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SolitaryThinker
2025-07-31 08:10:19 +00:00
parent 0422d18377
commit daea023383
4 changed files with 36 additions and 34 deletions
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examples/inference/gradio/gradio_frontend.py
+9 -9
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@@ -486,7 +486,7 @@ def main():
help="URL of the Ray Serve backend")
parser.add_argument("--t2v_model_path",
type=str,
default="Wan-AI/Wan2.2-TI2V-5B-Diffusers",
default="FastVideo/FastWan2.1-T2V-1.3B-Diffusers",
help="Path to the T2V model (for default parameters)")
parser.add_argument("--i2v_model_path",
type=str,
@@ -505,18 +505,18 @@ def main():
# Load default parameters from the models
# try:
# default_params = SamplingParam.from_pretrained(args.t2v_model_path)
default_params = SamplingParam.from_pretrained(args.t2v_model_path)
# except Exception as e:
# print(f"Warning: Could not load default parameters from {args.t2v_model_path}: {e}")
# print("Using fallback default parameters...")
# # Create fallback default parameters
default_params = SamplingParam()
default_params.height = 448
default_params.width = 832
default_params.num_frames = 21
default_params.guidance_scale = 7.5
default_params.num_inference_steps = 20
default_params.seed = 1024
# default_params = SamplingParam()
# default_params.height = 448
# default_params.width = 832
# default_params.num_frames = 21
# default_params.guidance_scale = 7.5
# default_params.num_inference_steps = 20
# default_params.seed = 1024
# Create and launch the interface
demo = create_gradio_interface(args.backend_url, default_params)
examples/inference/gradio/ray_serve_backend.py
+10 -10
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@@ -167,7 +167,7 @@ class FastVideoAPI:
print("✅ I2V model initialized successfully")
@app.post("/generate_video", response_model=VideoGenerationResponse)
@limiter.limit("2/minute") # Allow 2 requests per minute per IP
@limiter.limit("50/minute") # Allow 2 requests per minute per IP
async def generate_video(self, request: Request, video_request: VideoGenerationRequest) -> VideoGenerationResponse:
try:
# Select the appropriate model and parameters based on model_type
@@ -183,15 +183,15 @@ class FastVideoAPI:
# Update parameters with request values
params.prompt = video_request.prompt
# Only override negative prompt if user explicitly opts in
# if video_request.use_negative_prompt:
# params.negative_prompt = video_request.negative_prompt
if video_request.use_negative_prompt:
params.negative_prompt = video_request.negative_prompt
# params.seed = video_request.seed
# params.guidance_scale = video_request.guidance_scale
# params.num_frames = video_request.num_frames
# params.height = video_request.height
# params.width = video_request.width
# params.num_inference_steps = video_request.num_inference_steps
params.seed = video_request.seed
params.guidance_scale = video_request.guidance_scale
params.num_frames = video_request.num_frames
params.height = video_request.height
params.width = video_request.width
params.num_inference_steps = video_request.num_inference_steps
# Handle seed randomization
if video_request.randomize_seed:
@@ -297,7 +297,7 @@ if __name__ == "__main__":
parser = argparse.ArgumentParser(description="FastVideo Ray Serve Backend")
parser.add_argument("--t2v_model_path",
type=str,
default="Wan-AI/Wan2.2-TI2V-5B-Diffusers",
default="FastVideo/FastWan2.1-T2V-1.3B-Diffusers",
help="Path to the T2V model")
parser.add_argument("--i2v_model_path",
type=str,
fastvideo/pipelines/stages/denoising.py
+15 -14
View File
@@ -10,7 +10,6 @@ from collections.abc import Iterable
from typing import Any
import torch
import torchvision.transforms.functional as TF
from einops import rearrange
from tqdm.auto import tqdm
@@ -31,8 +30,7 @@ from fastvideo.pipelines.stages.base import PipelineStage
from fastvideo.pipelines.stages.validators import StageValidators as V
from fastvideo.pipelines.stages.validators import VerificationResult
from fastvideo.platforms import AttentionBackendEnum
from fastvideo.utils import dict_to_3d_list, masks_like, best_output_size
from PIL import Image
from fastvideo.utils import dict_to_3d_list, masks_like
try:
from fastvideo.attention.backends.sliding_tile_attn import (
@@ -200,12 +198,11 @@ class DenoisingStage(PipelineStage):
logger.info(f"z shape: {z.shape}")
logger.info(f"latent_model_input shape: {latent_model_input.shape}")
latent_model_input = latent_model_input.squeeze(0)
mask1, mask2 = masks_like([latent_model_input],
zero=True)
mask1, mask2 = masks_like([latent_model_input], zero=True)
# logger.info(f"mask1 shape: {mask1.shape}")
# logger.info(f"mask2 shape: {mask2.shape}")
latent_model_input = (
1. - mask2[0]) * z + mask2[0] * latent_model_input
latent_model_input = (1. -
mask2[0]) * z + mask2[0] * latent_model_input
# latent_model_input = latent_model_input.unsqueeze(0)
latent_model_input = latent_model_input.to(get_local_torch_device())
latents = latent_model_input
@@ -213,9 +210,10 @@ class DenoisingStage(PipelineStage):
temporal_scale = fastvideo_args.pipeline_config.vae_config.arch_config.scale_factor_temporal
spatial_scale = fastvideo_args.pipeline_config.vae_config.arch_config.scale_factor_spatial
patch_size = fastvideo_args.pipeline_config.dit_config.arch_config.patch_size
seq_len = ((F - 1) // temporal_scale + 1) * (
batch.height // spatial_scale) * (batch.width // spatial_scale) // (
patch_size[1] * patch_size[2])
seq_len = ((F - 1) // temporal_scale +
1) * (batch.height // spatial_scale) * (
batch.width // spatial_scale) // (patch_size[1] *
patch_size[2])
import math
seq_len = int(math.ceil(seq_len / sp_world_size)) * sp_world_size
logger.info("latents shape: %s", latents.shape)
@@ -239,11 +237,14 @@ class DenoisingStage(PipelineStage):
if fastvideo_args.pipeline_config.ti2v_task and batch.pil_image is not None:
logger.info(f"before ti2v timestep: {t}")
timestep = [t]
timestep = torch.stack(timestep).to(get_local_torch_device())
timestep = torch.stack(timestep).to(
get_local_torch_device())
logger.info(f"mask2 shape: {mask2[0].shape}")
logger.info(f"mask[0][0] shape: {mask2[0][0].shape}")
logger.info(f"mask[0][0][:, ::2, ::2] shape: {mask2[0][0][:, ::2, ::2].shape}")
logger.info(
f"mask[0][0][:, ::2, ::2] shape: {mask2[0][0][:, ::2, ::2].shape}"
)
temp_ts = (mask2[0][0][:, ::2, ::2] * timestep).flatten()
logger.info(f"temp_ts: {temp_ts}")
logger.info(f"temp_ts shape: {temp_ts.shape}")
@@ -257,8 +258,8 @@ class DenoisingStage(PipelineStage):
t = timestep
# else:
t_expand = t.repeat(latent_model_input.shape[0])
logger.info(f"t_expand shape: {t_expand.shape}")
logger.info(f"t_expand: {t_expand}")
# logger.info(f"t_expand shape: {t_expand.shape}")
# logger.info(f"t_expand: {t_expand}")
assert torch.isnan(latent_model_input).sum() == 0
latent_model_input = self.scheduler.scale_model_input(
fastvideo/worker/multiproc_executor.py
+2 -1
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@@ -40,7 +40,8 @@ class MultiprocExecutor(Executor):
logger.info("Using provided master port: %s", self.master_port)
else:
# Auto-find available port
for port in range(29503, 65535):
import random
for port in range(29503 + random.randint(0, 10000), 65535):
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
if s.connect_ex(('localhost', port)) != 0:
self.master_port = port