# Thank AK. https://huggingface.co/spaces/akhaliq/cool-japan-diffusion-2-1-0/blob/main/app.py from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, EulerAncestralDiscreteScheduler from transformers import CLIPFeatureExtractor import gradio as gr import torch from PIL import Image model_id = 'aipicasso/cool-japan-diffusion-2-1-1-beta' scheduler = EulerAncestralDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler") feature_extractor = CLIPFeatureExtractor.from_pretrained(model_id) 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, requires_safety_checker=False, safety_checker=None, feature_extractor=feature_extractor ) 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, image_size="Square", seed=0, img=None, strength=0.5, neg_prompt="", cool_japan_type="Anime", disable_auto_prompt_correction=False): generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None if(not disable_auto_prompt_correction): prompt,neg_prompt=auto_prompt_correction(prompt,neg_prompt,cool_japan_type) if(image_size=="Portrait"): height=768 width=576 elif(image_size=="Landscape"): height=576 width=768 else: height=512 width=512 print(prompt,neg_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 auto_prompt_correction(prompt_ui,neg_prompt_ui,cool_japan_type_ui): # auto prompt correction cool_japan_type=str(cool_japan_type_ui) if(cool_japan_type=="Manga"): cool_japan_type="manga, monochrome, white and black manga" elif(cool_japan_type=="Game"): cool_japan_type="game" else: cool_japan_type="anime" prompt=str(prompt_ui) neg_prompt=str(neg_prompt_ui) prompt=prompt.lower() neg_prompt=neg_prompt.lower() if(prompt=="" and neg_prompt==""): prompt=f"{cool_japan_type}, masterpiece, a portrait of a girl, good pupil, 4k, detailed" neg_prompt=f"(((deformed))), blurry, ((((bad anatomy)))), {neg_prompt}, bad pupil, disfigured, poorly drawn face, mutation, mutated, (extra limb), (ugly), (poorly drawn hands), bad hands, fused fingers, messy drawing, broken legs censor, low quality, (mutated hands and fingers:1.5), (long body :1.3), (mutation, poorly drawn :1.2), ((bad eyes)), ui, error, missing fingers, fused fingers, one hand with more than 5 fingers, one hand with less than 5 fingers, one hand with more than 5 digit, one hand with less than 5 digit, extra digit, fewer digits, fused digit, missing digit, bad digit, liquid digit, long body, uncoordinated body, unnatural body, lowres, jpeg artifacts, 3d, cg, text" splited_prompt=prompt.replace(","," ").replace("_"," ").split(" ") splited_prompt=["a person" if p=="solo" else p for p in splited_prompt] splited_prompt=["girl" if p=="1girl" else p for p in splited_prompt] splited_prompt=["a couple of girls" if p=="2girls" else p for p in splited_prompt] splited_prompt=["a couple of boys" if p=="2boys" else p for p in splited_prompt] human_words=["girl","maid","maids","female","woman","girls","a couple of girls","women","boy","boys","a couple of boys","male","man","men","guy","guys"] for word in human_words: if( word in splited_prompt): prompt=f"{cool_japan_type}, masterpiece, {prompt}, good pupil, 4k, detailed" neg_prompt=f"(((deformed))), blurry, ((((bad anatomy)))), {neg_prompt}, bad pupil, disfigured, poorly drawn face, mutation, mutated, (extra limb), (ugly), (poorly drawn hands), bad hands, fused fingers, messy drawing, broken legs censor, low quality, (mutated hands and fingers:1.5), (long body :1.3), (mutation, poorly drawn :1.2), ((bad eyes)), ui, error, missing fingers, fused fingers, one hand with more than 5 fingers, one hand with less than 5 fingers, one hand with more than 5 digit, one hand with less than 5 digit, extra digit, fewer digits, fused digit, missing digit, bad digit, liquid digit, long body, uncoordinated body, unnatural body, lowres, jpeg artifacts, 3d, cg, text" animal_words=["cat","dog","bird"] for word in animal_words: if( word in splited_prompt): prompt=f"{cool_japan_type}, a {word}, 4k, detailed" neg_prompt=f"(((deformed))), blurry, ((((bad anatomy)))), {neg_prompt}, bad pupil, disfigured, poorly drawn face, mutation, mutated, (extra limb), (ugly), (poorly drawn hands), bad hands, fused fingers, messy drawing, broken legs censor, low quality, (mutated hands and fingers:1.5), (long body :1.3), (mutation, poorly drawn :1.2), ((bad eyes)), ui, error, missing fingers, fused fingers, one hand with more than 5 fingers, one hand with less than 5 fingers, one hand with more than 5 digit, one hand with less than 5 digit, extra digit, fewer digits, fused digit, missing digit, bad digit, liquid digit, long body, uncoordinated body, unnatural body, lowres, jpeg artifacts, 3d, cg, text" background_words=["mount fuji","mt. fuji","building", "buildings", "tokyo", "kyoto", "nara", "shibuya", "shinjuku"] for word in background_words: if( word in splited_prompt): prompt=f"{cool_japan_type}, shinkai makoto, {word}, 4k, 8k, highly detailed" neg_prompt=f"(((deformed))), {neg_prompt}, girl, boy, photo, people, low quality, ui, error, lowres, jpeg artifacts, 2d, 3d, cg, text" return prompt,neg_prompt 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] 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"""

Cool Japan Diffusion 2.1.1 Beta

Demo for Cool Japan Diffusion 2.1.1 Beta Stable Diffusion model.

sample prompt1 : girl, kimono

sample prompt2 : boy, school uniform

日本語の取扱説明書.

Running on {"GPU 🔥" if torch.cuda.is_available() else f"CPU 🥶. For faster inference it is recommended to upgrade to GPU in Settings"}
""" ) with gr.Row(): with gr.Column(scale=55): with gr.Group(): with gr.Row(): cool_japan_type=gr.Radio(["Anime", "Manga", "Game"]) cool_japan_type.show_label=False cool_japan_type.value="Anime" with gr.Row(): prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder="[your prompt]").style(container=False) generate = gr.Button(value="Generate").style(rounded=(False, True, True, False)) image_out = gr.Image(height=768,width=576) 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") disable_auto_prompt_correction = gr.Checkbox(label="Disable auto prompt corretion.") with gr.Row(): image_size=gr.Radio(["Portrait","Landscape","Square"]) image_size.show_label=False image_size.value="Portrait" with gr.Row(): guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15) steps = gr.Slider(label="Steps", value=20, minimum=2, maximum=75, step=1) 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 = [prompt, guidance, steps, image_size, seed, image, strength, neg_prompt, cool_japan_type, disable_auto_prompt_correction] outputs = [image_out, error_output] prompt.submit(inference, inputs=inputs, outputs=outputs) generate.click(inference, inputs=inputs, outputs=outputs) gr.HTML("""

This space was created using SD Space Creator.

""") demo.queue(concurrency_count=1) demo.launch()