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update controlnet_frames, downloads

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This commit is contained in:
tc2000731
2024-10-31 17:38:57 +08:00
parent 900a1c095f
commit 9377214518
2 changed files with 39 additions and 29 deletions
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65
examples/ControlNet/flux_controlnet_quantization.py
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@@ -6,10 +6,10 @@ import numpy as np
def example_1():
download_models(["FLUX.1-dev", "jasperai/Flux.1-dev-Controlnet-Upscaler"])
model_manager = ModelManager(
torch_dtype=torch.bfloat16,
device="cpu" # To reduce VRAM required, we load models to RAM.
# device="cuda" # To reduce VRAM required, we load models to RAM.
device="cpu"
)
model_manager.load_models([
"models/FLUX/FLUX.1-dev/text_encoder/model.safetensors",
@@ -18,11 +18,11 @@ def example_1():
])
model_manager.load_models(
["models/FLUX/FLUX.1-dev/flux1-dev.safetensors"],
torch_dtype=torch.float8_e4m3fn # Load the DiT model in FP8 format.
torch_dtype=torch.float8_e4m3fn
)
model_manager.load_models(
["models/ControlNet/jasperai/Flux.1-dev-Controlnet-Upscaler/diffusion_pytorch_model.safetensors"],
torch_dtype=torch.float8_e4m3fn # Load the DiT model in FP8 format.
torch_dtype=torch.float8_e4m3fn
)
pipe = FluxImagePipeline.from_model_manager(model_manager, controlnet_config_units=[
ControlNetConfigUnit(
@@ -55,10 +55,10 @@ def example_1():
def example_2():
download_models(["FLUX.1-dev", "jasperai/Flux.1-dev-Controlnet-Upscaler"])
model_manager = ModelManager(
torch_dtype=torch.bfloat16,
device="cpu" # To reduce VRAM required, we load models to RAM.
# device="cuda" # To reduce VRAM required, we load models to RAM.
device="cpu"
)
model_manager.load_models([
"models/FLUX/FLUX.1-dev/text_encoder/model.safetensors",
@@ -67,11 +67,11 @@ def example_2():
])
model_manager.load_models(
["models/FLUX/FLUX.1-dev/flux1-dev.safetensors"],
torch_dtype=torch.float8_e4m3fn # Load the DiT model in FP8 format.
torch_dtype=torch.float8_e4m3fn
)
model_manager.load_models(
["models/ControlNet/jasperai/Flux.1-dev-Controlnet-Upscaler/diffusion_pytorch_model.safetensors"],
torch_dtype=torch.float8_e4m3fn # Load the DiT model in FP8 format.
torch_dtype=torch.float8_e4m3fn
)
pipe = FluxImagePipeline.from_model_manager(model_manager, controlnet_config_units=[
ControlNetConfigUnit(
@@ -102,10 +102,10 @@ def example_2():
def example_3():
download_models(["FLUX.1-dev", "InstantX/FLUX.1-dev-Controlnet-Union-alpha"])
model_manager = ModelManager(
torch_dtype=torch.bfloat16,
device="cpu" # To reduce VRAM required, we load models to RAM.
# device="cuda" # To reduce VRAM required, we load models to RAM.
device="cpu"
)
model_manager.load_models([
"models/FLUX/FLUX.1-dev/text_encoder/model.safetensors",
@@ -114,11 +114,11 @@ def example_3():
])
model_manager.load_models(
["models/FLUX/FLUX.1-dev/flux1-dev.safetensors"],
torch_dtype=torch.float8_e4m3fn # Load the DiT model in FP8 format.
torch_dtype=torch.float8_e4m3fn
)
model_manager.load_models(
["models/ControlNet/InstantX/FLUX.1-dev-Controlnet-Union-alpha/diffusion_pytorch_model.safetensors"],
torch_dtype=torch.float8_e4m3fn # Load the DiT model in FP8 format.
torch_dtype=torch.float8_e4m3fn
)
pipe = FluxImagePipeline.from_model_manager(model_manager, controlnet_config_units=[
ControlNetConfigUnit(
@@ -153,10 +153,10 @@ def example_3():
def example_4():
download_models(["FLUX.1-dev", "InstantX/FLUX.1-dev-Controlnet-Union-alpha"])
model_manager = ModelManager(
torch_dtype=torch.bfloat16,
device="cpu" # To reduce VRAM required, we load models to RAM.
# device="cuda" # To reduce VRAM required, we load models to RAM.
device="cpu"
)
model_manager.load_models([
"models/FLUX/FLUX.1-dev/text_encoder/model.safetensors",
@@ -165,11 +165,11 @@ def example_4():
])
model_manager.load_models(
["models/FLUX/FLUX.1-dev/flux1-dev.safetensors"],
torch_dtype=torch.float8_e4m3fn # Load the DiT model in FP8 format.
torch_dtype=torch.float8_e4m3fn
)
model_manager.load_models(
["models/ControlNet/InstantX/FLUX.1-dev-Controlnet-Union-alpha/diffusion_pytorch_model.safetensors"],
torch_dtype=torch.float8_e4m3fn # Load the DiT model in FP8 format.
torch_dtype=torch.float8_e4m3fn
)
pipe = FluxImagePipeline.from_model_manager(model_manager, controlnet_config_units=[
ControlNetConfigUnit(
@@ -205,10 +205,10 @@ def example_4():
def example_5():
download_models(["FLUX.1-dev", "alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta"])
model_manager = ModelManager(
torch_dtype=torch.bfloat16,
device="cpu" # To reduce VRAM required, we load models to RAM.
# device="cuda" # To reduce VRAM required, we load models to RAM.
device="cpu"
)
model_manager.load_models([
"models/FLUX/FLUX.1-dev/text_encoder/model.safetensors",
@@ -217,11 +217,11 @@ def example_5():
])
model_manager.load_models(
["models/FLUX/FLUX.1-dev/flux1-dev.safetensors"],
torch_dtype=torch.float8_e4m3fn # Load the DiT model in FP8 format.
torch_dtype=torch.float8_e4m3fn
)
model_manager.load_models(
["models/ControlNet/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta/diffusion_pytorch_model.safetensors"],
torch_dtype=torch.float8_e4m3fn # Load the DiT model in FP8 format.
torch_dtype=torch.float8_e4m3fn
)
pipe = FluxImagePipeline.from_model_manager(model_manager, controlnet_config_units=[
ControlNetConfigUnit(
@@ -257,10 +257,14 @@ def example_5():
def example_6():
download_models([
"FLUX.1-dev",
"jasperai/Flux.1-dev-Controlnet-Surface-Normals",
"alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta"
])
model_manager = ModelManager(
torch_dtype=torch.bfloat16,
device="cpu" # To reduce VRAM required, we load models to RAM.
# device="cuda" # To reduce VRAM required, we load models to RAM.
device="cpu"
)
model_manager.load_models([
"models/FLUX/FLUX.1-dev/text_encoder/model.safetensors",
@@ -269,12 +273,12 @@ def example_6():
])
model_manager.load_models(
["models/FLUX/FLUX.1-dev/flux1-dev.safetensors"],
torch_dtype=torch.float8_e4m3fn # Load the DiT model in FP8 format.
torch_dtype=torch.float8_e4m3fn
)
model_manager.load_models(
["models/ControlNet/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta/diffusion_pytorch_model.safetensors",
"models/ControlNet/jasperai/Flux.1-dev-Controlnet-Surface-Normals/diffusion_pytorch_model.safetensors"],
torch_dtype=torch.float8_e4m3fn # Load the DiT model in FP8 format.
torch_dtype=torch.float8_e4m3fn
)
pipe = FluxImagePipeline.from_model_manager(model_manager, controlnet_config_units=[
ControlNetConfigUnit(
@@ -314,10 +318,15 @@ def example_6():
def example_7():
download_models([
"FLUX.1-dev",
"InstantX/FLUX.1-dev-Controlnet-Union-alpha",
"alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta",
"jasperai/Flux.1-dev-Controlnet-Upscaler",
])
model_manager = ModelManager(
torch_dtype=torch.bfloat16,
device="cpu" # To reduce VRAM required, we load models to RAM.
# device="cuda" # To reduce VRAM required, we load models to RAM.
device="cpu"
)
model_manager.load_models([
"models/FLUX/FLUX.1-dev/text_encoder/model.safetensors",
@@ -326,13 +335,13 @@ def example_7():
])
model_manager.load_models(
["models/FLUX/FLUX.1-dev/flux1-dev.safetensors"],
torch_dtype=torch.float8_e4m3fn # Load the DiT model in FP8 format.
torch_dtype=torch.float8_e4m3fn
)
model_manager.load_models(
["models/ControlNet/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta/diffusion_pytorch_model.safetensors",
"models/ControlNet/InstantX/FLUX.1-dev-Controlnet-Union-alpha/diffusion_pytorch_model.safetensors",
"models/ControlNet/jasperai/Flux.1-dev-Controlnet-Upscaler/diffusion_pytorch_model.safetensors"],
torch_dtype=torch.float8_e4m3fn # Load the DiT model in FP8 format.
torch_dtype=torch.float8_e4m3fn
)
pipe = FluxImagePipeline.from_model_manager(model_manager, controlnet_config_units=[
ControlNetConfigUnit(