| from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler | |
| import gradio as gr | |
| import torch | |
| from PIL import Image | |
| model_id = 'Fictiverse/Stable_Diffusion_PaperCut_Model' | |
| prefix = 'PaperCut,' | |
| scheduler = DPMSolverMultistepScheduler.from_pretrained(model_id, subfolder="scheduler") | |
| 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 _parse_args(prompt, generator): | |
| parser = argparse.ArgumentParser( | |
| description="making it work." | |
| ) | |
| parser.add_argument( | |
| "--no-half-vae", help="no half vae" | |
| ) | |
| cmdline_args = parser.parse_args() | |
| command = cmdline_args.command | |
| conf_file = cmdline_args.conf_file | |
| conf_args = Arguments(conf_file) | |
| opt = conf_args.readArguments() | |
| if cmdline_args.config_overrides: | |
| for config_override in cmdline_args.config_overrides.split(";"): | |
| config_override = config_override.strip() | |
| if config_override: | |
| var_val = config_override.split("=") | |
| assert ( | |
| len(var_val) == 2 | |
| ), f"Config override '{var_val}' does not have the form 'VAR=val'" | |
| conf_args.add_opt(opt, var_val[0], var_val[1], force_override=True) | |
| 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 result.images[0] | |
| 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 result.images[0] | |
| def fake_safety_checker(images, **kwargs): | |
| return result.images[0], [False] * len(images) | |
| pipe.safety_checker = fake_safety_checker | |
| 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 style="color:green;">✂️ PaperCut V1.0 📃</h1> | |
| </div> | |
| <p> | |
| Demo for <a href="https://huggingface.co/Fictiverse/Stable_Diffusion_PaperCut_Model">PaperCut V1.0</a> | |
| Stable Diffusion model by <a href="https://huggingface.co/Fictiverse/"><abbr title="Fictiverse">Fictiverse</abbr></a>. {"" if prefix else ""} | |
| Running on {"<b>GPU 🔥</b>" if torch.cuda.is_available() else f"<b>CPU ⚡</b>"}. | |
| </p> | |
| <p>Please use the prompt template below to achieve the desired result: | |
| </p> | |
| <b>Prompt</b>: | |
| <details><code> | |
| PaperCut, <b>* the subject * </b>, | |
| <br> | |
| <br> | |
| <q><i>Example: | |
| </i></q> | |
| </code></details> | |
| <br> | |
| <b>Negative Prompt</b>: | |
| <details><code> | |
| lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, out of frame, extra fingers, deformed, blurry, bad anatomy, bad proportions, extra limbs, cloned face, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature | |
| </code></details> | |
| <br> | |
| Have Fun & Enjoy ⚡ <a href="https://www.thafx.com"><abbr title="Website">//THAFX</abbr></a> | |
| <br> | |
| </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 (mdjrny-v4 style,)", 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) | |
| demo.queue(concurrency_count=1) | |
| demo.launch() | |