import os import random import sys from typing import Sequence, Mapping, Any, Union import torch import gradio as gr from huggingface_hub import hf_hub_download import spaces from datetime import datetime import spaces from huggingface_hub import hf_hub_download hf_hub_download(repo_id="Comfy-Org/HunyuanVideo_repackaged",filename="hunyuan_video_t2v_720p_bf16.safetensors",local_dir="models/diffusion_models") hf_hub_download(repo_id="Comfy-Org/HunyuanVideo_repackaged", filename=" clip_l.safetensors", local_dir="models/text_encoders") hf_hub_download(repo_id="Comfy-Org/HunyuanVideo_repackaged", filename=" llava_llama3_fp8_scaled.safetensors", local_dir="models/text_encoders") hf_hub_download(repo_id="Comfy-Org/HunyuanVideo_repackaged", filename=" hunyuan_video_vae_bf16.safetensors", local_dir="models/vae") def get_value_at_index(obj: Union[Sequence, Mapping], index: int) -> Any: """Returns the value at the given index of a sequence or mapping. If the object is a sequence (like list or string), returns the value at the given index. If the object is a mapping (like a dictionary), returns the value at the index-th key. Some return a dictionary, in these cases, we look for the "results" key Args: obj (Union[Sequence, Mapping]): The object to retrieve the value from. index (int): The index of the value to retrieve. Returns: Any: The value at the given index. Raises: IndexError: If the index is out of bounds for the object and the object is not a mapping. """ try: return obj[index] except KeyError: return obj["result"][index] def find_path(name: str, path: str = None) -> str: """ Recursively looks at parent folders starting from the given path until it finds the given name. Returns the path as a Path object if found, or None otherwise. """ # If no path is given, use the current working directory if path is None: path = os.getcwd() # Check if the current directory contains the name if name in os.listdir(path): path_name = os.path.join(path, name) print(f"{name} found: {path_name}") return path_name # Get the parent directory parent_directory = os.path.dirname(path) # If the parent directory is the same as the current directory, we've reached the root and stop the search if parent_directory == path: return None # Recursively call the function with the parent directory return find_path(name, parent_directory) def add_comfyui_directory_to_sys_path() -> None: """ Add 'ComfyUI' to the sys.path """ comfyui_path = find_path("ComfyUI") if comfyui_path is not None and os.path.isdir(comfyui_path): sys.path.append(comfyui_path) print(f"'{comfyui_path}' added to sys.path") def add_extra_model_paths() -> None: """ Parse the optional extra_model_paths.yaml file and add the parsed paths to the sys.path. """ try: from main import load_extra_path_config except ImportError: print( "Could not import load_extra_path_config from main.py. Looking in utils.extra_config instead." ) from utils.extra_config import load_extra_path_config extra_model_paths = find_path("extra_model_paths.yaml") if extra_model_paths is not None: load_extra_path_config(extra_model_paths) else: print("Could not find the extra_model_paths config file.") add_comfyui_directory_to_sys_path() add_extra_model_paths() def import_custom_nodes() -> None: """Find all custom nodes in the custom_nodes folder and add those node objects to NODE_CLASS_MAPPINGS This function sets up a new asyncio event loop, initializes the PromptServer, creates a PromptQueue, and initializes the custom nodes. """ import asyncio import execution from nodes import init_extra_nodes import server # Creating a new event loop and setting it as the default loop loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) # Creating an instance of PromptServer with the loop server_instance = server.PromptServer(loop) execution.PromptQueue(server_instance) # Initializing custom nodes init_extra_nodes() from nodes import NODE_CLASS_MAPPINGS @spaces.GPU(duration=400) def generate_image(prompt,frames,lora_strenth,width,height,frame_rate): import_custom_nodes() with torch.inference_mode(): vaeloader = NODE_CLASS_MAPPINGS["VAELoader"]() vaeloader_10 = vaeloader.load_vae(vae_name="hunyuan_video_vae_bf16.safetensors") dualcliploader = NODE_CLASS_MAPPINGS["DualCLIPLoader"]() dualcliploader_11 = dualcliploader.load_clip( clip_name1="clip_l.safetensors", clip_name2="llava_llama3_fp8_scaled.safetensors", type="hunyuan_video", device="default", ) unetloader = NODE_CLASS_MAPPINGS["UNETLoader"]() unetloader_12 = unetloader.load_unet( unet_name="hunyuan_video_720_cfgdistill_bf16.safetensors", weight_dtype="fp8_e4m3fn", ) ksamplerselect = NODE_CLASS_MAPPINGS["KSamplerSelect"]() ksamplerselect_16 = ksamplerselect.get_sampler( sampler_name="gradient_estimation" ) randomnoise = NODE_CLASS_MAPPINGS["RandomNoise"]() randomnoise_25 = randomnoise.get_noise(noise_seed=random.randint(1, 2**64)) cliptextencode = NODE_CLASS_MAPPINGS["CLIPTextEncode"]() cliptextencode_44 = cliptextencode.encode( text=prompt, clip=get_value_at_index(dualcliploader_11, 0), ) int_literal = NODE_CLASS_MAPPINGS["Int Literal"]() int_literal_295 = int_literal.get_int(int=frames) hunyuanvideoloraloader = NODE_CLASS_MAPPINGS["HunyuanVideoLoraLoader"]() modelsamplingsd3 = NODE_CLASS_MAPPINGS["ModelSamplingSD3"]() fluxguidance = NODE_CLASS_MAPPINGS["FluxGuidance"]() basicguider = NODE_CLASS_MAPPINGS["BasicGuider"]() basicscheduler = NODE_CLASS_MAPPINGS["BasicScheduler"]() emptyhunyuanlatentvideo = NODE_CLASS_MAPPINGS["EmptyHunyuanLatentVideo"]() samplercustomadvanced = NODE_CLASS_MAPPINGS["SamplerCustomAdvanced"]() big_latent_switch_dream = NODE_CLASS_MAPPINGS["Big Latent Switch [Dream]"]() vaedecodetiled = NODE_CLASS_MAPPINGS["VAEDecodeTiled"]() imagesharpen = NODE_CLASS_MAPPINGS["ImageSharpen"]() vhs_videocombine = NODE_CLASS_MAPPINGS["VHS_VideoCombine"]() anything_everywhere3 = NODE_CLASS_MAPPINGS["Anything Everywhere3"]() easy_cleangpuused = NODE_CLASS_MAPPINGS["easy cleanGpuUsed"]() for q in range(1): hunyuanvideoloraloader_255 = hunyuanvideoloraloader.load_lora( lora_name="model.safetensors", strength=lora_strenth, blocks_type="all", model=get_value_at_index(unetloader_12, 0), ) modelsamplingsd3_67 = modelsamplingsd3.patch( shift=9, model=get_value_at_index(hunyuanvideoloraloader_255, 0) ) fluxguidance_26 = fluxguidance.append( guidance=12, conditioning=get_value_at_index(cliptextencode_44, 0) ) basicguider_22 = basicguider.get_guider( model=get_value_at_index(modelsamplingsd3_67, 0), conditioning=get_value_at_index(fluxguidance_26, 0), ) basicscheduler_17 = basicscheduler.get_sigmas( scheduler="simple", steps=40, denoise=1, model=get_value_at_index(hunyuanvideoloraloader_255, 0), ) emptyhunyuanlatentvideo_232 = emptyhunyuanlatentvideo.generate( width=width, height=height, length=get_value_at_index(int_literal_295, 0), batch_size=1, ) samplercustomadvanced_13 = samplercustomadvanced.sample( noise=get_value_at_index(randomnoise_25, 0), guider=get_value_at_index(basicguider_22, 0), sampler=get_value_at_index(ksamplerselect_16, 0), sigmas=get_value_at_index(basicscheduler_17, 0), latent_image=get_value_at_index(emptyhunyuanlatentvideo_232, 0), ) big_latent_switch_dream_243 = big_latent_switch_dream.pick( select=0, on_missing="next", input_2=get_value_at_index(samplercustomadvanced_13, 1), ) vaedecodetiled_73 = vaedecodetiled.decode( tile_size=128, overlap=64, temporal_size=64, temporal_overlap=8, samples=get_value_at_index(big_latent_switch_dream_243, 0), vae=get_value_at_index(vaeloader_10, 0), ) imagesharpen_106 = imagesharpen.sharpen( sharpen_radius=1, sigma=0.43, alpha=0.5, image=get_value_at_index(vaedecodetiled_73, 0), ) vhs_videocombine_82 = vhs_videocombine.combine_video( frame_rate=frame_rate, loop_count=0, filename_prefix="HunyuanVideo", format="video/h264-mp4", pix_fmt="yuv420p", crf=10, save_metadata=True, trim_to_audio=False, pingpong=False, save_output=True, images=get_value_at_index(imagesharpen_106, 0), vae=get_value_at_index(vaeloader_10, 0), unique_id=3348895206324303610, ) anything_everywhere3_180 = anything_everywhere3.func( CLIP=get_value_at_index(dualcliploader_11, 0), VAE=get_value_at_index(vaeloader_10, 0), ) easy_cleangpuused_182 = easy_cleangpuused.empty_cache( anything=get_value_at_index(big_latent_switch_dream_243, 0), unique_id=16583500820061639415, ) #saved_path = f"output/{vhs_videocombine_82['ui']['filename_prefix'][0]}" #return saved_path def get_latest_video(directory="output"): files = [os.path.join(directory, f) for f in os.listdir(directory) if f.endswith(".mp4")] if not files: raise FileNotFoundError("No video files found in the output directory.") latest_file = max(files, key=os.path.getmtime) # Get file with the latest modification time return latest_file # Get the latest video file based on modification time saved_path = get_latest_video() return saved_path import gradio as gr # Placeholder functions for demonstration purposes def generate_video(prompt, frames, lora_strength, width, height, frame_rate): # Integrate with your generation logic here return "Generated Video Placeholder" with gr.Blocks(theme="soft") as app: gr.Markdown("# 🌟 FLUX Style Shaping 🌟") with gr.Row(): with gr.Column(): prompt_input = gr.Textbox(label="📝 Prompt", placeholder="Enter your prompt here...") frames_input = gr.Number(label="🎞 Frames", value=30, minimum=1) lora_strength_input = gr.Slider(label="🔧 LoRA Strength", minimum=0.0, maximum=1.0, value=0.5, step=0.01) width_input = gr.Number(label="📏 Width", value=512, minimum=256, step=64) height_input = gr.Number(label="📐 Height", value=512, minimum=256, step=64) frame_rate_input = gr.Number(label="🎯 Frame Rate", value=24, minimum=1) generate_btn = gr.Button("🚀 Generate Video") with gr.Column(): output_video = gr.Video(label="🎬 Generated Video", interactive=False) generate_btn.click( fn=generate_image, inputs=[prompt_input, frames_input, lora_strength_input, width_input, height_input, frame_rate_input], outputs=[output_video] ) app.launch(share=True)