egutierrez
a802f59f55
- cmd/fn/doctor.go - cmd/fn/main.go - cpp/apps/primitives_gallery/playground/tables/CMakeLists.txt - cpp/apps/primitives_gallery/playground/tables/data_table.cpp - cpp/apps/primitives_gallery/playground/tables/data_table_logic.cpp - cpp/apps/primitives_gallery/playground/tables/data_table_logic.h - cpp/apps/primitives_gallery/playground/tables/self_test.cpp - cpp/apps/primitives_gallery/playground/tables/tql.cpp - cpp/apps/primitives_gallery/playground/tables/viz.cpp - cpp/apps/primitives_gallery/playground/tables/viz.h - ... Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
99 lines
3.2 KiB
Python
99 lines
3.2 KiB
Python
"""diffusers_generate — ejecuta inferencia con un pipeline diffusers y retorna ImageGenResult."""
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from __future__ import annotations
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import sys
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import os
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import time
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from typing import Any
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sys.path.insert(0, os.path.dirname(__file__))
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from generation_config import GenerationConfig
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from image_gen_result import ImageGenResult
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from genconfig_to_diffusers_kwargs import genconfig_to_diffusers_kwargs
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def diffusers_generate(pipe: Any, cfg: GenerationConfig) -> ImageGenResult:
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"""Ejecuta inferencia con un pipeline diffusers y retorna ImageGenResult.
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Convierte el GenerationConfig a kwargs via genconfig_to_diffusers_kwargs,
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crea un torch.Generator con la semilla configurada, mide duracion y pico
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de VRAM (si CUDA disponible). El campo meta del resultado incluye backend,
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modelo, sampler, seed y steps usados.
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Args:
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pipe: Pipeline diffusers cargado (resultado de diffusers_load_pipeline).
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Debe ser callable: pipe(prompt=..., ...) -> objeto con .images[0].
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cfg: Parametros de generacion. cfg.seed se usa para torch.Generator.
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cfg.model.name se incluye en meta. cfg.sampler se incluye en meta
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pero NO se aplica aqui — usar diffusers_set_scheduler antes si se
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quiere cambiar el sampler.
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Returns:
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ImageGenResult con image=PIL.Image, meta con keys backend/model/sampler/
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actual_steps/seed, duration_ms y vram_peak_mb (None si no hay CUDA).
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Raises:
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ImportError: Si torch o diffusers no estan instalados.
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RuntimeError: Si la inferencia falla (OOM, modelo incompatible, etc.).
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"""
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try:
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import torch
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except ImportError as exc:
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raise ImportError(
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"diffusers_generate requiere torch. "
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"Instalar con: pip install torch"
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) from exc
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# Determinar device del pipeline
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device = "cpu"
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if hasattr(pipe, "device"):
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device = str(pipe.device)
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# Medir VRAM solo en CUDA
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cuda_available = torch.cuda.is_available()
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if cuda_available:
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torch.cuda.reset_peak_memory_stats()
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# Construir kwargs desde GenerationConfig
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kwargs = genconfig_to_diffusers_kwargs(cfg)
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# Crear generator con semilla
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seed = cfg.seed if cfg.seed >= 0 else int(time.time()) % (2**32)
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generator = torch.Generator(device=device).manual_seed(seed)
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kwargs["generator"] = generator
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# Inferencia
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t0 = time.perf_counter()
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result = pipe(**kwargs)
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t1 = time.perf_counter()
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duration_ms = int((t1 - t0) * 1000)
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# VRAM peak
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vram_peak_mb: int | None = None
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if cuda_available:
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vram_peak_mb = torch.cuda.max_memory_allocated() // 1024 // 1024
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# Nombre del modelo
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model_name = cfg.model.name if cfg.model and hasattr(cfg.model, "name") else "unknown"
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meta: dict[str, Any] = {
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"backend": "diffusers",
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"model": model_name,
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"sampler": cfg.sampler,
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"actual_steps": cfg.steps,
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"seed": seed,
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"width": cfg.width,
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"height": cfg.height,
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"cfg_scale": cfg.cfg_scale,
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}
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return ImageGenResult(
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image=result.images[0],
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meta=meta,
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duration_ms=duration_ms,
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vram_peak_mb=vram_peak_mb,
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)
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