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Merged
merged 7 commits into from
May 8, 2025
Merged

Support period in lora name #76

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7cd8092
added outputs directory viewer, resolution height and width for laten…
AvatoR-S-K May 5, 2025
5c925fb
outputs directory viewer
AvatoR-S-K May 5, 2025
1bede7e
LoRA weights fix
AvatoR-S-K May 5, 2025
54ad9fa
LoRA weights fix for f1
AvatoR-S-K May 5, 2025
ecbe3ee
comment out new tab for now
May 5, 2025
3924e6b
Support period in lora name
arledesma May 6, 2025
82d3f10
Replace period with _DOT_ in module name
arledesma May 6, 2025
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14 changes: 12 additions & 2 deletions diffusers_helper/lora_utils.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
from pathlib import Path
from pathlib import Path, PurePath
from typing import Dict, List, Optional, Union
from diffusers.loaders.lora_pipeline import _fetch_state_dict
from diffusers.loaders.lora_conversion_utils import _convert_hunyuan_video_lora_to_diffusers
Expand Down Expand Up @@ -33,7 +33,17 @@ def load_lora(transformer, lora_path: Path, weight_name: Optional[str] = "pytorc

state_dict = _convert_hunyuan_video_lora_to_diffusers(state_dict)

adapter_name = weight_name.split(".")[0]
# should weight_name even be Optional[str] or just str?
# For now, we assume it is never None
# The module name in the state_dict must not include a . in the name
# See https://github.com/pytorch/pytorch/pull/6639/files#diff-4be56271f7bfe650e3521c81fd363da58f109cd23ee80d243156d2d6ccda6263R133-R134
adapter_name = PurePath(str(weight_name).replace('_DOT_', '.')).stem.replace('.', '_DOT_')
if '_DOT_' in adapter_name:
print(
f"LoRA file '{weight_name}' contains a '.' in the name. " +
'This may cause issues. Consider renaming the file.' +
f" Using '{adapter_name}' as the adapter name to be safe."
)

# Check if adapter already exists and delete it if it does
if hasattr(transformer, 'peft_config') and adapter_name in transformer.peft_config:
Expand Down
126 changes: 105 additions & 21 deletions modules/interface.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,7 @@
import time
import datetime
import random
import json
import os
from typing import List, Dict, Any, Optional
from PIL import Image
Expand Down Expand Up @@ -125,7 +126,8 @@ def create_interface(
height=420,
elem_classes="contain-image"
)



with gr.Accordion("Latent Image Options", open=False):
latent_type = gr.Dropdown(
["Black", "White", "Noise", "Green Screen"], label="Latent Image", value="Black", info="Used as a starting point if no image is provided"
Expand All @@ -142,11 +144,21 @@ def create_interface(
with gr.Row():
steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=25, step=1)
total_second_length = gr.Slider(label="Video Length (Seconds)", minimum=1, maximum=120, value=6, step=0.1)
with gr.Row():
resolution = gr.Slider(
label="Output Resolution (Width)", minimum=128, maximum=768, value=640,
step=32, info="Nearest valid bucket size will be used. Height will be adjusted automatically."
with gr.Row("Resolution"):
resolutionW = gr.Slider(
label="Width", minimum=128, maximum=768, value=640, step=32,
info="Nearest valid width will be used."
)
resolutionH = gr.Slider(
label="Height", minimum=128, maximum=768, value=640, step=32,
info="Nearest valid height will be used."
)
def on_input_image_change(img):
if img is not None:
return gr.update(info="Nearest valid bucket size will be used. Height will be adjusted automatically."), gr.update(visible=False)
else:
return gr.update(info="Nearest valid width will be used."), gr.update(visible=True)
input_image.change(fn=on_input_image_change, inputs=[input_image], outputs=[resolutionW, resolutionH])
with gr.Row("LoRAs"):
lora_selector = gr.Dropdown(
choices=lora_names,
Expand All @@ -155,7 +167,7 @@ def create_interface(
value=[],
info="Select one or more LoRAs to use for this job"
)

lora_names_states = gr.State(lora_names)
lora_sliders = {}
for lora in lora_names:
lora_sliders[lora] = gr.Slider(
Expand All @@ -173,20 +185,18 @@ def create_interface(
save_metadata = gr.Checkbox(label="Save Metadata", value=True, info="Save to JSON file")
with gr.Row("TeaCache"):
use_teacache = gr.Checkbox(label='Use TeaCache', value=True, info='Faster speed, but often makes hands and fingers slightly worse.')

n_prompt = gr.Textbox(label="Negative Prompt", value="", visible=False) # Not used

with gr.Row():
seed = gr.Number(label="Seed", value=31337, precision=0)
randomize_seed = gr.Checkbox(label="Randomize", value=False, info="Generate a new random seed for each job")


with gr.Accordion("Advanced Parameters", open=False):
latent_window_size = gr.Slider(label="Latent Window Size", minimum=1, maximum=33, value=9, step=1, visible=True, info='Change at your own risk, very experimental') # Should not change
cfg = gr.Slider(label="CFG Scale", minimum=1.0, maximum=32.0, value=1.0, step=0.01, visible=False) # Should not change
gs = gr.Slider(label="Distilled CFG Scale", minimum=1.0, maximum=32.0, value=10.0, step=0.01)
rs = gr.Slider(label="CFG Re-Scale", minimum=0.0, maximum=1.0, value=0.0, step=0.01, visible=False) # Should not change
gpu_memory_preservation = gr.Slider(label="GPU Inference Preserved Memory (GB) (larger means slower)", minimum=6, maximum=128, value=6, step=0.1, info="Set this number to a larger value if you encounter OOM. Larger value causes slower speed.")
gpu_memory_preservation = gr.Slider(label="GPU Inference Preserved Memory (GB) (larger means slower)", minimum=1, maximum=128, value=6, step=0.1, info="Set this number to a larger value if you encounter OOM. Larger value causes slower speed.")
with gr.Accordion("Output Parameters", open=False):
mp4_crf = gr.Slider(label="MP4 Compression", minimum=0, maximum=100, value=16, step=1, info="Lower means better quality. 0 is uncompressed. Change to 16 if you get black outputs. ")
clean_up_videos = gr.Checkbox(
Expand Down Expand Up @@ -217,6 +227,7 @@ def create_interface(
elem_classes="contain-image"
)


with gr.Accordion("Latent Image Options", open=False):
f1_latent_type = gr.Dropdown(
["Black", "White", "Noise", "Green Screen"], label="Latent Image", value="Black", info="Used as a starting point if no image is provided"
Expand All @@ -233,11 +244,21 @@ def create_interface(
with gr.Row():
f1_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=25, step=1)
f1_total_second_length = gr.Slider(label="Video Length (Seconds)", minimum=1, maximum=120, value=5, step=0.1)
with gr.Row():
f1_resolution = gr.Slider(
label="Output Resolution (Width)", minimum=128, maximum=768, value=640,
step=32, info="Nearest valid bucket size will be used. Height will be adjusted automatically."
with gr.Row("Resolution"):
f1_resolutionW = gr.Slider(
label="Width", minimum=128, maximum=768, value=640, step=32,
info="Nearest valid width will be used."
)
f1_resolutionH = gr.Slider(
label="Height", minimum=128, maximum=768, value=640, step=32,
info="Nearest valid height will be used."
)
def f1_on_input_image_change(img):
if img is not None:
return gr.update(info="Nearest valid bucket size will be used. Height will be adjusted automatically."), gr.update(visible=False)
else:
return gr.update(info="Nearest valid width will be used."), gr.update(visible=True)
f1_input_image.change(fn=f1_on_input_image_change, inputs=[f1_input_image], outputs=[f1_resolutionW, f1_resolutionH])
with gr.Row("LoRAs"):
f1_lora_selector = gr.Dropdown(
choices=lora_names,
Expand All @@ -246,7 +267,7 @@ def create_interface(
value=[],
info="Select one or more LoRAs to use for this job"
)

f1_lora_names_states = gr.State(lora_names)
f1_lora_sliders = {}
for lora in lora_names:
f1_lora_sliders[lora] = gr.Slider(
Expand Down Expand Up @@ -275,7 +296,7 @@ def create_interface(
f1_cfg = gr.Slider(label="CFG Scale", minimum=1.0, maximum=32.0, value=1.0, step=0.01, visible=False)
f1_gs = gr.Slider(label="Distilled CFG Scale", minimum=1.0, maximum=32.0, value=10.0, step=0.01)
f1_rs = gr.Slider(label="CFG Re-Scale", minimum=0.0, maximum=1.0, value=0.0, step=0.01, visible=False)
f1_gpu_memory_preservation = gr.Slider(label="GPU Inference Preserved Memory (GB) (larger means slower)", minimum=6, maximum=128, value=6, step=0.1, info="Set this number to a larger value if you encounter OOM. Larger value causes slower speed.")
f1_gpu_memory_preservation = gr.Slider(label="GPU Inference Preserved Memory (GB) (larger means slower)", minimum=1, maximum=128, value=6, step=0.1, info="Set this number to a larger value if you encounter OOM. Larger value causes slower speed.")
with gr.Accordion("Output Parameters", open=False):
f1_mp4_crf = gr.Slider(label="MP4 Compression", minimum=0, maximum=100, value=16, step=1, info="Lower means better quality. 0 is uncompressed. Change to 16 if you get black outputs. ")
f1_clean_up_videos = gr.Checkbox(
Expand Down Expand Up @@ -339,6 +360,67 @@ def create_interface(
object-fit: cover;
}
"""
# with gr.TabItem("Outputs"):
# outputDirectory = settings.get("output_dir", settings.default_settings['output_dir'])
# def get_gallery_items():
# items = []
# for f in os.listdir(outputDirectory):
# if f.endswith(".png"):
# prefix = os.path.splitext(f)[0]
# latest_video = get_latest_video_version(prefix)
# if latest_video:
# video_path = os.path.join(outputDirectory, latest_video)
# mtime = os.path.getmtime(video_path)
# preview_path = os.path.join(outputDirectory, f)
# items.append((preview_path, prefix, mtime))
# items.sort(key=lambda x: x[2], reverse=True)
# return [(i[0], i[1]) for i in items]
# def get_latest_video_version(prefix):
# max_number = -1
# selected_file = None
# for f in os.listdir(outputDirectory):
# if f.startswith(prefix + "_") and f.endswith(".mp4"):
# num = int(f.replace(prefix + "_", '').replace(".mp4", ''))
# if num > max_number:
# max_number = num
# selected_file = f
# return selected_file
# def load_video_and_info_from_prefix(prefix):
# video_file = get_latest_video_version(prefix)
# if not video_file:
# return None, "JSON not found."
# video_path = os.path.join(outputDirectory, video_file)
# json_path = os.path.join(outputDirectory, prefix) + ".json"
# info = {"description": "no info"}
# if os.path.exists(json_path):
# with open(json_path, "r", encoding="utf-8") as f:
# info = json.load(f)
# return video_path, json.dumps(info, indent=2, ensure_ascii=False)
# gallery_items_state = gr.State(get_gallery_items())
# with gr.Row():
# with gr.Column(scale=2):
# thumbs = gr.Gallery(
# # value=[i[0] for i in get_gallery_items()],
# columns=[4],
# allow_preview=False,
# object_fit="cover",
# height="auto"
# )
# refresh_button = gr.Button("Update")
# with gr.Column(scale=5):
# video_out = gr.Video(sources=[], autoplay=True, loop=True, visible=False)
# with gr.Column(scale=1):
# info_out = gr.Textbox(label="Generation info", visible=False)
# def refresh_gallery():
# new_items = get_gallery_items()
# return gr.update(value=[i[0] for i in new_items]), new_items
# refresh_button.click(fn=refresh_gallery, outputs=[thumbs, gallery_items_state])

# def on_select(evt: gr.SelectData, gallery_items):
# prefix = gallery_items[evt.index][1]
# video, info = load_video_and_info_from_prefix(prefix)
# return gr.update(value=video, visible=True), gr.update(value=info, visible=True)
# thumbs.select(fn=on_select, inputs=[gallery_items_state], outputs=[video_out, info_out])

with gr.Tab("Settings"):
with gr.Row():
Expand Down Expand Up @@ -430,7 +512,7 @@ def cleanup_temp_files():
# Connect the main process function (wrapper for adding to queue)
def process_with_queue_update(model_type, *args):
# Extract all arguments (ensure order matches inputs lists)
input_image, prompt_text, n_prompt, seed_value, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, use_teacache, mp4_crf, randomize_seed_checked, save_metadata_checked, blend_sections, latent_type, clean_up_videos, selected_loras, resolution, *lora_args = args
input_image, prompt_text, n_prompt, seed_value, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, use_teacache, mp4_crf, randomize_seed_checked, save_metadata_checked, blend_sections, latent_type, clean_up_videos, selected_loras, resolutionW, resolutionH, *lora_args = args

# DO NOT parse the prompt here. Parsing happens once in the worker.

Expand All @@ -439,7 +521,7 @@ def process_with_queue_update(model_type, *args):
# Pass the model_type and the ORIGINAL prompt_text string to the backend process function
result = process_fn(model_type, input_image, prompt_text, n_prompt, seed_value, total_second_length, # Pass original prompt_text string
latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation,
use_teacache, mp4_crf, save_metadata_checked, blend_sections, latent_type, clean_up_videos, selected_loras, resolution, *lora_args)
use_teacache, mp4_crf, save_metadata_checked, blend_sections, latent_type, clean_up_videos, selected_loras, resolutionW, resolutionH, *lora_args)

# If randomize_seed is checked, generate a new random seed for the next job
new_seed_value = None
Expand Down Expand Up @@ -492,7 +574,9 @@ def end_process_with_update():
latent_type,
clean_up_videos,
lora_selector,
resolution
resolutionW,
resolutionH,
lora_names_states
]
# Add LoRA sliders to the input list
ips.extend([lora_sliders[lora] for lora in lora_names])
Expand All @@ -518,7 +602,9 @@ def end_process_with_update():
f1_latent_type,
f1_clean_up_videos,
f1_lora_selector,
f1_resolution
f1_resolutionW,
f1_resolutionH,
f1_lora_names_states
]
# Add F1 LoRA sliders to the input list
f1_ips.extend([f1_lora_sliders[lora] for lora in lora_names])
Expand Down Expand Up @@ -626,8 +712,6 @@ def load_metadata_from_json(json_path):
return [gr.update()] * (2 + num_orig_sliders)

try:
import json

with open(json_path, 'r') as f:
metadata = json.load(f)

Expand Down
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