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import gradio as gr
from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
import torch
from PIL import Image
import random
model_id = "CompVis/stable-diffusion-v1-4" # Daha hafif bir model
lora_model_id = "codermert/mert_flux" # Your LoRA model
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float32)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to("cpu") # CPU'ya taşıyoruz
pipe.load_lora_weights(lora_model_id)
pipe.safety_checker = None # Safety checker'ı devre dışı bırakıyoruz
def generate_image(prompt, negative_prompt, steps, cfg_scale, seed, strength):
if seed == -1:
seed = random.randint(1, 1000000000)
generator = torch.Generator().manual_seed(seed)
with torch.no_grad():
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
num_inference_steps=steps,
guidance_scale=cfg_scale,
generator=generator,
cross_attention_kwargs={"scale": strength},
).images[0]
return image, seed
css = """
#app-container {
max-width: 800px;
margin-left: auto;
margin-right: auto;
}
"""
examples = [
["A beautiful landscape with mountains and a lake", "ugly, deformed"],
["A futuristic cityscape at night", "daytime, rural"],
["A portrait of a smiling person in a colorful outfit", "monochrome, frowning"],
]
with gr.Blocks(theme='default', css=css) as app:
gr.HTML("<center><h1>Mert Flux LoRA Explorer (CPU Version)</h1></center>")
with gr.Column(elem_id="app-container"):
with gr.Row():
text_prompt = gr.Textbox(label="Prompt", placeholder="Enter a prompt here", lines=2)
negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="What to avoid in the image", lines=2)
with gr.Row():
with gr.Column():
steps = gr.Slider(label="Sampling steps", value=30, minimum=10, maximum=50, step=1)
cfg_scale = gr.Slider(label="CFG Scale", value=7.5, minimum=1, maximum=15, step=0.5)
with gr.Column():
strength = gr.Slider(label="LoRA Strength", value=0.75, minimum=0, maximum=1, step=0.01)
seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=1000000000, step=1)
with gr.Row():
generate_button = gr.Button("Generate", variant='primary')
with gr.Row():
image_output = gr.Image(type="pil", label="Generated Image", show_download_button=True)
with gr.Row():
seed_output = gr.Number(label="Seed Used")
gr.Examples(examples=examples, inputs=[text_prompt, negative_prompt])
generate_button.click(
generate_image,
inputs=[text_prompt, negative_prompt, steps, cfg_scale, seed, strength],
outputs=[image_output, seed_output]
)
app.launch() |