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| from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler, AutoencoderKL | |
| import gradio as gr | |
| import torch | |
| from PIL import Image | |
| from huggingface_hub import hf_hub_download | |
| from safetensors.torch import load_file | |
| import os | |
| os.environ['CUDA_LAUNCH_BLOCKING'] = '1' | |
| def convert_safetensors_to_bin(pipeline, state_dict, alpha = 0.4): | |
| LORA_PREFIX_UNET = 'lora_unet' | |
| LORA_PREFIX_TEXT_ENCODER = 'lora_te' | |
| visited = [] | |
| # directly update weight in diffusers model | |
| for key in state_dict: | |
| # it is suggested to print out the key, it usually will be something like below | |
| # "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight" | |
| # as we have set the alpha beforehand, so just skip | |
| if '.alpha' in key or key in visited: | |
| continue | |
| if 'text' in key: | |
| layer_infos = key.split('.')[0].split(LORA_PREFIX_TEXT_ENCODER + '_')[-1].split('_') | |
| curr_layer = pipeline.text_encoder | |
| else: | |
| layer_infos = key.split('.')[0].split(LORA_PREFIX_UNET + '_')[-1].split('_') | |
| curr_layer = pipeline.unet | |
| # find the target layer | |
| temp_name = layer_infos.pop(0) | |
| while len(layer_infos) > -1: | |
| try: | |
| curr_layer = curr_layer.__getattr__(temp_name) | |
| if len(layer_infos) > 0: | |
| temp_name = layer_infos.pop(0) | |
| elif len(layer_infos) == 0: | |
| break | |
| except Exception: | |
| if len(temp_name) > 0: | |
| temp_name += '_' + layer_infos.pop(0) | |
| else: | |
| temp_name = layer_infos.pop(0) | |
| # org_forward(x) + lora_up(lora_down(x)) * multiplier | |
| pair_keys = [] | |
| if 'lora_down' in key: | |
| pair_keys.append(key.replace('lora_down', 'lora_up')) | |
| pair_keys.append(key) | |
| else: | |
| pair_keys.append(key) | |
| pair_keys.append(key.replace('lora_up', 'lora_down')) | |
| # update weight | |
| if len(state_dict[pair_keys[0]].shape) == 4: | |
| weight_up = state_dict[pair_keys[0]].squeeze(3).squeeze(2).to(torch.float32) | |
| weight_down = state_dict[pair_keys[1]].squeeze(3).squeeze(2).to(torch.float32) | |
| curr_layer.weight.data += alpha * torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3) | |
| else: | |
| weight_up = state_dict[pair_keys[0]].to(torch.float32) | |
| weight_down = state_dict[pair_keys[1]].to(torch.float32) | |
| curr_layer.weight.data += alpha * torch.mm(weight_up, weight_down) | |
| # update visited list | |
| for item in pair_keys: | |
| visited.append(item) | |
| return pipeline | |
| model_id = 'andite/anything-v4.0' | |
| prefix = '' | |
| lora_path = hf_hub_download( | |
| "showee/showee-lora-v1.0", "showee-any4.0.safetensors" | |
| ) | |
| vae_path = "./anything-v4.0-vae/diffusion_pytorch_model.bin" | |
| scheduler = DPMSolverMultistepScheduler.from_pretrained(model_id, subfolder="scheduler") | |
| pipe = StableDiffusionPipeline.from_pretrained( | |
| model_id, | |
| torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32, | |
| scheduler=scheduler) | |
| pipe.vae.load_state_dict(torch.load(vae_path)) | |
| state_dict = load_file(lora_path) | |
| pipe = convert_safetensors_to_bin(pipe, state_dict, 0.3) | |
| pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained( | |
| model_id, | |
| torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32, | |
| scheduler=scheduler) | |
| pipe_i2i.vae.load_state_dict(torch.load(vae_path)) | |
| state_dict_i2i = load_file(lora_path) | |
| pipe_i2i = convert_safetensors_to_bin(pipe, state_dict_i2i, 0.3) | |
| 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, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt="", auto_prefix=False): | |
| if torch.cuda.is_available(): | |
| generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None | |
| else: | |
| generator = torch.Generator().manual_seed(seed) if seed != 0 else None | |
| prompt = f"{prefix} {prompt}" if auto_prefix else 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 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>Showee V1.0</h1> | |
| </div> | |
| <p> | |
| Demo for <a href="https://huggingface.co/showee/showee-lora-v1.0">Showee V1.0</a> LoRA adaption weights fine-tuned from <a href="https://huggingface.co/andite/anything-v4.0">Anything V4.0</a> Stable Diffusion model.<br> | |
| {"Add the following tokens to your prompts for the model to work properly: <b>prefix</b>" if prefix else ""} | |
| </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/showee/showee-v1.0/settings'>Settings</a></b>"} after duplicating the space<br><br> | |
| <a style="display:inline-block" href="https://huggingface.co/spaces/showee/showee-v1.0?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a> | |
| </div> | |
| """ | |
| ) | |
| with gr.Row(): | |
| with gr.Column(scale=55): | |
| with gr.Group(): | |
| with gr.Row(): | |
| prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder=f"{prefix} [your prompt]").style(container=False) | |
| generate = gr.Button(value="Generate").style(rounded=(False, True, True, False)) | |
| image_out = gr.Image(height=512) | |
| 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", | |
| value="NSFW, lowres, ((bad anatomy)), ((bad hands)), text, missing finger, " | |
| "extra digits, fewer digits, blurry, ((mutated hands and fingers)), " | |
| "(poorly drawn face), ((mutation)), ((deformed face)), (ugly), " | |
| "((bad proportions)), ((extra limbs)), extra face, (double head), " | |
| "(extra head), ((extra feet)), monster, logo, cropped, worst quality, " | |
| "low quality, normal quality, jpeg, humpbacked, long body, long neck, " | |
| "((jpeg artifacts))") | |
| auto_prefix = gr.Checkbox(label="Prefix styling tokens automatically ()", value=prefix, visible=prefix) | |
| with gr.Row(): | |
| guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15) | |
| steps = gr.Slider(label="Steps", value=25, minimum=2, maximum=75, step=1) | |
| with gr.Row(): | |
| width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8) | |
| height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8) | |
| 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) | |
| gr.Examples( | |
| [[ | |
| "masterpiece, best quality, ultra-detailed, illustration, portrait, 1girl, solo, white hair, green eyes, " | |
| "aqua_eyes, cat_ears, :3, ahoge, dress, red_jacket, long_sleeves, bangs, black_legwear, hair_ornament, " | |
| "hairclip", 8, 25, 768, 1024, 909198616 | |
| ], | |
| [ | |
| "masterpiece, best quality, ultra-detailed, illustration, portrait, 1girl, :3, animal_ears, aqua_eyes, ahoge, " | |
| "asymmetrical_legwear, bangs, black_footwear, black_skirt, breasts, cleavage, hair_ornament, hairclip, " | |
| "long_hair, navel, thighhighs, smile", 7.5, 25, 512, 768, 9 | |
| ], | |
| [ | |
| "masterpiece, best quality, ultra-detailed, illustration, portrait, 1girl, :3, animal_ears, aqua_eyes, ahoge, seaside," | |
| "asymmetrical_legwear, bangs, black_footwear, black_skirt, breasts, cleavage, hair_ornament, hairclip, " | |
| "long_hair, navel, thighhighs", 7.5, 25, 512, 512, 353573117 | |
| ]], | |
| [prompt, guidance, steps, width, height, seed], | |
| ) | |
| auto_prefix.change(lambda x: gr.update(placeholder=f"{prefix} [your prompt]" if x else "[Your prompt]"), inputs=auto_prefix, outputs=prompt, queue=False) | |
| inputs = [prompt, guidance, steps, width, height, seed, image, strength, neg_prompt, auto_prefix] | |
| outputs = [image_out, error_output] | |
| prompt.submit(inference, inputs=inputs, outputs=outputs) | |
| generate.click(inference, inputs=inputs, outputs=outputs) | |
| gr.HTML(""" | |
| <div style="border-top: 1px solid #303030;"> | |
| <br> | |
| <p>This space was created using <a href="https://huggingface.co/spaces/anzorq/sd-space-creator">SD Space Creator</a>.</p> | |
| </div> | |
| """) | |
| demo.queue(concurrency_count=1) | |
| demo.launch() | |