Flux and LTX pipelines do not forward the generator to stochastic scheduler steps #15163

import torch
from diffusers import (
    AutoencoderKL, FlowMatchEulerDiscreteScheduler,
    FluxPipeline, FluxTransformer2DModel,
)

torch.set_num_threads(1)
torch.manual_seed(0)

transformer = FluxTransformer2DModel(
    patch_size=1, in_channels=4, num_layers=1, num_single_layers=1,
    attention_head_dim=16, num_attention_heads=2, joint_attention_dim=32,
    pooled_projection_dim=32, axes_dims_rope=[4, 4, 8],
).eval()

vae = AutoencoderKL(
    sample_size=32, in_channels=3, out_channels=3, block_out_channels=(4,),
    layers_per_block=1, latent_channels=1, norm_num_groups=1,
    use_quant_conv=False, use_post_quant_conv=False,
    shift_factor=0.0609, scaling_factor=1.5035,
).eval()

pipe = FluxPipeline(
    transformer=transformer, vae=vae,
    scheduler=FlowMatchEulerDiscreteScheduler(),
    text_encoder=None, tokenizer=None,
    text_encoder_2=None, tokenizer_2=None,
)
pipe.set_progress_bar_config(disable=True)

prompt_embeds = torch.randn(1, 16, 32)
pooled_prompt_embeds = torch.randn(1, 32)

for stochastic in (False, True):
    pipe.scheduler = FlowMatchEulerDiscreteScheduler(
        stochastic_sampling=stochastic
    )
    outputs = []
    for global_seed in (13, 99):
        torch.manual_seed(global_seed)
        outputs.append(pipe(
            prompt_embeds=prompt_embeds,
            pooled_prompt_embeds=pooled_prompt_embeds,
            height=8, width=8, guidance_scale=1.0,
            num_inference_steps=4,
            generator=torch.Generator().manual_seed(17),
            output_type="latent",
        ).images)

    difference = (outputs[0] - outputs[1]).abs().max().item()
    print(f"stochastic_sampling={stochastic}: max difference = {difference:.6f}")