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import os
import tempfile
import fal_client
import gradio as gr
import numpy as np
import requests
from dotenv import load_dotenv
from huggingface_hub import InferenceClient
load_dotenv()
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048
TOKEN = None
FAL_KEY = None
def download_locally(url: str, local_path: str = "downloaded_file.png") -> str:
"""Download an image or a video from a URL to a local path.
Args:
url (str): The URL of the image to download. Must be an http(s) URL.
local_path (str, optional): The path (including filename) where the file should be saved. Defaults to "downloaded_file.png".
Returns:
str: The filesystem path of the saved file – suitable for returning to a **gr.File** output, or as an MCP tool response.
"""
if local_path == "":
local_path = "downloaded_file.png"
response = requests.get(url, timeout=30)
response.raise_for_status()
# If the caller passed only a filename, save into a temporary directory to avoid permission issues
if os.path.dirname(local_path) == "":
tmp_dir = tempfile.gettempdir()
local_path = os.path.join(tmp_dir, local_path)
with open(local_path, "wb") as f:
f.write(response.content)
return local_path
def login_hf(oauth_token: gr.OAuthToken | None):
"""
Login to Hugging Face and check initial key statuses.
Args:
oauth_token (gr.OAuthToken | None): The OAuth token from Hugging Face.
"""
global TOKEN
if oauth_token and oauth_token.token:
print("Received OAuth token, logging in...")
TOKEN = oauth_token.token
else:
print("No OAuth token provided, using environment variable HF_TOKEN.")
TOKEN = os.environ.get("HF_TOKEN")
print("TOKEN: ", TOKEN)
def login_fal(fal_key_from_ui: str | None):
"""
Sets the FAL API key from the UI.
Args:
fal_key_from_ui (str | None): The FAL key from the UI textbox.
"""
global FAL_KEY
if fal_key_from_ui and fal_key_from_ui.strip():
FAL_KEY = fal_key_from_ui.strip()
os.environ["FAL_KEY"] = FAL_KEY
print("FAL_KEY has been set from UI input.")
else:
FAL_KEY = os.environ.get("FAL_KEY")
print("FAL_KEY is configured from environment variable.")
print("FAL_KEY: ", FAL_KEY)
def generate_image(prompt: str, seed: int = 42, width: int = 1024, height: int = 1024, num_inference_steps: int = 25):
"""
Generate an image from a prompt.
Args:
prompt (str):
The prompt to generate an image from.
seed (int, default=42):
Seed for the random number generator.
height (int, default=1024):
The height in pixels of the output image
width (int, default=1024):
The width in pixels of the output image
num_inference_steps (int, default=25):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
"""
client = InferenceClient(provider="fal-ai", token=TOKEN)
image = client.text_to_image(
prompt=prompt,
width=width,
height=height,
num_inference_steps=num_inference_steps,
seed=seed,
model="black-forest-labs/FLUX.1-dev",
)
return image, seed
def generate_video_from_image(
image_filepath: str, # This will be the path to the image from gr.Image output
video_prompt: str,
duration: str, # "5" or "10"
aspect_ratio: str, # "16:9", "9:16", "1:1"
video_negative_prompt: str,
cfg_scale_video: float,
progress=gr.Progress(track_tqdm=True),
):
"""
Generates a video from an image using fal-ai/kling-video API.
"""
if not FAL_KEY:
gr.Error("FAL_KEY is not set. Cannot generate video.")
return None
if not image_filepath:
gr.Warning("No image provided to generate video from.")
return None
if not os.path.exists(image_filepath):
gr.Error(f"Image file not found at: {image_filepath}")
return None
print(f"Video generation started for image: {image_filepath}")
progress(0, desc="Preparing for video generation...")
try:
progress(0.1, desc="Uploading image...")
print("Uploading image to fal.ai storage...")
print("FAL_KEY: ", os.environ.get("FAL_KEY"))
image_url = fal_client.upload_file(image_filepath)
print(f"Image uploaded, URL: {image_url}")
progress(0.3, desc="Image uploaded. Submitting video request...")
def on_queue_update(update):
if isinstance(update, fal_client.InProgress):
if update.logs:
for log in update.logs:
print(f"[fal-ai log] {log['message']}")
# Try to update progress description if logs are available
# progress(progress.current_progress_value, desc=f"Video processing: {log['message'][:50]}...")
print("Subscribing to fal-ai/kling-video/v2.1/master/image-to-video...")
api_result = fal_client.subscribe(
"fal-ai/kling-video/v2.1/master/image-to-video",
arguments={
"prompt": video_prompt,
"image_url": image_url,
"duration": duration,
"aspect_ratio": aspect_ratio,
"negative_prompt": video_negative_prompt,
"cfg_scale": cfg_scale_video,
},
with_logs=True, # Get logs
on_queue_update=on_queue_update, # Callback for logs
)
progress(0.9, desc="Video processing complete.")
video_output_url = api_result.get("video", {}).get("url")
if video_output_url:
print(f"Video generated successfully: {video_output_url}")
progress(1, desc="Video ready!")
return video_output_url
else:
print(f"Video generation failed or no URL in response. API Result: {api_result}")
gr.Error("Video generation failed or no video URL returned.")
return None
except Exception as e:
print(f"Error during video generation: {e}")
gr.Error(f"An error occurred: {str(e)}")
return None
examples = [
"a tiny astronaut hatching from an egg on the moon",
"a cat holding a sign that says hello world",
"an anime illustration of a wiener schnitzel",
]
css = """
#col-container {
margin: 0 auto;
max-width: 520px;
}
"""
with gr.Blocks(css=css) as demo:
demo.load(login_hf, inputs=None, outputs=None)
demo.load(login_fal, inputs=None, outputs=None)
with gr.Sidebar():
gr.Markdown("# Authentication")
gr.Markdown(
"Sign in with Hugging Face for image generation. Separately, set your fal.ai API Key for image to video generation."
)
gr.Markdown("### Hugging Face Login")
hf_login_button = gr.LoginButton("Sign in with Hugging Face")
# When hf_login_button is clicked, it provides an OAuthToken or None to the login function.
hf_login_button.click(fn=login_hf, inputs=[hf_login_button], outputs=None)
gr.Markdown("### FAL Login (for Image to Video)")
fal_key_input = gr.Textbox(
label="FAL API Key",
placeholder="Enter your FAL API Key here",
type="password",
value=os.environ.get("FAL_KEY", ""), # Pre-fill if loaded from env
)
set_fal_key_button = gr.Button("Set FAL Key")
set_fal_key_button.click(fn=login_fal, inputs=[fal_key_input], outputs=None)
with gr.Column(elem_id="col-container"):
gr.Markdown(
"""# Text to Image to Video with fal‑ai through HF Inference Providers ⚡\nLearn more about HF Inference Providers [here](https://huggingface.co/docs/inference-providers/index)"""
"""## Text to Image uses [FLUX.1 [dev]](https://fal.ai/models/fal-ai/flux/dev) with fal‑ai through HF Inference Providers"""
"""## Image to Vide uses [kling-video v2.1](https://fal.ai/models/fal-ai/kling-video/v2.1/master/image-to-video/playground) with fal‑ai directly (you will need to set your `FAL_KEY`)."""
)
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.Image(label="Generated Image", show_label=False, format="png", type="filepath")
download_btn = gr.DownloadButton(
label="Download result image",
visible=False,
value=None,
variant="primary",
)
seed_number = gr.Number(label="Seed", precision=0, value=42, interactive=False)
with gr.Accordion("Advanced Settings", open=False):
seed_slider = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=42,
)
with gr.Row():
width_slider = gr.Slider(
label="Width",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
height_slider = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
steps_slider = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=50,
step=1,
value=25,
)
gr.Examples(
examples=examples,
fn=generate_image,
inputs=[prompt],
outputs=[result, seed_number],
cache_examples="lazy",
)
def update_image_outputs(image_pil, seed_val):
return {
result: image_pil,
seed_number: seed_val,
download_btn: gr.DownloadButton(value=image_pil, visible=True)
if image_pil
else gr.DownloadButton(visible=False),
}
run_button.click(
fn=generate_image,
inputs=[prompt, seed_slider, width_slider, height_slider, steps_slider],
outputs=[result, seed_number],
).then(
lambda img_path, vid_accordion, vid_btn: {
vid_accordion: gr.Accordion(open=True),
vid_btn: gr.Button(interactive=True),
},
inputs=[result],
outputs=[],
)
video_result_output = gr.Video(label="Generated Video", show_label=False)
with gr.Accordion("Video Generation from Image", open=False) as video_gen_accordion:
video_prompt_input = gr.Text(
label="Prompt for Video",
placeholder="Describe the animation or changes for the video (e.g., 'camera zooms out slowly')",
value="A gentle breeze rustles the leaves, subtle camera movement.", # Default prompt
)
with gr.Row():
video_duration_input = gr.Dropdown(label="Duration (seconds)", choices=["5", "10"], value="5")
video_aspect_ratio_input = gr.Dropdown(
label="Aspect Ratio",
choices=["16:9", "9:16", "1:1"],
value="16:9", # Default from API
)
video_negative_prompt_input = gr.Text(
label="Negative Prompt for Video",
value="blur, distort, low quality", # Default from API
)
video_cfg_scale_input = gr.Slider(
label="CFG Scale for Video",
minimum=0.0,
maximum=10.0,
value=0.5,
step=0.1,
)
generate_video_btn = gr.Button("Generate Video", interactive=False)
generate_video_btn.click(
fn=generate_video_from_image,
inputs=[
result,
video_prompt_input,
video_duration_input,
video_aspect_ratio_input,
video_negative_prompt_input,
video_cfg_scale_input,
],
outputs=[video_result_output],
)
run_button.click(
fn=generate_image,
inputs=[prompt, seed_slider, width_slider, height_slider, steps_slider],
outputs=[result, seed_number],
).then(
lambda image_filepath: {
video_gen_accordion: gr.Accordion(open=True),
generate_video_btn: gr.Button(interactive=True if image_filepath else False),
download_btn: gr.DownloadButton(value=image_filepath, visible=True if image_filepath else False),
},
inputs=[result],
outputs=[video_gen_accordion, generate_video_btn, download_btn],
)
with gr.Accordion("Download Image from URL", open=False):
image_url_input = gr.Text(label="Image URL", placeholder="Enter image URL (e.g., http://.../image.png)")
filename_input = gr.Text(
label="Filename (optional)",
placeholder=" Filename",
)
download_from_url_btn = gr.DownloadButton(label="Download Image")
download_from_url_btn.click(
fn=download_locally,
inputs=[image_url_input, filename_input],
outputs=[download_from_url_btn],
)
if __name__ == "__main__":
demo.launch(mcp_server=True)
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