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support SD3

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Artiprocher
2024-07-04 16:08:39 +08:00
parent 237daa2048
commit 9920b8d975
26 changed files with 329648 additions and 2 deletions
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66
diffsynth/models/__init__.py
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@@ -16,6 +16,11 @@ from .sdxl_unet import SDXLUNet
from .sdxl_vae_decoder import SDXLVAEDecoder
from .sdxl_vae_encoder import SDXLVAEEncoder
from .sd3_text_encoder import SD3TextEncoder1, SD3TextEncoder2, SD3TextEncoder3
from .sd3_dit import SD3DiT
from .sd3_vae_decoder import SD3VAEDecoder
from .sd3_vae_encoder import SD3VAEEncoder
from .sd_controlnet import SDControlNet
from .sd_motion import SDMotionModel
@@ -90,6 +95,13 @@ preset_models_on_modelscope = {
"StableDiffusionXL_Turbo": [
("AI-ModelScope/sdxl-turbo", "sd_xl_turbo_1.0_fp16.safetensors", "models/stable_diffusion_xl_turbo"),
],
# Stable Diffusion 3
"StableDiffusion3": [
("AI-ModelScope/stable-diffusion-3-medium", "sd3_medium_incl_clips_t5xxlfp16.safetensors", "models/stable_diffusion_3"),
],
"StableDiffusion3_without_T5": [
("AI-ModelScope/stable-diffusion-3-medium", "sd3_medium_incl_clips.safetensors", "models/stable_diffusion_3"),
],
# ControlNet
"ControlNet_v11f1p_sd15_depth": [
("AI-ModelScope/ControlNet-v1-1", "control_v11f1p_sd15_depth.pth", "models/ControlNet"),
@@ -171,6 +183,8 @@ Preset_model_id: TypeAlias = Literal[
"opus-mt-zh-en",
"IP-Adapter-SD",
"IP-Adapter-SDXL",
"StableDiffusion3",
"StableDiffusion3_without_T5"
]
Preset_model_website: TypeAlias = Literal[
"HuggingFace",
@@ -297,7 +311,8 @@ class ModelManager:
def is_hunyuan_dit_t5_text_encoder(self, state_dict):
param_name = "encoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight"
return param_name in state_dict
param_name_ = "decoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight"
return param_name in state_dict and param_name_ in state_dict
def is_hunyuan_dit(self, state_dict):
param_name = "final_layer.adaLN_modulation.1.weight"
@@ -311,6 +326,23 @@ class ModelManager:
param_name = "blocks.185.positional_embedding.embeddings"
return param_name in state_dict
def is_stable_diffusion_3(self, state_dict):
param_names = [
"text_encoders.clip_l.transformer.text_model.encoder.layers.9.self_attn.v_proj.weight",
"text_encoders.clip_g.transformer.text_model.encoder.layers.9.self_attn.v_proj.weight",
"model.diffusion_model.joint_blocks.9.x_block.mlp.fc2.weight",
"first_stage_model.encoder.mid.block_2.norm2.weight",
"first_stage_model.decoder.mid.block_2.norm2.weight",
]
for param_name in param_names:
if param_name not in state_dict:
return False
return True
def is_stable_diffusion_3_t5(self, state_dict):
param_name = "encoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight"
return param_name in state_dict
def load_stable_video_diffusion(self, state_dict, components=None, file_path="", add_positional_conv=None):
component_dict = {
"image_encoder": SVDImageEncoder,
@@ -520,6 +552,34 @@ class ModelManager:
self.model["unet"].load_state_dict(state_dict, strict=False)
self.model["unet"].to(self.torch_dtype).to(self.device)
def load_stable_diffusion_3(self, state_dict, components=None, file_path=""):
component_dict = {
"sd3_text_encoder_1": SD3TextEncoder1,
"sd3_text_encoder_2": SD3TextEncoder2,
"sd3_text_encoder_3": SD3TextEncoder3,
"sd3_dit": SD3DiT,
"sd3_vae_decoder": SD3VAEDecoder,
"sd3_vae_encoder": SD3VAEEncoder,
}
if components is None:
components = ["sd3_text_encoder_1", "sd3_text_encoder_2", "sd3_text_encoder_3", "sd3_dit", "sd3_vae_decoder", "sd3_vae_encoder"]
for component in components:
if component == "sd3_text_encoder_3":
if "text_encoders.t5xxl.transformer.encoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight" not in state_dict:
continue
self.model[component] = component_dict[component]()
self.model[component].load_state_dict(self.model[component].state_dict_converter().from_civitai(state_dict))
self.model[component].to(self.torch_dtype).to(self.device)
self.model_path[component] = file_path
def load_stable_diffusion_3_t5(self, state_dict, file_path=""):
component = "sd3_text_encoder_3"
model = SD3TextEncoder3()
model.load_state_dict(model.state_dict_converter().from_civitai(state_dict))
model.to(self.torch_dtype).to(self.device)
self.model[component] = model
self.model_path[component] = file_path
def search_for_embeddings(self, state_dict):
embeddings = []
for k in state_dict:
@@ -587,6 +647,10 @@ class ModelManager:
self.load_diffusers_vae(state_dict, file_path=file_path)
elif self.is_ExVideo_StableVideoDiffusion(state_dict):
self.load_ExVideo_StableVideoDiffusion(state_dict, file_path=file_path)
elif self.is_stable_diffusion_3(state_dict):
self.load_stable_diffusion_3(state_dict, components=components, file_path=file_path)
elif self.is_stable_diffusion_3_t5(state_dict):
self.load_stable_diffusion_3_t5(state_dict, file_path=file_path)
def load_models(self, file_path_list, lora_alphas=[]):
for file_path in file_path_list:

783
diffsynth/models/sd3_dit.py Normal file
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@@ -0,0 +1,783 @@
import torch
from einops import rearrange
from .svd_unet import TemporalTimesteps
from .tiler import TileWorker
class PatchEmbed(torch.nn.Module):
def __init__(self, patch_size=2, in_channels=16, embed_dim=1536, pos_embed_max_size=192):
super().__init__()
self.pos_embed_max_size = pos_embed_max_size
self.patch_size = patch_size
self.proj = torch.nn.Conv2d(in_channels, embed_dim, kernel_size=(patch_size, patch_size), stride=patch_size)
self.pos_embed = torch.nn.Parameter(torch.zeros(1, self.pos_embed_max_size, self.pos_embed_max_size, 1536))
def cropped_pos_embed(self, height, width):
height = height // self.patch_size
width = width // self.patch_size
top = (self.pos_embed_max_size - height) // 2
left = (self.pos_embed_max_size - width) // 2
spatial_pos_embed = self.pos_embed[:, top : top + height, left : left + width, :].flatten(1, 2)
return spatial_pos_embed
def forward(self, latent):
height, width = latent.shape[-2:]
latent = self.proj(latent)
latent = latent.flatten(2).transpose(1, 2)
pos_embed = self.cropped_pos_embed(height, width)
return latent + pos_embed
class TimestepEmbeddings(torch.nn.Module):
def __init__(self, dim_in, dim_out):
super().__init__()
self.time_proj = TemporalTimesteps(num_channels=dim_in, flip_sin_to_cos=True, downscale_freq_shift=0)
self.timestep_embedder = torch.nn.Sequential(
torch.nn.Linear(dim_in, dim_out), torch.nn.SiLU(), torch.nn.Linear(dim_out, dim_out)
)
def forward(self, timestep, dtype):
time_emb = self.time_proj(timestep).to(dtype)
time_emb = self.timestep_embedder(time_emb)
return time_emb
class AdaLayerNorm(torch.nn.Module):
def __init__(self, dim, single=False):
super().__init__()
self.single = single
self.linear = torch.nn.Linear(dim, dim * (2 if single else 6))
self.norm = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
def forward(self, x, emb):
emb = self.linear(torch.nn.functional.silu(emb))
if self.single:
scale, shift = emb.unsqueeze(1).chunk(2, dim=2)
x = self.norm(x) * (1 + scale) + shift
return x
else:
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = emb.unsqueeze(1).chunk(6, dim=2)
x = self.norm(x) * (1 + scale_msa) + shift_msa
return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
class JointAttention(torch.nn.Module):
def __init__(self, dim_a, dim_b, num_heads, head_dim, only_out_a=False):
super().__init__()
self.num_heads = num_heads
self.head_dim = head_dim
self.only_out_a = only_out_a
self.a_to_qkv = torch.nn.Linear(dim_a, dim_a * 3)
self.b_to_qkv = torch.nn.Linear(dim_b, dim_b * 3)
self.a_to_out = torch.nn.Linear(dim_a, dim_a)
if not only_out_a:
self.b_to_out = torch.nn.Linear(dim_b, dim_b)
def forward(self, hidden_states_a, hidden_states_b):
batch_size = hidden_states_a.shape[0]
qkv = torch.concat([self.a_to_qkv(hidden_states_a), self.b_to_qkv(hidden_states_b)], dim=1)
qkv = qkv.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2)
q, k, v = qkv.chunk(3, dim=1)
hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v)
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim)
hidden_states = hidden_states.to(q.dtype)
hidden_states_a, hidden_states_b = hidden_states[:, :hidden_states_a.shape[1]], hidden_states[:, hidden_states_a.shape[1]:]
hidden_states_a = self.a_to_out(hidden_states_a)
if self.only_out_a:
return hidden_states_a
else:
hidden_states_b = self.b_to_out(hidden_states_b)
return hidden_states_a, hidden_states_b
class JointTransformerBlock(torch.nn.Module):
def __init__(self, dim, num_attention_heads):
super().__init__()
self.norm1_a = AdaLayerNorm(dim)
self.norm1_b = AdaLayerNorm(dim)
self.attn = JointAttention(dim, dim, num_attention_heads, dim // num_attention_heads)
self.norm2_a = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
self.ff_a = torch.nn.Sequential(
torch.nn.Linear(dim, dim*4),
torch.nn.GELU(approximate="tanh"),
torch.nn.Linear(dim*4, dim)
)
self.norm2_b = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
self.ff_b = torch.nn.Sequential(
torch.nn.Linear(dim, dim*4),
torch.nn.GELU(approximate="tanh"),
torch.nn.Linear(dim*4, dim)
)
def forward(self, hidden_states_a, hidden_states_b, temb):
norm_hidden_states_a, gate_msa_a, shift_mlp_a, scale_mlp_a, gate_mlp_a = self.norm1_a(hidden_states_a, emb=temb)
norm_hidden_states_b, gate_msa_b, shift_mlp_b, scale_mlp_b, gate_mlp_b = self.norm1_b(hidden_states_b, emb=temb)
# Attention
attn_output_a, attn_output_b = self.attn(norm_hidden_states_a, norm_hidden_states_b)
# Part A
hidden_states_a = hidden_states_a + gate_msa_a * attn_output_a
norm_hidden_states_a = self.norm2_a(hidden_states_a) * (1 + scale_mlp_a) + shift_mlp_a
hidden_states_a = hidden_states_a + gate_mlp_a * self.ff_a(norm_hidden_states_a)
# Part B
hidden_states_b = hidden_states_b + gate_msa_b * attn_output_b
norm_hidden_states_b = self.norm2_b(hidden_states_b) * (1 + scale_mlp_b) + shift_mlp_b
hidden_states_b = hidden_states_b + gate_mlp_b * self.ff_b(norm_hidden_states_b)
return hidden_states_a, hidden_states_b
class JointTransformerFinalBlock(torch.nn.Module):
def __init__(self, dim, num_attention_heads):
super().__init__()
self.norm1_a = AdaLayerNorm(dim)
self.norm1_b = AdaLayerNorm(dim, single=True)
self.attn = JointAttention(dim, dim, num_attention_heads, dim // num_attention_heads, only_out_a=True)
self.norm2_a = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
self.ff_a = torch.nn.Sequential(
torch.nn.Linear(dim, dim*4),
torch.nn.GELU(approximate="tanh"),
torch.nn.Linear(dim*4, dim)
)
def forward(self, hidden_states_a, hidden_states_b, temb):
norm_hidden_states_a, gate_msa_a, shift_mlp_a, scale_mlp_a, gate_mlp_a = self.norm1_a(hidden_states_a, emb=temb)
norm_hidden_states_b = self.norm1_b(hidden_states_b, emb=temb)
# Attention
attn_output_a = self.attn(norm_hidden_states_a, norm_hidden_states_b)
# Part A
hidden_states_a = hidden_states_a + gate_msa_a * attn_output_a
norm_hidden_states_a = self.norm2_a(hidden_states_a) * (1 + scale_mlp_a) + shift_mlp_a
hidden_states_a = hidden_states_a + gate_mlp_a * self.ff_a(norm_hidden_states_a)
return hidden_states_a, hidden_states_b
class SD3DiT(torch.nn.Module):
def __init__(self):
super().__init__()
self.pos_embedder = PatchEmbed(patch_size=2, in_channels=16, embed_dim=1536, pos_embed_max_size=192)
self.time_embedder = TimestepEmbeddings(256, 1536)
self.pooled_text_embedder = torch.nn.Sequential(torch.nn.Linear(2048, 1536), torch.nn.SiLU(), torch.nn.Linear(1536, 1536))
self.context_embedder = torch.nn.Linear(4096, 1536)
self.blocks = torch.nn.ModuleList([JointTransformerBlock(1536, 24) for _ in range(23)] + [JointTransformerFinalBlock(1536, 24)])
self.norm_out = AdaLayerNorm(1536, single=True)
self.proj_out = torch.nn.Linear(1536, 64)
def tiled_forward(self, hidden_states, timestep, prompt_emb, pooled_prompt_emb, tile_size=128, tile_stride=64):
# Due to the global positional embedding, we cannot implement layer-wise tiled forward.
hidden_states = TileWorker().tiled_forward(
lambda x: self.forward(x, timestep, prompt_emb, pooled_prompt_emb),
hidden_states,
tile_size,
tile_stride,
tile_device=hidden_states.device,
tile_dtype=hidden_states.dtype
)
return hidden_states
def forward(self, hidden_states, timestep, prompt_emb, pooled_prompt_emb, tiled=False, tile_size=128, tile_stride=64):
if tiled:
return self.tiled_forward(hidden_states, timestep, prompt_emb, pooled_prompt_emb, tile_size, tile_stride)
conditioning = self.time_embedder(timestep, hidden_states.dtype) + self.pooled_text_embedder(pooled_prompt_emb)
prompt_emb = self.context_embedder(prompt_emb)
height, width = hidden_states.shape[-2:]
hidden_states = self.pos_embedder(hidden_states)
for block in self.blocks:
hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning)
hidden_states = self.norm_out(hidden_states, conditioning)
hidden_states = self.proj_out(hidden_states)
hidden_states = rearrange(hidden_states, "B (H W) (P Q C) -> B C (H P) (W Q)", P=2, Q=2, H=height//2, W=width//2)
return hidden_states
def state_dict_converter(self):
return SD3DiTStateDictConverter()
class SD3DiTStateDictConverter:
def __init__(self):
pass
def from_diffusers(self, state_dict):
rename_dict = {
"context_embedder": "context_embedder",
"pos_embed.pos_embed": "pos_embedder.pos_embed",
"pos_embed.proj": "pos_embedder.proj",
"time_text_embed.timestep_embedder.linear_1": "time_embedder.timestep_embedder.0",
"time_text_embed.timestep_embedder.linear_2": "time_embedder.timestep_embedder.2",
"time_text_embed.text_embedder.linear_1": "pooled_text_embedder.0",
"time_text_embed.text_embedder.linear_2": "pooled_text_embedder.2",
"norm_out.linear": "norm_out.linear",
"proj_out": "proj_out",
"norm1.linear": "norm1_a.linear",
"norm1_context.linear": "norm1_b.linear",
"attn.to_q": "attn.a_to_q",
"attn.to_k": "attn.a_to_k",
"attn.to_v": "attn.a_to_v",
"attn.to_out.0": "attn.a_to_out",
"attn.add_q_proj": "attn.b_to_q",
"attn.add_k_proj": "attn.b_to_k",
"attn.add_v_proj": "attn.b_to_v",
"attn.to_add_out": "attn.b_to_out",
"ff.net.0.proj": "ff_a.0",
"ff.net.2": "ff_a.2",
"ff_context.net.0.proj": "ff_b.0",
"ff_context.net.2": "ff_b.2",
}
state_dict_ = {}
for name, param in state_dict.items():
if name in rename_dict:
if name == "pos_embed.pos_embed":
param = param.reshape((1, 192, 192, 1536))
state_dict_[rename_dict[name]] = param
elif name.endswith(".weight") or name.endswith(".bias"):
suffix = ".weight" if name.endswith(".weight") else ".bias"
prefix = name[:-len(suffix)]
if prefix in rename_dict:
state_dict_[rename_dict[prefix] + suffix] = param
elif prefix.startswith("transformer_blocks."):
names = prefix.split(".")
names[0] = "blocks"
middle = ".".join(names[2:])
if middle in rename_dict:
name_ = ".".join(names[:2] + [rename_dict[middle]] + [suffix[1:]])
state_dict_[name_] = param
return state_dict_
def from_civitai(self, state_dict):
rename_dict = {
"model.diffusion_model.context_embedder.bias": "context_embedder.bias",
"model.diffusion_model.context_embedder.weight": "context_embedder.weight",
"model.diffusion_model.final_layer.linear.bias": "proj_out.bias",
"model.diffusion_model.final_layer.linear.weight": "proj_out.weight",
"model.diffusion_model.joint_blocks.0.context_block.adaLN_modulation.1.bias": "blocks.0.norm1_b.linear.bias",
"model.diffusion_model.joint_blocks.0.context_block.adaLN_modulation.1.weight": "blocks.0.norm1_b.linear.weight",
"model.diffusion_model.joint_blocks.0.context_block.attn.proj.bias": "blocks.0.attn.b_to_out.bias",
"model.diffusion_model.joint_blocks.0.context_block.attn.proj.weight": "blocks.0.attn.b_to_out.weight",
"model.diffusion_model.joint_blocks.0.context_block.attn.qkv.bias": ['blocks.0.attn.b_to_q.bias', 'blocks.0.attn.b_to_k.bias', 'blocks.0.attn.b_to_v.bias'],
"model.diffusion_model.joint_blocks.0.context_block.attn.qkv.weight": ['blocks.0.attn.b_to_q.weight', 'blocks.0.attn.b_to_k.weight', 'blocks.0.attn.b_to_v.weight'],
"model.diffusion_model.joint_blocks.0.context_block.mlp.fc1.bias": "blocks.0.ff_b.0.bias",
"model.diffusion_model.joint_blocks.0.context_block.mlp.fc1.weight": "blocks.0.ff_b.0.weight",
"model.diffusion_model.joint_blocks.0.context_block.mlp.fc2.bias": "blocks.0.ff_b.2.bias",
"model.diffusion_model.joint_blocks.0.context_block.mlp.fc2.weight": "blocks.0.ff_b.2.weight",
"model.diffusion_model.joint_blocks.0.x_block.adaLN_modulation.1.bias": "blocks.0.norm1_a.linear.bias",
"model.diffusion_model.joint_blocks.0.x_block.adaLN_modulation.1.weight": "blocks.0.norm1_a.linear.weight",
"model.diffusion_model.joint_blocks.0.x_block.attn.proj.bias": "blocks.0.attn.a_to_out.bias",
"model.diffusion_model.joint_blocks.0.x_block.attn.proj.weight": "blocks.0.attn.a_to_out.weight",
"model.diffusion_model.joint_blocks.0.x_block.attn.qkv.bias": ['blocks.0.attn.a_to_q.bias', 'blocks.0.attn.a_to_k.bias', 'blocks.0.attn.a_to_v.bias'],
"model.diffusion_model.joint_blocks.0.x_block.attn.qkv.weight": ['blocks.0.attn.a_to_q.weight', 'blocks.0.attn.a_to_k.weight', 'blocks.0.attn.a_to_v.weight'],
"model.diffusion_model.joint_blocks.0.x_block.mlp.fc1.bias": "blocks.0.ff_a.0.bias",
"model.diffusion_model.joint_blocks.0.x_block.mlp.fc1.weight": "blocks.0.ff_a.0.weight",
"model.diffusion_model.joint_blocks.0.x_block.mlp.fc2.bias": "blocks.0.ff_a.2.bias",
"model.diffusion_model.joint_blocks.0.x_block.mlp.fc2.weight": "blocks.0.ff_a.2.weight",
"model.diffusion_model.joint_blocks.1.context_block.adaLN_modulation.1.bias": "blocks.1.norm1_b.linear.bias",
"model.diffusion_model.joint_blocks.1.context_block.adaLN_modulation.1.weight": "blocks.1.norm1_b.linear.weight",
"model.diffusion_model.joint_blocks.1.context_block.attn.proj.bias": "blocks.1.attn.b_to_out.bias",
"model.diffusion_model.joint_blocks.1.context_block.attn.proj.weight": "blocks.1.attn.b_to_out.weight",
"model.diffusion_model.joint_blocks.1.context_block.attn.qkv.bias": ['blocks.1.attn.b_to_q.bias', 'blocks.1.attn.b_to_k.bias', 'blocks.1.attn.b_to_v.bias'],
"model.diffusion_model.joint_blocks.1.context_block.attn.qkv.weight": ['blocks.1.attn.b_to_q.weight', 'blocks.1.attn.b_to_k.weight', 'blocks.1.attn.b_to_v.weight'],
"model.diffusion_model.joint_blocks.1.context_block.mlp.fc1.bias": "blocks.1.ff_b.0.bias",
"model.diffusion_model.joint_blocks.1.context_block.mlp.fc1.weight": "blocks.1.ff_b.0.weight",
"model.diffusion_model.joint_blocks.1.context_block.mlp.fc2.bias": "blocks.1.ff_b.2.bias",
"model.diffusion_model.joint_blocks.1.context_block.mlp.fc2.weight": "blocks.1.ff_b.2.weight",
"model.diffusion_model.joint_blocks.1.x_block.adaLN_modulation.1.bias": "blocks.1.norm1_a.linear.bias",
"model.diffusion_model.joint_blocks.1.x_block.adaLN_modulation.1.weight": "blocks.1.norm1_a.linear.weight",
"model.diffusion_model.joint_blocks.1.x_block.attn.proj.bias": "blocks.1.attn.a_to_out.bias",
"model.diffusion_model.joint_blocks.1.x_block.attn.proj.weight": "blocks.1.attn.a_to_out.weight",
"model.diffusion_model.joint_blocks.1.x_block.attn.qkv.bias": ['blocks.1.attn.a_to_q.bias', 'blocks.1.attn.a_to_k.bias', 'blocks.1.attn.a_to_v.bias'],
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"model.diffusion_model.joint_blocks.9.x_block.mlp.fc2.weight": "blocks.9.ff_a.2.weight",
"model.diffusion_model.pos_embed": "pos_embedder.pos_embed",
"model.diffusion_model.t_embedder.mlp.0.bias": "time_embedder.timestep_embedder.0.bias",
"model.diffusion_model.t_embedder.mlp.0.weight": "time_embedder.timestep_embedder.0.weight",
"model.diffusion_model.t_embedder.mlp.2.bias": "time_embedder.timestep_embedder.2.bias",
"model.diffusion_model.t_embedder.mlp.2.weight": "time_embedder.timestep_embedder.2.weight",
"model.diffusion_model.x_embedder.proj.bias": "pos_embedder.proj.bias",
"model.diffusion_model.x_embedder.proj.weight": "pos_embedder.proj.weight",
"model.diffusion_model.y_embedder.mlp.0.bias": "pooled_text_embedder.0.bias",
"model.diffusion_model.y_embedder.mlp.0.weight": "pooled_text_embedder.0.weight",
"model.diffusion_model.y_embedder.mlp.2.bias": "pooled_text_embedder.2.bias",
"model.diffusion_model.y_embedder.mlp.2.weight": "pooled_text_embedder.2.weight",
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"model.diffusion_model.joint_blocks.23.context_block.adaLN_modulation.1.bias": "blocks.23.norm1_b.linear.bias",
"model.diffusion_model.final_layer.adaLN_modulation.1.weight": "norm_out.linear.weight",
"model.diffusion_model.final_layer.adaLN_modulation.1.bias": "norm_out.linear.bias",
}
state_dict_ = {}
for name in state_dict:
if name in rename_dict:
param = state_dict[name]
if name.startswith("model.diffusion_model.joint_blocks.23.context_block.adaLN_modulation.1."):
param = torch.concat([param[1536:], param[:1536]], axis=0)
elif name.startswith("model.diffusion_model.final_layer.adaLN_modulation.1."):
param = torch.concat([param[1536:], param[:1536]], axis=0)
elif name == "model.diffusion_model.pos_embed":
param = param.reshape((1, 192, 192, 1536))
if isinstance(rename_dict[name], str):
state_dict_[rename_dict[name]] = param
else:
name_ = rename_dict[name][0].replace(".a_to_q.", ".a_to_qkv.").replace(".b_to_q.", ".b_to_qkv.")
state_dict_[name_] = param
return state_dict_

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diffsynth/models/sd3_text_encoder.py Normal file
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import torch
from .sd_vae_decoder import VAEAttentionBlock, SDVAEDecoderStateDictConverter
from .sd_unet import ResnetBlock, UpSampler
from .tiler import TileWorker
class SD3VAEDecoder(torch.nn.Module):
def __init__(self):
super().__init__()
self.scaling_factor = 1.5305 # Different from SD 1.x
self.shift_factor = 0.0609 # Different from SD 1.x
self.conv_in = torch.nn.Conv2d(16, 512, kernel_size=3, padding=1) # Different from SD 1.x
self.blocks = torch.nn.ModuleList([
# UNetMidBlock2D
ResnetBlock(512, 512, eps=1e-6),
VAEAttentionBlock(1, 512, 512, 1, eps=1e-6),
ResnetBlock(512, 512, eps=1e-6),
# UpDecoderBlock2D
ResnetBlock(512, 512, eps=1e-6),
ResnetBlock(512, 512, eps=1e-6),
ResnetBlock(512, 512, eps=1e-6),
UpSampler(512),
# UpDecoderBlock2D
ResnetBlock(512, 512, eps=1e-6),
ResnetBlock(512, 512, eps=1e-6),
ResnetBlock(512, 512, eps=1e-6),
UpSampler(512),
# UpDecoderBlock2D
ResnetBlock(512, 256, eps=1e-6),
ResnetBlock(256, 256, eps=1e-6),
ResnetBlock(256, 256, eps=1e-6),
UpSampler(256),
# UpDecoderBlock2D
ResnetBlock(256, 128, eps=1e-6),
ResnetBlock(128, 128, eps=1e-6),
ResnetBlock(128, 128, eps=1e-6),
])
self.conv_norm_out = torch.nn.GroupNorm(num_channels=128, num_groups=32, eps=1e-6)
self.conv_act = torch.nn.SiLU()
self.conv_out = torch.nn.Conv2d(128, 3, kernel_size=3, padding=1)
def tiled_forward(self, sample, tile_size=64, tile_stride=32):
hidden_states = TileWorker().tiled_forward(
lambda x: self.forward(x),
sample,
tile_size,
tile_stride,
tile_device=sample.device,
tile_dtype=sample.dtype
)
return hidden_states
def forward(self, sample, tiled=False, tile_size=64, tile_stride=32, **kwargs):
# For VAE Decoder, we do not need to apply the tiler on each layer.
if tiled:
return self.tiled_forward(sample, tile_size=tile_size, tile_stride=tile_stride)
# 1. pre-process
hidden_states = sample / self.scaling_factor + self.shift_factor
hidden_states = self.conv_in(hidden_states)
time_emb = None
text_emb = None
res_stack = None
# 2. blocks
for i, block in enumerate(self.blocks):
hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack)
# 3. output
hidden_states = self.conv_norm_out(hidden_states)
hidden_states = self.conv_act(hidden_states)
hidden_states = self.conv_out(hidden_states)
return hidden_states
def state_dict_converter(self):
return SDVAEDecoderStateDictConverter()

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diffsynth/models/sd3_vae_encoder.py Normal file
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import torch
from .sd_unet import ResnetBlock, DownSampler
from .sd_vae_encoder import VAEAttentionBlock, SDVAEEncoderStateDictConverter
from .tiler import TileWorker
from einops import rearrange
class SD3VAEEncoder(torch.nn.Module):
def __init__(self):
super().__init__()
self.scaling_factor = 1.5305 # Different from SD 1.x
self.shift_factor = 0.0609 # Different from SD 1.x
self.conv_in = torch.nn.Conv2d(3, 128, kernel_size=3, padding=1)
self.blocks = torch.nn.ModuleList([
# DownEncoderBlock2D
ResnetBlock(128, 128, eps=1e-6),
ResnetBlock(128, 128, eps=1e-6),
DownSampler(128, padding=0, extra_padding=True),
# DownEncoderBlock2D
ResnetBlock(128, 256, eps=1e-6),
ResnetBlock(256, 256, eps=1e-6),
DownSampler(256, padding=0, extra_padding=True),
# DownEncoderBlock2D
ResnetBlock(256, 512, eps=1e-6),
ResnetBlock(512, 512, eps=1e-6),
DownSampler(512, padding=0, extra_padding=True),
# DownEncoderBlock2D
ResnetBlock(512, 512, eps=1e-6),
ResnetBlock(512, 512, eps=1e-6),
# UNetMidBlock2D
ResnetBlock(512, 512, eps=1e-6),
VAEAttentionBlock(1, 512, 512, 1, eps=1e-6),
ResnetBlock(512, 512, eps=1e-6),
])
self.conv_norm_out = torch.nn.GroupNorm(num_channels=512, num_groups=32, eps=1e-6)
self.conv_act = torch.nn.SiLU()
self.conv_out = torch.nn.Conv2d(512, 32, kernel_size=3, padding=1)
def tiled_forward(self, sample, tile_size=64, tile_stride=32):
hidden_states = TileWorker().tiled_forward(
lambda x: self.forward(x),
sample,
tile_size,
tile_stride,
tile_device=sample.device,
tile_dtype=sample.dtype
)
return hidden_states
def forward(self, sample, tiled=False, tile_size=64, tile_stride=32, **kwargs):
# For VAE Decoder, we do not need to apply the tiler on each layer.
if tiled:
return self.tiled_forward(sample, tile_size=tile_size, tile_stride=tile_stride)
# 1. pre-process
hidden_states = self.conv_in(sample)
time_emb = None
text_emb = None
res_stack = None
# 2. blocks
for i, block in enumerate(self.blocks):
hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack)
# 3. output
hidden_states = self.conv_norm_out(hidden_states)
hidden_states = self.conv_act(hidden_states)
hidden_states = self.conv_out(hidden_states)
hidden_states = hidden_states[:, :16]
hidden_states = (hidden_states - self.shift_factor) * self.scaling_factor
return hidden_states
def encode_video(self, sample, batch_size=8):
B = sample.shape[0]
hidden_states = []
for i in range(0, sample.shape[2], batch_size):
j = min(i + batch_size, sample.shape[2])
sample_batch = rearrange(sample[:,:,i:j], "B C T H W -> (B T) C H W")
hidden_states_batch = self(sample_batch)
hidden_states_batch = rearrange(hidden_states_batch, "(B T) C H W -> B C T H W", B=B)
hidden_states.append(hidden_states_batch)
hidden_states = torch.concat(hidden_states, dim=2)
return hidden_states
def state_dict_converter(self):
return SDVAEEncoderStateDictConverter()

1
diffsynth/pipelines/__init__.py
View File

@@ -4,3 +4,4 @@ from .stable_diffusion_video import SDVideoPipeline, SDVideoPipelineRunner
from .stable_diffusion_xl_video import SDXLVideoPipeline
from .stable_video_diffusion import SVDVideoPipeline
from .hunyuan_dit import HunyuanDiTImagePipeline
from .stable_diffusion_3 import SD3ImagePipeline

130
diffsynth/pipelines/stable_diffusion_3.py Normal file
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@@ -0,0 +1,130 @@
from ..models import ModelManager, SD3TextEncoder1, SD3TextEncoder2, SD3TextEncoder3, SD3DiT, SD3VAEDecoder, SD3VAEEncoder
from ..prompts import SD3Prompter
from ..schedulers import FlowMatchScheduler
import torch
from tqdm import tqdm
from PIL import Image
import numpy as np
class SD3ImagePipeline(torch.nn.Module):
def __init__(self, device="cuda", torch_dtype=torch.float16):
super().__init__()
self.scheduler = FlowMatchScheduler()
self.prompter = SD3Prompter()
self.device = device
self.torch_dtype = torch_dtype
# models
self.text_encoder_1: SD3TextEncoder1 = None
self.text_encoder_2: SD3TextEncoder2 = None
self.text_encoder_3: SD3TextEncoder3 = None
self.dit: SD3DiT = None
self.vae_decoder: SD3VAEDecoder = None
self.vae_encoder: SD3VAEEncoder = None
def fetch_main_models(self, model_manager: ModelManager):
self.text_encoder_1 = model_manager.sd3_text_encoder_1
self.text_encoder_2 = model_manager.sd3_text_encoder_2
if "sd3_text_encoder_3" in model_manager.model:
self.text_encoder_3 = model_manager.sd3_text_encoder_3
self.dit = model_manager.sd3_dit
self.vae_decoder = model_manager.sd3_vae_decoder
self.vae_encoder = model_manager.sd3_vae_encoder
def fetch_prompter(self, model_manager: ModelManager):
self.prompter.load_from_model_manager(model_manager)
@staticmethod
def from_model_manager(model_manager: ModelManager):
pipe = SD3ImagePipeline(
device=model_manager.device,
torch_dtype=model_manager.torch_dtype,
)
pipe.fetch_main_models(model_manager)
pipe.fetch_prompter(model_manager)
return pipe
def preprocess_image(self, image):
image = torch.Tensor(np.array(image, dtype=np.float32) * (2 / 255) - 1).permute(2, 0, 1).unsqueeze(0)
return image
def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=32):
image = self.vae_decoder(latent.to(self.device), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0]
image = image.cpu().permute(1, 2, 0).numpy()
image = Image.fromarray(((image / 2 + 0.5).clip(0, 1) * 255).astype("uint8"))
return image
@torch.no_grad()
def __call__(
self,
prompt,
negative_prompt="bad quality, poor quality, doll, disfigured, jpg, toy, bad anatomy, missing limbs, missing fingers, 3d, cgi",
cfg_scale=4.5,
input_image=None,
denoising_strength=1.0,
height=1024,
width=1024,
num_inference_steps=20,
tiled=False,
tile_size=128,
tile_stride=64,
progress_bar_cmd=tqdm,
progress_bar_st=None,
):
# Prepare scheduler
self.scheduler.set_timesteps(num_inference_steps, denoising_strength)
# Prepare latent tensors
if input_image is not None:
image = self.preprocess_image(input_image).to(device=self.device, dtype=self.torch_dtype)
latents = self.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
noise = torch.randn((1, 16, height//8, width//8), device=self.device, dtype=self.torch_dtype)
latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0])
else:
latents = torch.randn((1, 16, height//8, width//8), device=self.device, dtype=self.torch_dtype)
# Encode prompts
prompt_emb_posi, pooled_prompt_emb_posi = self.prompter.encode_prompt(
self.text_encoder_1, self.text_encoder_2, self.text_encoder_3,
prompt,
device=self.device, positive=True
)
prompt_emb_nega, pooled_prompt_emb_nega = self.prompter.encode_prompt(
self.text_encoder_1, self.text_encoder_2, self.text_encoder_3,
negative_prompt,
device=self.device, positive=False
)
# Denoise
for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
timestep = torch.Tensor((timestep,)).to(self.device)
# Classifier-free guidance
noise_pred_posi = self.dit(
latents, timestep, prompt_emb_posi, pooled_prompt_emb_posi,
tiled=tiled, tile_size=tile_size, tile_stride=tile_stride
)
noise_pred_nega = self.dit(
latents, timestep, prompt_emb_nega, pooled_prompt_emb_nega,
tiled=tiled, tile_size=tile_size, tile_stride=tile_stride
)
noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega)
# DDIM
latents = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], latents)
# UI
if progress_bar_st is not None:
progress_bar_st.progress(progress_id / len(self.scheduler.timesteps))
# Decode image
image = self.decode_image(latents, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
return image

1
diffsynth/prompts/__init__.py
View File

@@ -1,3 +1,4 @@
from .sd_prompter import SDPrompter
from .sdxl_prompter import SDXLPrompter
from .sd3_prompter import SD3Prompter
from .hunyuan_dit_prompter import HunyuanDiTPrompter

84
diffsynth/prompts/sd3_prompter.py Normal file
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@@ -0,0 +1,84 @@
from .utils import Prompter
from transformers import CLIPTokenizer, T5TokenizerFast
import os, torch
class SD3Prompter(Prompter):
def __init__(
self,
tokenizer_1_path=None,
tokenizer_2_path=None,
tokenizer_3_path=None
):
if tokenizer_1_path is None:
base_path = os.path.dirname(os.path.dirname(__file__))
tokenizer_1_path = os.path.join(base_path, "tokenizer_configs/stable_diffusion_3/tokenizer_1")
if tokenizer_2_path is None:
base_path = os.path.dirname(os.path.dirname(__file__))
tokenizer_2_path = os.path.join(base_path, "tokenizer_configs/stable_diffusion_3/tokenizer_2")
if tokenizer_3_path is None:
base_path = os.path.dirname(os.path.dirname(__file__))
tokenizer_3_path = os.path.join(base_path, "tokenizer_configs/stable_diffusion_3/tokenizer_3")
super().__init__()
self.tokenizer_1 = CLIPTokenizer.from_pretrained(tokenizer_1_path)
self.tokenizer_2 = CLIPTokenizer.from_pretrained(tokenizer_2_path)
self.tokenizer_3 = T5TokenizerFast.from_pretrained(tokenizer_3_path)
def encode_prompt_using_clip(self, prompt, text_encoder, tokenizer, max_length, device):
input_ids = tokenizer(
prompt,
return_tensors="pt",
padding="max_length",
max_length=max_length,
truncation=True
).input_ids.to(device)
pooled_prompt_emb, prompt_emb = text_encoder(input_ids)
return pooled_prompt_emb, prompt_emb
def encode_prompt_using_t5(self, prompt, text_encoder, tokenizer, max_length, device):
input_ids = tokenizer(
prompt,
return_tensors="pt",
padding="max_length",
max_length=max_length,
truncation=True,
add_special_tokens=True,
).input_ids.to(device)
prompt_emb = text_encoder(input_ids)
prompt_emb = prompt_emb.reshape((1, prompt_emb.shape[0]*prompt_emb.shape[1], -1))
return prompt_emb
def encode_prompt(
self,
text_encoder_1,
text_encoder_2,
text_encoder_3,
prompt,
positive=True,
device="cuda"
):
prompt = self.process_prompt(prompt, positive=positive)
# CLIP
pooled_prompt_emb_1, prompt_emb_1 = self.encode_prompt_using_clip(prompt, text_encoder_1, self.tokenizer_1, 77, device)
pooled_prompt_emb_2, prompt_emb_2 = self.encode_prompt_using_clip(prompt, text_encoder_2, self.tokenizer_2, 77, device)
# T5
if text_encoder_3 is None:
prompt_emb_3 = torch.zeros((1, 256, 4096), dtype=prompt_emb_1.dtype, device=device)
else:
prompt_emb_3 = self.encode_prompt_using_t5(prompt, text_encoder_3, self.tokenizer_3, 256, device)
prompt_emb_3 = prompt_emb_3.to(prompt_emb_1.dtype) # float32 -> float16
# Merge
prompt_emb = torch.cat([
torch.nn.functional.pad(torch.cat([prompt_emb_1, prompt_emb_2], dim=-1), (0, 4096 - 768 - 1280)),
prompt_emb_3
], dim=-2)
pooled_prompt_emb = torch.cat([pooled_prompt_emb_1, pooled_prompt_emb_2], dim=-1)
return prompt_emb, pooled_prompt_emb

3
diffsynth/prompts/utils.py
View File

@@ -87,7 +87,8 @@ class Prompter:
tokens, _ = textual_inversion_dict[keyword]
additional_tokens += tokens
self.keyword_dict[keyword] = " " + " ".join(tokens) + " "
self.tokenizer.add_tokens(additional_tokens)
if self.tokenizer is not None:
self.tokenizer.add_tokens(additional_tokens)
def load_beautiful_prompt(self, model, model_path):
model_folder = os.path.dirname(model_path)

1
diffsynth/schedulers/__init__.py
View File

@@ -1,2 +1,3 @@
from .ddim import EnhancedDDIMScheduler
from .continuous_ode import ContinuousODEScheduler
from .flow_match import FlowMatchScheduler

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diffsynth/schedulers/flow_match.py Normal file
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@@ -0,0 +1,42 @@
import torch
class FlowMatchScheduler():
def __init__(self, num_inference_steps=100, num_train_timesteps=1000, shift=3.0, sigma_max=1.0, sigma_min=0.003/1.002):
self.num_train_timesteps = num_train_timesteps
self.shift = shift
self.sigma_max = sigma_max
self.sigma_min = sigma_min
self.set_timesteps(num_inference_steps)
def set_timesteps(self, num_inference_steps=100, denoising_strength=1.0):
sigma_start = self.sigma_min + (self.sigma_max - self.sigma_min) * denoising_strength
self.sigmas = torch.linspace(sigma_start, self.sigma_min, num_inference_steps)
self.sigmas = self.shift * self.sigmas / (1 + (self.shift - 1) * self.sigmas)
self.timesteps = self.sigmas * self.num_train_timesteps
def step(self, model_output, timestep, sample, to_final=False):
timestep_id = torch.argmin((self.timesteps - timestep).abs())
sigma = self.sigmas[timestep_id]
if to_final or timestep_id + 1 >= len(self.timesteps):
sigma_ = 0
else:
sigma_ = self.sigmas[timestep_id + 1]
prev_sample = sample + model_output * (sigma_ - sigma)
return prev_sample
def return_to_timestep(self, timestep, sample, sample_stablized):
# This scheduler doesn't support this function.
pass
def add_noise(self, original_samples, noise, timestep):
timestep_id = torch.argmin((self.timesteps - timestep).abs())
sigma = self.sigmas[timestep_id]
sample = (1 - sigma) * original_samples + sigma * noise
return sample

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diffsynth/tokenizer_configs/stable_diffusion_3/tokenizer_1/special_tokens_map.json Normal file
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@@ -0,0 +1,30 @@
{
"bos_token": {
"content": "<|startoftext|>",
"lstrip": false,
"normalized": true,
"rstrip": false,
"single_word": false
},
"eos_token": {
"content": "<|endoftext|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"pad_token": {
"content": "<|endoftext|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"unk_token": {
"content": "<|endoftext|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
}
}

30
diffsynth/tokenizer_configs/stable_diffusion_3/tokenizer_1/tokenizer_config.json Normal file
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@@ -0,0 +1,30 @@
{
"add_prefix_space": false,
"added_tokens_decoder": {
"49406": {
"content": "<|startoftext|>",
"lstrip": false,
"normalized": true,
"rstrip": false,
"single_word": false,
"special": true
},
"49407": {
"content": "<|endoftext|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
}
},
"bos_token": "<|startoftext|>",
"clean_up_tokenization_spaces": true,
"do_lower_case": true,
"eos_token": "<|endoftext|>",
"errors": "replace",
"model_max_length": 77,
"pad_token": "<|endoftext|>",
"tokenizer_class": "CLIPTokenizer",
"unk_token": "<|endoftext|>"
}

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diffsynth/tokenizer_configs/stable_diffusion_3/tokenizer_1/vocab.json Normal file
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diffsynth/tokenizer_configs/stable_diffusion_3/tokenizer_2/special_tokens_map.json Normal file
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@@ -0,0 +1,30 @@
{
"bos_token": {
"content": "<|startoftext|>",
"lstrip": false,
"normalized": true,
"rstrip": false,
"single_word": false
},
"eos_token": {
"content": "<|endoftext|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"pad_token": {
"content": "!",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"unk_token": {
"content": "<|endoftext|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
}
}

38
diffsynth/tokenizer_configs/stable_diffusion_3/tokenizer_2/tokenizer_config.json Normal file
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@@ -0,0 +1,38 @@
{
"add_prefix_space": false,
"added_tokens_decoder": {
"0": {
"content": "!",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"49406": {
"content": "<|startoftext|>",
"lstrip": false,
"normalized": true,
"rstrip": false,
"single_word": false,
"special": true
},
"49407": {
"content": "<|endoftext|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
}
},
"bos_token": "<|startoftext|>",
"clean_up_tokenization_spaces": true,
"do_lower_case": true,
"eos_token": "<|endoftext|>",
"errors": "replace",
"model_max_length": 77,
"pad_token": "!",
"tokenizer_class": "CLIPTokenizer",
"unk_token": "<|endoftext|>"
}

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diffsynth/tokenizer_configs/stable_diffusion_3/tokenizer_3/special_tokens_map.json Normal file
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@@ -0,0 +1,125 @@
{
"additional_special_tokens": [
"<extra_id_0>",
"<extra_id_1>",
"<extra_id_2>",
"<extra_id_3>",
"<extra_id_4>",
"<extra_id_5>",
"<extra_id_6>",
"<extra_id_7>",
"<extra_id_8>",
"<extra_id_9>",
"<extra_id_10>",
"<extra_id_11>",
"<extra_id_12>",
"<extra_id_13>",
"<extra_id_14>",
"<extra_id_15>",
"<extra_id_16>",
"<extra_id_17>",
"<extra_id_18>",
"<extra_id_19>",
"<extra_id_20>",
"<extra_id_21>",
"<extra_id_22>",
"<extra_id_23>",
"<extra_id_24>",
"<extra_id_25>",
"<extra_id_26>",
"<extra_id_27>",
"<extra_id_28>",
"<extra_id_29>",
"<extra_id_30>",
"<extra_id_31>",
"<extra_id_32>",
"<extra_id_33>",
"<extra_id_34>",
"<extra_id_35>",
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"<extra_id_37>",
"<extra_id_38>",
"<extra_id_39>",
"<extra_id_40>",
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"<extra_id_95>",
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"<extra_id_99>"
],
"eos_token": {
"content": "</s>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"pad_token": {
"content": "<pad>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"unk_token": {
"content": "<unk>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
}
}

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diffsynth/tokenizer_configs/stable_diffusion_3/tokenizer_3/tokenizer_config.json Normal file
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{
"add_prefix_space": true,
"added_tokens_decoder": {
"0": {
"content": "<pad>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
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"lstrip": false,
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},
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}

30
examples/image_synthesis/sd3_text_to_image.py Normal file
View File

@@ -0,0 +1,30 @@
from diffsynth import ModelManager, SD3ImagePipeline, download_models
import torch
# Download models (automatically)
# `models/stable_diffusion_3/sd3_medium_incl_clips.safetensors`: [link](https://huggingface.co/stabilityai/stable-diffusion-3-medium/resolve/main/sd3_medium_incl_clips.safetensors)
download_models(["StableDiffusion3_without_T5"])
model_manager = ModelManager(torch_dtype=torch.float16, device="cuda",
file_path_list=["models/stable_diffusion_3/sd3_medium_incl_clips.safetensors"])
pipe = SD3ImagePipeline.from_model_manager(model_manager)
torch.manual_seed(0)
image = pipe(
prompt="a white cat, colorful ink painting, cyberpunk, unreal",
negative_prompt="bad quality, poor quality, doll, disfigured, jpg, toy, bad anatomy, missing limbs, missing fingers, 3d, cgi",
cfg_scale=4.5,
num_inference_steps=50, width=1024, height=1024,
)
image.save("image_1024.jpg")
image = pipe(
prompt="a white cat, colorful ink painting, cyberpunk, unreal",
negative_prompt="bad quality, poor quality, doll, disfigured, jpg, toy, bad anatomy, missing limbs, missing fingers, 3d, cgi",
input_image=image.resize((2048, 2048)), denoising_strength=0.5,
cfg_scale=4.5,
num_inference_steps=50, width=2048, height=2048,
tiled=True
)
image.save("image_2048.jpg")

0
models/stable_diffusion_3/Put Stable Diffusion 3 checkpoints here.txt Normal file
View File