FLUX.1-dev-torchao-fp8

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diffusers
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Quick Summary

Visual comparison of Flux-dev model outputs using BF16 and torchao float8weightonly quantization To use this quantized FLUX.

Code Examples

python
from diffusers import FluxPipeline
pipe = FluxPipeline.from_pretrained(
    "diffusers/FLUX.1-dev-torchao-fp8",
    torch_dtype=torch.bfloat16,
    use_safetensors=False,
    device_map="balanced"
)
prompt = "Baroque style, a lavish palace interior with ornate gilded ceilings, intricate tapestries, and dramatic lighting over a grand staircase."
pipe_kwargs = {
    "prompt": prompt,
    "height": 1024,
    "width": 1024,
    "guidance_scale": 3.5,
    "num_inference_steps": 50,
    "max_sequence_length": 512,
}
image = pipe(
    **pipe_kwargs, generator=torch.manual_seed(0),
).images[0]
image.save("flux.png")
python
from diffusers import FluxPipeline
pipe = FluxPipeline.from_pretrained(
    "diffusers/FLUX.1-dev-torchao-fp8",
    torch_dtype=torch.bfloat16,
    use_safetensors=False,
    device_map="balanced"
)
prompt = "Baroque style, a lavish palace interior with ornate gilded ceilings, intricate tapestries, and dramatic lighting over a grand staircase."
pipe_kwargs = {
    "prompt": prompt,
    "height": 1024,
    "width": 1024,
    "guidance_scale": 3.5,
    "num_inference_steps": 50,
    "max_sequence_length": 512,
}
image = pipe(
    **pipe_kwargs, generator=torch.manual_seed(0),
).images[0]
image.save("flux.png")
python
from diffusers import FluxPipeline
pipe = FluxPipeline.from_pretrained(
    "diffusers/FLUX.1-dev-torchao-fp8",
    torch_dtype=torch.bfloat16,
    use_safetensors=False,
    device_map="balanced"
)
prompt = "Baroque style, a lavish palace interior with ornate gilded ceilings, intricate tapestries, and dramatic lighting over a grand staircase."
pipe_kwargs = {
    "prompt": prompt,
    "height": 1024,
    "width": 1024,
    "guidance_scale": 3.5,
    "num_inference_steps": 50,
    "max_sequence_length": 512,
}
image = pipe(
    **pipe_kwargs, generator=torch.manual_seed(0),
).images[0]
image.save("flux.png")
python
from diffusers import FluxPipeline
pipe = FluxPipeline.from_pretrained(
    "diffusers/FLUX.1-dev-torchao-fp8",
    torch_dtype=torch.bfloat16,
    use_safetensors=False,
    device_map="balanced"
)
prompt = "Baroque style, a lavish palace interior with ornate gilded ceilings, intricate tapestries, and dramatic lighting over a grand staircase."
pipe_kwargs = {
    "prompt": prompt,
    "height": 1024,
    "width": 1024,
    "guidance_scale": 3.5,
    "num_inference_steps": 50,
    "max_sequence_length": 512,
}
image = pipe(
    **pipe_kwargs, generator=torch.manual_seed(0),
).images[0]
image.save("flux.png")
python
from diffusers import FluxPipeline
pipe = FluxPipeline.from_pretrained(
    "diffusers/FLUX.1-dev-torchao-fp8",
    torch_dtype=torch.bfloat16,
    use_safetensors=False,
    device_map="balanced"
)
prompt = "Baroque style, a lavish palace interior with ornate gilded ceilings, intricate tapestries, and dramatic lighting over a grand staircase."
pipe_kwargs = {
    "prompt": prompt,
    "height": 1024,
    "width": 1024,
    "guidance_scale": 3.5,
    "num_inference_steps": 50,
    "max_sequence_length": 512,
}
image = pipe(
    **pipe_kwargs, generator=torch.manual_seed(0),
).images[0]
image.save("flux.png")
python
from diffusers import FluxPipeline
pipe = FluxPipeline.from_pretrained(
    "diffusers/FLUX.1-dev-torchao-fp8",
    torch_dtype=torch.bfloat16,
    use_safetensors=False,
    device_map="balanced"
)
prompt = "Baroque style, a lavish palace interior with ornate gilded ceilings, intricate tapestries, and dramatic lighting over a grand staircase."
pipe_kwargs = {
    "prompt": prompt,
    "height": 1024,
    "width": 1024,
    "guidance_scale": 3.5,
    "num_inference_steps": 50,
    "max_sequence_length": 512,
}
image = pipe(
    **pipe_kwargs, generator=torch.manual_seed(0),
).images[0]
image.save("flux.png")
python
from diffusers import FluxPipeline
pipe = FluxPipeline.from_pretrained(
    "diffusers/FLUX.1-dev-torchao-fp8",
    torch_dtype=torch.bfloat16,
    use_safetensors=False,
    device_map="balanced"
)
prompt = "Baroque style, a lavish palace interior with ornate gilded ceilings, intricate tapestries, and dramatic lighting over a grand staircase."
pipe_kwargs = {
    "prompt": prompt,
    "height": 1024,
    "width": 1024,
    "guidance_scale": 3.5,
    "num_inference_steps": 50,
    "max_sequence_length": 512,
}
image = pipe(
    **pipe_kwargs, generator=torch.manual_seed(0),
).images[0]
image.save("flux.png")
python
from diffusers import FluxPipeline
pipe = FluxPipeline.from_pretrained(
    "diffusers/FLUX.1-dev-torchao-fp8",
    torch_dtype=torch.bfloat16,
    use_safetensors=False,
    device_map="balanced"
)
prompt = "Baroque style, a lavish palace interior with ornate gilded ceilings, intricate tapestries, and dramatic lighting over a grand staircase."
pipe_kwargs = {
    "prompt": prompt,
    "height": 1024,
    "width": 1024,
    "guidance_scale": 3.5,
    "num_inference_steps": 50,
    "max_sequence_length": 512,
}
image = pipe(
    **pipe_kwargs, generator=torch.manual_seed(0),
).images[0]
image.save("flux.png")
python
from diffusers import FluxPipeline
pipe = FluxPipeline.from_pretrained(
    "diffusers/FLUX.1-dev-torchao-fp8",
    torch_dtype=torch.bfloat16,
    use_safetensors=False,
    device_map="balanced"
)
prompt = "Baroque style, a lavish palace interior with ornate gilded ceilings, intricate tapestries, and dramatic lighting over a grand staircase."
pipe_kwargs = {
    "prompt": prompt,
    "height": 1024,
    "width": 1024,
    "guidance_scale": 3.5,
    "num_inference_steps": 50,
    "max_sequence_length": 512,
}
image = pipe(
    **pipe_kwargs, generator=torch.manual_seed(0),
).images[0]
image.save("flux.png")
python
from diffusers import FluxPipeline
pipe = FluxPipeline.from_pretrained(
    "diffusers/FLUX.1-dev-torchao-fp8",
    torch_dtype=torch.bfloat16,
    use_safetensors=False,
    device_map="balanced"
)
prompt = "Baroque style, a lavish palace interior with ornate gilded ceilings, intricate tapestries, and dramatic lighting over a grand staircase."
pipe_kwargs = {
    "prompt": prompt,
    "height": 1024,
    "width": 1024,
    "guidance_scale": 3.5,
    "num_inference_steps": 50,
    "max_sequence_length": 512,
}
image = pipe(
    **pipe_kwargs, generator=torch.manual_seed(0),
).images[0]
image.save("flux.png")
python
from diffusers import FluxPipeline
pipe = FluxPipeline.from_pretrained(
    "diffusers/FLUX.1-dev-torchao-fp8",
    torch_dtype=torch.bfloat16,
    use_safetensors=False,
    device_map="balanced"
)
prompt = "Baroque style, a lavish palace interior with ornate gilded ceilings, intricate tapestries, and dramatic lighting over a grand staircase."
pipe_kwargs = {
    "prompt": prompt,
    "height": 1024,
    "width": 1024,
    "guidance_scale": 3.5,
    "num_inference_steps": 50,
    "max_sequence_length": 512,
}
image = pipe(
    **pipe_kwargs, generator=torch.manual_seed(0),
).images[0]
image.save("flux.png")
python
from diffusers import FluxPipeline
pipe = FluxPipeline.from_pretrained(
    "diffusers/FLUX.1-dev-torchao-fp8",
    torch_dtype=torch.bfloat16,
    use_safetensors=False,
    device_map="balanced"
)
prompt = "Baroque style, a lavish palace interior with ornate gilded ceilings, intricate tapestries, and dramatic lighting over a grand staircase."
pipe_kwargs = {
    "prompt": prompt,
    "height": 1024,
    "width": 1024,
    "guidance_scale": 3.5,
    "num_inference_steps": 50,
    "max_sequence_length": 512,
}
image = pipe(
    **pipe_kwargs, generator=torch.manual_seed(0),
).images[0]
image.save("flux.png")
python
from diffusers import FluxPipeline
pipe = FluxPipeline.from_pretrained(
    "diffusers/FLUX.1-dev-torchao-fp8",
    torch_dtype=torch.bfloat16,
    use_safetensors=False,
    device_map="balanced"
)
prompt = "Baroque style, a lavish palace interior with ornate gilded ceilings, intricate tapestries, and dramatic lighting over a grand staircase."
pipe_kwargs = {
    "prompt": prompt,
    "height": 1024,
    "width": 1024,
    "guidance_scale": 3.5,
    "num_inference_steps": 50,
    "max_sequence_length": 512,
}
image = pipe(
    **pipe_kwargs, generator=torch.manual_seed(0),
).images[0]
image.save("flux.png")
python
from diffusers import FluxPipeline
pipe = FluxPipeline.from_pretrained(
    "diffusers/FLUX.1-dev-torchao-fp8",
    torch_dtype=torch.bfloat16,
    use_safetensors=False,
    device_map="balanced"
)
prompt = "Baroque style, a lavish palace interior with ornate gilded ceilings, intricate tapestries, and dramatic lighting over a grand staircase."
pipe_kwargs = {
    "prompt": prompt,
    "height": 1024,
    "width": 1024,
    "guidance_scale": 3.5,
    "num_inference_steps": 50,
    "max_sequence_length": 512,
}
image = pipe(
    **pipe_kwargs, generator=torch.manual_seed(0),
).images[0]
image.save("flux.png")
python
from diffusers import FluxPipeline
pipe = FluxPipeline.from_pretrained(
    "diffusers/FLUX.1-dev-torchao-fp8",
    torch_dtype=torch.bfloat16,
    use_safetensors=False,
    device_map="balanced"
)
prompt = "Baroque style, a lavish palace interior with ornate gilded ceilings, intricate tapestries, and dramatic lighting over a grand staircase."
pipe_kwargs = {
    "prompt": prompt,
    "height": 1024,
    "width": 1024,
    "guidance_scale": 3.5,
    "num_inference_steps": 50,
    "max_sequence_length": 512,
}
image = pipe(
    **pipe_kwargs, generator=torch.manual_seed(0),
).images[0]
image.save("flux.png")
python
from diffusers import FluxPipeline
pipe = FluxPipeline.from_pretrained(
    "diffusers/FLUX.1-dev-torchao-fp8",
    torch_dtype=torch.bfloat16,
    use_safetensors=False,
    device_map="balanced"
)
prompt = "Baroque style, a lavish palace interior with ornate gilded ceilings, intricate tapestries, and dramatic lighting over a grand staircase."
pipe_kwargs = {
    "prompt": prompt,
    "height": 1024,
    "width": 1024,
    "guidance_scale": 3.5,
    "num_inference_steps": 50,
    "max_sequence_length": 512,
}
image = pipe(
    **pipe_kwargs, generator=torch.manual_seed(0),
).images[0]
image.save("flux.png")
python
from diffusers import FluxPipeline
pipe = FluxPipeline.from_pretrained(
    "diffusers/FLUX.1-dev-torchao-fp8",
    torch_dtype=torch.bfloat16,
    use_safetensors=False,
    device_map="balanced"
)
prompt = "Baroque style, a lavish palace interior with ornate gilded ceilings, intricate tapestries, and dramatic lighting over a grand staircase."
pipe_kwargs = {
    "prompt": prompt,
    "height": 1024,
    "width": 1024,
    "guidance_scale": 3.5,
    "num_inference_steps": 50,
    "max_sequence_length": 512,
}
image = pipe(
    **pipe_kwargs, generator=torch.manual_seed(0),
).images[0]
image.save("flux.png")
python
from diffusers import FluxPipeline
pipe = FluxPipeline.from_pretrained(
    "diffusers/FLUX.1-dev-torchao-fp8",
    torch_dtype=torch.bfloat16,
    use_safetensors=False,
    device_map="balanced"
)
prompt = "Baroque style, a lavish palace interior with ornate gilded ceilings, intricate tapestries, and dramatic lighting over a grand staircase."
pipe_kwargs = {
    "prompt": prompt,
    "height": 1024,
    "width": 1024,
    "guidance_scale": 3.5,
    "num_inference_steps": 50,
    "max_sequence_length": 512,
}
image = pipe(
    **pipe_kwargs, generator=torch.manual_seed(0),
).images[0]
image.save("flux.png")
python
from diffusers import FluxPipeline
pipe = FluxPipeline.from_pretrained(
    "diffusers/FLUX.1-dev-torchao-fp8",
    torch_dtype=torch.bfloat16,
    use_safetensors=False,
    device_map="balanced"
)
prompt = "Baroque style, a lavish palace interior with ornate gilded ceilings, intricate tapestries, and dramatic lighting over a grand staircase."
pipe_kwargs = {
    "prompt": prompt,
    "height": 1024,
    "width": 1024,
    "guidance_scale": 3.5,
    "num_inference_steps": 50,
    "max_sequence_length": 512,
}
image = pipe(
    **pipe_kwargs, generator=torch.manual_seed(0),
).images[0]
image.save("flux.png")
python
from diffusers import FluxPipeline
pipe = FluxPipeline.from_pretrained(
    "diffusers/FLUX.1-dev-torchao-fp8",
    torch_dtype=torch.bfloat16,
    use_safetensors=False,
    device_map="balanced"
)
prompt = "Baroque style, a lavish palace interior with ornate gilded ceilings, intricate tapestries, and dramatic lighting over a grand staircase."
pipe_kwargs = {
    "prompt": prompt,
    "height": 1024,
    "width": 1024,
    "guidance_scale": 3.5,
    "num_inference_steps": 50,
    "max_sequence_length": 512,
}
image = pipe(
    **pipe_kwargs, generator=torch.manual_seed(0),
).images[0]
image.save("flux.png")
python
from diffusers import FluxPipeline
pipe = FluxPipeline.from_pretrained(
    "diffusers/FLUX.1-dev-torchao-fp8",
    torch_dtype=torch.bfloat16,
    use_safetensors=False,
    device_map="balanced"
)
prompt = "Baroque style, a lavish palace interior with ornate gilded ceilings, intricate tapestries, and dramatic lighting over a grand staircase."
pipe_kwargs = {
    "prompt": prompt,
    "height": 1024,
    "width": 1024,
    "guidance_scale": 3.5,
    "num_inference_steps": 50,
    "max_sequence_length": 512,
}
image = pipe(
    **pipe_kwargs, generator=torch.manual_seed(0),
).images[0]
image.save("flux.png")

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