⚠️Running on CPU, This may not work on CPU.
" MAX_SEED = np.iinfo(np.int32).max CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES", "0") == "1" MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "2048")) USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1" ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1" device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") #Compile if torch.cuda.is_available(): pipe = StableDiffusionXLPipeline.from_pretrained( "SG161222/RealVisXL_V4.0_Lightning", #(or) use --- SG161222/RealVisXL_V4.0 / SG161222/RealVisXL_V4.0_Lightning --- for better results. torch_dtype=torch.float16, use_safetensors=True, add_watermarker=False, variant="fp16" ).to(device) if ENABLE_CPU_OFFLOAD: pipe.enable_model_cpu_offload() else: pipe.to(device) print("Loaded on Device!") if USE_TORCH_COMPILE: pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True) print("Model Compiled!") def save_image(img, path): img.save(path) #seeding def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: if randomize_seed: seed = random.randint(0, MAX_SEED) return seed #Load the HTML content #html_file_url = "https://prithivmlmods-hamster-static.static.hf.space/index.html" #html_content = f'' #html_file_url = "https://prithivmlmods-static-loading-theme.static.hf.space/index.html" #html_file_url = "" #html_content = f'' #js_func = """ # #""" @spaces.GPU(enable_queue=True) def generate( prompt: str, negative_prompt: str = "", use_negative_prompt: bool = False, style: str = DEFAULT_STYLE_NAME, collage_style: str = DEFAULT_COLLAGE_STYLE_NAME, filter_name: str = DEFAULT_FILTER_NAME, grid_size: str = "2x2", seed: int = 0, width: int = 1024, height: int = 1024, guidance_scale: float = 3, randomize_seed: bool = False, use_resolution_binning: bool = True, progress=gr.Progress(track_tqdm=True), ): if check_text(prompt, negative_prompt): raise ValueError("Prompt contains restricted words.") if collage_style != "No Style": prompt, negative_prompt = apply_style(collage_style, prompt, negative_prompt) elif filter_name != "No Filter": prompt, negative_prompt = apply_style(filter_name, prompt, negative_prompt) else: prompt, negative_prompt = apply_style(style, prompt, negative_prompt) seed = int(randomize_seed_fn(seed, randomize_seed)) generator = torch.Generator().manual_seed(seed) if not use_negative_prompt: negative_prompt = "" # type: ignore negative_prompt += default_negative grid_sizes = { "2x1": (2, 1), "1x2": (1, 2), "2x2": (2, 2), "2x3": (2, 3), "3x2": (3, 2), "1x1": (1, 1) } grid_size_x, grid_size_y = grid_sizes.get(grid_size, (2, 2)) num_images = grid_size_x * grid_size_y options = { "prompt": prompt, "negative_prompt": negative_prompt, "width": width, "height": height, "guidance_scale": guidance_scale, "num_inference_steps": 20, "generator": generator, "num_images_per_prompt": num_images, "use_resolution_binning": use_resolution_binning, "output_type": "pil", } torch.cuda.empty_cache() # Clear GPU memory images = pipe(**options).images grid_img = Image.new('RGB', (width * grid_size_x, height * grid_size_y)) for i, img in enumerate(images[:num_images]): grid_img.paste(img, (i % grid_size_x * width, i // grid_size_x * height)) unique_name = str(uuid.uuid4()) + ".png" save_image(grid_img, unique_name) return [unique_name], seed def load_predefined_images1(): predefined_images1 = [ "Tones/1.png", "Tones/2.png", "Tones/3.png", "Tones/4.png", "Tones/5.png", "Tones/6.png", "Tones/7.png", "Tones/8.png", "Tones/9.png", ] return predefined_images1 def load_predefined_images(): predefined_images = [ "assets/11.png", "assets/22.png", "assets/33.png", "assets/44.png", "assets/55.png", "assets/66.png", "assets/77.png", "assets/88.png", "assets/99.png", ] return predefined_images examples = [ "Chocolate dripping from a donut against a yellow background, in the style of brocore, hyper-realistic oil --ar 2:3 --q 2 --s 750 --v 5 --ar 2:3 --q 2 --s 750 --v 5", "3d image, cute girl, in the style of Pixar --ar 1:2 --stylize 750, 4K resolution highlights, Sharp focus, octane render, ray tracing, Ultra-High-Definition, 8k, UHD, HDR, (Masterpiece:1.5), (best quality:1.5)", "Cold coffee in a cup bokeh --ar 85:128 --v 6.0 --style raw5, 4K, Photo-Realistic", "Food photography of a milk shake with flying strawberrys against a pink background, professionally studio shot with cinematic lighting. The image is in the style of a professional studio shot --ar 85:128 --v 6.0 --style raw" ] css = ''' .gradio-container{max-width: 600px !important} h1{text-align:center} ''' with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo: gr.Markdown(DESCRIPTION) gr.DuplicateButton( value="Duplicate Space for private use", elem_id="duplicate-button", visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1", ) with gr.Group(): with gr.Row(): prompt = gr.Text( label="Prompt", show_label=False, max_lines=1, placeholder="Enter your prompt", container=False, ) run_button = gr.Button("Run") result = gr.Gallery(label="Grid", columns=1, preview=True) with gr.Row(visible=True): grid_size_selection = gr.Dropdown( choices=["2x1", "1x2", "2x2", "2x3", "3x2", "1x1"], value="1x1", label="Grid Size" ) with gr.Row(visible=True): filter_selection = gr.Radio( show_label=True, container=True, interactive=True, choices=FILTER_NAMES, value=DEFAULT_FILTER_NAME, label="Filter Type", ) with gr.Row(visible=True): collage_style_selection = gr.Radio( show_label=True, container=True, interactive=True, choices=COLLAGE_STYLE_NAMES, value=DEFAULT_COLLAGE_STYLE_NAME, label="Collage Template + Duotone Canvas", ) with gr.Row(visible=True): style_selection = gr.Radio( show_label=True, container=True, interactive=True, choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME, label="Quality Style", ) with gr.Accordion("Advanced options", open=False): use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True, visible=True) negative_prompt = gr.Text( label="Negative prompt", max_lines=1, placeholder="Enter a negative prompt", value="(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation", visible=True, ) with gr.Row(): num_inference_steps = gr.Slider( label="Steps", minimum=10, maximum=30, step=1, value=15, ) with gr.Row(): num_images_per_prompt = gr.Slider( label="Images", minimum=1, maximum=5, step=1, value=2, ) seed = gr.Slider( label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, visible=True ) randomize_seed = gr.Checkbox(label="Randomize seed", value=True) with gr.Row(visible=True): width = gr.Slider( label="Width", minimum=512, maximum=2048, step=8, value=1024, ) height = gr.Slider( label="Height", minimum=512, maximum=2048, step=8, value=1024, ) with gr.Row(): guidance_scale = gr.Slider( label="Guidance Scale", minimum=0.1, maximum=20.0, step=0.1, value=6, ) gr.Examples( examples=examples, inputs=prompt, outputs=[result, seed], fn=generate, #cache_examples=True, cache_examples=CACHE_EXAMPLES, ) use_negative_prompt.change( fn=lambda x: gr.update(visible=x), inputs=use_negative_prompt, outputs=negative_prompt, api_name=False, ) gr.on( triggers=[ prompt.submit, negative_prompt.submit, run_button.click, ], fn=generate, inputs=[ prompt, negative_prompt, use_negative_prompt, style_selection, collage_style_selection, filter_selection, grid_size_selection, seed, width, height, guidance_scale, randomize_seed, ], outputs=[result, seed], api_name="run", ) gr.Markdown("### Duotone Canvas") predefined_gallery = gr.Gallery(label="Duotone Canvas", columns=3, show_label=False, value=load_predefined_images1()) gr.Markdown("### Image Gallery") predefined_gallery = gr.Gallery(label="Image Gallery", columns=3, show_label=False, value=load_predefined_images()) gr.Markdown(DESCRIPTIONy) gr.Markdown("**Disclaimer/Note:**") gr.Markdown("This is the demo space for generating images using Stable Diffusion with grids, filters, templates, quality styles, and types. Try the sample prompts to generate higher quality images. Try the sample prompts for generating higher quality images.Try prompts.") #gr.HTML(html_content) if __name__ == "__main__": demo.queue(max_size=40).launch()