app.py

import gradio as gr
import numpy as np
import random
import spaces
import torch
from diffusers import FlowMatchEulerDiscreteScheduler, AutoencoderTiny, AutoencoderKL
from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
from huggingface_hub import hf_hub_download
from optimum.quanto import freeze, qfloat8, quantize
from live_preview_helpers import flux_pipe_call_that_returns_an_iterable_of_images
import os

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048
# Set up environment variables and device
huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"

# Load VAE models
taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
good_vae = AutoencoderKL.from_pretrained(
    "black-forest-labs/FLUX.1-dev",
    subfolder="vae",
    torch_dtype=dtype,
    token=huggingface_token
).to(device)

# Initialize FluxPipeline instead of DiffusionPipeline
from pipelines import FluxPipeline

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev",
    torch_dtype=dtype,
    vae=taef1,
    token=huggingface_token
).to(device)

# Load and fuse LoRA BEFORE quantizing
print('Loading and fusing LoRA, please wait...')
lora_path = hf_hub_download("gokaygokay/Flux-Game-Assets-LoRA-v2", "game_asst.safetensors")
pipe.load_lora_weights(lora_path)
pipe.fuse_lora(lora_scale=0.125)
pipe.unload_lora_weights()

# Quantize the transformer
print("Quantizing transformer")
quantize(pipe.transformer, weights=qfloat8)
freeze(pipe.transformer)

# Quantize the T5 text encoder
print("Quantizing T5 text encoder")
quantize(pipe.text_encoder_2, weights=qfloat8)
freeze(pipe.text_encoder_2)

# Move quantized components to device (if not already)
pipe.transformer.to(device)
pipe.text_encoder_2.to(device)

# Move other components to device
pipe.text_encoder.to(device, dtype=dtype)
torch.cuda.empty_cache()

@spaces.GPU(duration=75)
def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    generator = torch.Generator().manual_seed(seed)
    
    for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
            prompt=prompt,
            guidance_scale=guidance_scale,
            num_inference_steps=num_inference_steps,
            width=width,
            height=height,
            generator=generator,
            output_type="pil",
            good_vae=good_vae,
        ):
            yield img, seed

examples = [
    "wbgmsst, a cat, white background",
    "wbgmsst, a warrior, white background",
    "wbgmsst, an anime girl, white background",
]

css = """
#col-container {
    margin: 0 auto;
    max-width: 520px;
}
"""

with gr.Blocks(css=css) as demo:
    
    with gr.Column(elem_id="col-container"):
        gr.Markdown(f"""# FLUX.1 [dev]
12B param rectified flow transformer guidance-distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/)  
[[non-commercial license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)] [[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-dev)]
        """)
        
        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,
                )
                
                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,
                )
            
            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance Scale",
                    minimum=1,
                    maximum=15,
                    step=0.1,
                    value=3.5,
                )
  
                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=28,
                )
        
        gr.Examples(
            examples=examples,
            fn=infer,
            inputs=[prompt],
            outputs=[result, seed],
            cache_examples="lazy"
        )

    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
        outputs=[result, seed]
    )

demo.launch()