Spaces:
Running
on
Zero
Running
on
Zero
| from diffusers import ( | |
| StableDiffusionPipeline, | |
| DPMSolverMultistepScheduler, | |
| DiffusionPipeline, | |
| ) | |
| import gradio as gr | |
| import torch | |
| from PIL import Image | |
| import time | |
| import psutil | |
| import random | |
| from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker | |
| start_time = time.time() | |
| current_steps = 25 | |
| SAFETY_CHECKER = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker", torch_dtype=torch.float16) | |
| UPSCALER = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16) | |
| UPSCALER.to("cuda") | |
| UPSCALER.enable_xformers_memory_efficient_attention() | |
| class Model: | |
| def __init__(self, name, path=""): | |
| self.name = name | |
| self.path = path | |
| if path != "": | |
| self.pipe_t2i = StableDiffusionPipeline.from_pretrained( | |
| path, torch_dtype=torch.float16, safety_checker=SAFETY_CHECKER | |
| ) | |
| self.pipe_t2i.scheduler = DPMSolverMultistepScheduler.from_config( | |
| self.pipe_t2i.scheduler.config | |
| ) | |
| else: | |
| self.pipe_t2i = None | |
| models = [ | |
| Model("Stable Diffusion v1-4", "CompVis/stable-diffusion-v1-4"), | |
| # Model("Stable Diffusion v1-5", "runwayml/stable-diffusion-v1-5"), | |
| # Model("anything-v4.0", "andite/anything-v4.0"), | |
| ] | |
| MODELS = {m.name: m for m in models} | |
| device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶" | |
| def error_str(error, title="Error"): | |
| return ( | |
| f"""#### {title} | |
| {error}""" | |
| if error | |
| else "" | |
| ) | |
| def inference( | |
| model_name, | |
| prompt, | |
| guidance, | |
| steps, | |
| seed=0, | |
| neg_prompt="", | |
| ): | |
| print(psutil.virtual_memory()) # print memory usage | |
| if seed == 0: | |
| seed = random.randint(0, 2147483647) | |
| generator = torch.Generator("cuda").manual_seed(seed) | |
| try: | |
| low_res_image, up_res_image = txt_to_img( | |
| model_name, | |
| prompt, | |
| neg_prompt, | |
| guidance, | |
| steps, | |
| generator, | |
| ) | |
| return low_res_image, up_res_image, f"Done. Seed: {seed}", | |
| except Exception as e: | |
| return None, None, error_str(e) | |
| def txt_to_img( | |
| model_name, | |
| prompt, | |
| neg_prompt, | |
| guidance, | |
| steps, | |
| generator, | |
| ): | |
| pipe = MODELS[model_name].pipe_t2i | |
| if torch.cuda.is_available(): | |
| pipe = pipe.to("cuda") | |
| pipe.enable_xformers_memory_efficient_attention() | |
| low_res_latents = pipe( | |
| prompt, | |
| negative_prompt=neg_prompt, | |
| num_inference_steps=int(steps), | |
| guidance_scale=guidance, | |
| generator=generator, | |
| output_type="latent", | |
| ).images | |
| with torch.no_grad(): | |
| low_res_image = pipe.decode_latents(low_res_latents) | |
| low_res_image = pipe.numpy_to_pil(low_res_image) | |
| up_res_image = UPSCALER( | |
| prompt=prompt, | |
| negative_prompt=neg_prompt, | |
| image=low_res_latents, | |
| num_inference_steps=20, | |
| guidance_scale=0, | |
| generator=generator, | |
| ).images | |
| pipe.to("cpu") | |
| torch.cuda.empty_cache() | |
| return low_res_image[0], up_res_image[0] | |
| 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 | |
| with gr.Blocks(css="style.css") as demo: | |
| gr.HTML( | |
| f""" | |
| <div class="finetuned-diffusion-div"> | |
| <div> | |
| <h1>Stable Diffusion Latent Upscaler</h1> | |
| </div> | |
| <p> | |
| Demo for <a href="https://huggingface.co/stabilityai/sd-x2-latent-upscaler">Latent Diffusion Upscaling</a> | |
| </p> | |
| <p> | |
| Running on <b>{device}</b> | |
| </p> | |
| <p>You can also duplicate this space and upgrade to gpu by going to settings:<br> | |
| <a style="display:inline-block" href="https://huggingface.co/spaces/patrickvonplaten/finetuned_diffusion?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></p> | |
| </div> | |
| """ | |
| ) | |
| with gr.Row(): | |
| with gr.Column(scale=55): | |
| with gr.Group(): | |
| model_name = gr.Dropdown( | |
| label="Model", | |
| choices=[m.name for m in models], | |
| value=models[0].name, | |
| ) | |
| with gr.Row(): | |
| prompt = gr.Textbox( | |
| label="Prompt", | |
| show_label=False, | |
| max_lines=2, | |
| placeholder="Enter prompt.", | |
| ).style(container=False) | |
| generate = gr.Button(value="Generate").style( | |
| rounded=(False, True, True, False) | |
| ) | |
| low_res_image = gr.Image(label="512px Image", shape=(512, 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", | |
| ) | |
| with gr.Row(): | |
| guidance = gr.Slider( | |
| label="Guidance scale", value=7.5, maximum=15 | |
| ) | |
| steps = gr.Slider( | |
| label="Steps", | |
| value=current_steps, | |
| minimum=2, | |
| maximum=75, | |
| step=1, | |
| ) | |
| seed = gr.Slider( | |
| 0, 2147483647, label="Seed (0 = random)", value=0, step=1 | |
| ) | |
| up_res_image = gr.Image(label="1024px Image", shape=(1024, 1024)) | |
| inputs = [ | |
| model_name, | |
| prompt, | |
| guidance, | |
| steps, | |
| seed, | |
| neg_prompt, | |
| ] | |
| outputs = [low_res_image, up_res_image, error_output] | |
| prompt.submit(inference, inputs=inputs, outputs=outputs) | |
| generate.click(inference, inputs=inputs, outputs=outputs) | |
| # ex = gr.Examples( | |
| # [ | |
| # [models[0].name, "a photo of an astronaut high resolution, unreal engine, ultra realistic", 7.5, 50, 33, ""] | |
| # ], | |
| # inputs=[model_name, prompt, guidance, steps, seed, neg_prompt], | |
| # outputs=outputs, | |
| # fn=inference, | |
| # cache_examples=False, | |
| # ) | |
| gr.HTML( | |
| """ | |
| <div style="border-top: 1px solid #303030;"> | |
| <br> | |
| <p>Models by 🤗 Hugging Face and others. ❤️</p> | |
| <p>This space uses the <a href="https://github.com/LuChengTHU/dpm-solver">DPM-Solver++</a> sampler by <a href="https://arxiv.org/abs/2206.00927">Cheng Lu, et al.</a>.</p> | |
| <p>This is a Demo Space For:<br> | |
| <a href="https://huggingface.co/stabilityai/sd-x2-latent-upscaler">Stability AI's Latent Upscaler</a> | |
| </div> | |
| """ | |
| ) | |
| print(f"Space built in {time.time() - start_time:.2f} seconds") | |
| demo.queue(concurrency_count=1) | |
| demo.launch() | |