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1ff42f6 e07843c 1ff42f6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 | # app.py — Wan 2.2 Lite Edition (8GB RAM Safe)
import os
import spaces
import torch
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
import gradio as gr
import tempfile
import numpy as np
from PIL import Image
import random
import gc
import aoti
from torchao.quantization import quantize_
from torchao.quantization import (
Float8DynamicActivationFloat8WeightConfig,
Int8WeightOnlyConfig
)
# =====================================================================
# BASIC CONFIG FOR LOW-RAM SYSTEMS
# =====================================================================
MODEL_ID = "Wan-AI/Wan2.2-I2V-A14B-Diffusers"
from dotenv import load_dotenv
load_dotenv()
HF_TOKEN = os.environ.get("HF_TOKEN")
MAX_DIM = 480
MIN_DIM = 320
MULTIPLE_OF = 16
MAX_SEED = np.iinfo(np.int32).max
FPS = 12
MIN_FRAMES = 8
MAX_FRAMES = 200 # around 2 seconds max
# =====================================================================
# LOAD PIPELINE — STRIPPED DOWN (NO TRANSFORMER_2)
# =====================================================================
pipe = WanImageToVideoPipeline.from_pretrained(
MODEL_ID,
transformer=WanTransformer3DModel.from_pretrained(
MODEL_ID,
subfolder="transformer",
torch_dtype=torch.bfloat16,
device_map="cuda",
token=HF_TOKEN,
),
torch_dtype=torch.bfloat16,
).to("cuda")
# =====================================================================
# QUANTIZATION FOR 8GB VRAM
# =====================================================================
quantize_(pipe.text_encoder, Int8WeightOnlyConfig())
quantize_(pipe.transformer, Float8DynamicActivationFloat8WeightConfig())
# AOTI compile small blocks
aoti.aoti_blocks_load(pipe.transformer, "zerogpu-aoti/Wan2", variant="fp8da")
# =====================================================================
# IMAGE RESIZE — 480p MAX
# =====================================================================
def resize_image(image):
w, h = image.size
ratio = w / h
if w > h:
new_w = MAX_DIM
new_h = int(new_w / ratio)
else:
new_h = MAX_DIM
new_w = int(new_h * ratio)
new_w = max(MIN_DIM, round(new_w / MULTIPLE_OF) * MULTIPLE_OF)
new_h = max(MIN_DIM, round(new_h / MULTIPLE_OF) * MULTIPLE_OF)
return image.resize((new_w, new_h), Image.LANCZOS)
# =====================================================================
# FRAME COUNT
# =====================================================================
def get_num_frames(seconds):
return np.clip(int(seconds * FPS), MIN_FRAMES, MAX_FRAMES)
# =====================================================================
# MAIN GENERATE FUNCTION
# =====================================================================
@spaces.GPU()
def generate_video(
input_image,
prompt,
steps=1,
negative_prompt="",
duration_seconds=1.5,
seed=42,
randomize_seed=False,
):
if input_image is None:
raise gr.Error("Upload an input image.")
gc.collect()
torch.cuda.empty_cache()
num_frames = get_num_frames(duration_seconds)
seed = random.randint(0, MAX_SEED) if randomize_seed else int(seed)
img = resize_image(input_image)
out = pipe(
image=img,
prompt=prompt,
negative_prompt=negative_prompt,
num_frames=num_frames,
height=img.height,
width=img.width,
guidance_scale=0.5,
num_inference_steps=int(steps),
generator=torch.Generator("cuda").manual_seed(seed),
)
frames = out.frames[0]
with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmp:
path = tmp.name
export_to_video(frames, path, fps=FPS)
gc.collect()
torch.cuda.empty_cache()
return path, seed
# =====================================================================
# GRADIO UI — LIGHT VERSION
# =====================================================================
with gr.Blocks() as demo:
gr.Markdown("## 🚀 Wan 2.2 — Lite Edition (8GB RAM Optimized)")
gr.Markdown("Runs at max 480p, 2-second videos, FP8 + INT8 optimized.")
with gr.Row():
with gr.Column():
img = gr.Image(label="Input Image", type="pil")
prompt_box = gr.Textbox("make this image move smoothly")
duration = gr.Slider(0.8, 2.0, 1.5, label="Duration (seconds)")
steps = gr.Slider(1, 3, 1, step=1, label="Steps (1 = fastest)")
seed_box = gr.Slider(0, MAX_SEED, 42, label="Seed")
rand_seed = gr.Checkbox(True, label="Randomize Seed")
btn = gr.Button("Generate Video", variant="primary")
with gr.Column():
out_video = gr.Video(label="Result")
btn.click(
generate_video,
inputs=[img, prompt_box, steps, None, duration, seed_box, rand_seed],
outputs=[out_video, seed_box],
)
if __name__ == "__main__":
demo.queue().launch()
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