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from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler |
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import gradio as gr |
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import torch |
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from PIL import Image |
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model_id = 'SG161222/Realistic_Vision_V5.0_noVAE' |
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prefix = 'RAW photo,' |
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scheduler = DPMSolverMultistepScheduler.from_pretrained(model_id, subfolder="scheduler") |
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pipe = StableDiffusionPipeline.from_pretrained( |
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model_id, |
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torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32, |
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scheduler=scheduler) |
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pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained( |
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model_id, |
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torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32, |
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scheduler=scheduler) |
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if torch.cuda.is_available(): |
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pipe = pipe.to("cuda") |
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pipe_i2i = pipe_i2i.to("cuda") |
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def error_str(error, title="Error"): |
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return f"""#### {title} |
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{error}""" if error else "" |
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def _parse_args(prompt, generator): |
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parser = argparse.ArgumentParser( |
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description="making it work." |
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) |
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parser.add_argument( |
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"--no-half-vae", help="no half vae" |
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) |
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cmdline_args = parser.parse_args() |
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command = cmdline_args.command |
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conf_file = cmdline_args.conf_file |
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conf_args = Arguments(conf_file) |
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opt = conf_args.readArguments() |
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if cmdline_args.config_overrides: |
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for config_override in cmdline_args.config_overrides.split(";"): |
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config_override = config_override.strip() |
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if config_override: |
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var_val = config_override.split("=") |
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assert ( |
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len(var_val) == 2 |
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), f"Config override '{var_val}' does not have the form 'VAR=val'" |
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conf_args.add_opt(opt, var_val[0], var_val[1], force_override=True) |
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def inference(prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt="", auto_prefix=False): |
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generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None |
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prompt = f"{prefix} {prompt}" if auto_prefix else prompt |
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try: |
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if img is not None: |
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return img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator), None |
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else: |
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return txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator), None |
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except Exception as e: |
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return None, error_str(e) |
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def txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator): |
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result = pipe( |
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prompt, |
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negative_prompt = neg_prompt, |
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num_inference_steps = int(steps), |
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guidance_scale = guidance, |
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width = width, |
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height = height, |
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generator = generator) |
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return result.images[0] |
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def img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator): |
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ratio = min(height / img.height, width / img.width) |
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img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS) |
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result = pipe_i2i( |
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prompt, |
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negative_prompt = neg_prompt, |
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init_image = img, |
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num_inference_steps = int(steps), |
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strength = strength, |
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guidance_scale = guidance, |
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width = width, |
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height = height, |
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generator = generator) |
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return result.images[0] |
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def fake_safety_checker(images, **kwargs): |
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return result.images[0], [False] * len(images) |
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pipe.safety_checker = fake_safety_checker |
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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} |
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""" |
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with gr.Blocks(css=css) as demo: |
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gr.HTML( |
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f""" |
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<div class="main-div"> |
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<div> |
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<h1 style="color:orange;">📷 Realistic Vision V5.0 📸</h1> |
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</div> |
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<p> |
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Demo for <a href="https://huggingface.co/SG161222/Realistic_Vision_V5.0_noVAE">Realistic Vision V5.0</a> |
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Stable Diffusion model by <a href="https://huggingface.co/SG161222/"><abbr title="SG1611222">Eugene</abbr></a>. {"" if prefix else ""} |
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Running on {"<b>GPU 🔥</b>" if torch.cuda.is_available() else f"<b>CPU ⚡</b>"}. |
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</p> |
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<p>Please use the prompt template below to get an example of the desired generation results: |
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</p> |
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<b>Prompt</b>: |
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<details><code> |
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* subject *, (high detailed skin:1.2), 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3 |
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<br> |
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<br> |
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<q><i> |
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Example: a close up portrait photo of 26 y.o woman in wastelander clothes, long haircut, pale skin, slim body, background is city ruins, <br> |
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(high detailed skin:1.2), 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3 |
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</i></q> |
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</code></details> |
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<br> |
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<b>Negative Prompt</b>: |
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<details><code> |
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(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime:1.4), text, close up, cropped, out of frame, worst quality, <br> |
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low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, <br> |
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dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, <br> |
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extra legs, fused fingers, too many fingers, long neck |
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</code></details> |
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<br> |
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Have Fun & Enjoy ⚡ <a href="https://www.thafx.com"><abbr title="Website">//THAFX</abbr></a> |
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<br> |
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</div> |
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""" |
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) |
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with gr.Row(): |
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with gr.Column(scale=55): |
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with gr.Group(): |
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with gr.Row(): |
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prompt = gr.Textbox(label="Prompt", show_label=False,max_lines=2,placeholder=f"{prefix} [your prompt]").style(container=False) |
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generate = gr.Button(value="Generate").style(rounded=(False, True, True, False)) |
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image_out = gr.Image(height=512) |
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error_output = gr.Markdown() |
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with gr.Column(scale=45): |
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with gr.Tab("Options"): |
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with gr.Group(): |
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neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image") |
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auto_prefix = gr.Checkbox(label="Prefix styling tokens automatically (RAW photo,)", value=prefix, visible=prefix) |
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with gr.Row(): |
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guidance = gr.Slider(label="Guidance scale", value=5, maximum=15) |
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steps = gr.Slider(label="Steps", value=20, minimum=2, maximum=75, step=1) |
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with gr.Row(): |
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width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8) |
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height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8) |
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seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1) |
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with gr.Tab("Image to image"): |
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with gr.Group(): |
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image = gr.Image(label="Image", height=256, tool="editor", type="pil") |
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strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5) |
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auto_prefix.change(lambda x: gr.update(placeholder=f"{prefix} [your prompt]" if x else "[Your prompt]"), inputs=auto_prefix, outputs=prompt, queue=False) |
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inputs = [prompt, guidance, steps, width, height, seed, image, strength, neg_prompt, auto_prefix] |
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outputs = [image_out, error_output] |
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prompt.submit(inference, inputs=inputs, outputs=outputs) |
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generate.click(inference, inputs=inputs, outputs=outputs) |
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demo.queue(concurrency_count=1) |
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demo.launch() |
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