import gradio as gr import numpy as np import random from diffusers import AuraFlowPipeline import torch import spaces device = "cuda" if torch.cuda.is_available() else "cpu" #torch.set_float32_matmul_precision("high") #torch._inductor.config.conv_1x1_as_mm = True #torch._inductor.config.coordinate_descent_tuning = True #torch._inductor.config.epilogue_fusion = False #torch._inductor.config.coordinate_descent_check_all_directions = True pipe = AuraFlowPipeline.from_pretrained( "fal/AuraFlow", torch_dtype=torch.float16 ).to("cuda") #pipe.transformer.to(memory_format=torch.channels_last) #pipe.vae.to(memory_format=torch.channels_last) #pipe.transformer = torch.compile(pipe.transformer, mode="max-autotune", fullgraph=True) #pipe.vae.decode = torch.compile(pipe.vae.decode, mode="max-autotune", fullgraph=True) MAX_SEED = np.iinfo(np.int32).max MAX_IMAGE_SIZE = 1024 @spaces.GPU def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, 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, negative_prompt = negative_prompt, width=width, height=height, guidance_scale = guidance_scale, num_inference_steps = num_inference_steps, generator = generator ).images[0] return image, seed examples = [ "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", "An astronaut riding a green horse", "A delicious ceviche cheesecake slice", ] css=""" #col-container { margin: 0 auto; max-width: 520px; } """ if torch.cuda.is_available(): power_device = "GPU" else: power_device = "CPU" with gr.Blocks(css=css) as demo: with gr.Column(elem_id="col-container"): gr.Markdown(f""" # AuraFlow 0.1 Demo of the [AuraFlow 0.1](https://huggingface.co/fal/AuraFlow) 5.6B parameters open source diffusion transformer model [[blog](https://blog.fal.ai/auraflow/)] [[model](https://huggingface.co/fal/AuraFlow)] [[fal](https://fal.ai/models/fal-ai/aura-flow)] """) 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): negative_prompt = gr.Text( label="Negative prompt", max_lines=1, placeholder="Enter a negative prompt", ) 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=0.0, maximum=10.0, step=0.1, value=5.0, ) num_inference_steps = gr.Slider( label="Number of inference steps", minimum=1, maximum=50, step=1, value=28, ) gr.Examples( examples = examples, inputs = [prompt], cache_examples=False ) gr.on( triggers=[run_button.click, prompt.submit, negative_prompt.submit], fn = infer, inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps], outputs = [result, seed] ) demo.queue().launch()