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gradio_app.py
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gradio_app.py
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import os
os.environ['HF_HOME'] = os.path.join(os.path.dirname(__file__), 'hf_download')
result_dir = os.path.join('./', 'results')
os.makedirs(result_dir, exist_ok=True)
import functools
import os
import random
import gradio as gr
import numpy as np
import torch
import wd14tagger
import memory_management
import uuid
from PIL import Image
from diffusers_helper.code_cond import unet_add_coded_conds
from diffusers_helper.cat_cond import unet_add_concat_conds
from diffusers_helper.k_diffusion import KDiffusionSampler
from diffusers import AutoencoderKL, UNet2DConditionModel
from diffusers.models.attention_processor import AttnProcessor2_0
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers_vdm.pipeline import LatentVideoDiffusionPipeline
from diffusers_vdm.utils import resize_and_center_crop, save_bcthw_as_mp4
class ModifiedUNet(UNet2DConditionModel):
@classmethod
def from_config(cls, *args, **kwargs):
m = super().from_config(*args, **kwargs)
unet_add_concat_conds(unet=m, new_channels=4)
unet_add_coded_conds(unet=m, added_number_count=1)
return m
model_name = 'lllyasviel/paints_undo_single_frame'
tokenizer = CLIPTokenizer.from_pretrained(model_name, subfolder="tokenizer")
text_encoder = CLIPTextModel.from_pretrained(model_name, subfolder="text_encoder").to(torch.float16)
vae = AutoencoderKL.from_pretrained(model_name, subfolder="vae").to(torch.bfloat16) # bfloat16 vae
unet = ModifiedUNet.from_pretrained(model_name, subfolder="unet").to(torch.float16)
unet.set_attn_processor(AttnProcessor2_0())
vae.set_attn_processor(AttnProcessor2_0())
video_pipe = LatentVideoDiffusionPipeline.from_pretrained(
'lllyasviel/paints_undo_multi_frame',
fp16=True
)
memory_management.unload_all_models([
video_pipe.unet, video_pipe.vae, video_pipe.text_encoder, video_pipe.image_projection, video_pipe.image_encoder,
unet, vae, text_encoder
])
k_sampler = KDiffusionSampler(
unet=unet,
timesteps=1000,
linear_start=0.00085,
linear_end=0.020,
linear=True
)
def find_best_bucket(h, w, options):
min_metric = float('inf')
best_bucket = None
for (bucket_h, bucket_w) in options:
metric = abs(h * bucket_w - w * bucket_h)
if metric <= min_metric:
min_metric = metric
best_bucket = (bucket_h, bucket_w)
return best_bucket
@torch.inference_mode()
def encode_cropped_prompt_77tokens(txt: str):
memory_management.load_models_to_gpu(text_encoder)
cond_ids = tokenizer(txt,
padding="max_length",
max_length=tokenizer.model_max_length,
truncation=True,
return_tensors="pt").input_ids.to(device=text_encoder.device)
text_cond = text_encoder(cond_ids, attention_mask=None).last_hidden_state
return text_cond
@torch.inference_mode()
def pytorch2numpy(imgs):
results = []
for x in imgs:
y = x.movedim(0, -1)
y = y * 127.5 + 127.5
y = y.detach().float().cpu().numpy().clip(0, 255).astype(np.uint8)
results.append(y)
return results
@torch.inference_mode()
def numpy2pytorch(imgs):
h = torch.from_numpy(np.stack(imgs, axis=0)).float() / 127.5 - 1.0
h = h.movedim(-1, 1)
return h
def resize_without_crop(image, target_width, target_height):
pil_image = Image.fromarray(image)
resized_image = pil_image.resize((target_width, target_height), Image.LANCZOS)
return np.array(resized_image)
@torch.inference_mode()
def interrogator_process(x):
return wd14tagger.default_interrogator(x)
@torch.inference_mode()
def process(input_fg, prompt, input_undo_steps, image_width, image_height, seed, steps, n_prompt, cfg,
progress=gr.Progress()):
rng = torch.Generator(device=memory_management.gpu).manual_seed(int(seed))
memory_management.load_models_to_gpu(vae)
fg = resize_and_center_crop(input_fg, image_width, image_height)
concat_conds = numpy2pytorch([fg]).to(device=vae.device, dtype=vae.dtype)
concat_conds = vae.encode(concat_conds).latent_dist.mode() * vae.config.scaling_factor
memory_management.load_models_to_gpu(text_encoder)
conds = encode_cropped_prompt_77tokens(prompt)
unconds = encode_cropped_prompt_77tokens(n_prompt)
memory_management.load_models_to_gpu(unet)
fs = torch.tensor(input_undo_steps).to(device=unet.device, dtype=torch.long)
initial_latents = torch.zeros_like(concat_conds)
concat_conds = concat_conds.to(device=unet.device, dtype=unet.dtype)
latents = k_sampler(
initial_latent=initial_latents,
strength=1.0,
num_inference_steps=steps,
guidance_scale=cfg,
batch_size=len(input_undo_steps),
generator=rng,
prompt_embeds=conds,
negative_prompt_embeds=unconds,
cross_attention_kwargs={'concat_conds': concat_conds, 'coded_conds': fs},
same_noise_in_batch=True,
progress_tqdm=functools.partial(progress.tqdm, desc='Generating Key Frames')
).to(vae.dtype) / vae.config.scaling_factor
memory_management.load_models_to_gpu(vae)
pixels = vae.decode(latents).sample
pixels = pytorch2numpy(pixels)
pixels = [fg] + pixels + [np.zeros_like(fg) + 255]
return pixels
@torch.inference_mode()
def process_video_inner(image_1, image_2, prompt, seed=123, steps=25, cfg_scale=7.5, fs=3, progress_tqdm=None):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
frames = 16
target_height, target_width = find_best_bucket(
image_1.shape[0], image_1.shape[1],
options=[(320, 512), (384, 448), (448, 384), (512, 320)]
)
image_1 = resize_and_center_crop(image_1, target_width=target_width, target_height=target_height)
image_2 = resize_and_center_crop(image_2, target_width=target_width, target_height=target_height)
input_frames = numpy2pytorch([image_1, image_2])
input_frames = input_frames.unsqueeze(0).movedim(1, 2)
memory_management.load_models_to_gpu(video_pipe.text_encoder)
positive_text_cond = video_pipe.encode_cropped_prompt_77tokens(prompt)
negative_text_cond = video_pipe.encode_cropped_prompt_77tokens("")
memory_management.load_models_to_gpu([video_pipe.image_projection, video_pipe.image_encoder])
input_frames = input_frames.to(device=video_pipe.image_encoder.device, dtype=video_pipe.image_encoder.dtype)
positive_image_cond = video_pipe.encode_clip_vision(input_frames)
positive_image_cond = video_pipe.image_projection(positive_image_cond)
negative_image_cond = video_pipe.encode_clip_vision(torch.zeros_like(input_frames))
negative_image_cond = video_pipe.image_projection(negative_image_cond)
memory_management.load_models_to_gpu([video_pipe.vae])
input_frames = input_frames.to(device=video_pipe.vae.device, dtype=video_pipe.vae.dtype)
input_frame_latents, vae_hidden_states = video_pipe.encode_latents(input_frames, return_hidden_states=True)
first_frame = input_frame_latents[:, :, 0]
last_frame = input_frame_latents[:, :, 1]
concat_cond = torch.stack([first_frame] + [torch.zeros_like(first_frame)] * (frames - 2) + [last_frame], dim=2)
memory_management.load_models_to_gpu([video_pipe.unet])
latents = video_pipe(
batch_size=1,
steps=int(steps),
guidance_scale=cfg_scale,
positive_text_cond=positive_text_cond,
negative_text_cond=negative_text_cond,
positive_image_cond=positive_image_cond,
negative_image_cond=negative_image_cond,
concat_cond=concat_cond,
fs=fs,
progress_tqdm=progress_tqdm
)
memory_management.load_models_to_gpu([video_pipe.vae])
video = video_pipe.decode_latents(latents, vae_hidden_states)
return video, image_1, image_2
@torch.inference_mode()
def process_video(keyframes, prompt, steps, cfg, fps, seed, progress=gr.Progress()):
result_frames = []
cropped_images = []
for i, (im1, im2) in enumerate(zip(keyframes[:-1], keyframes[1:])):
im1 = np.array(Image.open(im1[0]))
im2 = np.array(Image.open(im2[0]))
frames, im1, im2 = process_video_inner(
im1, im2, prompt, seed=seed + i, steps=steps, cfg_scale=cfg, fs=3,
progress_tqdm=functools.partial(progress.tqdm, desc=f'Generating Videos ({i + 1}/{len(keyframes) - 1})')
)
result_frames.append(frames[:, :, :-1, :, :])
cropped_images.append([im1, im2])
video = torch.cat(result_frames, dim=2)
video = torch.flip(video, dims=[2])
uuid_name = str(uuid.uuid4())
output_filename = os.path.join(result_dir, uuid_name + '.mp4')
Image.fromarray(cropped_images[0][0]).save(os.path.join(result_dir, uuid_name + '.png'))
video = save_bcthw_as_mp4(video, output_filename, fps=fps)
video = [x.cpu().numpy() for x in video]
return output_filename, video
block = gr.Blocks().queue()
with block:
gr.Markdown('# Paints-Undo')
with gr.Accordion(label='Step 1: Upload Image and Generate Prompt', open=True):
with gr.Row():
with gr.Column():
input_fg = gr.Image(sources=['upload'], type="numpy", label="Image", height=512)
with gr.Column():
prompt_gen_button = gr.Button(value="Generate Prompt", interactive=False)
prompt = gr.Textbox(label="Output Prompt", interactive=True)
with gr.Accordion(label='Step 2: Generate Key Frames', open=True):
with gr.Row():
with gr.Column():
input_undo_steps = gr.Dropdown(label="Operation Steps", value=[400, 600, 800, 900, 950, 999],
choices=list(range(1000)), multiselect=True)
seed = gr.Slider(label='Stage 1 Seed', minimum=0, maximum=50000, step=1, value=12345)
image_width = gr.Slider(label="Image Width", minimum=256, maximum=1024, value=512, step=64)
image_height = gr.Slider(label="Image Height", minimum=256, maximum=1024, value=640, step=64)
steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=50, step=1)
cfg = gr.Slider(label="CFG Scale", minimum=1.0, maximum=32.0, value=3.0, step=0.01)
n_prompt = gr.Textbox(label="Negative Prompt",
value='lowres, bad anatomy, bad hands, cropped, worst quality')
with gr.Column():
key_gen_button = gr.Button(value="Generate Key Frames", interactive=False)
result_gallery = gr.Gallery(height=512, object_fit='contain', label='Outputs', columns=4)
with gr.Accordion(label='Step 3: Generate All Videos', open=True):
with gr.Row():
with gr.Column():
i2v_input_text = gr.Text(label='Prompts', value='1girl, masterpiece, best quality')
i2v_seed = gr.Slider(label='Stage 2 Seed', minimum=0, maximum=50000, step=1, value=123)
i2v_cfg_scale = gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='CFG Scale', value=7.5,
elem_id="i2v_cfg_scale")
i2v_steps = gr.Slider(minimum=1, maximum=60, step=1, elem_id="i2v_steps",
label="Sampling steps", value=50)
i2v_fps = gr.Slider(minimum=1, maximum=30, step=1, elem_id="i2v_motion", label="FPS", value=4)
with gr.Column():
i2v_end_btn = gr.Button("Generate Video", interactive=False)
i2v_output_video = gr.Video(label="Generated Video", elem_id="output_vid", autoplay=True,
show_share_button=True, height=512)
with gr.Row():
i2v_output_images = gr.Gallery(height=512, label="Output Frames", object_fit="contain", columns=8)
input_fg.change(lambda: ["", gr.update(interactive=True), gr.update(interactive=False), gr.update(interactive=False)],
outputs=[prompt, prompt_gen_button, key_gen_button, i2v_end_btn])
prompt_gen_button.click(
fn=interrogator_process,
inputs=[input_fg],
outputs=[prompt]
).then(lambda: [gr.update(interactive=True), gr.update(interactive=True), gr.update(interactive=False)],
outputs=[prompt_gen_button, key_gen_button, i2v_end_btn])
key_gen_button.click(
fn=process,
inputs=[input_fg, prompt, input_undo_steps, image_width, image_height, seed, steps, n_prompt, cfg],
outputs=[result_gallery]
).then(lambda: [gr.update(interactive=True), gr.update(interactive=True), gr.update(interactive=True)],
outputs=[prompt_gen_button, key_gen_button, i2v_end_btn])
i2v_end_btn.click(
inputs=[result_gallery, i2v_input_text, i2v_steps, i2v_cfg_scale, i2v_fps, i2v_seed],
outputs=[i2v_output_video, i2v_output_images],
fn=process_video
)
dbs = [
['./imgs/1.jpg', 12345, 123],
['./imgs/2.jpg', 37000, 12345],
['./imgs/3.jpg', 3000, 3000],
]
gr.Examples(
examples=dbs,
inputs=[input_fg, seed, i2v_seed],
examples_per_page=1024
)
block.queue().launch(server_name='0.0.0.0')