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lllyasviel
2024-01-30 10:32:54 -08:00
parent e3dcb2c5a3
commit f20f6d9cc6
6 changed files with 166 additions and 46 deletions
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84
extensions-builtin/forge_legacy_preprocessors/legacy_preprocessors/preprocessor_compiled.py
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@@ -860,48 +860,48 @@ legacy_preprocessors = {
"Reference"
]
},
"revision_clipvision": {
"label": "revision_clipvision",
"call_function": functools.partial(clip, config='clip_g'),
"unload_function": functools.partial(unload_clip, config='clip_g'),
"managed_model": None,
"model_free": True,
"no_control_mode": True,
"resolution": None,
"slider_1": {
"label": "Noise Augmentation",
"value": 0.0,
"minimum": 0.0,
"maximum": 1.0
},
"slider_2": None,
"slider_3": None,
"priority": 100,
"tags": [
"Revision"
]
},
"revision_ignore_prompt": {
"label": "revision_ignore_prompt",
"call_function": functools.partial(clip, config='clip_g'),
"unload_function": functools.partial(unload_clip, config='clip_g'),
"managed_model": None,
"model_free": True,
"no_control_mode": True,
"resolution": None,
"slider_1": {
"label": "Noise Augmentation",
"value": 0.0,
"minimum": 0.0,
"maximum": 1.0
},
"slider_2": None,
"slider_3": None,
"priority": 0,
"tags": [
"Revision"
]
},
# "revision_clipvision": {
# "label": "revision_clipvision",
# "call_function": functools.partial(clip, config='clip_g'),
# "unload_function": functools.partial(unload_clip, config='clip_g'),
# "managed_model": None,
# "model_free": True,
# "no_control_mode": True,
# "resolution": None,
# "slider_1": {
# "label": "Noise Augmentation",
# "value": 0.0,
# "minimum": 0.0,
# "maximum": 1.0
# },
# "slider_2": None,
# "slider_3": None,
# "priority": 100,
# "tags": [
# "Revision"
# ]
# },
# "revision_ignore_prompt": {
# "label": "revision_ignore_prompt",
# "call_function": functools.partial(clip, config='clip_g'),
# "unload_function": functools.partial(unload_clip, config='clip_g'),
# "managed_model": None,
# "model_free": True,
# "no_control_mode": True,
# "resolution": None,
# "slider_1": {
# "label": "Noise Augmentation",
# "value": 0.0,
# "minimum": 0.0,
# "maximum": 1.0
# },
# "slider_2": None,
# "slider_3": None,
# "priority": 0,
# "tags": [
# "Revision"
# ]
# },
"scribble_hed": {
"label": "scribble_hed",
"call_function": scribble_hed,

4
extensions-builtin/forge_preprocessor_clipvision/scripts/preprocessor_clipvision.py
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@@ -22,12 +22,12 @@ add_supported_preprocessor(PreprocessorClipVisionForIPAdapter(
add_supported_preprocessor(PreprocessorClipVisionForIPAdapter(
name='CLIP-ViT-bigG (IPAdapter)',
url='https://huggingface.co/h94/IP-Adapter/resolve/main/models/image_encoder/model.safetensors',
url='https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/image_encoder/model.safetensors',
filename='CLIP-ViT-bigG.safetensors'
))
add_supported_preprocessor(PreprocessorClipVisionForIPAdapter(
name='CLIP-ViT-L (IPAdapter)',
url='https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/pytorch_model.bin',
filename='CLIP-ViT-bigG.safetensors'
filename='CLIP-ViT-L.safetensors'
))

101
extensions-builtin/forge_preprocessor_revision/scripts/preprocessor_revision.py Normal file
View File

@@ -0,0 +1,101 @@
import torch
import copy
from modules_forge.supported_preprocessor import PreprocessorClipVision, PreprocessorParameter
from modules_forge.shared import add_supported_preprocessor
def revision_conditioning_modifier(model, x, timestep, uncond, cond, cond_scale, model_options, seed):
revision_conditions = model_options['revision_conditions']
noise_augmentor = model.noise_augmentor
noise_augment_merge = 0.0
ignore_prompt = False
adm_inputs = []
weights = []
noise_aug = []
for revision_condition in revision_conditions:
adm_cond = revision_condition['cond'].image_embeds
weight = revision_condition["weight"]
noise_augment = revision_condition["noise_aug"]
noise_level = round((noise_augmentor.max_noise_level - 1) * noise_augment)
c_adm, noise_level_emb = noise_augmentor(adm_cond.to(x.device),
noise_level=torch.tensor([noise_level], device=x.device), seed=seed)
adm_out = torch.cat((c_adm, noise_level_emb), 1) * weight
weights.append(weight)
noise_aug.append(noise_augment)
adm_inputs.append(adm_out)
if revision_condition["ignore_prompt"]:
ignore_prompt = True
if len(noise_aug) > 1:
adm_out = torch.stack(adm_inputs).sum(0)
noise_augment = noise_augment_merge
noise_level = round((noise_augmentor.max_noise_level - 1) * noise_augment)
c_adm, noise_level_emb = noise_augmentor(adm_out[:, :noise_augmentor.time_embed.dim],
noise_level=torch.tensor([noise_level], device=x.device))
adm_out = torch.cat((c_adm, noise_level_emb), 1)
cond = copy.deepcopy(cond)
uncond = copy.deepcopy(uncond)
for c in cond:
a = 0
for c in uncond:
a = 0
if ignore_prompt:
for c in cond + uncond:
a = 0
return model, x, timestep, uncond, cond, cond_scale, model_options, seed
class PreprocessorClipVisionForRevision(PreprocessorClipVision):
def __init__(self, name, url, filename, ignore_prompt=False):
super().__init__(name, url, filename)
self.tags = ['Revision']
self.model_filename_filters = ['Revision']
self.do_not_need_model = True
self.ignore_prompt = ignore_prompt
self.slider_1 = PreprocessorParameter(
label="Noise Augmentation", minimum=0.0, maximum=1.0, value=0.0, visible=True)
def process_before_every_sampling(self, process, cond, *args, **kwargs):
unit = kwargs['unit']
weight = float(unit.weight)
noise_aug = float(unit.threshold_a)
unet = process.sd_model.forge_objects.unet.clone()
if 'revision_conditions' not in unet.model_options:
unet.model_options['revision_conditions'] = []
unet.model_options['revision_conditions'].append(dict(
cond=cond,
weight=weight,
noise_aug=noise_aug,
ignore_prompt=self.ignore_prompt
))
unet.add_conditioning_modifier(revision_conditioning_modifier, ensure_uniqueness=True)
process.sd_model.forge_objects.unet = unet
return
add_supported_preprocessor(PreprocessorClipVisionForRevision(
name='CLIP-G (Revision)',
url='https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/image_encoder/model.safetensors',
filename='CLIP-ViT-bigG.safetensors',
ignore_prompt=False
))
add_supported_preprocessor(PreprocessorClipVisionForRevision(
name='CLIP-G (Revision ignore prompt)',
url='https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/image_encoder/model.safetensors',
filename='CLIP-ViT-bigG.safetensors',
ignore_prompt=True
))

10
extensions-builtin/sd_forge_controlnet/scripts/controlnet.py
View File

@@ -23,6 +23,7 @@ import functools
from PIL import Image
from modules_forge.shared import try_load_supported_control_model
from modules_forge.supported_controlnet import ControlModelPatcher
# Gradio 3.32 bug fix
import tempfile
@@ -455,9 +456,14 @@ class ControlNetForForgeOfficial(scripts.Script):
params.control_cond_for_hr_fix = preprocessor_output
p.extra_result_images.append(input_image)
model_filename = global_state.get_controlnet_filename(unit.model)
if preprocessor.do_not_need_model:
model_filename = 'Not Needed'
params.model = ControlModelPatcher()
else:
model_filename = global_state.get_controlnet_filename(unit.model)
params.model = cached_controlnet_loader(model_filename)
assert params.model is not None, logger.error(f"Recognizing Control Model failed: {model_filename}")
params.model = cached_controlnet_loader(model_filename)
params.preprocessor = preprocessor
params.preprocessor.process_after_running_preprocessors(process=p, params=params, **kwargs)

3
modules_forge/forge_sampler.py
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@@ -76,6 +76,9 @@ def forge_sample(self, denoiser_params, cond_scale, cond_composition):
for h in cond + uncond:
h['control'] = control
for modifier in model_options.get('conditioning_modifiers', []):
model, x, timestep, uncond, cond, cond_scale, model_options, seed = modifier(model, x, timestep, uncond, cond, cond_scale, model_options, seed)
denoised = sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options, seed)
return denoised

10
modules_forge/unet_patcher.py
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@@ -33,3 +33,13 @@ class UnetPatcher(ModelPatcher):
results.append(pointer)
pointer = pointer.previous_controlnet
return results
def add_conditioning_modifier(self, modifier, ensure_uniqueness=False):
if 'conditioning_modifiers' not in self.model_options:
self.model_options['conditioning_modifiers'] = []
if ensure_uniqueness and modifier in self.model_options['conditioning_modifiers']:
return
self.model_options['conditioning_modifiers'].append(modifier)
return