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Update __init__.py

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tin2tin 9 months ago committed by GitHub
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1 changed files with 118 additions and 32 deletions
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  1. 150
      __init__.py

150
__init__.py
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@ -685,6 +685,7 @@ def install_modules(self):
# resemble-enhance:
subprocess.call([pybin, "-m", "pip", "install", "git+https://github.com/daswer123/resemble-enhance-windows.git", "--no-dependencies", "--upgrade"])
deep_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),"deepspeed/deepspeed-0.12.4+unknown-py3-none-any.whl")
print(deep_speed)
import_module(self, "deepspeed", deep_path)
import_module(self, "librosa", "librosa")
import_module(self, "celluloid", "celluloid")
@ -1142,6 +1143,8 @@ class GeneratorAddonPreferences(AddonPreferences):
"Stable Diffusion XL 1.0 (1024x1024)",
"stabilityai/stable-diffusion-xl-base-1.0",
),
("ByteDance/SDXL-Lightning", "SDXL-Lightning 2 Step (1024 x 1024)", "ByteDance/SDXL-Lightning"),
# ("stabilityai/stable-cascade", "Stable Cascade (1024 x 1024)", "stabilityai/stable-cascade"),
# ("thibaud/sdxl_dpo_turbo", "SDXL DPO TURBO (1024x1024)", "thibaud/sdxl_dpo_turbo"),
# (
# "stabilityai/sdxl-turbo",
@ -1173,11 +1176,11 @@ class GeneratorAddonPreferences(AddonPreferences):
# "Miniaturus_PotentiaV1.2 (1024x1024)",
# "dataautogpt3/Miniaturus_PotentiaV1.2",
# ),#
# (
# "dataautogpt3/ProteusV0.2",
# "Proteus (1024x1024)",
# "dataautogpt3/ProteusV0.2",
# ),
(
"dataautogpt3/ProteusV0.3",
"Proteus (1024x1024)",
"dataautogpt3/ProteusV0.3",
),
("dataautogpt3/OpenDalleV1.1", "OpenDalle (1024 x 1024)", "dataautogpt3/OpenDalleV1.1"),
# ("h94/IP-Adapter", "IP-Adapter (512 x 512)", "h94/IP-Adapter"),
#("PixArt-alpha/PixArt-XL-2-1024-MS", "PixArt (1024 x 1024)", "PixArt-alpha/PixArt-XL-2-1024-MS"),
@ -1692,7 +1695,7 @@ class SEQUENCER_PT_pallaidium_panel(Panel): # UI
"svd_decode_chunk_size",
text="Decode Frames",
)
if bpy.context.scene.sequence_editor is not None and image_model_card != "diffusers/controlnet-canny-sdxl-1.0-small":
if bpy.context.scene.sequence_editor is not None and image_model_card != "diffusers/controlnet-canny-sdxl-1.0-small" and image_model_card != "ByteDance/SDXL-Lightning":
if len(bpy.context.scene.sequence_editor.sequences) > 0:
if input == "input_strips" and type == "image":
col.prop_search(
@ -1818,9 +1821,12 @@ class SEQUENCER_PT_pallaidium_panel(Panel): # UI
)
else:
col.prop(
context.scene, "movie_num_inference_steps", text="Quality Steps"
)
if type == "image" and image_model_card == "ByteDance/SDXL-Lightning":
pass
else:
col.prop(
context.scene, "movie_num_inference_steps", text="Quality Steps"
)
if (
type == "movie"
@ -1833,6 +1839,9 @@ class SEQUENCER_PT_pallaidium_panel(Panel): # UI
scene.use_lcm and not (
type == "image"
and image_model_card == "Lykon/dreamshaper-8"
) and not (
type == "image"
and image_model_card == image_model_card == "ByteDance/SDXL-Lightning"
)
):
pass
@ -3133,6 +3142,7 @@ class SEQUENCER_OT_generate_image(Operator):
# )
# from compel import Compel
except ModuleNotFoundError:
print("Dependencies needs to be installed in the add-on preferences.")
self.report(
@ -3173,6 +3183,7 @@ class SEQUENCER_OT_generate_image(Operator):
and not image_model_card == "monster-labs/control_v1p_sdxl_qrcode_monster"
and not image_model_card == "Salesforce/blipdiffusion"
and not image_model_card == "Lykon/dreamshaper-8"
and not image_model_card == "ByteDance/SDXL-Lightning"
)
do_convert = (
(scene.image_path or scene.movie_path)
@ -3182,6 +3193,7 @@ class SEQUENCER_OT_generate_image(Operator):
and not image_model_card == "h94/IP-Adapter"
and not image_model_card == "monster-labs/control_v1p_sdxl_qrcode_monster"
and not image_model_card == "Salesforce/blipdiffusion"
and not image_model_card == "ByteDance/SDXL-Lightning"
and not do_inpaint
)
do_refine = scene.refine_sd and not do_convert
@ -3746,14 +3758,45 @@ class SEQUENCER_OT_generate_image(Operator):
torch_dtype=torch.float16,
local_files_only=local_files_only,
)
elif image_model_card == "dataautogpt3/ProteusV0.2":
elif image_model_card == "ByteDance/SDXL-Lightning":
import torch
from diffusers import (
AutoPipelineForText2Image,
StableDiffusionXLPipeline,
KDPM2AncestralDiscreteScheduler,
AutoencoderKL
from diffusers import StableDiffusionXLPipeline, EulerDiscreteScheduler
from huggingface_hub import hf_hub_download
base = "stabilityai/stable-diffusion-xl-base-1.0"
repo = "ByteDance/SDXL-Lightning"
ckpt = "sdxl_lightning_2step_lora.pth" # Use the correct ckpt for your step setting!
# Load model.
pipe = StableDiffusionXLPipeline.from_pretrained(base, torch_dtype=torch.float16, variant="fp16").to("cuda")
pipe.load_lora_weights(hf_hub_download(repo, ckpt))
pipe.fuse_lora()
# Ensure sampler uses "trailing" timesteps.
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
elif image_model_card == "dataautogpt3/ProteusV0.3":
from diffusers import StableDiffusionXLPipeline
# from diffusers import AutoencoderKL
# vae = AutoencoderKL.from_pretrained(
# "madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16
# )
pipe = StableDiffusionXLPipeline.from_single_file(
"dataautogpt3/ProteusV0.3",
#vae=vae,
torch_dtype=torch.float16,
#variant="fp16",
)
# from diffusers import DPMSolverMultistepScheduler
# pipe.scheduler = DPMSolverMultistepScheduler.from_config(
# pipe.scheduler.config
# )
if low_vram():
pipe.enable_model_cpu_offload()
else:
pipe.to(gfx_device)
# # Load VAE component
# vae = AutoencoderKL.from_pretrained(
@ -3771,6 +3814,13 @@ class SEQUENCER_OT_generate_image(Operator):
# )
#pipe.scheduler = KDPM2AncestralDiscreteScheduler.from_config(pipe.scheduler.config)
elif image_model_card == "stabilityai/stable-cascade":
import torch
from diffusers import StableCascadeDecoderPipeline, StableCascadePriorPipeline
# prior = StableCascadePriorPipeline.from_pretrained("stabilityai/stable-cascade-prior", torch_dtype=torch.bfloat16).to(gfx_device)
# decoder = StableCascadeDecoderPipeline.from_pretrained("stabilityai/stable-cascade", torch_dtype=torch.float16).to(gfx_device)
elif image_model_card == "dataautogpt3/Miniaturus_PotentiaV1.2":
from diffusers import AutoPipelineForText2Image
pipe = AutoPipelineForText2Image.from_pretrained(
@ -3831,28 +3881,27 @@ class SEQUENCER_OT_generate_image(Operator):
scene.movie_num_guidance = 0
pipe.load_lora_weights("segmind/Segmind-VegaRT")
pipe.fuse_lora()
elif image_model_card != "PixArt-alpha/PixArt-XL-2-1024-MS" and image_model_card != "Lykon/dreamshaper-8":
print("Use LCM: False")
elif image_model_card != "PixArt-alpha/PixArt-XL-2-1024-MS" and image_model_card != "Lykon/dreamshaper-8" and image_model_card != "stabilityai/stable-cascade":
pipe.scheduler = DPMSolverMultistepScheduler.from_config(
pipe.scheduler.config
)
if image_model_card != "stabilityai/stable-cascade":
pipe.watermark = NoWatermark()
pipe.watermark = NoWatermark()
if low_vram():
# torch.cuda.set_per_process_memory_fraction(0.95) # 6 GB VRAM
pipe.enable_model_cpu_offload()
# pipe.enable_vae_slicing()
else:
pipe.to(gfx_device)
if low_vram():
# torch.cuda.set_per_process_memory_fraction(0.95) # 6 GB VRAM
pipe.enable_model_cpu_offload()
# pipe.enable_vae_slicing()
else:
pipe.to(gfx_device)
# # FreeU
# if scene.use_freeU and pipe: # Free Lunch
# # -------- freeu block registration
# print("Process: FreeU")
# register_free_upblock2d(pipe, b1=1.1, b2=1.2, s1=0.6, s2=0.4)
# register_free_crossattn_upblock2d(pipe, b1=1.1, b2=1.2, s1=0.6, s2=0.4)
# # -------- freeu block registration
# # FreeU
# if scene.use_freeU and pipe: # Free Lunch
# # -------- freeu block registration
# print("Process: FreeU")
# register_free_upblock2d(pipe, b1=1.1, b2=1.2, s1=0.6, s2=0.4)
# register_free_crossattn_upblock2d(pipe, b1=1.1, b2=1.2, s1=0.6, s2=0.4)
# # -------- freeu block registration
# LoRA
if (
@ -4247,6 +4296,43 @@ class SEQUENCER_OT_generate_image(Operator):
generator=generator,
).images[0]
elif image_model_card == "ByteDance/SDXL-Lightning":
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
height=y,
width=x,
guidance_scale=0.0,
output_type="pil",
num_inference_steps=2,
).images[0]
decoder = None
elif image_model_card == "stabilityai/stable-cascade":
#import torch
prior = StableCascadePriorPipeline.from_pretrained("stabilityai/stable-cascade-prior", torch_dtype=torch.bfloat16)
prior.enable_model_cpu_offload()
prior_output = prior(
prompt=prompt,
height=y,
width=x,
negative_prompt=negative_prompt,
guidance_scale=image_num_guidance,
#num_images_per_prompt=num_images_per_prompt,
num_inference_steps=image_num_inference_steps,
)
prior = None
decoder = StableCascadeDecoderPipeline.from_pretrained("stabilityai/stable-cascade", torch_dtype=torch.float16)
decoder.enable_model_cpu_offload()
image = decoder(
image_embeddings=prior_output.image_embeddings.half(),
prompt=prompt,
negative_prompt=negative_prompt,
guidance_scale=0.0,
output_type="pil",
num_inference_steps=int(image_num_inference_steps/2),
).images[0]
decoder = None
# Inpaint
elif do_inpaint:
print("Process: Inpaint")

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