app_fast.py

import os
import spaces
import torch
from diffusers import AutoencoderKLWan, WanPipeline, WanImageToVideoPipeline, UniPCMultistepScheduler
from diffusers.utils import export_to_video
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"

HF_MODEL = os.environ.get("HF_UPLOAD_REPO", "rahul7star/wan22TITV5B-image-analysis")

def upload_image_and_prompt(input_image, prompt_text) -> str:
    """
    Upload an image and a prompt text to Hugging Face Hub in a date-based folder.
    
    Args:
        input_image (PIL.Image.Image or path-like): The image to upload.
        prompt_text (str): Text prompt or summary associated with the image.

    Returns:
        str: Hugging Face folder path where the image and prompt were uploaded.
    """
    import tempfile
    import os
    import uuid
    from datetime import datetime
    from huggingface_hub import upload_file

    # Create a date-based folder on HF
    today_str = datetime.now().strftime("%Y-%m-%d")
    unique_subfolder = f"Upload-Image-{uuid.uuid4().hex[:8]}"
    hf_folder = f"{today_str}/{unique_subfolder}"

    # Save the image temporarily
    with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp_img:
        if isinstance(input_image, str):
            # If path provided, just copy
            import shutil
            shutil.copy(input_image, tmp_img.name)
        else:
            # PIL.Image.Image
            input_image.save(tmp_img.name, format="PNG")
        tmp_img_path = tmp_img.name

    # Upload image
    image_filename = "input_image.png"
    image_hf_path = f"{hf_folder}/{image_filename}"
    upload_file(
        path_or_fileobj=tmp_img_path,
        path_in_repo=image_hf_path,
        repo_id=HF_MODEL,
        repo_type="model",
        token=os.environ.get("HUGGINGFACE_HUB_TOKEN"),
    )

    # Upload prompt as summary.txt
    summary_file = tempfile.NamedTemporaryFile(delete=False, suffix=".txt").name
    with open(summary_file, "w", encoding="utf-8") as f:
        f.write(prompt_text)
    summary_hf_path = f"{hf_folder}/summary.txt"
    upload_file(
        path_or_fileobj=summary_file,
        path_in_repo=summary_hf_path,
        repo_id=HF_MODEL,
        repo_type="model",
        token=os.environ.get("HUGGINGFACE_HUB_TOKEN"),
    )

    # Cleanup
    os.remove(tmp_img_path)
    os.remove(summary_file)

    return hf_folder


#MODEL_ID ="linoyts/Wan2.2-VACE-Fun-14B-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=8.0)
    pipe.to("cuda")




##Lora testing
# LORA_REPO_ID = "JERRYNPC/WAN2.2-LORA-NSFW"
# 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"
# 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()


## works

# LORA_REPO_ID = "UnifiedHorusRA/Beauty_of_rain_Wan_2.1_2.2"
# LORA_FILENAME = "beauty_of_rain_wan2_2_ti2v_5B.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()



## works

# LORA_REPO_ID = "rahul7star/wan2.2Lora"
# LORA_FILENAME = "DR34ML4Y_TI2V_5B_V1.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()


## woeks very well

LORA_REPO_ID = "UnifiedHorusRA/Missionary_POV_Wan_2.2_5B_LoRA"
LORA_FILENAME = "missionary-pov-wan2.2_5b-v1.0-vfxai.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()

## bad

# LORA_REPO_ID = "rahul7star/wan2.2Lora"
# LORA_FILENAME = "wan2.2_5b_missionary_000005000.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()




# Constants
MOD_VALUE = 32
DEFAULT_H_SLIDER_VALUE = 896
DEFAULT_W_SLIDER_VALUE = 896
NEW_FORMULA_MAX_AREA = 720 * 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 = 24
MIN_FRAMES_MODEL = 25
MAX_FRAMES_MODEL = 193

default_prompt_i2v = "make this image come alive, cinematic motion, smooth animation"
default_negative_prompt = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards, watermark, text, signature"

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 > 4 and duration_seconds > 4:
        return 90
    elif steps > 4 or duration_seconds > 4:
        return 75
    else:
        return 60

@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)
    print("prompt is")
    print(prompt)
    # After generating or receiving input image
    #hf_folder = upload_image_and_prompt(input_image, prompt)


    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=False):
                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]
    )
    #upload_image_and_prompt(input_image_component, prompt_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()