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- .claude/CLAUDE.md - .claude/commands/subagentes.md - .claude/rules/INDEX.md - .mcp.json - bash/functions/cybersecurity/analyze_dns.md - bash/functions/cybersecurity/audit_http_headers.md - bash/functions/cybersecurity/audit_ssh_config.md - bash/functions/cybersecurity/check_firewall.md - bash/functions/cybersecurity/detect_suspicious_users.md - bash/functions/cybersecurity/encrypt_file.md - ... Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
72 lines
2.6 KiB
Markdown
72 lines
2.6 KiB
Markdown
---
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name: diffusers_generate
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kind: function
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lang: py
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domain: ml
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version: "1.0.0"
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purity: impure
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signature: "def diffusers_generate(pipe: Any, cfg: GenerationConfig) -> ImageGenResult"
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description: "Ejecuta inferencia con un pipeline diffusers usando GenerationConfig. Mide duracion y pico de VRAM. Retorna ImageGenResult con imagen PIL, meta y metricas."
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tags: [diffusers, ml, image-generation, inference, vram, metrics, pendiente-usar]
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uses_functions: [genconfig_to_diffusers_kwargs_py_ml]
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uses_types: [generation_config_py_ml, image_gen_result_py_ml]
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returns: [image_gen_result_py_ml]
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returns_optional: false
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error_type: "error_go_core"
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imports: [torch, diffusers]
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params:
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- name: pipe
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desc: "Pipeline diffusers cargado y listo para inferencia (resultado de diffusers_load_pipeline, opcionalmente con scheduler y LoRA configurados)."
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- name: cfg
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desc: "Parametros de generacion. cfg.seed >= 0 para semilla fija; -1 usa time-based. cfg.sampler se incluye en meta pero no se aplica aqui (usar diffusers_set_scheduler antes)."
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output: "ImageGenResult con image=PIL.Image.Image, meta={backend, model, sampler, actual_steps, seed, width, height, cfg_scale}, duration_ms en entero milisegundos, vram_peak_mb (None si no hay CUDA)."
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tested: true
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tests:
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- "genera imagen retorna ImageGenResult"
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test_file_path: "python/functions/ml/tests/test_diffusers_backend.py"
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file_path: "python/functions/ml/diffusers_generate.py"
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---
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## Ejemplo
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```python
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from diffusers_load_pipeline import diffusers_load_pipeline
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from diffusers_generate import diffusers_generate
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from generation_config import GenerationConfig
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from model_ref import ModelRef
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model = ModelRef(
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name="sd-turbo",
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model_type="sd15",
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path="/home/lucas/vaults/imagegen_models/diffusers/sd-turbo",
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)
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cfg = GenerationConfig(
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prompt="a photo of a cat",
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seed=42,
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steps=1,
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cfg_scale=0.0,
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sampler="euler",
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width=512,
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height=512,
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model=model,
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)
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pipe = diffusers_load_pipeline(model, device="cuda", dtype="fp16")
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result = diffusers_generate(pipe, cfg)
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# result.image -> PIL.Image.Image 512x512
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# result.duration_ms -> int > 0
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# result.meta["backend"] -> "diffusers"
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```
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## Notas
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`cfg.seed = -1` genera seed aleatorio basado en `time.time()` (reproducible si
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se guarda en `result.meta["seed"]`).
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VRAM: `torch.cuda.reset_peak_memory_stats()` antes de inferencia,
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`torch.cuda.max_memory_allocated() // 1024 // 1024` despues.
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`genconfig_to_diffusers_kwargs` omite generator=None; esta funcion lo reemplaza
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con `torch.Generator(device=device).manual_seed(seed)`.
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Import lazy de torch — ImportError descriptivo si no instalado.
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