app.py

import os
import torch
import gradio as gr
from diffusers import FluxTransformer2DModel, FluxPipeline, BitsAndBytesConfig
from transformers import T5EncoderModel, BitsAndBytesConfig as BitsAndBytesConfigTF

def generate_image(prompt, negative_prompt="", num_inference_steps=30, guidance_scale=7.5):
    # Initialize Flux pipeline
    device = "cuda" if torch.cuda.is_available() else "cpu"
    
    dtype = torch.bfloat16
    single_file_base_model = "camenduru/FLUX.1-dev-diffusers"
    file_url = "https://huggingface.co/lodestones/Chroma/resolve/main/chroma-unlocked-v31.safetensors"
    
    quantization_config_tf = BitsAndBytesConfigTF(load_in_8bit=True, bnb_8bit_compute_dtype=torch.bfloat16)
    text_encoder_2 = T5EncoderModel.from_pretrained(single_file_base_model, subfolder="text_encoder_2", torch_dtype=dtype, config=single_file_base_model, quantization_config=quantization_config_tf)
    
    quantization_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=torch.bfloat16)
    transformer = FluxTransformer2DModel.from_single_file(file_url, subfolder="transformer", torch_dtype=dtype, config=single_file_base_model, quantization_config=quantization_config)
    
    flux_pipeline = FluxPipeline.from_pretrained(single_file_base_model, transformer=transformer, text_encoder_2=text_encoder_2, torch_dtype=dtype)
    flux_pipeline.to(device)
    
    # Generate image
    image = flux_pipeline(
        prompt=prompt,
        negative_prompt=negative_prompt,
        num_inference_steps=num_inference_steps,
        guidance_scale=guidance_scale
    ).images[0]
    
    return image

# Create Gradio interface
iface = gr.Interface(
    fn=generate_image,
    inputs=[
        gr.Textbox(label="Prompt", placeholder="Enter your prompt here..."),
        gr.Textbox(label="Negative Prompt", placeholder="Enter negative prompt here...", value=""),
        gr.Slider(minimum=1, maximum=100, value=30, step=1, label="Number of Inference Steps"),
        gr.Slider(minimum=1.0, maximum=20.0, value=7.5, step=0.1, label="Guidance Scale")
    ],
    outputs=gr.Image(label="Generated Image"),
    title="Chroma Image Generator",
    description="Generate images using the Chroma model with FLUX pipeline",
    examples=[
        ["A beautiful sunset over mountains, photorealistic, 8k", "blurry, low quality, distorted", 30, 7.5],
        ["A futuristic cityscape at night, neon lights, cyberpunk style", "ugly, deformed, low resolution", 30, 7.5]
    ]
)

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