import random import tempfile import time import gradio as gr import numpy as np import torch import math import re from gradio import inputs from diffusers import ( AutoencoderKL, DDIMScheduler, UNet2DConditionModel, ) from modules.model import ( CrossAttnProcessor, StableDiffusionPipeline, ) from torchvision import transforms from transformers import CLIPTokenizer, CLIPTextModel from PIL import Image from pathlib import Path from safetensors.torch import load_file import modules.safe as _ from modules.lora import LoRANetwork models = [ ("AbyssOrangeMix2", "Korakoe/AbyssOrangeMix2-HF", 2), ("Pastal Mix", "andite/pastel-mix", 2), ("Basil Mix", "nuigurumi/basil_mix", 2), ("Counterfeit-V2.5", "gsdf/Counterfeit-V2.5", 2) ] keep_vram = ["Korakoe/AbyssOrangeMix2-HF", "andite/pastel-mix"] base_name, base_model, clip_skip = models[0] samplers_k_diffusion = [ ("Euler a", "sample_euler_ancestral", {}), ("Euler", "sample_euler", {}), ("LMS", "sample_lms", {}), ("Heun", "sample_heun", {}), ("DPM2", "sample_dpm_2", {"discard_next_to_last_sigma": True}), ("DPM2 a", "sample_dpm_2_ancestral", {"discard_next_to_last_sigma": True}), ("DPM++ 2S a", "sample_dpmpp_2s_ancestral", {}), ("DPM++ 2M", "sample_dpmpp_2m", {}), ("DPM++ SDE", "sample_dpmpp_sde", {}), ("LMS Karras", "sample_lms", {"scheduler": "karras"}), ("DPM2 Karras", "sample_dpm_2", {"scheduler": "karras", "discard_next_to_last_sigma": True}), ("DPM2 a Karras", "sample_dpm_2_ancestral", {"scheduler": "karras", "discard_next_to_last_sigma": True}), ("DPM++ 2S a Karras", "sample_dpmpp_2s_ancestral", {"scheduler": "karras"}), ("DPM++ 2M Karras", "sample_dpmpp_2m", {"scheduler": "karras"}), ("DPM++ SDE Karras", "sample_dpmpp_sde", {"scheduler": "karras"}), ] # samplers_diffusers = [ # ("DDIMScheduler", "diffusers.schedulers.DDIMScheduler", {}) # ("DDPMScheduler", "diffusers.schedulers.DDPMScheduler", {}) # ("DEISMultistepScheduler", "diffusers.schedulers.DEISMultistepScheduler", {}) # ] start_time = time.time() timeout = 90 scheduler = DDIMScheduler.from_pretrained( base_model, subfolder="scheduler", ) vae = AutoencoderKL.from_pretrained( "stabilityai/sd-vae-ft-ema", torch_dtype=torch.float16 ) text_encoder = CLIPTextModel.from_pretrained( base_model, subfolder="text_encoder", torch_dtype=torch.float16, ) tokenizer = CLIPTokenizer.from_pretrained( base_model, subfolder="tokenizer", torch_dtype=torch.float16, ) unet = UNet2DConditionModel.from_pretrained( base_model, subfolder="unet", torch_dtype=torch.float16, ) pipe = StableDiffusionPipeline( text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, vae=vae, scheduler=scheduler, ) unet.set_attn_processor(CrossAttnProcessor) pipe.setup_text_encoder(clip_skip, text_encoder) if torch.cuda.is_available(): pipe = pipe.to("cuda") def get_model_list(): return models te_cache = { base_model: text_encoder } unet_cache = { base_model: unet } lora_cache = { base_model: LoRANetwork(text_encoder, unet) } te_base_weight_length = text_encoder.get_input_embeddings().weight.data.shape[0] original_prepare_for_tokenization = tokenizer.prepare_for_tokenization current_model = base_model def setup_model(name, lora_state=None, lora_scale=1.0): global pipe, current_model keys = [k[0] for k in models] model = models[keys.index(name)][1] if model not in unet_cache: unet = UNet2DConditionModel.from_pretrained(model, subfolder="unet", torch_dtype=torch.float16) text_encoder = CLIPTextModel.from_pretrained(model, subfolder="text_encoder", torch_dtype=torch.float16) unet_cache[model] = unet te_cache[model] = text_encoder lora_cache[model] = LoRANetwork(text_encoder, unet) if current_model != model: if current_model not in keep_vram: # offload current model unet_cache[current_model].to("cpu") te_cache[current_model].to("cpu") lora_cache[current_model].to("cpu") current_model = model local_te, local_unet, local_lora, = te_cache[model], unet_cache[model], lora_cache[model] local_unet.set_attn_processor(CrossAttnProcessor()) local_lora.reset() clip_skip = models[keys.index(name)][2] if torch.cuda.is_available(): local_unet.to("cuda") local_te.to("cuda") if lora_state is not None and lora_state != "": local_lora.load(lora_state, lora_scale) local_lora.to(local_unet.device, dtype=local_unet.dtype) pipe.text_encoder, pipe.unet = local_te, local_unet pipe.setup_unet(local_unet) pipe.tokenizer.prepare_for_tokenization = original_prepare_for_tokenization pipe.tokenizer.added_tokens_encoder = {} pipe.tokenizer.added_tokens_decoder = {} pipe.setup_text_encoder(clip_skip, local_te) return pipe def error_str(error, title="Error"): return ( f"""#### {title} {error}""" if error else "" ) def make_token_names(embs): all_tokens = [] for name, vec in embs.items(): tokens = [f'emb-{name}-{i}' for i in range(len(vec))] all_tokens.append(tokens) return all_tokens def setup_tokenizer(tokenizer, embs): reg_match = [re.compile(fr"(?:^|(?<=\s|,)){k}(?=,|\s|$)") for k in embs.keys()] clip_keywords = [' '.join(s) for s in make_token_names(embs)] def parse_prompt(prompt: str): for m, v in zip(reg_match, clip_keywords): prompt = m.sub(v, prompt) return prompt def prepare_for_tokenization(self, text: str, is_split_into_words: bool = False, **kwargs): text = parse_prompt(text) r = original_prepare_for_tokenization(text, is_split_into_words, **kwargs) return r tokenizer.prepare_for_tokenization = prepare_for_tokenization.__get__(tokenizer, CLIPTokenizer) return [t for sublist in make_token_names(embs) for t in sublist] def convert_size(size_bytes): if size_bytes == 0: return "0B" size_name = ("B", "KB", "MB", "GB", "TB", "PB", "EB", "ZB", "YB") i = int(math.floor(math.log(size_bytes, 1024))) p = math.pow(1024, i) s = round(size_bytes / p, 2) return "%s %s" % (s, size_name[i]) def inference( prompt, guidance, steps, width=512, height=512, seed=0, neg_prompt="", state=None, g_strength=0.4, img_input=None, i2i_scale=0.5, hr_enabled=False, hr_method="Latent", hr_scale=1.5, hr_denoise=0.8, sampler="DPM++ 2M Karras", embs=None, model=None, lora_state=None, lora_scale=None, ): if seed is None or seed == 0: seed = random.randint(0, 2147483647) pipe = setup_model(model, lora_state, lora_scale) generator = torch.Generator("cuda").manual_seed(int(seed)) start_time = time.time() sampler_name, sampler_opt = None, None for label, funcname, options in samplers_k_diffusion: if label == sampler: sampler_name, sampler_opt = funcname, options tokenizer, text_encoder = pipe.tokenizer, pipe.text_encoder if embs is not None and len(embs) > 0: ti_embs = {} for name, file in embs.items(): if str(file).endswith(".pt"): loaded_learned_embeds = torch.load(file, map_location="cpu") else: loaded_learned_embeds = load_file(file, device="cpu") loaded_learned_embeds = loaded_learned_embeds["string_to_param"]["*"] if "string_to_param" in loaded_learned_embed else loaded_learned_embed ti_embs[name] = loaded_learned_embeds if len(ti_embs) > 0: tokens = setup_tokenizer(tokenizer, ti_embs) added_tokens = tokenizer.add_tokens(tokens) delta_weight = torch.cat([val for val in ti_embs.values()], dim=0) assert added_tokens == delta_weight.shape[0] text_encoder.resize_token_embeddings(len(tokenizer)) token_embeds = text_encoder.get_input_embeddings().weight.data token_embeds[-delta_weight.shape[0]:] = delta_weight config = { "negative_prompt": neg_prompt, "num_inference_steps": int(steps), "guidance_scale": guidance, "generator": generator, "sampler_name": sampler_name, "sampler_opt": sampler_opt, "pww_state": state, "pww_attn_weight": g_strength, "start_time": start_time, "timeout": timeout, } if img_input is not None: ratio = min(height / img_input.height, width / img_input.width) img_input = img_input.resize( (int(img_input.width * ratio), int(img_input.height * ratio)), Image.LANCZOS ) result = pipe.img2img(prompt, image=img_input, strength=i2i_scale, **config) elif hr_enabled: result = pipe.txt2img( prompt, width=width, height=height, upscale=True, upscale_x=hr_scale, upscale_denoising_strength=hr_denoise, **config, **latent_upscale_modes[hr_method], ) else: result = pipe.txt2img(prompt, width=width, height=height, **config) end_time = time.time() vram_free, vram_total = torch.cuda.mem_get_info() print(f"done: model={model}, res={width}x{height}, step={steps}, time={round(end_time-start_time, 2)}s, vram_alloc={convert_size(vram_total-vram_free)}/{convert_size(vram_total)}") return gr.Image.update(result[0][0], label=f"Initial Seed: {seed}") color_list = [] def get_color(n): for _ in range(n - len(color_list)): color_list.append(tuple(np.random.random(size=3) * 256)) return color_list def create_mixed_img(current, state, w=512, h=512): w, h = int(w), int(h) image_np = np.full([h, w, 4], 255) if state is None: state = {} colors = get_color(len(state)) idx = 0 for key, item in state.items(): if item["map"] is not None: m = item["map"] < 255 alpha = 150 if current == key: alpha = 200 image_np[m] = colors[idx] + (alpha,) idx += 1 return image_np # width.change(apply_new_res, inputs=[width, height, global_stats], outputs=[global_stats, sp, rendered]) def apply_new_res(w, h, state): w, h = int(w), int(h) for key, item in state.items(): if item["map"] is not None: item["map"] = resize(item["map"], w, h) update_img = gr.Image.update(value=create_mixed_img("", state, w, h)) return state, update_img def detect_text(text, state, width, height): if text is None or text == "": return None, None, gr.Radio.update(value=None), None t = text.split(",") new_state = {} for item in t: item = item.strip() if item == "": continue if state is not None and item in state: new_state[item] = { "map": state[item]["map"], "weight": state[item]["weight"], "mask_outsides": state[item]["mask_outsides"], } else: new_state[item] = { "map": None, "weight": 0.5, "mask_outsides": False } update = gr.Radio.update(choices=[key for key in new_state.keys()], value=None) update_img = gr.update(value=create_mixed_img("", new_state, width, height)) update_sketch = gr.update(value=None, interactive=False) return new_state, update_sketch, update, update_img def resize(img, w, h): trs = transforms.Compose( [ transforms.ToPILImage(), transforms.Resize(min(h, w)), transforms.CenterCrop((h, w)), ] ) result = np.array(trs(img), dtype=np.uint8) return result def switch_canvas(entry, state, width, height): if entry == None: return None, 0.5, False, create_mixed_img("", state, width, height) return ( gr.update(value=None, interactive=True), gr.update(value=state[entry]["weight"] if entry in state else 0.5), gr.update(value=state[entry]["mask_outsides"] if entry in state else False), create_mixed_img(entry, state, width, height), ) def apply_canvas(selected, draw, state, w, h): if selected in state: w, h = int(w), int(h) state[selected]["map"] = resize(draw, w, h) return state, gr.Image.update(value=create_mixed_img(selected, state, w, h)) def apply_weight(selected, weight, state): if selected in state: state[selected]["weight"] = weight return state def apply_option(selected, mask, state): if selected in state: state[selected]["mask_outsides"] = mask return state # sp2, radio, width, height, global_stats def apply_image(image, selected, w, h, strgength, mask, state): if selected in state: state[selected] = { "map": resize(image, w, h), "weight": strgength, "mask_outsides": mask } return state, gr.Image.update(value=create_mixed_img(selected, state, w, h)) # [ti_state, lora_state, ti_vals, lora_vals, uploads] def add_net(files, ti_state, lora_state): if files is None: return ti_state, "", lora_state, None for file in files: item = Path(file.name) stripedname = str(item.stem).strip() if item.suffix == ".pt": state_dict = torch.load(file.name, map_location="cpu") else: state_dict = load_file(file.name, device="cpu") if any("lora" in k for k in state_dict.keys()): lora_state = file.name else: ti_state[stripedname] = file.name return ( ti_state, lora_state, gr.Text.update(f"{[key for key in ti_state.keys()]}"), gr.Text.update(f"{lora_state}"), gr.Files.update(value=None), ) # [ti_state, lora_state, ti_vals, lora_vals, uploads] def clean_states(ti_state, lora_state): return ( dict(), None, gr.Text.update(f""), gr.Text.update(f""), gr.File.update(value=None), ) latent_upscale_modes = { "Latent": {"upscale_method": "bilinear", "upscale_antialias": False}, "Latent (antialiased)": {"upscale_method": "bilinear", "upscale_antialias": True}, "Latent (bicubic)": {"upscale_method": "bicubic", "upscale_antialias": False}, "Latent (bicubic antialiased)": { "upscale_method": "bicubic", "upscale_antialias": True, }, "Latent (nearest)": {"upscale_method": "nearest", "upscale_antialias": False}, "Latent (nearest-exact)": { "upscale_method": "nearest-exact", "upscale_antialias": False, }, } css = """ .finetuned-diffusion-div div{ display:inline-flex; align-items:center; gap:.8rem; font-size:1.75rem; padding-top:2rem; } .finetuned-diffusion-div div h1{ font-weight:900; margin-bottom:7px } .finetuned-diffusion-div p{ margin-bottom:10px; font-size:94% } .box { float: left; height: 20px; width: 20px; margin-bottom: 15px; border: 1px solid black; clear: both; } a{ text-decoration:underline } .tabs{ margin-top:0; margin-bottom:0 } #gallery{ min-height:20rem } .no-border { border: none !important; } """ with gr.Blocks(css=css) as demo: gr.HTML( f"""
Hso @ nyanko.sketch2img.gradio
Will use the following formula: w = scale * token_weight_martix * log(1 + sigma) * max(qk).