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 json
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_presets(repo_id, subfolder):
    """
    Fetches the list of available LoRA presets by looking for .lset files.
    This is more robust as it ensures a preset and prompt info exists.
    """
    try:
        # Fetch all files from the repo to maintain compatibility with older library versions.
        all_files = list_repo_files(repo_id=repo_id, repo_type='model')

        # Manually filter for .lset files and get their names without the extension.
        subfolder_path = f"{subfolder}/"
        lset_files = [
            os.path.splitext(f.split('/')[-1])[0] # Get filename without extension
            for f in all_files
            if f.startswith(subfolder_path) and f.endswith('.lset')
        ]
        print(f"✅ Discovered {len(lset_files)} LoRA presets in {repo_id}/{subfolder}")
        return ["None"] + sorted(lset_files)
    except Exception as e:
        print(f"⚠️ Warning: Could not fetch LoRA presets from {repo_id}. LoRA selection will be disabled. Error: {e}")
        return ["None"]

available_i2v_presets = get_available_presets(I2V_LORA_REPO_ID, I2V_LORA_SUBFOLDER) if i2v_pipe else ["None"]


# --- Constants and Configuration ---
MOD_VALUE = 16 # Changed to 16 for model compatibility
DEFAULT_H_SLIDER_VALUE = 480 # Default to 480p height
DEFAULT_W_SLIDER_VALUE = 640 # Default to 640p width
NEW_FORMULA_MAX_AREA = 640.0 * 480.0 # Default area for new images

LORA_MAX_AREA = 640.0 * 480.0 # Max area when using a LoRA

SLIDER_MIN_H, SLIDER_MAX_H = 128, 1024
SLIDER_MIN_W, SLIDER_MAX_W = 128, 1024
MAX_SEED = np.iinfo(np.int32).max

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"

# --- LoRA Preset Helper Functions ---
def parse_lset_prompt(lset_prompt):
    """Parses a .lset prompt, resolving variables and highlighting them."""
    # Find all variable declarations like ! {Subject}="woman"
    variables = dict(re.findall(r'! \{(\w+)\}="([^"]+)"', lset_prompt))
    
    # Remove the declaration lines to get the clean prompt template
    prompt_template = re.sub(r'! \{\w+\}="[^"]+"\n?', '', lset_prompt).strip()
    
    # Replace placeholders with their default values, highlighted with markdown
    resolved_prompt = prompt_template
    for key, value in variables.items():
        # Highlight the default value to indicate it's a replaceable variable
        highlighted_value = f"__{value}__"
        resolved_prompt = resolved_prompt.replace(f"{{{key}}}", highlighted_value)
        
    return resolved_prompt

def handle_lora_selection_change(preset_name, current_prompt, current_h, current_w, aspect_ratio):
    """
    When a preset is selected, this function finds the corresponding .lset file,
    parses it, appends the prompt, and resizes dimensions if they are too large.
    """
    # Initialize updates to avoid changing UI elements unnecessarily
    prompt_update = gr.update()
    h_update = gr.update()
    w_update = gr.update()

    if not preset_name or preset_name == "None":
        return prompt_update, h_update, w_update

    # --- Handle Prompt ---
    try:
        lset_filename = f"{preset_name}.lset"
        lset_path = hf_hub_download(
            repo_id=I2V_LORA_REPO_ID, filename=lset_filename,
            subfolder=I2V_LORA_SUBFOLDER, repo_type='model'
        )
        with open(lset_path, 'r', encoding='utf-8') as f:
            lset_data = json.load(f)
        
        if lset_prompt_raw := lset_data.get("prompt"):
            resolved_prompt = parse_lset_prompt(lset_prompt_raw)
            new_prompt = f"{current_prompt}\n\n{resolved_prompt}".strip()
            gr.Info(f"✅ Appended prompt from '{lset_filename}'. Replace highlighted text like __this__.")
            prompt_update = gr.update(value=new_prompt)
    except Exception as e:
        print(f"Info: Could not process .lset for '{preset_name}'. Reason: {e}")
        gr.Info(f"ℹ️ Error processing preset '{preset_name}'.")

    # --- Handle Resolution ---
    if current_h * current_w > LORA_MAX_AREA:
        gr.Info(f"Resolution too high for LoRA. Scaling down to a 640x480 equivalent area.")
        # aspect_ratio is W/H
        if aspect_ratio > 0:
            # Calculate ideal dimensions based on area, without premature rounding
            calc_w = np.sqrt(LORA_MAX_AREA * aspect_ratio)
            calc_h = np.sqrt(LORA_MAX_AREA / aspect_ratio)
            
            # Round to the nearest multiple of MOD_VALUE
            new_h = max(MOD_VALUE, round(calc_h / MOD_VALUE) * MOD_VALUE)
            new_w = max(MOD_VALUE, round(calc_w / MOD_VALUE) * MOD_VALUE)
            
            h_update = gr.update(value=new_h)
            w_update = gr.update(value=new_w)
        else: # Fallback if aspect ratio is invalid
            h_update = gr.update(value=480)
            w_update = gr.update(value=640)

    return prompt_update, h_update, w_update

# --- 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
    
    # Calculate ideal dimensions based on area, without premature rounding
    calc_h = np.sqrt(calculation_max_area * aspect_ratio)
    calc_w = np.sqrt(calculation_max_area / aspect_ratio)

    # Round to the nearest multiple of mod_val
    calc_h = max(mod_val, round(calc_h / mod_val) * mod_val)
    calc_w = max(mod_val, round(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,                       
                       preset_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)
    
    # If a LoRA is used, enforce max resolution as a safety net
    if preset_name and preset_name != "None":
        if target_h * target_w > LORA_MAX_AREA:
            print(f"⚠️ Warning: Resolution {target_w}x{target_h} is too high for LoRA. Rescaling to fit max area.")
            aspect_ratio = target_w / target_h if target_h > 0 else 1.0
            
            # Re-calculate w and h based on max area, without premature rounding
            calc_w = np.sqrt(LORA_MAX_AREA * aspect_ratio)
            calc_h = np.sqrt(LORA_MAX_AREA / aspect_ratio)

            # Snap to MOD_VALUE by rounding to the nearest multiple
            target_h = max(MOD_VALUE, round(calc_h / MOD_VALUE) * MOD_VALUE)
            target_w = max(MOD_VALUE, round(calc_w / MOD_VALUE) * MOD_VALUE)
            print(f"   - Rescaled to: {target_w}x{target_h}")

    # 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"

    lora_filename = None # Will be extracted from the .lset file
    adapter_name = "i2v_lora"
    try:
        # If a preset is selected, load the corresponding LoRA
        if preset_name and preset_name != "None":
            lset_filename = f"{preset_name}.lset"
            print(f"🚀 Processing preset: {preset_name}")
            try:
                lset_path = hf_hub_download(
                    repo_id=I2V_LORA_REPO_ID,
                    filename=lset_filename,
                    subfolder=I2V_LORA_SUBFOLDER,
                    repo_type='model'
                )
                with open(lset_path, 'r', encoding='utf-8') as f:
                    lset_data = json.load(f)
                
                # Extract the LoRA filename from the .lset file
                loras_list = lset_data.get("loras")
                if not loras_list or not isinstance(loras_list, list) or len(loras_list) == 0:
                    raise gr.Error(f"Preset file '{lset_filename}' is invalid or does not specify a LoRA file.")
                
                lora_filename = loras_list[0] # Use the first LoRA in the list
                print(f"   - Found LoRA file: {lora_filename}")
                
                i2v_pipe.load_lora_weights(
                    I2V_LORA_REPO_ID,
                    weight_name=lora_filename,
                    adapter_name=adapter_name,
                    subfolder=I2V_LORA_SUBFOLDER
                )
                i2v_pipe.set_adapters([adapter_name], adapter_weights=[float(lora_weight)])
                print(f"   - LoRA '{lora_filename}' loaded successfully with weight {lora_weight}.")
            except Exception as e:
                raise gr.Error(f"Failed to load LoRA for preset '{preset_name}'. Reason: {e}")

        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_filename and hasattr(i2v_pipe, "unload_lora_weights"):
            print(f"🧹 Unloading LoRA: {lora_filename}")
            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("# Wan 2.1 Video Suite with Dynamic LoRA Presets")
        gr.Markdown(
            """
            Welcome! This space allows you to generate videos from images using the powerful Wan 2.1 model, enhanced with dynamic LoRA presets.
            
            **How to use:**
            1.  Start in the **Image-to-Video** tab and upload your starting image.
            2.  Select a **LoRA Preset** from the dropdown to apply a unique style and automatically add a suggested prompt.
            3.  Customize the prompt, adjust settings like duration and resolution, and click **Generate I2V**!
            """
        )
        
        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_preset_name = gr.Dropdown(label="🎨 LoRA Preset", choices=available_i2v_presets, value="None", info="Select a preset to apply a LoRA and a suggested prompt.", interactive=len(available_i2v_presets) > 1)
                        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_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_preset_name.change(
        fn=handle_lora_selection_change,
        inputs=[i2v_preset_name, i2v_prompt, i2v_height, i2v_width, i2v_aspect_ratio],
        outputs=[i2v_prompt, i2v_height, i2v_width]
    )
    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_preset_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()