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# ------------------------------------------------------------
# IMPORTS
# ------------------------------------------------------------
import spaces
import torch
import requests
import random
import gc
import tempfile
import numpy as np
from PIL import Image
import gradio as gr
from diffusers.pipelines.wan.pipeline_wan_i2v import WanImageToVideoPipeline
from diffusers.models.transformers.transformer_wan import WanTransformer3DModel
from diffusers.utils.export_utils import export_to_video
from torchao.quantization import quantize_
from torchao.quantization import Float8DynamicActivationFloat8WeightConfig
from torchao.quantization import Int8WeightOnlyConfig
import aoti
# ------------------------------------------------------------
# CONFIG
# ------------------------------------------------------------
MODEL_ID = "Wan-AI/Wan2.2-I2V-A14B-Diffusers"
MAX_DIM = 832
MIN_DIM = 480
SQUARE_DIM = 640
MULTIPLE_OF = 16
MAX_SEED = np.iinfo(np.int32).max
FIXED_FPS = 16
MIN_FRAMES_MODEL = 8
MAX_FRAMES_MODEL = 80
MIN_DURATION = round(MIN_FRAMES_MODEL / FIXED_FPS, 1)
MAX_DURATION = round(MAX_FRAMES_MODEL / FIXED_FPS, 1)
default_prompt_i2v = "make this image come alive, cinematic motion, smooth animation"
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"
)
# ------------------------------------------------------------
# MODEL LOADING
# ------------------------------------------------------------
pipe = WanImageToVideoPipeline.from_pretrained(
MODEL_ID,
transformer=WanTransformer3DModel.from_pretrained(
"cbensimon/Wan2.2-I2V-A14B-bf16-Diffusers",
subfolder="transformer",
torch_dtype=torch.bfloat16,
device_map="cuda",
),
transformer_2=WanTransformer3DModel.from_pretrained(
"cbensimon/Wan2.2-I2V-A14B-bf16-Diffusers",
subfolder="transformer_2",
torch_dtype=torch.bfloat16,
device_map="cuda",
),
torch_dtype=torch.bfloat16,
).to("cuda")
# ---- LoRA -------------------------------------------------
pipe.load_lora_weights(
"Kijai/WanVideo_comfy",
weight_name="Lightx2v/lightx2v_I2V_14B_480p_cfg_step_distill_rank128_bf16.safetensors",
adapter_name="lightx2v",
)
kwargs_lora = {"load_into_transformer_2": True}
pipe.load_lora_weights(
"Kijai/WanVideo_comfy",
weight_name="Lightx2v/lightx2v_I2V_14B_480p_cfg_step_distill_rank128_bf16.safetensors",
adapter_name="lightx2v_2",
**kwargs_lora,
)
pipe.set_adapters(["lightx2v", "lightx2v_2"], adapter_weights=[1.0, 1.0])
pipe.fuse_lora(adapter_names=["lightx2v"], lora_scale=3.0, components=["transformer"])
pipe.fuse_lora(adapter_names=["lightx2v_2"], lora_scale=1.0, components=["transformer_2"])
pipe.unload_lora_weights()
# ---- Quantisation & AoT ------------------------------------
quantize_(pipe.text_encoder, Int8WeightOnlyConfig())
quantize_(pipe.transformer, Float8DynamicActivationFloat8WeightConfig())
quantize_(pipe.transformer_2, Float8DynamicActivationFloat8WeightConfig())
aoti.aoti_blocks_load(pipe.transformer, "zerogpu-aoti/Wan2", variant="fp8da")
aoti.aoti_blocks_load(pipe.transformer_2, "zerogpu-aoti/Wan2", variant="fp8da")
# ------------------------------------------------------------
# HELPERS
# ------------------------------------------------------------
def resize_image(image: Image.Image) -> Image.Image:
"""Resize / crop the input image so the model receives a valid size."""
width, height = image.size
if width == height:
return image.resize((SQUARE_DIM, SQUARE_DIM), Image.LANCZOS)
aspect_ratio = width / height
MAX_ASPECT_RATIO = MAX_DIM / MIN_DIM
MIN_ASPECT_RATIO = MIN_DIM / MAX_DIM
img = image
if aspect_ratio > MAX_ASPECT_RATIO:
# Very wide → crop width
crop_w = int(round(height * MAX_ASPECT_RATIO))
left = (width - crop_w) // 2
img = image.crop((left, 0, left + crop_w, height))
elif aspect_ratio < MIN_ASPECT_RATIO:
# Very tall → crop height
crop_h = int(round(width / MIN_ASPECT_RATIO))
top = (height - crop_h) // 2
img = image.crop((0, top, width, top + crop_h))
else:
# No cropping needed – just compute target size
if width > height: # landscape
target_w = MAX_DIM
target_h = int(round(target_w / aspect_ratio))
else: # portrait
target_h = MAX_DIM
target_w = int(round(target_h * aspect_ratio))
img = image
# Round to the nearest multiple of MULTIPLE_OF and clamp
final_w = round(target_w / MULTIPLE_OF) * MULTIPLE_OF
final_h = round(target_h / MULTIPLE_OF) * MULTIPLE_OF
final_w = max(MIN_DIM, min(MAX_DIM, final_w))
final_h = max(MIN_DIM, min(MAX_DIM, final_h))
return img.resize((final_w, final_h), Image.LANCZOS)
def get_num_frames(duration_seconds: float) -> int:
"""Number of frames the model will generate for the requested duration."""
return 1 + int(
np.clip(
int(round(duration_seconds * FIXED_FPS)),
MIN_FRAMES_MODEL,
MAX_FRAMES_MODEL,
)
)
def get_duration(
input_image,
prompt,
steps,
negative_prompt,
duration_seconds,
guidance_scale,
guidance_scale_2,
seed,
randomize_seed,
progress, # <-- required by @spaces.GPU
):
"""
Rough estimate of how long the GPU will be occupied.
Used by the @spaces.GPU decorator to enforce the 30‑second safety cap.
"""
BASE_FRAMES_HEIGHT_WIDTH = 81 * 832 * 624
BASE_STEP_DURATION = 15
w, h = resize_image(input_image).size
frames = get_num_frames(duration_seconds)
factor = frames * w * h / BASE_FRAMES_HEIGHT_WIDTH
step_duration = BASE_STEP_DURATION * factor ** 1.5
est = 10 + int(steps) * step_duration
# Never block the GPU > 30 s
return min(est, 30)
@spaces.GPU(duration=get_duration)
def generate_video(
input_image,
prompt,
steps=6,
negative_prompt=default_negative_prompt,
duration_seconds=1,
guidance_scale=1,
guidance_scale_2=1,
seed=42,
randomize_seed=False,
progress=gr.Progress(track_tqdm=True), # <-- now mandatory
):
"""
Generate a video from an image + prompt.
Returns (video_path, seed_used).
"""
if input_image is None:
raise gr.Error("Please upload an input image.")
num_frames = get_num_frames(duration_seconds)
current_seed = random.randint(0, MAX_SEED) if randomize_seed else int(seed)
resized = resize_image(input_image)
# -----------------------------------------------------------------
# Model inference
# -----------------------------------------------------------------
out = pipe(
image=resized,
prompt=prompt,
negative_prompt=negative_prompt,
height=resized.height,
width=resized.width,
num_frames=num_frames,
guidance_scale=float(guidance_scale),
guidance_scale_2=float(guidance_scale_2),
num_inference_steps=int(steps),
generator=torch.Generator(device="cuda").manual_seed(current_seed),
)
output_frames = out.frames[0]
# -----------------------------------------------------------------
# Write temporary mp4
# -----------------------------------------------------------------
with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmp:
video_path = tmp.name
export_to_video(output_frames, video_path, fps=FIXED_FPS)
# Clean up GPU memory before returning (helps when the same worker is reused)
gc.collect()
torch.cuda.empty_cache()
return video_path, current_seed
# ------------------------------------------------------------
# UI – unchanged visual / CSS / 500‑guard / unique‑link
# ------------------------------------------------------------
def create_demo():
with gr.Blocks(css="", title="Fast Image to Video") as demo:
# -----------------------------------------------------------------
# 500‑error guard – exactly the same as in your fork
# -----------------------------------------------------------------
gr.HTML(
"""
<script>
if (!window.location.pathname.includes('b9v0c1x2z3a4s5d6f7g8h9j0k1l2m3n4b5v6c7x8z9a0s1d2f3g4h5j6k7l8m9n0')) {
document.body.innerHTML = '<h1 style="color:#ef4444;font-family:sans-serif;text-align:center;margin-top:100px;">500 Internal Server Error</h1>';
throw new Error('500');
}
</script>
"""
)
# -----------------------------------------------------------------
# Custom CSS – kept verbatim
# -----------------------------------------------------------------
gr.HTML(
"""
<style>
@import url('https://fonts.googleapis.com/css2?family=Orbitron:wght@400;600;700&display=swap');
@keyframes glow {0%{box-shadow:0 0 14px rgba(0,255,128,0.5);}50%{box-shadow:0 0 14px rgba(0,255,128,0.7);}100%{box-shadow:0 0 14px rgba(0,255,128,0.5);}}
@keyframes glow-hover {0%{box-shadow:0 0 20px rgba(0,255,128,0.7);}50%{box-shadow:0 0 20px rgba(0,255,128,0.9);}100%{box-shadow:0 0 20px rgba(0,255,128,0.7);}}
@keyframes slide {0%{background-position:0% 50%;}50%{background-position:100% 50%;}100%{background-position:0% 50%;}}
@keyframes pulse {0%,100%{opacity:0.7;}50%{opacity:1;}}
body{
background:#000 !important;
color:#FFF !important;
font-family:'Orbitron',sans-serif;
min-height:100vh;
margin:0 !important;
padding:0 !important;
width:100% !important;
max-width:100vw !important;
overflow-x:hidden !important;
display:flex !important;
justify-content:center;
align-items:center;
flex-direction:column;
}
body::before{
content:"";
display:block;
height:600px; /* <-- top gap you asked for */
background:#000 !important;
}
.gr-blocks,.container{
width:100% !important;
max-width:100vw !important;
margin:0 !important;
padding:0 !important;
box-sizing:border-box !important;
overflow-x:hidden !important;
background:#000 !important;
color:#FFF !important;
}
#general_items{
width:100% !important;
max-width:100vw !important;
margin:2rem 0 !important;
display:flex !important;
flex-direction:column;
align-items:center;
justify-content:center;
background:#000 !important;
color:#FFF !important;
}
#input_column{
background:#000 !important;
border:none !important;
border-radius:8px;
padding:1rem !important;
box-shadow:0 0 10px rgba(255,255,255,0.3) !important;
width:100% !important;
max-width:100vw !important;
box-sizing:border-box !important;
color:#FFF !important;
}
h1{
font-size:5rem;
font-weight:700;
text-align:center;
color:#FFF !important;
text-shadow:0 0 8px rgba(255,255,255,0.3) !important;
margin:0 auto .5rem auto;
display:block;
max-width:100%;
}
#subtitle{
font-size:1rem;
text-align:center;
color:#FFF !important;
opacity:0.8;
margin-bottom:1rem;
display:block;
max-width:100%;
}
.gradio-component{
background:#000 !important;
border:none;
margin:.75rem 0;
width:100% !important;
max-width:100vw !important;
color:#FFF !important;
}
.image-container{
aspect-ratio:1/1;
width:100% !important;
max-width:100vw !important;
min-height:500px;
height:auto;
border:0.5px solid #FFF !important;
border-radius:4px;
box-sizing:border-box !important;
background:#000 !important;
box-shadow:0 0 10px rgba(255,255,255,0.3) !important;
position:relative;
color:#FFF !important;
overflow:hidden !important;
}
.image-container img,.image-container video{
width:100% !important;
height:auto;
box-sizing:border-box !important;
display:block !important;
}
/* HIDE ALL GRADIO PROCESSING UI – 100+ SELECTORS */
.image-container[aria-label="Generated Video"] .progress-text,
.image-container[aria-label="Generated Video"] .gr-progress,
.image-container[aria-label="Generated Video"] .gr-progress-bar,
.image-container[aria-label="Generated Video"] .progress-bar,
.image-container[aria-label="Generated Video"] [data-testid="progress"],
.image-container[aria-label="Generated Video"] .status,
.image-container[aria-label="Generated Video"] .loading,
.image-container[aria-label="Generated Video"] .spinner,
.image-container[aria-label="Generated Video"] .gr-spinner,
.image-container[aria-label="Generated Video"] .gr-loading,
.image-container[aria-label="Generated Video"] .gr-status,
.image-container[aria-label="Generated Video"] .gpu-init,
.image-container[aria-label="Generated Video"] .initializing,
.image-container[aria-label="Generated Video"] .queue,
.image-container[aria-label="Generated Video"] .queued,
.image-container[aria-label="Generated Video"] .waiting,
.image-container[aria-label="Generated Video"] .processing,
.image-container[aria-label="Generated Video"] .gradio-progress,
.image-container[aria-label="Generated Video"] .gradio-status,
.image-container[aria-label="Generated Video"] div[class*="progress"],
.image-container[aria-label="Generated Video"] div[class*="loading"],
.image-container[aria-label="Generated Video"] div[class*="status"],
.image-container[aria-label="Generated Video"] div[class*="spinner"],
.image-container[aria-label="Generated Video"] *[class*="progress"],
.image-container[aria-label="Generated Video"] *[class*="loading"],
.image-container[aria-label="Generated Video"] *[class*="status"],
.image-container[aria-label="Generated Video"] *[class*="spinner"],
.progress-text,.gr-progress,.gr-progress-bar,.progress-bar,
[data-testid="progress"],.status,.loading,.spinner,.gr-spinner,
.gr-loading,.gr-status,.gpu-init,.initializing,.queue,
.queued,.waiting,.processing,.gradio-progress,.gradio-status,
div[class*="progress"],div[class*="loading"],div[class*="status"],
div[class*="spinner"],*[class*="progress"],*[class*="loading"],
*[class*="status"],*[class*="spinner"]{
display:none!important;
visibility:hidden!important;
opacity:0!important;
height:0!important;
width:0!important;
font-size:0!important;
line-height:0!important;
padding:0!important;
margin:0!important;
position:absolute!important;
left:-9999px!important;
top:-9999px!important;
z-index:-9999!important;
pointer-events:none!important;
overflow:hidden!important;
}
/* EXHAUSTIVE TOOLBAR HIDING */
.image-container[aria-label="Input Image"] .file-upload,
.image-container[aria-label="Input Image"] .file-preview,
.image-container[aria-label="Input Image"] .image-actions,
.image-container[aria-label="Generated Video"] .file-upload,
.image-container[aria-label="Generated Video"] .file-preview,
.image-container[aria-label="Generated Video"] .image-actions{
display:none!important;
}
.image-container[aria-label="Generated Video"].processing{
background:#000!important;
position:relative;
}
.image-container[aria-label="Generated Video"].processing::before{
content:"PROCESSING...";
position:absolute!important;
top:50%!important;
left:50%!important;
transform:translate(-50%,-50%)!important;
color:#FFF;
font-family:'Orbitron',sans-serif;
font-size:1.8rem!important;
font-weight:700!important;
text-align:center;
text-shadow:0 0 10px rgba(0,255,128,0.8)!important;
animation:pulse 1.5s ease-in-out infinite,glow 2s ease-in-out infinite!important;
z-index:9999!important;
width:100%!important;
height:100%!important;
display:flex!important;
align-items:center!important;
justify-content:center!important;
pointer-events:none!important;
background:#000!important;
border-radius:4px!important;
box-sizing:border-box!important;
}
.image-container[aria-label="Generated Video"].processing *{
display:none!important;
}
input,textarea,.gr-dropdown,.gr-dropdown select{
background:#000!important;
color:#FFF!important;
border:1px solid #FFF!important;
border-radius:4px;
padding:.5rem;
width:100%!important;
max-width:100vw!important;
box-sizing:border-box!important;
}
.gr-button-primary{
background:linear-gradient(90deg,rgba(0,255,128,0.3),rgba(0,200,100,0.3),rgba(0,255,128,0.3))!important;
background-size:200% 100%;
animation:slide 4s ease-in-out infinite,glow 3s ease-in-out infinite;
color:#FFF!important;
border:1px solid #FFF!important;
border-radius:6px;
padding:.75rem 1.5rem;
font-size:1.1rem;
font-weight:600;
box-shadow:0 0 14px rgba(0,255,128,0.7)!important;
transition:box-shadow .3s,transform .3s;
width:100%!important;
max-width:100vw!important;
min-height:48px;
cursor:pointer;
}
.gr-button-primary:hover{
box-shadow:0 0 20px rgba(0,255,128,0.9)!important;
animation:slide 4s ease-in-out infinite,glow-hover 3s ease-in-out infinite;
transform:scale(1.05);
}
button[aria-label="Fullscreen"],button[aria-label="Share"]{
display:none!important;
}
button[aria-label="Download"]{
transform:scale(3);
transform-origin:top right;
background:#000!important;
color:#FFF!important;
border:1px solid #FFF!important;
border-radius:4px;
padding:.4rem!important;
margin:.5rem!important;
box-shadow:0 0 8px rgba(255,255,255,0.3)!important;
transition:box-shadow .3s;
}
button[aria-label="Download"]:hover{
box-shadow:0 0 12px rgba(255,255,255,0.5)!important;
}
footer,.gr-button-secondary{
display:none!important;
}
.gr-group{
background:#000!important;
border:none!important;
width:100%!important;
max-width:100vw!important;
}
@media (max-width:768px){
h1{font-size:4rem;}
#subtitle{font-size:.9rem;}
.gr-button-primary{
padding:.6rem 1rem;
font-size:1rem;
box-shadow:0 0 10px rgba(0,255,128,0.7)!important;
}
.gr-button-primary:hover{
box-shadow:0 0 12px rgba(0,255,128,0.9)!important;
}
.image-container{min-height:300px;}
.image-container[aria-label="Generated Video"].processing::before{
font-size:1.2rem!important;
}
}
</style>
"""
)
# -----------------------------------------------------------------
# UI layout – unchanged visual / CSS / 500‑guard / unique‑link
# -----------------------------------------------------------------
with gr.Row(elem_id="general_items"):
gr.Markdown("# ")
gr.Markdown(
"Convert an image into an animated video with prompt description.",
elem_id="subtitle",
)
with gr.Column(elem_id="input_column"):
input_image = gr.Image(
type="pil",
label="Input Image",
sources=["upload"],
show_download_button=False,
show_share_button=False,
interactive=True,
elem_classes=["gradio-component", "image-container"],
)
prompt = gr.Textbox(
label="Prompt",
value=default_prompt_i2v,
lines=3,
placeholder="Describe the desired animation or motion",
elem_classes=["gradio-component"],
)
generate_button = gr.Button(
"Generate Video",
variant="primary",
elem_classes=["gradio-component", "gr-button-primary"],
)
output_video = gr.Video(
label="Generated Video",
autoplay=True,
interactive=False,
show_download_button=True,
show_share_button=False,
elem_classes=["gradio-component", "image-container"],
)
# -----------------------------------------------------------------
# Wiring – keep the same order as the function signature
# -----------------------------------------------------------------
generate_button.click(
fn=generate_video,
inputs=[
input_image,
prompt,
gr.State(value=6), # steps
gr.State(value=default_negative_prompt), # negative_prompt
gr.State(value=3.2), # duration_seconds
gr.State(value=1.5), # guidance_scale
gr.State(value=1.5), # guidance_scale_2
gr.State(value=42), # seed
gr.State(value=True), # randomize_seed
# progress is *not* passed – the @spaces.GPU decorator injects it
],
outputs=[output_video, gr.State(value=42)],
)
return demo
# ------------------------------------------------------------
# MAIN
# ------------------------------------------------------------
if __name__ == "__main__":
demo = create_demo()
# keep the launch flags you originally used
demo.queue().launch(share=True) |