker
input_video_pixels = frames_pt.cpu()
# 20250506 pftq: Move to device
print(f"Moving tensor to device: {device}")
frames_pt = frames_pt.to(device)
print("Tensor moved to device")
# 20250506 pftq: Move VAE to device
print(f"Moving VAE to device: {device}")
vae.to(device)
print("VAE moved to device")
# 20250506 pftq: Encode frames in batches
print(f"Encoding input video frames in VAE batch size {vae_batch_size} (reduce if memory issues here or if forcing video resolution)")
latents = []
vae.eval()
with torch.no_grad():
for i in tqdm(range(0, frames_pt.shape[2], vae_batch_size), desc="Encoding video frames", mininterval=0.1):
#print(f"Encoding batch {i//vae_batch_size + 1}: frames {i} to {min(i + vae_batch_size, frames_pt.shape[2])}")
batch = frames_pt[:, :, i:i + vae_batch_size] # Shape: (1, channels, batch_size, height, width)
try:
# 20250506 pftq: Log GPU memory before encoding
if device == "cuda":
free_mem = torch.cuda.memory_allocated() / 1024**3
#print(f"GPU memory before encoding: {free_mem:.2f} GB")
batch_latent = vae_encode(batch, vae)
# 20250506 pftq: Synchronize CUDA to catch issues
if device == "cuda":
torch.cuda.synchronize()
#print(f"GPU memory after encoding: {torch.cuda.memory_allocated() / 1024**3:.2f} GB")
latents.append(batch_latent)
#print(f"Batch encoded, latent shape: {batch_latent.shape}")
except RuntimeError as e:
print(f"Error during VAE encoding: {str(e)}")
if device == "cuda" and "out of memory" in str(e).lower():
print("CUDA out of memory, try reducing vae_batch_size or using CPU")
raise
# 20250506 pftq: Concatenate latents
print("Concatenating latents...")
history_latents = torch.cat(latents, dim=2) # Shape: (1, channels, frames, height//8, width//8)
print(f"History latents shape: {history_latents.shape}")
# 20250506 pftq: Get first frame's latent
start_latent = history_latents[:, :, :1] # Shape: (1, channels, 1, height//8, width//8)
print(f"Start latent shape: {start_latent.shape}")
# 20250506 pftq: Move VAE back to CPU to free GPU memory
if device == "cuda":
vae.to(cpu)
torch.cuda.empty_cache()
print("VAE moved back to CPU, CUDA cache cleared")
return start_latent, input_image_np, history_latents, fps, target_height, target_width, input_video_pixels
except Exception as e:
print(f"Error in video_encode: {str(e)}")
raise
# 20250508 pftq: for saving prompt to mp4 metadata comments
def set_mp4_comments_imageio_ffmpeg(input_file, comments):
try:
# Get the path to the bundled FFmpeg binary from imageio-ffmpeg
ffmpeg_path = imageio_ffmpeg.get_ffmpeg_exe()
# Check if input file exists
if not os.path.exists(input_file):
print(f"Error: Input file {input_file} does not exist")
return False
# Create a temporary file path
temp_file = tempfile.NamedTemporaryFile(suffix='.mp4', delete=False).name
# FFmpeg command using the bundled binary
command = [
ffmpeg_path, # Use imageio-ffmpeg's FFmpeg
'-i', input_file, # input file
'-metadata', f'comment={comments}', # set comment metadata
'-c:v', 'copy', # copy video stream without re-encoding
'-c:a', 'copy', # copy audio stream without re-encoding
'-y', # overwrite output file if it exists
temp_file # temporary output file
]
# Run the FFmpeg command
result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
if result.returncode == 0:
# Replace the original file with the modified one
shutil.move(temp_file, input_file)
print(f"Successfully added comments to {input_file}")
return True
else:
# Clean up temp file if FFmpeg fails
if os.path.exists(temp_file):
os.remove(temp_file)
print(f"Error: FFmpeg failed with message:\n{result.stderr}")
return False
except Exception as e:
# Clean up temp file in case of other errors
if 'temp_file' in locals() and os.path.exists(temp_file):
os.remove(temp_file)
print(f"Error saving prompt to video metadata, ffmpeg may be required: "+str(e))
return False
@torch.no_grad()
def worker(input_image, prompts, n_prompt, seed, resolution, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, enable_preview, use_teacache, mp4_crf):
def encode_prompt(prompt, n_prompt):
llama_vec, clip_l_pooler = encode_prompt_conds(prompt, text_encoder, text_encoder_2, tokenizer, tokenizer_2)
if cfg == 1:
llama_vec_n, clip_l_pooler_n = torch.zeros_like(llama_vec), torch.zeros_like(clip_l_pooler)
else:
llama_vec_n, clip_l_pooler_n = encode_prompt_conds(n_prompt, text_encoder, text_encoder_2, tokenizer, tokenizer_2)
llama_vec, llama_attention_mask = crop_or_pad_yield_mask(llama_vec, length=512)
llama_vec_n, llama_attention_mask_n = crop_or_pad_yield_mask(llama_vec_n, length=512)
llama_vec = llama_vec.to(transformer.dtype)
llama_vec_n = llama_vec_n.to(transformer.dtype)
clip_l_pooler = clip_l_pooler.to(transformer.dtype)
clip_l_pooler_n = clip_l_pooler_n.to(transformer.dtype)
return [llama_vec, clip_l_pooler, llama_vec_n, clip_l_pooler_n, llama_attention_mask, llama_attention_mask_n]
total_latent_sections = (total_second_length * 30) / (latent_window_size * 4)
total_latent_sections = int(max(round(total_latent_sections), 1))
job_id = generate_timestamp()
stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'Starting ...'))))
try:
# Clean GPU
if not high_vram:
unload_complete_models(
text_encoder, text_encoder_2, image_encoder, vae, transformer
)
# Text encoding
stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'Text encoding ...'))))
if not high_vram:
fake_diffusers_current_device(text_encoder, gpu) # since we only encode one text - that is one model move and one encode, offload is same time consumption since it is also one load and one encode.
load_model_as_complete(text_encoder_2, target_device=gpu)
prompt_parameters = []
for prompt_part in prompts:
prompt_parameters.append(encode_prompt(prompt_part, n_prompt))
# Processing input image
stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'Image processing ...'))))
H, W, C = input_image.shape
height, width = find_nearest_bucket(H, W, resolution=resolution)
input_image_np = resize_and_center_crop(input_image, target_width=width, target_height=height)
Image.fromarray(input_image_np).save(os.path.join(outputs_folder, f'{job_id}.png'))
input_image_pt = torch.from_numpy(input_image_np).float() / 127.5 - 1
input_image_pt = input_image_pt.permute(2, 0, 1)[None, :, None]
# VAE encoding
stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'VAE encoding ...'))))
if not high_vram:
load_model_as_complete(vae, target_device=gpu)
start_latent = vae_encode(input_image_pt, vae)
# CLIP Vision
stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'CLIP Vision encoding ...'))))
if not high_vram:
load_model_as_complete(image_encoder, target_device=gpu)
image_encoder_output = hf_clip_vision_encode(input_image_np, feature_extractor, image_encoder)
image_encoder_last_hidden_state = image_encoder_output.last_hidden_state
# Dtype
image_encoder_last_hidden_state = image_encoder_last_hidden_state.to(transformer.dtype)
# Sampling
stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'Start sampling ...'))))
rnd = torch.Generator("cpu").manual_seed(seed)
history_latents = torch.zeros(size=(1, 16, 16 + 2 + 1, height // 8, width // 8), dtype=torch.float32).cpu()
history_pixels = None
history_latents = torch.cat([history_latents, start_latent.to(history_latents)], dim=2)
total_generated_latent_frames = 1
if enable_preview:
def callback(d):
preview = d['denoised']
preview = vae_decode_fake(preview)
preview = (preview * 255.0).detach().cpu().numpy().clip(0, 255).astype(np.uint8)
preview = einops.rearrange(preview, 'b c t h w -> (b h) (t w) c')
if stream.input_queue.top() == 'end':
stream.output_queue.push(('end', None))
raise KeyboardInterrupt('User ends the task.')
current_step = d['i'] + 1
percentage = int(100.0 * current_step / steps)
hint = f'Sampling {current_step}/{steps}'
desc = f'Total generated frames: {int(max(0, total_generated_latent_frames * 4 - 3))}, Video length: {max(0, (total_generated_latent_frames * 4 - 3) / 30) :.2f} seconds (FPS-30), Resolution: {height}px * {width}px. The video is being extended now ...'
stream.output_queue.push(('progress', (preview, desc, make_progress_bar_html(percentage, hint))))
return
else:
def callback(d):
return
indices = torch.arange(0, sum([1, 16, 2, 1, latent_window_size])).unsqueeze(0)
clean_latent_indices_start, clean_latent_4x_indices, clean_latent_2x_indices, clean_latent_1x_indices, latent_indices = indices.split([1, 16, 2, 1, latent_window_size], dim=1)
clean_latent_indices = torch.cat([clean_latent_indices_start, clean_latent_1x_indices], dim=1)
for section_index in range(total_latent_sections):
if stream.input_queue.top() == 'end':
stream.output_queue.push(('end', None))
return
print(f'section_index = {section_index}, total_latent_sections = {total_latent_sections}')
if len(prompt_parameters) > 0:
[llama_vec, clip_l_pooler, llama_vec_n, clip_l_pooler_n, llama_attention_mask, llama_attention_mask_n] = prompt_parameters.pop(0)
if not high_vram:
unload_complete_models()
move_model_to_device_with_memory_preservation(transformer, target_device=gpu, preserved_memory_gb=gpu_memory_preservation)
if use_teacache:
transformer.initialize_teacache(enable_teacache=True, num_steps=steps)
else:
transformer.initialize_teacache(enable_teacache=False)
clean_latents_4x, clean_latents_2x, clean_latents_1x = history_latents[:, :, -sum([16, 2, 1]):, :, :].split([16, 2, 1], dim=2)
clean_latents = torch.cat([start_latent.to(history_latents), clean_latents_1x], dim=2)
generated_latents = sample_hunyuan(
transformer=transformer,
sampler='unipc',
width=width,
height=height,
frames=latent_window_size * 4 - 3,
real_guidance_scale=cfg,
distilled_guidance_scale=gs,
guidance_rescale=rs,
# shift=3.0,
num_inference_steps=steps,
generator=rnd,
prompt_embeds=llama_vec,
prompt_embeds_mask=llama_attention_mask,
prompt_poolers=clip_l_pooler,
negative_prompt_embeds=llama_vec_n,
negative_prompt_embeds_mask=llama_attention_mask_n,
negative_prompt_poolers=clip_l_pooler_n,
device=gpu,
dtype=torch.bfloat16,
image_embeddings=image_encoder_last_hidden_state,
latent_indices=latent_indices,
clean_latents=clean_latents,
clean_latent_indices=clean_latent_indices,
clean_latents_2x=clean_latents_2x,
clean_latent_2x_indices=clean_latent_2x_indices,
clean_latents_4x=clean_latents_4x,
clean_latent_4x_indices=clean_latent_4x_indices,
callback=callback,
)
total_generated_latent_frames += int(generated_latents.shape[2])
history_latents = torch.cat([history_latents, generated_latents.to(history_latents)], dim=2)
if not high_vram:
offload_model_from_device_for_memory_preservation(transformer, target_device=gpu, preserved_memory_gb=8)
load_model_as_complete(vae, target_device=gpu)
real_history_latents = history_latents[:, :, -total_generated_latent_frames:, :, :]
if history_pixels is None:
history_pixels = vae_decode(real_history_latents, vae).cpu()
else:
section_latent_frames = latent_window_size * 2
overlapped_frames = latent_window_size * 4 - 3
current_pixels = vae_decode(real_history_latents[:, :, -section_latent_frames:], vae).cpu()
history_pixels = soft_append_bcthw(history_pixels, current_pixels, overlapped_frames)
if not high_vram:
unload_complete_models()
if enable_preview or section_index == total_latent_sections - 1:
output_filename = os.path.join(outputs_folder, f'{job_id}_{total_generated_latent_frames}.mp4')
save_bcthw_as_mp4(history_pixels, output_filename, fps=30, crf=mp4_crf)
print(f'Decoded. Current latent shape {real_history_latents.shape}; pixel shape {history_pixels.shape}')
stream.output_queue.push(('file', output_filename))
except:
traceback.print_exc()
if not high_vram:
unload_complete_models(
text_encoder, text_encoder_2, image_encoder, vae, transformer
)
stream.output_queue.push(('end', None))
return
def get_duration(input_image, prompt, generation_mode, n_prompt, randomize_seed, seed, resolution, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, enable_preview, use_teacache, mp4_crf):
return total_second_length * 60 * (0.9 if use_teacache else 1.5) * (2**((resolution - 640) / 640)) * (1 + ((steps - 25) / 100))
@spaces.GPU(duration=get_duration)
def process(input_image, prompt,
generation_mode="image",
n_prompt="",
randomize_seed=True,
seed=31337,
resolution=640,
total_second_length=5,
latent_window_size=9,
steps=25,
cfg=1.0,
gs=10.0,
rs=0.0,
gpu_memory_preservation=6,
enable_preview=True,
use_teacache=False,
mp4_crf=16
):
global stream
if torch.cuda.device_count() == 0:
gr.Warning('Set this space to GPU config to make it work.')
yield gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), gr.update()
return
if randomize_seed:
seed = random.randint(0, np.iinfo(np.int32).max)
prompts = prompt.split(";")
# assert input_image is not None, 'No input image!'
if generation_mode == "text":
default_height, default_width = 640, 640
input_image = np.ones((default_height, default_width, 3), dtype=np.uint8) * 255
print("No input image provided. Using a blank white image.")
yield None, None, '', '', gr.update(interactive=False), gr.update(interactive=True)
stream = AsyncStream()
async_run(worker, input_image, prompts, n_prompt, seed, resolution, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, enable_preview, use_teacache, mp4_crf)
output_filename = None
while True:
flag, data = stream.output_queue.next()
if flag == 'file':
output_filename = data
yield output_filename, gr.update(), gr.update(), gr.update(), gr.update(interactive=False), gr.update(interactive=True)
if flag == 'progress':
preview, desc, html = data
yield gr.update(), gr.update(visible=True, value=preview), desc, html, gr.update(interactive=False), gr.update(interactive=True)
if flag == 'end':
yield output_filename, gr.update(visible=False), gr.update(), 'To make all your generated scenes consistent, you can then apply a face swap on the main character.', gr.update(interactive=True), gr.update(interactive=False)
break
# 20250506 pftq: Modified worker to accept video input and clean frame count
@spaces.GPU()
@torch.no_grad()
def worker_video(input_video, prompts, n_prompt, seed, batch, resolution, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, enable_preview, use_teacache, no_resize, mp4_crf, num_clean_frames, vae_batch):
def encode_prompt(prompt, n_prompt):
llama_vec, clip_l_pooler = encode_prompt_conds(prompt, text_encoder, text_encoder_2, tokenizer, tokenizer_2)
if cfg == 1:
llama_vec_n, clip_l_pooler_n = torch.zeros_like(llama_vec), torch.zeros_like(clip_l_pooler)
else:
llama_vec_n, clip_l_pooler_n = encode_prompt_conds(n_prompt, text_encoder, text_encoder_2, tokenizer, tokenizer_2)
llama_vec, llama_attention_mask = crop_or_pad_yield_mask(llama_vec, length=512)
llama_vec_n, llama_attention_mask_n = crop_or_pad_yield_mask(llama_vec_n, length=512)
llama_vec = llama_vec.to(transformer.dtype)
llama_vec_n = llama_vec_n.to(transformer.dtype)
clip_l_pooler = clip_l_pooler.to(transformer.dtype)
clip_l_pooler_n = clip_l_pooler_n.to(transformer.dtype)
return [llama_vec, clip_l_pooler, llama_vec_n, clip_l_pooler_n, llama_attention_mask, llama_attention_mask_n]
stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'Starting ...'))))
try:
# Clean GPU
if not high_vram:
unload_complete_models(
text_encoder, text_encoder_2, image_encoder, vae, transformer
)
# Text encoding
stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'Text encoding ...'))))
if not high_vram:
fake_diffusers_current_device(text_encoder, gpu) # since we only encode one text - that is one model move and one encode, offload is same time consumption since it is also one load and one encode.
load_model_as_complete(text_encoder_2, target_device=gpu)
prompt_parameters = []
for prompt_part in prompts:
prompt_parameters.append(encode_prompt(prompt_part, n_prompt))
# 20250506 pftq: Processing input video instead of image
stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'Video processing ...'))))
# 20250506 pftq: Encode video
start_latent, input_image_np, video_latents, fps, height, width, input_video_pixels = video_encode(input_video, resolution, no_resize, vae, vae_batch_size=vae_batch, device=gpu)
# CLIP Vision
stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'CLIP Vision encoding ...'))))
if not high_vram:
load_model_as_complete(image_encoder, target_device=gpu)
image_encoder_output = hf_clip_vision_encode(input_image_np, feature_extractor, image_encoder)
image_encoder_last_hidden_state = image_encoder_output.last_hidden_state
# Dtype
image_encoder_last_hidden_state = image_encoder_last_hidden_state.to(transformer.dtype)
total_latent_sections = (total_second_length * fps) / (latent_window_size * 4)
total_latent_sections = int(max(round(total_latent_sections), 1))
if enable_preview:
def callback(d):
preview = d['denoised']
preview = vae_decode_fake(preview)
preview = (preview * 255.0).detach().cpu().numpy().clip(0, 255).astype(np.uint8)
preview = einops.rearrange(preview, 'b c t h w -> (b h) (t w) c')
if stream.input_queue.top() == 'end':
stream.output_queue.push(('end', None))
raise KeyboardInterrupt('User ends the task.')
current_step = d['i'] + 1
percentage = int(100.0 * current_step / steps)
hint = f'Sampling {current_step}/{steps}'
desc = f'Total frames: {int(max(0, total_generated_latent_frames * 4 - 3))}, Video length: {max(0, (total_generated_latent_frames * 4 - 3) / fps) :.2f} seconds (FPS-{fps}), Resolution: {height}px * {width}px, Seed: {seed}, Video {idx+1} of {batch}. The video is generating part {section_index+1} of {total_latent_sections}...'
stream.output_queue.push(('progress', (preview, desc, make_progress_bar_html(percentage, hint))))
return
else:
def callback(d):
return
for idx in range(batch):
if batch > 1:
print(f"Beginning video {idx+1} of {batch} with seed {seed} ")
#job_id = generate_timestamp()
job_id = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")+f"_framepackf1-videoinput_{width}-{total_second_length}sec_seed-{seed}_steps-{steps}_distilled-{gs}_cfg-{cfg}" # 20250506 pftq: easier to read timestamp and filename
# Sampling
stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'Start sampling ...'))))
rnd = torch.Generator("cpu").manual_seed(seed)
# 20250506 pftq: Initialize history_latents with video latents
history_latents = video_latents.cpu()
total_generated_latent_frames = history_latents.shape[2]
# 20250506 pftq: Initialize history_pixels to fix UnboundLocalError
history_pixels = None
previous_video = None
for section_index in range(total_latent_sections):
if stream.input_queue.top() == 'end':
stream.output_queue.push(('end', None))
return
print(f'section_index = {section_index}, total_latent_sections = {total_latent_sections}')
if len(prompt_parameters) > 0:
[llama_vec, clip_l_pooler, llama_vec_n, clip_l_pooler_n, llama_attention_mask, llama_attention_mask_n] = prompt_parameters.pop(0)
if not high_vram:
unload_complete_models()
move_model_to_device_with_memory_preservation(transformer, target_device=gpu, preserved_memory_gb=gpu_memory_preservation)
if use_teacache:
transformer.initialize_teacache(enable_teacache=True, num_steps=steps)
else:
transformer.initialize_teacache(enable_teacache=False)
# 20250506 pftq: Use user-specified number of context frames, matching original allocation for num_clean_frames=2
available_frames = history_latents.shape[2] # Number of latent frames
max_pixel_frames = min(latent_window_size * 4 - 3, available_frames * 4) # Cap at available pixel frames
adjusted_latent_frames = max(1, (max_pixel_frames + 3) // 4) # Convert back to latent frames
# Adjust num_clean_frames to match original behavior: num_clean_frames=2 means 1 frame for clean_latents_1x
effective_clean_frames = max(0, num_clean_frames - 1) if num_clean_frames > 1 else 0
effective_clean_frames = min(effective_clean_frames, available_frames - 2) if available_frames > 2 else 0 # 20250507 pftq: changed 1 to 2 for edge case for <=1 sec videos
num_2x_frames = min(2, max(1, available_frames - effective_clean_frames - 1)) if available_frames > effective_clean_frames + 1 else 0 # 20250507 pftq: subtracted 1 for edge case for <=1 sec videos
num_4x_frames = min(16, max(1, available_frames - effective_clean_frames - num_2x_frames)) if available_frames > effective_clean_frames + num_2x_frames else 0 # 20250507 pftq: Edge case for <=1 sec
total_context_frames = num_4x_frames + num_2x_frames + effective_clean_frames
total_context_frames = min(total_context_frames, available_frames) # 20250507 pftq: Edge case for <=1 sec videos
indices = torch.arange(0, sum([1, num_4x_frames, num_2x_frames, effective_clean_frames, adjusted_latent_frames])).unsqueeze(0) # 20250507 pftq: latent_window_size to adjusted_latent_frames for edge case for <=1 sec videos
clean_latent_indices_start, clean_latent_4x_indices, clean_latent_2x_indices, clean_latent_1x_indices, latent_indices = indices.split(
[1, num_4x_frames, num_2x_frames, effective_clean_frames, adjusted_latent_frames], dim=1 # 20250507 pftq: latent_window_size to adjusted_latent_frames for edge case for <=1 sec videos
)
clean_latent_indices = torch.cat([clean_latent_indices_start, clean_latent_1x_indices], dim=1)
# 20250506 pftq: Split history_latents dynamically based on available frames
fallback_frame_count = 2 # 20250507 pftq: Changed 0 to 2 Edge case for <=1 sec videos
context_frames = clean_latents_4x = clean_latents_2x = clean_latents_1x = history_latents[:, :, :fallback_frame_count, :, :]
if total_context_frames > 0:
context_frames = history_latents[:, :, -total_context_frames:, :, :]
split_sizes = [num_4x_frames, num_2x_frames, effective_clean_frames]
split_sizes = [s for s in split_sizes if s > 0] # Remove zero sizes
if split_sizes:
splits = context_frames.split(split_sizes, dim=2)
split_idx = 0
if num_4x_frames > 0:
clean_latents_4x = splits[split_idx]
split_idx = 1
if clean_latents_4x.shape[2] < 2: # 20250507 pftq: edge case for <=1 sec videos
print("Edge case for <=1 sec videos 4x")
clean_latents_4x = clean_latents_4x.expand(-1, -1, 2, -1, -1)
if num_2x_frames > 0 and split_idx < len(splits):
clean_latents_2x = splits[split_idx]
if clean_latents_2x.shape[2] < 2: # 20250507 pftq: edge case for <=1 sec videos
print("Edge case for <=1 sec videos 2x")
clean_latents_2x = clean_latents_2x.expand(-1, -1, 2, -1, -1)
split_idx += 1
elif clean_latents_2x.shape[2] < 2: # 20250507 pftq: edge case for <=1 sec videos
clean_latents_2x = clean_latents_4x
if effective_clean_frames > 0 and split_idx < len(splits):
clean_latents_1x = splits[split_idx]
clean_latents = torch.cat([start_latent.to(history_latents), clean_latents_1x], dim=2)
# 20250507 pftq: Fix for <=1 sec videos.
max_frames = min(latent_window_size * 4 - 3, history_latents.shape[2] * 4)
generated_latents = sample_hunyuan(
transformer=transformer,
sampler='unipc',
width=width,
height=height,
frames=max_frames,
real_guidance_scale=cfg,
distilled_guidance_scale=gs,
guidance_rescale=rs,
num_inference_steps=steps,
generator=rnd,
prompt_embeds=llama_vec,
prompt_embeds_mask=llama_attention_mask,
prompt_poolers=clip_l_pooler,
negative_prompt_embeds=llama_vec_n,
negative_prompt_embeds_mask=llama_attention_mask_n,
negative_prompt_poolers=clip_l_pooler_n,
device=gpu,
dtype=torch.bfloat16,
image_embeddings=image_encoder_last_hidden_state,
latent_indices=latent_indices,
clean_latents=clean_latents,
clean_latent_indices=clean_latent_indices,
clean_latents_2x=clean_latents_2x,
clean_latent_2x_indices=clean_latent_2x_indices,
clean_latents_4x=clean_latents_4x,
clean_latent_4x_indices=clean_latent_4x_indices,
callback=callback,
)
total_generated_latent_frames += int(generated_latents.shape[2])
history_latents = torch.cat([history_latents, generated_latents.to(history_latents)], dim=2)
if not high_vram:
offload_model_from_device_for_memory_preservation(transformer, target_device=gpu, preserved_memory_gb=8)
load_model_as_complete(vae, target_device=gpu)
real_history_latents = history_latents[:, :, -total_generated_latent_frames:, :, :]
if history_pixels is None:
history_pixels = vae_decode(real_history_latents, vae).cpu()
else:
section_latent_frames = latent_window_size * 2
overlapped_frames = min(latent_window_size * 4 - 3, history_pixels.shape[2])
current_pixels = vae_decode(real_history_latents[:, :, -section_latent_frames:], vae).cpu()
history_pixels = soft_append_bcthw(history_pixels, current_pixels, overlapped_frames)
if not high_vram:
unload_complete_models()
if enable_preview or section_index == total_latent_sections - 1:
output_filename = os.path.join(outputs_folder, f'{job_id}_{total_generated_latent_frames}.mp4')
# 20250506 pftq: Use input video FPS for output
save_bcthw_as_mp4(history_pixels, output_filename, fps=fps, crf=mp4_crf)
print(f"Latest video saved: {output_filename}")
# 20250508 pftq: Save prompt to mp4 metadata comments
set_mp4_comments_imageio_ffmpeg(output_filename, f"Prompt: {prompts} | Negative Prompt: {n_prompt}");
print(f"Prompt saved to mp4 metadata comments: {output_filename}")
# 20250506 pftq: Clean up previous partial files
if previous_video is not None and os.path.exists(previous_video):
try:
os.remove(previous_video)
print(f"Previous partial video deleted: {previous_video}")
except Exception as e:
print(f"Error deleting previous partial video {previous_video}: {e}")
previous_video = output_filename
print(f'Decoded. Current latent shape {real_history_latents.shape}; pixel shape {history_pixels.shape}')
stream.output_queue.push(('file', output_filename))
seed = (seed + 1) % np.iinfo(np.int32).max
except:
traceback.print_exc()
if not high_vram:
unload_complete_models(
text_encoder, text_encoder_2, image_encoder, vae, transformer
)
stream.output_queue.push(('end', None))
return
def get_duration_video(input_video, prompt, n_prompt, randomize_seed, seed, batch, resolution, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, enable_preview, use_teacache, no_resize, mp4_crf, num_clean_frames, vae_batch):
return total_second_length * 60 * (0.9 if use_teacache else 2.3) * (2**((resolution - 640) / 640)) * (1 + ((steps - 25) / 100))
# 20250506 pftq: Modified process to pass clean frame count, etc from video_encode
@spaces.GPU(duration=get_duration_video)
def process_video(input_video, prompt, n_prompt, randomize_seed, seed, batch, resolution, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, enable_preview, use_teacache, no_resize, mp4_crf, num_clean_frames, vae_batch):
global stream, high_vram
if torch.cuda.device_count() == 0:
gr.Warning('Set this space to GPU config to make it work.')
yield gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), gr.update()
return
if randomize_seed:
seed = random.randint(0, np.iinfo(np.int32).max)
prompts = prompt.split(";")
# 20250506 pftq: Updated assertion for video input
assert input_video is not None, 'No input video!'
yield None, None, '', '', gr.update(interactive=False), gr.update(interactive=True)
# 20250507 pftq: Even the H100 needs offloading if the video dimensions are 720p or higher
if high_vram and (no_resize or resolution>640):
print("Disabling high vram mode due to no resize and/or potentially higher resolution...")
high_vram = False
vae.enable_slicing()
vae.enable_tiling()
DynamicSwapInstaller.install_model(transformer, device=gpu)
DynamicSwapInstaller.install_model(text_encoder, device=gpu)
# 20250508 pftq: automatically set distilled cfg to 1 if cfg is used
if cfg > 1:
gs = 1
stream = AsyncStream()
# 20250506 pftq: Pass num_clean_frames, vae_batch, etc
async_run(worker_video, input_video, prompts, n_prompt, seed, batch, resolution, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, enable_preview, use_teacache, no_resize, mp4_crf, num_clean_frames, vae_batch)
output_filename = None
while True:
flag, data = stream.output_queue.next()
if flag == 'file':
output_filename = data
yield output_filename, gr.update(), gr.update(), gr.update(), gr.update(interactive=False), gr.update(interactive=True)
if flag == 'progress':
preview, desc, html = data
#yield gr.update(), gr.update(visible=True, value=preview), desc, html, gr.update(interactive=False), gr.update(interactive=True)
yield output_filename, gr.update(visible=True, value=preview), desc, html, gr.update(interactive=False), gr.update(interactive=True) # 20250506 pftq: Keep refreshing the video in case it got hidden when the tab was in the background
if flag == 'end':
yield output_filename, gr.update(visible=False), desc+' Video complete. To make all your generated scenes consistent, you can then apply a face swap on the main character.', '', gr.update(interactive=True), gr.update(interactive=False)
break
def end_process():
stream.input_queue.push('end')
timeless_prompt_value = [""]
timed_prompts = {}
def handle_prompt_number_change():
timed_prompts.clear()
return []
def handle_timeless_prompt_change(timeless_prompt):
timeless_prompt_value[0] = timeless_prompt
return refresh_prompt()
def handle_timed_prompt_change(timed_prompt_id, timed_prompt):
timed_prompts[timed_prompt_id] = timed_prompt
return refresh_prompt()
def refresh_prompt():
dict_values = {k: v for k, v in timed_prompts.items()}
sorted_dict_values = sorted(dict_values.items(), key=lambda x: x[0])
array = []
for sorted_dict_value in sorted_dict_values:
if timeless_prompt_value[0] is not None and len(timeless_prompt_value[0]) and sorted_dict_value[1] is not None and len(sorted_dict_value[1]):
array.append(timeless_prompt_value[0] + ". " + sorted_dict_value[1])
else:
array.append(timeless_prompt_value[0] + sorted_dict_value[1])
print(str(array))
return ";".join(array)
title_html = """
FramePack
Generate videos from text/image/video freely, without account, without watermark and download it
This space is ready to work on ZeroGPU and GPU and has been tested successfully on ZeroGPU. Please leave a message in discussion if you encounter issues.
"""
js = """
function createGradioAnimation() {
window.addEventListener("beforeunload", function (e) {
if (document.getElementById('end-button') && !document.getElementById('end-button').disabled) {
var confirmationMessage = 'A process is still running. '
+ 'If you leave before saving, your changes will be lost.';
(e || window.event).returnValue = confirmationMessage;
}
return confirmationMessage;
});
return 'Animation created';
}
"""
css = make_progress_bar_css()
block = gr.Blocks(css=css, js=js).queue()
with block:
if torch.cuda.device_count() == 0:
with gr.Row():
gr.HTML("""
⚠️To use FramePack, duplicate this space and set a GPU with 30 GB VRAM.
You can't use FramePack directly here because this space runs on a CPU, which is not enough for FramePack. Please provide feedback if you have issues.
""")
gr.HTML(title_html)
local_storage = gr.BrowserState(default_local_storage)
with gr.Row():
with gr.Column():
generation_mode = gr.Radio([["Text-to-Video", "text"], ["Image-to-Video", "image"], ["Video Extension", "video"]], elem_id="generation-mode", label="Generation mode", value = "image")
text_to_video_hint = gr.HTML("I discourage to use the Text-to-Video feature. You should rather generate an image with Flux and use Image-to-Video. You will save time.")
input_image = gr.Image(sources='upload', type="numpy", label="Image", height=320)
input_video = gr.Video(sources='upload', label="Input Video", height=320)
timeless_prompt = gr.Textbox(label="Timeless prompt", info='Used on the whole duration of the generation', value='', placeholder="The creature starts to move, fast motion, fixed camera, focus motion, consistent arm, consistent position, mute colors, insanely detailed")
prompt_number = gr.Slider(label="Timed prompt number", minimum=0, maximum=1000, value=0, step=1, info='Prompts will automatically appear')
@gr.render(inputs=prompt_number)
def show_split(prompt_number):
for digit in range(prompt_number):
timed_prompt_id = gr.Textbox(value="timed_prompt_" + str(digit), visible=False)
timed_prompt = gr.Textbox(label="Timed prompt #" + str(digit + 1), elem_id="timed_prompt_" + str(digit), value="")
timed_prompt.change(fn=handle_timed_prompt_change, inputs=[timed_prompt_id, timed_prompt], outputs=[final_prompt])
final_prompt = gr.Textbox(label="Final prompt", value='', info='Use ; to separate in time')
total_second_length = gr.Slider(label="Video Length to Generate (seconds)", minimum=1, maximum=120, value=2, step=0.1)
with gr.Row():
start_button = gr.Button(value="🎥 Generate", variant="primary")
start_button_video = gr.Button(value="🎥 Generate", variant="primary")
end_button = gr.Button(elem_id="end-button", value="End Generation", variant="stop", interactive=False)
with gr.Accordion("Advanced settings", open=False):
enable_preview = gr.Checkbox(label='Enable preview', value=True, info='Display a preview around each second generated but it costs 2 sec. for each second generated.')
use_teacache = gr.Checkbox(label='Use TeaCache', value=False, info='Faster speed, but often makes hands and fingers slightly worse.')
n_prompt = gr.Textbox(label="Negative Prompt", value="Missing arm, unrealistic position, impossible contortion, blurred, blurry", info='Requires using normal CFG (undistilled) instead of Distilled (set Distilled=1 and CFG > 1).')
latent_window_size = gr.Slider(label="Latent Window Size", minimum=1, maximum=33, value=9, step=1, info='Generate more frames at a time (larger chunks). Less degradation and better blending but higher VRAM cost. Should not change.')
steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=25, step=1, info='Increase for more quality, especially if using high non-distilled CFG. Changing this value is not recommended.')
with gr.Row():
no_resize = gr.Checkbox(label='Force Original Video Resolution (no Resizing)', value=False, info='Might run out of VRAM (720p requires > 24GB VRAM).')
resolution = gr.Dropdown([
640,
672,
704,
768,
832,
864,
960
], value=640, label="Resolution (max width or height)")
# 20250506 pftq: Reduced default distilled guidance scale to improve adherence to input video
cfg = gr.Slider(label="CFG Scale", minimum=1.0, maximum=32.0, value=1.0, step=0.01, info='Use this instead of Distilled for more detail/control + Negative Prompt (make sure Distilled set to 1). Doubles render time. Should not change.')
gs = gr.Slider(label="Distilled CFG Scale", minimum=1.0, maximum=32.0, value=10.0, step=0.01, info='Prompt adherence at the cost of less details from the input video, but to a lesser extent than Context Frames; 3=follow the prompt but blurred motions & unsharped, 10=focus motion; changing this value is not recommended')
rs = gr.Slider(label="CFG Re-Scale", minimum=0.0, maximum=1.0, value=0.0, step=0.01, info='Should not change')
# 20250506 pftq: Renamed slider to Number of Context Frames and updated description
num_clean_frames = gr.Slider(label="Number of Context Frames", minimum=2, maximum=10, value=5, step=1, info="Retain more video details but increase memory use. Reduce to 2 to avoid memory issues or to give more weight to the prompt.")
default_vae = 32
if high_vram:
default_vae = 128
elif free_mem_gb>=20:
default_vae = 64
vae_batch = gr.Slider(label="VAE Batch Size for Input Video", minimum=4, maximum=256, value=default_vae, step=4, info="Reduce if running out of memory. Increase for better quality frames during fast motion.")
gpu_memory_preservation = gr.Slider(label="GPU Inference Preserved Memory (GB) (larger means slower)", minimum=6, maximum=128, value=6, step=0.1, info="Set this number to a larger value if you encounter OOM. Larger value causes slower speed.")
mp4_crf = gr.Slider(label="MP4 Compression", minimum=0, maximum=100, value=16, step=1, info="Lower means better quality. 0 is uncompressed. Change to 16 if you get black outputs. ")
batch = gr.Slider(label="Batch Size (Number of Videos)", minimum=1, maximum=1000, value=1, step=1, info='Generate multiple videos each with a different seed.')
with gr.Row():
randomize_seed = gr.Checkbox(label='Randomize seed', value=True, info='If checked, the seed is always different')
seed = gr.Slider(label="Seed", minimum=0, maximum=np.iinfo(np.int32).max, step=1, randomize=True)
with gr.Column():
preview_image = gr.Image(label="Next Latents", height=200, visible=False)
result_video = gr.Video(label="Finished Frames", autoplay=True, show_share_button=False, height=512, loop=True)
progress_desc = gr.Markdown('', elem_classes='no-generating-animation')
progress_bar = gr.HTML('', elem_classes='no-generating-animation')
# 20250506 pftq: Updated inputs to include num_clean_frames
ips = [input_image, final_prompt, generation_mode, n_prompt, randomize_seed, seed, resolution, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, enable_preview, use_teacache, mp4_crf]
ips_video = [input_video, final_prompt, n_prompt, randomize_seed, seed, batch, resolution, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, enable_preview, use_teacache, no_resize, mp4_crf, num_clean_frames, vae_batch]
def save_preferences(preferences, value):
preferences["generation-mode"] = value
return preferences
def load_preferences(saved_prefs):
saved_prefs = init_preferences(saved_prefs)
return saved_prefs["generation-mode"]
def init_preferences(saved_prefs):
if saved_prefs is None:
saved_prefs = default_local_storage
return saved_prefs
def check_parameters(generation_mode, input_image, input_video):
if generation_mode == "image" and input_image is None:
raise gr.Error("Please provide an image to extend.")
if generation_mode == "video" and input_video is None:
raise gr.Error("Please provide a video to extend.")
return gr.update(interactive=True)
prompt_number.change(fn=handle_prompt_number_change, inputs=[], outputs=[])
timeless_prompt.change(fn=handle_timeless_prompt_change, inputs=[timeless_prompt], outputs=[final_prompt])
start_button.click(fn = check_parameters, inputs = [
generation_mode, input_image, input_video
], outputs = [end_button], queue = False, show_progress = False).success(fn=process, inputs=ips, outputs=[result_video, preview_image, progress_desc, progress_bar, start_button, end_button])
start_button_video.click(fn = check_parameters, inputs = [
generation_mode, input_image, input_video
], outputs = [end_button], queue = False, show_progress = False).success(fn=process_video, inputs=ips_video, outputs=[result_video, preview_image, progress_desc, progress_bar, start_button_video, end_button])
end_button.click(fn=end_process)
generation_mode.change(fn = save_preferences, inputs = [
local_storage,
generation_mode,
], outputs = [
local_storage
])
with gr.Row(elem_id="image_examples", visible=False):
gr.Examples(
examples = [
[
"./img_examples/Example1.png", # input_image
"A dolphin emerges from the water, photorealistic, realistic, intricate details, 8k, insanely detailed",
"image", # generation_mode
"Missing arm, unrealistic position, impossible contortion, blurred, blurry", # n_prompt
True, # randomize_seed
42, # seed
672, # resolution
1, # total_second_length
9, # latent_window_size
50, # steps
1.0, # cfg
10.0, # gs
0.0, # rs
6, # gpu_memory_preservation
False, # enable_preview
False, # use_teacache
16 # mp4_crf
],
[
"./img_examples/Example1.png", # input_image
"View of the sea as far as the eye can see, from the seaside, a piece of land is barely visible on the horizon at the middle, the sky is radiant, reflections of the sun in the water, photorealistic, realistic, intricate details, 8k, insanely detailed",
"image", # generation_mode
"Missing arm, unrealistic position, impossible contortion, blurred, blurry", # n_prompt
True, # randomize_seed
42, # seed
672, # resolution
1, # total_second_length
9, # latent_window_size
35, # steps
1.0, # cfg
10.0, # gs
0.0, # rs
6, # gpu_memory_preservation
False, # enable_preview
False, # use_teacache
16 # mp4_crf
],
],
run_on_click = True,
fn = process,
inputs = ips,
outputs = [result_video, preview_image, progress_desc, progress_bar, start_button, end_button],
cache_examples = torch.cuda.device_count() > 0,
)
with gr.Row(elem_id="video_examples", visible=False):
gr.Examples(
examples = [
[
"./img_examples/Example1.mp4", # input_video
"View of the sea as far as the eye can see, from the seaside, a piece of land is barely visible on the horizon at the middle, the sky is radiant, reflections of the sun in the water, photorealistic, realistic, intricate details, 8k, insanely detailed",
"Missing arm, unrealistic position, blurred, blurry", # n_prompt
True, # randomize_seed
42, # seed
1, # batch
672, # resolution
1, # total_second_length
9, # latent_window_size
50, # steps
1.0, # cfg
10.0, # gs
0.0, # rs
6, # gpu_memory_preservation
False, # enable_preview
False, # use_teacache
False, # no_resize
16, # mp4_crf
5, # num_clean_frames
default_vae
],
[
"./img_examples/Example1.mp4", # input_video
"View of the sea as far as the eye can see, from the seaside, a piece of land is barely visible on the horizon at the middle, the sky is radiant, reflections of the sun in the water, photorealistic, realistic, intricate details, 8k, insanely detailed",
"Missing arm, unrealistic position, blurred, blurry", # n_prompt
True, # randomize_seed
42, # seed
1, # batch
640, # resolution
1, # total_second_length
9, # latent_window_size
35, # steps
1.0, # cfg
10.0, # gs
0.0, # rs
6, # gpu_memory_preservation
False, # enable_preview
False, # use_teacache
False, # no_resize
16, # mp4_crf
5, # num_clean_frames
default_vae
],
],
run_on_click = True,
fn = process_video,
inputs = ips_video,
outputs = [result_video, preview_image, progress_desc, progress_bar, start_button_video, end_button],
cache_examples = torch.cuda.device_count() > 0,
)
gr.Examples(
examples = [
[
"./img_examples/Example1.png", # input_image
"A dolphin emerges from the water, photorealistic, realistic, intricate details, 8k, insanely detailed",
"image", # generation_mode
"Missing arm, unrealistic position, impossible contortion, blurred, blurry", # n_prompt
True, # randomize_seed
42, # seed
640, # resolution
1, # total_second_length
9, # latent_window_size
25, # steps
1.0, # cfg
10.0, # gs
0.0, # rs
6, # gpu_memory_preservation
False, # enable_preview
False, # use_teacache
16 # mp4_crf
]
],
run_on_click = True,
fn = process,
inputs = ips,
outputs = [result_video, preview_image, progress_desc, progress_bar, start_button, end_button],
cache_examples = False,
)
gr.Examples(
examples = [
[
"./img_examples/Example1.mp4", # input_video
"View of the sea as far as the eye can see, from the seaside, a piece of land is barely visible on the horizon at the middle, the sky is radiant, reflections of the sun in the water, photorealistic, realistic, intricate details, 8k, insanely detailed",
"Missing arm, unrealistic position, blurred, blurry", # n_prompt
True, # randomize_seed
42, # seed
1, # batch
640, # resolution
1, # total_second_length
9, # latent_window_size
25, # steps
1.0, # cfg
10.0, # gs
0.0, # rs
6, # gpu_memory_preservation
False, # enable_preview
False, # use_teacache
False, # no_resize
16, # mp4_crf
5, # num_clean_frames
default_vae
]
],
run_on_click = True,
fn = process_video,
inputs = ips_video,
outputs = [result_video, preview_image, progress_desc, progress_bar, start_button_video, end_button],
cache_examples = False,
)
def handle_generation_mode_change(generation_mode_data):
if generation_mode_data == "text":
return [gr.update(visible = True), gr.update(visible = False), gr.update(visible = False), gr.update(visible = True), gr.update(visible = False), gr.update(visible = False), gr.update(visible = False), gr.update(visible = False), gr.update(visible = False)]
elif generation_mode_data == "image":
return [gr.update(visible = False), gr.update(visible = True), gr.update(visible = False), gr.update(visible = True), gr.update(visible = False), gr.update(visible = False), gr.update(visible = False), gr.update(visible = False), gr.update(visible = False)]
elif generation_mode_data == "video":
return [gr.update(visible = False), gr.update(visible = False), gr.update(visible = True), gr.update(visible = False), gr.update(visible = True), gr.update(visible = True), gr.update(visible = True), gr.update(visible = True), gr.update(visible = True)]
generation_mode.change(
fn=handle_generation_mode_change,
inputs=[generation_mode],
outputs=[text_to_video_hint, input_image, input_video, start_button, start_button_video, no_resize, batch, num_clean_frames, vae_batch]
)
# Update display when the page loads
block.load(
fn=handle_generation_mode_change, inputs = [
generation_mode
], outputs = [
text_to_video_hint, input_image, input_video, start_button, start_button_video, no_resize, batch, num_clean_frames, vae_batch
]
)
# Load saved preferences when the page loads
block.load(
fn=load_preferences, inputs = [
local_storage
], outputs = [
generation_mode
]
)
block.launch(mcp_server=True, ssr_mode=False)