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Merge pull request AUTOMATIC1111#4 from likholat/lora_multiplier
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Multiplier for Lora
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@ynimmaga
ynimmaga committed Aug 4, 2023
2 parents ac9c9e1 + d7e45a8 commit befa012
Showing 1 changed file with 42 additions and 54 deletions.
96 changes: 42 additions & 54 deletions scripts/openvino_accelerate.py
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Original file line number Diff line number Diff line change
@@ -1,7 +1,6 @@
# Copyright (C) 2023 Intel Corporation
# SPDX-License-Identifier: AGPL-3.0

import math
import cv2
import os
import torch
Expand All @@ -18,17 +17,17 @@
from modules import images, devices, extra_networks, masking, shared
from modules.processing import (
StableDiffusionProcessing, Processed, apply_overlay, apply_color_correction,
get_fixed_seed, create_random_tensors, create_infotext, setup_color_correction,
get_fixed_seed, create_infotext, setup_color_correction,
process_images
)
from modules.sd_models import CheckpointInfo
from modules.shared import Shared, opts, state
from modules.shared import opts, state

from PIL import Image, ImageOps
from pathlib import Path

import openvino.frontend.pytorch.torchdynamo.backend
from openvino.frontend.pytorch.torchdynamo.execute import partitioned_modules, compiled_cache
import openvino.frontend.pytorch.torchdynamo.backend # noqa: F401
from openvino.frontend.pytorch.torchdynamo.execute import partitioned_modules, compiled_cache # noqa: F401
from openvino.runtime import Core

from diffusers import (
Expand Down Expand Up @@ -82,7 +81,7 @@ def from_single_file(self, pretrained_model_link_or_path, **kwargs):
text_encoder = kwargs.pop("text_encoder", None)
tokenizer = kwargs.pop("tokenizer", None)
local_config_file = kwargs.pop("local_config_file", None)

torch_dtype = kwargs.pop("torch_dtype", None)

use_safetensors = kwargs.pop("use_safetensors", None if is_safetensors_available() else False)
Expand Down Expand Up @@ -135,6 +134,7 @@ def from_single_file(self, pretrained_model_link_or_path, **kwargs):
if file_path.startswith("main/"):
file_path = file_path[len("main/") :]

from huggingface_hub import hf_hub_download
pretrained_model_link_or_path = hf_hub_download(
repo_id,
filename=file_path,
Expand Down Expand Up @@ -323,7 +323,6 @@ def init_new(self, all_prompts, all_seeds, all_subseeds):
imgs.append(image)

if len(imgs) == 1:
batch_images = np.expand_dims(imgs[0], axis=0).repeat(self.batch_size, axis=0)
if self.overlay_images is not None:
self.overlay_images = self.overlay_images * self.batch_size

Expand All @@ -332,7 +331,6 @@ def init_new(self, all_prompts, all_seeds, all_subseeds):

elif len(imgs) <= self.batch_size:
self.batch_size = len(imgs)
batch_images = np.array(imgs)
else:
raise RuntimeError(f"bad number of images passed: {len(imgs)}; expecting {self.batch_size} or less")

Expand All @@ -354,6 +352,7 @@ def process_images_openvino(p: StableDiffusionProcessing, sampler_name, enable_c
subseed = get_fixed_seed(p.subseed)

comments = {}
custom_inputs = {}

p.setup_prompts()

Expand Down Expand Up @@ -423,11 +422,13 @@ def infotext(iteration=0, position_in_batch=0):
with devices.autocast():
extra_networks.activate(p, p.extra_network_data)

# TODO: support multiplier
if ('lora' in modules.extra_networks.extra_network_registry):
import lora
for lora_model in lora.loaded_loras:
# TODO: multiple Loras aren't supported for Diffusers now, needs to add warning
if lora.loaded_loras:
lora_model = lora.loaded_loras[0]
shared.sd_diffusers_model.load_lora_weights(os.path.join(os.getcwd(), "models", "Lora"), weight_name=lora_model.name + ".safetensors")
custom_inputs.update(cross_attention_kwargs={"scale" : lora_model.te_multiplier})

if p.scripts is not None:
p.scripts.process_batch(p, batch_number=n, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds)
Expand All @@ -438,7 +439,6 @@ def infotext(iteration=0, position_in_batch=0):
# strength, which is saved as "Model Strength: 1.0" in the infotext
if n == 0:
with open(os.path.join(paths.data_path, "params.txt"), "w", encoding="utf8") as file:
processed = Processed(p, [], p.seed, "")
file.write(create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, comments=[], position_in_batch=0 % p.batch_size, iteration=0 // p.batch_size))

if p.n_iter > 1:
Expand All @@ -449,50 +449,38 @@ def infotext(iteration=0, position_in_batch=0):
time_stamps = []

def callback(iter, t, latents):
time_stamps.append(time.time())
time_stamps.append(time.time()) # noqa: B023

time_stamps.append(time.time())

if (mode == 0):
output = shared.sd_diffusers_model(
prompt=p.prompts,
negative_prompt=p.negative_prompts,
num_inference_steps=p.steps,
guidance_scale=p.cfg_scale,
width = p.width,
height = p.height,
generator=generator,
output_type="np",
callback = callback,
callback_steps = 1,
)
custom_inputs.update({
'width': p.width,
'height': p.height,
})
elif (mode == 1):
output = shared.sd_diffusers_model(
prompt=p.prompts,
negative_prompt=p.negative_prompts,
num_inference_steps=p.steps,
guidance_scale=p.cfg_scale,
image = p.init_images,
strength = p.denoising_strength,
generator=generator,
output_type="np",
callback = callback,
callback_steps = 1,
)
custom_inputs.update({
'image': p.init_images,
'strength':p.denoising_strength,
})
else:
output = shared.sd_diffusers_model(
custom_inputs.update({
'image': p.init_images,
'strength':p.denoising_strength,
'mask_image': p.mask,
})
output = shared.sd_diffusers_model(
prompt=p.prompts,
negative_prompt=p.negative_prompts,
num_inference_steps=p.steps,
guidance_scale=p.cfg_scale,
mask_image = p.mask,
image = p.init_images,
strength = p.denoising_strength,
generator=generator,
output_type="np",
callback = callback,
callback_steps = 1,
)

**custom_inputs
)

model_state.recompile = 0

warmup_duration = time_stamps[1] - time_stamps[0]
Expand Down Expand Up @@ -616,21 +604,21 @@ def show(self, is_img2img):

def ui(self, is_img2img):
core = Core()
openvino_device = gr.Dropdown(label="Select a device", choices=[device for device in core.available_devices], value=model_state.device)
openvino_device = gr.Dropdown(label="Select a device", choices=list(core.available_devices), value=model_state.device)
override_sampler = gr.Checkbox(label="Override the sampling selection from the main UI (Recommended as only below sampling methods have been validated for OpenVINO)", value=True)
sampler_name = gr.Radio(label="Select a sampling method", choices=["Euler a", "Euler", "LMS", "Heun", "DPM++ 2M", "LMS Karras", "DPM++ 2M Karras", "DDIM", "PLMS"], value="Euler a")
enable_caching = gr.Checkbox(label="Cache the compiled models on disk for faster model load in subsequent launches (Recommended)", value=True, elem_id=self.elem_id("enable_caching"))
warmup_status = gr.Textbox(label="Device", interactive=False, visible=False)
warmup_note = gr.Markdown(
"""
###
### Note:
First inference involves compilation of the model for best performance.
Excluding the first inference (or warm up inference) is recommended for
performance measurements. When resolution, batchsize, or device is changed,
or samplers like DPM++ or Karras are selected, model is recompiled. Subsequent
iterations use the cached compiled model for faster inference.
""")
gr.Markdown(
"""
###
### Note:
First inference involves compilation of the model for best performance.
Excluding the first inference (or warm up inference) is recommended for
performance measurements. When resolution, batchsize, or device is changed,
or samplers like DPM++ or Karras are selected, model is recompiled. Subsequent
iterations use the cached compiled model for faster inference.
""")

def device_change(choice):
if (model_state.device == choice):
Expand Down Expand Up @@ -663,7 +651,7 @@ def run(self, p, openvino_device, override_sampler, sampler_name, enable_caching
processed = process_images_openvino(p, p.sampler_name, enable_caching, openvino_device, mode)
else:
if p.image_mask is None:
mode = 1
mode = 1
else:
mode = 2
p.init = functools.partial(init_new, p)
Expand Down

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