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import gradio as gr
import spaces
import torch
from diffusers import FluxPipeline
from diffusers import FluxImg2ImgPipeline
from diffusers.utils import load_image
from huggingface_hub.utils import RepositoryNotFoundError
pipeline = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.float16).to("cuda")
pipelineImg = FluxImg2ImgPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.float16).to("cuda")
@spaces.GPU(duration=70)
def generate(image, prompt, negative_prompt, width, height, sample_steps, lora_id):
try:
# pipeline.load_lora_weights(lora_id)
init_image = load_image(image).resize((1024, 1024))
pipelineImg.load_lora_weights(lora_id)
except RepositoryNotFoundError:
raise ValueError(f"Recieved invalid FLUX LoRA.")
return pipeline(prompt=f"{prompt}\nDO NOT INCLUDE {negative_prompt}", image=init_image, width=width, height=height, num_inference_steps=sample_steps, generator=torch.Generator("cpu").manual_seed(42), guidance_scale=7).images[0]
with gr.Blocks() as interface:
with gr.Column():
with gr.Row():
with gr.Column():
image = gr.Image(label="Input image", show_label=False, type="filepath")
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)
lora_id = gr.Textbox(label="Adapter Repository", info="ID of the FLUX LoRA", value="pepper13/fluxfw")
generate_button.click(fn=generate, inputs=[image, prompt, negative_prompt, width, height, sampling_steps, lora_id], outputs=[output])
if __name__ == "__main__":
interface.launch() |