FLUX.1-schnell-SDNQ-uint4-svd-r32
85
license:apache-2.0
by
Disty0
Image Model
OTHER
New
85 downloads
Early-stage
Edge AI:
Mobile
Laptop
Server
Unknown
Mobile
Laptop
Server
Quick Summary
4 bit (UINT4 with SVD rank 32) quantization of black-forest-labs/FLUX.
Code Examples
text
pip install git+https://github.com/Disty0/sdnqtext
pip install git+https://github.com/Disty0/sdnqtext
pip install git+https://github.com/Disty0/sdnqtext
pip install git+https://github.com/Disty0/sdnqtext
pip install git+https://github.com/Disty0/sdnqtext
pip install git+https://github.com/Disty0/sdnqtext
pip install git+https://github.com/Disty0/sdnqtext
pip install git+https://github.com/Disty0/sdnqtext
pip install git+https://github.com/Disty0/sdnqtext
pip install git+https://github.com/Disty0/sdnqtext
pip install git+https://github.com/Disty0/sdnqtext
pip install git+https://github.com/Disty0/sdnqtext
pip install git+https://github.com/Disty0/sdnqtext
pip install git+https://github.com/Disty0/sdnqtext
pip install git+https://github.com/Disty0/sdnqtext
pip install git+https://github.com/Disty0/sdnqtext
pip install git+https://github.com/Disty0/sdnqtext
pip install git+https://github.com/Disty0/sdnqtext
pip install git+https://github.com/Disty0/sdnqtext
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pip install git+https://github.com/Disty0/sdnqtext
pip install git+https://github.com/Disty0/sdnqtext
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pip install git+https://github.com/Disty0/sdnqtext
pip install git+https://github.com/Disty0/sdnqtext
pip install git+https://github.com/Disty0/sdnqtext
pip install git+https://github.com/Disty0/sdnqtext
pip install git+https://github.com/Disty0/sdnqtext
pip install git+https://github.com/Disty0/sdnqtext
pip install git+https://github.com/Disty0/sdnqtext
pip install git+https://github.com/Disty0/sdnqpythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")pythonpytorch
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.FluxPipeline.from_pretrained("Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
generator=torch.manual_seed(0)
).images[0]
image.save("flux-dev-sdnq-uint4-svd-r32.png")Deploy This Model
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