xinyun/ComfyUI-CogVideoXWrapper
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kijai 2024-11-17 00:48:01 +02:00
parent 4374273138
commit 15aa68c95d
3 changed files with 18 additions and 11 deletions
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5
cogvideox_fun/pipeline_cogvideox_inpaint.py
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@ -739,6 +739,8 @@ class CogVideoX_Fun_Pipeline_Inpaint(DiffusionPipeline):
num_channels_transformer = self.transformer.config.in_channels num_channels_transformer = self.transformer.config.in_channels
return_image_latents = num_channels_transformer == num_channels_latents return_image_latents = num_channels_transformer == num_channels_latents
self.vae.to(device)
latents_outputs = self.prepare_latents( latents_outputs = self.prepare_latents(
batch_size * num_videos_per_prompt, batch_size * num_videos_per_prompt,
num_channels_latents, num_channels_latents,
@ -840,6 +842,9 @@ class CogVideoX_Fun_Pipeline_Inpaint(DiffusionPipeline):
mask = rearrange(mask, "b c f h w -> b f c h w") mask = rearrange(mask, "b c f h w -> b f c h w")
inpaint_latents = None inpaint_latents = None
self.vae.to(torch.device("cpu"))
if comfyui_progressbar: if comfyui_progressbar:
pbar.update(1) pbar.update(1)

10
model_loading.py
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@ -121,7 +121,7 @@ class DownloadAndLoadCogVideoModel:
"precision": (["fp16", "fp32", "bf16"], "precision": (["fp16", "fp32", "bf16"],
{"default": "bf16", "tooltip": "official recommendation is that 2b model should be fp16, 5b model should be bf16"} {"default": "bf16", "tooltip": "official recommendation is that 2b model should be fp16, 5b model should be bf16"}
), ),
"fp8_transformer": (['disabled', 'enabled', 'fastmode', 'torchao_fp8dq', "torchao_fp6"], {"default": 'disabled', "tooltip": "enabled casts the transformer to torch.float8_e4m3fn, fastmode is only for latest nvidia GPUs and requires torch 2.4.0 and cu124 minimum"}), "fp8_transformer": (['disabled', 'enabled', 'fastmode', 'torchao_fp8dq', "torchao_fp8dqrow", "torchao_int8dq", "torchao_fp6"], {"default": 'disabled', "tooltip": "enabled casts the transformer to torch.float8_e4m3fn, fastmode is only for latest nvidia GPUs and requires torch 2.4.0 and cu124 minimum"}),
"compile": (["disabled","onediff","torch"], {"tooltip": "compile the model for faster inference, these are advanced options only available on Linux, see readme for more info"}), "compile": (["disabled","onediff","torch"], {"tooltip": "compile the model for faster inference, these are advanced options only available on Linux, see readme for more info"}),
"enable_sequential_cpu_offload": ("BOOLEAN", {"default": False, "tooltip": "significantly reducing memory usage and slows down the inference"}), "enable_sequential_cpu_offload": ("BOOLEAN", {"default": False, "tooltip": "significantly reducing memory usage and slows down the inference"}),
"block_edit": ("TRANSFORMERBLOCKS", {"default": None}), "block_edit": ("TRANSFORMERBLOCKS", {"default": None}),
@ -301,7 +301,8 @@ class DownloadAndLoadCogVideoModel:
from torchao.quantization import ( from torchao.quantization import (
quantize_, quantize_,
fpx_weight_only, fpx_weight_only,
float8_dynamic_activation_float8_weight float8_dynamic_activation_float8_weight,
int8_dynamic_activation_int8_weight
) )
except: except:
raise ImportError("torchao is not installed, please install torchao to use fp8dq") raise ImportError("torchao is not installed, please install torchao to use fp8dq")
@ -316,6 +317,11 @@ class DownloadAndLoadCogVideoModel:
quant_func = fpx_weight_only(3, 2) quant_func = fpx_weight_only(3, 2)
elif "fp8dq" in fp8_transformer: #very fast on 4090 when compiled elif "fp8dq" in fp8_transformer: #very fast on 4090 when compiled
quant_func = float8_dynamic_activation_float8_weight() quant_func = float8_dynamic_activation_float8_weight()
elif 'fp8dqrow' in fp8_transformer:
from torchao.quantization.quant_api import PerRow
quant_func = float8_dynamic_activation_float8_weight(granularity=PerRow())
elif 'int8dq' in fp8_transformer:
quant_func = int8_dynamic_activation_int8_weight()
for i, block in enumerate(pipe.transformer.transformer_blocks): for i, block in enumerate(pipe.transformer.transformer_blocks):
if "CogVideoXBlock" in str(block): if "CogVideoXBlock" in str(block):

12
nodes.py
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@ -1101,10 +1101,6 @@ class CogVideoXFunSampler:
assert "fun" in base_path.lower(), "'Unfun' models not supported in 'CogVideoXFunSampler', use the 'CogVideoSampler'" assert "fun" in base_path.lower(), "'Unfun' models not supported in 'CogVideoXFunSampler', use the 'CogVideoSampler'"
assert "pose" not in base_path.lower(), "'Pose' models not supported in 'CogVideoXFunSampler', use the 'CogVideoXFunControlSampler'" assert "pose" not in base_path.lower(), "'Pose' models not supported in 'CogVideoXFunSampler', use the 'CogVideoXFunControlSampler'"
if not pipeline["cpu_offloading"]:
pipe.enable_model_cpu_offload(device=device)
mm.soft_empty_cache() mm.soft_empty_cache()
#vid2vid #vid2vid
@ -1123,8 +1119,8 @@ class CogVideoXFunSampler:
else: else:
raise ValueError(f"Unknown scheduler: {scheduler}") raise ValueError(f"Unknown scheduler: {scheduler}")
#if not pipeline["cpu_offloading"]: if not pipeline["cpu_offloading"] and pipeline["manual_offloading"]:
# pipe.transformer.to(device) pipe.transformer.to(device)
if context_options is not None: if context_options is not None:
context_frames = context_options["context_frames"] // 4 context_frames = context_options["context_frames"] // 4
@ -1184,8 +1180,8 @@ class CogVideoXFunSampler:
noise_aug_strength = noise_aug_strength, noise_aug_strength = noise_aug_strength,
strength = vid2vid_denoise, strength = vid2vid_denoise,
) )
#if not pipeline["cpu_offloading"]: if not pipeline["cpu_offloading"] and pipeline["manual_offloading"]:
# pipe.transformer.to(offload_device) pipe.transformer.to(offload_device)
#clear FasterCache #clear FasterCache
if fastercache is not None: if fastercache is not None:
for block in pipe.transformer.transformer_blocks: for block in pipe.transformer.transformer_blocks: