Create app.py

app.py

@@ -0,0 +1,170 @@
+import os
+import io
+import random
+import requests
+import gradio as gr
+import numpy as np
+from PIL import Image
+
+
+MAX_SEED = np.iinfo(np.int32).max
+
+API_TOKEN = os.getenv("HF_TOKEN")
+headers = {"Authorization": f"Bearer {API_TOKEN}"}
+timeout = 100
+
+
+def split_image(input_image, num_splits=4):
+    output_images = []
+
+    # Define the coordinates for the 2x2 grid (left-to-right, top-to-bottom)
+    boxes = [
+        (0, 0, 512, 512),      # Top-left
+        (512, 0, 1024, 512),   # Top-right
+        (0, 512, 512, 1024),   # Bottom-left
+        (512, 512, 1024, 1024) # Bottom-right
+    ]
+
+    # Crop the input image based on the defined boxes
+    for box in boxes:
+        output_images.append(input_image.crop(box))
+
+    return output_images
+
+
+# Function to export split images to GIF
+def export_to_gif(images, output_path, fps=4):
+    # Calculate duration per frame in milliseconds based on fps
+    duration = int(1000 / fps)
+
+    # Create a GIF from the list of images
+    images[0].save(
+        output_path,
+        save_all=True,
+        append_images=images[1:],
+        duration=duration,  # Duration between frames
+        loop=0              # Loop forever
+    )
+
+
+def predict(prompt, seed=-1, randomize_seed=True, guidance_scale=3.5, num_inference_steps=28, lora_id="black-forest-labs/FLUX.1-dev", progress=gr.Progress(track_tqdm=True)):
+    prompt_template = f"""a 2x2 total 4 grid of frames, showing consecutive stills from a looped gif of {prompt}"""
+
+    if lora_id.strip() == "" or lora_id == None:
+        lora_id = "black-forest-labs/FLUX.1-dev"
+
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+
+    key = random.randint(0, 999)
+
+    API_URL = "https://api-inference.huggingface.co/models/"+ lora_id.strip()
+    
+    API_TOKEN = random.choice([os.getenv("HF_TOKEN")])
+    headers = {"Authorization": f"Bearer {API_TOKEN}"}
+
+    payload = {
+        "inputs": prompt_template,
+        "steps": num_inference_steps,
+        "cfg_scale": guidance_scale,
+        "seed": seed,
+    }
+
+    response = requests.post(API_URL, headers=headers, json=payload, timeout=timeout)
+    if response.status_code != 200:
+        print(f"Error: Failed to get image. Response status: {response.status_code}")
+        print(f"Response content: {response.text}")
+        if response.status_code == 503:
+            raise gr.Error(f"{response.status_code} : The model is being loaded")
+        raise gr.Error(f"{response.status_code}")
+    
+    try:
+        image_bytes = response.content
+        image = Image.open(io.BytesIO(image_bytes))
+        print(f'\033[1mGeneration {key} completed!\033[0m ({prompt})')
+        
+        split_images = split_image(image, num_splits=4)
+        
+        # Path to save the GIF
+        gif_path = "output.gif"
+    
+        # Export the split images to GIF
+        export_to_gif(split_images, gif_path, fps=4)
+        return gif_path, image, seed
+    except Exception as e:
+        print(f"Error when trying to open the image: {e}")
+        return None
+
+
+demo = gr.Interface(fn=predict, inputs="text", outputs="image")
+
+css="""
+#col-container {
+    margin: 0 auto;
+    max-width: 520px;
+}
+#stills{max-height:160px}
+"""
+examples = [
+    "a cat waving its paws in the air",
+    "a panda moving their hips from side to side",
+    "a flower going through the process of blooming"
+]
+
+with gr.Blocks(css=css) as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown("# FLUX Gif Generator")
+        gr.Markdown("Create GIFs with Flux-dev. Based on @dn6's [space](https://huggingface.co/spaces/dn6/FLUX-GIFs) and @fofr's [tweet](https://x.com/fofrAI/status/1828910395962343561).")
+        gr.Markdown("Add LoRA (if needed) in Advanced Settings. For better results, include a description of the motion in your prompt.")
+        # gr.Markdown("For better results include a description of the motion in your prompt")
+        # with gr.Row():
+        #     with gr.Column():
+        with gr.Row():
+            prompt = gr.Text(label="Prompt", show_label=False, max_lines=4, show_copy_button = True, placeholder="Enter your prompt", container=False)
+            submit = gr.Button("Submit", scale=0)
+
+        output = gr.Image(label="GIF", show_label=False)
+        output_stills = gr.Image(label="stills", show_label=False, elem_id="stills")
+        with gr.Accordion("Advanced Settings", open=False):
+            custom_lora = gr.Textbox(label="Custom LoRA", info="LoRA Hugging Face path (optional)", placeholder="multimodalart/vintage-ads-flux")
+            seed = gr.Slider(
+                label="Seed",
+                minimum=0,
+                maximum=MAX_SEED,
+                step=1,
+                value=0,
+            )
+
+            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+
+            with gr.Row():
+                guidance_scale = gr.Slider(
+                    label="Guidance Scale",
+                    minimum=1,
+                    maximum=15,
+                    step=0.1,
+                    value=3.5,
+                )
+                num_inference_steps = gr.Slider(
+                    label="Number of inference steps",
+                    minimum=1,
+                    maximum=50,
+                    step=1,
+                    value=28,
+                )
+
+        gr.Examples(
+            examples=examples,
+            fn=predict,
+            inputs=[prompt],
+            outputs=[output, output_stills, seed],
+            cache_examples="lazy"
+        )
+        gr.on(
+            triggers=[submit.click, prompt.submit],
+            fn=predict,
+            inputs=[prompt, seed, randomize_seed, guidance_scale, num_inference_steps, custom_lora],
+            outputs = [output, output_stills, seed]
+        )
+
+demo.launch()