Update app.py

app.py

@@ -3,120 +3,173 @@ import numpy as np
 import random
 import spaces
 import torch
-from diffusers import DiffusionPipeline
+import time
+from diffusers import DiffusionPipeline, AutoencoderTiny
+from diffusers.models.attention_processor import AttnProcessor2_0
+from custom_pipeline import FluxWithCFGPipeline
 
-dtype = torch.bfloat16
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=dtype).to(device)
+torch.backends.cuda.matmul.allow_tf32 = True
 
+# Constants
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 2048
+DEFAULT_WIDTH = 1024
+DEFAULT_HEIGHT = 1024
+DEFAULT_INFERENCE_STEPS = 1
+
+# Device and model setup
+dtype = torch.float16
+pipe = FluxWithCFGPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-schnell", torch_dtype=dtype
+)
+pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype)
+pipe.to("cuda")
+pipe.load_lora_weights('hugovntr/flux-schnell-realism', weight_name='schnell-realism_v2.3.safetensors', adapter_name="better")
+pipe.set_adapters(["better"], adapter_weights=[1.0])
+pipe.fuse_lora(adapter_name=["better"], lora_scale=1.0)
+pipe.unload_lora_weights()
 
-@spaces.GPU()
-def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, progress=gr.Progress(track_tqdm=True)):
+torch.cuda.empty_cache()
+from io import BytesIO
+from PIL import Image
+# Inference function
+@spaces.GPU(duration=25)
+def generate_image(prompt, seed=24, width=DEFAULT_WIDTH, height=DEFAULT_HEIGHT, randomize_seed=False, num_inference_steps=2, 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, 
-            width = width,
-            height = height,
-            num_inference_steps = num_inference_steps, 
-            generator = generator,
-            guidance_scale=0.0
-    ).images[0] 
-    return image, seed
- 
+    generator = torch.Generator().manual_seed(int(float(seed)))
+
+    start_time = time.time()
+
+    # Only generate the last image in the sequence
+    img = pipe.generate_images( 
+            prompt=prompt,
+            width=width,
+            height=height,
+            num_inference_steps=num_inference_steps,
+            generator=generator
+        )
+    latency = f"Latency: {(time.time()-start_time):.2f} seconds"
+    
+    return img, seed, latency
+
+# Example prompts
 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",
+    "a cute white cat holding a sign that says hello world",
+    "an anime illustration of Steve Jobs",
+    "Create image of Modern house in minecraft style",
+    "photo of a woman on the beach, shot from above. She is facing the sea, while wearing a white dress. She has long blonde hair",
+    "Selfie photo of a wizard with long beard and purple robes, he is apparently in the middle of Tokyo. Probably taken from a phone.",
+    "Photo of a young woman with long, wavy brown hair tied in a bun and glasses. She has a fair complexion and is wearing subtle makeup, emphasizing her eyes and lips. She is dressed in a black top. The background appears to be an urban setting with a building facade, and the sunlight casts a warm glow on her face.",
 ]
 
-css="""
-#col-container {
-    margin: 0 auto;
-    max-width: 520px;
-}
-"""
+# --- Gradio UI ---
+with gr.Blocks() as demo:
+    with gr.Column(elem_id="app-container"):
+        gr.Markdown("# 🎨 Realtime FLUX Image Generator")
+        gr.Markdown("Generate stunning images in real-time with Modified Flux.Schnell pipeline.")
+        gr.Markdown("<span style='color: red;'>Note: Sometimes it stucks or stops generating images (I don't know why). In that situation just refresh the site.</span>")
 
-with gr.Blocks(css=css) as demo:
-    
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""# FLUX.1 [schnell]
-12B param rectified flow transformer distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/) for 4 step generation
-[[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-schnell)]
-        """)
-        
         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="Result", show_label=False)
-        
-        with gr.Accordion("Advanced Settings", open=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,
+            with gr.Column(scale=2.5):
+                result = gr.Image(label="Generated Image", show_label=False, interactive=False)
+            with gr.Column(scale=1):
+                prompt = gr.Text(
+                    label="Prompt",
+                    placeholder="Describe the image you want to generate...",
+                    lines=3,
+                    show_label=False,
+                    container=False,
                 )
-                
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,
-                )
-            
-            with gr.Row():
-                
-  
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=4,
-                )
-        
-        gr.Examples(
-            examples = examples,
-            fn = infer,
-            inputs = [prompt],
-            outputs = [result, seed],
-            cache_examples="lazy"
-        )
+                generateBtn = gr.Button("🖼️ Generate Image")
+                # enhanceBtn = gr.Button("🚀 Enhance Image")
+
+                with gr.Column("Advanced Options"):
+                    with gr.Row():
+                        realtime = gr.Checkbox(label="Realtime Toggler", info="If TRUE then uses more GPU but create image in realtime.", value=False)
+                        latency = gr.Text(label="Latency")
+                    with gr.Row():
+                        seed = gr.Number(label="Seed", value=42)
+                        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=DEFAULT_WIDTH)
+                        height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=DEFAULT_HEIGHT)
+                        num_inference_steps = gr.Slider(label="Inference Steps", minimum=1, maximum=4, step=1, value=DEFAULT_INFERENCE_STEPS)
+
+        with gr.Row():
+            gr.Markdown("### 🌟 Inspiration Gallery")
+        with gr.Row():
+            gr.Examples(
+                examples=examples,
+                fn=generate_image,
+                inputs=[prompt],
+                outputs=[result, seed, latency],
+                cache_examples="lazy" 
+            )
+
+    # enhanceBtn.click(
+    #     fn=generate_image,
+    #     inputs=[prompt, seed, width, height],
+    #     outputs=[result, seed, latency],
+    #     show_progress="full",
+    #     queue=False,
+    #     concurrency_limit=None
+    # )
 
     gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn = infer,
-        inputs = [prompt, seed, randomize_seed, width, height, num_inference_steps],
-        outputs = [result, seed]
+        triggers=[generateBtn.click, prompt.submit],
+        fn=generate_image,
+         inputs=[prompt, seed, width, height, randomize_seed, num_inference_steps],
+        outputs=[result, seed, latency],
     )
 
+    # generateBtn.click(
+    #     fn=generate_image,
+    #     inputs=[prompt, seed, width, height, randomize_seed, num_inference_steps],
+    #     outputs=[result, seed, latency],
+    #     show_progress="full",
+    #     api_name="RealtimeFlux",
+    #     queue=False
+    # )
+
+    # def update_ui(realtime_enabled):
+    #     return {
+    #         prompt: gr.update(interactive=True),
+    #         generateBtn: gr.update(visible=not realtime_enabled)
+    #     }
+
+    # realtime.change(
+    #     fn=update_ui,
+    #     inputs=[realtime],
+    #     outputs=[prompt, generateBtn],
+    #     queue=False,
+    #     concurrency_limit=None
+    # )
+
+    # def realtime_generation(*args):
+        # if args[0]:  # If realtime is enabled
+        #     return next(generate_image(*args[1:]))
+
+    # prompt.submit(
+    #     fn=generate_image,
+    #     inputs=[prompt, seed, width, height, randomize_seed, num_inference_steps],
+    #     outputs=[result, seed, latency],
+    #     show_progress="full",
+    #     queue=False,
+    #     concurrency_limit=None
+    # )
+
+    # for component in [prompt, width, height, num_inference_steps]:
+    #     component.input(
+    #         fn=realtime_generation,
+    #         inputs=[realtime, prompt, seed, width, height, randomize_seed, num_inference_steps],
+    #         outputs=[result, seed, latency],
+    #         show_progress="hidden",
+    #         trigger_mode="always_last",
+    #         queue=False,
+    #         concurrency_limit=None
+    #     )
+
+# Launch the app
 demo.launch()