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Add: Proteus - RunDiffusion 

https://huggingface.co/dataautogpt3/Proteus-RunDiffusion
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  1. 135
      __init__.py

135
__init__.py
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@ -1113,6 +1113,7 @@ class GeneratorAddonPreferences(AddonPreferences):
# ), # ),
# ("VideoCrafter/Image2Video-512", "VideoCrafter v1 (512x512)", "VideoCrafter/Image2Video-512"), # ("VideoCrafter/Image2Video-512", "VideoCrafter v1 (512x512)", "VideoCrafter/Image2Video-512"),
("wangfuyun/AnimateLCM", "AnimateLCM", "wangfuyun/AnimateLCM"),
( (
"cerspense/zeroscope_v2_XL", "cerspense/zeroscope_v2_XL",
"Zeroscope XL (1024x576x24)", "Zeroscope XL (1024x576x24)",
@ -1188,18 +1189,18 @@ class GeneratorAddonPreferences(AddonPreferences):
# "dataautogpt3/Miniaturus_PotentiaV1.2", # "dataautogpt3/Miniaturus_PotentiaV1.2",
# ),# # ),#
( (
"dataautogpt3/ProteusV0.3", "dataautogpt3/Proteus-RunDiffusion",
"Proteus (1024x1024)", "Proteus-RunDiffusion (1024x1024)",
"dataautogpt3/ProteusV0.3", "dataautogpt3/Proteus-RunDiffusion",
), ),
("dataautogpt3/ProteusV0.3-Lightning", "ProteusV0.3-Lightning (1024 x 1024)", "dataautogpt3/ProteusV0.3-Lightning"), ("dataautogpt3/Proteus-RunDiffusion-Lightning", "ProteusV0.3-Lightning (1024 x 1024)", "dataautogpt3/Proteus-RunDiffusion-Lightning"),
("dataautogpt3/OpenDalleV1.1", "OpenDalle (1024 x 1024)", "dataautogpt3/OpenDalleV1.1"), ("dataautogpt3/OpenDalleV1.1", "OpenDalle (1024 x 1024)", "dataautogpt3/OpenDalleV1.1"),
# ("h94/IP-Adapter", "IP-Adapter (512 x 512)", "h94/IP-Adapter"), # ("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"), #("PixArt-alpha/PixArt-XL-2-1024-MS", "PixArt (1024 x 1024)", "PixArt-alpha/PixArt-XL-2-1024-MS"),
### ("ptx0/terminus-xl-gamma-v1", "Terminus XL Gamma v1", "ptx0/terminus-xl-gamma-v1"), ### ("ptx0/terminus-xl-gamma-v1", "Terminus XL Gamma v1", "ptx0/terminus-xl-gamma-v1"),
# ("warp-ai/wuerstchen", "Würstchen (1024x1024)", "warp-ai/wuerstchen"), # ("warp-ai/wuerstchen", "Würstchen (1024x1024)", "warp-ai/wuerstchen"),
("imagepipeline/JuggernautXL-v8", "JuggernautXL-v8 (1024x1024)", "imagepipeline/JuggernautXL-v8"), ("imagepipeline/JuggernautXL-v8", "JuggernautXL-v8 (1024x1024)", "imagepipeline/JuggernautXL-v8"),
### ("lrzjason/playground-v2-1024px-aesthetic-fp16", "Playground v2 (1024x1024)", "lrzjason/playground-v2-1024px-aesthetic-fp16"), ("playgroundai/playground-v2.5-1024px-aesthetic", "Playground v2.5 (1024x1024)", "playgroundai/playground-v2.5-1024px-aesthetic"),
# ( # (
# "playgroundai/playground-v2-1024px-aesthetic", # "playgroundai/playground-v2-1024px-aesthetic",
# "Playground v2 (1024x1024)", # "Playground v2 (1024x1024)",
@ -1212,19 +1213,19 @@ class GeneratorAddonPreferences(AddonPreferences):
), ),
( (
"diffusers/controlnet-canny-sdxl-1.0-small", "diffusers/controlnet-canny-sdxl-1.0-small",
"Canny (512x512)", "Canny ControlNet",
"diffusers/controlnet-canny-sdxl-1.0-small", "diffusers/controlnet-canny-sdxl-1.0-small",
), ),
# Disabled - has log-in code. # Disabled - has log-in code.
# ("DeepFloyd/IF-I-M-v1.0", "DeepFloyd/IF-I-M-v1.0", "DeepFloyd/IF-I-M-v1.0"), # ("DeepFloyd/IF-I-M-v1.0", "DeepFloyd/IF-I-M-v1.0", "DeepFloyd/IF-I-M-v1.0"),
( (
"monster-labs/control_v1p_sdxl_qrcode_monster", "monster-labs/control_v1p_sdxl_qrcode_monster",
"Illusion (512x512)", "Illusion ControlNet",
"monster-labs/control_v1p_sdxl_qrcode_monster", "monster-labs/control_v1p_sdxl_qrcode_monster",
), ),
( (
"lllyasviel/sd-controlnet-openpose", "lllyasviel/sd-controlnet-openpose",
"OpenPose (512x512)", "OpenPose ControlNet",
"lllyasviel/sd-controlnet-openpose", "lllyasviel/sd-controlnet-openpose",
), ),
# ( # (
@ -1840,7 +1841,7 @@ class SEQUENCER_PT_pallaidium_panel(Panel): # UI
else: else:
if (type == "image" and image_model_card == "ByteDance/SDXL-Lightning" or if (type == "image" and image_model_card == "ByteDance/SDXL-Lightning" or
type == "image" and image_model_card == "dataautogpt3/ProteusV0.3-Lightning" or type == "image" and image_model_card == "dataautogpt3/Proteus-RunDiffusion-Lightning" or
type == "image" and image_model_card == "Lykon/dreamshaper-xl-lightning" type == "image" and image_model_card == "Lykon/dreamshaper-xl-lightning"
): ):
pass pass
@ -1863,7 +1864,7 @@ class SEQUENCER_PT_pallaidium_panel(Panel): # UI
) and not ( ) and not (
type == "image" type == "image"
and image_model_card == image_model_card == "ByteDance/SDXL-Lightning" or and image_model_card == image_model_card == "ByteDance/SDXL-Lightning" or
type == "image" and image_model_card == "dataautogpt3/ProteusV0.3-Lightning" or type == "image" and image_model_card == "dataautogpt3/Proteus-RunDiffusion-Lightning" or
type == "image" and image_model_card == "Lykon/dreamshaper-xl-lightning" type == "image" and image_model_card == "Lykon/dreamshaper-xl-lightning"
) )
): ):
@ -2139,6 +2140,16 @@ class SEQUENCER_OT_generate_movie(Operator):
elif (movie_model_card == "stabilityai/stable-video-diffusion-img2vid" or movie_model_card == "stabilityai/stable-video-diffusion-img2vid-xt"): # or movie_model_card == "vdo/stable-video-diffusion-img2vid-fp16"): elif (movie_model_card == "stabilityai/stable-video-diffusion-img2vid" or movie_model_card == "stabilityai/stable-video-diffusion-img2vid-xt"): # or movie_model_card == "vdo/stable-video-diffusion-img2vid-fp16"):
from diffusers import StableVideoDiffusionPipeline from diffusers import StableVideoDiffusionPipeline
from diffusers.utils import load_image, export_to_video from diffusers.utils import load_image, export_to_video
# from .lcm_scheduler import AnimateLCMSVDStochasticIterativeScheduler
# noise_scheduler = AnimateLCMSVDStochasticIterativeScheduler(
# num_train_timesteps=40,
# sigma_min=0.002,
# sigma_max=700.0,
# sigma_data=1.0,
# s_noise=1.0,
# rho=7,
# clip_denoised=False,
# )
if movie_model_card == "stabilityai/stable-video-diffusion-img2vid": if movie_model_card == "stabilityai/stable-video-diffusion-img2vid":
# Version 1.1 - too heavy # Version 1.1 - too heavy
@ -2161,12 +2172,23 @@ class SEQUENCER_OT_generate_movie(Operator):
variant="fp16", variant="fp16",
local_files_only=local_files_only, local_files_only=local_files_only,
) )
# model_select = "AnimateLCM-SVD-xt-1.1.safetensors"
# refinere.unet.cpu()
# file_path = os.path.join("./safetensors", model_select)
# state_dict = {}
# with safe_open(file_path, framework="pt", device="cpu") as f:
# for key in f.keys():
# state_dict[key] = f.get_tensor(key)
# missing, unexpected = refiner.unet.load_state_dict(state_dict, strict=True)
# pipe.unet.cuda()
# del state_dict
if low_vram(): if low_vram():
refiner.enable_model_cpu_offload() refiner.enable_model_cpu_offload()
refiner.unet.enable_forward_chunking() refiner.unet.enable_forward_chunking()
else: else:
refiner.to(gfx_device) refiner.to(gfx_device)
else: # vid2vid / img2vid else: # vid2vid / img2vid
if ( if (
movie_model_card == "cerspense/zeroscope_v2_dark_30x448x256" movie_model_card == "cerspense/zeroscope_v2_dark_30x448x256"
@ -2230,6 +2252,28 @@ class SEQUENCER_OT_generate_movie(Operator):
else: else:
pipe.to(gfx_device) pipe.to(gfx_device)
elif movie_model_card == "wangfuyun/AnimateLCM":
import torch
from diffusers import AnimateDiffPipeline, LCMScheduler, MotionAdapter
from diffusers.utils import export_to_gif
adapter = MotionAdapter.from_pretrained("wangfuyun/AnimateLCM", torch_dtype=torch.float16)
#pipe = AnimateDiffPipeline.from_pretrained("dataautogpt3/OpenDalleV1.1", motion_adapter=adapter, torch_dtype=torch.float16, variant="fp16",)
#pipe = AnimateDiffPipeline.from_pretrained("lykon/dreamshaper-8", motion_adapter=adapter, torch_dtype=torch.float16, variant="fp16",)
pipe = AnimateDiffPipeline.from_pretrained("emilianJR/epiCRealism", motion_adapter=adapter, torch_dtype=torch.float16)
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config, beta_schedule="linear")
pipe.load_lora_weights("wangfuyun/AnimateLCM", weight_name="AnimateLCM_sd15_t2v_lora.safetensors", adapter_name="lcm-lora")
pipe.set_adapters(["lcm-lora"], [0.8])
if low_vram():
pipe.enable_vae_slicing()
pipe.enable_model_cpu_offload()
# pipe.enable_vae_slicing()
else:
pipe.to(gfx_device)
elif movie_model_card == "VideoCrafter/Image2Video-512": elif movie_model_card == "VideoCrafter/Image2Video-512":
from diffusers import StableDiffusionPipeline from diffusers import StableDiffusionPipeline
@ -2446,6 +2490,24 @@ class SEQUENCER_OT_generate_movie(Operator):
generator=generator, generator=generator,
).frames[0] ).frames[0]
elif movie_model_card == "wangfuyun/AnimateLCM":
video_frames = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
num_inference_steps=movie_num_inference_steps,
guidance_scale=movie_num_guidance,
height=y,
width=x,
num_frames=duration,
generator=generator,
#num_frames=16,
#guidance_scale=2.0,
#num_inference_steps=6,
#generator=torch.Generator("cpu").manual_seed(0),
)
#frames = output.frames[0]
#export_to_gif(frames, "animatelcm.gif")
elif movie_model_card != "guoyww/animatediff-motion-adapter-v1-5-2": elif movie_model_card != "guoyww/animatediff-motion-adapter-v1-5-2":
if scene.movie_path: if scene.movie_path:
print("Process: Video to video") print("Process: Video to video")
@ -3206,7 +3268,7 @@ class SEQUENCER_OT_generate_image(Operator):
and not image_model_card == "Salesforce/blipdiffusion" and not image_model_card == "Salesforce/blipdiffusion"
and not image_model_card == "Lykon/dreamshaper-8" and not image_model_card == "Lykon/dreamshaper-8"
and not image_model_card == "ByteDance/SDXL-Lightning" and not image_model_card == "ByteDance/SDXL-Lightning"
and not image_model_card == "dataautogpt3/ProteusV0.3-Lightning" and not image_model_card == "dataautogpt3/Proteus-RunDiffusion-Lightning"
and not image_model_card == "Lykon/dreamshaper-xl-lightning" and not image_model_card == "Lykon/dreamshaper-xl-lightning"
) )
do_convert = ( do_convert = (
@ -3218,7 +3280,7 @@ class SEQUENCER_OT_generate_image(Operator):
and not image_model_card == "monster-labs/control_v1p_sdxl_qrcode_monster" and not image_model_card == "monster-labs/control_v1p_sdxl_qrcode_monster"
and not image_model_card == "Salesforce/blipdiffusion" and not image_model_card == "Salesforce/blipdiffusion"
and not image_model_card == "ByteDance/SDXL-Lightning" and not image_model_card == "ByteDance/SDXL-Lightning"
and not image_model_card == "dataautogpt3/ProteusV0.3-Lightning" and not image_model_card == "dataautogpt3/Proteus-RunDiffusion-Lightning"
and not image_model_card == "Lykon/dreamshaper-xl-lightning" and not image_model_card == "Lykon/dreamshaper-xl-lightning"
and not do_inpaint and not do_inpaint
) )
@ -3581,17 +3643,26 @@ class SEQUENCER_OT_generate_image(Operator):
# dreamshaper-xl-lightning # dreamshaper-xl-lightning
elif do_convert == False and image_model_card == "Lykon/dreamshaper-xl-lightning": elif do_convert == False and image_model_card == "Lykon/dreamshaper-xl-lightning":
from diffusers import AutoPipelineForText2Image, AutoencoderKL from diffusers import AutoPipelineForText2Image, AutoencoderKL
from diffusers import DPMSolverMultistepScheduler from diffusers import DPMSolverMultistep
#from diffusers import DPMSolverMultistepScheduler
#from diffusers import EulerAncestralDiscreteScheduler
vae = AutoencoderKL.from_pretrained( vae = AutoencoderKL.from_pretrained(
"madebyollin/sdxl-vae-fp16-fix", "madebyollin/sdxl-vae-fp16-fix",
torch_dtype=torch.float16, torch_dtype=torch.float16,
local_files_only=local_files_only, local_files_only=local_files_only,
) )
#from diffusers import DPMSolverMultistepScheduler
#from diffusers import EulerAncestralDiscreteScheduler #from diffusers import EulerAncestralDiscreteScheduler
pipe = AutoPipelineForText2Image.from_pretrained('Lykon/dreamshaper-xl-lightning', torch_dtype=torch.float16, variant="fp16", vae=vae) pipe = AutoPipelineForText2Image.from_pretrained('Lykon/dreamshaper-xl-lightning', torch_dtype=torch.float16, variant="fp16", vae=vae)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) #pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
#pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) #pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config, algorithm_type="sde-dpmsolver++")
#pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
pipe.scheduler = DPMSolverMultistep.from_config(pipe.scheduler.config)
pipe = pipe.to(gfx_device) pipe = pipe.to(gfx_device)
# Wuerstchen # Wuerstchen
@ -3735,6 +3806,24 @@ class SEQUENCER_OT_generate_image(Operator):
else: else:
stage_3.to(gfx_device) stage_3.to(gfx_device)
# playground
elif image_model_card == "playgroundai/playground-v2.5-1024px-aesthetic":
from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained(
"playgroundai/playground-v2.5-1024px-aesthetic",
torch_dtype=torch.float16,
variant="fp16",
)
from diffusers import EDMDPMSolverMultistepScheduler
pipe.scheduler = EDMDPMSolverMultistepScheduler()
if low_vram():
pipe.enable_model_cpu_offload()
else:
pipe.to(gfx_device)
# sdxl_dpo_turbo # sdxl_dpo_turbo
elif image_model_card == "thibaud/sdxl_dpo_turbo": elif image_model_card == "thibaud/sdxl_dpo_turbo":
from diffusers import StableDiffusionXLPipeline from diffusers import StableDiffusionXLPipeline
@ -3821,7 +3910,7 @@ class SEQUENCER_OT_generate_image(Operator):
# Ensure sampler uses "trailing" timesteps. # Ensure sampler uses "trailing" timesteps.
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing") pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
elif image_model_card == "dataautogpt3/ProteusV0.3-Lightning": elif image_model_card == "dataautogpt3/Proteus-RunDiffusion-Lightning":
import torch import torch
from diffusers import ( from diffusers import (
@ -3838,14 +3927,14 @@ class SEQUENCER_OT_generate_image(Operator):
# Configure the pipeline # Configure the pipeline
pipe = StableDiffusionXLPipeline.from_pretrained( pipe = StableDiffusionXLPipeline.from_pretrained(
"dataautogpt3/ProteusV0.3-Lightning", "dataautogpt3/Proteus-RunDiffusion-Lightning",
vae=vae, vae=vae,
torch_dtype=torch.float16 torch_dtype=torch.float16
) )
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.to('cuda') pipe.to('cuda')
elif image_model_card == "dataautogpt3/ProteusV0.3": elif image_model_card == "dataautogpt3/Proteus-RunDiffusion":
from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
from diffusers import AutoencoderKL from diffusers import AutoencoderKL
@ -3853,7 +3942,7 @@ class SEQUENCER_OT_generate_image(Operator):
"madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16 "madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16
) )
pipe = StableDiffusionXLPipeline.from_pretrained( pipe = StableDiffusionXLPipeline.from_pretrained(
"dataautogpt3/ProteusV0.3", "dataautogpt3/Proteus-RunDiffusion",
vae=vae, vae=vae,
torch_dtype=torch.float16, torch_dtype=torch.float16,
#variant="fp16", #variant="fp16",
@ -4193,12 +4282,12 @@ class SEQUENCER_OT_generate_image(Operator):
image = pipe( image = pipe(
prompt=prompt, prompt=prompt,
negative_prompt=negative_prompt, negative_prompt=negative_prompt,
num_inference_steps=4, num_inference_steps=5,
guidance_scale=image_num_guidance, guidance_scale=image_num_guidance,
height=y, height=y,
width=x, width=x,
generator=generator, generator=generator,
#output_type="pil", output_type="pil",
).images[0] ).images[0]
# OpenPose # OpenPose
@ -4374,7 +4463,7 @@ class SEQUENCER_OT_generate_image(Operator):
).images[0] ).images[0]
decoder = None decoder = None
elif image_model_card == "dataautogpt3/ProteusV0.3-Lightning": elif image_model_card == "dataautogpt3/Proteus-RunDiffusion-Lightning":
image = pipe( image = pipe(
prompt=prompt, prompt=prompt,
negative_prompt=negative_prompt, negative_prompt=negative_prompt,
@ -4411,7 +4500,7 @@ class SEQUENCER_OT_generate_image(Operator):
).images[0] ).images[0]
decoder = None decoder = None
elif image_model_card == "dataautogpt3/ProteusV0.3": elif image_model_card == "dataautogpt3/Proteus-RunDiffusion":
image = pipe( image = pipe(
# prompt_embeds=prompt, # for compel - long prompts # prompt_embeds=prompt, # for compel - long prompts
prompt, prompt,

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