app_fast.py

import os
os.system('pip install --upgrade --pre --extra-index-url https://download.pytorch.org/whl/nightly/cu126 "torch<2.9" spaces')
os.system('sudo modprobe -r nvidia_uvm && sudo modprobe nvidia_uvm" spaces')

import spaces
import torch
from diffusers import AutoencoderKLWan, WanPipeline, WanImageToVideoPipeline, UniPCMultistepScheduler
from diffusers.utils import export_to_video
#from diffusers.hooks import apply_first_block_cache, FirstBlockCacheConfig
import gradio as gr
import tempfile
from huggingface_hub import hf_hub_download
import numpy as np
from PIL import Image
import random

MODEL_ID = "FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers"
vae = AutoencoderKLWan.from_pretrained(MODEL_ID, subfolder="vae", torch_dtype=torch.float32)

# Initialize pipelines
text_to_video_pipe = WanPipeline.from_pretrained(MODEL_ID, vae=vae, torch_dtype=torch.bfloat16)
image_to_video_pipe = WanImageToVideoPipeline.from_pretrained(MODEL_ID, vae=vae, torch_dtype=torch.bfloat16)


for pipe in [text_to_video_pipe, image_to_video_pipe]:
    pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, flow_shift=5.0)
    pipe.to("cuda")    
    
##Lora testing
#vae = AutoencoderKLWan.from_pretrained("Kijai/WanVideo_comfy", filename="Wan2_2_VAE_bf16.safetensors", torch_dtype=torch.bfloat16)
# LORA_REPO_ID = "JERRYNPC/WAN2.2-LORA-NSFW"
#apply_first_block_cache(pipe.transformer, FirstBlockCacheConfig(threshold=0.2))

# LORA_FILENAME= "jerry_HIGH-nsfw-V10E800.safetensors"

# causvid_path = hf_hub_download(repo_id=LORA_REPO_ID, filename=LORA_FILENAME)
# pipe.load_lora_weights(causvid_path, adapter_name="causvid_lora")
# pipe.set_adapters(["causvid_lora"], adapter_weights=[0.95])
# pipe.fuse_lora()

#LORA_REPO_ID = "AlekseyCalvin/HSToric_Color_Wan2.2_5B_LoRA_BySilverAgePoets"
#LORA_FILENAME = "HSToric_color_Wan22_5b_LoRA.safetensors"

LORA_REPO_ID = "AlekseyCalvin/HSToric_Color_Wan2.2_5B_LoRA_BySilverAgePoets"
LORA_FILENAME = "HSTcolor_Wan5b_LoRA_Rank64_PowerEMAsigmaRel020.safetensors"
causvid_path = hf_hub_download(repo_id=LORA_REPO_ID, filename=LORA_FILENAME)
pipe.load_lora_weights(causvid_path, adapter_name="wan_lora")
pipe.set_adapters(["wan_lora"], adapter_weights=[1.0])
pipe.fuse_lora()

# Constants
MOD_VALUE = 32
DEFAULT_H_SLIDER_VALUE = 832
DEFAULT_W_SLIDER_VALUE = 832
NEW_FORMULA_MAX_AREA = 1024 * 1024
SLIDER_MIN_H, SLIDER_MAX_H = 256, 1024
SLIDER_MIN_W, SLIDER_MAX_W = 256, 1024
MAX_SEED = np.iinfo(np.int32).max
FIXED_FPS = 16
MIN_FRAMES_MODEL = 17
MAX_FRAMES_MODEL = 193

default_prompt_i2v = "make this image come alive, cinematic motion, smooth animation"
default_negative_prompt = "dull, overexposed, flashing, stuttering, static, blurred, vapid, banal, static, overall gray, worst, low, JPEG compression residue, incomplete, extra,  error, missing, vanishing, lapse, broken, wrong, deformed, disfigured, misshapen, fused fingers, still, messy, watermark"

def _calculate_new_dimensions_wan(pil_image, mod_val, calculation_max_area, min_slider_h, max_slider_h, min_slider_w, max_slider_w, default_h, default_w):
    orig_w, orig_h = pil_image.size
    if orig_w <= 0 or orig_h <= 0:
        return default_h, default_w
    aspect_ratio = orig_h / orig_w

    calc_h = round(np.sqrt(calculation_max_area * aspect_ratio))
    calc_w = round(np.sqrt(calculation_max_area / aspect_ratio))
    calc_h = max(mod_val, (calc_h // mod_val) * mod_val)
    calc_w = max(mod_val, (calc_w // mod_val) * mod_val)

    new_h = int(np.clip(calc_h, min_slider_h, (max_slider_h // mod_val) * mod_val))
    new_w = int(np.clip(calc_w, min_slider_w, (max_slider_w // mod_val) * mod_val))

    return new_h, new_w

def handle_image_upload_for_dims_wan(uploaded_pil_image, current_h_val, current_w_val):
    if uploaded_pil_image is None:
        return gr.update(value=DEFAULT_H_SLIDER_VALUE), gr.update(value=DEFAULT_W_SLIDER_VALUE)
    try:
        new_h, new_w = _calculate_new_dimensions_wan(
            uploaded_pil_image, MOD_VALUE, NEW_FORMULA_MAX_AREA,
            SLIDER_MIN_H, SLIDER_MAX_H, SLIDER_MIN_W, SLIDER_MAX_W,
            DEFAULT_H_SLIDER_VALUE, DEFAULT_W_SLIDER_VALUE
        )
        return gr.update(value=new_h), gr.update(value=new_w)
    except Exception as e:
        gr.Warning("Error attempting to calculate new dimensions")
        return gr.update(value=DEFAULT_H_SLIDER_VALUE), gr.update(value=DEFAULT_W_SLIDER_VALUE)
        
def get_duration(input_image, prompt, height, width, 
                   negative_prompt, duration_seconds,
                   guidance_scale, steps,
                   seed, randomize_seed, 
                   progress):
    if steps > 5 and duration_seconds > 5:
        return 60
    elif steps > 5 or duration_seconds > 5:
        return 50
    else:
        return 40

@spaces.GPU(duration=get_duration)
def generate_video(input_image, prompt, height, width, negative_prompt=default_negative_prompt, duration_seconds=2, guidance_scale=0, steps=4, seed=44, randomize_seed=False, progress=gr.Progress(track_tqdm=True)):
    target_h = max(MOD_VALUE, (int(height) // MOD_VALUE) * MOD_VALUE)
    target_w = max(MOD_VALUE, (int(width) // MOD_VALUE) * MOD_VALUE)

    num_frames = np.clip(int(round(duration_seconds * FIXED_FPS)), MIN_FRAMES_MODEL, MAX_FRAMES_MODEL)

    current_seed = random.randint(0, MAX_SEED) if randomize_seed else int(seed)

    if input_image is not None:
        resized_image = input_image.resize((target_w, target_h))
        with torch.inference_mode():
            output_frames_list = image_to_video_pipe(
                image=resized_image, prompt=prompt, negative_prompt=negative_prompt,
                height=target_h, width=target_w, num_frames=num_frames,
                guidance_scale=float(guidance_scale), num_inference_steps=int(steps),
                generator=torch.Generator(device="cuda").manual_seed(current_seed)
            ).frames[0]
    else:
        with torch.inference_mode():
            output_frames_list = text_to_video_pipe(
                prompt=prompt, negative_prompt=negative_prompt,
                height=target_h, width=target_w, num_frames=num_frames,
                guidance_scale=float(guidance_scale), num_inference_steps=int(steps),
                generator=torch.Generator(device="cuda").manual_seed(current_seed)
            ).frames[0]

    with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmpfile:
        video_path = tmpfile.name
    export_to_video(output_frames_list, video_path, fps=FIXED_FPS)
    return video_path, current_seed

with gr.Blocks() as demo:
    gr.Markdown("# Fast Wan 2.2 TI2V 5B Demo")
    gr.Markdown("""This Demo is using [FastWan2.2-TI2V-5B](https://huggingface.co/FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers) which is fine-tuned with Sparse-distill method which allows wan to generate high quality videos in 3-5 steps.""")

    with gr.Row():
        with gr.Column():
            input_image_component = gr.Image(type="pil", label="Input Image (optional, auto-resized to target H/W)")
            prompt_input = gr.Textbox(label="Prompt", value=default_prompt_i2v)
            duration_seconds_input = gr.Slider(minimum=round(MIN_FRAMES_MODEL/FIXED_FPS,1), maximum=round(MAX_FRAMES_MODEL/FIXED_FPS,1), step=0.1, value=2, label="Duration (seconds)", info=f"Clamped to model's {MIN_FRAMES_MODEL}-{MAX_FRAMES_MODEL} frames at {FIXED_FPS}fps.")

            with gr.Accordion("Advanced Settings", open=True):
                negative_prompt_input = gr.Textbox(label="Negative Prompt", value=default_negative_prompt, lines=3)
                seed_input = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=42, interactive=True)
                randomize_seed_checkbox = gr.Checkbox(label="Randomize seed", value=True, interactive=True)
                with gr.Row():
                    height_input = gr.Slider(minimum=SLIDER_MIN_H, maximum=SLIDER_MAX_H, step=MOD_VALUE, value=DEFAULT_H_SLIDER_VALUE, label=f"Output Height (multiple of {MOD_VALUE})")
                    width_input = gr.Slider(minimum=SLIDER_MIN_W, maximum=SLIDER_MAX_W, step=MOD_VALUE, value=DEFAULT_W_SLIDER_VALUE, label=f"Output Width (multiple of {MOD_VALUE})")
                steps_slider = gr.Slider(minimum=1, maximum=8, step=1, value=4, label="Inference Steps")
                guidance_scale_input = gr.Slider(minimum=0.0, maximum=5.0, step=0.01, value=0.0, label="Guidance Scale")
            generate_button = gr.Button("Generate Video", variant="primary")
        with gr.Column():
            video_output = gr.Video(label="Generated Video", autoplay=True, interactive=False)

    input_image_component.upload(
        fn=handle_image_upload_for_dims_wan,
        inputs=[input_image_component, height_input, width_input],
        outputs=[height_input, width_input]
    )

    input_image_component.clear(
        fn=handle_image_upload_for_dims_wan,
        inputs=[input_image_component, height_input, width_input],
        outputs=[height_input, width_input]
    )

    ui_inputs = [
        input_image_component, prompt_input, height_input, width_input,
        negative_prompt_input, duration_seconds_input,
        guidance_scale_input, steps_slider, seed_input, randomize_seed_checkbox
    ]
    generate_button.click(fn=generate_video, inputs=ui_inputs, outputs=[video_output, seed_input])

    gr.Examples(
        examples=[ 
            [None, "A person eating spaghetti", 1024, 720],
            ["cat.png", "The cat removes the glasses from its eyes.", 1088, 800],
            [None, "a penguin playfully dancing in the snow, Antarctica", 1024, 720],
            ["peng.png", "a penguin running towards camera joyfully, Antarctica", 896, 512],
        ],
        
        inputs=[input_image_component, prompt_input, height_input, width_input], outputs=[video_output, seed_input], fn=generate_video, cache_examples="lazy"
    )

if __name__ == "__main__":
    demo.queue().launch()