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import gradio as gr
from diffusers import StableDiffusionPipeline
import torch
from huggingface_hub import HfFolder
def generate_image(prompt, guidance_scale, num_steps, lora_scale):
# Lade das Base Model
pipe = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
torch_dtype=torch.float16
).to("cuda")
# Lade dein LoRA
pipe.load_lora_weights(
"Scalino84/my-flux-face-v2",
weight_name="flux_train_replicate.safetensors"
)
# Generiere das Bild
image = pipe(
prompt=prompt,
num_inference_steps=num_steps,
guidance_scale=guidance_scale,
cross_attention_kwargs={"scale": lora_scale}
).images[0]
return image
# Erstelle das Gradio Interface
with gr.Blocks() as demo:
gr.Markdown("# Flux Face Generator")
with gr.Row():
with gr.Column():
prompt = gr.Textbox(
label="Prompt",
value="a photo of xyz person, professional headshot",
lines=3
)
guidance = gr.Slider(
label="Guidance Scale",
minimum=1,
maximum=20,
value=7.5,
step=0.5
)
steps = gr.Slider(
label="Inference Steps",
minimum=20,
maximum=100,
value=30,
step=1
)
lora_scale = gr.Slider(
label="LoRA Scale",
minimum=0.1,
maximum=1.0,
value=0.8,
step=0.1
)
generate = gr.Button("Generate Image")
with gr.Column():
output = gr.Image(label="Generated Image")
generate.click(
fn=generate_image,
inputs=[prompt, guidance, steps, lora_scale],
outputs=output
)
demo.launch()
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