app.py

import spaces  # Import this first to avoid CUDA initialization issues
import os
import gradio as gr
import numpy as np
import gradio as gr
import torch
import random
import time
from PIL import Image
from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler, FluxTransformer2DModel
from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast

dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"

# Use the 'waffles' environment variable as the access token
hf_token = os.getenv('waffles')

# Ensure the token is loaded correctly
if not hf_token:
    raise ValueError("Hugging Face API token not found. Please set the 'waffles' environment variable.")


pipe = DiffusionPipeline.from_pretrained("AlekseyCalvin/RCA_Agitprop_Manufactory", torch_dtype=dtype, token=hf_token).to(device)

@spaces.GPU()
def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, progress=gr.Progress(track_tqdm=True)):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    generator = torch.Generator().manual_seed(seed)
    image = pipe(
            prompt = prompt, 
            width = width,
            height = height,
            num_inference_steps = num_inference_steps, 
            generator = generator,
            guidance_scale=0.0
    ).images[0] 
    return image, seed


examples = [
    "RCA style communist party poster with the words Ready for REVOLUTION? in large black consistent constructivist font alongside a red Soviet hammer and a red Soviet sickle over the background of planet earth, over the North American continent",
]

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"""# RCA Agitprop Manufactory""")
        
        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():
                
  
                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"
        )

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

demo.launch()