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+98)}" 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+98)}" 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, 4k, detailed" neg_prompt=f"{nfixer},(((deformed))), blurry, ((((bad anatomy)))), 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" 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, 4k, detailed" neg_prompt=f"{nfixer},(((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" return prompt, neg_prompt animal_words=["cat","dog","bird"] 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}, 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" 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}, 4k, 8k, 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) + cacoe's model.

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" 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()