app.py

# app.py
from diffusers import StableDiffusionPipeline
import torch
import gradio as gr
from huggingface_hub import hf_hub_download # Keep this import
import os

# Define the model ID for the specific checkpoint
MODEL_REPO_ID = "uhralk/Indigo_Furry_mix"
CHECKPOINT_FILENAME = "indigo_Furrymix_v120_hybrid_fin_fp16.safetensors"

# Determine the device (GPU or CPU)
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float16 if device == "cuda" else torch.float32

# --- NEW LOADING LOGIC ---
# 1. Download the specific .safetensors file to the Hugging Face cache
try:
    print(f"Downloading checkpoint: {CHECKPOINT_FILENAME} from {MODEL_REPO_ID}")
    checkpoint_local_path = hf_hub_download(repo_id=MODEL_REPO_ID, filename=CHECKPOINT_FILENAME)
    print(f"Checkpoint downloaded to: {checkpoint_local_path}")
except Exception as e:
    print(f"Failed to download checkpoint: {e}")
    exit() # Critical failure if we can't even download the file

# 2. Load the pipeline from the downloaded single file
try:
    print(f"Attempting to load pipeline from single file: {checkpoint_local_path} on {device}")
    pipe = StableDiffusionPipeline.from_single_file(
        checkpoint_local_path, # Pass the local file path here
        torch_dtype=dtype,
        use_safetensors=True,
        # You might need to specify base model parameters if they're not fully embedded.
        # For a full merged checkpoint like this, often default inference components
        # from Stable Diffusion 1.5 are compatible.
        # It usually tries to infer these or fall back to SD 1.5 defaults.
        # If it complains about missing components, we might add:
        # vae="runwayml/stable-diffusion-v1-5",
        # text_encoder="runwayml/stable-diffusion-v1-5",
        # tokenizer="runwayml/stable-diffusion-v1-5",
        # scheduler="runwayml/stable-diffusion-v1-5",
    )
    pipe.to(device)
    print(f"Model loaded successfully from single file on {device}")

except Exception as e:
    print(f"Error loading model directly from single file on {device}: {e}")
    print("Attempting to load on CPU with float32 as a fallback (may be very slow)...")
    try:
        pipe = StableDiffusionPipeline.from_single_file(
            checkpoint_local_path, # Pass the local file path here again
            torch_dtype=torch.float32, # Force float32 for CPU
            use_safetensors=True,
        )
        pipe.to("cpu")
        print("Model forced loaded on CPU.")
    except Exception as e_cpu:
        print(f"Critical error: Failed to load model even from single file on CPU: {e_cpu}")
        exit() # Exit if model cannot be loaded at all

# 3. Define the Gradio inference function
def generate_image(prompt, negative_prompt, num_inference_steps, guidance_scale, seed):
    if not prompt:
        return None, "Please enter a prompt."

    generator = torch.Generator(device=device).manual_seed(seed) if seed != -1 else None

    try:
        image = pipe(
            prompt=prompt,
            negative_prompt=negative_prompt,
            num_inference_steps=int(num_inference_steps),
            guidance_scale=float(guidance_scale),
            generator=generator
        ).images[0]
        return image, None
    except Exception as e:
        return None, f"Error during image generation: {e}"

# 4. Create the Gradio Interface
iface = gr.Interface(
    fn=generate_image,
    inputs=[
        gr.Textbox(label="Prompt", placeholder="A furry creature with indigo fur, in a magical forest, whimsical art, highly detailed"),
        gr.Textbox(label="Negative Prompt (optional)", placeholder="blurry, ugly, deformed, low quality, bad anatomy"),
        gr.Slider(minimum=10, maximum=100, value=25, step=1, label="Inference Steps"),
        gr.Slider(minimum=1.0, maximum=20.0, value=7.5, step=0.5, label="Guidance Scale"),
        gr.Number(label="Seed (-1 for random)", value=-1, precision=0)
    ],
    outputs=[
        gr.Image(type="pil", label="Generated Image"),
        gr.Textbox(label="Status/Error")
    ],
    title="Indigo Furry Mix - Text-to-Image Generator",
    description="Generate images of furry characters with the Indigo Furry Mix model.",
    examples=[
        ["An indigo furry warrior, wielding a glowing sword, intricate armor, epic fantasy art", "blurry, low quality"],
        ["A cute indigo furry wizard casting a spell, magical effects, cartoon style", "disfigured, ugly"],
    ]
)

if __name__ == "__main__":
    iface.launch()