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"""
import os
import random
import cv2
import gradio as gr
import numpy as np
import torch
from omegaconf import OmegaConf
from PIL import Image
from safetensors import safe_open
from ..data.bucket_sampler import ASPECT_RATIO_512, get_closest_ratio
from ..models import (AutoencoderKLWan, AutoTokenizer, CLIPModel,
WanT5EncoderModel, WanTransformer3DModel)
from ..models.cache_utils import get_teacache_coefficients
from ..pipeline import WanFunInpaintPipeline, WanFunPipeline, WanFunControlPipeline
from ..utils.fp8_optimization import (convert_model_weight_to_float8,
convert_weight_dtype_wrapper,
replace_parameters_by_name)
from ..utils.lora_utils import merge_lora, unmerge_lora
from ..utils.utils import (filter_kwargs, get_image_to_video_latent,
get_video_to_video_latent, save_videos_grid)
from .controller import (Fun_Controller, Fun_Controller_Client,
all_cheduler_dict, css, ddpm_scheduler_dict,
flow_scheduler_dict, gradio_version,
gradio_version_is_above_4)
from .ui import (create_cfg_and_seedbox,
create_fake_finetune_models_checkpoints,
create_fake_height_width, create_fake_model_checkpoints,
create_fake_model_type, create_finetune_models_checkpoints,
create_generation_method,
create_generation_methods_and_video_length,
create_height_width, create_model_checkpoints,
create_model_type, create_prompts, create_samplers,
create_ui_outputs)
class Wan_Fun_Controller(Fun_Controller):
def update_diffusion_transformer(self, diffusion_transformer_dropdown):
print("Update diffusion transformer")
self.diffusion_transformer_dropdown = diffusion_transformer_dropdown
if diffusion_transformer_dropdown == "none":
return gr.update()
self.vae = AutoencoderKLWan.from_pretrained(
os.path.join(diffusion_transformer_dropdown, self.config['vae_kwargs'].get('vae_subpath', 'vae')),
additional_kwargs=OmegaConf.to_container(self.config['vae_kwargs']),
).to(self.weight_dtype)
# Get Transformer
self.transformer = WanTransformer3DModel.from_pretrained(
os.path.join(diffusion_transformer_dropdown, self.config['transformer_additional_kwargs'].get('transformer_subpath', 'transformer')),
transformer_additional_kwargs=OmegaConf.to_container(self.config['transformer_additional_kwargs']),
low_cpu_mem_usage=True,
torch_dtype=self.weight_dtype,
)
# Get Tokenizer
self.tokenizer = AutoTokenizer.from_pretrained(
os.path.join(diffusion_transformer_dropdown, self.config['text_encoder_kwargs'].get('tokenizer_subpath', 'tokenizer')),
)
# Get Text encoder
self.text_encoder = WanT5EncoderModel.from_pretrained(
os.path.join(diffusion_transformer_dropdown, self.config['text_encoder_kwargs'].get('text_encoder_subpath', 'text_encoder')),
additional_kwargs=OmegaConf.to_container(self.config['text_encoder_kwargs']),
low_cpu_mem_usage=True,
torch_dtype=self.weight_dtype,
)
self.text_encoder = self.text_encoder.eval()
if self.transformer.config.in_channels != self.vae.config.latent_channels:
# Get Clip Image Encoder
self.clip_image_encoder = CLIPModel.from_pretrained(
os.path.join(diffusion_transformer_dropdown, self.config['image_encoder_kwargs'].get('image_encoder_subpath', 'image_encoder')),
).to(self.weight_dtype)
self.clip_image_encoder = self.clip_image_encoder.eval()
else:
self.clip_image_encoder = None
Choosen_Scheduler = self.scheduler_dict[list(self.scheduler_dict.keys())[0]]
self.scheduler = Choosen_Scheduler(
**filter_kwargs(Choosen_Scheduler, OmegaConf.to_container(self.config['scheduler_kwargs']))
)
# Get pipeline
if self.model_type == "Inpaint":
if self.transformer.config.in_channels != self.vae.config.latent_channels:
self.pipeline = WanFunInpaintPipeline(
vae=self.vae,
tokenizer=self.tokenizer,
text_encoder=self.text_encoder,
transformer=self.transformer,
scheduler=self.scheduler,
clip_image_encoder=self.clip_image_encoder,
)
else:
self.pipeline = WanFunPipeline(
vae=self.vae,
tokenizer=self.tokenizer,
text_encoder=self.text_encoder,
transformer=self.transformer,
scheduler=self.scheduler,
)
else:
self.pipeline = WanFunControlPipeline(
vae=self.vae,
tokenizer=self.tokenizer,
text_encoder=self.text_encoder,
transformer=self.transformer,
scheduler=self.scheduler,
clip_image_encoder=self.clip_image_encoder,
)
if self.ulysses_degree > 1 or self.ring_degree > 1:
self.transformer.enable_multi_gpus_inference()
if self.GPU_memory_mode == "sequential_cpu_offload":
replace_parameters_by_name(self.transformer, ["modulation",], device=self.device)
self.transformer.freqs = self.transformer.freqs.to(device=self.device)
self.pipeline.enable_sequential_cpu_offload(device=self.device)
elif self.GPU_memory_mode == "model_cpu_offload_and_qfloat8":
convert_model_weight_to_float8(self.transformer, exclude_module_name=["modulation",])
convert_weight_dtype_wrapper(self.transformer, self.weight_dtype)
self.pipeline.enable_model_cpu_offload(device=self.device)
elif self.GPU_memory_mode == "model_cpu_offload":
self.pipeline.enable_model_cpu_offload(device=self.device)
else:
self.pipeline.to(self.device)
print("Update diffusion transformer done")
return gr.update()
def generate(
self,
diffusion_transformer_dropdown,
base_model_dropdown,
lora_model_dropdown,
lora_alpha_slider,
prompt_textbox,
negative_prompt_textbox,
sampler_dropdown,
sample_step_slider,
resize_method,
width_slider,
height_slider,
base_resolution,
generation_method,
length_slider,
overlap_video_length,
partial_video_length,
cfg_scale_slider,
start_image,
end_image,
validation_video,
validation_video_mask,
control_video,
denoise_strength,
seed_textbox,
is_api = False,
):
self.clear_cache()
self.input_check(
resize_method, generation_method, start_image, end_image, validation_video,control_video, is_api
)
is_image = True if generation_method == "Image Generation" else False
if self.base_model_path != base_model_dropdown:
self.update_base_model(base_model_dropdown)
if self.lora_model_path != lora_model_dropdown:
self.update_lora_model(lora_model_dropdown)
self.pipeline.scheduler = self.scheduler_dict[sampler_dropdown].from_config(self.pipeline.scheduler.config)
if resize_method == "Resize according to Reference":
height_slider, width_slider = self.get_height_width_from_reference(
base_resolution, start_image, validation_video, control_video,
)
if self.lora_model_path != "none":
# lora part
self.pipeline = merge_lora(self.pipeline, self.lora_model_path, multiplier=lora_alpha_slider)
coefficients = get_teacache_coefficients(self.base_model_path) if self.enable_teacache else None
if coefficients is not None:
print(f"Enable TeaCache with threshold {self.teacache_threshold} and skip the first {self.num_skip_start_steps} steps.")
self.pipeline.transformer.enable_teacache(
coefficients, sample_step_slider, self.teacache_threshold, num_skip_start_steps=self.num_skip_start_steps, offload=self.teacache_offload
)
if int(seed_textbox) != -1 and seed_textbox != "": torch.manual_seed(int(seed_textbox))
else: seed_textbox = np.random.randint(0, 1e10)
generator = torch.Generator(device=self.device).manual_seed(int(seed_textbox))
if self.enable_riflex:
latent_frames = (int(length_slider) - 1) // self.vae.config.temporal_compression_ratio + 1
self.pipeline.transformer.enable_riflex(k = self.riflex_k, L_test = latent_frames if not is_image else 1)
try:
if self.model_type == "Inpaint":
if self.transformer.config.in_channels != self.vae.config.latent_channels:
if validation_video is not None:
input_video, input_video_mask, ref_image, clip_image = get_video_to_video_latent(validation_video, length_slider if not is_image else 1, sample_size=(height_slider, width_slider), validation_video_mask=validation_video_mask, fps=16)
else:
input_video, input_video_mask, clip_image = get_image_to_video_latent(start_image, end_image, length_slider if not is_image else 1, sample_size=(height_slider, width_slider))
sample = self.pipeline(
prompt_textbox,
negative_prompt = negative_prompt_textbox,
num_inference_steps = sample_step_slider,
guidance_scale = cfg_scale_slider,
width = width_slider,
height = height_slider,
num_frames = length_slider if not is_image else 1,
generator = generator,
video = input_video,
mask_video = input_video_mask,
clip_image = clip_image
).videos
else:
sample = self.pipeline(
prompt_textbox,
negative_prompt = negative_prompt_textbox,
num_inference_steps = sample_step_slider,
guidance_scale = cfg_scale_slider,
width = width_slider,
height = height_slider,
num_frames = length_slider if not is_image else 1,
generator = generator
).videos
else:
input_video, input_video_mask, ref_image, clip_image = get_video_to_video_latent(control_video, length_slider if not is_image else 1, sample_size=(height_slider, width_slider), fps=16)
sample = self.pipeline(
prompt_textbox,
negative_prompt = negative_prompt_textbox,
num_inference_steps = sample_step_slider,
guidance_scale = cfg_scale_slider,
width = width_slider,
height = height_slider,
num_frames = length_slider if not is_image else 1,
generator = generator,
control_video = input_video,
).videos
except Exception as e:
self.clear_cache()
if self.lora_model_path != "none":
self.pipeline = unmerge_lora(self.pipeline, self.lora_model_path, multiplier=lora_alpha_slider)
if is_api:
return "", f"Error. error information is {str(e)}"
else:
return gr.update(), gr.update(), f"Error. error information is {str(e)}"
self.clear_cache()
# lora part
if self.lora_model_path != "none":
self.pipeline = unmerge_lora(self.pipeline, self.lora_model_path, multiplier=lora_alpha_slider)
save_sample_path = self.save_outputs(
is_image, length_slider, sample, fps=16
)
if is_image or length_slider == 1:
if is_api:
return save_sample_path, "Success"
else:
if gradio_version_is_above_4:
return gr.Image(value=save_sample_path, visible=True), gr.Video(value=None, visible=False), "Success"
else:
return gr.Image.update(value=save_sample_path, visible=True), gr.Video.update(value=None, visible=False), "Success"
else:
if is_api:
return save_sample_path, "Success"
else:
if gradio_version_is_above_4:
return gr.Image(visible=False, value=None), gr.Video(value=save_sample_path, visible=True), "Success"
else:
return gr.Image.update(visible=False, value=None), gr.Video.update(value=save_sample_path, visible=True), "Success"
Wan_Fun_Controller_Host = Wan_Fun_Controller
Wan_Fun_Controller_Client = Fun_Controller_Client
def ui(GPU_memory_mode, scheduler_dict, config_path, ulysses_degree, ring_degree, enable_teacache, teacache_threshold, num_skip_start_steps, teacache_offload, enable_riflex, riflex_k, weight_dtype, savedir_sample=None):
controller = Wan_Fun_Controller(
GPU_memory_mode, scheduler_dict, model_name=None, model_type="Inpaint",
config_path=config_path, ulysses_degree=ulysses_degree, ring_degree=ring_degree,
enable_teacache=enable_teacache, teacache_threshold=teacache_threshold,
num_skip_start_steps=num_skip_start_steps, teacache_offload=teacache_offload,
enable_riflex=enable_riflex, riflex_k=riflex_k, weight_dtype=weight_dtype,
savedir_sample=savedir_sample,
)
with gr.Blocks(css=css) as demo:
gr.Markdown(
"""
# Wan-Fun:
A Wan with more flexible generation conditions, capable of producing videos of different resolutions, around 6 seconds, and fps 8 (frames 1 to 81), as well as image generated videos.
[Github](https://github.com/aigc-apps/CogVideoX-Fun/)
"""
)
with gr.Column(variant="panel"):
model_type = create_model_type(visible=True)
diffusion_transformer_dropdown, diffusion_transformer_refresh_button = \
create_model_checkpoints(controller, visible=True)
base_model_dropdown, lora_model_dropdown, lora_alpha_slider, personalized_refresh_button = \
create_finetune_models_checkpoints(controller, visible=True)
with gr.Column(variant="panel"):
prompt_textbox, negative_prompt_textbox = create_prompts(negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走")
with gr.Row():
with gr.Column():
sampler_dropdown, sample_step_slider = create_samplers(controller)
resize_method, width_slider, height_slider, base_resolution = create_height_width(
default_height = 480, default_width = 832, maximum_height = 1344,
maximum_width = 1344,
)
generation_method, length_slider, overlap_video_length, partial_video_length = \
create_generation_methods_and_video_length(
["Video Generation", "Image Generation"],
default_video_length=81,
maximum_video_length=81,
)
image_to_video_col, video_to_video_col, control_video_col, source_method, start_image, template_gallery, end_image, validation_video, validation_video_mask, denoise_strength, control_video = create_generation_method(
["Text to Video (文本到视频)", "Image to Video (图片到视频)"], prompt_textbox
)
cfg_scale_slider, seed_textbox, seed_button = create_cfg_and_seedbox(gradio_version_is_above_4)
generate_button = gr.Button(value="Generate (生成)", variant='primary')
result_image, result_video, infer_progress = create_ui_outputs()
model_type.change(
fn=controller.update_model_type,
inputs=[model_type],
outputs=[]
)
def upload_generation_method(generation_method):
if generation_method == "Video Generation":
return [gr.update(visible=True, maximum=81, value=81, interactive=True), gr.update(visible=False), gr.update(visible=False)]
elif generation_method == "Image Generation":
return [gr.update(minimum=1, maximum=1, value=1, interactive=False), gr.update(visible=False), gr.update(visible=False)]
else:
return [gr.update(visible=True, maximum=1344), gr.update(visible=True), gr.update(visible=True)]
generation_method.change(
upload_generation_method, generation_method, [length_slider, overlap_video_length, partial_video_length]
)
def upload_source_method(source_method):
if source_method == "Text to Video (文本到视频)":
return [gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(value=None), gr.update(value=None), gr.update(value=None), gr.update(value=None), gr.update(value=None)]
elif source_method == "Image to Video (图片到视频)":
return [gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(), gr.update(), gr.update(value=None), gr.update(value=None), gr.update(value=None)]
elif source_method == "Video to Video (视频到视频)":
return [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(value=None), gr.update(value=None), gr.update(), gr.update(), gr.update(value=None)]
else:
return [gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), gr.update(value=None), gr.update(value=None), gr.update(value=None), gr.update(value=None), gr.update()]
source_method.change(
upload_source_method, source_method, [
image_to_video_col, video_to_video_col, control_video_col, start_image, end_image,
validation_video, validation_video_mask, control_video
]
)
def upload_resize_method(resize_method):
if resize_method == "Generate by":
return [gr.update(visible=True), gr.update(visible=True), gr.update(visible=False)]
else:
return [gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)]
resize_method.change(
upload_resize_method, resize_method, [width_slider, height_slider, base_resolution]
)
generate_button.click(
fn=controller.generate,
inputs=[
diffusion_transformer_dropdown,
base_model_dropdown,
lora_model_dropdown,
lora_alpha_slider,
prompt_textbox,
negative_prompt_textbox,
sampler_dropdown,
sample_step_slider,
resize_method,
width_slider,
height_slider,
base_resolution,
generation_method,
length_slider,
overlap_video_length,
partial_video_length,
cfg_scale_slider,
start_image,
end_image,
validation_video,
validation_video_mask,
control_video,
denoise_strength,
seed_textbox,
],
outputs=[result_image, result_video, infer_progress]
)
return demo, controller
def ui_host(GPU_memory_mode, scheduler_dict, model_name, model_type, config_path, ulysses_degree, ring_degree, enable_teacache, teacache_threshold, num_skip_start_steps, teacache_offload, enable_riflex, riflex_k, weight_dtype, savedir_sample=None):
controller = Wan_Fun_Controller_Host(
GPU_memory_mode, scheduler_dict, model_name=model_name, model_type=model_type,
config_path=config_path, ulysses_degree=ulysses_degree, ring_degree=ring_degree,
enable_teacache=enable_teacache, teacache_threshold=teacache_threshold,
num_skip_start_steps=num_skip_start_steps, teacache_offload=teacache_offload,
enable_riflex=enable_riflex, riflex_k=riflex_k, weight_dtype=weight_dtype,
savedir_sample=savedir_sample,
)
with gr.Blocks(css=css) as demo:
gr.Markdown(
"""
# Wan-Fun:
A Wan with more flexible generation conditions, capable of producing videos of different resolutions, around 6 seconds, and fps 8 (frames 1 to 81), as well as image generated videos.
[Github](https://github.com/aigc-apps/CogVideoX-Fun/)
"""
)
with gr.Column(variant="panel"):
model_type = create_fake_model_type(visible=True)
diffusion_transformer_dropdown = create_fake_model_checkpoints(model_name, visible=True)
base_model_dropdown, lora_model_dropdown, lora_alpha_slider = create_fake_finetune_models_checkpoints(visible=True)
with gr.Column(variant="panel"):
prompt_textbox, negative_prompt_textbox = create_prompts(negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走")
with gr.Row():
with gr.Column():
sampler_dropdown, sample_step_slider = create_samplers(controller)
resize_method, width_slider, height_slider, base_resolution = create_height_width(
default_height = 480, default_width = 832, maximum_height = 1344,
maximum_width = 1344,
)
generation_method, length_slider, overlap_video_length, partial_video_length = \
create_generation_methods_and_video_length(
["Video Generation", "Image Generation"],
default_video_length=81,
maximum_video_length=81,
)
image_to_video_col, video_to_video_col, control_video_col, source_method, start_image, template_gallery, end_image, validation_video, validation_video_mask, denoise_strength, control_video = create_generation_method(
["Text to Video (文本到视频)", "Image to Video (图片到视频)"], prompt_textbox
)
cfg_scale_slider, seed_textbox, seed_button = create_cfg_and_seedbox(gradio_version_is_above_4)
generate_button = gr.Button(value="Generate (生成)", variant='primary')
result_image, result_video, infer_progress = create_ui_outputs()
def upload_generation_method(generation_method):
if generation_method == "Video Generation":
return gr.update(visible=True, minimum=1, maximum=81, value=81, interactive=True)
elif generation_method == "Image Generation":
return gr.update(minimum=1, maximum=1, value=1, interactive=False)
generation_method.change(
upload_generation_method, generation_method, [length_slider]
)
def upload_source_method(source_method):
if source_method == "Text to Video (文本到视频)":
return [gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(value=None), gr.update(value=None), gr.update(value=None), gr.update(value=None), gr.update(value=None)]
elif source_method == "Image to Video (图片到视频)":
return [gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(), gr.update(), gr.update(value=None), gr.update(value=None), gr.update(value=None)]
elif source_method == "Video to Video (视频到视频)":
return [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(value=None), gr.update(value=None), gr.update(), gr.update(), gr.update(value=None)]
else:
return [gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), gr.update(value=None), gr.update(value=None), gr.update(value=None), gr.update(value=None), gr.update()]
source_method.change(
upload_source_method, source_method, [
image_to_video_col, video_to_video_col, control_video_col, start_image, end_image,
validation_video, validation_video_mask, control_video
]
)
def upload_resize_method(resize_method):
if resize_method == "Generate by":
return [gr.update(visible=True), gr.update(visible=True), gr.update(visible=False)]
else:
return [gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)]
resize_method.change(
upload_resize_method, resize_method, [width_slider, height_slider, base_resolution]
)
generate_button.click(
fn=controller.generate,
inputs=[
diffusion_transformer_dropdown,
base_model_dropdown,
lora_model_dropdown,
lora_alpha_slider,
prompt_textbox,
negative_prompt_textbox,
sampler_dropdown,
sample_step_slider,
resize_method,
width_slider,
height_slider,
base_resolution,
generation_method,
length_slider,
overlap_video_length,
partial_video_length,
cfg_scale_slider,
start_image,
end_image,
validation_video,
validation_video_mask,
control_video,
denoise_strength,
seed_textbox,
],
outputs=[result_image, result_video, infer_progress]
)
return demo, controller
def ui_client(scheduler_dict, model_name, savedir_sample=None):
controller = Wan_Fun_Controller_Client(scheduler_dict, savedir_sample)
with gr.Blocks(css=css) as demo:
gr.Markdown(
"""
# Wan-Fun:
A Wan with more flexible generation conditions, capable of producing videos of different resolutions, around 6 seconds, and fps 8 (frames 1 to 81), as well as image generated videos.
[Github](https://github.com/aigc-apps/CogVideoX-Fun/)
"""
)
with gr.Column(variant="panel"):
diffusion_transformer_dropdown = create_fake_model_checkpoints(model_name, visible=True)
base_model_dropdown, lora_model_dropdown, lora_alpha_slider = create_fake_finetune_models_checkpoints(visible=True)
with gr.Column(variant="panel"):
prompt_textbox, negative_prompt_textbox = create_prompts(negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走")
with gr.Row():
with gr.Column():
sampler_dropdown, sample_step_slider = create_samplers(controller, maximum_step=50)
resize_method, width_slider, height_slider, base_resolution = create_fake_height_width(
default_height = 480, default_width = 832, maximum_height = 1344,
maximum_width = 1344,
)
generation_method, length_slider, overlap_video_length, partial_video_length = \
create_generation_methods_and_video_length(
["Video Generation", "Image Generation"],
default_video_length=81,
maximum_video_length=81,
)
image_to_video_col, video_to_video_col, control_video_col, source_method, start_image, template_gallery, end_image, validation_video, validation_video_mask, denoise_strength, control_video = create_generation_method(
["Text to Video (文本到视频)", "Image to Video (图片到视频)"], prompt_textbox
)
cfg_scale_slider, seed_textbox, seed_button = create_cfg_and_seedbox(gradio_version_is_above_4)
generate_button = gr.Button(value="Generate (生成)", variant='primary')
result_image, result_video, infer_progress = create_ui_outputs()
def upload_generation_method(generation_method):
if generation_method == "Video Generation":
return gr.update(visible=True, minimum=5, maximum=81, value=49, interactive=True)
elif generation_method == "Image Generation":
return gr.update(minimum=1, maximum=1, value=1, interactive=False)
generation_method.change(
upload_generation_method, generation_method, [length_slider]
)
def upload_source_method(source_method):
if source_method == "Text to Video (文本到视频)":
return [gr.update(visible=False), gr.update(visible=False), gr.update(value=None), gr.update(value=None), gr.update(value=None), gr.update(value=None)]
elif source_method == "Image to Video (图片到视频)":
return [gr.update(visible=True), gr.update(visible=False), gr.update(), gr.update(), gr.update(value=None), gr.update(value=None)]
else:
return [gr.update(visible=False), gr.update(visible=True), gr.update(value=None), gr.update(value=None), gr.update(), gr.update()]
source_method.change(
upload_source_method, source_method, [image_to_video_col, video_to_video_col, start_image, end_image, validation_video, validation_video_mask]
)
def upload_resize_method(resize_method):
if resize_method == "Generate by":
return [gr.update(visible=True), gr.update(visible=True), gr.update(visible=False)]
else:
return [gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)]
resize_method.change(
upload_resize_method, resize_method, [width_slider, height_slider, base_resolution]
)
generate_button.click(
fn=controller.generate,
inputs=[
diffusion_transformer_dropdown,
base_model_dropdown,
lora_model_dropdown,
lora_alpha_slider,
prompt_textbox,
negative_prompt_textbox,
sampler_dropdown,
sample_step_slider,
resize_method,
width_slider,
height_slider,
base_resolution,
generation_method,
length_slider,
cfg_scale_slider,
start_image,
end_image,
validation_video,
validation_video_mask,
denoise_strength,
seed_textbox,
],
outputs=[result_image, result_video, infer_progress]
)
return demo, controller |