| |
| import gradio as gr |
| import random |
| import spaces |
| import torch |
| import uuid |
| import os |
|
|
| from diffusers import StableDiffusionXLPipeline, ControlNetModel |
| from diffusers.models import AutoencoderKL |
|
|
| |
| DEVICE = "auto" |
| if DEVICE == "auto": |
| DEVICE = "cuda" if torch.cuda.is_available() else "cpu" |
| print(f"[SYSTEM] | Using {DEVICE} type compute device.") |
|
|
| |
| MAX_SEED = 9007199254740991 |
| DEFAULT_INPUT = "" |
| DEFAULT_NEGATIVE_INPUT = "EasyNegative, deformed, distorted, disfigured, disconnected, disgusting, mutation, mutated, blur, blurry, scribble, abstract, watermark, ugly, amputation, limb, limbs, leg, legs, foot, feet, toe, toes, arm, arms, hand, hands, finger, fingers, head, heads, exposed, porn, nude, nudity, naked, nsfw" |
| DEFAULT_HEIGHT = 1024 |
| DEFAULT_WIDTH = 1024 |
|
|
| REPO = "hsalf-lxds/ytinummoc-ds"[::-1] |
|
|
| vae = AutoencoderKL.from_pretrained("xif-61pf-eav-lxds/nilloybedam"[::-1], torch_dtype=torch.float16) |
| controlnet = ControlNetModel.from_pretrained("k031-sdnah-dedocne-tenlortnoc/naPikaM"[::-1], torch_dtype=torch.float16) |
|
|
| model = StableDiffusionXLPipeline.from_pretrained(REPO, vae=vae, controlnet=controlnet, torch_dtype=torch.float16, use_safetensors=True, add_watermarker=False) |
| model.load_lora_weights("2v-lx-3-ellad/urofotsirhe"[::-1], adapter_name="base") |
| model.set_adapters(["base"], adapter_weights=[0.7]) |
| model.to(DEVICE) |
|
|
| css = ''' |
| .gradio-container{max-width: 560px !important} |
| h1{text-align:center} |
| footer { |
| visibility: hidden |
| } |
| ''' |
|
|
| |
| def save_image(img, seed): |
| name = f"{seed}-{uuid.uuid4()}.png" |
| img.save(name) |
| return name |
| |
| def get_seed(seed): |
| seed = seed.strip() |
| if seed.isdigit(): |
| return int(seed) |
| else: |
| return random.randint(0, MAX_SEED) |
|
|
| @spaces.GPU(duration=30) |
| def generate(input=DEFAULT_INPUT, negative_input=DEFAULT_NEGATIVE_INPUT, height=DEFAULT_HEIGHT, width=DEFAULT_WIDTH, steps=1, guidance=0, number=1, seed=None): |
| |
| seed = get_seed(seed) |
|
|
| print(input, negative_input, height, width, steps, guidance, number, seed) |
|
|
| model.to(DEVICE) |
| parameters = { |
| "prompt": input, |
| "negative_prompt": negative_input, |
| "height": height, |
| "width": width, |
| "num_inference_steps": steps, |
| "guidance_scale": guidance, |
| "num_images_per_prompt": number, |
| "controlnet_conditioning_scale": 1, |
| "cross_attention_kwargs": {"scale": 1}, |
| "generator": torch.Generator().manual_seed(seed), |
| "use_resolution_binning": True, |
| "output_type":"pil", |
| } |
| |
| images = model(**parameters).images |
| image_paths = [save_image(img, seed) for img in images] |
| print(image_paths) |
| return image_paths |
|
|
| def cloud(): |
| print("[CLOUD] | Space maintained.") |
|
|
|
|
| |
| with gr.Blocks(css=css) as main: |
| with gr.Column(): |
| gr.Markdown("🪄 Generate high quality images on all styles between 10 to 20 seconds.") |
| |
| with gr.Column(): |
| input = gr.Textbox(lines=1, value=DEFAULT_INPUT, label="Input") |
| negative_input = gr.Textbox(lines=1, value=DEFAULT_NEGATIVE_INPUT, label="Input Negative") |
| height = gr.Slider(minimum=1, maximum=2160, step=1, value=DEFAULT_HEIGHT, label="Height") |
| width = gr.Slider(minimum=1, maximum=2160, step=1, value=DEFAULT_WIDTH, label="Width") |
| steps = gr.Slider(minimum=0, maximum=100, step=1, value=16, label="Steps") |
| guidance = gr.Slider(minimum=0, maximum=100, step=0.001, value=3, label = "Guidance") |
| number = gr.Slider(minimum=1, maximum=4, step=1, value=1, label="Number") |
| seed = gr.Textbox(lines=1, value="", label="Seed (Blank for random)") |
| submit = gr.Button("▶") |
| maintain = gr.Button("☁️") |
|
|
| with gr.Column(): |
| images = gr.Gallery(columns=1, label="Image") |
| |
| submit.click(generate, inputs=[input, negative_input, height, width, steps, guidance, number, seed], outputs=[images], queue=False) |
| maintain.click(cloud, inputs=[], outputs=[], queue=False) |
|
|
| main.launch(show_api=True) |