# app_wan.py import os # PyTorch 2.8 (temporary hack) — manter conforme app original #os.system('pip install --upgrade --pre --extra-index-url https://download.pytorch.org/whl/nightly/cu126 "torch<2.9" spaces') import gradio as gr import tempfile import numpy as np from PIL import Image from gradio_client import Client, handle_file # === Constantes (espelhando o app original) === MODEL_ID = "Wan-AI/Wan2.2-I2V-A14B-Diffusers" # Dimensões MAX_DIMENSION = 832 MIN_DIMENSION = 480 DIMENSION_MULTIPLE = 16 SQUARE_SIZE = 480 # Geração MAX_SEED = np.iinfo(np.int32).max FIXED_FPS = 16 MIN_FRAMES_MODEL = 8 MAX_FRAMES_MODEL = 81 MIN_DURATION = round(MIN_FRAMES_MODEL / FIXED_FPS, 1) MAX_DURATION = round(MAX_FRAMES_MODEL / FIXED_FPS, 1) default_negative_prompt = ( "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，" "JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，" "手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走,过曝，" ) # === Importa o serviço de geração (manager) === from aduc_framework.managers.wan_manager import WanManager wan_manager = WanManager() # === Utilidades de UI === def switch_to_upload_tab(): # Atualiza o Tabs existente para a aba "Upload" return gr.Tabs.update(selected="upload_tab") def generate_end_frame(start_img, gen_prompt, progress=gr.Progress(track_tqdm=True)): """ Chama uma API Gradio externa para gerar uma imagem (end frame). """ if start_img is None: raise gr.Error("Please provide a Start Frame first.") hf_token = os.getenv("HF_TOKEN") if not hf_token: raise gr.Error("HF_TOKEN not found in environment variables. Please set it in your Space secrets.") with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmpfile: start_img.save(tmpfile.name) tmp_path = tmpfile.name progress(0.1, desc="Connecting to image generation API...") client = Client("multimodalart/nano-banana") progress(0.5, desc=f"Generating with prompt: '{gen_prompt}'...") try: result = client.predict( prompt=gen_prompt, images=[{"image": handle_file(tmp_path)}], manual_token=hf_token, api_name="/unified_image_generator", ) finally: try: os.remove(tmp_path) except: pass progress(1.0, desc="Done!") return result # Wrapper: a UI monta images_condition_items e delega ao serviço def ui_generate_video( start_image_pil, start_frame_text, handle_image_pil, handle_frame_text, handle_peso, end_image_pil, end_frame_text, end_peso, prompt, negative_prompt=default_negative_prompt, duration_seconds=2.1, steps=8, guidance_scale=1.0, guidance_scale_2=1.0, seed=42, randomize_seed=False, progress=gr.Progress(track_tqdm=True), ): # Conversões simples def to_int_safe(v, default=0): try: return int(v) except: return default def to_float_safe(v, default=1.0): try: return float(v) except: return default # Peso da imagem inicial é fixo 1.0 start_item = [start_image_pil, to_int_safe(start_frame_text, 0), 1.0] # Handle é opcional: se não houver imagem, não enviar item items = [start_item] if handle_image_pil is not None: items.append([handle_image_pil, to_int_safe(handle_frame_text, 4), to_float_safe(handle_peso, 1.0)]) # Último item (end) sempre presente items.append([end_image_pil, to_int_safe(end_frame_text, MAX_FRAMES_MODEL - 1), to_float_safe(end_peso, 1.0)]) return wan_manager.generate_video_from_conditions( images_condition_items=items, prompt=prompt, negative_prompt=negative_prompt, duration_seconds=float(duration_seconds), steps=int(steps), guidance_scale=float(guidance_scale), guidance_scale_2=float(guidance_scale_2), seed=int(seed), randomize_seed=bool(randomize_seed), ) # === UI Gradio === css = ''' .fillable{max-width: 1100px !important} .dark .progress-text {color: white} #general_items{margin-top: 2em} #group_all{overflow:visible} #group_all .styler{overflow:visible} #group_tabs .tabitem{padding: 0} .tab-wrapper{margin-top: -33px;z-index: 999;position: absolute;width: 100%;background-color: var(--block-background-fill);padding: 0;} #component-9-button{width: 50%;justify-content: center} #component-11-button{width: 50%;justify-content: center} #or_item{text-align: center; padding-top: 1em; padding-bottom: 1em; font-size: 1.1em;margin-left: .5em;margin-right: .5em;width: calc(100% - 1em)} #fivesec{margin-top: 5em;margin-left: .5em;margin-right: .5em;width: calc(100% - 1em)} ''' with gr.Blocks(theme=gr.themes.Citrus(), css=css) as app: gr.Markdown("# Wan 2.2 Aduca-sdr") with gr.Row(elem_id="general_items"): with gr.Column(): with gr.Group(elem_id="group_all"): with gr.Row(): # Coluna: Start (peso fixo 1.0) with gr.Column(): start_image = gr.Image(type="pil", label="Start Frame", sources=["upload", "clipboard"]) start_frame_tb = gr.Textbox(label="Start Frame (int)", value="0") # Coluna: Handle (opcional) with gr.Column(): handle_image = gr.Image(type="pil", label="Handle Image", sources=["upload", "clipboard"]) handle_frame_tb = gr.Textbox(label="Handle Frame (int)", value="4") handle_peso_sl = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, value=1.0, label="Handle Peso") # Coluna: End (Upload/Generate) with gr.Tabs(elem_id="group_tabs") as tabs: with gr.TabItem("Upload", id="upload_tab"): with gr.Column(): end_image = gr.Image(type="pil", label="End Frame", sources=["upload", "clipboard"]) end_frame_tb = gr.Textbox(label="End Frame (int)", value=str(MAX_FRAMES_MODEL - 1)) end_peso_sl = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, value=1.0, label="End Peso") with gr.TabItem("Generate", id="generate_tab"): generate_5seconds = gr.Button("Generate scene 5 seconds in the future", elem_id="fivesec") gr.Markdown( "Generate a custom end-frame with an edit model like Nano Banana or Qwen Image Edit", elem_id="or_item", ) prompt = gr.Textbox(label="Prompt", info="Describe the transition between the two images") with gr.Accordion("Advanced Settings", open=False): duration_seconds_input = gr.Slider( minimum=MIN_DURATION, maximum=MAX_DURATION, step=0.1, value=2.1, label="Video Duration (seconds)", info=f"Clamped to model's {MIN_FRAMES_MODEL}-{MAX_FRAMES_MODEL} frames at {FIXED_FPS}fps.", ) negative_prompt_input = gr.Textbox(label="Negative Prompt", value=default_negative_prompt, lines=3) steps_slider = gr.Slider(minimum=1, maximum=30, step=1, value=8, label="Inference Steps") guidance_scale_input = gr.Slider( minimum=0.0, maximum=10.0, step=0.5, value=1.0, label="Guidance Scale - high noise" ) guidance_scale_2_input = gr.Slider( minimum=0.0, maximum=10.0, step=0.5, value=1.0, label="Guidance Scale - low noise" ) with gr.Row(): seed_input = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=42) randomize_seed_checkbox = gr.Checkbox(label="Randomize seed", value=True) generate_button = gr.Button("Generate Video", variant="primary") with gr.Column(): output_video = gr.Video(label="Generated Video", autoplay=True) # Inputs/outputs para o wrapper ui_inputs = [ start_image, start_frame_tb, handle_image, handle_frame_tb, handle_peso_sl, end_image, end_frame_tb, end_peso_sl, prompt, negative_prompt_input, duration_seconds_input, steps_slider, guidance_scale_input, guidance_scale_2_input, seed_input, randomize_seed_checkbox, ] ui_outputs = [output_video, seed_input] generate_button.click(fn=ui_generate_video, inputs=ui_inputs, outputs=ui_outputs) # Cadeia “5 seconds”: alterna aba, gera end frame e dispara render generate_5seconds.click( fn=switch_to_upload_tab, inputs=None, outputs=[tabs] ).then( fn=lambda img: generate_end_frame( img, "this image is a still frame from a movie. generate a new frame with what happens on this scene 5 seconds in the future" ), inputs=[start_image], outputs=[end_image] ).success( fn=ui_generate_video, inputs=ui_inputs, outputs=ui_outputs ) if __name__ == "__main__": os.makedirs("examples", exist_ok=True) try: Image.new('RGB', (832, 480), color=(73, 109, 137)).save("examples/frame_1.png") Image.new('RGB', (832, 480), color=(173, 109, 237)).save("examples/frame_2.png") Image.new('RGB', (832, 480), color=(255, 255, 0)).save("examples/frame_3.png") except: pass app.launch(server_name="0.0.0.0", server_port=7860, show_error=True)