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| 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) | |
| pipe.load_lora_weights("lainlives/l2d-tex") | |
| torch.cuda.empty_cache() | |
| MAX_SEED = np.iinfo(np.int32).max | |
| MIN_IMAGE_SIZE = 1024 | |
| 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) | |
| 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 = [ | |
| "guijiaoxiansheng, live2d, simple background, white background, solo, full body, separate head, separate feet, separate hand", | |
| ] | |
| css=""" | |
| #col-container { | |
| margin: 0 auto; | |
| max-width: 520px; | |
| } | |
| """ | |
| with gr.Blocks(css=css) as demo: | |
| with gr.Column(elem_id="col-container"): | |
| with gr.Row(): | |
| prompt = gr.Text( | |
| label="Prompt", | |
| show_label=False, | |
| max_lines=10, | |
| value="guijiaoxiansheng, live2d, simple background, white background, solo, full body, separate head, separate feet, separate hand", | |
| 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=MIN_IMAGE_SIZE, | |
| maximum=MAX_IMAGE_SIZE, | |
| step=32, | |
| value=1024, | |
| ) | |
| height = gr.Slider( | |
| label="Height", | |
| minimum=MIN_IMAGE_SIZE, | |
| 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=True, | |
| cache_mode="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() |