import os import random import uuid import json import gradio as gr import numpy as np from PIL import Image import spaces import torch from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler #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 = "https://prithivhamster.vercel.app/" #html_content = f'' DESCRIPTIONx = """## STABLE HAMSTER 🐹 """ DESCRIPTIONy = """

badge-github-stars

""" css = ''' .gradio-container{max-width: 560px !important} h1{text-align:center} footer { visibility: hidden } ''' examples = [ "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", "Vector illustration of a horse, vector graphic design with flat colors on an brown background in the style of vector art, using simple shapes and graphics with simple details, professionally designed as a tshirt logo ready for print on a white background. --ar 89:82 --v 6.0 --style raw", "Man in brown leather jacket posing for camera, in the style of sleek and stylized, clockpunk, subtle shades, exacting precision, ferrania p30 --ar 67:101 --v 5", "Commercial photography, giant burger, white lighting, studio light, 8k octane rendering, high resolution photography, insanely detailed, fine details, on white isolated plain, 8k, commercial photography, stock photo, professional color grading, --v 4 --ar 9:16 " ] #examples = [ # ["file/1.png", "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)"], # ["file/2.png", "Cold coffee in a cup bokeh --ar 85:128 --v 6.0 --style raw5, 4K"], #["file/3.png", "Vector illustration of a horse, vector graphic design with flat colors on a brown background in the style of vector art, using simple shapes and graphics with simple details, professionally designed as a tshirt logo ready for print on a white background. --ar 89:82 --v 6.0 --style raw"], #["file/4.png", "Man in brown leather jacket posing for the camera, in the style of sleek and stylized, clockpunk, subtle shades, exacting precision, ferrania p30 --ar 67:101 --v 5"], #["file/5.png", "Commercial photography, giant burger, white lighting, studio light, 8k octane rendering, high resolution photography, insanely detailed, fine details, on a white isolated plain, 8k, commercial photography, stock photo, professional color grading, --v 4 --ar 9:16"] #] #Set an os.Getenv variable #set VAR_NAME=”VALUE” #Fetch an environment variable #echo %VAR_NAME% MODEL_ID = os.getenv("MODEL_REPO") #Use SDXL Model as "MODEL_REPO" --------->>> ”VALUE”. MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "4096")) USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1" ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1" BATCH_SIZE = int(os.getenv("BATCH_SIZE", "1")) # Allow generating multiple images at once #Load model outside of function device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") pipe = StableDiffusionXLPipeline.from_pretrained( MODEL_ID, torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32, use_safetensors=True, add_watermarker=False, ).to(device) pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) # if USE_TORCH_COMPILE: pipe.compile() # Offloading capacity (RAM) if ENABLE_CPU_OFFLOAD: pipe.enable_model_cpu_offload() MAX_SEED = np.iinfo(np.int32).max def save_image(img): unique_name = str(uuid.uuid4()) + ".png" img.save(unique_name) return unique_name def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: if randomize_seed: seed = random.randint(0, MAX_SEED) return seed @spaces.GPU(duration=60, enable_queue=True) def generate( prompt: str, negative_prompt: str = "", use_negative_prompt: bool = False, seed: int = 1, width: int = 1024, height: int = 1024, guidance_scale: float = 3, num_inference_steps: int = 25, randomize_seed: bool = False, use_resolution_binning: bool = True, num_images: int = 1, # Number of images to generate progress=gr.Progress(track_tqdm=True), ): seed = int(randomize_seed_fn(seed, randomize_seed)) generator = torch.Generator(device=device).manual_seed(seed) #Options options = { "prompt": [prompt] * num_images, "negative_prompt": [negative_prompt] * num_images if use_negative_prompt else None, "width": width, "height": height, "guidance_scale": guidance_scale, "num_inference_steps": num_inference_steps, "generator": generator, "output_type": "pil", } #VRAM usage Lesser if use_resolution_binning: options["use_resolution_binning"] = True #Images potential batches images = [] for i in range(0, num_images, BATCH_SIZE): batch_options = options.copy() batch_options["prompt"] = options["prompt"][i:i+BATCH_SIZE] if "negative_prompt" in batch_options: batch_options["negative_prompt"] = options["negative_prompt"][i:i+BATCH_SIZE] images.extend(pipe(**batch_options).images) image_paths = [save_image(img) for img in images] return image_paths, seed #Main gr.Block with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo: gr.Markdown(DESCRIPTIONx) 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", scale=0) result = gr.Gallery(label="Result", columns=1, show_label=False) with gr.Accordion("Advanced options", open=False, visible=False): num_images = gr.Slider( label="Number of Images", minimum=1, maximum=4, step=1, value=1, ) with gr.Row(): use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True) negative_prompt = gr.Text( label="Negative prompt", max_lines=5, lines=4, 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, ) seed = gr.Slider( label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, ) randomize_seed = gr.Checkbox(label="Randomize seed", value=True) with gr.Row(visible=True): width = gr.Slider( label="Width", minimum=512, maximum=MAX_IMAGE_SIZE, step=64, value=1024, ) height = gr.Slider( label="Height", minimum=512, maximum=MAX_IMAGE_SIZE, step=64, value=1024, ) with gr.Row(): guidance_scale = gr.Slider( label="Guidance Scale", minimum=0.1, maximum=6, step=0.1, value=3.0, ) num_inference_steps = gr.Slider( label="Number of inference steps", minimum=1, maximum=25, step=1, value=23, ) gr.Examples( examples=examples, inputs=prompt, cache_examples=False ) 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, seed, width, height, guidance_scale, num_inference_steps, randomize_seed, num_images ], outputs=[result, seed], api_name="run", ) gr.Markdown(DESCRIPTIONy) gr.Markdown("**Disclaimer:**") gr.Markdown("⚠️ users are accountable for the content they generate and are responsible for ensuring it meets appropriate ethical standards.") #gr.HTML(html_content) if __name__ == "__main__": demo.queue(max_size=40).launch()