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mirror of https://github.com/vladmandic/automatic synced 2026-09-20 01:31:13 +02:00
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add flux inpaint and img2img

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Vladimir Mandic
2024-09-04 18:20:40 -04:00
parent 2427f922ae
commit 25a301105f
5 changed files with 27 additions and 12 deletions
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scripts/cogvideo.py
+10 -7
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@@ -60,10 +60,11 @@ class Script(scripts.Script):
return [model, sampler, frames, guidance, offload, override, video_type, duration, loop, pad, interpolate, image, video]
def load(self, model, txt):
if shared.sd_model_type != 'cogvideox' and model != 'None':
if (shared.sd_model_type != 'cogvideox' or shared.sd_model.sd_model_checkpoint != model) and model != 'None':
sd_models.unload_model_weights('model')
shared.log.info(f'CogVideoX load: model="{model}"')
try:
shared.sd_model = None
shared.sd_model = diffusers.CogVideoXPipeline.from_pretrained(model, torch_dtype=devices.dtype, cache_dir=shared.opts.diffusers_dir)
shared.sd_model.sd_checkpoint_info = sd_models.CheckpointInfo(model)
shared.sd_model.sd_model_checkpoint = model
@@ -71,7 +72,6 @@ class Script(scripts.Script):
shared.log.error(f'Loading CogVideoX: {e}')
if debug:
errors.display(e, 'CogVideoX')
devices.torch_gc()
if shared.sd_model_type == 'cogvideox' and model != 'None':
shared.sd_model = sd_models.switch_pipe(diffusers.CogVideoXPipeline if txt else diffusers.CogVideoXVideoToVideoPipeline, shared.sd_model)
shared.sd_model.set_progress_bar_config(bar_format='Progress {rate_fmt}{postfix} {bar} {percentage:3.0f}% {n_fmt}/{total_fmt} {elapsed} {remaining} ' + '\x1b[38;5;71m', ncols=80, colour='#327fba')
@@ -80,13 +80,14 @@ class Script(scripts.Script):
shared.log.info(f'CogVideoX unload: model={model}')
shared.sd_model = None
devices.torch_gc(force=True)
devices.torch_gc()
def offload(self, offload):
if shared.sd_model_type != 'cogvideox':
return
if offload == 'none':
sd_models.move_model(shared.sd_model, devices.device)
shared.log.info(f'CogVideoX: offload={offload}')
shared.log.debug(f'CogVideoX: offload={offload}')
if offload == 'balanced':
sd_models.apply_balanced_offload(shared.sd_model)
if offload == 'model':
@@ -97,6 +98,7 @@ class Script(scripts.Script):
shared.sd_model.vae.enable_slicing()
shared.sd_model.vae.enable_tiling()
"""
def prepare(self, p, video):
import imageio # TODO dont use imageio
from torchvision import transforms
@@ -108,6 +110,7 @@ class Script(scripts.Script):
tensor = torch.stack(frames).to(devices.device).permute(1, 0, 2, 3).unsqueeze(0).to(devices.dtype)
encoded = shared.sd_model.vae.encode(tensor)[0].sample()
return encoded
"""
def generate(self, p: processing.StableDiffusionProcessing):
if shared.sd_model_type != 'cogvideox':
@@ -136,10 +139,10 @@ class Script(scripts.Script):
callback_on_step_end_tensor_inputs=['latents'],
)
if getattr(p, 'image', False):
raise ValueError('CogVideoX: image not supported')
raise ValueError('CogVideoX: image not supported') # TODO image2video
# args['latents'] = self.prepare(p, [p.image])
elif getattr(p, 'video', False):
raise ValueError('CogVideoX: video not supported')
raise ValueError('CogVideoX: video not supported') # TODO video2video
# args['video'] = self.prepare(p, p.video)
else:
args['num_frames'] = p.frames # only txt2vid has num_frames
@@ -180,8 +183,8 @@ class Script(scripts.Script):
self.load(model, txt)
self.offload(offload)
frames = self.generate(p)
info = 'whatever'
processed = processing.Processed(p, images_list=frames, info=info)
devices.torch_gc()
processed = processing.Processed(p, images_list=frames)
shared.state.end()
return processed