mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2026-02-10 05:22:34 +08:00
Merge branch 'comfyanonymous:master' into feat/is_change_object_storage
This commit is contained in:
Dr.Lt.Data
GitHub
commit
86cf3db5b0
@ -66,6 +66,9 @@ class BatchedBrownianTree:
|
||||
"""A wrapper around torchsde.BrownianTree that enables batches of entropy."""
|
||||
|
||||
def __init__(self, x, t0, t1, seed=None, **kwargs):
|
||||
self.cpu_tree = True
|
||||
if "cpu" in kwargs:
|
||||
self.cpu_tree = kwargs.pop("cpu")
|
||||
t0, t1, self.sign = self.sort(t0, t1)
|
||||
w0 = kwargs.get('w0', torch.zeros_like(x))
|
||||
if seed is None:
|
||||
@ -77,7 +80,10 @@ class BatchedBrownianTree:
|
||||
except TypeError:
|
||||
seed = [seed]
|
||||
self.batched = False
|
||||
self.trees = [torchsde.BrownianTree(t0.cpu(), w0.cpu(), t1.cpu(), entropy=s, **kwargs) for s in seed]
|
||||
if self.cpu_tree:
|
||||
self.trees = [torchsde.BrownianTree(t0.cpu(), w0.cpu(), t1.cpu(), entropy=s, **kwargs) for s in seed]
|
||||
else:
|
||||
self.trees = [torchsde.BrownianTree(t0, w0, t1, entropy=s, **kwargs) for s in seed]
|
||||
|
||||
@staticmethod
|
||||
def sort(a, b):
|
||||
@ -85,7 +91,11 @@ class BatchedBrownianTree:
|
||||
|
||||
def __call__(self, t0, t1):
|
||||
t0, t1, sign = self.sort(t0, t1)
|
||||
w = torch.stack([tree(t0.cpu().float(), t1.cpu().float()).to(t0.dtype).to(t0.device) for tree in self.trees]) * (self.sign * sign)
|
||||
if self.cpu_tree:
|
||||
w = torch.stack([tree(t0.cpu().float(), t1.cpu().float()).to(t0.dtype).to(t0.device) for tree in self.trees]) * (self.sign * sign)
|
||||
else:
|
||||
w = torch.stack([tree(t0, t1) for tree in self.trees]) * (self.sign * sign)
|
||||
|
||||
return w if self.batched else w[0]
|
||||
|
||||
|
||||
@ -104,10 +114,10 @@ class BrownianTreeNoiseSampler:
|
||||
internal timestep.
|
||||
"""
|
||||
|
||||
def __init__(self, x, sigma_min, sigma_max, seed=None, transform=lambda x: x):
|
||||
def __init__(self, x, sigma_min, sigma_max, seed=None, transform=lambda x: x, cpu=False):
|
||||
self.transform = transform
|
||||
t0, t1 = self.transform(torch.as_tensor(sigma_min)), self.transform(torch.as_tensor(sigma_max))
|
||||
self.tree = BatchedBrownianTree(x, t0, t1, seed)
|
||||
self.tree = BatchedBrownianTree(x, t0, t1, seed, cpu=cpu)
|
||||
|
||||
def __call__(self, sigma, sigma_next):
|
||||
t0, t1 = self.transform(torch.as_tensor(sigma)), self.transform(torch.as_tensor(sigma_next))
|
||||
@ -544,7 +554,7 @@ def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=N
|
||||
"""DPM-Solver++ (stochastic)."""
|
||||
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
|
||||
seed = extra_args.get("seed", None)
|
||||
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed) if noise_sampler is None else noise_sampler
|
||||
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler
|
||||
extra_args = {} if extra_args is None else extra_args
|
||||
s_in = x.new_ones([x.shape[0]])
|
||||
sigma_fn = lambda t: t.neg().exp()
|
||||
@ -616,7 +626,7 @@ def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
|
||||
|
||||
seed = extra_args.get("seed", None)
|
||||
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
|
||||
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed) if noise_sampler is None else noise_sampler
|
||||
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler
|
||||
extra_args = {} if extra_args is None else extra_args
|
||||
s_in = x.new_ones([x.shape[0]])
|
||||
|
||||
@ -651,3 +661,18 @@ def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
|
||||
old_denoised = denoised
|
||||
h_last = h
|
||||
return x
|
||||
|
||||
@torch.no_grad()
|
||||
def sample_dpmpp_2m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'):
|
||||
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
|
||||
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
|
||||
return sample_dpmpp_2m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type)
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def sample_dpmpp_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2):
|
||||
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
|
||||
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
|
||||
return sample_dpmpp_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, r=r)
|
||||
|
||||
|
||||
|
||||
@ -483,8 +483,8 @@ def encode_adm(model, conds, batch_size, width, height, device, prompt_type):
|
||||
class KSampler:
|
||||
SCHEDULERS = ["normal", "karras", "exponential", "simple", "ddim_uniform"]
|
||||
SAMPLERS = ["euler", "euler_ancestral", "heun", "dpm_2", "dpm_2_ancestral",
|
||||
"lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde",
|
||||
"dpmpp_2m", "dpmpp_2m_sde", "ddim", "uni_pc", "uni_pc_bh2"]
|
||||
"lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu",
|
||||
"dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "ddim", "uni_pc", "uni_pc_bh2"]
|
||||
|
||||
def __init__(self, model, steps, device, sampler=None, scheduler=None, denoise=None, model_options={}):
|
||||
self.model = model
|
||||
|
||||
127
comfy/sd.py
127
comfy/sd.py
@ -59,35 +59,6 @@ LORA_CLIP_MAP = {
|
||||
"self_attn.out_proj": "self_attn_out_proj",
|
||||
}
|
||||
|
||||
LORA_UNET_MAP_ATTENTIONS = {
|
||||
"proj_in": "proj_in",
|
||||
"proj_out": "proj_out",
|
||||
}
|
||||
|
||||
transformer_lora_blocks = {
|
||||
"transformer_blocks.{}.attn1.to_q": "transformer_blocks_{}_attn1_to_q",
|
||||
"transformer_blocks.{}.attn1.to_k": "transformer_blocks_{}_attn1_to_k",
|
||||
"transformer_blocks.{}.attn1.to_v": "transformer_blocks_{}_attn1_to_v",
|
||||
"transformer_blocks.{}.attn1.to_out.0": "transformer_blocks_{}_attn1_to_out_0",
|
||||
"transformer_blocks.{}.attn2.to_q": "transformer_blocks_{}_attn2_to_q",
|
||||
"transformer_blocks.{}.attn2.to_k": "transformer_blocks_{}_attn2_to_k",
|
||||
"transformer_blocks.{}.attn2.to_v": "transformer_blocks_{}_attn2_to_v",
|
||||
"transformer_blocks.{}.attn2.to_out.0": "transformer_blocks_{}_attn2_to_out_0",
|
||||
"transformer_blocks.{}.ff.net.0.proj": "transformer_blocks_{}_ff_net_0_proj",
|
||||
"transformer_blocks.{}.ff.net.2": "transformer_blocks_{}_ff_net_2",
|
||||
}
|
||||
|
||||
for i in range(10):
|
||||
for k in transformer_lora_blocks:
|
||||
LORA_UNET_MAP_ATTENTIONS[k.format(i)] = transformer_lora_blocks[k].format(i)
|
||||
|
||||
|
||||
LORA_UNET_MAP_RESNET = {
|
||||
"in_layers.2": "resnets_{}_conv1",
|
||||
"emb_layers.1": "resnets_{}_time_emb_proj",
|
||||
"out_layers.3": "resnets_{}_conv2",
|
||||
"skip_connection": "resnets_{}_conv_shortcut"
|
||||
}
|
||||
|
||||
def load_lora(lora, to_load):
|
||||
patch_dict = {}
|
||||
@ -188,39 +159,9 @@ def load_lora(lora, to_load):
|
||||
print("lora key not loaded", x)
|
||||
return patch_dict
|
||||
|
||||
def model_lora_keys(model, key_map={}):
|
||||
def model_lora_keys_clip(model, key_map={}):
|
||||
sdk = model.state_dict().keys()
|
||||
|
||||
counter = 0
|
||||
for b in range(12):
|
||||
tk = "diffusion_model.input_blocks.{}.1".format(b)
|
||||
up_counter = 0
|
||||
for c in LORA_UNET_MAP_ATTENTIONS:
|
||||
k = "{}.{}.weight".format(tk, c)
|
||||
if k in sdk:
|
||||
lora_key = "lora_unet_down_blocks_{}_attentions_{}_{}".format(counter // 2, counter % 2, LORA_UNET_MAP_ATTENTIONS[c])
|
||||
key_map[lora_key] = k
|
||||
up_counter += 1
|
||||
if up_counter >= 4:
|
||||
counter += 1
|
||||
for c in LORA_UNET_MAP_ATTENTIONS:
|
||||
k = "diffusion_model.middle_block.1.{}.weight".format(c)
|
||||
if k in sdk:
|
||||
lora_key = "lora_unet_mid_block_attentions_0_{}".format(LORA_UNET_MAP_ATTENTIONS[c])
|
||||
key_map[lora_key] = k
|
||||
counter = 3
|
||||
for b in range(12):
|
||||
tk = "diffusion_model.output_blocks.{}.1".format(b)
|
||||
up_counter = 0
|
||||
for c in LORA_UNET_MAP_ATTENTIONS:
|
||||
k = "{}.{}.weight".format(tk, c)
|
||||
if k in sdk:
|
||||
lora_key = "lora_unet_up_blocks_{}_attentions_{}_{}".format(counter // 3, counter % 3, LORA_UNET_MAP_ATTENTIONS[c])
|
||||
key_map[lora_key] = k
|
||||
up_counter += 1
|
||||
if up_counter >= 4:
|
||||
counter += 1
|
||||
counter = 0
|
||||
text_model_lora_key = "lora_te_text_model_encoder_layers_{}_{}"
|
||||
clip_l_present = False
|
||||
for b in range(32):
|
||||
@ -244,69 +185,23 @@ def model_lora_keys(model, key_map={}):
|
||||
lora_key = "lora_te_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #TODO: test if this is correct for SDXL-Refiner
|
||||
key_map[lora_key] = k
|
||||
|
||||
return key_map
|
||||
|
||||
#Locon stuff
|
||||
ds_counter = 0
|
||||
counter = 0
|
||||
for b in range(12):
|
||||
tk = "diffusion_model.input_blocks.{}.0".format(b)
|
||||
key_in = False
|
||||
for c in LORA_UNET_MAP_RESNET:
|
||||
k = "{}.{}.weight".format(tk, c)
|
||||
if k in sdk:
|
||||
lora_key = "lora_unet_down_blocks_{}_{}".format(counter // 2, LORA_UNET_MAP_RESNET[c].format(counter % 2))
|
||||
key_map[lora_key] = k
|
||||
key_in = True
|
||||
for bb in range(3):
|
||||
k = "{}.{}.op.weight".format(tk[:-2], bb)
|
||||
if k in sdk:
|
||||
lora_key = "lora_unet_down_blocks_{}_downsamplers_0_conv".format(ds_counter)
|
||||
key_map[lora_key] = k
|
||||
ds_counter += 1
|
||||
if key_in:
|
||||
counter += 1
|
||||
|
||||
counter = 0
|
||||
for b in range(3):
|
||||
tk = "diffusion_model.middle_block.{}".format(b)
|
||||
key_in = False
|
||||
for c in LORA_UNET_MAP_RESNET:
|
||||
k = "{}.{}.weight".format(tk, c)
|
||||
if k in sdk:
|
||||
lora_key = "lora_unet_mid_block_{}".format(LORA_UNET_MAP_RESNET[c].format(counter))
|
||||
key_map[lora_key] = k
|
||||
key_in = True
|
||||
if key_in:
|
||||
counter += 1
|
||||
|
||||
counter = 0
|
||||
us_counter = 0
|
||||
for b in range(12):
|
||||
tk = "diffusion_model.output_blocks.{}.0".format(b)
|
||||
key_in = False
|
||||
for c in LORA_UNET_MAP_RESNET:
|
||||
k = "{}.{}.weight".format(tk, c)
|
||||
if k in sdk:
|
||||
lora_key = "lora_unet_up_blocks_{}_{}".format(counter // 3, LORA_UNET_MAP_RESNET[c].format(counter % 3))
|
||||
key_map[lora_key] = k
|
||||
key_in = True
|
||||
for bb in range(3):
|
||||
k = "{}.{}.conv.weight".format(tk[:-2], bb)
|
||||
if k in sdk:
|
||||
lora_key = "lora_unet_up_blocks_{}_upsamplers_0_conv".format(us_counter)
|
||||
key_map[lora_key] = k
|
||||
us_counter += 1
|
||||
if key_in:
|
||||
counter += 1
|
||||
def model_lora_keys_unet(model, key_map={}):
|
||||
sdk = model.state_dict().keys()
|
||||
|
||||
for k in sdk:
|
||||
if k.startswith("diffusion_model.") and k.endswith(".weight"):
|
||||
key_lora = k[len("diffusion_model."):-len(".weight")].replace(".", "_")
|
||||
key_map["lora_unet_{}".format(key_lora)] = k
|
||||
|
||||
diffusers_keys = utils.unet_to_diffusers(model.model_config.unet_config)
|
||||
for k in diffusers_keys:
|
||||
if k.endswith(".weight"):
|
||||
key_lora = k[:-len(".weight")].replace(".", "_")
|
||||
key_map["lora_unet_{}".format(key_lora)] = "diffusion_model.{}".format(diffusers_keys[k])
|
||||
return key_map
|
||||
|
||||
|
||||
class ModelPatcher:
|
||||
def __init__(self, model, load_device, offload_device, size=0):
|
||||
self.size = size
|
||||
@ -506,8 +401,8 @@ class ModelPatcher:
|
||||
self.backup = {}
|
||||
|
||||
def load_lora_for_models(model, clip, lora, strength_model, strength_clip):
|
||||
key_map = model_lora_keys(model.model)
|
||||
key_map = model_lora_keys(clip.cond_stage_model, key_map)
|
||||
key_map = model_lora_keys_unet(model.model)
|
||||
key_map = model_lora_keys_clip(clip.cond_stage_model, key_map)
|
||||
loaded = load_lora(lora, key_map)
|
||||
new_modelpatcher = model.clone()
|
||||
k = new_modelpatcher.add_patches(loaded, strength_model)
|
||||
|
||||
@ -8,6 +8,7 @@ class SDXLClipG(sd1_clip.SD1ClipModel):
|
||||
super().__init__(device=device, freeze=freeze, textmodel_json_config=textmodel_json_config, textmodel_path=textmodel_path)
|
||||
self.empty_tokens = [[49406] + [49407] + [0] * 75]
|
||||
self.text_projection = torch.nn.Parameter(torch.empty(1280, 1280))
|
||||
self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055))
|
||||
self.layer_norm_hidden_state = False
|
||||
if layer == "last":
|
||||
pass
|
||||
|
||||
@ -118,6 +118,7 @@ class SDXLRefiner(supported_models_base.BASE):
|
||||
|
||||
state_dict = utils.transformers_convert(state_dict, "conditioner.embedders.0.model.", "cond_stage_model.clip_g.transformer.text_model.", 32)
|
||||
keys_to_replace["conditioner.embedders.0.model.text_projection"] = "cond_stage_model.clip_g.text_projection"
|
||||
keys_to_replace["conditioner.embedders.0.model.logit_scale"] = "cond_stage_model.clip_g.logit_scale"
|
||||
|
||||
state_dict = supported_models_base.state_dict_key_replace(state_dict, keys_to_replace)
|
||||
return state_dict
|
||||
@ -153,6 +154,7 @@ class SDXL(supported_models_base.BASE):
|
||||
replace_prefix["conditioner.embedders.0.transformer.text_model"] = "cond_stage_model.clip_l.transformer.text_model"
|
||||
state_dict = utils.transformers_convert(state_dict, "conditioner.embedders.1.model.", "cond_stage_model.clip_g.transformer.text_model.", 32)
|
||||
keys_to_replace["conditioner.embedders.1.model.text_projection"] = "cond_stage_model.clip_g.text_projection"
|
||||
keys_to_replace["conditioner.embedders.1.model.logit_scale"] = "cond_stage_model.clip_g.logit_scale"
|
||||
|
||||
state_dict = supported_models_base.state_dict_prefix_replace(state_dict, replace_prefix)
|
||||
state_dict = supported_models_base.state_dict_key_replace(state_dict, keys_to_replace)
|
||||
|
||||
117
comfy/utils.py
117
comfy/utils.py
@ -70,6 +70,123 @@ def transformers_convert(sd, prefix_from, prefix_to, number):
|
||||
sd[k_to] = weights[shape_from*x:shape_from*(x + 1)]
|
||||
return sd
|
||||
|
||||
UNET_MAP_ATTENTIONS = {
|
||||
"proj_in.weight",
|
||||
"proj_in.bias",
|
||||
"proj_out.weight",
|
||||
"proj_out.bias",
|
||||
"norm.weight",
|
||||
"norm.bias",
|
||||
}
|
||||
|
||||
TRANSFORMER_BLOCKS = {
|
||||
"norm1.weight",
|
||||
"norm1.bias",
|
||||
"norm2.weight",
|
||||
"norm2.bias",
|
||||
"norm3.weight",
|
||||
"norm3.bias",
|
||||
"attn1.to_q.weight",
|
||||
"attn1.to_k.weight",
|
||||
"attn1.to_v.weight",
|
||||
"attn1.to_out.0.weight",
|
||||
"attn1.to_out.0.bias",
|
||||
"attn2.to_q.weight",
|
||||
"attn2.to_k.weight",
|
||||
"attn2.to_v.weight",
|
||||
"attn2.to_out.0.weight",
|
||||
"attn2.to_out.0.bias",
|
||||
"ff.net.0.proj.weight",
|
||||
"ff.net.0.proj.bias",
|
||||
"ff.net.2.weight",
|
||||
"ff.net.2.bias",
|
||||
}
|
||||
|
||||
UNET_MAP_RESNET = {
|
||||
"in_layers.2.weight": "conv1.weight",
|
||||
"in_layers.2.bias": "conv1.bias",
|
||||
"emb_layers.1.weight": "time_emb_proj.weight",
|
||||
"emb_layers.1.bias": "time_emb_proj.bias",
|
||||
"out_layers.3.weight": "conv2.weight",
|
||||
"out_layers.3.bias": "conv2.bias",
|
||||
"skip_connection.weight": "conv_shortcut.weight",
|
||||
"skip_connection.bias": "conv_shortcut.bias",
|
||||
"in_layers.0.weight": "norm1.weight",
|
||||
"in_layers.0.bias": "norm1.bias",
|
||||
"out_layers.0.weight": "norm2.weight",
|
||||
"out_layers.0.bias": "norm2.bias",
|
||||
}
|
||||
|
||||
def unet_to_diffusers(unet_config):
|
||||
num_res_blocks = unet_config["num_res_blocks"]
|
||||
attention_resolutions = unet_config["attention_resolutions"]
|
||||
channel_mult = unet_config["channel_mult"]
|
||||
transformer_depth = unet_config["transformer_depth"]
|
||||
num_blocks = len(channel_mult)
|
||||
if not isinstance(num_res_blocks, list):
|
||||
num_res_blocks = [num_res_blocks] * num_blocks
|
||||
|
||||
transformers_per_layer = []
|
||||
res = 1
|
||||
for i in range(num_blocks):
|
||||
transformers = 0
|
||||
if res in attention_resolutions:
|
||||
transformers = transformer_depth[i]
|
||||
transformers_per_layer.append(transformers)
|
||||
res *= 2
|
||||
|
||||
transformers_mid = unet_config.get("transformer_depth_middle", transformers_per_layer[-1])
|
||||
|
||||
diffusers_unet_map = {}
|
||||
for x in range(num_blocks):
|
||||
n = 1 + (num_res_blocks[x] + 1) * x
|
||||
for i in range(num_res_blocks[x]):
|
||||
for b in UNET_MAP_RESNET:
|
||||
diffusers_unet_map["down_blocks.{}.resnets.{}.{}".format(x, i, UNET_MAP_RESNET[b])] = "input_blocks.{}.0.{}".format(n, b)
|
||||
if transformers_per_layer[x] > 0:
|
||||
for b in UNET_MAP_ATTENTIONS:
|
||||
diffusers_unet_map["down_blocks.{}.attentions.{}.{}".format(x, i, b)] = "input_blocks.{}.1.{}".format(n, b)
|
||||
for t in range(transformers_per_layer[x]):
|
||||
for b in TRANSFORMER_BLOCKS:
|
||||
diffusers_unet_map["down_blocks.{}.attentions.{}.transformer_blocks.{}.{}".format(x, i, t, b)] = "input_blocks.{}.1.transformer_blocks.{}.{}".format(n, t, b)
|
||||
n += 1
|
||||
for k in ["weight", "bias"]:
|
||||
diffusers_unet_map["down_blocks.{}.downsamplers.0.conv.{}".format(x, k)] = "input_blocks.{}.0.op.{}".format(n, k)
|
||||
|
||||
i = 0
|
||||
for b in UNET_MAP_ATTENTIONS:
|
||||
diffusers_unet_map["mid_block.attentions.{}.{}".format(i, b)] = "middle_block.1.{}".format(b)
|
||||
for t in range(transformers_mid):
|
||||
for b in TRANSFORMER_BLOCKS:
|
||||
diffusers_unet_map["mid_block.attentions.{}.transformer_blocks.{}.{}".format(i, t, b)] = "middle_block.1.transformer_blocks.{}.{}".format(t, b)
|
||||
|
||||
for i, n in enumerate([0, 2]):
|
||||
for b in UNET_MAP_RESNET:
|
||||
diffusers_unet_map["mid_block.resnets.{}.{}".format(i, UNET_MAP_RESNET[b])] = "middle_block.{}.{}".format(n, b)
|
||||
|
||||
num_res_blocks = list(reversed(num_res_blocks))
|
||||
transformers_per_layer = list(reversed(transformers_per_layer))
|
||||
for x in range(num_blocks):
|
||||
n = (num_res_blocks[x] + 1) * x
|
||||
l = num_res_blocks[x] + 1
|
||||
for i in range(l):
|
||||
c = 0
|
||||
for b in UNET_MAP_RESNET:
|
||||
diffusers_unet_map["up_blocks.{}.resnets.{}.{}".format(x, i, UNET_MAP_RESNET[b])] = "output_blocks.{}.0.{}".format(n, b)
|
||||
c += 1
|
||||
if transformers_per_layer[x] > 0:
|
||||
c += 1
|
||||
for b in UNET_MAP_ATTENTIONS:
|
||||
diffusers_unet_map["up_blocks.{}.attentions.{}.{}".format(x, i, b)] = "output_blocks.{}.1.{}".format(n, b)
|
||||
for t in range(transformers_per_layer[x]):
|
||||
for b in TRANSFORMER_BLOCKS:
|
||||
diffusers_unet_map["up_blocks.{}.attentions.{}.transformer_blocks.{}.{}".format(x, i, t, b)] = "output_blocks.{}.1.transformer_blocks.{}.{}".format(n, t, b)
|
||||
if i == l - 1:
|
||||
for k in ["weight", "bias"]:
|
||||
diffusers_unet_map["up_blocks.{}.upsamplers.0.conv.{}".format(x, k)] = "output_blocks.{}.{}.conv.{}".format(n, c, k)
|
||||
n += 1
|
||||
return diffusers_unet_map
|
||||
|
||||
def convert_sd_to(state_dict, dtype):
|
||||
keys = list(state_dict.keys())
|
||||
for k in keys:
|
||||
|
||||
Loading…
Reference in New Issue
Block a user