import spaces
import gradio as gr
import torch
from PIL import Image
from diffusers import DiffusionPipeline


# Constants
MAX_SEED = 2**32 - 1
MAX_IMAGE_SIZE = 2048
dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"

# Load FLUX model
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 print_model_shapes(pipe):
    print("Model component shapes:")
    print(f"VAE Encoder: {pipe.vae.encoder}")
    print(f"VAE Decoder: {pipe.vae.decoder}")
    print(f"x_embedder shape: {pipe.transformer.x_embedder.weight.shape}")
    print(f"First transformer block shape: {pipe.transformer.transformer_blocks[0].attn.to_q.weight.shape}")

print_model_shapes(pipe)

@spaces.GPU()
def infer(prompt, init_image=None, seed=None, width=1024, height=1024, num_inference_steps=4, guidance_scale=0.0):
    generator = torch.Generator(device=device).manual_seed(seed) if seed is not None else None

    try:
        if init_image is None:
            # text2img case
            print("Running text-to-image generation")
            image = pipe(
                prompt=prompt,
                height=height,
                width=width,
                num_inference_steps=num_inference_steps,
                generator=generator,
                guidance_scale=guidance_scale
            ).images[0]
        else:
            # img2img case
            print("Running image-to-image generation")
            init_image = init_image.convert("RGB").resize((width, height))
            image = pipe(
                prompt=prompt,
                image=init_image,
                num_inference_steps=num_inference_steps,
                generator=generator,
                guidance_scale=guidance_scale
            ).images[0]

        return image, seed
    except RuntimeError as e:
        if "mat1 and mat2 shapes cannot be multiplied" in str(e):
            print("Matrix multiplication error detected. Tensor shapes:")
            print(e)
            # Here you could add code to print shapes of specific tensors if needed
        else:
            print(f"RuntimeError during inference: {e}")
        import traceback
        traceback.print_exc()
        return Image.new("RGB", (width, height), (255, 0, 0)), seed
    except Exception as e:
        print(f"Unexpected error during inference: {e}")
        import traceback
        traceback.print_exc()
        return Image.new("RGB", (width, height), (255, 0, 0)), seed

# Gradio interface
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.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=None)
        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)
        guidance_scale = gr.Slider(label="Guidance scale", minimum=0, maximum=20, step=0.1, value=0.0)

    generate.click(
        infer,
        inputs=[prompt, init_image, seed, width, height, num_inference_steps, guidance_scale],
        outputs=[result, seed_output]
    )

demo.launch()