apextoaster
/
onnx-web
1
0
Fork 0
Code Actions

dtype logging for potential fp16 params

This commit is contained in:
Sean Sube 2023-03-19 09:59:01 -05:00
parent 243a2d9df6
commit bbd4c0fd72
Signed by: ssube
GPG Key ID: 3EED7B957D362AF1
1 changed files with 66 additions and 43 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

109
api/onnx_web/diffusers/load.py
View File

@ -107,48 +107,6 @@ def get_tile_latents(
return full_latents[:, :, y:yt, x:xt]
def optimize_pipeline(
server: ServerContext,
pipe: StableDiffusionPipeline,
) -> None:
if "diffusers-attention-slicing" in server.optimizations:
logger.debug("enabling attention slicing on SD pipeline")
try:
pipe.enable_attention_slicing()
except Exception as e:
logger.warning("error while enabling attention slicing: %s", e)
if "diffusers-vae-slicing" in server.optimizations:
logger.debug("enabling VAE slicing on SD pipeline")
try:
pipe.enable_vae_slicing()
except Exception as e:
logger.warning("error while enabling VAE slicing: %s", e)
if "diffusers-cpu-offload-sequential" in server.optimizations:
logger.debug("enabling sequential CPU offload on SD pipeline")
try:
pipe.enable_sequential_cpu_offload()
except Exception as e:
logger.warning("error while enabling sequential CPU offload: %s", e)
elif "diffusers-cpu-offload-model" in server.optimizations:
# TODO: check for accelerate
logger.debug("enabling model CPU offload on SD pipeline")
try:
pipe.enable_model_cpu_offload()
except Exception as e:
logger.warning("error while enabling model CPU offload: %s", e)
if "diffusers-memory-efficient-attention" in server.optimizations:
# TODO: check for xformers
logger.debug("enabling memory efficient attention for SD pipeline")
try:
pipe.enable_xformers_memory_efficient_attention()
except Exception as e:
logger.warning("error while enabling memory efficient attention: %s", e)
def load_pipeline(
server: ServerContext,
pipeline: DiffusionPipeline,
@ -330,9 +288,74 @@ def load_pipeline(
# monkey-patch pipeline
if not lpw:
pipe._encode_prompt = expand_prompt.__get__(pipe, pipeline)
patch_pipeline(server, pipe, pipeline)
server.cache.set("diffusion", pipe_key, pipe)
server.cache.set("scheduler", scheduler_key, components["scheduler"])
return pipe
def optimize_pipeline(
server: ServerContext,
pipe: StableDiffusionPipeline,
) -> None:
if "diffusers-attention-slicing" in server.optimizations:
logger.debug("enabling attention slicing on SD pipeline")
try:
pipe.enable_attention_slicing()
except Exception as e:
logger.warning("error while enabling attention slicing: %s", e)
if "diffusers-vae-slicing" in server.optimizations:
logger.debug("enabling VAE slicing on SD pipeline")
try:
pipe.enable_vae_slicing()
except Exception as e:
logger.warning("error while enabling VAE slicing: %s", e)
if "diffusers-cpu-offload-sequential" in server.optimizations:
logger.debug("enabling sequential CPU offload on SD pipeline")
try:
pipe.enable_sequential_cpu_offload()
except Exception as e:
logger.warning("error while enabling sequential CPU offload: %s", e)
elif "diffusers-cpu-offload-model" in server.optimizations:
# TODO: check for accelerate
logger.debug("enabling model CPU offload on SD pipeline")
try:
pipe.enable_model_cpu_offload()
except Exception as e:
logger.warning("error while enabling model CPU offload: %s", e)
if "diffusers-memory-efficient-attention" in server.optimizations:
# TODO: check for xformers
logger.debug("enabling memory efficient attention for SD pipeline")
try:
pipe.enable_xformers_memory_efficient_attention()
except Exception as e:
logger.warning("error while enabling memory efficient attention: %s", e)
def patch_pipeline(
server: ServerContext,
pipe: StableDiffusionPipeline,
pipeline: Any,
) -> None:
logger.debug("patching SD pipeline")
pipe._encode_prompt = expand_prompt.__get__(pipe, pipeline)
original_unet = pipe.unet.__call__
original_vae = pipe.vae_decoder.__call__
def unet_call(sample=None, timestep=None, encoder_hidden_states=None):
logger.info("UNet parameter types: %s, %s", sample.dtype, timestep.dtype)
return original_unet(sample=sample, timestep=timestep, encoder_hidden_states=encoder_hidden_states)
def vae_call(latent_sample=None):
logger.info("VAE parameter types: %s", latent_sample.dtype)
return original_vae(latent_sample=latent_sample)
pipe.unet.__call__ = unet_call
pipe.vae_decoder.__call__ = vae_call