@ -196,6 +196,19 @@ def closest_divisible_32(num):
else :
return max ( num + ( 32 - remainder ) , 192 )
def closest_divisible_128 ( num ) :
# Determine the remainder when num is divided by 128
remainder = ( num % 128 )
# If the remainder is less than or equal to 16, return num - remainder,
# but ensure the result is not less than 192
if remainder < = 64 :
result = num - remainder
return max ( result , 256 )
# Otherwise, return num + (32 - remainder)
else :
return max ( num + ( 64 - remainder ) , 256 )
def find_first_empty_channel ( start_frame , end_frame ) :
for ch in range ( 1 , len ( bpy . context . scene . sequence_editor . sequences_all ) + 1 ) :
@ -460,7 +473,7 @@ def low_vram():
for i in range ( torch . cuda . device_count ( ) ) :
properties = torch . cuda . get_device_properties ( i )
total_vram + = properties . total_memory
return ( total_vram / ( 1024 * * 3 ) ) < 24 .1 # Y/N under 6.1 GB?
return ( total_vram / ( 1024 * * 3 ) ) < 1 2.1# Y/N under 6.1 GB?
def import_module ( self , module , install_module ) :
@ -582,7 +595,6 @@ def install_modules(self):
import_module ( self , " scipy " , " scipy " )
import_module ( self , " IPython " , " IPython " )
import_module ( self , " bark " , " git+https://github.com/suno-ai/bark.git " )
#import_module(self, "bark", "git+https://github.com/suno-ai/bark.git")
import_module ( self , " xformers " , " xformers " )
import_module ( self , " imageio " , " imageio " )
import_module ( self , " imwatermark " , " invisible-watermark>=0.2.0 " )
@ -811,6 +823,7 @@ class GeneratorAddonPreferences(AddonPreferences):
" Stable Diffusion XL 1.0 (1024x1024) " ,
" stabilityai/stable-diffusion-xl-base-1.0 " ,
) ,
( " warp-ai/wuerstchen " , " Würstchen (1024x1024) " , " warp-ai/wuerstchen " ) ,
( " DeepFloyd/IF-I-M-v1.0 " , " DeepFloyd/IF-I-M-v1.0 " , " DeepFloyd/IF-I-M-v1.0 " ) ,
( " lllyasviel/sd-controlnet-canny " , " ControlNet (512x512) " , " lllyasviel/sd-controlnet-canny " ) ,
( " lllyasviel/sd-controlnet-openpose " , " OpenPose (512x512) " , " lllyasviel/sd-controlnet-openpose " ) ,
@ -1189,12 +1202,12 @@ class SEQEUNCER_PT_generate_ai(Panel): # UI
col . use_property_split = True
col . use_property_decorate = False
if type != " audio " :
col = col . box ( )
col = col . column ( )
col = col . box ( )
col = col . column ( )
col . prop ( context . scene , " input_strips " , text = " Input " )
col . prop ( context . scene , " input_strips " , text = " Input " )
if type != " audio " :
if image_model_card != " lllyasviel/sd-controlnet-canny " and image_model_card != " lllyasviel/sd-controlnet-openpose " :
if input == " input_strips " and not scene . inpaint_selected_strip :
@ -1395,7 +1408,7 @@ class SEQUENCER_OT_generate_movie(Operator):
pipe . watermark = NoWatermark ( )
if low_vram :
if low_vram ( ) :
pipe . enable_model_cpu_offload ( )
#pipe.unet.enable_forward_chunking(chunk_size=1, dim=1) # Heavy
#pipe.enable_vae_slicing()
@ -1412,7 +1425,7 @@ class SEQUENCER_OT_generate_movie(Operator):
variant = " fp16 " ,
)
if low_vram :
if low_vram ( ) :
refiner . enable_model_cpu_offload ( )
#refiner.enable_vae_tiling()
#refiner.enable_vae_slicing()
@ -1432,7 +1445,7 @@ class SEQUENCER_OT_generate_movie(Operator):
#
# # local: pipe = pipeline(task='image-to-video', model='C:/Users/45239/.cache/modelscope/hub/damo/Image-to-Video', model_revision='v1.1.0')
# if low_vram:
# if low_vram() :
# pipe.enable_model_cpu_offload()
# pipe.enable_vae_tiling()
# pipe.enable_vae_slicing()
@ -1456,7 +1469,7 @@ class SEQUENCER_OT_generate_movie(Operator):
upscale . scheduler = DPMSolverMultistepScheduler . from_config ( upscale . scheduler . config )
if low_vram :
if low_vram ( ) :
#torch.cuda.set_per_process_memory_fraction(0.98)
upscale . enable_model_cpu_offload ( )
upscale . enable_vae_tiling ( )
@ -1477,7 +1490,7 @@ class SEQUENCER_OT_generate_movie(Operator):
from diffusers import DPMSolverMultistepScheduler
pipe . scheduler = DPMSolverMultistepScheduler . from_config ( pipe . scheduler . config )
if low_vram :
if low_vram ( ) :
pipe . enable_model_cpu_offload ( )
pipe . enable_vae_slicing ( )
else :
@ -1496,7 +1509,7 @@ class SEQUENCER_OT_generate_movie(Operator):
upscale . scheduler = DPMSolverMultistepScheduler . from_config ( upscale . scheduler . config )
if low_vram :
if low_vram ( ) :
upscale . enable_model_cpu_offload ( )
upscale . unet . enable_forward_chunking ( chunk_size = 1 , dim = 1 ) #Heavy
upscale . enable_vae_slicing ( )
@ -1819,10 +1832,11 @@ class SEQUENCER_OT_generate_audio(Operator):
pipe . scheduler = DPMSolverMultistepScheduler . from_config ( pipe . scheduler . config )
if low_vram :
if low_vram ( ) :
pipe . enable_model_cpu_offload ( )
pipe . enable_vae_slicing ( )
pipe . to ( " cuda " )
else :
pipe . to ( " cuda " )
elif addon_prefs . audio_model_card == " facebook/audiogen-medium " :
pipe = AudioGen . get_pretrained ( " facebook/audiogen-medium " )
@ -1975,6 +1989,11 @@ def get_strip_path(strip):
return None
def clamp_value ( value , min_value , max_value ) :
# Ensure value is within the specified range
return max ( min ( value , max_value ) , min_value )
class SEQUENCER_OT_generate_image ( Operator ) :
""" Generate Image """
@ -2061,7 +2080,7 @@ class SEQUENCER_OT_generate_image(Operator):
pipe . watermark = NoWatermark ( )
if low_vram :
if low_vram ( ) :
#torch.cuda.set_per_process_memory_fraction(0.99)
pipe . enable_model_cpu_offload ( )
#pipe.enable_vae_slicing()
@ -2078,7 +2097,7 @@ class SEQUENCER_OT_generate_image(Operator):
# use_safetensors=True,
# variant="fp16",
# )
# if low_vram:
# if low_vram() :
# refiner.enable_model_cpu_offload()
# refiner.enable_vae_slicing()
# else:
@ -2097,7 +2116,7 @@ class SEQUENCER_OT_generate_image(Operator):
pipe . scheduler = UniPCMultistepScheduler . from_config ( pipe . scheduler . config )
if low_vram :
if low_vram ( ) :
pipe . enable_model_cpu_offload ( )
pipe . enable_vae_slicing ( )
else :
@ -2127,13 +2146,28 @@ class SEQUENCER_OT_generate_image(Operator):
pipe . scheduler = UniPCMultistepScheduler . from_config ( pipe . scheduler . config )
if low_vram :
if low_vram ( ) :
pipe . enable_xformers_memory_efficient_attention ( )
pipe . enable_model_cpu_offload ( )
pipe . enable_vae_slicing ( )
else :
pipe . to ( " cuda " )
# Wuerstchen
elif image_model_card == " warp-ai/wuerstchen " :
from diffusers import AutoPipelineForText2Image
from diffusers . pipelines . wuerstchen import DEFAULT_STAGE_C_TIMESTEPS
pipe = AutoPipelineForText2Image . from_pretrained ( " warp-ai/wuerstchen " , torch_dtype = torch . float16 )
if low_vram ( ) :
#torch.cuda.set_per_process_memory_fraction(0.95) # 6 GB VRAM
pipe . enable_model_cpu_offload ( )
#pipe.enable_vae_slicing()
#pipe.enable_forward_chunking(chunk_size=1, dim=1)
else :
pipe . to ( " cuda " )
# DeepFloyd
elif image_model_card == " DeepFloyd/IF-I-M-v1.0 " :
@ -2147,7 +2181,7 @@ class SEQUENCER_OT_generate_image(Operator):
stage_1 = DiffusionPipeline . from_pretrained (
" DeepFloyd/IF-I-M-v1.0 " , variant = " fp16 " , torch_dtype = torch . float16
)
if low_vram :
if low_vram ( ) :
stage_1 . enable_model_cpu_offload ( )
# here: stage_1.unet.enable_forward_chunking(chunk_size=1, dim=1)
#stage_1.enable_vae_slicing()
@ -2161,7 +2195,7 @@ class SEQUENCER_OT_generate_image(Operator):
variant = " fp16 " ,
torch_dtype = torch . float16 ,
)
if low_vram :
if low_vram ( ) :
stage_2 . enable_model_cpu_offload ( )
# stage_2.unet.enable_forward_chunking(chunk_size=1, dim=1)
#stage_2.enable_vae_slicing()
@ -2179,7 +2213,7 @@ class SEQUENCER_OT_generate_image(Operator):
* * safety_modules ,
torch_dtype = torch . float16 ,
)
if low_vram :
if low_vram ( ) :
stage_3 . enable_model_cpu_offload ( )
# stage_3.unet.enable_forward_chunking(chunk_size=1, dim=1)
#stage_3.enable_vae_slicing()
@ -2205,7 +2239,7 @@ class SEQUENCER_OT_generate_image(Operator):
converter . watermark = NoWatermark ( )
if low_vram :
if low_vram ( ) :
converter . enable_model_cpu_offload ( )
#refiner.enable_vae_tiling()
converter . enable_vae_slicing ( )
@ -2216,10 +2250,10 @@ class SEQUENCER_OT_generate_image(Operator):
else :
from diffusers import AutoencoderKL
if image_model_card == " stabilityai/stable-diffusion-xl-base-1.0 " :
#vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16 )
vae = AutoencoderKL . from_pretrained ( " madebyollin/sdxl-vae-fp16-fix " , torch_dtype = torch . float16 )
pipe = DiffusionPipeline . from_pretrained (
image_model_card ,
#vae=vae ,
vae = vae ,
torch_dtype = torch . float16 ,
variant = " fp16 " ,
)
@ -2233,7 +2267,7 @@ class SEQUENCER_OT_generate_image(Operator):
pipe . watermark = NoWatermark ( )
if low_vram :
if low_vram ( ) :
#torch.cuda.set_per_process_memory_fraction(0.95) # 6 GB VRAM
pipe . enable_model_cpu_offload ( )
pipe . enable_vae_slicing ( )
@ -2247,25 +2281,23 @@ class SEQUENCER_OT_generate_image(Operator):
print ( " Refine Model: " + " stabilityai/stable-diffusion-xl-refiner-1.0 " )
from diffusers import StableDiffusionXLImg2ImgPipeline , AutoencoderKL
vae = AutoencoderKL . from_pretrained ( " madebyollin/sdxl-vae-fp16-fix " , torch_dtype = torch . float16 )
#vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16 )
refiner = StableDiffusionXLImg2ImgPipeline . from_pretrained (
" stabilityai/stable-diffusion-xl-refiner-1.0 " ,
#text_encoder_2=pipe.text_encoder_2,
#vae=pipe.vae,
vae = vae ,
#vae=vae,
torch_dtype = torch . float16 ,
variant = " fp16 " ,
)
refiner . watermark = NoWatermark ( )
#refiner.watermark = NoWatermark( )
if low_vram :
refiner . enable_model_cpu_offload ( )
#refiner.enable_vae_tiling()
refiner . enable_vae_slicing ( )
else :
refiner . to ( " cuda " )
# if low_vram() :
refiner . enable_model_cpu_offload ( )
# #refiner.enable_vae_tiling()
# #refiner.enable_vae_slicing( )
# else :
#refiner.to("cuda" )
# Main Generate Loop:
for i in range ( scene . movie_num_batch ) :
@ -2341,6 +2373,22 @@ class SEQUENCER_OT_generate_image(Operator):
# image[0].save("./if_stage_III.png")
image = image [ 0 ]
elif image_model_card == " warp-ai/wuerstchen " :
scene . generate_movie_y = y = closest_divisible_128 ( y )
scene . generate_movie_x = x = closest_divisible_128 ( x )
print ( " Generate: Image with Würstchen " )
image = pipe (
prompt ,
negative_prompt = negative_prompt ,
num_inference_steps = image_num_inference_steps ,
decoder_guidance_scale = 0.0 ,
#prior_guidance_scale=image_num_guidance,
prior_timesteps = DEFAULT_STAGE_C_TIMESTEPS ,
height = y ,
width = x ,
generator = generator ,
) . images [ 0 ]
# ControlNet
elif image_model_card == " lllyasviel/sd-controlnet-canny " :
print ( " Process: ControlNet " )
@ -2370,7 +2418,7 @@ class SEQUENCER_OT_generate_image(Operator):
negative_prompt = negative_prompt ,
image = canny_image ,
num_inference_steps = image_num_inference_steps , #Should be around 50
guidance_scale = image_num_guidance , # Should be between 3 and 5.
guidance_scale = clamp_value ( image_num_guidance , 3 , 5 ) , # Should be between 3 and 5.
guess_mode = True ,
height = y ,
width = x ,
@ -2442,7 +2490,6 @@ class SEQUENCER_OT_generate_image(Operator):
mask_image = mask_image ,
num_inference_steps = image_num_inference_steps ,
guidance_scale = image_num_guidance ,
#strength=1.00 - scene.image_power, #not supported.
height = y ,
width = x ,
generator = generator ,
@ -2504,7 +2551,7 @@ class SEQUENCER_OT_generate_image(Operator):
image = refiner (
prompt ,
negative_prompt = negative_prompt ,
num_inference_steps = image_num_inference_steps ,
num_inference_steps = clamp_value ( int ( image_num_inference_steps / 2 ) , 1 , 5 ) ,
denoising_start = 0.8 ,
guidance_scale = image_num_guidance ,
image = image ,
@ -2534,11 +2581,7 @@ class SEQUENCER_OT_generate_image(Operator):
scene . sequence_editor . active_strip . frame_final_start
)
strip . use_proxy = True
bpy . ops . sequencer . rebuild_proxy ( )
# Redraw UI to display the new strip. Remove this if Blender crashes:
# https://docs.blender.org/api/current/info_gotcha.html#can-i-redraw-during-script-execution
bpy . ops . wm . redraw_timer ( type = " DRAW_WIN_SWAP " , iterations = 1 )
#bpy.ops.sequencer.rebuild_proxy()
else :
print ( " No resulting file found. " )
@ -2546,6 +2589,26 @@ class SEQUENCER_OT_generate_image(Operator):
if torch . cuda . is_available ( ) :
torch . cuda . empty_cache ( )
# Redraw UI to display the new strip. Remove this if Blender crashes:
# https://docs.blender.org/api/current/info_gotcha.html#can-i-redraw-during-script-execution
#bpy.ops.wm.redraw_timer(type="DRAW_WIN_SWAP", iterations=1)
for window in bpy . context . window_manager . windows :
screen = window . screen
for area in screen . areas :
if area . type == " SEQUENCE_EDITOR " :
from bpy import context
with context . temp_override ( window = window , area = area ) :
if i > 0 :
scene . frame_current = (
scene . sequence_editor . active_strip . frame_final_start
)
# Redraw UI to display the new strip. Remove this if Blender crashes: https://docs.blender.org/api/current/info_gotcha.html#can-i-redraw-during-script-execution
bpy . ops . wm . redraw_timer ( type = " DRAW_WIN_SWAP " , iterations = 1 )
break
bpy . ops . renderreminder . play_notification ( )
scene . frame_current = current_frame
@ -2787,15 +2850,15 @@ def register():
bpy . types . Scene . generate_movie_x = bpy . props . IntProperty (
name = " generate_movie_x " ,
default = 1024 ,
step = 64 ,
min = 19 2,
step = 128 ,
min = 256 ,
max = 1536 ,
)
bpy . types . Scene . generate_movie_y = bpy . props . IntProperty (
name = " generate_movie_y " ,
default = 512 ,
step = 64 ,
min = 19 2,
step = 128 ,
min = 256 ,
max = 1536 ,
)
# The number of frames to be generated.
tin2tin
1 year ago
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