rgbguy101/OpenSource101

1import gradio as gr2import numpy as np3import random4import spaces5import torch6from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler, FluxTransformer2DModel7from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast8 9dtype = torch.bfloat1610device = "cuda" if torch.cuda.is_available() else "cpu"11 12pipe = DiffusionPipeline.from_pretrained("sayakpaul/FLUX.1-merged", torch_dtype=dtype).to(device)13 14MAX_SEED = np.iinfo(np.int32).max15MAX_IMAGE_SIZE = 204816 17@spaces.GPU()18def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.5, num_inference_steps=8, progress=gr.Progress(track_tqdm=True)):19    if randomize_seed:20        seed = random.randint(0, MAX_SEED)21    generator = torch.Generator().manual_seed(seed)22    image = pipe(23        prompt = prompt, 24        width = width,25        height = height,26        num_inference_steps = num_inference_steps, 27        generator = generator,28        guidance_scale=guidance_scale29    ).images[0] 30    return image, seed31 32examples = [33    "a coder learning about AI in his room",34    "a man playing basketball",35    "a disney style statue of liberty",36]37 38css="""39#col-container {40    margin: 0 auto;41    max-width: 520px;42}43"""44 45with gr.Blocks(css=css) as demo:46    47    with gr.Column(elem_id="col-container"):48        gr.Markdown(f"""# [FLUX.1 [merged]](https://huggingface.co/sayakpaul/FLUX.1-merged)49Merge by [Sayak Paul](https://huggingface.co/sayakpaul) of 2 of the 12B param rectified flow transformers [FLUX.1 [dev]](https://huggingface.co/black-forest-labs/FLUX.1-dev) and [FLUX.1 [schnell]](https://huggingface.co/black-forest-labs/FLUX.1-schnell) by [Black Forest Labs](https://blackforestlabs.ai/)50        """)51        52        with gr.Row():53            54            prompt = gr.Text(55                label="Prompt",56                show_label=False,57                max_lines=1,58                placeholder="Enter your prompt",59                container=False,60            )61            run_button = gr.Button("Run", scale=0)62        63        num_inference_steps = gr.Slider(64            label="Number of inference steps",65            minimum=1,66            maximum=50,67            step=1,68            value=8,69        )70        71        result = gr.Image(label="Result", show_label=False)72        73        with gr.Accordion("Advanced Settings", open=False):74            75            seed = gr.Slider(76                label="Seed",77                minimum=0,78                maximum=MAX_SEED,79                step=1,80                value=0,81            )82            83            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)84            85            with gr.Row():86                87                width = gr.Slider(88                    label="Width",89                    minimum=256,90                    maximum=MAX_IMAGE_SIZE,91                    step=32,92                    value=1024,93                )94                95                height = gr.Slider(96                    label="Height",97                    minimum=256,98                    maximum=MAX_IMAGE_SIZE,99                    step=32,100                    value=1024,101                )102            103            with gr.Row():104 105                guidance_scale = gr.Slider(106                    label="Guidance Scale",107                    minimum=1,108                    maximum=15,109                    step=0.1,110                    value=3.5,111                )112        113        gr.Examples(114            examples = examples,115            fn = infer,116            inputs = [prompt],117            outputs = [result, seed],118            cache_examples="lazy"119        )120 121    gr.on(122        triggers=[run_button.click, prompt.submit],123        fn = infer,124        inputs = [prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],125        outputs = [result, seed]126    )127 128demo.launch()