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| import random | |
| import gradio as gr | |
| import numpy as np | |
| import spaces | |
| import torch | |
| from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler | |
| from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast | |
| from gradio_imagefeed import ImageFeed | |
| dtype = torch.bfloat16 | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to(device) | |
| MAX_SEED = np.iinfo(np.int32).max | |
| MAX_IMAGE_SIZE = 2048 | |
| LICENSE=f"""# Better UI for FLUX.1 [dev] [[non-commercial license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)]""" | |
| CSS = "#col-container { margin: 0 auto; max-width: 900px; }" | |
| EXAMPLES = ["a tiny elephant hatching from a turtle egg in the palm of a human hand, highly detailed textures, close-up"] | |
| def infer(prompt, seed=99999, randomize_seed=True, width=896, height=1152, guidance_scale=5.0, 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) | |
| image = pipe( | |
| prompt = prompt, | |
| width = width, | |
| height = height, | |
| num_inference_steps = num_inference_steps, | |
| generator = generator, | |
| guidance_scale=guidance_scale).images[0] | |
| yield image, seed | |
| with gr.Blocks(css=CSS) as demo: | |
| with gr.Column(elem_id="col-container"): | |
| gr.Markdown(LICENSE) | |
| with gr.Row(): | |
| prompt = gr.Text( | |
| label="Prompt", | |
| show_label=False, | |
| max_lines=5, | |
| placeholder="Prompt", | |
| container=False) | |
| run_button = gr.Button("Run", scale=0) | |
| result = ImageFeed(label="Result", show_label=False) | |
| # 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=random.randint(0, MAX_SEED)) | |
| randomize_seed = gr.Checkbox(label="Randomize", value=True) | |
| with gr.Row(): | |
| width = gr.Slider( | |
| label="Width", | |
| minimum=256, | |
| maximum=MAX_IMAGE_SIZE, | |
| step=32, | |
| value=896) | |
| height = gr.Slider( | |
| label="Height", | |
| minimum=256, | |
| maximum=MAX_IMAGE_SIZE, | |
| step=32, | |
| value=1152) | |
| with gr.Row(): | |
| guidance_scale = gr.Slider( | |
| label="Guidance Scale", | |
| minimum=1, | |
| maximum=15, | |
| step=0.1, | |
| value=3) | |
| num_inference_steps = gr.Slider( | |
| label="Inference Steps", | |
| minimum=1, | |
| maximum=50, | |
| step=1, | |
| value=28) | |
| 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], | |
| outputs = [result, seed] | |
| ) | |
| demo.launch() |