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import gradio as gr
import torch
d = "cuda" if torch.cuda.is_available() else False
if d:
import spaces
from diffusers import FluxPipeline
pipeline = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.float16).to(d)
#pipeline.enable_model_cpu_offload()
@spaces.GPU(duration=70)
def generate(prompt, negative_prompt, width, height, sample_steps):
return pipeline(prompt=f"{prompt}\nDO NOT INCLUDE {negative_prompt}", width=width, height=height, num_inference_steps=sample_steps, guidance_scale=7).images[0]
with gr.Blocks() as demo:
with gr.Column():
with gr.Row():
with gr.Column():
prompt = gr.Textbox(label="Prompt", info="What do you want?", value="Keanu Reeves holding a neon sign reading 'Hello, world!', 32k HDR, paparazzi", lines=4, interactive=True)
negative_prompt = gr.Textbox(label="Negative Prompt", info="What do you want to exclude from the image?", value="ugly, low quality", lines=4, interactive=True)
with gr.Column():
generate_button = gr.Button("Generate")
output = gr.Image()
with gr.Row():
with gr.Accordion(label="Advanced Settings", open=False):
with gr.Row():
with gr.Column():
width = gr.Slider(label="Width", info="The width in pixels of the generated image.", value=512, minimum=128, maximum=4096, step=64, interactive=True)
height = gr.Slider(label="Height", info="The height in pixels of the generated image.", value=512, minimum=128, maximum=4096, step=64, interactive=True)
with gr.Column():
sampling_steps = gr.Slider(label="Sampling Steps", info="The number of denoising steps.", value=20, minimum=4, maximum=50, step=1, interactive=True)
generate_button.click(fn=generate, inputs=[prompt, negative_prompt, width, height, sampling_steps], outputs=[output])
else:
def show_message():
return "# This is the legacy space. To access the app, [click here](https://huggingface.co/spaces/nroggendorff/flux-lora-tester)"
demo = gr.Interface(fn=show_message,
inputs=None,
outputs="markdown")
if __name__ == "__main__":
demo.launch() |