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| from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler | |
| import gradio as gr | |
| import torch | |
| from PIL import Image | |
| model_id = 'MirageML/lowpoly-environment' | |
| prefix = 'lowpoly_environment' | |
| scheduler = DPMSolverMultistepScheduler( | |
| beta_start=0.00085, | |
| beta_end=0.012, | |
| beta_schedule="scaled_linear", | |
| num_train_timesteps=1000, | |
| trained_betas=None, | |
| predict_epsilon=True, | |
| thresholding=False, | |
| algorithm_type="dpmsolver++", | |
| solver_type="midpoint", | |
| lower_order_final=True, | |
| ) | |
| pipe = StableDiffusionPipeline.from_pretrained( | |
| model_id, | |
| torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32, | |
| scheduler=scheduler) | |
| pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained( | |
| model_id, | |
| torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32, | |
| scheduler=scheduler) | |
| if torch.cuda.is_available(): | |
| pipe = pipe.to("cuda") | |
| pipe_i2i = pipe_i2i.to("cuda") | |
| def error_str(error, title="Error"): | |
| return f"""#### {title} | |
| {error}""" if error else "" | |
| def inference(prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt="", auto_prefix=False): | |
| generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None | |
| prompt = f"{prefix} {prompt}" if auto_prefix else prompt | |
| try: | |
| if img is not None: | |
| return img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator), None | |
| else: | |
| return txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator), None | |
| except Exception as e: | |
| return None, error_str(e) | |
| def txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator): | |
| result = pipe( | |
| prompt, | |
| negative_prompt = neg_prompt, | |
| num_inference_steps = int(steps), | |
| guidance_scale = guidance, | |
| width = width, | |
| height = height, | |
| generator = generator) | |
| return replace_nsfw_images(result) | |
| def img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator): | |
| ratio = min(height / img.height, width / img.width) | |
| img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS) | |
| result = pipe_i2i( | |
| prompt, | |
| negative_prompt = neg_prompt, | |
| init_image = img, | |
| num_inference_steps = int(steps), | |
| strength = strength, | |
| guidance_scale = guidance, | |
| width = width, | |
| height = height, | |
| generator = generator) | |
| return replace_nsfw_images(result) | |
| def replace_nsfw_images(results): | |
| for i in range(len(results.images)): | |
| if results.nsfw_content_detected[i]: | |
| results.images[i] = Image.open("nsfw.png") | |
| return results.images[0] | |
| css = """.main-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.main-div div h1{font-weight:900;margin-bottom:7px}.main-div p{margin-bottom:10px;font-size:94%}a{text-decoration:underline}.tabs{margin-top:0;margin-bottom:0}#gallery{min-height:20rem} | |
| """ | |
| with gr.Blocks(css=css) as demo: | |
| gr.HTML( | |
| f""" | |
| <div class="main-div"> | |
| <div> | |
| <h1>Lowpoly Environment</h1> | |
| </div> | |
| <p> | |
| Demo for <a href="https://huggingface.co/MirageML/lowpoly-environment">Lowpoly Environment</a> Stable Diffusion model.<br> | |
| {"Add the following tokens to your prompts for the model to work properly: <b>prefix</b>" if prefix else ""} | |
| </p> | |
| Running on {"<b>GPU 🔥</b>" if torch.cuda.is_available() else f"<b>CPU 🥶</b>. For faster inference it is recommended to <b>upgrade to GPU in <a href='https://huggingface.co/spaces/amankishore/lowpoly-environment/settings'>Settings</a></b>"}<br><br> | |
| <a style="display:inline-block" href="https://huggingface.co/spaces/amankishore/lowpoly-environment?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a> | |
| </div> | |
| """ | |
| ) | |
| with gr.Row(): | |
| with gr.Column(scale=55): | |
| with gr.Group(): | |
| with gr.Row(): | |
| prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder=f"{prefix} [your prompt]").style(container=False) | |
| generate = gr.Button(value="Generate").style(rounded=(False, True, True, False)) | |
| image_out = gr.Image(height=512) | |
| error_output = gr.Markdown() | |
| with gr.Column(scale=45): | |
| with gr.Tab("Options"): | |
| with gr.Group(): | |
| neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image") | |
| auto_prefix = gr.Checkbox(label="Prefix styling tokens automatically (lowpoly_environment)", value=prefix, visible=prefix) | |
| with gr.Row(): | |
| guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15) | |
| steps = gr.Slider(label="Steps", value=25, minimum=2, maximum=75, step=1) | |
| with gr.Row(): | |
| width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8) | |
| height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8) | |
| seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1) | |
| with gr.Tab("Image to image"): | |
| with gr.Group(): | |
| image = gr.Image(label="Image", height=256, tool="editor", type="pil") | |
| strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5) | |
| auto_prefix.change(lambda x: gr.update(placeholder=f"{prefix} [your prompt]" if x else "[Your prompt]"), inputs=auto_prefix, outputs=prompt, queue=False) | |
| inputs = [prompt, guidance, steps, width, height, seed, image, strength, neg_prompt, auto_prefix] | |
| outputs = [image_out, error_output] | |
| prompt.submit(inference, inputs=inputs, outputs=outputs) | |
| generate.click(inference, inputs=inputs, outputs=outputs) | |
| gr.HTML(""" | |
| <div style="border-top: 1px solid #303030;"> | |
| <br> | |
| <p>This space was created using <a href="https://huggingface.co/spaces/anzorq/sd-space-creator">SD Space Creator</a>.</p> | |
| </div> | |
| """) | |
| demo.queue(concurrency_count=1) | |
| demo.launch() | |