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Running
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Zero
| from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, EulerAncestralDiscreteScheduler | |
| from transformers import CLIPFeatureExtractor | |
| import gradio as gr | |
| import torch | |
| from PIL import Image | |
| import random | |
| import os | |
| from huggingface_hub import hf_hub_download | |
| model_id = 'aipicasso/picasso-diffusion-1-1' | |
| auth_token=os.environ.get("ACCESS_TOKEN") | |
| scheduler = EulerAncestralDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler", use_auth_token=auth_token) | |
| feature_extractor = CLIPFeatureExtractor.from_pretrained(model_id, use_auth_token=auth_token) | |
| pipe_merged = StableDiffusionPipeline.from_pretrained( | |
| model_id, | |
| torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32, | |
| scheduler=scheduler, use_auth_token=auth_token) | |
| pipe_i2i_merged = 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, use_auth_token=auth_token | |
| ) | |
| pipe=pipe_merged.to("cuda") | |
| pipe_i2i=pipe_i2i_merged.to("cuda") | |
| pipe.enable_xformers_memory_efficient_attention() | |
| pipe_i2i.enable_xformers_memory_efficient_attention() | |
| embeddings_path=hf_hub_download(repo_id=model_id, filename="embeddings/negative/nfixer.pt", use_auth_token=auth_token) | |
| embeddings_dict=torch.load(embeddings_path) | |
| print(embeddings_dict) | |
| if "string_to_param" in embeddings_dict: | |
| embeddings = next(iter(embeddings_dict['string_to_param'].values())) | |
| nfixer = "" | |
| for i, emb in enumerate(embeddings): | |
| token = f"sksd{chr(i+65)}" | |
| nfixer += token | |
| pipe.tokenizer.add_tokens(token) | |
| pipe.text_encoder.resize_token_embeddings(len(pipe.tokenizer)) | |
| token_id = pipe.tokenizer.convert_tokens_to_ids(token) | |
| pipe.text_encoder.get_input_embeddings().weight.data[token_id] = emb | |
| else: | |
| nfixer = list(embeddings_dict.keys())[0] | |
| embeddings = embeddings_dict[nfixer].to(pipe.text_encoder.get_input_embeddings().weight.dtype) | |
| pipe.tokenizer.add_tokens(placeholder_token) | |
| pipe.text_encoder.resize_token_embeddings(len(pipe.tokenizer)) | |
| placeholder_token_id = pipe.tokenizer.convert_tokens_to_ids(placeholder_token) | |
| pipe.text_encoder.get_input_embeddings().weight.data[placeholder_token_id] = embeddings | |
| embeddings_path=hf_hub_download(repo_id=model_id, filename="embeddings/positive/embellish1.pt", use_auth_token=auth_token) | |
| embeddings_dict=torch.load(embeddings_path) | |
| print(embeddings_dict) | |
| if "string_to_param" in embeddings_dict: | |
| embeddings = next(iter(embeddings_dict['string_to_param'].values())) | |
| embellish1 = "" | |
| for i, emb in enumerate(embeddings): | |
| token = f"kskd{chr(i%26+65)}{chr(i//26+65)}" | |
| embellish1 += token | |
| pipe.tokenizer.add_tokens(token) | |
| pipe.text_encoder.resize_token_embeddings(len(pipe.tokenizer)) | |
| token_id = pipe.tokenizer.convert_tokens_to_ids(token) | |
| pipe.text_encoder.get_input_embeddings().weight.data[token_id] = emb | |
| else: | |
| embellish1 = list(embeddings_dict.keys())[0] | |
| embeddings = embeddings_dict[embellish2].to(pipe.text_encoder.get_input_embeddings().weight.dtype) | |
| pipe.tokenizer.add_tokens(placeholder_token) | |
| pipe.text_encoder.resize_token_embeddings(len(pipe.tokenizer)) | |
| placeholder_token_id = pipe.tokenizer.convert_tokens_to_ids(placeholder_token) | |
| pipe.text_encoder.get_input_embeddings().weight.data[placeholder_token_id] = embeddings | |
| 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="", disable_auto_prompt_correction=False, image_style="Animetic", original_model=False): | |
| global pipe,pipe_i2i | |
| generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None | |
| prompt,neg_prompt=auto_prompt_correction(prompt,neg_prompt,disable_auto_prompt_correction,image_style) | |
| if(image_size=="Portrait"): | |
| height=1024 | |
| width=768 | |
| elif(image_size=="Landscape"): | |
| height=768 | |
| width=1024 | |
| elif(image_size=="Highreso."): | |
| height=1024 | |
| width=1024 | |
| else: | |
| height=768 | |
| width=768 | |
| 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,disable_auto_prompt_correction,image_style): | |
| # auto prompt correction | |
| prompt=str(prompt_ui) | |
| neg_prompt=str(neg_prompt_ui) | |
| prompt=prompt.lower() | |
| neg_prompt=neg_prompt.lower() | |
| if(image_style=="Animetic"): | |
| style="anime" | |
| else: | |
| style=f"anime,{embellish1}" | |
| if(disable_auto_prompt_correction): | |
| prompt=f"{style}, {prompt}" | |
| return prompt, neg_prompt | |
| if(prompt=="" and neg_prompt==""): | |
| prompt=f"{style}, masterpiece, portrait, a girl with flowers, good pupil, detailed" | |
| neg_prompt=f"{nfixer},(((deformed))), blurry, ((((bad anatomy)))),3d, cg, text , 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" | |
| return prompt, neg_prompt | |
| splited_prompt=prompt.replace(","," ").replace("_"," ").split(" ") | |
| human_words=["1girl","girl","maid","maids","female","1woman","woman","girls","2girls","3girls","4girls","5girls","a couple of girls","women","1boy","boy","boys","a couple of boys","2boys","male","1man","1handsome","1bishounen","man","men","guy","guys"] | |
| for word in human_words: | |
| if( word in splited_prompt): | |
| prompt=f"{style}, masterpiece, {prompt}, good pupil, detailed" | |
| neg_prompt=f"{nfixer},(((deformed))), blurry, ((((bad anatomy)))), {neg_prompt}, 3d, cg, text, 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" | |
| return prompt, neg_prompt | |
| animal_words=["cat","dog","bird","pigeon","rabbit","bunny","horse"] | |
| for word in animal_words: | |
| if( word in splited_prompt): | |
| prompt=f"{style}, a {prompt}, 4k, detailed" | |
| neg_prompt=f"{nfixer}, girl, (((deformed))), blurry, ((((bad anatomy)))), {neg_prompt}, 3d, cg, text, 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" | |
| return prompt, neg_prompt | |
| 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"{style}, shinkai makoto, {word}, highly detailed" | |
| neg_prompt=f"girl, (((deformed))), {neg_prompt}, girl, boy, photo, people, low quality, ui, error, lowres, jpeg artifacts, 2d, 3d, cg, text" | |
| return prompt, neg_prompt | |
| 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""" | |
| <div class="main-div"> | |
| <div> | |
| <h1>Picasso Diffusion 1.1 Demo</h1> | |
| </div> | |
| <p> | |
| Demo for <a href="https://huggingface.co/alfredplpl/picasso-diffusion-1-1">Picasso Diffusion 1.1</a><br> | |
| </p> | |
| <p> | |
| サンプル： そのままGenerateボタンを押してください。<br> | |
| sample : Click "Generate" button without any prompts. | |
| </p> | |
| <p> | |
| sample prompt1 : girl, kimono | |
| </p> | |
| <p> | |
| sample prompt2 : boy, armor | |
| </p> | |
| Running on {"<b>GPU 🔥</b>" if torch.cuda.is_available() else f"<b>CPU 🥶</b>. For faster inference it is recommended to <b>upgrade to GPU in <a href='https://huggingface.co/spaces/akhaliq/cool-japan-diffusion-2-1-0/settings'>Settings</a></b>"} <br> | |
| <a style="display:inline-block" href="https://huggingface.co/spaces/aipicasso/picasso-diffusion-latest-demo?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a> to say goodbye from waiting for the generating. | |
| </div> | |
| """ | |
| ) | |
| with gr.Row(): | |
| with gr.Column(scale=55): | |
| with gr.Group(): | |
| with gr.Row(): | |
| image_style=gr.Radio(["Realistic","Animetic"]) | |
| image_style.show_label=False | |
| image_style.value="Animetic" | |
| 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=768) | |
| 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.") | |
| #original_model = gr.Checkbox(label="Change the model into the original model.") | |
| with gr.Row(): | |
| image_size=gr.Radio(["Portrait","Landscape","Square","Highreso."]) | |
| image_size.show_label=False | |
| image_size.value="Square" | |
| 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, disable_auto_prompt_correction,image_style]#, original_model] | |
| outputs = [image_out, error_output] | |
| prompt.submit(inference, inputs=inputs, outputs=outputs) | |
| generate.click(inference, inputs=inputs, outputs=outputs,api_name="generate") | |
| demo.queue(concurrency_count=1) | |
| demo.launch() | |