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Remove img2img SDXL.

Pallaidium_MacOS
tin2tin 12 months ago committed by GitHub
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1 changed files with 99 additions and 98 deletions
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  1. 197
      __init__.py

197
__init__.py
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@ -1011,11 +1011,11 @@ class GeneratorAddonPreferences(AddonPreferences):
# ("hotshotco/Hotshot-XL", "Hotshot-XL (512x512)", "Hotshot-XL (512x512)"),
("strangeman3107/animov-0.1.1", "Animov (448x384)", "Animov (448x384)"),
("strangeman3107/animov-512x", "Animov (512x512)", "Animov (512x512)"),
(
"stabilityai/stable-diffusion-xl-base-1.0",
"Img2img SD XL 1.0 Refine (1024x1024)",
"Stable Diffusion XL 1.0",
),
# (
# "stabilityai/stable-diffusion-xl-base-1.0",
# "Img2img SD XL 1.0 Refine (1024x1024)",
# "Stable Diffusion XL 1.0",
# ),
("camenduru/potat1", "Potat v1 (1024x576)", "Potat (1024x576)"),
# ("VideoCrafter/Image2Video-512", "VideoCrafter v1 (512x512)", "VideoCrafter/Image2Video-512"),
(
@ -1087,11 +1087,6 @@ class GeneratorAddonPreferences(AddonPreferences):
"Segmind SSD-1B (1024x1024)",
"segmind/SSD-1B",
),
(
"stabilityai/sdxl-turbo",
"SDXL Turbo (512x512)",
"stabilityai/sdxl-turbo",
),
(
"runwayml/stable-diffusion-v1-5",
"Stable Diffusion 1.5 (512x512)",
@ -1102,6 +1097,11 @@ class GeneratorAddonPreferences(AddonPreferences):
"Stable Diffusion 2 (768x768)",
"stabilityai/stable-diffusion-2",
),
(
"stabilityai/sdxl-turbo",
"Stable Diffusion Turbo (512x512)",
"stabilityai/sdxl-turbo",
),
(
"stabilityai/stable-diffusion-xl-base-1.0",
"Stable Diffusion XL 1.0 (1024x1024)",
@ -1857,11 +1857,11 @@ class SEQUENCER_PT_pallaidium_panel(Panel): # UI
row = col.row(align=True)
row.scale_y = 1.2
if type == "movie":
if movie_model_card == "stabilityai/stable-diffusion-xl-base-1.0":
row.operator(
"sequencer.text_to_generator", text="Generate from Strips"
)
else:
# if movie_model_card == "stabilityai/stable-diffusion-xl-base-1.0":
# row.operator(
# "sequencer.text_to_generator", text="Generate from Strips"
# )
# else:
row.operator("sequencer.generate_movie", text="Generate")
if type == "image":
row.operator("sequencer.generate_image", text="Generate")
@ -1952,52 +1952,52 @@ class SEQUENCER_OT_generate_movie(Operator):
and input == "input_strips"
and movie_model_card != "guoyww/animatediff-motion-adapter-v1-5-2"
):
# if (
# movie_model_card == "stabilityai/stable-diffusion-xl-base-1.0"
# ): # img2img
# from diffusers import StableDiffusionXLImg2ImgPipeline, AutoencoderKL
# vae = AutoencoderKL.from_pretrained(
# "madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16
# )
# pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(
# movie_model_card,
# torch_dtype=torch.float16,
# variant="fp16",
# vae=vae,
# )
# from diffusers import DPMSolverMultistepScheduler
# pipe.scheduler = DPMSolverMultistepScheduler.from_config(
# pipe.scheduler.config
# )
# pipe.watermark = NoWatermark()
# if low_vram():
# pipe.enable_model_cpu_offload()
# # pipe.unet.enable_forward_chunking(chunk_size=1, dim=1) # Heavy
# # pipe.enable_vae_slicing()
# else:
# pipe.to("cuda")
# from diffusers import StableDiffusionXLImg2ImgPipeline
# refiner = StableDiffusionXLImg2ImgPipeline.from_pretrained(
# "stabilityai/stable-diffusion-xl-refiner-1.0",
# text_encoder_2=pipe.text_encoder_2,
# vae=pipe.vae,
# torch_dtype=torch.float16,
# variant="fp16",
# )
# if low_vram():
# refiner.enable_model_cpu_offload()
# # refiner.enable_vae_tiling()
# # refiner.enable_vae_slicing()
# else:
# refiner.to("cuda")
if (
movie_model_card == "stabilityai/stable-diffusion-xl-base-1.0"
): # img2img
from diffusers import StableDiffusionXLImg2ImgPipeline, AutoencoderKL
vae = AutoencoderKL.from_pretrained(
"madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16
)
pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(
movie_model_card,
torch_dtype=torch.float16,
variant="fp16",
vae=vae,
)
from diffusers import DPMSolverMultistepScheduler
pipe.scheduler = DPMSolverMultistepScheduler.from_config(
pipe.scheduler.config
)
pipe.watermark = NoWatermark()
if low_vram():
pipe.enable_model_cpu_offload()
# pipe.unet.enable_forward_chunking(chunk_size=1, dim=1) # Heavy
# pipe.enable_vae_slicing()
else:
pipe.to("cuda")
from diffusers import StableDiffusionXLImg2ImgPipeline
refiner = StableDiffusionXLImg2ImgPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-refiner-1.0",
text_encoder_2=pipe.text_encoder_2,
vae=pipe.vae,
torch_dtype=torch.float16,
variant="fp16",
)
if low_vram():
refiner.enable_model_cpu_offload()
# refiner.enable_vae_tiling()
# refiner.enable_vae_slicing()
else:
refiner.to("cuda")
elif (
movie_model_card == "stabilityai/stable-video-diffusion-img2vid"
or movie_model_card == "stabilityai/stable-video-diffusion-img2vid-xt"
):
@ -2201,44 +2201,45 @@ class SEQUENCER_OT_generate_movie(Operator):
):
video_path = scene.movie_path
# img2img
if movie_model_card == "stabilityai/stable-diffusion-xl-base-1.0":
print("Process: Frame by frame (SD XL)")
# # img2img
# if movie_model_card == "stabilityai/stable-diffusion-xl-base-1.0":
# print("Process: Frame by frame (SD XL)")
# input_video_path = video_path
# output_video_path = solve_path("temp_images")
# if scene.movie_path:
# frames = process_video(input_video_path, output_video_path)
# elif scene.image_path:
# frames = process_image(
# scene.image_path, int(scene.generate_movie_frames)
# )
# video_frames = []
# # Iterate through the frames
# for frame_idx, frame in enumerate(
# frames
# ): # would love to get this flicker free
# print(str(frame_idx + 1) + "/" + str(len(frames)))
# image = refiner(
# prompt,
# negative_prompt=negative_prompt,
# num_inference_steps=movie_num_inference_steps,
# strength=1.00 - scene.image_power,
# guidance_scale=movie_num_guidance,
# image=frame,
# generator=generator,
# ).images[0]
# video_frames.append(image)
# if torch.cuda.is_available():
# torch.cuda.empty_cache()
# video_frames = np.array(video_frames)
input_video_path = video_path
output_video_path = solve_path("temp_images")
if scene.movie_path:
frames = process_video(input_video_path, output_video_path)
elif scene.image_path:
frames = process_image(
scene.image_path, int(scene.generate_movie_frames)
)
video_frames = []
# Iterate through the frames
for frame_idx, frame in enumerate(
frames
): # would love to get this flicker free
print(str(frame_idx + 1) + "/" + str(len(frames)))
image = refiner(
prompt,
negative_prompt=negative_prompt,
num_inference_steps=movie_num_inference_steps,
strength=1.00 - scene.image_power,
guidance_scale=movie_num_guidance,
image=frame,
generator=generator,
).images[0]
video_frames.append(image)
if torch.cuda.is_available():
torch.cuda.empty_cache()
video_frames = np.array(video_frames)
# vid2vid / img2vid
elif (
if (
movie_model_card == "stabilityai/stable-video-diffusion-img2vid"
or movie_model_card
== "stabilityai/stable-video-diffusion-img2vid-xt"
@ -4230,7 +4231,7 @@ def register():
bpy.types.Scene.movie_num_inference_steps = bpy.props.IntProperty(
name="movie_num_inference_steps",
default=25,
min=2,
min=1,
max=100,
)
# The number of videos to generate.
@ -4414,9 +4415,9 @@ def register():
# SVD motion_bucket_id
bpy.types.Scene.svd_motion_bucket_id = bpy.props.IntProperty(
name="svd_motion_bucket_id",
default=127,
default=30,
min=1,
max=255,
max=512,
)
for cls in classes:

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