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| from diffusers import ( | |
| StableDiffusionPipeline, | |
| StableDiffusionImg2ImgPipeline, | |
| DPMSolverMultistepScheduler, | |
| ) | |
| 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) | |
| 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 | |
| ) | |
| self.pipe_i2i = StableDiffusionImg2ImgPipeline(**self.pipe_t2i.components, safety_checker=SAFETY_CHECKER) | |
| else: | |
| self.pipe_t2i = None | |
| self.pipe_i2i = None | |
| models = [ | |
| Model("2.2", "darkstorm2150/Protogen_v2.2_Official_Release"), | |
| Model("3.4", "darkstorm2150/Protogen_x3.4_Official_Release"), | |
| Model("5.3", "darkstorm2150/Protogen_v5.3_Official_Release"), | |
| Model("5.8", "darkstorm2150/Protogen_x5.8_Official_Release"), | |
| Model("Dragon", "darkstorm2150/Protogen_Dragon_Official_Release"), | |
| ] | |
| 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, | |
| n_images=1, | |
| width=512, | |
| height=512, | |
| seed=0, | |
| img=None, | |
| strength=0.5, | |
| 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: | |
| if img is not None: | |
| return ( | |
| img_to_img( | |
| model_name, | |
| prompt, | |
| n_images, | |
| neg_prompt, | |
| img, | |
| strength, | |
| guidance, | |
| steps, | |
| width, | |
| height, | |
| generator, | |
| seed, | |
| ), | |
| f"Done. Seed: {seed}", | |
| ) | |
| else: | |
| return ( | |
| txt_to_img( | |
| model_name, | |
| prompt, | |
| n_images, | |
| neg_prompt, | |
| guidance, | |
| steps, | |
| width, | |
| height, | |
| generator, | |
| seed, | |
| ), | |
| f"Done. Seed: {seed}", | |
| ) | |
| except Exception as e: | |
| return None, error_str(e) | |
| def txt_to_img( | |
| model_name, | |
| prompt, | |
| n_images, | |
| neg_prompt, | |
| guidance, | |
| steps, | |
| width, | |
| height, | |
| generator, | |
| seed, | |
| ): | |
| pipe = MODELS[model_name].pipe_t2i | |
| if torch.cuda.is_available(): | |
| pipe = pipe.to("cuda") | |
| pipe.enable_xformers_memory_efficient_attention() | |
| result = pipe( | |
| prompt, | |
| negative_prompt=neg_prompt, | |
| num_images_per_prompt=n_images, | |
| num_inference_steps=int(steps), | |
| guidance_scale=guidance, | |
| width=width, | |
| height=height, | |
| generator=generator, | |
| ) | |
| pipe.to("cpu") | |
| return replace_nsfw_images(result) | |
| def img_to_img( | |
| model_name, | |
| prompt, | |
| n_images, | |
| neg_prompt, | |
| img, | |
| strength, | |
| guidance, | |
| steps, | |
| width, | |
| height, | |
| generator, | |
| seed, | |
| ): | |
| pipe = MODELS[model_name].pipe_i2i | |
| if torch.cuda.is_available(): | |
| pipe = pipe.to("cuda") | |
| pipe.enable_xformers_memory_efficient_attention() | |
| ratio = min(height / img.height, width / img.width) | |
| img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS) | |
| result = pipe( | |
| prompt, | |
| negative_prompt=neg_prompt, | |
| num_images_per_prompt=n_images, | |
| image=img, | |
| num_inference_steps=int(steps), | |
| strength=strength, | |
| guidance_scale=guidance, | |
| generator=generator, | |
| ) | |
| pipe.to("cpu") | |
| 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 | |
| with gr.Blocks(css="style.css") as demo: | |
| gr.HTML( | |
| """ | |
| <div class="finetuned-diffusion-div"> | |
| <div> | |
| <h1>Protogen Diffusion</h1> | |
| </div> | |
| <p> | |
| Demo for multiple fine-tuned Protogen Stable Diffusion models. | |
| </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.Box(visible=False) as custom_model_group: | |
| custom_model_path = gr.Textbox( | |
| label="Custom model path", | |
| placeholder="Path to model, e.g. darkstorm2150/Protogen_x3.4_Official_Release", | |
| interactive=True, | |
| ) | |
| gr.HTML( | |
| "<div><font size='2'>Custom models have to be downloaded first, so give it some time.</font></div>" | |
| ) | |
| 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) | |
| ) | |
| # image_out = gr.Image(height=512) | |
| gallery = gr.Gallery( | |
| label="Generated images", show_label=False, elem_id="gallery" | |
| ).style(grid=[2], height="auto") | |
| state_info = gr.Textbox(label="State", show_label=False, max_lines=2).style( | |
| container=False | |
| ) | |
| 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", | |
| ) | |
| n_images = gr.Slider( | |
| label="Images", value=1, minimum=1, maximum=4, step=1 | |
| ) | |
| 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, | |
| ) | |
| 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, | |
| ) | |
| inputs = [ | |
| model_name, | |
| prompt, | |
| guidance, | |
| steps, | |
| n_images, | |
| width, | |
| height, | |
| seed, | |
| image, | |
| strength, | |
| neg_prompt, | |
| ] | |
| outputs = [gallery, error_output] | |
| prompt.submit(inference, inputs=inputs, outputs=outputs) | |
| generate.click(inference, inputs=inputs, outputs=outputs) | |
| ex = gr.Examples( | |
| [ | |
| [models[0].name, "portrait of a beautiful alyx vance half life", 10, 25], | |
| [models[1].name, "Brad Pitt with sunglasses, highly realistic", 7.5, 25], | |
| ], | |
| inputs=[model_name, prompt, guidance, steps], | |
| outputs=outputs, | |
| fn=inference, | |
| cache_examples=False, | |
| ) | |
| gr.HTML( | |
| """ | |
| <div style="border-top: 1px solid #303030;"> | |
| <br> | |
| <p>Models by <a href="https://huggingface.co/darkstorm2150">@darkstorm2150</a> 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>Space by: Darkstorm (Victor Espinoza)<br> | |
| <a href="https://www.instagram.com/officialvictorespinoza/">Instagram</a> | |
| </div> | |
| """ | |
| ) | |
| print(f"Space built in {time.time() - start_time:.2f} seconds") | |
| demo.queue(concurrency_count=1) | |
| demo.launch() | |