Delete example_unified.py

example_unified.py

@@ -1,97 +0,0 @@
-"""Exercise all three workflows of the unified Ideogram4 AutoBlocks from ONE pipeline instance.
-
-This is the integration check: t2i / img2img / differential share a single scheduler + components, so we run
-them in sequence to confirm there's no state leakage (e.g. begin_index) between workflows.
-"""
-
-import sys
-
-import torch
-
-
-sys.path.insert(0, r"C:\projects\modular_diffusers")
-
-from sdnq import SDNQConfig  # noqa: E402,F401
-from sdnq.common import use_torch_compile as triton_is_available  # noqa: E402
-from sdnq.loader import apply_sdnq_options_to_model  # noqa: E402
-
-import diffusers  # noqa: E402
-from diffusers.utils import load_image  # noqa: E402
-from ideogram4_custom_blocks import Ideogram4AutoBlocks  # noqa: E402
-
-
-MODEL_ID = "Disty0/Ideogram-4-SDNQ-4bit-dynamic-hadamard"
-H = W = 1024
-STEPS = 20
-SEED = 0
-REFERENCE = (
-    "https://huggingface.co/datasets/OzzyGT/testing-resources/resolve/main/differential/20240329211129_4024911930.png"
-)
-CHANGE_MAP = "https://huggingface.co/datasets/OzzyGT/testing-resources/resolve/main/differential/gradient_mask.png"
-
-T2I_PROMPT = "a photo of a red apple on a black background, studio lighting, water droplets"
-EDIT_PROMPT = "a photo of a snowy mountain landscape at sunset, dramatic clouds, alpenglow on the peaks"
-
-
-def main():
-    pipe_std = diffusers.Ideogram4Pipeline.from_pretrained(MODEL_ID, torch_dtype=torch.bfloat16)
-    if triton_is_available and (torch.cuda.is_available() or torch.xpu.is_available()):
-        pipe_std.transformer = apply_sdnq_options_to_model(pipe_std.transformer, use_quantized_matmul=True)
-        pipe_std.unconditional_transformer = apply_sdnq_options_to_model(
-            pipe_std.unconditional_transformer, use_quantized_matmul=True
-        )
-        pipe_std.text_encoder = apply_sdnq_options_to_model(pipe_std.text_encoder, use_quantized_matmul=True)
-
-    from diffusers_mm import managed
-
-    pipe_std = managed(pipe_std, strategy="auto", device="cuda")
-    print(f"diffusers-mm applied strategy: {getattr(pipe_std.mm, 'applied_strategy', None)}")
-
-    pipe = Ideogram4AutoBlocks().init_pipeline()
-    pipe.update_components(
-        text_encoder=pipe_std.text_encoder,
-        tokenizer=pipe_std.tokenizer,
-        transformer=pipe_std.transformer,
-        unconditional_transformer=pipe_std.unconditional_transformer,
-        vae=pipe_std.vae,
-        scheduler=pipe_std.scheduler,
-    )
-    if torch.cuda.is_available():
-        type(pipe)._execution_device = property(lambda self: torch.device("cuda"))
-
-    reference = load_image(REFERENCE)
-    change_map = load_image(CHANGE_MAP)
-    common = {
-        "height": H,
-        "width": W,
-        "num_inference_steps": STEPS,
-        "guidance_schedule": [7.0] * STEPS,
-        "output": "images",
-    }
-
-    # Run in an order that stresses scheduler state: img2img (sets begin_index) -> differential (full schedule)
-    # -> text2image. If state leaked, t2i/differential would start from a trimmed schedule and look wrong.
-    print("running img2img...")
-    pipe(prompt=T2I_PROMPT, image=reference, strength=0.6, generator=torch.manual_seed(SEED), **common)[0].save(
-        "ideogram4-unified-img2img.png"
-    )
-
-    print("running differential...")
-    pipe(
-        prompt=EDIT_PROMPT,
-        image=reference,
-        diffdiff_map=change_map,
-        generator=torch.manual_seed(SEED),
-        **common,
-    )[0].save("ideogram4-unified-diffdiff.png")
-
-    print("running text2image...")
-    pipe(prompt=T2I_PROMPT, generator=torch.manual_seed(SEED), **common)[0].save("ideogram4-unified-t2i.png")
-
-    if torch.cuda.is_available():
-        print(f"peak VRAM: {torch.cuda.max_memory_allocated() / 1024**3:.2f} GB")
-    print("done: saved ideogram4-unified-{img2img,diffdiff,t2i}.png")
-
-
-if __name__ == "__main__":
-    main()