app.py

import gradio as gr
import numpy as np
import random
import spaces
import torch
from diffusers import  DiffusionPipeline, FlowMatchEulerDiscreteScheduler, AutoencoderTiny, AutoencoderKL
from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast
from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images

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

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).to(device)
pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=dtype, vae=taef1).to(device)
torch.cuda.empty_cache()

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048

pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)

@spaces.GPU()
def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.5, num_inference_steps=28, lora_id=None, lora_scale=0.95, 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

    # Handle LoRA loading
    # Load LoRA weights and prepare joint_attention_kwargs
    if lora_id and lora_id.strip() != "":
        pipe.unload_lora_weights()
        pipe.load_lora_weights(lora_id.strip())
        joint_attention_kwargs = {"scale": lora_scale}
    else:
        joint_attention_kwargs = None
    
    try:
        # Call the custom pipeline function with the correct keyword argument
        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,  # Assuming good_vae is defined elsewhere
            joint_attention_kwargs=joint_attention_kwargs,  # Fixed parameter name
        ):
            yield img, seed, seed
    finally:
        # Unload LoRA weights if they were loaded
        if lora_id:
            pipe.unload_lora_weights()
    
examples = [
    "a tiny astronaut hatching from an egg on the moon",
    "a cat holding a sign that says hello world",
    "an anime illustration of a wiener schnitzel",
]

# 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] LoRA
# 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=8,
#                     value=1024,
#                 )
                
#                 height = gr.Slider(
#                     label="Height",
#                     minimum=256,
#                     maximum=MAX_IMAGE_SIZE,
#                     step=8,
#                     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,
#                 )

#             with gr.Row():
#                 lora_id = gr.Textbox(
#                     label="LoRA Model ID (HuggingFace path)",
#                     placeholder="username/lora-model",
#                     max_lines=1
#                 )
#                 lora_scale = gr.Slider(
#                     label="LoRA Scale",
#                     minimum=0,
#                     maximum=2,
#                     step=0.01,
#                     value=0.95,
#                 )
        
#         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,lora_id,lora_scale],
#         outputs = [result, seed]
#     )

# demo.launch()
    
css = """
#col-container {
   margin: 0 auto;
   max-width: 960px;
}
.generate-btn {
   background: linear-gradient(90deg, #4B79A1 0%, #283E51 100%) !important;
   border: none !important;
   color: white !important;
}
.generate-btn:hover {
   transform: translateY(-2px);
   box-shadow: 0 5px 15px rgba(0,0,0,0.2);
}
"""

with gr.Blocks(css=css) as app:
    gr.HTML("<center><h1>FLUX.1-Dev with LoRA support</h1></center>")
    with gr.Column(elem_id="col-container"):
        with gr.Row():
            with gr.Column():
                with gr.Row():
                    text_prompt = gr.Textbox(label="Prompt", placeholder="Enter a prompt here", lines=3, elem_id="prompt-text-input")
                with gr.Row():
                    custom_lora = gr.Textbox(label="Custom LoRA", info="LoRA Hugging Face path (optional)", placeholder="multimodalart/vintage-ads-flux")
                with gr.Row():
                    with gr.Accordion("Advanced Settings", open=False):
                        lora_scale = gr.Slider(
                            label="LoRA Scale",
                            minimum=0,
                            maximum=2,
                            step=0.01,
                            value=0.95,
                        )
                        with gr.Row():
                            width = gr.Slider(label="Width", value=1024, minimum=64, maximum=1216, step=8)
                            height = gr.Slider(label="Height", value=1024, minimum=64, maximum=1216, step=8)
                        seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=4294967296, step=1)
                        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
                        with gr.Row():
                            steps = gr.Slider(label="Inference steps steps", value=28, minimum=1, maximum=100, step=1)
                            cfg = gr.Slider(label="Guidance Scale", value=3.5, minimum=1, maximum=20, step=0.5)
                        # method = gr.Radio(label="Sampling method", value="DPM++ 2M Karras", choices=["DPM++ 2M Karras", "DPM++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM"])

                with gr.Row():
                    # text_button = gr.Button("Run", variant='primary', elem_id="gen-button")
                    text_button = gr.Button("✨ Generate Image", variant='primary', elem_classes=["generate-btn"])
            with gr.Column():
                with gr.Row():
                    image_output = gr.Image(type="pil", label="Image Output", elem_id="gallery")
                with gr.Row():
                    seed_output = gr.Textbox(label="Seed Used", show_copy_button = True)
        
        # gr.Markdown(article_text)
        with gr.Column():
            gr.Examples(
                examples = examples,
                inputs = [text_prompt],
            )
    gr.on(
        triggers=[text_button.click, text_prompt.submit],
        fn = infer,
        inputs=[text_prompt, seed, randomize_seed, width, height, cfg, steps, custom_lora, lora_scale], 
        outputs=[image_output,seed_output, seed]
    )
        
        # text_button.click(query, inputs=[custom_lora, text_prompt, steps, cfg, randomize_seed, seed, width, height], outputs=[image_output,seed_output, seed])
        # text_button.click(infer, inputs=[text_prompt, seed, randomize_seed, width, height, cfg, steps, custom_lora, lora_scale], outputs=[image_output,seed_output, seed])

app.launch(share=True)