Create app.py

app.py

@@ -0,0 +1,135 @@
+import os
+import io
+import random
+import requests
+import gradio as gr
+import numpy as np
+from PIL import Image
+import replicate
+
+
+MAX_SEED = np.iinfo(np.int32).max
+
+
+def predict(replicate_api, prompt, lora_id, lora_scale=0.95, aspect_ratio="1:1", seed=-1, randomize_seed=True, guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
+
+    # Validate API key and prompt
+    if not replicate_api or not prompt:
+        return "Error: Missing necessary inputs.", -1
+    
+    # Set the seed if randomize_seed is True
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+
+    # Set the Replicate API token in the environment variable
+    os.environ["REPLICATE_API_TOKEN"] = replicate_api
+
+    # Construct the input for the replicate model
+    input_params = {
+        "prompt": prompt,
+        "output_format": "jpg",
+        "aspect_ratio": aspect_ratio,
+        "num_inference_steps": num_inference_steps,
+        "guidance_scale": guidance_scale,
+        "seed": seed,
+        "disable_safety_checker": True
+    }
+
+    # If lora_id is provided, include it in the input
+    if lora_id:
+        input_params["hf_lora"] = lora_id
+        input_params["lora_scale"] = lora_scale
+
+    try:
+        # Run the model using the user's API token from the environment variable
+        output = replicate.run(
+            "lucataco/flux-dev-lora:a22c463f11808638ad5e2ebd582e07a469031f48dd567366fb4c6fdab91d614d",
+            input=input_params
+        )
+        return output[0], seed  # Return the generated image and seed
+
+    except Exception as e:
+        # Catch any exceptions, such as invalid API token or lack of credits
+        return f"Error: {str(e)}", -1
+
+    finally:
+        # Always remove the API key from the environment
+        if "REPLICATE_API_TOKEN" in os.environ:
+            del os.environ["REPLICATE_API_TOKEN"]
+
+    
+
+demo = gr.Interface(fn=predict, inputs="text", outputs="image")
+
+css="""
+#col-container {
+    margin: 0 auto;
+    max-width: 520px;
+}
+"""
+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",
+]
+
+with gr.Blocks(css=css) as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown("# FLUX Dev with LoRA Support using Replicate API")
+        
+        replicate_api = gr.Text(label="Replicate API", show_label=True, max_lines=1, placeholder="Enter Replicate API", container=False)
+        prompt = gr.Text(label="Prompt", show_label=True, max_lines=4, show_copy_button = True, placeholder="Enter your prompt", container=False)
+        with gr.Accordion("Advanced Settings", open=False):
+            with gr.Row():
+                custom_lora = gr.Textbox(label="Custom LoRA", info="LoRA Hugging Face path (optional)", placeholder="multimodalart/vintage-ads-flux")
+                lora_scale = gr.Slider(
+                    label="LoRA Scale",
+                    minimum=0,
+                    maximum=1,
+                    step=0.1,
+                    value=0.95,
+                )
+            aspect_ratio = gr.Radio(label="Aspect ratio", value="1:1", choices=["1:1", "4:5", "2:3", "3:4","9:16", "4:3", "16:9"])
+            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,
+                )
+        submit = gr.Button("Generate Image", scale=1)
+
+        output = gr.Image(label="Output Image", show_label=True)
+        
+
+        gr.Examples(
+            examples=examples,
+            fn=predict,
+            inputs=[prompt]
+        )
+        gr.on(
+            triggers=[submit.click, prompt.submit],
+            fn=predict,
+            inputs=[replicate_api, prompt, custom_lora, lora_scale, aspect_ratio, seed, randomize_seed, guidance_scale, num_inference_steps],
+            outputs = [output, seed]
+        )
+
+demo.launch()