simplification

app.py

@@ -1,6 +1,4 @@
-# Import spaces first to avoid CUDA initialization conflicts
 import spaces
-
 import gradio as gr
 import numpy as np
 import random
@@ -9,36 +7,19 @@ from PIL import Image
 from torchvision import transforms
 from diffusers import DiffusionPipeline, AutoencoderKL
 
-# Define constants
-flux_dtype = torch.bfloat16
-vae_dtype = torch.float32
+# Constants
+dtype = torch.bfloat16
+device = "cuda" if torch.cuda.is_available() else "cpu"
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 2048
 
-# Move device selection after spaces import
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-def load_models():
-    # Load the initial VAE model for preprocessing in float32
-    vae_model_name = "runwayml/stable-diffusion-v1-5"
-    vae = AutoencoderKL.from_pretrained(vae_model_name, subfolder="vae").to(device).to(vae_dtype)
-    
-    # Load the FLUX diffusion pipeline with bfloat16
-    pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=flux_dtype)
-    pipe.enable_model_cpu_offload()
-    pipe.vae.enable_slicing()
-    pipe.vae.enable_tiling()
-    pipe.to(device)
-    
-    return vae, pipe
-
-# Defer model loading until it's needed
-vae, pipe = None, None
+# Load models
+pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=dtype).to(device)
+pipe.enable_model_cpu_offload()
+pipe.vae.enable_slicing()
+pipe.vae.enable_tiling()
 
-def ensure_models_loaded():
-    global vae, pipe
-    if vae is None or pipe is None:
-        vae, pipe = load_models()
+vae = AutoencoderKL.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="vae").to(device)
 
 def preprocess_image(image, image_size):
     preprocess = transforms.Compose([
@@ -46,70 +27,46 @@ def preprocess_image(image, image_size):
         transforms.ToTensor(),
         transforms.Normalize([0.5], [0.5])
     ])
-    image = preprocess(image).unsqueeze(0).to(device, dtype=vae_dtype)
-    print("Image processed successfully.")
+    image = preprocess(image).unsqueeze(0).to(device, dtype=torch.float32)
     return image
 
-def encode_image(image, vae):
-    try:
-        with torch.no_grad():
-            latents = vae.encode(image).latent_dist.sample() * 0.18215
-        print("Image encoded successfully.")
-        return latents
-    except RuntimeError as e:
-        print(f"Error during image encoding: {e}")
-        raise
+def encode_image(image):
+    with torch.no_grad():
+        latents = vae.encode(image).latent_dist.sample() * 0.18215
+    return latents
 
 @spaces.GPU()
 def infer(prompt, init_image=None, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, progress=gr.Progress(track_tqdm=True)):
-    ensure_models_loaded()
-    
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
     generator = torch.Generator(device=device).manual_seed(seed)
 
-    fallback_image = Image.new("RGB", (width, height), (255, 0, 0))  # Red image as a fallback
-
     try:
         if init_image is None:
             # text2img case
-            result = pipe(
+            image = pipe(
                 prompt=prompt,
                 height=height,
                 width=width,
                 num_inference_steps=num_inference_steps,
                 generator=generator,
-                guidance_scale=0.0,
-                max_sequence_length=256
-            )
+                guidance_scale=0.0
+            ).images[0]
         else:
             # img2img case
-            print("Initial image provided, starting preprocessing...")
-            vae_image_size = 1024  # Using FLUX VAE sample size for preprocessing
             init_image = init_image.convert("RGB")
-            init_image = preprocess_image(init_image, vae_image_size)
-            
-            print("Starting encoding of the image...")
-            latents = encode_image(init_image, vae)
+            init_image = preprocess_image(init_image, 1024)  # Using 1024 as FLUX VAE sample size
+            latents = encode_image(init_image)
             
-            print(f"Latents shape after encoding: {latents.shape}")
-            
-            # Ensure the latents size matches the expected input size for the FLUX model
-            print("Interpolating latents to match model's input size...")
             latents = torch.nn.functional.interpolate(latents, size=(height // 8, width // 8), mode='bilinear')
             
-            latent_channels = latents.shape[1]
-            print(f"Latent channels from VAE: {latent_channels}, expected by FLUX model: {pipe.vae.config.latent_channels}")
-            
-            if latent_channels != pipe.vae.config.latent_channels:
-                print(f"Adjusting latent channels from {latent_channels} to {pipe.vae.config.latent_channels}")
-                conv = torch.nn.Conv2d(latent_channels, pipe.vae.config.latent_channels, kernel_size=1).to(device, dtype=flux_dtype)
-                latents = conv(latents.to(flux_dtype))
+            if latents.shape[1] != pipe.vae.config.latent_channels:
+                conv = torch.nn.Conv2d(latents.shape[1], pipe.vae.config.latent_channels, kernel_size=1).to(device, dtype=dtype)
+                latents = conv(latents.to(dtype))
 
             latents = latents.permute(0, 2, 3, 1).contiguous().view(-1, pipe.vae.config.latent_channels)
-            print(f"Latents shape after permutation: {latents.shape}")
 
-            result = pipe(
+            image = pipe(
                 prompt=prompt,
                 height=height,
                 width=width,
@@ -117,129 +74,37 @@ def infer(prompt, init_image=None, seed=42, randomize_seed=False, width=1024, he
                 generator=generator,
                 guidance_scale=0.0,
                 latents=latents
-            )
-        
-        image = result.images[0]
+            ).images[0]
+
         return image, seed
     except Exception as e:
         print(f"Error during inference: {e}")
-        return fallback_image, seed
-
-# ... (rest of the Gradio interface code remains the same)
-
-# Define 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",
-]
-
-# CSS styling for the Japanese-inspired interface
-css = """
-body {
-    background-color: #fff;
-    font-family: 'Noto Sans JP', sans-serif;
-    color: #333;
-}
-#col-container {
-    margin: 0 auto;
-    max-width: 520px;
-    border: 2px solid #000;
-    padding: 20px;
-    background-color: #f7f7f7;
-    border-radius: 10px;
-}
-.gr-button {
-    background-color: #e60012;
-    color: #fff;
-    border: 2px solid #000;
-}
-.gr-button:hover {
-    background-color: #c20010;
-}
-.gr-slider, .gr-checkbox, .gr-textbox {
-    border: 2px solid #000;
-}
-.gr-accordion {
-    border: 2px solid #000;
-    background-color: #fff;
-}
-.gr-image {
-    border: 2px solid #000;
-}
-"""
-
-# Create the Gradio interface
-with gr.Blocks(css=css) as demo:
-    
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown("""
-        # 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.Textbox(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-            run_button = gr.Button("Run", scale=0)
+        return Image.new("RGB", (width, height), (255, 0, 0)), seed  # Red fallback image
 
-        with gr.Row():
-            init_image = gr.Image(label="Initial Image (optional)", type="pil")
-            result = gr.Image(label="Result", show_label=False)
+# Gradio interface setup
+with gr.Blocks() as demo:
+    with gr.Row():
+        prompt = gr.Textbox(label="Prompt")
+        init_image = gr.Image(label="Initial Image (optional)", type="pil")
 
-        with gr.Accordion("Advanced Settings", open=False):
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                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=1024,
-                )
-                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"
-        )
-
-    run_button.click(
+    with gr.Row():
+        generate = gr.Button("Generate")
+        
+    with gr.Row():
+        result = gr.Image(label="Result")
+        seed_output = gr.Number(label="Seed")
+
+    with gr.Accordion("Advanced Settings", open=False):
+        seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=42)
+        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+        width = gr.Slider(label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024)
+        height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024)
+        num_inference_steps = gr.Slider(label="Number of inference steps", minimum=1, maximum=50, step=1, value=4)
+
+    generate.click(
         infer,
         inputs=[prompt, init_image, seed, randomize_seed, width, height, num_inference_steps],
-        outputs=[result, seed]
+        outputs=[result, seed_output]
     )
 
-demo.launch()
+demo.launch()