Add gradio local inference demo
This commit is contained in:
ainsley
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</svg>
|
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|
After Width: | Height: | Size: 41 KiB |
@@ -0,0 +1,169 @@
|
||||
import argparse
|
||||
import os
|
||||
from copy import deepcopy
|
||||
|
||||
import gradio as gr
|
||||
import torch
|
||||
|
||||
from fastvideo import VideoGenerator
|
||||
from fastvideo.configs.sample.base import SamplingParam
|
||||
|
||||
if __name__ == "__main__":
|
||||
os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "VIDEO_SPARSE_ATTN"
|
||||
parser = argparse.ArgumentParser(description="FastVideo Gradio Demo")
|
||||
parser.add_argument("--model_path",
|
||||
type=str,
|
||||
default="FastVideo/FastWan2.1-T2V-1.3B-Diffusers",
|
||||
help="Path to the model")
|
||||
parser.add_argument("--num_gpus",
|
||||
type=int,
|
||||
default=1,
|
||||
help="Number of GPUs to use")
|
||||
parser.add_argument("--output_path",
|
||||
type=str,
|
||||
default="my_videos/",
|
||||
help="Path to save generated videos")
|
||||
parsed_args = parser.parse_args()
|
||||
|
||||
|
||||
generator = VideoGenerator.from_pretrained(
|
||||
model_path=parsed_args.model_path, num_gpus=parsed_args.num_gpus)
|
||||
|
||||
default_params = SamplingParam.from_pretrained(parsed_args.model_path)
|
||||
|
||||
def generate_video(
|
||||
prompt,
|
||||
negative_prompt,
|
||||
use_negative_prompt,
|
||||
seed,
|
||||
guidance_scale,
|
||||
num_frames,
|
||||
height,
|
||||
width,
|
||||
num_inference_steps,
|
||||
randomize_seed=False,
|
||||
):
|
||||
params = deepcopy(default_params)
|
||||
params.prompt = prompt
|
||||
params.negative_prompt = negative_prompt
|
||||
params.seed = seed
|
||||
params.guidance_scale = guidance_scale
|
||||
params.num_frames = num_frames
|
||||
params.height = height
|
||||
params.width = width
|
||||
params.num_inference_steps = num_inference_steps
|
||||
|
||||
if randomize_seed:
|
||||
params.seed = torch.randint(0, 1000000, (1, )).item()
|
||||
|
||||
if use_negative_prompt and negative_prompt:
|
||||
params.negative_prompt = negative_prompt
|
||||
else:
|
||||
params.negative_prompt = default_params.negative_prompt
|
||||
|
||||
generator.generate_video(prompt=prompt, sampling_param=params,output_path=parsed_args.output_path,save_video=True)
|
||||
|
||||
output_path = os.path.join(parsed_args.output_path,
|
||||
f"{params.prompt[:100]}.mp4")
|
||||
|
||||
return output_path, params.seed
|
||||
|
||||
examples = [
|
||||
"A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes wide with interest.",
|
||||
]
|
||||
|
||||
with gr.Blocks() as demo:
|
||||
gr.Markdown("# FastVideo Inference Demo")
|
||||
|
||||
with gr.Group():
|
||||
with gr.Row():
|
||||
prompt = gr.Text(
|
||||
label="Prompt",
|
||||
show_label=False,
|
||||
max_lines=1,
|
||||
placeholder="Enter your prompt",
|
||||
container=False,
|
||||
)
|
||||
run_button = gr.Button("Run", scale=0)
|
||||
result = gr.Video(label="Result", show_label=False)
|
||||
|
||||
with gr.Accordion("Advanced options", open=False):
|
||||
with gr.Group():
|
||||
with gr.Row():
|
||||
height = gr.Slider(
|
||||
label="Height",
|
||||
minimum=256,
|
||||
maximum=1024,
|
||||
step=32,
|
||||
value=default_params.height,
|
||||
)
|
||||
width = gr.Slider(label="Width",
|
||||
minimum=256,
|
||||
maximum=1024,
|
||||
step=32,
|
||||
value=default_params.width)
|
||||
|
||||
with gr.Row():
|
||||
num_frames = gr.Slider(
|
||||
label="Number of Frames",
|
||||
minimum=21,
|
||||
maximum=163,
|
||||
value=default_params.num_frames,
|
||||
)
|
||||
guidance_scale = gr.Slider(
|
||||
label="Guidance Scale",
|
||||
minimum=1,
|
||||
maximum=12,
|
||||
value=default_params.guidance_scale,
|
||||
)
|
||||
num_inference_steps = gr.Slider(
|
||||
label="Inference Steps",
|
||||
minimum=3,
|
||||
maximum=100,
|
||||
value=default_params.num_inference_steps,
|
||||
)
|
||||
|
||||
with gr.Row():
|
||||
use_negative_prompt = gr.Checkbox(
|
||||
label="Use negative prompt", value=False)
|
||||
negative_prompt = gr.Text(
|
||||
label="Negative prompt",
|
||||
max_lines=1,
|
||||
placeholder="Enter your negative prompt",
|
||||
visible=False,
|
||||
)
|
||||
|
||||
seed = gr.Slider(label="Seed",
|
||||
minimum=0,
|
||||
maximum=1000000,
|
||||
step=1,
|
||||
value=default_params.seed)
|
||||
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
|
||||
seed_output = gr.Number(label="Used Seed")
|
||||
|
||||
gr.Examples(examples=examples, inputs=prompt)
|
||||
|
||||
use_negative_prompt.change(
|
||||
fn=lambda x: gr.update(visible=x),
|
||||
inputs=use_negative_prompt,
|
||||
outputs=negative_prompt,
|
||||
)
|
||||
|
||||
run_button.click(
|
||||
fn=generate_video,
|
||||
inputs=[
|
||||
prompt,
|
||||
negative_prompt,
|
||||
use_negative_prompt,
|
||||
seed,
|
||||
guidance_scale,
|
||||
num_frames,
|
||||
height,
|
||||
width,
|
||||
num_inference_steps,
|
||||
randomize_seed,
|
||||
],
|
||||
outputs=[result, seed_output],
|
||||
)
|
||||
|
||||
demo.queue(max_size=20).launch(server_name="0.0.0.0", server_port=8888)
|
||||
@@ -0,0 +1,656 @@
|
||||
import argparse
|
||||
import os
|
||||
import base64
|
||||
import time
|
||||
|
||||
import gradio as gr
|
||||
from fastvideo.entrypoints.video_generator import VideoGenerator
|
||||
from fastvideo.configs.sample.base import SamplingParam
|
||||
from copy import deepcopy
|
||||
|
||||
|
||||
MODEL_PATH_MAPPING = {
|
||||
"FastWan2.1-T2V-1.3B": "FastVideo/FastWan2.1-T2V-1.3B-Diffusers",
|
||||
"FastWan2.2-TI2V-5B-FullAttn": "FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers",
|
||||
}
|
||||
|
||||
def create_timing_display(inference_time, total_time, stage_execution_times, num_frames):
|
||||
dit_denoising_time = f"{stage_execution_times[5]:.2f}s" if len(stage_execution_times) > 5 else "N/A"
|
||||
|
||||
timing_html = f"""
|
||||
<div style="margin: 10px 0;">
|
||||
<h3 style="text-align: center; margin-bottom: 10px;">⏱️ Timing Breakdown</h3>
|
||||
<div style="display: grid; grid-template-columns: repeat(5, 1fr); gap: 10px; margin-bottom: 10px;">
|
||||
<div class="timing-card timing-card-highlight">
|
||||
<div style="font-size: 20px;">🚀</div>
|
||||
<div style="font-weight: bold; margin: 3px 0; font-size: 14px;">DiT Denoising</div>
|
||||
<div style="font-size: 16px; color: #ffa200; font-weight: bold;">{dit_denoising_time}</div>
|
||||
</div>
|
||||
<div class="timing-card">
|
||||
<div style="font-size: 20px;">🧠</div>
|
||||
<div style="font-weight: bold; margin: 3px 0; font-size: 14px;">E2E (w. vae/text encoder)</div>
|
||||
<div style="font-size: 16px; color: #2563eb;">{inference_time:.2f}s</div>
|
||||
</div>
|
||||
<div class="timing-card">
|
||||
<div style="font-size: 20px;">🎬</div>
|
||||
<div style="font-weight: bold; margin: 3px 0; font-size: 14px;">Video Encoding</div>
|
||||
<div style="font-size: 16px; color: #dc2626;">N/A</div>
|
||||
</div>
|
||||
<div class="timing-card">
|
||||
<div style="font-size: 20px;">🌐</div>
|
||||
<div style="font-weight: bold; margin: 3px 0; font-size: 14px;">Network Transfer</div>
|
||||
<div style="font-size: 16px; color: #059669;">N/A</div>
|
||||
</div>
|
||||
<div class="timing-card">
|
||||
<div style="font-size: 20px;">📊</div>
|
||||
<div style="font-weight: bold; margin: 3px 0; font-size: 14px;">Total Processing</div>
|
||||
<div style="font-size: 18px; color: #0277bd;">{total_time:.2f}s</div>
|
||||
</div>
|
||||
</div>"""
|
||||
|
||||
if inference_time > 0:
|
||||
fps = num_frames / inference_time
|
||||
timing_html += f"""
|
||||
<div class="performance-card" style="margin-top: 15px;">
|
||||
<span style="font-weight: bold;">Generation Speed: </span>
|
||||
<span style="font-size: 18px; color: #6366f1; font-weight: bold;">{fps:.1f} frames/second</span>
|
||||
</div>"""
|
||||
|
||||
return timing_html + "</div>"
|
||||
def setup_model_environment(model_path: str) -> None:
|
||||
if "fullattn" in model_path.lower():
|
||||
os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "FLASH_ATTN"
|
||||
else:
|
||||
os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "VIDEO_SPARSE_ATTN"
|
||||
os.environ["FASTVIDEO_STAGE_LOGGING"] = "1"
|
||||
|
||||
def load_example_prompts():
|
||||
def contains_chinese(text):
|
||||
return any('\u4e00' <= char <= '\u9fff' for char in text)
|
||||
|
||||
def load_from_file(filepath):
|
||||
prompts, labels = [], []
|
||||
try:
|
||||
with open(filepath, "r", encoding='utf-8') as f:
|
||||
for line in f:
|
||||
line = line.strip()
|
||||
if line and not contains_chinese(line):
|
||||
label = line[:100] + "..." if len(line) > 100 else line
|
||||
labels.append(label)
|
||||
prompts.append(line)
|
||||
except Exception as e:
|
||||
print(f"Warning: Could not read {filepath}: {e}")
|
||||
return prompts, labels
|
||||
|
||||
examples, example_labels = load_from_file("prompts/prompts_final.txt")
|
||||
|
||||
if not examples:
|
||||
examples = ["A crowded rooftop bar buzzes with energy, the city skyline twinkling like a field of stars in the background."]
|
||||
example_labels = ["Crowded rooftop bar at night"]
|
||||
|
||||
return examples, example_labels
|
||||
|
||||
|
||||
def create_gradio_interface(default_params: dict[str, SamplingParam], generators: dict[str, VideoGenerator]):
|
||||
def generate_video(
|
||||
prompt, negative_prompt, use_negative_prompt, seed, guidance_scale,
|
||||
num_frames, height, width, randomize_seed, model_selection, progress
|
||||
):
|
||||
model_path = MODEL_PATH_MAPPING.get(model_selection, "FastVideo/FastWan2.1-T2V-1.3B-Diffusers")
|
||||
setup_model_environment(model_path)
|
||||
try:
|
||||
if progress:
|
||||
progress(0.1, desc="Loading model for local inference...")
|
||||
|
||||
generator = generators[model_path]
|
||||
params = deepcopy(default_params[model_path])
|
||||
total_start_time = time.time()
|
||||
if progress:
|
||||
progress(0.2, desc="Configuring parameters...")
|
||||
|
||||
params.prompt = prompt
|
||||
params.seed = int(seed)
|
||||
params.guidance_scale = guidance_scale
|
||||
params.num_frames = int(num_frames)
|
||||
params.height = int(height)
|
||||
params.width = int(width)
|
||||
|
||||
if randomize_seed:
|
||||
params.seed = torch.randint(0, 1000000, (1, )).item()
|
||||
|
||||
if use_negative_prompt and negative_prompt:
|
||||
params.negative_prompt = negative_prompt
|
||||
else:
|
||||
params.negative_prompt = default_params[model_path].negative_prompt
|
||||
|
||||
if progress:
|
||||
progress(0.4, desc="Generating video locally...")
|
||||
|
||||
output_dir = "outputs/"
|
||||
os.makedirs(output_dir, exist_ok=True)
|
||||
start_time = time.time()
|
||||
result = generator.generate_video(prompt=prompt, sampling_param=params, save_video=False, return_frames=False)
|
||||
inference_time = time.time() - start_time
|
||||
logging_info = result.get("logging_info", None)
|
||||
if logging_info:
|
||||
stage_names = logging_info.get_execution_order()
|
||||
stage_execution_times = [
|
||||
logging_info.get_stage_info(stage_name).get("execution_time", 0.0)
|
||||
for stage_name in stage_names
|
||||
]
|
||||
else:
|
||||
stage_names = []
|
||||
stage_execution_times = []
|
||||
total_time = time.time() - total_start_time
|
||||
timing_details=create_timing_display(inference_time=inference_time, total_time=total_time, stage_execution_times=stage_execution_times, num_frames=params.num_frames)
|
||||
safe_prompt = params.prompt[:100].replace(' ', '_').replace('/', '_').replace('\\', '_')
|
||||
video_filename = f"{params.prompt[:100]}.mp4"
|
||||
output_path = os.path.join(output_dir, video_filename)
|
||||
|
||||
if progress:
|
||||
progress(1.0, desc="Generation complete!")
|
||||
|
||||
return output_path, params.seed, timing_details
|
||||
|
||||
except Exception as e:
|
||||
print(f"An error occurred during local generation: {e}")
|
||||
return None, f"Generation failed: {str(e)}", ""
|
||||
|
||||
examples, example_labels = load_example_prompts()
|
||||
|
||||
theme = gr.themes.Base().set(
|
||||
button_primary_background_fill="#2563eb",
|
||||
button_primary_background_fill_hover="#1d4ed8",
|
||||
button_primary_text_color="white",
|
||||
slider_color="#2563eb",
|
||||
checkbox_background_color_selected="#2563eb",
|
||||
)
|
||||
|
||||
def get_default_values(model_name):
|
||||
model_path = MODEL_PATH_MAPPING.get(model_name)
|
||||
if model_path and model_path in default_params:
|
||||
params = default_params[model_path]
|
||||
return {
|
||||
'height': params.height,
|
||||
'width': params.width,
|
||||
'num_frames': params.num_frames,
|
||||
'guidance_scale': params.guidance_scale,
|
||||
'seed': params.seed,
|
||||
}
|
||||
|
||||
return {
|
||||
'height': 448,
|
||||
'width': 832,
|
||||
'num_frames': 61,
|
||||
'guidance_scale': 3.0,
|
||||
'seed': 1024,
|
||||
}
|
||||
|
||||
initial_values = get_default_values("FastWan2.1-T2V-1.3B")
|
||||
|
||||
with gr.Blocks(title="FastWan", theme=theme) as demo:
|
||||
gr.Image("fastvideo-logos/main/svg/full.svg", show_label=False, container=False, height=80)
|
||||
|
||||
gr.HTML("""
|
||||
<div style="text-align: center; margin-bottom: 10px;">
|
||||
<p style="font-size: 18px;"> Make Video Generation Go Blurrrrrrr </p>
|
||||
<p style="font-size: 18px;"> <a href="https://github.com/hao-ai-lab/FastVideo/tree/main" target="_blank">Code</a> | <a href="https://hao-ai-lab.github.io/blogs/fastvideo_post_training/" target="_blank">Blog</a> | <a href="https://hao-ai-lab.github.io/FastVideo/" target="_blank">Docs</a> </p>
|
||||
</div>
|
||||
""")
|
||||
|
||||
with gr.Accordion("🎥 What Is FastVideo?", open=False):
|
||||
gr.HTML("""
|
||||
<div style="padding: 20px; line-height: 1.6;">
|
||||
<p style="font-size: 16px; margin-bottom: 15px;">
|
||||
FastVideo is an inference and post-training framework for diffusion models. It features an end-to-end unified pipeline for accelerating diffusion models, starting from data preprocessing to model training, finetuning, distillation, and inference. FastVideo is designed to be modular and extensible, allowing users to easily add new optimizations and techniques. Whether it is training-free optimizations or post-training optimizations, FastVideo has you covered.
|
||||
</p>
|
||||
</div>
|
||||
""")
|
||||
|
||||
with gr.Row():
|
||||
model_selection = gr.Dropdown(
|
||||
choices=list(MODEL_PATH_MAPPING.keys()),
|
||||
value="FastWan2.1-T2V-1.3B",
|
||||
label="Select Model",
|
||||
interactive=True
|
||||
)
|
||||
|
||||
with gr.Row():
|
||||
example_dropdown = gr.Dropdown(
|
||||
choices=example_labels,
|
||||
label="Example Prompts",
|
||||
value=None,
|
||||
interactive=True,
|
||||
allow_custom_value=False
|
||||
)
|
||||
|
||||
with gr.Row():
|
||||
with gr.Column(scale=6):
|
||||
prompt = gr.Text(
|
||||
label="Prompt",
|
||||
show_label=False,
|
||||
max_lines=3,
|
||||
placeholder="Describe your scene...",
|
||||
container=False,
|
||||
lines=3,
|
||||
autofocus=True,
|
||||
)
|
||||
with gr.Column(scale=1, min_width=120, elem_classes="center-button"):
|
||||
run_button = gr.Button("Run", variant="primary", size="lg")
|
||||
|
||||
with gr.Row():
|
||||
with gr.Column():
|
||||
error_output = gr.Text(label="Error", visible=False)
|
||||
timing_display = gr.Markdown(label="Timing Breakdown", visible=False)
|
||||
|
||||
with gr.Row(equal_height=True, elem_classes="main-content-row"):
|
||||
with gr.Column(scale=1, elem_classes="advanced-options-column"):
|
||||
with gr.Group():
|
||||
gr.HTML("<div style='margin: 0 0 15px 0; text-align: center; font-size: 16px;'>Advanced Options</div>")
|
||||
with gr.Row():
|
||||
height = gr.Number(
|
||||
label="Height",
|
||||
value=initial_values['height'],
|
||||
interactive=False,
|
||||
container=True
|
||||
)
|
||||
width = gr.Number(
|
||||
label="Width",
|
||||
value=initial_values['width'],
|
||||
interactive=False,
|
||||
container=True
|
||||
)
|
||||
|
||||
with gr.Row():
|
||||
num_frames = gr.Number(
|
||||
label="Number of Frames",
|
||||
value=initial_values['num_frames'],
|
||||
interactive=False,
|
||||
container=True
|
||||
)
|
||||
guidance_scale = gr.Slider(
|
||||
label="Guidance Scale",
|
||||
minimum=1,
|
||||
maximum=12,
|
||||
value=initial_values['guidance_scale'],
|
||||
)
|
||||
|
||||
with gr.Row():
|
||||
use_negative_prompt = gr.Checkbox(
|
||||
label="Use negative prompt", value=False)
|
||||
negative_prompt = gr.Text(
|
||||
label="Negative prompt",
|
||||
max_lines=3,
|
||||
lines=3,
|
||||
placeholder="Enter a negative prompt",
|
||||
visible=False,
|
||||
)
|
||||
|
||||
seed = gr.Slider(
|
||||
label="Seed",
|
||||
minimum=0,
|
||||
maximum=1000000,
|
||||
step=1,
|
||||
value=initial_values['seed'],
|
||||
)
|
||||
randomize_seed = gr.Checkbox(label="Randomize seed", value=False)
|
||||
seed_output = gr.Number(label="Used Seed")
|
||||
|
||||
with gr.Column(scale=1, elem_classes="video-column"):
|
||||
result = gr.Video(
|
||||
label="Generated Video",
|
||||
show_label=True,
|
||||
height=466,
|
||||
width=600,
|
||||
container=True,
|
||||
elem_classes="video-component"
|
||||
)
|
||||
|
||||
gr.HTML("""
|
||||
<style>
|
||||
.center-button {
|
||||
display: flex !important;
|
||||
justify-content: center !important;
|
||||
height: 100% !important;
|
||||
padding-top: 1.4em !important;
|
||||
}
|
||||
|
||||
.gradio-container {
|
||||
max-width: 1200px !important;
|
||||
margin: 0 auto !important;
|
||||
}
|
||||
|
||||
.main {
|
||||
max-width: 1200px !important;
|
||||
margin: 0 auto !important;
|
||||
}
|
||||
|
||||
.gr-form, .gr-box, .gr-group {
|
||||
max-width: 1200px !important;
|
||||
}
|
||||
|
||||
.gr-video {
|
||||
max-width: 500px !important;
|
||||
margin: 0 auto !important;
|
||||
}
|
||||
|
||||
.main-content-row {
|
||||
display: flex !important;
|
||||
align-items: flex-start !important;
|
||||
min-height: 500px !important;
|
||||
gap: 20px !important;
|
||||
}
|
||||
|
||||
.advanced-options-column,
|
||||
.video-column {
|
||||
display: flex !important;
|
||||
flex-direction: column !important;
|
||||
flex: 1 !important;
|
||||
min-height: 400px !important;
|
||||
align-items: stretch !important;
|
||||
}
|
||||
|
||||
.video-column > * {
|
||||
margin-top: 0 !important;
|
||||
}
|
||||
|
||||
.video-column .gr-video,
|
||||
.video-component {
|
||||
margin-top: 0 !important;
|
||||
padding-top: 0 !important;
|
||||
}
|
||||
|
||||
.video-column .gr-video .gr-form {
|
||||
margin-top: 0 !important;
|
||||
}
|
||||
|
||||
.advanced-options-column .gr-group,
|
||||
.video-column .gr-video {
|
||||
margin-top: 0 !important;
|
||||
vertical-align: top !important;
|
||||
}
|
||||
|
||||
.advanced-options-column > *:last-child,
|
||||
.video-column > *:last-child {
|
||||
flex-grow: 0 !important;
|
||||
}
|
||||
|
||||
@media (max-width: 1400px) {
|
||||
.main-content-row {
|
||||
min-height: 600px !important;
|
||||
}
|
||||
|
||||
.advanced-options-column,
|
||||
.video-column {
|
||||
min-height: 600px !important;
|
||||
}
|
||||
}
|
||||
|
||||
@media (max-width: 1200px) {
|
||||
.main-content-row {
|
||||
flex-direction: column !important;
|
||||
align-items: stretch !important;
|
||||
}
|
||||
|
||||
.advanced-options-column,
|
||||
.video-column {
|
||||
min-height: auto !important;
|
||||
width: 100% !important;
|
||||
}
|
||||
}
|
||||
|
||||
.timing-card {
|
||||
background: var(--background-fill-secondary) !important;
|
||||
border: 1px solid var(--border-color-primary) !important;
|
||||
color: var(--body-text-color) !important;
|
||||
padding: 10px;
|
||||
border-radius: 8px;
|
||||
text-align: center;
|
||||
min-height: 80px;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
justify-content: center;
|
||||
}
|
||||
|
||||
.timing-card-highlight {
|
||||
background: var(--background-fill-primary) !important;
|
||||
border: 2px solid var(--color-accent) !important;
|
||||
}
|
||||
|
||||
.performance-card {
|
||||
background: var(--background-fill-secondary) !important;
|
||||
border: 1px solid var(--border-color-primary) !important;
|
||||
color: var(--body-text-color) !important;
|
||||
padding: 10px;
|
||||
border-radius: 6px;
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
.gr-number input[readonly] {
|
||||
background-color: var(--background-fill-secondary) !important;
|
||||
border: 1px solid var(--border-color-primary) !important;
|
||||
color: var(--body-text-color-subdued) !important;
|
||||
cursor: default !important;
|
||||
text-align: center !important;
|
||||
font-weight: 500 !important;
|
||||
}
|
||||
</style>
|
||||
""")
|
||||
|
||||
def on_example_select(example_label):
|
||||
if example_label and example_label in example_labels:
|
||||
index = example_labels.index(example_label)
|
||||
return examples[index]
|
||||
return ""
|
||||
|
||||
example_dropdown.change(
|
||||
fn=on_example_select,
|
||||
inputs=example_dropdown,
|
||||
outputs=prompt,
|
||||
)
|
||||
|
||||
gr.HTML("""
|
||||
<div style="text-align: center; margin-top: 10px; margin-bottom: 15px;">
|
||||
<p style="font-size: 16px; margin: 0;">The compute for this demo is generously provided by <a href="https://www.gmicloud.ai/" target="_blank">GMI Cloud</a>. Note that this demo is meant to showcase FastWan's quality and that under a large number of requests, generation speed may be affected. We are also rate-limiting users to 3 requests per minute.</p>
|
||||
</div>
|
||||
""")
|
||||
|
||||
use_negative_prompt.change(
|
||||
fn=lambda x: gr.update(visible=x),
|
||||
inputs=use_negative_prompt,
|
||||
outputs=negative_prompt,
|
||||
)
|
||||
|
||||
def on_model_selection_change(selected_model):
|
||||
if not selected_model:
|
||||
selected_model = "FastWan2.1-T2V-1.3B"
|
||||
|
||||
model_path = MODEL_PATH_MAPPING.get(selected_model)
|
||||
|
||||
if model_path and model_path in default_params:
|
||||
params = default_params[model_path]
|
||||
return (
|
||||
gr.update(value=params.height),
|
||||
gr.update(value=params.width),
|
||||
gr.update(value=params.num_frames),
|
||||
gr.update(value=params.guidance_scale),
|
||||
gr.update(value=params.seed),
|
||||
)
|
||||
|
||||
return (
|
||||
gr.update(value=448),
|
||||
gr.update(value=832),
|
||||
gr.update(value=61),
|
||||
gr.update(value=3.0),
|
||||
gr.update(value=1024),
|
||||
)
|
||||
|
||||
model_selection.change(
|
||||
fn=on_model_selection_change,
|
||||
inputs=model_selection,
|
||||
outputs=[height, width, num_frames, guidance_scale, seed],
|
||||
)
|
||||
|
||||
def handle_generation(*args, progress=None, request: gr.Request = None):
|
||||
model_selection, prompt, negative_prompt, use_negative_prompt, seed, guidance_scale, num_frames, height, width, randomize_seed = args
|
||||
|
||||
result_path, seed_or_error, timing_details = generate_video(
|
||||
prompt, negative_prompt, use_negative_prompt, seed, guidance_scale,
|
||||
num_frames, height, width, randomize_seed, model_selection, progress
|
||||
)
|
||||
if result_path and os.path.exists(result_path):
|
||||
return (
|
||||
result_path,
|
||||
seed_or_error,
|
||||
gr.update(visible=False),
|
||||
gr.update(visible=True, value=timing_details),
|
||||
)
|
||||
else:
|
||||
return (
|
||||
None,
|
||||
seed_or_error,
|
||||
gr.update(visible=True, value=seed_or_error),
|
||||
gr.update(visible=False),
|
||||
)
|
||||
|
||||
run_button.click(
|
||||
fn=handle_generation,
|
||||
inputs=[
|
||||
model_selection,
|
||||
prompt,
|
||||
negative_prompt,
|
||||
use_negative_prompt,
|
||||
seed,
|
||||
guidance_scale,
|
||||
num_frames,
|
||||
height,
|
||||
width,
|
||||
randomize_seed,
|
||||
],
|
||||
outputs=[result, seed_output, error_output, timing_display],
|
||||
concurrency_limit=20,
|
||||
)
|
||||
|
||||
return demo
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="FastVideo Gradio Frontend")
|
||||
parser.add_argument("--t2v_model_paths", type=str,
|
||||
default="FastVideo/FastWan2.1-T2V-1.3B-Diffusers",
|
||||
help="Comma separated list of paths to the T2V model(s)")
|
||||
parser.add_argument("--host", type=str, default="0.0.0.0",
|
||||
help="Host to bind to")
|
||||
parser.add_argument("--port", type=int, default=7860,
|
||||
help="Port to bind to")
|
||||
args = parser.parse_args()
|
||||
generators = {}
|
||||
default_params = {}
|
||||
model_paths = args.t2v_model_paths.split(",")
|
||||
for model_path in model_paths:
|
||||
print(f"Loading model: {model_path}")
|
||||
setup_model_environment(model_path)
|
||||
generators[model_path] = VideoGenerator.from_pretrained(model_path)
|
||||
default_params[model_path] = SamplingParam.from_pretrained(model_path)
|
||||
demo = create_gradio_interface(default_params, generators)
|
||||
print(f"Starting Gradio frontend at http://{args.host}:{args.port}")
|
||||
print(f"T2V Models: {args.t2v_model_paths}")
|
||||
|
||||
from fastapi import FastAPI, Request, HTTPException
|
||||
from fastapi.responses import HTMLResponse, FileResponse
|
||||
import uvicorn
|
||||
|
||||
app = FastAPI()
|
||||
|
||||
@app.get("/logo.png")
|
||||
def get_logo():
|
||||
return FileResponse(
|
||||
"fastvideo-logos/main/svg/full.svg",
|
||||
media_type="image/svg+xml",
|
||||
headers={
|
||||
"Cache-Control": "public, max-age=3600",
|
||||
"Access-Control-Allow-Origin": "*"
|
||||
}
|
||||
)
|
||||
|
||||
@app.get("/favicon.ico")
|
||||
def get_favicon():
|
||||
favicon_path = "fastvideo-logos/main/svg/icon-simple.svg"
|
||||
|
||||
if os.path.exists(favicon_path):
|
||||
return FileResponse(
|
||||
favicon_path,
|
||||
media_type="image/svg+xml",
|
||||
headers={
|
||||
"Cache-Control": "public, max-age=3600",
|
||||
"Access-Control-Allow-Origin": "*"
|
||||
}
|
||||
)
|
||||
else:
|
||||
raise HTTPException(status_code=404, detail="Favicon not found")
|
||||
|
||||
@app.get("/", response_class=HTMLResponse)
|
||||
def index(request: Request):
|
||||
base_url = str(request.base_url).rstrip('/')
|
||||
return f"""
|
||||
<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="UTF-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
|
||||
<title>FastWan</title>
|
||||
<meta name="title" content="FastWan">
|
||||
<meta name="description" content="Make video generation go blurrrrrrr">
|
||||
<meta name="keywords" content="FastVideo, video generation, AI, machine learning, FastWan">
|
||||
|
||||
<meta property="og:type" content="website">
|
||||
<meta property="og:url" content="{base_url}/">
|
||||
<meta property="og:title" content="FastWan">
|
||||
<meta property="og:description" content="Make video generation go blurrrrrrr">
|
||||
<meta property="og:image" content="{base_url}/logo.png">
|
||||
<meta property="og:image:width" content="1200">
|
||||
<meta property="og:image:height" content="630">
|
||||
<meta property="og:site_name" content="FastWan">
|
||||
|
||||
<meta property="twitter:card" content="summary_large_image">
|
||||
<meta property="twitter:url" content="{base_url}/">
|
||||
<meta property="twitter:title" content="FastWan">
|
||||
<meta property="twitter:description" content="Make video generation go blurrrrrrr">
|
||||
<meta property="twitter:image" content="{base_url}/logo.png">
|
||||
<link rel="icon" type="image/png" sizes="32x32" href="/favicon.ico">
|
||||
<link rel="icon" type="image/png" sizes="16x16" href="/favicon.ico">
|
||||
<link rel="apple-touch-icon" href="/favicon.ico">
|
||||
<style>
|
||||
body, html {{
|
||||
margin: 0;
|
||||
padding: 0;
|
||||
height: 100%;
|
||||
overflow: hidden;
|
||||
}}
|
||||
iframe {{
|
||||
width: 100%;
|
||||
height: 100vh;
|
||||
border: none;
|
||||
}}
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<iframe src="/gradio" width="100%" height="100%" style="border: none;"></iframe>
|
||||
</body>
|
||||
</html>
|
||||
"""
|
||||
|
||||
app = gr.mount_gradio_app(
|
||||
app,
|
||||
demo,
|
||||
path="/gradio",
|
||||
allowed_paths=[os.path.abspath("outputs"), os.path.abspath("fastvideo-logos")]
|
||||
)
|
||||
|
||||
uvicorn.run(app, host=args.host, port=args.port)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
main()
|
||||
Reference in New Issue
Block a user