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Update app.py
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app.py
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import gradio as gr
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import numpy as np
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import random
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import torch
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from diffusers import DiffusionPipeline
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import os
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# Constants
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MAX_SEED = np.iinfo(np.int32).max
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MAX_IMAGE_SIZE = 2048
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DEFAULT_IMAGE_SIZE = 1024
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# Model setup
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dtype = torch.bfloat16
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device = "cuda" if torch.cuda.is_available() else "cpu"
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huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
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torch_dtype=dtype,
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token=huggingface_token
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).to(device)
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if randomize_seed:
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seed = random.randint(0, MAX_SEED)
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generator = torch.Generator().manual_seed(
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image = pipe(
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prompt=prompt,
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width=width,
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).images[0]
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return image, seed
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css = """
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body {
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background-color: #f4faff;
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color: #005662;
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font-family: 'Poppins', sans-serif;
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}
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margin: 0 auto;
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max-width:
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padding: 20px;
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}
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.gr-button {
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.gr-button:hover {
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background-color: #0277bd;
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}
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.gr-
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border-radius: 12px;
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border: 1px solid #eeeeee;
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}
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"""
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with gr.Blocks(css=css, theme=gr.themes.Soft(primary_hue="blue", secondary_hue="gray")) as demo:
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gr.Markdown("""
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# FLUX.1 [dev] | A Text-To-Image Rectified Flow 12B Transformer
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Enter a text prompt below to generate an image. Click 'Generate' to create your image.
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""")
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with gr.Row():
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with gr.Column(scale=4):
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prompt = gr.Text(
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label="Prompt",
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placeholder="Enter your prompt here",
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lines=3
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)
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with gr.Column(scale=1):
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generate_button = gr.Button("Generate", variant="primary")
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with gr.Accordion("Advanced Settings", open=False):
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seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
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randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
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with gr.Row():
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guidance_scale = gr.Slider(label="Guidance Scale", minimum=1, maximum=15, step=0.1, value=3.5)
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num_inference_steps = gr.Slider(label="Number of inference steps", minimum=1, maximum=50, step=1, value=28)
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inputs=[prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
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outputs=[result, seed]
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)
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demo.launch(share=True)
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import gradio as gr
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import numpy as np
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import random
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import spaces
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import torch
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from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler
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from PIL import Image
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import io
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import os
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dtype = torch.bfloat16
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device = "cuda" if torch.cuda.is_available() else "cpu"
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# Set your Hugging Face API token
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huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
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# Load the diffusion pipeline with the Hugging Face API token
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pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=dtype, token=huggingface_token).to(device)
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MAX_SEED = np.iinfo(np.int32).max
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MAX_IMAGE_SIZE = 2048
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@spaces.GPU(duration=200)
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def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=5.0, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
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if randomize_seed:
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seed = random.randint(0, MAX_SEED)
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generator = torch.Generator().manual_seed(seed)
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image = pipe(
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prompt=prompt,
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width=width,
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).images[0]
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return image, seed
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def download_image(image, file_format):
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img_byte_arr = io.BytesIO()
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image.save(img_byte_arr, format=file_format)
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img_byte_arr = img_byte_arr.getvalue()
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return img_byte_arr
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examples = [
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"a galaxy swirling with vibrant blue and purple hues",
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"a futuristic cityscape under a dark sky",
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"a serene forest with a magical glowing tree",
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"a futuristic cityscape with sleek skyscrapers and flying cars",
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"a portrait of a smiling woman with a colorful floral crown",
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"a fantastical creature with the body of a dragon and the wings of a butterfly",
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]
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css = """
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body {
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background-color: #f4faff;
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color: #005662;
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font-family: 'Poppins', sans-serif;
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}
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#col-container {
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margin: 0 auto;
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max-width: 100%;
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padding: 20px;
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}
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.gr-button {
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.gr-button:hover {
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background-color: #0277bd;
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}
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.gr-examples-card {
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border: 1px solid #eeeeee;
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border-radius: 12px;
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padding: 16px;
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margin-bottom: 12px;
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}
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.gr-examples-card:hover {
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background-color: #f4faf2;
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border-color: #0277bd;
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color: #005662;
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}
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.gr-progress-bar, .gr-progress-bar-fill {
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background-color: #0288d1 !important;
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}
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.gr-slider, .gr-slider-track {
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background-color: #0288d1 !important;
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}
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.gr-slider-thumb {
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background-color: #005662 !important;
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}
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.gr-text-input, .gr-image {
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width: 100%;
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box-sizing: border-box;
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margin-bottom: 10px;
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}
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"""
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with gr.Blocks(css=css, theme=gr.themes.Soft(primary_hue="blue", secondary_hue="gray")) as demo:
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with gr.Column(elem_id="col-container"):
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gr.Markdown(f"""# FLUX.1 [dev] | A Text-To-Image Rectified Flow 12B Transformer
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<a href="https://huggingface.co/black-forest-labs/FLUX.1-dev" style="text-decoration:none;">
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<div class="gr-examples-card">
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<h3>View Model Details</h3>
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<p>Explore more about this model on Hugging Face.</p>
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</div>
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</a>
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""")
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with gr.Row():
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with gr.Column(scale=4):
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prompt = gr.Text(
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label="Prompt",
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placeholder="Enter your prompt here",
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lines=2
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)
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with gr.Column(scale=1):
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generate_button = gr.Button("Generate", variant="primary")
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result = gr.Image(label="Generated Image", type="pil")
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with gr.Accordion("Advanced Settings", open=False):
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seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
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randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
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with gr.Row():
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width = gr.Slider(
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label="Width",
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minimum=256,
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maximum=MAX_IMAGE_SIZE,
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step=32,
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value=1024,
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)
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height = gr.Slider(
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label="Height",
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minimum=256,
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maximum=MAX_IMAGE_SIZE,
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step=32,
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value=1024,
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)
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with gr.Row():
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guidance_scale = gr.Slider(
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label="Guidance Scale",
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minimum=1,
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maximum=15,
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step=0.1,
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value=3.5,
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)
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num_inference_steps = gr.Slider(
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label="Number of inference steps",
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minimum=1,
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maximum=50,
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step=1,
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value=28,
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)
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download_format = gr.Radio(
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label="Download Format",
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choices=["PNG", "JPEG", "SVG", "WEBP"],
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value="PNG",
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type="value",
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)
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download_button = gr.Button("Download Image")
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download_button.click(
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fn=download_image,
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inputs=[result, download_format],
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outputs=gr.File(label="Download"),
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)
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gr.Examples(
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examples=examples,
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fn=infer,
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inputs=[prompt],
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outputs=[result, seed],
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cache_examples="lazy"
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)
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gr.on(
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triggers=[run_button.click, prompt.submit],
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fn=infer,
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inputs=[prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
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outputs=[result, seed]
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)
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demo.load(
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fn=lambda: None,
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inputs=None,
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outputs=None
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)
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demo.launch(share=True)
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