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Unverified Commit ad8097b9 authored by Muyang Li's avatar Muyang Li Committed by GitHub
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Release v0.2.0 

Ready to release v0.2.0
parents 804a6d30 998192ca
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comfyui/workflows/svdq-flux.1-depth.json deleted 100644 → 0
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examples/flux.1-canny-dev-lora.py 0 → 100644
View file @ ad8097b9
import torch
from controlnet_aux import CannyDetector
from diffusers import FluxControlPipeline
from diffusers.utils import load_image
from nunchaku import NunchakuFluxTransformer2dModel
from nunchaku.utils import get_precision
precision = get_precision() # auto-detect your precision is 'int4' or 'fp4' based on your GPU
transformer = NunchakuFluxTransformer2dModel.from_pretrained(f"mit-han-lab/svdq-{precision}-flux.1-dev")
pipe = FluxControlPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev", transformer=transformer, torch_dtype=torch.bfloat16
).to("cuda")
### LoRA Related Code ###
transformer.update_lora_params(
"black-forest-labs/FLUX.1-Canny-dev-lora/flux1-canny-dev-lora.safetensors"
) # Path to your LoRA safetensors, can also be a remote HuggingFace path
transformer.set_lora_strength(0.85) # Your LoRA strength here
### End of LoRA Related Code ###
prompt = (
"A robot made of exotic candies and chocolates of different kinds. "
"The background is filled with confetti and celebratory gifts."
)
control_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/robot.png")
processor = CannyDetector()
control_image = processor(
control_image, low_threshold=50, high_threshold=200, detect_resolution=1024, image_resolution=1024
)
image = pipe(
prompt=prompt, control_image=control_image, height=1024, width=1024, num_inference_steps=50, guidance_scale=30.0
).images[0]
image.save(f"flux.1-canny-dev-lora-{precision}.png")
examples/int4-flux.1-canny-dev.py → examples/flux.1-canny-dev.py
View file @ ad8097b9
......@@ -4,13 +4,18 @@ from diffusers import FluxControlPipeline
from diffusers.utils import load_image
from nunchaku import NunchakuFluxTransformer2dModel
from nunchaku.utils import get_precision
transformer = NunchakuFluxTransformer2dModel.from_pretrained("mit-han-lab/svdq-int4-flux.1-canny-dev")
precision = get_precision() # auto-detect your precision is 'int4' or 'fp4' based on your GPU
transformer = NunchakuFluxTransformer2dModel.from_pretrained(f"mit-han-lab/svdq-{precision}-flux.1-canny-dev")
pipe = FluxControlPipeline.from_pretrained(
"black-forest-labs/FLUX.1-Canny-dev", transformer=transformer, torch_dtype=torch.bfloat16
).to("cuda")
prompt = "A robot made of exotic candies and chocolates of different kinds. The background is filled with confetti and celebratory gifts."
prompt = (
"A robot made of exotic candies and chocolates of different kinds. "
"The background is filled with confetti and celebratory gifts."
)
control_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/robot.png")
processor = CannyDetector()
......@@ -21,4 +26,4 @@ control_image = processor(
image = pipe(
prompt=prompt, control_image=control_image, height=1024, width=1024, num_inference_steps=50, guidance_scale=30.0
).images[0]
image.save("flux.1-canny-dev.png")
image.save(f"flux.1-canny-dev-{precision}.png")
examples/flux.1-depth-dev-lora.py 0 → 100644
View file @ ad8097b9
import torch
from diffusers import FluxControlPipeline
from diffusers.utils import load_image
from image_gen_aux import DepthPreprocessor
from nunchaku import NunchakuFluxTransformer2dModel
from nunchaku.utils import get_precision
precision = get_precision() # auto-detect your precision is 'int4' or 'fp4' based on your GPU
transformer = NunchakuFluxTransformer2dModel.from_pretrained(f"mit-han-lab/svdq-{precision}-flux.1-dev")
pipe = FluxControlPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev", transformer=transformer, torch_dtype=torch.bfloat16
).to("cuda")
### LoRA Related Code ###
transformer.update_lora_params(
"black-forest-labs/FLUX.1-Depth-dev-lora/flux1-depth-dev-lora.safetensors"
) # Path to your LoRA safetensors, can also be a remote HuggingFace path
transformer.set_lora_strength(0.85) # Your LoRA strength here
### End of LoRA Related Code ###
control_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/robot.png")
processor = DepthPreprocessor.from_pretrained("LiheYoung/depth-anything-large-hf")
control_image = processor(control_image)[0].convert("RGB")
image = pipe(
prompt="A robot made of exotic candies and chocolates of different kinds. The background is filled with confetti and celebratory gifts.", # noqa: E501
control_image=control_image,
height=1024,
width=1024,
num_inference_steps=30,
guidance_scale=10.0,
generator=torch.Generator().manual_seed(42),
).images[0]
image.save(f"flux.1-depth-dev-lora-{precision}.png")
examples/int4-flux.1-depth-dev.py → examples/flux.1-depth-dev.py
View file @ ad8097b9
......@@ -4,8 +4,10 @@ from diffusers.utils import load_image
from image_gen_aux import DepthPreprocessor
from nunchaku import NunchakuFluxTransformer2dModel
from nunchaku.utils import get_precision
transformer = NunchakuFluxTransformer2dModel.from_pretrained("mit-han-lab/svdq-int4-flux.1-depth-dev")
precision = get_precision() # auto-detect your precision is 'int4' or 'fp4' based on your GPU
transformer = NunchakuFluxTransformer2dModel.from_pretrained(f"mit-han-lab/svdq-{precision}-flux.1-depth-dev")
pipe = FluxControlPipeline.from_pretrained(
"black-forest-labs/FLUX.1-Depth-dev",
......@@ -13,7 +15,10 @@ pipe = FluxControlPipeline.from_pretrained(
torch_dtype=torch.bfloat16,
).to("cuda")
prompt = "A robot made of exotic candies and chocolates of different kinds. The background is filled with confetti and celebratory gifts."
prompt = (
"A robot made of exotic candies and chocolates of different kinds. "
"The background is filled with confetti and celebratory gifts."
)
control_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/robot.png")
processor = DepthPreprocessor.from_pretrained("LiheYoung/depth-anything-large-hf")
......@@ -22,4 +27,4 @@ control_image = processor(control_image)[0].convert("RGB")
image = pipe(
prompt=prompt, control_image=control_image, height=1024, width=1024, num_inference_steps=30, guidance_scale=10.0
).images[0]
image.save("flux.1-depth-dev.png")
image.save(f"flux.1-depth-dev-{precision}.png")
examples/flux.1-dev-cache.py 0 → 100644
View file @ ad8097b9
import torch
from diffusers import FluxPipeline
from nunchaku import NunchakuFluxTransformer2dModel
from nunchaku.caching.diffusers_adapters import apply_cache_on_pipe
from nunchaku.utils import get_precision
precision = get_precision() # auto-detect your precision is 'int4' or 'fp4' based on your GPU
transformer = NunchakuFluxTransformer2dModel.from_pretrained(f"mit-han-lab/svdq-{precision}-flux.1-dev")
pipeline = FluxPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev", transformer=transformer, torch_dtype=torch.bfloat16
).to("cuda")
apply_cache_on_pipe(
pipeline, residual_diff_threshold=0.12
) # Set the first-block cache threshold. Increasing the value enhances speed at the cost of quality.
image = pipeline(["A cat holding a sign that says hello world"], num_inference_steps=50).images[0]
image.save(f"flux.1-dev-cache-{precision}.png")
examples/flux.1-dev-controlnet-union-pro.py 0 → 100644
View file @ ad8097b9
import torch
from diffusers import FluxControlNetModel, FluxControlNetPipeline
from diffusers.models import FluxMultiControlNetModel
from diffusers.utils import load_image
from nunchaku import NunchakuFluxTransformer2dModel
from nunchaku.caching.diffusers_adapters.flux import apply_cache_on_pipe
from nunchaku.utils import get_precision
base_model = "black-forest-labs/FLUX.1-dev"
controlnet_model_union = "Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro"
controlnet_union = FluxControlNetModel.from_pretrained(controlnet_model_union, torch_dtype=torch.bfloat16)
controlnet = FluxMultiControlNetModel([controlnet_union]) # we always recommend loading via FluxMultiControlNetModel
precision = get_precision()
transformer = NunchakuFluxTransformer2dModel.from_pretrained(
f"mit-han-lab/svdq-{precision}-flux.1-dev", torch_dtype=torch.bfloat16
)
transformer.set_attention_impl("nunchaku-fp16")
pipeline = FluxControlNetPipeline.from_pretrained(
base_model, transformer=transformer, controlnet=controlnet, torch_dtype=torch.bfloat16
).to("cuda")
# apply_cache_on_pipe(
# pipeline, residual_diff_threshold=0.1
# ) # Uncomment this line to enable first-block cache to speedup generation
prompt = "A anime style girl with messy beach waves."
control_image_depth = load_image(
"https://huggingface.co/Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro/resolve/main/assets/depth.jpg"
)
control_mode_depth = 2
control_image_canny = load_image(
"https://huggingface.co/Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro/resolve/main/assets/canny.jpg"
)
control_mode_canny = 0
width, height = control_image_depth.size
image = pipeline(
prompt,
control_image=[control_image_depth, control_image_canny],
control_mode=[control_mode_depth, control_mode_canny],
width=width,
height=height,
controlnet_conditioning_scale=[0.3, 0.1],
num_inference_steps=28,
guidance_scale=3.5,
generator=torch.manual_seed(233),
).images[0]
image.save(f"flux.1-dev-controlnet-union-pro-{precision}.png")
examples/fp4-flux.1-dev.py → examples/flux.1-dev-fp16attn.py
View file @ ad8097b9
......@@ -2,10 +2,13 @@ import torch
from diffusers import FluxPipeline
from nunchaku import NunchakuFluxTransformer2dModel
from nunchaku.utils import get_precision
transformer = NunchakuFluxTransformer2dModel.from_pretrained("mit-han-lab/svdq-fp4-flux.1-dev", precision="fp4")
precision = get_precision() # auto-detect your precision is 'int4' or 'fp4' based on your GPU
transformer = NunchakuFluxTransformer2dModel.from_pretrained(f"mit-han-lab/svdq-{precision}-flux.1-dev")
transformer.set_attention_impl("nunchaku-fp16") # set attention implementation to fp16
pipeline = FluxPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev", transformer=transformer, torch_dtype=torch.bfloat16
).to("cuda")
image = pipeline("A cat holding a sign that says hello world", num_inference_steps=50, guidance_scale=3.5).images[0]
image.save("flux.1-dev.png")
image = pipeline(["A cat holding a sign that says hello world"], num_inference_steps=50).images[0]
image.save(f"flux.1-dev-cache-{precision}.png")
examples/int4-flux.1-dev-lora.py → examples/flux.1-dev-lora.py
View file @ ad8097b9
......@@ -2,22 +2,24 @@ import torch
from diffusers import FluxPipeline
from nunchaku import NunchakuFluxTransformer2dModel
from nunchaku.utils import get_precision
transformer = NunchakuFluxTransformer2dModel.from_pretrained("mit-han-lab/svdq-int4-flux.1-dev")
precision = get_precision() # auto-detect your precision is 'int4' or 'fp4' based on your GPU
transformer = NunchakuFluxTransformer2dModel.from_pretrained(f"mit-han-lab/svdq-{precision}-flux.1-dev")
pipeline = FluxPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev", transformer=transformer, torch_dtype=torch.bfloat16
).to("cuda")
### LoRA Related Code ###
transformer.update_lora_params(
"mit-han-lab/svdquant-lora-collection/svdq-int4-flux.1-dev-ghibsky.safetensors"
) # Path to your converted LoRA safetensors, can also be a remote HuggingFace path
"aleksa-codes/flux-ghibsky-illustration/lora.safetensors"
) # Path to your LoRA safetensors, can also be a remote HuggingFace path
transformer.set_lora_strength(1) # Your LoRA strength here
### End of LoRA Related Code ###
image = pipeline(
"GHIBSKY style, cozy mountain cabin covered in snow, with smoke curling from the chimney and a warm, inviting light spilling through the windows",
"GHIBSKY style, cozy mountain cabin covered in snow, with smoke curling from the chimney and a warm, inviting light spilling through the windows", # noqa: E501
num_inference_steps=25,
guidance_scale=3.5,
).images[0]
image.save("flux.1-dev-ghibsky.png")
image.save(f"flux.1-dev-ghibsky-{precision}.png")
examples/flux.1-dev-multiple-lora.py 0 → 100644
View file @ ad8097b9
import torch
from diffusers import FluxPipeline
from nunchaku import NunchakuFluxTransformer2dModel
from nunchaku.lora.flux.compose import compose_lora
from nunchaku.utils import get_precision
precision = get_precision() # auto-detect your precision is 'int4' or 'fp4' based on your GPU
transformer = NunchakuFluxTransformer2dModel.from_pretrained(f"mit-han-lab/svdq-{precision}-flux.1-dev")
pipeline = FluxPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev", transformer=transformer, torch_dtype=torch.bfloat16
).to("cuda")
### LoRA Related Code ###
composed_lora = compose_lora(
[
("aleksa-codes/flux-ghibsky-illustration/lora.safetensors", 1),
("alimama-creative/FLUX.1-Turbo-Alpha/diffusion_pytorch_model.safetensors", 1),
]
) # set your lora strengths here when using composed lora
transformer.update_lora_params(composed_lora)
### End of LoRA Related Code ###
image = pipeline(
"GHIBSKY style, cozy mountain cabin covered in snow, with smoke curling from the chimney and a warm, inviting light spilling through the windows", # noqa: E501
num_inference_steps=8,
guidance_scale=3.5,
).images[0]
image.save(f"flux.1-dev-turbo-ghibsky-{precision}.png")
examples/int4-flux.1-dev-offload.py → examples/flux.1-dev-offload.py
View file @ ad8097b9
import torch
from diffusers import FluxPipeline
from nunchaku import NunchakuFluxTransformer2dModel, NunchakuT5EncoderModel
from nunchaku import NunchakuFluxTransformer2dModel
from nunchaku.utils import get_precision
precision = get_precision() # auto-detect your precision is 'int4' or 'fp4' based on your GPU
transformer = NunchakuFluxTransformer2dModel.from_pretrained(
"mit-han-lab/svdq-int4-flux.1-dev", offload=True
f"mit-han-lab/svdq-{precision}-flux.1-dev", offload=True
) # set offload to False if you want to disable offloading
pipeline = FluxPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev", transformer=transformer, torch_dtype=torch.bfloat16
)
pipeline.enable_sequential_cpu_offload() # remove this line if you want to disable the CPU offloading
) # no need to set the device here
pipeline.enable_sequential_cpu_offload() # diffusers' offloading
image = pipeline("A cat holding a sign that says hello world", num_inference_steps=50, guidance_scale=3.5).images[0]
image.save("flux.1-dev.png")
image.save(f"flux.1-dev-{precision}.png")
examples/int4-flux.1-dev-qencoder.py → examples/flux.1-dev-qencoder.py
View file @ ad8097b9
......@@ -2,8 +2,10 @@ import torch
from diffusers import FluxPipeline
from nunchaku import NunchakuFluxTransformer2dModel, NunchakuT5EncoderModel
from nunchaku.utils import get_precision
transformer = NunchakuFluxTransformer2dModel.from_pretrained("mit-han-lab/svdq-int4-flux.1-dev")
precision = get_precision() # auto-detect your precision is 'int4' or 'fp4' based on your GPU
transformer = NunchakuFluxTransformer2dModel.from_pretrained(f"mit-han-lab/svdq-{precision}-flux.1-dev")
text_encoder_2 = NunchakuT5EncoderModel.from_pretrained("mit-han-lab/svdq-flux.1-t5")
pipeline = FluxPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev",
......@@ -12,4 +14,4 @@ pipeline = FluxPipeline.from_pretrained(
torch_dtype=torch.bfloat16,
).to("cuda")
image = pipeline("A cat holding a sign that says hello world", num_inference_steps=50, guidance_scale=3.5).images[0]
image.save("flux.1-dev.png")
image.save(f"flux.1-dev-{precision}.png")
examples/int4-flux.1-dev-qencoder-offload.py → examples/flux.1-dev-turing.py
View file @ ad8097b9
import torch
from diffusers import FluxPipeline
from nunchaku import NunchakuFluxTransformer2dModel, NunchakuT5EncoderModel
from nunchaku import NunchakuFluxTransformer2dModel
from nunchaku.utils import get_precision
precision = get_precision() # auto-detect your precision is 'int4' or 'fp4' based on your GPU
transformer = NunchakuFluxTransformer2dModel.from_pretrained(
"mit-han-lab/svdq-int4-flux.1-dev", offload=True
f"mit-han-lab/svdq-{precision}-flux.1-dev",
offload=True,
torch_dtype=torch.float16, # Turing GPUs only support fp16 precision
) # set offload to False if you want to disable offloading
text_encoder_2 = NunchakuT5EncoderModel.from_pretrained("mit-han-lab/svdq-flux.1-t5")
transformer.set_attention_impl("nunchaku-fp16") # Turing GPUs only support fp16 attention
pipeline = FluxPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev", text_encoder_2=text_encoder_2, transformer=transformer, torch_dtype=torch.bfloat16
).to("cuda")
pipeline.enable_sequential_cpu_offload() # remove this line if you want to disable the CPU offloading
"black-forest-labs/FLUX.1-dev", transformer=transformer, torch_dtype=torch.float16
) # no need to set the device here
pipeline.enable_sequential_cpu_offload() # diffusers' offloading
image = pipeline("A cat holding a sign that says hello world", num_inference_steps=50, guidance_scale=3.5).images[0]
image.save("flux.1-dev.png")
image.save(f"flux.1-dev-{precision}.png")
examples/int4-flux.1-dev.py → examples/flux.1-dev.py
View file @ ad8097b9
......@@ -2,10 +2,12 @@ import torch
from diffusers import FluxPipeline
from nunchaku import NunchakuFluxTransformer2dModel
from nunchaku.utils import get_precision
transformer = NunchakuFluxTransformer2dModel.from_pretrained("mit-han-lab/svdq-int4-flux.1-dev")
precision = get_precision() # auto-detect your precision is 'int4' or 'fp4' based on your GPU
transformer = NunchakuFluxTransformer2dModel.from_pretrained(f"mit-han-lab/svdq-{precision}-flux.1-dev")
pipeline = FluxPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev", transformer=transformer, torch_dtype=torch.bfloat16
).to("cuda")
image = pipeline("A cat holding a sign that says hello world", num_inference_steps=50, guidance_scale=3.5).images[0]
image.save("flux.1-dev.png")
image.save(f"flux.1-dev-{precision}.png")
examples/int4-flux.1-fill-dev.py → examples/flux.1-fill-dev.py
View file @ ad8097b9
......@@ -3,11 +3,13 @@ from diffusers import FluxFillPipeline
from diffusers.utils import load_image
from nunchaku import NunchakuFluxTransformer2dModel
from nunchaku.utils import get_precision
image = load_image("https://huggingface.co/mit-han-lab/svdq-int4-flux.1-fill-dev/resolve/main/example.png")
mask = load_image("https://huggingface.co/mit-han-lab/svdq-int4-flux.1-fill-dev/resolve/main/mask.png")
transformer = NunchakuFluxTransformer2dModel.from_pretrained("mit-han-lab/svdq-int4-flux.1-fill-dev")
precision = get_precision() # auto-detect your precision is 'int4' or 'fp4' based on your GPU
transformer = NunchakuFluxTransformer2dModel.from_pretrained(f"mit-han-lab/svdq-{precision}-flux.1-fill-dev")
pipe = FluxFillPipeline.from_pretrained(
"black-forest-labs/FLUX.1-Fill-dev", transformer=transformer, torch_dtype=torch.bfloat16
).to("cuda")
......@@ -21,4 +23,4 @@ image = pipe(
num_inference_steps=50,
max_sequence_length=512,
).images[0]
image.save("flux.1-fill-dev.png")
image.save(f"flux.1-fill-dev-{precision}.png")
examples/int4-flux.1-redux-dev.py → examples/flux.1-redux-dev.py
View file @ ad8097b9
......@@ -3,11 +3,13 @@ from diffusers import FluxPipeline, FluxPriorReduxPipeline
from diffusers.utils import load_image
from nunchaku import NunchakuFluxTransformer2dModel
from nunchaku.utils import get_precision
precision = get_precision()
pipe_prior_redux = FluxPriorReduxPipeline.from_pretrained(
"black-forest-labs/FLUX.1-Redux-dev", torch_dtype=torch.bfloat16
).to("cuda")
transformer = NunchakuFluxTransformer2dModel.from_pretrained("mit-han-lab/svdq-int4-flux.1-dev")
transformer = NunchakuFluxTransformer2dModel.from_pretrained(f"mit-han-lab/svdq-{precision}-flux.1-dev")
pipe = FluxPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev",
text_encoder=None,
......@@ -19,4 +21,4 @@ pipe = FluxPipeline.from_pretrained(
image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/robot.png")
pipe_prior_output = pipe_prior_redux(image)
images = pipe(guidance_scale=2.5, num_inference_steps=50, **pipe_prior_output).images
images[0].save("flux.1-redux-dev.png")
images[0].save(f"flux.1-redux-dev-{precision}.png")
examples/int4-flux.1-schnell.py → examples/flux.1-schnell.py
View file @ ad8097b9
......@@ -2,12 +2,14 @@ import torch
from diffusers import FluxPipeline
from nunchaku import NunchakuFluxTransformer2dModel
from nunchaku.utils import get_precision
transformer = NunchakuFluxTransformer2dModel.from_pretrained("mit-han-lab/svdq-int4-flux.1-schnell")
precision = get_precision() # auto-detect your precision is 'int4' or 'fp4' based on your GPU
transformer = NunchakuFluxTransformer2dModel.from_pretrained(f"mit-han-lab/svdq-{precision}-flux.1-schnell")
pipeline = FluxPipeline.from_pretrained(
"black-forest-labs/FLUX.1-schnell", transformer=transformer, torch_dtype=torch.bfloat16
).to("cuda")
image = pipeline(
"A cat holding a sign that says hello world", width=1024, height=1024, num_inference_steps=4, guidance_scale=0
).images[0]
image.save("flux.1-schnell.png")
image.save(f"flux.1-schnell-{precision}.png")
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