app.py

import torch
from diffusers import UniPCMultistepScheduler, FlowMatchEulerDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler
from diffusers import WanPipeline, AutoencoderKLWan  # Use Wan-specific VAE
# from diffusers.hooks import apply_first_block_cache, FirstBlockCacheConfig
from para_attn.first_block_cache.diffusers_adapters import apply_cache_on_pipe
from diffusers.models import UNetSpatioTemporalConditionModel
from transformers import T5EncoderModel, T5Tokenizer
from huggingface_hub import hf_hub_download

from PIL import Image
import numpy as np

import gradio as gr
import spaces

model_id = "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
pipe = WanPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
flow_shift = 1.0 #5.0  1.0 for image, 5.0 for 720P, 3.0 for 480P
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, flow_shift=flow_shift)

# Configure DDIMScheduler with a beta schedule
# pipe.scheduler = DDIMScheduler.from_config(
#     pipe.scheduler.config,
#     beta_start=0.00085,  # Starting beta value
#     beta_end=0.012,      # Ending beta value
#     beta_schedule="linear",  # Linear beta schedule (other options: "scaled_linear", "squaredcos_cap_v2")
#     num_train_timesteps=1000,  # Number of timesteps
#     flow_shift=flow_shift
# )


# Configure FlowMatchEulerDiscreteScheduler
# pipe.scheduler = FlowMatchEulerDiscreteScheduler.from_config(
#     pipe.scheduler.config,
#     flow_shift=flow_shift           # Retain flow_shift for WanPipeline compatibility
# )

# --- LoRA State Management ---
# Define unique names for our adapters
DEFAULT_LORA_NAME = "causvid_lora"
CUSTOM_LORA_NAME = "custom_lora"
# Track which custom LoRA is currently loaded to avoid reloading
CURRENTLY_LOADED_CUSTOM_LORA = None

# Load the default base LoRA ONCE at startup
print("Loading base LoRA...")
CAUSVID_LORA_REPO = "Kijai/WanVideo_comfy"
CAUSVID_LORA_FILENAME = "Wan21_CausVid_bidirect2_T2V_1_3B_lora_rank32.safetensors"
try:
    causvid_path = hf_hub_download(repo_id=CAUSVID_LORA_REPO, filename=CAUSVID_LORA_FILENAME)
    pipe.load_lora_weights(causvid_path, adapter_name=DEFAULT_LORA_NAME)
    print(f"✅ Default LoRA '{DEFAULT_LORA_NAME}' loaded successfully.")
except Exception as e:
    print(f"⚠️ Default LoRA could not be loaded: {e}")
    DEFAULT_LORA_NAME = None

print("Initialization complete. Gradio is starting...")



@spaces.GPU()
def generate(prompt, negative_prompt, width=1024, height=1024, num_inference_steps=30, lora_id=None, progress=gr.Progress(track_tqdm=True)):
    # if lora_id and lora_id.strip() != "":
    #     pipe.unload_lora_weights()
    #     pipe.load_lora_weights(lora_id.strip())

    print("Loading base LoRA for this run...")
    causvid_path = hf_hub_download(repo_id=CAUSVID_LORA_REPO, filename=CAUSVID_LORA_FILENAME)
    pipe.load_lora_weights(causvid_path, adapter_name=DEFAULT_LORA_NAME)

    # If a custom LoRA is provided, load it as well.
    if clean_lora_id:
        print(f"Loading custom LoRA '{clean_lora_id}' for this run...")
        pipe.load_lora_weights(clean_lora_id, adapter_name=CUSTOM_LORA_NAME)
        # If a custom LoRA is present, activate both.
        pipe.set_adapters([DEFAULT_LORA_NAME, CUSTOM_LORA_NAME], adapter_weights=[1.0, 1.0])
    else:
        # If no custom LoRA, just activate the base one.
        print("Activating base LoRA only.")
        pipe.set_adapters([DEFAULT_LORA_NAME], adapter_weights=[1.0])


    pipe.to("cuda")
    # apply_first_block_cache(pipe.transformer, FirstBlockCacheConfig(threshold=0.2))
    apply_cache_on_pipe(
        pipe,
        # residual_diff_threshold=0.2,
    )
    try:
        output = pipe(
            prompt=prompt,
            negative_prompt=negative_prompt,
            height=height,
            width=width,
            num_frames=1,
            num_inference_steps=num_inference_steps,
            guidance_scale=1.0, #5.0
        )
        image = output.frames[0][0]
        image = (image * 255).astype(np.uint8)
        return Image.fromarray(image)
    finally:
        # if lora_id and lora_id.strip() != "":
        #     pass
            # pipe.unload_lora_weights()
        # if clean_lora_id:
        #     print(f"Unloading '{CUSTOM_LORA_NAME}' from this run.")
        #     pipe.unload_lora_weights(CUSTOM_LORA_NAME)

        # # Always disable all active LoRAs to reset the state.
        # pipe.disable_lora()
        print("Unloading all LoRAs to clean up.")
        pipe.unload_lora_weights()


iface = gr.Interface(
    fn=generate,
    inputs=[
        gr.Textbox(label="Input prompt"),
    ],
    additional_inputs = [
        gr.Textbox(label="Negative prompt", value = "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"),
        gr.Slider(label="Width", minimum=480, maximum=1280, step=16, value=1024),
        gr.Slider(label="Height", minimum=480, maximum=1280, step=16, value=1024),
        gr.Slider(minimum=1, maximum=80, step=1, label="Inference Steps", value=30),
        gr.Textbox(label="LoRA ID"),
    ],
    outputs=gr.Image(label="output"),
)

iface.launch()