KenSchae
/
Pallaidium
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity
Browse Source

Update __init__.py

pull/101/head
tin2tin 9 months ago committed by GitHub
parent
3301c9e65b
commit
a576e76d55
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
1 changed files with 118 additions and 32 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 116
      __init__.py

116
__init__.py
Unescape View File

@ -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(
@ -1817,6 +1820,9 @@ class SEQUENCER_PT_pallaidium_panel(Panel): # UI
context.scene, "movie_num_inference_steps", text="Quality Steps"
)
else:
if type == "image" and image_model_card == "ByteDance/SDXL-Lightning":
pass
else:
col.prop(
context.scene, "movie_num_inference_steps", text="Quality Steps"
@ -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(
@ -3831,12 +3881,11 @@ 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()
if low_vram():
@ -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")

Write Preview
Loading…
Cancel
Save