Update app.py

app.py

@@ -1,47 +1,95 @@
+import spaces
 import gradio as gr
 import torch
-from huggingface_hub import hf_hub_download, login
-from llama_cpp import Llama
-from peft import PeftModel, PeftConfig
-import os
-
-# Authenticate with Hugging Face
-if 'HF_TOKEN' in os.environ:
-    login(token=os.environ['HF_TOKEN'])
-else:
-    raise ValueError("HF_TOKEN not found in environment variables. Please add it to your Space's secrets.")
-
-# Download GGUF model
-model_path = hf_hub_download(repo_id="city96/FLUX.1-dev-gguf", filename="flux1-dev-Q4_K_S.gguf")
-
-# Initialize the GGUF model
-llm = Llama(model_path=model_path, n_ctx=2048, n_gpu_layers=-1)
-
-# Load your LoRA weights
-lora_path = "sagar007/sagar_flux"
-config = PeftConfig.from_pretrained(lora_path)
-model = PeftModel.from_pretrained(llm, lora_path)
-
-def generate_image(prompt, guidance_scale, num_inference_steps):
-    # This is a placeholder for the actual image generation logic
-    # You'll need to implement the correct way to use your model for image generation
-    output = model.generate(prompt, max_new_tokens=100)
-    # For now, we'll return a placeholder image
-    return gr.Image.update(value=None, visible=True)
-
-# Gradio interface
+from PIL import Image
+from diffusers import DiffusionPipeline
+import random
+
+# Initialize the base model and specific LoRA
+base_model = "city96/FLUX.1-dev-gguf/flux1-dev-Q4_K_S.gguf"
+pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)
+pipe.to("cpu")
+
+lora_repo = "sagar007/sagar_flux"
+trigger_word = ""  # Leave trigger_word blank if not used.
+pipe.load_lora_weights(lora_repo)
+
+MAX_SEED = 2**32-1
+
+
+def run_lora(prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora_scale, progress=gr.Progress(track_tqdm=True)):
+    # Set random seed for reproducibility
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    generator = torch.Generator(device="cuda").manual_seed(seed)
+
+    # Update progress bar (0% saat mulai)
+    progress(0, "Starting image generation...")
+
+    # Generate image with progress updates
+    for i in range(1, steps + 1):
+        # Simulate the processing step (in a real scenario, you would integrate this with your image generation process)
+        if i % (steps // 10) == 0:  # Update every 10% of the steps
+            progress(i / steps * 100, f"Processing step {i} of {steps}...")
+
+    # Generate image using the pipeline
+    image = pipe(
+        prompt=f"{prompt} {trigger_word}",
+        num_inference_steps=steps,
+        guidance_scale=cfg_scale,
+        width=width,
+        height=height,
+        generator=generator,
+        joint_attention_kwargs={"scale": lora_scale},
+    ).images[0]
+
+    # Final update (100%)
+    progress(100, "Completed!")
+
+    yield image, seed
+
+# Example cached image and settings
+example_image_path = "example0.webp"  # Replace with the actual path to the example image
+example_prompt = """A Jelita Sukawati speaker is captured mid-speech. She has long, dark brown hair that cascades over her shoulders, framing her radiant, smiling face. Her Latina features are highlighted by warm, sun-kissed skin and bright, expressive eyes. She gestures with her left hand, displaying a delicate ring on her pinky finger, as she speaks passionately.
+The woman is wearing a colorful, patterned dress with a green lanyard featuring multiple badges and logos hanging around her neck. The lanyard prominently displays the "CagliostroLab" text.
+Behind her, there is a blurred background with a white banner containing logos and text, indicating a professional or conference setting. The overall scene captures the energy and vibrancy of her presentation."""
+example_cfg_scale = 3.2
+example_steps = 32
+example_width = 1152
+example_height = 896
+example_seed = 3981632454
+example_lora_scale = 0.85
+
+def load_example():
+    # Load example image from file
+    example_image = Image.open(example_image_path)
+    return example_prompt, example_cfg_scale, example_steps, False, example_seed, example_width, example_height, example_lora_scale, example_image
+
 with gr.Blocks() as app:
-    gr.Markdown("# GGUF FLUX Model with Sagar LoRA Image Generator")
+    gr.Markdown("# Flux RealismLora Image Generator")
     with gr.Row():
-        with gr.Column():
-            prompt = gr.Textbox(label="Prompt", placeholder="Enter your image description here")
-            guidance_scale = gr.Slider(minimum=1, maximum=20, value=7.5, label="Guidance Scale")
-            num_inference_steps = gr.Slider(minimum=1, maximum=100, value=50, label="Number of Inference Steps")
-            generate_btn = gr.Button("Generate Image")
-        with gr.Column():
-            output = gr.Image(label="Generated Image")
+        with gr.Column(scale=3):
+            prompt = gr.TextArea(label="Prompt", placeholder="Type a prompt", lines=5)
+            generate_button = gr.Button("Generate")
+            cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=example_cfg_scale)
+            steps = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=example_steps)
+            width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=example_width)
+            height = gr.Slider(label="Height", minimum=256, maximum=1536, step=64, value=example_height)
+            randomize_seed = gr.Checkbox(False, label="Randomize seed")
+            seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=example_seed)
+            lora_scale = gr.Slider(label="LoRA Scale", minimum=0, maximum=1, step=0.01, value=example_lora_scale)
+        with gr.Column(scale=1):
+            result = gr.Image(label="Generated Image")
+            gr.Markdown("Generate images using RealismLora and a text prompt.\n[[non-commercial license, Flux.1 Dev](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)]")
+
+    # Automatically load example data and image when the interface is launched
+    app.load(load_example, inputs=[], outputs=[prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora_scale, result])
     
-    generate_btn.click(generate_image, inputs=[prompt, guidance_scale, num_inference_steps], outputs=output)
+    generate_button.click(
+        run_lora,
+        inputs=[prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora_scale],
+        outputs=[result, seed]
+    )
 
-# Launch the app
+app.queue()
 app.launch()