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-1-0-demo' 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="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="embellish2.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())) embellish2 = "" for i, emb in enumerate(embeddings): token = f"kskd{chr(i%26+65)}{chr(i//26+65)}" embellish2 += 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: embellish2 = 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="Realistic", 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,{embellish2}" 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"""

Picasso Diffusion 1.0 +α Demo

Demo for Picasso Diffusion 1.0 (Comming soon) + Illumi. Diffusion.

サンプル： そのままGenerateボタンを押してください。
sample : Click "Generate" button without any prompts.

sample prompt1 : girl, kimono

sample prompt2 : boy, armor

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(): image_style=gr.Radio(["Realistic","Animetic"]) image_style.show_label=False image_style.value="Realistic" 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()