Jeffsun's picture
Update app.py
42ceab6
# code from @nyanko7
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 = [
("LSPV2-anime", "Jeffsun/LSPV2", 2),
("LSPV3-real", "Jeffsun/LSPV3", 2)
]
keep_vram = ["Jeffsun/LSPV2","Jeffsun/LSPV3"]
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"""
<div class="finetuned-diffusion-div">
<div>
<h1>Demo For LSP model</h1>
"""
)
global_stats = gr.State(value={})
with gr.Row():
with gr.Column(scale=55):
model = gr.Dropdown(
choices=[k[0] for k in get_model_list()],
label="Model",
value=base_name,
)
image_out = gr.Image(height=512)
# gallery = gr.Gallery(
# label="Generated images", show_label=False, elem_id="gallery"
# ).style(grid=[1], height="auto")
with gr.Column(scale=45):
with gr.Group():
with gr.Row():
with gr.Column(scale=70):
prompt = gr.Textbox(
label="Prompt",
value="best quality, masterpiece, highres , 1girl,real photo , beautiful face, magic clothes",
show_label=True,
max_lines=4,
placeholder="Enter prompt.",
)
neg_prompt = gr.Textbox(
label="Negative Prompt",
value="simple background,monochrome ,lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits,twisting jawline, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, lowres, bad anatomy, bad hands, text, error, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, ugly,pregnant,vore,duplicate,morbid,mut ilated,tran nsexual, hermaphrodite,long neck,mutated hands,poorly drawn hands,poorly drawn face,mutation,deformed,blurry,bad anatomy,bad proportions,malformed limbs,extra limbs,cloned face,disfigured,gross proportions, missing arms, missing legs, extra arms,extra legs,pubic hair, plump,bad legs,error legs,username,blurry,bad feet",
show_label=True,
max_lines=4,
placeholder="Enter negative prompt.",
)
generate = gr.Button(value="Generate").style(
rounded=(False, True, True, False)
)
with gr.Tab("Options"):
with gr.Group():
# n_images = gr.Slider(label="Images", value=1, minimum=1, maximum=4, step=1)
with gr.Row():
guidance = gr.Slider(
label="Guidance scale", value=7.5, maximum=15
)
steps = gr.Slider(
label="Steps", value=25, minimum=2, maximum=50, step=1
)
with gr.Row():
width = gr.Slider(
label="Width", value=512, minimum=64, maximum=1024, step=64
)
height = gr.Slider(
label="Height", value=512, minimum=64, maximum=1024, step=64
)
sampler = gr.Dropdown(
value="DPM++ 2M Karras",
label="Sampler",
choices=[s[0] for s in samplers_k_diffusion],
)
seed = gr.Number(label="Seed (0 = random)", value=0)
with gr.Tab("Image to image"):
with gr.Group():
inf_image = gr.Image(
label="Image", height=256, tool="editor", type="pil"
)
inf_strength = gr.Slider(
label="Transformation strength",
minimum=0,
maximum=1,
step=0.01,
value=0.5,
)
def res_cap(g, w, h, x):
if g:
return f"Enable upscaler: {w}x{h} to {int(w*x)}x{int(h*x)}"
else:
return "Enable upscaler"
with gr.Tab("Hires fix"):
with gr.Group():
hr_enabled = gr.Checkbox(label="Enable upscaler", value=False)
hr_method = gr.Dropdown(
[key for key in latent_upscale_modes.keys()],
value="Latent",
label="Upscale method",
)
hr_scale = gr.Slider(
label="Upscale factor",
minimum=1.0,
maximum=2.0,
step=0.1,
value=1.5,
)
hr_denoise = gr.Slider(
label="Denoising strength",
minimum=0.0,
maximum=1.0,
step=0.1,
value=0.8,
)
hr_scale.change(
lambda g, x, w, h: gr.Checkbox.update(
label=res_cap(g, w, h, x)
),
inputs=[hr_enabled, hr_scale, width, height],
outputs=hr_enabled,
queue=False,
)
hr_enabled.change(
lambda g, x, w, h: gr.Checkbox.update(
label=res_cap(g, w, h, x)
),
inputs=[hr_enabled, hr_scale, width, height],
outputs=hr_enabled,
queue=False,
)
with gr.Tab("Embeddings/Loras"):
ti_state = gr.State(dict())
lora_state = gr.State()
with gr.Group():
with gr.Row():
with gr.Column(scale=90):
ti_vals = gr.Text(label="Loaded embeddings")
with gr.Row():
with gr.Column(scale=90):
lora_vals = gr.Text(label="Loaded loras")
with gr.Row():
uploads = gr.Files(label="Upload new embeddings/lora")
with gr.Column():
lora_scale = gr.Slider(
label="Lora scale",
minimum=0,
maximum=2,
step=0.01,
value=1.0,
)
btn = gr.Button(value="Upload")
btn_del = gr.Button(value="Reset")
btn.click(
add_net,
inputs=[uploads, ti_state, lora_state],
outputs=[ti_state, lora_state, ti_vals, lora_vals, uploads],
queue=False,
)
btn_del.click(
clean_states,
inputs=[ti_state, lora_state],
outputs=[ti_state, lora_state, ti_vals, lora_vals, uploads],
queue=False,
)
# error_output = gr.Markdown()
gr.HTML(
f"""
<div class="finetuned-diffusion-div">
<div>
<h1>Paint with words</h1>
</div>
<p>
Will use the following formula: w = scale * token_weight_martix * log(1 + sigma) * max(qk).
</p>
</div>
"""
)
with gr.Row():
with gr.Column(scale=55):
rendered = gr.Image(
invert_colors=True,
source="canvas",
interactive=False,
image_mode="RGBA",
)
with gr.Column(scale=45):
with gr.Group():
with gr.Row():
with gr.Column(scale=70):
g_strength = gr.Slider(
label="Weight scaling",
minimum=0,
maximum=0.8,
step=0.01,
value=0.4,
)
text = gr.Textbox(
lines=2,
interactive=True,
label="Token to Draw: (Separate by comma)",
)
radio = gr.Radio([], label="Tokens")
sk_update = gr.Button(value="Update").style(
rounded=(False, True, True, False)
)
# g_strength.change(lambda b: gr.update(f"Scaled additional attn: $w = {b} \log (1 + \sigma) \std (Q^T K)$."), inputs=g_strength, outputs=[g_output])
with gr.Tab("SketchPad"):
sp = gr.Image(
image_mode="L",
tool="sketch",
source="canvas",
interactive=False,
)
mask_outsides = gr.Checkbox(
label="Mask other areas",
value=False
)
strength = gr.Slider(
label="Token strength",
minimum=0,
maximum=0.8,
step=0.01,
value=0.5,
)
sk_update.click(
detect_text,
inputs=[text, global_stats, width, height],
outputs=[global_stats, sp, radio, rendered],
queue=False,
)
radio.change(
switch_canvas,
inputs=[radio, global_stats, width, height],
outputs=[sp, strength, mask_outsides, rendered],
queue=False,
)
sp.edit(
apply_canvas,
inputs=[radio, sp, global_stats, width, height],
outputs=[global_stats, rendered],
queue=False,
)
strength.change(
apply_weight,
inputs=[radio, strength, global_stats],
outputs=[global_stats],
queue=False,
)
mask_outsides.change(
apply_option,
inputs=[radio, mask_outsides, global_stats],
outputs=[global_stats],
queue=False,
)
with gr.Tab("UploadFile"):
sp2 = gr.Image(
image_mode="L",
source="upload",
shape=(512, 512),
)
mask_outsides2 = gr.Checkbox(
label="Mask other areas",
value=False,
)
strength2 = gr.Slider(
label="Token strength",
minimum=0,
maximum=0.8,
step=0.01,
value=0.5,
)
apply_style = gr.Button(value="Apply")
apply_style.click(
apply_image,
inputs=[sp2, radio, width, height, strength2, mask_outsides2, global_stats],
outputs=[global_stats, rendered],
queue=False,
)
width.change(
apply_new_res,
inputs=[width, height, global_stats],
outputs=[global_stats, rendered],
queue=False,
)
height.change(
apply_new_res,
inputs=[width, height, global_stats],
outputs=[global_stats, rendered],
queue=False,
)
# color_stats = gr.State(value={})
# text.change(detect_color, inputs=[sp, text, color_stats], outputs=[color_stats, rendered])
# sp.change(detect_color, inputs=[sp, text, color_stats], outputs=[color_stats, rendered])
inputs = [
prompt,
guidance,
steps,
width,
height,
seed,
neg_prompt,
global_stats,
g_strength,
inf_image,
inf_strength,
hr_enabled,
hr_method,
hr_scale,
hr_denoise,
sampler,
ti_state,
model,
lora_state,
lora_scale,
]
outputs = [image_out]
prompt.submit(inference, inputs=inputs, outputs=outputs)
generate.click(inference, inputs=inputs, outputs=outputs)
print(f"Space built in {time.time() - start_time:.2f} seconds")
# demo.launch(share=True)
demo.launch(enable_queue=True, server_name="0.0.0.0", server_port=7860)