John6666

/

flux-asian-realistic-v10-fp8-flux

like 2

The model is FLUX, not SDXL, and it only has a transformer, so the loading method is special. You should be able to load it as follows.

import spaces
import os
import gradio as gr
import torch
from diffusers import FluxPipeline, FluxTransformer2DModel

# Access token for HF if needed
HF_TOKEN = os.getenv("HF_TOKEN")
print(HF_TOKEN)

# --------------------------------------------------------------------
# 1) MODEL + PIPELINE SETUP
# --------------------------------------------------------------------
base_model = "https://huggingface.co/soulfulmachine/Ghibli/blob/main/IllustrationJuanerGhibli_v20.safetensors"
diffusers_base_model = "camenduru/FLUX.1-dev-diffusers"

device = "cuda"  # or "cpu" if you don't have a GPU

# Load the FLUX transformer (DiT) from a single .safetensors file
base_model = base_model.replace("/resolve/main/", "/blob/main/").replace("?download=true", "")
transformer = FluxTransformer2DModel.from_single_file(base_model, subfolder="transformer", torch_dtype=torch.bfloat16, config=diffusers_base_model, token=HF_TOKEN)

# Load the FLUX model with already loaded transformer
pipe = FluxPipeline.from_single_file(
    diffusers_base_model,
    transformer=transformer,
    torch_dtype=torch.bfloat16,
    token=HF_TOKEN,
).to(device)

# --------------------------------------------------------------------
# 2) INFERENCE FUNCTION
# --------------------------------------------------------------------
@spaces.GPU
def generate_image(prompt, negative_prompt, steps, scale, width, height):
    """
    Generates images using the loaded FLUX pipeline.
    Returns a list of 2 images by default.
    """
    images = pipe(
        prompt=prompt,
        num_images_per_prompt=2,
        guidance_scale=scale,
        width=width,
        height=height,
        num_inference_steps=steps,
    ).images
    return images  # Gradio can display multiple images if returned as a list

# --------------------------------------------------------------------
# 3) BUILD GRADIO INTERFACE
# --------------------------------------------------------------------
<Gradio code is truncated>

Thank you for the response! Really appreciate all your work porting models!!

I tried your code above but getting a different error now. I tried various approaches but could not get it to work. Could you please check only when you have some time?

runtime error
Exit code: 1. Reason: 1:15, 156MB/s]

IllustrationJuanerGhibli_v20.safetensors: 11%|█ | 1.27G/11.9G [00:02<00:14, 714MB/s]

IllustrationJuanerGhibli_v20.safetensors: 23%|██▎ | 2.79G/11.9G [00:03<00:08, 1.08GB/s]

IllustrationJuanerGhibli_v20.safetensors: 35%|███▌ | 4.20G/11.9G [00:04<00:06, 1.21GB/s]

IllustrationJuanerGhibli_v20.safetensors: 47%|████▋ | 5.60G/11.9G [00:05<00:04, 1.27GB/s]

IllustrationJuanerGhibli_v20.safetensors: 60%|█████▉ | 7.14G/11.9G [00:06<00:03, 1.36GB/s]

IllustrationJuanerGhibli_v20.safetensors: 74%|███████▍ | 8.85G/11.9G [00:07<00:02, 1.45GB/s]

IllustrationJuanerGhibli_v20.safetensors: 88%|████████▊ | 10.5G/11.9G [00:08<00:00, 1.51GB/s]
IllustrationJuanerGhibli_v20.safetensors: 100%|█████████▉| 11.9G/11.9G [00:08<00:00, 1.34GB/s]

transformer/config.json: 0%| | 0.00/378 [00:00<?, ?B/s]
transformer/config.json: 100%|██████████| 378/378 [00:00<00:00, 2.86MB/s]
Traceback (most recent call last):
File "/home/user/app/app.py", line 23, in
pipe = FluxPipeline.from_single_file(
File "/usr/local/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(*args, **kwargs)
File "/usr/local/lib/python3.10/site-packages/diffusers/loaders/single_file.py", line 378, in from_single_file
checkpoint = load_single_file_checkpoint(
File "/usr/local/lib/python3.10/site-packages/diffusers/loaders/single_file_utils.py", line 386, in load_single_file_checkpoint
repo_id, weights_name = _extract_repo_id_and_weights_name(pretrained_model_link_or_path)
File "/usr/local/lib/python3.10/site-packages/diffusers/loaders/single_file_utils.py", line 340, in _extract_repo_id_and_weights_name
raise ValueError("Invalid pretrained_model_name_or_path provided. Please set it to a valid URL.")
ValueError: Invalid pretrained_model_name_or_path provided. Please set it to a valid URL.

My full updated code:

import spaces
import os
import gradio as gr
import torch
from diffusers import FluxPipeline, FluxTransformer2DModel

# Access token for HF if needed
HF_TOKEN = os.getenv("HF_TOKEN")

# --------------------------------------------------------------------
# 1) MODEL + PIPELINE SETUP
# --------------------------------------------------------------------
base_model = "https://huggingface.co/soulfulmachine/Ghibli/blob/main/IllustrationJuanerGhibli_v20.safetensors"
diffusers_base_model = "camenduru/FLUX.1-dev-diffusers"

device = "cuda"  # or "cpu" if you don't have a GPU

# Load the FLUX transformer (DiT) from a single .safetensors file
base_model = base_model.replace("/resolve/main/", "/blob/main/").replace("?download=true", "")
transformer = FluxTransformer2DModel.from_single_file(base_model, subfolder="transformer", torch_dtype=torch.bfloat16, config=diffusers_base_model, token=HF_TOKEN)

# Load the FLUX model with already loaded transformer
pipe = FluxPipeline.from_single_file(
    diffusers_base_model,
    transformer=transformer,
    torch_dtype=torch.bfloat16,
    token=HF_TOKEN,
).to(device)

# --------------------------------------------------------------------
# 2) INFERENCE FUNCTION
# --------------------------------------------------------------------
@spaces.GPU
def generate_image(prompt, negative_prompt, steps, scale, width, height):
    """
    Generates images using the loaded FLUX pipeline.
    Returns a list of 2 images by default.
    """
    images = pipe(
        prompt=prompt,
        num_images_per_prompt=2,
        guidance_scale=scale,
        width=width,
        height=height,
        num_inference_steps=steps,
    ).images
    return images  # Gradio can display multiple images if returned as a list


# --------------------------------------------------------------------
# 3) BUILD GRADIO INTERFACE
# --------------------------------------------------------------------
def build_demo():
    with gr.Blocks() as demo:
        gr.Markdown("## Gradio Demo")
        gr.Markdown(
            "Enter your **prompt**, optional **negative prompt**, and adjust the generation parameters below."
        )

        with gr.Row():
            prompt = gr.Textbox(
                label="Prompt", 
                placeholder="e.g. A futuristic Asian utopian city, cinematic lighting..."
            )
            negative_prompt = gr.Textbox(
                label="Negative Prompt", 
                placeholder="e.g. lowres, bad anatomy, blurred..."
            )

        with gr.Row():
            steps = gr.Slider(
                minimum=1, 
                maximum=100, 
                value=30, 
                step=1, 
                label="Number of Inference Steps"
            )
            scale = gr.Slider(
                minimum=1.0,
                maximum=20.0,
                value=7.5,
                step=0.1,
                label="Guidance Scale (CFG)"
            )

        with gr.Row():
            width = gr.Slider(
                minimum=64, 
                maximum=2048, 
                value=1024, 
                step=64, 
                label="Width"
            )
            height = gr.Slider(
                minimum=64, 
                maximum=2048, 
                value=1024, 
                step=64, 
                label="Height"
            )

        generate_button = gr.Button("Generate Image")

        gallery = gr.Gallery(
            label="Generated Images", 
            show_label=False
        ).style(grid=[2], height="auto")

        # Wire up the button
        generate_button.click(
            fn=generate_image, 
            inputs=[prompt, negative_prompt, steps, scale, width, height],
            outputs=gallery
        )

    return demo

# --------------------------------------------------------------------
# 4) LAUNCH THE DEMO
# --------------------------------------------------------------------
if __name__ == "__main__":
    demo_app = build_demo()
    demo_app.launch(server_name="0.0.0.0", server_port=7860)

Sorry, I made a mistake!😅

# Load the FLUX model with already loaded transformer
#pipe = FluxPipeline.from_single_file(
pipe = FluxPipeline.from_pretrained(

Thank you. I updated as you suggested. I also had to add the following to my requirements.txt file:
accelerate
sentencepiece

I was then able to generate images with the Flux checkpoint file. Thank you so much !!!

Here is the final code:

import spaces
import os
import gradio as gr
import torch
from diffusers import FluxPipeline, FluxTransformer2DModel

# Access token for HF if needed
HF_TOKEN = os.getenv("HF_TOKEN")

# --------------------------------------------------------------------
# 1) MODEL + PIPELINE SETUP
# --------------------------------------------------------------------
base_model = "https://huggingface.co/soulfulmachine/Ghibli/blob/main/IllustrationJuanerGhibli_v20.safetensors"
diffusers_base_model = "camenduru/FLUX.1-dev-diffusers"

device = "cuda"  # or "cpu" if you don't have a GPU

# Load the FLUX transformer (DiT) from a single .safetensors file
base_model = base_model.replace("/resolve/main/", "/blob/main/").replace("?download=true", "")
transformer = FluxTransformer2DModel.from_single_file(base_model, subfolder="transformer", torch_dtype=torch.bfloat16, config=diffusers_base_model, token=HF_TOKEN)

# Load the FLUX model with already loaded transformer
pipe = FluxPipeline.from_pretrained(
    diffusers_base_model,
    transformer=transformer,
    torch_dtype=torch.bfloat16,
    token=HF_TOKEN,
).to(device)

# --------------------------------------------------------------------
# 2) INFERENCE FUNCTION
# --------------------------------------------------------------------
@spaces.GPU
def generate_image(prompt, negative_prompt, steps, scale, width, height):
    """
    Generates images using the loaded FLUX pipeline.
    Returns a list of 2 images by default.
    """
    images = pipe(
        prompt=prompt,
        num_images_per_prompt=2,
        guidance_scale=scale,
        width=width,
        height=height,
        num_inference_steps=steps,
    ).images
    return images  # Gradio can display multiple images if returned as a list


# --------------------------------------------------------------------
# 3) BUILD GRADIO INTERFACE
# --------------------------------------------------------------------
def build_demo():
    with gr.Blocks() as demo:
        gr.Markdown("## Gradio Demo")
        gr.Markdown(
            "Enter your **prompt**, optional **negative prompt**, and adjust the generation parameters below."
        )

        with gr.Row():
            prompt = gr.Textbox(
                label="Prompt", 
                placeholder="e.g. A futuristic Asian utopian city, cinematic lighting..."
            )
            negative_prompt = gr.Textbox(
                label="Negative Prompt", 
                placeholder="e.g. lowres, bad anatomy, blurred..."
            )

        with gr.Row():
            steps = gr.Slider(
                minimum=1, 
                maximum=100, 
                value=30, 
                step=1, 
                label="Number of Inference Steps"
            )
            scale = gr.Slider(
                minimum=1.0,
                maximum=20.0,
                value=7.5,
                step=0.1,
                label="Guidance Scale (CFG)"
            )

        with gr.Row():
            width = gr.Slider(
                minimum=64, 
                maximum=2048, 
                value=1024, 
                step=64, 
                label="Width"
            )
            height = gr.Slider(
                minimum=64, 
                maximum=2048, 
                value=1024, 
                step=64, 
                label="Height"
            )

        generate_button = gr.Button("Generate Image")

        gallery = gr.Gallery(label="Generated Images", show_label=False)

        # Wire up the button
        generate_button.click(
            fn=generate_image, 
            inputs=[prompt, negative_prompt, steps, scale, width, height],
            outputs=gallery
        )

    return demo

# --------------------------------------------------------------------
# 4) LAUNCH THE DEMO
# --------------------------------------------------------------------
if __name__ == "__main__":
    demo_app = build_demo()
    demo_app.launch(server_name="0.0.0.0", server_port=7860)