| import gradio as gr |
| import numpy as np |
| import random |
| from diffusers import DiffusionPipeline |
| import torch |
| from PIL import Image |
|
|
| device = "cuda" if torch.cuda.is_available() else "cpu" |
|
|
| LORA=False |
|
|
| if torch.cuda.is_available(): |
| torch.cuda.max_memory_allocated(device=device) |
| pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16", use_safetensors=True) |
| pipe.enable_xformers_memory_efficient_attention() |
| pipe = pipe.to(device) |
| else: |
| pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", use_safetensors=True) |
| pipe = pipe.to(device) |
|
|
| MAX_SEED = np.iinfo(np.int32).max |
| MAX_IMAGE_SIZE = 1024 |
|
|
|
|
|
|
|
|
|
|
| def infer(prompt, negative_prompt, seed, randomize_seed, image_style,cfg,lora_scale): |
| pipe.unload_lora_weights() |
| if image_style == "No style": |
| pipe.unload_lora_weights() |
| |
| elif image_style == "style_detailed": |
| pipe.load_lora_weights("ohkarim/LoRA_logos", weight_name="OH_logos.safetensors", adapter_name="OH_logos") |
| prompt=prompt+", detailed, close up, unique background color, OH_logos" |
| |
| elif image_style == "style_blacknwhite": |
| pipe.load_lora_weights("ohkarim/lora_logo_blacknwhite", weight_name="bel_blacknwhite_lora.safetensors", adapter_name="bel_blacknwhite") |
| prompt=prompt+", black and white, balck and white, minimalist, unique background color, bel_blacknwhite" |
|
|
| elif image_style == "style_cartoon": |
| pipe.load_lora_weights("ohkarim/lora_logo_modern", weight_name="lora_modern.safetensors", adapter_name="oh_bel_modern") |
| prompt=prompt+", simple, modern, unique background color, oh_bel_modern" |
|
|
|
|
| if randomize_seed: |
| seed = random.randint(0, MAX_SEED) |
| |
| generator = torch.Generator().manual_seed(seed) |
| |
| griddd = [] |
| for _ in range(4): |
| image = pipe( |
| prompt = prompt, |
| negative_prompt = negative_prompt, |
| cross_attention_kwargs={"scale": lora_scale}, |
| guidance_scale = cfg, |
| num_inference_steps = 20, |
| width = 512, |
| height = 512, |
| generator = generator |
| ).images[0] |
| griddd.append(image) |
| |
| return griddd |
|
|
|
|
| css=""" |
| #col-container { |
| margin: 0 auto; |
| max-width: 520px; |
| } |
| """ |
|
|
| if torch.cuda.is_available(): |
| power_device = "GPU" |
| else: |
| power_device = "CPU" |
|
|
|
|
| |
| def read_css_from_file(filename): |
| with open(filename, "r") as file: |
| return file.read() |
|
|
| |
| css = read_css_from_file("style.css") |
|
|
| PTI_SD_DESCRIPTION = ''' |
| <div id="content_align"> |
| <span style="color:darkred;font-size:32px;font-weight:bold"> |
| Create your own logo now! |
| </span> |
| </div> |
| <div id="content_align"> |
| <span style="color:blue;font-size:16px;font-weight:bold"> |
| There is 3 styles so far, detailed, black & white and cartoon |
| </span> |
| </div> |
| <div id="content_align" style="margin-top: 10px;"> |
| </div> |
| ''' |
|
|
| |
| with gr.Blocks(css=css) as demo: |
| gr.Markdown(PTI_SD_DESCRIPTION) |
| with gr.Row(): |
| with gr.Column(): |
| text_prompt = gr.Textbox(label="Input Prompt", placeholder="Example: logo of a coffee shop, cup of coffee, mug, brown shades ", lines=2) |
| negative_prompt = gr.Textbox(label="Negative Prompt (optional)", placeholder="Example: blurry, unfocused, complicated", lines=2) |
| image_style = gr.Dropdown(label="Choose a style", choices=["No style", "style_detailed", "style_blacknwhite", "style_cartoon"], value="No LoRA") |
|
|
| with gr.Column(): |
| cfg = gr.Slider(label="Prompt guidance", minimum=1, maximum=20, step=1, value=4) |
| lora_scale = gr.Slider(label="Style guidance", minimum=0, maximum=2, step=0.01, value=0.9) |
| seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0) |
| |
| randomize_seed = gr.Checkbox(label="Randomize seed", value=True) |
| |
| generate_button = gr.Button("Generate Image", variant='primary') |
| |
| with gr.Row(): |
| output_image1 = gr.Image(type="pil", label="Image 1", width=256, height=400) |
| output_image2 = gr.Image(type="pil", label="Image 2", width=256, height=400) |
| output_image3 = gr.Image(type="pil", label="Image 3", width=256, height=400) |
| output_image4 = gr.Image(type="pil", label="Image 4", width=256, height=400) |
|
|
| generate_button.click( |
| fn = infer, |
| inputs = [text_prompt, negative_prompt, seed, randomize_seed,image_style,cfg,lora_scale], |
| outputs = [output_image1,output_image2,output_image3,output_image4], |
| show_progress=True |
| ) |
| demo.queue().launch() |
