Public/automatic
1
0
Fork 0
mirror of https://github.com/vladmandic/automatic synced 2026-09-17 08:19:11 +02:00
Code Issues Packages Projects Releases Wiki Activity

mark legacy scripts

Browse Source 
Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
2025-04-02 17:22:45 -04:00
parent 8e3ef40014
commit 4457ea2e51
9 changed files with 154 additions and 23 deletions
Download Patch File Download Diff File Expand all files Collapse all files
CHANGELOG.md
+1
View File
@@ -13,6 +13,7 @@ Models...And support for new models: **CogView-4**, **SANA 1.5**,
*Plus...*
- New **Prompt Enhance** using LLM,
- New pipelines such as **InfiniteYou**
- New **CLiP** models, improvements to **remote VAE**, additional wiki/docs/guides
- More quantization options and granular control
- Pretty big performance updates to a) Any model using DiT based architecture due to new caching methods, b) ZLUDA with new attention methods, c) LoRA with much lower memory usage
TODO.md
+16 -17
View File
@@ -6,10 +6,7 @@ Main ToDo list can be found at [GitHub projects](https://github.com/users/vladma
### Issues/Limitations
- Video: Hunyuan Video I2V: requires `transformers==4.47.1` <https://github.com/huggingface/diffusers/issues/11118>
- Video: CogVideoX 1.5 5B T2V/I2V: all-gray output
- Video: Allegro T2V: all-gray output
- Video: Latte1 T2V: garbage output
N/A
## Future Candidates
@@ -25,17 +22,19 @@ Main ToDo list can be found at [GitHub projects](https://github.com/users/vladma
## Code TODO
- control: support scripts via api
- enable ROCm for windows when available
- fc: autodetect distilled based on model
- fc: autodetect tensor format based on model
- hypertile: vae breaks when using non-standard sizes
- infotext: handle using regex instead
- lora: add other quantization types
- lora: force-reloading entire model as loading transformers only leads to massive memory usage
- lora: required for flux to reapply offload after lora has been applied, but fails with oom
- lora: support pre-quantized flux
- model loader: implement model in-memory caching
- modernui: monkey-patch for missing tabs.select event
- processing: remove duplicate mask params
> pnpm lint | grep W0511 | awk -F'TODO ' '{print "- "$NF}' | sed 's/ (fixme)//g'
- install: enable ROCm for windows when available
- resize image: enable full VAE mode for resize-latent
- infotext: handle using regex instead
- fc: autodetect tensor format based on model
- fc: autodetect distilled based on model
- processing: remove duplicate mask params
- model loader: implement model in-memory caching
- hypertile: vae breaks when using non-standard sizes
- model load: force-reloading entire model as loading transformers only leads to massive memory usage
- lora: add other quantization types
- lora: maybe force imediate quantization
- modules/lora/lora_extract.py:185:9: W0511: TODO: lora support pre-quantized flux
- control: support scripts via api
- modernui: monkey-patch for missing tabs.select event
scripts/allegrovideo.py
+1 -1
View File
@@ -31,7 +31,7 @@ def hijack_encode_prompt(*args, **kwargs):
class Script(scripts.Script):
def title(self):
return 'Video: Allegro'
return 'Video: Allegro (Legacy)'
def show(self, is_img2img):
return not is_img2img if shared.native else False
scripts/cogvideo.py
+1 -1
View File
@@ -22,7 +22,7 @@ debug = (os.environ.get('SD_LOAD_DEBUG', None) is not None) or (os.environ.get('
class Script(scripts.Script):
def title(self):
return 'Video: CogVideoX'
return 'Video: CogVideoX (Legacy)'
def show(self, is_img2img):
return shared.native
scripts/flux_prompt_enhance.py
+1 -1
View File
@@ -27,7 +27,7 @@ class Script(scripts.Script):
prompt: gr.Textbox = None
def title(self):
return 'Prompt enhance'
return 'Flux Prompt enhance (Legacy)'
def show(self, is_img2img):
return shared.native
scripts/hunyuanvideo.py
+1 -1
View File
@@ -60,7 +60,7 @@ def hijack_encode_prompt(*args, **kwargs):
class Script(scripts.Script):
def title(self):
return 'Video: Hunyuan Video'
return 'Video: Hunyuan Video (Legacy)'
def show(self, is_img2img):
return not is_img2img if shared.native else False
scripts/legacy_allegrovideo.py
+131
View File
@@ -0,0 +1,131 @@
import time
import gradio as gr
import transformers
import diffusers
from modules import scripts, processing, shared, images, devices, sd_models, sd_checkpoint, model_quant, timer
repo_id = 'rhymes-ai/Allegro'
def hijack_decode(*args, **kwargs):
t0 = time.time()
vae: diffusers.AutoencoderKLAllegro = shared.sd_model.vae
shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model, exclude=['vae'])
res = shared.sd_model.vae.orig_decode(*args, **kwargs)
t1 = time.time()
timer.process.add('vae', t1-t0)
shared.log.debug(f'Video: vae={vae.__class__.__name__} time={t1-t0:.2f}')
return res
def hijack_encode_prompt(*args, **kwargs):
t0 = time.time()
res = shared.sd_model.vae.orig_encode_prompt(*args, **kwargs)
t1 = time.time()
timer.process.add('te', t1-t0)
shared.log.debug(f'Video: te={shared.sd_model.text_encoder.__class__.__name__} time={t1-t0:.2f}')
shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model)
return res
class Script(scripts.Script):
def title(self):
return 'Video: Allegro (Legacy)'
def show(self, is_img2img):
return not is_img2img if shared.native else False
# return signature is array of gradio components
def ui(self, is_img2img):
with gr.Row():
gr.HTML('<a href="https://huggingface.co/rhymes-ai/Allegro">&nbsp Allegro Video</a><br>')
with gr.Row():
num_frames = gr.Slider(label='Frames', minimum=4, maximum=88, step=1, value=22)
with gr.Row():
override_scheduler = gr.Checkbox(label='Override scheduler', value=True)
with gr.Row():
from modules.ui_sections import create_video_inputs
video_type, duration, gif_loop, mp4_pad, mp4_interpolate = create_video_inputs(tab='img2img' if is_img2img else 'txt2img')
return [num_frames, override_scheduler, video_type, duration, gif_loop, mp4_pad, mp4_interpolate]
def run(self, p: processing.StableDiffusionProcessing, num_frames, override_scheduler, video_type, duration, gif_loop, mp4_pad, mp4_interpolate): # pylint: disable=arguments-differ, unused-argument
# set params
num_frames = int(num_frames)
p.width = 8 * int(p.width // 8)
p.height = 8 * int(p.height // 8)
p.do_not_save_grid = True
p.ops.append('video')
# load model
if shared.sd_model.__class__ != diffusers.AllegroPipeline:
sd_models.unload_model_weights()
t0 = time.time()
quant_args = model_quant.create_config()
transformer = diffusers.AllegroTransformer3DModel.from_pretrained(
repo_id,
subfolder="transformer",
torch_dtype=devices.dtype,
cache_dir=shared.opts.hfcache_dir,
**quant_args
)
shared.log.debug(f'Video: module={transformer.__class__.__name__}')
text_encoder = transformers.T5EncoderModel.from_pretrained(
repo_id,
subfolder="text_encoder",
cache_dir=shared.opts.hfcache_dir,
torch_dtype=devices.dtype,
**quant_args
)
shared.log.debug(f'Video: module={text_encoder.__class__.__name__}')
shared.sd_model = diffusers.AllegroPipeline.from_pretrained(
repo_id,
# transformer=transformer,
# text_encoder=text_encoder,
cache_dir=shared.opts.hfcache_dir,
torch_dtype=devices.dtype,
**quant_args
)
t1 = time.time()
shared.log.debug(f'Video: load cls={shared.sd_model.__class__.__name__} repo="{repo_id}" dtype={devices.dtype} time={t1-t0:.2f}')
sd_models.set_diffuser_options(shared.sd_model)
shared.sd_model.sd_checkpoint_info = sd_checkpoint.CheckpointInfo(repo_id)
shared.sd_model.sd_model_hash = None
shared.sd_model.vae.orig_decode = shared.sd_model.vae.decode
shared.sd_model.vae.orig_encode_prompt = shared.sd_model.encode_prompt
shared.sd_model.vae.decode = hijack_decode
shared.sd_model.encode_prompt = hijack_encode_prompt
shared.sd_model.vae.enable_tiling()
# shared.sd_model.vae.enable_slicing()
shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model)
devices.torch_gc(force=True)
processing.fix_seed(p)
if override_scheduler:
p.sampler_name = 'Default'
p.steps = 100
p.task_args['num_frames'] = num_frames
p.task_args['output_type'] = 'pil'
p.task_args['clean_caption'] = False
p.all_prompts, p.all_negative_prompts = shared.prompt_styles.apply_styles_to_prompts([p.prompt], [p.negative_prompt], p.styles, [p.seed])
p.task_args['prompt'] = p.all_prompts[0]
p.task_args['negative_prompt'] = p.all_negative_prompts[0]
# w = shared.sd_model.transformer.config.sample_width * shared.sd_model.vae_scale_factor_spatial
# h = shared.sd_model.transformer.config.sample_height * shared.sd_model.vae_scale_factor_spatial
# n = shared.sd_model.transformer.config.sample_frames * shared.sd_model.vae_scale_factor_temporal
# run processing
t0 = time.time()
shared.state.disable_preview = True
shared.log.debug(f'Video: cls={shared.sd_model.__class__.__name__} width={p.width} height={p.height} frames={num_frames}')
processed = processing.process_images(p)
shared.state.disable_preview = False
t1 = time.time()
if processed is not None and len(processed.images) > 0:
shared.log.info(f'Video: frames={len(processed.images)} time={t1-t0:.2f}')
if video_type != 'None':
images.save_video(p, filename=None, images=processed.images, video_type=video_type, duration=duration, loop=gif_loop, pad=mp4_pad, interpolate=mp4_interpolate)
return processed
scripts/ltxvideo.py
+1 -1
View File
@@ -52,7 +52,7 @@ def hijack_encode_prompt(*args, **kwargs):
class Script(scripts.Script):
def title(self):
return 'Video: LTX Video'
return 'Video: LTX Video (Legacy)'
def show(self, is_img2img):
return shared.native
scripts/mochivideo.py
+1 -1
View File
@@ -10,7 +10,7 @@ repo_id = 'genmo/mochi-1-preview'
class Script(scripts.Script):
def title(self):
return 'Video: Mochi.1 Video'
return 'Video: Mochi.1 Video (Legacy)'
def show(self, is_img2img):
return not is_img2img if shared.native else False