feat: migrate from Streamlit to Gradio for UI

This commit updates the application to use Gradio instead of Streamlit
for the user interface. Changes include:

- Updated README.md to reflect the new UI framework.
- Replaced Streamlit code in app.py with Gradio code.
- Updated requirements.txt to include necessary Gradio dependencies.

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
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+*.pt filter=lfs diff=lfs merge=lfs -text
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README.md

@@ -1,20 +1,13 @@
 ---
-title: FLUX Text-to-Image Generator
-emoji: 🖼️
-colorFrom: indigo
-colorTo: pink
-sdk: streamlit
-python_version: 3.10
+title: Text To Image
+emoji: 🖼
+colorFrom: purple
+colorTo: red
+sdk: gradio
+sdk_version: 5.0.1
 app_file: app.py
-fullWidth: true
-header: default
-short_description: Generate images from text with FLUX.1-dev.
-tags:
-  - text-to-image
-  - Streamlit
-  - HuggingFace
-pinned: true
+pinned: false
 ---
 
-# FLUX Text-to-Image Generator
-This is a Streamlit-based application that uses FLUX.1-dev to generate high-quality images from text prompts. Designed to run seamlessly on Hugging Face Spaces.
+# Text-to-Image Generator
+This is a Gradle-based application that uses DiffusionPipeline to generate high-quality images from text prompts. Designed to run seamlessly on Hugging Face Spaces.

app.py

@@ -1,32 +1,154 @@
-import streamlit as st
-from PIL import Image
-from flux import FLUXModel  # Assuming FLUX.1-dev provides this
-
-# Initialize the FLUX model (load only once)
-@st.cache_resource
-def load_model():
-    return FLUXModel.from_pretrained("flux-1-dev")
-
-model = load_model()
-
-# Streamlit GUI
-st.title("Text-to-Image Generator with FLUX.1-dev")
-st.subheader("Enter a prompt to generate stunning images!")
-
-# Text input
-prompt = st.text_input("Enter your prompt", "")
-
-# Generate image when button is clicked
-if st.button("Generate Image"):
-    if prompt:
-        with st.spinner("Generating image... Please wait."):
-            try:
-                image = model.generate_image(prompt)  # Generate image
-                st.image(image, caption="Generated Image", use_column_width=True)
-            except Exception as e:
-                st.error(f"Error generating image: {e}")
-    else:
-        st.warning("Please enter a prompt!")
-
-# Footer
-st.write("Powered by FLUX.1-dev and Streamlit")
+import gradio as gr
+import numpy as np
+import random
+
+# import spaces #[uncomment to use ZeroGPU]
+from diffusers import DiffusionPipeline
+import torch
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
+
+if torch.cuda.is_available():
+    torch_dtype = torch.float16
+else:
+    torch_dtype = torch.float32
+
+pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
+pipe = pipe.to(device)
+
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 1024
+
+
+# @spaces.GPU #[uncomment to use ZeroGPU]
+def infer(
+    prompt,
+    negative_prompt,
+    seed,
+    randomize_seed,
+    width,
+    height,
+    guidance_scale,
+    num_inference_steps,
+    progress=gr.Progress(track_tqdm=True),
+):
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+
+    generator = torch.Generator().manual_seed(seed)
+
+    image = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps,
+        width=width,
+        height=height,
+        generator=generator,
+    ).images[0]
+
+    return image, seed
+
+
+examples = [
+    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
+    "An astronaut riding a green horse",
+    "A delicious ceviche cheesecake slice",
+]
+
+css = """
+#col-container {
+    margin: 0 auto;
+    max-width: 640px;
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown(" # Text-to-Image Gradio Template")
+
+        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, variant="primary")
+
+        result = gr.Image(label="Result", show_label=False)
+
+        with gr.Accordion("Advanced Settings", open=False):
+            negative_prompt = gr.Text(
+                label="Negative prompt",
+                max_lines=1,
+                placeholder="Enter a negative prompt",
+                visible=False,
+            )
+
+            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():
+                width = gr.Slider(
+                    label="Width",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=1024,  # Replace with defaults that work for your model
+                )
+
+                height = gr.Slider(
+                    label="Height",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=1024,  # Replace with defaults that work for your model
+                )
+
+            with gr.Row():
+                guidance_scale = gr.Slider(
+                    label="Guidance scale",
+                    minimum=0.0,
+                    maximum=10.0,
+                    step=0.1,
+                    value=0.0,  # Replace with defaults that work for your model
+                )
+
+                num_inference_steps = gr.Slider(
+                    label="Number of inference steps",
+                    minimum=1,
+                    maximum=50,
+                    step=1,
+                    value=2,  # Replace with defaults that work for your model
+                )
+
+        gr.Examples(examples=examples, inputs=[prompt])
+    gr.on(
+        triggers=[run_button.click, prompt.submit],
+        fn=infer,
+        inputs=[
+            prompt,
+            negative_prompt,
+            seed,
+            randomize_seed,
+            width,
+            height,
+            guidance_scale,
+            num_inference_steps,
+        ],
+        outputs=[result, seed],
+    )
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -1,9 +1,6 @@
-torch>=2.0.0
-transformers>=4.28.0
-diffusers>=0.15.0
-accelerate>=0.19.0
-pillow
-numpy
-scipy
-flux-1-dev
-streamlit
+accelerate
+diffusers
+invisible_watermark
+torch
+transformers
+xformers