app.py

import spaces
import torch
from diffusers import AutoencoderKLWan, WanImageToVideoPipeline, UniPCMultistepScheduler, WanTransformer3DModel, AutoModel, DiffusionPipeline
from diffusers.utils import export_to_video
from transformers import CLIPVisionModel, UMT5EncoderModel, CLIPTextModel, CLIPImageProcessor
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import tempfile
import re
import os
import traceback
from huggingface_hub import list_repo_files
from huggingface_hub import hf_hub_download
import numpy as np
from PIL import Image
import gradio as gr
import random

# --- I2V (Image-to-Video) Configuration ---
I2V_BASE_MODEL_ID = "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers" # Used for VAE/encoder components
I2V_FUSIONX_REPO_ID = "vrgamedevgirl84/Wan14BT2VFusioniX"
I2V_FUSIONX_FILENAME = "Wan14Bi2vFusioniX.safetensors"

# --- I2V LoRA Configuration ---
I2V_LORA_REPO_ID = "DeepBeepMeep/Wan2.1"
I2V_LORA_SUBFOLDER = "loras_i2v"

# --- Load Pipelines ---
print("🚀 Loading I2V pipeline from single file...")
i2v_pipe = None
try:
    # Load ALL components needed for the pipeline from the base model repo
    i2v_image_encoder = CLIPVisionModel.from_pretrained(I2V_BASE_MODEL_ID, subfolder="image_encoder", torch_dtype=torch.float32)
    i2v_vae = AutoencoderKLWan.from_pretrained(I2V_BASE_MODEL_ID, subfolder="vae", torch_dtype=torch.float32)
    i2v_text_encoder = UMT5EncoderModel.from_pretrained(I2V_BASE_MODEL_ID, subfolder="text_encoder", torch_dtype=torch.bfloat16)
    i2v_tokenizer = AutoTokenizer.from_pretrained(I2V_BASE_MODEL_ID, subfolder="tokenizer")
    i2v_image_processor = CLIPImageProcessor.from_pretrained(I2V_BASE_MODEL_ID, subfolder="image_processor")
    
    # Create scheduler with custom flow_shift
    scheduler_config = UniPCMultistepScheduler.load_config(I2V_BASE_MODEL_ID, subfolder="scheduler")
    scheduler_config['flow_shift'] = 8.0
    i2v_scheduler = UniPCMultistepScheduler.from_config(scheduler_config)
    
    # Load the main transformer from the repo and filename
    i2v_transformer = WanTransformer3DModel.from_single_file(
        "https://huggingface.co/vrgamedevgirl84/Wan14BT2VFusioniX/blob/main/Wan14Bi2vFusioniX.safetensors",
        torch_dtype=torch.bfloat16
    )

    # Manually assemble the pipeline with the custom transformer
    i2v_pipe = WanImageToVideoPipeline(
        vae=i2v_vae,
        text_encoder=i2v_text_encoder,
        tokenizer=i2v_tokenizer,
        image_encoder=i2v_image_encoder,
        image_processor=i2v_image_processor,
        scheduler=i2v_scheduler,
        transformer=i2v_transformer
    )
    i2v_pipe.to("cuda")
    print("✅ I2V pipeline loaded successfully from single file.")
except Exception as e:
    print(f"❌ Critical Error: Failed to load I2V pipeline from single file.")
    traceback.print_exc()

# --- LoRA Discovery ---
def get_available_loras(repo_id, subfolder):
    """Fetches the list of available LoRA files from a Hugging Face Hub repo subfolder."""
    try:
        files = list_repo_files(repo_id=repo_id, repo_type='model', subfolder=subfolder)
        # Filter for .safetensors and get just the filename
        safetensors_files = [f.split('/')[-1] for f in files if f.endswith('.safetensors')]
        print(f"✅ Discovered {len(safetensors_files)} LoRAs in {repo_id}/{subfolder}")
        return ["None"] + sorted(safetensors_files)
    except Exception as e:
        print(f"⚠️ Warning: Could not fetch LoRAs from {repo_id}. LoRA selection will be disabled. Error: {e}")
        return ["None"]

available_i2v_loras = get_available_loras(I2V_LORA_REPO_ID, I2V_LORA_SUBFOLDER) if i2v_pipe else ["None"]


# --- Constants and Configuration ---
MOD_VALUE = 8
DEFAULT_H_SLIDER_VALUE = 512
DEFAULT_W_SLIDER_VALUE = 768
NEW_FORMULA_MAX_AREA = 768.0 * 512.0

SLIDER_MIN_H, SLIDER_MAX_H = 128, 896
SLIDER_MIN_W, SLIDER_MAX_W = 128, 896
MAX_SEED = np.iinfo(np.int32).max

FIXED_FPS = 16
T2V_FIXED_FPS = 16
MIN_FRAMES_MODEL = 8
MAX_FRAMES_MODEL = 81

# --- Default Prompts ---
default_prompt_i2v = "Cinematic motion, smooth animation, detailed textures, dynamic lighting, professional cinematography"
default_negative_prompt = "Static image, no motion, blurred details, overexposed, underexposed, low quality, worst quality, JPEG artifacts, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, watermark, text, signature, three legs, many people in the background, walking backwards"

# --- Helper Functions ---
def sanitize_prompt_for_filename(prompt: str, max_len: int = 60) -> str:
    """Sanitizes a prompt string to be used as a valid filename."""
    if not prompt:
        prompt = "video"
    sanitized = re.sub(r'[^\w\s_-]', '', prompt).strip()
    sanitized = re.sub(r'[\s_-]+', '_', sanitized)
    return sanitized[:max_len]

def update_linked_dimension(driving_value, other_value, aspect_ratio, mod_val, mode):
    """Updates a dimension slider based on the other, maintaining aspect ratio."""
    # aspect_ratio is stored as W/H
    if aspect_ratio is None or aspect_ratio == 0:
        return gr.update() # Do nothing if aspect ratio is not set

    if mode == 'h_drives_w':
        # new_w = h * (W/H)
        new_other_value = driving_value * aspect_ratio
    else: # 'w_drives_h'
        # new_h = w / (W/H)
        new_other_value = driving_value / aspect_ratio

    # Round to the nearest multiple of mod_val
    new_other_value = max(mod_val, (round(new_other_value / mod_val)) * mod_val)

    # Return an update only if the value has changed to prevent infinite loops
    return gr.update(value=new_other_value) if int(new_other_value) != int(other_value) else gr.update()

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):
    default_aspect = DEFAULT_W_SLIDER_VALUE / DEFAULT_H_SLIDER_VALUE
    if uploaded_pil_image is None:
        return gr.update(value=DEFAULT_H_SLIDER_VALUE), gr.update(value=DEFAULT_W_SLIDER_VALUE), default_aspect
    try:
        # This function calculates initial slider positions based on a max area
        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
        )

        # We need the original image's true aspect ratio (W/H) for locking the sliders
        orig_w, orig_h = uploaded_pil_image.size
        aspect_ratio = orig_w / orig_h if orig_h > 0 else default_aspect

        return gr.update(value=new_h), gr.update(value=new_w), aspect_ratio
    except Exception as e:
        gr.Warning("Error calculating new dimensions. Resetting to default.")
        return gr.update(value=DEFAULT_H_SLIDER_VALUE), gr.update(value=DEFAULT_W_SLIDER_VALUE), default_aspect

# --- GPU Duration Estimators for @spaces.GPU ---
def get_i2v_duration(steps, duration_seconds):
    """Estimates GPU time for Image-to-Video generation."""
    if steps > 8 and duration_seconds > 3: return 600
    elif steps > 8 or duration_seconds > 3: return 300
    else: return 150

def get_t2v_duration(steps, duration_seconds):
    """Estimates GPU time for Text-to-Video generation."""
    if steps > 15 and duration_seconds > 4: return 700
    elif steps > 15 or duration_seconds > 4: return 400
    else: return 200

# --- Core Generation Functions ---

@spaces.GPU(duration_from_args=get_i2v_duration)
def generate_i2v_video(input_image, prompt, height, width,
                      negative_prompt, duration_seconds,
                      guidance_scale, steps, seed, randomize_seed,
                      lora_name, lora_weight,
                      progress=gr.Progress(track_tqdm=True)):
    """Generates a video from an initial image and a prompt."""
    if input_image is None:
        raise gr.Error("Please upload an input image for Image-to-Video generation.")
    if i2v_pipe is None:
        raise gr.Error("Image-to-Video pipeline is not available due to a loading error.")

    target_h = max(MOD_VALUE, (int(height) // MOD_VALUE) * MOD_VALUE)
    target_w = max(MOD_VALUE, (int(width) // MOD_VALUE) * MOD_VALUE)
    
    # Calculate and adjust num_frames to be compatible with video codecs
    target_frames = int(round(duration_seconds * FIXED_FPS))
    adjusted_frames = 4 * round((target_frames - 1) / 4) + 1
    num_frames = int(np.clip(adjusted_frames, MIN_FRAMES_MODEL, MAX_FRAMES_MODEL))
    current_seed = random.randint(0, MAX_SEED) if randomize_seed else int(seed)
    resized_image = input_image.resize((target_w, target_h))
    enhanced_prompt = f"{prompt}, cinematic quality, smooth motion, detailed animation, dynamic lighting"

    adapter_name = "i2v_lora"
    try:
        # Dynamically load the selected LoRA
        if lora_name and lora_name != "None":
            print(f"🚀 Loading LoRA: {lora_name} with weight {lora_weight}")
            i2v_pipe.load_lora_weights(
                I2V_LORA_REPO_ID,
                weight_name=lora_name,
                adapter_name=adapter_name,
                subfolder=I2V_LORA_SUBFOLDER
            )
            i2v_pipe.set_adapters([adapter_name], adapter_weights=[float(lora_weight)])

        with torch.inference_mode():
            output_frames_list = i2v_pipe(
                image=resized_image,
                prompt=enhanced_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]
    finally:
        # Unload the LoRA to ensure a clean state for the next run
        if lora_name and lora_name != "None" and hasattr(i2v_pipe, "unload_lora_weights"):
            print(f"🧹 Unloading LoRA: {lora_name}")
            i2v_pipe.unload_lora_weights()
        # Clear GPU cache to free up memory for the next run
        if torch.cuda.is_available():
            torch.cuda.empty_cache()

    sanitized_prompt = sanitize_prompt_for_filename(prompt)
    filename = f"i2v_{sanitized_prompt}_{current_seed}.mp4"
    temp_dir = tempfile.mkdtemp()
    video_path = os.path.join(temp_dir, filename)
    export_to_video(output_frames_list, video_path, fps=FIXED_FPS)
    
    return video_path, current_seed, gr.File(value=video_path, visible=True, label=f"📥 Download: {filename}")

# --- Gradio UI Layout ---
with gr.Blocks() as demo:
    with gr.Column(elem_classes=["main-container"]):
        i2v_aspect_ratio = gr.State(value=DEFAULT_W_SLIDER_VALUE / DEFAULT_H_SLIDER_VALUE)
        gr.Markdown("# ⚡ FusionX Enhanced Wan 2.1 Video Suite")
        
        with gr.Tabs(elem_classes=["gr-tabs"]):
            # --- Image-to-Video Tab ---
            with gr.TabItem("🖼️ Image-to-Video", id="i2v_tab"):
                with gr.Row():
                    with gr.Column(elem_classes=["input-container"]):
                        i2v_input_image = gr.Image(
                            type="pil",
                            label="🖼️ Input Image (auto-resizes H/W sliders)",
                            elem_classes=["image-upload"]
                        )
                        i2v_prompt = gr.Textbox(
                            label="✏️ Prompt",
                            value=default_prompt_i2v, lines=3
                        )
                        i2v_duration = 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"Generates {MIN_FRAMES_MODEL}-{MAX_FRAMES_MODEL} frames at {FIXED_FPS}fps."
                        )
                        with gr.Accordion("⚙️ Advanced Settings", open=False):
                            i2v_neg_prompt = gr.Textbox(label="❌ Negative Prompt", value=default_negative_prompt, lines=4)
                            i2v_seed = gr.Slider(label="🎲 Seed", minimum=0, maximum=MAX_SEED, step=1, value=42, interactive=True)
                            i2v_rand_seed = gr.Checkbox(label="🔀 Randomize seed", value=True, interactive=True)
                            i2v_lora_name = gr.Dropdown(label="🎨 LoRA Style", choices=available_i2v_loras, value="None", info="Dynamically loaded from Hugging Face.", interactive=len(available_i2v_loras) > 1)
                            i2v_lora_weight = gr.Slider(label="💪 LoRA Weight", minimum=0.0, maximum=2.0, step=0.1, value=0.8, interactive=True)
                            with gr.Row():
                                i2v_height = gr.Slider(minimum=SLIDER_MIN_H, maximum=SLIDER_MAX_H, step=MOD_VALUE, value=DEFAULT_H_SLIDER_VALUE, label=f"📏 Height ({MOD_VALUE}px steps)")
                                i2v_width = gr.Slider(minimum=SLIDER_MIN_W, maximum=SLIDER_MAX_W, step=MOD_VALUE, value=DEFAULT_W_SLIDER_VALUE, label=f"📐 Width ({MOD_VALUE}px steps)")
                            gr.Markdown("<p style='color: #ffcc00; font-size: 0.9em;'>⚠️ High resolutions can lead to out-of-memory errors. If generation fails, try a smaller size.</p>")
                            i2v_steps = gr.Slider(minimum=1, maximum=20, step=1, value=8, label="🚀 Inference Steps", info="8-10 recommended for great results.")
                            i2v_guidance = gr.Slider(minimum=0.0, maximum=20.0, step=0.5, value=1.0, label="🎯 Guidance Scale", visible=False)
                        
                        i2v_generate_btn = gr.Button("🎬 Generate I2V", variant="primary", elem_classes=["generate-btn"])

                    with gr.Column(elem_classes=["output-container"]):
                        i2v_output_video = gr.Video(label="🎥 Generated Video", autoplay=True, interactive=False)
                        i2v_download = gr.File(label="📥 Download Video", visible=False)



    # --- Event Handlers ---
    # I2V Handlers
    i2v_input_image.upload(
        fn=handle_image_upload_for_dims_wan,
        inputs=[i2v_input_image],
        outputs=[i2v_height, i2v_width, i2v_aspect_ratio]
    )
    i2v_input_image.clear(
        fn=lambda: (DEFAULT_H_SLIDER_VALUE, DEFAULT_W_SLIDER_VALUE, DEFAULT_W_SLIDER_VALUE / DEFAULT_H_SLIDER_VALUE),
        inputs=[],
        outputs=[i2v_height, i2v_width, i2v_aspect_ratio]
    )
    i2v_generate_btn.click(
        fn=generate_i2v_video,
        inputs=[i2v_input_image, i2v_prompt, i2v_height, i2v_width, i2v_neg_prompt, i2v_duration, i2v_guidance, i2v_steps, i2v_seed, i2v_rand_seed, i2v_lora_name, i2v_lora_weight],
        outputs=[i2v_output_video, i2v_seed, i2v_download]
    )
    i2v_height.release(
        fn=update_linked_dimension,
        inputs=[i2v_height, i2v_width, i2v_aspect_ratio, gr.State(MOD_VALUE), gr.State('h_drives_w')],
        outputs=[i2v_width]
    )
    i2v_width.release(
        fn=update_linked_dimension,
        inputs=[i2v_width, i2v_height, i2v_aspect_ratio, gr.State(MOD_VALUE), gr.State('w_drives_h')],
        outputs=[i2v_height]
    )



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