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import gradio as gr
import requests
API_URL = "https://rahul7star-FramePack-F1-DiffusionForce.hf.space/api/generate/"
def call_framepack_api(
input_image,
prompt,
t2v,
n_prompt,
seed,
total_second_length,
latent_window_size,
steps,
cfg,
gs,
rs,
gpu_memory_preservation,
use_teacache,
mp4_crf,
lora_file,
lora_multiplier,
fp8_optimization,
):
files = {}
data = {
"prompt": prompt,
"t2v": str(t2v).lower(),
"n_prompt": n_prompt,
"seed": int(seed),
"total_second_length": float(total_second_length),
"latent_window_size": int(latent_window_size),
"steps": int(steps),
"cfg": float(cfg),
"gs": float(gs),
"rs": float(rs),
"gpu_memory_preservation": float(gpu_memory_preservation),
"use_teacache": str(use_teacache).lower(),
"mp4_crf": int(mp4_crf),
"lora_multiplier": float(lora_multiplier),
"fp8_optimization": str(fp8_optimization).lower(),
}
if input_image:
files["input_image"] = ("input.png", input_image, "image/png")
if lora_file:
files["lora_file"] = (lora_file.name, lora_file, "application/octet-stream")
try:
response = requests.post(API_URL, data=data, files=files)
if response.status_code == 200:
result = response.json()
video_url = result.get("video_url")
preview_url = result.get("preview_image_url")
return video_url, preview_url, str(result)
else:
return None, None, f"API Error: {response.status_code} - {response.text}"
except Exception as e:
return None, None, f"Exception: {str(e)}"
with gr.Blocks() as demo:
gr.Markdown("# FramePack API Client with Full Options")
with gr.Row():
with gr.Column():
input_image = gr.File(label="Input Image (PNG/JPG) — optional", file_types=[".png", ".jpg", ".jpeg"])
lora_file = gr.File(label="LoRA File (optional)", file_types=[".safetensors", ".pt", ".bin"])
prompt = gr.Textbox(label="Prompt")
n_prompt = gr.Textbox(label="Negative Prompt (optional)", value="")
t2v = gr.Checkbox(label="Text-to-Video", value=True)
seed = gr.Number(label="Seed", value=31337, precision=0)
total_second_length = gr.Slider(label="Video Length (seconds)", minimum=1, maximum=120, value=5, step=0.1)
latent_window_size = gr.Slider(label="Latent Window Size", minimum=1, maximum=33, value=9, step=1)
steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=25, step=1)
cfg = gr.Slider(label="CFG Scale", minimum=1.0, maximum=32.0, value=1.0, step=0.01)
gs = gr.Slider(label="Distilled CFG Scale", minimum=1.0, maximum=32.0, value=10.0, step=0.01)
rs = gr.Slider(label="CFG Re-Scale", minimum=0.0, maximum=1.0, value=0.0, step=0.01)
gpu_memory_preservation = gr.Slider(label="GPU Inference Preserved Memory (GB)", minimum=6, maximum=128, value=6, step=0.1)
use_teacache = gr.Checkbox(label="Use TeaCache", value=True)
mp4_crf = gr.Slider(label="MP4 Compression", minimum=0, maximum=100, value=16, step=1)
lora_multiplier = gr.Slider(label="LoRA Multiplier", minimum=0.0, maximum=1.0, value=0.8, step=0.1)
fp8_optimization = gr.Checkbox(label="FP8 Optimization", value=False)
generate_btn = gr.Button("Generate")
with gr.Column():
video_output = gr.Video(label="Generated Video", autoplay=True)
preview_output = gr.Image(label="Preview Image")
api_response = gr.Textbox(label="API JSON Response", lines=10)
generate_btn.click(
fn=call_framepack_api,
inputs=[
input_image,
prompt,
t2v,
n_prompt,
seed,
total_second_length,
latent_window_size,
steps,
cfg,
gs,
rs,
gpu_memory_preservation,
use_teacache,
mp4_crf,
lora_file,
lora_multiplier,
fp8_optimization,
],
outputs=[video_output, preview_output, api_response],
)
demo.launch()
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